Forum Discussion

Dwittkop's avatar
Dwittkop
New Member
8 months ago
Solved

Importing Survey Monkey data

I am able to connect to and import some Survey Monkey data, but we use it to by creating branching surveys which create very large datasets and exceed the amount allowed to import by Power BI. I have...
  • Anonymous's avatar
    Anonymous
    8 months ago

    Hi Dwittkop,

     

    Thank you for reaching out to the Microsoft Fabric Forum Community, and special thanks to amitchandak  for prompt and helpful responses.

     

    Don’t import the full SurveyMonkey dataset at once filter only the surveys and dates you need, remove unused columns, use incremental refresh, and if the connector still fails, pull data via the API into SharePoint/Azure first and load from there into Power BI.

     

    Thanks & Regards,

    Prasanna Kumar

  • Nabha-Ahmed's avatar
    8 months ago

    Hi Dwittkop 

    Solutions / Workarounds

     

    1.  Use DirectQuery instead of Import
    • If supported by your connector or API, use DirectQuery mode so Power BI queries SurveyMonkey live rather than importing all data.

    • This avoids hitting the import size limit, but may have performance considerations for very large surveys.

    2. Pre-process / Aggregate the Data

    • Use Power Query or an intermediate storage (Azure Data Lake, SQL, or Dataverse) to flatten or aggregate survey responses before loading into Power BI.

    • For branching surveys, consider summarizing at the question level rather than importing every row/response.

    3. Incremental Data Loading

    • Load data in chunks (e.g., by survey period, survey ID, or response batches) and append into Power BI over time.

    • Use parameters in Power Query to filter the dataset dynamically.

    4. Consider External ETL

    • Use tools like Azure Data Factory, Power Automate, or Python scripts to extract, clean, and flatten SurveyMonkey data into a storage location.

    • Power BI can then connect to this storage instead of directly to SurveyMonkey, reducing size and complexity.