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

Summary

In this chapter, we explored Tableau Prep Conductor. This capability allows us to schedule the flows we create in Tableau Prep Builder and the web client. We can schedule our flows to run as single or linked tasks, we can subscribe to flows, automate messages when our flow fails, and check the status of our flows when they have run.

We learned that the data catalog and data lineage allow for building trust in our data source, allowing the people who view and interact with Tableau visualizations to see data definitions, where data originates, and where the data is being used in the organization.

Data quality warnings allow us to alert users when we have issues with the data in our data models. These warnings can be created manually or created to trigger when data extracts fail.

We also learned that certifying data sources signals to authors and consumers using published data sources that they can be used with confidence.

In the next chapter, we will be looking at...