Healthcare ETL/Data Pipelines
Enterprise Data Integration at Scale
Architected secure data pipelines for extracting, transforming, and loading massive volumes of patient records from legacy EHRs to modern platforms.
Business Problem
Migrating millions of patient records from legacy EHRs to new platforms while maintaining absolute data fidelity and HIPAA compliance.
Technical Challenges
Parsing inconsistent legacy formats, handling massive file volumes, and ensuring 'idempotent' loads to prevent data duplication.
Architecture
A cloud-native ETL pipeline using Azure Blob Storage for landing, Python for transformation/validation, and SQL Server for the final warehouse.
Implementation
Built custom parsers for complex clinical documents (CCDA) and implemented a YAML-based configuration system for flexible mapping.
Scalability
Successfully migrated millions of clinical records with complete data fidelity.
Results / Impact
Eliminated dependency on costly legacy infrastructure, resulting in substantial annual licensing fee savings.
Lessons Learned
Always assume source data is wrong. Validation is the most important step in any ETL process.
Interested in the technical implementation?
Let's discuss how this architecture can be applied to your specific healthcare challenges.