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

Summary

In this chapter on data warehousing in the cloud, we covered key concepts and approaches to data warehousing, including the normalized approach by Bill Inmon and the dimensional approach by Ralph Kimball. We also explored building a data warehouse in the cloud using Azure SQL Database and Synapse SQL, including dedicated pools and serverless pools with the medallion architecture.

By the end of this chapter, readers should have a solid understanding of the different data warehousing approaches and the benefits of building a data warehouse in the cloud. They should also be able to build a data warehouse using Azure SQL Database and Synapse SQL. These skills are essential for data architects looking to design and implement data warehousing solutions in the cloud.

The semantic layer is the next logical step in building a data platform after the data warehouse. It enables users to easily access and analyze data without needing to understand the complex underlying data model...