Operations | Monitoring | ITSM | DevOps | Cloud

Never lose quorum in Apache Kafka - and what to do when you do

Apache Kafka's documentation and operational lore for KRaft are clear on one point: you shall never lose controller quorum. It is good advice, because a Raft quorum that can no longer elect a leader cannot commit metadata, and a Kafka cluster without a functioning controller cannot create topics, change ACLs, elect partition leaders, or otherwise make progress on its control plane.

Manage Context with Aiven DataHub

Aiven DataHub is a managed data catalog that helps you and your agents find, understand and reason on top of your data in data sources like PostgreSQL, ClickHouse, Kafka or BI Tools. In this video Stan dives into Aiven DataHub and shows how to set it up so context is derived from metadata, or the lineage between services. He also dives into examples on how to get deeper insights about your data through prompting Aiven DataHub.

How to Build an App on Base44 with a Production-Ready Aiven Database

Base44 is fast at the part that used to take a week. Describe an application, and you have a working interface in minutes. Base44's built-in Cloud backend, enabled by default, is a reasonable place to start. But it doesn't put your data in an account you already own, in the cloud and region you picked, next to the rest of your data platform. That's the gap this post closes.

Know Your data, Trust Your AI: Aiven DataHub is now GA

Ask a simple question "who are our most profitable customers?" and things fall apart. The data lives in six systems, nobody agrees which table is canonical, the column called profit is actually revenue, and the business rules that matter live in someone's head or a Confluence page nobody's touched since 2023. Now point an AI agent at that same mess.

Deploy Your Apps and Agents Where Your Data Lives With Aiven Runtime

Everything that makes an app or agent real happens after it works on your machine. Locally coding an app is a joy: hot reload, a seeded database, a mocked API key. Then you go to ship it, and "deploy" quietly expands into a Dockerfile that behaves in CI, somewhere to actually run the container, a database it can reach, TLS, secrets wired into the environment, and a pipeline to hold it all together. The feature took an afternoon. The plumbing takes the rest of the week.

Let Builders Build, and Agents Cook.

Software used to be built by engineers. Not any more. Low-code tools brought in domain teams. AI copilots brought in everyone else. And now agents are building and acting alongside humans; marketing, finance, HR and legal are all shipping the apps they used to file tickets for. The number of people (and things) building on your data has exploded, and it isn't slowing down. And there's no single, standardized way to build, there likely never will be.

Your users already know what's relevant. Are you listening?

TL;DR If you work on search relevance, you know the feeling. You ship a synonym. You boost a field. You add a vector model. You stare at a judgment set that was labeled six months ago and hope the next NDCG number moves in the right direction. Somewhere between offline metrics and production traffic, a quiet gap opens: you optimized for what you think users want, not for what they actually do when the results appear. That gap is not a failure of effort. It is a missing feedback loop.

Diskless Topics on Aiven for Apache Kafka

Diskless moves Kafka replication into object storage, removing cross-AZ fees. See how diskless topics work, then create one live on Aiven for Apache Kafka. AIVEN DATA PLATFORM The Aiven Platform 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.

Tame the data chaos with Sumo Logic's Data Pipelines

Security and operations teams are collecting more telemetry than ever, and AI is accelerating that curve. IDC projects the world will generate 393.9 zettabytes of data in 2028, up from 149 zettabytes in 2024, with AI and machine learning workloads driving much of that growth. That growth forces a hard trade-off. Ingest everything, and you pay for it. Filter aggressively, and you risk missing the signal that matters.

How to Scrape Google Maps at Scale: A Practical Data Pipeline Guide

Collecting a few Google Maps listings is easy. The engineering work starts when the same job has to run across dozens of cities, multiple business categories, and a recurring schedule. Take a project covering 30 cities and 10 categories. That already creates 300 search combinations before neighborhoods or alternate keywords are added. If the dataset needs to be refreshed every week, the job quickly moves beyond a one-time export. You have to keep searches consistent, track failures, avoid duplicate records, and make sure each run still looks like the last one.