Every development team needs realistic data to test against, and almost every team is told it cannot use production data as it is. Customer names, card numbers and health records do not belong in a QA database, a developer laptop or a CI runner. Copying a full production database is also slow and expensive, so environments drift, tests run against stale data and bugs slip through.
Test data management tools solve both problems. They find sensitive columns, mask them, cut large databases down to consistent subsets, generate synthetic test data where production data is off limits, and deliver copies to environments quickly. In 2026 the category moved fast: vendors added AI-assisted configuration, natural-language data generation and MCP servers so that coding agents can request test data directly. If you are also tightening access to production, our guide to database security and compliance auditing tools covers the monitoring side.
Here are 10 test data management tools and data masking tools worth evaluating for 2027, ranked on depth of masking and provisioning, database coverage and how well each fits modern development workflows.
Test Data Management And Data Masking Tools At A Glance
Tool
Best For
Core Approach
Pricing Model
Perforce Delphix
Large enterprises with many data sources
Virtualization plus masking and AI synthetic data
Quote-based
K2view Test Data Management
Test data spread across many systems
Entity-based masking, subsetting and generation
Quote-based
Tonic.ai
Developer-led teams and AI workloads
De-identification and synthetic data
Free Fabricate tier; Structural quote-based
Redgate Test Data Manager
SQL Server, Oracle, PostgreSQL and MySQL shops
Subsetting, masking and database cloning
Quote-based
Broadcom Test Data Manager
Mainframe-heavy enterprises
Masking, synthetic data, find and reserve
Quote-based
IBM Optim Test Data Management
IBM and Db2 estates
Business-object subsetting and policy masking
Quote-based subscription
Synthesized
SAP and enterprise application testing
YAML-defined masking, subsetting and generation
Quote-based
GenRocket
Test automation teams going synthetic-first
Rule-based synthetic data generation
Quote-based; consumption pricing for DataConnect
DataSunrise
Masking across many database engines
Static and dynamic masking
Quote-based; free download
Bytebase
Controlling what engineers see in production
Dynamic data masking in a SQL workspace
Free Community; masking on Enterprise
Best Test Data Management And Data Masking Tools In 2027
1. Perforce Delphix — Virtualized, Masked Data From 170+ Sources
Perforce Delphix is the broadest platform on this list. It combines database virtualization, sensitive data discovery, masking and synthetic data generation under one governance layer, and it targets enterprises that need compliant copies of many databases delivered to many teams.
Delphix virtualizes databases so that teams can provision, refresh and rewind environments on demand and wire data delivery into CI/CD pipelines. Masking finds and obfuscates personal and health data before it reaches non-production, and it extends to data warehouses, data lakes and ETL pipelines. Perforce cites compliant delivery across more than 170 sources and support for GDPR, CCPA, HIPAA, PCI DSS and DORA. On 30 September 2026 it launched Delphix Synthetic Data, which scans schemas and metadata, lets users describe the data they need in natural language, keeps referential integrity, supports bring-your-own LLM and is reachable through a UI, APIs and MCP-enabled workflows. It runs on premises, in private or public clouds, or in hybrid setups.
The trade-off is weight. Delphix is an enterprise platform with enterprise pricing and a real implementation effort, and smaller teams that only need to mask one database will be paying for capability they never use.
Best for: Large organizations that want virtualization, masking and synthetic data from one vendor across a mixed estate.
K2view organizes test data around business entities, such as a customer, account or order, rather than around tables. Each entity pulls together the records that belong to it from every connected system, so a tester who asks for one customer gets a consistent view across CRM, billing and order databases.
Masking happens in flight as data is ingested, backed by PII discovery, a library of masking functions, custom functions and preserved referential integrity. Subsetting works on business terms such as account status or balance instead of hand-written joins. Synthetic data can be generated with AI, with rules that support weighted value distributions, or by cloning entities. Testers can reserve datasets so others do not overwrite them, take snapshots and roll back to a known-good state. Sources include enterprise applications, relational and NoSQL databases, mainframes, files and cloud data stores, and the platform deploys on premises, in the cloud or in hybrid architectures.
