Kruzr
Real-Time Driving Behavior Platform
Backend processing for sensor data and low-latency driving behavior analysis.
Realtime systems expose architectural weaknesses quickly. This project required low-latency processing, tighter service boundaries, and more practical tradeoffs around cloud dependence and responsiveness.
PythonFlaskKafkaDocker
Problem
The platform needed fast, synchronized processing of sensor events to drive real-time intervention and personalization.
Approach
Moved from a monolith toward microservices, improved processing flow design, and reduced cloud dependence where possible.
Impact
Improved responsiveness and architectural resilience for real-time user behavior analysis.
Key Takeaways
- Realtime flows force clarity around latency and processing boundaries.
- Microservice decomposition only helps when it reduces real bottlenecks.
- Architecture should support intervention speed, not just model sophistication.