CSV Validator Configurator
Define per-column rules (type, required, custom regex) and instantly validate any CSV against them, with a row-by-row error report. Useful for checking data before importing it into another system. Runs entirely in your browser.
Reviewed by Vincenzo Manto, Founder & Lead Developer · Last updated September 27, 2026
How it works
- 1
Add a rule for each column: its name, type, and whether it's required.
- 2
For custom checks, pick the regex type and enter your own pattern.
- 3
Paste or drop the CSV you want to validate.
- 4
Get a row-by-row report of every missing column and failed cell.
Catch bad data before it reaches your system
CSV exports from spreadsheets, third-party tools, or manual data entry frequently contain missing values, malformed emails, or the wrong data type in a column. Defining your expected schema once and validating against it lets you catch every broken row before importing it into a database or another application.
No code needed for a schema-based CSV check
Instead of writing a validation script for every new dataset, this tool lets you describe column rules through a simple form and get a structured, row-by-row error report right away. That works for one-off data checks as well as repeated data-cleaning workflows.
Frequently asked questions
Which column types can I validate against?
String, number, integer, email, date (expected as YYYY-MM-DD), and a custom regex pattern for anything else. That covers most real-world CSV validation needs without writing code.
What happens if a column I configured is missing from the CSV entirely?
It's reported separately as a missing column, distinct from per-cell errors, so you immediately know whether the problem is a wrong header name versus bad data inside an existing column.
How does it detect the delimiter?
It checks the header row: if it contains semicolons but no commas, it treats the file as semicolon-delimited, otherwise it defaults to commas.
Is my CSV data uploaded anywhere during validation?
No. The parsing and rule-checking logic runs in your browser, so nothing about your data or your validation rules is sent to a server.
What if my custom regex pattern is invalid?
That column will report a configuration error instead of silently passing every row, so a typo in your own regex doesn't produce a false sense of clean data.
Can I reuse the same rule set on multiple CSV files?
Yes, the rules stay configured in the interface as you swap in a new CSV, so you can validate several files one after another against the same schema.