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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

Summary

In this chapter, we zoomed in on two Microsoft frameworks, the WAF and part of the CAF.

We discussed the five pillars of the WAF. Although this framework is designed for all workloads, we curated the relevant principles for data and AI workloads. We explored design principles to optimize reliability, build airtight security, architect for cost-effectiveness, maximize operational excellence, and boost the performance efficiency of an Azure data solution.

Next, the concept of landing zones was introduced, as part of the CAF. After an introduction to some fundamental Azure concepts, such as subscriptions and resource groups, we examined both the data management landing zone and the data landing zone. Leveraging these landing zones is a great way to get started quickly with any data solution at scale on the Azure cloud.

In the next chapter, we will focus on data ingestion into the cloud.

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