How to Link Data Sources in Tableau

Bringing different sets of data together in Tableau is essential for creating a complete picture of your business performance. Instead of looking at website traffic in one report and sales data in another, you can link them to see exactly which campaigns drive revenue. This guide will walk you through the two primary methods for linking data sources in Tableau: joining and blending, explaining when and how to use each.

Understanding Your Options: Joining vs. Blending

Before you start dragging fields onto your worksheet, it’s important to understand the two main ways Tableau combines data. They sound similar, but they work in fundamentally different ways and are used for different scenarios.

What are Joins?

A join merges tables of data at the row level by matching values in one or more common fields (columns). Think of it like creating one single, wide master spreadsheet from two smaller ones. You define a rule, like "match these tables wherever the 'Order ID' is the same," and Tableau creates a new, combined table based on that rule.

Joins are performed on the data source page before you start building your visualizations. They are ideal when you are combining tables from the same data source, such as two different sheets in an Excel workbook or multiple tables within a single SQL database.

There are four types of joins:

• Inner Join: Only returns rows where the key field exists in both tables. If a row in one table doesn't have a match in the other, it's excluded.
• Left Join: Returns all rows from the left table and any matching rows from the right table. If there's no match, the columns from the right table will show as null.
• Right Join: The opposite of a left join. It returns all rows from the right table and any matching rows from the left.
• Full Outer Join: Returns every row from both tables. If a row doesn't have a match in the other table, the columns from that other table will show null values.

What is Data Blending?

Data blending works a bit differently. It queries data from multiple, separate data sources and combines the aggregated results in your view. Unlike a join, it doesn’t create a single new table of row-level data. Instead, it queries each data source independently and then "links" them within the visualization based on a common field.

This method is necessary when you want to combine data from different types of sources, like linking your Google Analytics data with sales numbers from a Salesforce report or a financial forecast in a Google Sheet.

In a data blend, one data source is designated as the primary source (indicated by a blue checkmark), and the others are secondary (orange checkmark). The view is built around the primary source, and the secondary source adds supplemental information to it.

When to Use Joins vs. Blends

Here’s a simple cheat sheet:

• Use a Join when:
• Use Data Blending when:

How to Join Data in Tableau: A Step-by-Step Guide

Joining is a fundamental skill in Tableau. The interface makes it surprisingly intuitive once you get the hang of it. Let’s walk through the process using a common example: joining an "Orders" table with a "Returns" table.

Step 1: Connect to Your Data Source

In Tableau, go to Connect to Data and select your source (e.g., Microsoft Excel). Navigate to your file and open it. Your tables (or sheets) will appear in the left-hand panel of the Data Source page.

Step 2: Add Your First Table to the Canvas

Drag your primary table, in this case, "Orders", from the side panel onto the canvas area that says "Drag tables here." You'll see a preview of your data appear in the grid below.

Step 3: Add the Second Table to Create a Join

Now, drag your second table, "Returns", onto the canvas next to the "Orders" table. Tableau is pretty smart and will often detect a common column name (like "Order ID") and automatically create an inner join for you. You'll see a Venn diagram icon appear between the tables, indicating the join.

Step 4: Configure the Join Type

If Tableau’s default choice of an inner join isn't what you need, just click on the Venn diagram icon. A configuration box will pop up where you can select the join type that fits your analysis — Inner, Left, Right, or Full Outer.

For example, if you want to see all orders and find out if they were returned, a Left Join from Orders to Returns would be perfect. This keeps every order and adds return information only where a match exists.

Step 5: Verify the Join Clause

The join clause tells Tableau which columns to use for matching rows. Tableau usually gets this right if the column names are identical. Below the join type selection, you'll see the fields used for the "Data source" (left table) and the "Returns" (right table). Ensure they are the correct linking fields, like "Order ID". If not, you can click the dropdown menus to select the correct fields.

Step 6: Review Your Combined Data

Once your join is configured, look at the data grid at the bottom of the screen. Scroll left and right to see the columns from both tables. This instant preview helps you confirm that the join worked as expected and that your data looks correct before you move on to building your charts.

How to Blend Data in Tableau: A Step-by-Step Guide

Blending is necessary for cross-platform analysis, like combining Facebook Ads spend with Shopify sales. This process happens on a worksheet, not the Data Source page.

Step 1: Connect to Both Data Sources

First, connect to each of your data sources separately. For instance, connect to your Google Sheet with ads spend data. Then, click the ‘Add’ button next to 'Connections' in the Data Source tab to connect to your Shopify data.

Step 2: Establish the Primary Data Source

Go to a new worksheet. In the 'Data' pane on the left, select your first data source (let's say, Shopify). Build a simple view by dragging a dimension, like Date, onto the Rows shelf. The data source you use first automatically becomes the primary source for this sheet, marked with a small blue checkmark icon.

Step 3: Switch to the Secondary Data Source

Now, click on your second data source (the Ads Spend Google Sheet) in the Data pane. This will become the secondary source for your view. Fields from this source can now be added to your existing view.

Step 4: Identify and Activate the Linking Field

Tableau tries to find a field with the same name in both sources to use as the link. For our example, if both data sources have a "Date" field, a small orange chain-link icon will appear next to it in the secondary source's dimension list. This indicates an active linking field.

If the fields have different names (e.g., "Date" and "Day") but contain the same information, you'll need to create a custom relationship. Go to Data > Edit Blend Relationships and specify which fields to link.

Step 5: Add a Measure From the Secondary Source

With the link active, you can now drag a measure, like "Spend", from your secondary (Ads Spend) source into the view — for instance, onto the Columns shelf. Tableau 'blends' the data, summarizing the Spend value for each corresponding Date from the primary source. Your secondary data source will now have an orange checkmark next to it.

One common hiccup with blends is seeing an asterisk (*) where data should be. This usually means there are multiple matching values in the secondary source for a single mark in the primary source. To fix this, ensure the linking fields are at the same level of granularity or add more linking dimensions to make the connection more specific.

Quick Tips for Linking Data Like a Pro

• Keep Naming Consistent: A "Date" column in one source and a "Transaction Day" in another can confuse Tableau. Standardize common field names before you connect them to make linking effortless.
• Understand Your Granularity: "Granularity" just refers to the level of detail. Is your data measured daily, weekly, or monthly? Joining data with different granularities (e.g., daily events and monthly budgets) can lead to duplicates and incorrect calculations. This is a classic case where blending is superior.
• Use Extracts for Performance: When working with large data sets, creating Tableau Extracts for your data sources can dramatically speed up query performance. An extract is a saved snapshot of the data that's optimized for Tableau.

Final Thoughts

Knowing how to properly use joins and data blends in Tableau is fundamental to moving beyond simple charts and creating truly insightful, integrated dashboards. Joins are perfect for merging row-level data from the same source, while blends give you the flexibility to combine aggregated data from completely different platforms.

As powerful as these features are, the setup process can still feel manual and time-consuming, especially for teams who just want quick answers. We designed Graphed to remove this technical hurdle completely. Instead of configuring joins or blending data sources one by one, you just connect your platforms — like Google Ads, Shopify, and Salesforce — and then ask questions in plain English. Graphed handles all the connections in the background, allowing you to build cross-platform dashboards in seconds, not hours.