Real-time Streaming Analytics API

An industrial manufacturing facility needed to monitor high-frequency sensor data from their production line to catch equipment failures before they occurred. Delay in processing this data (even by a few minutes) meant potential thousands of dollars in downtime and wasted materials.

Existing analytical tools were batch-oriented and couldn't handle the sub-second latency required for true real-time alerting. The sensor volumes were reaching over 10,000 events per second, overwhelming their legacy servers.

I built a high-throughput Streaming Analytics API using a Flask web server backed by Redis for ultra-low latency data caching. The system ingests sensor streams and performs real-time 'Moving Average' and 'Peak Detection' calculations.

The front-end uses WebSockets to push live updates to a 'Factory Floor Dashboard', providing operators with instant visual feedback and automated haptic alerts if sensor readings exceed safety parameters.

To handle the high throughput, I implemented a custom 'Debouncing' and 'Aggregation' layer that ensures only meaningful changes are pushed to the UI, while every raw data point is asynchronously stored in a time-series database for later troubleshooting.