What is Append Queries in Power BI?

Combining data from different spreadsheets or tables is one of the most common tasks you'll face in data analysis. Whether you have monthly sales reports, weekly traffic exports, or regional performance data, you often need to stack them together to see the big picture. This is exactly what Power BI's "Append Queries" feature is designed for. This article will walk you through what appending is, common scenarios where it's useful, and a step-by-step guide on how to do it.

What Are Append Queries? The Core Concept

Think of appending as vertically stacking sheets of paper. If you have a separate report for January, February, and March, appending them means placing the February report directly underneath January, and the March report directly underneath February. The result is a single, continuous pile with all your data from Q1.

In Power BI, the Append Queries function does the same thing with your data tables. It takes two or more tables and combines them into one single table by adding the rows from one table to the bottom of another. This is a crucial data transformation performed within the Power Query Editor, the engine room of Power BI where you clean and shape your data before visualizing it.

Append vs. Merge: What's the Difference?

It’s easy to mix up Appending and Merging Queries, but they serve very different purposes. It's a fundamental distinction to understand:

• Append Queries: Adds rows to a table. You use this when you have tables with the same (or very similar) column structure, and you want to stack them vertically. For example, combining Sales 2022 and Sales 2023.
• Merge Queries: Adds columns to a table. You use this to join two different tables side-by-side based on a related column, similar to a VLOOKUP in Excel or a JOIN in SQL. For example, merging a Sales table with a Products table using the ProductID to bring in product details like name and category.

In short: Append = more rows. Merge = more columns. This article focuses exclusively on appending.

When Should You Use Append Queries?

The append operation is your go-to tool whenever you have data of the same kind that has been split into separate tables. This happens more often than you’d think. Here are a few common scenarios where appending is the perfect solution.

1. Consolidating Data Over Time

Businesses often generate reports on a recurring schedule. You might have separate CSV files or spreadsheet tabs for each month’s sales, website traffic by week, or ad performance by day. To analyze trends over a quarter or a full year, you need all of that data in one place.

Example: You have January_Sales.csv, February_Sales.csv, and March_Sales.csv. You can load each file into Power BI as a separate query and then append them to create a single Q1_Sales table for your reports.

2. Combining Data from Different Regions or Segments

Companies that operate in multiple locations or have distinct business units often maintain separate datasets for each one. This could be sales data from different stores, regional marketing campaign results, or performance metrics for different product lines.

Example: Your company has separate tables tracking sales for its North_America, Europe, and Asia regions. To get a global view of sales performance, you can append these three tables into a Global_Sales table.

3. Aggregating Data from Similar Sources

Sometimes you’re pulling data from different platforms that report on the same type of information. Think about social media analytics: you might export post performance from Facebook, LinkedIn, and X (Twitter) separately. While the column headers might differ slightly, the core data (impressions, likes, shares, date) is conceptually the same.

Example: After a bit of cleaning to standardize column names (Likes vs. Favorites), you can append your Facebook_Data and Twitter_Data queries to create a unified Social_Media_Performance table.

4. Working with Incremental Data Loads

In many systems, you work with a "live" data table and periodically archive older data to improve performance. For a complete historical analysis, you may need to combine the current data with the archived data.

Example: You could append your Current_Customer_List table with an Archived_Customer_List table to build a comprehensive view of all customers, past and present.

A Step-by-Step Guide to Appending Queries in Power BI

Let's walk through the process inside the Power Query Editor. For our example, imagine we have two tables representing sales from the first two quarters of the year: Sales_Q1 and Sales_Q2.

Both tables have the same structure:

• OrderID
• OrderDate
• ProductID
• Revenue

Step 1: Open the Power Query Editor

First, you need to be in the Power Query Editor. If you’re in your main Power BI Desktop window, go to the Home tab on the top ribbon and click on Transform data. This will open the Power Query Editor, where you should see your loaded queries (tables) in the left-hand pane.

Step 2: Choose Your Append Method

Before you click the button, you have a key decision to make. Power BI offers two ways to append:

• Append Queries: This option adds the rows of the second table directly into the first table you have selected. Your base query (e.g., Sales_Q1) will be permanently altered (within Power Query) to include the data from Sales_Q2. The original Sales_Q2 query still exists, but you'll probably hide it from your report view later.
• Append Queries as New: This option leaves your original source queries (Sales_Q1 and Sales_Q2) completely untouched. Instead, it creates a brand new third query that contains the combined data.

