Tableau 10.0 best practices develop a deep understanding of Tableau 10.0 and get to know tricks to understand your data

291 Pages·2016·26.667 MB·English

by  Zhang, Jenny (technology professional)

Tableau 10.0 Best Practices

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Jenny Zhang

BIRMINGHAM - MUMBAI
Tableau 10.0 Best Practices

Copyright © 2016 Packt Publishing

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First published: December 2016
Production reference: 1091216

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ISBN 978-1-78646-009-7
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Credits
Author Copy Editor

Jenny Zhang Manisha Sinha
Reviewers Project Coordinator

Ravi Ratanlal Mistry Nidhi Joshi
Sneha Vijay
Commissioning Editor Proofreader

Veena Pagare Safis Editing
Acquisition Editor Indexer

Vinay Argekar Mariammal Chettiyar
Content Development Editor Graphics

Rahul Popat Disha Haria
Technical Editor Production Coordinator

Danish Shaikh Nilesh Mohite
About the Author
Jenny Zhang is a technology professional with 6+ years' experience of data and analytics
and currently working at JW Plater as Business Analytics Manager. She is a data strategist
and technologist, Tableau and Alteryx community advocate, blogger. She had a series of
blog posts about Tableau best practices at (cid:73)(cid:85)(cid:85)(cid:81)(cid:28)(cid:17)(cid:17)(cid:75)(cid:70)(cid:79)(cid:79)(cid:90)(cid:89)(cid:74)(cid:66)(cid:80)(cid:91)(cid:73)(cid:66)(cid:79)(cid:72)(cid:16)(cid:68)(cid:80)(cid:78)(cid:17)(cid:85)(cid:66)(cid:72)(cid:17)(cid:85)(cid:66)(cid:67)(cid:77)(cid:70)(cid:66)(cid:86)(cid:17).
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Jenny is also passion about Big data. She had a series of blog posts about Big Data, NoSQL,
Spark, Hadoop, and Yarn at (cid:73)(cid:85)(cid:85)(cid:81)(cid:28)(cid:17)(cid:17)(cid:75)(cid:70)(cid:79)(cid:79)(cid:90)(cid:89)(cid:74)(cid:66)(cid:80)(cid:91)(cid:73)(cid:66)(cid:79)(cid:72)(cid:16)(cid:68)(cid:80)(cid:78)(cid:17)(cid:68)(cid:66)(cid:85)(cid:70)(cid:72)(cid:80)(cid:83)(cid:90)(cid:17)(cid:67)(cid:74)(cid:72)(cid:15)(cid:69)(cid:66)(cid:85)(cid:66)(cid:17).
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I would like to thank you all the people who helped me with this book. Thank you all those
who read, offered comments and helped in the editing and design. I would like to show
special gratitude to Siddhesh Salvi, Vinay Argekar, Milton Dsouza from Packt publishing
for enabling me to publish this book.
About the Reviewers
Ravi Ratanlal Mistry is an avid technology enthusiast and loves learning new concepts as
well as teaching others. He holds a bachelor's degree in Information Technology and is a
self-taught programmer.
I would like to thank my family and friends for their ongoing support, especially my
mother for always believing in me.

Sneha Vijay has a well-rounded consulting background in domains of Data and Analytics
with specialization in Tableau. Having over 4 years of experience and successful track
record in Consulting, Analytics, Building sophisticated reporting technologies, Data Mining
and Tableau visualizations, she provide users with the ability to examine information and
uncover hidden trends and anomalies. Currently, Sneha works at Deloitte US Consulting
based out of Gurgaon, India. Sneha's passions are spending time with his family, swimming
and enjoying music to the fullest.
She has previously reviewed Tableau 10 Business Intelligence Cookbook with Packt Publishing.
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Table of Contents
Preface
1
Chapter 1: Data Extraction
5
Different ways of creating Tableau data extracts 5
Direct connect to original data sources 5
Duplicate of an extract 5
Connecting to a Tableau extract file 9
Technical details of how a Tableau data extract works 9
Tableau data extract's design principle 9
Benefits of using Tableau data extracts 10
Creating extract with large volume of data efficiently 11
Loading a very large Excel file to Tableau 12
Aggregating the values to higher dimension 12
Using data source filter 13
Hiding unused fields 14
Uploading and managing Tableau data extract in Tableau online 14
Creating workbook just for extracts 14
Using default project 14
Making sure Tableau online/server has enough space 15
Refreshing Tableau data extracts 15
Refreshing published extracts locally 15
Schedule data extract refresh in Tableau Online 20
Incremental refresh 20
Using Tableau web connector to create data extract 20
What is Tableau web connector? 20
How to use Tableau web connector? 21
Is the Tableau web connection live? 21
Are there any Tableau web connection available? 21
Summary 21
Chapter 2: Data Blending
22
Primary versus secondary data source in data blending 22
Data blending versus join 27
When to use data blending instead of joining 27
Differences between data blending and join 29
Potential issues of using data blending and quick fix 30
Blend on date 30
Use table calculations to aggregate blended data 31
Use cases of solving different business problems by blending the same
data in different ways 33
Performing Blend on single field versus multiple fields 33
Self-blending 35
Domain padding 37
Using Alteryx to blend large volume of data efficiently 39
Getting started with Alteryx 39
Benefit of Alteryx 39
Summary 39
Chapter 3: Calculation/Parameter
40
Table calculations 40
Overview 40
Key concepts 41
Understand addressing and partitioning 41
Understanding At the Level 48
Understanding At the Level options 50
Table calculation functions 56
Understand rank functions 57
LOD calculations 77
Overview 78
Key Concepts 78
LOD Functions 78
LOD Use Cases 79
LOD Remix 79
LOD and Totals 80
LOD nesting 84
LOD limitations 84
Date calculations 85
Using Date Tableau calculation to hide parts of the date 86
String calculations 88
Getting last name from an email 89
Getting street number from address 89
Counting number of words in a string 90
Total for count distinct 90
Alternatives for count distinct 92
Data blending limitation with COUNTD 93
Custom Total 93
Custom Grand Total 93
[ ii ]
Another Way of Custom Grand Total 95
Dynamic parameter 97
Summary 98
Chapter 4: Sort and Filter
99
Different types of sorting 100
Sort by calculated field 100
Different types of filters 107
Data source filter 107
Context filter 108
Traditional filter 108
Filter order 108
Filtering by calculated fields 109
Top N Rank 109
Top and bottom percentage 112
Filter without losing context 114
Filter with self-blending 118
Filter, group and set 120
Cascading filter 121
Dynamic set and filter 125
Summary 129
Chapter 5: Formatting
130
Tooltip 130
Custom logo in a tooltip 130
Chart in a tooltip 135
Formatting individual measure 141
Colour code individual measure 141
Fill cell with different color 145
Date formatting 147
Time range 148
Reference line 154
Reference line with 45 degree 155
Sheet selection 163
Dashboard actions 167
Action on blended field 167
Exclude filter action 173
Tips for color blind 176
Summary 177
Chapter 6: Visualization
178
[ iii ]