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.
Table of Contents (18 chapters)

Working with big data

Tableau Prep works with big data volumes and big data tools, such as Snowflake, Redshift, and Amazon EMR. You can refer to Chapter 10, Tableau for Big Data and connect existing accounts of Amazon Redshift, Amazon EMR, or Snowflake.

Tableau Prep allows us to work with big data sets by leveraging sampling. However, it processes data on your local machine and if you want to create, extract, or export data into a CSV using a huge dataset, it can fail due to lack of memory. We learned that Tableau Desktop works with big data by rendering results using a live connection. We don't want to create an extract when working with big data. In the case of Tableau Prep, we can learn our dataset and then use filters to split the dataset and work with part of it.

Here's another solution: we can launch a powerful AWS EC2 instance and install Tableau Prep there, where...