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

Tools for the semantic layer

There are several different tools for implementing the semantic layer, as follows:

  • SQL Server Analysis Services (SSAS): SSAS is part of the SQL Server services. It is primarily deployed on-premises, meaning it is hosted within an organization’s local infrastructure. SSAS offers a robust and scalable platform for building multidimensional and tabular models. Initially, only multidimensional models were supported, but due to popular demand, tabular model support was added later. Multidimensional models are designed for complex analytical scenarios and support features such as hierarchies, calculated members, and advanced aggregation capabilities. Tabular models, on the other hand, provide a simpler and more intuitive approach to data modeling, allowing users to create analytical models using tables and relationships. SSAS also includes advanced features such as data mining and data visualization.

The following architecture diagram (Figure...