We extract apparel listings, dynamic size-colour availability, pricing signals, material compositions, and reviews from bonprix.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 bonprix.de. All fields typed and schema-versioned.
"article_number": "93847295", "title": "Strickpullover mit V-Ausschnitt", "brand": "bpc bonprix collection", "price": 19.99, "original_price": 24.99, "currency": "EUR", "discount_pct": 20, "material": "100% Baumwolle"
| # | article_number | title | brand | category_path | price | original_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Size & Colour Matrix objects from bonprix.de. All fields typed and schema-versioned.
"article_number": "93847295", "variant_id": "93847295-BL-42", "colour": "Dunkelblau", "size": "40/42", "in_stock": true, "stock_level": "low", "delivery_time": "2-3 Werktage"
| # | article_number | variant_id | colour | colour_code | size | size_system |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from bonprix.de. All fields typed and schema-versioned.
"review_id": "REV-837492", "article_number": "93847295", "rating": 4, "review_title": "Schöner Pullover", "date": "2026-03-14", "fit_rating": "Fällt normal aus", "helpful_votes": 12
| # | review_id | article_number | reviewer_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Navigation objects from bonprix.de. All fields typed and schema-versioned.
"category_id": "cat_10293", "category_name": "Pullover & Strickjacken", "parent_category": "Damenbekleidung", "product_count": 1452, "breadcrumbs": "['Damen', 'Bekleidung', 'Pullover']", "gender": "Female"
| # | category_id | category_name | parent_category | url | product_count | breadcrumbs |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Cross-Sell Items objects from bonprix.de. All fields typed and schema-versioned.
"article_number": "93847295", "recommended_article_number": "91238475", "recommendation_type": "Dazu passt", "position": 1, "title": "Stretch-Jeans Straight", "price": 29.99, "in_stock": true
| # | article_number | recommended_article_number | recommendation_type | position | title | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Fashion scraping requires handling complex variant matrices. We unroll Bonprix's nested size and colour structures into flat, queryable database records.
Title, description, material composition, care instructions, and brand details scraped across all Bonprix categories.
Extract every combination of size and colour as an independent record, complete with specific stock availability and price overrides.
Capture base prices, promotional discounts, sale badges, and currency details directly from the German storefront.
Extract customer reviews, star ratings, and specific fit feedback (e.g., 'fällt klein aus') to analyse product perception.
Capture CDN URLs for all product images, mapped to specific colour variants for accurate visual databases.
Extract estimated delivery windows and stock warnings for specific variants to monitor supply chain signals.
Map 'Dazu passt' (Matches with) and 'Kunden kauften auch' (Customers also bought) recommendations to build relationship graphs.
Normalise German material descriptions (Baumwolle, Elasthan) and sizing formats into standardised data structures.
Run daily diffs to track new product launches, stock dropouts, and price changes without re-downloading the entire catalogue.
Brief in. Clean data out.
Provide Bonprix categories, specific article numbers, or search terms. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for bonprix.de.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Bonprix relies heavily on JavaScript for variant loading and employs standard e-commerce anti-bot measures. Here is how we maintain stable pipelines.
Bonprix loads specific size availability and colour variants via asynchronous JavaScript requests. We use Playwright to execute the frontend logic, ensuring we capture the true stock state for every single variant combination rather than just the default view.
To avoid geo-blocking and ensure accurate regional pricing and delivery estimates, all requests are routed through German residential proxies with appropriate headers and TLS fingerprints.
Apparel data is deeply nested (Product -> Colour -> Size -> Stock). Our pipeline automatically unrolls these hierarchies into flat, relational records, making the output immediately queryable in SQL environments.
Category pages often feature infinite scroll or complex pagination logic. We simulate user scroll behaviour and intercept API calls to guarantee 100% coverage of category listings without missing items.
Prices are extracted as floats, discount percentages as integers, and stock states as booleans. We strip currency symbols and German formatting (commas for decimals) before delivery.
Retailers track Bonprix's pricing strategies, discount depths, and seasonal sale timing to adjust their own positioning.
Fashion analysts monitor category growth, colour trends, and new material adoptions to inform upcoming collections.
Supply chain analysts track which sizes and colours sell out fastest to model consumer demand and optimise inventory planning.
Computer vision teams use extracted high-res images and mapped textual attributes to train visual search and tagging models.
Agencies aggregate review data and fit metrics to understand consumer preferences and quality perceptions in the DACH region.
Third-party brands selling on Otto Group platforms monitor their product visibility, reviews, and cross-sell placements.
"Bonprix.de holds a massive, highly structured catalogue of European fast fashion — but extracting the true size-colour stock matrix requires executing complex frontend JavaScript."
Apparel scraping is notoriously difficult due to nested variant structures. A single Bonprix product might have 40 size-colour combinations, each with independent stock states and dynamic pricing. We handle the JavaScript execution, residential proxy rotation, and nested JSON structuring so you get flat, queryable tables ready for immediate analysis.
Everything supported by our bonprix.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across DE regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 bonprix.de scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and review data from Bonprix is generally permissible. DataFlirt extracts only public, non-authenticated information. We do not extract personal user data or circumvent login walls. Clients should review platform terms of service and consult legal counsel for their specific use cases.
We use Playwright to execute the JavaScript responsible for loading variant data. Our pipeline intercepts the underlying API responses or parses the hydrated DOM to capture the exact stock state and price for every size-colour combination, delivering it as flattened, relational records.
We extract the raw data exactly as it appears on bonprix.de (German). However, we can implement post-processing steps to map standard German terms (e.g., colours, materials) to English equivalents if required by your schema.
For targeted SKU lists, we can configure high-frequency pipelines to check stock levels multiple times a day. Full catalogue refreshes are typically run daily or weekly depending on your requirements.
Yes. We extract both the original list price and the current selling price, calculating the exact discount percentage. We also capture any sale badges or promotional text associated with the product.
Yes. Every pipeline run produces timestamped snapshots. We can maintain time-series tables for pricing and stock levels, allowing you to track exactly when an item went on sale or sold out.
We extract the high-resolution CDN URLs for all product images. We do not host the image files directly, but provide the direct links mapped to the corresponding product and colour variant in your JSON/CSV output.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or a continuous daily feed of pricing and stock changes — we scope, build, and operate the pipeline. Tell us what you need.
