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

Before doing any work in Tableau, we must first connect to the data we'll be working on. In the first recipe in this book, Connecting to data, we learned how to connect to a data source. In this chapter, we'll build on that knowledge to become more skillful at manipulating, joining, unioning, and transforming data sources. In the last recipe of this chapter, Preparing data, we'll also address the topic of data preparation and getting our data ready for visualizing and further analysis.

In this chapter, we'll be using multiple datasets. In the first recipe, Joining data sources, we'll be using two datasets, Public_Schools_1.csv and Public_Schools_2.csv, which contain data on Boston public schools for the school year 2018/2019. The dataset originally comes from Kaggle's official site. In the Adding a secondary data source and Data blending...