What causes COLUMN_NOT_FOUND?

A column was renamed or dropped in the source table after the query was written. The query uses a hardcoded column name with incorrect casing (Spark SQL is case-insensitive but schema drift can cause mismatches). A DataFrame transformation uses a column name that does not exist in the current schema. A wildcard SELECT followed by a specific column reference misses an aliased column

How do I fix COLUMN_NOT_FOUND?

Step 1: Run DESCRIBE TABLE catalog.schema.table_name to see current column names and data types.. Step 2: Update the query or transformation to use the current column name.. Step 3: If the column was removed upstream, decide whether to add a default value or remove the reference from the query.. Step 4: Add schema validation tests to catch column renames before they reach production queries.. Step 5: For DataFrame code, use df.columns to inspect the schema at runtime and add assertions.

Medium severitycompilation

Power BI Refresh Error:
COLUMN_NOT_FOUND

What does this error mean?

A query or transformation referenced a column name that does not exist in the target table or DataFrame schema.

Common causes

1A column was renamed or dropped in the source table after the query was written
2The query uses a hardcoded column name with incorrect casing (Spark SQL is case-insensitive but schema drift can cause mismatches)
3A DataFrame transformation uses a column name that does not exist in the current schema
4A wildcard SELECT followed by a specific column reference misses an aliased column

How to fix it

1Step 1: Run DESCRIBE TABLE catalog.schema.table_name to see current column names and data types.
2Step 2: Update the query or transformation to use the current column name.
3Step 3: If the column was removed upstream, decide whether to add a default value or remove the reference from the query.
4Step 4: Add schema validation tests to catch column renames before they reach production queries.
5Step 5: For DataFrame code, use df.columns to inspect the schema at runtime and add assertions.

Frequently asked questions

How can I prevent column rename errors in production?

Use Delta Lake schema evolution with column mapping enabled — this allows column renames without breaking existing readers. Enable with ALTER TABLE ... SET TBLPROPERTIES ('delta.columnMapping.mode' = 'name').

Does Spark SQL resolve columns case-insensitively?

Yes — Spark SQL column resolution is case-insensitive by default, so 'MyColumn' and 'mycolumn' refer to the same column. However, if the column simply does not exist, the error occurs regardless of casing.