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Microsoft Certified Azure Data Fundamentals (Exam DP-900) Certification Guide

You're reading from   Microsoft Certified Azure Data Fundamentals (Exam DP-900) Certification Guide The comprehensive guide to passing the DP-900 exam on your first attempt

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781803240633
Length 300 pages
Edition 1st Edition
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Author (1):
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Marcelo Leite Marcelo Leite
Author Profile Icon Marcelo Leite
Marcelo Leite
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Core Data Concepts
2. Chapter 1: Understanding the Core Data Terminologies FREE CHAPTER 3. Chapter 2: Exploring the Roles and Responsibilities in Data Domain 4. Chapter 3: Working with Relational Data 5. Chapter 4: Working with Non-Relational Data 6. Chapter 5: Exploring Data Analytics Concepts 7. Part 2: Relational Data in Azure
8. Chapter 6: Integrating Relational Data on Azure 9. Chapter 7: Provisioning and Configuring Relational Database Services in Azure 10. Chapter 8: Querying Relational Data in Azure 11. Part 3: Non-Relational Data in Azure
12. Chapter 9: Exploring Non-Relational Data Offerings in Azure 13. Chapter 10: Provisioning and Configuring Non-Relational Data Services in Azure 14. Part 4: Analytics Workload on Azure
15. Chapter 11: Components of a Modern Data Warehouse 16. Chapter 12: Provisioning and Configuring Large-Scale Data Analytics in Azure 17. Chapter 13: Working with Power BI 18. Chapter 14: DP-900 Mock Exam 19. Index 20. Other Books You May Enjoy

Exploring an analytical data model

Analytical data models allow you to organize the data so that it can be analyzed.

Models are defined by the quantitative values that you want to analyze or report (known as measures) and the entities by which you want to aggregate them and are based on connected data tables (known as dimensions).

A model can, for example, include a table with numerical sales measures (such as revenue or quantity), as well as dimensions for products, customers, and time. This would combine sales data of one or more dimensions (for example, to identify total revenue per customer or total items sold per product per month).

The model is conceptually a multidimensional structure known as a cube, where each location in which the dimensions meet represents an added value.

To understand this cubed data organization, let’s look at the following figure:

Figure 5.3 – Cube data model

In the example, we are analyzing a fact (Sales...

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