How do you implement Kubernetes cluster monitoring and observability?

Kubernetes cluster monitoring requires comprehensive observability across infrastructure, applications, and business metrics using multiple tools and strategies.\n\nMonitoring Stack Components:\n• Prometheus: Metrics collection and alerting\n• Grafana: Visualization and dashboards\n• Jaeger/Zipkin: Distributed tracing\n• ELK/EFK Stack: Log aggregation and analysis\n• Node Exporter: Node-level metrics\n\nKey Metrics to Monitor:\n• Cluster health: Node status, API server latency\n• Resource utilization: CPU, memory, storage, network\n• Application metrics: Request rate, error rate, duration\n• Business metrics: User activity, revenue impact\n\nImplementation Example:\n# Deploy Prometheus Operator\nkubectl apply -f prometheus-operator.yaml\n\n# Create ServiceMonitor for app\napiVersion: monitoring.coreos.com/v1\nkind: ServiceMonitor\nmetadata:\n name: app-monitor\nspec:\n selector:\n matchLabels:\n app: myapp\n\nBest Practices:\n• Implement SLIs and SLOs\n• Set up intelligent alerting\n• Use distributed tracing\n• Monitor golden signals\n• Regular capacity planning