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
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18
Index

Learning about Joins, Blends, and Data Structures

Connecting Tableau to data often means more than connecting to a single table in a single data source. You may need to use Tableau to join multiple tables from a single data source. For this purpose, we can use joins, which combine a dataset row with another dataset’s row if a given key value matches. You can also join tables from disparate data sources or union data with a similar metadata structure.

Sometimes, you may need to merge data that does not share a common row-level key, meaning if you were to match two datasets on a row level like in a join, you would duplicate data because the row data in one dataset is of much greater detail (for example, cities) than the other dataset (which might contain countries). In such cases, you will need to blend the data. This functionality allows you to, for example, show the count of cities per country without changing the city dataset to a country level.

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