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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

Moving to self-service BI

Over the years, BI has undergone significant changes, with new generations of BI systems emerging to meet the evolving needs of businesses. The three waves of BI, namely technical BI, self-service BI, and end user BI, are key milestones in the history of BI.

Let’s take a look at how each wave changed the way we work in BI:

  • First wave: Technical BI refers to the early stages of BI when IT staff, developers, and data analysts relied on technical tools to manage and analyze data. These tools were often expensive and required significant technical expertise to use.
  • Second wave: Self-service BI refers to the emergence of BI systems that provided business users with more accessible tools to access and analyze data without relying on IT staff or data analysts. Power BI is an example of a self-service BI tool that provides drag-and-drop interfaces and data visualizations to make it easier for non-technical users to work with data.
  • Third wave...

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