6 min readSEYON Insights
Healthcare data engineering is ordinary data engineering with stricter consequences. The useful pattern is still easy to draw: source systems, ingestion, transformation, data quality, a warehouse or lakehouse, and analytics. Each stage should have an owner and a way to rerun.
Ingestion is not the model
Landing files, database extracts, or API responses in a raw area preserves what arrived. Transformation then conforms identifiers, dates, and codes. The published warehouse model should match the questions analysts ask, at a grain the source can support. A lake is a reasonable place for raw and intermediate data. It is not a substitute for a defined reporting model.
Quality checks before publication
Check row counts, required keys, accepted code values, and referential integrity before a table is marked current. Batch processing fits many clinical and financial extracts. Near-real-time processing fits operational feeds that lose value overnight. Pick the latency the decision needs, then design for it.
- Keep raw data immutable for the retention window you agreed.
- Version transformations so a measure can be explained later.
- Fail a publish step when a quality rule fails.
- Document the source of each published column.