The entity model pays off at scale but demands more upfront design, integration work and governance decisions than a table-level masking tool. Pricing is not published.
Best for: Enterprises whose test data must stay consistent across many connected systems, including mainframes.
3. Tonic.ai — Structural, Fabricate And Textual For Structured And Unstructured Data
Tonic.ai sells three products that work alone or together. Tonic Structural de-identifies and synthesizes structured and semi-structured data while keeping schemas and referential integrity intact. Tonic Fabricate generates synthetic databases from a plain-language prompt with no production data needed. Tonic Textual redacts or synthesizes sensitive entities in unstructured content such as emails, transcripts, clinical notes and contracts.
Structural connects to MySQL, PostgreSQL, SQL Server, Oracle, Snowflake, Databricks, BigQuery, MongoDB, Salesforce and file sources. It offers sensitivity scanning with custom rules, subsetting that gives each developer an isolated dataset, a Privacy Report, role-based access control, SSO, audit logging, a conversational configuration agent and an MCP server. Fabricate adds a data agent with a plan mode for complex schemas, a validation agent that reviews output, mock API endpoints and unstructured output such as PDFs. Textual uses proprietary NER models and ships a Python SDK and a Snowflake Native App. Structural and Fabricate offer EU hosting in Frankfurt.
Tonic is table-centric and developer-friendly, which makes it quick to adopt, but it is less suited than entity-based platforms to keeping one customer consistent across many separate systems. Fabricate has a free tier with monthly credits and a Plus plan at $29 per month; Structural is custom-priced based on plan and connected source data.
Best for: Engineering teams that want fast de-identification, synthetic data for AI work and coverage of unstructured text.
Redgate Test Data Manager brings the company’s long experience with SQL Server tooling to a multi-database test data product. It supports SQL Server, Oracle, PostgreSQL and MySQL, and it is aimed at development teams that want production-like databases in every environment without exposing personal data.
The product covers three jobs. Subsetting builds a smaller, representative slice of a database from a chosen starting table and filters while keeping referential integrity. Anonymization discovers and classifies PII automatically and applies deterministic masking, so a replaced value stays consistent everywhere it appears. Redgate Clone then provisions lightweight virtualized copies of whole database instances, with optional masking, so each developer or pipeline run gets its own copy. Test Data Manager runs on Windows and Linux and is CLI-first, which means anything in the GUI can be scripted into a CI/CD pipeline.
Coverage stops at four relational engines, so teams with mainframe, NoSQL or SaaS application data will need something broader. Pricing is quote-based.
Best for: Teams on SQL Server, Oracle, PostgreSQL or MySQL that want masking, subsetting and per-developer clones from a vendor they may already use.
Broadcom Test Data Manager, formerly CA Test Data Manager, is a long-standing enterprise product with particular strength on the mainframe. It covers masking, synthetic generation, subsetting, virtual copies and a self-service portal.
Masking is consistent and format-preserving, with NIST-compliant format-preserving encryption to keep masked fields usable. Synthetic generation preserves statistical patterns and relationships, and AI can create synthetic data rules automatically, with bring-your-own model support and natural-language interfaces. Adaptive subsetting selects only the data a test needs. The Database Virtualization Engine creates storage-efficient virtual clones that can be snapshotted and rolled back. The find and reserve portal lets testers locate, reserve and provision datasets without waiting on DBAs. Sources include relational and NoSQL databases, Db2, IMS and VSAM on the mainframe, and file systems, and masking runs natively on z/OS so data never leaves the platform.
Broadcom’s licensing and sales model is built around large enterprise agreements, and the product feels heavier than developer-first tools for teams that do not need mainframe support.
