DataflowsStagingLakehouse Size

Hi nicolasf​,

The main thing I would clarify is that DataflowsStagingLakehouse is an internal Dataflow Gen2 staging artifact, not a normal Lakehouse that you should manage manually.

Microsoft documents that these staging items are created and managed automatically by Dataflow Gen2 and strongly advises users not to modify, rename or delete them directly because that can cause unexpected behavior.

The staging Lakehouse stores intermediate data used during Dataflow Gen2 transformations, so if you have many dataflows with staging enabled, especially with large intermediate datasets, the storage can become significant.

Microsoft explains the staging behavior here in the Dataflow Gen2 staging documentation.

I would also be careful with this command:

ALTER DATABASE DataflowsStagingLakehouse
SET TIME_TRAVEL_RETENTION_PERIOD = 7 DAYS;

Microsoft documents TIME_TRAVEL_RETENTION_PERIOD as a Fabric Warehouse retention setting. I would not use it to try to manage the internal Dataflow Gen2 staging Lakehouse.

The safer approach is:

• leave DataflowsStagingLakehouse itself untouched
• review which Dataflows Gen2 have staging enabled
• disable staging where it is not providing a performance benefit
• use the Dataflow Gen2 performance guidance to reduce unnecessary staging
• monitor storage growth after those changes

Microsoft’s current guidance also notes that staging is not always needed. For example, when loading directly to some destinations, staging can be disabled, and it is most useful when transformations cannot fold efficiently to the source.

So I would optimize the Dataflows that generate the staging data rather than trying to clean or change retention settings on the system staging Lakehouse itself.

AI-assisted drafting: AI was used to help structure and phrase this response. I reviewed and validated the technical content before posting.

Hi nicolasf​,

Have you had a chance to review the solution shared by tayloramy​ ShivekMaharaj​ codeautomation​ ? If the issue persists, feel free to reply so we can help further.

Thank you.

Hi nicolasf​,

The staging lakehouse is used for staging for dataflow gen 2. If you have lots of dataflows, then this staging will get rather big. 

It is strongly advised that you do not touch this lakehouse at all. 

Data in Dataflow Gen2 staging - Microsoft Fabric | Microsoft Learn

Tx tayloramy​ . But i've done this :

ALTER DATABASE DataflowsStagingLakehouse 

SET TIME_TRAVEL_RETENTION_PERIOD  = 7 DAYS;

... and the storage seems to be moved on soft delete storage.
I don't understand why this instruction had effect on a lakehouse.. I thought it was specific for warehouses

Is it possible to perform a comprehensive storage assessment of my Microsoft Fabric environment (Lakehouse and/or Warehouse) to identify the main drivers of storage consumption and explain any unusually high storage usage?

The assessment should provide:

• An overall breakdown of storage consumption across data, system files, metadata, Delta logs, version history, and other storage components.
• Identification of the largest storage consumers (tables, datasets, files, or objects).
• Analysis of the impact of retention settings, including Time Travel and historical version retention.
• Assessment of the storage impact of auditing, logging, monitoring, and governance-related features.
• Detection of potential inefficiencies or anomalies, such as excessive version accumulation, obsolete files, fragmented storage, or a large number of small files.
• Trends and factors contributing to storage growth over time.
• Recommendations and optimization opportunities to reduce storage consumption while maintaining operational and compliance requirements.

The objective is to obtain a global diagnostic of the Microsoft Fabric storage footprint, understand the root causes of storage usage, and identify opportunities for optimization.

Hi nicolasf​ 

103 GB can happen if many Dataflows Gen2 have staging enabled, because intermediate/staged data can accumulate over time, especially with frequent refreshes and larger transformations.

I’d first check which Dataflows are using staging and whether all of them really need it. For simple flows, disabling staging can reduce the footprint.

Also review refresh history and data volume, because repeated refreshes of large datasets can increase temporary/staging storage significantly.

As a good practice, I’d suggest:

• enable staging only where it improves performance or is required
• monitor large or frequently refreshed Dataflows
• review unused Dataflows and old workloads
• avoid treating the staging lakehouse as permanent business storage

If the size keeps growing unexpectedly, it may be worth opening a support case so Microsoft can confirm whether old staging artifacts are being cleaned up correctly.