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

Tableau virtual connections

Prior to the Tableau 2021.4 release, data model creation started with a direct connection to the underlying data source. This meant that data modelers would most often connect directly to enterprise database tables in their organization. This approach works well when the data modeler understands databases, but it falls short in several important ways, especially when the organization wants to delegate the role of data modeling to less technical users in the business.

If the organization is going to delegate data model creation to business users, the information technology, data engineering, and security teams will often want to ensure that the data modeler:

  • Cannot have access to all the data in the database for confidentiality reasons
  • Limits access to the number of tables in the database
  • Ensures that analytics are not run against live tables, that is, ensures that data is extracted to an analytics store so impacting performance of the...