How to Append Data in Tableau

Combining data from different files, tables, or sheets is one of the most common tasks in data analysis. If you're managing monthly sales reports, weekly campaign performance data, or regional customer logs, you'll eventually need to bring them all together. This article will walk you through exactly how to append data in Tableau by using unions, a straightforward way to stack your datasets on top of one another.

What Exactly Is Appending Data (Union vs. Join)?

Before we get into the "how," let's quickly iron out the "what." When we talk about appending data, we mean taking rows from one table and adding them to the bottom of another. Imagine you have two separate spreadsheets: one for January sales and one for February sales. Appending them would mean creating a single, longer list that contains all the sales from both months combined.

In Tableau, this process is called a union. A union essentially "stacks" your data vertically. For a union to work smoothly, your tables should have a similar structure. Ideally, this means:

• They have the same number of columns.
• Corresponding columns have the same name.
• Corresponding columns have the same data type (e.g., text, number, date).

This is different from a join, which combines data horizontally by adding new columns from another table based on a related field. Think of it this way:

• Union (Appending): Stacks rows. Used when tables have the same columns but different rows of data (e.g., sales data for different months).
• Join: Adds columns. Used when tables have related data you want to display side-by-side (e.g., adding customer details to a sales transaction table).

If you're dealing with monthly sales reports that have identical columns - like Date, Product ID, Customer Name, and Sales Amount - a union is exactly what you need.

How to Union Data in Tableau: The Drag-and-Drop Method

The most direct way to create a union is by manually dragging tables onto the canvas. This method is perfect when you have just a few files or sheets to combine and you want full control over the process.

Let's use an example. Imagine you have an Excel workbook with three separate sheets: Q1_Sales, Q2_Sales, and Q3_Sales. Each sheet has the same columns.

Step-by-Step Instructions:

1. Connect to Your Data: Open Tableau Desktop and connect to your data source. In this case, it would be the Excel file containing our sales sheets.
2. Drag Your First Table to the Canvas: Once connected, you’ll see the available sheets listed on the left panel of the Data Source page. Drag the first sheet, Q1_Sales, into the view pane (the big empty area that says "Drag tables here").
3. Drag the Second Table to Create the Union: Now, drag your second sheet, Q2_Sales, and hover it directly below the Q1_Sales table in the canvas. You'll see an orange box appear with the text "Drag table to union." Drop the sheet there to create the union.
4. Add More Tables (Optional): Repeat the process for any remaining sheets. Drag Q3_Sales to the same union box. You can continue adding as many tables as you need this way.

After you create the union, look at the data grid at the bottom. Tableau automatically adds two new fields to your data source:

• Table Name: This field lists the name of the original sheet or table each row came from (e.g., 'Q1_Sales', 'Q2_Sales').
• Sheet/Path: Shows the source sheet or file name, helping you trace your data back to its origin.

These fields are incredibly useful. For instance, you could now build a chart comparing total sales by quarter just by dragging the "Sales Amount" to your view and the "Table Name" field to the Columns shelf. No complex calculations needed!

Automating Unions with a Wildcard Search

The manual drag-and-drop method is great for a few files, but what if you get a new sales file every single month? You don't want to open Tableau and manually add the new file to the union each time. This is where the wildcard union comes in. It lets you automatically append files based on a shared naming pattern.

A wildcard union is perfect if your files are named consistently, for example:

• Sales_Data_2023_Jan.csv
• Sales_Data_2023_Feb.csv
• Sales_Data_2023_Mar.csv

Step-by-Step Instructions:

1. Start as usual: Connect to your data. Let's assume these are separate CSV files in a folder. Connect to one of them, like Sales_Data_2023_Jan.csv, and drag it to the canvas.
2. Convert to Union: In the view pane, click the down arrow on your table name and select "Convert to Union."
3. Configure the Wildcard Search: A dialog box will pop up. Instead of dragging and dropping files manually, switch to the "Wildcard" tab. Here you can define the naming pattern for the files you want to include.
4. Apply the Settings: Click "OK." Tableau will find all files in the directory that match this pattern and automatically union them together.

The real power of this feature is what happens next. When you add Sales_Data_2023_Apr.csv to that same folder, Tableau will automatically include it in your data source the next time you refresh it. Your dashboard becomes an automated reporting machine, always staying up-to-date with no extra work from you.

What If My Columns Don't Match Perfectly?

In a perfect world, all your files would have identical column names. But reality is often messy. You might have one file with a column named Sale_Date and another with Transaction Date. Or maybe someone misspelled a header as ProducdtID.

When Tableau encounters mismatched column names during a union, it doesn’t throw an error. Instead, it creates separate columns for each unique name and populates the non-applicable rows with 'null' values. This gives you data, but it’s not clean or usable for analysis.

How to Fix Mismatched Columns

Thankfully, Tableau makes this an easy fix. In the Data Source preview pane (where you see your data at the bottom), you can merge these mismatched fields.

1. Identify the columns that should be the same. For example, you might see Sale_Date and Transaction Date as two separate fields.
2. Hold down the Ctrl (or Command on Mac) key and select both columns in the field view.
3. Right-click on one of the selected columns and choose "Merge Mismatched Fields."

Tableau will instantly combine them into a single column, cleaning up all those unwanted nulls and giving you a single, unified field for your analysis.

Advanced Tip: Performing Cross-Database Unions

Sometimes your data lives in entirely different systems. You might have Q1 sales data in a SQL Server database and Q2 sales data in a Google Sheet. Traditionally, combining these would require a dedicated data engineer to pull everything into a central warehouse.

With Tableau, you can perform a cross-database union directly in the Data Source tab.

The process is very similar to a standard union:

1. Connect to Your First Source: Start by connecting to your first data source (e.g., SQL Server) and dragging your table (e.g., dbo.Q1_Sales) to the canvas.
2. Add a New Connection: In the Connections section on the left panel, click the "Add" button. Find and connect to your second data source (e.g., a Google Sheet).
3. Create the Union: You will now see both connections listed. From the Google Sheet connection, find the correct sheet (e.g., Q2_Sales) and drag it under the SQL server table just like you did with a single-source union. The orange "Drag table to union" box will appear.

Tableau handles the work of querying both systems and stacking the results for you. While extremely powerful, be mindful of performance. Unions across large, different databases can be slower since Tableau has to communicate with multiple systems at once.

Final Thoughts

Understanding how to append data using unions is a fundamental skill that dramatically speeds up your reporting workflow in Tableau. Whether you're combining a few Excel sheets with the manual method, automating monthly reporting with a wildcard union, or even bridging data across different databases, the process gives you a unified view of your information so you can get to the interesting analysis faster.

Manually preparing and combining files is often the most time-consuming part of analytics, especially for sales and marketing teams juggling data from a dozen platforms. At Graphed, we built our AI data analyst to eliminate this friction entirely. Instead of configuring unions and managing data sources, you just connect your platforms like Google Analytics, Shopify, and Facebook Ads once. Then, you can ask for a dashboard in plain English - like "create a report showing sessions from Google Analytics and sales from Shopify by day for this month." Our AI handles the data connections and builds the dashboard in seconds, turning hours of data prep into a short conversation.