We turned a handful of sensors and a Raspberry Pi into a fully operational Fabric Real-Time Intelligence pipeline, and in this session, we show you exactly how.

We'll walk through a live demo of an IoT pipeline built from scratch: connecting light, temperature, and humidity sensors directly into Fabric RTI. You'll see the full data journey: sensor wiring, edge data capture, event streaming, Real-Time Dashboards and Activators. Learn about our implementation journey, hard-won best practices, and a blueprint you can replicate tomorrow.

This is a live demo instead of slides-only session. We will physically alter the environment in real-time: changing light colors and humidity levels in front of the audience and watch the entire pipeline react end-to-end. From the moment a sensor detects a change, you'll see the data propagate through the Raspberry Pi edge layer, flow into Fabric Eventstream, land in the KQL Database, update the Real-Time Dashboard, and trigger Activator alerts — all within seconds.

In this session we will cover:
- Capture environmental readings by connecting physical sensors of light (LDR/RGB), temperature and humidity to a Raspberry Pi interface.
- Edge data collection through a lightweight Python service running on Raspberry Pi, responsible for polling sensors, normalizing readings, and managing local buffering during connectivity gaps.
- Ingest event streaming into Fabric RTI using Eventstream with schema enforcement and timestamp alignment handled at the edge.
- Persist data into KQL Database where it can queried and visualized in Real-Time Dashboards to enabling live monitoring.
- Trigger automated alerts and actions using Activator when the defined conditions are met. Which closes the loop from raw data to intelligent response.

Key Takeaways :
- A clear architectural blueprint for Raspberry Pi to Fabric RTI pipelines.
- Practical guidance on hardware implementation, data flow from sensors to Fabric RTI, and Eventstream configuration.
- A realistic picture of the trade-offs between cost, latency, and reliability in IoT + Fabric scenarios.
- Code snippets and a GitHub reference repository shared post-session.