We extract commercial kitchen equipment catalogues, bulk pricing tiers, manufacturer specifications, and inventory levels from Wasserstrom. 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 Equipment Listings objects from wasserstrom.com. All fields typed and schema-versioned.
"sku": "104829", "mfr_part_number": "TRCB-52", "title": "True TRCB-52 52 Inch Refrigerated Chef Base", "brand": "True Refrigeration", "base_price": 5429.0, "currency": "USD", "category": "Refrigeration", "sub_category": "Chef Bases"
| # | sku | mfr_part_number | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Stock objects from wasserstrom.com. All fields typed and schema-versioned.
"sku": "104829", "unit_price": 5429.0, "in_stock": true, "stock_status_message": "Usually Ships in 1 to 2 Weeks", "lead_time_days": 14, "ships_from_mfr": true, "units_per_case": 1
| # | sku | unit_price | case_price | pallet_price | units_per_case | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Specs & Certifications objects from wasserstrom.com. All fields typed and schema-versioned.
"sku": "104829", "voltage": 115, "amps": 8.1, "phase": 1, "width_inches": 51.875, "depth_inches": 32.125, "height_inches": 20.375, "nsf_certified": true
| # | sku | voltage | wattage | amps | phase | width_inches |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Taxonomy objects from wasserstrom.com. All fields typed and schema-versioned.
"category_id": "cat10023", "category_name": "Commercial Ovens", "parent_category": "Cooking Equipment", "breadcrumb_path": "Home > Cooking Equipment > Commercial Ovens", "total_products": 1248, "url": "https://www.wasserstrom.com/restaurant-supplies-equipment/commercial-ovens"
| # | category_id | category_name | parent_category | breadcrumb_path | total_products | featured_brands |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Replacement Parts objects from wasserstrom.com. All fields typed and schema-versioned.
"part_sku": "602911", "part_name": "Hobart 00-294650-00002 Agitator Shaft", "manufacturer": "Hobart", "compatible_models": "['HL200', 'HL200C']", "price": 142.5, "in_stock": true
| # | part_sku | part_name | manufacturer | compatible_models | part_category | price |
|---|---|---|---|---|---|---|
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Our Wasserstrom scraper navigates complex B2B catalogues, extracts tabular specifications from PDF cut sheets, and captures dynamic bulk pricing tiers.
Extract electrical requirements, dimensions, capacities, and materials for heavy equipment and smallwares.
Capture base price, case price, and pallet pricing tiers. Normalise unit costs across different packaging formats.
Automatically download manufacturer PDF cut sheets and extract tabular specification data into structured JSON.
Standardise manufacturer names and part numbers across thousands of distinct foodservice brands.
Track stock availability, factory lead times, and direct-from-manufacturer shipping statuses.
Flag NSF, Energy Star, UL, and CE certifications required for commercial health code compliance.
Navigate deeply nested category trees to ensure full catalogue coverage without missing orphan SKUs.
Map OEM replacement parts to their compatible base equipment models for service catalogues.
Maintain a hash index of catalogue state. Push only changed records to reduce downstream processing load.
Brief in. Clean data out.
Provide categories, manufacturer filters, or specific SKU lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and PDF parsing modules specific to the Wasserstrom catalogue.
Schema validation, unit normalisation checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Wholesale distributors use complex taxonomy and heterogeneous data formats. Here is how we standardise the extraction.
Critical equipment specifications often exist only in attached manufacturer PDFs. We deploy OCR and tabular data extraction models to parse these documents and merge the variables back into the primary product record.
B2B catalogues feature deep, overlapping category trees. Our crawlers map the full breadcrumb path and normalise categories so you can filter products precisely by functional group.
We use US-based residential ISP proxies with realistic browser fingerprints and randomised request timing to prevent IP bans during large-scale catalogue extraction.
For catalogues exceeding 300,000 SKUs, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and storage bloat.
Every run emits structured logs to our observability stack. We alert on null-rate spikes or coverage drops, responding before you notice missing data.
Restaurant supply dealers monitor wholesale pricing and bulk discount tiers to maintain market parity.
Hospitality groups aggregate equipment specifications and pricing to negotiate better terms across their supply chain.
Manufacturers track category placement, brand representation, and retail pricing of their equipment versus competitors.
Machine learning teams use structured specification data to train procurement assistants and kitchen design models.
Analysts monitor lead times and out-of-stock indicators across major brands to predict supply chain bottlenecks.
Niche B2B retailers build comprehensive product databases by extracting specifications and imagery for dropship fulfillment.
"Wasserstrom holds the definitive catalogue for commercial foodservice equipment, but extracting standardised specifications across thousands of manufacturers requires serious pipeline engineering."
B2B distributors present unique challenges: data is often locked in PDF cut sheets, pricing varies by case size, and taxonomies are deeply nested. DataFlirt handles PDF parsing, unit normalisation, and bot mitigation so your procurement teams get clean, queryable data.
Everything supported by our wasserstrom.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 for dynamic inventory checks. Combined via custom middleware.
Dedicated microservices download, OCR, and parse manufacturer PDFs, extracting specification tables and merging them into the primary JSON payload.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About wasserstrom.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available catalogue 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. Clients should consult legal counsel for specific use cases.
Our pipeline identifies linked PDF cut sheets, downloads them, and passes them through a dedicated parsing microservice. We extract tabular specification data and append it directly to the product's JSON record.
Yes. We capture base unit prices, case prices, and pallet prices, normalising the data so you can compare unit costs accurately across different packaging formats.
Catalogue refresh cadences depend on your requirements. We can configure daily runs for full catalogue updates, or hourly runs for targeted high-velocity SKUs to monitor lead time changes.
No. DataFlirt extracts public wholesale and retail pricing. Negotiated contract pricing requires authenticated sessions, which falls outside our standard managed service scope.
Yes. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality before committing.
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 300K SKUs, we scope, build, and operate the pipeline. Tell us what you need.
