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

Determining how the data will be used

The aforementioned data types are stored in either a data lake or a database. How the data will be used will determine in which service the data needs to be stored.

As described in the previous chapters, a data lake is a centralized repository that allows data to be stored in its raw format without the need for predefined schemas. Data lakes are often used for big data and analytics workloads, as they enable storing and processing large amounts of data from various sources in a flexible way.

A database, on the other hand, can store structured (and, in some cases, semi-structured) data that is organized in a specific way, typically with a defined schema and defined relationships between the data. This form of organization makes it easy to search, sort, and manipulate the data, and is often used for transactional workloads.

Relational databases

Structured data is often stored and queried using relational databases. These databases utilize...