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)

Chapter 5: Combining Data

Besides cleaning up your data inputs, Tableau Prep can be used to increase the value of your new dataset by augmenting it with complementary data. This can be done by extending the dataset vertically, adding more rows, or horizontally, by adding new data columns. Performing such data preparation tasks within Tableau Prep allows you to create a dataset that includes key data from multiple inputs, making the end result a comprehensive dataset for analysis.

In this chapter, you will learn how to combine different datasets by using a variety of different methods. Combining data is one of the most common actions in data preparation. Most organizations source data from multiple systems and combining that data into a holistic dataset allows more insightful analysis than looking at each dataset in isolation.

In this chapter, you'll find the following recipes to help you combine your data for analytics:

  • Combining data with Union
  • Combining data...