Tableau 2019.x Cookbook E-Book

Dmitry Anoshin

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 390

Veröffentlichungsjahr: 2019

Beschreibung

Perform advanced dashboard, visualization, and analytical techniques with Tableau Desktop, Tableau Prep, and Tableau Server

Key Features

• Unique problem-solution approach to aid effective business decision-making
  
  


• Create interactive dashboards and implement powerful business intelligence solutions
  
  


• Includes best practices on using Tableau with modern cloud analytics services

Book Description

Tableau has been one of the most popular business intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. Tableau 2019.x Cookbook is full of useful recipes from industry experts, who will help you master Tableau skills and learn each aspect of Tableau's ecosystem.

This book is enriched with features such as Tableau extracts, Tableau advanced calculations, geospatial analysis, and building dashboards. It will guide you with exciting data manipulation, storytelling, advanced filtering, expert visualization, and forecasting techniques using real-world examples. From basic functionalities of Tableau to complex deployment on Linux, you will cover it all. Moreover, you will learn advanced features of Tableau using R, Python, and various APIs. You will learn how to prepare data for analysis using the latest Tableau Prep. In the concluding chapters, you will learn how Tableau fits the modern world of analytics and works with modern data platforms such as Snowflake and Redshift. In addition, you will learn about the best practices of integrating Tableau with ETL using Matillion ETL.

By the end of the book, you will be ready to tackle business intelligence challenges using Tableau's features.

What you will learn

• Understand the basic and advanced skills of Tableau Desktop
  
  


• Implement best practices of visualization, dashboard, and storytelling
  
  


• Learn advanced analytics with the use of build in statistics
  
  


• Deploy the multi-node server on Linux and Windows
  
  


• Use Tableau with big data sources such as Hadoop, Athena, and Spectrum
  
  


• Cover Tableau built-in functions for forecasting using R packages
  
  


• Combine, shape, and clean data for analysis using Tableau Prep
  
  


• Extend Tableau's functionalities with REST API and R/Python
  
  

Who this book is for

Tableau 2019.x Cookbook is for data analysts, data engineers, BI developers, and users who are looking for quick solutions to common and not-so-common problems faced while using Tableau products. Put each recipe into practice by bringing the latest offerings of Tableau 2019.x to solve real-world analytics and business intelligence challenges. Some understanding of BI concepts and Tableau is required.

Leseprobe

Tableau 2019.x Cookbook

Over 115 recipes to build end-to-end analytical solutions using Tableau

Dmitry Anoshin

Teodora Matic

Slaven Bogdanovic

Tania Lincoln 

Dmitrii Shirokov

BIRMINGHAM - MUMBAI

Tableau 2019.x Cookbook

Copyright © 2019 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Sunith ShettyAcquisition Editor: Yogesh DeokarContent Development Editor: Nathanya DiasTechnical Editor: Vibhuti GawdeCopy Editor: Safis EditingProject Coordinator: Kirti PisatProofreader: Safis EditingIndexer: Rekha NairGraphics: Jisha ChirayilProduction Coordinator: Deepika Naik

First published: January 2019

Production reference: 1310119

Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.

ISBN 978-1-78953-338-5

www.packtpub.com

 

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Foreword

This book is different from most Tableau books. Previuosly, Tableau books tried to cover the default Tableau functionality with simple examples, or covered the principles of data visualization. This book focuses on end-to-end BI solutions based on Tableau. It includes Tableau Server, both on Linux and Windows; a new tool for data preparation, Tableau Prep; complex use cases with the Tableau REST API, and more. Moreover, this book goes beyond Tableau use cases and covers BI solutions in general and explains the concepts of integration between ETL based on Matillion and BI. Finally, you will learn about working with big data and modern data platforms such as Redshift and Snowflake.

