Operations | Monitoring | ITSM | DevOps | Cloud

Launch Week Day 3: Introducing Discover Infrastructure Your dashboard looks perfect. APIs responding in 80ms, background jobs processing smoothly, error rates at 0.02%. Everything's green. Then production breaks. "Why is checkout so slow?" "The payment service keeps timing out!" You run kubectl get pods and discover payment-service pods restarting every 3 minutes due to OOM kills. Then you check your database host—CPU at 98% because someone forgot the new ML training job runs there too.

Azure Kubernetes Service (AKS) continuously generates a high volume of telemetry, ranging from node-level CPU and memory usage to request latencies and error rates within individual pods and services. Without a structured monitoring strategy, this flood of metrics can easily become noise, leaving teams blind to early warning signs. Effective monitoring in AKS is about identifying the right signals, correlating them across layers, and acting before they impact application performance or cluster stability.

This is a guest post by Simon Grimm, founder of Galaxies.dev, a platform dedicated to helping developers master React Native through hands-on courses, expert guidance, and personal support. React Native performance matters more in 2025 than ever before. With the New Architecture now stable and apps competing against lightning-fast native experiences, users expect sub-second load times and buttery-smooth 60fps interactions.

Obviously nobody is disagreeing with this. It’s just that during ongoing development and while focusing on features and bug-fixes, testing often falls behind in priority, especially when developers would need to write tests for existing or legacy code, teams can be hesitant to invest the time. C++ applications have to run a diverse set up target environments, varying in OS, compilers, C/C++ standard libraries and dependency versions.

Let's be provocative for a moment. You probably don't know what is actually on your network. You have the CMDB, spreadsheets, diagrams from the last big refresh, and the institutional knowledge of your veteran engineers. But is this information accurate? Is it complete? Answering that question with absolute certainty can be difficult for many who manage complex IT environments.

Predictive Analytics Models and Algorithms are an important component of eG Enterprise’s AIOps engine for proactive observability. eG Enterprise collects and analyses metrics, events, logs and traces and the data including real usage data is used to make intelligent predictions to forecast future system behavior and IT resource metric levels.

I was thrilled to host our latest tech talk, where we got to do a deep dive into vmanomaly with the best possible guests: Fred Navruzov, the actual team lead for the product, and Co-Host, Matthias Palmersheim. We covered a ton of ground, from high-level concepts to the nitty-gritty of configuration. For everyone who couldn’t make it, I wanted to share my personal recap of the most important technical takeaways from our conversation.

Service Level Objectives (SLOs) are a cornerstone of modern software engineering. Defining alerts around SLOs has become standard practice, but many of the common patterns in use today miss the early signals that can tell a customer before an SLO breach has occurred.

In today’s technological landscape, mobile applications are on the rise, boosting efficiency, portability and accessibility in daily life, across a spectrum of industries, from financial services to food delivery. As mobile apps become more essential, the quality of their features, performance, and user experience is critical.l.