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 datasets using a left or right join

In the Combining datasets using an inner join recipe, we combined data that was complementary and complete, orders, and associated customer information. However you may find a use case where complementary data is present for some records, and not for others. An example of this that we'll use in this recipe involves two data sources, one with sales data from a department store, and another data source with information from customers who checked out with a loyalty card. Of course, not all customers may have a loyalty card, and so we cannot expect to match every row in the data. This is where a left join comes into play.

In a left join, we pass through all the data from the first dataset, that is, the left data source, and only those records from the second, right data source that we were able to match. This means that any sales records that did not involve a loyalty card will still pass through, but the additional fields from the...