We extract fabric listings, pricing per metre, haberdashery inventory, and roll specifications from Fabricland. 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 Fabric Listings objects from fabricland.co.uk. All fields typed and schema-versioned.
"sku": "FBL-COT-042", "title": "Printed Cotton Poplin Floral", "category": "Cotton Fabrics", "price_per_metre": 6.99, "composition": "100% Cotton", "width_cm": 112, "colour": "Navy/Pink", "in_stock": true
| # | sku | title | category | sub_category | price_per_metre | composition |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Haberdashery objects from fabricland.co.uk. All fields typed and schema-versioned.
"sku": "HAB-ZIP-891", "title": "YKK Concealed Zip 22 inch", "brand": "YKK", "category": "Zips", "price": 2.5, "colour": "Black", "in_stock": true
| # | sku | title | brand | category | price | pack_size |
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Complete list of extractable fields for Sewing Patterns objects from fabricland.co.uk. All fields typed and schema-versioned.
"pattern_number": "M7969", "brand": "McCall's", "garment_type": "Dresses", "difficulty": "Easy", "price": 10.5, "size_range": "XS-M", "in_stock": true
| # | pattern_number | brand | garment_type | difficulty | price | size_range |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Stock objects from fabricland.co.uk. All fields typed and schema-versioned.
"sku": "FBL-COT-042", "base_price": 6.99, "unit_of_measure": "metre", "bulk_discount_threshold": 10, "bulk_price": 5.99, "stock_status": "In Stock", "currency": "GBP"
| # | sku | base_price | unit_of_measure | bulk_discount_threshold | bulk_price | stock_status |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Categories & Taxonomy objects from fabricland.co.uk. All fields typed and schema-versioned.
"category_id": "cat_104", "name": "Fleece Fabrics", "parent_category": "Dress Fabrics", "url": "https://fabricland.co.uk/product-category/fleece/", "product_count": 142, "breadcrumb_trail": "['Home', 'Dress Fabrics', 'Fleece Fabrics']"
| # | category_id | name | parent_category | url | product_count | description |
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Our scraper maps the complete Fabricland catalogue: from complex fabric roll specifications to haberdashery stock levels, handling unstructured legacy HTML and variant matrices.
Extract composition percentages, width in centimetres and inches, weight in GSM, and care instructions from unstructured product descriptions.
Standardise pricing and dimensions across listings. Convert yards to metres and normalise price per unit automatically.
Link parent products to all available colour options, capturing specific variant SKUs and associated image assets.
Capture discrete items like threads, zips, and buttons, including pack sizes, thread lengths, and brand associations.
Extract pattern brands, garment types, difficulty levels, size ranges, and recommended fabric requirements.
Identify bulk discount thresholds and wholesale pricing tiers for full roll purchases versus cut-to-length orders.
Monitor out of stock indicators and low stock warnings across all variants to inform procurement decisions.
Reconstruct the full site hierarchy, mapping products to their precise sub-categories and breadcrumb trails.
Run daily or weekly pipelines that output only changed records, minimising downstream processing overhead.
Brief in. Clean data out.
Provide target categories, search terms, or full catalogue requirements. We design the extraction schema together.
We configure Scrapy crawlers, DOM parsing rules, unit normalisation logic, and residential proxy rotation.
Schema validation, null-rate checks, and unit conversion verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Fabricland's architecture presents specific extraction challenges. Here is how our infrastructure maintains data integrity.
Fabricland often embeds critical specifications like composition and width within raw text descriptions rather than structured tables. We deploy targeted regex and NLP patterns to extract and normalise these values into distinct schema fields.
Textile retail frequently mixes imperial and metric units. Our pipeline automatically detects yards, inches, metres, and centimetres, converting them to a unified metric standard for your database.
Older eCommerce platforms exhibit inconsistent DOM structures. Our selector strategy uses multiple fallback chains per field, ensuring data extraction succeeds even when page layouts vary between categories.
Products with multiple colour options are unrolled into flat, distinct records. Each variant receives its specific SKU, price, and stock status, preventing nested data complexity in your warehouse.
To prevent IP bans and ensure reliable access, we route requests through UK-based residential proxies with conservative concurrency limits tailored to the target server capacity.
Independent fabric retailers track pricing per metre across core categories to maintain competitive market positioning.
Procurement teams monitor out-of-stock trends on staple fabrics like calico and muslin to predict supply chain bottlenecks.
Analysts track new product additions and category expansion to identify trending prints, materials, and seasonal shifts.
Garment manufacturers identify bulk discount thresholds and roll availability for large-scale production runs.
Craft and sewing search engines ingest product feeds to build comprehensive textile discovery catalogues.
Textile researchers analyse historical pricing data and material composition trends within the UK retail market.
"Fabricland holds critical pricing signals for the UK textile market, but extracting structured data from legacy retail architecture requires specialised parsing."
Most teams waste engineering cycles writing brittle regex for unstructured product descriptions. DataFlirt deploys resilient XPath chains, normalises measurement units automatically, and delivers clean schema-validated records directly to your warehouse.
Everything supported by our fabricland.co.uk scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
High-throughput asynchronous crawling handles the entire Fabricland catalogue efficiently. Custom middleware manages request retries and proxy rotation.
Post-extraction processing steps apply regex and NLP to clean unstructured text, normalise units of measure, and validate schema constraints.
Pipelines run on Kubernetes clusters. Airflow manages scheduling and dependency execution. All runs emit detailed metrics to Grafana for SLA monitoring.
Data delivered to where your team already works — no new tooling required.
About fabricland.co.uk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and product information is generally permissible under UK law. DataFlirt extracts only public, non-authenticated retail data. We do not bypass login walls to access trade pricing or extract personal customer data.
We use custom regex patterns and text parsing logic to identify specifications like '100% Cotton' or '112cm wide' buried in paragraph text, mapping them to structured schema fields.
Yes. Our pipeline detects imperial measurements (inches, yards) and automatically converts them to metric equivalents (centimetres, metres) to ensure your database remains consistent.
For catalogues of this size (~25,000 products), we typically run daily or weekly pipelines. We can configure specific categories for higher-frequency extraction if required.
Yes. If a fabric has 10 colour options, our pipeline unrolls this matrix and delivers 10 distinct records, each with its specific image URL and stock status.
Our selectors use multiple fallback chains. If a primary XPath fails due to a DOM update, the system attempts secondary and tertiary selectors. Our monitoring stack alerts us to schema drift immediately.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete textile catalogue export or continuous price monitoring across core fabrics — we scope, build, and operate the pipeline. Tell us what you need.
