Error: Writing data into Warehouse using a pyspark notebook

[sholy29]

Hi everyone

I currently have a pyspark notebook that writes data into Warehouse using the synapsesql (df_pivoted.write.mode("overwrite").synapsesql("Data Reporting Status WH.dbo.bus_mgt_data_reporting_status") ). The code has been working however for the past two days i have been getting the following errors in the picture below (Py4JJavaError: An error occurred while calling o6923.synapsesql. : com.microsoft.spark.fabric.tds.write.error.FabricSparkTDSWriteError: Write orchestration failed.)

Can anyone help me on how tp resolve this error.

Thanks

Thank you

[sholy29]

Thanks for the recomendation.

Schema mismatch

I have checked both the source dataframe (bus_mgt_data_reporting_status) and the destination table in the warehouse. Both of them have the same structure in terms column name, column order and data type.

Without stopping the current session, I tried writing the dataframe (bus_mgt_data_reporting_status) into a new table (with an new table name) in the warehouse but I get the same error.

However, when I stop the current session and use a dummy data to write into a new table in the warehouse It went pretty well.

My guess is that it has to do with the session as at when writing the bus_mgt_data_reporting_status table to the warehouse. What do you recommend.

[sholy29]

Hi 

Thanks, I tried your recommendation but I still got the same error. This is really futrating because two days ago, it worked.

[v-hashadapu]

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

Based on your description, the issue you're facing is almost certainly tied to a corrupted or unstable Spark session. The fact that writing fails even to a new table within the same session but works fine after restarting rules out schema mismatch and points directly to stale execution plans or internal memory/cache inconsistencies that affect write orchestration in Fabric's Spark runtime.

To resolve this without restarting your session, force Spark to fully evaluate and materialize your DataFrame before the write. Do this by adding .cache() followed by .count() before the write call:

df_pivoted = df_pivoted.cache() df_pivoted.count() df_pivoted.write.mode("overwrite").synapsesql("WH.dbo.bus_mgt_data_reporting_status")

This ensures that all transformations are resolved ahead of the write and breaks any lingering state that might interfere with orchestration. If you're working with a large dataset, you can optionally add .repartition(n) before the write to help avoid shuffle-related failures.

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.

[v-hashadapu]

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.