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)

Combining data ingest and Union actions

When you have multiple data sources that need to be vertically stacked using a union, you may opt to perform that action during ingestion. This avoids you having to create a separate Union step in Tableau Prep. Furthermore, you can use wildcards in your input, such that the input becomes dynamic, and new data files can be ingested as they are added. A typical scenario for this would be an automated process that exports data on a recurring schedule, which you then need to union with prior data exports.

In this recipe, we'll use a special type of union that is part of the input step, rather than a step by itself. Using the Union functionality during input allows you to ingest and union multiple input files simultaneously.

Getting ready

To follow along with this recipe, download the Sample Files 5.2 folder from this book's GitHub repository. This folder contains sales data that has been exported every month, and so we have one...