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Mastering Tableau 2026

Mastering Tableau 2026 - Fifth Edition

By : Marleen Meier
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Mastering Tableau 2026

Mastering Tableau 2026

By: Marleen Meier

Overview of this book

Master the full capabilities of Tableau to design, build, and scale modern data visualization and business intelligence solutions. This book takes you beyond the basics to help you create advanced Tableau dashboards, perform efficient data preparation with Tableau Prep Builder, and deliver impactful analytics across your organization. Through practical examples, you’ll learn how to turn raw data into meaningful insights using proven Tableau data visualization techniques. As you progress, you’ll work with calculated fields and LOD expressions to build more flexible and powerful analyses. You’ll also explore performance optimization, deployment, and collaboration using Tableau Server, along with best practices for data governance and security in enterprise environments. In addition, the book covers AI-powered Tableau features that enhance analysis and accelerate insight discovery. You’ll apply advanced techniques such as time series analysis, geospatial analytics, and data modeling, and extend Tableau’s capabilities through Python and R integration for more sophisticated analytics workflows. By the end of this book, you’ll be equipped to build scalable, high-performance Tableau analytics solutions, enabling you to solve complex business problems with confidence.
Table of Contents (19 chapters)
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15
Chapter 15: The Intelligent Era of Tableau AI, Pulse, and Next
18
Index

Summary

We began this chapter with an introduction to relationships, followed by a discussion on joins, and discovered the queries Tableau uses to generate the respective data. Unions come in handy if identically formatted data, stored in multiple sheets or data sources, needs to be appended.

Then, we reviewed data blending to clearly understand how it differs from joining. We discovered that the primary limitation in data blending is that no dimensions are allowed from a secondary source; however, we also discovered that there are exceptions to this rule. We also discussed scaffolding, which can make data blending surprisingly fruitful.

Finally, we discussed data structures and learned how pivoting can make difficult or impossible visualizations easy. Having completed our second data-centric discussion, in the next chapter, we will discuss table calculations, partitioning, and addressing.

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