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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

Fundamental concepts of data warehousing

An (enterprise) data warehouse, often abbreviated as DW or DWH, is a specialized system utilized for analyzing and reporting data. It acts as a centralized hub where data from different sources is consolidated and organized, serving as a vital component of business intelligence (BI). Businesses need to make informed decisions by learning from data from the past as well as examining present data. To accomplish this, both sets of data are stored in a single location called the data warehouse. Operational systems such as customer relationship management (CRM) systems (sales) or marketing are often data sources of a data warehouse and may require cleansing and curating before they can be utilized for analysis and reporting.

The design of a data warehouse includes two essential concepts: extract, transform, and load (ETL) and extract, load, and transform (ELT). These processes involve extracting data from source systems and transforming it into...

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