Data engineering

Pipelines are production systems whose failures are quiet. A broken service pages someone; a broken pipeline produces plausible numbers that people act on for a week.

This is movement and transformation. Schema and semantics belong to data-analytics:data-modeling, policy and stewardship to data-analytics:data-governance.

Land raw, transform downstream

Keep an immutable copy of source data exactly as received. Transformation logic will be wrong at some point, and raw data is what lets you reprocess rather than re-request from a source that may no longer have it.

Business logic belongs downstream where it is visible and testable, not buried in ingestion. The exception is transformation required for privacy — minimization, pseudonymization, dropping fields you have no basis to hold — which belongs at ingest precisely because raw storage is what the obligation attaches to. See legal-risk:privacy-and-data-protection.