datgen

Datgen is a term used for software tools and libraries designed to generate synthetic or anonymized data for testing, development, benchmarking, and research. The goal is to produce datasets that resemble real data in structure and distribution while avoiding exposure of sensitive information. The name is used by several independent projects across programming languages, each with its own implementation details.

Core capabilities typically include a schema-driven generator that accepts a data model describing entities, fields, data types, and relationships. Generators support common types such as integers, decimals, strings, dates, timestamps, and booleans, with configurable distributions and constraints. Users can model relational data by defining primary and foreign keys, and can create realistic relational graphs with controlled cardinality. Seeding provides deterministic output for reproducibility, while templates or seed data libraries speed up common scenarios (for example, users, orders, and products). Privacy-oriented options may include data masking, redaction, and anonymization. Output formats often include CSV, JSON, SQL insert statements, Parquet, or direct database sinks, and tools may offer streaming or batched generation.

Operation typically follows a workflow in which a user defines a data model in a schema file, configures volume and performance parameters, and runs the generation tool via a command line interface or API. The generated data can be loaded into test environments, used to benchmark systems, or supplied for machine learning pipelines. Many datgen tools provide extensibility through plugins or custom generators and can integrate with CI/CD processes.

Limitations include the challenge of achieving high realism, potential bias in generated patterns, and performance considerations for very large datasets. Datagen outputs should be validated against real-world statistics and privacy requirements. When used responsibly, datgen accelerates development, testing, and experimentation by providing controllable, reproducible data without exposing sensitive information.