We extract fabric specifications, yardage pricing, bolt inventory, designer collections, and reviews from Fabric.com. 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 fabric.com. All fields typed and schema-versioned.
"sku": "0401825", "title": "Kaufman Kona Cotton Solid Black", "brand": "Robert Kaufman", "fiber_content": "100% Cotton", "width": "44 inches", "price_per_yard": 8.99, "in_stock": true, "stock_yardage": 450.5
| # | sku | title | brand | designer | fiber_content | width |
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Complete list of extractable fields for Pricing & Inventory objects from fabric.com. All fields typed and schema-versioned.
"sku": "0401825", "price_per_yard": 8.99, "sale_price": 7.49, "swatch_price": 3.0, "in_stock": true, "stock_yardage": 450.5, "minimum_cut": 0.5, "currency": "USD"
| # | sku | price_per_yard | bulk_discount_tiers | sale_price | list_price | swatch_price |
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Complete list of extractable fields for Reviews & Ratings objects from fabric.com. All fields typed and schema-versioned.
"review_id": "REV-993812", "sku": "0401825", "rating": 5, "review_title": "Perfect quilting solid", "review_date": "2026-03-12", "helpful_votes": 14, "verified_buyer": true, "project_type": "Quilting"
| # | review_id | sku | reviewer_name | rating | review_title | review_body |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Material Specs objects from fabric.com. All fields typed and schema-versioned.
"sku": "0401825", "fiber_content": "100% Cotton", "weight_oz": 4.3, "width_inches": 44.0, "stretch_pct": 0, "care_instructions": "Machine Wash Cold/Tumble Dry Low", "weave_type": "Broadcloth", "opacity": "Opaque"
| # | sku | fiber_content | weight_oz | width_inches | stretch_pct | care_instructions |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Categories & Collections objects from fabric.com. All fields typed and schema-versioned.
"category_id": "CAT-1029", "category_name": "Quilting Cottons", "parent_category": "Cotton Fabric", "designer_name": "Kaffe Fassett", "collection_name": "Artisan", "theme": "Floral", "pattern": "Abstract", "release_year": 2025
| # | category_id | category_name | parent_category | designer_name | collection_name | theme |
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Our Fabric.com scraper handles every layer of the platform: product listings, yardage pricing, material specifications, and inventory levels — with JavaScript rendering and anti-bot circumvention built in.
Title, material composition, width, weight, designer, and theme — scraped at the SKU level with swatch and yardage mapping.
Capture price per yard, sale prices, swatch costs, and bulk discount tiers — timestamped per crawl.
Extract available stock yardage, bolt quantities, and backorder status across thousands of SKUs.
Full review text, star ratings, helpful vote counts, and verified buyer flags — paginated across all review pages.
Group fabrics by designer, collection, and release season to track brand assortments and textile trends.
Normalise unstructured fiber content strings (e.g., '95% Rayon / 5% Spandex') into structured, queryable fields.
Capture primary product images, pattern repeats, and ruler-scale images for computer vision and pattern analysis.
Extract washing instructions, opacity, stretch percentages, and recommended project types.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, designer lists, or search terms. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for fabric.com.
Schema validation, null-rate checks, price-outlier detection, and sample records before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
eCommerce sites invest heavily in scraping detection. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Bot detection operates on TLS fingerprints, browser headers, and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management.
Product pages and search results are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution to capture dynamic pricing and inventory widgets.
DOM structures change frequently. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, and text-pattern matching — so a layout change doesn't break your data pipeline.
Fabric specifications are often unstructured text. We apply regex and NLP rules to extract exact fiber percentages, weight in ounces, and width in inches into typed numeric fields.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops — and respond before you notice.
Retailers and wholesalers monitor yardage pricing, bulk discount thresholds, and promotional sales to adjust their own pricing.
Apparel manufacturers track stock depth and availability of specific materials to anticipate supply chain bottlenecks.
Fashion analysts aggregate data on popular colours, themes, and designer collections to forecast upcoming seasonal trends.
Textile brands audit competitor assortments, material compositions, and customer reviews to identify product gaps.
Computer vision teams use high-resolution fabric images and pattern descriptions to train textile recognition models.
Investors track category growth, new designer launches, and review velocity to evaluate market demand.
"Fabric.com holds the definitive taxonomy of textiles, material compositions, and yardage pricing — but standardising it requires parsing thousands of unstructured descriptions."
Most teams underestimate the investment required: reliable textile scraping requires handling complex product variants (swatches vs yards vs bolts), parsing inconsistent material specs, and monitoring dynamic inventory levels. DataFlirt absorbs that complexity so your engineers can focus on analysis.
Everything supported by our fabric.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. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 fabric.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use custom regex pipelines and NLP models to parse strings like '95% Cotton / 5% Lycra' into structured JSON objects, separating the fiber type from the percentage.
Yes. We extract the exact stock yardage available for each SKU, updating the count on each pipeline run to help you monitor depletion rates.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined SKU set. Full catalogue refreshes at daily cadence complete within a 6-12 hour window.
Our smallest packages start at a defined category list (typically 1,000-50,000 SKUs) with weekly delivery. For larger catalogues, we price based on volume and delivery frequency.
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, 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 product catalogue dump or a continuous price-monitoring feed across 100K SKUs — we scope, build, and operate the pipeline. Tell us what you need.
