Generate synthetic data from JSON Schema

Create browser-local sample records from a JSON Schema for demos, QA fixtures, and API contract testing without using real customer data.

Open this example in Synthetic Data Generator

Open the tool, then paste the sample input below. Everything runs locally in your browser.

Open this example in Synthetic Data Generator →

The problem

Teams often need realistic-looking payloads for testing, demos, and examples, but production data can contain personal or sensitive values. A schema-based generator lets you create representative records without copying real data.

Sample input

JSON Schema
{
  "type": "object",
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "email": { "type": "string", "format": "email" },
    "active": { "type": "boolean" }
  },
  "required": ["id", "email"]
}

Expected output

Generated sample record
{
  "id": "8b6f1a34-418f-4d4c-b967-0a8f2d7fd921",
  "email": "user@example.com",
  "active": true
}

How to do it

  1. Paste a JSON Schema.
  2. Choose the number of records to generate.
  3. Set optional-field behavior if available.
  4. Generate the sample data.
  5. Copy or download the fixture for tests.

Common mistakes

  • Using real customer exports as seed data.
  • Forgetting required fields when hand-writing examples.
  • Generating too many records for a browser-based fixture.
  • Assuming synthetic data validates business rules not present in the schema.

Related tools

FAQ

Is synthetic data the same as anonymized production data?

No. Synthetic data is generated from rules or schema shape and should not contain real user values.

Can I use generated records in automated tests?

Yes. They are useful as fixtures when your tests need structurally valid payloads.

Is the schema uploaded?

No. Generation runs locally in your browser.

Is my data uploaded anywhere?

No. This workflow runs locally in your browser unless you explicitly copy or share the result yourself.

This guide uses browser-local tooling. Avoid pasting production secrets unless you understand what the tool displays and shares.

Explore related tools

Continue with adjacent browser-based tools for the same workflow.

View data cleanup and QA tools →

Generate schema-shaped test data: quick answer

Use this when demos, QA fixtures, screenshots, or contract examples need realistic data without using production records. Paste or load the artifact into the linked tool, run the local check, then copy only the safe result or summary into your PR, ticket, or test notes.

What to verify

Review required fields, enum values, min/max constraints, nested arrays, and whether generated values could look like real secrets. If a result will be shared outside your team, run a privacy or secret scan first and replace real values with safe examples.

Recommended next steps

Practical example and expected result

Create browser-local sample records from a JSON Schema for demos, QA fixtures, and API contract testing without using real customer data. In practice, this is most useful when you need a quick, repeatable check on a JSON payload before adding it to a ticket, pull request, test fixture, or support note.

A realistic input for this workflow is { "type": "object", "properties": { "id": { "type": "string", "format": "uuid" }, "email": { "type": "string", "format": "email" }, "active".... The expected result should resemble { "id": "8b6f1a34-418f-4d4c-b967-0a8f2d7fd921", "email": "user@example.com", "active": true }, with the same important values preserved.

Troubleshooting checklist

  • Confirm you copied the complete JSON payload and not only a partial line or truncated preview.
  • Run the local tool once with a safe sample, then repeat with the real data only if your team policy allows it.
  • Check quoting, escaping, whitespace, encoding, timestamps, and environment-specific values before trusting the result.
  • Before sharing output, remove secrets, tokens, cookies, customer data, and production hostnames that are not needed for the review.

Next useful steps