Forum Discussion
Dataflow: Traceback Invalid Cell Value Errors to Source
I was able to resolve this issue by inserting a temporary step after the folder of excel workbooks is loaded into the query, which filtered the workbooks down by name into batches (beginning with). Using this I was able to test batches of about 5-10 at a time, until I narrowed it down to the two files which were throwing the "#REF!" errors. I opened them up, cleared up the formulas generating the errors, and now the dataflow is working again.
Identifying the exact workbook, sheet, row/column, and cell causing errors in Power BI dataflows can be a bit challenging, but it is possible with a systematic approach. Here are some steps you can follow to pinpoint the source of the errors:
Check Data Sources:
- First, verify the data sources you are pulling from. Ensure that the data is well-structured and does not contain any issues like #N/A or #REF! errors. Sometimes these errors originate in the source data.
Dataflow Query Errors:
- In Power BI, when you load data into a dataflow, the Power Query Editor performs transformations on the data. Check the query editor for each dataflow. Look for any steps that might introduce errors like dividing by zero, referencing invalid cells, or handling missing values incorrectly. Fix any such issues within the Power Query Editor.
Column Profiling:
- Use the "Column Profiling" feature in Power Query Editor to analyze the data and identify anomalies. This can help you identify which columns might be causing issues. To use this feature, select a column and go to the "View" tab in Power Query Editor, then choose "Column Profiling."
Data Lineage:
- In Power BI, data lineage can help you understand where data is coming from and where it's going. Use the "Data lineage view" to trace back to the data source and identify the problematic cell in Excel. This view might show you the path from the source data to the error in the pipeline.
Error Handling in Power Query:
- In Power Query, you can implement custom error handling to capture and record problematic cells. For example, you can add custom columns that flag rows containing errors or invalid values. This can make it easier to identify the source.
Testing Individual Steps:
- You mentioned that none of the steps in Power Query are showing errors. It's possible that the issues are arising during the merge or transformation steps. You may need to isolate and test individual steps to identify which one introduces the error.
Excel Workbook Audit:
- If the issue persists, you might have to audit the Excel workbooks manually. Open the Excel files used as data sources, and look for the specific cells or formulas that contain #N/A or #REF!. Once you identify these cells, you can decide how to handle them in your Power Query transformations.
Data Validation:
- Implement data validation in your Excel workbooks. This can help prevent invalid values from getting into your dataflow in the first place. You can use Excel's data validation features to restrict the input to acceptable values.
Error Logging:
- Consider implementing error logging within Power Query or in the data loading process to track and capture errors as they occur. This can help you pinpoint the exact location of problematic data.
Consult Power BI Community:
- If you are still facing difficulties, consider posting your issue on the Power BI community forums. Experienced Power BI users might be able to provide specific advice based on your report and dataflow structure.
Identifying the exact source of these errors may require a combination of these methods, as well as careful examination of both the Power Query steps and the source Excel files.