Use the generated data freely. Keep its synthetic nature clear.

MockRows output is built for analysis practice, demos, prototypes, portfolios, testing, and other legitimate personal or commercial work.

Generated Data License

MockRows grants you a worldwide, royalty-free, non-exclusive license to use, copy, modify, combine, publish, and distribute files that you generate with the public workbench, including in commercial projects. Attribution is appreciated but not required.

This license covers the generated dataset output. It does not transfer ownership of the MockRows name, website, interface, source code, documentation, or brand assets.

Good uses

Generated files are intended for work where realistic structure is useful but real records are unnecessary or inappropriate.

  • Dashboard, spreadsheet, SQL, and data-modeling practice
  • Portfolio case studies that disclose the source as synthetic
  • Application demos, QA fixtures, prototypes, and internal training
  • Examples, tutorials, and commercial deliverables that do not imply the records are real

Use limits

Do not present MockRows output as evidence of real people, companies, payments, employment, support interactions, or campaign performance.

  • Do not use generated records for identity, eligibility, credit, employment, medical, legal, or other high-impact decisions.
  • Do not claim that a generated name, identifier, location, or transaction describes a real subject.
  • Review the output before publication; synthetic data can still contain implausible combinations or resemble real-world values by coincidence.

Quality contract

MockRows protects the documented row grain, field order, identifiers, lifecycle relationships, calculations, and scenario rules for each dataset. Those rules make the output useful for analysis, but they are not a warranty that every row matches every real business or regulatory requirement.

The dataset reference and Quality tab are the source of truth for interpreting repeated identifiers, blanks, ratios, dates, and additive measures.