We extract shoe specifications, stack heights, clearance pricing, and inventory depth from Running Warehouse. 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 Footwear Specs objects from runningwarehouse.com. All fields typed and schema-versioned.
"product_id": "ASNK24M", "brand": "ASICS", "model": "Nimbus 24", "gender": "Men", "weight_oz": 10.2, "heel_drop_mm": 10, "stack_height_heel": 36, "price": 159.95
| # | product_id | brand | model | gender | surface | weight_oz |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Apparel & Gear objects from runningwarehouse.com. All fields typed and schema-versioned.
"product_id": "PUMST1", "brand": "Puma", "product_name": "Seasons Singlet", "category": "Apparel", "sub_category": "Singlets", "fit": "Athletic", "price": 45.0, "sizes_available": "['S', 'M', 'L']"
| # | product_id | brand | product_name | category | sub_category | material |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Inventory objects from runningwarehouse.com. All fields typed and schema-versioned.
"sku": "ASNK24M-001-105", "product_id": "ASNK24M", "base_price": 159.95, "clearance_price": 119.88, "discount_pct": 25, "in_stock": true, "sizes_in_stock": "['9.0', '9.5', '10.0', '11.0']", "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | product_id | base_price | clearance_price | discount_pct | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from runningwarehouse.com. All fields typed and schema-versioned.
"review_id": "REV98234", "product_id": "ASNK24M", "rating": 5, "reviewer_name": "Marathon Mike", "date": "2026-04-18", "title": "Great daily trainer", "helpful_votes": 12, "verified_buyer": true
| # | review_id | product_id | rating | reviewer_name | date | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search & Category objects from runningwarehouse.com. All fields typed and schema-versioned.
"keyword": "carbon plated shoes", "category_path": "Men's Running Shoes > Racing Shoes", "position": 3, "brand": "Nike", "product_name": "Vaporfly 3", "price": 250.0, "rating": 4.7, "review_count": 342
| # | keyword | category_path | position | product_name | brand | price |
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Our Running Warehouse scraper targets specific technical specifications, dynamic pricing grids, and size availability matrices across their entire catalogue.
Capture stack heights, heel drops, weights, pronation categories, and surface types for every shoe model in the catalogue.
Monitor base prices versus markdown prices across different colourways and sizes, timestamped per crawl.
Extract real-time stock status for specific size and width combinations (Standard, Wide, Extra Wide).
Map individual SKUs to their respective colourway names and image URLs.
Extract review text, star ratings, and verified buyer status to analyse sentiment on specific shoe updates.
Extract embedded YouTube URLs for Running Warehouse staff shoe reviews and overviews.
Scrape data across Running Warehouse US, Europe, and Australia storefronts to track regional inventory.
Extract fabric compositions, fit types, and category taxonomies for running apparel, hydration packs, and electronics.
Run pipelines daily or weekly to output only changes in price or stock availability.
Brief in. Clean data out.
Provide target categories, brands, or specific product URLs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for runningwarehouse.com.
Schema validation, null-rate checks, and data normalisation for technical specs before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Running Warehouse uses dynamic size grids and regional storefronts. Here is how we ensure data accuracy.
Shoe availability and pricing often change based on the selected size and width. We use Playwright to iterate through these selection matrices, capturing the exact price and stock status for every variant.
Running Warehouse redirects users based on IP location to regional sites (US, EU, AU). We force specific regional residential proxies to ensure we scrape the intended catalogue and currency.
Product descriptions often contain unstructured technical data. We use regex and NLP to parse weights, stack heights, and drops into clean, typed numerical fields in your database.
We maintain state across runs to detect when a product moves from full price to clearance, emitting a webhook or diff file immediately.
Retail sites employ rate limiting. Our crawlers use residential ISP proxies with realistic browser fingerprints to maintain high concurrency without triggering blocks.
Specialty running stores monitor clearance pricing and discount strategies to adjust their own retail pricing.
Footwear brands audit retail listings to ensure Minimum Advertised Price compliance across current season models.
Publishers aggregate technical specifications (stack height, drop, weight) to build comparison tools for runners.
Analysts track the proliferation of carbon-plated shoes and maximalist stack heights across different brands over time.
Brands track competitor size availability to understand which models and sizes are selling out fastest.
Product teams analyse review text to understand runner feedback on specific upper materials or midsole foam updates.
"Running Warehouse holds the most detailed technical specification dataset for running footwear on the internet, but extracting it requires navigating complex variant grids and dynamic pricing."
Most teams underestimate the investment required: reliable retail scraping requires residential proxies, full JavaScript rendering for size grids, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our runningwarehouse.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 for dynamic size and colourway selection grids.
We maintain pools of residential ISP proxies across US, EU, and AU regions to bypass geo-blocks and rate limits.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About runningwarehouse.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from retail sites is generally permissible. DataFlirt targets only public product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use Playwright to interact with the DOM, systematically selecting each size and width combination to capture the exact stock status and price for every variant.
Yes. We parse the unstructured product descriptions and specification lists to extract weight (in oz/g), heel drop (in mm), and stack heights into clean, numerical database columns.
Yes. We use geo-targeted residential proxies to access the regional storefronts, ensuring we capture the correct local currency and inventory.
We can configure pipelines to run daily or multiple times a day for specific product categories to catch flash sales and markdown events.
Yes. We extract the embedded YouTube URLs for the Running Warehouse staff reviews present on product pages.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across the entire site, we scope, build, and operate the pipeline. Tell us what you need.
