Book Image

Data Modeling with Tableau

By : Kirk Munroe
Book Image

Data Modeling with Tableau

By: Kirk Munroe

Overview of this book

Tableau is unlike most other BI platforms that have a single data modeling tool and enterprise data model (for example, LookML from Google’s Looker). That doesn’t mean Tableau doesn’t have enterprise data governance; it is both robust and highly flexible. This book will help you effectively use Tableau governance models to build a data-driven organization. Data Modeling with Tableau is an extensive guide, complete with step-by-step explanations of essential concepts, practical examples, and hands-on exercises. As you progress through the chapters, you’ll learn the role that Tableau Prep Builder and Tableau Desktop each play in data modeling. You’ll also explore the components of Tableau Server and Tableau Cloud that make data modeling more robust, secure, and performant. Moreover, by extending data models for Ask and Explain Data, you’ll gain the knowledge required to extend analytics to more people in their organizations, leading to better data-driven decisions. Finally, this book will guide you through the entire Tableau stack and the techniques required to build the right level of governance into Tableau data models for the correct use cases. By the end of this Tableau book, you’ll have a firm understanding of how to leverage data modeling in Tableau to benefit your organization.
Table of Contents (22 chapters)
1
Part 1: Data Modeling on the Tableau Platform
4
Part 2: Tableau Prep Builder for Data Modeling
9
Part 3: Tableau Desktop for Data Modeling
14
Part 4: Data Modeling with Tableau Server and Online

Inserting data science models

In this section, we will explore how we can incorporate data science models into our flows. Your organization might already have data-cleaning code written in R or Python. Your organization might also be using R, Python, or Einstein Discovery and Prediction Builder to score data. For example, you might have a model that looks at customer data and, using an ML algorithm, scores a customer’s propensity to churn. Within a Tableau Prep flow, you can pass your data to any of these technologies to get back new or transformed data and then continue with your flow in Tableau Prep Builder.

It is beyond the scope of this textbook to create and integrate with R, Python, or Einstein models, as each of these technologies requires an extensive combination of installation and/or configuration. For this reason, we will look at the steps to add the models into a flow in the user interface without creating a connection. This will enable you to understand the process...