Book Image

Mastering Tableau

By : David Baldwin
Book Image

Mastering Tableau

By: David Baldwin

Overview of this book

Tableau has emerged as one of the most popular Business Intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. This book will empower you to become a master in Tableau by exploiting the many new features introduced in Tableau 10.0. You will embark on this exciting journey by getting to know the valuable methods of utilizing advanced calculations to solve complex problems. These techniques include creative use of different types of calculations such as row-level, aggregate-level, and more. You will discover how almost any data visualization challenge can be met in Tableau by getting a proper understanding of the tool’s inner workings and creatively exploring possibilities. You’ll be armed with an arsenal of advanced chart types and techniques to enable you to efficiently and engagingly present information to a variety of audiences through the use of clear, efficient, and engaging dashboards. Explanations and examples of efficient and inefficient visualization techniques, well-designed and poorly designed dashboards, and compromise options when Tableau consumers will not embrace data visualization will build on your understanding of Tableau and how to use it efficiently. By the end of the book, you will be equipped with all the information you need to create effective dashboards and data visualization solutions using Tableau.
Table of Contents (18 chapters)
Mastering Tableau
Credits
About the Author
www.Packtpub.com
Preface

Data blending


In a nutshell, data blending allows you to merge multiple, disparate data sources in a single view. Understanding the four following points will give you a basic grasp of data blending:

  • Data blending is typically used to merge data from multiple data sources:

    • Although as of Tableau 10, joining is possible between multiple data sources, there are still cases where data blending is the only possible option to merge data from two or more sources. The following Pants & Shirts example demonstrates such a case.

  • Data blending requires a shared dimension:

    • A date dimension is often a good candidate for blending multiple data sources.

  • Data blending aggregates and then matches. Joining matches and then aggregates:

    • This point will be covered in detail in the following section

  • Data blending does not enable dimensions from a secondary data source:

    • Attempting to use dimensions from a secondary data source will result in a * or null in the view. There is an exception to this rule, which...