We extract designer metadata, material specifications, variant pricing, and stock availability from hay.dk. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 hay.dk. All fields typed and schema-versioned.
"product_id": "HAY-M-1002", "sku": "100293", "title": "Mags Sofa", "designer": "HAY Studio", "category": "Furniture > Sofas", "base_price": 18499.0, "currency": "DKK", "dimensions": "W268.5 x D95.5 x H67 cm"
| # | product_id | sku | title | designer | collection | category |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Variant Data objects from hay.dk. All fields typed and schema-versioned.
"variant_id": "VAR-8392", "parent_sku": "100293", "colour": "Steelcut Trio 133", "material": "Kvadrat Fabric", "price": 21499.0, "in_stock": true, "ean": "5710441238492"
| # | variant_id | parent_sku | colour | material | finish | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Designers objects from hay.dk. All fields typed and schema-versioned.
"designer_id": "DES-042", "name": "Ronan & Erwan Bouroullec", "origin_country": "France", "collection_count": 4, "product_skus": "['10492', '10493', '10494']", "active_years": "2004-Present"
| # | designer_id | name | bio | profile_image_url | collection_count | product_skus |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Materials objects from hay.dk. All fields typed and schema-versioned.
"material_id": "MAT-091", "name": "FSC Certified Oak", "type": "Wood", "finish": "Matt Lacquered", "sustainability_cert": "FSC Mix", "care_guide": "Wipe with damp cloth"
| # | material_id | name | type | finish | sustainability_cert | care_guide |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Categories objects from hay.dk. All fields typed and schema-versioned.
"category_id": "CAT-012", "name": "Lounge Chairs", "parent_category": "Furniture", "product_count": 42, "url": "https://hay.dk/category/furniture/lounge-chairs", "metadata_tags": "['seating', 'living room', 'lounge']"
| # | category_id | name | parent_category | url | product_count | description |
|---|---|---|---|---|---|---|
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Our infrastructure navigates the complex front end of modern eCommerce platforms. We capture exact material finishes, dynamic pricing, and high resolution assets without missing a single variant.
Map every fabric, colour, and finish combination back to its parent product with accurate SKU relationships.
Extract and normalise height, width, and depth metrics into structured numeric fields for spatial analysis.
Capture biographical data, collection associations, and origin details for every featured designer.
Extract direct URLs for uncompressed product images, lifestyle shots, and specific variant textures.
Monitor inventory status across different regional storefronts and warehouse locations.
Capture region specific pricing, tax inclusions, and currency conversions across European markets.
Identify and extract URLs for AR models and CAD files embedded within product pages.
Extract sustainability certifications, care instructions, and exact composite percentages for fabrics.
Configure continuous pipelines at weekly or daily cadences to capture new collection drops immediately.
Brief in. Clean data out.
Provide target categories, designer profiles, or specific regional storefronts. We design the schema together.
We configure Scrapy and Playwright crawlers to handle dynamic variant loading and geo-location routing.
Schema validation, null-rate checks, and variant integrity tests before full production launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake stage on an agreed schedule.
Design brands use complex JavaScript frameworks to render variants and regional pricing. Here is how we extract clean data.
Furniture variants load dynamically based on user interaction. We run full Playwright browser sessions to trigger JavaScript events, ensuring every fabric and frame combination is captured.
We bypass compressed thumbnails to locate the source URLs of high resolution assets and 3D models, delivering clean links ready for your internal DAM systems.
Pricing and availability change based on the visitor location. Our residential proxy pools target specific European regions to capture accurate local market data.
We utilise multiple fallback chains for field extraction, combining CSS selectors with structured JSON-LD data to survive minor site updates.
We maintain a hash index of last seen values. Subsequent runs only push modifications to pricing or stock, reducing downstream processing load.
Retailers track pricing, material choices, and collection launches to benchmark their own product lines.
Marketplaces ingest structured dimension and material data to build comprehensive search filters for design professionals.
Analysts track the introduction of new materials, colours, and sustainability certifications across seasons.
Machine learning teams use structured metadata paired with high resolution imagery to train spatial recognition and style matching models.
Procurement teams monitor stock levels across regional stores to predict manufacturing constraints.
Distributors track cross border pricing disparities to optimise their purchasing strategy.
"Hay.dk holds the definitive digital record for contemporary Danish design, but extracting structured variant data requires navigating complex front end architecture."
Extracting furniture catalogues involves more than parsing text. We capture high resolution imagery, exact material specifications, designer metadata, and dynamic variant pricing across multiple regional storefronts. DataFlirt manages the complete extraction lifecycle so your team can focus on analysis.
Everything supported by our hay.dk 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 variant interaction flows.
We maintain proxy pools across European regions to ensure accurate capture of localised pricing and availability.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About hay.dk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product information is generally permissible. DataFlirt extracts only public catalogue data, pricing, and specifications. We do not bypass authentication walls or extract personal user data. Clients should review target terms of service and consult legal counsel.
We utilise headless browsers to interact with the page exactly as a user would, clicking through every available fabric, colour, and finish option to capture the specific SKU, price, and image associated with that combination.
Yes. If the product page embeds links to .glb, .gltf, or .usdz files for augmented reality viewing, our pipeline identifies and extracts those source URLs for your use.
For a complete catalogue of this size, daily or weekly runs are standard. We can configure specific categories to run at higher frequencies if you are tracking limited edition drops or flash sales.
No. Trade pricing on Hay.dk requires an authenticated dealer login. We only extract the publicly visible retail pricing available to standard consumers.
We parse raw text strings like 'W268.5 x D95.5 x H67 cm' into discrete numeric fields for width, depth, and height, standardising the unit of measurement across the dataset.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete catalogue export or continuous tracking of new collections and pricing changes. Tell us your requirements.
