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Azure IoT: Turn Device Data into Insights and Action

Name: Azure IoT: Turn Device Data into Insights and Action
Rating: 5.0 (7 reviews)

Hands-on guide to sending, processing, and turning IoT data into actionable insights in Azure

Created byBart van Uden

Last updated 3/2026

English

English [Auto],

What you'll learn

Connect devices to Azure IoT Hub and send telemetry to the cloud
Store IoT data long-term with the cold path (Azure Storage)
Learn about device-to-cloud messages
Query and visualize telemetry with Azure Data Explorer and KQL
Build a complete end-to-end IoT solution in Azure without coding
Detect critical conditions in near real-time with Stream Analytics
Route IoT messages to Azure services using IoT Hub message routing
Understand cold, warm and hot path architecture and when to use each

Course content

8 sections • 58 lectures • 4h 15m total length

Introduction2:33
Turn cloud-stored IoT data into actionable insights and actions. Learn end-to-end cloud architecture, data storage, processing, querying, dashboards, and automated actions in Azure, designed for absolute beginners.

Introducing the Cloud and Azure7:35
Introduction to IoT - What, Why, Where?7:26
Internet of Things - Big picture view2:15
IoT devices - The things6:50
IoT Hub2:18
Explore Azure IoT Hub, a secure two-way cloud gateway that scales to millions of devices, routes messages to other Azure services, and stores data for cold, warm, and hot paths.
IoT Architecture - Cold, warm and hot path7:46
Understand the cold, warm, and hot data paths in IoT architecture, where hot data enables near real-time actions, warm path supports recent data analysis, and cold path archives raw data.
IoT Architecture in Azure2:31
Module recap1:29

Module intro1:30
Getting Azure2:42
Sign up for azure free trial or pay-as-you-go, explore free resources like IoT Hub, and log into portal.azure.com after linking a Microsoft account; you won’t be charged unless you switch.
A quick word on tools1:31
Demo: Creating an IoT Hub8:51
Provision an IoT Hub in the Azure portal, organize it with a resource group and naming convention, choose a region and tier, and explore the IoT Hub overview.
Getting an IoT device3:59
Set up a device and connect it to the IoT Hub using a browser-based Raspberry Pi simulator, sending BME280 readings with a Node.js app.
Demo: Connect a device to IoT Hub5:32
Demo: Explore messages in IoT Hub - with Azure IoT Explorer8:36
Module recap1:50

Module intro1:38
Message protocols5:13
Learn how MQTT and AMQP provide reliable, low-power, two-way IoT communication with persistent connections and quality of service guarantees via queueing, versus HTTP, illustrated by a thermostat and JSON messages.
Introduction to JSON6:25
IoT Hub messages6:52
Demo: Send a Cloud-to-Device message - part 15:44
Demo: Send a Cloud-to-Device message - part 23:56
Demonstrates using cloud-to-device messages to control an IoT device by setting a LEDState property to on or off, updating the receive handler, and gating LED blinking.
IoT Hub message routing7:19
Module recap1:26

Module intro1:15
Cold path - a quick refresher4:09
Explore the cold path in IoT architecture, focusing on storing raw data with no processing for archiving and historical analysis using Azure Storage Account and IoT Hub message routing.
Demo: Create Azure Storage Account4:23
Create an azure storage account in the portal with a consistent prefix and region, then create a blob container named raw data for locally redundant message routing.
Demo: Creating a message route6:37
Demo: Validating the cold path8:58
Demo: Installing Azure CLI with IoT Extension4:11
Demo: Sending messages with Azure CLI7:57
Demo: Re-enable fallback route5:17
Re-enable routing by adding a built-in endpoint alongside the cold path with route query set to true, ensuring every device-to-cloud message reaches both endpoints.
Module recap1:27
Built the cold path, stored raw data for future analysis, explored the Azure Storage Account and IoT Hub routes, and introduced the Azure CLI to make the message body readable.

