Project Overview
Designed and built a high-throughput data pipeline engine that processes and analyzes massive amounts of streaming data in real-time. The system handles complex ETL workflows and provides real-time analytics for business intelligence.
Key Features
- Stream processing of 1B+ daily events
- Real-time data transformation and enrichment
- Dynamic workflow orchestration
- Automated data quality checks
- Real-time analytics dashboards
Technical Implementation
- Built on Apache Kafka for event streaming
- Used Apache Spark for distributed processing
- Implemented custom Python operators for data transformation
- Created a workflow engine using Apache Airflow
- Developed monitoring using Prometheus and Grafana
Impact
- Reduced data processing latency from hours to seconds
- Improved data quality through automated validation
- Enabled real-time business insights
- Scaled to handle 5x increase in data volume