Book Image

Cloud Analytics with Microsoft Azure - Second Edition

By : Has Altaiar, Jack Lee, Michael Peña
Book Image

Cloud Analytics with Microsoft Azure - Second Edition

By: Has Altaiar, Jack Lee, Michael Peña

Overview of this book

Cloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data. The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse. Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts. By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization.
Table of Contents (7 chapters)

Internet of Things (IoT)

IoT plays a vital role in scaling your application to go beyond your current data sources. IoT is simply an interconnection of devices that are embedded to serve a single purpose in objects around us to send and receive data. IoT allows us to constantly gather data about "things" without manually encoding them into a database.

A smartwatch is a good example of an IoT device that constantly measures your body's vital signs. Instead of getting a measuring device and encoding it to a system, a smartwatch allows you to record your data automatically. Another good example is a device tracker for an asset that captures location, temperature, and humidity information. This allows logistics companies to monitor their items in transit, ensuring the quality and efficiency of their services.

At scale, these IoT devices generate anywhere from gigabytes to terabytes of data. This data is usually stored in a data lake in a raw, unstructured format, and is later analyzed to derive business insights. A data lake is a centralized repository of all structured, semi-structured, and unstructured data. In the example of the logistic company mentioned previously, patterns (such as the best delivery routes) could be generated. The data could also be used to understand anomalies such as data leakage or suspected fraudulent activities.