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)

Extracting substrings

More often than not, data is delivered to us in a less-than-ideal state, with multiple values being held in a single field. We saw how to split data into multiple fields in the Splitting columns with multiple values recipe, in Chapter 3, Cleaning Transformations. However, splitting fields relies on the data being organized, and will never leave out any of the data. In this recipe, we'll look at extracting substrings, which will result in new fields as well. However, unlike splitting fields, we'll be able to more narrowly define what data we want to include in our new field. Furthermore, extracting substrings is non-destructive, that is, the original field will remain unaffected. In this recipe, we'll load a dataset into Tableau Prep that has a field with multiple values in it. We'll then proceed to extract each value and create separate fields for each.

Getting ready

To follow along with this recipe, download the Sample Files 7.3 folder...