Operations | Monitoring | ITSM | DevOps | Cloud

What does a typical day look like for the people working behind the scenes of a data centre? From hardware installations and cabling to troubleshooting, customer requests and supporting critical works, every shift brings something different. In this 'day in the life', Leon Strong, Data Centre Services Engineer for Maidenhead, shares what it’s like to work in a hands-on technical role, what they enjoy most and their advice for anyone considering a career in data centre engineering.

Low disk space remediation is the process of diagnosing storage consumption, safely recovering capacity, and confirming that the affected system and its dependent services are healthy. Automated remediation begins when a monitoring or AIOps platform detects a threshold breach and triggers a governed workflow.

Kafka is a crucial and widely used technology: 80% of the Fortune 100 rely on the event streaming platform as part of their stack, according to Apache. But Kafka issues can be complex to manage and even more difficult to troubleshoot, as the same symptom can point to very different problems. Suppose consumer lag on your checkout-events topic suddenly exceeds its SLA.

When Datadog acquires a company, we usually rebuild the product rather than plugging it in as is. That’s exactly what we did with Eppo, an experimentation and feature-management platform. Eppo’s feature-management capabilities became Datadog Feature Flags, while experimentation became Datadog Experiments. This post focuses on the experimentation platform and four changes we made to help you get to a decision faster.

AI agents can help investigate failed builds, fix flaky tests and automate other development tasks. But setting up those agents has required more Pipelines configuration than it should. Agent-powered steps often need different compute, permissions and runtime settings from ordinary build and test steps. Until now, teams have either repeated those settings across every agent-powered step or tried to make one set of global defaults work for everything.

The best API monitoring tools are Hyperping for HTTP and API checks with on-call and status pages, Checkly for API monitoring as code, Postman Monitors for teams that already keep collections in Postman, Datadog for connecting failed checks to traces and logs, Grafana Cloud for teams using k6, Better Stack for checks inside a broader incident workflow, and UptimeRobot for inexpensive availability checks.

Somewhere in every compliance program is a person who spends the week before an audit pulling logs out of several different systems, reconstructing who had access to what, and hoping the screenshots match what the auditor actually asks for. None of this work makes the system more secure. It just makes the existing security visible to someone who's checking. That gap, between the controls that are actually in place and the evidence that proves it, is where most audit prep time goes.

We’re back with another major SIGNL4 update! This release brings new features and improvements across Call Routing, User Profiles and Management, Stakeholder Communication, and AI.

How much of your database delivery can you actually see? For most teams, the picture is scattered. Deployment status lives in CI logs and scripts. Drift appears outside the process. And with AI multiplying the volume and speed of changes moving through pipelines, the risk that comes with low visibility rises with every release.