Real-Time Data Visualization Dashboard for an IoT Fleet
Overview
What this challenge is about.
Build a deck.gl scatterplot layer rendering 38,000 turbine positions on a map base layer. Color-code by status (operational / degraded / offline) updating from a WebSocket stream every 2 seconds. Implement on-hover detail popovers without dropping below 60fps. Add a time-range slider that re-colors based on historical status. Test on a mid-range laptop (Intel Iris-class integrated graphics) and a low-end Chromebook. Deliver source, the running dashboard, a performance benchmark report, and a 6-page design document explaining the GPU-rendering approach.
The Brief
What you'll do, and what you'll demonstrate.
Build a WebGL-accelerated dashboard rendering 38,000 IoT sensors at 60fps with live WebSocket updates and on-hover detail.
Earning criteria — what you'll demonstrate
- Use GPU-accelerated rendering for large-fleet data visualization
- Manage WebSocket-driven state updates without re-rendering everything
- Profile WebGL performance on multiple hardware tiers
- Communicate GPU-rendering trade-offs to a non-graphics stakeholder
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career mappings coming soon.