We extract product listings, pricing signals, Eco-Design metrics, store-level stock, and reviews from Decathlon. 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 decathlon.com. All fields typed and schema-versioned.
"product_id": "8559098", "model_code": "8559098", "title": "Men's Waterproof Hiking Jacket - MH500", "brand": "Quechua", "sport_category": "Hiking", "price": 79.99, "currency": "GBP", "eco_design": true, "rating": 4.6
| # | product_id | model_code | title | brand | sport_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Stock objects from decathlon.com. All fields typed and schema-versioned.
"product_id": "8559098", "price": 79.99, "list_price": 99.99, "discount_pct": 20, "in_stock": true, "store_id": "1245", "store_name": "London Surrey Quays", "click_and_collect": true
| # | product_id | price | list_price | discount_pct | in_stock | stock_qty |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from decathlon.com. All fields typed and schema-versioned.
"review_id": "REV-992384", "product_id": "8559098", "rating": 5, "title": "Excellent jacket for the price", "date": "2023-10-14", "reviewer_sport_level": "Intermediate", "helpful_votes": 12, "verified_purchase": true
| # | review_id | product_id | rating | title | body | date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Technical Specs objects from decathlon.com. All fields typed and schema-versioned.
"product_id": "8559098", "warranty_years": 2, "weight": "540g in size L", "waterproofing_mm": "10000", "breathability_ret": "RET < 9", "user_level": "Intermediate", "composition": "Main fabric 100% Polyester"
| # | product_id | composition | care_instructions | weight | warranty_years | waterproofing_mm |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Second Life objects from decathlon.com. All fields typed and schema-versioned.
"product_id": "8559098-SL", "condition": "Excellent", "original_price": 79.99, "refurbished_price": 55.0, "discount_pct": 31, "store_location": "Milton Keynes", "warranty_included": true, "availability_status": "In Stock"
| # | product_id | condition | original_price | refurbished_price | discount_pct | defect_description |
|---|---|---|---|---|---|---|
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Our Decathlon scraper handles the entire catalogue: multi-sport categories, Eco-Design attributes, technical specifications, and store-level stock APIs.
Extract product data mapped to Decathlon's specific sport taxonomy, from Alpinism to Yoga.
Capture base price, discounted price, and clearance rates across all sizes and colours.
Query Decathlon's stock APIs to extract exact inventory levels for specific physical retail locations.
Track sustainability data, material composition, and Eco-Design flags for ESG reporting.
Extract product reviews including the reviewer's self-reported sport level and usage frequency.
Capture detailed engineering data: waterproofing ratings, breathability, weight, and warranty periods.
Scrape decathlon.fr, decathlon.co.uk, decathlon.de, and other regional storefronts with local pricing.
Monitor refurbished inventory, condition grades, and secondary market pricing.
Map complex parent-child relationships across multiple sizes, colours, and models.
Brief in. Clean data out.
Provide category URLs, sport types, or specific store IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for Decathlon's frontend.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Sporting goods retail sites rely on heavy frontend frameworks and dynamic stock APIs. Here is how we extract the data reliably.
Decathlon uses complex frontend frameworks. We run full Playwright browser sessions with JavaScript execution to hydrate product variants, pricing, and stock availability widgets.
Where possible, we intercept Decathlon's backend GraphQL and REST calls for store inventory and pricing, bypassing DOM parsing for faster, more reliable data extraction.
Retail sites deploy strict rate limits. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain access.
For large product catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and storage bloat.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and schema drift automatically.
Sporting goods retailers monitor Decathlon's aggressive pricing and clearance events to adjust their own strategies.
Brands analyse category depth, size availability, and colour variations across 80+ sports to identify market gaps.
ESG analysts track the growth of Eco-Design products and Second Life refurbished inventory over time.
Machine learning teams use technical specifications and user reviews to train product recommendation engines.
Supply chain teams correlate store-level stock depletion rates with external factors to model local demand.
Consultancies track private label penetration versus third-party brands within Decathlon's ecosystem.
"Decathlon holds the most comprehensive technical specification dataset for sporting goods globally, spanning over 80 distinct sports categories."
Most teams underestimate the investment required to extract sporting goods data reliably. Decathlon relies on complex React frontends, dynamic store-level inventory APIs, and strict rate limits. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our decathlon.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 and interaction flows for complex product pages.
We maintain pools of residential ISP proxies across multiple regions. Rotation happens per request to ensure high success rates.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About decathlon.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and store data is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal data or bypass authentication walls. Clients should review terms of service and consult legal counsel.
Yes. We can query Decathlon's stock systems using specific store IDs to return exact inventory status for local retail locations.
Yes. We extract material composition, care instructions, and specific Eco-Design flags present on the product pages.
We support major regions including decathlon.fr, decathlon.co.uk, decathlon.de, decathlon.es, and decathlon.it, handling local currencies and languages.
We map all child variants to the parent product, ensuring you have complete visibility into price differences or stock shortages for specific sizes or colours.
Our packages start at defined category lists with weekly delivery. For full catalogue tracking across multiple regions, we price based on volume and frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off category export or continuous store-level inventory tracking, we scope, build, and operate the pipeline. Tell us what you need.
