Reusable YAML configurations to create realistic production-like databases easily integrated into CI/CD pipelines.
Right-sized database creation
Automatically meet data quality requirements with data generation, subsetting, and masking for complex databases while preserving referential integrity and database logic.
"Data as Code" approach enables you to codify complex compliance requirements into concrete data transformations.
Optimised test data coverage
Synthesized helps to measure test data coverage of your test cases via static SQL analysis and enables to generate optimised test data with 100% coverage.
Data quality automation
Automate high-quality data creation using machine learning and common workflows.
Database generation preserving distributions in a table and cross tables. Preserve primary and foreign key relationships and configure non-explicit relationships. Preserve logical relationships across tables including constraints, procedures, views, sequences and more.
Configure parameters on a database level such as the size ratio of the synthesized database. Mimic business rules and logic for specific generated tables or columns. Synthesize data for only selected columns in a table and use existing data for other columns.