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

Summary

In this chapter, we learned about the more advanced capabilities of Tableau Prep Builder, including adding new rows, pivoting rows to columns, and integrating with data science models.

Sometimes, the level of detail of the source data is higher than the level we need to analyze. Specifically, there are cases where the data has a range of values that are contained in the same row, based on the value of one of the fields. In these cases, to make analysis easier in Tableau, we can add new rows to expand the data to a lower level of detail.

We also learned about pivoting rows to columns. Data sources sometimes have multiple conditional measures contained in a single column. Pivoting the rows within these fields into their own columns, each representing a unique field, allows for much easier analysis in Tableau.

In the final section of the chapter, we learned that Tableau Prep Builder can extend its capabilities to include data science models created in R, Python, or Salesforce...