Case Study - Building a Scalable ETL Framework
Architected and developed a comprehensive ETL framework using AWS microservices, Python, and PostgreSQL to enhance data management capabilities and system scalability.
- Client
- Enterprise Data Integration
- Specialty
- Data Engineering
- Service
- Cloud Architecture
Overview
In a rapidly growing fintech environment, managing the increasing volume of data flowing between various systems became a significant challenge. The existing processes were manual, error-prone, and couldn't scale with the business growth.
I was tasked with designing and implementing a comprehensive ETL (Extract, Transform, Load) framework that could handle diverse data sources, ensure data integrity, and automate data processing workflows.
Technical Approach
The solution I architected leveraged AWS services combined with Python and PostgreSQL to create a scalable, reliable data pipeline:
- Serverless Architecture: Utilized AWS Lambda functions to create a cost-effective, event-driven processing pipeline
- Data Validation Engine: Implemented comprehensive validation rules ensuring data quality and compliance
- Workflow Orchestration: Implemented state machines to manage complex and inter-dependent data processing workflows
- Monitoring & Alerting: Integrated Sentry for real-time system health monitoring
What I Delivered
- Cloud Architecture (AWS)
- Python Data Processing
- PostgreSQL Optimization
- API Integrations
The ETL framework transformed our data management processes, making them more efficient and reliable. The solution continues to deliver substantial value through improved processing efficiency and enhanced pipeline reliability.
- Reduced onboarding time
- 50%
- System reliability
- 99%
- Data processing capacity
- Increased
- Automated validation
- Improved