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Learning Tableau 2022

You're reading from   Learning Tableau 2022 Create effective data visualizations, build interactive visual analytics, and improve your data storytelling capabilities

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801072328
Length 568 pages
Edition 5th Edition
Tools
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Author (1):
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Joshua N. Milligan Joshua N. Milligan
Author Profile Icon Joshua N. Milligan
Joshua N. Milligan
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Table of Contents (20) Chapters Close

Preface 1. Taking Off with Tableau FREE CHAPTER 2. Connecting to Data in Tableau 3. Moving Beyond Basic Visualizations 4. Starting an Adventure with Calculations and Parameters 5. Leveraging Level of Detail Calculations 6. Diving Deep with Table Calculations 7. Making Visualizations that Look Great and Work Well 8. Telling a Data Story with Dashboards 9. Visual Analytics: Trends, Clustering, Distributions, and Forecasting 10. Advanced Visualizations 11. Dynamic Dashboards 12. Exploring Mapping and Advanced Geospatial Features 13. Integrating Advanced Features: Extensions, Scripts, and AI 14. Understanding the Tableau Data Model, Joins, and Blends 15. Structuring Messy Data to Work Well in Tableau 16. Taming Data with Tableau Prep 17. Sharing Your Data Story 18. Other Books You May Enjoy
19. Index

When to use a data model, joins, or blends

In one sense, every data source you create using the latest versions of Tableau will use a data model. Even data sources using one physical table will have a corresponding object in the logical layer of a data model. But when should you relate tables using the data model, when should you join them together in the physical layer, and when should you employ blending?

Most of the time, there’s no single right or wrong answer. However, here are some general guidelines to help you think through when it’s appropriate to use a given approach.

In general, use a data model to relate tables:

  • When joins would make correct aggregations impossible or require complex LOD expressions to get accurate results
  • When joins would result in unwanted duplication of data
  • When you need flexibility in showing full domains of dimensions versus only values that match across relationships
  • When you are uncertain of a...
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