We extract bike geometry, groupset specifications, variant pricing, and stock depths from Tredz. 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 Product Listings objects from tredz.co.uk. All fields typed and schema-versioned.
"sku": "248912", "title": "Specialized Tarmac SL7 Comp 2024 - Road Bike", "brand": "Specialized", "price": 4500.0, "rrp": 5000.0, "in_stock": true, "rating": 4.8, "review_count": 14
| # | sku | title | brand | category | price | rrp |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Technical Specifications objects from tredz.co.uk. All fields typed and schema-versioned.
"sku": "248912", "frame_material": "Carbon", "groupset": "Shimano 105 Di2 12-Speed", "brakes": "Hydraulic Disc", "model_year": 2024, "wheel_size": "700c", "suspension_type": "Rigid"
| # | sku | frame_material | groupset | brakes | fork | wheel_size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Stock objects from tredz.co.uk. All fields typed and schema-versioned.
"parent_sku": "248912", "variant_sku": "248912-56-RED", "size": "56cm", "colour": "Gloss Red", "price": 4500.0, "in_stock": true, "stock_message": "In stock - usually dispatched within 24 hours", "dispatch_time": "24h"
| # | parent_sku | variant_sku | size | colour | price | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from tredz.co.uk. All fields typed and schema-versioned.
"review_id": "REV-99214", "sku": "248912", "star_rating": 5, "review_title": "Fast and responsive", "review_date": "2023-11-14", "verified_buyer": true, "helpful_votes": 3
| # | review_id | sku | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Navigation objects from tredz.co.uk. All fields typed and schema-versioned.
"category_id": "cat-road-bikes", "category_name": "Road Bikes", "parent_category": "Bikes", "product_count": 1240, "url": "https://www.tredz.co.uk/road-bikes", "top_brands": "['Specialized', 'Giant', 'Trek', 'Cannondale']", "scraped_at": "2024-05-12T08:00:00Z"
| # | category_id | category_name | parent_category | breadcrumb | url | product_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Tredz scraper handles complex variant structures, dynamic stock levels, and detailed technical specifications with JavaScript rendering and anti-bot circumvention built in.
Extract groupsets, frame materials, brake types, and model years. We map unstructured specification tables into clean key-value pairs.
Parse geometry tables for reach, stack, top tube length, and head angles across all frame sizes.
Capture availability and pricing for every size and colour combination. We monitor stock status messages and dispatch estimates.
Monitor RRP, current price, and discount percentages. Track clearance items and seasonal promotional pricing.
Extract full review text, star ratings, and verified buyer flags across all paginated review sections.
Track total SKU counts, new additions, and discontinued lines for specific brands like Specialized, Giant, or Shimano.
Track organic ranking positions for specific components or bike types across Tredz search results.
Monitor the Tredz clearance section for deep discounts and end-of-line stock liquidations.
Run daily stock sweeps or weekly catalogue refreshes with change-detection diffing to minimise payload size.
Brief in. Clean data out.
Provide category URLs, brand names, or specific SKUs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for tredz.co.uk.
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.
Cycling retailers use complex nested variant structures and dynamic stock loading. Here is how we ensure reliable extraction.
Tredz loads specific size and colour availability via asynchronous requests when users interact with dropdowns. We run full Playwright browser sessions to trigger these events and capture true stock depths for every variant.
We route requests through UK-based residential ISP proxies with realistic browser fingerprints. This prevents geographic blocking and rate-limiting from the retailer's CDN and WAF layers.
Bike specifications vary wildly between brands. We use heuristic mapping to normalise inconsistent specification tables into standard fields for groupset, brakes, and frame material.
For large brand catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load for stock and price updates.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops. SLA uptime is contractual.
Independent bike shops and rival retailers track Tredz pricing and discount strategies to maintain competitive positioning.
Cycling brands monitor Tredz to ensure adherence to Minimum Advertised Price policies across current season stock.
Distributors track out-of-stock rates across specific frame sizes and components to predict market demand.
Analysts aggregate specifications and pricing to identify trends in groupset adoption, e-bike penetration, and category pricing.
Deal sites and affiliate marketers consume clearance pricing feeds to automate promotional content.
ML teams use structured bike specifications and geometry tables to train product recommendation engines.
"Tredz holds the most comprehensive technical cycling catalogue in the UK, but mapping geometry to variant stock requires a dedicated infrastructure."
Most teams underestimate the investment required: reliable Tredz scraping requires residential proxies, full JavaScript rendering for dynamic stock grids, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our tredz.co.uk 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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows for dynamic stock grids.
We maintain pools of UK residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 tredz.co.uk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from UK retail sites is generally permissible for non-personal data. DataFlirt targets only public product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use Playwright to simulate user interactions with size and colour dropdowns on the product page. This triggers the required XHR requests, allowing us to capture accurate stock levels for every specific variant.
Yes. We map disparate specification tables into a unified schema. A Shimano 105 rear derailleur will populate the same 'rear_derailleur' field whether the bike is a Specialized or a Giant.
Stock and price sweeps can be configured to run daily or sub-daily for targeted SKU lists. Full catalogue refreshes typically run weekly.
Yes. We capture the RRP, current selling price, and calculate the discount percentage. We also flag items explicitly marked as clearance.
Our smallest packages start at a defined category or brand list with weekly delivery. For full catalogue extraction, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 products as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across 50K SKUs, we scope, build, and operate the pipeline. Tell us what you need.
