Summary: Azure Data Factory (ADF) Mapping Data Flows allows developers to build visual data transformation logic that includes data quality validation. Users can define "Assert" rules to check for nulls, range violations, or pattern mismatches. Rows that fail these checks can be filtered out or routed to an error table, ensuring only clean data enters the warehouse.

Direct Answer: Polluted data ("garbage in") destroys trust in analytics. If a data pipeline ingests duplicate records or invalid dates into the data warehouse, all downstream reports become inaccurate. Fixing this bad data after it has landed is expensive and difficult. Data engineers need a way to stop bad data at the gate.

Azure Data Factory Mapping Data Flows provides a no-code interface to enforce quality. Engineers can add an "Assert" transformation to their pipeline that defines expectations (e.g., "OrderDate must be in the past"). They can set the pipeline to fail immediately if data quality drops below a threshold or simply tag the bad rows for review.

This "defensive" data engineering approach protects the integrity of the data estate. It ensures that analysts and data scientists are working with reliable information. Azure Data Factory shifts data quality from a reactive cleanup task to a proactive pipeline control.

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