We extract shoe catalogues, size-level inventory signals, material specifications, and dynamic pricing from Reno.de. 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 reno.de. All fields typed and schema-versioned.
"product_id": "RN-8472910", "brand": "Rieker", "title": "Classic Leather Ankle Boots", "category": "Women > Boots > Ankle Boots", "price": 69.95, "list_price": 89.95, "colour": "Brown", "upper_material": "Leather"
| # | product_id | sku | brand | title | category | target_gender |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Size & Inventory objects from reno.de. All fields typed and schema-versioned.
"product_id": "RN-8472910", "variant_id": "RN-8472910-39", "size_eu": "39", "in_stock": true, "stock_level": 4, "low_stock_warning": true, "price_for_size": 69.95
| # | product_id | variant_id | size_eu | size_uk | in_stock | stock_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from reno.de. All fields typed and schema-versioned.
"product_id": "RN-8472910", "current_price": 69.95, "original_price": 89.95, "discount_pct": 22, "sale_badge": "Winter Sale", "currency": "EUR", "scraped_at": "2023-11-04T10:15:22Z"
| # | product_id | current_price | original_price | discount_pct | discount_abs | campaign_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Navigation objects from reno.de. All fields typed and schema-versioned.
"category_id": "cat_women_boots", "category_name": "Ankle Boots", "parent_category": "Boots", "breadcrumb_path": "Home > Women > Boots > Ankle Boots", "total_products": 412, "url": "https://reno.de/damen/stiefel/stiefeletten/"
| # | category_id | category_name | parent_category | breadcrumb_path | url | total_products |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from reno.de. All fields typed and schema-versioned.
"review_id": "rev_99281", "product_id": "RN-8472910", "rating": 4, "review_text": "Comfortable but runs slightly small.", "review_date": "2023-10-12", "verified_purchase": true, "fit_feedback": "Runs small"
| # | review_id | product_id | author | rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Reno.de scraper handles dynamic size availability, category pagination, and real-time pricing updates. Built with JavaScript rendering to capture inventory states that headless HTTP clients miss.
Extract availability across all EU/UK size variants per model. Map parent-child relationships for colours and specific shoe sizes.
Capture base price, discounted price, and promotional campaign badges. Timestamped per crawl for historical pricing analysis.
Monitor in-stock status, low-stock warnings, and estimated delivery timelines for every individual size variant.
Traverse the complete category tree. Extract brand names, target demographics (men, women, kids), and detailed breadcrumb paths.
Parse structured details including upper material, lining, sole composition, fastening type, and heel height.
Extract high-resolution product image URLs, multiple angles, and lifestyle shots associated with the footwear listings.
Run continuous pipelines and receive only records that have changed since the last run. Optimise downstream processing.
Configure hourly tracking on high-velocity items or fast-moving sale categories to catch stockouts and price drops.
Requests routed through German residential IPs to ensure accurate local pricing, tax inclusion, and regional stock availability.
Brief in. Clean data out.
Provide target categories, specific brands, or full catalogue requirements. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, German proxy rotation, and session management for reno.de.
Schema validation, null-rate checks, and size-variant mapping tests before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern eCommerce sites rely heavily on client-side rendering for inventory and pricing. Here is how we ensure data completeness.
Footwear sizes and specific stock levels on Reno.de are often loaded asynchronously based on user interaction. We run full Playwright browser sessions to trigger these network requests and capture the true inventory state for every size.
To prevent blocking and ensure accurate VAT-inclusive pricing, all requests are routed through verified German residential IPs. This mimics legitimate local traffic and avoids geofencing restrictions.
eCommerce platforms frequently update their frontend frameworks. We use multiple fallback chains per field, including JSON-LD structured data extraction, ensuring pipeline stability during Reno.de site updates.
A single shoe model may have 4 colours and 12 sizes, resulting in 48 variants. Our pipeline flattens this matrix into structured, queryable rows, mapping each specific SKU to its precise price and stock status.
For daily catalogue syncs, we hash the state of every variant. You receive a clean changelog of price adjustments and stock changes rather than processing the entire 40K+ product catalogue repeatedly.
Footwear retailers track Reno.de pricing and discount strategies to adjust their own promotional calendars and maintain competitiveness.
Merchandising teams analyse brand coverage, category depth, and size availability to identify gaps in their own product ranges.
Shoe manufacturers audit Reno.de listings to ensure compliance with Minimum Advertised Price (MAP) agreements across all variants.
Analysts track new arrivals, category expansion, and out-of-stock velocity to determine consumer demand for specific styles and colours.
Resellers monitor high-demand sizes and clearance sections to identify arbitrage opportunities in the secondary footwear market.
Computer vision teams use extracted product images mapped to structured material and category attributes to train classification models.
"Footwear eCommerce data is uniquely complex. A single shoe model requires tracking dozens of size and colour combinations to understand true inventory state."
Scraping Reno.de requires more than simple HTTP GET requests. Size availability and specific variant pricing are dynamically rendered. DataFlirt manages the JavaScript execution, proxy rotation, and variant matrix flattening so you receive clean, relational data ready for analysis.
Everything supported by our reno.de 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, cookie sessions, and interaction flows required for size availability.
We maintain pools of residential ISP proxies specific to Germany. Rotation happens per-request to ensure accurate local pricing and prevent IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About reno.de scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Reno.de is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and stock data. We do not extract personal data or circumvent authentication walls.
Our pipeline maps the parent product to every available child variant. You receive a flattened record for each specific combination (e.g., Brown Leather Boot, Size 42) with its exact price and stock status.
Yes. We track the boolean in-stock status for every size variant. If an item drops out of stock between runs, the delta export will reflect this change immediately.
We can configure pipelines to run daily for full catalogue refreshes, or hourly for specific high-priority categories or brands to track fast-moving inventory.
Yes. We parse the product details section to extract structured fields for upper material, inner lining, sole material, and fastening mechanisms.
Engagements typically start at a defined category or brand list with daily delivery. For full-site extraction, we price based on the required frequency and total variant volume.
Yes. We provide a sample run of up to 500 products as part of the scoping process so you can validate the variant mapping and schema fit before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily sync of specific shoe brands or continuous tracking of the entire Reno.de catalogue — we build and operate the infrastructure. Tell us what you need.
