Tableau 2019.x Cookbook: Over 115 recipes to build end-to-end analytical solutions using Tableau

Tableau 2019.x Cookbook: Over 115 recipes to build end-to-end analytical solutions using Tableau
Tableau 2019.x Cookbook: Over 115 recipes to build end-to-end analytical solutions using Tableau by Dmitry Anoshin
English | 2019 | ISBN: 1789533385 | 670 Pages | EPUB | 232 MB

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