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

Classifying the data type

First, we will explore how the architect can classify different types of data. Data can be classified into three different types:

  • Structured data
  • Semi-structured data
  • Unstructured data

We will also examine various file types associated with each type of data, as different file formats have their own characteristics, benefits, and drawbacks. For each data type, a solid understanding of these file types and their features can help to optimize storage costs, retrieval speeds, and scalability.

Note that there can be some ambiguity on which file format falls under which data type. In particular, file formats such as CSV and Avro are often classified as either structured or semi-structured, depending on whom you ask and what their exact definition is. However, this exact classification is not of importance to the data architect. What is important is knowing which file type is optimal in which scenario.

Structured data

Structured data...