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
Other Books You May Enjoy
18
Index

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 (which is the row limit in Microsoft 365, Excel 2019, Excel 2016, Excel 2013, Excel 2010, and Excel 2007), while others would only apply the term to datasets in the multiple-petabyte range. Definitions found on Wikipedia (https://en.wikipedia.org/wiki/Big_data) are so broad as to encompass both of these examples.

True, it is probably simplistic to consider data that merely exceeds Excel’s row limitation as big data; nevertheless, from the perspective of an individual for whom Excel is the traditional data-processing application, the preceding definition fits.

Talking about big data goes hand in hand with parallel processing. For Tableau and working with big data, it is very important to know the partitions that your IT team has put in place for...