We extract independent designs, fabric pricing matrices, artist portfolios, and category trends from Spoonflower. 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 Designs & Patterns objects from spoonflower.com. All fields typed and schema-versioned.
"design_id": "12345678", "title": "Mid Century Modern Geometric", "artist_name": "retro_patterns_co", "tags": "['midcentury', 'geometric', 'retro', 'mustard']", "repeat_type": "Basic", "favourites_count": 1432, "date_uploaded": "2024-02-14T10:30:00Z"
| # | design_id | title | artist_name | artist_url | tags | primary_colours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fabric Pricing objects from spoonflower.com. All fields typed and schema-versioned.
"design_id": "12345678", "fabric_type": "Petal Signature Cotton", "width_inches": 42, "weight_gsm": 145, "price_per_yard": 19.0, "fat_quarter_price": 9.0, "currency": "USD"
| # | design_id | fabric_type | width_inches | weight_gsm | price_per_yard | price_per_metre |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Artist Profiles objects from spoonflower.com. All fields typed and schema-versioned.
"username": "retro_patterns_co", "display_name": "Retro Patterns Co.", "location": "London, UK", "join_date": "2019-11-04", "total_designs": 452, "followers_count": 8904, "storefront_url": "https://www.spoonflower.com/profiles/retro_patterns_co"
| # | artist_id | username | display_name | location | bio | join_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Wallpaper Variants objects from spoonflower.com. All fields typed and schema-versioned.
"design_id": "12345678", "product_category": "Wallpaper", "material_type": "Peel and Stick", "roll_length_feet": 12, "roll_width_inches": 24, "price": 108.0, "currency": "USD"
| # | design_id | product_category | material_type | roll_length_feet | roll_width_inches | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Trends objects from spoonflower.com. All fields typed and schema-versioned.
"keyword": "floral wallpaper", "category": "Wallpaper", "rank_position": 4, "design_id": "87654321", "title": "Dark Moody Florals", "is_bestseller": true, "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | category | rank_position | design_id | title | artist |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Spoonflower scraper handles the complete visual marketplace: design metadata, fabric-specific pricing, artist portfolios, and search rankings — with image variant mapping and anti-bot circumvention built in.
Extract title, tags, description, repeat type, primary colours, and favourites count for millions of independent designs.
Capture dynamic pricing matrices across Petal Signature Cotton, Minky, Chiffon, and 20+ other fabric types.
Extract artist biographies, follower counts, design catalogues, and location data to map the creator ecosystem.
Extract user-generated tags and taxonomy data to understand search behaviour and surface design trends.
Capture URLs for watermarked pattern previews and product-specific mockups (pillows, curtains, wallpaper rolls).
Extract dimensions, material types (Peel and Stick, Pre-pasted), and pricing for non-fabric product lines.
Track organic position for any keyword or colour palette to identify bestsellers and trending patterns.
Monitor design popularity over time by tracking favourites and collection inclusions to forecast demand.
Scrape curated design challenges and user-created collections to identify grouped aesthetic trends.
Brief in. Clean data out.
Provide artist profiles, keyword sets, or category URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for spoonflower.com.
Schema validation, null-rate checks, price-outlier detection, and mockup image verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Spoonflower relies on complex visual rendering and dynamic pricing matrices. Here is how we extract structured data reliably.
Spoonflower prices change dynamically based on substrate selection (e.g., Cotton vs. Silk) and yardage. We use Playwright to execute JavaScript, simulate dropdown interactions, and capture the complete pricing matrix for every design.
Artist storefronts and search results rely on infinite scrolling and dynamic asset loading. Our crawlers intercept XHR requests and simulate scroll events to ensure complete extraction of deep pattern catalogues.
A single design generates dozens of mockups across fabric, wallpaper, and home decor. We extract the base image identifiers and reconstruct the CDN URLs for every product variant without downloading heavy image payloads.
Scraping highly visual sites often triggers rate limits and CAPTCHAs. We route requests through residential ISP proxies with realistic browser fingerprints to maintain continuous pipeline execution.
Frontend frameworks update frequently. We use multiple fallback chains per field — CSS selectors, XPath, and JSON-LD extraction — to ensure your data pipeline remains stable during site updates.
Textile designers and fashion brands monitor keyword velocity, colour trends, and popular tags to inform upcoming collections.
Print-on-demand platforms track Spoonflower's substrate pricing, bulk discounts, and shipping tiers to maintain competitive margins.
Agencies and brands identify top-performing independent artists based on follower counts, design volume, and engagement metrics.
Design studios scan the marketplace to detect unauthorised reproductions of their proprietary patterns and artwork.
Creators analyse specific niches (e.g., 'dinosaur nursery wallpaper') to identify low-competition, high-demand design opportunities.
Machine learning teams use structured metadata, tags, and pattern previews to train generative surface design models.
"Spoonflower holds the largest repository of independent surface design and fabric pricing — but none of it is queryable unless you build the pipeline."
Most teams underestimate the complexity of scraping visual marketplaces: reliable Spoonflower extraction requires residential proxies, full JavaScript rendering for dynamic pricing matrices, and complex image variant mapping. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our spoonflower.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/UK 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 spoonflower.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Spoonflower is generally permissible under applicable law. DataFlirt targets only public, non-authenticated design metadata, pricing, and artist profiles. We do not extract private designs, circumvent authentication walls, or download unwatermarked proprietary source files. Clients should consult legal counsel for specific use cases.
We use Playwright to execute JavaScript and simulate substrate selections. This allows us to capture the complete pricing matrix for every design across all fabric types, wallpaper materials, and home decor items.
No. We extract the URLs for publicly visible, watermarked pattern previews and product mockups. We do not bypass security measures to access original, high-resolution source files uploaded by artists.
Search ranking and tag velocity pipelines can be configured to run daily or weekly. Full catalogue refreshes depend on the target artist or category size, but typically complete within a 6-12 hour window.
Yes. We extract public artist profiles including biographies, location data, follower counts, and their complete public design catalogue.
Our smallest packages start at a defined artist list or category set with weekly delivery. For continuous marketplace monitoring, we price based on volume and delivery frequency. Contact us with your requirements for a scoped quote.
Absolutely. We provide a sample run of up to 500 designs or 50 search result pages 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 artist catalogue dump or continuous trend monitoring across millions of patterns — we scope, build, and operate the pipeline. Tell us what you need.
