We extract product specifications, vehicle fitment databases, volume pricing, and inventory from superbrightleds.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 Specifications objects from superbrightleds.com. All fields typed and schema-versioned.
"sku": "1157-W27-T", "name": "1157 LED Bulb - Dual Function 27 SMD LED Tower", "category": "Automotive", "lumens": 450, "colour_temperature": "6500K", "operating_voltage": "9-14V DC", "price": 14.95
| # | sku | name | category | sub_category | lumens | colour_temperature |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Vehicle Fitment objects from superbrightleds.com. All fields typed and schema-versioned.
"base_type": "BAY15D", "cross_reference": "['1157', '2057', '2357', '7528']", "year_start": 1998, "year_end": 2012, "make": "Ford", "model": "F-150", "position": "Tail Light"
| # | base_type | cross_reference | year_start | year_end | make | model |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Inventory objects from superbrightleds.com. All fields typed and schema-versioned.
"sku": "1157-W27-T", "base_price": 14.95, "volume_tier_1_qty": 10, "volume_tier_1_price": 13.5, "volume_tier_2_qty": 50, "volume_tier_2_price": 12.0, "in_stock": true
| # | sku | base_price | volume_tier_1_qty | volume_tier_1_price | volume_tier_2_qty | volume_tier_2_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from superbrightleds.com. All fields typed and schema-versioned.
"review_id": "REV-89214", "sku": "1157-W27-T", "rating": 5, "verified_buyer": true, "review_date": "2024-01-15", "review_text": "Much brighter than the OEM halogen bulbs.", "helpful_votes": 12
| # | review_id | sku | reviewer_name | rating | review_date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Commercial Lighting objects from superbrightleds.com. All fields typed and schema-versioned.
"sku": "UFO-150W-50K", "dlc_listed": true, "ul_listed": true, "wattage_equivalent": "400W MH", "cri": 80, "dimmable": "0-10V", "mounting_type": "Hook Mount"
| # | sku | product_series | dlc_listed | ul_listed | wattage_equivalent | cri |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper handles the complex data structures of superbrightleds.com: nested specification tables, AJAX-driven vehicle fitment selectors, and multi-tier volume pricing.
Extract lumens, colour temperature, operating voltage, beam angle, and IP ratings directly from product tables into typed JSON fields.
Map LED bulbs to exact vehicle applications via the Year/Make/Model selector. Capture all compatible OEM cross-references.
Capture base price and all bulk discount thresholds for commercial and B2B cataloguing.
Track stock availability and lead times for high-demand commercial fixtures and automotive bulbs.
Extract DLC, UL, and RoHS compliance markers for commercial and industrial lighting products.
Extract customer reviews, star ratings, and verified buyer tags to analyse product reliability and fitment accuracy.
Preserve the full breadcrumb trail from main category down to specific bulb bases and applications.
Run daily diffs to identify price changes, new product additions, and discontinued SKUs.
Capture high-resolution product images, dimensional diagrams, photometric data sheets, and IES files.
Brief in. Clean data out.
Provide category URLs, vehicle ranges, or commercial lighting segments. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for superbrightleds.com.
Schema validation, null-rate checks, and cross-reference standardisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Lighting cataloguing requires deep parsing of unstructured data. Here is how we extract clean records from complex frontend components.
The vehicle bulb finder uses AJAX requests to load compatible bulbs. Our pipeline intercepts these backend API calls directly, mapping exact SKU compatibility across thousands of vehicle permutations without brittle UI automation.
Product specifications are often displayed in inconsistent HTML tables. We use rules-based parsing to normalise fields like 'Colour Temperature' and 'Operating Voltage' into strict numeric and categorical types.
A single LED bulb can replace dozens of legacy incandescent models (e.g., 1157, 2057, 2357). We extract and normalise these comma-separated strings into clean JSON arrays for easy database joins.
High-volume scraping triggers perimeter defences. We route requests through US-based residential proxies, managing cookies and TLS fingerprints to maintain uninterrupted access to the catalogue.
We hash the specification and pricing payload of every SKU. Subsequent runs only deliver records where pricing, stock status, or technical specifications have changed, reducing your processing load.
Auto parts retailers use fitment data and cross-references to build internal bulb-finder tools for their customers.
Lighting distributors track base prices and volume tiers to optimise their own pricing strategies.
Wholesalers ingest technical specifications directly into their ERP systems to enrich their product listings.
Manufacturers analyse category breadth and review sentiment to identify missing products in their own portfolios.
Data providers aggregate year-make-model mappings to build master ACES/PIES compatible databases.
Contractors extract DLC and UL certified fixture data to automate lighting upgrade proposals and rebate calculations.
"Superbrightleds.com holds an incredibly dense cross-reference database mapping modern LED equivalents to decades of legacy automotive and commercial bulb standards."
Building a comprehensive lighting catalogue requires more than simple web scraping. You must navigate complex vehicle year-make-model selectors, parse dense technical specification tables, and normalise hundreds of bulb base cross-references. DataFlirt manages this pipeline end-to-end, delivering structured data ready for your application.
Everything supported by our superbrightleds.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 executes JavaScript for dynamic pricing widgets and vehicle fitment APIs.
We maintain pools of residential ISP proxies to bypass perimeter blocks, rotating IPs per request while maintaining necessary session cookies.
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 superbrightleds.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product data, specifications, and pricing is generally permissible under applicable law. DataFlirt targets only public, non-authenticated catalogue data. We do not circumvent authentication walls or extract personal user data.
We intercept the backend API calls used by the frontend Year/Make/Model selector. This allows us to map every vehicle permutation to its compatible SKUs efficiently and accurately.
Yes. We parse the comma-separated cross-reference strings on the product pages and output them as clean JSON arrays, making it simple to query for legacy bulb equivalents like '1157' or 'H11'.
Yes. We capture the direct URLs to all attached PDF specification sheets, dimensional diagrams, and downloadable IES files for commercial lighting fixtures.
Pipelines can be configured to run daily or weekly depending on your requirements. Our change-detection system ensures you only process records where prices or stock levels have shifted.
Yes. We can flatten the nested JSON structures into tabular CSV files, formatting the output to align with ACES and PIES automotive standards if required.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of commercial fixtures or a continuous feed of automotive fitment data - we scope, build, and operate the pipeline. Tell us what you need.
