Observability on Kubernetes — OpenTelemetry, Grafana Stack

Welcome to the Observability on Kubernetes workshop! This training covers end-to-end observability using OpenTelemetry, Prometheus, Grafana, Loki, Tempo, Mimir, and alerting on Azure AKS.

Materials

OpenTelemetry & Instrumentation

• Observability vs Monitoring — Why classic monitoring is no longer enough
• Czym jest Observability — Observability, OpenTelemetry overview
• Logi — Log formats, best practices, architecture
• Metryki — Metric types, PromQL, naming conventions
• Trace’y — Distributed tracing, spans, sampling
• Profile (eBPF) — Continuous profiling, flame graphs
• Architektura OpenTelemetry — Collector, instrumentation, Grafana Stack
• Cost Optimization — Data volume, sampling, retention, storage costs

Prometheus (separate sidebar group)

• Overview — Architecture, data flow, pull model
• Service Discovery — Kubernetes SD, relabeling, annotations
• Federation — Hierarchical monitoring, cross-datacenter
• Remote Write — Long-term storage, queue tuning, HA
• Native Histograms — Sparse buckets, migration, PromQL
• Naming Conventions — Metric names, labels, base units
• Recording Rules — Pre-aggregation, hierarchical rules
• Internal Mechanisms — TSDB, WAL, compaction, security
• Metric Cardinality — High cardinality, refactoring, analysis
• Push Gateway — When to use, encoding, API
• Exporters — Writing exporters, naming, deployment

Loki (separate sidebar group)

• Overview — Components, deployment modes, chunk format, labels
• LogQL — Index filtering, content filtering, exercises

Tempo (separate sidebar group)

• Overview — Architecture, components, protocols
• Integrations & TraceQL — Metrics generator, traces to logs/profiles, MCP
• Configuration & Deployment — Helm values, Grafana, retention

Exercises

• Loki — Log querying exercises
• Tempo — Distributed tracing exercises
• Prometheus — Metrics and PromQL exercises