Data Observability

The practice of understanding the health of data systems through freshness, volume, schema, lineage, quality, and distribution signals.

Why it matters

Data Observability helps data teams design systems that are reliable, understandable, governable, and efficient at scale.

Design considerations

• Define ownership, contracts, and expected consumers.
• Make failure, replay, compatibility, and observability behavior explicit.
• Measure quality, freshness, cost, and operational burden rather than optimizing only throughput.