We extract product specifications, dynamic pricing, size-level inventory, and customer reviews from evo.com. 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 evo.com. All fields typed and schema-versioned.
"product_id": "EVO-89214", "title": "Burton Custom Snowboard 2024", "brand": "Burton", "price": 659.95, "sale_price": 593.95, "discount_pct": 10, "category": "Snowboard"
| # | product_id | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Technical Specs objects from evo.com. All fields typed and schema-versioned.
"product_id": "EVO-89214", "terrain": "All-Mountain", "ability_level": "Intermediate-Advanced", "rocker_type": "Camber", "shape": "Directional Twin", "flex_rating": "Medium", "warranty": "3 Years"
| # | product_id | terrain | ability_level | rocker_type | shape | flex_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Inventory & Sizing objects from evo.com. All fields typed and schema-versioned.
"product_id": "EVO-89214", "sku": "BUR-CUS-154-24", "size": "154 cm", "colour": "Black", "in_stock": true, "stock_status": "Low Stock", "price_for_size": 593.95
| # | product_id | sku | size | colour | in_stock | stock_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews objects from evo.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "product_id": "EVO-89214", "author": "Shredder99", "rating": 5, "title": "Best board ever", "date": "2023-12-15", "verified_purchase": true
| # | review_id | product_id | author | rating | title | body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Used Gear objects from evo.com. All fields typed and schema-versioned.
"used_id": "UG-4412", "product_id": "EVO-89214", "condition": "Good", "original_price": 659.95, "used_price": 350.0, "defects_noted": "Minor top sheet scratches", "year_model": "2023"
| # | used_id | product_id | condition | original_price | used_price | defects_noted |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Evo scraper handles every layer of the platform: technical gear specifications, dynamic sizing inventory, seasonal pricing, and the review corpus with full anti-bot circumvention built in.
Title, description, high-resolution imagery, and all metadata fields extracted across ski, snowboard, and bike categories.
Extract complex spec tables including flex ratings, rocker profiles, terrain suitability, and core materials.
Capture stock status and availability for every size and colour combination to monitor demand.
Track MSRP, sale prices, and clearance markdowns across all variants with timestamped records.
Full review text, star ratings, helpful vote counts, and verified purchase flags paginated across all reviews.
Monitor the used gear section for condition grades, defect notes, and secondary market pricing.
Map brand hierarchies and sub-categories accurately to maintain clean taxonomy in your database.
Extract image URLs for all product angles and colourways for visual machine learning pipelines.
Run bulk exports or configure continuous pipelines at hourly cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, brand sets, or keyword lists. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for evo.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Outdoor gear sites rely on complex variant rendering and strict bot protection. Here is how we maintain steady extraction.
Retail bot protection looks for datacenter IPs and headless browser signatures. We use US residential proxies with realistic TLS fingerprints and human-like interaction timing.
Evo loads size availability and dynamic pricing via JavaScript. We run full Playwright browser sessions to trigger these network requests and capture the hydrated data.
Gear pricing often changes based on size and colour. Our crawlers iterate through all variant selectors to build a complete matrix of SKUs and their exact prices.
Evo updates its frontend frequently. We use multiple fallback selectors including CSS, XPath, and JSON-LD structured data to ensure extraction does not fail on layout updates.
For large product catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Retailers monitor competitor pricing, seasonal sales, and clearance markdowns to adjust their own pricing strategies.
Outdoor brands audit retail partners for Minimum Advertised Price compliance across all product variants.
Merchandising teams track category saturation and brand representation to identify gaps in their own catalogues.
Analysts aggregate technical specs like flex ratings and rocker profiles to map industry trends.
Supply chain teams correlate stockouts in specific sizes with seasonal trends to improve procurement models.
Machine learning teams use structured gear specifications and imagery to train recommendation engines.
"Evo maintains one of the most detailed technical specification databases for action sports gear on the internet, but it remains locked behind complex frontend rendering."
Most teams underestimate the investment required: reliable Evo scraping requires residential proxies, full JavaScript rendering for variant selection, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our evo.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 retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows for dynamic variants.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required to prevent blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About evo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from evo.com is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use US residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass standard retail bot mitigation.
Yes. We parse the detailed specification tables on Evo product pages, extracting structured fields for flex ratings, rocker types, core materials, and binding compatibility.
Yes. We can monitor the used and outlet sections, capturing condition grades, original pricing, used pricing, and specific defect notes listed by Evo.
Pipelines can be configured to run daily or at custom intervals. For specific high-priority SKUs, we can configure sub-hourly checks for stock and price changes.
Yes. Our crawlers iterate through all available size and colour dropdowns to capture the exact price and stock status for every specific variant combination.
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 100K SKUs, we scope, build, and operate the pipeline. Tell us what you need.
