Test Data Generator

The tool generates synthetic records based on a user-defined schema. You specify field data types (numeric, string, date, boolean, etc.), value ranges, distributions (uniform, normal, Bernoulli), and the total number of records to produce. A seed parameter ensures deterministic results for reproducibility across runs, enabling reliable comparisons between environments and testing cycles.
During generation, each field is produced according to its rules; optional cross-field constraints can be approximated with deterministic IDs or date relationships by deriving values from a shared seed. The engine supports output formats such as JSON, CSV, and SQL insert statements, and can stream records in batches. Advanced options include null probability per field, value masking for sensitive data, locale-aware formatting, and controlled skew to simulate real-world distributions.
Intended users include QA engineers validating data pipelines, data scientists testing models, software developers simulating realistic inputs, and CI teams creating repeatable fixtures. Concrete examples include a customers table with id, signup_date, age, and status; a transactions log with amount, currency, and timestamp; and a product catalog with category hierarchies. The tool also provides metadata about the generated dataset, such as field histograms and sample previews.
Key differentiators are reproducible seeds, expressive per-field definitions, multi-format exports, and a lightweight runtime designed for quick iteration in development and testing. It avoids reliance on production data while preserving realistic value distributions, enabling safe experimentation, benchmarking, and regression testing across pipelines.