We extract fabric listings, material composition, stretch metrics, weight, pricing, and stock status from Moodfabrics. 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 moodfabrics.com. All fields typed and schema-versioned.
"sku": "329581", "title": "Italian Black Silk Crepe de Chine", "price_per_yard": 29.99, "composition": "100% Silk", "width": "54 inches", "weight": "Lightweight", "color": "Black", "stock_status": "In Stock"
| # | sku | title | price_per_yard | composition | width | weight |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Swatches objects from moodfabrics.com. All fields typed and schema-versioned.
"sku": "329581", "price_per_yard": 29.99, "swatch_price": 1.5, "swatch_available": true, "discount_pct": 0, "sale_badge": false, "minimum_yardage": 1.0, "stock_level": "High"
| # | sku | price_per_yard | wholesale_price | swatch_price | swatch_available | discount_pct |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Material Specs objects from moodfabrics.com. All fields typed and schema-versioned.
"sku": "329581", "primary_fiber": "Silk", "weave_type": "Crepe de Chine", "gsm": "60", "stretch_width": "0%", "stretch_length": "0%", "opacity": "Opaque", "drape": "Fluid"
| # | sku | primary_fiber | secondary_fiber | weave_type | gsm | stretch_width |
|---|---|---|---|---|---|---|
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| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from moodfabrics.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "sku": "329581", "reviewer_name": "Sarah J.", "star_rating": 5, "review_date": "2023-11-14", "review_text": "Beautiful drape, perfect for a bias cut dress.", "helpful_votes": 12, "verified_buyer": true
| # | review_id | sku | reviewer_name | star_rating | review_date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category & Taxonomy objects from moodfabrics.com. All fields typed and schema-versioned.
"sku": "329581", "primary_category": "Fashion Fabrics", "sub_category": "Silk", "tags": "['crepe', 'eveningwear', 'drapey']", "collection": "Italian Imports", "season": "All Season", "usage_recommendations": "['Dresses', 'Blouses']", "url": "https://www.moodfabrics.com/italian-black-silk-crepe-de-chine-329581"
| # | sku | primary_category | sub_category | tags | collection | season |
|---|---|---|---|---|---|---|
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Our Moodfabrics scraper handles the entire textile catalogue: composition specs, yardage pricing, swatch availability, and designer deadstock alerts - with JavaScript rendering and anti-bot circumvention built in.
Extract material composition percentages, GSM, width, stretch metrics, and opacity directly from product spec tables.
Capture price per yard, sale discounts, and minimum yardage requirements timestamped per crawl.
Monitor swatch pricing and availability status across the entire catalogue to track sampling trends.
Track limited-run designer fabrics and deadstock inventory levels before they sell out.
Extract colour families, specific hex representations, and pattern classifications like floral, geometric, or abstract.
Parse washing, drying, and ironing instructions to normalise care data across material types.
Extract customer feedback, star ratings, and verified buyer status to gauge fabric quality and workability.
Map the full navigation path, tags, and recommended usage categories for every SKU.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide SKU lists, category URLs, or fabric types. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for moodfabrics.com.
Schema validation, null-rate checks, price-outlier detection, and composition parsing checks before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Scraping eCommerce textile catalogues requires precise handling of variant structures and dynamic stock levels. Here is how we maintain data integrity.
eCommerce platforms heavily monitor traffic patterns. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management - trained on real user behaviour patterns.
Swatch availability and dynamic pricing widgets rely on JavaScript. We run full Playwright browser sessions with JavaScript execution to capture data that headless HTTP clients miss entirely.
Textile specifications often appear in varied formats. Our selector strategy uses regex and text-pattern matching to parse unstructured composition strings into normalised JSON fields.
For the full fabric catalogue, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops - and respond before you notice.
Textile retailers monitor per-yard pricing, sale events, and wholesale tiers to adjust their own pricing strategies.
Trend analysts track the velocity of specific colours, patterns, and materials selling out to predict upcoming seasonal trends.
Apparel manufacturers monitor deadstock availability and material composition options for rapid sourcing and procurement.
Designers ingest catalogue data via API to automatically filter fabrics meeting strict stretch, weight, and composition criteria.
Machine learning teams use high-resolution fabric images paired with structured composition data to train computer vision models.
Textile industry analysts aggregate pricing across thousands of SKUs to track inflation and raw material cost impacts on retail pricing.
"Moodfabrics holds the most comprehensive structured dataset of designer deadstock and material compositions on the web - accessible only if you build the pipeline."
Extracting textile data requires parsing unstructured composition strings, handling dynamic yardage pricing, and tracking volatile deadstock inventory. DataFlirt absorbs that complexity so your engineering team can focus on analysis, not infrastructure.
Everything supported by our moodfabrics.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. 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 moodfabrics.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from eCommerce sites 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 residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes do not break the pipeline.
Yes. We extract the raw composition string (e.g., '95% Cotton, 5% Spandex') and parse it into structured JSON objects with primary and secondary fiber percentages.
Yes. Swatch availability is often managed dynamically via JavaScript. Our Playwright integration ensures we accurately capture whether a swatch can currently be added to the cart.
Full catalogue refreshes at daily cadence complete within a 4-8 hour window depending on size. Sub-sets of high-priority SKUs can be tracked at hourly intervals.
Our smallest packages start at a defined SKU list with weekly delivery. For full catalogue extraction or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
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 40K SKUs, we scope, build, and operate the pipeline. Tell us what you need.
