Data Modeling with Tableau

Data Modeling with Tableau

by Kirk Munroe
Released December 2022
Publisher(s): Packt Publishing
ISBN: 9781803248028

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

Save time analyzing volumes of data using best practices to extract, model, and create insights from your data

Key Features

  • Master best practices in data modeling with Tableau Prep Builder and Tableau Desktop
  • Apply Tableau Server and Cloud to create and extend data models
  • Build organizational data models based on data and content governance best practices

Book Description

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 very flexible. This book will help you build a data-driven organization with the proper use of Tableau governance models.

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 will 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 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 get into the entire Tableau stack and get the techniques required to build the right level of governance into Tableau data models for the right 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.

What you will learn

  • Showcase Tableau published data sources and embedded connections
  • Apply Ask Data in data cataloging and natural language query
  • Exhibit features of Tableau Prep Builder with hands-on exercises
  • Model data with Tableau Desktop through examples
  • Formulate a governed data strategy using Tableau Server and Cloud
  • Optimize data models for Ask and Explain Data

Who this book is for

This book is for data analysts and business analysts who are looking to expand their data skills, offering a broad foundation to build better data models in Tableau for easier analysis and better query performance.

It will also benefit individuals responsible for making trusted and secure data available to their organization through Tableau, such as data stewards and others who work to take enterprise data and make it more accessible to business analysts.

Table of contents

  1. Data Modeling with Tableau
  2. Contributors
  3. About the author
  4. About the reviewers
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Download the example code files
    5. Conventions used
    6. Get in touch
    7. Share Your Thoughts
    8. Download a free PDF copy of this book
  6. Part 1: Data Modeling on the Tableau Platform
  7. Chapter 1: Introducing Data Modeling in Tableau
    1. Technical requirements
    2. What happens when you connect to data in Tableau Desktop?
    3. The ideal data format for Tableau – table format
    4. Shaping data for Tableau
    5. Connecting multiple tables to add new columns
    6. Summary
  8. Chapter 2: Licensing Considerations and Types of Data Models
    1. Tableau roles – Viewer, Explorer, and Creator
    2. Tableau Data Management
    3. Tableau virtual connections
    4. Tableau published data sources
      1. Working with published data sources on Tableau Server and Tableau Cloud
    5. Tableau embedded data sources
    6. Live versus extracted data
    7. The Tableau Hyper engine
    8. Summary
  9. Part 2: Tableau Prep Builder for Data Modeling
  10. Chapter 3: Data Preparation with Tableau Prep Builder
    1. Using Tableau Prep Builder to connect to data
    2. Profiling, cleaning, and grouping data
    3. Row-level calculations and hiding and removing fields
    4. Recommendations and changes
    5. Summary
  11. Chapter 4: Data Modeling Functions with Tableau Prep Builder
    1. Adding rows to our data model with unions and wildcard unions
    2. Adding new columns by joining data
    3. Dealing with data when columns contain values and not distinct fields
    4. Aggregating data
    5. Summary
  12. Chapter 5: Advanced Modeling Functions in Tableau Prep Builder
    1. Adding new rows
    2. Pivoting rows to columns
    3. Inserting data science models
    4. Summary
  13. Chapter 6: Data Output from Tableau Prep Builder
    1. Outputting our data models to files
    2. Outputting our data models to published data sources
    3. Outputting our data models to database tables
    4. Summary
  14. Part 3: Tableau Desktop for Data Modeling
  15. Chapter 7: Connecting to Data in Tableau Desktop
    1. Technical requirements
    2. Connecting to files in Tableau Desktop
      1. Getting data from Microsoft Excel files
      2. Getting data from text (or delimited) files
      3. Importing geospatial file types to allow for visual analysis with maps
      4. Creating data models from statistical files
      5. Creating data models from JSON files
      6. Getting data from tables in PDF files
    3. Dealing with preformatted reporting files with data interpreter and pivoting columns to rows
    4. Connecting to servers through installed connectors
    5. Connecting to servers through other connectors
      1. Additional connectors
      2. Web data connectors
      3. Connecting to databases without a listed connection
    6. Connecting to the Tableau data server
    7. Summary
  16. Chapter 8: Building Data Models Using Relationships
    1. Technical requirements
    2. Using relationships to combine tables at the logical layer
      1. Many use cases with a single data model
      2. Ability to handle tables at different levels of aggregation
    3. Understanding the differences between relationships and joins
    4. Setting performance options for relationships
    5. Creating manual and wildcard unions in Tableau Desktop to add additional rows of data
      1. Manual union
    6. Summary
  17. Chapter 9: Building Data Models at the Physical Level
    1. Technical requirements
    2. Opening relationships to join at the physical layer through database joins
      1. Single use case and using the join for a filter
    3. Geospatial join type to drive map-based analysis
    4. Using joins to create a data model with row-level security
    5. Understanding custom SQL – when to use it and the pitfalls of using it
    6. Summary
  18. Chapter 10: Sharing and Extending Tableau Data Models
    1. Technical requirements
    2. Understanding live connections and extracts – scenarios for using each
    3. Creating extracts with the Tableau Hyper engine
    4. Understanding extracts and data source filters
    5. Understanding the implications of an embedded data source versus a published data source
    6. Creating a published data source from the web interface of Tableau Server or Cloud
    7. Extending the Tableau data model with calculations, folders, hierarchies, grouping, and descriptions
    8. Summary
  19. Part 4: Data Modeling with Tableau Server and Online
  20. Chapter 11: Securing Data
    1. Technical requirements
    2. Adding users and groups to Tableau Server and Cloud
    3. Using Tableau projects to manage data model security
    4. Adding user-based security using a user filter
    5. Adding user-based security inside a published data source using an entitlements table
    6. Using Tableau virtual connections to manage access and security
    7. Leveraging database security features for both row and column-level security
    8. Summary
  21. Chapter 12: Data Modeling Considerations for Ask Data and Explain Data
    1. Technical requirements
    2. Visual analytics through natural language search with Ask Data
    3. Creating a lens for Ask Data, including field exclusions, renaming, and creating aliases
    4. Uncovering outliers in your data with Explain Data
    5. Curating data sources for Explain Data by telling the model which columns to use and ignore
    6. Summary
  22. Chapter 13: Data Management with Tableau Prep Conductor
    1. Technical requirements
    2. Scheduling Tableau Prep flows from Tableau Prep Conductor
    3. Data catalog, data lineage, data quality warnings, and certified data sources
    4. Summary
  23. Chapter 14: Scheduling Extract Refreshes
    1. Technical requirements
    2. How to set up and run schedules
    3. Using schedules with subscriptions
    4. Tableau Bridge – what it is and when to use it
    5. Summary
  24. Chapter 15: Data Modeling Strategies by Audience and Use Case
    1. When to use Tableau Prep Builder versus Tableau Desktop for creating our data models
    2. Use case 1 – finance user with quarterly financial reporting
    3. Use case 2 – sales performance management dashboards
    4. Use case 3 – information systems analytics of internal employee intranet site visits
    5. Use case 4 – marketing analytics of social media campaigns
    6. Summary
  25. Index
    1. Why subscribe?
  26. Other Books You May Enjoy
    1. Packt is searching for authors like you
    2. Share Your Thoughts
    3. Download a free PDF copy of this book

Product information

  • Title: Data Modeling with Tableau
  • Author(s): Kirk Munroe
  • Release date: December 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781803248028