We extract apparel listings, sizing matrices, pricing history, and fabric compositions from FatFace. 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 Listings objects from fatface.com. All fields typed and schema-versioned.
"product_id": "984213", "sku": "FF-M-SHIRT-042", "title": "Airlie Graphic T-Shirt", "category": "Mens", "sub_category": "T-Shirts", "price": 28.0, "currency": "GBP"
| # | product_id | sku | title | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from fatface.com. All fields typed and schema-versioned.
"sku": "FF-M-SHIRT-042", "current_price": 28.0, "original_price": 35.0, "discount_pct": 20, "promo_badge": "Sale", "promo_text": "20% off selected tees", "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | current_price | original_price | discount_pct | discount_abs | promo_badge |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Sizing objects from fatface.com. All fields typed and schema-versioned.
"sku": "FF-M-SHIRT-042-NVY-L", "colour": "Navy", "size": "Large", "in_stock": true, "low_stock_warning": true, "stock_level": "Low", "scraped_at": "2026-05-12T09:14:33Z"
| # | sku | parent_product_id | colour | size | in_stock | stock_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Details objects from fatface.com. All fields typed and schema-versioned.
"product_id": "984213", "fit_type": "Classic Fit", "fabric_composition": "100% Cotton", "sustainability_tags": "['Better Cotton Initiative']", "care_instructions": "Machine washable at 30 degrees", "model_height": "6ft 1in", "model_wears_size": "Medium"
| # | product_id | description | fit_type | neckline | sleeve_length | fabric_composition |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from fatface.com. All fields typed and schema-versioned.
"review_id": "REV-847291", "product_id": "984213", "star_rating": 5, "review_title": "Great quality and fit", "review_date": "2026-04-18", "recommended": true, "fit_rating": "True to size"
| # | review_id | product_id | reviewer_nickname | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our FatFace scraper maps complex variant grids, tracks dynamic stock levels across sizes, and extracts granular fabric and sustainability data required for retail analytics.
Capture categories, sub-categories, product titles, descriptions, and high-resolution image URLs across the entire FatFace site.
Extract complex parent-child variant relationships. Map every colourway to its available size range.
Monitor inventory status for specific size and colour combinations. Capture low stock warnings and out-of-stock flags.
Track current prices, original prices, discount percentages, and promotional text across all departments.
Extract material breakdowns and care instructions for ESG reporting and product attribute analysis.
Capture eco-friendly product badges and sustainability claims associated with specific SKUs.
Collect customer sentiment, star ratings, and specific fit feedback from product review sections.
Monitor product rankings and visibility for specific search terms within the FatFace internal search engine.
Run daily or hourly pipelines that only output changed pricing or stock levels to reduce processing overhead.
Brief in. Clean data out.
Provide target categories or specific product URLs. We design the extraction schema together.
We configure Scrapy crawlers, handle pagination, and manage variant grid extraction for fatface.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Apparel sites use dynamic frontends and complex API endpoints for stock verification. We handle the technical overhead.
FatFace loads specific size availability via background API calls when a user selects a colour. We intercept these XHR requests to capture true stock status without relying on DOM scraping alone.
Apparel data is heavily nested. We flatten multi-dimensional arrays of colours and sizes into tabular formats, ensuring every unique SKU combination has its own row with accurate pricing.
To prevent IP blocking and ensure accurate regional pricing, we route requests through UK-based residential proxies with appropriate request throttling and header rotation.
We bypass thumbnail images and construct URLs for the highest resolution product assets from the content delivery network, useful for visual AI training.
For daily tracking, we maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs for price changes or stock movements.
Retailers track FatFace markdown cadences and promotional events to optimise their own pricing strategies.
Merchandising teams analyse category depth, colour trends, and sizing distributions to inform future buying decisions.
Fashion analysts track new product introductions and out-of-stock velocities to identify emerging consumer preferences.
Researchers aggregate fabric composition data to monitor the adoption of sustainable materials across retail brands.
Machine learning teams use structured high-resolution image datasets mapped to specific apparel categories to train computer vision models.
Pricing algorithms consume historical discount data to predict optimal markdown timing for seasonal inventory.
"Apparel intelligence requires more than scraping titles. You need the full matrix of colours, sizes, and stock depth to understand retail velocity."
Extracting data from modern fashion retailers involves navigating dynamic frontends, intercepting inventory APIs, and flattening complex parent-child variant structures. DataFlirt manages this complexity, delivering clean, normalised retail data ready for immediate analysis. Your engineering team avoids the maintenance burden of broken selectors and blocked IPs.
Everything supported by our fatface.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 and dynamic API interception for stock checks.
We maintain pools of residential ISP proxies across UK regions. Rotation happens per-request to prevent blocking and ensure accurate regional data.
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 fatface.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product and pricing information is generally permissible. DataFlirt targets only public, non-authenticated catalogue data. We do not extract personal user data or circumvent authentication walls.
Yes. Our pipeline iterates through all available variant combinations on a product page, intercepting the necessary API calls to determine stock status for each specific SKU.
We can configure pipelines to run at daily or hourly cadences depending on your requirements. Delta exports ensure you only process records where prices have changed.
Yes. We extract all structured metadata from the product details section, including fabric percentages, fit types, and washing instructions.
Our selector strategy uses multiple fallback chains. We monitor for null-rate spikes in real time and update extraction logic before it impacts your data delivery.
Yes. We provide a sample run of up to 500 products during the scoping process so you can validate the schema and data quality.
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 SKUs, we build and operate the pipeline. Tell us what you need.
