Power BI is lying to you.
Those neat, orderly “Applied Steps” you trust are a façade.
Power Query is secretly rearranging, deferring, collapsing and sometimes ignoring the way you wrote your transform steps — which is why your filters don’t filter, your merges get weird, and refreshes suddenly go from 20 seconds to 10 minutes.
The only way to stop getting blindsided is to understand the invisible engine + query folding… because THAT is the real execution order — not the thing you see in the pane.

Many users experience frustration when their Power BI Query fails. These failures can disrupt your workflow and lead to delays. Understanding the root causes of these issues is crucial. It allows you to implement targeted troubleshooting steps. For example, simplifying complex data models and optimizing storage can enhance performance. Monitoring your Power BI Query performance leads to proactive troubleshooting, minimizing disruptions. By grasping these concepts, you can resolve issues more efficiently and improve your overall experience with Power BI.

Key Takeaways

Check your connection strings for typos and invalid characters to avoid frustrating errors.
Monitor network settings to ensure your Power BI queries can connect without interruptions.
Align data types correctly in Power BI to prevent performance bottlenecks and improve efficiency.
Review failed steps in the Query Editor to identify and resolve issues quickly.
Optimize your queries by simplifying complex structures and using techniques like query folding.
Regularly update your Power BI version to avoid compatibility issues with data sources.
Manage user permissions effectively to ensure all users can access necessary datasets without errors.
Implement security settings like Row-Level Security to protect data while allowing user access.

Connection Issues in Power BI Query

Incorrect Connection Strings

Common Mistakes

Incorrect connection strings often lead to frustrating errors in Power BI. You might encounter messages indicating issues with your connection. Here are some common mistakes to watch for:

Error Message	Description
Error connecting: Connection name can only contain letters, numbers, underscores, hyphens, and spaces	This error indicates that the auto-generated connection name derived from the connection string contains invalid characters, leading to a validation failure.

These errors can stem from typos or incorrect formatting in your connection strings. Always double-check your strings for accuracy.

How to Fix

To fix connection string issues, follow these steps:

Review your connection string for any typos or invalid characters.
Ensure that the connection name adheres to the required format.
Test the connection after making adjustments to confirm it works correctly.

Network Problems

Network problems can also disrupt your Power BI queries. Identifying these issues is crucial for maintaining a smooth workflow.

Identifying Connectivity Issues

Common network problems that can affect your Power BI query connections include:

Firewall and proxy misconfigurations blocking required ports (e.g., ports 443, 5671-5672, 9350-9354).
Network latency or packet loss affecting Azure Relay connectivity.
VPNs or proxies that restrict communication may prevent the gateway from connecting properly.
Incorrect gateway or data source credentials leading to authentication failures.
Resource limitations on the gateway machine (CPU, memory) causing disconnections.
Azure service interruptions impacting connectivity.

To diagnose these issues, you can use developer tools in your browser. Open the tools by pressing F12, then navigate to the Network tab. Enable 'Preserve log' to capture connectivity issues as they occur.

Solutions to Network Problems

To resolve network problems, consider the following solutions:

Ensure the gateway is online and running.
Verify that the data source credentials are correct and up-to-date.
Confirm that the gateway server can connect to the data source over the network without firewall restrictions.
Check that necessary ports are open and accessible.
Avoid placing the gateway behind restrictive VPNs or proxies.
Analyze gateway logs for errors such as timeouts or authentication failures.
Keep the gateway software updated to the latest version to avoid known connectivity issues.

By addressing these connection and network issues, you can significantly reduce the likelihood of encountering power query errors in your Power BI experience.

Transformation Issues and Power Query Errors

Invalid Data Types

Recognizing Data Type Mismatches

Incorrect data types can significantly disrupt your data transformation processes in Power BI. When you use the wrong type, it can lead to performance bottlenecks. For example, a manufacturing firm had “Order Amount” stored as text. Once a 3M-row table went live, measures became sluggish, and refresh times ballooned from 10 seconds to nearly five minutes. After converting to Numeric, the report size dropped by 60%, and performance improved by 320%.

Performance bottlenecks are often caused not just by large datasets — it’s about how those datasets are structured. The wrong type can make a model three times heavier and vastly more sluggish.

When Power BI loads data, it tries to convert the data types of source columns into types that support more efficient storage, calculations, and data visualization. Therefore, ensuring that your data types align with their intended use is crucial for optimal performance.

