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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

Modes in tabular models

By default, tabular models use an Import mode to load data into memory. An ETL tool such as Power Query extracts the data from data sources, transforms it, and loads it into memory. Afterward, DAX queries can be performed against the in-memory database to calculate and aggregate the data. When tabular models query data residing in memory, processing can be very fast, but the data also needs to be refreshed with the ETL tool every now and then to reflect the most recent changes.

DirectQuery mode is very different from Import mode. Queries are run against the underlying data sources instead of the in-memory database. This means the data is always up-to-date and no refreshes of the in-memory database need to be scheduled, but latencies are higher, meaning performance is worse. Another benefit of DirectQuery mode is that the data model can grow beyond the memory size limits as no copy of the data is kept in memory.

When performing a DAX query against a tabular...

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