ETL Pipeline System
Scalable ETL (Extract, Transform, Load) pipeline system for processing large volumes of data from multiple sources.
Problem Statement
A client needed a robust ETL system to process millions of records daily from various sources, transform the data, and load it into a data warehouse for analytics.
Solution Approach
We built a scalable ETL pipeline system with:
- Multi-source data extraction
- Data transformation workflows
- Automated scheduling
- Error handling and retry logic
- Data quality validation
- Monitoring and alerting
Technologies Used
- Python - ETL scripts
- Apache Airflow - Workflow orchestration
- PostgreSQL - Data warehouse
- Apache Spark - Large-scale processing
- Docker - Containerization
Key Achievements
- ✅ Process 10M+ records daily
- ✅ 99.9% uptime
- ✅ Automated error recovery
- ✅ Data quality validation
- ✅ Comprehensive monitoring
Impact
- Processes millions of records daily
- Reduced data processing time by 70%
- Improved data quality through validation
- Automated error recovery
- Enabled real-time analytics
Lessons Learned
- ETL pipelines require robust error handling
- Data quality validation prevents downstream issues
- Monitoring is essential for production systems
- Scalability requires careful architecture
- Automated retry logic improves reliability