We extract product specifications, variant matrices, regional pricing, and stock status from Scarpa. 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 scarpa.com. All fields typed and schema-versioned.
"product_id": "87501-200", "name": "Ribelle Tech 2.0 HD", "category": "Mountaineering", "price": 449.0, "currency": "USD", "weight_grams": 640, "membrane": "HDry", "gender": "Unisex"
| # | product_id | name | category | sub_category | gender | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Variants & Sizing objects from scarpa.com. All fields typed and schema-versioned.
"product_id": "87501-200", "sku": "87501-200-BLK-42", "colour_name": "Black/Orange", "size_eu": 42.0, "stock_status": "In Stock", "price": 449.0, "discount_price": "None"
| # | product_id | sku | colour_name | colour_code | size_eu | size_us |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Technical Specs objects from scarpa.com. All fields typed and schema-versioned.
"product_id": "70053-000", "upper_material": "Microsuede", "outsole": "Vibram XS Edge", "crampon_compatibility": "None", "vegan_friendly": true, "resoleable": true, "primary_activity": "Bouldering"
| # | product_id | upper_material | lining | insole | outsole | rand |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Dealer Locations objects from scarpa.com. All fields typed and schema-versioned.
"store_id": "DL-4819", "store_name": "Mountain Gear Exchange", "city": "Boulder", "state": "CO", "country": "USA", "latitude": 40.015, "longitude": -105.2705
| # | store_id | store_name | address | city | state | zip |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews objects from scarpa.com. All fields typed and schema-versioned.
"review_id": "REV-9921", "product_id": "70053-000", "rating": 5, "title": "Best bouldering shoe", "date_posted": "2023-10-14", "verified_buyer": true, "recommended": true
| # | review_id | product_id | author | rating | title | body |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Scarpa scraper pulls deep technical specifications, variant matrices, and regional pricing data. Built with rendering capabilities to handle dynamic stock status and sizing availability.
Deep capture of materials, sole types, weight metrics, and crampon compatibility across all product categories.
Map every colourway and half-size to its specific SKU and current stock status.
Capture pricing across different geographic zones and currencies using targeted proxies.
Extract physical retail networks, including coordinates, addresses, and contact details.
Capture primary, alternate, and technical diagram image URLs for catalogue enrichment.
Maintain the hierarchy from broad activities like Skiing down to specific boot sub-categories.
Identify products using specific third-party technologies like Vibram, Gore-Tex, or BOA fit systems.
Track inventory depth across sizes to understand demand patterns and stockouts.
Extract customer feedback, aggregate ratings, and specific fit recommendations.
Brief in. Clean data out.
Provide target regions, categories, or specific product lines. We design the extraction schema together.
We configure Scrapy crawlers, session management, and proxy routing for scarpa.com.
Schema validation, null-rate checks, and specification accuracy verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Extracting technical footwear data requires navigating complex variant matrices and regional site variations.
Scarpa loads size and colour availability dynamically based on user selection. We use Playwright to execute these state changes and capture the full SKU matrix.
Scarpa directs users to regional subdomains based on IP. We use targeted residential proxies to lock the crawler to specific locales for accurate local pricing.
Footwear specifications vary wildly between climbing shoes and ski boots. Our schema normalises these fields into a consistent JSON structure.
Store locators rely on external API calls. We intercept the underlying XHR requests to extract raw JSON coordinates rather than scraping the DOM map.
We hash product records and only emit updates when prices, stock levels, or specifications change, reducing downstream processing load.
Outdoor retailers track Scarpa's direct-to-consumer pricing to optimise their own margin strategies.
Merchandisers analyse size availability trends to forecast demand for specific models and half-sizes.
Product teams monitor the adoption rates of specific technologies like Vibram XS Grip2 or HDry membranes across the catalogue.
Competing brands extract retail locations to identify distribution gaps and wholesale opportunities.
Retailers integrate accurate weight, last, and material specifications directly into their own product display pages.
Analysts compare Scarpa's pricing tiers against competitors in the mountaineering and climbing verticals.
"Technical footwear data is incredibly dense. Extracting accurate sizing matrices and material specifications requires a pipeline built for complex variant structures."
Most generic scrapers fail on product pages with three-dimensional variant matrices covering model, colour, and half-size. DataFlirt handles the JavaScript execution required to hydrate these states, ensuring you capture the exact stock status and price for a size 42.5 climbing shoe, not just the base product.
Everything supported by our scarpa.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.
Executes the client-side JavaScript required to load size matrices and dynamic stock indicators.
Utilises regional residential IPs to bypass geo-redirects and capture accurate local pricing.
Scales dynamically on AWS infrastructure to handle full catalogue crawls without triggering rate limits.
Data delivered to where your team already works — no new tooling required.
About scarpa.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available catalogue and pricing data is generally permissible. We do not bypass authentication to access B2B or Pro Program portals.
Yes. Our pipeline executes the necessary JavaScript to cycle through all variant combinations, capturing specific SKUs, prices, and stock statuses.
We use geo-targeted residential proxies to access scarpa.com from specific regions, capturing accurate local currencies and pricing tiers.
Yes. We capture detailed specs including upper materials, lining, insoles, outsoles, weight, and last shapes, normalising them into structured fields.
Yes. We intercept the backend API calls used by the dealer map to extract clean JSON records of store locations, addresses, and coordinates.
We support daily or weekly runs for full catalogue extraction, and higher frequency runs for targeted stock monitoring on specific product lines.
We extract the URLs for high-resolution primary images, alternate angles, and technical diagrams. We do not host the image files directly.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous stock monitoring across the entire product line — we scope, build, and operate the pipeline. Tell us what you need.
