Generating realistic, production-like data for development, testing, and CI/CD pipelines often requires exposing sensitive information or creating datasets manually, which is time-consuming and inconsistent. Existing data generation methods may lack the ability to accurately represent complex data models and relationships, leading to inadequate testing and potential production issues.

Synth is an open-source, data-as-code tool that enables users to generate consistent, scalable, and realistic datasets using a declarative configuration language. It allows defining data models as code, specifying constraints, relations, and semantic types to create anonymized data that mimics production environments without exposing sensitive information. Synth supports importing data from existing sources to automatically create accurate data models and is database agnostic, working with both SQL and NoSQL databases. The platform provides a CLI workflow for generating data and supports thousands of semantic data types, such as credit card numbers and email addresses.

Declarative configuration language for defining data models as code

CLI workflow for generating consistent data in a scalable way

Anonymization of sensitive production data

Support for generating time series and relational data

Easy import of data from existing sources

Database agnostic, supporting SQL and NoSQL databases

Generation of thousands of semantic data types

Synth is primarily targeted towards software developers, data engineers, and QA teams who need to generate realistic, anonymized data for development, testing, and continuous integration purposes.