When working inside the Power Query Editor, you will frequently need to combine multiple database tables into a single consolidated view. For instance, you might have separate files for different sales territories, or your customer details might live apart from your transactional data.
To bring these datasets together, Power BI offers two primary consolidation tools: Merge Queries and Append Queries. While both operations combine data, they function in completely opposite directions. Mixing them up can result in a disorganized data model and slow dashboard performance. Let us explore exactly how these tools operate and when to use each one.
Append Queries: Vertical Table Stacking
Think of an Append operation as stacking sheets of paper directly on top of each other. This process combines tables vertically by adding rows from one table to the bottom of another.
When to Use Append
You should use Append when you have multiple tables that share the exact same structural columns, but contain entirely different records. For example, if your regional managers send you separate monthly sales spreadsheets—such as Sales_January, Sales_February, and Sales_March—you append them together. This operation unifies the monthly sheets into a single, comprehensive Annual_Sales master table.
Critical Structural Requirements
For an Append operation to run smoothly, your tables must share identical column names and matching data types. If Table A has a column labeled Transaction_Date and Table B labels it Date, Power Query will create two separate columns in your final output, resulting in messy null values.
Merge Queries: Horizontal Table Joining
Unlike Appends, a Merge operation expands your data canvas horizontally. This process looks for a shared relational key column between two separate tables. Then, it pulls matching columns from the secondary table into the primary table side by side.
When to Use Merge
You should use Merge when you want to enrich a transaction log with more descriptive fields from a lookup table. For instance, if your core Sales table contains a ProductID column but lacks details like product names or cost prices, you can merge it with your Product_Dim master catalog. Power Query matches the IDs and brings the product names directly into your sales rows.
Understanding SQL-Style Joins
When you click the Merge button, the interface requires you to choose a Join Kind dropdown. This choice determines how unmatched rows are handled:
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Left Outer (Default): Keeps all rows from your first table and only pulls matching records from the second table.
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Inner Join: Discards all unmatched entries, retaining only the rows where the shared key exists in both files perfectly.
Core Structural Differences Summary
| Operational Feature | Append Queries | Merge Queries |
| Expansion Direction | Vertical (Adds more data rows to the bottom). | Horizontal (Adds new column fields to the side). |
| Table Requirements | Requires identical column headers and structures. | Requires at least one matching relational key column. |
| SQL Database Equivalent | Functions exactly like a UNION ALL statement. |
Functions exactly like a relational JOIN clause. |
| Primary Use Case | Combining separate regional or monthly CSV files. | Linking transaction IDs to master product categories. |
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