Python-DuckDB Pipeline Shows How to Anonymize PII Data Before Analytics Use

A technical guide demonstrates how to build a PII-safe data pipeline using Python and DuckDB, without requiring cloud infrastructure or Docker. The system separates data into three zones — raw, curated, and vault — each with distinct access controls and protection levels. Personal identifiers are pseudonymized using HMAC hashing, phone numbers are masked, and quasi-identifiers like zip codes and birth years are generalized before reaching the analytics layer. A companion code repository processes 300 sample rows, confirming that deterministic HMAC keys correctly merge duplicate contact references into a single join key per person. The guide also acknowledges production limitations, such as DuckDB's lack of native access-control policies, and flags where heavier infrastructure would be needed in real deployments.