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

Aggregating data

To create impactful data models in Tableau, it is important to understand the level of detail in your data sources. In the previous sections of this chapter, we looked at sales data. This sales data had a row for every product sold in each sales transaction. That is, if a customer had an order that had 11 products in it, that would generate 11 rows of data. That creates the level of detail of the data source.

In the previous section of this chapter, we pivoted data to create a row of sales targets for each country for each month. This defines the level of detail of the data.

For an analyst, understanding the level of detail is essential to know what answers you can get from your data model. As someone creating data models, you need to understand the level of detail when combining data sources into a single data model. To join two or more data sources into a single data model, they typically need to be at the same level of detail.

Let’s imagine that...