Data Observability

Purpose

Design comprehensive data observability across pipelines: freshness, volume, schema, quality, lineage, and incident management.

Agent Protocol

Trigger

Exact user phrases: "data observability", "data quality monitoring", "data profiling", "data health", "freshness check", "row count anomaly", "schema drift", "data incident", "data lineage", "data monitoring", "observability platform".

Input Context

• Data stack (warehouse, lake, pipelines, BI tools)
• Number of tables/datasets to monitor
• Existing quality checks and monitoring
• Team size and on-call rotation
• SLAs for data freshness and quality
• Incident management workflow
• Monitoring budget and tooling preferences