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The BigQuery Sink connector is a critical piece of Kafka infrastructure that allows you to offload your Kafka topic data into BigQuery in real time. It is the third most-used connector among Kafka users (after the Google Cloud Managed Service for Apache Kafka and the original WePay sink connector), but it's not without its fair share of plot twists. Here's the story of how this connector switched hands three times and we ultimately ended up helping to re-build it.

Aiven is proud to launch the newest version of PostgreSQL, version 18, alongside with the open source community as the first managed PostgreSQL provider to support the latest version. This year we had three Aiveners join in on contributing to this major release, a trend which we hope to only see increase. Congrats to Patrick Stählin, Ronan Dunklau, and Thomas Krennwallner for your contributions to the codebase.

For many years Redis has been the default for caching, message queues, and fast data storage. However recent changes to its licensing mean that companies who want truly open-source tools need to make a switch. This is why Valkey was created. We are leading the way in offering a smooth path to move your current Redis setups to a fully open-source managed Valkey service.

We’ve added Tiered Storage to Diskless Kafka—using plain old KIP-405 as the read-optimizer, Diskless Kafka materializes fast-to-read segments—unifying Tiered and Diskless into a single path. This leverages production-grade Tiered Storage plugin, removes the need for bespoke components, and simplifies the community discussion. We’ve also upgraded KIP-1150 and KIP-1163 to address the community’s most pressing questions such as transactions and queues support.

Understand how Kafka integrates with Apache Iceberg and experiment locally with Docker and Spark The streaming data landscape is evolving rapidly, and one of the most exciting developments is the integration between Apache Kafka and Apache Iceberg. While Kafka excels at real-time data streaming, organizations often struggle with the complexity of moving streaming data into analytical systems.

Set aside the data masking features of PostgreSQL Anonymizer. This plugin can save the day during development by simplifying your workflow and generating schema-accurate, privacy-compliant test data. In a previous post, we discussed using static and dynamic masking to anonymize data. I spent the last two weeks trying to write followups for the anonymization posts. It's time to confess... I'm an over-engineerer.

Developers love tools that make their lives easier. Whether it’s debugging, visualizing data, or managing clusters, the right tools can transform hours of manual work into just a few clicks. When it comes to Kafka - one of the most powerful but complex streaming platforms - having the right interface is critical. That’s exactly why we built our new Kafka UI Plugin.

The promise of large language models (LLMs) brings new performance gains to development teams, sharing information and context to your team. How is this information stored?

In today’s interconnected world, data sovereignty has evolved from a legal concept into a strategic imperative. For some European businesses and institutions, it’s no longer just about regulatory compliance – it’s about trust, resilience, and long-term digital independence.

For many growing businesses, data infrastructure grows and evolves organically. This often results in teams running one technology for log analytics, like a self-managed ELK cluster, and a completely separate technology for application search. While functional, these disparate tech stacks begin to eat into the bottom line. Businesses grapple with fragmented skill sets, inconsistent security models, multiple vendors, and a constant operational tax.