Enable schema validation on a Feast feature view so materialization fails fast on data quality issues
domain: docs.feast.dev · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Add `enable_validation=True` to the FeatureView (or BatchFeatureView/StreamFeatureView) definition
Ensure the schema's declared Field names match the columns actually present in the source
Run `feast apply` then materialize/retrieve historical features to trigger validation
Handle the raised ValueError when a required column is missing from the input data
Review logged warnings for type mismatches, which do not block execution
Known gotchas
Missing required columns raise a hard ValueError, but type mismatches only log a warning and let execution continue — don't assume validation blocks on both classes of issue
Validation is supported on Local, Spark, and Ray compute engines only
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