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Azure Data and AI Architect Handbook

Azure Data and AI Architect Handbook

By : Olivier Mertens, Breght Van Baelen
4.5 (13)
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Azure Data and AI Architect Handbook

Azure Data and AI Architect Handbook

4.5 (13)
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)
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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

Building a data warehouse in the cloud

Data warehouses can be built with different Azure services. Traditional data warehouses used to be built on-premises with databases in SQL servers. When moving to the cloud, this changed to either SQL server on Azure VMs (Infrastructure as a Service, or IaaS) or Azure SQL Database or Managed Instance (Platform as a Service, or PaaS), depending on how Microsoft-managed the database needed to be. Building SQL databases feel very familiar to building data warehouses as they are also often used operationally as a backend for applications. However, data warehouses are built for analytical purposes, not operational purposes, and thus have different needs, as outlined here:

  • Queries against operational databases are often frequent and simple in nature (small reads and writes), whereas queries against analytical data warehouses are infrequent and complex in nature (often with lots of joins and aggregates).
  • Data warehouses are often nonvolatile...

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