Data refresh failure, connecting to Google Big Query

Hi whiteBirdie_258 It sounds like you're facing a common challenge when dealing with data refreshes in Power BI, especially with memory limitations on a Pro license. Here are some strategies that might help you mitigate this issue without needing to upgrade to a Premium license:

Optimize Your Data Model

1. Reduce Data Volume:

   • Query Folding: Ensure that your queries are being folded back to BigQuery, so the data transformation happens on the server side. This reduces the amount of data transferred and processed in Power BI.
   • Filtering: Apply more aggressive filtering in your data source queries to reduce the dataset size.
   • Aggregations: Use aggregations to reduce the detail level of your data.
     
     

2. Optimize Columns:

   • Remove unnecessary columns and only load the data that you need for your reports.
   • Use data types that are more memory-efficient (e.g., integers instead of strings where possible).
     
     

3. Calculated Columns and Measures:

   • Move calculated columns and complex transformations to BigQuery where possible. This can reduce the memory footprint in Power BI.

Alternative Solutions

• Incremental Refresh: If your dataset supports it, set up incremental refreshes. This way, only new or changed data is processed rather than the entire dataset.
• Split the Dataset: If feasible, split your dataset into smaller chunks and process them separately.

Explore Other Tools

Since you are not planning to upgrade to a Premium license, you might also consider exploring other data integration tools that can handle large datasets more efficiently. For instance, Windsor.ai offers data connectors that might help streamline your data processes and reduce memory consumption when exporting your data into Power BI

I hope these can be helpful for you!