Data Validation Generator
Turn sample data into validation rules. The generator infers each field’s type, whether it is always present, formats such as email, URL and date, non-negative numbers, and enums for columns with a few repeated values. It writes the rules as JSON Schema (draft 2020-12), a Zod schema for TypeScript, a Pydantic model for Python or SQL CHECK constraints, with a table explaining every rule.
- Runs in your browser
- No sign-up
- Free to use
Processed entirely in your browser – your data is not uploaded. Files up to 50 MB.
How to use Data Validation Generator
- Paste a representative sample.
- Choose the output format and type name.
- Review the inferred rules.
- Copy the schema into your project.
Data Validation Generator features
Four outputs
JSON Schema, Zod, Pydantic, SQL.
Formats
email, uri and date.
Enums
For small sets of values.
Required fields
From columns that are never empty.
Rule table
Explains each inference.
Private
Runs in your browser; nothing is uploaded.
When to use Data Validation Generator
- Validating imports and API payloads.
- Bootstrapping TypeScript or Python types.
- Documenting data contracts.
- Adding database constraints.
Data Validation Generator FAQ
Are the rules complete?
They reflect the sample. Use a representative sample and review enums and limits.
Why are length limits higher than the sample?
They are set 50% above the longest value so normal data is not rejected.
Which JSON Schema version?
Draft 2020-12.
How large should the sample be?
Large enough to include typical variations – a few hundred rows is often enough to infer formats and optional fields reliably.
Which validation formats are produced?
JSON Schema, which most programming languages and API tools can use, plus readable rule summaries for documentation.
Should every field be required?
Only fields that must always be present. The generator marks fields that were always filled in your sample; review them against the real specification.
Can it detect enums?
Columns with a few repeated values are suggested as enums. Add any allowed values that did not appear in the sample.
Validate at the boundary
Rejecting bad data when it enters a system is cheaper than repairing it later.
Validation rules written by hand often miss edge cases; inferring them from real sample data gives a starting point that matches what you actually receive. The generator detects types, required fields, formats such as email, URL and date, non-negative numbers and enums for columns with a few repeated values.
Review the rules before using them: a sample may contain only a few of the allowed values, or a field that happened to be always present but is optional in reality. Loosen or tighten them to match the real specification.
Use the JSON Schema output in API validation, form handling or data pipelines, so bad records are rejected at the door instead of discovered later. The sample is analysed in your browser.
Validating data at the point where it enters your system – an API, an upload form, an import job – is far cheaper than cleaning it later. The generated schema gives you a starting point; add business rules such as allowed ranges or patterns that only you know.