AI TOOL PROFILE
Synthesized Review: Test Data Management Software
- Software Development
- Test Data Generation
- Software development companies
- Enterprise IT departments
- QA and testing teams
- Organizations with high data privacy requirements
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
At a glance
- Best for
- Software development companies, Enterprise IT departments, QA and testing teams, Organizations with high data privacy requirements
- Key use cases
- Privacy-Preserving Testing, CI/CD Pipeline Integration, Reducing Test Environment Size, Enterprise App Validation
- Integrations
- AWS, Azure, GCP, Docker, Kubernetes
- Official website
- Visit synthesized official website
How AI is used
Synthesized is a Test Data Management (TDM) platform designed to provide development and QA teams with high-fidelity, production-like data. Instead of using real production databases, which may pose security and compliance risks, the tool uses AI to generate synthetic datasets that mirror the statistical properties of the original data.
The platform supports various database environments, including SAP HANA, Oracle, and SQL Server. It provides data operations such as subsetting for smaller test environments and PII masking to protect sensitive information.
Buyers should note that the platform is designed for technical environments, utilizing YAML configurations and integrating into CI/CD pipelines. Organizations should confirm if their specific database versions and cloud infrastructure align with the supported deployment options.
Key Features
Generative AI Data Generation
Creates synthetic datasets that mirror original data without containing real individual records.
PII Masking
Identifies and masks personally identifiable information to support regulatory standards like GDPR and HIPAA.
Data Subsetting
Extracts specific portions of production data for use in smaller development or testing environments.
Referential Integrity Preservation
Maintains the relationships and foreign key links between tables across generated or masked datasets.
Data as Code
Uses YAML configurations and Python DSL to define data requirements and transformation jobs.
Use Cases
Privacy-Preserving Testing
Creating synthetic versions of production databases to allow testing and analysis without exposing real user data.
CI/CD Pipeline Integration
Automating the provisioning of test data as part of a continuous integration and deployment workflow.
Reducing Test Environment Size
Using subsetting to create smaller, role-specific datasets for faster deployment than full databases.
Enterprise App Validation
Generating compliant test data for complex environments such as SAP S/4HANA and Oracle Fusion.
Integrations
- AWS
- Azure
- GCP
- Docker
- Kubernetes
- GitHub Actions
FAQ
What does Synthesized do?
- It uses generative AI to create synthetic datasets that mirror production data without containing real individual records, which helps in testing without risking privacy breaches.
Which databases are supported by Synthesized?
- The platform supports several databases including SAP HANA, PostgreSQL, SQL Server, Oracle, DB2, and MySQL.
How is the software deployed?
- Synthesized can be deployed using Docker Compose or Kubernetes (via Helm charts), and it is compatible with AWS, Azure, and GCP environments.
Source category: Software Development
Source subcategory: Test Data Generation
More tools in Software Development
Other published listings in the Software Development category.
More tools in the Test Data Generation software type
Related listings that share the same software type for comparison and shortlisting.