Correcting Data Types

To fix incorrect data types, follow these steps:

Open the Power Query Editor.
Select the column with the incorrect data type.
In the ribbon, click on the "Transform" tab.
Choose the correct data type from the "Data Type" dropdown menu.
Apply changes and refresh your query.

By correcting data types, you can enhance the efficiency of your Power BI query and improve overall performance.

Failed Steps in Query Editor

Identifying Failed Steps

Failed steps in the Query Editor can also lead to frustrating power query errors. Users often report failures when refreshing PBIX files due to issues with 'Append Queries' steps, especially when combining data from different sources. The error message may indicate that the database operation was canceled due to an earlier failure linked to the 'Append Queries' step.

Additionally, the Formula.Firewall error occurs when queries access data sources with incompatible privacy levels, which is a frequent cause of failed steps.

How to Resolve Step Failures

To resolve failed steps, consider these actions:

Review the error message for specific details about the failure.
Check the privacy levels of your data sources and adjust them if necessary.
Ensure that all data sources are accessible and correctly configured.
Test each step individually to identify where the failure occurs.

By systematically addressing these issues, you can effectively troubleshoot power query errors and ensure smoother data transformation processes.

Performance Problems in Power BI Queries

Slow Query Performance

Factors Affecting Performance

Slow query performance can frustrate users and hinder productivity. Several factors contribute to this issue:

Data Volume: Large datasets can significantly slow down your queries. The more data you process, the longer it takes to retrieve results.
Query Complexity: Complex queries with multiple joins and subqueries can take longer to execute. Simplifying your queries can lead to faster performance.
Indexing: Proper indexing of your databases can drastically improve query performance. Without indexes, databases must scan entire tables, which is time-consuming.
Data Architecture: The structure of your data warehouse or database plays a crucial role. Efficient data architecture can facilitate faster queries.
Inadequate Hardware: Insufficient memory, CPU power, or storage can bottleneck the performance of your Power BI dashboards.
Real-Time Processing: Handling real-time data can strain query performance due to constant updates and quick processing needs.

To enhance your Power BI experience, you should address these performance issues proactively.

Tips for Optimization

You can implement several techniques to optimize your Power BI queries:

Optimization Technique	Description
Query Folding	Push transformations to the data source, reducing computational load and improving refresh efficiency.
Star Schema	Simplifying complex data models to a star schema can improve performance by 5-10 times.
Remove Unused Columns	Eliminating unnecessary columns can save memory and processing power.
Optimize Data Loading & Transformations	Performing heavy calculations in Power Query rather than DAX can significantly enhance performance.
Implement Incremental Refresh	Configuring incremental refresh for large datasets can drastically reduce refresh times.

By applying these techniques, you can significantly improve the performance of your Power BI queries and reduce the likelihood of encountering power query errors.

Resource Limitations

Understanding Resource Constraints

Resource limitations, such as memory and CPU constraints, can severely impact your Power BI query execution. When your system lacks sufficient resources, it struggles to process data efficiently. This can lead to slow performance and even query failures.

Solutions for Resource Management

To manage resources effectively, consider these best practices:

Keep Your Data Clean and Updated: Ensure that your data sources are accurate and current.
Use Standardized Metrics: Clearly define key metrics for consistent interpretation.
Automate Data Refresh: Schedule data refreshes to maintain up-to-date information.
Disable Load for Intermediate Queries: This can optimize memory usage and improve performance.
Train Your Users: Provide training to ensure effective use of the dashboard.

By implementing these strategies, you can mitigate the impact of memory limitations and enhance the overall performance of your Power BI environment.

Compatibility and Syntax Errors

Version Mismatches

Checking Power BI Versions

Version mismatches can lead to frustrating power query errors. You may encounter issues when your Power BI version does not align with your data sources. To avoid these problems, regularly check your Power BI version. Here are some steps to ensure compatibility:

Refer to the Power Query documentation for details on connector capabilities and limitations.
Each data source article in the documentation describes whether features like DirectQuery are supported.

Common version mismatches that lead to query failures include:

Text values inside a number column
Non-date values inside a date column
Error values from formulas (#N/A, #VALUE, #REF)
Characters such as “–”, “.”, or empty strings that are not truly empty

By staying informed about your version and its compatibility, you can prevent many issues before they arise.

Updating to Compatible Versions

If you discover that your version is outdated or incompatible, updating it is essential. Follow these steps to ensure you have the latest version:

Visit the official Microsoft Power BI website.
Download the latest version available.
Install the update and restart Power BI.

Keeping your software up to date helps you avoid compatibility issues and enhances your overall experience.

