Analyze and visualize your IoT data

This overview introduces the key concepts around the options to analyze and visualize your IoT data. Each section includes links to content that provides further detail and guidance.

In Azure IoT, analysis and visualization services are used to identify and display business insights derived from your IoT data. For example, you can use a machine learning model to analyze device telemetry and predict when maintenance should be carried out on an industrial asset. You can also use a visualization tool to display a map of the location of your devices.

Edge-based solution

The following diagram shows a high-level view of the components in a typical edge-based IoT solution. This article focuses on the areas relevant to analyzing and visualizing your IoT data:

Cloud-based solution

The following diagram shows a high-level view of the components in a typical cloud-based IoT solution. This article focuses on the areas relevant to analyzing and visualizing your IoT data:

Azure Digital Twins

The Azure Digital Twins service lets you build and maintain models that are live, up-to-date representations of the real world. You can query, analyze, and generate visualizations from these models to extract business insights. An example model might be a representation of a building that includes information about the rooms, the devices in the rooms, and the relationships between the rooms and devices. The real-world data that populates these models is typically collected from IoT devices and sent through an IoT hub.

IoT Central

IoT Central provides a rich set of features that you can use to analyze and visualize your IoT data. The following articles and tutorials show some examples of how to use IoT Central to analyze and visualize IoT data:

• How to use IoT Central data explorer to analyze device data
• Create and manage IoT Central dashboards

External services

There are many services you can use to analyze and visualize your IoT data. Some services are designed to work with streaming IoT data, while others are more general-purpose. The following services are some of the most common ones used for analysis and visualization in IoT solutions:

Microsoft Fabric

Microsoft Fabric is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a comprehensive suite of services such as Data Science and Real-Time Analytics.

The following articles show some examples of how to use Microsoft Fabric to analyze and visualize IoT data:

• Tutorial: Get insights from your processed data
• Tutorial: Send data from an OPC UA server to Azure Data Lake Storage Gen 2

Azure Data Explorer

Azure Data Explorer is a fully managed, high-performance, big-data analytics platform that makes it easy to analyze high volumes of data in near real time. The following articles and tutorials show some examples of how to use Azure Data Explorer to analyze and visualize IoT data:

• IoT Hub data connection (Azure Data Explorer)
• Export IoT data to Azure Data Explorer (IoT Central)
• Azure Digital Twins query plugin for Azure Data Explorer

Power BI

Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Power BI lets you easily connect to your data sources, visualize, and discover what's important, and share reports with anyone or everyone you want.

• Visualize real-time sensor data from Azure IoT Hub using Power BI
• Export data from Azure IoT Central and visualize insights in Power BI

Grafana

Grafana is visualization and analytics software. It allows you to query, visualize, alert on, and explore your metrics, logs, and traces no matter where they're stored. It provides you with tools to turn your time-series database data into insightful graphs and visualizations. Azure Managed Grafana is a fully managed service for analytics and monitoring solutions. To learn more about using Grafana in your IoT solution, see Cloud IoT dashboards using Grafana with Azure IoT.

Databricks

Use Azure Databricks to process, store, clean, share, analyze, model, and monetize datasets with solutions from BI to machine learning. Use the Azure Databricks platform to build and deploy data engineering workflows, machine learning models, analytics dashboards, and more.

Use structured streaming with Azure Event Hubs and Azure Databricks clusters. You can connect a Databricks workspace to the Event Hubs-compatible endpoint on an IoT hub to read data from IoT devices.

Azure Stream Analytics

Azure Stream Analytics is a fully managed stream processing engine that is designed to analyze and process large volumes of streaming data with low latency. Patterns and relationships can be identified in data that originates from various input sources including applications, devices, and sensors. You can use these patterns to trigger actions and initiate workflows such as creating alerts or feeding information to a reporting tool. In a cloud-based solution, Stream Analytics is also available on the Azure IoT Edge runtime, enabling data processing directly on the edge.

• Build an IoT solution by using Stream Analytics
• Real-time data visualization of data from Azure IoT Hub
• Extend Azure IoT Central with custom rules and notifications
• Deploy Azure Stream Analytics as an IoT Edge module

Azure Maps

Azure Maps is a collection of geospatial services and SDKs that use fresh mapping data to provide geographic context to web and mobile applications. For an IoT example, see Integrate with Azure Maps (Azure Digital Twins).