We extract product listings, size-level stock matrices, pricing signals, and brand catalogues from Sarenza. 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 sarenza.com. All fields typed and schema-versioned.
"sku": "SA-98214-BLK", "brand": "Nike", "model_name": "Air Force 1 '07", "category": "Trainers", "gender": "Women", "colour": "Black", "upper_material": "Leather", "heel_height": "3 cm", "season": "Autumn/Winter"
| # | sku | url | brand | model_name | category | gender |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Offers objects from sarenza.com. All fields typed and schema-versioned.
"sku": "SA-98214-BLK", "base_price": 119.99, "current_price": 89.99, "discount_pct": 25, "currency": "EUR", "flash_sale_flag": true, "sale_name": "Autumn Mid-Season Sale", "price_timestamp": "2026-10-14T08:12:00Z"
| # | sku | base_price | current_price | discount_pct | currency | flash_sale_flag |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Size & Inventory objects from sarenza.com. All fields typed and schema-versioned.
"sku": "SA-98214-BLK", "variant_id": "VAR-98214-38", "size_eu": "38", "size_uk": "5", "in_stock": true, "stock_level": 4, "low_stock_warning": true, "scraped_at": "2026-10-14T08:12:05Z"
| # | sku | variant_id | size_eu | size_uk | in_stock | stock_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Fit objects from sarenza.com. All fields typed and schema-versioned.
"review_id": "REV-884120", "sku": "SA-98214-BLK", "star_rating": 4.5, "size_fit_rating": "Runs small", "comfort_rating": 5.0, "review_title": "Great everyday trainer", "review_text": "Very comfortable, but I recommend ordering a half size up.", "review_date": "2026-09-22", "verified_purchase": true
| # | review_id | sku | reviewer_name | star_rating | comfort_rating | size_fit_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Brand Catalogue objects from sarenza.com. All fields typed and schema-versioned.
"brand_id": "BR-104", "brand_name": "Veja", "brand_url": "https://www.sarenza.com/veja", "active_models_count": 312, "new_arrivals_count": 45, "average_price": 135.5, "top_category": "Eco-friendly Trainers", "scraped_at": "2026-10-14T09:00:00Z"
| # | brand_id | brand_name | brand_url | active_models_count | new_arrivals_count | average_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Sarenza scraper navigates complex sizing grids, European bot mitigation, and dynamic pricing rules to deliver structured footwear data ready for analysis.
Extract materials (upper, lining, sole), heel heights, closure types, and seasonal collections directly from the product metadata.
Capture stock availability and low-stock warnings across all EU, UK, and US size variants for every shoe model.
Track base prices, discount percentages, and temporary flash sale flags across Sarenza's entire catalogue.
Group multiple colourways under a single parent model, ensuring your database reflects the true product hierarchy.
Extract standard star ratings alongside Sarenza's specific 'size fit' (e.g., runs small/large) and comfort metrics.
Monitor brand-level metrics including active SKU counts, new arrival velocity, and average price points.
Extract data from Sarenza's regional domains (France, UK, Germany, etc.) with localised currency and sizing formats.
Capture URLs for all product angles, lifestyle shots, and 360-degree spin assets.
Run pipelines that only emit records when a price changes or a specific size goes out of stock.
Brief in. Clean data out.
Provide target brands, categories, or specific product URLs. We map the extraction schema to your requirements.
We configure Scrapy and Playwright crawlers, proxy rotation, and bot-mitigation bypass for Sarenza's infrastructure.
Schema validation, null-rate checks on sizing grids, and price-outlier detection before full deployment.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on an agreed schedule.
European fashion retailers deploy strict rate limits and complex frontend architectures. Here is how we maintain pipeline stability.
Sarenza uses aggressive bot protection that blocks standard data center IPs. We route requests through EU-based residential proxies, managing TLS fingerprints and session cookies to mimic legitimate shopper traffic.
Size availability is not present in the static HTML. We use Playwright to execute Sarenza's JavaScript, triggering the size selection dropdowns to accurately capture in-stock status for every specific EU/UK size.
Fashion retailers frequently change their site layout for seasonal campaigns and sales events. Our extraction logic relies on underlying JSON data objects (next.js props) rather than fragile CSS classes.
A single shoe model can have 10 colours and 15 sizes. Our pipeline normalises this hierarchy, ensuring you receive a clean relational dataset linking every SKU back to its parent model.
During flash sales, stock levels change by the minute. Our change-detection engine monitors specific target SKUs continuously, emitting webhook payloads the moment a size drops out of stock or a price changes.
Retailers monitor Sarenza's discount depth and flash sale timing to optimise their own promotional calendars.
Merchandising teams analyse brand catalogues and new arrivals to identify gaps in their own footwear offerings.
Inventory planners track which sizes sell out first across different brands to optimise their purchasing size ratios.
Footwear brands audit Sarenza's pricing to ensure compliance with Minimum Advertised Price agreements across Europe.
Fashion analysts aggregate metadata on heel heights, colours, and materials to quantify emerging footwear trends.
eCommerce managers compare their brand coverage and price positioning directly against Sarenza's catalogue.
"Sarenza holds one of Europe's most structured footwear catalogues, but extracting reliable size-level inventory requires bypassing aggressive bot mitigation."
Most teams underestimate the complexity of apparel scraping: sizing grids are heavily nested in JavaScript, and European retailers deploy strict rate limits. DataFlirt manages the residential proxies, SPA rendering, and daily selector maintenance so your engineers can focus on pricing models and inventory forecasting, not broken pipelines.
Everything supported by our sarenza.com 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 orchestrates the broad crawl across category pages, while Playwright handles the JavaScript-heavy product pages to extract accurate size matrices.
We maintain dedicated pools of European residential proxies to bypass regional blocking and ensure compliance with localised pricing displays.
Airflow schedules continuous validation checks against the extracted data, alerting our engineers via Prometheus if Sarenza alters their frontend structure.
Data delivered to where your team already works — no new tooling required.
About sarenza.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline interacts with the sizing dropdowns on Sarenza product pages to extract the exact stock status (in stock, low stock, out of stock) for every available EU or UK size.
We utilise European residential proxies, realistic browser fingerprinting via Playwright, and automated solver integrations. This mimics human browsing patterns and prevents IP bans.
Yes. We can configure the pipeline to target specific regional domains (e.g., Sarenza.fr, Sarenza.de) to capture localised pricing, currencies, and availability.
For full catalogue extractions, we recommend daily or weekly cadences. For targeted monitoring of specific brands or high-velocity SKUs, we can configure sub-hourly pipelines.
Yes. We extract standard text reviews and star ratings, as well as Sarenza's specific metadata regarding sizing accuracy (e.g., 'runs large') and comfort levels.
Our minimum engagement typically covers a defined list of target brands or categories. We scope the pricing based on the total SKU volume and the required extraction frequency. Contact us for a precise quote.
Yes. We can configure webhooks to send an immediate payload to your systems if a tracked SKU drops below a specific price threshold or enters a flash sale.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop wrestling with bot mitigation and complex sizing grids. Tell us which brands and categories you need, and we will deliver clean, structured footwear data directly to your warehouse.