Thank you to my beautiful wife, Svetlana, who supports me during my professional journey and always has a positive attitude. In addition, I want to hug my kids, Vasily, Anna, and Michael and hope one day they will be proud of their dad who planted trees in the backyard and wrote a couple of books. PS for sure, we will go on vacation soon and have some fun together!                                                                                                                                                                                                                                                                                                                     —Dmitry Anoshin

To our baby, with love. We had a great time writing this book and waiting for you!                                                                                                                       —Teodora and Slaven

Thank you Justin and Sydney for your support. I owe you some nice dinners and playground time!  Appreciations to my fellow co-authors for giving me this opportunity.                                                                                                                      —Tania Lincoln

Contributors

About the authors

Dmitry Anoshin is an expert in analytics with 10 years of experience. He started using Tableau as primary BI tool in 2011 as a BI consultant at Teradata. He is certified with both Tableau Desktop and Server. He leads probably the biggest Tableau user community, with more than 2,000 active users. This community has 2-3 Tableau talks every month led by top Tableau experts, Tableau Zen Masters, Viz Champions, and so on. In addition, Dmitry has previously written three books with Packt and reviewed more than seven books. Finally, he is an active speaker at data conferences and helps people to adopt cloud analytics.

Teodora Matic is a data analyst with a strong background in statistics and more than 5 years of experience in data analytics and reporting. She has been using Tableau since 2014. She has been working as a project manager and data analyst for leading market research companies, such as Ipsos and EyeSee Research, levering the power of Tableau to bring business insights to clients. She currently does data analysis and reporting at theInternational Committee of the Red Cross.

Slaven Bogdanovic has more than 10 years of experience in data analysis and reporting within both business and academia. His expertise covers complex statistical analysis and insight communication. He has been using Tableau since 2013. Currently, he works as a BI/big data developer at NCR Corporation. Previously, he was a senior research executive at Ipsos. Also, Slaven is a PhD candidate and a member of the Laboratory for Research of Individual Differences at the University of Belgrade. In addition, Slaven is the author of six articles published in academic and professional journals.

Tania Lincoln has over 12 years of development experience in BI and data analytics domain. She has a strong SQL, visualization, and analytics skill set, and has demonstrated the ability to mentor others on new technologies and process improvements. She is also experienced in taking a product from inception to launch and managing post-launch growth.

Dmitrii Shirokov has over 11 years of design and development of data-driven solutions.  He has been using Tableau since 2011 in the majority of analytics projects. His expertise covers building data warehouses and sophisticated analytical solutions. Currently, he works as a solutions architect at Rock Your Data consulting company. Previously, he was a big data architect at Sberbank and also worked as a professional service consultant at Teradata.

About the reviewers

Shweta Savale is the cofounder and head of client engagements at Syvylyze (pronounced civilize) Analytics. Being one of the leading experts on Tableau, she has translated her expertise to successfully rendering analytics and data visualization services for numerous clients across a wide range of industry verticals. She has successfully trained over 2,200 participants across 150+ companies on Tableau, and is also an empaneled trainer for Tableau APAC and conducts public and private Tableau training across Singapore, Malaysia, Hong Kong, Thailand, Australia, and India.

In addition to being an entrepreneur and trainer, she has also authored Tableau Cookbook – Recipes of Data Visualization, published by Packt Publishing, UK.

Lana Anoshina is mom of three kids and she enjoys working with data and solving complex business issues with data visualization. She enjoys driving her Audi on West Coast and spending quality time with her family near the ocean.

Dave Dwyer has a BS in Information Systems from RIT (Rochester Institute of Technology), MBA from Drexel University, certified Six Sigma Black Belt and PMP. In his 20+ years as an IT professional, he has worked in a wide range of technical and leadership roles, in companies ranging from startups to Fortune 100 enterprises. A chance introduction to reporting and analytics 10 years ago hooked him and he never left. Dave feels the data science landscape of analytics, visualization, big data, and machine learning will drive more real changes in business over the next 10 years than any other area.

Manideep Bhattacharyya is a Tableau enthusiast and Tableau certified professional with more than 16 years of industry experience. He graduated from science college Calcutta in 2003. He started his career at IBM as a Siebel certified professional and worked for 7 years and contributed to many global multinational projects across the world. Later on, he joined an Indian conglomerate and implemented Tableau with a large-scale multi-billion row dataset, and set a new standard for data discovery and visualization for CXOs and top management.

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Table of Contents

Title Page

Copyright and Credits

Tableau 2019.x Cookbook

About Packt

Why subscribe?

