We extract product specifications, fabric permutations, pricing signals, and inventory status from Arhaus. 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 arhaus.com. All fields typed and schema-versioned.
"sku": "15KIP84SFA", "name": "Kipton Sofa", "category": "Living", "sub_category": "Sofas", "base_price": 3299.0, "collection_name": "Kipton", "dimensions": "84" W X 40" D X 35" H", "materials": "Hardwood frame, Crypton fabric"
| # | sku | name | category | sub_category | base_price | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Fabrics objects from arhaus.com. All fields typed and schema-versioned.
"parent_sku": "15KIP84SFA", "variant_sku": "15KIP84SFA-NF01", "fabric_grade": "Performance", "fabric_name": "Nomad Snow", "colour_family": "White", "price_adjustment": 400.0, "lead_time_weeks": "8-10", "in_stock": false
| # | parent_sku | variant_sku | finish_name | fabric_grade | fabric_name | colour_family |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Stock objects from arhaus.com. All fields typed and schema-versioned.
"sku": "15KIP84SFA-NF01", "current_price": 3699.0, "original_price": 4299.0, "discount_pct": 14, "is_clearance": false, "stock_status": "Made to Order", "white_glove_eligible": true, "last_checked": "2026-05-12T09:14:00Z"
| # | sku | current_price | original_price | discount_pct | is_clearance | stock_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from arhaus.com. All fields typed and schema-versioned.
"review_id": "REV-982341", "sku": "15KIP84SFA", "rating": 4.8, "reviewer_name": "Sarah M.", "review_date": "2025-11-04", "title": "Beautiful and comfortable", "verified_buyer": true, "helpful_votes": 12
| # | review_id | sku | rating | reviewer_name | review_date | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locations objects from arhaus.com. All fields typed and schema-versioned.
"store_id": "STR-042", "name": "Arhaus Chicago", "city": "Chicago", "state": "IL", "zip": "60614", "latitude": 41.9112, "longitude": -87.6525, "design_services_available": true
| # | store_id | name | address | city | state | zip |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Arhaus scraper handles complex configurators, extracting every fabric grade, finish, and dimension permutation with JavaScript rendering and session management built in.
Title, description, dimensions, materials, care instructions, and collection mapping scraped at the SKU level.
Iterate through JavaScript configurators to capture every finish, fabric grade, and colour family combination.
Extract and normalise width, depth, and height specifications into structured numerical fields for spatial planning.
Capture base price, variant upcharges, original price, and clearance status timestamped per crawl.
Extract stock status, made-to-order lead times, and shipping surcharges for every configuration.
Scrape showroom locations, hours, contact details, and available in-store design services.
Capture URLs for high-resolution product imagery and room scenes across all available angles.
Extract star ratings, review text, verified buyer flags, and helpful votes across product pages.
Run bulk exports or configure continuous pipelines at weekly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs or specific collections. We design the extraction schema together.
We configure Playwright crawlers, handle dynamic configurators, and map variant permutations.
Schema validation, null-rate checks, and variant completeness testing before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Luxury furniture sites rely on heavy frontend JavaScript to render thousands of custom options. Here is how we extract structured data from complex DOMs.
Arhaus product pages use complex JavaScript to update pricing and images based on fabric and finish selections. We run full Playwright browser sessions to iterate through these options, capturing data that headless HTTP clients miss.
A single sofa can have over 100 fabric options affecting price and lead time. Our crawlers systematically click through dropdowns and swatches to build a complete matrix of child SKUs.
We use fallback chains for CSS and XPath selectors to ensure extraction continues even when frontend developers update the site layout or component class names.
High-resolution product images are often lazy-loaded. Our pipeline scrolls and triggers intersection observers to ensure all image URLs are captured before the session closes.
We alert on null-rate spikes in pricing or dimension fields, ensuring you receive complete records. SLA uptime is contractual.
Furniture retailers track pricing, sales events, and clearance discounts to optimise their own pricing strategies.
Merchandising teams analyse fabric grades, colour trends, and material usage across luxury collections.
Analysts monitor made-to-order lead times and stock availability to gauge supply chain health and consumer demand.
Design platforms ingest structured dimension and material data to build spatial planning tools.
Firms track collection launches and category expansion to identify trends in the luxury home decor sector.
ML teams use structured dimension data and room scene imagery to train generative interior design models.
"Extracting luxury furniture data requires parsing thousands of fabric and finish permutations hidden behind complex JavaScript configurators."
Most teams underestimate the investment required: reliable Arhaus scraping requires full browser rendering to evaluate fabric grade price adjustments, handling lazy-loaded high-resolution imagery, and maintaining selectors against frequent frontend updates. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our arhaus.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 and deduplication. Playwright handles JavaScript rendering and interaction flows for product configurators. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent IP bans during deep variant iteration.
Pipelines run on AWS Lambda and ECS. 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 arhaus.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from retail websites is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and store data. We do not extract personal data or circumvent authentication walls. Clients should consult legal counsel for specific use cases.
We use Playwright to simulate user interactions, iterating through available fabric grades, colours, and finishes. This ensures we capture the exact price upcharge and lead time associated with every specific permutation.
Full catalogue refreshes typically run weekly or daily depending on your requirements. The extraction window completes within 4-8 hours depending on the depth of variant iteration requested.
Yes. We parse the raw dimension strings into structured fields (width, depth, height) to facilitate ingestion into spatial planning software or database schemas.
Yes. Every pipeline run produces timestamped snapshots. We can maintain a time-series table per SKU to track base price changes and promotional events over time.
Our packages start at a defined category scope with weekly delivery. For full catalogue extraction including all fabric permutations, we price based on compute volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across all product variants, we scope, build, and operate the pipeline. Tell us what you need.
