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

Designing AI solutions

In this part, we will talk about the design of AI solutions, including qualification, strategy, and the responsible use of AI. Infusing AI into architecture has to be the result of some strategic consideration. The data architect should ask themself a series of questions, and find a substantiated answer, to end up with an optimal architecture.

The first set of questions is regarding the qualification of a use case.

Is AI the right solution?

This can be further refined to the necessity of an inductive solution, compared to a deductive one. Business rulesets are deductive; machine learning is inductive. Business rules will provide you with a solid answer if the condition for that rule is met. Machine learning models will provide you with answers that have a high probability but not certain ones.

The big advantage of machine learning is its ability to cover cases in a much more granular manner, whereas business rules must group various cases within a...