Data Safety Suite

Audit your dbt project for the test and documentation gaps that let bad data ship. Flags models with no unique or not_null tests, sources missing freshness config or tests, likely keys without a not_null test, models missing descriptions, SELECT * in models, and raw table references that should use ref() or source(). Each finding comes with a suggested tests: YAML snippet to drop into schema.yml.

Scan your schemas, seed data, config, and logs for personal data before it leaks. Detects PII-indicating column and key names (email, ssn, phone, address) across SQL, CSV, and JSON, plus PII in the data itself: email addresses, SSN-like numbers, credit-card-like numbers, phone numbers, and PII written into log files. Each finding is flagged with its location and a GDPR-style review note. Heuristic by design: it surfaces what to review, not a compliance guarantee.

Catch the dangerous migration before it locks or wrecks your production database. Scans SQL migration files for destructive and risky operations: DROP and TRUNCATE, drops without IF EXISTS, lossy column-type changes, NOT NULL added without a default, DELETE or UPDATE with no WHERE, non-concurrent index builds, dropped constraints, renames, and data backfills mixed into schema changes. Each finding is ranked by severity with a safer rewrite. Postgres, MySQL, and SQLite.