Unsupported Data Sources

Identifying Unsupported Sources

Unsupported data sources can also cause significant problems in Power BI. You might see error messages like:

Couldn't connect to a service or resource. Check your connection.
The file you're trying to access could not be found.
The server is not responding and may be undergoing maintenance.

These messages indicate that the data source you are trying to use is not supported. Additionally, you may encounter specific errors such as:

Column Not Found Error: This occurs when a step references a column that no longer exists in the source data.
Formula Firewall Error: This happens when combining data from multiple sources with different privacy levels.
Data Type Conversion Errors: These occur when attempting to convert data into an incompatible type.

Alternatives for Data Sources

If you find that your data source is unsupported, consider these alternatives:

Creating a custom connector in Power BI allows integration with unique or proprietary data sources not supported by built-in connectors.
Implementing data fabric and data mesh architectures can help integrate disconnected data sources, improving governance and access.

By exploring these options, you can enhance your Power BI capabilities and reduce the likelihood of encountering power query errors.

User Permissions and Security Issues

User permissions play a crucial role in the successful execution of Power BI queries. When you lack the necessary permissions, you may face significant obstacles. Insufficient permissions can restrict access to essential datasets. This limitation prevents successful query execution. You might encounter errors like 'Cannot load model,' indicating issues with loading the model schema.

Insufficient Permissions

Checking User Roles

To ensure smooth query execution, you must verify user roles. Here are some steps to check user roles effectively:

Confirm that users have the appropriate permissions to access the datasets.
Review the roles assigned to each user in your organization.
Ensure that users can connect to and interact with composite models and underlying datasets.

Granting Necessary Permissions

If you find that users lack the necessary permissions, follow these steps to grant them:

Sign in to the Microsoft 365 Admin Center.
Navigate to 'Active users.'
Select the user you want to grant admin access to.
Go to the 'Roles' section in the user's details pane.
Assign the 'Power BI Administrator' role.
Save your changes.

By managing access effectively, you can prevent permission-related issues that disrupt your Power BI experience.

Security Settings

Security settings are vital for protecting your data and ensuring proper access control. Understanding these settings helps you maintain a secure environment while allowing users to perform their tasks.

Understanding Security Implications

Several critical security settings impact Power BI query execution:

Row-Level Security (RLS): RLS enforces user-level data filtering within Power BI models. This is especially important when using DirectQuery, as backend databases often cannot differentiate users without Kerberos delegation.
Performance Considerations: RLS filters should support query folding. Underlying tables should be indexed on RLS columns to enhance performance.
Continuous Monitoring: Regular auditing and integration with tools like Microsoft Purview are necessary to maintain security beyond the initial RLS setup.

Adjusting Security Settings

To balance security requirements with user access needs, consider these strategies:

Require multifactor authentication for some or all users.
Allow only devices that comply with organizational policies.
Block connectivity from non-domain-joined machines.
Conditionally allow or deny access to Power BI for specific users.

By implementing these measures, you can create a secure environment that meets your organization's needs while enabling users to access the data they require.

Power BI queries often break due to connection problems, transformation errors, performance bottlenecks, compatibility issues, or permission restrictions. You can improve reliability by focusing on key areas like query folding, which boosts performance by pushing work to the data source. Applying best practices such as star schema modeling, incremental data loading, and simplifying visuals helps speed up your reports.

Tip: Regularly monitor your queries using tools like Power Query Diagnostics and Performance Analyzer to catch issues early and keep your reports running smoothly.

By following these steps, you will enhance your Power BI experience and reduce query failures.

FAQ

What are common error messages in Power BI queries?

You may encounter various error messages, such as "Expression syntax errors" or "Missing column names." These indicate issues with your data processing or transformations. Identifying these messages helps you troubleshoot effectively.

How can I fix data processing errors?

To fix data processing errors, review your query steps. Check for incorrect data types, missing column names, or wrong source location. Adjusting these elements often resolves the issues.

What should I do if I see an expression syntax error?

If you see an expression syntax error, double-check your DAX formulas. Ensure that you use the correct syntax and that all referenced columns exist in your source.

How can I identify the wrong source location?

You can identify the wrong source location by reviewing your data source settings in Power BI. Ensure that the path or URL matches the actual location of your data files.

Are there any workarounds for common Power BI issues?

Yes, several workarounds exist for common Power BI issues. For example, if you face performance problems, consider simplifying your queries or using query folding to optimize data processing.

How do I manage user permissions effectively?

