We extract bicycle specifications, geometry charts, component lists, and real-time stock levels from Rutland Cycling. 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 Bicycles & E-Bikes objects from rutlandcycling.com. All fields typed and schema-versioned.
"sku": "RC-TREK-MARLIN-8-2024", "title": "Trek Marlin 8 Gen 3 Mountain Bike 2024", "brand": "Trek", "model_year": 2024, "frame_material": "Aluminium", "groupset": "SRAM SX Eagle 12-speed", "price": 1150.0, "url": "https://www.rutlandcycling.com/bikes/mountain-bikes/trek-marlin-8-gen-3-mountain-bike-2024_48291"
| # | sku | title | brand | model_year | category | frame_material |
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Complete list of extractable fields for Pricing & Inventory objects from rutlandcycling.com. All fields typed and schema-versioned.
"sku": "RC-TREK-MARLIN-8-2024", "variant_id": "VAR-89214", "size": "M/L", "colour": "Crimson", "stock_status": "In Stock", "price": 1150.0, "rrp": 1250.0, "discount_pct": 8.0, "click_collect_eligible": true
| # | sku | variant_id | size | colour | stock_status | stock_quantity |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Geometry Data objects from rutlandcycling.com. All fields typed and schema-versioned.
"sku": "RC-TREK-MARLIN-8-2024", "frame_size": "M/L", "rider_height_min": "173cm", "rider_height_max": "180cm", "reach": "440mm", "stack": "602mm", "head_tube_angle": "66.5", "seat_tube_angle": "74.0"
| # | sku | frame_size | rider_height_min | rider_height_max | reach | stack |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Clothing & Accessories objects from rutlandcycling.com. All fields typed and schema-versioned.
"sku": "RC-SPEC-ALIGN-2", "title": "Specialized Align II MIPS Helmet", "category": "Helmets", "brand": "Specialized", "size": "M/L", "colour": "Matte Black", "price": 45.0, "rrp": 45.0
| # | sku | title | category | brand | gender | size |
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Complete list of extractable fields for Reviews & Ratings objects from rutlandcycling.com. All fields typed and schema-versioned.
"review_id": "REV-98214", "sku": "RC-SPEC-ALIGN-2", "reviewer_name": "James T.", "rating": 5, "title": "Great fit and value", "body": "Comfortable helmet, MIPS included at this price point is excellent.", "date": "2024-03-12", "verified_purchase": true
| # | review_id | sku | reviewer_name | rating | title | body |
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Our Rutland Cycling scraper handles the complex hierarchy of bicycle retail: nested geometry tables, component specifications, variant-level stock by frame size, and dynamic pricing updates.
Extract frame materials, suspension travel, groupset details, brake types, and wheel sizes from structured and unstructured product descriptions.
Convert complex HTML geometry charts into clean JSON arrays, mapping frame sizes to reach, stack, and tube angles.
Capture motor torque, battery capacity, display types, and estimated range for electric mountain, road, and hybrid bikes.
Monitor inventory levels across all frame size and colour combinations. Detect stock-outs and restocks per variant.
Track RRP, current sale price, clearance discounts, and Cycle to Work scheme pricing across the entire catalogue.
Extract sizing guides, material compositions, and compatibility information for cycling clothing, helmets, and replacement parts.
Extract store availability flags and estimated delivery timelines for specific postcode regions.
Extract customer reviews, star ratings, and verified purchase status across all product categories.
Run daily or hourly pipelines that emit only changed records, reducing downstream processing for price and stock updates.
Brief in. Clean data out.
Provide target categories, brands, or specific URLs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and HTML parsers specific to Rutland Cycling's DOM structure.
Schema validation, null-rate checks, and variant mapping verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Bicycle retail sites use complex variation models and heavily nested specification tables. Here is how we maintain data integrity.
Bicycles often have 5+ sizes and multiple colours per model. We extract the full matrix of variants, ensuring that stock status and pricing are mapped accurately to the specific SKU combination, not just the parent product.
Geometry data is presented in complex HTML tables that vary by brand. We use custom parsers to map arbitrary table structures into a normalised schema, ensuring 'Reach' and 'Stack' always land in the correct database columns.
During sales events, popular frame sizes sell out in hours. Our pipelines can run at high frequencies to capture accurate stock statuses, using diff-based delivery to minimise payload size.
Retail sites employ rate limiting and bot protection. We route requests through UK-based residential proxies to maintain consistent access without triggering security blocks.
Product pages for different brands (e.g., Specialized vs Trek) often use different HTML templates on the same site. We use multi-layered selectors to ensure reliable extraction regardless of the brand layout.
Cycling retailers track Rutland Cycling pricing to adjust their own RRP and sale prices dynamically.
Bicycle manufacturers monitor retail prices to ensure compliance with Minimum Advertised Price agreements.
Market analysts track stock-outs by frame size and model to estimate sales velocity and consumer demand trends.
Brands analyse the assortment of components and e-bike motors to understand market penetration and category growth.
Retailers and insurers extract geometry tables and technical specifications to enrich their own internal databases.
Product teams mine review data across helmets, apparel, and components to identify common failure points or design flaws.
"Rutland Cycling holds critical inventory and pricing data for the UK cycling market - but without a pipeline, it remains locked in HTML tables."
Most teams underestimate the investment required: reliable cycling retail extraction demands complex variant mapping for frame sizes, parsing nested geometry tables, and tracking high-velocity stock changes. DataFlirt absorbs that complexity so your engineers can focus on analysis - not infrastructure.
Everything supported by our rutlandcycling.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 and interaction flows for dynamic stock widgets.
We maintain pools of residential ISP proxies across UK regions. Rotation happens per-request to ensure uninterrupted extraction.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About rutlandcycling.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline maps the parent product to all child variants, extracting the stock status, price, and SKU for every frame size and colour combination available.
Bicycle brands use varying formats for geometry tables. We build custom parsing logic to normalise these tables into a consistent schema, ensuring metrics like 'Reach' and 'Stack' are always accurately captured.
Pipelines can be configured to run daily, hourly, or at custom intervals. For high-velocity sales periods, we can increase the crawl frequency on targeted categories.
Yes. We extract motor brand, torque output, battery capacity, display type, and estimated range from e-bike product pages.
Yes. We maintain a hash index of last-seen values. Subsequent runs can be configured to push only the diffs, providing a clean changelog of price or stock updates.
Our packages start at defined category extractions with weekly delivery. For full-site catalogues or custom schema requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off specification dump or a continuous price-monitoring feed across the catalogue - we scope, build, and operate the pipeline. Tell us what you need.
