Case Study
Here's the Case-Study of some of our works we'd like you to see...
Cook Medical
Cook Medical is a global leader in healthcare innovation, delivering advanced medical solutions worldwide. The company needed a reliable, scalable approach to ingest, validate, and process large volumes of data received from multiple Oracle systems.
Our team built an automated ETL framework using Master Data Services (MDS) for metadata management and BIML for dynamic SSIS package generation. This solution streamlined the ingestion of Tier 1 and Tier 2 data files and prepared clean, structured outputs for API simulations.
The Challenge
Cook Medical’s existing data ingestion process faced several challenges:
Complex File Structures: Hundreds of Oracle files came with inconsistent formats, missing fields, and structural variations. Even minor mismatches caused failures during SSIS package execution.
High Dependency Between Tiers: Tier 2 processing relied heavily on the accuracy of Tier 1 loads. Any errors in Tier 1—such as incorrect data mappings or missing validations—propagated downstream and created widespread processing issues.
Lack of Standardized Metadata: Managing diverse file definitions was time-consuming. Configuring MDS manually for each new file required extensive effort and caused delays in onboarding new data sources.
Validation & Transformation Complexity : Business rules, lookups, and relationship mappings require precise validations to ensure consistent and accurate transformations across all files.
The Solution
To address these challenges, we built a fully automated, metadata-driven ETL framework that significantly improved scalability and reliability. Metadata was standardized within Master Data Services (MDS), allowing consistent file definitions and seamless updates without manual intervention.
Using BIML, SSIS packages were automatically generated and regenerated whenever file layouts changed, eliminating repetitive development work. We introduced robust pre-validation checks to detect formatting issues early and implemented strong transformation logic, lookup validations, and cleansing rules to ensure accurate Tier 1 and Tier 2 loads. Synonym-based stored procedures were added to prevent deployment conflicts and maintain stable data flow.
Together, these enhancements enable consistent processing of large datasets, simplified onboarding of new file types, and delivered clean, API-ready outputs with comprehensive logging and high performance.
The solution delivered to Cook Medical significantly improved their data ingestion and transformation processes. By leveraging MDS for metadata standardization and BIML for automated SSIS generation, we eliminated repetitive development work, reduced failures, and enhanced overall data quality.
The automated Tier 1 and Tier 2 workflows now support reliable API simulations and provide a scalable foundation for future data integrations for long-term operational efficiency and accuracy.
Technology Stack
