We extract lighting fixtures, technical specifications, pricing signals, brand catalogues, and stock availability from capitollighting.com. 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 capitollighting.com. All fields typed and schema-versioned.
"sku": "VC-1234-BZ", "title": "Darlana Medium Lantern", "brand": "Visual Comfort", "category": "Ceiling Lights", "sub_category": "Pendants", "price": 849.0, "stock_status": "In Stock", "rating": 4.8, "review_count": 42
| # | sku | title | brand | category | sub_category | finish_options |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Technical Specs objects from capitollighting.com. All fields typed and schema-versioned.
"sku": "VC-1234-BZ", "bulb_type": "E12 Candelabra", "max_wattage": "60W", "voltage": "120V", "ul_rating": "Damp Location", "material": "Brass", "style": "Transitional", "collection": "Darlana"
| # | sku | bulb_type | max_wattage | voltage | ul_rating | material |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Offers objects from capitollighting.com. All fields typed and schema-versioned.
"sku": "VC-1234-BZ", "price": 849.0, "list_price": 999.0, "discount_pct": 15, "map_pricing_flag": true, "lead_time": "Ships in 3 to 5 days", "currency": "USD", "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | price | list_price | discount_pct | sale_badge | map_pricing_flag |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from capitollighting.com. All fields typed and schema-versioned.
"review_id": "REV-89213", "sku": "VC-1234-BZ", "reviewer_name": "Sarah J.", "star_rating": 5, "review_title": "Beautiful statement piece", "review_date": "2026-04-18", "helpful_votes": 12, "verified_purchase": true
| # | review_id | sku | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Categories & Brands objects from capitollighting.com. All fields typed and schema-versioned.
"brand_name": "Visual Comfort", "category_path": "Brands > Visual Comfort", "product_count": 1240, "brand_url": "https://www.capitollighting.com/brands/visual-comfort", "avg_price": 650.0, "active_listings": 1185
| # | brand_name | collection_name | category_path | product_count | brand_url | top_seller_sku |
|---|---|---|---|---|---|---|
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Our pipeline handles every layer of the Capitol Lighting platform: product catalogues, technical specifications, dynamic pricing, and stock status, with JavaScript rendering and session management built in.
SKUs, titles, descriptions, finishes, dimensions, weights, and high-resolution images scraped at the individual product level.
Capture bulb types, max wattage, voltage, UL ratings, material composition, and installation instructions for every fixture.
Monitor retail price, list price, discount percentages, and Minimum Advertised Price (MAP) compliance across all brands.
Extract parent-child relationships for fixtures with multiple finish or size options, ensuring accurate SKU-level data.
Track inventory status, backorder notices, and estimated shipping lead times for accurate supply chain modelling.
Map entire catalogues from premium brands like Visual Comfort, Kichler, Hinkley, and Minka Lavery.
Extract full review text, star ratings, helpful vote counts, and verified purchase flags across the product catalogue.
Crawl hierarchical category trees from chandeliers and pendants to outdoor lighting and ceiling fans.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences with change-detection diffing.
Brief in. Clean data out.
Provide brand URLs, category paths, or specific SKUs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for capitollighting.com.
Schema validation, null-rate checks, price-outlier detection, and sample data review before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
eCommerce scraping requires constant maintenance. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Retail sites employ bot detection based on TLS fingerprints and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management.
Product variants, pricing updates, and stock statuses often load via JavaScript. We run full Playwright browser sessions to trigger lazy-loads and hydrate dynamic widgets.
Retail DOM structures change frequently. Our strategy uses multiple fallback chains per field, including structured data extraction, so a layout update does not break your pipeline.
For large lighting catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and schema drift, responding before you notice.
Lighting brands audit retail listings for Minimum Advertised Price violations and unauthorised discounts.
Retailers track assortment, pricing, and promotional events across premium lighting categories.
Analysts track finish trends, new collection launches, and category saturation to identify design shifts.
Computer vision teams use high-resolution fixture images and technical metadata to train style-matching models.
eCommerce operators synchronise stock availability and lead times to prevent out-of-stock orders.
Design platforms aggregate technical specifications and dimensions to build comprehensive specification tools.
"Capitol Lighting holds one of the most comprehensive digital catalogues of premium lighting fixtures, but extracting structured technical specifications requires a dedicated pipeline."
Most teams underestimate the complexity of scraping lighting eCommerce sites. Extracting accurate dimensions, bulb requirements, finish variants, and MAP pricing requires residential proxies, full JavaScript rendering, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our capitollighting.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, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About capitollighting.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information 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.
We use residential ISP proxies, full Playwright browser sessions, and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
Yes. We parse the specification tables on every product page to extract bulb type, max wattage, UL rating, dimensions, and materials into structured JSON fields.
We map parent-child variant relationships. If a chandelier comes in Brass, Nickel, and Bronze, we extract each finish as a unique SKU with its corresponding price and image.
Pipelines can be configured for daily or weekly runs depending on your requirements. Daily refreshes capture MAP pricing changes and promotional events accurately.
Yes. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process so you can validate schema fit and field completeness.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across thousands of SKUs, we scope, build, and operate the pipeline. Tell us what you need.
