We began this chapter with a discussion on complex joins and discovered that, when possible, Tableau uses join culling to generate efficient queries to the data source. A secondary join, however, limits Tableau's ability to employ join culling. An extract results in a materialized, flattened view that eliminates the need for joins to be included in any queries. Unions come in handy if identically-formatted data, stored in multiple sheets or data sources, needs to be appended. We showed how to do so in this chapter. Then, we reviewed data blending to clearly understand how it differs from joining. We discovered that the primary limitation in data blending is that no dimensions are allowed from a secondary source; however, we also discovered that there are exceptions to this rule. We also discussed scaffolding, which can make data blending surprisingly fruitful. Finally...
Mastering Tableau 2019.1 - Second Edition
By :
Mastering Tableau 2019.1 - Second Edition
By:
Overview of this book
Tableau is one of the leading business intelligence (BI) tools used to solve BI and analytics challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain.
This book is also the second edition of the popular Mastering Tableau series, with new features, examples, and updated code. The book covers essential Tableau concepts and its advanced functionalities. Using Tableau Hyper and Tableau Prep, you’ll be able to handle and prepare data easily. You’ll gear up to perform complex joins, spatial joins, union, and data blending tasks using practical examples. Following this, you’ll learn how to perform data densification to make displaying granular data easier. Next, you’ll explore expert-level examples to help you with advanced calculations, mapping, and visual design using various Tableau extensions. With the help of examples, you’ll also learn about improving dashboard performance, connecting Tableau Server, and understanding data visualizations. In the final chapters, you’ll cover advanced use cases such as Self-Service Analytics, Time Series Analytics, and Geo-Spatial Analytics, and learn to connect Tableau to R, Python, and MATLAB.
By the end of this book, you’ll have mastered the advanced offerings of Tableau and be able to tackle common and not-so-common challenges faced in the BI domain.
Table of Contents (20 chapters)
Preface
Getting Up to Speed - A Review of the Basics
All About Data - Getting Your Data Ready
Tableau Prep
All About Data - Joins, Blends, and Data Structures
All About Data - Data Densification, Cubes, and Big Data
Table Calculations
Level of Detail Calculations
Section 2: Advanced Calculations, Mapping, Visualizations
Beyond the Basic Chart Types
Mapping
Tableau for Presentations
Visualization Best Practices and Dashboard Design
Advanced Analytics
Improving Performance
Section 3: Connecting Tableau to R, Python, and Matlab
Interacting with Tableau Server
Programming Tool Integration
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