Soflab G.A.L.L.

Soflab G.A.L.L.

Soflab Technology Sp. z o.o.
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About

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.

About

The Soflab G.A.L.L. application is designed to anonymize sensitive data in non-production environments, enabling the generation of high-quality synthetic data that remains consistent with real data and supports reliable testing. At the same time, it ensures full protection of sensitive information, effectively preventing data leaks. Reduced data breach risk by replacing real data with artificial equivalents and detecting sensitive or erroneous records. Lower legal and financial exposure while protecting customer transactional data. Unified anonymization across non-production systems ensures a consistent data model and preserved production relationships. Synthetic data, generated from key production attributes, maintains statistical consistency for BI and AI. A central test data repository enables controlled reuse, lowers maintenance costs, accelerates deployments (up to 5 days), and supports simulation and reusable scenarios.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Companies and professionals seeking a solution to emulate and generate synthetic patterns for tabular data

Audience

All which have to deal with anonymization sensitive data in non-production environments and to ensure full protection of sensitive information by preventing data leaks.

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

Free
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

DataCebo
sdv.dev/

Company Information

Soflab Technology Sp. z o.o.
Founded: 2008
Poland
soflab.pl/en/

Alternatives

Alternatives

Subsalt

Subsalt

Subsalt Inc.

Categories

Categories

Integrations

Python

Integrations

Python
Claim DataCebo Synthetic Data Vault (SDV) and update features and information
Claim DataCebo Synthetic Data Vault (SDV) and update features and information
Claim Soflab G.A.L.L. and update features and information
Claim Soflab G.A.L.L. and update features and information