How to Connect Tables in Tableau

Connecting data from different tables is where you unlock the real power of analytics in Tableau. This guide will show you exactly how to do it using the platform’s two main methods: flexible Relationships and classic Joins. You'll learn the difference between them, when to use each, and how to set them up step-by-step.

Why Do You Need to Connect Tables?

Your data is rarely stored in a single, neat file. More often, it’s spread across multiple tables, each describing a different part of your business. For instance, you might have one table for sales transactions, another for customer details, and a third for product information.

• Your Sales Table might contain OrderID, CustomerID, ProductID, and SaleAmount.
• Your Customers Table might have CustomerID, CustomerName, and State.
• Your Products Table could include ProductID, ProductName, and Category.

Each table is useful on its own, but the most powerful questions require combining them. If you want to see which product categories are most popular in California, you need to connect all three tables to get the complete picture. This process of linking tables based on shared fields is fundamental to data analysis.

Understanding The Core Concepts: Relationships vs. Joins

Tableau offers two ways to connect tables: Relationships and Joins. While they might seem similar, they work very differently. Relationships are Tableau's newer, recommended method, offering much more flexibility.

Relationships: The Flexible Way

Think of Relationships as a smart, flexible agreement between your tables. You tell Tableau how two tables are related to each other (e.g., this CustomerID field here connects to the CustomerID field there), and Tableau handles the details. You'll see this visualized as a flexible line, fondly called a "noodle," connecting your tables in the data source pane.

The magic of Relationships is that they don’t merge your tables into a single, permanent new table beforehand. Instead, Tableau keeps the tables separate but understands how they connect. When you drag fields into a worksheet to build a chart, Tableau intelligently looks at what you’re trying to build and creates the appropriate join on the fly, just for that specific visualization.

Key Benefits of Relationships:

• They're contextual: Joins are determined by the visualization, not upfront. This prevents common issues like data duplication or lost records that can happen with traditional joins.
• They preserve detail: Each table maintains its own level of granularity. You can have daily sales data and monthly customer sign-ups related in the same model without one messing up the other.
• They're simpler to set up: You drag your tables onto the canvas, define the connecting fields, and Tableau does most of the heavy lifting.

Joins: The Classic, Fixed Approach

Joins are the more traditional way of combining data, common in SQL databases and older versions of Tableau. When you create a join, you are physically merging tables into a single new table based on a specific rule. This new, combined table is the only thing Tableau will "see" for your analysis.

There are four main types of joins, which are easiest to understand using a Venn diagram analogy:

1. Inner Join

An inner join returns only the matching rows from both tables. If a customer in your Customers table has never made a purchase, they will not appear in the resulting joined table because there is no matching CustomerID in the Sales table.

• Use Case: You want to analyze sales data only for customers who have made a purchase and for products that have been sold.

2. Left Join

A left join returns all the rows from the left table and only the matching rows from the right table. If a customer in your Customers table (the left table) has never purchased anything, they will still appear in the final dataset, but the fields from the Sales table (the right table) will be null (empty).

• Use Case: You want a list of all your customers, along with their sales data if they have any. This is great for finding which customers haven't purchased anything yet.

3. Right Join

A right join is the opposite of a left join. It returns all the rows from the right table and only the matching rows from the left table.

• Use Case: You want a list of all sales transactions and want to include customer information for those sales. This is functionally similar to a left join, just depends on which table you start with.

4. Full Outer Join

A full outer join returns all rows from both tables. If there's a match, it will combine the rows. If there's no match, it will fill in the missing side with null values. You'll see every customer (even those with no sales) and every sale (even if, for some strange reason, it wasn't linked to a customer).

• Use Case: You need a complete, comprehensive view of all records in both tables, regardless of whether they have a match in the other.

How to Connect Tables Using Relationships (The Modern Way)

Using Relationships should be your default choice in modern versions of Tableau. It's simpler and more powerful for the vast majority of use cases. Here’s how to do it.

1. Connect to Your Data: First, connect to your data source (e.g., an Excel file, a database). You’ll be taken to the Data Source page.
2. Drag Your First Table: From the sidebar on the left, find your first table (our "base" table, like Orders) and drag it into the canvas area that says "Drag tables here." This establishes the first "logical table" in your data model.
3. Drag a Second Table: Now, find your second table (e.g., Customers) and drag it onto the canvas near the first one. Tableau will automatically try to detect the relationship based on common field names and draw a flexible "noodle" between them.
4. Configure the Relationship: Click the noodle connecting the two tables. A dialog box will pop up where you can configure the relationship.
5. Add More Tables: Repeat the process by dragging more tables onto the canvas and establishing their relationships to your existing tables. For example, you can drag the Products table and relate it to the Orders table on the ProductID field.

That’s it! Your tables are now related. You can go to a worksheet and start building visualizations, and Tableau will handle the rest behind the scenes.

How to Create a Join (The Classic Way)

Sometimes you might need to create a specific join, perhaps because your analytics problem demands a fixed, flat table structure. To create joins, you need to enter the "physical layer" of your data model.

1. Drag Your First Table: Just like with relationships, start by dragging your first table (Orders, for example) onto the canvas.
2. Open the Physical Layer: This is the key step. Double-click the logical table block you just created on the canvas. This will open up a new view which looks like the classic join interface from older Tableau versions.
3. Drag Your Second Table: Drag your second table (e.g., Customers) onto this new canvas, to the right of your first table. A Venn diagram icon will appear between them, indicating a join is being created (by default, it’s usually an inner join).
4. Configure the Join: Click on the Venn diagram icon. This opens the join configuration pane.

Once you close the physical layer view, you will see a single logical table block on your canvas that represents this new, pre-joined dataset. All your analysis will now be based on this fixed table.

When Should You Use Relationships vs. Joins?

Here’s a simple cheat sheet to help you decide which method to use:

Use Relationships if:

• You are using a recent version of Tableau (99% of the time, this is the best and recommended method).
• You are combining tables with different levels of detail (e.g., daily sales, weekly ad spend, monthly goals).
• You want a flexible data source you can adapt for many different analyses instead of one rigid structure.
• You want to avoid data duplication issues commonly found with joins.

Use Joins if:

• You are working with a single data source that only supports joins.
• You know for a fact that you need a specific, fixed, combined table structure for your analysis before you begin building visuals.
• You need to perform a specific data shaping task that can only be done with a particular join type upfront (this is an advanced and rare use case).

Final Thoughts

Learning how to connect tables is an essential skill for getting meaningful results from your data in Tableau. While both Relationships and Joins serve to combine your data, starting with Relationships is the modern, flexible approach that will save you from major headaches and reporting errors in the long run.

Wrestling with data connections and complex BI tools is a common hurdle that takes hours away from actual analysis. At Graphed, we designed an AI data analyst to eliminate this friction entirely. Instead of configuring relationships or choosing join types, you simply connect your marketing and sales platforms (like Google Analytics, Shopify, Facebook Ads), and then ask questions in plain English like, “show me sales by product category from Shopify for last quarter.” We build the charts and dashboards for you automatically, using live data without you ever needing to think about what happens behind the scenes.