Can You Have Multiple Data Sources in Tableau?

The short answer is a resounding yes. Being able to combine multiple data sources in a single dashboard isn't just a feature in Tableau, it's one of its core strengths. You can pull Salesforce opportunities, Google Analytics traffic data, and an ad-spend spreadsheet into one view to get a complete picture of your funnel. This article will show you the two main methods for doing this - relationships and data blending - and guide you on when and how to use each one.

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Data from Different Places on One Dashboard: Why Bother?

Before diving into the "how," let's quickly touch on the "why." In an ideal world, all your business data would live in one pristine, perfectly organized data warehouse. In reality, it's scattered across a dozen different apps, databases, and spreadsheets.

Your website traffic is in Google Analytics. Your sales data is in Salesforce. Your product inventory is in a SQL database. Your marketing team’s budget is in a shared Google Sheet. Each platform tells one chapter of the story, but isolating them makes it impossible to see the whole plot. Combining them allows you to answer critical, cross-functional questions like:

• Which marketing campaigns from our Facebook Ads data are driving the most sales in our Shopify store?
• How does website user behavior from Google Analytics correlate with customer lifetime value in our Stripe data?
• Are sales reps meeting the quarterly targets we've set in a separate Excel file?

Tableau makes answering these questions possible by allowing you to create a virtual, unified view of your scattered data.

The Two Core Methods: Relationships vs. Blending

When you want to combine data in Tableau, you have two primary options: establishing relationships or using data blending. For years, blending was the go-to, but with recent Tableau updates, relationships have become the smarter, more flexible, and default method for most situations.

What Are Relationships?

Relationships are Tableau's modern way of combining data. Think of them as a "smart join." Instead of mashing your tables together into one giant, flat table (like a traditional join), relationships keep your tables separate but tell Tableau how they are related to each other through a common field.

Imagine you have two tables:

1. Orders Data: Contains Order ID, Customer ID, and Sales Amount.
2. Customer Info: Contains Customer ID, Customer Name, and State.

By creating a relationship on the Customer ID field, you're not merging the tables. You’re simply telling Tableau, "If you need the Customer's Name for a given Order, use the Customer ID to look it up in the Customer Info table."

Tableau manages the joins behind the scenes at the right level of detail based on what fields you bring into your visualization. This is highly flexible and avoids many of the data duplication and aggregation issues common with older methods.

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What is Data Blending?

Data blending is the older method, used to combine data from sources that are at different levels of detail or granularity. It works by querying each data source independently and then aggregating and combining the results in the final view.

With blending, you always have a primary data source and one or more secondary data sources. The primary source dictates the level of detail for the visualization. Let's say your primary source contains sales data, detailed down to the individual transaction. Your secondary source is a Google Sheet with monthly sales goals for each region. You can't do a row-level join because "monthly goals" don't match up with "daily transactions." Data blending solves this. It will first query your primary source and aggregate sales to the visualization level (e.g., total sales per region). Then, it will query your secondary source, find the goal for each region, and "blend" it in. If a matching value is found in the secondary source, it's displayed, if not, it appears as a null.

So, Which One Should I Use?

Here’s a simple rule of thumb:

• Use Relationships (most of the time): If you are connecting to different tables within the same database or different sheets in an Excel file where there is a common key or field to connect them, relationships are almost always the right choice. It's the default, it's more flexible, and it's better for performance.
• Use Data Blending (in specific situations): When your data is at different levels of granularity that can’t be rectified easily (like daily sales vs. monthly targets), or when you need to combine data from different published data sources on a Tableau Server. It's a great tool for a very specific problem.

A Step-by-Step Guide to Using Relationships

Let's walk through a common business scenario: combining sales orders with product information.

Scenario: You have an Excel file named Orders with sales transaction data. You also have a CSV file named Products that contains details about each product, including its category. You want to see total sales by product category.

Step 1: Connect to Your First Data Source

Open Tableau and select "Microsoft Excel" from the "Connect" pane. Navigate to your Orders.xlsx file and select it. Tableau will now show you the sheets in that file on the Data Source page.

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Step 2: Add Your Second Data Source

With your first data source loaded, look at the top of the connection pane on the left. You'll see "Connections" with your Excel file listed. Click the Add button (it looks like a cylinder with a plus sign).

