Book Image

Azure Data and AI Architect Handbook

By : Olivier Mertens, Breght Van Baelen
Book Image

Azure Data and AI Architect Handbook

By: Olivier Mertens, Breght Van Baelen

Overview of this book

With data’s growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution. You’ll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you’ll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you’ll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you’ll discover how to add data governance, compliance, and security to solutions. By the end of this book, you’ll have gained the expertise needed to become a well-rounded Azure Data & AI architect.
Table of Contents (18 chapters)
1
Part 1: Introduction to Azure Data Architect
4
Part 2: Data Engineering on Azure
8
Part 3: Data Warehousing and Analytics
13
Part 4: Data Security, Governance, and Compliance

Data Warehousing

As businesses collect ever-increasing amounts of data, data warehousing has become a critical component in managing and analyzing data. Cloud-based data warehousing provides a flexible and cost-effective solution for building systems that support data analytics as well as reporting. Here, we’ll explore the fundamental concepts of data warehousing, as well as the different approaches to designing a data warehouse.

We will start by discussing the two main approaches to data warehousing: the normalized approach by Bill Inmon (Inmon, William H. (1992). Building the Data Warehouse. Boston: QED Technical Pub. Group. ISBN 0-89435-404-3. OCLC 24846118) and the dimensional approach by Ralph Kimball (Ralph Kimball and Margy Ross (26 April 2002). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Second ed.). Wiley. ISBN 0-471-20024-7). Inmon’s approach emphasizes the importance of data integration and consistency, while Kimball’s...