Validate the Schema | NVIDIA Kumo

Check the schema after configuring table metadata and relationships. Validation checks structural compatibility; it cannot determine whether the schema accurately represents the business problem.

Review the complete schema

Inspect table metadata and relationships together:

print(schema)
for name, table in schema.tables.items():
    print(table)
print(schema.relationships)

Confirm the following:

Each table represents one entity, event, or junction concept.
Every primary key is non-null and unique.
Event tables use the correct time column.
Identifier, feature, text, and timestamp columns have appropriate semantic types.
Every foreign key points to the intended primary-key table.
Related key columns use compatible physical types.
The schema contains the relational paths required by the predictive query.

Run schema validation

schema.check_consistent()

check_consistent() refuses any column whose storage and meaning contradict each other, and any relationship that cannot join. It raises StructuredDataError with a message naming the problem; resolve it before submitting a prediction request. After correcting metadata or relationships, run check_consistent() again.

materialize() also calls check_consistent() and the engine’s own graph-construction checks before sampling, so an inconsistency is caught there even if you skip the explicit call — but checking early, right after you finish editing a schema, gives you a faster and more specific failure.

There is no visualize(), print_metadata(), or print_links() on Schema in this client. Inspect a schema by printing it, iterating schema.tables, and reading schema.relationships, as shown above.

Successful validation does not guarantee that a timestamp, relationship, or historical window is appropriate for your prediction task. Review those choices with someone who understands the source data and business question.

When validation succeeds and the schema reflects the intended prediction task, continue to Make Predictions.