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

Applying enterprise-level data governance

Not every cloud data architect will have to be as well-versed in the following part, as the implementation of data governance is something that happens organization-wide, rather than on the level of a single data solution. It would mainly involve a chief architect, CDO, or CIO.

Data governance forms an essential component for any data estate at scale, yet we cannot implement data governance blindly. As we will see later, data governance can be implemented too late or too early.

Neglecting the need for governance over a longer period of time, until the data platform reaches full-scale maturity, will cause many issues in the long run. The longer governance, compliance, and data management are neglected, the more technical debt is built up. The data landscape will turn into an unmanageable swamp, making it hard or near impossible to clean up and organize. Therefore, the key takeaway here is to think about data governance from the start.

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