You cannot fix what you cannot see. Production Kubernetes clusters generate thousands of metrics, logs, and events every second — and without the right observability stack, incidents become guesswork. This pillar covers how engineering teams at Hotstar, Swiggy, and Zerodha monitor, debug, and automatically scale their Kubernetes workloads.
What This Pillar Covers
- Setting up Prometheus for metrics collection and alert rules
- Building Grafana dashboards for pod CPU, memory, and request rate visibility
- Configuring Alertmanager routing to Slack and PagerDuty
- Horizontal Pod Autoscaler (HPA) for automatic pod scaling based on CPU and custom metrics
- Cluster Autoscaler for automatic node provisioning on AWS EKS
- Troubleshooting OOMKilled, CrashLoopBackOff, and ImagePullBackOff errors in production
Who This Is For
Site reliability engineers, DevOps engineers, and platform engineers responsible for maintaining uptime, diagnosing production incidents, and ensuring Kubernetes workloads scale reliably under traffic spikes.