The Challenge
While serving as Product Owner leading a team migrating legacy systems to a new Enterprise Data Warehouse, we encountered persistent ETL pipeline failures. HEDIS data was periodically sent to us with unannounced layout changes, and the healthcare system's IT department was unable to provide advance notice of these schema shifts. These unexpected modifications continuously broke our Extract, Transfer, Load (ETL) processes, causing data delays and requiring costly, time-consuming manual intervention from engineers to remap the data.
Architectural Approach
To resolve the pipeline bottleneck, I leveraged my Python expertise to design a dynamic data ingestion tool. Within a single week, I developed a working prototype that could dynamically parse and ingest incoming data regardless of structural layout changes. Furthermore, I engineered the script to automatically generate an Excel spreadsheet to serve as the required Source-to-Target Mapping documentation. By providing this prototype to my engineering team, we eliminated the downtime associated with manual re-mapping and significantly accelerated our EDW migration efforts.
Project Repository: https://github.com/MLAbram/python-dynamic-data-ingestion-framework
GitHub Profile: https://github.com/MLAbram
Project Repository: https://github.com/MLAbram/python-dynamic-data-ingestion-framework
GitHub Profile: https://github.com/MLAbram
Outcomes & Impact
~90%
Reduction in Pipeline Maintenance
Eliminated the recurring manual engineering hours traditionally required to rewrite ETL scripts following unannounced HEDIS data layout changes.
40+ Hours
Saved Per Schema Shift
By dynamically auto-generating the Source-to-Target Mapping Excel documentation, engineers were spared from manually reverse-engineering layout changes.
1 Week
Rapid Prototype Delivery
Designed, developed, and delivered a fully functional dynamic ingestion prototype to the engineering team to accelerate the EDW migration.