Datafaker
Datafaker: A free, open-source JVM library for schema-driven synthetic test data. Ranked #17 of 28 in Test Data Generation Tools by our editors (7.0/10); pricing: Free plan; best for java and Kotlin schema transformations.
At a glance
- Editor score7.0 / 10
- PricingFree plan
- Best forJava and Kotlin schema transformations
- Free planYes
- Paid fromFree
- Generation modesSynthetic
- Facts checked20 Sep 2026
Where it wins
- Supports Java and Kotlin fake-data providers with locale-specific generation
- Transforms schemas into CSV, JSON, SQL, YAML, XML, Java objects, and TOML
- Includes collections, streams, expressions, and custom data providers
Where it doesn't
- It is a developer library rather than a hosted test-data platform
- Direct database connectors are not included
- Its primary focus is JVM application development
Our verdict on Datafaker
Datafaker is an open-source Java and Kotlin library for generating synthetic data in JVM applications. It is aimed at developers creating test data, populating databases, stress testing applications, anonymizing production data, and producing sample data during application development. Its fake-data providers cover domain-specific values, while locale support, expressions, collections, streams, and custom providers give teams several ways to shape generated output. The library can also populate Java and Kotlin objects from schemas.
Schema transformation is the product’s standout capability. Datafaker can generate or transform schemas into CSV, JSON, SQL, YAML, XML, Java objects, and TOML, with SQL INSERT generation and database dialect support. Expression-based generation includes letterify, numerify, and templating, which can help developers define repeatable patterns alongside provider-based values. These features make it a strong fit for JVM teams that need generated fixtures in multiple formats or want to map structured data into application objects.
Datafaker is free and open source, with a self-hosted library model rather than hosted workspace pricing or paid service tiers. That keeps the product aligned with code-driven development, but it also means teams looking for a managed interface, a hosted data-generation service, or direct database connectors should choose another type of tool. Pick Datafaker when Java or Kotlin integration, schema transformation, locale data, and programmable generation are central requirements. Choose an alternative when database connectivity or a hosted test-data workflow matters more than JVM-native control.
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Datafaker pricing
Datafaker fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Generation modes | Synthetic |
| Relational data | Not verified |
| API data generation | Yes |
| Supported data formats | CSV, JSON, SQL, YAML, XML, Java Object, TOML |
| Deployment | Self-hosted |
| Generation limit | Not verified |
| Database connectors | Not verified |
| Deployment | Self-hosted |
| Support | Docs |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan |
| Website | datafaker.net |
| Facts checked | 20 Sep 2026 |
Alternatives to Datafaker
- MOSTLY AIA broad synthetic-data platform for enterprise relational data teams.9.4
- SynthesizedA database-focused platform for schema-faithful synthetic data and protected test environments.9.2
- Tonic FabricateA flexible choice for AI-assisted relational test data with API and export workflows.9.0
See all Datafaker alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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