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Datafaker
Datafaker is an Apache-2.0 fake data library for the JVM (Java, Kotlin, Groovy), a modern fork of Java Faker with updated dependencies, many more providers and schema-based generation to CSV, JSON, SQL and more.
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Introduction
Datafaker is a modern fork of Java Faker with up-to-date libraries and several newly added fake generators, according to its README. It generates fake data for the JVM, including Java, Kotlin and Groovy, similar to Ruby’s Faker gem, Python’s Faker and the JavaScript Faker library.
Datafaker offers hundreds of providers, from names and addresses to domain-specific data, plus expression-based generation and schema transformations that output data as CSV, JSON, SQL, YAML and other formats. Locales are supported for realistic regional data.
Datafaker 2.x requires Java 17; 1.x runs on Java 8 but is no longer maintained. Version 2.7.0 was released in June 2026, with commits in November 2026.
In the quality loop it supports unit and integration tests and database seeding on the JVM.
The source code is at datafaker-net/datafaker on GitHub under the Apache-2.0 licence, with about 1,800 stars; the most recent commit was on 9 November 2026.
Features
- Many providers: Hundreds of data types.
- Expressions: Template-based values.
- Schema output: CSV, JSON, SQL, YAML and more.
- Locales: Regional data.
- JVM languages: Java, Kotlin, Groovy.
Pros
- Maintained Java Faker successor: Updated dependencies.
- Free to use: Open source under the Apache-2.0 licence, so the core tool costs nothing to use.
- Actively maintained: The repository had commits as recent as 9 November 2026.
Cons
- Java 17 for 2.x: Older JVMs need unmaintained 1.x.
Usage and Links (Example)
Faker faker = new Faker();
String name = faker.name().fullName();
String street = faker.address().streetAddress();
Resources:
Pricing
Datafaker is free and open source under the Apache-2.0 licence.
Recommended for?
- JVM teams needing fake data.
- Not a fit for Java 8-only projects wanting maintained releases.
Conclusion
Datafaker is the maintained choice for fake data on the JVM.
On CheckOps, Datafaker is filed under the Unit testing and Integration testing stages. Related tools listed here include Instancio, Easy Random, Faker (Python), Faker (JavaScript), Bogus and Mockaroo. Before choosing, compare them on licence, supported languages and platforms, hosting model, and how well each fits your existing CI pipeline. Facts on this page were checked against the official website, documentation and repository on 11 November 2026; statements the vendor makes about its own product are marked as vendor claims.
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Roundups
Alternatives
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IInstancioInstancio is an Apache-2.0 Java library that automatically creates and populates objects, including nested objects and collections, for unit tests, with a fluent API to customise values.Compare with Datafaker
EEasy RandomEasy Random is an MIT-licensed Java library that generates random Java beans and records for tests; it has been in maintenance mode since 2020 but released v6 with record support in 2026.Compare with Datafaker
FFaker (Python)Faker is an MIT-licensed Python package that generates fake but realistic data such as names, addresses, text, dates and identifiers, with dozens of locales, a pytest fixture and seeding for reproducible test data.Compare with Datafaker
FFaker (JavaScript)Faker is an MIT-licensed JavaScript and TypeScript library that generates large amounts of fake but realistic data, such as people, addresses, dates, finance and commerce values, in over 70 locales, in Node.js and browsers.Compare with Datafaker
BBogusBogus is an MIT-licensed fake data generator for .NET (C#, F# and VB.NET), a port of faker.js with a fluent API for building realistic objects for tests and seed data.Compare with Datafaker
MMockarooMockaroo is a commercial web service for generating realistic test data in CSV, JSON, SQL and Excel formats, with schemas, mock APIs, datasets and a Docker image for private deployment; up to 1,000 rows are free.Compare with Datafaker