SDV Enterprise, by DataCebo
Welcome! If you are here, you have bought a license to access the SDV Enterprise, the enterprise edition of the Synthetic Data Vault. Or you are considering purchasing a license to SDV Enterprise.
We have created this documentation specifically for our enterprise users, covering a range of topics including on-prem installation of our package, technical requirements for SDV Enterprise, access to our enterprise-only forum, ways to contact us, and how to request enterprise-specific features.
Quick Links
On this page, you'll find the technical and architectural requirements for installing SDV Enterprise, including supported operating systems, Python versions, and system configurations.
On this page, you'll find the instructions to install the SDV Enterprise package, verify and debug any installation issues.
This page outlines the industry-standard security checks our team performs before releasing and distributing the SDV Enterprise package, along with instructions on how to access the security scan results from within in your package.
This page provides a comprehensive list of all DataCebo packages, as well as commercially permissible third-party open source packages, included within the SDV Enterprise.
This page provides release notes from all of our past releases.
SDV Enterprise is released on a regular schedule, with release dates predetermined to ensure greater transparency. This page provides the release dates for the current year.
This page contains the information required to join our enterprise-users-only forum, along with guidelines for obtaining technical support from the DataCebo team.
If you are executing a proof-of-concept for your project using SDV Enterprise, this page outlines the necessary preparatory steps.
About DataCebo
The Synthetic Data Vault Project, first created at MIT's Data to AI Lab in 2016. After 4 years of research and traction with enterprise, we created DataCebo in 2020 with the goal of growing the project.
Today, DataCebo is the proud developer and maintainer of the SDV, the largest open source ecosystem for synthetic data generation & evaluation.
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