Network Security ETL Pipeline
End-to-end ETL pipeline for network security monitoring
Client
Security Client
Industry
Security
Services
Tech Stack
Challenge
Network security generates massive amounts of data that needs to be processed, analyzed, and stored for threat detection. A security client needed a complete ETL pipeline that could ingest network logs, process them in real-time, store them efficiently, and provide visualization for security analysts—all while maintaining data versioning and ML experiment tracking.
Our Contribution
Our team built a comprehensive ETL pipeline for network security data. We created data ingestion from multiple sources, implemented stream processing for real-time analysis, set up data versioning with DVC, and built ML experiment tracking with MLFlow. We also deployed monitoring with Grafana and Prometheus.
Scope of Work
- Built connectors for multiple network log sources
- Implemented real-time ingestion with Kafka
- Created data validation and schema enforcement
- Set up S3 for raw data storage
Solution
We delivered a complete ETL and ML platform for network security that processes millions of events per second. The system ingests data from diverse sources, processes it in real-time for threat detection, and stores it efficiently for historical analysis. With proper data versioning and experiment tracking, security teams can continuously improve detection models.
Impact
The platform reduced threat detection time from hours to seconds and enabled security teams to respond to incidents in real-time.