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What Delphix does
Delphix provides an enterprise data-management platform. Its capabilities include data virtualization, data masking, automation, governance, compliance, and on-demand access to data across the application lifecycle. Organizations can use the platform to support software testing, analytics, AI modeling, and cloud data-environment workflows.
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No More Development Without Data An Introduction to Delphix Virtual Databases: The Complete Guide to... | $6.24 | Buy on Amazon |
Why Perforce acquired a data-virtualization company
Perforce presented the acquisition as a way to strengthen infrastructure automation, self-service test environments, and continuous testing. Access to suitable test data can help development teams test applications earlier in the delivery process—a practice often called shift-left testing—while masking and governance features address the need to handle sensitive information appropriately.
Perforce’s announcement also included two company-reported figures. They describe Perforce’s claims, not independently established results:
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| Perforce-reported figure | What it indicates | Qualification |
|---|---|---|
| Up to 200% faster application releases | Perforce’s stated potential release-speed improvement associated with Delphix-supported data practices | Company claim; no independently verified benchmark or measurement methodology is provided in the announcement. |
| More than 30% of Fortune 100 companies | Perforce’s stated Delphix customer adoption figure | Company-reported figure; the announcement does not provide its methodology. |
What happened to Delphix after the acquisition
Delphix’s product line remains active. In September 2026, Perforce announced Delphix Synthetic Data, extending the offering toward AI-oriented test-data generation. That signals continued product investment, although the announcement alone does not establish how broadly the new capability is available or how customers are using it.
For organizations evaluating the combined offering, the practical questions are whether its data virtualization and masking fit existing governance requirements, how it connects to infrastructure automation and continuous-testing workflows, and whether its support for analytics, AI modeling, and synthetic data meets the team’s specific needs.
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