Operations | Monitoring | ITSM | DevOps | Cloud

The cloud is often spoken of as a separate realm where data exists safely away from the messy realities of the physical world. But as the events of March 2026 have reminded us, the cloud has a physical home, and that home is susceptible to the same disruptions as any other infrastructure. Here’s how diversified monitoring across independent data centers can keep visibility intact when cloud services go down.

‍Flaky tests are automated tests that pass or fail inconsistently without changes to the code. In this guide, you’ll learn why flaky tests happen, how to detect them automatically in CI pipelines, and how modern platforms prevent them from slowing teams down. Your test went well three times yesterday. It didn't work this morning. You ran it again without changing anything, and now it works. Congratulations, you've just passed a flaky test, and now someone's day is going to be ruined.

Modern software teams are under constant pressure to ship faster without breaking production. That’s why CI/CD best practices have become essential for high-performing DevOps organizations. Continuous integration and continuous delivery (CI/CD) help automate builds, testing, and deployments — but simply installing a pipeline tool isn’t enough. Without the right practices, pipelines become slow, flaky, and difficult to govern.

Redgate’s annual “State of the Database Landscape” survey has been published. Like every other year, it paints a really interesting picture. Personally, I love looking through this in order to better understand where people are experiencing pain in the management of their data. If you know where people are experiencing pain, as a technical person, you know where to focus your own skill development.

While it has taken longer than some people expected, Matter is finally going mainstream. Brands including Ikea, Kwikset, and Bosch have shipped matter devices, and matter hubs can increasingly be found in people’s homes. Many dev kits out there are matter compatible, and if you want to build a simple application you can find good example code and get started quickly. This is fine if your use case fits neatly within existing Matter clusters, but direct internet communication is not straightforward.

Enterprises rarely notice the moment complexity begins to reshape their environment. Growth initiatives move forward. New cloud services are adopted. Modernization programs introduce new architectures. Business units implement tools that solve immediate problems. Acquisitions add their own ecosystems. Each change is logical in isolation. The cumulative effect becomes something else entirely.

So, you've been tasked with automating one or more of your tedious, time-consuming IT processes… but what exactly does that mean? And perhaps more importantly, where on earth do you start? IT automation and orchestration can cover a broad spectrum of potential use-cases, ranging from the Service Desk to the NOC, to Infrastructure, and well beyond.

An incident response platform helps organizations manage, track, and resolve IT incidents quickly and efficiently. With the right platform, teams can minimize downtime, reduce the impact of incidents, and lower their Mean Time to Resolution (MTTR). ‍ In this article, we’ll explore the top 5 incident response platforms for 2026, helping you choose the best solution for your needs. ‍

Root cause analysis tools are designed to help engineering teams understand why failures happen in production and other remote environments. As modern systems become more distributed and input-dependent, many incidents cannot be reproduced outside live environments. The stakes are significant: high-impact IT outages cost organizations a median of $2 million per hour, with annual downtime costs reaching $76 million per organization.

A runtime aware AI SRE extends existing AI SRE approaches by moving beyond telemetry correlation into runtime-validated reliability. While the majority of AI SRE tools accelerate incident triage using logs, metrics, and traces, they cannot confirm execution behavior if critical runtime signals were never captured. By generating on-demand evidence inside running services, AI SRES can eliminate slow redeploy cycles, ensuring your distributed systems remain resilient under real-world traffic conditions.