Book Image

Mastering Tableau 2019.1 - Second Edition

By : Marleen Meier, David Baldwin
Book Image

Mastering Tableau 2019.1 - Second Edition

By: Marleen Meier, David Baldwin

Overview of this book

Tableau is one of the leading business intelligence (BI) tools used to solve BI and analytics challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. This book is also the second edition of the popular Mastering Tableau series, with new features, examples, and updated code. The book covers essential Tableau concepts and its advanced functionalities. Using Tableau Hyper and Tableau Prep, you’ll be able to handle and prepare data easily. You’ll gear up to perform complex joins, spatial joins, union, and data blending tasks using practical examples. Following this, you’ll learn how to perform data densification to make displaying granular data easier. Next, you’ll explore expert-level examples to help you with advanced calculations, mapping, and visual design using various Tableau extensions. With the help of examples, you’ll also learn about improving dashboard performance, connecting Tableau Server, and understanding data visualizations. In the final chapters, you’ll cover advanced use cases such as Self-Service Analytics, Time Series Analytics, and Geo-Spatial Analytics, and learn to connect Tableau to R, Python, and MATLAB. By the end of this book, you’ll have mastered the advanced offerings of Tableau and be able to tackle common and not-so-common challenges faced in the BI domain.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Tableau Concepts, Basics
9
Section 2: Advanced Calculations, Mapping, Visualizations
16
Section 3: Connecting Tableau to R, Python, and Matlab

Boolean/Numbers > Date > String

As the header suggests, calculations that use Boolean values or numbers are more performant than those that use dates. Calculations that use dates, in turn, are more performant than those using strings. This is not only true of Tableau but also in computer science as a whole.

Based on this information, Scenario II listed in the table above is more performant than Scenario I. Scenario I causes Tableau to create a query that requires the data source engine to handle strings for reporting profitability, whereas Scenario II sends only 1s and 0s to determine profitability. The third step for Scenario II (that is, aliasing 'True' and 'False' to 'Profitable' and 'Unprofitable') is merely a labelling change that happens after the aggregate dataset is returned from the data source. Labelling is quick and easy...