This time, select "Text File" from the options and navigate to your Products.csv file.

Step 3: Create the Relationship

You’ll now see both data sources in the left pane. Let's assume your Excel file has a sheet called "Orders" and your CSV has "Products."

1. Drag the "Orders" sheet onto the canvas in the center.
2. Now, drag the "Products" table onto the canvas.

Tableau will automatically try to identify a common field between the two. If both tables have a field named Product ID, you'll see a light orange line - affectionately called a "noodle" - connecting them. This noodle represents the relationship.

You can click on the noodle itself to edit the relationship settings, such as the fields being used and the cardinality (e.g., one-to-many), though Tableau's default settings are often sufficient.

Step 4: Build Your Visualization

Navigate to a worksheet. In the "Data" pane on the left, you'll see fields organized under their respective tables ("Orders" and "Products").

Now you can build your viz by pulling fields from both sources seamlessly:

• Drag Category from the "Products" table to the "Rows" shelf.
• Drag Sales from the "Orders" table to the "Columns" shelf.

Voila! You now have a bar chart showing total sales by product category, a visualization that required data from two separate files.

A Quick Guide to Data Blending

Now for a scenario where blending is the right tool for the job.

Scenario: You're connected to a live Salesforce database that has all your opportunity data (sales amounts and sales reps). In a separate Google Sheet, you have the monthly sales quotas for each rep. You want to compare rep performance against their quota.

Step 1: Connect to the Primary Source and Build a View

First, connect to Salesforce. Go to a new worksheet and build a simple view. For example, put Sales Rep on the Rows shelf and SUM(Sales Amount) on the Columns shelf. This automatically makes Salesforce your primary data source for this worksheet. You can tell which source is primary because it will have a little blue checkmark next to it in the Data pane.

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Step 2: Connect to the Secondary Source

Go to Data > New Data Source and connect to your Google Sheet with the sales quotas.

Step 3: Activate the Blend

Once connected, you will see both data sources listed in your Data pane. Tableau will look for a common dimension to link them. If both sources have a field named Sales Rep, you’ll see a little gray chain-link icon next to that dimension in the secondary data source.

Click on this gray link icon. It will turn orange to indicate that the link - the blend - is now active for this worksheet.

Step 4: Add Fields from the Secondary Source

Your Google Sheet will now have an orange checkmark, indicating it's the active secondary source. You can now drag the Quota field from your Google Sheets data source into the view. Tableau will aggregate a rep’s monthly quotas and display them alongside their total sales.

You can then use this to create calculated fields, like [Sales] / [Quota] to see performance percentage.

Best Practices and Potential Pitfalls

Working with multiple data sources introduces complexity. Keep these tips in mind to avoid headaches:

• Keep Linking Fields Clean: Ensure the fields you use to link your sources (via relationships or blending) are consistent. "USA" in one table and "United States" in another will not match up.
• Understand Granularity: Be mindful of the level of detail in your datasets. Mismatched granularity is the #1 cause of issues, leading to duplicated data with relationships or asterisks (*) in blending, which indicate multiple possible values from the secondary source.
• Choose Extracts for Performance: When combining large data sources, especially from slow cloud-based apps, consider creating Tableau Extracts (.hyper files). The performance gain over live connections can be enormous as Tableau queries the optimized local file instead of pulling fresh data for every interaction.
• Filter Smartly: Understand how filters apply. A data source filter will apply before the data is combined, which can impact your results.

Final Thoughts

In short, Tableau excels at weaving together data from multiple different platforms to give you a single, cohesive view of your business. By mastering the distinction between relationships (for flexible, row-level joins) and data blending (for combining aggregated data), you can move beyond simple, single-source charts and start creating truly insightful interactive dashboards.

Of course, even with powerful tools like Tableau, there’s still a learning curve and manual effort involved in connecting sources, fiddling with settings, and designing effective reports. That friction is why we built Graphed. We automate the entire process for your key marketing and sales platforms like Google Analytics, Shopify, and Salesforce. Instead of dragging and dropping, you just describe the dashboard you want in simple instructions - like "Show me a chart of Facebook Ads spend versus Shopify revenue by campaign" - and it's built for you instantly, on a live and real-time dashboard.