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)

What this book covers

Chapter 1, Getting Started with Tableau Prep, runs through the various use cases in which you can employ Tableau Prep as a tool for data analysis.

Chapter 2, Extract and Load Processes, explains how to connect to various data source types and write your output to different destinations.

Chapter 3, Cleaning Transformations, covers how to apply cleaning operations to your dataset, including filtering, validation, and splitting columns.

Chapter 4, Data Aggregation, looks at how to aggregate data using aggregation and grouping functions.

Chapter 5, Combining Data, addresses how to join various disparate datasets using join and union tools.

Chapter 6, Pivoting Data, looks at how to pivot the orientation of your data from columns to rows and vice versa.

Chapter 7, Creating Powerful Calculations, explains how to add calculated fields to your dataset.

Chapter 8, Data Science in Tableau Prep Builder, covers how to add R or Python code to your Tableau Prep data flow.

Chapter 9, Creating Prep Flows in Various Business Scenarios, lets you practice a full end-to-end flow creation using a variety of methods discussed in this book.