To manage user permissions effectively, regularly review user roles in Power BI. Ensure that users have the necessary access to datasets and adjust permissions as needed to prevent errors.

What are some solutions for improving query performance?

Solutions for improving query performance include optimizing data models, removing unused columns, and implementing incremental refresh. These strategies enhance data processing efficiency and reduce refresh times.

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Opening: The Lie Your Power BI Query Tells YouYou think Power BI runs your query exactly as you wrote it. It doesn’t. It quietly reorders your steps like a bureaucrat with a clipboard—efficient, humorless, and entirely convinced it knows better than you. You ask it to filter first, then merge, then expand a column. Power BI nods politely, jots that down, and proceeds to do those steps in whatever internal order it feels like. The result? Your filters get ignored, refresh times stretch into geological eras, and you start doubting every dashboard you’ve ever published.The truth hiding underneath your Apply Steps pane is that Power Query doesn’t actually execute those steps in the visual order you see. It’s a logical description, not a procedural recipe. Behind the scenes, there’s a hidden execution engine shuffling, deferring, and optimizing your operations. By the end of this, you’ll finally see why your query breaks—and how to make it obey you.Section 1: The Illusion of Control – Logical vs. Physical ExecutionHere’s the first myth to kill: the idea that Power Query executes your steps top to bottom like a loyal script reader. It doesn’t. Those “Applied Steps” you see on the right are nothing but a neatly labeled illusion. They represent the logical order—your narrative. But the physical execution order—what the engine actually does—is something else entirely. Think of it as filing taxes: you write things in sequence, but behind the curtain, an auditor reshuffles them according to whatever rules increase efficiency and reduce pain—for them, not for you.Power Query is that auditor. It builds a dependency tree, not a checklist. Each step isn’t executed immediately; it’s defined. The engine looks at your query, figures out which steps rely on others, and schedules real execution later—often reordering those operations. When you hit Close & Apply, that’s when the theater starts. The M engine runs its optimized plan, sometimes skipping entire layers if it can fold logic back into the source system.The visual order is comforting, like a child’s bedtime story—predictable and clean. But the real story is messier. A step you wrote early may execute last; another may never execute at all if no downstream transformation references it. Essentially, you’re writing declarative code that describes what you want, not how it’s performed. Sound familiar? Yes, it’s the same principle that underlies SQL.In SQL, you write SELECT, then FROM, then WHERE, then maybe a GROUP BY and ORDER BY. But internally, the database flips it. The real order starts with FROM (gather data), then WHERE (filter), then GROUP BY (aggregate), then HAVING, finally SELECT, and only then ORDER BY. Power Query operates under a similar sleight of hand—it reads your instructions, nods, then rearranges them for optimal performance, or occasionally, catastrophic inefficiency.Picture Power Query as a government department that “optimizes” paperwork by shuffling it between desks. You submit your forms labeled A through F; the department decides F actually needs to be processed first, C can be combined with D, and B—well, B is being “held for review.” Every applied step is that form, and M—the language behind Power Query—is the policy manual telling the clerk exactly how to ignore your preferred order in pursuit of internal efficiency.Dependencies, not decoration, determine that order. If your custom column depends on a transformed column created two steps above, sure, those two will stay linked. But steps without direct dependencies can slide around. That’s why inserting an innocent filter early doesn’t always “filter early.” The optimizer might push it later—particularly if it detects that folding back to the source would be more efficient. In extreme cases, your early filter does nothing until the very end, after a million extra rows have already been fetched.So when someone complains their filters “don’t work,” they’re not wrong—they just don’t understand when they work. M code only defines transformations. Actual execution happens when the engine requests data—often once, late, and in bulk. Everything before that? A list of intentions, not actions.Understanding this logical-versus-physical divide is the first real step toward fixing “broken” Power BI queries. If the Apply Steps pane is the script, the engine is the director—rewriting scenes, reordering shots, and often cutting entire subplots you thought were essential. The result may still load, but it won’t perform well unless you understand the director’s vision. And that vision, my friend, is query folding.Section 2: Query Folding – The Hidden OptimizerQuery folding is where Power Query reveals its true personality—an obsessive efficiency addict that prefers delegation to labor. In simple terms, folding means pushing your transformations back down to the source system—SQL Server, a Fabric Lakehouse, an Excel file, wherever the data lives—so that all the heavy computation happens there. The Power Query engine acts more like a project manager than a worker: it drafts the list of tasks, then hands them to someone else to execute, ideally a faster someone.Think of folding as teleportation. Rather than Power BI downloading a million rows, filtering them