You now understand data observability:
- pillars: freshness, volume, schema, distribution, lineage
- Lineage traces data flow for impact analysis and debugging
- Anomaly detection catches unexpected changes using statistical methods
- Tools range from DIY to enterprise (Elementary, Soda, Monte Carlo)
- Incident response follows detect, triage, communicate, investigate, fix, postmortem
Observability separates reactive fire-fighting from proactive data operations. Build it before you need it.