Tableau 2019.x Cookbook

▶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

▶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

▶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.

▶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