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

Tableau and big data

Perhaps the first challenge of big data is defining it adequately. It's a term so widely used as to be almost meaningless. For example, some may refer to data exceeding 1,048,576 rows as big data (that is, the row limit in Excel 2010 and 2013) while others would only apply the term to datasets in the multiple petabyte range. Definitions found on Wikipedia and Webopedia are so broad as to encompass both of these examples. You can find them in the following links, https://en.wikipedia.org/wiki/Big_data and https://www.webopedia.com/TERM/B/big_data.html.

True, we should probably not consider data that merely exceeds Excel's row limitation as big data; nevertheless, from the perspective of the individual for whom Excel is the traditional data-processing application, the preceding definitions fit.

Rather than try to provide an adequately narrow definition...