Generate realistic JSON mock data for testing and development. Create fake users, addresses, names, emails, and more with customizable schemas and data types.
Generate realistic test data including names, emails, addresses, phone numbers, and custom data types with configurable templates and patterns.
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Built-in Templates
Pre-configured templates for common data structures like users, products, orders, and companies with realistic field relationships and constraints.
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Custom Schema Builder
Design custom data schemas with multiple data types, validation rules, and complex nested structures for specific testing requirements.
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Localized Data
Generate location-specific data including localized addresses, phone numbers, names, and cultural variations for international testing.
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Volume Control
Generate single objects or large arrays with configurable count, batch processing, and performance optimization for different testing scales.
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Data Relationships
Create related data with foreign key references, parent-child relationships, and referential integrity for comprehensive testing scenarios.
JSON Mock Data Generation Quick Guide
What it does:
Generates realistic, structured test data that mimics production patterns for development, testing, and demonstration purposes while maintaining privacy and data consistency.
Quick Start:
Choose a built-in template (users, products, orders) or create custom schema
Configure data generation options like count, localization, and relationships
Specify field types, constraints, and realistic value patterns
Click "Generate Mock Data" to create realistic test data
Download as JSON file or copy to clipboard for immediate use
Best for:
API Development: Create test endpoints with realistic response data
Frontend Development: Build UI components without backend dependencies
Performance Testing: Generate large datasets for scalability testing
Demo Environments: Create compelling demonstrations with realistic data
Privacy Protection: Replace sensitive data with synthetic alternatives
Pro Tips:
Use realistic constraints and patterns for believable test data
Generate related data with proper foreign key relationships
Include edge cases and boundary conditions in your schemas
Consider localization needs for international applications
{{firstName}}, {{lastName}}: Realistic personal names
{{email}}: Valid email addresses with various domains
{{integer(min, max)}}: Random numbers within range
{{randomChoice(array)}}: Pick from predefined options
{{boolean}}: Random true/false values
Step 3: Configure Relationships
// Related data with foreign keys
{
"users": [/* generated users */],
"orders": [
{
"id": "{{uuid}}",
"userId": "{{relatedId('users')}}",
"amount": "{{decimal(10, 1000)}}",
"items": "{{array(1, 5, 'orderItem')}}"
}
]
}
Step 4: Generate and Validate
Specify the number of records to generate
Choose output format and download options
Validate generated data meets your requirements
Test integration with your development workflow
Real-World Use Cases
🛒 E-commerce Development
Scenario: Building an online store without access to real product data or customer information.
Solution: Generate realistic product catalogs with prices, descriptions, categories, and customer profiles with order histories. Create varied pricing structures and inventory levels for comprehensive testing.
Result: Complete development and testing environment with realistic data patterns enabling thorough feature validation.
📱 Social Media Platform
Scenario: Developing social features requiring user interactions, posts, and engagement data.
Solution: Generate mock user profiles with realistic social connections, posts with engagement metrics, comments, and activity feeds. Create varied content types and interaction patterns.
Result: Rich test environment enabling development of recommendation algorithms, feed optimization, and social interaction features.
🏦 Financial Application Testing
Scenario: Testing banking software with transaction data while maintaining privacy compliance.
Solution: Generate synthetic transaction records, account information, and financial portfolios that maintain realistic patterns without using real customer data. Include various transaction types and account states.
Result: Comprehensive testing environment enabling validation of financial calculations, reporting, and analytics while ensuring privacy compliance.
Frequently Asked Questions
Q: How do I ensure mock data doesn't accidentally contain real information?
A: Use clearly synthetic templates, avoid real name/address databases, add obvious fake indicators like "test-" prefixes, and implement automated scanning for potentially real data patterns.
Q: How much mock data should I generate for testing?
A: Start with 10-100 records for unit tests, 1,000-10,000 for integration tests, and 100,000+ for performance testing. Scale based on your application's expected production volume.
Q: How do I maintain data consistency across related entities?
A: Generate parent entities first, then use their IDs for child entities. Implement referential integrity checks and use consistent generation seeds for reproducible relationships.
Q: Can I use mock data in production environments?
A: Mock data should only be used in development, testing, and demo environments. Never use mock data in production systems that handle real user data or business operations.
Q: How do I create realistic business logic in mock data?
A: Use conditional generation based on other field values, implement realistic constraints and relationships, study real data patterns, and include edge cases and boundary conditions in your schemas.