datafaker
Datafaker is a Java library for generating realistic fake data (names, addresses, dates, etc.) useful for testing and development. It's a maintained fork of java-faker with modern dependencies, Java 17+ requirement, and support for multiple JVM languages including Kotlin and Groovy.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | datafaker-net/datafaker |
| Owner | datafaker-net |
| Primary language | Java |
| License | Apache-2.0 — OSI-approved |
| Stars | 1.8k |
| Forks | 243 |
| Open issues | 10 |
| Latest release | 2.7.0 (2026-06-24) |
| Last updated | 2026-07-06 |
| Source | https://github.com/datafaker-net/datafaker |
What datafaker is
Apache 2.0 licensed JVM library providing fluent API for fake data generation via providers (name, address, date, etc.), expressions, collections, streams, and schema-based transformations (CSV, JSON, XML, YAML). Requires Java 17+ for 2.x branch; 1.x legacy branch targets Java 8 but is unmaintained.
Get the datafaker source
Clone the repository and explore it locally.
git clone https://github.com/datafaker-net/datafaker.gitcd datafaker# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Java 17+ is mandatory for 2.x; confirm runtime environment before adoption.
- Instantiate Faker() once and reuse or use thread-safe patterns if generating data concurrently.
- Localization support exists via Faker constructors; verify locale data coverage for your use case.
- Custom providers can be added; review documentation for extension patterns to match domain models.
- Snapshot builds (2.8.0-SNAPSHOT) available from Sonatype; only use in dev; prefer stable releases for production testing.
When to avoid it — and what to weigh
- Production Data Masking — Not designed for de-identification or anonymization of real production datasets; use specialized privacy tools instead.
- Java 8 or Earlier Environments — Current 2.x version requires Java 17+. Legacy 1.x branch (Java 8) is unmaintained; upgrade path recommended.
- Cryptographically Secure Randomness — Faker uses standard JVM randomization; do not rely on it for security-sensitive operations (tokens, secrets, etc.).
- Real-Time, High-Throughput Data Streaming — Suitable for test/dev scenarios but not optimized for production-scale, low-latency fake data pipelines.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution under stated terms (preservation of copyright/license notice required).
Apache 2.0 is a permissive OSI license generally recognized as safe for commercial use in proprietary/closed-source applications provided License and copyright notice are retained. No commercial support or warranty is provided by the project; rely on Apache 2.0 terms and community support.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
Datafaker uses standard JVM pseudorandom number generation; unsuitable for cryptographic or security-sensitive operations (tokens, secrets). Data generated is deterministic if seeded; review seeding behavior if reproducibility is required in sensitive contexts. No known security disclosures mentioned in available data. Follow Maven Central vulnerability scanning practices.
Alternatives to consider
java-faker (original)
Original Faker port for Java; datafaker is its maintained modern fork with updated dependencies and new generators, making datafaker the recommended choice.
TestData (com.flextrade.jfixture)
Alternative fixture generation library; smaller scope. Consider if you need deeper reflection-based object construction without explicit provider setup.
Instancio
Newer reflection-driven arbitrary object generator for JVM; useful if you prefer annotation-based or generic object generation over explicit fake providers.
Build on datafaker with DEV.co software developers
Integrate Datafaker into your JVM project to accelerate test suite setup and reduce manual mock data creation. Contact our team to discuss implementation for your testing strategy.
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datafaker FAQ
Can I use datafaker 1.x with Java 8?
Is datafaker suitable for generating production data?
Can I extend datafaker with custom generators?
What JVM languages does datafaker support?
Custom software development services
Need help beyond evaluating datafaker? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source testing integrations — and maintain them long-term.
Ready to streamline your test data generation?
Integrate Datafaker into your JVM project to accelerate test suite setup and reduce manual mock data creation. Contact our team to discuss implementation for your testing strategy.