Book Image

Mastering Tableau 2023 - Fourth Edition

By : Marleen Meier
Book Image

Mastering Tableau 2023 - Fourth Edition

By: Marleen Meier

Overview of this book

This edition of the bestselling Tableau guide will teach you how to leverage Tableau's newest features and offerings in various paradigms of the BI domain. Updated with fresh topics, including the newest features in Tableau Server, Prep, and Desktop, as well as up-to-date examples, this book will take you from mastering essential Tableau concepts to advance functionalities. A chapter on data governance has also been added. Throughout this book, you'll learn how to use Tableau Hyper files and Prep Builder to easily perform data preparation and handling, as well as complex joins, spatial joins, unions, and data blending tasks using practical examples. You'll also get to grips with executing data densification and explore other expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. Later chapters will teach you all about improving dashboard performance, connecting to Tableau Server, and understanding data visualization with examples. Finally, you'll cover advanced use cases, such as self-service analysis, time series analysis, geo-spatial analysis, and how to connect Tableau to Python and R to implement programming functionalities within Tableau. By the end of this book, you'll have mastered Tableau 2023 and be able to tackle common and advanced challenges in the BI domain.
Table of Contents (19 chapters)
17
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18
Index

Blends

Relationships make data blending a little less needed and it can be seen as legacy functionality. But for the sake of completeness and for older Tableau versions (below 2020.2), let’s consider a summary of data blending in the following sections. In a nutshell, data blending allows you to merge multiple disparate data sources into a single view. Understanding the following four points will give you a grasp of the main points regarding data blending:

  • Data blending is typically used to merge data from multiple data sources. Although as of Tableau 10, joins are possible between multiple data sources, there are still cases when data blending is the only feasible option to merge data from two or more sources. In the following sections, we will see a practical example that demonstrates such a case.
  • Data blending requires a shared dimension. A date dimension is often a viable candidate for blending multiple data sources.
  • Data blending aggregates and then...