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Data & Analytics · Intermediate

Data Observability

Continuous monitoring of data health across pipelines (freshness, volume, schema, distribution and lineage) to detect and resolve issues quickly.

Definition

Data observability applies the ideas of software observability to data. Platforms such as Monte Carlo and Databricks Lakehouse Monitoring raise anomaly alerts and support root cause analysis using lineage.

Exam tip

The five pillars are freshness, volume, schema, distribution and lineage.

Related terms