Pro Tip: For clarity, maintainability, and easier debugging, it is almost always best practice to use Append Queries as New. It keeps your data transformation steps clean and preserves your original source data, making it easier to troubleshoot problems down the line.

Step 3: Appending Two Tables (The Easy Way)

We'll start by using our preferred method, "Append Queries as New."

1. Make sure your Sales_Q1 and Sales_Q2 tables are loaded and visible in the Queries pane on the left.
2. Click on either query (it doesn't matter which). On the Home ribbon, click the dropdown arrow next to Append Queries and select Append Queries as New.
3. An "Append" dialog box will appear. Since we're combining just two tables, the "Two tables" radio button will be selected.
4. The Primary table will be whatever query you had selected. From the second dropdown, select the other table you want to append. For our example, set one to Sales_Q1 and the other to Sales_Q2.
5. Click OK.

Instantly, a new query named Append1 will appear in the Queries pane. This is your new combined table! Good practice is to immediately rename it to something descriptive, like Sales_H1. Now you have a clean table with all sales data from the first half of the year.

Step 4: Appending Three or More Tables

What if you had four separate tables for Sales_Q1, Sales_Q2, Sales_Q3, and Sales_Q4? The process is very similar.

1. Go to the Home ribbon > Append Queries > Append Queries as New.
2. In the Append dialog box, this time select the Three or more tables radio button.
3. The view will change, showing a list of "Available tables" on the left and an empty "Tables to append" list on the right.
4. Select the tables you want to combine from the left list (you can hold Ctrl to select multiple) and click the Add > button to move them into the right list.
5. You can reorder the tables in the right list if the order matters for some reason, but generally, it does not impact the final outcome. All rows will be included regardless of order.
6. Click OK.

Just like before, a new query is created with all tables stacked. Rename it to Total_Annual_Sales and you're ready to start building visuals from this complete dataset.

Pro Tips and Best Practices to Avoid Pitfalls

Appending seems straightforward, but a few common issues can trip you up. Here’s how to handle them.

Correcting Mismatched Column Names

What happens if your Sales_Q1 table has a column named ProductID but the Sales_Q2 table calls it Product_ID? When you append these tables, Power BI will treat them as two entirely separate columns. You'll end up with a combined ProductID column (with nulls for the Q2 rows) and a separate Product_ID column (with nulls for the Q1 rows). Not what you want!

The Fix: Before you append, go into each query and make sure the column names match perfectly. In your Sales_Q2 query, right-click the Product_ID column header and select "Rename." Change it to ProductID. Now, when you append, Power Query will correctly stack the data into a single, unified ProductID column.

Ensuring Consistent Data Types

Similar to column names, data types also need to be consistent. If the Revenue column is a Decimal Number in one table but formatted as Text in another, you could run into errors or unexpected results during the append operation. Power BI does its best but can struggle with these mismatched types.

The Fix: Check the data type for each column in all your source queries. You can see the data type icon (e.g., 1.2 for Decimal, ABC for Text) in the column header. Click the icon to change the type if needed. Standardizing these before appending will save you a major headache.

Handling Missing or Extra Columns

What if Sales_Q2 has an extra Discount column that Sales_Q1 does not? No problem. The append operation will proceed smoothly. The final table will include the Discount column. For all the rows that came from the Sales_Q1 data, the value in the Discount column will be null, as there was no data to pull from. This is often the desired behavior.

If you need those nulls to be zeroes instead, you can simply right-click the Discount column in the final appended query and use the Replace Values option to replace null with 0.

Final Thoughts

Mastering Append Queries is a fundamental step in becoming proficient with Power BI. It’s a powerful but simple operation for consolidating data, simplifying your data model, and making holistic analysis possible. By understanding when to use it and how to manage common issues like mismatched column names, you can build cleaner, more robust, and more scalable Power BI reports.

Of course, getting all your data - whether from separate spreadsheets or entirely different platforms like Google Analytics, Shopify, and Facebook Ads - into one place is often the most time-consuming part of analysis. We built Graphed to dramatically simplify this process. Instead of manually loading, cleaning, and appending data, you can connect your sources with a few clicks and use natural language to analyze them. For example, instead of manually appending Q1 and Q2 sales data, you could just ask, "Show me my total sales broken down by month this year," and get an interactive chart in seconds, without ever needing to open a query editor.