About CSV Schema Validator

Validate CSV data against a column schema definition. Part of the DevTools Surf developer suite. Browse more tools in the Data / SQL collection.

Use Cases

• Data engineers validating ETL pipeline output before loading
• Analysts checking uploaded CSV files against expected schemas
• API developers verifying CSV export format consistency
• QA teams automating data quality checks on batch imports

Tips

• Define column types like string, number, date for strict checks
• Set required columns to catch missing fields in uploads
• Add regex patterns for custom validation on specific columns

Fun Facts

• CSV predates personal computers — IBM used comma-separated values on Fortran punch cards in the early 1960s, though the format wasn't standardized until RFC 4180 in 2005.
• RFC 4180 is only 7 pages long and still leaves ambiguities — there's no universal agreement on whether CSV should use commas, semicolons, or tabs as delimiters.
• Excel's CSV handling varies by locale — European versions often use semicolons instead of commas because commas are used as decimal separators in many European countries.

FAQ

What schema format?

Simple column-name: type list. Types: string, integer, number, boolean, date, email, url. Optional regex for custom patterns.

What does it flag?

Type mismatches (string in integer column), missing required values, duplicate unique values, format violations (bad email).

Custom validation rules?

Regex per column for string fields. Ranges (min/max) for numeric. Date formats (ISO, MM/DD/YYYY, etc.) for date columns.

Row-level or document-level?

Both. Each row is validated; also aggregate checks (unique constraints across all rows).