What fields does each product have?

Each row includes an id, a generated name, a SKU, a category, a price, a discount percentage and resulting sale price, stock quantity, an in-stock boolean, a rating out of five, a review count, and a short SKU-based description. Prices and discounts are internally consistent.

Is the sale price correct relative to the discount?

Yes. The sale price is computed as price times one minus the discount percentage, rounded to two decimals. So a 50 priced item with a 20 percent discount shows a sale price of 40, keeping the dataset arithmetically valid for analytics tests.

Can I export to CSV for a spreadsheet?

Yes. CSV mode produces a header row followed by one line per product, with fields quoted where needed so names containing commas do not break columns. Paste it straight into a spreadsheet or a bulk-import tool.

Is the data realistic?

Names combine real adjectives, materials, and product nouns, categories are drawn from common retail departments, and prices, ratings, and stock follow plausible ranges. It is synthetic but reads like a believable catalog for demos and screenshots.

Does any data leave my browser?

No. The dataset is generated locally in JavaScript and serialised on the page, so nothing is uploaded. It is safe for confidential demos and works offline.

Sample E-commerce Dataset Generator

This generator produces a believable synthetic e-commerce catalog — products with names, SKUs, prices, discounts, stock levels, and ratings — that you can drop into demos, analytics dashboards, seed scripts, and import pipelines. Every row is internally consistent, so the data behaves correctly when your tooling does maths on it.

How it works

For each product the generator composes a name from adjective, material, and noun word pools, assigns a category, and derives the commercial fields:

price      = random within a category-plausible range
discount   = random percentage (often 0)
salePrice  = round(price × (1 − discount / 100), 2)
inStock    = stock quantity > 0
rating     = random 3.0–5.0, reviews scale with rating
sku        = CATEGORY-prefix + zero-padded sequence

Because salePrice is computed from price and discount rather than being random, any revenue or margin calculation you run against the dataset gives sensible results. Export as JSON for APIs and seed files, or as CSV with a header row and proper quoting for spreadsheets and bulk importers.

Tips and notes

Use a small row count for quick UI screenshots and a few hundred rows to stress-test tables, pagination, and chart rendering. The CSV exporter quotes any field containing a comma or quote so it never corrupts columns. Regenerate for a fresh catalog, or copy and save a dataset you want to reuse as a fixed fixture in tests.