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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

Fundamental concepts of data warehousing

An (enterprise) data warehouse, often abbreviated as DW or DWH, is a specialized system utilized for analyzing and reporting data. It acts as a centralized hub where data from different sources is consolidated and organized, serving as a vital component of business intelligence (BI). Businesses need to make informed decisions by learning from data from the past as well as examining present data. To accomplish this, both sets of data are stored in a single location called the data warehouse. Operational systems such as customer relationship management (CRM) systems (sales) or marketing are often data sources of a data warehouse and may require cleansing and curating before they can be utilized for analysis and reporting.

The design of a data warehouse includes two essential concepts: extract, transform, and load (ETL) and extract, load, and transform (ELT). These processes involve extracting data from source systems and transforming it into...

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