Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes troubleshooting traditionally requires deep platform expertise. Understanding pod lifecycle, decoding error messages, correlating events across resources, and identifying root cause all demand experience that takes years to build. This expertise gap creates a bottleneck where only senior engineers can handle production issues, limiting how quickly teams can resolve incidents.

We recently released the 2026 State of DevSecOps study, in which we analyzed tens of thousands of applications and their respective supply chain and build system dependencies. Our research revealed trends in security posture and best practices across the software development life cycle.

Modern database design is no longer a one-time activity but an ongoing process that evolves as business needs, scale, and system behavior change. Instead of large redesigns, teams rely on incremental and backward-compatible schema changes, such as adding columns, indexes, or new tables, to safely adapt the database without disrupting production.

Egress fees can quietly drive cloud costs. Learn practical ways to reduce your cloud egress fees in 2026 without redesigning everything. Cloud egress fees can sneak up on you. One month your cloud bill can look reasonable, and the next it’s clear that data movement is causing your cloud spend to fluctuate. For many network teams, egress is still treated as a fixed cost or something you only revisit during a major architecture change, but that approach doesn’t hold up in 2026.

As anyone managing one or more Kubernetes clusters knows by now, scaling can introduce an exponentially growing number of problems. The sheer volume of metrics, logs and other data can become an obstacle, rather than an asset, to effective troubleshooting and overall cluster management. Fragmented tools and manual troubleshooting processes introduce operational complexity leading to the inevitable security gaps and extended downtime.

Continuous delivery is only as good as your ability to understand what’s happening inside your pipelines. When a build is slow, flaky, or burning through capacity, you need more than a green/red status and a wall of logs — you need traces. Bitbucket Pipelines now exposes pipeline execution as OpenTelemetry (OTel) traces via webhook events. This lets you stream detailed pipeline spans into your own observability stack and correlate them with the rest of your system. This post walks through.

Seven developer-requested features. Tighter control over branches, history, and large repos. No CLI detours required. If you have been using GitKraken Desktop in a complex repo, you already know what it feels like when the commit graph turns into a wall of branches. When rebasing requires more ceremony than it should. When you just need one file back from three commits ago but have to orchestrate a whole checkout to get it. GitKraken Desktop 11.10 is built for those moments.

New Australian research reveals poor website search is costing businesses revenue as AI raises the bar.

Somewhere between a model’s first demo and its first production workload, the cost conversation changes completely. Training is a big number, but it’s a finite one. Inference isn’t. Every user interaction, every query, every API call triggers compute behind the scenes — and unlike training, inference never stops billing. That shift from one-time expense to ongoing operational cost is where inference economics begins.

Years in the FinOps trenches taught me one thing: The data has never been the problem. The data exists. It’s out there, scattered across provider invoices, buried in tagging gaps, locked behind dashboards that maybe three people in your org actually know how to navigate. The real problem? Nobody can get to it when they need it. Engineers ship features without understanding what they cost the business, let alone whether they improved margin.