Fake Data Generator

About the Random Data Generator

The Random Data Generator creates realistic synthetic datasets directly in your browser. Whether you need mock records for database seeding, QA testing, application demos, or frontend prototyping, this tool produces structured data with a single click – no server uploads and no account required.

Define your schema by adding fields for names, emails, phone numbers, dates, addresses, UUIDs, numeric ranges, and test credit card numbers. Configure each field’s options, set a row count up to 10,000, and export the result as JSON, CSV, or SQL insert statements.

Key Features

• Drag-and-Drop Schema Builder – Add, remove, rename, and reorder fields to match your target table or API payload.
• 11 Field Types – Row Number, First Name, Last Name, Full Name, Email, Phone, Date, Address, Number, UUID, and Credit Card.
• Per-Field Options – Set min/max ranges with decimal precision for numbers, choose date presets or custom ranges, pick UUID v1 or v4, and select Visa, Mastercard, or Amex card brands.
• Blank Rate Control – Introduce intentional null values at a configurable percentage per field for realistic data quality testing.
• Live Preview – See a table preview of the first 12 rows alongside the full raw output before copying or downloading.
• Three Export Formats – JSON, CSV (with optional header row), and SQL INSERT statements with a configurable table name.
• 100% Client-Side – All generation happens in your browser. No data ever leaves your machine.

How It Works

The generator uses purely client-side JavaScript to produce synthetic values. Name fields draw from curated first-name and last-name pools. Email addresses combine name slugs with test-safe domains like example.com and testmail.dev. Phone numbers follow the US format with randomized area codes and line numbers. Dates resolve against preset windows (past month, past year, next month, next year) or a custom start/end range. Addresses combine street numbers, names, types, and randomly selected US city/state/zip tuples. Credit card numbers use realistic brand prefixes and pass the Luhn checksum algorithm, making them structurally valid for test gateways without being real card numbers.

Once the schema and row count are defined, the tool produces all rows in a single pass, then formats the output to JSON, CSV (via PapaParse), or parameterized SQL INSERT statements with properly escaped identifiers and values.

How to Use the Random Data Generator

• Step 1: Review the default schema or click “Reset” to restore the starter template with common fields.
• Step 2: Add fields via the “Add Field” button, which opens a searchable picker organized by category.
• Step 3: Rename field keys to match your database column names, select the desired type, and configure field-specific options (ranges, formats, brands).
• Step 4: Set the row count (1–10,000), choose an output format (JSON, CSV, or SQL), and optionally adjust the SQL table name or toggle the CSV header row.
• Step 5: Click “Generate” to produce the dataset, then preview the table, copy the raw output to your clipboard, or download the file.

Common Use Cases

• Database Seeding – Generate SQL INSERT statements or CSV files to populate development and staging databases with realistic test records.
• API Prototyping – Export JSON arrays to serve as mock API responses while backend endpoints are still in development.
• QA and Load Testing – Create thousands of rows to stress-test search filters, pagination, sorting, and data-export features.
• UI and Frontend Development – Feed generated data into table components, charts, or form auto-fill to evaluate layout, responsive behavior, and edge-case handling.
• Demo and Presentation Data – Quickly build convincing sample datasets for product demos, sales decks, and stakeholder reviews without exposing real customer information.
• Data Privacy Compliance – Replace production data with synthetic equivalents when sharing datasets across teams or with external partners.

Best Practices

• Match Schema to Target – Rename field keys to exactly match your database columns or API contract to avoid mapping errors during import.
• Use Realistic Row Counts – Start with a small count (50–100 rows) to validate the schema, then scale up to thousands for load testing.
• Test with Nulls – Set a small blank rate (5–10%) on optional fields to verify your application handles missing data gracefully.
• Never Use Test Cards in Production – Generated credit card numbers are structurally valid but entirely fake. Use them only in sandbox or test-mode payment gateways.
• Validate SQL Before Importing – Review the generated INSERT statements and ensure the table name and column order match your target schema.

Frequently Asked Questions

How many rows can I generate at once?

Up to 10,000 rows per generation. The tool runs entirely in your browser, so performance depends on your device.

Is the generated data truly random?

Values are pseudo-random, drawn from curated pools for names, cities, and domains to ensure realistic output. Numeric and date fields use JavaScript’s Math.random within configured ranges.

Are the generated credit card numbers real?

No. They follow valid brand prefixes and pass the Luhn checksum, but they are not connected to any real account. Use them only for testing.

Is my data sent to a server?

No. All processing runs locally in your browser. Nothing is uploaded or stored remotely.

Can I generate data with custom columns?

Yes. Add any combination of the 11 supported field types and rename the keys to whatever your project needs.

What is the blank rate setting?

It controls the percentage chance that a field value will be null instead of a generated value, useful for testing how your application handles missing or incomplete data.

Can I reorder the columns?

Yes. Drag and drop fields in the schema builder to change column order. The export respects the field order you set.

Related Tools

You might also find these tools useful:

• CSV Editor — Edit and transform your generated CSV data.
• Run SQL on CSV — Query your generated data with SQL syntax.
• Diff Checker — Compare different data generation outputs.