Windocks
Windocks is a leader in cloud native database DevOps, recognized by Gartner as a Cool Vendor, and as an innovator by Bloor research in Test Data Management. Novartis, DriveTime, American Family Insurance, and other enterprises rely on Windocks for on-demand database environments for development, testing, and DevOps. Windocks software is easily downloaded for evaluation on standard Linux and Windows servers, for use on-premises or cloud, and for data delivery of SQL Server, Oracle, PostgreSQL, and MySQL to Docker containers or conventional database instances.
Windocks database orchestration allows for code-free end to end automated delivery. This includes masking, synthetic data, Git operations and access controls, as well as secrets management.
Windocks can be installed on standard Linux or Windows servers in minutes. It can also run on any public cloud infrastructure or on-premise infrastructure. One VM can host up 50 concurrent database environments.
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DATPROF
DATPROF Test Data Platform is a complete test data management solution that helps software teams create, protect, provision, and automate high-quality test data. The platform combines data masking, synthetic test data generation, data subsetting, test data provisioning, and automation in one integrated solution.
DATPROF enables organizations to safely use realistic, production-like data for development, testing, QA, and CI/CD pipelines without exposing sensitive or privacy-related information. It helps companies comply with regulations such as GDPR, PCI, and HIPAA while improving software delivery speed and reducing manual test data work.
DATPROF is a software company specialized in test data management. Its mission is to help organizations make test data available faster, safer, and more efficiently, especially in complex enterprise and regulated environments.
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YData
Adopting data-centric AI has never been easier with automated data quality profiling and synthetic data generation. We help data scientists to unlock data's full potential. YData Fabric empowers users to easily understand and manage data assets, synthetic data for fast data access, and pipelines for iterative and scalable flows. Better data, and more reliable models delivered at scale. Automate data profiling for simple and fast exploratory data analysis. Upload and connect to your datasets through an easily configurable interface. Generate synthetic data that mimics the statistical properties and behavior of the real data. Protect your sensitive data, augment your datasets, and improve the efficiency of your models by replacing real data or enriching it with synthetic data. Refine and improve processes with pipelines, consume the data, clean it, transform your data, and work its quality to boost machine learning models' performance.
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DataCebo Synthetic Data Vault (SDV)
The Synthetic Data Vault (SDV) is a Python library designed to be your one-stop shop for creating tabular synthetic data. The SDV uses a variety of machine learning algorithms to learn patterns from your real data and emulate them in synthetic data. The SDV offers multiple models, ranging from classical statistical methods (GaussianCopula) to deep learning methods (CTGAN). Generate data for single tables, multiple connected tables, or sequential tables. Compare the synthetic data to the real data against a variety of measures. Diagnose problems and generate a quality report to get more insights. Control data processing to improve the quality of synthetic data, choose from different types of anonymization, and define business rules in the form of logical constraints. Use synthetic data in place of real data for added protection, or use it in addition to your real data as an enhancement. The SDV is an overall ecosystem for synthetic data models, benchmarks, and metrics.
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