What is Data Observability? Definition, Pillars & Tools

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A professional-level summary covering key definitions, frameworks, and exam-relevant points.

The Five Pillars of Data Observability

Pillar	What It Monitors	Example Issue Detected
Freshness	Data update recency	Sales table not updated for 6 hours (pipeline failure)
Distribution	Value distributions and anomalies	Average order value drops from $150 to $0.01 (data error)
Volume	Record counts and completeness	Daily transaction table has 100 records instead of 10,000 (partial load)
Schema	Structural changes	Column "customer_id" renamed to "cust_id" without warning (breaking change)
Lineage	Data flow and dependencies	Upstream source change impacts 15 downstream reports (impact analysis)

CDMP Exam Relevance

Data observability is an emerging topic in the CDMP exam, primarily relevant to the Data Quality knowledge area (11%) and Data Integration & Interoperability (6%). Key exam topics include: the definition of data observability and its five pillars, the difference between data observability and data quality monitoring, and the role of data lineage in observability. As data observability becomes more mainstream, its presence in the CDMP exam is likely to increase.

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