Operations | Monitoring | ITSM | DevOps | Cloud

Learn how AWS Cloud WAN works, key benefits, limitations, use cases, and adoption best practices. As AWS environments grow across multiple regions and accounts, networking can become increasingly difficult to manage. What starts as a handful of virtual private clouds (VPCs) can quickly evolve into a complex web of connectivity, routing policies, and security requirements.

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

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.

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.

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.

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.

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.