Mock Data Generator 🎲

Realistic, configurable test data — users, companies, products, addresses, custom schemas

Every developer needs realistic test data — for prototyping UIs, seeding databases, generating API test payloads, or creating convincing demo environments. Manually crafting hundreds of records with realistic names, emails, addresses, and product data is tedious and inconsistent. Mock Data Generator produces structured, lifelike records using the Faker library, supporting five built-in record types and fully custom schemas with typed fields and range constraints.

Generate up to 1,000 records at a time in JSON or CSV format, with deterministic seeding so the same input always produces identical output across environments and CI runs. Batch mode lets you generate multiple record types in a single execution.

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✨ Features

• Five built-in record types — User, Company, Product, Address, and Custom — each with 10–15 realistic fields covering names, emails, phones, addresses, prices, ratings, and more

• Custom schemas — Define your own record structure with 8 field types: string, integer, float, boolean, uuid, email, phone, and date; each field supports min/max constraints for numeric types

• High volume generation — Generate up to 1,000 records at a time, suitable for database seeding, load testing, and large-scale API test data

• Realistic data generation — Powered by the Faker library: names, company names, catch phrases, addresses, phone numbers, emails, URLs, and ISO dates all follow real-world distribution patterns

• Deterministic output — Same input parameters produce identical output every time via MD5-seeded Faker initialization, enabling reproducible test data across environments and CI/CD pipelines

• Multiple output formats — JSON for programmatic consumption (APIs, database inserts) or CSV for spreadsheet import, data analysis, and bulk loading

• Batch mode — Generate multiple record types in a single run with independent configurations per batch, including different counts, formats, and custom schemas

• Sequential IDs — Every record receives a unique, human-readable ID like usr_000001, prd_000042, or cst_000100 for easy cross-referencing

• Comprehensive summaries — Every run appends a summary row with total records generated, per-type breakdown, and error count

🚀 Quick Start

Generate 10 User Records

{
  "recordType": "user",
  "count": 10,
  "outputFormat": "json"
}

Response example (user record):

{
  "record_type": "user",
  "id": "usr_000001",
  "name": "Sarah Johnson",
  "email": "sarah.johnson@example.com",
  "phone": "555-0123-4567",
  "username": "sarahj",
  "job": "Software Engineer",
  "company": "Acme Corp",
  "address": "123 Main Street, Springfield, IL 62701",
  "city": "Springfield",
  "country": "United States",
  "zip_code": "62701",
  "website": "https://www.sarahjohnson.dev",
  "created_at": "2025-11-15T08:30:00+00:00"
}

Generate 50 Products as CSV

{
  "recordType": "product",
  "count": 50,
  "outputFormat": "csv"
}

Custom Schema — Mixed Types

{
  "recordType": "custom",
  "customFields": [
    { "name": "id", "type": "uuid" },
    { "name": "score", "type": "integer", "min": 0, "max": 100 },
    { "name": "active", "type": "boolean" },
    { "name": "email", "type": "email" },
    { "name": "amount", "type": "float", "min": 10.0, "max": 999.99 }
  ],
  "count": 20,
  "outputFormat": "json"
}

Response example (custom record):

{
  "record_type": "custom",
  "id": "cst_000001",
  "uuid": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "score": 73,
  "active": true,
  "email": "user@example.net",
  "amount": 456.78
}

Generate Company Records

{
  "recordType": "company",
  "count": 5,
  "outputFormat": "json"
}

Response example (company record):

{
  "record_type": "company",
  "id": "cmp_000001",
  "name": "Globex Corporation",
  "suffix": "Inc",
  "catch_phrase": "Synergizing enterprise solutions",
  "bs": "Innovate next-generation platforms",
  "email": "info@globexcorp.com",
  "phone": "555-987-6543",
  "address": "456 Oak Avenue, Portland, OR 97201",
  "city": "Portland",
  "country": "United States",
  "zip_code": "97201",
  "website": "https://www.globexcorp.com",
  "industry": "Information Technology",
  "employee_count": 1250,
  "founded_year": 2012
}

Batch Mode — Multiple Record Types

{
  "batchMode": true,
  "batchData": [
    { "recordType": "user", "count": 5, "outputFormat": "json" },
    { "recordType": "company", "count": 3, "outputFormat": "json" },
    { "recordType": "product", "count": 15, "outputFormat": "csv" }
  ]
}

📋 Input Parameters

Parameter	Type	Default	Description
recordType	string	"user"	Type of data to generate: user, company, product, address, or custom
count	integer	10	Number of records to generate (1–1000). Clamped to valid range.
outputFormat	string	"json"	Output format: json for structured data or csv for spreadsheet-compatible output
customFields	array	[]	Custom field definitions for the custom record type. Each field: {name, type, min?, max?}. Ignored for built-in record types.
batchMode	boolean	false	Enable batch processing for multiple independent record generation jobs in a single run
batchData	array	[]	Array of per-job input objects, each with its own recordType, count, outputFormat, and optionally customFields

Custom Field Types Reference

Type	Description	Min/Max	Example Output
string	Random word	—	"synthesize"
integer	Random integer in range	✅ min / max	73
float	Random float in range (4 decimal places)	✅ min / max	456.7800
boolean	True or false	—	true
uuid	Random UUID v4	—	"a1b2c3d4-e5f6-7890-abcd-ef1234567890"
email	Realistic email address	—	"user@example.net"
phone	Phone number (international format)	—	"+1-555-0123-4567"
date	Random date (YYYY-MM-DD)	—	"2024-06-15"

📤 Output Format

Each generated record is pushed as a separate dataset item with fields appropriate to the record type:

