Error: Writing data into Warehouse using a pyspark notebook

Hi sholy29 , Thank you for reaching out to the Microsoft Community Forum.

This typically indicates an issue during the write orchestration phase, which often comes down to one of three causes: a schema mismatch between the DataFrame and the Warehouse table, a locked or corrupted target table or a temporary platform-level issue in Fabric.

The most common culprit is a schema mismatch. Fabric Warehouse doesn't support automatic schema evolution when writing via Spark, so if the DataFrame's structure has changed (new columns, different types or casing mismatches), your write will fail. Use df_pivoted.printSchema() to inspect your DataFrame and compare it to the Warehouse table definition using:

SELECT COLUMN_NAME, DATA_TYPE FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = 'bus_mgt_data_reporting_status';

If the schemas don’t match exactly (including column order and casing), align them manually or recreate the table. A quick way to isolate this is to try writing to a new or temporary table. If that succeeds, the issue lies with the target table schema or state.

Another common issue is a locked or corrupted table, especially if the notebook previously failed mid-write. Restart your Spark session to clear cached metadata and ensure no one else is querying or modifying the table.

If this helped solve the issue, please consider marking it “Accept as Solution” so others with similar queries may find it more easily. If not, please share the details, always happy to help.
Thank you.