Operations | Monitoring | ITSM | DevOps | Cloud

Modern engineering teams are drowning in telemetry data. A mid-sized Kubernetes cluster running 50 microservices can generate millions of log lines per minute. Add distributed traces, Prometheus metrics, cloud provider events, and application-level instrumentation and you're looking at terabytes of observability data every day. The problem isn't just volume. It's what you do with it.

The NoSQL Storm explores core Database DevOps concepts and the real-world challenges teams face while managing modern NoSQL environments. It highlights the complexities of schema evolution, scaling distributed systems, and maintaining operational reliability as applications grow.

Redgate Monitor ships new features every month and the past few months have brought some exciting new additions to empower your workflows. Spanning AI-powered tooling, cloud deployment, cross-database platform support and enterprise security, these updates reflect some of the biggest areas shaping how database teams work today. Whether you're managing compliance requirements, trying to get on top of alert management or looking to get a better grip on cloud costs, there's something here for you.

Runtime guardrails and control towers govern AI activity — but without a financial control plane connecting spend to outcomes, enterprises can't tell which AI bets are worth it. Most enterprises can answer exactly one question about their AI rollout: what did we spend?

Canonical, the publisher of Ubuntu, today announced the general availability (GA) of Managed Kubeflow on the Microsoft Azure Marketplace. This solution enables AI teams to get a fully managed, production-ready MLOps platform in their own tenant. Upstream Kubeflow is a powerful tool for machine learning, but it remains notoriously challenging to deploy and maintain.

The infrastructure industry spent two decades chasing a single pane of glass. The future looks different: domain-expert AI platforms that reason deeply within their own data, connected through tool chaining when problems cross boundaries.

I’ve yet to meet a developer that enjoys working with metered AI APIs. The need to pay for every API call in development works in direct opposition to the ethos of rapid iteration, and it’s easy for the costs to get out of hand. That’s why Canonical has created a different approach to building AI-powered applications; one where the model lives inside your app, not behind a pay-per-token HTTP call.

How to say good-bye to your assets in a secure and safe way.

Networks generate the most data of any system in your stack and have the least patience for stale dashboards. Interface counters tick every second. BGP sessions flap. Flow records arrive in bursts. When something goes wrong, you don’t have 10 seconds to wait for an aggregation to finish.

Last year, I wrote about how AI-driven search trends reshaped my digital marketing strategy in ways I hadn’t seen in two decades. At the time, the story was mostly observational: traffic patterns were changing, conversions were holding, and AI-generated search answers were clearly influencing buyer behavior. Fast-forward to the first quarter of 2026, and one thing is clear — this shift didn’t slow down; it accelerated.