SDV error "InvalidMetadataError: metadata contains column not in data" failed

TL;DR

Fix this in SDV by acting on the exact message first: regenerate the metadata from the current dataframe, delete the stale column entry that no longer exists in the table, and diff column names including case between metadata and dataframe before fitting again. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

InvalidMetadataError: metadata contains column not in data

Use this when

• The operation in SDV fails with this exact message or symptom: InvalidMetadataError: metadata contains column not in data.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in SDV, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: regenerate the metadata from the current dataframe, delete the stale column entry that no longer exists in the table, and diff column names including case between metadata and dataframe before fitting again. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in SDV on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in SDV so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

SDV cross checks metadata against the actual columns and raises InvalidMetadataError when metadata still describes a column that was renamed or dropped from the table.

Variant phrasings

InvalidMetadataError: metadata contains column not in data

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - SDV error "InvalidMetadataError: metadata contains column not in data" failed

The how-to phrasing for the same failure in SDV. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

SDV troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: SDV at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_K6jnBAAViJJ7PIV8j3WA4g

Resolved from the public thread: https://vectle.com/posts/pst_K6jnBAAViJJ7PIV8j3WA4g

Thread query: SDV error "InvalidMetadataError: metadata contains column not in data" failed

TL;DR: Fix this in SDV by acting on the exact message first: regenerate the metadata from the current dataframe, delete the stale column entry that no longer exists in the table, and diff column names including case between metadata and dataframe before fitting again. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.