Operations | Monitoring | ITSM | DevOps | Cloud

In our previous article we looked at the basics of: In this article, we are going to take a deeper dive into some of the complexities of configuration as well as some of the nuances of analysing Claude telemetry. Before we dive into the code, let us just remind ourselves that our telemetry pipeline looks like this: That is, we are emitting Claude Code telemetry to an OpenTelemetry Collector. The telemetry is then exported to an Application Insights endpoint and stored in Log Analytics tables.

Your dashboards may be telling a different story than what the customers are experiencing There's a version of a website problem that nobody talks about enough—the one where everything is technically fine. The site is up. The server is responding. No alerts have fired. And yet, somewhere out there, a user is watching a spinner rotate for the fifth second in a row, quietly losing faith in your product. This is what makes response time latency the most deceptive problem in web operations.

Every engineer has a story about the outage that came out of nowhere. One moment everything is green. The next, your monitoring dashboard lights up red, your inbox fills faster than you can read it, and somewhere a customer is staring at a blank screen wondering if your business still exists.

Alibaba Cloud monitoring isn't AWS or Azure monitoring with a different logo. The way its services scale, absorb load, and send early warning signals follows its own logic and if you're watching the wrong things, you'll find out too late. Cloud monitoring conversations often follow patterns set by AWS and Azure. The metrics are familiar, dashboards look the same, and operational playbooks are built around expected infrastructure behavior.

Datadog Kubernetes monitoring tells an SRE team what failed, which pod failed, and when. It does so within seconds of the alert firing. The investigation then stalls at the same point every time: nothing in the dashboard layer can prove why a specific request behaved the way it did inside a running JVM at the moment of failure. Variable values, feature flag evaluations, and code branches are never captured.

You’re no longer writing code. You’re managing a pipeline of agents writing it for you. If you’ve been running two, three, or four AI coding agents in parallel, you already know the problem. The agents are fast. The orchestration is chaos. You’re bouncing between terminal windows, manually rebasing branches, cleaning up messy commits, and trying to remember which agent is touching which repo.

This article examines DORA's requirements for database change management and explains how Redgate Flyway Enterprise addresses them. The EU's Digital Operational Resilience Act (DORA) came into full effect in January 2025. It is designed to strengthen the ability of financial institutions to withstand operational disruption, whether caused by technology failures, data corruption, human error, or a cyberattack.

Kellyn Gorman is a Database and AI Advocate and Engineer at Redgate She's the previous director of Data and AI at Silk, and the Oracle SME in Azure at Microsoft. With a robust background in cloud technology and a passion for promoting its merits and potential, I am thrilled to spearhead conversations and actions that help shape the future of this industry. Kellyn has authored numerous technical books, white papers and solution repositories in GitHub on database, AI and engineering topics.

Observability has always been important, and much like any core capability in your business, the value needs to be understood. For years, the value of observability was predictable. It was uptime, error rates, MTTR, and likely tool consolidation. That was enough to be able to show progress. These are foundational, tablestakes metrics—and they still matter, but they aren’t enough.

Cost monitoring, exploration, investigation, and reporting used to live in separate places, so following a single cost question meant hopping between tools.