Packt.com

Foreword

Dedication

Contributors

About the authors

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Sections

Getting ready

How to do it…

How it works…

There's more…

See also

Get in touch

Reviews

Getting Started with Tableau Software

Technical requirements

Introduction to Tableau

Connecting to the data

Getting ready

How to do it…

How it works...

There's more…

See also                    

Building a bar chart using Show Me

Getting ready

How to do it…

Creating a chart using Show Me

Sorting the chart        

How it works...

There's more…

See also

Building a text table

Getting ready

How to do it…

How it works...

There's more…

See also

Adding filters

Getting ready

How to do it…

How it works...

There's more…

See also

Adding color        

Getting ready

How to do it…

How it works...

There's more…

See also

Building a tree map

Getting ready

How to do it...

How it works...

There's more…

See also

Building a map

Getting ready

How to do it…

Creating a map with circles

Creating a map with a color gradient

How it works...

There's more…

See also

Building a dual-axis map

Getting ready

How to do it…

How it works...

There's more…

See also

Customizing tooltips

Getting ready

How to do it…

How it works...

There's more…

See also

Data Manipulation

Technical requirements           

Introduction

Joining data sources

Getting ready

How to do it…

How it works...

There's more…

See also

Adding a secondary data source

Getting ready

How to do it…

How it works...

There's more…

See also

Data blending

Getting ready

How to do it…

How it works...

There's more…

 See also

Data union

Getting ready

How to do it…

How it works...

There's more…

See also

Using Tableau Pivot

Getting ready

How to do it…

How it works...

There's more…

See also

Preparing data

Getting ready

How to do it…

Splitting fields

Converting measures into dimensions

Renaming fields

Adding aliases

How it works...

There's more…

See also

Tableau Extracts

Introduction

Overview of different file formats in Tableau

How to do it...

Tableau Workbook (TWB)

Tableau Packaged Workbook 

Tableau Bookmark 

Tableau Data Extract

Hyper

How it works...

Tableau Workbook 

Tableau Packaged Workbook (TWBX)

Tableau Bookmark (TBM)

Creating a data source extract

Getting ready

How to do it...

Connecting to data

Creating an extract

Optional settings for extracts

How it works...

There's more...

See also

Configuring an incremental extract

Getting ready

How to do it...

How it works...

There's more...

See also

Upgrading to Hyper

Getting ready

How to do it...

How it works...

See also

Creating extracts using cross-database joins

Getting ready

How to do it...

How it works...

See also

Troubleshooting extracts with Tableau Server

Getting ready

How to do it...

How it works...

There's more...

See also

Tableau Desktop Advanced Calculations

Technical requirements

Introduction

Creating calculated fields

Getting ready

How to do it…

How it works...

There's more…

See also

Implementing quick table calculations

Getting ready

How to do it…

How it works...

There's more…

See also    

Creating and using groups

Getting ready

How to do it…

How it works...

There's more…

See also

Creating and using sets

Getting ready

How to do it…

How it works...

There's more…

See also

Creating and using parameters

Getting ready

How to do it…

How it works...

There's more…

See also

Implementing the basics of level of detail expressions

Getting ready

How to do it…

How it works...

There's more…

See also                                   

Using custom geocoding       

Getting ready

How to do it…

How it works...

There's more…

See also

Using polygons for analytics

Getting ready

How to do it…                                                    

How it works...

There's more…

See also

Tableau Desktop Advanced Filtering

Technical requirements

Introduction

Implementing a top N filter

Getting ready

How to do it…

How it works...

There's more…

See also

Adding filters to context

Getting ready

How to do it…

How it works...

There's more…

See also

Creating a measure filter

Getting ready

How to do it…

How it works...

There’s more…

See also

Creating date range filters

Getting ready

How to do it…

How it works...

There's more…

See also

Creating relative dates filters

Getting ready

How to do it…

How it works...

There's more…

See also

Implementing table calculation filters

Getting ready 

How to do it…

How it works...

There's more…

See also

Implementing action filters 

Getting ready

How to do it…

How it works...

There's more…

See also

Building Dashboards

Technical requirements

Introduction

Creating a dashboard

Getting ready

How to do it…

How it works...

There's more…

See also

Formatting a dashboard

Getting ready

How to do it…

Setting and formatting dashboard title

Formatting worksheet titles

Formatting text objects

Formatting the dashboard background

How it works...

