Automatically generate realistic test data with Faker.js integration
Mock data is realistic, randomly generated test data that simulates real database records. It allows you to develop and test your frontend without needing a working backend or database.
Mock API Builder uses Faker.js, a powerful library with 100+ data generators for creating realistic mock data across various categories:
• Street addresses
• Cities, states, countries
• ZIP codes
• Coordinates (lat/lng)
• Timezones
• Product names
• Prices, departments
• Colors, materials
• Product descriptions
• ISBN, barcodes
• Company names
• Catch phrases
• BS phrases
• Industry types
• Corporate suffixes
• Email addresses
• Usernames
• URLs, domains
• IP addresses
• User agents, emoji
• Past/future dates
• Recent/soon dates
• Birthdates
• Month, weekday names
• Timestamps
• Credit card numbers
• Account numbers
• Currency codes
• Bitcoin addresses
• IBAN, BIC codes
• Lorem ipsum text
• Paragraphs, sentences
• Words, lines
• Slugs
• Random text
Create your data structure with field names and types
The system automatically detects field purpose based on name
• "email" → faker.internet.email()
• "firstName" → faker.person.firstName()
• "city" → faker.location.city()
Choose how many records to generate (1 to 10,000+)
Data is generated and stored in your database
The system intelligently maps field names to appropriate Faker.js generators:
Field Name:
firstNameGenerated Value:
"John"Also detects: first_name, name, fullName, lastName
Field Name:
emailGenerated Value:
"john@example.com"Also detects: emailAddress, email_address
Field Name:
avatarGenerated Value:
"https://..."Also detects: image, photo, thumbnail, url, website
Field Name:
createdAtGenerated Value:
"2025-01-15T10:30:00Z"Also detects: updatedAt, birthdate, date, timestamp
Let the system automatically generate data based on your schema
Best for:
Specify exact Faker.js methods for precise control
Best for:
Manually create specific test cases or seed data
Best for:
Clear field names help auto-detection work better:
firstName,emailAddress,phoneNumber
data1,field2,value
Generate 10-20 records first to verify the structure, then scale up to hundreds or thousands.
Auto-generate most data, then manually create a few edge cases (empty fields, very long text, special characters).
After updating your schema, regenerate mock data to match the new structure and avoid inconsistencies.
Create multiple endpoints with different data scenarios:
/users - 100 normal users/users/empty - Empty array (edge case)/users/large - 10,000 users (performance)