Azure IoT Edge

Azure IoT Edge is a service from Microsoft Azure that allows you to run cloud workloads locally on edge devices. It is part of the broader Azure IoT platform and is closely integrated with Azure IoT Hub. The goal of Azure IoT Edge is to enable data processing closer to where the data is generated — at the edge — instead of sending everything to the cloud. 

This is especially useful in environments where: 

• Internet connectivity is limited or intermittent 

• Latency is critical 

• Data privacy or cost of bandwidth is a concern 

Azure IoT Edge makes it possible to run AI models, stream processing, data filtering, and other logic locally, even when the device is offline. 

Key Components of Azure IoT Edge 

Azure IoT Edge has three main parts: Modules, the Runtime, and the Cloud Interface.

1. IoT Edge Modules 

Modules are Docker-compatible containers that run on the edge device. Each module performs a specific task — for example, collecting sensor data, processing it, or applying a machine learning model. 

Modules can: 

• Be pre-built by Microsoft (e.g., Azure Stream Analytics, Azure Functions, or ML models) 

• Be custom modules written in languages like Python, C#, or Node.js 

• Communicate with each other to create a processing pipeline 

Modules are the building blocks of edge workloads. You can mix and match them based on your use case. 

For example, a typical pipeline might look like: 

1. A module reads temperature data from a sensor. 

2. Another module filters or aggregates the data. 

3. A third module sends only relevant insights to the cloud. 

This reduces unnecessary data transfer and allows for quicker local decisions. 

2. IoT Edge Runtime 

The Azure IoT Edge Runtime runs on each device. It acts as the manager for all edge modules and ensures the system behaves as expected. 

The runtime is responsible for: 

• Installing and updating modules 

• Starting, stopping, or restarting containers as needed 

• Managing security, including module identity and communication 

• Ensuring the edge device reports health status to Azure IoT Hub 

• Supporting offline operation with data buffering and sync 

It’s designed to be lightweight and runs on devices like industrial PCs, gateways, or even single-board computers like the Raspberry Pi (for development). 

The runtime uses the container engine (usually Moby or Docker) to manage modules and a local agent to control behavior. 

3. Cloud Interface (via Azure IoT Hub) 

The cloud interface provides a way to manage IoT Edge devices remotely through Azure IoT Hub. Using this interface, you can: 

• Deploy and configure modules to devices 

• Monitor device status and logs 

• Check module performance and health 

• View telemetry or metrics 

• Trigger software updates 

From the Azure Portal, CLI, or REST APIs, admins can roll out workloads to hundreds or thousands of devices in a consistent way. 

This centralized control is especially useful for large-scale deployments where field devices are distributed across locations. 

Typical Use Cases for Azure IoT Edge 

• Industrial Automation: Process sensor data on-site and use ML to detect anomalies in equipment. 

• Retail: Run object detection models locally in stores for shelf analytics or customer tracking. 

• Agriculture: Monitor environmental data in remote areas with poor connectivity. 

• Smart Cities: Handle camera or sensor data locally for traffic analysis or environmental monitoring. 

 Offline Capabilities and Cloud Sync 

A key feature of Azure IoT Edge is that it works offline. Modules can continue running even when the cloud connection is unavailable. When the connection is restored, data is synced automatically to the cloud. 

This makes it well-suited for edge computing scenarios where uptime and reliability are critical. 

Learn more: https://azure.microsoft.com/de-de/products/iot-edge