Best for: Banks, insurers and other mainframe estates that must mask Db2, IMS and VSAM data in place.
IBM Optim Test Data Management, long sold as InfoSphere Optim, is one of the oldest products in the category. It provisions related data as complete business objects across databases, so a subset keeps its referential integrity even when relationships span systems.
Optim builds right-sized datasets for specific scenarios and edge cases, applies predefined masking policies through a centralized engine and offers self-service, reusable workflows with CI/CD integration. The companion IBM Optim Data Privacy product adds context-aware masking that preserves formats and relationships, user-defined functions for masking data at rest, and policy support for GDPR, HIPAA, SOX and PCI DSS. Optim Test Data Management 2.0 introduces a containerized deployment with flows for subsetting and masking, data comparison across sources, LDAP integration and a REST API, while distributed and z/OS editions of the classic product remain available.
Optim is dependable in IBM-centric shops but shows its age next to newer tools, and it does not lead on synthetic data generation. Pricing is quote-based.
Best for: Organizations already invested in IBM and Db2 that need proven subsetting and policy-based masking.
7. Synthesized — YAML-Defined Masking, Subsetting And Generation For SAP And Databases
Synthesized is a test data platform that masks, subsets and generates production-like data, with a strong focus on enterprise applications. Alongside databases it offers dedicated test data for SAP, including S/4HANA and ECC, and integrations with UiPath and Tricentis Tosca.
Workflows are defined in human-readable YAML, written in a no-code editor or through the API, and can be previewed in a sandbox database before approval. Because the rules live in version-controlled YAML, compliance settings are auditable and reusable. A Java engine runs the transformations, preserves primary and foreign keys and approximates column distributions, and the CLI drops into GitHub Actions, GitLab CI, Jenkins or Azure DevOps. Documented sources include PostgreSQL, Oracle, MySQL, SQLite, SQL Server, Snowflake, Db2 LUW, SAP HANA, CSV and XML, and the platform also masks documents and free text.
Synthesized is a smaller vendor than the enterprise incumbents, and pricing is only available through a demo, so plan for a proof of concept before committing.
Best for: Teams testing SAP and other enterprise applications that want test data rules stored as code.
8. GenRocket — Design-Driven Synthetic Test Data With MCP Delivery
GenRocket takes a synthetic-first view of test data. Rather than copying and masking production, teams design test data cases from business rules and generate exactly the scenarios and edge cases they need.
The platform generates data in real time inside CI/CD pipelines, scales to billions of rows and can blend reference data pulled from a production database with synthetic records. Prebuilt formats include healthcare X12 EDI and financial services MX message data, and there are prebuilt Salesforce test data cases. GenRocket also covers in-place masking for teams that still use production copies. In June 2026 it launched DataConnect, a synthetic data service that delivers rule-based data to agentic testing systems through REST APIs and MCP, with consumption-based pricing.
The design-driven model takes upfront effort: someone has to model the data rules, and it is not the tool to reach for when you simply need a masked copy of production by Friday.
Best for: Test automation teams that want controlled, repeatable synthetic test data for complex scenarios.
9. DataSunrise — Static And Dynamic Masking Across 30+ Data Platforms
DataSunrise is a database security suite whose masking module covers both static and dynamic masking.
Dynamic masking applies role-based rules to production results in real time, leaving source data unchanged and requiring no application changes. Static masking supports in-place masking and ETL-style masking, where columns are masked as data is loaded into a target server for development, testing or training. DataSunrise lists 32 supported platforms, including 27 SQL databases, Elasticsearch, MongoDB, Amazon DynamoDB, Neo4j and Amazon S3, and it runs on premises or in the cloud. The vendor maps its controls to GDPR, SOX, PCI DSS and HIPAA, and the wider suite adds auditing and sensitive data discovery.
DataSunrise is a masking and security product rather than a full test data management platform: it does not offer the subsetting, reservation or synthetic generation found higher on this list. Pricing goes through sales, with a free download to evaluate.
