Operations | Monitoring | ITSM | DevOps | Cloud

Clusters get retired. A test cluster is torn down, a workload moves to a new environment, a provider contract ends. Icinga for Kubernetes does not know about any of that. Since v0.3.0, several clusters can write into one shared database, and Icinga for Kubernetes Web shows them side by side. But stopping or uninstalling the daemon for a cluster leaves all of its data in place.

NOC monitoring decides how fast a network problem turns into a fix. If your NOC watches the wrong layers, it misses the fault. If it alerts on everything, the real fault gets buried. According to Uptime Institute's Annual Outage Analysis 2026, 57% of operators said their most recent major outage cost more than $100,000. Most teams already collect plenty of data through network monitoring. The hard part is turning that data into a short queue of alerts that the right person acts on.

When a CVSS 10.0 vulnerability is published, the expectation is that affected software gets patched within days. When it involves an open-source broker that processes business-critical transactions in enterprise environments, the expectation is that the security teams responsible for those brokers treat it as an emergency. “The attacks exploit a maximum-severity security flaw in Apache.

This Apache ActiveMQ CVE guide is the maintained reference for every significant Apache ActiveMQ vulnerability: what it does, how it is scored, which versions it affects, which release fixes it, and whether it is being exploited.

Organizations can use Oracle AI Database@AWS, Oracle AI Database@Azure, and Oracle AI Database@Google Cloud to run Oracle databases co-located with the cloud applications they support rather than managing the databases on premises. However, running applications in a cloud provider’s environment while hosting databases on Oracle Cloud Infrastructure (OCI) can lead to visibility gaps. When application performance degrades, identifying the source of the problem can be difficult.

Running one A/B test well is manageable. Scaling A/B testing to hundreds of experiments across dozens of teams is harder, because teams may make different decisions about metrics, randomization, rollout, and analysis. Experiment protocols in Datadog Experiments turn an approved methodology into a reusable configuration, giving experiments of the same type a consistent starting point. In this post, you’ll learn how to.