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On the recordRead 7 Oct 202618 facts
Mage Data Test Data Management
New York, New York, United States
Plans and prices
Test Data Management
Priced on request
Pricing by request; contact Mage Data for pricing
Read 7 Oct 2026 · magedata.ai
On the record
- Automation
- The product can automate test data workflows via APIs.magedata.ai · read 7 Oct 2026
- Company history
- Mage Data's CEO profile says Rajesh K. Parthasarathy founded MENTIS in 2004; the profile does not state that Mage Data itself was founded that year.magedata.ai · read 7 Oct 2026
- Data handling
- Mage Data says its repository holds metadata and policies, not a copy of production data, and that virtual copies live on the customer's servers.magedata.ai · read 7 Oct 2026
- Data preparation
- The product supports in-place masking, Extract-Mask-Load pipelines from production, and instant virtual copies.magedata.ai · read 7 Oct 2026
- Deployment
- Mage Data says the platform is installed in the customer's environment on-premises, in their cloud account, or in a hybrid deployment, and is not a SaaS service.magedata.ai · read 7 Oct 2026
- Integrations
- Mage Data lists Jenkins, GitLab CI, GitHub Actions, and Azure DevOps Pipelines as CI/CD integrations, and says any orchestrator that can call a REST API can connect.magedata.ai · read 7 Oct 2026
- Masking
- Static Data Masking permanently masks sensitive data in non-production environments while maintaining consistency.magedata.ai · read 7 Oct 2026
- Masking consistency
- Its context-preserving masking algorithms are designed to maintain referential integrity and statistical consistency so data remains usable for testing.magedata.ai · read 7 Oct 2026
- Natural language
- A conversational interface lets users request datasets and manage provisioning using natural language.magedata.ai · read 7 Oct 2026
- Pricing
- The contact page invites prospective customers to request pricing; it does not state a price.magedata.ai · read 7 Oct 2026
- Purpose
- Test Data Management automates delivery of secure, high-quality data for CI/CD pipelines.magedata.ai · read 7 Oct 2026
- Subsetting
- Intelligent Subsetting extracts referentially intact production data subsets for testing.magedata.ai · read 7 Oct 2026
- Support
- Mage Data says its contact team will respond within one business day to discuss requirements or arrange a personalized demo.magedata.ai · read 7 Oct 2026
- Supported sources
- Representative supported sources include Oracle, Microsoft SQL Server, IBM Db2, MongoDB, Amazon RDS, Snowflake, Google BigQuery, and Databricks; Mage Data says its connector list is not exhaustive.magedata.ai · read 7 Oct 2026
- Target users
- The product is positioned for enterprises and their developers who need secure test data for rapid release and CI/CD workflows.magedata.ai · read 7 Oct 2026
- Virtualization
- Database Virtualization creates space-efficient virtual database copies in minutes to reduce storage costs and accelerate cycles.magedata.ai · read 7 Oct 2026
