We extract furniture listings, dimension specs, material details, store-level inventory, and pricing from Living Spaces. 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 Product Listings objects from livingspaces.com. All fields typed and schema-versioned.
"sku": "234567", "title": "Alton Cherry Queen Panel Bed", "category": "Bedroom", "price": 495.0, "colour": "Cherry", "material": "Wood", "dimensions": "65W x 85D x 54H", "assembly_required": true
| # | sku | title | category | sub_category | price | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Inventory & Stores objects from livingspaces.com. All fields typed and schema-versioned.
"sku": "234567", "store_name": "Fremont", "zip_code": "94538", "in_stock": true, "floor_model_available": true, "pickup_available": true, "delivery_estimate": "2-3 business days"
| # | sku | store_id | store_name | zip_code | in_stock | stock_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from livingspaces.com. All fields typed and schema-versioned.
"review_id": "REV-99812", "sku": "234567", "star_rating": 4.5, "review_title": "Solid bed frame", "review_date": "2025-11-12", "verified_purchase": true, "helpful_votes": 12
| # | review_id | sku | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Room Ideas objects from livingspaces.com. All fields typed and schema-versioned.
"room_id": "RM-445", "room_name": "Modern Coastal Living", "room_type": "Living Room", "style_category": "Coastal", "total_room_price": 2450.0, "included_skus": "['112233', '445566', '778899']", "designer_name": "In-house Studio"
| # | room_id | room_name | room_type | style_category | designer_name | total_room_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Clearance & Offers objects from livingspaces.com. All fields typed and schema-versioned.
"sku": "998877", "original_price": 895.0, "clearance_price": 450.0, "discount_pct": 49, "condition_notes": "Floor model - minor scratches", "store_location": "Irvine", "final_sale": true
| # | sku | original_price | clearance_price | discount_pct | condition_notes | store_location |
|---|---|---|---|---|---|---|
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Our Living Spaces scraper handles the entire catalogue: furniture specifications, dynamic inventory by zip code, clearance pricing, and room collections. We bypass bot protection and render JavaScript to capture accurate local stock.
Extract dimensions, weight, materials, care instructions, and assembly requirements for every SKU.
Track in-stock status, floor model availability, and pickup times across specific retail locations and zip codes.
Capture base prices, sale events, and store-specific clearance markdowns with condition notes.
Pull all customer reviews, star ratings, and verified purchase flags to analyse product sentiment.
Map special order fabric options, pricing tiers, and extended delivery timelines for custom configurations.
Scrape curated 'Shop the Room' pages to map individual SKUs to lifestyle imagery and total room pricing.
Extract zip-code specific delivery windows and shipping costs for large freight items.
Receive only changed records. We track modifications in price, stock, and delivery dates to reduce payload size.
Capture direct links to high-resolution product photography, lifestyle shots, and dimension diagrams.
Brief in. Clean data out.
Provide category URLs, specific SKUs, or target zip codes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for livingspaces.com.
Schema validation, null-rate checks, and inventory accuracy testing across multiple store locations.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Furniture retail sites rely heavily on location-based state and dynamic rendering. Here is how we maintain data integrity.
Living Spaces pricing and availability change based on the user's location. Our crawlers inject specific zip codes into the session state, allowing us to extract accurate store-level stock and delivery estimates across multiple regions simultaneously.
Custom upholstery options and 'Shop the Room' features rely on heavy client-side rendering. We run full Playwright browser sessions to trigger these dynamic elements and capture data that basic HTTP requests miss.
Retail sites actively block datacenter IPs. We route requests through US-based residential ISP proxies with realistic browser fingerprints, preventing IP bans and ensuring uninterrupted daily crawls.
E-commerce DOM structures shift during sales events. Our extraction logic uses multiple fallback chains per field, ensuring that a promotional banner or layout update does not break the data pipeline.
Every run emits structured logs to our observability stack. We alert on null-rate spikes in critical fields like price or dimensions, responding before the data reaches your warehouse.
Furniture retailers monitor Living Spaces pricing, promotional events, and clearance markdowns to adjust their own pricing strategies.
Merchandising teams analyse material trends, colour popularity, and category depth to inform their own product development.
Logistics firms and competitors track delivery timeframes and restock dates to gauge supply chain health and factory lead times.
Analysts aggregate review sentiment and rating velocity to identify top-performing product categories and quality issues.
Secondary market sellers track floor model clearance and deep discounts across specific store locations for profitable resale.
Computer vision teams use high-resolution furniture imagery and room scenes to train interior design and spatial recognition models.
"Furniture retail data is complex—dimensions, custom fabrics, and location-based inventory require a pipeline built for dynamic state, not just static HTML."
Extracting data from Living Spaces requires managing session state across different zip codes and rendering heavy JavaScript for custom configurations. DataFlirt handles the proxy rotation, session management, and DOM parsing so your team receives clean, normalised catalogue data without maintaining the infrastructure.
Everything supported by our livingspaces.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 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 livingspaces.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Living Spaces is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and inventory data. We do not extract personal data or circumvent authentication walls.
Living Spaces requires a zip code to display accurate delivery times and local store stock. We configure the pipeline to inject your target zip codes into the session cookies, allowing us to extract data for specific geographic regions.
We use US-based residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and randomised request timing. This mimics genuine human browsing behaviour and prevents IP blacklisting.
We support daily, weekly, or custom cadences. For clearance and inventory tracking across specific high-value SKUs, we can configure sub-hourly pipelines.
Our smallest packages start at a defined category list or SKU set with weekly delivery. For full-site daily crawls across multiple zip codes, we price based on compute volume and delivery frequency.
Yes. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process. This allows your engineering team to validate schema fit and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous inventory feed across multiple zip codes — we scope, build, and operate the pipeline. Tell us what you need.
