Validate CSV data against custom rules
Define validation rules for each column, required fields, allowed values, numeric or length ranges, regex patterns, format checks (email, URL, phone, date), and unique constraints, and the tool checks every row against them. You get a detailed error report showing exactly which cells fail and why, making it easy to fix issues before data goes into production.
Initializing in your browser…
Before an import you must guarantee every email is well-formed and every age is a positive integer.
Rules
email: regex · age: integer 0–120 · country: in [US,DE,FR]
Report
17 rows failed: 11 bad email, 4 age out of range, 2 unknown country Failing rows listed with line numbers
You declare per-column rules and get back exactly which rows and cells violate them, with line numbers, turning a vague "the import keeps failing" into a precise fix list. Validating before load is far cheaper than rolling back a bad import.
Define validation rules for each column, required fields, allowed values, numeric or length ranges, regex patterns, format checks (email, URL, phone, date), and unique constraints, and the tool checks every row against them. You get a detailed error report showing exactly which cells fail and why, making it easy to fix issues before data goes into production.
Data validation is the gatekeeping step between raw CSV data and trusted datasets. This tool lets you build a set of column rules without writing code. The supported rule types are: required (no blanks), regex pattern match, minimum and maximum length, minimum and maximum numeric value, email, URL, numeric, integer, date, phone, unique (no repeated values in the column), and in-list (value must be one of an allowed set). You can apply more than one rule to the same column.
To get started fast, one-click templates assemble common rule sets, Contact Info (required + email + phone), Numeric Data (required + numeric + min), Text Fields (required + length bounds), Date Fields, and Unique IDs. After running validation you get a summary (total rows, valid rows, invalid rows, total errors) and a per-cell error list showing the row, column, failing value, and the reason. Two downloads are available: a cleaned CSV containing only the rows that passed every rule, and a plain-text validation report listing the rules and the individual errors.
Validate vendor or partner data deliveries against agreed-upon rules before accepting them.
Catch constraint violations (nulls in required columns, out-of-range values) before loading into a database.
Ensure datasets meet format and completeness requirements mandated by regulatory standards.
Validate bulk form responses exported as CSV to catch invalid emails, phone numbers, or missing fields.
Quickly run a set of column rules against a fresh export to confirm it meets the format and completeness you expect.
Yes. One-click templates load common rule sets such as Contact Info, Numeric Data, Text Fields, Date Fields, and Unique IDs, which you can then adjust per column.
Required, regex pattern, min/max length, min/max numeric value, email, URL, numeric, integer, date, phone, unique values, and in-list (value must be one of an allowed set). You can combine several rules on one column.
A summary of total, valid, and invalid rows plus the total error count, and a per-cell list of failures with the row, column, value, and reason. You can download a cleaned CSV of only the passing rows or a plain-text report of the errors.
Yes. Validation runs in your browser and handles files with hundreds of thousands of rows.
Rows and columns are parsed and transformed in memory in your browser. No record ever reaches a server.