Book Image

Mastering Tableau 2019.1 - Second Edition

By : Marleen Meier, David Baldwin
Book Image

Mastering Tableau 2019.1 - Second Edition

By: Marleen Meier, David Baldwin

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.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Tableau Concepts, Basics
Section 1: Tableau Concepts, Basics
2
Getting Up to Speed - A Review of the Basics
Getting Up to Speed - A Review of the Basics
Creating worksheets and dashboards
Connecting Tableau to your data
Connecting to Tableau Server
Connecting to saved data sources
Measure Names and Measure Values
Three essential Tableau concepts
Exporting data to other devices
Summary
3
All About Data - Getting Your Data Ready
All About Data - Getting Your Data Ready
Understanding Tableau's data handling engine, hyper
The Tableau data handling engine
Hyper takeaways
Data mining and knowledge discovery process models
CRISP–DM
Focus on data preparation
Surveying data
Cleaning the data
Summary
4
Tableau Prep
Tableau Prep
Connecting to data
The Tableau Prep GUI
Prepping data
Exporting data
Summary
5
All About Data - Joins, Blends, and Data Structures
All About Data - Joins, Blends, and Data Structures
Introduction to joins
Introduction to complex joins
Exercise: observing join culling
Introduction to join calculations
Introduction to spatial joins
Introduction to unions
Understanding data blending
Order of operations
No dimensions from a secondary source
Introduction to scaffolding
Introduction to data structures
Exercise: adjusting the data structure for different questions
Summary
6
All About Data - Data Densification, Cubes, and Big Data
All About Data - Data Densification, Cubes, and Big Data
Introduction to data densification
Domain completion
Deployment of domain completion
Usefulness of domain completion
Domain padding
Working with cubes
The deprecation of cubes
Tableau and big data
Addressing Excel's row limitation
Massively parallel processing
Building a visualization with Google BigQuery
Summary
7
Table Calculations
Table Calculations
A definition and two questions
Introduction to functions
Directional and non-directional table calculations
Application of functions
Summary
8
Level of Detail Calculations
Level of Detail Calculations
Building playgrounds
Playground I: FIXED and EXCLUDE
Playground II: INCLUDE
Practical application
Exercise: practical FIXED
Exercise: practical INCLUDE
Exercise: practical EXCLUDE
Summary
9
Section 2: Advanced Calculations, Mapping, Visualizations
Section 2: Advanced Calculations, Mapping, Visualizations
10
Beyond the Basic Chart Types
Beyond the Basic Chart Types
Improving popular visualizations
Custom background images
Tableau extensions
Summary
11
Mapping
Mapping
Extending Tableau's mapping capabilities without leaving Tableau
Extending Tableau mapping with other technology
Exercise: connecting to a WMS server
Exploring the TMS file
Exploring Mapbox
Accessing different maps with a dashboard
Creating custom polygons
Converting shape files for Tableau
Exercise: polygons for Texas
Heatmaps
Summary
12
Tableau for Presentations
Tableau for Presentations
Getting the best images out of Tableau
From Tableau to PowerPoint
Embedding Tableau in PowerPoint
Animating Tableau
Story points and dashboards for Presentations
Summary
13
Visualization Best Practices and Dashboard Design
Visualization Best Practices and Dashboard Design
Visualization design theory
Formatting rules
Color rules
Visualization type rules
Compromises
Keeping visualizations simple
Dashboard design
Dashboard layout
Sheet selection
Summary
14
Advanced Analytics
Advanced Analytics
Self-service Analytics
Use case – Self-service Analytics
Use case – Geo-spatial Analytics
Summary
15
Improving Performance
Improving Performance
Understanding the performance-recording dashboard
Exercise: exploring performance recording in Tableau desktop
Performance-recording dashboard events
Behind the scenes of the performance- recording dashboard
Hardware and on-the-fly techniques
Hardware considerations
On-the-fly-techniques
Single Data Source > Joining > Blending
Three ways Tableau connects to data
Using referential integrity when joining
Advantages of blending
Efficiently working with data sources
Tuning data sources
Working efficiently with large data sources
Intelligent extracts
Understanding the Tableau data extract
Constructing an extract for optimal performance
Exercise: summary aggregates for improved performance
Optimizing extracts
Exercise: materialized calculations
Using filters wisely
Extract filter performance
Data source filter performance
Context filters
Dimension and measure filters
Table-calculation filters
Efficient calculations
Boolean/Numbers > Date > String
Additional performance considerations
Avoid overcrowding a dashboard
Fixing dashboard sizing
Setting expectations
Summary
16
Section 3: Connecting Tableau to R, Python, and Matlab
Section 3: Connecting Tableau to R, Python, and Matlab
17
Interacting with Tableau Server
Interacting with Tableau Server
Tableau file types
Tableau data source
Tableau packaged data source
Tableau workbook
Tableau packaged workbook
Other file types
Tableau Server architecture
Tableau Server architecture approaches to avoid
Tableau Server architecture approaches to adopt
Tableau Server revision history
Tableau Server web-authoring environment
Basic web-authoring instructions
Capabilities and limitations of web-authoring
Comparing Tableau Desktop and web-authoring
User filters
Performance-recording dashboard
Exercise: exploring Performance Recording on Tableau Server
More Tableau Server settings
Summary
18
Programming Tool Integration
Programming Tool Integration
The architecture
R installation and integration
Installation
Using R functions
Introduction to correlations
Introduction to regression analysis
Introduction to clustering
Introduction to quantiles
Troubleshooting in Tableau and R
Examples of troubleshooting in Tableau and R
R scripts and Tableau table calculations
Python installation and integration
Using Python functions
Introduction to sentiment analysis
MATLAB installation and integration
Summary
19
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Tableau packaged data source

Take a look at the following Tableau data file details:

  • File format type: Compressed.
  • What it contains: Metadata and a data extract.
  • Why it's useful: The .tdsx file is useful because it can be accessed for both metadata and data. Tableau authors can access a .tdsx file located on Tableau Server as a data source, thus eliminating the need for a workbook to connect directly to an external data source. A published .tdsx file can be placed on a schedule so that it is regularly updated from the underlying data source.
  • How it's generated: A .tdsx file can be generated by right-clicking on a data source in the Data pane and selecting Add to Saved Data Sources..., followed by selecting Tableau Packaged Data Source in the resulting dialog box. Like the .tds file, the .tdsx file can also be generated when publishing to Tableau server via Server ...
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