Operations | Monitoring | ITSM | DevOps | Cloud

When your Kubernetes app suddenly has traffic spikes in a distant country, it can be difficult to determine why. Let’s say, for example, we have an e-commerce app that started to receive an unusual surge of visitors from Australia — something we never anticipated. We search for answers in our logs, but without geographic context, we don’t have the full insights we need.

You push a release that touches the checkout flow. Now you’re glued to dashboards and checking Slack, hoping you didn’t introduce a regression that breaks the payment path. You can’t tell if you’ve just shipped a blocker that’s stalling every cart—or some edge case quietly making users bail.

You’ve probably run a lot of Docker containers, but do you know what happens behind the scenes? The Docker container lifecycle is the path a container follows from being created to running, stopping, and finally getting removed. Understanding these steps helps you figure out why a container might not start or when to restart it instead of creating a new one.

If you’ve worked with Kubernetes, you know logs are essential for understanding what’s happening inside your clusters. However, unlike traditional servers, Kubernetes logs present their unique challenges. Pods frequently start and stop, containers restart regularly, and logs stored locally can be lost quickly. Because of this, managing logs in Kubernetes requires a different approach.

Industrial systems constantly generate time series data—streams of time-stamped values like temperature, flow rate, vibration, or power load. This data powers real-time monitoring, performance tracking, and long-term forecasting across critical infrastructure, energy systems, and manufacturing environments.

Your systems are getting faster. More complex. More distributed. But your tools are still waiting for something to go wrong before they do anything about it. That’s the real limitation of most AIOps platforms. They highlight issues. They suggest next steps. But they stop short of action—leaving your team to connect the dots, chase down context, and manually fix what broke. Agentic AIOps doesn’t wait. It acts.

This is a guest blogpost by Markus Opolka from the Icinga Enterprise Partner NETWAYS. After forking the Grafana Module for Icinga Web last year, we started thinking about alternative ways to display Icinga performance data graphically in the web interface. Running a separate Grafana instance just to render graphs is a lot of overhead and adds operational complexity — no matter how much you like Grafana. Plus, installing the grafana-image-renderer isn’t always straightforward.