Module intro2:14
Explore the warm path for near real-time analytics with Azure Data Explorer, linking to Azure IoT Hub, ingesting and querying data with KQL, and visualizing on ADX dashboards.
The warm path revisited2:58
Introducing Azure Data Explorer (ADX)4:01
Demo: Create Azure Data Explorer cluster5:32
Demo: Create Azure Data Explorer table5:39
Demo: Connect IoT Hub to ADX5:35
IoT Hub Consumer groups2:23
Demo: Connect IoT Hub to ADX - the right way3:17
Demo: Kusto Query Language basics7:06
Demo: Visualize your data with KQL5:24
Module recap1:50

Module intro2:09
Revisiting the hot path6:03
Introducing Azure Stream Analytics3:34
Explore Azure Stream Analytics to process real-time IoT data by building a job with input, query, and output, using SQL-based filtering, aggregation, and routing results to actions.
Demo: Create Stream Analytics Job2:46
Demo: Link IoT Hub to Stream Analytics Job3:56
Connect the stream analytics job to IoT Hub by configuring a stream input, creating a dedicated ASA consumer group, and setting JSON UTF-8 encoding for telemetry in the job topology.
Demo: Stream Analytics Queries - part 1 - examples5:28
Demo: Stream Analytics Queries - part 2 - Alert condition detection3:13
Detect high temperature messages above 30 degrees Celsius using a where clause and isFirst over a 10-minute window, partition by device ID, returning first alert per device every 10 minutes.
Demo: Create Event Hub3:08
Demo: Create Event Hub Output4:37
Hook up the Event Hub output in the Stream Analytics job, set JSON UTF-8 encoding, and verify the one message per device every 10 minutes rule with the simulator.
Demo: Create Logic App8:55
Set up an Azure logic app to trigger on Event Hub data, parse JSON, and send email notifications via Outlook, using a copied connection string and parsed schema.
Module recap1:22

Requirements

No programming experience needed
No cloud or IoT experience needed
No IoT hardware needed
An Azure subscription is recommended to follow along with the demos, but not required. We'll use free services where possible, and any paid services easily fit within the $100 credit you get with a new account — so your bill should be close to zero. No subscription? No problem, you can still follow along and learn everything!

Description

Most IoT tutorials stop at the device. They show you how to connect hardware, send some data to the cloud, and call it done. And that's often where it ends.

In this course, that's just the beginning.

By the end, you'll have built a complete, working IoT data pipeline in Azure, from a connected device all the way to real-time dashboards and automated alerts. And no, you won't need any hardware. Or much code, for that matter.

The architecture you'll build is based on a real pattern used in production IoT solutions: the cold, warm, and hot path. Each path handles data differently depending on how urgent it is. A sensor value that might indicate a machine is overheating? That needs to be acted on immediately. Historical data for trend analysis? That can wait. By the end of the course, you'll understand the difference and know how to build both.

How the course works

Throughout the course we follow one concrete scenario: a device that sends temperature and humidity readings to the cloud. Simple enough to follow, but realistic enough to be useful.

We start by getting that device connected and sending its first messages, and this might surprise you: that part is easier than most people expect. From there, we gradually build out the full architecture. Each module adds another layer to the solution, so by the end you have a complete picture rather than a collection of isolated demos.

The course is demo-heavy by design. Theory is kept to the minimum needed to understand what you're building and why. No heavy coding either. Any scripts or queries used in the demos are available in the GitHub repository that comes with the course.

Who this course is for

This course is a good fit if you're a developer or cloud professional who wants to understand how IoT data processing actually works in Azure, and build it yourself. You don't need prior IoT or cloud experience to get started.

If you're working with data or AI in Azure and want to understand where that data comes from and how IoT pipelines feed into those systems, this course gives you that foundation.

It's not the right fit if you're looking for embedded systems or hardware setup. This course focuses on the cloud side of things.

What's covered

Eight modules, more than four hours of video content:

Course introduction
Introduction to Azure IoT — the cloud, IoT concepts, and the cold/warm/hot architecture
Getting started hands-on — create an IoT Hub, connect a device, send your first messages
IoT Hub messaging — message format, protocols, cloud-to-device messages, and message routing
Cold path — long-term storage with Azure Storage
Warm path — querying and visualizing data with Azure Data Explorer and KQL
Hot path — real-time detection and automated alerts with Stream Analytics, Event Hub, and Logic Apps
Final solution recap — the complete architecture end-to-end

All scripts, queries, and CLI commands used in the demos are available in the GitHub repository that comes with the course.

Who this course is for:

Beginner IoT developers who want to learn not just how to send data to the cloud, but how to actually do something useful with it.
Architects who want to design systems for processing IoT data in the cloud
Data engineers who want to expand their knowledge to IoT systems

Azure IoT: Turn Device Data into Insights and Action

What you'll learn

Explore related topics

Course content

Course introduction1 lecture • 3min

Introduction to Azure and IoT8 lectures • 38min

Getting started, hands-on8 lectures • 35min

IoT Messages8 lectures • 39min

Cold path9 lectures • 44min

Warm path11 lectures • 46min

Hot path11 lectures • 45min

Final Solution Recap2 lectures • 6min

Requirements

Description

Who this course is for: