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New Feature Friday: Ask Your Engineering Metrics Anything | Cortex Engineering Intelligence for MCP
What if you could analyze your engineering metrics — just by asking a question? Now you can. Introducing Engineering Intelligence for MCP, a new Cortex feature that lets you query your engineering data through AI systems like Claude or Cursor. No dashboards. No BI setup. Just ask: “What’s driving our cycle time?” “Show me Q3 deployment trends.” “Where are our bottlenecks?” Cortex connects your real engineering data to your favorite AI client, so you can get instant, conversational insights on delivery, quality, and team performance — all without leaving your IDE.
DevOps & Observability for Digital Catalogs: faster releases, fewer outages
Digital catalogs have become a core sales engine, not just a glossy PDF on a server. They power discovery, merchandising, and conversion across web and mobile experiences. When a catalog powers real revenue, the way you build and run it starts to look a lot like modern software delivery. That's where DevOps and observability enter the picture: practices that shorten release cycles, reduce risk, and keep customer experiences fast and available even on your biggest traffic days.
The Burn Down: October 2025
All the latest updates from FireHydrant, including more powerful ways to use AI and Incident Management upgrades.
Enhanced Flexibility and Security Monitoring - New in DataStream
This update delivers significant advances in operational flexibility and security monitoring capabilities. It addresses the evolving needs of security teams across diverse deployment environments, from air-gapped networks to those prioritizing automation and simplicity, while expanding integration options and improving visibility into data flows.
Fix flaky tests in your sleep with Chunk by CircleCI
A test fails. You rerun it and it passes. You shrug and move on. This is how most teams deal with flaky tests. The “rerun until green” approach works in the moment, and rerunning from failed tests is a useful way to confirm whether a failure is real. But reruns don’t fix the underlying issue. Over time, they burn CI resources and can hide real instability in your code. On the other hand, fixing flaky tests can mean hours of work.
What Is Business Continuity?
A single outage can stop operations, affect customers, and impact trust. In a world of pandemics, cyberattacks, weather events, and supply chain delays, your team cannot pray that something does not break. Business continuity drives your team to stay ready, recover earlier, and keep downtime lower. In this blog, we’ll explain what business continuity means, how to create a solid business continuity plan, and which approaches help teams keep operational during a disruption event.
Simple Talk Podcast - Coffee Chat with Lee Brownhill
Steve sits down with Lee Brownhill, who by day helps clients optimize their SQL workloads in Azure and AWS at Cloud Rede, but is also a Redgate Ambassador, blogger and aspiring speaker. Lee talks about his interest in giving back to the SQL Server community through writing and speaking, having taken inspiration from others online and in-person at events, and naturally the conversation also touches upon AI, the cloud, and more.
What Is Incident Response Lifecycle?
The Incident Response Lifecycle is a step-by-step process that helps engineering teams detect, respond to, and recover from unexpected system disruptions or outages. It includes a series of six practical stages: Detection, Analysis, Impact Mitigation, Incident Resolution, Service Restoration, and Post-Incident Analysis. By following this lifecycle, teams can minimize downtime, reduce business impact, and continuously strengthen system reliability.
Why your Kubernetes clusters and GPUs should live under one roof
The world remains abuzz with AI hype, but the reality is that most modern applications aren’t purely AI workloads. The average company will have web services, APIs, databases, and background jobs running alongside its machine learning inference or training components. An architecture question everyone faces: should your Kubernetes cluster and GPU compute live in the same data center, or can you split them across providers?