Datafaker Documentation

Datafaker organizes its fake data generation capabilities into several logical provider groups. Depending on your use case, you can access specialized data from the following categories:

• Base: Providers for everyday data (e.g., names, addresses, dates).
• Entertainment: Providers for movies, shows, books, etc.
• Food: Providers for various types of food.
• Healthcare: Providers for diseases, medications, procedures, etc.
• Sport: Providers for different types of sports.
• Videogame: Providers for video game related data.

A Schema is a set of rules used to transform data from Datafaker's internal representation into supported formats (CSV, JSON, SQL, etc.). You can use a schema to either generate data from scratch or transform existing data collections.

Schemas support nested (composite) fields using compositeField.

Schema<String, String> schema =
    Schema.of(
        field("first_name", () -> faker.name().firstName()),
        field("last_name", () -> faker.name().lastName()),
        field("address", () -> faker.address().streetAddress()));

The documentation site is built using MkDocs and Material for MkDocs. The repository structure is organized as follows:

• mkdocs.yml: Site configuration, theme features, and plugin definitions.
• requirements-docs.txt: Pinned Python dependencies (Material, plugins).
• docs/: Markdown content and static assets.
  • docs/assets/images/: Favicon and hero illustrations.
  • docs/stylesheets/extra.css: Site-specific CSS.
• material/overrides/: Jinja template overrides only (e.g., home.html, main.html).
• .github/workflows/deploy-docs.yml: CI/CD workflow for production deployment.

Important: material/overrides/ is the only content that belongs in the material/ directory. Base templates, partials, icons, and bundled JS/CSS are provided by the pip-installed theme.

Since version 1.2.0, Datafaker allows you to pass a format string as an argument to date and time methods to control the output format of generated dates and timestamps. This works with standard Java date-time pattern strings.

Faker faker = new Faker();
System.out.println(faker.timeAndDate().future(1, TimeUnit.HOURS, "yyyy MM.dd mm:hh:ss"));
System.out.println(faker.timeAndDate().past(1, TimeUnit.HOURS, "yyyy-MM-dd mm:hh:ss"));
System.out.println(faker.timeAndDate().birthday(1, 99, "yyyy/MM/dd"));

To generate fake data in Java, instantiate a Faker object and access various provider methods (e.g., name(), address()) to retrieve generated strings.

import net.datafaker.Faker;

Faker faker = new Faker();

String name = faker.name().fullName(); // Miss Samanta Schmidt
String firstName = faker.name().firstName(); // Emory
String lastName = faker.name().lastName(); // Barton

String streetAddress = faker.address().streetAddress(); // 60018 Sawayn Brooks Suite 449

The SqlTransformer generates INSERT statements. It supports two modes:

1. Non-batch: One statement per row.
2. Batch: One statement containing multiple rows (configured via .batch(n)).

You can specify the table name and a SqlDialect (e.g., SqlDialect.POSTGRES, SqlDialect.ORACLE) to handle identifier quoting and syntax specifics.

SqlTransformer<String> transformer =
    new SqlTransformer.SqlTransformerBuilder<String>()
        .batch(5)
        .tableName("MY_TABLE")
        .dialect(SqlDialect.POSTGRES)
        .build();

String output = transformer.generate(schema, 10);

To publish the Datafaker project to Maven Central, use the Maven deploy command from the project root.

mvn deploy

Follow these guidelines to maintain a clean and upgradeable documentation codebase:

• CSS: Prefer using docs/stylesheets/extra.css and Material CSS variables over vendoring or minifying CSS bundles.
• JS/CSS Integration: Use extra_css and extra_javascript in mkdocs.yml instead of creating custom JS bundles whenever possible.
• Templates: Keep overrides in material/overrides/ minimal. Always extend existing theme templates (like base.html or main.html) rather than copying the entire file.