We extract boot listings, sneaker catalogues, size availability, pricing signals, and customer reviews from Sorel. 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 sorel.com. All fields typed and schema-versioned.
"style_number": "2058561", "title": "Women's Kinetic Breakthru Tech Sneaker", "category": "Sneakers", "gender": "Women", "price": 130.0, "list_price": 130.0, "weather_rating": "Breathable", "materials": "Eco-friendly materials"
| # | style_number | title | category | gender | price | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Variants & Colourways objects from sorel.com. All fields typed and schema-versioned.
"style_number": "2058561", "colour_name": "Sea Salt, Honest Yellow", "colour_code": "125", "size_range": "5 - 12", "available_sizes": "['6', '7', '8', '9']", "out_of_stock_sizes": "['5', '10', '11', '12']", "width_options": "['Standard']"
| # | style_number | colour_name | colour_code | image_urls | size_range | available_sizes |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Inventory objects from sorel.com. All fields typed and schema-versioned.
"style_number": "2058561", "sku": "2058561125", "base_price": 130.0, "sale_price": 99.9, "discount_pct": 23, "in_stock": true, "low_stock_warning": false, "currency": "USD"
| # | style_number | sku | base_price | sale_price | discount_pct | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from sorel.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "style_number": "2058561", "reviewer_name": "Sarah T.", "star_rating": 5, "review_title": "Extremely comfortable", "review_body": "Walked 10 miles in these right out of the box.", "review_date": "2026-02-14", "verified_buyer": true
| # | review_id | style_number | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Technical Specs objects from sorel.com. All fields typed and schema-versioned.
"style_number": "2058561", "upper_material": "Layered mesh with eco-friendly overlays", "lining_material": "Textile lining", "footbed_material": "Removable molded EVA", "midsole_material": "Lightweight molded Livelyfoam", "outsole_material": "Molded rubber", "heel_height": "1 1/2 in", "platform_height": "1 1/4 in"
| # | style_number | upper_material | lining_material | footbed_material | midsole_material | outsole_material |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Sorel scraper handles dynamic variant loading, colourway mapping, and size availability tracking with JavaScript rendering and session management built in.
Title, description, style numbers, and category metadata mapped across all footwear lines.
Monitor stock depth and availability for every size and width option per variant.
Extract colour names, internal colour codes, and associated high-resolution image URLs.
Capture base price, sale price, discount percentages, and promotional pricing.
Extract technical data including waterproof ratings, seam-sealed construction, and insulation weight.
Parse upper, lining, footbed, midsole, and outsole material specifications.
Extract star ratings, review text, verified buyer status, and review dates.
Traverse Men's, Women's, and Kids' categories automatically.
Run one-off bulk exports or configure continuous pipelines with change-detection diffing.
Brief in. Clean data out.
Provide category URLs or style numbers. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for sorel.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
eCommerce platforms heavily utilise dynamic inventory loading and bot mitigation. Here is how we maintain stable extraction.
Retail sites block datacentre IPs. We route requests through residential proxies with realistic TLS fingerprints to ensure uninterrupted access to Sorel's catalogue.
Size availability and colour-specific pricing load dynamically. We execute full browser sessions to trigger these events and capture the resulting DOM state.
Sorel updates site layouts seasonally. We implement multi-layered CSS and XPath selectors to ensure data extraction continues even when UI elements change.
We maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing downstream processing load.
We capture out-of-stock states for specific sizes and colours, providing accurate inventory signals rather than just top-level product availability.
Retailers track Sorel's direct-to-consumer pricing and promotional discounts to inform their own pricing strategies.
Merchandisers analyse Sorel's product mix, colourway offerings, and size ranges to identify market gaps.
Fashion analysts monitor new releases and material specifications to predict upcoming footwear trends.
Analysts track size-level stockouts to estimate demand velocity for specific boot and sneaker models.
Machine learning teams use structured footwear descriptions and technical specs to train retail classification models.
Brands track retail pricing to ensure compliance with Minimum Advertised Price policies across channels.
"Sorel's catalogue contains critical footwear spec data and pricing signals, but capturing accurate size-level availability requires constant pipeline maintenance."
Most teams underestimate the investment required to build reliable eCommerce scrapers. Sorel.com uses dynamic variant loading, requiring full JavaScript rendering and residential proxies to extract accurate pricing and inventory data without triggering bot defences. DataFlirt manages this complexity so you can focus on retail analysis.
Everything supported by our sorel.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 handles crawl orchestration and deduplication. Playwright handles JavaScript rendering and variant selection.
We maintain pools of residential ISP proxies. Rotation happens per-request to avoid datacentre IP blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management.
Data delivered to where your team already works — no new tooling required.
About sorel.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline iterates through all available colourways and size options to capture specific pricing and stock status for every variant.
We use JavaScript rendering to capture the exact price displayed to the user after variant selection, including sale prices and discounts.
Yes. We record the stock status for every size and colour, allowing you to track inventory depletion over time.
Pipelines can be configured to run daily, weekly, or at custom intervals depending on your requirements.
Yes. We parse the details section to extract materials, waterproof ratings, insulation details, and physical measurements like heel height.
Yes. We provide a sample run of up to 100 products during the scoping process.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous inventory monitoring, we build and operate the pipeline. Tell us your requirements.
