Forbidden error using saveAsTable

[PeterDaniels]

I have a dataframe. Trying to simply saveAsTable (delta). I get a strange Forbidden error. I am admin in the workspace and I'm the one that created the lakehouse. I've tried just using a table name, using lakehouse name dot table name, and using dbo in the middle. Baffled. 

[PontusPersson]

I have the same issue, and I know this has worked before. It stopped working yesterday (about 16-17 hours before this post was when I noticed it). Reading tables (e.g. spark.read.table("lakehouse.schema.table") does not work, but reading using path (e.g. spark.read.format("delta").load("Tables/schema/table")) does work. And same for writing. Receiving 403 forbidden error. Only happens on schema enabled lakehouse.

[Anonymous]

Hi @PeterDaniels ,

Thank you for your reply from frithjof_v .

I was able to reproduce your error with the preview schema feature enabled, and the error occurs seemingly because of an architecture mismatch.

Using the statement “SaveAsTable” works fine, you can try it:

df = spark.read.format(“csv”).option(“header”, “false”).load( “Files/2019.csv")

# Create a new table
df.write.format(“delta”).saveAsTable(“test2”)

If you have any other questions please feel free to contact me.

Best Regards,
Yang
Community Support Team

If there is any post helps, then please consider Accept it as the solution  to help the other members find it more quickly.
If I misunderstand your needs or you still have problems on it, please feel free to let us know. Thanks a lot!

[frithjof_v]

Schema enabled Lakehouse?

Same issue here, it seems: https://community.fabric.microsoft.com/t5/Data-Engineering/Error-when-writing-data-into-schema-enabl...

A possible solution was proposed.

Anyway, schema enabled Lakehouses have big limitations at the moment. It's a preview feature still.

[PeterDaniels · Accepted solution]

Yeah. I think the solution for now is to not use schema enabled lakehouses. Hopefully that feature will get more robust as it heads towards GA