We extract footwear listings, apparel catalogues, size-colour matrices, stock depth, and pricing signals from Under Armour. 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 Catalogue objects from underarmour.com. All fields typed and schema-versioned.
"product_id": "3026121", "style_number": "3026121-001", "title": "Men's UA HOVR™ Phantom 3 Running Shoes", "gender": "Men", "category": "Shoes", "fit_type": "Standard", "price": 140.0, "currency": "USD", "tech_features": "['UA HOVR™', 'SpeedForm® 2.0']", "available_sizes": "['7', '7.5', '8', '8.5', '9', '9.5', '10', '11', '12']"
| # | product_id | style_number | title | gender | category | sub_category |
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Complete list of extractable fields for Inventory & Pricing objects from underarmour.com. All fields typed and schema-versioned.
"style_number": "3026121-001", "sku": "3026121-001-9.5", "colour_name": "Black / Black / Metallic Silver", "size": "9.5", "price": 105.0, "list_price": 140.0, "discount_pct": 25, "in_stock": true, "stock_status": "Low Stock", "outlet_status": true
| # | style_number | sku | colour_id | colour_name | size | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Fit Data objects from underarmour.com. All fields typed and schema-versioned.
"review_id": "rev_982341", "product_id": "3026121", "rating": 4.5, "title": "Great cushioning for long runs", "verified_buyer": true, "date_posted": "2026-03-14", "fit_feedback": "Runs True to Size", "comfort_rating": 5, "quality_rating": 4, "helpful_votes": 12
| # | review_id | product_id | rating | title | body | reviewer_nickname |
|---|---|---|---|---|---|---|
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Our Under Armour scraper extracts multi-dimensional product grids: mapping every colourway and size variation to its specific SKU, price, and stock status — bypassing retail anti-bot systems automatically.
Extract apparel, footwear, and accessories across Men's, Women's, and Kids' categories — including outlet and new arrivals.
Map parent style numbers to child SKUs for every colourway and size permutation. Capture specific imagery per colour.
Monitor in-stock status, low-stock warnings, and out-of-stock variants at the SKU level to track inventory depth.
Capture base price, markdown price, discount percentage, and promo code eligibility. Track pricing across regional domains.
Extract proprietary technology tags — HeatGear®, ColdGear®, UA RUSH™, and UA HOVR™ — along with fabric compositions.
Extract customer reviews including specific fit feedback (runs small/large), comfort ratings, and verified buyer flags.
Run continuous pipelines that output clean diffs — highlighting only new markdowns, restocks, or newly launched styles.
Brief in. Clean data out.
Provide category URLs, specific style numbers, or regional domains. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for underarmour.com.
Schema validation, null-rate checks, price-outlier detection, and variant mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Retail sites deploy aggressive bot protection and complex frontend hydration. Here is how we maintain stable extraction.
Under Armour uses enterprise bot protection (like Akamai/Datadome) that flags headless browsers. Our crawlers use residential ISP proxies, realistic browser fingerprints, and randomised request timing to maintain high success rates without triggering blocks.
Size and colour selections often require JavaScript execution to fetch the corresponding SKU price and stock status. We run full Playwright sessions to trigger these DOM updates, capturing data that static HTML parsers miss.
Retailers frequently A/B test product page layouts. We use fallback chains — CSS, XPath, and LD+JSON structured data — ensuring that a frontend redesign does not break your data feed.
For large SKU catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load for your data engineering team.
Every run emits structured logs to our observability stack. We alert on null-rate spikes in critical fields like price or stock status, catching schema drift before it pollutes your warehouse.
Athletic apparel brands monitor Under Armour's pricing strategies, outlet markdowns, and promotional calendars to optimise their own pricing.
Retailers analyse category depth, colourway availability, and size distributions to identify market gaps and inform merchandising decisions.
Apparel analysts track the adoption of proprietary materials (e.g., UA HOVR) and fit types across new product lines.
Brands and distributors audit third-party pricing against Under Armour's direct-to-consumer prices to enforce Minimum Advertised Price policies.
Machine learning teams use structured review data — specifically fit feedback and comfort ratings — to train sizing recommendation algorithms.
Wholesalers monitor outlet inventory and deep discount events to source authentic apparel for secondary markets.
"Under Armour's catalogue holds critical signals for athletic wear pricing and material trends — but navigating their dynamic product matrices requires dedicated infrastructure."
Extracting apparel data at scale means handling multi-dimensional SKU variants, dynamic stock updates, and aggressive anti-bot measures. DataFlirt manages the proxy rotation, JavaScript hydration, and schema maintenance so your engineers receive clean, normalised datasets without the operational overhead.
Everything supported by our underarmour.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows for dynamic SKU matrices.
We maintain pools of residential ISP proxies to route around retail WAFs. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About underarmour.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from retail websites is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data, circumvent authentication walls, or violate GDPR.
We map the parent style number to every available child SKU. Our pipeline extracts the specific price, stock status, and imagery for each size and colour combination, normalising it into a structured matrix.
Yes. We capture boolean in-stock flags and parse low-stock indicators (e.g., 'Only 2 left') directly from the product page or underlying API responses.
Yes. We can target specific regional domains (e.g., underarmour.co.uk vs underarmour.com) using geo-located proxies to capture accurate local pricing and currency data.
Full catalogue refreshes typically run daily. For specific high-priority SKUs, we can configure intraday pipelines to monitor flash sales or outlet markdowns with sub-hour latency.
Absolutely. We provide a sample run of up to 500 products as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off apparel catalogue dump or a continuous price-monitoring feed across thousands of SKUs — we scope, build, and operate the pipeline. Tell us what you need.
