We extract product listings, pricing signals, EU size availability, brand intelligence, and material specifications from Humanic. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
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 humanic.net. All fields typed and schema-versioned.
"sku": "HU-94821045", "title": "Nike Air Max 270", "brand": "Nike", "category": "Sneakers", "colour": "Black/White", "price": 159.95, "currency": "EUR", "target_demographic": "Men"
| # | sku | title | brand | category | sub_category | colour |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from humanic.net. All fields typed and schema-versioned.
"sku": "HU-94821045", "current_price": 119.95, "original_price": 159.95, "discount_pct": 25, "currency": "EUR", "sale_badge": true, "club_price_eligible": false, "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | current_price | original_price | discount_pct | discount_abs | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Sizes objects from humanic.net. All fields typed and schema-versioned.
"sku": "HU-94821045", "size_eu": "43", "size_uk": "9", "in_stock": true, "low_stock_warning": true, "delivery_time_days": "2-4", "stock_status_text": "Nur noch wenige Artikel verfügbar"
| # | sku | size_eu | size_uk | in_stock | low_stock_warning | store_availability |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Materials & Specs objects from humanic.net. All fields typed and schema-versioned.
"sku": "HU-94821045", "upper_material": "Textile/Synthetic", "inner_material": "Textile", "sole_material": "Rubber", "closure_type": "Laces", "shoe_width": "Standard", "waterproof": false
| # | sku | upper_material | inner_material | sole_material | insole_material | heel_height_cm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from humanic.net. All fields typed and schema-versioned.
"keyword": "running shoes", "position": 4, "sku": "HU-94821045", "title": "Nike Air Max 270", "brand": "Nike", "price": 119.95, "sale_flag": true, "new_arrival_flag": false
| # | keyword | category_path | position | sku | title | brand |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Humanic scraper handles the complexities of European footwear retail data: regional storefronts, complex size matrices, material specifications, and dynamic pricing.
Extract titles, brands, article numbers, demographic targeting, and high-resolution image URLs for every shoe in the catalogue.
Capture availability across all EU and UK size variants, including low-stock warnings and out-of-stock indicators per size.
Monitor current prices, original prices, discount percentages, and sale badges across the DACH region storefronts.
Extract structured data for upper materials, inner linings, sole compositions, closure types, and heel heights.
Track click-and-collect availability and store-level stock indicators where surfaced on the product details page.
Map brand assortments and category hierarchies to understand inventory distribution across brands like Nike, Adidas, and Tommy Hilfiger.
Scrape data across Humanic's Austrian (AT), German (DE), and Swiss (CH) storefronts with correct regional pricing and taxes.
Detect seasonal sales, clearance events, and promotional tags to optimise your own pricing strategies.
Run daily or weekly pipelines to track stock depletion rates and price changes over time.
Brief in. Clean data out.
Provide target categories, brands, or specific SKUs. We configure the extraction schema for the target regions.
We configure Scrapy crawlers, proxy routing for DACH regions, and logic to hydrate complex size matrices.
Schema validation, null-rate checks on material fields, and price anomaly detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed schedule.
Extracting accurate footwear data requires navigating variant hydration and regional bot protection. Here is how we maintain stable pipelines.
Footwear data is useless without size-level stock. We execute the necessary API calls and DOM interactions to hydrate availability for every EU size variant on a product page, rather than just capturing the generic product status.
Humanic serves different content, pricing, and stock based on the user's location (Austria, Germany, Switzerland). We route requests through region-specific residential proxies to ensure accurate local data capture.
Shoe specifications are often presented in complex HTML lists or unstructured text blocks. Our parsers clean and normalise these into strictly typed fields for upper material, lining, and sole composition.
Retail sites often cap pagination visibility. We bypass these limits using targeted parameter injection and sub-category traversal to ensure 100% catalogue extraction without missing long-tail products.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs for price changes or stock shifts, reducing downstream processing load for your data engineering team.
Footwear retailers monitor Humanic's pricing and discount strategies across the DACH region to adjust their own pricing algorithms.
Merchandising teams analyse Humanic's brand mix and category depth to identify missing brands or over-represented styles in their own catalogues.
Footwear brands audit how their products are presented, priced, and discounted on Humanic to ensure MAP compliance and brand alignment.
Supply chain analysts track size-level stock depletion rates to predict demand curves for specific styles and colourways.
Retail strategists track the ratio of full-price to discounted items to gauge Humanic's inventory health and promotional intensity.
Fashion analysts aggregate material specs, colours, and new arrivals to identify emerging footwear trends in the European market.
"Humanic.net holds a massive footprint of the European footwear market, but extracting accurate size-level stock data requires navigating complex variant matrices and regional storefronts."
Most teams underestimate the investment required: reliable Humanic scraping requires residential proxies spanning DACH regions, handling complex size-variant hydration, 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 humanic.net 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 and deduplication. Playwright handles JavaScript rendering for dynamic size grids and interaction flows.
We maintain pools of residential ISP proxies across AT, DE, and CH regions. Rotation happens per-request to ensure accurate local pricing.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About humanic.net scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing, product, and inventory information is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal data or circumvent authentication walls. Clients should consult legal counsel for their specific use cases.
Yes. Our pipeline iterates through the size matrix for each product, capturing the stock status, low-stock warnings, and delivery estimates for every available EU or UK size.
Yes. We can configure pipelines for Humanic AT, DE, and CH, ensuring that currency, local pricing, and region-specific stock levels are captured accurately.
We use DACH-region residential proxies, appropriate request delays, and full browser rendering with realistic TLS fingerprints to maintain high success rates without triggering blocks.
We can run daily or twice-daily pipelines for full catalogue refreshes. For targeted SKUs (e.g., high-velocity sneaker releases), we can configure higher frequency polling.
Our smallest packages start at a defined category or brand list with weekly delivery. For full-catalogue daily extraction, we price based on compute volume and proxy bandwidth.
Absolutely. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process so you can validate field completeness and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or a continuous inventory monitoring feed across 80,000 SKUs, we scope, build, and operate the pipeline. Tell us what you need.
