Azure Digital Twins (ADT) gives that data context . It knows that Sensor 47 belongs to Room 312 , which is on the North Wing of Floor 3 , and that room contains a Server Rack . If the temperature rises, ADT understands the impact .
# Update the twin property patch = ["op": "replace", "path": "/temperature", "value": new_temp] service_client.update_digital_twin(sensor_id, patch) hands-on azure digital twins read online
Your graph is now alive. Think of it as a mini-Google Knowledge Graph for your warehouse. Unlike SQL (tables) or NoSQL (documents), ADT uses a graph query language similar to SQL but with RELATED and IS_OF_MODEL . Query 1: Find all sensors in the Receiving Zone az dt twin query --adt-name adt-warehouse-<unique> \ --query-command "SELECT sensor FROM digitaltwins zone JOIN sensor RELATED zone.hasSensor WHERE zone.\$dtId = 'ZoneReceiving'" Result: Returns TempSensor-Rcv . Query 2: Traverse two hops (Warehouse → Zone → Sensor) az dt twin query --adt-name adt-warehouse-<unique> \ --query-command "SELECT sensor, zone FROM digitaltwins wh JOIN zone RELATED wh.contains JOIN sensor RELATED zone.hasSensor WHERE wh.\$dtId = 'WarehouseMain'" This is powerful. In a real app, this query would run in milliseconds, even across 100,000+ nodes. Step 6: Simulate Telemetry and Compute Changes Here’s where it gets truly hands-on. Azure Digital Twins itself does not ingest telemetry directly. Instead, you use Azure Functions or IoT Hub to route data in. Azure Digital Twins (ADT) gives that data context
The Problem: You have a building, a factory, or a logistics center. Sensors are streaming data, but the data is "dumb"—it’s just a timestamp and a number. A temperature of 22°C in isolation means nothing. # Update the twin property patch = ["op":
Azure Digital Twins isn't just a database—it's the of your physical world. Go build something intelligent.
# Upload models az dt model create --adt-name adt-warehouse-<unique> \ --models models/*.json az dt model list --adt-name adt-warehouse-<unique> -o table