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

All About Data – 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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