datafaker

Datafaker is a family of open-source software libraries designed to generate synthetic data for software development, testing, and data privacy workflows. Similar in concept to Faker, datafaker provides a set of functions to produce realistic-looking names, addresses, contact details, company information, internet-related data, and generic text. There is no single canonical implementation; multiple independent projects across programming languages use the name datafaker, often borrowing API ideas from Faker and offering locale-specific data sets to improve realism.

Typical features include locale selection to tailor data to a language and region; deterministic seeding to reproduce results; a wide range of data providers such as person names, locations, emails, phone numbers, dates, numbers, text, credit card numbers, and passwords; and the ability to export data in JSON, CSV, or direct database seeds. Some implementations support custom providers and user-defined data sets.

Common use cases are populating development and test databases with synthetic data, anonymizing real datasets for sharing, generating sample records for demos, and seeding application fixtures. Datafaker libraries are generally easy to integrate with test frameworks or scripts and may be lightweight or larger depending on the ecosystem.

Licensing and quality vary by project. Users should verify locale coverage and data provenance, particularly for sensitive information. While datafaker aims to produce plausible data, it should not be mistaken for real personal information or used to misrepresent individuals.

See also: Faker, Synthetic data, Data anonymization, Test data generation.