azure-digital-twins

DTDL modeling for Azure Digital Twins involves defining twin types, properties, relationships, and components to represent physical environments. This guidance covers schema design, inheritance, and best practices for building scalable twin models that map to your IoT topology.

Integrating IoT Hub with Azure Digital Twins routes telemetry through Azure Functions to update twin properties and trigger graph events. This guidance covers routing endpoints, function bindings, and data flow patterns for syncing live device data into your twin graph.

Querying twin graphs in Azure Digital Twins uses a SQL-like query language to traverse relationships, filter properties, and select twins across the graph. This guidance helps you write efficient queries, optimize traversal performance, and troubleshoot result accuracy.

Azure Digital Twins enforces quotas on twin counts, relationship limits, and API throttling that vary by region and subscription tier. This guidance covers deployment considerations, quota planning, and architectural patterns to scale within service constraints.

Azure Functions integrates with Azure Digital Twins through bindings and SDK calls to process lifecycle events, update property values, and orchestrate downstream actions. This guidance covers event routing, function triggers, and security configurations for end-to-end workflows.

Azure Digital Twins deployment security failures often stem from misconfigured managed identities, role assignments, or network isolation settings. This guidance covers best practices for authentication, RBAC permissions, and securing cross-service integrations with IoT Hub and Functions.