Backend system processing 100K+ events/min with real-time analytics dashboards
EventStream Analytics is a high-performance backend system designed to handle large-scale event streams while providing real-time analytics dashboards. The system is built to process 100,000+ events per minute, aggregate metrics, and expose them via REST APIs and WebSocket streams for real-time monitoring.
This project demonstrates scalable backend architecture, queue-based processing, database design, and observability—making it a great portfolio piece for backend engineering and data-driven systems.
- High-throughput event ingestion via REST API and WebSocket
- Real-time analytics metrics and dashboards
- Backend event processing pipelines for aggregation and transformations
- Scalable storage using PostgreSQL and Redis for counters and caching
- Event simulation scripts to generate test data at 100K+ events/min
- Dockerized setup for easy deployment and local testing
- Unit tests for event processors and APIs
| Layer | Technology |
|---|---|
| Backend Language | Node.js (or Python) |
| Event Streaming | Kafka / Redis Streams |
| Database | PostgreSQL, Redis |
| Real-time Dashboard | WebSocket, REST API |
| Containerization | Docker, Docker Compose |
| Testing | Jest / Pytest |
- Clone the repository
git clone https://github.com/abhirawatt786/eventstream-analytics.git
cd eventstream-analytics