Operations | Monitoring | ITSM | DevOps | Cloud

The Jira plugin is one of our most-used integrations and for good reason. Teams rely on it daily to stay on top of work, manage issues, and ship on time. As more people leaned on it, we saw a chance to make the experience even smoother. So, we gave it an upgrade. We’ve refreshed the out-of-the-box dashboards, simplified the data streams, and improved the overall experience. So, let’s take a closer look at what’s changed.

The cloud gave us agility, but it also introduced fragmentation. And in most companies, no one’s fully owning the sprawl. One team deploys a new service in a hurry. Another forgets to shut down a dev environment. Meanwhile, batch jobs run 24/7 on oversized instances. And no one quite knows why your bill is $10K higher this month. The result? A growing source of cost overruns, performance headaches, and operational inefficiencies. This is exactly why cloud workload management is so crucial.

Ever spent a week wrangling log pipelines just to get your SIEM to stop screaming about missing fields? Wasted way too much time stripping out noisy events and reformatting data for analytics? You’re not the only one. If you work in Security or ITOps, you know the pain: every new data source means another round of schema headaches, more manual mapping, endless field transformations, and a quick prayer that you didn’t break something critical (or let in a flood of junk events).

When your Docker container isn't behaving the way you expect, you need to get inside and see what's going on. Maybe your app is throwing errors, a service won't start, or you just need to check some configuration files. Getting into a running Docker container is simpler than you might think, but there are several ways to do it depending on your situation. This guide shows you exactly how to log into Docker containers, troubleshoot common issues, and debug your applications effectively.

Datadog may be the default choice for all-in-one observability, but its logging experience takes a back seat to the broader platform. Logs are primarily designed to feed into metrics and traces, which leads to tradeoffs such as slower search, complex workflows, and a UI that isn’t optimized for log investigations. As a result, Datadog doesn’t align with how developers actually troubleshoot.

Last week saw several huge milestones for BigPanda. We launched the BigPanda agentic IT operations platform, a sweeping evolution of our product offerings. As part of this launch, we also introduced two new AI solutions, BigPanda AI Detection and Response and BigPanda AI Incident Assistant. These powerful new capabilities bring agentic AI into IT operations, transforming how enterprises automate the manual and time-intensive workflows of ITOps, L1 response, and incident management.