Observability Engineering

A book-shaped table of contents for observability engineering: foundations through architecture, metrics, logging, tracing, profiling, OpenTelemetry, instrumentation, Kubernetes/cloud, data platforms, visualization, alerting, SRE integration, cost, security, platform engineering, AI-driven operations, and MAANG interview preparation — cross-linking existing prometheus/grafana-cloud/kubernetes/sre/platform-engineering notes instead of duplicating them.

1 — AIOps / Agentic RCA

What's actually new versus a static runbook — an investigation loop, not a fixed trigger-action mapping — why it depends on everything earlier in this book already being solid, and the read-vs-write safety line most real deployments draw.

5 — Continuous Profiling

What makes always-on, sampling-based profiling cheap enough to run in production continuously, why it earns that cost mainly for hot or expensive services, and how a profile correlates back to the one trace that was running during the sample.

4 — Observability-Driven Development

The TDD analogy taken seriously: SLOs and instrumentation defined at design time as acceptance criteria, not retrofitted after an incident — and why this only sticks as a launch gate, not a guideline.

3 — Anomaly Detection

Covers statistical and ML-based anomaly detection on time series, and its false-positive tradeoff against static thresholds.