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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

Data transformations in data lake tiers

As we saw in Chapter 3, we dump raw data into the bronze layer. It serves as the primary source of raw, unrefined data for the warehouse. This layer contains all the original data as it is received from various sources, including transactional systems, log files, and external data feeds. The purpose of the bronze layer is to provide a centralized location for raw data to be stored, and to make it available for further processing in the higher layers of the warehouse. Data is transformed into the silver and gold layers.

Bronze-to-silver transformations

When moving from the bronze layer to the silver layer, a series of transformations are applied to make the data more usable for analysis. Some examples of transformations that are typically done in the silver layer include the following:

  • Data cleansing: Removing any duplicates and correcting errors and inconsistencies in the data.
  • Data integration: Combining data from multiple...

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