Book Image

Tableau 2019.x Cookbook

By : Dmitry Anoshin, Teodora Matic, Slaven Bogdanovic, Tania Lincoln, Dmitrii Shirokov
Book Image

Tableau 2019.x Cookbook

By: Dmitry Anoshin, Teodora Matic, Slaven Bogdanovic, Tania Lincoln, Dmitrii Shirokov

Overview of this book

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.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
Free Chapter
1
Getting Started with Tableau Software
Getting Started with Tableau Software
Technical requirements
Introduction to Tableau
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
Building a dual-axis map
Customizing tooltips
2
Data Manipulation
Data Manipulation
Technical requirements
Introduction
Joining data sources
Adding a secondary data source
Data blending
Data union
Using Tableau Pivot
Preparing data
3
Tableau Extracts
Tableau Extracts
Introduction
Overview of different file formats in Tableau
Creating a data source extract
Configuring an incremental extract
Upgrading to Hyper
Creating extracts using cross-database joins
Troubleshooting extracts with Tableau Server
4
Tableau Desktop Advanced Calculations
Tableau Desktop Advanced Calculations
Technical requirements
Introduction
Creating calculated fields
Implementing quick table calculations
Creating and using groups
Creating and using sets
Creating and using parameters
Implementing the basics of level of detail expressions
Using custom geocoding
Using polygons for analytics
5
Tableau Desktop Advanced Filtering
Tableau Desktop Advanced Filtering
Technical requirements
Introduction
Implementing a top N filter
Adding filters to context
Creating a measure filter
Creating date range filters
Creating relative dates filters
Implementing table calculation filters
Implementing action filters
6
Building Dashboards
Building Dashboards
Technical requirements
Introduction
Creating a dashboard
Formatting a dashboard
Setting filters
Setting filters across various data sources
Adding highlight actions
Setting layouts
Building a self-service dashboard
7
Telling a Story with Tableau
Telling a Story with Tableau
Technical requirements
Introduction
Creating a Tableau story
Setting the narrative of the Story
Choosing the right charts
Writing effective headlines
Recommendation and executive summary
Formatting the Story
8
Tableau Visualization
Tableau Visualization
Technical requirements
Introduction
Dual axis waterfall chart
Pareto chart
Bump chart
Sparklines chart
Donut chart
Motion chart
9
Tableau Advanced Visualization
Tableau Advanced Visualization
Technical requirements
Introduction
Lollipop charts
Sankey diagrams
Marimekko charts
Hex-Tile maps
Waffle charts
10
Tableau for Big Data
Tableau for Big Data
Technical requirements
Introduction
Connecting with Amazon Redshift
Launching an Amazon Redshift cluster
Connecting a Redshift cluster
Loading sample data into the Redshift cluster
Connecting Redshift with Tableau
Creating a Tableau report
Tuning Redshift for efficient Tableau performance
Connecting to Amazon Redshift Spectrum
Connecting to Snowflake
Using SnowSQL CLI
Connecting Tableau to Snowflake
Connecting big data
Accessing semi–structured data
Connecting Amazon Elastic MapReduce with Apache Hive
Creating sample data
Connect Tableau with Apache Hive
11
Forecasting with Tableau
Forecasting with Tableau
Technical requirements
Introduction
Basic forecasting and statistical inference
Forecasting on a dataset with outliers
Using R within Tableau
Forecasting based on multiple regression
Regression with random forest
Time series forecasting
12
Advanced Analytics with Tableau
Advanced Analytics with Tableau
Technical requirements
Introduction
Running segmentation analysis
Discovering the latent structure of the dataset
Extracting the structure beneath discrete variables
Data mining with tree-based models
Identifying anomalies in data
13
Deploy Tableau Server
Deploy Tableau Server
Technical requirements
Introduction
Deploying Tableau Server in Windows
Deploying to Tableau Server
Deploying Tableau Server on Linux using AWS
Getting started with Tabcmd
14
Tableau Troubleshooting
Tableau Troubleshooting
Technical requirements
Introduction
Performance recording
Performance troubleshooting and best practices
Troubleshooting through log files
15
Preparing Data for Analysis with Tableau Prep
Preparing Data for Analysis with Tableau Prep
Introduction
Technical requirements
Installing Tableau Prep
Building the first flow with Tableau Prep
Working with big data
16
ETL Best Practices for Tableau
ETL Best Practices for Tableau
Introduction
Technical requirements
Getting started with Matillion ETL
Deploying Tabcmd on Linux
Creating Matillion Shared Jobs
17
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Implementing the basics of level of detail expressions

Level of detail (LOD) expressions are a very useful feature of Tableau. In order to understand what LOD expressions do, we must first understand what LOD is. When we use one dimension in our view, Tableau aggregates the measures along this dimension; so, we say that this dimension provides the LOD. If we add another dimension to the view, measures will be additionally disaggregated along this dimension as well, leading to an even more granular LOD. LOD expressions allow us to control the LOD that will be used in our view independently of the fields that are included in the view itself. We can make it either more or less granular than the LOD provided by the fields included in the visualization itself, which provides for flexibility in creating our views.

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