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 — Cost Drivers

Covers the ingest-volume, cardinality, and retention-window levers that actually drive observability platform cost.

3 — Downsampling

Covers reducing metric resolution over time as data ages, and the query-accuracy tradeoff that comes with it.

4 — Retention Policies

Covers setting differentiated retention per signal type and per tier, driven by actual debugging-lookback needs rather than defaults.

5 — Compression

Covers the compression techniques (chunk encoding, columnar compression) that let TSDBs and log stores shrink storage cost per sample.