User Records

Field	Type	Description
record_type	string	Always "user"
id	string	Sequential ID: usr_000001, usr_000002, ...
name	string	Full name (first + last)
email	string	Realistic email address
phone	string	International phone number
username	string	Username derived from name
job	string	Job title
company	string	Company name
address	string	Full address (one line)
city	string	City name
country	string	Country name
zip_code	string	Postal/ZIP code
website	string	Personal or work URL
created_at	string	ISO 8601 timestamp

Company Records

Field	Type	Description
record_type	string	Always "company"
id	string	Sequential ID: cmp_000001, cmp_000002, ...
name	string	Company name
suffix	string	Legal suffix (Inc, LLC, Ltd, etc.)
catch_phrase	string	Marketing catch phrase
bs	string	Business jargon phrase
email	string	Company email address
phone	string	Phone number
address	string	Full address (one line)
city	string	City
country	string	Country
zip_code	string	Postal code
website	string	Company website URL
industry	string	Industry/sector
employee_count	integer	Employee count (10–50,000)
founded_year	string	Year founded

Product Records

Field	Type	Description
record_type	string	Always "product"
id	string	Sequential ID: prd_000001, prd_000002, ...
name	string	Product name
description	string	Short product description (up to 200 chars)
price	float	Price in USD
currency	string	Currency code (USD)
category	string	Product category
brand	string	Brand name
sku	string	Stock keeping unit (8-char uppercase)
in_stock	boolean	Stock availability (80% chance true)
rating	float	Customer rating (1.0–5.0)
reviews_count	integer	Number of reviews (0–5000)
created_at	string	ISO 8601 timestamp

Address Records

Field	Type	Description
record_type	string	Always "address"
id	string	Sequential ID: adr_000001, adr_000002, ...
street	string	Street address
city	string	City
state	string	State/province full name
state_abbr	string	State abbreviation
zip_code	string	Postal/ZIP code
country	string	Country name
country_code	string	Two-letter country code
latitude	float	Latitude coordinate
longitude	float	Longitude coordinate
timezone	string	Timezone identifier
full_address	string	Complete address on one line

Custom Records

User-defined fields as specified in the customFields input parameter, plus record_type: "custom" and a sequential ID prefixed with cst_.

Summary Row

A _summary row is appended at the end with aggregate statistics:

Field	Type	Description
_summary	boolean	Always true for the summary row
total_records_generated	integer	Total number of records pushed to the dataset
record_type_breakdown	object	Per-type counts (e.g., {"user": 10, "company": 5})
error_count	integer	Number of generation jobs that failed

⚙️ Technical Details

Deterministic Seeding

The actor generates an MD5 hash from the concatenation of recordType, count, and a sorted JSON representation of customFields. This hash is used to seed the Faker random generator, ensuring that identical inputs always produce identical outputs. This is critical for:

• Reproducible test data across CI/CD pipeline runs
• Consistent database seeds across development and staging environments
• Regression testing where input → output mapping must remain stable

Record Type Generators

Each built-in record type has a dedicated generator function that produces 10–15 realistic fields:

• User: Uses fake.name(), fake.email(), fake.job(), fake.company(), fake.address(), fake.city(), fake.country(), fake.url(), fake.iso8601() and more
• Company: Uses fake.company(), fake.catch_phrase(), fake.bs(), fake.company_email(), fake.year(), and fake.random_int()
• Product: Uses fake.catch_phrase() for names, fake.text() for descriptions, fake.boolean() for stock status, and fake.random.uniform() for ratings
• Address: Uses fake.street_address(), fake.city(), fake.state(), fake.country_code(), fake.latitude(), fake.longitude(), and fake.timezone()
• Custom: Iterates over user-defined field definitions, dispatching to the appropriate Faker method for each field type

Volume and Performance

Generation is CPU-bound rather than network-bound. For 1,000 records of a single type, expect 3–10 seconds of processing time. Custom schemas with many fields may add marginal overhead. The apify-default-dataset-item charge event is fired per-record to cover dataset storage costs.

🎯 Use Cases

• Frontend prototyping — Populate UI components, tables, dashboards, and data visualizations with realistic user and product data before backend API integration is complete

• Database seeding — Fill development and staging databases with structured test data that mirrors production patterns, complete with realistic distributions across all columns

• API testing — Generate varied payloads for integration tests, contract tests, and load testing; deterministic seeding ensures tests are reproducible across environments and CI runs

• Demo environment creation — Create convincing demo data for sales presentations, trade show booths, and client walkthroughs with realistic company names, product catalogs, and customer records

• Schema validation — Test data model boundaries by generating edge-case records: minimum/maximum values, different field combinations, and high-volume cardinality checks

• Performance benchmarking — Generate large datasets (1,000 records) to benchmark database query performance, API response times, and rendering pipeline efficiency under load

❓ FAQ & Troubleshooting

Q: Are the generated records guaranteed to be unique? A: Each record gets a unique sequential ID, but the underlying Faker values (names, emails, etc.) follow probabilistic distributions — duplicates are unlikely but not mathematically guaranteed. For strict uniqueness, generate records with count set to your exact requirement.

Q: Why do I get the same records every time? A: That's by design. The actor uses deterministic seeding — the same input always produces the same output. Change any input parameter (recordType, count, customFields) to get different data. For truly random output across runs, change the count or add a field.

Q: Can I generate records localized to a specific country? A: The current implementation uses the default Faker locale (en_US), producing English-language data with US-format addresses and phone numbers. Localized locales can be added in future versions.

Q: What happens if I set count to 0 or a negative number? A: The count is clamped to the range 1–1000. A value of 0 or negative defaults to 1 record.

Q: Is custom schema a premium feature? A: Yes. Using recordType: "custom" triggers a premium charge event (custom-schema). Built-in record types (user, company, product, address) use the standard charge model without additional events.

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