Project: Real-time Data Pipeline
Category: Data Engineering
Technologies: Python, Apache Kafka, Spark, Hadoop, PostgreSQL
Impact: Processing petabyte-scale data with sub-second latency
Overview
Built a high-performance real-time data pipeline for enterprise scale analytics, processing millions of events daily while maintaining sub-second latency and 99.99% reliability.
Architecture
Data Ingestion Layer
- Apache Kafka for event streaming
- Multi-datacenter deployment for redundancy
- Support for 100K+ events per second
Stream Processing
- Apache Spark Streaming for real-time aggregations
- Windowed operations for time-series analytics
- Exactly-once processing semantics
Storage Layer
- PostgreSQL for relational data
- Hadoop HDFS for archival storage
- Redis for hot cache
Performance Metrics
- Throughput: 1M+ events per second
- Latency: <500ms end-to-end
- Reliability: 99.99% uptime
- Data Volume: 10TB+ daily ingestion
Technologies & Best Practices
- Python for all data processing logic
- Containerization with Docker for deployment
- Infrastructure as Code using Terraform
- Comprehensive monitoring with Prometheus and Grafana
- Extensive logging and tracing for debugging