Forum Discussion
DF Staging Q
Hi, smpa01
When publishing your Dataflow (DF) table to a Data destination like Lakehouse, it is generally recommended to disable staging to improve performance. When staging is enabled, ingestion will take more time. By default, staging is disabled when loading data into the Lakehouse or other non-warehouse destinations. This means that the data is directly written to the data destination without using staging.
Data Factory Spotlight: Dataflow Gen2 | Microsoft Fabric Blog | Microsoft Fabric
Best Regards,
Community Support Team _Charlotte
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There is some information in this comment which is new to me:
"However, the data might not be as clean or organized as it would be with staging enabled."
"However, there might be a risk of inconsistencies or incomplete data updates since staging helps in managing incremental changes and ensuring data integrity."
"Enabling staging helps in managing and organizing the data before it is published to the destination. This process reduces the risk of data duplication and ensures that the data is clean and consistent."
"In summary, enabling staging is generally recommended to ensure data integrity, avoid duplication, and manage incremental updates effectively."
Where did you find this information?
This information is surprising to me and I would like to get more information about this. Can you please explain more about why there is a risk of inconsistencies or incomplete data when the enable staging option is disabled?
Disable staging is, after all, the default setting in Dataflows Gen2 when loading to Lakehouse: https://learn.microsoft.com/en-us/fabric/data-factory/dataflow-gen2-data-destinations-and-managed-settings#using-staging-before-loading-to-a-destination
I thought the primary purpose of enable/disable staging was related to performance optimization of the Dataflow Gen2.
Thanks for this frithjof_v . yes, the default is Disable staging
Dataflow Gen2 is not even comparable to notebook in terms of performance. I am not surprised by that and I don't expect PQ to be faster than notebook's distributed processing.
However, there are situations when I don't have an option other than to rely on DFGen2 (e.g. sharepoint). In future, if I can procure accees to Graph API, I can discard this option.Anonymous miguel can you please validate the comments from frithjof_v
Two things, I care about most if I have to rely of DF Gen2, in terms of priority
a. What do I need to do to ensure data is not duplicated + Incremental Refresh (willing to overlook performance) in destination (e.g. lakehouse)? (Staging or Disable Staging)
b. if a is satisifed, what are the possible performance tuning available?
