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 rows to columns

When preparing data that has been generated by transactional systems, you may encounter data structures that appear nonsensical from a reporting and analytics perspective. Take a sales order as an example. A sale may be for one or multiple items and the total sales amount may be affected by things such as a loyalty card, discount, referral code, and of course sales tax. Depending on which system you are working with, such information may be reported separately, that is, in columns. However, it's quite likely to see multiple rows in your dataset for the same transaction. In this recipe, we'll look at pivoting data from rows to columns, which will resolve any issues arising from such a data structure. Broadly, these steps are similar to pivoting columns to rows, with some important differences, as we'll see.

Getting ready

To follow along with this recipe, download the Sample Files 6.3 folder from this book's GitHub repository.