Common Power Query Errors and How to Fix Them

Power Query is a great tool for data transformation and preparation in Excel and Power BI. However, like any tool, it comes with its own set of challenges and errors that can disrupt workflows and data analysis. Understanding these errors and knowing how to resolve them is crucial for maintaining smooth and efficient data processing.

Read on as we take an in-depth look at common Power Query errors and offer detailed solutions to fix them.

Understanding Power Query Errors

Errors in Power Query can arise from various issues, including syntax errors, data source problems, and transformation conflicts. These errors are generally categorized into three main types: expression syntax errors, data source errors, and data transformation errors.

What These Errors Can Lead To

1. Inaccurate Data Analysis: Errors can result in incorrect data being loaded into the model, leading to flawed analysis and decision-making.
2. Incomplete Data Sets: Transformation errors, in particular, can cause data to be omitted from the final data set, resulting in incomplete information.
3. Increased Processing Time: Troubleshooting and resolving errors can significantly increase the time required to process and prepare data, impacting productivity.
4. Failed Data Refreshes: Persistent errors can cause scheduled data refreshes to fail, leading to outdated data being used for reporting and analysis.
5. User Frustration: Frequent and unresolved errors can cause frustration among users, reducing confidence in the data and the tools being used.

Expression Syntax Errors

Symptoms: Expression syntax errors occur when there is a mistake in the formula or code within the Power Query editor. These errors are typically highlighted by the system, indicating the problematic line of code.

Common Causes:

• Typographical errors.
• Incorrect use of functions or operators.
• Missing or misplaced commas, parentheses, or other syntax elements.

Example: A common syntax error might involve a misspelled data type, such as writing “tet” instead of “text.” The error message would highlight this mistake, allowing you to correct it by changing “tet” to “text.”

Solution:

1. Carefully review the error message and locate the highlighted line.
2. Check for common syntax mistakes, such as missing commas or incorrect function names.
3. Use the “Advanced Editor” to examine the code and make necessary corrections.

Formula.Firewall Error

Symptoms: This error occurs when combining data from multiple sources with different privacy levels. The error message typically states that the query is accessing data sources with incompatible privacy settings.

Common Causes:

• Combining internal and external data sources.
• Using dynamic data sources that define the source of another query.

Solution:

1. Set Appropriate Privacy Levels:
   • Navigate to File > Options and Settings > Data Source Settings.
   • Edit the permissions to align privacy levels.
2. Ignore Privacy Levels:
   • If data sensitivity allows, you can choose to ignore privacy levels by selecting File > Options and Settings > Query Options > Privacy > Always Ignore Privacy Level settings.

Data Source Errors

Symptoms: These errors occur when Power Query cannot access the specified data source. This could be due to a file location change, server issues, or incorrect source paths.

Common Causes:

• Moved or deleted files.
• Server downtime or changes in database locations.
• Incorrect file paths or URL changes.

Solution:

1. Update Source Location:
   • Navigate to the affected query, click on the error message, and choose to edit settings to update the source path.
2. Ensure Accessibility:
   • Make sure that the data source is available and accessible.

Missing or Renamed Columns

Symptoms: Errors occur when the query references a column that no longer exists or has been renamed. The error message will indicate that a specific column was not found.

Common Causes:

• Renamed or deleted columns in the data source after the query was created.
• Changes in the data structure that were not updated in the query.

Solution:

1. Verify Column Names:
   • Check the data source to confirm column names and adjust the query to match any changes.
2. Use Dynamic Column Referencing:
   • Avoid hardcoding column names that might change.

Data Type Conversion Errors

Symptoms: These errors occur when converting data types, such as changing text to a number. The error message will indicate that conversion failed for specific values.

Common Causes:

• Non-numeric values in a column being converted to a number.
• Inconsistent data types within the same column.

Solution:

1. Data Profiling Tools:
   • Use data profiling tools to identify problematic values before conversion.
2. Replace Errors:
   • Apply a “Replace Errors” transformation to substitute errors with a default value.

Cell-Level Errors

Symptoms: Individual cells display errors due to issues like invalid data or unsupported operations. These errors are shown as “Error” in the cell.

Common Causes:

• Invalid data entries such as “NA” in a numeric column.
• Unsupported operations like multiplying text values.

Solution:

1. Remove Errors:
   • Select the column with errors and choose Home > Remove Rows > Remove Errors.
2. Replace Errors:
   • Select the column, then Transform > Replace Values > Replace Errors.
3. Keep Errors:
   • Select the column, then Home > Keep Rows > Keep Errors.

Step-Level Errors

Symptoms: Errors occur at a specific step in the query process, often due to issues in transformations or applied steps.

Common Causes:

• Changes in the data structure that invalidate previous steps.
• Logical errors in the transformation sequence.

