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Provenance

As explained in 100 SQL Server Mistakes and How to Avoid Them

Data lineage is the trace of a data item’s origin and transformations through systems and storage locations.

Data lineage records the path a data item takes before reaching its current table. Keep it in a data dictionary alongside attribute meaning, business rules, example data, data types, and upstream dependencies. Without understanding those business meanings, a schema can embed false assumptions. section 4.1.3

Tracing a marketing identifier

For an internet-marketing database, trace a cookie identifier from Tracking ID in an Impressions CSV supplied by a cookie provider, to TrackingID in a staging Cookies table, to CookieID in a marketing Cookies table. This makes the origin and each storage step explicit. section 4.1.3

Use lineage as part of schema design:

  • Work with a business analyst or application owner to establish each attribute’s business meaning and rules before recording its path. section 4.1.3
  • Record upstream dependencies and the successive names and locations of an item as it moves into its destination table. section 4.1.3

How 100 SQL Server Mistakes and How to Avoid Them covers it

How deep it goes
Defines lineage and gives a three-step CSV-to-staging-to-marketing-table example without describing automated capture.
Examples
Traces a cookie-provider Tracking ID through an Impressions CSV, a staging Cookies table, and a marketing Cookies table.
What it assumes
Assumes collaboration with business analysts or application owners to establish attribute meaning and business rules.
What you can do after
Apply it by documenting an attribute’s origin, intermediate locations, and destination table.
How it connects
Connects data lineage with data dictionaries, upstream dependencies, business rules, and database normalization.

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This explainer was generated by Manning's knowledge graph from the text of 100 SQL Server Mistakes and How to Avoid Them. The section links point to the parts of the book it draws on.