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

Understanding data blending

In a nutshell, data blending allows you to merge multiple, disparate data sources into a single view. Understanding the four following points will give you a basic grasp on data blending.

  • Data blending is typically used to merge data from multiple data sources. Although as of Tableau 10, joining is possible between multiple data sources, there are still cases when data blending is the only possible option to merge data from two or more sources. In the following sections, we will see a pants and shirts example that demonstrates such a case.
  • Data blending requires a shared dimension. A date dimension is often a good candidate for blending multiple data sources.
  • Data blending aggregates and then matches. Joining matches and then aggregates. This point will be covered in detail in a later section.
  • Data blending does not enable dimensions from a secondary...