"@id": "dtmi:handsOn:Zone;1", "@type": "Interface", "displayName": "Zone", "contents": [ "@type": "Property", "name": "temperature", "schema": "double", "writable": true , "@type": "Relationship", "name": "contains", "target": "dtmi:handsOn:Shelf;1" , "@type": "Relationship", "name": "hasSensor", "target": "dtmi:handsOn:Sensor;1" ]
Now create the actual twins (instances):
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 . 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 .
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"@id": "dtmi:handsOn:Zone;1", "@type": "Interface", "displayName": "Zone", "contents": [ "@type": "Property", "name": "temperature", "schema": "double", "writable": true , "@type": "Relationship", "name": "contains", "target": "dtmi:handsOn:Shelf;1" , "@type": "Relationship", "name": "hasSensor", "target": "dtmi:handsOn:Sensor;1" ]
Now create the actual twins (instances):
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 . 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 .