When working with Power BI, understanding the difference between Duplicate and Reference is crucial for building clean and efficient data models. While they might seem similar on the surface, they serve very different purposes, and confusing them can lead to head-scratching issues. Let’s unpack this.
1. Duplicate: A Complete Copy
When you duplicate a query in Power BI, you create an exact replica of the original query. The duplicate query functions independently, meaning any changes you make to the duplicate will not impact the original query, and vice versa.
Key Characteristics of Duplicate:
Creates a separate, independent copy of the query.
Changes in the duplicate do not affect the original.
Useful when you need multiple versions of the same query for different purposes.
Example Use Case:
Imagine you have a sales table. By duplicating it, you can create separate tables for:
Sales by Region
Sales by Product
Sales by Time Period
Each duplicated table can then have its own set of transformations without interfering with the original dataset.
2. Reference: A Dependent Link
When you reference a query, you create a linked version of the original query. Any transformation made in the original query will flow down to the referenced query.
Key Characteristics of Reference:
Creates a dependent copy that inherits changes from the original query.
Ideal for breaking down large transformations into smaller, manageable steps.
Saves memory and improves performance since it doesn’t duplicate data.
3. Detailed Example
Use Case 1:
Imagine you have an Excel file with 20 different sheets, and you only need data from 10 sheets.
If you load each sheet individually into Power Query, every sheet becomes an original query. This means if the file path or location changes, you’ll have to manually update the file path in all 10 queries.
Instead, you can create a single source query named Z Source that connects to the Excel file.
From Z Source, you can create 10 referenced queries, each extracting data from a specific sheet.
To optimize performance, you can disable the "Enable Load" option for the Z Source table.
Benefits of This Approach:
If the file location changes, you only need to update the path in one place (Z Source) instead of 10 different queries.
If you decide to move the Excel file to SharePoint or Google Sheets, again, you’ll only need to update the connection in one place (Z Source).
This approach keeps your data model cleaner and minimizes redundant connections.
Use Case 2:
Imagine you need to create sorting on a Sales table for the Country column, where the sorting is based on Sales Amount (Descending Order).
Achieving this directly in Power BI charts might cause inconsistencies.
Instead, you can create a Duplicate table of Sales.
Group the data by Country.
Sum the Sales Amount.
Add an Index Column for custom sorting.
Once the Index is ready, you can merge it back into the original Sales table.
Why Not Use a Reference Table Here?
If you try to use a Referenced table instead of a Duplicate table, you will hit a limitation.
You cannot merge a referenced table back into its original source table because it creates a circular dependency.
This scenario highlights a perfect use case for a Duplicate Table, where independence is required to avoid dependency conflicts.
4. When to Use Duplicate vs. Reference
Scenario | Duplicate | Reference |
You need an independent version of a query | ✅ Yes | ❌ No |
You need to create multiple stages of transformations | ❌ No | ✅ Yes |
Changes in one query shouldn't affect the other | ✅ Yes | ❌ No |
You want to save memory and processing power | ❌ No | ✅ Yes |
5. Best Practices for Using Duplicate and Reference
Use Duplicate: When your changes are specific to one query and should not impact others.
Use Reference: When your transformations are part of a staged process or need to remain in sync with the source query.
Avoid Overusing References: Excessive references can make the dependency chain difficult to manage.
6. Why Merge/Append Query Won’t Work Between Referenced Tables
When two queries reference each other directly or indirectly in Power Query, you simply won’t get the option to merge or append them.
Power Query prevents circular dependencies from happening by not offering the operation in the first place. Unlike Power BI, where you might encounter an error indicating a circular dependency, Power Query simply avoids creating the issue in the first place.
If you encounter such a limitation, the best approach is to rethink your data transformation steps or introduce an intermediate duplicate table to break the dependency cycle.
7. Conclusion
Understanding Duplicate vs. Reference isn’t just about knowing which button to click. It’s about logically structuring your query dependencies to avoid paradoxes and ensure smooth data processing.
Next time you realize you can’t merge or append referenced queries in Power Query, pause and visualize the data dependency chain.
Happy querying!
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