locally, then calculating averages like a sweaty intern with a calculator, it simply sends instructions to the database: “Do this for me and only return what’s needed.” The result appears the same, but the journey is radically different. One path sends microscopic data requests that feel instantaneous; the other drags entire datasets through the network because the engine decided your latest custom column “isn’t compatible.”Most users first encounter query folding by accident. They open a native SQL view, add a filter, and everything is smooth—refreshes in seconds. Then they add one more transform, say a conditional column or an uppercase conversion, and suddenly the refresh time triples. It’s not superstition. That one unsupported step snapped the delicate chain of delegation. Folding broke, and with it, your performance.In folding-friendly mode, Power Query behaves like an air traffic controller—it issues concise commands, and the data source handles the flights. When folding breaks, Power Query becomes a delivery driver who insists on personally flying overseas to collect each parcel before delivering it back by hand. You can guess which one burns more time and fuel.Now, when exactly does folding work? Primarily with simple, relational operations that the source system natively understands: filters, merges (that resemble SQL joins), renames, column removals, and basic calculations. These are cheap for the engine to describe and easy for a source like SQL Server to execute. As long as the M code compiles into a recognizable SQL equivalent, folding proceeds.The moment you introduce nonlinear or complex operations—custom functions, text manipulations, or bizarre index logic—the engine decides, “Nope, can’t delegate that,” and pulls the data back to handle it locally. It’s like a translator who gives up halfway through a speech because the other side doesn’t support sarcasm. The result: partial folding, where only the first few steps get delegated, and the rest are processed in memory on your machine.You can actually see this hierarchy in action. Right-click any step and choose “View Native Query.” If that option is grayed out, congratulations, folding just died at that point. Diagnostics will show earlier steps executed at the source but later ones marked as engine-only. Every broken link in that chain multiplies the time and data volume needed.The consequence of folding breaks isn’t subtle—it’s catastrophic. Instead of letting SQL Server apply a filter that returns five thousand rows, Power BI now pulls fifty million and filters them locally. The refresh that once ran in twenty seconds now takes ten minutes. Your CPU fans spin like jet turbines, and you start questioning Microsoft’s life choices. But the blame belongs to the M function that triggered execution on the client.Most real-world “why is my query slow” complaints are just folding issues disguised as mystery bugs. Users assume Power BI is inherently sluggish. In reality, they’ve forced it to perform database-scale transformations in a lightweight ETL layer. It’s like forcing Excel to play the role of a data warehouse—it’ll try, but it resents you deeply the whole time.Let’s trace a classic failure case. You build a table connection to SQL Server. You remove a few columns, apply a filter on Date > 202, and everything folds beautifully. Then, feeling creative, you add a custom column that uses Text.Contains to flag names with “Inc.” Suddenly, folding collapses. That one string function isn’t supported by the SQL provider, so Power Query retrieves all rows locally, executes the function row by row, and only then filters. You’ve effectively asked your laptop to simulate a server farm—using caffeine and willpower.This is why query folding is less about coding style and more about translation compatibility. Power Query speaks M; your data source speaks SQL or another language. The folding process is the interpreter turning those M expressions into native commands. As long as both sides understand the vocabulary, folding continues. The moment you introduce an idiom—like a custom function—the interpreter shrugs and switches to manual translation mode.Performance tuning, in this context, becomes less about computation and more about diplomacy. You’re negotiating with the data source: “How much of this work can you handle?” The smartest Power BI developers design queries that are easy for the source to understand. They filter early, avoid exotic transformations, and check folding integrity regularly.You can even think of folding fidelity as a status indicator for your professional competence: if your transformations still fold at the end of construction, you’ve engineered harmony between Power BI and its

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Mirko Peters

Founder of m365.fm, m365.show and m365con.net

Mirko Peters is a Microsoft 365 expert, content creator, and founder of m365.fm, a platform dedicated to sharing practical insights on modern workplace technologies. His work focuses on Microsoft 365 governance, security, collaboration, and real-world implementation strategies.

Through his podcast and written content, Mirko provides hands-on guidance for IT professionals, architects, and business leaders navigating the complexities of Microsoft 365. He is known for translating complex topics into clear, actionable advice, often highlighting common mistakes and overlooked risks in real-world environments.

With a strong emphasis on community contribution and knowledge sharing, Mirko is actively building a platform that connects experts, shares experiences, and helps organizations get the most out of their Microsoft 365 investments.

Your Power BI Model Looks Fine but Your Queries Are Breaking It