Compare Top 20 Test Data Management Tools

Test data management tools (TDM) ensure quick delivery of high-quality test datasets to development environments, supporting the shift to agile DevOps methodologies. Compare the top test data management tools to choose the best test data management solution for your enterprise:

What is test data management?

Test data management (TDM) is an essential practice in modern software development. It is defined as the process of sourcing, securing, and provisioning test data for software testing and quality assurance.

For example, several data masking and subsetting activities are executed in parallel rather than sequentially to minimize the provisioning time of test datasets. The TDM process can be applied to detect data dependencies because it understands source system schemas and target environment requirements by using comprehensive data profiling techniques.

Without effective test data management, development and testing teams struggle to get the high quality test data needed to validate application functionality, leading to delays and poor application quality. 

Commercial test data management tools

1. Avo iTDM

Avo iTDM is a cloud-based TDM tool within the Avo Automation suite. It provides automated data discovery, masking, and AI-generated synthetic data. The tool supports on-demand data provisioning and includes analysis functions to evaluate test data coverage.

Avo iTDM integrates with databases through connectors and exposes APIs for CI/CD workflows. It is a commercial solution with customizable pricing.

Key features include:

• Automated discovery of sensitive data
• Data masking and obfuscation
• AI-based synthetic data generation
• Cross-environment provisioning
• CI/CD integration

Best for: Teams seeking cloud-based, AI-driven test data generation and provisioning.

2. BMC Compuware File-AID

File-AID is a TDM solution tailored for mainframe environments. It provides extraction, transformation, masking, and generation of test data from IMS, DB2, VSAM, and other mainframe datasets. The tool maintains referential integrity and includes specialized formats for COBOL copybooks and other legacy structures.

File-AID supports synthetic data creation and format-preserving masking suited to mainframe requirements. It is deployed on-premises and integrates with ISPF and modern interfaces such as Topaz. It is a commercial product commonly used in mainframe-heavy organizations.

Key features include:

• Masking and subsetting for mainframe datasets
• Support for COBOL copybooks and legacy formats
• Synthetic data generation
• Referential integrity preservation
• Integration with ISPF and Topaz.

Best for: Enterprises with mainframe systems needing secure, right-sized test datasets.

3. Datamaker by Broadcom CA

CA Test Data Manager is an on-premises TDM suite used to generate, manage, and secure test data across the testing lifecycle. It provides data masking, subsetting, and synthetic data generation, and can produce realistic datasets while maintaining referential integrity. The tool also supports self-service test data provisioning, allowing teams to refresh or extract subsets on demand.

CA TDM integrates with a wide range of development and testing tools and has strong support for legacy systems, including mainframe data sources. It is commonly deployed in industries with strict security and compliance requirements. The product is licensed commercially and is typically run on-premises, with optional deployment on cloud VMs.

Key features include:

• Data masking for sensitive fields
• Data subsetting with referential integrity
• Synthetic data generation
• Self-service provisioning
• Integration with modern and legacy systems

Best for: Enterprises requiring secure, on-premises TDM with legacy system support.

4. DATPROF

DATPROF enables users to create smaller, anonymized test databases from large production sets, and also generate fake data where needed. DATPROF supports multiple database platforms (SQL Server, Oracle, PostgreSQL, etc.) and can integrate into various testing environments.

It is typically deployed on-premises by mid-size entperirsces enterprises or teams needing a more affordable TDM solution compared to the larger suites.

It provides a central management console to:

• Subsetting filters
• Designing masking rules
• CI/CD-ready DevOps integration.

Best For: Small to mid-sized teams using modern DevOps workflows.

5. GenRocket

GenRocket is a TDM platform centered on synthetic test data generation. It uses a model-based approach to produce high-volume, conditioned data for automated and continuous testing environments. The tool supports generating data for databases via JDBC and multiple file formats.

GenRocket provides rule-driven data scenarios, maintains referential integrity, and offers APIs for retrieving synthetic data during test execution. It can be used through a cloud portal or installed on-premises for controlled environments. It is a commercial solution with subscription-based licensing.

Key features include:

• Synthetic data generation with model-based design
• Rule-driven data scenarios
• API-based data retrieval for CI/CD
• Database and file format support via JDBC
• On-premises or cloud deployment

Best for: Teams needing controlled synthetic data generation for automated and continuous testing.

6. IBM InfoSphere Optim TDM

IBM’s InfoSphere Optim is a long-standing enterprise TDM suite for data subsetting, masking, and archiving across multiple database systems, such as: DB2, Oracle and SQL Server. It can generate large volumes of test data and create optimized, right-sized datasets. 