There's more…

See also

Setting filters

Getting ready

How to do it…

Setting filters through the dashboard

Setting filters through the worksheet

Filtering by worksheets in the dashboard – action filters

How it works...

There's more…

See also

Setting filters across various data sources

Getting ready

How to do it…

How it works...

There's more…

See also

Adding highlight actions

Getting ready

How to do it…

How it works...

There's more…

See also

Setting layouts

Getting ready

How to do it…

Setting a fixed size

Setting the automatic size

Setting the range size

Adding a device layout

Customizing the device layout

How it works...

There's more…

See also

Building a self-service dashboard

Getting ready

How to do it…

Switching between dimensions with parameters

Adding a hyperlink to an image object

Adding a web page to the dashboard

Adding an action filter

How it works...

There's more…

See also

Telling a Story with Tableau

Technical requirements

Introduction

Creating a Tableau story

Getting ready

How to do it…

How it works...

There's more...

See also

Setting the narrative of the Story

Getting ready

Conflict

How to do it…

How it works...

Development 

How to do it…

How it works...

Climax

How to do it…

How it works...

Resolution

How to do it…

How it works...

There's more...

See also

Choosing the right charts

Getting ready

How to do it…

How it works

There’s more…

See also 

Writing effective headlines

Getting ready

How to do it…

How it works...

Recommendation and executive summary

Getting ready

How to do it…

How it works...

There's more…

See also

Formatting the Story

Getting ready

How to do it…

How it works...

There's more...

See also

Tableau Visualization

Technical requirements

Introduction

Dual axis waterfall chart

Getting ready

How to do it...

How it works...

There's more...

See also

Pareto chart

Getting ready

How to do it...

How it works...

There's more...

See also

Bump chart

Getting ready

How to do it...

How it works...

There's more...

See also

Sparklines chart

Getting ready

How to do it...

How it works...

There's more...

See also

Donut chart

Getting ready

How to do it...

How it works...

There's more...

See also

Motion chart

Getting ready

How to do it...

How it works...

There's more...

See also 

Tableau Advanced Visualization

Technical requirements

Introduction

Lollipop charts

Getting ready

How to do it...

How it works...

There's more...

See also

Sankey diagrams

Getting ready

How to do it...

How it works...

See also

Marimekko charts

Getting ready

How to do it...

How it works...

There's more...

Hex-Tile maps

Getting ready

How to do it...

How it works...

See also

Waffle charts

Getting ready

How to do it...

How it works...

There's more...

See also

Tableau for Big Data

Technical requirements

Introduction

Connecting with Amazon Redshift

Getting ready

How to do it…

Creating an AWS account

Creating an IAM role

How it works…

Launching an Amazon Redshift cluster

How to do it…

How it works…

There's more…

Connecting a Redshift cluster

How to do it…

How it works…

There's more…

Loading sample data into the Redshift cluster

How to do it…

How it works…

There's more…

Connecting Redshift with Tableau

How to do it…

How it works…

Creating a Tableau report

How to do it…

How it works…

There's more…

Tuning Redshift for efficient Tableau performance

How to do it...

How it works...

There's more…

See also

Connecting to Amazon Redshift Spectrum

Getting ready

How to do it…

How it works...

There's more…

See also

Connecting to Snowflake

Getting ready

How to do it…

How it works...

Using SnowSQL CLI

How to do it…

How it works…

Connecting Tableau to Snowflake

How to do it…

How it works…

Connecting big data

How to do it…

How it works…

There's more…

Accessing semi–structured data

How to do it…

How it works...

There's more…

See also

Connecting Amazon Elastic MapReduce with Apache Hive

Getting ready

How to do it...

How it works...

Creating sample data

How to do it...

How it works...

There’s more…

See also

Connect Tableau with  Apache Hive

How to do it…

How it works…

There's more…

See also

Forecasting with Tableau

Technical requirements

Introduction

Basic forecasting and statistical inference

Getting ready

How to do it…

How it works...

There's more…

See also

Forecasting on a dataset with outliers

Getting ready

How to do it…

How it works...

There's more…

See also

Using R within Tableau

Getting ready

How to do it…

How it works...

There's more...

See also

Forecasting based on multiple regression

Getting ready

How to do it…

How it works...

