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

Sharing and Extending Tableau Data Models

We discussed creating data models using Tableau Prep Builder in Chapters 4 to 6 and using Tableau Desktop in Chapters 7 to 9. This chapter focuses on sharing and extending Tableau data models using published data sources and extending the model using hierarchies, folders, descriptions, grouping, and calculations. This chapter also explores the details and implications of live versus extracted data and embedded versus published data sources. Understanding the concepts in this chapter is key to being able to create data models that can be scaled and leveraged by the entire organization.

In this chapter, we’re going to cover the following topics:

  • Understanding live connections and extracts – scenarios of when to use each of them
  • Creating extracts with the Tableau Hyper engine
  • Understanding data sources and extract filters and their use
  • Understanding the implications of an embedded data source versus a published...