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features
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Description

The Synthetic Data Vault (SDV) is a comprehensive Python library crafted for generating synthetic tabular data with ease. It employs various machine learning techniques to capture and replicate the underlying patterns present in actual datasets, resulting in synthetic data that mirrors real-world scenarios. The SDV provides an array of models, including traditional statistical approaches like GaussianCopula and advanced deep learning techniques such as CTGAN. You can produce data for individual tables, interconnected tables, or even sequential datasets. Furthermore, it allows users to assess the synthetic data against real data using various metrics, facilitating a thorough comparison. The library includes diagnostic tools that generate quality reports to enhance understanding and identify potential issues. Users also have the flexibility to fine-tune data processing for better synthetic data quality, select from various anonymization techniques, and establish business rules through logical constraints. Synthetic data can be utilized as a substitute for real data to increase security, or as a complementary resource to augment existing datasets. Overall, the SDV serves as a holistic ecosystem for synthetic data models, evaluations, and metrics, making it an invaluable resource for data-driven projects. Additionally, its versatility ensures it meets a wide range of user needs in data generation and analysis.

Description

Enterprise synthetic test data solutions. It is essential that test data accurately reflects the structure of your database or application. This means it must be easy for you to model and maintain each project. Respect the referential integrity of parent/child/sibling relations across data domains within an app database or across multiple databases used for multiple applications. Ensure consistency and integrity of synthetic attributes across applications, data sources, and targets. A customer name must match the same customer ID across multiple transactions simulated by real-time synthetic information generation. Customers need to quickly and accurately build their data model for a test project. GenRocket offers ten methods to set up your data model. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Cloud Foundry No 
Cognizant No 
Decision Moments No 
Delphix No 
Eclipse IDE No 
FitNesse No 
Git No 
HCL Accelerate No 
IBM Rational Build Forge No 
IBM WebSphere Application Server No 
JUnit No 
Kubernetes No 
Maven No 
Mercurial No 
Microsoft Azure No 
Puppet Enterprise No 
SaltStack No 
Scratchpad No 
Vagrant No 
Visual Studio No 

Integrations

Cloud Foundry Yes 
Cognizant Yes 
Decision Moments Yes 
Delphix Yes 
Eclipse IDE Yes 
FitNesse Yes 
Git Yes 
HCL Accelerate Yes 
IBM Rational Build Forge Yes 
IBM WebSphere Application Server Yes 
JUnit Yes 
Kubernetes Yes 
Maven Yes 
Mercurial Yes 
Microsoft Azure Yes 
Puppet Enterprise Yes 
SaltStack Yes 
Scratchpad Yes 
Vagrant Yes 
Visual Studio Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

DataCebo

Website

sdv.dev/

Vendor Details

Company Name

GenRocket

Founded

2012

Country

United States

Website

www.genrocket.com/enterprise-features/

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