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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

The importance of data governance and compliance

Businesses are becoming more and more data-driven as time goes on. They have good reason to do so, as revenue, time-to-market, profits, and customer satisfaction increase greatly for data-driven businesses. Given this trend, the need for data governance is higher than ever.

The goals of data governance can be summarized as follows:

  • Managing an ever-growing data landscape
  • Overcoming data silos
  • Increasing data agility
  • Complying with data regulations

More data-driven organizations mean a faster growth of the global data volume, but also a larger data landscape per organization. The latter will cause serious issues in the long run when managed incorrectly. With a strong data governance strategy alongside the right tools and services, a data landscape will remain clear and structured at scale. The data landscape tends to grow exponentially with the business. When the business grows, new subsidiaries or acquisitions...

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