Solution:

1. Review Applied Steps:
   • Examine the applied steps in the Query Editor to identify the step causing the error.
2. Modify or Remove Step:
   • Adjust or delete the problematic step and re-apply the correct transformation.

Best Practices for Avoiding Power Query Errors

1. Use Dynamic References

Avoid hardcoding file paths and column names when possible. Use parameters and dynamic referencing to accommodate changes.

2. Regularly Update Queries

Regularly review and update queries to align with changes in data sources and structures.

3. Error Handling Functions

Utilize Power Query’s error handling functions like try and otherwise to manage errors gracefully.

4. Data Profiling Tools

Use data profiling tools to identify and resolve data quality issues early in the query process.

Advanced Error Handling Techniques

1. Custom Error Handling

Power Query allows you to create custom error-handling logic using the try and otherwise functions. For instance, if you expect certain types of errors, you can catch these errors and handle them in a specific way.

let Source = Excel.Workbook(File.Contents(“C:\Path\To\Your\File.xlsx”), null, true), SalesData = Source{[Name=”Sales”]}[Data], AddedTotalSales = Table.AddColumn(SalesData, “Total Sales”, each try [Quantity] * [UnitPrice] otherwise 0) in AddedTotalSales

Note: In this example:

• Source loads the Excel workbook from the specified path.
• SalesData refers to the table named “Sales” in the workbook.
• AddedTotalSales adds a new column called “Total Sales” to the SalesData table, where each value is calculated as [Quantity] * [UnitPrice]. If there is an error in the calculation (e.g., if either Quantity or UnitPrice is missing or not a number), the try…otherwise construct will return 0 as the default value.

2. Splitting and Merging Data

When dealing with large datasets or complex transformations, splitting data into smaller, manageable parts can help identify and resolve errors more efficiently. Similarly, merging data back together should be done carefully to ensure data integrity.

3. Debugging Complex Queries

For complex queries with multiple steps, debugging can be challenging. One effective method is to break down the query into smaller parts and validate each part independently. This approach helps isolate errors and understand their root causes.

Common Scenarios and Solutions

Scenario 1: Combining Data from Different Sources

Combining data from different sources is a common task in Power Query, but it can lead to compatibility issues due to privacy levels or data formats. These issues can prevent successful merging and cause errors in the query.

Solution:

• Privacy Levels: Ensure that the privacy levels for each data source are set correctly. Power Query uses privacy levels to determine how data should be combined, and mismatched levels can cause errors. Adjust privacy levels in the Query Options under Privacy.
• Data Formats: Verify that data formats are compatible across sources. For instance, if one source uses text for dates and another uses date values, you need to standardize these formats before merging. Use the ‘Change Type’ option to ensure consistency.

Scenario 2: Transforming Data Types

Transforming data types, such as converting text to numbers or dates, can lead to errors if the data contains incompatible values. These errors can disrupt the query and result in incorrect data being loaded into your model.

Solution:

• Data Profiling Tools: Use Power Query’s data profiling tools to identify incompatible values. These tools can highlight data quality issues such as errors, empty values, or mismatched types.
• Appropriate Transformations: Apply appropriate transformations to handle incompatible values. For example, use the ‘Replace Errors’ function to substitute error values with a default value or remove problematic rows entirely. Additionally, consider using the ‘Detect Data Type’ feature to automate type conversions where possible.

Scenario 3: Updating Data Sources

When a data source is updated or moved or its structure changes, existing queries can break. This can lead to errors in data retrieval and processing, requiring manual updates to the query settings.

Solution:

• Update Source Location: Always update the source location in the query settings when a data source is moved or its URL changes. Navigate to the ‘Data Source Settings’ and update the connection information to reflect the new location.
• Ensure Structural Consistency: Ensure that the new data source has the same structure as the old one. If there are changes in the data schema, such as renamed columns or additional fields, update the query steps accordingly. Use the ‘Manage Queries’ feature to modify and test each step to confirm that the updated source integrates smoothly.

Scenario 4: Handling Large Data Sets

Working with large data sets can cause performance issues and lead to memory errors in Power Query. Efficient data handling techniques are essential to avoid these problems.

Solution:

• Query Folding: Use query folding to push data transformations back to the data source, decreasing the amount of data processed locally. This can significantly improve performance and reduce memory usage.
• Filter Early: Apply filters as early as possible in your query to limit the amount of data being processed. This can help in managing large data sets more effectively.
• Use Efficient Data Types: Optimize data types for performance. For instance, using integer types instead of text for numeric data can improve processing speed and reduce memory consumption.

Conclusion

Power Query is a robust tool for data transformation, but errors are inevitable due to the dynamic nature of data sources and transformations. Understanding common Power Query errors and their solutions can significantly enhance your ability to manage and troubleshoot data processing tasks. By following best practices and leveraging advanced error-handling techniques, you can create more reliable and efficient data workflows.