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Data Modeling with Tableau

You're reading from   Data Modeling with Tableau A practical guide to building data models using Tableau Prep and Tableau Desktop

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803248028
Length 356 pages
Edition 1st Edition
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Author (1):
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Kirk Munroe Kirk Munroe
Author Profile Icon Kirk Munroe
Kirk Munroe
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Table of Contents (22) Chapters Close

Preface 1. Part 1: Data Modeling on the Tableau Platform
2. Chapter 1: Introducing Data Modeling in Tableau FREE CHAPTER 3. Chapter 2: Licensing Considerations and Types of Data Models 4. Part 2: Tableau Prep Builder for Data Modeling
5. Chapter 3: Data Preparation with Tableau Prep Builder 6. Chapter 4: Data Modeling Functions with Tableau Prep Builder 7. Chapter 5: Advanced Modeling Functions in Tableau Prep Builder 8. Chapter 6: Data Output from Tableau Prep Builder 9. Part 3: Tableau Desktop for Data Modeling
10. Chapter 7: Connecting to Data in Tableau Desktop 11. Chapter 8: Building Data Models Using Relationships 12. Chapter 9: Building Data Models at the Physical Level 13. Chapter 10: Sharing and Extending Tableau Data Models 14. Part 4: Data Modeling with Tableau Server and Online
15. Chapter 11: Securing Data 16. Chapter 12: Data Modeling Considerations for Ask Data and Explain Data 17. Chapter 13: Data Management with Tableau Prep Conductor 18. Chapter 14: Scheduling Extract Refreshes 19. Chapter 15: Data Modeling Strategies by Audience and Use Case 20. Index 21. Other Books You May Enjoy

Aggregating data

To create impactful data models in Tableau, it is important to understand the level of detail in your data sources. In the previous sections of this chapter, we looked at sales data. This sales data had a row for every product sold in each sales transaction. That is, if a customer had an order that had 11 products in it, that would generate 11 rows of data. That creates the level of detail of the data source.

In the previous section of this chapter, we pivoted data to create a row of sales targets for each country for each month. This defines the level of detail of the data.

For an analyst, understanding the level of detail is essential to know what answers you can get from your data model. As someone creating data models, you need to understand the level of detail when combining data sources into a single data model. To join two or more data sources into a single data model, they typically need to be at the same level of detail.

Let’s imagine that...

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