PythonApache AirflowPostgreSQLDocker
Data Pipeline Framework
A comprehensive data pipeline framework built to handle high-volume data processing with reliability and observability. The system processes millions of records daily from various sources, transforming and loading them into our data warehouse while maintaining data quality and consistency.
Key Features
- Automated data ingestion from 15+ sources
- Real-time data quality monitoring
- Incremental processing with checkpointing
- Custom alerting for pipeline failures
- Self-healing retry mechanisms
Challenges
- Handling schema changes gracefully
- Managing dependencies between pipelines
- Optimizing for both latency and throughput
Outcome
Reduced data latency from 24 hours to under 15 minutes while processing 3x more data volume with 99.9% reliability.