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)

Introduction

In Chapter 1, Getting Started with Tableau, we encountered filters and learned how to implement basic ones. Filters allow us to use just part of the rows in a dataset, and this basic principle is common to all the filters we will be working with.

However, Tableau's filtering capabilities extend far beyond the basic filtering you have learned so far. In this chapter, you will become comfortable with implementing top N filters, measure filters, different kinds of date filters, table calculation filters, and action filters.

In this chapter, we will mostly be using the Winery.csv dataset, originally found on Kaggle.com. It contains data on wines—which winery they belong to, which province they originate from, number of points, price of the wine, and the name of the wine taster who rated them, among other details.

In the two recipes dealing with date filters...