Fake Product Dataset Generator
Create reproducible synthetic product datasets with names, categories, pricing, inventory status, and ratings. This data is fictional and must not be used as real product data.
Also known as: mock products · dummy products · fake catalog
seeded · synthetic data
Presets
Output
About this tool, tips & examples
What it does
The Fake Product Dataset Generator creates reproducible synthetic catalog records — product names, categories, prices in your chosen currency, inventory status, and ratings — up to 10,000 rows per run. Filter to a category, inject nulls to simulate incomplete feeds, and reuse the seed to regenerate the identical catalog. Exports as CSV, JSON, NDJSON, TSV, or Markdown.
Common use cases
- E-commerce development — product grids, detail pages, search, and filtering against a realistic catalog.
- Catalog import testing — feed files with a controlled null rate to exercise validation, defaults, and error reporting.
- Analytics examples — price distributions, category mix, and rating charts with plausible inputs.
- Currency handling — the same catalog priced in EUR vs USD flushes out formatting and conversion assumptions (a preset covers EUR).
Settings
- Rows — 1 to 10,000 products.
- Category — Electronics, Apparel, Home, or All for a mix.
- Currency — the denomination for prices.
- Null rate — 0 to 1; randomly blanks nullable fields to simulate messy supplier feeds (a preset demonstrates it).
- Seed — identical seed + settings = identical catalog.
Privacy note
Products are generated locally in your browser and never uploaded. Everything is fictional — names, prices, and ratings are synthesized — and must not be presented as a real catalog or real inventory.
FAQ
What does the null rate actually do? Each nullable field has that probability of being empty per row. At 0.1, roughly one field in ten is missing — exactly the mess your import validation should survive.
Can I reproduce the same catalog in CI? Yes — pin the seed and settings; the rows regenerate identically on every machine, so snapshot tests stay stable.
How does this differ from the Synthetic Dataset tool? This one is a focused product-catalog generator; Synthetic Dataset is the general tool with field selection, renames, and many templates. Start here for catalogs, go there for custom schemas.