It’s known for compliance features, like:

• Advanced data masking for compliance (e.g. masking credit card numbers or personal details)
• Archiving
• Reporting.

Best for: Security-conscious enterprises with large, regulated datasets.

7. Informatica TDM

Informatica offers a comprehensive TDM solution as a part of its data management suite. It focuses on secure data provisioning, allowing teams to discover and create targeted data subsets for testing. 

Informatica TDM supports a wide range of databases and file systems since it integrates with Informatica’s ETL platform. It can be deployed on-premises or via Informatica’s cloud services.

Its key features are:

• Data masking to anonymize sensitive customer data (meeting GDPR, HIPAA, etc.)
• Data subsetting to create smaller and optimized test databases, role-based access
• Cloud integrations.

It is best for: Cloud-native enterprises with existing Informatica skillsets.

8. K2tdm by K2View

K2View’s TDM solution can quickly provision consistent test data subsets from production across many systems while maintaining referential integrity. It supports a variety of technologies (relational databases, mainframe sources, etc.) and scales to large, fragmented environments with many apps. K2View TDM can embed into DevOps CI/CD pipelines for automated data refresh (“DataOps” for testing). 

Its key features include: 

• Patented entity-based data subsetting
• PII discovery
• Synthetic data generation
• Instant data rewind & reserve.

It is best for: Enterprises with complex, multi-source data environments in the industries such as telecom or banking that need to supply synchronized test data across heterogeneous systems. 

9. Micro Focus Data Express

Data Express is a TDM solution focused on mainframe and distributed systems. It automates data extraction, subsetting, and masking to create referentially intact test datasets. The tool supports mainframe data sources such as DB2 and VSAM and maintains relationships across multiple datasets.

Data Express also provides mapping and association capabilities to align fields from diverse sources, supporting consolidation efforts. It is designed to reduce CPU, storage, and bandwidth usage by optimizing test dataset size. Deployment is primarily on-premises and integrates with z/OS and distributed databases.

Key features include:

• Data subsetting with referential integrity
• Masking for mainframe and distributed systems
• Field mapping across heterogeneous data sources
• Storage and resource optimization
• Mainframe integration

Best for: Enterprises with mainframe workloads requiring efficient, masked test datasets.

10. Oracle Data Masking and Subsetting Pack

Oracle’s Data Masking and Subsetting Pack provides masking and subsetting capabilities for Oracle databases as part of Oracle Enterprise Manager. It allows defining masking rules, generating realistic replacement values, and creating referentially intact subsets for testing. The pack includes templates for Oracle applications such as E-Business Suite.

Masking can be applied in-place or during data export. While connectors exist for non-Oracle sources, the tool is primarily optimized for Oracle environments. It is available for on-premises Oracle Database and Oracle Cloud deployments and requires separate licensing.

Key features include:

• Data masking with predefined templates
• Subsetting for Oracle databases
• In-place or export-time masking
• Application-specific templates
• Oracle Cloud and on-premises support

Best for: Oracle-centric environments requiring secure, reduced test datasets.

11. Perforce Delphix TDM

Delphix is a DevOps-oriented test data platform known for database virtualization and integrated data masking. It allows teams to clone entire databases as virtual instances (“virtual databases”) in minutes, dramatically reducing storage use and provisioning time.

Delphix supports connecting to most enterprise databases, such as Oracle, SQL Server, PostgreSQL or DB2 as well as some NoSQL and cloud data sources. Delphix is typically deployed as a virtual appliance, which can run in a private data center or in public cloud infrastructure. This makes it a hybrid solution.

Some of its key features include:

• Data virtualization
• Instant rollback
• Provisioning
• Built-in data masking, ensuring sanitization of any cloned data (masking PII/PHI) while preserving realism.
• Compliance enforcement.

Best For: Enterprises requiring fast data refresh and versioning for CI/CD pipelines.

12. Redgate SQL Provision

SQL Provision is a TDM solution designed for Microsoft SQL Server environments. It combines SQL Clone and Data Masker to create masked, size-reduced database clones for development and testing. The tool provides one-click cloning, masking templates, and integration into CI pipelines.

SQL Provision supports subsetting and works closely with SQL Server Management Studio and Azure SQL. Its scope is limited to SQL Server-based systems. It is licensed commercially and typically deployed in Windows environments.

Key features include:

• Lightweight SQL Server database cloning
• Built-in data masking templates
• Subsetting for reduced storage and faster provisioning
• CI integration
• SSMS and Azure SQL compatibility

Best for: Organizations using SQL Server that require efficient cloning and masking for test environments.

