We extract lighting, furniture, and decor listings from Bellacor. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your schedule.
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 bellacor.com. All fields typed and schema-versioned.
"sku": "765432", "title": "Hudson Valley Lighting Mitzi Stella", "brand": "Hudson Valley", "category": "Lighting", "price": 298.0, "in_stock": true, "currency": "USD"
| # | sku | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Specifications objects from bellacor.com. All fields typed and schema-versioned.
"sku": "765432", "finish": "Aged Brass", "bulb_type": "E26 Medium Base", "max_wattage": "60W", "voltage": "120V", "ul_rating": "Damp Location", "weight": "4.5 lbs"
| # | sku | dimensions | weight | material | finish | bulb_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Availability objects from bellacor.com. All fields typed and schema-versioned.
"sku": "765432", "current_price": 298.0, "msrp": 350.0, "discount_pct": 15, "sale_badge": true, "clearance_flag": false, "lead_time": "2-3 Business Days"
| # | sku | current_price | msrp | discount_pct | sale_badge | clearance_flag |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from bellacor.com. All fields typed and schema-versioned.
"review_id": "REV-99821", "sku": "765432", "rating": 5, "reviewer_name": "Jane D.", "review_date": "2023-11-12", "review_title": "Beautiful sconce", "verified_buyer": true
| # | review_id | sku | rating | reviewer_name | review_date | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category & Brand Data objects from bellacor.com. All fields typed and schema-versioned.
"brand_name": "Hudson Valley", "category_name": "Wall Sconces", "total_products": 412, "breadcrumb_trail": "Lighting > Wall Lights > Wall Sconces", "page_number": 1, "scraped_at": "2023-11-15T08:00:00Z"
| # | brand_name | brand_url | category_name | category_url | total_products | breadcrumb_trail |
|---|---|---|---|---|---|---|
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Our Bellacor scraper parses complex specification tables, handles dynamic stock availability, and extracts high-resolution image URLs across thousands of lighting and furniture SKUs.
Extract bulb types, maximum wattage, UL ratings, voltage, and fixture dimensions directly from specification tables.
Capture height, width, depth, weight, and assembly requirements for all furniture and decor items.
Track MSRP, current sale prices, discount percentages, and clearance flags across the entire assortment.
Monitor stock availability, estimated shipping windows, and lead time variations by finish or size.
Extract complete brand assortments for manufacturers like Quoizel, Uttermost, and Hudson Valley Lighting.
Scrape all product gallery image URLs, including lifestyle shots and dimensional diagrams.
Extract star ratings, review text, helpful votes, and verified buyer status across product pages.
Capture finish options (e.g., Aged Brass, Polished Nickel) and map them to their corresponding SKUs.
Traverse deep category trees and filtered search results to ensure 100% catalogue coverage.
Run continuous pipelines that only output records when price, stock, or specifications change.
Brief in. Clean data out.
Provide Bellacor category URLs, brand names, or specific SKU lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and parsing logic for Bellacor's specific DOM structure.
Schema validation, null-rate checks, and sample data review before full production launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on your defined cadence.
Home furnishings websites present unique scraping challenges due to nested specifications and complex product variants. Here is how we ensure reliable data extraction.
Lighting and furniture specifications are often formatted inconsistently across different brands on Bellacor. Our parsers map variable table rows (e.g., 'Bulb Type', 'Max Wattage', 'UL Listing') into a strict, normalised JSON schema.
Products often have multiple finishes or sizes. We extract the parent-child relationship, ensuring every finish variant is captured as a distinct record with its specific price and stock status.
Retailers block aggressive data collection. We use residential ISP proxies with realistic request timing and header rotation to maintain uninterrupted access to bellacor.com category pages.
For large product catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing downstream processing load for your data engineering team.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, layout changes, and coverage drops, fixing DOM selectors before you notice missing data.
Home decor retailers track Bellacor's pricing, discounts, and clearance events to optimise their own pricing strategies.
Merchandising teams analyse Bellacor's brand coverage and product depth to identify missing categories in their own catalogues.
Design platforms aggregate lighting and furniture specifications to build searchable databases for interior designers.
Manufacturers monitor Bellacor to ensure their products are not being sold below Minimum Advertised Price (MAP) agreements.
Analysts track new product additions, popular finishes, and review velocity to identify emerging trends in home decor.
Suppliers monitor stock availability and lead times across competing retailers to forecast macro demand for lighting fixtures.
"Bellacor holds a massive repository of structured lighting and furniture specifications, but extracting dimensions, finishes, and bulb requirements at scale requires dedicated infrastructure."
Most teams underestimate the complexity of scraping home decor sites. Reliable extraction requires handling deep category trees, parsing non-standard specification tables, and bypassing basic bot protection. DataFlirt manages the proxies, parsing logic, and pipeline orchestration so your team can focus on merchandising and pricing strategy.
Everything supported by our bellacor.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 bellacor.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Bellacor is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and specification data. We do not extract personal data or circumvent authentication walls.
Our parsers are designed to map non-standard HTML tables into a strict JSON schema. We normalise fields like 'Maximum Wattage' or 'UL Rating' regardless of how the specific brand formats the data on Bellacor.
Yes. We extract the stock status (e.g., 'In Stock', 'Out of Stock') and any estimated shipping or lead time text provided on the product page.
We can configure pipelines to run daily, weekly, or on a custom schedule. For large catalogues, we recommend daily diff runs to capture price and stock changes without unnecessary compute overhead.
We extract the direct URLs to the high-resolution images and gallery assets. We do not download and host the binary image files, but you can easily ingest the URLs into your own CDN or storage bucket.
No. Accessing Bellacor Pro trade pricing requires authenticated user sessions with approved credentials. DataFlirt only extracts publicly visible retail pricing.
Our minimum engagement typically starts with a defined category or brand list. We price based on the total number of SKUs tracked and the frequency of extraction. Contact us for a precise quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous price monitoring across thousands of SKUs — we scope, build, and operate the pipeline. Tell us what you need.
