Biedronka publishes the weekly gazetka as page images and its online catalogues as fields. We read both, add store cards and private label ranges, and hand you one clean feed.
A Biedronka scraper is scoped by surface, not by one crawl. We walk the online grocery catalogue category by category through its 32-item grid, its promotion filter and its price sort orders; we take Biedronka Home from its product sitemap and category tree; and we capture leaflet issues page by page with their validity dates, so every promotion can be tied to a window. Anti-bot handling, proxy rotation and CAPTCHA solving are part of the service, so blocking is our problem and never a line on your roadmap. Output comes as CSV, JSON, Excel or a direct database or S3 drop, on a cadence matched to the promotion calendar: weekly ahead of the Monday and Thursday changeovers, or daily where price movement is the whole point.
The two commerce domains are where a Biedronka product record is genuinely structured, because there price is a field and not a picture. On zakupy.biedronka.pl a grocery item carries a ten-digit zero-padded identifier, currently shaped like 0000008513, and the same id closes the URL slug, so the address is stable and easy to key on.
A grocery tile and its detail page together give:
Biedronka Home rows read differently. The identifier is an eighteen-digit padded number in the URL and a short numeric id on the page, and the page also prints the EAN barcode, so when we extract Biedronka non-food listings you get a key that joins straight to any other retailer. Around the price sit the unit price, the lowest price from the 30 days before the reduction, an explicit validity range in the form cena obowiazuje 05.08 - 30.09, the regular price outside the promotional period, courier lead time, a specification table with power, capacity, warranty years and certificates, and the manufacturer or importer block that EU product safety rules require.
The gazetka is what buyers ask about first, and it deserves a straight answer. Each issue lives at an address that packs the leaflet id and a title slug into the path under /pl/press, with the page number as an anchor; alcohol issues sit behind a separate age-gated path, and the title itself carries the start date. The viewer is a React widget that pulls a JSON document keyed by a leaflet UUID, and that document returns page image URLs and a layout flag, nothing more. A current food issue runs past fifty desktop spreads and about a hundred mobile pages. Leaflet prices therefore exist as pixels: turning them into rows means image capture plus OCR and layout reconstruction, not a field read.
Store data is far cleaner. The shop finder takes a city and street, and each store card holds the street with number, the postal code and separate opening hours for all seven weekdays, plus flags for a 24-hour shop, a lada tradycyjna (service counter), an in-store bakery, a franchise site, euro acceptance, tax free and Sunday opening. Poland restricts Sunday trading, so the trading-Sunday calendar is published as well and belongs in any store dataset. Roughly forty private label brands each get their own page, which makes own-brand versus national-brand splits straightforward.
Two things stay out of reach, and we say so plainly. The shelf price for one specific shop and the personalised Tylko dla Ciebie (only for you) offers live in the mobile app behind a loyalty card and are built from a shopper's own purchase history. We do not sign into consumer accounts and we do not collect personal data.
Biedronka is the discount grocery chain that Jeronimo Martins Polska runs in Poland, and its prices do not sit in a single catalogue. Three domains publish three different things. www.biedronka.pl carries the gazetka (the weekly promotional leaflet), the campaign calendar and the store finder. zakupy.biedronka.pl is a working online grocery shop with baskets, delivery slots and prices written as text. home.biedronka.pl is Biedronka Home, the non-food shop that ships by courier nationwide. Anyone planning to scrape Biedronka has to treat these as three sources with three shapes rather than one website.
Scale sets the stakes. The chain closed last year with roughly 3,900 stores spread over about 1,300 towns, so shelf coverage is a national question, not a city one. The offer engine runs on a trade-week calendar: leaflet artwork and campaign banners are filed under week codes such as T36A and T37A, food promotions open on Monday and on Thursday, and the non-food wave called Okazje tygodnia (deals of the week) runs in windows closer to a fortnight.
Store choice matters even on the marketing site, where a visitor is asked to pick a shop before the current offer appears. The chain also prints its own caveat next to every promotion: the regular price, the price before the reduction and the lowest price from the 30 days before it are nationwide figures set for the whole network, and the discount shown is a maximum that can differ from shop to shop.
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Poland is a discounter market and Biedronka sets the reference point inside it, which is why a Biedronka price feed replaces guesswork with dated rows. Typical questions it closes:
Because Biedronka scraping produces a dated series, the history outvalues any single snapshot. Repeat one category week after week and you get promotion frequency per item, average discount depth per brand, how often a product returns to the leaflet, and how fast a rival answers a cut. Range work behaves the same way: new entries and quiet delistings surface as arrivals and gaps in the category counts.
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If you sell online, run a marketplace, or advise e-commerce clients, you already know why eBay matters: it’s one of the few places where big retailers compete side by side with thousands of small merchants and private sellers.
E-commerce teams do not just need “some” competitor data anymore. They need a continuous stream of real prices, discounts, stock levels, reviews, and seller behavior from the platforms that actually shape their markets.
Amazon provides valuable information gathered in one place: products, reviews, ratings, exclusive offers, news, etc. So scraping data from Amazon will help solve the problems of the time-consuming process of extracting data from e-commerce.
ScrapeIt is a managed web scraping agency. You name the categories, the fields and the schedule; we build the crawler, repair it when the site shifts, and deliver clean rows on time. Nothing to license, no proxy pool to babysit, no in-house scraper to keep alive. Every Biedronka extract passes validation before it reaches you, so an empty price column or a broken unit-price parse is caught on our side rather than by your pricing team.
No. The leaflet viewer does call a JSON endpoint keyed by a leaflet id, but it answers with page image URLs and a layout flag, so it carries no product names, no prices and no article numbers. There is no documented Biedronka API for data buyers, and the loyalty account sits behind a login. Every structured field we deliver is therefore extracted from the public pages of the online grocery shop, Biedronka Home and the leaflet archive.
The chain states that the regular price, the price before the reduction and the lowest price from the previous 30 days are nationwide figures fixed for the whole network, and that the advertised discount is a maximum which may differ depending on the prices in a given shop. So leaflet numbers are a national reference, not a guaranteed shelf tag. Per-store shelf prices are only exposed through the in-app scanner, which reports the price for the nearest store or for the shop a customer has set as Moj sklep.
From the online grocery shop: id, name, brand, category path, price, unit price per kilo or litre, price before the reduction, discount percentage, promotion window, ingredients and allergens, country of origin, legal product name and availability. From Biedronka Home: id, EAN, price, unit price, lowest 30-day price, validity range, regular non-promotional price, specification table, warranty and manufacturer details. From the leaflet: issue id, title, start date, page count and page images. From the store finder: address, postal code, weekday opening hours and service flags.
Match the refresh to the promotion calendar. Food offers turn over on Monday and on Thursday, non-food deals of the week run in longer windows, and short bursts of two or three days appear around weekends. A weekly run just after each changeover captures the new leaflet and the fresh online prices; a daily run earns its keep when you track price movement, stock-outs or how quickly a rival reacts. Hourly is possible on a narrow watchlist, though grocery prices rarely move that fast.
We collect public commercial information only: product listings, prices, promotion windows, store addresses and opening hours, all of it visible to any visitor without an account. We do not log into the loyalty program, do not touch a customer's transaction history or e-receipts, and do not gather personal data of any kind. Personalised card offers are excluded for the same reason. If your legal team has constraints on volume, fields or retention, we build the crawl to fit them.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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