Validate CSV data against custom rules
Define validation rules for each column, required fields, allowed values, numeric ranges, regex patterns, 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 ranges, regex patterns, 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.
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.
Reuse saved validation profiles to check recurring data feeds on a regular schedule.
Data validation is the gatekeeping step between raw CSV data and trusted datasets. This tool lets you build a validation profile per column without writing code. Supported rule types include: required (no blanks), data type (must be integer, date, email, etc.), range (min/max for numbers or dates), allowed values (enumerated list), pattern (regex match), uniqueness (no duplicates), and cross-column rules (column A must be less than column B).
The error report groups failures by rule type and severity (error vs. warning), and you can download it as a CSV for integration into automated quality pipelines.
Yes. Save your rule set as a validation profile and load it the next time you receive a file with the same structure.
Errors indicate hard failures (data that must be fixed). Warnings flag potential issues worth reviewing but that may be acceptable.
Yes. Cross-column rules let you enforce relationships like "end_date must be after start_date" or "total must equal quantity times price."
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.