We extract furniture listings, designer collaborations, material specifications, and real-time inventory from CB2. 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 cb2.com. All fields typed and schema-versioned.
"sku": "439182", "name": "Boucle Sofa", "category": "Furniture", "sub_category": "Sofas", "base_price": 1999.0, "designer": "Gwyneth Paltrow", "colour": "Ivory", "dimensions": "84 W x 36 D x 30 H"
| # | sku | name | category | sub_category | base_price | designer |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Inventory objects from cb2.com. All fields typed and schema-versioned.
"sku": "439182", "current_price": 1799.0, "original_price": 1999.0, "clearance_flag": true, "in_stock": true, "stock_status_message": "In Stock and Ready to Ship", "delivery_estimate": "3-5 Business Days", "currency": "USD"
| # | sku | current_price | original_price | clearance_flag | in_stock | stock_status_message |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Designer Collections objects from cb2.com. All fields typed and schema-versioned.
"collection_name": "Goop x CB2", "designer_name": "Gwyneth Paltrow", "exclusive_collaboration": true, "item_count": 42, "description": "A curated collection of modern elegance.", "materials": "['Boucle', 'Brass', 'Marble']", "featured_skus": "['439182', '439185']"
| # | collection_name | designer_name | exclusive_collaboration | collection_url | item_count | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Lookbooks & Rooms objects from cb2.com. All fields typed and schema-versioned.
"lookbook_id": "LB-2023-Fall-04", "title": "Modern Parisian Living Room", "room_type": "Living Room", "aesthetic": "Modern Parisian", "tagged_skus": "['439182', '882104', '119283']", "total_room_cost": 4550.0, "season": "Fall 2023"
| # | lookbook_id | title | room_type | aesthetic | image_url | tagged_skus |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Specifications objects from cb2.com. All fields typed and schema-versioned.
"sku": "439182", "material": "Polyester Boucle", "finish": "Matte Black Legs", "care_instructions": "Spot clean with mild detergent", "assembly_required": false, "weight": "125 lbs", "origin": "Imported", "certifications": "['FSC Certified Wood']"
| # | sku | material | finish | care_instructions | assembly_required | weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our CB2 scraper navigates complex product variants, designer collections, and dynamic inventory systems. We handle the rendering and session management required to extract complete specification data.
Extract every colour, fabric, and configuration option for made-to-order furniture, linking parent SKUs to specific variant pricing and lead times.
Capture width, depth, height, and seat height as structured numeric fields rather than raw text blocks.
Track exclusive collections from Kravitz Design, Goop, Paul McCobb, and others, mapping items back to their respective campaigns.
Monitor stock availability, backorder dates, and delivery estimates based on specific zip codes and fulfillment centres.
Parse 'Shop the Room' and Lookbook pages to extract tagged SKUs, room aesthetics, and aggregate pricing for curated spaces.
Identify clearance items, promotional pricing, and limited-time discounts across the entire catalogue.
Extract detailed material compositions, finish types, care instructions, and origin data for every product.
Capture pricing variations across different geographic regions and shipping zones.
Receive only updated records when prices change, new items are added, or stock statuses shift, reducing processing overhead.
Brief in. Clean data out.
Provide CB2 categories, specific designer collections, or search terms. We define the schema together.
We configure crawlers to handle CB2's dynamic loading, variant selectors, and image galleries.
Schema validation, null-rate checks, and dimension parsing tests before full launch.
Structured data pushed to your S3 bucket, BigQuery dataset, or via Webhook on your defined schedule.
Extracting home decor data requires handling complex product configurations and visual-heavy pages. Here is how we maintain data integrity.
CB2 sofas and beds often have dozens of fabric and leg combinations. We execute JavaScript to trigger variant selections, capturing the specific price, SKU, and lead time for every possible configuration.
Furniture dimensions are often presented as unstructured text. Our pipeline uses regex and NLP to parse '84"Wx36"Dx30"H' into distinct, numeric width, depth, and height columns.
We extract the highest resolution image URLs for primary photos, lifestyle shots, and detailed fabric swatches, bypassing lazy-loaded thumbnails.
Stock status on CB2 varies by delivery location. We inject specific zip codes into the session to extract accurate delivery estimates and backorder dates for your target regions.
Retail sites update their front-end frequently. We rely on underlying JSON APIs and structured data objects where possible, using DOM parsing only as a secondary fallback.
Home decor retailers monitor CB2 pricing, clearance cycles, and promotional events to adjust their own merchandising strategies.
Merchandisers analyse category depth, material trends, and colour palettes across CB2 collections to inform product development.
Aggregators and design apps ingest CB2 product catalogues to offer accurate 3D modeling, pricing, and purchasing options to their users.
Analysts track the introduction of new designer collaborations and material shifts to identify emerging interior design trends.
Logistics teams monitor backorder dates and out-of-stock rates to gauge macroeconomic supply chain health in the furniture sector.
Affiliate sites and home goods aggregators maintain synchronised listings with accurate pricing and availability.
"In the furniture sector, dimensions, materials, and lead times are just as critical as price. Extracting this data accurately requires a pipeline built for complex retail structures."
Scraping a modern furniture retailer involves navigating endless variant combinations, dynamic inventory checks, and unstructured specification text. DataFlirt manages the JavaScript rendering, session state, and data normalisation required to deliver clean, structured interior design catalogues.
Everything supported by our cb2.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.
Executes full browser sessions to interact with fabric selectors and dynamic pricing modules, capturing data hidden from standard HTTP requests.
Custom Python parsing logic converts inconsistent retail text into strict numeric types for dimensions, weights, and pricing.
Airflow manages the dependency chain, ensuring categories are scraped, variants mapped, and diffs calculated before warehouse delivery.
Data delivered to where your team already works — no new tooling required.
About cb2.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline iterates through all available fabric and colour combinations on a product page, capturing the specific price, SKU, and lead time for each variant.
We use custom parsing logic to extract width, depth, height, and seat height from the unstructured text blocks on CB2, delivering them as clean numeric fields in your database.
Yes. We can inject target zip codes into the scraping session to extract accurate delivery estimates and stock availability for specific regions.
Yes. We map the curated lifestyle images to their tagged product SKUs, allowing you to reconstruct the room aesthetic and calculate aggregate room costs.
We can configure pipelines to run daily for the entire catalogue, or at higher frequencies for specific high-priority SKUs.
Yes. We extract star ratings, review text, submission dates, and helpful votes across all paginated review sections on a product page.
We begin tracking pricing history from the moment your pipeline is activated. We do not have access to historical pricing prior to pipeline initiation.
20-minute scoping call. Pilot dataset within the week. Production within two. From dimensional specs to real-time inventory, we build and manage the pipeline. Tell us your data requirements and delivery cadence.
