Linking Multiple Datasets in Tableau Public#

🎨 Data Visualization 📊 Tableau Lesson 014

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Combining sources for richer views#

Real analysis often draws on several datasets, and Tableau can combine them — linking multiple data sources so a single visualization draws on more than one table. This lesson covers linking datasets in Tableau Public, applying the data-combining concepts from the analysis section within the visualization tool.

How Tableau links data#

Tableau offers a few ways to combine data sources, mirroring the combining techniques already learned:

Joins — combining tables on a matching key, exactly the SQL JOIN from the analysis section, configured visually in Tableau. Tables are joined on a shared field (a key), producing a combined dataset with the join-type choices (inner, left, right, full) the JOIN lesson covered.
Relationships — Tableau’s flexible way to relate tables without a rigid up-front join, letting Tableau determine how to combine them per visualization.
Blending — combining data from different sources (say a spreadsheet and a database) at the visualization level, aggregating each and linking on a common field.
Unions — stacking tables with the same structure (appending rows), for combining like datasets (this month’s and last month’s data).

The concepts are the ones from the analysis section — joining on keys, combining sources — now performed inside Tableau to feed richer visualizations.

Why link datasets#

Linking data lets a visualization draw on information spread across tables — sales data joined to product details joined to regional information, visualized together. Just as the analysis section combined tables to answer richer questions, linking datasets in Tableau enables richer visualizations, showing relationships across data that no single table holds. It brings the relational-combine power into the visualization layer.

The caveat#

Combining data in Tableau carries exactly the hazards the analysis section flagged for joins, now one step removed and thus easier to get wrong unnoticed. Joining on a non-unique key multiplies rows and inflates the aggregates Tableau computes — the fan-out problem, now hidden inside a chart where the wrong numbers look authoritative. Mismatched keys silently drop data; blending aggregates before combining, which can produce subtly different results than a join. The discipline is the same: understand the relationship between the tables (one-to-one, one-to-many), verify that combined visualizations show the row counts and totals you expect, and treat a chart built on linked data with the same row-count skepticism as a SQL join. A visualization of wrongly-combined data misleads with a confident, polished face. The next lessons turn from building charts to telling stories with them.

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