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

Multidimensional versus tabular models

The semantic layer can be constructed using two different approaches: multi-dimensional models and tabular models. Multi-dimensional models, which represent an earlier technique, involve the creation of what are known as OLAP cubes. These cubes are designed to capture and express the multidimensional nature of the underlying data. To better understand this concept, one can envision an extension of a two-dimensional table in a spreadsheet, where data can be aggregated across not just two axes but multiple dimensions.

To illustrate the practical application of multi-dimensional models, let’s consider a scenario where a data warehouse contains detailed purchase records at the lowest level of granularity. The CEO of the organization wants to translate this data into a structure that allows them to report on revenue trends per country per product. In this case, a multidimensional cube would be created, aggregating the purchase data across...