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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 the analytical data store

After the ingestion process, the result set is stored in an analytical data store, which can be a relational database (the default in data warehouse solutions) or a standard object store in big data lakes.

It is important to evaluate these two types of storage. Let’s discuss them in detail.

Data warehouse

A data warehouse is a relational database with a predefined schema, designed for data analysis purposes rather than transactional workloads.

Analytics databases are typically denormalized in a scheme in which numeric values are stored in central fact tables, which are linked to one or more dimension tables that represent entities that can be aggregated.

A fact table can, for example, contain sales order data that can be grouped by customer, product, store, and time (allowing you, for example, to easily find the total monthly sales revenue per product for each store).

The star schema is a type of fact and dimension table...

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