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Azure Data and AI Architect Handbook

You're reading from   Azure Data and AI Architect Handbook Adopt a structured approach to designing data and AI solutions at scale on Microsoft Azure

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781803234861
Length 284 pages
Edition 1st Edition
Tools
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Authors (2):
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Olivier Mertens Olivier Mertens
Author Profile Icon Olivier Mertens
Olivier Mertens
Breght Van Baelen Breght Van Baelen
Author Profile Icon Breght Van Baelen
Breght Van Baelen
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1: Introduction to Azure Data Architect
2. Chapter 1: Introduction to Data Architectures FREE CHAPTER 3. Chapter 2: Preparing for Cloud Adoption 4. Part 2: Data Engineering on Azure
5. Chapter 3: Ingesting Data into the Cloud 6. Chapter 4: Transforming Data on Azure 7. Chapter 5: Storing Data for Consumption 8. Part 3: Data Warehousing and Analytics
9. Chapter 6: Data Warehousing 10. Chapter 7: The Semantic Layer 11. Chapter 8: Visualizing Data Using Power BI 12. Chapter 9: Advanced Analytics Using AI 13. Part 4: Data Security, Governance, and Compliance
14. Chapter 10: Enterprise-Level Data Governance and Compliance 15. Chapter 11: Introduction to Data Security 16. Index 17. Other Books You May Enjoy

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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