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

Live versus extracted data

Tableau broadly gives two options for connection types for the data behind your data models. These are Live and Extract.

If you choose a live connection, Tableau will query your data source every time a user interacts with a visualization when it needs to get additional data that isn’t in the view. If you choose to extract the data, Tableau will move the data from where the data is sourced to a high-performance analytical store.

The most basic use case for live connections is when the analysis being performed needs to occur on up-to-the-minute data. When the analysis is slightly delayed, as of the end of the close of business of the previous day, for example, an extract will often make the most sense as it allows for faster query time and less impact on operational databases. These use cases often simplify the many nuisances that determine the best option between live connections and extracts. We will explore each of these considerations as they...