Forum Discussion
Dataflow Gen2 Enable Staging
Has anyone started using the Enable Staging feature in Dataflow Gen 2 and noticed a substantial increase in CU usage? I used it for some of my more complex tables but the CU % jumped from roughly 50% of capacity beforehand to well over 1000% after (I double checked). I disabled staging and now am back to normal. So I wanted to see when staging tables in DFG2 is beneficial over not using it? And if anyone has a sense of how it impacts capacity usage.
For reference, we're on an F64 sku. And I've built out a pretty substantial set of dataflows and lakehouses. I was only using the Enable Staging on some of the more complicated tables that merge multiple tables created from other dataflows.
Hi GregMarbais ,
Staging is beneficial in the following situations:
- High Scale Compute is required, allowing for faster execution and efficient data processing.
- Temporal Consistency across downstream dataflows is needed to ensure data integrity.
- Reduced Load on Source Systems by minimizing the number of queries submitted to the data source.
- Data Reconciliation Processes where a copy of the source data is useful for validating transformations downstream.
Given your situation with an F64 SKU and substantial dataflows and lakehouses setup, it's important to balance the benefits of staging with the impact on CU usage. The substantial increase in CU usage you've observed suggests that the complexity and volume of your dataflows, when combined with Enable Staging, exceed the expected CU consumption for your SKU.
Action Plan:
- Review Dataflow Design: Consider optimizing your dataflows to reduce complexity where possible. This might involve simplifying transformations or splitting large dataflows into smaller, more manageable pieces.
- Monitor CU Consumption: Keep a close eye on CU usage patterns to identify specific dataflows or operations that contribute most to the increase. This can help in pinpointing areas for optimization.
- Consider SKU Adjustment: If the benefits of Enable Staging are critical for your scenarios, evaluating whether a different SKU might better accommodate your CU usage needs could be worthwhile.
For more insights on managing and optimizing dataflows in Power BI, I recommend reviewing the guidance on Power BI usage scenarios: Advanced data preparation - Power BI | Microsoft Learn
Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
1 Reply
Hi GregMarbais ,
Staging is beneficial in the following situations:
- High Scale Compute is required, allowing for faster execution and efficient data processing.
- Temporal Consistency across downstream dataflows is needed to ensure data integrity.
- Reduced Load on Source Systems by minimizing the number of queries submitted to the data source.
- Data Reconciliation Processes where a copy of the source data is useful for validating transformations downstream.
Given your situation with an F64 SKU and substantial dataflows and lakehouses setup, it's important to balance the benefits of staging with the impact on CU usage. The substantial increase in CU usage you've observed suggests that the complexity and volume of your dataflows, when combined with Enable Staging, exceed the expected CU consumption for your SKU.
Action Plan:
- Review Dataflow Design: Consider optimizing your dataflows to reduce complexity where possible. This might involve simplifying transformations or splitting large dataflows into smaller, more manageable pieces.
- Monitor CU Consumption: Keep a close eye on CU usage patterns to identify specific dataflows or operations that contribute most to the increase. This can help in pinpointing areas for optimization.
- Consider SKU Adjustment: If the benefits of Enable Staging are critical for your scenarios, evaluating whether a different SKU might better accommodate your CU usage needs could be worthwhile.
For more insights on managing and optimizing dataflows in Power BI, I recommend reviewing the guidance on Power BI usage scenarios: Advanced data preparation - Power BI | Microsoft Learn
Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.