Operations | Monitoring | ITSM | DevOps | Cloud

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?

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.

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.

We’re rolling out table-formatted topics for Apache Kafka, enabling open-source users to ingest, process, and emit data natively as Apache Iceberg tables. We’ve developed a new class of topics in Apache Kafka that stream data into lakehouse formats such as Apache Iceberg. The developer experience, similar to Diskless, is flexible, and the topics are opt-in via cluster configuration.

Finding yourself migrating and weighing the options when it comes to moving your entire Apache Kafka setup to a new cloud? This blog will point you to a solution to efficiently migrate your workloads to an Aiven for Apache Kafka cluster circumventing the headache that usually comes with cloud migration.

Diskless topics reward high-throughput workloads with large batches but can struggle with low-throughput patterns. Note: This analysis is based on testing with Diskless Kafka 4.0.0-rc15. Diskless topics are available for you to start experimenting with via the Inkless fork but the feature is still in development, and performance characteristics may change significantly as the technology matures. If you're: This post is for you!

Kafka and Iceberg is a costly marriage of inconvenience. If you write code for a living you’ve probably heard of Apache Iceberg - but you might not realise the detour your Kafka events must take to get there. Typically a Kafka message written to an Iceberg table must take a journey via a connector, rack up transfer fees, and idle in a sidecar before it appears as an Iceberg table—hardly the friction‑free flow open table formats promise.

TLDR; Aiven Stream Governance is our developer-centric approach to making trustworthy data the free and default experience, not a costly add-on. The cost? Nada. Because credible streaming is a right not a privilege.

In the cloud, Kafka’s promise of “never lose a byte” quietly morphs into “always pay for two.” Every time the leader syncs followers across zones, you get hit with premium egress charges that can dwarf compute costs. Diskless Kafka turns that upside-down: brokers replicate data straight into S3, so the pricey cross-zone hops vanish. Yes, object storage is slower than a local SSD, but the swap buys you on-demand elasticity and a bill that finally makes sense.