Big Data ETL for Healthcare Research

Our client is a major international pharmaceutical company conducting research and development across a broad range of human medical disorders, including neurological disorders, cancer, allergies, gastrointestinal diseases, and other therapeutic areas.

The client manages terabytes of biomedical and clinical data that must be continuously integrated, transformed, and analyzed to support research activities.

Key challenges included:

• Processing growing volumes of data within acceptable timeframes.
• Ensuring data quality and consistency across multiple sources.
• Reducing operational overheads through ETL automation.
• Building a scalable data foundation capable of supporting advanced analytics and future AI initiatives.

Software Country designed and implemented a cloud-native data platform for large-scale data integration and processing.

The solution leveraged AWS Redshift and Azure Databricks to provide scalable data warehousing and distributed analytics capabilities. Data quality was ensured through the Validation Analysis System, which automatically collected quality metrics and detected anomalies in loaded datasets.

Most ETL workflows were automated through cloud-based pipelines and CDM Builder — a serverless processing platform built on AWS Lambda and Azure Functions. CDM Builder transforms, validates, and loads data into the Common Data Model (CDM), enabling standardized and consistent data across research systems.

Technology Stack: AWS Redshift, Azure Databricks, AWS Lambda, Azure Functions, ETL/ELT Pipelines, Common Data Model (CDM), Data Quality Validation, Cloud Data Warehousing.

• Reduced data data processing times: from days or weeks to hours.
• Improved data quality due to automated validation and monitoring.
• Automation of the majority of data integration workflows.
• Increased scalability
• Reduced infrastructure management overheads
• An AI-ready data foundation for advanced analytics and research initiatives.