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In-depth guides, tutorials, and best practices for database design, data architecture, and healthcare data modeling.
Every healthcare data warehouse has a provider dimension table with NPI numbers. Bad NPIs cause claims rejections, failed eligibility checks, and broken provider analytics. This guide shows you how to validate NPIs programmatically — from format checking to Luhn algorithm to bulk Snowflake UDF deployment.
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Every healthcare data warehouse eventually develops naming drift — DOB in one model, birth_dt in another, member_birth_date in a third. The dbt-healthcare-standards package brings ISO-11179 column naming directly into your dbt project as installable macros and schema tests.
The complete data model for pharmacy claims, prescription management, and PBM data — including NDC normalization, DEA number validation, and dispensing fact table design for Snowflake and Databricks.
Pharmaceutical companies run the most complex data pipelines in any industry. Here is a complete 2026 competitive landscape comparing Snowflake, Databricks, BigQuery, Azure Synapse, and SAS for pharma-specific workloads: clinical trials, pharmacovigilance, real-world evidence, and commercial analytics.
Healthcare ETL has two very different schools of thought: Informatica, the enterprise incumbent used by health plans for 20+ years, and dbt, the modern SQL-first transformation tool taking healthcare data teams by storm. This guide breaks down exactly when each one wins.
Google BigQuery and Amazon Redshift are the two most widely used cloud data warehouses for healthcare claims analytics outside of Snowflake. This guide compares both platforms across HIPAA compliance, HEDIS reporting, FHIR integration, DDL syntax, and cost at scale.
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