13. Solix Enterprise Data Management Suite (EDMS)

Solix EDMS is an enterprise data management suite that includes capabilities for test data management, archiving, and governance. Its TDM functions include data masking, subsetting, and synthetic data creation. Solix supports compliance requirements by anonymizing sensitive data for regulations such as GDPR, HIPAA, and PCI-DSS.

The platform offers archiving and data retention features that help reduce test data size and preserve historical information for audit needs. It integrates with DevOps pipelines for automated provisioning and supports multiple databases and big data technologies. Deployment is available on-premises or in the cloud.

Key features include:

• Data masking and anonymization
• Data subsetting and synthetic data creation
• Archiving and retention management
• DevOps integration for automated provisioning
• On-premises or cloud deployment

Best for: Enterprises needing combined TDM, archiving, and compliance-driven data governance.

14. Tonic TDM

Tonic AI delivers a modern TDM platform, specialized in automated data synthesis and masking. It enables creating realistic and varied test datasets, using production data as a base while de-identifying sensitive information.

Tonic supports synthesizing data across many databases, such as: MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, DynamoDB, Snowflake and so on. It can generate fake data that maintains the statistical properties and relationships of production, which is useful for complex test scenarios.

Key features include:

• Flexible data subsetting
• Data masking functions with support for preserving formats, distributions
• Schema awareness
• privacy controls.
• Integration with cloud data warehouses and pipelines.

Best for: Teams focused on privacy and testing with synthetic data.

15. Tricentis Tosca (TDM)

Tosca’s TDM module supports test automation by providing centralized test data design, generation, and reuse. It enables creating synthetic data or retrieving data from connected systems based on constraints defined in test cases. Test data can be stored in a shared repository for reuse across automation workflows.

The tool integrates natively with Tosca’s test case design and service virtualization components. It supports common databases and can be deployed on-premises or through Tricentis cloud services. TDM is part of the broader Tosca licensing model.

Key features include:

• Centralized test data repository
• Constraint-based data generation
• Synthetic data support
• Integration with Tosca automation
• On-premises or cloud deployment

Best for: Organizations using Tricentis Tosca for automated testing requiring integrated test data capabilities.

Open-source test data management tools

Open-source TDM tools provide flexibility and no license cost, but most focus on a subset of TDM functions, such as subsetting, masking, or synthetic data generation, rather than offering a full end-to-end suite. They are typically self-hosted and require in-house support or customization. Below are notable open-source options:

16. Benerator by Databene

Benerator is an open-source framework for high-volume synthetic data generation. It can create realistic datasets in multiple formats and populate relational databases directly. Users define data models and rules that govern distributions, value ranges, and relationships.

The tool supports major databases and file formats and can be integrated into build pipelines. It is available under an open-source license, with an optional commercial license for advanced extensions.

Key features include:

• High-volume synthetic data generation
• Rule-based modeling for relational consistency
• Support for multiple databases and file formats
• Command-line and build pipeline integration

Best for: Generating synthetic datasets at scale or augmenting masked production subsets.

17. Databucket

Databucket is an open-source, self-hosted repository for managing and organizing test datasets. It provides a central location for storing test data, associated metadata, and scenario-based records across multiple projects and environments. Data is stored in flexible structures that support evolving schemas.

The tool offers a UI for editing and reviewing data, import/export functions, and an API for retrieving test data during automated test execution. It is most effective when a stable and reusable set of curated test records is needed rather than subsetting or generating data from production.

Key features include:

• Centralized repository for test datasets
• UI-based editing and metadata management
• API access for automation workflows
• Self-hosted deployment via Docker

Best for: Teams needing a shared repository of curated test data for manual or automated testing.

18. EPAM by TDspora

TDspora is an open-source test data management tool that supports data subsetting and synthetic data generation. It uses machine learning models to learn statistical patterns from production data and generate privacy-preserving synthetic datasets. Differential privacy options are available to control the balance between utility and anonymity.

TDspora can extract referentially consistent subsets from relational and non-relational data sources. It offers containerized deployment options for cloud and on-premises environments (such as AWS and GCP). The tool is maintained as an open-source project on GitHub.

Key features include:

• Synthetic data generation using learned patterns
• Differential privacy controls
• Data subsetting across relational and non-relational systems
• Containerized deployment options

Best for: Teams seeking an open-source, end-to-end TDM solution with synthetic data capabilities.

19. Generatedata

Generatedata is an open-source web-based tool for generating random test data in various formats. It provides predefined data types—such as names, addresses, dates, and numeric fields—and supports generating output in formats like CSV, SQL, JSON, XML, and Excel.

