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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
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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

Building a data warehouse in the cloud

Data warehouses can be built with different Azure services. Traditional data warehouses used to be built on-premises with databases in SQL servers. When moving to the cloud, this changed to either SQL server on Azure VMs (Infrastructure as a Service, or IaaS) or Azure SQL Database or Managed Instance (Platform as a Service, or PaaS), depending on how Microsoft-managed the database needed to be. Building SQL databases feel very familiar to building data warehouses as they are also often used operationally as a backend for applications. However, data warehouses are built for analytical purposes, not operational purposes, and thus have different needs, as outlined here:

  • Queries against operational databases are often frequent and simple in nature (small reads and writes), whereas queries against analytical data warehouses are infrequent and complex in nature (often with lots of joins and aggregates).
  • Data warehouses are often nonvolatile...
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