CSV schema validation as a first-class task
Schema checks stop bad data early and reduce manual triage. Instead of cleaning after imports, validate format, types, and constraints as soon as the file arrives.
Define schema rules as code
A schema contract should say which columns must exist, their expected types, and what constitutes an invalid value. If possible, store this in a shared configuration file and version control it.
- Required columns list
- Type constraints per column
- Allowed value ranges and enumerations
- Null allowances by field
Header-level checks
Header mismatches are the fastest way to corrupt downstream models. Check both spelling and position-sensitive expectations before any row transformations.
- Reject unexpected duplicate headers.
- Normalize case, trim, and remove odd characters.
- Fail fast when a mandatory header is missing.
Type checks for mixed formats
Mixed numeric and text formats in the same column are common in CSV exports. They should be flagged and corrected with explicit fallback rules.
- Type-assert integers and decimals separately.
- Isolate malformed rows into exceptions.
- Use canonical transformations and avoid silent coercion.
Business-rule checks
Generic schema rules catch syntax. Business rules catch reality errors: impossible dates, invalid status sequences, and cross-column contradictions.
- Validate date ordering and status transitions.
- Check foreign key reference fields where possible.
- Flag outlier amounts and negative values in constrained fields.
Keep exceptions visible
A clean pipeline should never silently drop errors. Keep a separated exceptions file with reason codes. This makes QA faster and prevents accidental data loss.