Observability

⬅ Back to Intro | Next → Part 3 – Understanding Sidecar Injection and Traffic Flow 💡 This post is part of my Istio Hands-on Series — a practical journey into Kubernetes Service Mesh. Each post builds on the previous one with hands-on labs, real command outputs, and clear explanations aimed at learning Istio by doing, not just reading. Objective In this post, we’ll set up our local playground for the Istio Hands-on series.You’ll learn how to: ...

The best way to learn is by doing — and sharing what you learn. 👋 Introduction Over the past few years, Kubernetes has become the de-facto standard for running microservices. It simplifies deployment, scaling, and management — but as applications grow, so does the complexity of how those services talk to each other. Things like observability , traffic control , and secure service-to-service communication suddenly become hard problems . That’s exactly where a Service Mesh comes in. ...

Building AI for Observability with AWS Bedrock In my previous post, I wrote about closing the last mile of observability with AI . The core idea was simple: we already have plenty of metrics, logs, and traces, but the real challenge is turning them into insights and answers that engineers can act on. In that post, I highlighted two main gaps: Connector layer – bridging multiple observability tools like Prometheus, Thanos, Elastic, etc. Insight layer – going beyond raw queries to provide real context and recommendations. Now, I’ve been experimenting with AWS Bedrock , and it feels like a natural way to solve both layers. ...

Over the years, observability has grown in ways I couldn’t have imagined when I first started working in this space. Thanks to OpenTelemetry, we now have a standard way to collect traces, metrics, and logs. Tools like Grafana, Prometheus, Jaeger and Elasticsearch make it easy to store and visualize that data. But here’s the truth I keep coming back to: Even with all the dashboards and alerts, something is still missing. ...

Summary Monitoring in Kubernetes is a complex task. The traditional monitoring framework is not sufficient to handle such a massive workload. Zabbix since version 6.0 provides a native way of integration for monitoring Kubernetes cluster. Zabbix-Kubernetes integration provides various templates to monitor kubernetes components like kube-controller-manager, kube-apiserver, kube-scheduler, kubelet, etc. It also supports automatic discovery of kubernetes nodes, pods and also collects metrics agentlessly. Why I don’t like the Zabbix’s direct way of monitoring Kubernetes cluster? Although Zabbix-Kubernetes integration looks promising in the beginning , it is not easy to use. ...