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Raw, Validated, Clean: Layered Cleaning

by LucidPlexus
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Three views between the raw file and the report, and proof of what the cleaning changed. Two A4 pages on cleaning in layers, written on the tables of Can I Trust This Data?, a teaching copy with faults put in on purpose.…
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Three views between the raw file and the report, and proof of what the cleaning changed.

Two A4 pages on cleaning in layers, written on the tables of Can I Trust This Data?, a teaching copy with faults put in on purpose. Every query was run, and its real result is printed under it.

It builds the raw, validated and clean views and says what belongs in each, keeps one flag column per rule, turns the flags into an issue log with one query, and sets out when to exclude, fix or flag a fault, with what each choice does to a real average.

It then joins tables kept at different grains without fan-out (aggregate first, or an ASOF join), reconciles raw to clean in rows and totals and per group, shows why a total over contested rows should be reported as a range, and gives a test to run on every load.

PDF to print, and an HTML version to read on screen. The SQL is DuckDB, as in the LucidPlexus workbench, with short notes on what to write in PostgreSQL, SQL Server or MySQL where a query depends on DuckDB.

#cheat-sheet #data-cleaning #data-quality #duckdb #joins #sql
Type Cheat Sheet
Pages 2
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