Forum Discussion
Data Upload Issue
1. Create a M Query parameter.
2. Reduce the amount of data that you load in to power bi desktop using a conditional logic based on the parameter value.
3. Complete your development activities.
4. Publish your report to power bi service.
5. Change the parameter value in power bi service. And supply necessary data source connection credentails or connection mapping.
6. Trigger an on demand data refresh.
7. Once the refresh is completed. If you want you can download the report again and perform any other activities in your file.
Checkout this page for sample steps: Chris Webb's BI Blog: Limit The Amount Of Data You Work With In Power BI Desktop Using Parameters And Deployment Pipelines
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Super UserHi,
From the screenshots, the issue looks like Power BI Desktop RAM/memory exhaustion during model refresh, not a problem with the dataflow itself. Since the problem started after adding one extra column to a table with ~16M rows, that new column may have significantly increased the model size (especially if it has high cardinality / many unique values).
A few things to check:
1. Check your machine RAM
Can you confirm:
- RAM size (8GB / 16GB / 32GB+)
- Whether you are using 64-bit Power BI Desktop
- Your license type (Pro / PPU / Fabric Capacity)
This helps identify if it is a local Desktop limitation or service capacity issue.
2. Disable parallel loading (important for large models)
Power BI may be trying to load multiple large tables at once, causing memory spikes.
Go to:
File → Options & Settings → Options → Current File → Data Load
Then under Parallel loading of tables, set it to One (disable parallel loading).
This often helps when importing very large tables.
3. Review the new column
Since refresh worked before adding the column:
- Check if the new column has high cardinality (many unique values)
- Ensure the datatype is optimized (Whole Number / Decimal / Date instead of Text where possible)
- Remove unnecessary text columns if not required in reporting
4. Reduce Desktop data during development
For development, try loading only recent data using a parameter/date filter, then load full data in the Service.
For example:
- Load only last 3–6 months in Desktop
- Publish to Service
- Refresh full dataset there
5. Incremental Refresh (recommended long-term)
Since your data is daily from 2019 onward, Incremental Refresh would be the best long-term solution:
- Keep historical partitions static
- Refresh only recent data
- Lower memory consumption
- Faster refresh in both Desktop and Service
Also, if you are on Power BI Pro, very large import models can hit memory/resource limits more quickly.
Could you share your RAM size and whether the newly added column is text with many unique values? That will help narrow down the exact cause.
Hope this helps.
Thanks!