We extract fragrance listings, cosmetic variants, pricing signals, ingredient lists, and stock levels from parfumdreams.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 parfumdreams.de. All fields typed and schema-versioned.
"sku": "PD-847291", "brand": "Dior", "title": "Sauvage Eau de Parfum", "category": "Fragrance", "sub_category": "Men", "base_price": 98.95, "size": "100 ml", "stock_status": "in_stock", "ean": "3348901368247"
| # | sku | brand | title | category | sub_category | base_price |
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Complete list of extractable fields for Pricing & Variants objects from parfumdreams.de. All fields typed and schema-versioned.
"sku": "PD-847291", "variant_id": "V-100ML", "size_ml": 100, "price": 98.95, "old_price": 115.0, "discount_pct": 14, "price_per_100ml": 98.95, "in_stock": true, "currency": "EUR"
| # | sku | variant_id | size_ml | price | old_price | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Ingredients & Specs objects from parfumdreams.de. All fields typed and schema-versioned.
"sku": "PD-847291", "fragrance_family": "Woody", "top_notes": "['Bergamot']", "heart_notes": "['Sichuan Pepper', 'Lavender']", "base_notes": "['Ambroxan', 'Vanilla']", "ingredients_inci": "ALCOHOL, PARFUM, AQUA, LIMONENE, LINALOOL", "vegan_flag": false
| # | sku | brand | fragrance_family | top_notes | heart_notes | base_notes |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from parfumdreams.de. All fields typed and schema-versioned.
"review_id": "REV-99382", "sku": "PD-847291", "rating": 5, "reviewer_name": "Markus T.", "review_date": "2026-02-14", "review_text": "Great projection and longevity.", "verified_purchase": true, "helpful_votes": 12
| # | review_id | sku | rating | reviewer_name | review_date | review_text |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Category & Search objects from parfumdreams.de. All fields typed and schema-versioned.
"keyword": "mens fragrance", "position": 3, "sku": "PD-847291", "brand": "Dior", "price": 98.95, "badge_premium": true, "badge_new": false, "badge_sale": true
| # | keyword | position | sku | brand | title | price |
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Our scraper handles the complete Parfumdreams catalogue: fragrance pyramids, INCI ingredient lists, dynamic pricing across sizes, and stock availability - with anti-bot circumvention built in.
Title, brand, description, category taxonomy, EAN, and image URLs scraped at the base product level.
Extract every bottle size (ml), weight (g), or colour variant mapped to its parent SKU with specific pricing and stock.
Capture current price, list price, discount percentages, and base price per 100ml for legal compliance tracking.
Extract structured olfactory data including top notes, heart notes, base notes, and fragrance family classifications.
Parse full ingredient lists (INCI) along with skin type suitability, application areas, and vegan or cruelty-free flags.
Monitor out-of-stock statuses and low-stock warnings across all variants to track assortment depth.
Full review text, star ratings, helpful vote counts, and verified purchase flags paginated across all reviews.
Track organic position for any keyword or category page, capturing premium, new, and sale badges.
Extract data from parfumdreams.de, parfumdreams.at, and other regional domains to compare cross-border pricing.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, keyword sets, brand lists, or EANs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for parfumdreams.de.
Schema validation, null-rate checks, price-outlier detection, and variant mapping review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Beauty retailers invest heavily in scraping detection to protect pricing data. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
Parfumdreams employs strict bot protection. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to blend in with legitimate German consumer traffic.
Product pages rely on JavaScript to load specific variant prices and stock levels when a different ml size is selected. We run full Playwright browser sessions to trigger these DOM changes and capture accurate variant data.
Retail DOM structures change frequently during promotional events. Our selector strategy uses multiple fallback chains per field - CSS selectors, XPath, and JSON-LD structured data - ensuring stable extraction.
For large brand catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops - and respond before you notice.
Beauty retailers and pharmacies track Parfumdreams pricing, discount campaigns, and base prices to optimise their own pricing strategies.
Cosmetic brands audit listings for minimum advertised price (MAP) violations and unauthorised discounting across the European market.
Category managers track new product listings, out-of-stock rates, and brand representation to identify gaps in their own catalogue.
Analysts track review velocity and category rankings to identify trending fragrance notes or rising skincare brands.
R&D teams extract INCI lists to track the prevalence of specific active ingredients (e.g., Niacinamide, Retinol) across product categories.
ML teams use structured fragrance pyramids and ingredient taxonomies to train recommendation engines and beauty-specific LLMs.
"Parfumdreams holds critical pricing and assortment data for the European beauty market - but extracting it requires navigating strict bot protection and complex variant structures."
Most teams underestimate the investment required: reliable Parfumdreams scraping requires residential proxies, full JavaScript rendering for variant hydration, 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 parfumdreams.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/AT 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 parfumdreams.de scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Parfumdreams is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should consult legal counsel for specific use cases.
We use German residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for WAF challenge spikes in real time and trigger solver queues automatically.
Yes. Our pipeline iterates through all available variant selectors (e.g., 30ml, 50ml, 100ml) on the product page to capture the specific price, base price, and stock status for each distinct SKU.
Yes. We extract structured fragrance pyramids (top, heart, base notes) and full INCI ingredient lists, parsing them into structured arrays for easy database ingestion.
Pipelines can be configured for daily full-catalogue refreshes or intra-day targeted crawls for specific high-priority brands to monitor flash sales and discount changes.
Yes. We support parfumdreams.de, parfumdreams.at, and other regional storefronts, utilising geo-located proxies to ensure accurate local pricing and availability.
Absolutely. We provide a sample run of up to 500 SKUs or 50 category pages as part of the pre-engagement scoping process - so you can validate schema fit, field completeness, and data quality before signing any contract.
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 80,000 SKUs - we scope, build, and operate the pipeline. Tell us what you need.
