Titles, identifiers, attributes, images, and prices from the sources you choose, normalized into one schema you can load and query.
Get a free assessment
Pull the same running shoe from three sources and you'll get three different products. One calls the color "Navy", another "navy blue", the third buries it in the title. One lists a GTIN, two don't. Sizes are an attribute here and five separate listings there. Multiply that by 40,000 SKUs and "we'll just scrape the catalogs" turns into a quarter of data cleaning before anyone can run an assortment query.
That cleanup is the actual product here. Datka delivers catalog and listing data that's already been through it.
Titles and descriptions. Identifiers: SKU, GTIN or EAN, and the source's own product ID, because matching products across retailers lives or dies on identifiers. Attributes like size, color, and material as structured fields rather than prose. Image URLs, category paths, current and list prices. We scope the exact field set to your use case, whether that's enriching your own catalog, building a competitor feed, or mapping a category's assortment, and we'll tell you plainly when a field simply isn't present on a given source.
| as listed at the source | as delivered | |
|---|---|---|
| color | "Navy", "navy blue", or only in the title | color: navy |
| identifier | GTIN in a spec table, or absent | gtin populated where it exists, flagged where it doesn't |
| variants | five near-identical listings | one product, five variants |
Illustrative. Normalization rules are agreed during scoping, so "clean" means what your team means by it.
The failure modes are always the same: variant duplication inflating product counts, attributes named and formatted differently on every source, identifiers scattered or missing. Normalization is where a pile of listings becomes a dataset you can join, deduplicate, and count.
A one-off catalog snapshot ages fast: products launch, sell out, get renamed, move categories. We re-collect on the rhythm you set and ship the same schema every time, validated before it reaches you, so the feeds and models downstream don't break on delivery day. That consistency across deliveries is most of what separates business-ready data from a successful scrape; we've unpacked the distinction in what business-ready data actually means, and the full collection and QA process is described on the product page.
The first step is short: name your sources and the fields you need, and we'll return a scoped free assessment with a sample of real, normalized records to inspect.
Have more questions?
See all FAQsYou'll get a feasibility read on your target sites plus a real data sample, free.