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Mage Data Test Data Management
What Mage Data Test Data Management is
Mage Data Test Data Management prepares data for software testing and automates its delivery into CI/CD pipelines. Teams can mask sensitive production data in place, run Extract-Mask-Load pipelines, or create virtual database copies. Static masking permanently obscures sensitive information in non-production environments while keeping values consistent. Intelligent subsetting extracts production data while preserving relationships between records. Virtualization creates space-efficient copies in minutes, helping reduce storage needs and shorten testing cycles. Context-preserving masking is designed to retain referential integrity and statistical consistency so masked data remains useful for tests.
Teams can request datasets and manage provisioning through a conversational interface, or automate data workflows with APIs. Listed CI/CD integrations include Jenkins, GitLab CI, GitHub Actions and Azure DevOps Pipelines; other orchestrators can connect if they can call a REST API. Mage Data says the platform is installed in a customer's on-premises environment, cloud account or hybrid setup, rather than delivered as SaaS. Its repository stores metadata and policies, not production data, and virtual copies stay on customer servers. Representative supported sources include Oracle, Microsoft SQL Server, IBM Db2, MongoDB, Amazon RDS, Snowflake, Google BigQuery and Databricks. Pricing is available by request.
Who it suits
It suits enterprises and developers who need secure test data for CI/CD and rapid-release workflows. It may fit teams that need to keep data and virtual copies in their own environment while automating provisioning.
What it does well
- Supports in-place masking and Extract-Mask-Load pipelines.
- Subsetting preserves production data relationships.
- Creates space-efficient virtual database copies in minutes.
- Offers natural-language provisioning and API automation.
- Lists Jenkins, GitLab CI, GitHub Actions and Azure DevOps integrations.
What to know first
- Pricing is available only by request.
- The platform is installed in customer environments, not offered as SaaS.
Plans
| Plan | Billed and limits | Price |
|---|---|---|
| Test Data Management | Pricing by request; contact Mage Data for pricing | On request |
As magedata.ai lists them, read 7 Oct 2026. The maker's own page has the final word.
Getting started
- Visit the Mage Data Test Data Management website.
- Contact Mage Data to discuss requirements or arrange a personalized demo.
- Request pricing from Mage Data.
- Plan installation in an on-premises environment, cloud account or hybrid deployment.
- Connect CI/CD tools or another orchestrator that can call a REST API.
Verdict
Choose Mage Data Test Data Management if your team needs to prepare and automate secure test data within its own on-premises, cloud or hybrid environment. Teams seeking a SaaS service or an upfront listed price should look elsewhere.
Questions
How much does Mage Data Test Data Management cost?
Pricing is available by request; the contact page invites prospective customers to request pricing.
Is the platform a SaaS service?
No. Mage Data says it is installed in the customer's on-premises environment, cloud account or hybrid deployment.
Which CI/CD tools are listed as integrations?
Mage Data lists Jenkins, GitLab CI, GitHub Actions and Azure DevOps Pipelines. Other orchestrators can connect if they can call a REST API.
What data sources are supported?
Representative sources include Oracle, Microsoft SQL Server, IBM Db2, MongoDB, Amazon RDS, Snowflake, Google BigQuery and Databricks.
Does Mage Data store production data?
Mage Data says its repository holds metadata and policies, not a copy of production data. Virtual copies live on customer servers.
How can a prospective customer learn more?
Mage Data says its contact team will respond within one business day to discuss requirements or arrange a personalized demo.
Facts from Mage Data Test Data Management's own pages
- Automation
- The product can automate test data workflows via APIs. · magedata.ai, 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, 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, 7 Oct 2026
- Data preparation
- The product supports in-place masking, Extract-Mask-Load pipelines from production, and instant virtual copies. · magedata.ai, 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, 7 Oct 2026
- Founded
- 2004 · magedata.ai, 28 Sept 2026
- Headquarters
- New York, New York, United States · magedata.ai, 28 Sept 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, 7 Oct 2026
- Masking
- Static Data Masking permanently masks sensitive data in non-production environments while maintaining consistency. · magedata.ai, 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, 7 Oct 2026
- Natural language
- A conversational interface lets users request datasets and manage provisioning using natural language. · magedata.ai, 7 Oct 2026
- Pricing
- The contact page invites prospective customers to request pricing; it does not state a price. · magedata.ai, 7 Oct 2026
- Purpose
- Test Data Management automates delivery of secure, high-quality data for CI/CD pipelines. · magedata.ai, 7 Oct 2026
- Subsetting
- Intelligent Subsetting extracts referentially intact production data subsets for testing. · magedata.ai, 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, 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, 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, 7 Oct 2026
- Virtualization
- Database Virtualization creates space-efficient virtual database copies in minutes to reduce storage costs and accelerate cycles. · magedata.ai, 7 Oct 2026
Where Mage Data Test Data Management stands on Freedom251
- Test Data Management Software#19 of 25