What is Synth?
Synth is an open-source data-as-code tool designed to generate consistent data efficiently and at scale. It provides a straightforward command-line interface (CLI) workflow, enabling users to anonymize sensitive production data effectively. The tool ensures the generated data maintains the characteristics and structure of the original production data, making it suitable for various testing and development purposes.
Furthermore, Synth facilitates the seeding of development, testing, and continuous integration environments with appropriate data fixtures. It also allows users to synthesize realistic data based on specific requirements, including constraints and relations, enabling the creation of data that accurately reflects desired scenarios. This capability is useful for generating data that tells a specific story or meets precise specifications for testing or demonstration purposes.
Furthermore, Synth facilitates the seeding of development, testing, and continuous integration environments with appropriate data fixtures. It also allows users to synthesize realistic data based on specific requirements, including constraints and relations, enabling the creation of data that accurately reflects desired scenarios. This capability is useful for generating data that tells a specific story or meets precise specifications for testing or demonstration purposes.
Features
- Data as Code: Define entire data models declaratively using Synth's configuration language.
- Easy Imports: Automatically create data models by importing directly from existing data sources.
- Database Agnostic: Compatible with both SQL and NoSQL databases, supporting semi-structured data.
- Semantic Data Types: Generate data for thousands of specific types like credit card numbers and email addresses.
- Anonymization: Create anonymized data that mirrors production data characteristics.
- Data Synthesis: Generate realistic data based on user-defined specifications, constraints, and relations.
Use Cases
- Anonymizing sensitive production data for testing or development.
- Seeding development and testing environments with realistic data.
- Populating continuous integration (CI) pipelines with consistent test data.
- Creating custom datasets with specific constraints and relationships for simulations or analysis.
- Generating test data fixtures for software development.
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