Book Image

Tableau Prep Cookbook

By : Hendrik Kleine
Book Image

Tableau Prep Cookbook

By: Hendrik Kleine

Overview of this book

Tableau Prep is a tool in the Tableau software suite, created specifically to develop data pipelines. This book will describe, in detail, a variety of scenarios that you can apply in your environment for developing, publishing, and maintaining complex Extract, Transform and Load (ETL) data pipelines. The book starts by showing you how to set up Tableau Prep Builder. You’ll learn how to obtain data from various data sources, including files, databases, and Tableau Extracts. Next, the book demonstrates how to perform data cleaning and data aggregation in Tableau Prep Builder. You’ll also gain an understanding of Tableau Prep Builder and how you can leverage it to create data pipelines that prepare your data for downstream analytics processes, including reporting and dashboard creation in Tableau. As part of a Tableau Prep flow, you’ll also explore how to use R and Python to implement data science components inside a data pipeline. In the final chapter, you’ll apply the knowledge you’ve gained to build two use cases from scratch, including a data flow for a retail store to prepare a robust dataset using multiple disparate sources and a data flow for a call center to perform ad hoc data analysis. By the end of this book, you’ll be able to create, run, and publish Tableau Prep flows and implement solutions to common problems in data pipelines.
Table of Contents (11 chapters)

Pivoting columns to rows

Data is often produced by systems in what the engineers building the system thought was the most efficient manner. Rarely do data processing and storage systems store data with visualization in mind. Similarly, you may have data available that is appropriate for one type of visualization but not another. In this recipe, we'll look at a sales dataset. This dataset has sales revenue values per category. The categories are Electronics, Groceries, and Household Appliances. Each of the categories has its own column, which prevents us from easily making a line chart with overall revenue. To resolve this, we're going to pivot the data such that these three individual columns become a single Category column, and values are placed in a single Revenue column.

Getting ready

To follow along with this recipe, download the Sample Files 6.1 folder from this book's GitHub repository.

How to do it…

Start by opening the Sales Data.csv file from...