We extract product listings, pricing signals, seller intelligence, and review data from Allegro. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from allegro.eu. All fields typed and schema-versioned.
"offer_id": "1234567890", "title": "Samsung Galaxy S23 Ultra 8/256GB Black", "brand": "Samsung", "ean": "8806094723145", "price": 5499.0, "currency": "PLN", "condition": "New", "allegro_smart": true
| # | offer_id | title | category_path | brand | ean | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Offers objects from allegro.eu. All fields typed and schema-versioned.
"offer_id": "1234567890", "price": 5499.0, "original_price": 5999.0, "discount_pct": 8, "lowest_price_30_days": 5499.0, "coins_reward": 50, "shipping_cost": 0.0
| # | offer_id | price | original_price | discount_pct | lowest_price_30_days | coins_reward |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Seller Intelligence objects from allegro.eu. All fields typed and schema-versioned.
"seller_id": "987654321", "seller_name": "ElectroStore_PL", "super_seller": true, "positive_feedback_pct": 99.8, "total_ratings": 45210, "company_name": "ElectroStore Sp. z o.o.", "vat_id": "PL1234567890"
| # | seller_id | seller_name | super_seller | positive_feedback_pct | total_ratings | response_time_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from allegro.eu. All fields typed and schema-versioned.
"review_id": "REV-8472910", "offer_id": "1234567890", "star_rating": 5, "review_text": "Fast shipping and perfect condition.", "review_date": "2026-03-14", "helpful_votes": 12, "verified_purchase": true
| # | review_id | offer_id | star_rating | review_text | review_date | helpful_votes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search Results objects from allegro.eu. All fields typed and schema-versioned.
"keyword": "smartfon", "position": 1, "offer_id": "1234567890", "title": "Samsung Galaxy S23 Ultra 8/256GB Black", "price": 5499.0, "sponsored": true, "allegro_smart": true
| # | keyword | position | offer_id | title | price | sponsored |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Allegro scraper handles every layer of the platform: storefront listings, dynamic pricing, Super Seller intelligence, and the review corpus — with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, parameters, description, EAN, images, and every metadata field Allegro surfaces — scraped at offer level.
Capture price, lowest price in 30 days (Omnibus directive), discounts, and coin rewards — timestamped per crawl.
Seller name, feedback score, Super Seller badge status, VAT ID, and response time — for every offer on a listing.
Full review text, star ratings, helpful vote counts, and verified purchase flags — paginated across all review pages.
Track which offers qualify for free delivery under the Allegro Smart! program, a critical conversion metric.
Track organic vs sponsored position for any keyword — with Coin reward and Smart! badge capture.
Extract parent-child variant relationships for colour, size, and storage capacity combinations.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Extract data across Allegro.pl, Allegro.cz, and Allegro.eu domains from a unified schema.
Brief in. Clean data out.
Provide offer IDs, category URLs, keyword sets, or seller IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for allegro.eu.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Allegro invests heavily in scraping detection. Here is how we stay resilient — and why teams choose managed infrastructure over DIY.
Allegro's bot detection operates on TLS fingerprints, browser headers, and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Allegro product pages and search results are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution, capturing data that headless HTTP clients miss entirely.
Allegro changes its DOM structure frequently. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, and text-pattern matching — so a layout change does not break your data pipeline overnight.
For large offer catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops — and respond before you notice.
eCommerce brands and 3P sellers monitor pricing across Allegro's EU domains to reprice and protect margin.
Brands audit third-party sellers for MAP violations, counterfeit listings, and unauthorised resellers.
Analysts track sales volume indicators, new entrant launches, and category saturation trends.
ML teams use Allegro datasets to train recommendation engines, NLP classifiers, and sentiment models.
Supply chain teams correlate review velocity and stock depth indicators with sales velocity.
PE firms track category leaders, Super Seller growth curves, and review ratios to evaluate marketplace companies.
"Allegro dominates Central European e-commerce, but extracting its pricing signals requires bypassing strict bot protection."
Most teams underestimate the investment required: reliable Allegro scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our allegro.eu scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies across EU regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About allegro.eu scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Allegro is generally permissible under applicable EU law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 503/CAPTCHA rate spikes in real time.
We support allegro.pl, allegro.cz, and allegro.eu from a unified schema, normalising currency and category structures.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined offer set. Full catalogue refreshes at daily cadence complete within a 6-12 hour window.
Our smallest packages start at a defined offer list (typically 1,000-50,000 offers) with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 offers or 50 search result pages as part of the pre-engagement scoping process.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off product catalogue dump or a continuous price-monitoring feed — we scope, build, and operate the pipeline. Tell us what you need.
