Book Image

Mastering Tableau 2021 - Third Edition

By : Marleen Meier, David Baldwin
Book Image

Mastering Tableau 2021 - Third Edition

By: Marleen Meier, David Baldwin

Overview of this book

Tableau is one of the leading business intelligence (BI) tools that can help you solve data analysis challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. Updated with fresh topics including Quick Level of Detail expressions, the newest Tableau Server features, Einstein Discovery, and more, this book covers essential Tableau concepts and advanced functionalities. Leveraging Tableau Hyper files and using Prep Builder, you’ll be able to perform data preparation and handling easily. You’ll gear up to perform complex joins, spatial joins, unions, and data blending tasks using practical examples. Next, you’ll learn how to execute data densification and further explore expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. You’ll also learn 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, and geo-spatial analysis, and connect Tableau to Python and R to implement programming functionalities within it. By the end of this Tableau book, you’ll have mastered the advanced offerings of Tableau 2021 and be able to tackle common and advanced challenges in the BI domain.
Table of Contents (18 chapters)
16
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17
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...