Content-addressable deduplication at scale: MD5 hash all columns (minus metadata) → find hash collisions → save duplicates to _log table → keep one record. Implemented as BigQuery stored procedures. Track everything, delete nothing permanently. Easy rollback if needed.
MD5(COALESCE(CONCAT( COALESCE(sanitize_text(column1), ''), COALESCE(sanitize_text(column2), ''), COALESCE(sanitize_text(column3), '') )))
Using LLMs for data standardization at scale:
Send messy province names to Claude with an official list → get back a mapping dictionary → validate programmatically → quarantine unmappable records to separate tables.
Traditional ETL: Write 50+ regex rules, maintain edge case dictionary, manual QA team reviews thousands of records.
LLM-powered ETL: One prompt template, automatic fuzzy matching, quarantine pattern for edge cases, 95%+ automation rate


