We would like Microsoft Fabric to support Spark write operations to Fabric Warehouse when Private Link is enabled, ideally including full read/write support under both tenant-level and workspace-level Private Link configurations.
Background
In our scenario, we need to perform multiple transformation steps with PySpark directly against Warehouse tables. At the moment, with Private Link enabled, this is not possible due to the documented product limitation.
Current Challenge
The currently possible workaround is a repeated flow like:
PySpark → Lakehouse → Warehouse → PySpark → Lakehouse → Warehouse → Power BI
This creates significant operational overhead, including:
additional data movement between engines
more complex orchestration and pipeline maintenance
increased runtime and storage duplication
more potential failure points
reduced architectural simplicity and efficiency
Why this matters
For enterprise environments, Private Link is often a mandatory security requirement. At the same time, organizations want to use PySpark as a central transformation engine while still leveraging the Warehouse as the serving layer for analytics and reporting.
Without direct Spark-to-Warehouse write support under Private Link, teams must choose between:
This is a major limitation for production-grade enterprise data platforms.
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