This edition of the bestselling Tableau guide will teach you how to leverage Tableau's newest features and offerings in various paradigms of the BI domain. Updated with fresh topics, including the newest features in Tableau Server, Tableau Prep Builder, and Desktop, and up-to-date examples, this book will take you from mastering essential Tableau concepts to advanced functionalities. A chapter on AI-powered analytics has also been added.

Throughout this book, you'll learn how to use Tableau Prep Builder to easily perform data preparation and handling, as well as complex joins, spatial joins and unions, using practical examples. You'll get to grips with OData or Google BigQuery, executing data densification and exploring other expert-level examples to help you with calculations, mappings, and visual designs.

Later chapters will teach you all about improving dashboard performance, Tableau Server, Tableau exchange, Data Governance and AI-powered features.. Finally, you'll cover advanced use cases, such as self-service analysis, time series analysis, geo-spatial analysis, and how to connect Tableau to Python and R to implement programming functionalities within Tableau.

By the end of this book, you'll have mastered Tableau 2026 and be able to tackle common and advanced challenges in the BI domain.

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Spis treści

Mastering Tableau 2026. Implement advanced data visualizations, BI techniques and AI-powered analytics with Tableau - Fifth Edition eBook -- spis treści

• 1. Reviewing the Basics
• 2. Getting Your Data Ready
• 3. Using Tableau Prep Builder
• 4. Learning about Joins, Blends, and Data Structures
• 5. Introducing Table Calculations
• 6. Utilizing OData, Data Densification, Big Data, and Google BigQuery
• 7. Practicing Level of Detail Calculations
• 8. Going Beyond the Basics
• 9. Working with Maps
• 10. Designing Dashboards and Best Practices for Visualizations
• 11. Leveraging Advanced Analytics
• 12. Improving Performance
• 13. Exploring Tableau Server and Tableau Cloud
• 14. Integrating Programming Languages
• 15. Developing Data Governance Practices