Data Validation Tool
Validate CSV data against rules you define per column — required, regex pattern, numeric range, or uniqueness — and see every violation.
Define Rules
How to use Data Validation Tool
- Start by providing the required input: Drop a CSV file. Use Load Sample to try example data first.
- Set the inputs and options shown here: Required (not empty), Must be unique.
- Run the tool with Run Validation.
- Review the results, then choose an output control: Export Violations.
Validating data against rules you actually define
Generic validators check for generic problems; real data often has specific requirements — this column must never be empty, that one must match a particular pattern, this ID column must be unique. Defining your own rules per column, rather than relying on built-in assumptions, catches the violations that actually matter for your data.
Combining multiple rule types on one column
A single column can be required, pattern-matched, and checked for uniqueness all at once — useful for something like an email or ID column that needs to satisfy several conditions simultaneously to be considered valid.
What regex syntax does the pattern rule use?
Standard JavaScript regular expression syntax — the same patterns used in most programming languages' regex engines.
Can I validate more than one column at once?
Yes — define rules independently for as many columns as you need; every rule is checked against every row in a single pass.
Don't want to configure rules manually?
The Auto Data Validator detects common formats (emails, dates, URLs) automatically without needing you to define anything.