Book Image

Learning Alteryx

By : Renato Baruti
Book Image

Learning Alteryx

By: Renato Baruti

Overview of this book

Alteryx, as a leading data blending and advanced data analytics platform, has taken self-service data analytics to the next level. Companies worldwide often find themselves struggling to prepare and blend massive datasets that are time-consuming for analysts. Alteryx solves these problems with a repeatable workflow designed to quickly clean, prepare, blend, and join your data in a seamless manner. This book will set you on a self-service data analytics journey that will help you create efficient workflows using Alteryx, without any coding involved. It will empower you and your organization to take well-informed decisions with the help of deeper business insights from the data.Starting with the fundamentals of using Alteryx such as data preparation and blending, you will delve into the more advanced concepts such as performing predictive analytics. You will also learn how to use Alteryx’s features to share the insights gained with the relevant decision makers. To ensure consistency, we will be using data from the Healthcare domain throughout this book. The knowledge you gain from this book will guide you to solve real-life problems related to Business Intelligence confidently. Whether you are a novice with Alteryx or an experienced data analyst keen to explore Alteryx’s self-service analytics features, this book will be the perfect companion for you.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Join and union

The ability to enable vital decisions with a few tools across multiple data tables is taking self-service analytics to the next level. The tools that we will review in this section include Join, Join Multiple, and Union. These core tools allow joining two inputs together using the Join tool, joining more than two inputs together using the Join Multiple tool, and combining two or more data streams together. Volumes of data can be streamlined within minutes using these helpful, powerful tools. Let's continue using the same workflow from the last section.


We'll begin this section by joining multiple files together. This can also be done using database tables, as the Join logic the same - the only difference is where the data comes from. The purpose is to identify the Average Age for all the cities in the Florida Local Data for Better Health.csv file. Let's go through how the Join tool can be applied in a workflow.

Step 1: Using an Input Data tool, connect to the file Florida...