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

Summary

In this chapter, we discussed how the semantic layer is used to create a more performant layer for reporting. We learned the semantic layer can be implemented using either multidimensional or tabular models. Multidimensional models are based on the concept of cubes and provide analytical capabilities by aggregating the data. Tabular models, on the other hand, utilize a columnar in-memory technology known as the VertiPaq engine, which enables faster processing and compression. Tabular models are well suited for scenarios where fast query performance and self-service analytics are paramount.

The VertiPaq engine was explained as a key component of tabular models and powers their impressive performance capabilities. By leveraging in-memory storage and columnar data structures, the VertiPaq engine optimizes data compression and enables efficient query execution. This engine plays a significant role in the success of tabular models, allowing for interactive and near-real-time...