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Synthetic Enterprise Labs

DataGen

Synthetic enterprise data generation for labs, validation, demos, exports, and downstream integration.

8guided walkthroughs
4cmdlet reference sections
1GitHub Pages-ready site
DataGen architecture and workflow overview

Documentation shaped around real usage

This site is organized for operators, lab builders, and SDK authors. It starts with scenarios and outputs, then moves into reference and extension surfaces.

Model complete enterprise worlds

Generate identity, infrastructure, repositories, applications, CMDB views, policies, access evidence, and external ecosystem data from a single scenario definition.

Drive labs and validation with the same dataset

Use the generated world to populate labs, seed exports, validate discovery tooling, and create richer demos without maintaining fragile hand-authored fixtures.

Stay scenario-first and plugin-safe

Author scenarios with templates, overlays, and the terminal wizard, then extend the dataset through plugins that add synthetic data rather than system-specific adapters.

Dial realism without losing structure

Keep enterprise richness intact while choosing a deviation profile that ranges from clean to aggressively messy for labs, demos, and regression suites.

A clean path from scenario to usable data

DataGen is intentionally focused on synthetic data generation. The docs emphasize a consistent flow so teams can build labs, exports, SDK extensions, and downstream adapters without guessing where each concern belongs.

DataGen flow from scenario authoring to world generation and exports