Synthesized Platform Documentation
Transform, generate, and subset production data with privacy-preserving data transformations. Deploy in minutes, scale efficiently, maintain referential integrity.
Getting Started
5-Minute Quick Start
Get up and running in minutes
- Install with
docker-compose up - Run your first masking transformation
- See results in 5 minutes
Connect Data Sources
Connect your databases quickly
- PostgreSQL, MySQL, Oracle, SQL Server
- JDBC URL examples
- Required permissions
Create Your First Workflow
Build and execute workflows
- YAML configuration basics
- Choose transformers
- Execute workflow
Core Concepts
Learn the fundamentals
- How masking works
- How generation works
- How subsetting works
Guides by Use Case
🎭 Masking Data
Anonymize PII and sensitive data for non-production environments while preserving data utility and relationships.
- Mask specific column types (email, SSN, credit cards)
- 35+ built-in transformers
- Preserve referential integrity
- Incremental masking support
✨ Generating Data
Create realistic synthetic test data that preserves statistical distributions and referential integrity.
- Categorical, numerical, and temporal generators
- Custom distributions and probabilities
- Maintain foreign key relationships
- Generate millions of rows
✂️ Subsetting Data
Extract 10%, 25%, or 50% of production data for development and testing while maintaining referential integrity.
- Filter by date, ID range, or custom conditions
- Preserve foreign key relationships
- Combine with masking
- Target specific tables
Deployment & Operations
Reference & Advanced
Popular Topics
Need Help?
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💬 FAQ - Frequently asked questions
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🐛 Report Issue - Bug reports and feature requests
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📧 Support - Get help from our team