Book Image

Azure Data and AI Architect Handbook

By : Olivier Mertens, Breght Van Baelen
Book Image

Azure Data and AI Architect Handbook

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)
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

Transforming data on Azure

As datasets continue to grow in size and complexity, it is increasingly important to have efficient ways of manipulating and processing this data. We will cover both batch and real-time transformation options.

For batch transformations, we will discuss the use of the following:

  • Mapping data flows
  • Spark notebooks
  • SQL scripts
  • SSIS

These tools can be used for shaping and cleaning large datasets and allow you to define complex data transformations using a visual interface or programming language, making it easy to handle even the most challenging data manipulation tasks.

For real-time transformations, we will look at the following:

  • Azure Stream Analytics
  • Azure Databricks

Both technologies allow you to process data with remarkably low latency, enabling real-time insights and decision-making. With these tools, you can process data streams from various sources in real time, transforming and analyzing the data as...