There's more…

See also

Regression with random forest

Getting ready

How to do it…

How it works...

 There's more…

See also

Time series forecasting

Getting ready

How to do it…

How it works...

 There's more…

See also

Advanced Analytics with Tableau

Technical requirements

Introduction

Running segmentation analysis

Getting ready

How to do it…

How it works...

There's more...

See also

Discovering the latent structure of the dataset

Getting ready

How to do it…

How it works...

There's more…

See also

Extracting the structure beneath discrete variables

Getting ready

How to do it…

How it works...

There's more...

See also

Data mining with tree-based models

Getting ready

How to do it…

How it works...

There's more

See also

Identifying anomalies in data

Getting ready

How to do it…

How it works...

There's more...

See also

Deploy Tableau Server

Technical requirements

Introduction

Deploying Tableau Server in Windows

Getting ready

How to do it...

How it works…

There's more…

See also

Deploying to Tableau Server

Getting ready

How to do it...

How it works...

There's more…

See also

Deploying Tableau Server on Linux using AWS

Getting ready

How to do it...

How it works...

There's more…

See also

Getting started with Tabcmd

Getting ready

How to do it...

How it works...

There's more…

See also

Tableau Troubleshooting

Technical requirements

Introduction

Performance recording

Getting ready

How to do it...

How it works...

There's more...

See also

Performance troubleshooting and best practices

How to do it...

Limiting your data source

Filtering out cases in the database

Filtering out variables

Making extracts

Being cautious with filters

Keeping an eye on the calculations

Optimizing your visualizations

How it works...

There's more...

See also

Troubleshooting through log files

How to do it...

Accessing logs

Submitting logs to the support team

There's more...

See also

Preparing Data for Analysis with Tableau Prep

Introduction

Technical requirements

Installing Tableau Prep

Getting ready

How to do it…

How it works…

There's more…

Building the first flow with Tableau Prep

Getting ready

How to do it...

Connecting the data

Transforming the data

Publishing the result

How it works…

There's more…

Working with big data

Getting ready

How to do it…

How it works…

There's more…

See also…

ETL Best Practices for Tableau

Introduction

Technical requirements

Getting started with Matillion ETL

How to do it... 

How it works... 

There's more... 

 Deploying Tabcmd on Linux

How to do it...

How it works...

There's more...

Creating Matillion Shared Jobs

How to do it...

How it works...

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Preface

Tableau is one of the most popular business intelligence (BI) solutions in recent times, thanks to its powerful and interactive data visualization capabilities. This comprehensive book is full of useful recipes from industry experts that will help you master Tableau skills and learn each aspect of Tableau's 2018.x offerings. This book is enriched with features such as extracts, Tableau advanced calculations, geospatial analysis, building dashboards, and much more. It will guide you to exciting techniques of data manipulation, storytelling, advanced filtering, expert visualization, and forecasting using real-world examples. It will help you on your learning journey from the basic functionalities of Tableau, all the way to complex deployment on Linux. Moreover, you will learn about the advanced features of Tableau by using R, Python, and various APIs. The complexity of tasks increases gradually, and you will be guided all the way to mastering advanced functionalities through bite-sized, detailed recipes. Furthermore, the book is packed with troubleshooting techniques to optimize your BI tasks. You will learn how to prepare data for analysis using the latest Tableau Prep. By the end of the book, you will be all ready to tackle BI challenges using Tableau's features.

Who this book is for

This book is for data analysts, data visualization, and BI users who are looking for quick solutions to common and not-so-common problems faced while using Tableau.

What this book covers

Chapter 1, Getting Started with Tableau Software, will consist of theory and recipes with a focus on the learning foundation of Tableau and allowing you to get familiar with the Tableau interface and basic tasks such as creating simple charts, tables, and filtering. You will come to understand the semantic layer of Tableau. You will learn through examples made with real data collected through a large market research study.

Chapter 2, Data Manipulation, will guide you through the process of manipulating data in Tableau using census data. From connecting to data sources, through adding multiple sources, joining them, and blending them—after practicing the recipes in this chapter, you will feel confident manipulating data sources in Tableau. Additionally, you will learn how to use the Tableau Pivot functionality, and set the semantic layer of your workbook to suit the requirement of the task by practicing converting measures to dimensions, continuous to discrete, and editing aliases.

