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Mastering Tableau 2023

Mastering Tableau 2023

By : Marleen Meier
4.7 (36)
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Mastering Tableau 2023

Mastering Tableau 2023

4.7 (36)
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)
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17
Other Books You May Enjoy
18
Index

Working with extracts

This section will discuss what a Tableau data extract is as well as how to efficiently construct an extract. A colleague of mine recently consulted with a relatively small mobile phone service provider. Even though the company was small, the volume could be in excess of 1,000,000 calls per day. Management at the company insisted on the ability to interface with detailed visualizations of individual calls in Tableau workbooks. The performance of the workbooks was, understandably, a problem. Was such low-level detail necessary? Might less detail and snappier workbooks have led to better business decisions?

In order to balance business needs with practical performance requirements, businesses often need to ascertain what level of detail is genuinely helpful for reporting. Often, detailed granularity is not necessary. When such is the case, a summary table may provide sufficient business insight while enabling quick performance. In the case of the mobile phone...

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