We extract aftermarket catalogues, OEM cross-reference numbers, Year/Make/Model fitment tables, and pricing signals from Partsgeek. 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 Part Listings objects from partsgeek.com. All fields typed and schema-versioned.
"part_number": "W0133-1928374", "brand": "Bosch", "title": "Bosch Alternator - Remanufactured", "price": 145.5, "core_charge": 45.0, "condition": "Remanufactured", "position": "Front", "in_stock": true
| # | part_number | sku | brand | title | price | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Fitment Data (YMM) objects from partsgeek.com. All fields typed and schema-versioned.
"part_number": "W0133-1928374", "year": 2018, "make": "Honda", "model": "Civic", "submodel": "EX", "engine": "2.0L 4 Cyl", "fitment_notes": "105 Amp; Includes Pulley"
| # | part_number | year | make | model | submodel | engine |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Cross-Reference objects from partsgeek.com. All fields typed and schema-versioned.
"part_number": "W0133-1928374", "oem_numbers": "['31100-RNA-A01', '31100-RNA-A01RM']", "interchange_numbers": "['AL1300X', '11311']", "upc": "028851543210", "weight_lbs": 12.4, "warranty": "12 Month"
| # | part_number | brand | oem_numbers | superseded_by | interchange_numbers | upc |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Availability objects from partsgeek.com. All fields typed and schema-versioned.
"part_number": "W0133-1928374", "price": 145.5, "core_charge": 45.0, "shipping_cost": 8.95, "stock_status": "In Stock", "price_timestamp": "2026-05-12T09:14:00Z", "currency": "USD"
| # | part_number | price | core_charge | shipping_cost | shipping_method | stock_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from partsgeek.com. All fields typed and schema-versioned.
"keyword": "alternator 2018 honda civic", "position": 1, "part_number": "W0133-1928374", "brand": "Bosch", "price": 145.5, "is_exact_fit": true, "scraped_at": "2026-05-12T09:15:33Z"
| # | keyword | position | part_number | title | brand | price |
|---|---|---|---|---|---|---|
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Extracting from Partsgeek requires navigating complex Year/Make/Model dropdowns, mapping OEM cross-references, and capturing accurate core charges. Our infrastructure handles the state management so you get clean, relational data.
Iterate through fitment selectors to map every part to its compatible vehicles, including submodels and engine specifications.
Capture original equipment manufacturer (OEM) part numbers and aftermarket interchange codes for precise cross-referencing.
Extract base price, list price, and mandatory core charges to calculate the true landed cost of remanufactured parts.
Scrape detailed fitment notes (e.g., 'Fits models with automatic transmission only') to prevent downstream catalogue errors.
Map Partsgeek's internal taxonomy into a clean category tree, standardising brand names across the aftermarket ecosystem.
Monitor inventory status and shipping estimates to detect supply chain shortages across specific part categories.
Input a list of internal SKUs or competitor part numbers and extract the exact Partsgeek equivalent and current price.
Extract high-resolution part images, diagrams, and schematic URLs to enrich your internal product information management (PIM) system.
Receive only records that have changed since the last run — ideal for high-frequency pricing updates without data bloat.
Brief in. Clean data out.
Provide target categories, brands, or specific Year/Make/Model combinations. We design the extraction schema.
We configure crawlers to navigate Partsgeek's fitment selectors, handle pagination, and manage proxy rotation.
Schema validation ensures core charges, OEM numbers, and fitment notes map correctly before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Automotive eCommerce sites rely on complex state management for fitment validation. Here is how we extract relational data accurately.
Partsgeek relies on sequential Year, Make, and Model dropdowns to filter parts. Our Playwright orchestrators maintain session state, iterating through these selectors systematically to build a complete fitment matrix without skipping submodels.
A single part number can fit hundreds of vehicle configurations. We extract the data relationally, providing a flat part catalogue alongside a separate, normalised fitment mapping table to keep your database clean.
Category pages load dynamically via AJAX requests. We intercept these backend API calls directly when possible, or use headless browsers to trigger scroll events, ensuring total capture of deep category trees.
Automotive pricing includes base prices, core charges, and shipping variations. We parse and type these fields explicitly as floats, preventing string-concatenation errors in your downstream pricing algorithms.
To extract millions of part-to-vehicle relationships, we distribute requests across a US-based residential proxy pool, preventing IP bans and ensuring continuous pipeline execution.
Aftermarket retailers track Partsgeek pricing and core charges to optimise their own pricing algorithms and maintain margin.
Distributors cross-reference their internal inventory against Partsgeek's taxonomy to identify missing brands or sub-categories.
eCommerce startups use extracted YMM data to populate their ACES/PIES-compatible fitment databases for accurate part matching.
Data teams build internal cross-reference tables linking expensive OEM part numbers to cheaper aftermarket alternatives.
Analysts monitor stock availability across specific brands (e.g., Bosch, Denso) to detect manufacturing delays and inventory shortages.
AI teams train natural language models on automotive part descriptions, fitment notes, and specifications to improve search relevance.
"Partsgeek holds one of the most comprehensive aftermarket and OEM auto parts catalogues online, but extracting accurate Year/Make/Model fitment data at scale requires complex state management."
Automotive data extraction fails when crawlers cannot maintain session state across complex Year/Make/Model dropdowns. DataFlirt orchestrates Playwright sessions to navigate fitment selectors, capture OEM cross-reference tables, and map aftermarket pricing — delivering clean, normalised catalogues to your warehouse.
Everything supported by our partsgeek.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 manages the complex JavaScript state required to navigate sequential automotive fitment dropdowns.
We route requests through US-based residential ISP proxies to avoid rate limits while scraping deep automotive category trees.
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 partsgeek.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We configure our crawlers to iterate through Partsgeek's fitment selectors, generating a relational mapping table that links specific part numbers to all compatible vehicle configurations.
Core charges are common in automotive parts (e.g., alternators, brake calipers). We parse the core charge separately from the base price, delivering both as distinct numeric fields so you can calculate total landed costs accurately.
Yes. You can provide a CSV of Manufacturer Part Numbers (MPNs) or OEM codes. We will script the pipeline to query Partsgeek's search engine and return the corresponding listings, prices, and availability.
Yes. We extract all available cross-reference data listed on the product page, including OEM numbers, superseded part numbers, and aftermarket interchange codes.
We support daily, weekly, or monthly cadences. For high-priority SKUs, we can configure sub-daily runs to monitor price fluctuations and stock availability.
Scraping publicly available, non-authenticated pricing and catalogue data is generally permissible. We do not bypass login walls to extract wholesale pricing or personal data. Clients should review Terms of Service and consult legal counsel for specific commercial use cases.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full catalogue extraction or daily price monitoring for specific part numbers — we scope, build, and operate the pipeline. Tell us what you need.
