We extract product specifications, dimensional tolerances, performance data, and cross-reference matrices from SKF. 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 Bearing Specifications objects from skf.com. All fields typed and schema-versioned.
"designation": "6205-2Z", "product_type": "Deep groove ball bearing", "inner_diameter_mm": 25.0, "outer_diameter_mm": 52.0, "width_mm": 15.0, "dynamic_load_rating_kn": 14.8, "limiting_speed_rpm": 14000, "mass_kg": 0.13
| # | designation | product_type | inner_diameter_mm | outer_diameter_mm | width_mm | dynamic_load_rating_kn |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Cross-References objects from skf.com. All fields typed and schema-versioned.
"skf_designation": "6205-2Z", "competitor_brand": "FAG", "competitor_designation": "6205-2Z", "match_type": "Exact Equivalent", "dimensional_match": true, "load_rating_delta_pct": 0.0, "verification_status": "Verified", "scraped_at": "2026-05-12T09:14:00Z"
| # | skf_designation | competitor_brand | competitor_designation | match_type | interchange_notes | dimensional_match |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Application Data objects from skf.com. All fields typed and schema-versioned.
"designation": "6205-2Z", "industry_target": "General Machinery", "mounting_type": "Interference fit", "operating_temp_min_c": -20, "operating_temp_max_c": 120, "recommended_lubricant": "SKF LGMT 2", "relubrication_interval_hrs": 3500
| # | designation | industry_target | mounting_type | housing_fit | shaft_fit | operating_temp_min_c |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for CAD & Media objects from skf.com. All fields typed and schema-versioned.
"designation": "6205-2Z", "image_url": "https://skf.com/img/6205-2z.jpg", "step_file_available": true, "iges_file_available": true, "pdf_datasheet_url": "https://skf.com/docs/6205-2z_specs.pdf", "cad_3d_url": "https://skf.com/cad/6205-2z.step", "media_timestamp": "2026-05-12T09:14:00Z"
| # | designation | image_url | cad_2d_url | cad_3d_url | step_file_available | iges_file_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Distributor Availability objects from skf.com. All fields typed and schema-versioned.
"designation": "6205-2Z", "region": "Europe", "distributor_name": "SKF Official Store", "stock_status": "In Stock", "quantity_available": 1450, "lead_time_days": 2, "list_price": 14.5, "currency": "EUR"
| # | designation | region | distributor_name | stock_status | quantity_available | lead_time_days |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our SKF scraper navigates complex product hierarchies, parametric search filters, and technical specification tables to extract exact engineering data for MRO and procurement systems.
Extract inner diameter, outer diameter, width, and chamfer dimensions with strict unit typing and normalisation.
Capture dynamic load ratings, static load ratings, and fatigue load limits directly from technical tables.
Parse reference speeds and limiting speeds for both grease and oil lubrication scenarios.
Map Timken, FAG, and NSK designations to their exact SKF equivalents using the interchange matrix.
Extract grease specifications, initial fill volumes, and calculated relubrication intervals per bearing type.
Record dimensional and running accuracy specifications required for high-precision applications.
Identify 2D and 3D model availability and extract direct asset links for STEP and IGES files.
Map individual designations back to parent categories like deep groove, angular contact, or spherical roller bearings.
Extract technical specifications from regional SKF catalogues in German, English, French, and Mandarin.
Monitor 'superseded by' flags and automatically map old designations to current active SKF part numbers.
Brief in. Clean data out.
Provide SKF designations, categories, or competitor cross-reference requirements. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for skf.com.
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.
Extracting engineering data requires precision. We handle parametric filters, dynamic table rendering, and complex unit normalisation.
SKF's catalogue relies heavily on parametric filtering. Our crawlers systematically iterate through bore size, outside diameter, and bearing type filters to ensure 100% catalogue coverage without missing edge-case SKUs.
Technical tables on SKF vary drastically between a deep groove ball bearing and a spherical roller thrust bearing. We normalise these varying column headers into a unified, queryable schema.
Engineering data is useless if units are mixed. Our pipeline explicitly parses metric (mm, kN, kg) and imperial values, ensuring all outputs adhere to your target system's unit requirements.
Many of SKF's calculation tools and CAD viewers require JavaScript to render. We use Playwright to execute these scripts, capturing data hidden behind interactive DOM elements.
SKF frequently updates part numbers. Our pipeline detects obsolescence notices and extracts the new recommended designation, maintaining the integrity of your internal item master.
Automate part standardisation and identify equivalent bearings across multiple manufacturing plants to consolidate spend.
Bearing manufacturers track SKF specifications, load ratings, and product portfolio gaps to position their own lines.
Enrich internal item masters with accurate SKF technical specifications, weights, and datasheet links.
Feed exact bearing frequencies, geometries, and load limits into vibration analysis machine learning models.
Populate asset management systems with precise physical dimensions and CAD metadata for facility modelling.
Track distributor listing prices and availability signals across regional markets to optimise procurement timing.
"SKF's digital catalogue contains the foundational engineering data for global industry - but integrating those specifications into your ERP requires a purpose-built extraction pipeline."
Industrial data extraction is about precision. Missing a tolerance class or misinterpreting a load rating renders the data useless. DataFlirt handles the complex parametric navigation, table normalisation, and unit standardisation required to turn SKF's website into a reliable database. Your engineers get clean, typed data ready for the item master.
Everything supported by our skf.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 renders JavaScript-heavy parametric search filters and application diagrams.
Custom Python pipelines handle unit conversion, standardising complex engineering tables into a predictable schema for your ERP.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About skf.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available specifications and product catalogues is generally permissible. DataFlirt targets only public, non-authenticated engineering data. We do not circumvent authentication walls to access proprietary distributor pricing or gated CAD downloads.
We use Playwright to interact with the JavaScript-rendered filters, methodically iterating through bore sizes and bearing types to ensure complete catalogue coverage without missing hidden SKUs.
Yes. When SKF marks a designation as obsolete, our pipeline extracts the obsolescence flag and maps the old part number to the new recommended designation.
Yes. We extract interchange data mapping competitor brands like FAG, Timken, and NSK to their SKF equivalents, including the match quality indicators.
For engineering specifications, a monthly or quarterly refresh is typically sufficient. If you are monitoring distributor stock or pricing signals, we configure weekly or daily pipelines.
We extract the URLs and metadata for CAD files and datasheets. Downloading the actual STEP or IGES files often requires an authenticated SKF account, which falls outside our public data extraction scope.
Yes. We provide a sample run of up to 500 SKUs to validate schema fit, unit normalisation, and field completeness before you commit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump for your ERP or continuous monitoring of cross-reference updates - we scope, build, and operate the pipeline. Tell us what you need.
