Healthcare Analytics ETL Pipeline
Project Overview
This healthcare analytics ETL pipeline was designed and implemented to process clinical, claims, and operational data from multiple sources into a unified data warehouse. Built using AWS Redshift, SQL, and DBT, the system incorporates automated data validation protocols to ensure data quality and HIPAA compliance while improving accessibility for analysis.
Key Features
- Optimized ETL processes for large-scale medical data
- Automated data validation protocols for compliance and accuracy
- Enhanced data modeling workflows in DBT
- Interactive AWS QuickSight dashboards
- Improved query efficiency and reduced reporting time
Technologies
- AWS Redshift for data warehousing
- SQL for data transformation and analysis
- DBT for data modeling and documentation
- AWS QuickSight for visualization
- Python for custom data processing
- AWS Lambda for automation
Results
- 50% reduction in data processing time
- 90% improvement in data quality accuracy
- Enhanced analytics capabilities for clinical decision-making
- Reduced compliance risks through automated validation
- Streamlined reporting workflow for healthcare administrators
Data Pipeline Architecture
Data Quality Monitoring
Technical Implementation Details
Data Warehouse Architecture
The solution utilized AWS Redshift as the core data warehouse, leveraging its columnar storage and massively parallel processing capabilities to efficiently handle large volumes of healthcare data. The warehouse was structured using a carefully designed star schema optimized for both analytical queries and data governance requirements. Distribution and sort keys were implemented to maximize query performance for the most common analytics patterns.
Data Validation Framework
A comprehensive automated data validation framework was developed to ensure accuracy and regulatory compliance. The system performs multi-level validation including data type checks, referential integrity verification, business rule validation, and pattern analysis to detect anomalies. All validation results are logged and monitored through a custom dashboard, with configurable alerting thresholds to notify data stewards of potential issues.
DBT Implementation
DBT (Data Build Tool) was implemented to manage the transformation layer, providing version-controlled, documented, and testable transformation logic. The modular design enabled reusable components, simplifying maintenance and extending functionality. Enhanced data modeling workflows improved query efficiency and reduced reporting time, while the documentation features created a self-service data dictionary that increased accessibility for clinical analysts and decision-makers.