stympy/faker
Faker
Faker is a synthetic data generation library used to create realistic but fake information, such as names, addresses, and phone numbers, for software testing and database population. It functions as a tool for producing synthetic test data to fill development databases with records that simulate production environments.
The library provides localized data generation, allowing synthetic information to be customized for specific geographic regions and language formats. It also includes a mechanism for unique value enforcement to prevent the repetition of generated data by tracking and rejecting duplicate entries.
To ensure repeatability and predictability in tests, the project utilizes deterministic data seeding via a pseudo-random number generator. This allows for the creation of consistent sequences of synthetic datasets across repeated runs.
Features
- General Synthetic Data Generators - Produces realistic but synthetic information such as names, addresses, and phone numbers for software testing.
- Synthetic Data Generators - Creates realistic fake information to populate databases for software testing and demonstrations.
- Database Seeding Tools - Fills development databases with unique, realistic records to simulate production environments.
- Test Data Generators - Generates realistic fake data for identities, locations, and credentials to populate databases and demos.
- Locale-Aware Data Generators - Produces synthetic information customized for specific geographic regions and local language formats.
- Uniqueness Enforcement - Enforces unique value generation by tracking and rejecting duplicate synthetic entries.
- Atomic Duplicate Prevention - Maintains a registry of emitted values to prevent repetitions when unique synthetic data is required.
- Locale Mappings - Provides mappings that link generic data categories to region-specific word lists and formatting patterns.
- Regional Data Localization - Allows customization of data formats and strings based on geographic regions to ensure regional realism.
- Seeding Utilities - Utilizes a seeded pseudo-random number generator to ensure the same sequence of fake data is produced across runs.
- Deterministic - Provides deterministic seeding for the random number generator to ensure repeatable test datasets.
- Ruby Frameworks and Tools - Generates fake data for testing.
- Testing - Generates fake data.
