Operations | Monitoring | ITSM | DevOps | Cloud

Modern systems are complex, distributed, and fast-changing, so keeping them reliable requires more than watching dashboards. Observability vs. Monitoring explains how teams gain the deep insight needed to detect, diagnose, and resolve issues. Monitoring collects predefined metrics and alerts you to known problems, while observability provides rich, contextual telemetry to investigate unknown failures.

In early October, Red Hat disclosed a breach of a GitLab system used by its Consulting division. Threat actors claim to have exfiltrated hundreds of gigabytes of project data — and while investigations are still underway, reports suggest consulting engagement artifacts may have been impacted. For the organizations involved, the concern isn’t limited to reputational damage.

I wanted to share a story of a recent engagement with a law firm to highlight the strategic importance of compliance in today’s legal sector. It started with a single email. A mid-sized law firm received a regulator’s request for evidence following a client complaint. The issue wasn’t malpractice; it was a missed filing deadline caused by a system slowdown. The firm had no audit trail to prove the delay was technical, not procedural.

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.

We are thrilled to announce a major update to our Python Connectors line. The release adds support for Python 3.14, PostgreSQL 18, MySQL 9, and MariaDB 12, introduces modern authentication and security options, and delivers notable performance gains across several connectors.

If there’s one thing every IT leader can agree on, it’s this: Manual Active Directory (AD) management never ends. There’s always one more access request, one more approval chain, and one more audit reminder flashing on your screen. By the time you’ve closed your last ticket of the day, there’s already another one waiting. For many teams, 2025 became the year of “we’ll automate next quarter.” But next quarter came and went without any automation.

Application configs change over time, often in small ways that are easy to miss. They may start simple — a few environment variables, one exporter, nothing unexpected. As your instrumentation grows, you add rules for filtering health check spans, adjust sampling based on attributes, or introduce environment-specific resource settings. Each change makes sense on its own. But months later, the picture can look different across dev, staging, and production.

Building and shipping an application is exciting, you watch your idea come alive and reach users. But once it’s out there, your real job begins: keeping it alive. An app in production isn’t just code running, it’s a living system. It needs monitoring to stay healthy and alerting to warn when something’s off. But there’s a catch: too few alerts, and you’ll miss real issues; too many, and you’ll drown in noise.