Chapter 3, Tableau Extracts, will cover how Tableau dashboard performance is boosted using extracts. You will be informed about the different types of Tableau file formats and types of extract. The chapter introduces you to Tableau's new in-memory, blazingly fast data engine technology called Hyper, which was released in October 2017. Step-by-step instructions will help users learn how extremely large datasets can be sliced and diced in seconds using Hyper and hence improve the speed of analysis. This chapter will enable you to optimize the performance of your Tableau dashboards using aggregated extracts, dimension reduction, extract filters, incremental extract refreshes, and cross-data joins.

Chapter 4, Tableau Desktop Advanced Calculations, will start to explore the rest of Tableau Desktop functionality, such as table calculations, calculated fields, parameters, sets, groups, and level of detail expressions. Steps by step, you will learn how to leverage the full power of Tableau. This chapter is full of useful recipes that help you to master Tableau Desktop skills, from simple table calculations to advanced level of detail expressions, helping you to become a more advanced Tableau developer. The chapter uses real-life marketing data and will cover population geospatial use cases.

Chapter 5, Tableau Desktop Advanced Filtering, covers filters from A to Z. After getting familiar with filtering in the first chapter, you will expand your skills. Through practical exercises that use data from the packaged food industry, you will have an opportunity to master all kinds of filters—you will learn about implementing date filters, measure filters, top N filters, table calculation filters, and action filters. This chapter will also teach you how to manage the relationship between multiple filters by adding them to context.

Chapter 6, Building Dashboards, will focus on dashboard design techniques. This chapter will introduce the concept of dashboards and go through the process of designing a dashboard. Using real-life data about internet usage, you will start by making a basic dashboard before building on it by adding custom formatting and advanced functionalities. Moreover, you will learn about the role of visualization and the importance of using the right design layout in order to use the full power of Tableau and create awesome dashboards. Finally, you will build a self-service dashboard.

Chapter 7, Telling a Story with Tableau, covers creating stories with data. Through practical examples made with real-life business data from the automotive industry, you will learn how to use Tableau functionality for making stories in a way that is engaging and accessible to the audience, while at the same time accurate in communicating the message.

Chapter 8, Tableau Visualization, introduces techniques for creating advanced visualizations with Tableau Desktop. Here we go beyond Tableau's Show Me feature and instead look at the exact technique for how to master advanced visualizations that can make your dashboard story stand out from the crowd. We cover multiple use cases and recommend the best practice for each visualization, along with detailed steps for creating each one of them. The use cases vary from identifying elements in the data to create the biggest impact, to creating ranks for different categories over a period of time to visually track goals for organizations, to comparing multiple measures for performance over time. This chapter uses multiple different datasets for each visualization, such as an American football dataset, an unsatisfactory customer service dataset from the hospitality industry, US state college rankings, a stock prices dataset, CO2 emissions from energy consumption, FY18 PMMR spending and budget data, and more.

Chapter 9, Tableau Advanced Visualization, builds on what was covered in the previous chapter. The use cases vary from comparing multiple categories with high values in the 80-90% range, identifying the dominant players in the flow, and creating part-to-whole relationships, to visually eliminating size Alaska Effect. This chapter uses multiple different datasets for each visualization, including football league data, Wikipedia clickstream data, ITA's market research data, retail sales marketing profit and cost data, and statewise US population distribution data.

Chapter 10, Tableau for Big Data, looks at how visualizing data is important—regardless of its volume, variety, and velocity! The approach to visualizing big data is especially vital, as the cost of storing, preparing, and querying data is much higher. Organizations must leverage well-architected data sources and rigorously apply best practices to allow workers to query big data directly. In this chapter, we address the challenges of visualizing big data; the best practices for leveraging Hadoop, S3, Athena, and Redshift Spectrum directly; and how you can deploy Tableau on big data at massive scale.

Chapter 11, Forecasting with Tableau, will cover Tableau's built-in functions for the forecasting and integration of R packages. Using real-life data from health behavior research, you will learn how to perform regression analysis on simple and more complex datasets, and how to correctly interpret the results of statistical tests. Also, you will learn how to implement time series models. Toward the end of the chapter, you will see a working example of regression that relies on machine learning.

