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Hello everyone,
I’m currently exploring Microsoft Fabric, especially Lakehouse shortcuts, and I have a question regarding schema behavior:
Do Lakehouse shortcuts automatically sync schema changes from the underlying source files, or do we need to manually refresh/update the metadata for the changes to reflect in the Lakehouse?
If anyone has seen this in real-world scenarios, I’d really appreciate your guidance.
Learning from experienced practitioners here has helped me a lot.
Thank you in advance!
— Youraj
Solved! Go to Solution.
Lakehouse shortcuts do not autosync schema changes. Remember that shortcut is just a pointer to files, it does not monitor or pull metadata updates on its own. If the underlying data’s schema changes, your fabric lakehouse will not pick it up until a metadata refresh happens. In practice, you usually trigger this by:
Running a spark read on the shortcut path so fabric reinfers the schema,
(or)
Rebuilding the table if you are using a managed table over the shortcut.
Practically...shortcuts pass through the data, not the schema. Any schema evolution must be re evaluated by spark or manually refreshed.
Hii @Youraj8271
No — Lakehouse shortcuts do not automatically sync schema changes.
You must manually refresh/update metadata (or recreate the shortcut / run a schema inference action) for new columns or structural changes to appear.
How to Refresh Metadata
Use “Refresh” option from the Lakehouse UI
Or access the shortcut files using a Spark notebook and run:
spark.read.format("delta").load("<shortcut-path>").schema
Lakehouse shortcuts do not autosync schema changes. Remember that shortcut is just a pointer to files, it does not monitor or pull metadata updates on its own. If the underlying data’s schema changes, your fabric lakehouse will not pick it up until a metadata refresh happens. In practice, you usually trigger this by:
Running a spark read on the shortcut path so fabric reinfers the schema,
(or)
Rebuilding the table if you are using a managed table over the shortcut.
Practically...shortcuts pass through the data, not the schema. Any schema evolution must be re evaluated by spark or manually refreshed.