We extract fabric catalogues, yardage pricing, material specs, and stock depths from OnlineFabricStore. 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 onlinefabricstore.net. All fields typed and schema-versioned.
"sku": "123456", "title": "Waverly Sun N Shade Seascape Caribbean Fabric", "brand": "Waverly", "price_per_yard": 14.95, "in_stock": true, "rating": 4.8, "review_count": 124
| # | sku | title | brand | category | sub_category | price_per_yard |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Variants objects from onlinefabricstore.net. All fields typed and schema-versioned.
"sku": "123456", "variant_type": "yard", "price": 14.95, "list_price": 19.95, "bulk_discount_threshold": 10, "bulk_discount_price": 12.95, "currency": "USD"
| # | sku | variant_type | price | list_price | bulk_discount_threshold | bulk_discount_price |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Material & Specs objects from onlinefabricstore.net. All fields typed and schema-versioned.
"sku": "123456", "material_composition": "100% Polyester", "width": "54 inches", "weight": "Medium", "pattern": "Nautical", "colour": "Blue / Green", "cleaning_code": "W (Water-based)"
| # | sku | material_composition | width | weight | pattern | colour |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Reviews & Ratings objects from onlinefabricstore.net. All fields typed and schema-versioned.
"review_id": "REV-98234", "sku": "123456", "rating": 5, "review_title": "Perfect for outdoor cushions", "review_body": "The colours are vibrant and it repels water perfectly.", "review_date": "2026-02-14", "helpful_votes": 12
| # | review_id | sku | reviewer_name | rating | review_title | review_body |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Categories & Applications objects from onlinefabricstore.net. All fields typed and schema-versioned.
"sku": "123456", "primary_application": "Outdoor", "secondary_applications": "['Upholstery', 'Pillows']", "collection_name": "Sun N Shade", "fabric_type": "Canvas", "weave_type": "Plain", "theme": "Coastal"
| # | sku | primary_application | secondary_applications | collection_name | designer | theme |
|---|---|---|---|---|---|---|
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Our pipeline handles the complexities of textile e-commerce: variant pricing matrices, heavy image payloads, category mapping, and dynamic stock indicators.
Extract and normalise composition percentages, width, weight, and repeat measurements into strict numeric and categorical fields.
Capture base pricing alongside bulk yardage discounts, sample cuts, and full bolt pricing structures.
Download, hash, and store uncompressed fabric textures directly to your S3 bucket without triggering bandwidth blocks.
Monitor inventory availability flags and low-stock warnings to forecast supply chain movements.
Isolate manufacturer data, designer collections, and exclusive lines across the entire catalogue.
Scrape full review text, star ratings, and verified buyer flags to analyse material performance and customer sentiment.
Extract primary colours, secondary tones, and pattern classifications (e.g. geometric, floral, damask).
Map fabrics to their intended uses, separating apparel, upholstery, drapery, and outdoor textiles.
Run continuous pipelines that only push updates when price, stock, or new variants are detected.
Brief in. Clean data out.
Provide target categories, brands, or specific SKUs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and image handling for onlinefabricstore.net.
Schema validation, null-rate checks, and image payload verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting fabric data requires managing complex variant structures and heavy media payloads. Here is how we maintain stability.
Fabric pricing is rarely flat. We map the relational structure between a base SKU and its purchasing variants, ensuring you receive a clean price-per-yard metric alongside sample costs and bulk discount thresholds.
Textile analysis relies on visual texture. Our pipelines separate HTML parsing from media downloading, asynchronously fetching high-resolution images via distributed workers to prevent pipeline bottlenecks.
E-commerce platforms block aggressive IP addresses. We route requests through US-based residential ISP proxies with realistic browser fingerprints and randomised request timing to mimic human browsing behaviour.
Material compositions are often unstructured text. We use regex and NLP pipelines to parse strings like '50% Cotton / 50% Poly' into queryable JSON objects with distinct fibre types and percentages.
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 track yardage pricing, bulk discount thresholds, and clearance events to optimise their own pricing strategies.
Computer vision teams extract high-resolution fabric images and material metadata to train generative design and rendering models.
E-commerce operators sync stock availability and pricing data to maintain accurate storefronts without manual data entry.
Fashion and interior design analysts monitor new category additions and colour trends to forecast seasonal demand.
Procurement teams track stock depth indicators across major brands to anticipate material shortages.
Interior design software platforms ingest specification data to build searchable libraries for their user base.
"OnlineFabricStore holds one of the most comprehensive textile catalogues on the web, but standardising material specifications and yardage pricing requires purpose-built infrastructure."
Textile data extraction involves more than just parsing HTML. You need to handle complex variant matrices for samples versus yardage, download gigabytes of high-resolution texture images without triggering rate limits, and normalise inconsistent material composition strings. DataFlirt manages this entire stack so your engineers can focus on analysis.
Everything supported by our onlinefabricstore.net 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 variant selection.
We maintain pools of residential proxies to ensure high success rates when scraping large volumes of product pages.
Pipelines run on AWS infrastructure with Airflow handling scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About onlinefabricstore.net scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and review data is generally permissible. DataFlirt targets only public, non-authenticated information. We do not extract personal data or circumvent authentication walls. Clients should review target site ToS and consult legal counsel for specific use cases.
We decouple HTML scraping from media extraction. Image URLs are pushed to a separate asynchronous worker queue that downloads the high-resolution files and streams them directly to your AWS S3 bucket, preventing the main crawler from bottlenecking.
Yes. We use parsing logic to convert unstructured text strings (e.g. 'Polyester 60%, Cotton 40%') into structured JSON fields, allowing you to filter and query by specific material percentages.
Pipelines can be configured to run daily or at custom intervals. We use change detection to quickly scan categories and only process full updates for SKUs that show modified timestamps or stock indicators.
Our schema maps a parent SKU to multiple variant objects. You receive distinct pricing records for a 1-yard cut, a sample swatch, and a full 15-yard bolt, complete with the respective bulk discount thresholds.
Our engagements typically start with a defined category list or a minimum of 10,000 SKUs delivered weekly. Contact us with your specific data requirements for a scoped quote.
Absolutely. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process so you can validate schema fit 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 catalogue extraction or continuous inventory monitoring across thousands of fabrics, we scope, build, and operate the pipeline. Tell us what you need.
