Synthetic Data Generator
PROGenerate realistic synthetic datasets for testing, AI model training, and privacy-compliant data sharing with configurable distributions, correlations, and domain templates.
Example Usage
Generate a synthetic e-commerce dataset with 5,000 records for testing a recommendation engine:
Domain: E-commerce Tables needed: customers, orders, order_items, products Requirements:
- Customer ages should follow a normal distribution centered at 35
- Order values should be right-skewed with a long tail
- 70% of customers should have 1-3 orders, 20% should have 4-10, 10% should have 10+
- Products should span 5 categories with realistic price ranges
- Include seasonal purchasing patterns (holiday spikes in Nov-Dec)
- Fully anonymized with no real PII
- Output as CSV files with a SQL schema file
Please generate the data with statistical validation summary.
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Suggested Customization
| Description | Default | Your Value |
|---|---|---|
| The business domain for the synthetic dataset (e-commerce, healthcare, finance, HR, SaaS) | e-commerce | |
| Number of records to generate in the dataset | 1000 | |
| Output format for the generated data (CSV, JSON, SQL, Parquet) | CSV | |
| Privacy level for generated data (fully anonymized, pseudonymized, realistic PII) | fully anonymized | |
| Statistical distribution model (realistic, uniform, normal, skewed, custom) | realistic |
Generate realistic synthetic datasets for testing, AI model training, and privacy-compliant data sharing. This premium skill supports configurable distributions, domain-specific templates, correlation preservation, and full privacy compliance validation.
What You’ll Get
- Domain-specific schema with realistic field types and relationships
- Configurable statistical distributions for every field
- Correlation preservation between related fields
- Privacy-compliant data generation (GDPR, HIPAA, CCPA)
- Edge case injection for robust testing
- Statistical validation report confirming data quality
- Multiple output formats (CSV, JSON, SQL, Parquet)
Ideal For
- Testing database applications without exposing real customer data
- Training machine learning models when real data is limited or restricted
- Sharing datasets across teams without privacy risk
- Building analytics dashboards with realistic demonstration data
- Load testing with production-scale synthetic workloads
Research Sources
This skill was built using research from these authoritative sources:
- Synthetic Data Vault Documentation Official documentation for the Synthetic Data Vault library covering data modeling, generation, and evaluation
- Gretel.ai Synthetic Data Guide Comprehensive guide to synthetic data generation techniques, privacy guarantees, and enterprise use cases
- MIT Sloan: AI and Data Science Trends 2026 MIT research on synthetic data as a critical enabler for AI model development and data democratization
- NIST Privacy Framework National Institute of Standards and Technology framework for managing privacy risk in data generation and sharing
- Google Research: Synthetic Data for ML Google's research publications on using synthetic data to improve machine learning model training and fairness
- Gartner: Synthetic Data Market Analysis Gartner's analysis of the synthetic data market projecting 60% of AI training data will be synthetic by 2030