Chapter 12, Advanced Analytics with Tableau, will cover advanced analytics with Tableau, using Tableau integration with R. Using real-life data from the telecommunication, automotive, banking, and fast-moving consumer goods industries, you will learn how to discover the underlying structure of data, how to identify market niches, how to classify similar cases in segments, and how to extrapolate results on larger data sets. Also, you will learn how to identify and interpret unusual cases and anomalies in data.

Chapter 13, Deploy Tableau Server, covers Tableau Server and its purpose. It contains the steps to download and deploy Tableau Server in Windows and Linux environments. You will also learn about how a Tableau Server backup is created, monitored, and scheduled. Further server usage monitoring is discussed along with Tableau Server automatization with tabcmd and tabadmin. Overall, this chapter aims to have you well versed with how to automatically update and publish Tableau dashboards on Tableau Server and create appropriate security for restricting access.

Chapter 14, Tableau Troubleshooting, covers troubleshooting Tableau Desktop and Tableau Server. This chapter aims to lay down the basic foundation for the steps to be followed whenever an issue is encountered during your Tableau journey. This chapter has been split into three sections: performance troubleshooting, technical troubleshooting, and logs.

Chapter 15, Preparing Data for Analysis with Tableau Prep, covers a new Tableau product: Tableau Prep. It is designed to help you quickly and confidently combine, shape, and clean your data for analysis. Prep allows end users to clean and organize data before creating a data source. You will learn about this product's use cases and best practices.

Chapter 16, ETL Best Practices for Tableau, introduces an integration between Tableau Server and modern ETL tool Matillion. The reader will learn how to install tabcmd for Linux and build integration between ETL pipeline and Tableau Server activities such as refreshing extracts and exporting PDFs. This approach could be used for any ETL tool.

Chapter 17, Meet Tableau SDK and API, is a detailed and practical step-by-step guide to installing the Tableau SDK and API. You will learn how to take any data and convert it into a Tableau extract file (.tde). This data can be from a database, added at defined intervals as new data comes in, or the result of a predictive model created using the powerful machine learning libraries of Python. Furthermore, this chapter elaborates on how the Tableau SDK can be utilized to read a Tableau extract file in Tableau Desktop and how it can be shared on Tableau Server for further visualization. This chapter shows how predictive models and visualizations can peacefully coexist as separate layers. For this chapter refer to: https:/​/www.​packtpub.​com/​sites/​default/​files/​downloads/​Tableau_2019_x_Cookbook.​pdf.

To get the most out of this book

You will need to download Tableau Desktop 2018. Some understanding of BI concepts and Tableau is required.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

Log in or register at

www.packt.com

.

Select the

SUPPORT

tab.

Click on

Code Downloads & Errata

.

Enter the name of the book in the

Search

box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

WinRAR/7-Zip for Windows

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7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Tableau-2019.x-Cookbook. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789533385_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "You will also need to save a local copy of the Baby_names.csv dataset to your device, as we will be using it in the recipes."

A block of code is set as follows:

install.packages('rpart',repos='http://cran.us.r-project.org')library(rpart)cars <- read.table("C:\\!Slaven\\6 KNJIGA\\4 Advanced analytics\\4 decision tree\\new_or_used_car.csv", header=T, sep=",")fit <- rpart(FuturePurchase ~ Age + Gender + Education + FamilyStatus + CurrentCar + AgeOfCurrentCar + MunicipalityType, method="class", data=cars)plot(fit, uniform=TRUE, main="Classification of new cars buyers")text(fit, all=TRUE, cex=.8)

Any command-line input or output is written as follows:

set enable_result_cache_for_session to off;

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "From the Connect pane on the left-hand side, choose the Text file option."

Warnings or important notes appear like this.

Tips and tricks appear like this.

Sections

In this book, you will find several headings that appear frequently (Getting ready, How to do it..., How it works..., There's more..., and See also).

To give clear instructions on how to complete a recipe, use these sections as follows:

Getting ready

This section tells you what to expect in the recipe and describes how to set up any software or any preliminary settings required for the recipe.

How to do it…

This section contains the steps required to follow the recipe.

