Azure IoT: Turn Device Data into Insights and Action

Explore the cloud concept, Azure as a platform, regions, SaaS, IaaS, and PaaS, and how pay-as-you-go, scaling, and cloud advantages differ from traditional hosting.

Explore what IoT is, why we use it, and where it fits, highlighting device-to-cloud communication and real-time data insights.

Take a 100,000-foot view of an IoT system, tracing data from devices to the cloud through a gateway, into storage, then analysis and actionable insights.

Explore how IoT devices like a smart thermostat, car, and fitness tracker send telemetry and device state data through gateways to cloud hubs, enabling insights and automated actions.

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.

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.

Outline Azure IoT architecture with cold, warm, hot paths: cold stores raw data; warm enables dashboards with Azure Data Explorer; hot analyzes data via Azure Stream Analytics and triggers alerts.

Summarize scalability and PaaS services, and explain how the internet of things enables device-to-cloud data exchange, plus the cold, warm, hot architecture and a hands-on Azure IoT Hub.

Start the module by setting up an IoT Hub, registering a device, and sending your first messages to the IoT Hub, then confirm delivery, with an Azure subscription.

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.

Explore Azure IoT tooling by using the Azure Portal, installing essential tools as needed, and creating your first IoT hub to turn device data into insights and action.

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.

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.

Register a new device in the IoT Hub identity registry as Raspberry Pi simulator, generate a symmetric key, and use the connection string to connect and monitor device-to-cloud messages.

Use Azure IoT Explorer to monitor telemetry from devices in an IoT Hub, inspect message bodies and properties, and understand system fields like enqueue time.

Set up an Azure subscription, create an IoT Hub, add a simulated device, and verify messages with IoT Explorer, laying groundwork for two-way device-to-cloud communication.

Explore common IoT protocols and the role of JSON in device messaging, then examine IoT Hub device-to-cloud and cloud-to-device messages and the hot, warm, and cold routing.

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.

Explore JSON, a lightweight, readable data format used in IoT messaging with the IoT Hub, and learn its syntax for strings, numbers, booleans, arrays, objects, and nesting.

Explore the two main IoT Hub message types—device-to-cloud and cloud-to-device—using the same format (system properties, application properties, and a JSON body) to enable routing and reliable delivery.

Send cloud-to-device messages using the azure portal and iot hub, validate delivery on a raspberry pi simulator, and extend the code to print message properties and trigger actions.

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.

Explore how IoT Hub routes device messages to built-in and custom endpoints using routing queries that evaluate message body, application properties, and system properties.

Explore IoT Hub messages, protocols, and json, and understand the structure with application and system properties and the body, plus cloud-to-device messaging and message routing to Azure services.

Master the cold path in IoT Hub by establishing Azure data storage, setting up routing with a custom endpoint and routing query, and validating long-term storage of messages.

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.

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.

Set up an IoT Hub message route to copy raw data to Azure Blob Storage using a storage endpoint named raw data, with 60-second batching, 100 MB, and JSON format.

Validate end-to-end data flow from the simulator to the IoT Hub and blob storage, verify batch writing, and decode base64 payloads to reveal JSON data for routing.

Explore how to install the azure cli and iot extension, sign in, and manage iot resources with commands like az iot hub list from any platform.

Demonstrates sending device-to-cloud messages with Azure command-line interface by building a command from az, IoT, and device groups, including device-id, hub-name, data, and system properties for content type and encoding.

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.

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.

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.

Explore the warm path in IoT architectures, enabling mid-term data storage, dashboards, and trend analysis with Azure Data Explorer that ingests, stores, and analyzes data from IoT Hub.

Discover Azure Data Explorer (ADX) for time series IoT data, ingesting from IoT Hub into a managed cluster with database and table, using Kusto Query Language for real-time analysis.

Provision an Azure Data Explorer cluster, create a warm data database, enable streaming ingestion, and map telemetry JSON to table columns.

Create a telemetry table in Azure Data Explorer with message id and device id, enable streaming ingestion, and set up a JSON mapping to link cloud-to-device messages to the table.

Connect IoT Hub to Azure Data Explorer to ingest telemetry into a JSON-formatted table via a data connection and mapping, decoding base64 messages to JSON for near real-time flow.

Learn how IoT Hub’s built-in endpoint uses consumer groups to fan out messages, and create a dedicated consumer group for Azure Data Explorer so each service gets its own copy.

Connect IoT Hub to ADX by creating a new consumer group, updating the data connection, and validating messages flow into ADX and IoT Explorer with Kusto Query Language coming up.

Learn the basics of Kusto Query Language for IoT using a telemetry source, the pipe operator, and commands like where, sort, project, and summarize to derive insights with aggregation functions.

Visualize time series data with KQL using the render operator to create line charts and dashboards, enabling real-time temperature and humidity insights on Azure Data Explorer.

Stream telemetry data from an IoT device into Azure Data Explorer via IoT Hub, analyze with KQL, and visualize results on auto-refresh dashboards.

Explore the hot path's speed in processing data with Azure Stream Analytics. Build an ASA job, connect IoT Hub, write a query, and trigger a real-time factory alert without coding.

Leverage the hotpath for near real-time alerts and automated responses by analyzing IoT Hub data with Azure Stream Analytics, then triggering emails via a low-code workflow.

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.

Provision an Azure Stream Analytics job in the Azure portal, verify subscription, resource group, naming, and region; deploy with cloud hosting, one-third streaming unit, and link to the IoT Hub.

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.

Learn to craft stream analytics queries with IoT Hub data, from simulating input and inspecting JSON properties to calculating per-device min, max, and average temperature in one-minute windows.

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.

Learn how to set up an Azure event hub in the IoT portal, including basic pricing, one throughput unit, and a single partition event hub with retention of one hour.

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.

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.

This module recap shows building a hot path with Azure Stream Analytics, and writing real-time queries for IoT data, with mentions of Azure Event Hub and Azure Logic Apps.

Review the Azure IoT architecture, tracing messages from IoT Hub to built-in and custom endpoints, with Azure Data Explorer and Azure Stream Analytics, plus an Event Hubs–triggered Logic App email.

Explore how Azure IoT Hub serves as the central cloud hub, enabling device management and secure bi-directional communication, and examine architecture variations with Stream Analytics or Event Hubs.