Streaming Radar API
Event-driven serving path from Kafka to low-latency APIs
The challenge
Some data systems are valuable only if they deliver information while it still matters. That means the architecture has to connect ingestion, processing, state management, and serving with low latency and operational clarity.
How we solved it
- - Use Kafka as the streaming backbone
- - Process events through Python producer and consumer services
- - Keep serving state in Redis
- - Expose the latest state through FastAPI endpoints
Execution story
Streaming events move through Kafka, are transformed by Python services, stored in Redis, and exposed via FastAPI as a practical real-time serving path.
Real-time as a business requirement
The project is framed around timeliness, not only technology. When the site talks about low-latency systems, this repository becomes the technical proof behind that claim.
Why it fits the platform
This is the kind of project that benefits from being connected to external references, because its value grows when it is placed inside a broader market conversation about streaming and operational responsiveness.