Operations | Monitoring | ITSM | DevOps | Cloud

In today’s landscape of digital transformation, companies are increasingly dependent on technologies that improve productivity and enhance the employee experience. However, measuring the real impact that each device, system, and configuration has on daily operations remains a significant challenge for many IT teams.

Data lock-in and freedom are a worldwide problem. As usual, the EU is the first to regulate it. The passing of the Data Act made clear that in Europe, companies are guaranteed: “fairness in the digital environment by clarifying who can create value from data and under which conditions.

Modern systems are wonderfully capable, but relentlessly complex. Debugging across microservices, frontends, and cloud edges often means switching between five or more tools, trying to stitch together “what changed” and “why it broke.” Honeycomb’s wide events model has proven to be a superpower for taming that complexity, by allowing you to easily observe and query end-to-end traces without worrying about how much granular data you attach to your events.

"Let us support different relational databases apart from SQLite. Nobody likes to run SQLite in production." This was one of the most requested features from our community. Your requests have been heard, and we've added support for different relational databases, starting with PostgreSQL. If you're self-hosting SigNoz, you no longer need to worry about SQLite's limitations. Let's dive into what we've built and why it matters for your production deployments.

We recently released Grafana Assistant into public preview for Grafana Cloud, and we’ve been excited to see how our customers have already made it part of their daily observability routines. At the same time, Assistant is becoming a go-to companion for developers right here at Grafana Labs, whether they’re debugging on-call issues, helping customers, or trying to remember tricky PromQL syntax.

Serverless computing helps teams move faster by removing the need to manage servers. Code runs only when needed, scaling up or down automatically. For DevOps engineers, this means quicker deployments and less infrastructure work. But serverless also brings new challenges. Functions run for short periods, making it hard to track errors, performance, and costs.

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.

At Sentry, we build in public and we move fast. But moving fast means we don’t always get everything right on the first try. That’s where feedback comes in: it helps us validate what’s working, spot what’s missing, and catch issues we wouldn’t always see through error tracking alone.