Building IoT
solutions with Azure:

A Developer's Guide

The Internet of Things (IoT) presents exciting opportunities for developers and businesses, enabling everything from predictive maintenance to smart cities to connected factories. Whatever you want to build—and regardless of your IoT experience—we’ve got you covered. This guide provides an overview of Azure services that address key IoT solution requirements, as well as a step-by- step progression you can use to build proficiency and move toward a fully functioning solution quickly and easily.

Why Microsoft for IoT?

Agile: Start in minutes, scale as needed

With fully-managed services, you can spend less time building and managing basic services and more time building value-added feature. Azure IoT works with a diverse ecosystem out of the box, including management tools tailored to accommodate a multitude of device classes, platforms, and protocols. Azure enables you to connect millions of devices and analyze terabytes of data in 50 regions worldwide—and Microsoft has years of experience delivering cloud services and device platforms.

Comprehensive: Everything in one solution

Microsoft is the only IoT solution provider with a complete platform spanning device to cloud, across big data, advanced analytics, and managed services. Tap into the power of the world’s largest power ecosystem and bring line-of-business and technology to life, across industries and around the world. Microsoft IoT allows you to connect anything, from legacy equipment to a vast ecosystem of certified hardware, and offers the ability to build your own devices across edge, mobile, and embedded systems.

Secure: End-to-end

From endpoint and connection through to data and the cloud, Azure IoT is built to secure devices and data in a wide range of use cases. This includes remote, infrequently accessed, and headless devices in business- and safety-critical scenarios.

What are developers building with Azure IoT?

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Schneider Electric

Empowering global operations

The Schneider Electric development team chose Azure Machine Learning and Azure IoT Edge to incorporate artificial intelligence (AI) and machine learning into its Realift pump monitoring system for predictive maintenance capabilities. Azure Machine Learning supported flexible model management and advanced predictive analytics, while IoT Edge provided an easy way to package, deploy, and run machine learning applications on devices and close to events. The company has the flexibility to run its tools in the cloud or at the edge.

Johnson Controls

Building efficiency with IoT

Johnson Controls developed the GLAS smart thermostat based on the Windows 10 IoT Core operating system. Connected to Microsoft Azure IoT Hub, GLAS gives building owners remote access through web and mobile apps to monitor and control features of the heating and cooling system. The solution can be intuitively managed through touch and voice commands. The company has also used Azure IoT to build Smart Connected Chillers, enabling a 66% reduction in unplanned emergency repairs.


Filtering the signal from the noise

Rolls-Royce uses Azure IoT capabilities including Azure IoT Hub to collect and process engine data from customers’ fleets. The company uses Azure analytics capabilities to create predictive maintenance models. It incorporates managed Azure services such as Azure Data Factory for orchestration and Azure HDInsight for high-level data aggregation and summarization, as well as using Azure SQL Database and Azure Blob Storage to handle varying storage needs. Using Azure IoT, the company can gather actionable insights around fuel usage and predictive maintenance and prevent unscheduled delays.


Transforming the urban landscape

By connecting thousands of sensors embedded in its elevators to the Microsoft cloud, ThyssenKrupp gained real-time visibility into product performance and rapid, remote diagnostic capabilities through HoloLens. Using Azure IoT Hub, ThyssenKrupp has reduced maintenance costs and elevator downtime by arming its 20,000 elevator service technicians with the ability to visualize and identify problems ahead of a job as well as have remote, hands-free access to technical and expert information when onsite.

Introducing Azure IoT

Azure IoT offers a wide range of tools to help you build IoT solutions. This list provides a quick overview of some of the most commonly used technologies. These will be explored in greater detail later in the guide.

Connecting devices

Azure IoT Hub

A platform as a service (PaaS) solution that enables reliable and highly secure bidirectional communication among millions of IoT devices and the Azure cloud.
With IoT Hub, you can implement:

  • High-volume device connectivity and management
  • High-volume telemetry ingestion
  • Device command and control
  • Enforcement of device security policies

Azure IoT Edge

Extend intelligence from the cloud to edge devices

Managing events

Azure Event Grid

Reliable event delivery at massive scale.

Unlocking insights

Azure Time Series

Instantly explore and analyze time-series data


Azure Stream Analytics

A fully managed event-processing engine for real-time analytic computations 
on streaming data.


Azure Machine Learning

Open and elastic AI development spanning the cloud and the edge.

Getting started quickly

Azure IoT preconfigured solutions

Enterprise-grade templates for custom IoT solutions

The universal IoT application pattern

In this section we’ll describe the three key elements that typical IoT solutions have in common— things, insights, and actions—and where Microsoft technologies fit into each.


The devices or sensors you want to connect to the cloud. These can be devices with processors capable of running an IoT client, or low-power devices that have no such capabilities. This element encompasses the full device lifecycle, including device security, monitoring, and management.


This sensor sends data about the status of a water pump.


The useful aspects of the data captured by the IoT devices, made possible by data processing and analytics capabilities. This can include “hot path” data that is analyzed in real time, and “cold path” data that is stored for batch processing later.


The data is analyzed using machine learning to determine when maintenance is needed.


What you ultimately do with the data, whether presenting it through a visualization tool, alerting a human to do something, connecting to a business process, or sending a message back to the device.

A field service technician receives an alert when maintenance is required, and a work order is automatically generated.

Connect with more resources and step-by-step guidance for getting started with your first IoT deployment. To access the full developer’s guide, please complete the following short profile.