We extract pattern catalogues, yarn specifications, designer portfolios, and public project metrics from Ravelry. 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 Patterns objects from ravelry.com. All fields typed and schema-versioned.
"pattern_id": "129481", "name": "Flax", "designer_name": "tincanknits", "category": "Clothing > Sweater > Pullover", "yarn_weight": "Aran (8 wpi)", "difficulty_rating": 2.4, "rating_count": 14205, "price": 0.0, "currency": "USD"
| # | pattern_id | name | designer_name | category | yarn_weight | gauge |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Yarns objects from ravelry.com. All fields typed and schema-versioned.
"yarn_id": "8472", "brand": "Malabrigo Yarn", "name": "Rios", "weight": "Worsted", "fibre_content": "['100% Merino']", "yardage": 210, "grams": 100, "rating": 4.8, "colourways_count": 412
| # | yarn_id | brand | name | weight | texture | fibre_content |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Designers objects from ravelry.com. All fields typed and schema-versioned.
"designer_id": "4829", "name": "Stephen West", "pattern_count": 342, "favorites_count": 158291, "projects_count": 89402, "website": "westknits.com", "location": "Amsterdam, Netherlands"
| # | designer_id | name | pattern_count | favorites_count | projects_count | website |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Colourways objects from ravelry.com. All fields typed and schema-versioned.
"yarn_id": "8472", "colour_name": "Teal Feather", "colour_number": "412", "tonal": true, "variegated": false, "stash_count": 1450, "projects_count": 890
| # | yarn_id | colour_name | colour_number | dye_type | tonal | variegated |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Public Projects objects from ravelry.com. All fields typed and schema-versioned.
"project_id": "948210", "pattern_id": "129481", "status": "Finished", "started_date": "2023-10-12", "completed_date": "2023-11-04", "rating": 5, "size_made": "Adult Medium", "helpful_votes": 12
| # | project_id | pattern_id | yarn_used | status | started_date | completed_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Ravelry scraper handles the deeply relational database structure: linking patterns to yarns, mapping fibre arrays, and normalising measurements across regions.
Extract gauge, yardage, needle sizes, difficulty scores, and category hierarchies for over 800,000 patterns.
Parse complex fibre content strings into structured JSON arrays, capturing texture, WPI, and weight categories.
Monitor designer popularity metrics, pattern output, and project completion rates over time.
Extract specific dye types, tonal indicators, and variegated flags across millions of yarn colourways.
Compile public project data to determine average completion times and actual yarn consumption per pattern.
Map physical retail locations, contact details, and the specific yarn brands stocked by each store.
Capture digital pattern prices and normalise currency values for global market analysis.
Convert imperial yardage to metric meters and US needle sizes to standard millimeter equivalents automatically.
Capture high-resolution image URLs for pattern examples, yarn swatches, and specific colourway lots.
Brief in. Clean data out.
Provide pattern categories, brand names, or designer IDs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and relationship mapping for ravelry.com.
Schema validation, null-rate checks, measurement conversions, and fibre parsing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Ravelry's database is deeply relational with complex nested attributes. Here is how we extract clean, flat records.
Ravelry data is highly relational. A single pattern links to multiple suggested yarns, a specific designer, and thousands of user projects. Our pipeline traverses these foreign keys and flattens the relationships into queryable warehouse tables.
Fibre composition is often inputted as unstructured text. We use regex and NLP parsing to convert strings into strict JSON arrays, ensuring percentages sum to 100 and material types are categorised correctly.
Knitting standards vary wildly between the US, UK, and Japan. Our pipeline automatically converts yardage to meters, ounces to grams, and regional needle sizes to standard metric millimeters.
We utilise residential proxies and strict concurrency limits to extract data reliably without triggering IP bans or placing undue load on the target infrastructure.
We maintain a hash index of last-seen values for patterns and yarns. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Fashion analysts identify rising yarn weights, popular colourways, and trending garment constructions before they hit mainstream retail.
Yarn manufacturers track competitor fibre blends, yardage-to-weight ratios, and market pricing to position new product lines.
Local Yarn Shop owners forecast stock requirements by analysing the specific yarns called for in trending patterns.
Independent designers identify underserved pattern categories, sizing gaps, and optimal price points for new digital releases.
Machine learning teams use structured textile attributes and fibre combinations to train generative design and classification models.
Textile producers correlate fibre popularity metrics with raw material procurement cycles to optimise manufacturing schedules.
"Ravelry is the definitive graph of the global fibre arts industry. Extracting its relational data maps exactly how raw materials become finished garments."
Scraping Ravelry requires untangling deeply nested relationships between designers, patterns, yarns, and user projects. We handle the complex schema mapping, measurement normalisation, and rate-limit circumvention so your data science team receives clean, queryable tables ready for immediate analysis.
Everything supported by our ravelry.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 high-throughput crawl orchestration, deduplication, and relationship mapping between patterns and yarns.
Custom Python middleware parses unstructured fibre strings and converts regional measurements into standard metric schemas.
Pipelines run on Kubernetes. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About ravelry.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Ravelry is generally permissible. DataFlirt targets only public, non-authenticated pattern, yarn, and designer catalogues. We do not extract private user stashes, forum posts, or circumvent authentication walls. Clients should review Terms of Service and consult legal counsel for specific use cases.
Ravelry users and brands often input fibre content as free text. Our parsing engine uses regex and NLP to extract percentages and material types, outputting a strict JSON array where values always sum to 100.
Yes. Our pipeline normalises all measurements to metric standard. Yardage becomes meters, ounces become grams, and US/UK needle sizes are converted to exact millimeter dimensions for consistent querying.
No. DataFlirt strictly adheres to extracting public data. Ravelry forums and individual user stashes are gated behind user authentication and carry strict privacy expectations.
We can configure pipelines to refresh specific designer portfolios or trending yarn categories on a daily or weekly cadence. Full catalogue sweeps typically run monthly due to the database size.
Yes. We provide a sample run of up to 1,000 patterns or 500 yarns as part of the pre-engagement scoping process so you can validate schema fit and parsing accuracy.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off yarn catalogue dump or a continuous trend-monitoring feed across 800K patterns, we scope, build, and operate the pipeline. Tell us what you need.
