Book Image

The Azure IoT Handbook

By : Dan Clark
Book Image

The Azure IoT Handbook

By: Dan Clark

Overview of this book

With the rise of cloud-based computing, deploying IoT systems has become more cost-effective for businesses. This transformation has led to developers and architects shouldering the responsibility of creating, managing, and securing these systems, even if they are new to the IoT technology. The Azure IoT Handbook is a comprehensive introduction to quickly bring you up to speed in this rapidly evolving landscape. Starting with the basic building blocks of any IoT system, this book guides you through mobile device management and data collection using an IoT hub. You’ll explore essential tools for system security and monitoring. Following data collection, you’ll delve into real-time data analytics using Azure Stream Analytics and view real-time streaming on a Power BI dashboard. Packed with real-world examples, this book covers common IoT use as well. By the end of this IoT book, you’ll know how to design and develop IoT solutions leveraging intelligent edge-to-cloud technologies implemented on Azure.
Table of Contents (18 chapters)
1
Part 1: Capturing Data from Remote Devices
7
Part 2: Processing the Data
12
Part 3: Processing the Data

Stream analytics use cases

Stream analytics, also known as real-time analytics or real-time data processing, involves analyzing and deriving insights from data that is generated continuously and in real time. Here are some common use cases for stream analytics:

  • Fraud detection: Stream analytics can be used to detect fraudulent activities in real time. By continuously analyzing incoming data from various sources, such as financial transactions or user behavior patterns, patterns indicative of fraud can be identified and appropriate actions can be taken immediately.
  • IoT data processing: IoT generates vast amounts of data from connected devices. Stream analytics can process and analyze this data in real time, enabling real-time monitoring, anomaly detection, predictive maintenance, and operational optimization.
  • Network monitoring and anomaly detection: Stream analytics can be used to monitor network traffic and detect anomalies or suspicious activities in real time. This...