We extract product listings, technical specifications, bulk pricing tiers, and stock depth from elightbulbs.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 Specs objects from elightbulbs.com. All fields typed and schema-versioned.
"sku": "PHL-45892", "brand": "Philips", "base_type": "E26 Medium", "bulb_shape": "A19", "wattage": 9.5, "lumens": 800, "colour_temp": 2700, "cri": 90
| # | sku | title | brand | base_type | bulb_shape | wattage |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Stock objects from elightbulbs.com. All fields typed and schema-versioned.
"sku": "PHL-45892", "base_price": 4.99, "case_qty": 24, "case_price": 105.36, "discount_pct": 12, "stock_status": "In Stock", "min_order_qty": 1, "currency": "USD"
| # | sku | base_price | case_qty | case_price | discount_pct | stock_status |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Taxonomy objects from elightbulbs.com. All fields typed and schema-versioned.
"sku": "PHL-45892", "primary_category": "LED Bulbs", "sub_category": "A-Shape LED", "technology": "LED", "energy_star": true, "rohs_compliant": true, "application": "General Purpose"
| # | sku | primary_category | sub_category | application | technology | energy_star |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Ballasts objects from elightbulbs.com. All fields typed and schema-versioned.
"sku": "ADV-IOP2P32N", "type": "Electronic Fluorescent", "start_method": "Instant Start", "num_lamps": 2, "input_voltage": "120-277V", "power_factor": 0.98, "ballast_factor": 0.88
| # | sku | type | start_method | num_lamps | input_voltage | power_factor |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Cross-Reference objects from elightbulbs.com. All fields typed and schema-versioned.
"sku": "PHL-45892", "manufacturer_part_num": "45892-7", "upc": "046677458921", "equivalent_wattage": 60, "replacement_for": "Incandescent 60W A19", "ansi_code": "A19", "unspsc_code": "39101628"
| # | sku | manufacturer_part_num | upc | equivalent_wattage | replacement_for | ansi_code |
|---|---|---|---|---|---|---|
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Our elightbulbs.com scraper extracts highly dimensional specification data, normalising inconsistent tables into clean schemas while capturing bulk pricing and stock depth.
Extract wattage, lumens, base type, CRI, colour temperature, and lifespan hours directly from product specification tables.
Capture base price, case quantities, pallet pricing, and volume discount percentages for B2B procurement modelling.
Extract ANSI codes, OEM part numbers, and equivalent wattage to map replacements across different manufacturers.
Monitor inventory levels, drop-ship status, and estimated lead times across the entire product catalogue.
Map products to specific applications, lighting technologies, and compliance standards like Energy Star and DLC.
Extract specific parameters for ballasts and fixtures, including start methods, input voltage, and lamp compatibility.
Capture URLs for PDF spec sheets, photometric data files, and high-resolution product images.
Run daily diffs to track pricing adjustments and stock outs without processing the entire catalogue.
Normalise specifications across Philips, Osram, GE, Sylvania, and hundreds of specialty lighting brands.
Brief in. Clean data out.
Provide categories, search terms, or manufacturer part numbers. We design the extraction schema together.
We configure Scrapy crawlers, handle pagination, and write normalisation logic for specification tables.
Schema validation, null-rate checks, and unit conversion verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Lighting data is notoriously difficult to parse. Here is how our pipeline handles the inconsistencies of B2B e-commerce.
Elightbulbs uses varying table structures depending on the product type (bulb vs ballast). Our parsers use heuristic mapping to identify keys like 'Color Temperature' or 'Kelvin' and normalise them into a single typed field.
To ensure total catalogue coverage, we traverse the site's parametric search filters rather than relying solely on sitemaps, capturing orphaned SKUs and category-specific metadata.
B2B pricing involves multiple tiers based on case or pallet quantities. We extract the full pricing matrix for each SKU, allowing you to model bulk procurement costs accurately.
For daily monitoring, we hash the pricing and availability fields. Subsequent runs only push data when a price changes or an item goes out of stock, reducing database load.
If elightbulbs.com redesigns their product pages, our telemetry detects the drop in field coverage and alerts our engineering team to patch the selectors immediately.
Lighting distributors monitor base prices and case discounts to optimise their own pricing strategies.
B2B suppliers extract structured specifications to populate their PIM systems and improve onsite search.
Energy consultants extract wattage and lumen data to calculate ROI for LED retrofit projects.
Procurement teams map OEM part numbers to generic equivalents to reduce supply chain costs.
Contractors monitor stock availability and lead times for large-scale commercial lighting installations.
Manufacturers track category saturation and the transition from fluorescent technologies to LED.
"Lighting specifications are highly dimensional. Without structured data on base types, lumens, and colour temperatures, catalogue mapping is impossible."
Extracting lighting data requires more than simple HTML parsing. Technical specifications are buried in inconsistent tables, pricing depends on case quantities, and cross-referencing OEM part numbers demands precise normalisation. DataFlirt handles the complexity of parametric extraction and schema enforcement, delivering clean, warehouse-ready records so your team can focus on catalogue enrichment.
Everything supported by our elightbulbs.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 high-throughput crawl orchestration and deduplication. Playwright handles dynamic content loading and complex navigation flows.
We maintain pools of US residential ISP proxies to distribute request load, preventing IP bans during full catalogue sweeps.
Pipelines run on AWS ECS. Airflow handles scheduling, dependency management, and SLA alerting. State is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About elightbulbs.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product specifications, pricing, and stock data is generally permissible. DataFlirt extracts only public, non-authenticated catalogue data. We do not circumvent authentication walls or extract proprietary customer information.
Yes. We map varying field names (e.g., 'Color Temp', 'Kelvin', 'CCT') into a single normalised schema, ensuring consistency across different brands and product categories.
We extract the entire pricing matrix for each SKU, delivering base price, case quantities, and calculated volume discounts as structured arrays.
Yes. We capture the direct URLs for manufacturer PDF specification sheets, photometric data files, and installation guides.
Pipelines can be configured to run daily or hourly. For high-frequency monitoring, we use change detection to only update records where stock status or pricing has shifted.
Yes. We extract cross-reference data, including ANSI codes, equivalent wattages, and replacement text strings to help you build mapping databases.
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 thousands of SKUs — we scope, build, and operate the pipeline. Tell us what you need.
