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High severityfabricMicrosoft Fabric

Microsoft Fabric Error:
Delta Schema Deserialization Error

What does this error mean?

Delta Lake cannot parse the schema stored in the transaction log for a Fabric Lakehouse table — the log entry is corrupted, malformed, or written by an incompatible Delta version. Reads and queries fail until the metadata is repaired.

Common causes

  • 1Interrupting a notebook mid-write leaves the transaction log in a partially written state
  • 2Schema evolution wrote a schema format that a lower Delta version cannot parse (version mismatch between writer and reader)
  • 3Manual edits or deletions of files inside the _delta_log directory
  • 4Storage-level corruption of the last Delta log file (e.g., due to a failed upload)

How to fix it

  1. 1Try reading the table with a RESTORE TABLE command to roll back to a known-good version: RESTORE TABLE <table> TO VERSION AS OF <version>.
  2. 2Check the _delta_log directory for the most recent .json checkpoint file and verify it is complete and valid JSON.
  3. 3Run DESCRIBE HISTORY <table> to find the last successful schema version before the corruption.
  4. 4If RESTORE is not possible, recreate the Delta table from the underlying Parquet files using delta.io conversion: CONVERT TO DELTA parquet.<path>.

Beyond the docs

Common practitioner solutions not covered in the official documentation.

  1. 1Ensure notebooks writing to the table complete without interruption — avoid killing Spark sessions mid-write

Frequently asked questions

Does this error affect Power BI reports in the same workspace?

Depends on the error type. Semantic model failures affect report freshness directly. Dataflow or pipeline failures may cascade into downstream semantic model failures.

How is debugging Fabric errors different from classic Power BI errors?

Fabric errors often require checking capacity utilization alongside the item-level error. The Fabric admin portal shows capacity pressure that Power BI Service doesn't expose.

Can Fabric errors be caused by capacity limits?

Yes — Fabric capacities have concurrent operation limits. An undersized capacity causes failures during peak usage.

Does this error appear in Power BI Desktop?

No — Fabric items are cloud-native. Desktop can connect to Fabric semantic models but cannot trigger or observe Fabric-specific errors.

Source · learn.microsoft.com/en-us/fabric/data-engineering/troubleshoot-lakehouse

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