Compare the Top Test Data Management Tools that integrate with Eclipse IDE as of October 2025

This a list of Test Data Management tools that integrate with Eclipse IDE. Use the filters on the left to add additional filters for products that have integrations with Eclipse IDE. View the products that work with Eclipse IDE in the table below.

What are Test Data Management Tools for Eclipse IDE?

Test data management tools enable IT professionals and developers to create non-production test data that simulates real company data in order to reliably test applications and systems with data that's similar to production data. Compare and read user reviews of the best Test Data Management tools for Eclipse IDE currently available using the table below. This list is updated regularly.

  • 1
    IRI FieldShield

    IRI FieldShield

    IRI, The CoSort Company

    IRI FieldShield® is powerful and affordable data discovery and masking software for PII in structured and semi-structured sources, big and small. Use FieldShield utilities in Eclipse to profile, search and mask data at rest (static data masking), and the FieldShield SDK to mask (or unmask) data in motion (dynamic data masking). Classify PII centrally, find it globally, and mask it consistently. Preserve realism and referential integrity via encryption, pseudonymization, redaction, and other rules for production and test environments. Delete, deliver, or anonymize data subject to DPA, FERPA, GDPR, GLBA, HIPAA, PCI, POPI, SOX, etc. Verify compliance via human- and machine-readable search reports, job audit logs, and re-identification risk scores. Optionally mask data as you map it. Apply FieldShield functions in IRI Voracity ETL, federation, migration, replication, subsetting, or analytic jobs. Or, run FieldShield from Actifio, Commvault or Windocks to mask DB clones.
  • 2
    GenRocket

    GenRocket

    GenRocket

    Enterprise synthetic test data solutions. In order to generate test data that accurately reflects the structure of your application or database, it must be easy to model and maintain each test data project as changes to the data model occur throughout the lifecycle of the application. Maintain referential integrity of parent/child/sibling relationships across the data domains within an application database or across multiple databases used by multiple applications. Ensure the consistency and integrity of synthetic data attributes across applications, data sources and targets. For example, a customer name must always match the same customer ID across multiple transactions simulated by real-time synthetic data generation. Customers want to quickly and accurately create their data model as a test data project. GenRocket offers 10 methods for data model setup. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.
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