Operations | Monitoring | ITSM | DevOps | Cloud

Your version and deploy health data no longer lives on separate pages. Versions and Deploys are now Dashboard cards, visible by default the next time you open Rollbar. Errors, versions, and deploys in one view, no more tab-hopping to piece together release health.

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.

So your database is slow. Now what? Grafana Cloud Database Observability already gives you visibility into your SQL queries with RED metrics, individual execution samples, wait event breakdowns, table schemas, and visual explain plans. But visibility is just the starting point. You can see that a query's P99 latency spiked, but what should you do about it? You can see wait events like wait/synch/mutex/innodb firing, but what does that actually mean?

Most people assume the modern enterprise runs cleanly on the dashboards and cloud consoles that dominate today’s digital workspaces. Anyone who operates these environments understands a more complicated truth. The real work happens beneath those surfaces, in systems few people notice until something slips. Across industries, engineers face the same recurring scenario: a routine shift disrupted by signals of degradation somewhere in the environment.

SQL Analytics has always been one of my favourite SquaredUp features. That's not just because I can use raw SQL to achieve complex data transformations. The fact that I can run SQL queries over data from all sorts of sources — not just relational databases, gives incredible power and flexibility. The great news is that SQL Analytics now ships with our AI-driven SmartAssist technology.

Operations teams have lived with the same frustrating tradeoff for years: the data exists, but getting to the right answer often takes too much time and too much expertise. Engineers are expected to know platform-specific query languages, navigate layers of dashboards, and understand exactly where the right visualization lives before they can even begin troubleshooting. That approach can work in smaller environments, but as infrastructure grows more distributed and complex, it becomes a bottleneck.

When an unexpected alert fires these days, most engineers' first move is to ask their AI assistant for help.You ask why your checkout service is slow and the assistant gets to work, but it can't get any meaningful insights—at least not quickly—without the proper guidance. So, the next thing you know you're sharing deals about your existing data sources, the services you have running, how they connect, which labels and metrics matter, and on and on.