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Data lineage analysis enables organizations to trace and analyze the flow and transformation of data across various systems. This helps improve data quality, ensure compliance, and build trust in data analytics.
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Executable approach: can be applied and produces an outcome.
What organizes, connects, or makes decisions possible.
Data lineage analysis reconstructs where data comes from, how it changes, and where it is used.
The approach grew from database administration, ETL documentation, and compliance demands for traceability. Distributed pipelines turned it into systematic analysis of sources, transformations, and consumers.
Follow a data value like a supply chain: backward to its source and forward to reports or services. Record each processing step as a node with a traceable connection.
The system or dataset from which a data value originates.
Processing that filters, computes, or combines values.
A link between a data object and its predecessor or successor.
Lineage speeds up fault analysis, impact analysis, and audits. Automated capture can be incomplete, especially for manual steps, dynamic SQL, or copied files.
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