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
Another Book You May Enjoy
17
Index

Summary

We began this chapter with a discussion of the performance-recording dashboard. This was important because many of the subsequent exercises utilized the performance-recording dashboard to examine underlying queries. Next, we discussed hardware and on-the-fly techniques, where the intent was to communicate hardware considerations for good Tableau performance and, in the absence of optimal hardware, techniques for squeezing the best possible performance out of any computer.

Then we covered working with data sources, including joining, blending, and efficiently working with data sources. This was followed by a discussion on generating and using extracts as efficiently as possible. By focusing on data sources for these three sections, we learned best practices and what to avoid when working with either remote datasets or extracts. The next sections explored performance implications for various types of filters and calculations. Lastly, we looked at additional performance considerations...