Modernizing Data Systems for a Leading U.S. Healthcare Company

A leading U.S. healthcare company moved its legacy mainframe data to a cloud platform. This change allowed for faster client onboarding and better data processing.

Business Challenges

A leading U.S. healthcare company had problems with its old Rxclaims system, which was built on a Mainframe architecture. The company is a major player in the U.S. healthcare sector, with extensive partnerships with insurance companies . As the largest pharmacy chain in the United States, it serves over 100 million people .

The main challenges were:

• Moving complex data and logic from a system with little documentation.

• Solving scaling issues to manage different data sizes from insurance clients.

• Needing a new platform to support more features and quicker partner onboarding.

Solution

A new data engineering platform was built on Google Cloud Platform (GCP) to solve these problems. The solution included several parts:

• PySpark Architecture: The main processing was moved to PySpark for modern data handling.

• Medallion Architecture: A layered data approach (Raw > Bronze > Silver > Gold) was used. This system cleans, adds to, and structures data for business dashboards.

• Low-Code Framework: A special Sparkflow framework was used. This lets the team make configuration changes through JSON. This reduces code changes and speeds up deployment for new clients.

  
  

Results

The project updated the company's data systems and produced clear results:

• The old Mainframe system for Rxclaims was fully moved to the new PySpark-based cloud platform.

• The system has grown through two production waves. It now serves six clients.

• The new architecture provides a base to add more clients in the future.