We extract footwear listings, size availability, brand matrices, and pricing signals from Pittarosso. Delivered as clean JSON, CSV, or Parquet to your S3 bucket or Snowflake instance 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 Listings objects from pittarosso.com. All fields typed and schema-versioned.
"product_id": "PR-849201", "sku": "10029481A", "title": "Nike Revolution 6", "brand": "Nike", "category": "Sneakers", "gender": "Men", "price": 49.99, "currency": "EUR"
| # | product_id | sku | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Sizing objects from pittarosso.com. All fields typed and schema-versioned.
"sku": "10029481A", "size_eu": "42", "size_uk": "8", "in_stock": true, "stock_level": "low", "colour_variant": "Black/White", "delivery_time": "2-3 days"
| # | sku | size_eu | size_uk | in_stock | stock_level | colour_variant |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from pittarosso.com. All fields typed and schema-versioned.
"sku": "10029481A", "current_price": 49.99, "original_price": 64.99, "discount_percentage": 23, "saldi_badge": true, "promo_code_eligible": false, "price_timestamp": "2026-05-12T10:14:00Z"
| # | sku | current_price | original_price | discount_percentage | promo_code_eligible | saldi_badge |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Specifications objects from pittarosso.com. All fields typed and schema-versioned.
"sku": "10029481A", "upper_material": "Synthetic Mesh", "lining_material": "Textile", "sole_material": "Rubber", "fastening_type": "Laces", "heel_height": "Flat", "waterproof": false
| # | sku | upper_material | lining_material | sole_material | fastening_type | heel_height |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Physical Stores objects from pittarosso.com. All fields typed and schema-versioned.
"store_id": "PT-042", "store_name": "Pittarosso Milano Fiori", "city": "Assago", "region": "Lombardia", "postal_code": "20090", "click_and_collect": true, "latitude": 45.4012
| # | store_id | store_name | address | city | region | postal_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Pittarosso scraper handles every layer of the platform including dynamic size matrices, promotional pricing, and category taxonomies, with full JavaScript rendering built in.
Title, brand, category, materials, and imagery across all Pittarosso departments.
Monitor stock availability across the entire EU size run for every colour variant.
Capture base prices, seasonal discount badges, and promotional mechanics during Italian sale periods.
Extract nested categories for major brands like Nike, Adidas, Puma, and Geox.
Isolate upper, lining, and sole materials into structured fields for attribute analysis.
Scrape physical store coordinates, opening hours, and click-and-collect availability.
Extract high-resolution product gallery URLs and structural mapping to specific colour variants.
Pipelines cover sneakers, boots, formal shoes, bags, and accessories in a single unified schema.
Run weekly catalogue dumps or configure daily pipelines for fast-moving inventory tracking.
Brief in. Clean data out.
Provide target categories, brands, or search URLs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, Italian residential proxies, and interaction flows for pittarosso.com.
Schema validation, null-rate checks, and size-matrix outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Retail sites deploy aggressive caching and dynamic inventory rendering. Here is how we maintain data integrity.
Pittarosso loads size availability and colour variants dynamically via frontend frameworks. We run full Playwright browser sessions to hydrate the DOM and extract accurate stock states.
To access accurate local pricing and prevent IP bans, our crawlers route traffic through residential ISP proxies geolocated in Italy, ensuring consistent access.
Retail platforms often truncate deep category pagination. We use programmatic filtering and sub-category traversal to ensure total catalogue capture without missing SKUs.
DOM structures for product specifications change frequently. Our selector strategy relies on fallback chains and JSON-LD extraction to maintain pipeline stability.
For large catalogues, we maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Retailers and brands track Pittarosso pricing, discount depth, and promotional periods to optimise their own pricing strategies.
Merchandising teams analyse brand mix, category depth, and new product introductions to identify market gaps.
Track size availability decay rates to estimate sales velocity and restock cadences for specific footwear categories.
Footwear brands audit Pittarosso listings to ensure adherence to Minimum Advertised Price guidelines during non-sale periods.
Analysts aggregate colour, material, and style attributes to detect shifting consumer preferences in the Italian footwear market.
Real estate and retail analysts extract store locator data to map Pittarosso physical presence and click-and-collect coverage.
"Footwear retail data is highly dimensional. Extracting a price is easy, but mapping exact size availability across a matrix of forty thousand SKUs requires dedicated infrastructure."
Most teams underestimate the complexity of retail scraping. Capturing dynamic size matrices, handling Italian geo restrictions, and maintaining clean schema mappings for complex material attributes requires dedicated engineering. DataFlirt absorbs that complexity so your data engineering team can focus on analysis, not infrastructure maintenance.
Everything supported by our pittarosso.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 for dynamic size matrices and interaction flows.
We maintain pools of residential ISP proxies in Italy. Rotation happens per request to prevent rate limiting and ensure accurate local pricing.
Pipelines run on AWS Lambda and Kubernetes. Airflow handles scheduling, dependency management, and alerting. State is stored in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About pittarosso.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available catalogue and pricing information is generally permissible. DataFlirt targets only public, non-authenticated product data. We do not extract personal data or circumvent authentication walls.
We use full Playwright browser sessions to execute JavaScript, triggering the frontend frameworks that load size and stock data for each colour variant.
Yes. We route requests through Italian residential proxies to ensure we receive the correct regional pricing, stock availability, and avoid geo-blocking.
Full catalogue refreshes can be configured at daily or weekly cadences. Specific high-priority categories can be tracked at higher frequencies depending on your requirements.
Yes. We can scope the pipeline to target specific brand URLs or specific categories rather than the entire Pittarosso catalogue.
Our packages start at defined category or brand lists with weekly delivery. For full catalogue extraction, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across thousands of footwear SKUs, we scope, build, and operate the pipeline. Tell us what you need.
