Synthesized
UnclaimedAutomate enterprise-grade test data provisioning with AI for faster, compliant software releases.
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TL;DR - Synthesized
- Automates production-like test data generation using AI.
- Ensures data compliance and security through intelligent masking and subsetting.
- Accelerates development cycles and reduces costs by streamlining test data provisioning.
Pricing: Paid only
Best for: Enterprises & pros
Pros & Cons
Pros
- Significantly reduces manual effort and time in test data provisioning.
- Enhances data security and regulatory compliance with codified masking policies.
- Accelerates software release cycles by providing reliable and up-to-date test data.
- Reduces costs associated with application development and testing lifecycles.
- Improves test coverage and helps identify bugs earlier in the development process.
Cons
- Requires integration into existing development and testing stacks.
- Initial setup and configuration may require technical expertise.
Preview
Key Features
AI-driven data generation for diverse datasetsIntelligent data masking for sensitive informationData subsetting for role-specific accessCloud-native test data provisioning running on KubernetesIntegration with CI/CD pipelines and testing frameworks"Data as Code" approach for codifying compliance requirementsSupport for PostgreSQL, SQL Server, Oracle, and SalesforceVisual editor and CLI for workflow creation
Pricing
Paid
Synthesized offers paid plans. Visit their website for current pricing details.
What is Synthesized?
Synthesized is an AI-powered platform designed to automate the creation, management, and provisioning of production-like test data. It addresses the critical need for high-quality, compliant, and readily available test data to accelerate development cycles and improve software quality. The platform utilizes generative AI (GenAI) and Large Language Models (LLMs) to generate diverse, realistic datasets, intelligently mask sensitive information, and subset large databases, ensuring data privacy and regulatory compliance.
This solution is ideal for engineering, QA, and DevOps teams in enterprises that require robust test data management. It helps unblock deployment pipelines, reduce manual testing efforts, and minimize compliance risks by providing a "Data as Code" approach. By integrating with CI/CD pipelines and supporting various databases like PostgreSQL, SQL Server, Oracle, and Salesforce, Synthesized enables faster bug identification, more stable releases, and efficient cloud/application migrations.
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Synthesized FAQ
How does Synthesized ensure test data compliance with regulations?
Synthesized encodes compliance rules directly into masking policies, which are then applied to test data. This 'Data as Code' approach ensures that sensitive information is protected according to regulatory standards, minimizing the risk of breaches and legal liabilities.
What types of databases and applications does Synthesized support for test data generation?
Synthesized provides comprehensive support for major databases and applications, including PostgreSQL, SQL Server, Oracle, and Salesforce, enabling automated test data provisioning across various enterprise environments.
Can Synthesized integrate with existing CI/CD pipelines?
Yes, Synthesized is designed for seamless integration with CI/CD pipelines. It allows for automated test data provisioning as part of continuous integration and delivery workflows, ensuring that development and testing teams always have access to current and compliant data.
How does Synthesized help in accelerating cloud and application migration?
Synthesized acts as a unified test data platform that breaks down data silos, providing a consistent approach to test data management. This streamlines the process of preparing and provisioning data for new cloud environments or migrated applications, ensuring stability and reliability during transitions.
What is the 'Data as Code' approach and how does it benefit users?
The 'Data as Code' approach allows users to codify complex compliance requirements into concrete data transformations using YAML configurations or Python DSL. This ensures that test data is automatically kept up-to-date, compliant, and as close to production as possible, reducing manual effort and potential errors.
Source: synthesized.io