The tool is suitable for producing standalone datasets or single-table test data. It does not enforce referential integrity between tables but can be customized or scripted for advanced scenarios. It can be self-hosted and accessed through a browser or its API.

Key features include:

• Browser-based test data generation
• Multiple output formats
• Customizable data types
• Self-hosted and API-enabled

Best for: Quickly generating standalone test datasets or individual tables for testing.

20. Jailer

Jailer is an open-source tool focused on database subsetting and anonymization. It extracts smaller, referentially consistent subsets from large relational databases through user-defined filters. Jailer maintains integrity across related tables and supports masking fields during export.

It works with many databases through JDBC and provides both a graphical interface and command-line options. Jailer is well-suited for creating reduced test databases from production systems, although it does not generate synthetic data. Any extended automation typically requires custom scripting.

Key features include:

• Database subsetting with referential integrity
• Basic data masking during export
• JDBC support for common relational databases
• UI and command-line interfaces

Best for: Teams needing consistent, downsized subsets of relational databases for testing.

Test data management in software testing

Test data management refers to the process of ensuring automated software tests have the right data at the right time. It’s the practice of providing, managing, and governing the data needed to execute test cases effectively.

While testing in production offers the “best” data, real and live data, most testing still occurs before deployment. It ensures pre-production tests have realistic, reliable, and compliant data in software development lifecycle.

The qualities of good test data

The role of TDM is to provide test data that meets several non-negotiable criteria. Without these qualities, tests will provide poor, misleading results.

• High quality: The data meets the expectations of the test. For example, it must be valid for “happy path” tests but invalid for negative testing(checking error handling). Ensuring the data’s format, type, and volume align with the test case’s intent.
• Available: Tests can easily access the data without technical blockers (e.g., authentication or network issues). Managing access controls and distribution of test data sets.
• Timely: The data is accessible instantly when the test needs it, without causing delays in the testing pipeline. Optimizing storage and retrieval mechanisms.
• Realistic: The data sets must accurately mimic real production data in terms of quantity, formats, and complexity. Sourcing, slicing, or generating data that faithfully represents the production environment.
• Compliant: The data adheres to all relevant data privacy regulations (like GDPR) to protect user information. Applying proper data obfuscation or masking techniques to sensitive production data.

Three essential categories of test data

Test data can be categorized in many ways (e.g., origin: production clone vs. synthetic; creation: manual vs. automatic), but one of the most practical approaches is based on the data values and their properties:

1. Valid data

This data adheres to the formats, values, and quantities expected by the system.

• Purpose: Testing the “happy path”—what happens when everything works as expected.

2. Invalid data

This data intentionally includes unexpected, wrong, or corrupted values.

• Purpose: Testing the “unhappy path”—how robustly the application handles errors, unexpected input, and security flaws (a key part of negative testing and chaos testing).

3. Extreme (boundary) data

Data that sits at or slightly outside the boundaries of acceptable values.

• Example: For an input field accepting values from 0 to 1000, the boundary values are 0 and 1000. Testing with −1and 1001 is testing outside the boundary.
• Purpose: Identifying errors that often occur at the edges of a system’s logic. This data can be both valid (the 0 and 1000) and invalid (the −1 and 1001).

Test data management challenges

While TDM offers immense benefits, it comes with significant practical challenges:

• Slow and costly production cloning: Copying an entire production database is often prohibitively slow, expensive, and storage-intensive. The best practice is data slicing, intelligently selecting and copying a representative subset of the data.
• Compliance risk (unmasked data): Using production data without proper masking or obfuscation is a massive risk. Failure to protect sensitive user data violates regulations like GDPR and can lead to severe financial penalties and legal trouble.
• Masking overhead: While necessary, the process of masking data isn’t free. It adds overhead, which can include the financial cost of tools, the time needed to set up and maintain the masking process, and the learning curve for new tools.
• Data outdatedness and availability: Ensuring data is always available, timely, and up-to-date is a constant battle. Test data can quickly become stale, forcing organizations to spend constant effort on renovation to keep it realistic.

Further reading

Explore more on data quality and data governance:

• AI Data Governance for Ethical Use
• Data Quality in AI: Challenges, Importance & Best Practices

Reference Links

1.

What is IBM InfoSphere Optim Data Privacy and use cases of IBM InfoSphere Optim Data Privacy? - DevOpsSchool.com

2.

Test Data Management Tools | K2view

K2view

3.

Perforce Delphix Test Data Management Solutions | Perforce

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