Book Image

Mastering Tableau 2021 - Third Edition

By : Marleen Meier, David Baldwin
Book Image

Mastering Tableau 2021 - Third Edition

By: Marleen Meier, David Baldwin

Overview of this book

Tableau is one of the leading business intelligence (BI) tools that can help you solve data analysis challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. Updated with fresh topics including Quick Level of Detail expressions, the newest Tableau Server features, Einstein Discovery, and more, this book covers essential Tableau concepts and advanced functionalities. Leveraging Tableau Hyper files and using Prep Builder, you’ll be able to perform data preparation and handling easily. You’ll gear up to perform complex joins, spatial joins, unions, and data blending tasks using practical examples. Next, you’ll learn how to execute data densification and further explore expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. You’ll also learn 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, and geo-spatial analysis, and connect Tableau to Python and R to implement programming functionalities within it. By the end of this Tableau book, you’ll have mastered the advanced offerings of Tableau 2021 and be able to tackle common and advanced challenges in the BI domain.
Table of Contents (18 chapters)
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Index

Three essential Tableau concepts

An important step on the road to mastering Tableau involves three essential concepts. In this section, we'll discuss each of them:

  • Dimensions and measures
  • Row-level, aggregate-level, and table-level calculations
  • Continuous and discrete

We'll start by defining dimensions and measures.

Dimensions and measures

Tableau categorizes every field from an underlying data source as either a dimension or a measure. A dimension is qualitative or, to use another word, categorical. A measure is quantitative or aggregable. A measure is usually a number but may be an aggregated, non-numeric field, such as MAX (Date). A dimension is usually a text, Boolean, or date field, but may also be a number, such as Number of Records. Dimensions provide meaning to numbers by slicing those numbers into separate parts/categories. Measures without dimensions are mostly meaningless.

Let's look at an example to understand...