Blog
In-depth guides, tutorials, and best practices for database design, data architecture, and healthcare data modeling.
Platform comparisons, TCO analysis, and governance frameworks — written for the people who approve the budget.
Showing 25–30 of 81 posts
A data contract is a formal agreement between the team that produces data and the teams that consume it — specifying schema, quality rules, SLAs, and ownership. In healthcare, where a schema change in the claims pipeline can break downstream HEDIS calculations, data contracts are a stability mechanism, not a formality.
A practical guide to designing the Snowflake and BigQuery data models that power CMS-HCC V28 risk adjustment — including RAF score calculation, member-HCC mapping tables, and the data quality checks that prevent audit findings.
How to validate NPI numbers using the Luhn algorithm, build a provider master data model, and reconcile against the NPPES registry — with SQL and Python examples for healthcare data engineers.
A DDL script is more than CREATE TABLE. In a healthcare data warehouse, it is your schema contract — defining data types, constraints, and indexes that determine whether your claims pipeline loads clean or fails silently.
The healthcare data modeling landscape has shifted in 2026. AI-native tools, FHIR R5 readiness, and LLM-assisted ERD generation have redefined what 'good' looks like. Here is how the leading platforms stack up — and why healthcare teams need a specialized category of their own.
SQL and Python are not competitors in healthcare data engineering — they are partners with clearly different responsibilities. SQL owns the warehouse: aggregations, HEDIS queries, claims analysis, and regulatory reporting. Python owns the pipeline: FHIR ingestion, PHI masking, ML model training, and clinical NLP. This guide shows exactly where each language wins, with real healthcare code examples for both.
Get the latest healthcare data architecture articles delivered to your inbox.