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Data Modeling with Tableau

Data Modeling with Tableau

By : Kirk Munroe
4.5 (14)
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Data Modeling with Tableau

Data Modeling with Tableau

4.5 (14)
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)
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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 learned about extending the data in the data model through unions and joins. Unions are a method of adding additional rows to our data model when two or more data sources have the same fields. Joins are a method of adding additional fields to our data model by linking one or more fields between data sources.

Additionally, we learned about pivoting columns to rows. Data sources are often in the format of crosstabs, which don’t work well with Tableau data models. Tableau likes the data to be in a table format. Pivoting the rows of a crosstab format neatly into columns allows Tableau to map the columns into fields for data model creation for easy analysis.

In the final section of the chapter, we learned about the level of detail of data sources and how we can aggregate the data source to join with other data sources at the same level of detail.

In the next chapter, we will add to the foundational knowledge we learned in this chapter by exploring...

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Data Modeling with Tableau
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