We extract running shoe specifications, apparel sizing, inventory availability, and pricing signals from Jackrabbit. Delivered as clean JSON or Parquet to your warehouse.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Footwear Specs objects from jackrabbit.com. All fields typed and schema-versioned.
"sku": "JR-SH-8821", "brand": "Brooks", "model": "Ghost 15", "gender": "Men", "drop_mm": 12, "cushion_level": "Medium", "price": 140.0, "support_type": "Neutral"
| # | sku | brand | model | gender | category | surface |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Apparel & Gear objects from jackrabbit.com. All fields typed and schema-versioned.
"sku": "JR-AP-993", "brand": "Nike", "product_name": "Dri-FIT Miler", "category": "Apparel", "material": "100% Polyester", "fit_type": "Standard", "price": 35.0, "clearance_flag": false
| # | sku | brand | product_name | category | sub_category | material |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Inventory objects from jackrabbit.com. All fields typed and schema-versioned.
"sku": "JR-SH-8821", "base_price": 140.0, "current_price": 119.95, "discount_pct": 14, "stock_status": "In Stock", "low_stock_warning": true, "timestamp": "2023-10-24T08:12:00Z"
| # | sku | base_price | current_price | discount_pct | stock_status | low_stock_warning |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from jackrabbit.com. All fields typed and schema-versioned.
"review_id": "REV-48291", "sku": "JR-SH-8821", "rating": 4.8, "review_date": "2023-09-15", "verified_buyer": true, "fit_rating": "True to size", "text": "Great daily trainer."
| # | review_id | sku | rating | reviewer_name | review_date | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category Mapping objects from jackrabbit.com. All fields typed and schema-versioned.
"brand_name": "HOKA", "category_path": "Running > Men > Shoes", "total_products": 142, "new_arrivals": 12, "sale_items": 34, "scraped_at": "2023-10-24T08:15:00Z"
| # | brand_id | brand_name | category_path | total_products | new_arrivals | sale_items |
|---|---|---|---|---|---|---|
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Our Jackrabbit scraper handles complex variant matrices, technical running specifications, and dynamic inventory systems to deliver accurate retail intelligence.
Capture heel-to-toe drop, stack height, weight, and cushion level attributes across all running shoe models.
Flatten multidimensional variants including shoe sizes, widths, and apparel dimensions into queryable formats.
Monitor base prices, clearance discounts, and brand-specific promotional pricing across the entire catalogue.
Extract specific SKUs, images, and inventory status for every individual colour variant of a product.
Detect out-of-stock sizes and low-stock warnings to identify supply chain gaps and demand trends.
Extract aggregated scores for fit, comfort, and quality alongside full text reviews.
Track new product launches, category dominance, and discontinued lines for major running brands.
Traverse deep category trees and faceted search filters to ensure complete catalogue coverage.
Identify site-wide banners, promo codes, and shipping thresholds applied to specific categories.
Brief in. Clean data out.
Provide target brands, categories, or specific SKUs. We map the required data fields.
We configure Scrapy crawlers, handle Jackrabbit's frontend rendering, and map the size-colour matrices.
Schema validation, price-outlier detection, and null-rate checks on technical specs.
JSON / CSV / Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Extracting from specialised retailers requires handling deep variant data and dynamic inventory. Here is our technical approach.
Running shoes have multidimensional variants: colour, size, and width. We flatten these matrices into queryable relational rows.
Stock availability per size is often loaded via client-side XHR. We intercept these API calls to capture accurate stock depth without rendering the full DOM.
E-commerce sites cap pagination. We use faceted search traversal, filtering by brand, size, and colour, to ensure 100% catalogue coverage.
Discounts are sometimes applied in-cart or via session-based banners. Our crawlers simulate user sessions to extract the true final price.
Different brands supply different spec formats. We normalise drop, weight, and cushion into standard numeric fields for cross-brand comparison.
Brands monitor retail prices across Jackrabbit to enforce Minimum Advertised Price agreements.
Retailers track brand overlap, new product launches, and category depth to inform their own buying strategies.
Analyse out-of-stock sizes and colours to identify supply constraints and optimise procurement.
Track the velocity of new colourways and clearance rates to predict upcoming seasonal trends.
Populate internal databases with accurate technical specifications for thousands of running shoe models.
Aggregate fit, comfort, and quality feedback across models to inform product development and marketing.
"Running retail relies on highly specific technical attributes like drop, stack height, and width. Extracting this consistently across thousands of SKUs requires precision parsing, not generic scraping."
Jackrabbit's catalogue presents unique extraction challenges: deep variant matrices (size, width, colour), dynamic inventory loading, and brand-specific technical specifications. DataFlirt engineers pipelines that normalise these attributes into a unified schema, delivering clean, warehouse-ready data. We handle the frontend complexity so your analysts can focus on pricing and assortment strategy.
Everything supported by our jackrabbit.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.
Playwright combined with Scrapy intercepts XHR requests for inventory data, bypassing heavy DOM rendering for faster execution.
Custom Python 3.12 parsers flatten nested JSON responses containing complex size, width, and colour matrices into tabular formats.
Prometheus and Grafana monitor null rates on critical fields like price and stock status, triggering Airflow retries automatically.
Data delivered to where your team already works — no new tooling required.
About jackrabbit.com scraping, legality, and pipeline operations.
Ask us directly →Extracting publicly available pricing, specification, and inventory data is generally permissible. We do not bypass authentication walls or extract personal user data.
We flatten complex product matrices. A single shoe model with 10 sizes, 2 widths, and 3 colours becomes 60 distinct rows, each with its own inventory status and SKU.
Yes. We capture specific stock status flags for every size and colour combination, allowing you to monitor inventory depletion rates.
We configure pipelines based on your requirements. Critical SKUs can be polled hourly, while full catalogue refreshes typically run daily or weekly.
Yes. Attributes like drop, weight, and cushion level are extracted and cast to standard numeric or categorical types for easy cross-brand comparison.
Yes. We paginate through all reviews, capturing text, star ratings, and specific sub-ratings for fit, comfort, and quality.
20-minute scoping call. Pilot dataset within the week. Production within two. From comprehensive catalogue dumps to daily price monitoring across key running brands. Tell us your data requirements, and we build the pipeline.
