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

Row-level calculations and hiding and removing fields

Creating calculations in our data model can be of great benefit to analysts, who will use it to get answers to questions they have about the data. It makes more sense for some calculations to happen during analysis. These types of calculations relate to use cases where the data modeler can’t predict the questions the analysts will have. Typical examples are running totals, differences between values, period-to-date, and percentage of totals. These calculations are hard to perform ahead of the analysis phase because we don’t know the filters and the level of aggregation the analyst will be using ahead of time. These aggregate calculations are usually best left to the analyst and not put in the data model. However, there are some exceptions to this rule, so we will be looking at aggregate calculations in Tableau Prep Builder in Chapter 4.

Many other calculations only need to be performed once. These calculations tend...