Book Image

Learning Tableau 2022 - Fifth Edition

By : Joshua N. Milligan
Book Image

Learning Tableau 2022 - Fifth Edition

By: Joshua N. Milligan

Overview of this book

Learning Tableau 2022 helps you get started with Tableau and data visualization, but it does more than just cover the basic principles. It helps you understand how to analyze and communicate data visually, and articulate data stories using advanced features. This new edition is updated with Tableau’s latest features, such as dashboard extensions, Explain Data, and integration with CRM Analytics (Einstein Analytics), which will help you harness the full potential of artificial intelligence (AI) and predictive modeling in Tableau. After an exploration of the core principles, this book will teach you how to use table and level of detail calculations to extend and alter default visualizations, build interactive dashboards, and master the art of telling stories with data. You’ll learn about visual statistical analytics and create different types of static and animated visualizations and dashboards for rich user experiences. We then move on to interlinking different data sources with Tableau’s Data Model capabilities, along with maps and geospatial visualization. You will further use Tableau Prep Builder’s ability to efficiently clean and structure data. By the end of this book, you will be proficient in implementing the powerful features of Tableau 2022 to improve the business intelligence insights you can extract from your data.
Table of Contents (20 chapters)
18
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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...