How it works…

This section usually consists of a detailed explanation of what happened in the previous section.

There's more…

This section consists of additional information about the recipe in order to make you more knowledgeable about the recipe.

See also

This section provides helpful links to other useful information for the recipe.

Get in touch

Feedback from our readers is always welcome.

General feedback: If you have questions about any aspect of this book, mention the book title in the subject of your message and email us at [email protected].

Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packt.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.

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For more information about Packt, please visit packt.com.

Getting Started with Tableau Software

In this chapter, we will cover the following recipes:

Connecting to the data

Building a bar chart using Show Me 

Building a text table 

Adding filters

Adding color

Building a tree map

Building a map

Customizing tooltips 

Building a dual axis map

Technical requirements

To follow the recipes in this chapter, you will need to have Tableau 2019.x installed. You will also need to save a local copy of the Baby_names.csv dataset to your device, as we will be using it in the recipes. You can download this dataset from the GitHub repository: (https://github.com/PacktPublishing/Tableau-2018-Dot-1-Cookbook/blob/master/Baby_names.csv).

Introduction to Tableau

Tableau is one of the fastest-evolving business intelligence (BI) and data visualization tools at the moment. The user-friendly interface, combined with powerful capabilities, makes it one of the most widely used and popular BI tools around the globe. Tableau offers many functionalities, and getting started with the basic ones is surprisingly easy. This chapter will get you familiar with Tableau basics and, by the end of it, you will have learned how to connect to a data source, and how to make simple visualizations.

To complete the recipes in this chapter, we will be using data on baby names in the US, which have been collected by the US Federal Social Security Administration (SSA). The Baby_names.csv dataset contains the most popular baby names (that have 100 or more registered appearances) in the US, from 2010 through 2017. The dataset contains information about the state, gender of the name, name itself, year, and number of babies with said name.

Connecting to the data

In this recipe, we will go through the basics of connecting to a data source. The first step you must take when you open Tableau, before you create any visualizations, is to connect to a data source. You will then use that data source to create your views and dashboards. 

Getting ready

In this recipe, we will be using the Baby_names.csv dataset. Make sure that you have a local copy of the dataset saved to your device.

How to do it…

Open Tableau.

From the

Connect

pane on the left-hand side, choose the

Text file

option:

A new window will open. Navigate to your local copy of the

Baby_names.csv

dataset, select it, and click

Open

.

Tableau has now opened the

Data Source

page for you, where the file you loaded has been selected as the data source, and where you can also preview it.

To begin making your first visualization, just click on the

Sheet 1

tab in the bottom of the workbook.

You are all set! 

How it works...

Tableau reads the file you connected to and recognizes fields and their respective data types. There are the following data types in Tableau:

Number (decimal)

Number (whole)

Date and time

Date

String

Boolean

After you have connected to the data source and you click on Sheet 1, you will see the Data pane on the left-hand side of the workspace, with all the fields from the data source listed, and their respective types marked by small symbols to the left of their names, as shown in the following screenshot:

The globe symbols in front of State, Longitude, and Latitude denotes the geographical roles of these fields, which are important when building maps. On the other hand, the Abc symbol signifies strings, while the # symbol denotes numerical values.

There's more…

Tableau allows users to connect to a wide range of data. You can connect to different types of files that are stored locally on your device, or data stored on the cloud or in relational or multidimensional databases. You can connect to the list of data that is available on the Start page, which opens when you launch Tableau Desktop, under Connect.

See also                    

For more on connecting to data, see the Tableau help resource on the topic at 

https://onlinehelp.tableau.com/current/pro/desktop/en-us/basicconnectoverview.html

Building a bar chart using Show Me

In this recipe, we will build a bar chart using Show Me. The Show Me option is a handy way to get started with building Tableau visualizations. To make a visualization, you don't need to know exactly how to do it, you just need to know what fields from your data source you would like to include in it. Tableau will suggest the appropriate visualizations.

Getting ready

To complete this recipe, you need to connect to the Baby_names.csv dataset and open a new blank worksheet.

How to do it…

We will now create the bar chart using the Show Me option, while referring to the given steps.

Creating a chart using Show Me

Hold the

Ctrl

key on your keyboard, then on

State

under

Dimensions

, and then choose 

Frequency

under