Best for: Security and DBA teams that need both static and dynamic masking across many database engines.
10. Bytebase — Dynamic Data Masking In A Governed SQL Workspace
Bytebase is a database DevSecOps platform covering schema change review, access control and a shared SQL editor. Its dynamic data masking feature tackles a related problem: engineers who query production directly to debug or support customers.
Masking rules can bind to a data classification, environment, project, table or column, and they apply to the SQL Editor, query results and exports. Engineers who need to see real values request unmasking through an approval flow, and every request is recorded in the audit log. Masking supports PostgreSQL, MySQL, SQL Server, Oracle, BigQuery and Snowflake. The Community plan is free for up to 20 users and 10 instances, Pro costs $20 per user per month, and dynamic data masking is part of the custom-priced Enterprise plan.
Masking covers only the human read path through Bytebase, not an application’s direct connection, and Bytebase does not subset, clone or generate test data. Treat it as a complement to a test data tool, not a replacement.
Best for: Teams that want to stop engineers seeing raw PII when they query production, with approvals and an audit trail.
How To Choose The Right Test Data Management Tool
Do You Need Static Masking, Dynamic Masking Or Both?
Static masking permanently rewrites sensitive values in a copy of the data, which is what you want for test, development and training environments. Dynamic data masking leaves the stored data alone and hides values at query time based on who is asking, which suits production support and analytics. Most test data platforms focus on static masking. If your main risk is engineers browsing production, a dynamic tool such as Bytebase or DataSunrise may close the gap faster, and pairs well with the monitoring covered in our SQL Server monitoring tools guide.
How Many Systems Does Your Test Data Span?
If most of your data lives in one or two relational databases, a table-level tool such as Redgate Test Data Manager or Tonic Structural is quicker to set up. If a single customer record is spread across CRM, billing, a mainframe and a SaaS application, you need a tool that keeps masked values and relationships consistent across all of them. That is where entity-based or business-object approaches from K2view, Delphix, Broadcom and IBM Optim earn their extra setup cost.
Should You Mask Production Or Generate Synthetic Test Data?
Masked production data reflects real distributions and real edge cases, but it still starts from sensitive records and carries some re-identification risk. Synthetic test data contains no real records and can model scenarios that do not exist yet, such as a new product line or a load test at ten times current volume. Many teams now use both: masked subsets for regression testing and synthetic data from tools such as Tonic Fabricate, GenRocket or Delphix Synthetic Data for new features, negative tests and AI work.
How Fast Do Environments Need To Be Ready?
Database subsetting shrinks what you copy, while database virtualization and cloning avoid full copies altogether. If every pull request should get its own database, look for virtual copies, such as Delphix, Redgate Clone or Broadcom’s virtualization engine, plus a CLI or API that your pipeline can call. Pair this with your schema workflow; our list of database DevOps and schema migration tools covers that half of the pipeline.
Which Regulations Do You Need To Evidence?
GDPR, HIPAA and PCI DSS all expect you to limit where personal, health and card data travels, and auditors will ask how you know non-production is clean. Favor tools with automatic sensitive data discovery, policies stored centrally or as code, and reports or audit logs you can hand to an assessor.
Conclusion
For large, mixed estates, Perforce Delphix and K2view offer the most complete combination of masking, subsetting, synthetic data and fast provisioning, with Broadcom and IBM Optim the safer choices where the mainframe and Db2 dominate. Tonic.ai and Redgate Test Data Manager are the quickest routes for development teams working in a handful of databases, while Synthesized and GenRocket suit teams that want test data defined as rules or code. DataSunrise and Bytebase close the dynamic masking gap in production.
Start with a pilot on your messiest schema, measure how long it takes to produce a masked, consistent environment, and confirm the audit evidence your compliance team needs. For the rest of the toolchain, browse our roundup of the best database tools.
