We extract fixture specifications, SKU variants, photometric data, and spec sheets from Maxim Lighting. 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 Fixture Details objects from maximlighting.com. All fields typed and schema-versioned.
"sku": "10041WTSN", "product_name": "Silo 1-Light Pendant", "collection": "Silo", "finish": "Satin Nickel", "width_inches": 5.5, "msrp": 148.0
| # | sku | product_name | collection | category | finish | glass_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Technical Specs objects from maximlighting.com. All fields typed and schema-versioned.
"sku": "10041WTSN", "bulb_type": "LED", "max_wattage": 8.0, "lumens": 560, "color_temp_k": 3000, "ul_rating": "Dry"
| # | sku | bulb_type | max_wattage | lumens | color_temp_k | cri |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Documents & Media objects from maximlighting.com. All fields typed and schema-versioned.
"sku": "10041WTSN", "primary_image_url": "https://maximlighting.com/images/products/10041WTSN.jpg", "spec_sheet_pdf": "https://maximlighting.com/pdfs/spec/10041WTSN.pdf", "install_guide_pdf": "https://maximlighting.com/pdfs/install/10041WTSN_install.pdf", "photometric_ies_url": "https://maximlighting.com/ies/10041WTSN.ies", "hi_res_zip_url": "https://maximlighting.com/hires/10041WTSN.zip"
| # | sku | primary_image_url | gallery_image_urls | spec_sheet_pdf | install_guide_pdf | photometric_ies_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variant Mapping objects from maximlighting.com. All fields typed and schema-versioned.
"parent_sku": "10041", "child_sku": "10041WTSN", "finish_code": "SN", "finish_name": "Satin Nickel", "stock_status": "In Stock", "price_differential": 0.0
| # | parent_sku | child_sku | finish_code | finish_name | glass_code | price_differential |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dealer Locator objects from maximlighting.com. All fields typed and schema-versioned.
"dealer_id": "DLR-8472", "dealer_name": "Lamps Plus", "city": "Los Angeles", "state": "CA", "zip_code": "90015", "dealer_type": "Showroom"
| # | dealer_id | dealer_name | address_line1 | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper navigates the entire Maxim Lighting and ET2 catalogue, extracting deep technical specifications, photometric files, and variant matrices while bypassing rate limits.
Dimensions, weight, UPC, and base metadata extracted directly from the fixture detail pages.
Lumens, CCT, CRI, dimmability, and voltage captured and normalised into strict data types.
Extracting static URLs for PDF spec sheets, IES photometric files, and installation guides.
Mapping finishes and glass types to specific child SKUs for accurate inventory representation.
Cross-referencing the ET2 Contemporary Lighting sub-brand within the same unified schema.
Capturing retail pricing and MAP guidelines where visible on public catalogue pages.
Extracting showroom and distributor locations across North America for market mapping.
Monitoring availability flags and lead time estimates for supply chain forecasting.
Run daily or weekly diffs to keep your Product Information Management system updated.
Brief in. Clean data out.
Provide target collections, brands, or full-site requirements. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and PDF parsers specifically for maximlighting.com.
Schema validation, null-rate checks, and spec sheet verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
B2B catalogues present unique extraction challenges, from unstructured PDFs to deep variant nesting. Here is how we build resilient pipelines.
Lighting data relies heavily on external files. We locate, extract, and validate URLs for PDF spec sheets, IES photometric files, and high-resolution image ZIPs, ensuring your database has direct links to all required assets.
Maxim Lighting displays multiple finishes and glass options on a single parent page. Our pipeline unrolls these dynamic matrices into discrete child SKUs, capturing unique pricing and imagery for each combination.
Raw catalogue text often mixes units and formats. We normalise dimensions into pure numeric values (inches/lbs) and parse electrical specs like CCT and lumens into strict integers for immediate queryability.
Scraping thousands of high-resolution images and PDFs triggers standard firewall rules. We route requests through US-based residential proxies to maintain high concurrency without IP bans.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load in your data warehouse.
Lighting distributors automate product information management updates, ensuring their local databases match manufacturer specifications.
Retailers and competing manufacturers track MSRP and MAP pricing across lighting brands to adjust their own positioning.
Software platforms ingest 3D models, IES files, and dimensional specs to power rendering engines and virtual staging tools.
Retailers rapidly populate Shopify or Magento storefronts with structured catalogue data, bypassing manual data entry.
Procurement teams monitor stock flags, availability status, and lead times to optimise inventory purchasing models.
Industry analysts track trends in color temperatures, popular finishes, and energy efficiency ratings across catalogue updates.
"Maxim Lighting's catalogue contains thousands of SKUs and critical photometric data, but integrating it requires a purpose-built pipeline to parse complex variant matrices and technical PDFs."
Extracting lighting data goes beyond simple web scraping. Electrical specifications, IES files, and dimensional data are often buried in PDFs or nested JavaScript tabs. DataFlirt handles the heavy lifting of parsing, normalising, and delivering this technical data so your engineering team can focus on core product development.
Everything supported by our maximlighting.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 manages crawl orchestration and deduplication, while Playwright handles JavaScript rendering for dynamic variant loading and dealer locator maps.
Custom parsers identify, validate, and extract asset URLs for PDFs and IES files, ensuring no dead links enter your downstream database.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management, pushing structured data directly to your warehouse.
Data delivered to where your team already works — no new tooling required.
About maximlighting.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available catalogue information is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and specification data. We do not extract personal data or circumvent authentication walls.
Yes. We capture the direct URLs for IES files, allowing your systems to automatically download and ingest them for rendering engines or CAD software.
We unroll variant matrices. If a single product page contains five finish options, our pipeline emits five discrete child SKUs, each with its specific pricing, image URL, and finish code.
Yes. Both Maxim Lighting and ET2 share similar underlying infrastructure, and our pipeline handles both brands within a unified output schema.
Our standard pipeline extracts the direct URLs for spec sheets. If your use case requires extracting text or tables from within the PDFs, we can configure custom OCR and parsing logic during the build phase.
We configure pipelines to match your requirements. Most catalogue syncs run on a weekly or daily cadence, while specific pricing or availability checks can run more frequently.
Engagements start at a defined category or brand list. Contact us with your specific data requirements for a scoped quote and timeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous inventory feed across thousands of SKUs, we scope, build, and operate the pipeline. Tell us what you need.
