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

Knowing the roles in data science

The Azure cloud offers an extensive range of services for use in advanced analytics and data science. Before we dive into these, it is crucial to understand the different roles in the data science ecosystem. In previous chapters, while always looking through the lens of a data architect, we saw workloads that are typically operationalized by data engineers, database administrators, and data analysts.

Up until now, the chapters followed the journey of data through a data platform, from ingestion to raw storage to transformation, data warehousing, and eventually, visualization and dashboarding. The advanced analytics component is more separated from the entire solution, in the sense that most data architectures can perform perfectly without it. This does not take away from the fact that adding advanced analytics such as machine learning predictions can be a valuable enhancement to a solution.

The environment for advanced analytics introduces some...