Operations | Monitoring | ITSM | DevOps | Cloud

Half a year ago, we launched a free tier cloud Kafka. We have 16,808 clusters so we got curious: what are these builders telling us about the state of Apache Kafka? The headlines this quarter suggest Kafka is dying because the streaming market is consolidating. At Aiven we see the opposite. Kafka is not shrinking. It is spreading outward from enterprise platform teams into the hands of individual builders. We are now seeing >200 new Kafka clusters created per day on the free tier.

Over the past few weeks, we have been driving a cross-ecosystem effort to replace the “monkey-patching” that powers all JavaScript APM tools today with something built into the runtime. Here is why, how, and where it stands. This applies to server-side JavaScript only (Node.js, Bun, Deno, Cloudflare Workers). Browsers do not have diagnostics_channel and lack the async context propagation primitives needed to polyfill it.

Most ActiveMQ outages are not sudden failures. They are visible in the metrics for minutes, sometimes hours, before they become incidents. A memory usage graph climbing past 60%. A queue depth that isn't draining. An enqueue time that doubled after a deployment. A consumer count that dropped from 3 to 1 at 2 AM.

Over the last decade, a great deal of data privacy regulations have been passed in the European Union. Like it or not, measures like GDPR, the Digital Services Act, and the upcoming Artificial Intelligence Act are exerting increasing influence across industries over how and especially where the data of European customers is stored. In this article, we will explore the ways to keep the simplicity of a Platform as a Service (PaaS) while utilizing only European providers.

I’ve been doing this for over thirty years. Sysadmin, ops lead, global teams, and more data centre migrations than I’d like to admit. Site to site, P2V, V2V, cloud, hybrid, all of it. Every migration gets sold as a clean, well-planned transition. None of them are. They go wrong in very predictable ways. Not because moving infrastructure is especially difficult, but because nobody ever has a clear, current view of what’s actually running, what’s changed, and what still matters.

A major new study from Harvard Medical School and Beth Israel Deaconess Medical Center has found that a large language model (LLM) outperformed physicians across a wide range of clinical reasoning tasks, including making emergency-room triage decisions from messy, real-world patient data. The findings, published April 30 in Science, represent one of the largest comparisons yet between AI and physicians on clinical tasks.

Many security teams are looking to move off Splunk, whether to reduce licensing costs, consolidate their SIEM, or take advantage of Google SecOps' built-in threat intelligence and YARA-L detection capabilities. But migrations aren’t easy, and no one wants to run blind while they evaluate and move to a new platform. With OpenTelemetry and Bindplane, you can easily make the switch to SecOps without impacting your existing stack.

How do you make search faster, smarter, and more scalable? During our recent webinar, I sat down with Luke Patterson, senior product manager at WP Engine, and Delphin Barankanira, independent software vendor partner engineering lead and data & AI specialist at Google Cloud, to answer that question. We dug into the mechanics behind WP Engine’s ability to deliver near-instant updates to over 2 million users.

Most platform and observability teams have logs they know are noise. These could be throwaway health check logs, forgotten DEBUG logs, or verbose INFO logs from little used services that only serve to inflate your bill. Regardless of what they contain and why they're there in the first place, the hard part is getting rid of them. Centralized teams want to easily and quickly prevent these logs from being ingested, without having to work with toilsome infrastructure change management to do so.

A few weeks ago, a group of engineering leaders I trade notes with got into it over a question none... A few weeks ago, a group of engineering leaders I trade notes with got into it over a question none of us has a clean answer to: How much should you let an engineer spend on AI? One SVP at a company of similar size and stage is in calibration mode and capping engineers at $200 per month. Hit the cap, you can self-bump by $100. Hit that, you need your manager. I told the thread our number. $5,000.