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What is Tonic?

Tonic offers an automated approach to creating mock data that preserves key characteristics of sensitive datasets, which allows developers, data scientists, and sales teams to work efficiently while maintaining confidentiality. By mimicking your production data, Tonic generates de-identified, realistic, and secure datasets that are ideal for testing scenarios. The data is engineered to mirror your actual production datasets, ensuring that the same narrative can be conveyed during testing. With Tonic, users gain access to safe and practical datasets designed to replicate real-world data on a large scale. This tool not only generates data that looks like production data but also acts in a similar manner, enabling secure sharing across teams, organizations, and international borders. It incorporates features for detecting, obfuscating, and transforming personally identifiable information (PII) and protected health information (PHI). Additionally, Tonic actively protects sensitive data through features like automatic scanning, real-time alerts, de-identification processes, and mathematical guarantees of data privacy. It also provides advanced subsetting options compatible with a variety of database types. Furthermore, Tonic enhances collaboration, compliance, and data workflows while delivering a fully automated experience to boost productivity. With its extensive range of features, Tonic emerges as a vital solution for organizations navigating the complexities of data security and usability, ensuring they can handle sensitive information with confidence. This makes Tonic not just a tool, but a critical component in the modern data management landscape.

What is GenRocket?

Solutions for synthetic test data in enterprises are crucial for ensuring that the test data mirrors the architecture of your database or application accurately. This necessitates that you can easily design and maintain your projects effectively. It's important to uphold the referential integrity of various relationships, such as parent, child, and sibling relations, across different data domains within a single application database or even across various databases used by multiple applications. Moreover, maintaining consistency and integrity of synthetic attributes across diverse applications, data sources, and targets is vital. For instance, a customer's name should consistently correspond to the same customer ID across numerous simulated transactions generated in real-time. Customers must be able to swiftly and accurately construct their data models for testing projects. GenRocket provides ten distinct methods for establishing your data model, including XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, and Salesforce, ensuring flexibility and adaptability in data management processes. These various methods empower users to choose the best fit for their specific testing needs and project requirements.

Media

Media

Integrations Supported

Amazon Redshift
Apache Spark
Google Cloud BigQuery

Integrations Supported

Amazon Web Services (AWS)
CA Flowdock
Cloud Foundry
Cognizant
Delphix
Eclipse IDE
Eightfold.ai
HCL Accelerate
Mercurial
Microsoft Azure
Nexus AP & Payments Automation
Rackspace OpenStack
Salesforce
SaltStack
Scratchpad
Vagrant
Wipro Cloud Studio

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
Linux

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

Tonic

Date Founded

2018

Company Location

United States

Company Website

www.tonic.ai/

Company Facts

Organization Name

GenRocket

Date Founded

2012

Company Location

United States

Company Website

www.genrocket.com/enterprise-features/

Categories and Features

Categories and Features

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