SQL vs Power Query: Where Should Logic Live in Power BI?

One of the most common — and most misunderstood — questions in Power BI projects is this:

Where should the logic live?
In SQL?
In Power Query?
Or inside Power BI itself?

There’s no single correct answer, and that’s exactly why this question causes so much confusion.

In real-world Power BI environments, performance issues, refresh failures, and maintenance headaches often trace back to logic being placed in the wrong layer. This article explains how to think about SQL and Power Query together, and how to decide where logic should live for long-term stability and performance.

Why This Question Matters More Than It Seems

At first glance, SQL and Power Query appear to do similar things:

• Filtering data
• Transforming columns
• Applying business rules

But they serve very different purposes in a Power BI architecture.

When logic is placed incorrectly:

• Refresh times increase
• Models become harder to debug
• Changes take longer to implement
• Ownership becomes unclear

Understanding the role of each layer is far more important than memorising features.

What SQL Is Best At (In Power BI Context)

SQL is strongest when it handles structural and foundational logic.

In Power BI projects, SQL is best used for:

• Extracting data from source systems
• Defining clean, consistent schemas
• Applying stable business rules
• Handling large data volumes efficiently
• Preparing fact and dimension tables

SQL works well when logic:

• Is unlikely to change frequently
• Needs to be shared across multiple reports
• Has performance implications

Examples of logic that fits well in SQL:

• Filtering out cancelled or invalid records
• Applying standard business definitions
• Creating conformed dimensions
• Pre-aggregating very large datasets

SQL is not about flexibility — it’s about consistency and scale.

Related Article:
SQL for Power BI Analysts: A Practical Guide for Real-World Reporting

What Power Query Is Best At

Power Query sits closer to Power BI and is designed for data shaping and transformation.

Power Query is ideal when logic:

• Is specific to a particular report
• May change over time
• Is part of data preparation rather than modelling
• Needs to be readable by analysts

Common Power Query use cases include:

• Renaming and reordering columns
• Splitting or merging fields
• Handling nulls and inconsistencies
• Light transformations for reporting

Power Query shines when logic needs to be transparent and adjustable.

Where Problems Usually Begin

Issues arise when SQL and Power Query are treated as interchangeable.

Common anti-patterns include:

• Putting all logic into SQL “because it’s faster”
• Rebuilding complex SQL logic again in Power Query
• Hiding business rules deep inside SQL views
• Using Power Query for heavy transformations on large datasets

These choices often lead to:

• Poor performance
• Difficult debugging
• Confusion about data ownership
• Fragile report refreshes

The question isn’t “Can I do this here?”
It’s “Should I?”

A Practical Rule of Thumb

While every project is different, a practical guideline is:

Use SQL for structure.
Use Power Query for shaping.
Use Power BI for analysis.
 

This separation helps ensure:

• SQL remains performant and stable
• Power Query remains readable and flexible
• Power BI remains focused on insights

This balance between SQL and Power BI is where many real-world reporting issues either get solved — or quietly created. For readers interested in seeing how this separation works in practice, this Power BI + SQL approach is explained here: Power BI with SQL

What About Performance?

Performance is often the deciding factor — but it shouldn’t be the only one.

Yes, SQL can handle large transformations efficiently.
But performance gains disappear quickly when:

• Logic becomes too complex
• Queries become unreadable
• Changes require database intervention for minor adjustments

In many cases, clear logic in Power Query + good SQL structure outperforms clever SQL alone.

Collaboration and Ownership Matter

Another overlooked aspect is who owns the logic.

• SQL logic often belongs to data or platform teams
• Power Query logic is usually owned by report developers

Placing logic in the wrong layer can slow down changes simply because the wrong team controls it.

Good Power BI solutions consider people and processes, not just tools.

Final Thought

SQL and Power Query are not competitors. They are partners — each strong in different areas.

The best Power BI solutions are built when:

• SQL provides a clean, reliable foundation
• Power Query shapes data for reporting
• Power BI focuses on modelling, analysis, and storytelling

Knowing where logic should live isn’t just a technical decision — it’s an architectural one.

Learning How SQL and Power Query Work Together

For those looking to understand this balance in real reporting environments, the Power BI + SQL course by ExcelGoodies focuses on practical design decisions rather than theory — covering SQL, Power Query, and Power BI as a single reporting ecosystem.

Check the Upcoming batch details
 

Editor’s Note

This article is based on recurring design discussions observed across Power BI projects where SQL, Power Query, and report development intersect. The focus is on practical separation of responsibilities rather than tool-specific features.

Insights compiled with inputs from the ExcelGoodies Trainers & Power Users Community.
 

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