Synthetic Data Generator

Pricing

$3.00/month + usage

Synthetic Data Generator

Generate realistic fake data for testing and development. Create profiles, addresses, companies, and transactions using Faker. 50+ locales, deterministic mode, custom schemas.

Pricing

$3.00/month + usage

Rating

0.0

(0)

Developer

👁 Web Harvester

Web Harvester

Maintained by Community

Actor stats

Bookmarked

Total users

Monthly active users

5 months ago

Last modified

🎯 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.

🏪 Apify Store Listing

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

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URL: https://apify.com/web.harvester/synthetic-data-generator