We extract footwear listings, leather variants, size availability, pricing changes, and fit reviews from Thursday Boots. 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 Products objects from thursdayboots.com. All fields typed and schema-versioned.
"product_id": "TB-CPT-RUG", "title": "Captain", "category": "Men's Boots", "base_price": 199.0, "fit_notes": "Order half size down", "construction_type": "Goodyear Welt", "sole_type": "Studded Rubber"
| # | product_id | title | category | base_price | description | fit_notes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants objects from thursdayboots.com. All fields typed and schema-versioned.
"variant_id": "VAR-84291", "colour": "Arizona Adobe", "material": "Rugged Resilient Leather", "size": "10.5", "width": "Standard", "in_stock": true, "price": 199.0
| # | variant_id | product_id | colour | material | size | width |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from thursdayboots.com. All fields typed and schema-versioned.
"review_id": "REV-9418", "rating": 5, "title": "Zero break-in required", "body": "Wore these straight out of the box.", "date": "2023-11-14", "verified_buyer": true, "fit_rating": "True to size"
| # | review_id | product_id | author | rating | title | body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Collections objects from thursdayboots.com. All fields typed and schema-versioned.
"collection_id": "COL-M-BOOTS", "name": "Men's Boots", "product_count": 42, "url": "/collections/mens-boots", "active_status": true, "seo_title": "Men's Leather Boots | Thursday Boot Company"
| # | collection_id | name | url | product_count | description | hero_image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Imagery objects from thursdayboots.com. All fields typed and schema-versioned.
"image_id": "IMG-9912", "variant_id": "VAR-84291", "url": "https://cdn.thursdayboots.com/example.jpg", "alt_text": "Captain in Arizona Adobe side profile", "position": 1, "width": 2048, "height": 2048
| # | image_id | product_id | variant_id | url | alt_text | position |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our pipeline handles the underlying storefront architecture, capturing variant-level stock states, high-resolution media, and third-party review widget data with full JavaScript execution.
Extract titles, descriptions, fit notes, and detailed construction specifications for every footwear model.
Extract every unique combination of size, width, and leather type mapped to specific SKUs.
Track in-stock status and backorder timelines per variant to monitor inventory velocity.
Pull customer ratings, text reviews, and specific fit feedback from embedded review widgets.
Capture high-resolution image URLs accurately mapped to their specific colourways.
Map individual products to specific collections like Captain, President, or Scout.
Monitor base prices and any promotional discounts applied to specific models or variants.
Extract recommended pairings, matching belts, and shoe care accessory links.
Parse Goodyear welt construction details, leather origins, and specific sole types.
Brief in. Clean data out.
Provide target collections or specific boot models. We design the extraction schema to match your requirements.
We configure Scrapy and Playwright crawlers to handle dynamic variant hydration and proxy rotation.
Schema validation, stock status checks, and data normalisation routines run before full launch.
JSON, CSV, or Parquet files pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on your cadence.
Extracting data from modern storefronts requires handling dynamic state, variant hydration, and asynchronous review loading.
Thursday Boots uses complex variant arrays for size, width, and leather combinations. We parse the underlying JSON state objects to map exact SKU availability without clicking every option manually.
Customer reviews load via third-party asynchronous widgets. Our Playwright instances intercept the underlying API calls to extract the raw, unpaginated review corpus directly.
Product galleries change dynamically based on the selected colourway. We map specific CDN image URLs to their corresponding variant IDs for accurate cataloguing.
E-commerce platforms deploy edge protection to block automated traffic. We route requests through residential proxies with standard browser fingerprints to maintain reliable access.
We hash variant states per run. You receive diffs when a specific size or colourway goes out of stock or returns to inventory, saving compute and storage costs.
Footwear brands monitor Thursday Boots pricing strategies across specific boot and sneaker categories.
Analysts track out-of-stock rates on core sizes to estimate sales velocity and supply chain health.
Product teams analyse review text to understand customer feedback on leather break-in periods and sizing accuracy.
Trend forecasters monitor new colourways and material introductions in the DTC boot market.
Fashion aggregators sync product metadata and imagery for unified search platforms.
Supply chain analysts track the introduction of new leather types, suedes, and weather-resistant materials.
"Understanding a direct-to-consumer brand's success requires tracking inventory velocity at the variant level. A sold-out size 10 in Rugged Resilient leather tells a clear story."
Extracting surface-level product titles is trivial. Mapping the exact matrix of sizes, widths, and materials against real-time stock availability requires deep integration with the storefront's underlying state. DataFlirt handles the JavaScript execution and variant expansion so you receive clean, relational data ready for analysis.
Everything supported by our thursdayboots.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 retry logic. Playwright handles JavaScript rendering, state hydration, and asynchronous API interception.
We maintain pools of residential ISP proxies to bypass edge protection. Rotation happens per-request to ensure continuous access to the catalogue.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management, storing all state in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About thursdayboots.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product and pricing information is generally permissible. DataFlirt extracts only public catalogue and review data. We do not extract personal user data or bypass authentication walls.
Yes. We intercept the underlying review widget APIs to extract the full unpaginated corpus, including fit ratings, text bodies, and verified buyer badges.
We map specific prices to exact size and colour combinations. If a specific suede variant costs more than the standard leather, that price difference is accurately captured.
Yes. We extract boolean stock flags for every single variant combination, allowing you to track inventory depletion rates accurately.
We configure pipeline runs at your required frequency. For stock velocity tracking, we can run extractions on hourly schedules.
We extract the high-resolution CDN URLs mapped to specific variants, allowing your downstream systems to ingest the media files directly.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily stock-check across all variants or a one-time export of the review corpus, we manage the infrastructure. Specify your requirements.
