Synthetic Data Generator

🎭 Generate realistic fake data for testing, development, and demos. Create thousands of profiles, addresses, companies, or transactions instantly using Faker.

🎯 What This Actor Does

Generate unlimited realistic fake data without manual creation:

• User Profiles - Names, emails, phones, addresses, jobs
• Companies - Business names, industries, contact info
• Transactions - Payment records, amounts, statuses
• Products - E-commerce items with prices, SKUs
• Custom Schemas - Define your own data structure

🚀 Use Cases

Use Case	Description
Testing	Populate databases with realistic test data
Development	Build UIs with sample data before backend is ready
Demos	Create compelling product demos with realistic data
Load Testing	Generate thousands of records for stress tests
Privacy	Replace real customer data with fake equivalents
Training	Create datasets for ML model development

📥 Input Examples

Generate 1000 User Profiles

{
    "count": 1000,
    "preset": "profiles",
    "locale": "en_US"
}

Generate German Companies

{
    "count": 500,
    "preset": "companies",
    "locale": "de_DE"
}

Custom Schema

{
    "count": 100,
    "preset": "custom",
    "schema": {
        "order_id": "uuid4",
        "customer_name": "name",
        "product": "catch_phrase",
        "quantity": "random_int(1, 10)",
        "total": "pyfloat(min_value=10, max_value=500, right_digits=2)",
        "status": "random_element(['pending', 'shipped', 'delivered'])"
    }
}

⚙️ Configuration

Parameter	Type	Default	Description
count	integer	100	Number of records (1-100,000)
locale	string	en_US	Language/region for data
preset	string	profiles	Data template to use
seed	integer	-	For reproducible results
schema	object	-	Custom field definitions

Available Presets

Preset	Fields
profiles	name, email, phone, address, job, company
companies	name, industry, address, phone, website, employees
addresses	street, city, state, postal_code, country, lat/lng
transactions	id, amount, merchant, category, timestamp, status
products	id, name, description, price, category, sku, rating
users	id, username, email, created_at, role, is_active

Supported Locales

🇺🇸 en_US, 🇬🇧 en_GB, 🇩🇪 de_DE, 🇫🇷 fr_FR, 🇪🇸 es_ES, 🇮🇹 it_IT, 🇧🇷 pt_BR, 🇯🇵 ja_JP, 🇨🇳 zh_CN, 🇰🇷 ko_KR, 🇷🇺 ru_RU, 🇸🇦 ar_SA, 🇮🇳 hi_IN, 🇳🇱 nl_NL, 🇵🇱 pl_PL, 🇸🇪 sv_SE, 🇩🇰 da_DK, 🇫🇮 fi_FI, 🇳🇴 no_NO, 🇹🇷 tr_TR

📤 Output

{
    "_index": 1,
    "first_name": "John",
    "last_name": "Smith",
    "email": "john.smith@example.com",
    "phone": "+1-555-123-4567",
    "birth_date": "1985-03-15",
    "address": "123 Main St, Apt 4B",
    "city": "New York",
    "country": "United States",
    "job": "Software Engineer",
    "company": "Tech Corp"
}

🎨 Custom Schema Field Types

Use any Faker provider:

{
    "name": "name",
    "email": "email",
    "phone": "phone_number",
    "address": "address",
    "company": "company",
    "job": "job",
    "text": "paragraph",
    "date": "date",
    "time": "time",
    "uuid": "uuid4",
    "url": "url",
    "ip": "ipv4",
    "price": "pricetag",
    "boolean": "boolean",
    "color": "color_name",
    "country": "country",
    "currency": "currency_code",
    "credit_card": "credit_card_number",
    "iban": "iban",
    "random_number": "random_int(1, 100)",
    "random_choice": "random_element(['A', 'B', 'C'])"
}

💰 Cost Estimation

Records	Approx. Time	Compute Units
1,000	~2 seconds	~0.001
10,000	~5 seconds	~0.003
100,000	~30 seconds	~0.02

🔧 Technical Details

• Language: Python 3.12
• Library: Faker 24.x
• Memory: 128MB-512MB
• Speed: ~50,000 records/second

📄 License

MIT License - see LICENSE for details.

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Keywords: fake data generator, synthetic data, test data, Faker, mock data, sample data, dummy data, data generation, testing data, development data, random data generator