Synthetic Data Generator

Generate realistic test datasets with customizable properties

Dataset Configuration

Number of Rows:

Number of Columns:

Use Preset Schema (Customer Data)

Column Configuration

Options

Include Headers Include Missing Values (~5%) Use Random Seed

Help

Data Types

Integer: Whole numbers (e.g., 1, 42, 100)
Float: Decimal numbers (e.g., 3.14, 42.5, 99.99)
String: Text values (random alphanumeric strings)
Name: Realistic person names
Email: Valid email addresses
Date: Random dates within a range
Boolean: True/False values
Category: Random selection from predefined categories
UUID: Unique identifiers

Distributions

Uniform: Equal probability across range
Normal: Bell curve distribution (requires mean and std dev)
Exponential: Decay distribution (requires lambda)
Poisson: Count data distribution (requires lambda)
Binomial: Success/failure distribution (requires n and p)

Use Cases

Testing: Generate test data for application development
Demos: Create sample datasets for presentations
Privacy: Replace sensitive data with synthetic data
Training: Generate data for machine learning experiments
Prototyping: Mock data for UI/UX development

Preset Schema

The customer preset generates a realistic customer dataset with:

Customer ID (UUID)
Name (Realistic person names)
Email (Valid email addresses)
Age (Normally distributed, mean=40, std=15)
Join Date (Random dates from 2020-2024)
Purchase Amount (Exponentially distributed)
Category (Bronze, Silver, Gold, Platinum)
Is Active (Boolean with 80% true rate)

Export Formats

CSV: Comma-separated values, compatible with Excel and most tools
JSON: JavaScript Object Notation, array of objects format
Copy: Copy current view to clipboard for pasting elsewhere