We extract gadget listings, hardware specifications, pricing signals, and stock availability from pearl.de. Delivered as clean JSON, CSV, or Parquet to AWS S3, BigQuery, or Snowflake on your schedule.
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 pearl.de. All fields typed and schema-versioned.
"item_id": "ZX-1234-919", "title": "revolt Solar-Powerbank", "brand": "revolt", "price": 29.99, "currency": "EUR", "in_stock": true, "rating": 4.2, "review_count": 145
| # | item_id | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Technical Specs objects from pearl.de. All fields typed and schema-versioned.
"item_id": "ZX-1234-919", "dimensions": "150 x 75 x 20 mm", "weight": "250g", "battery_capacity": "20000 mAh", "interfaces": "['USB-A', 'USB-C', 'Micro-USB']", "manual_url": "https://www.pearl.de/pdocs/ZX1234_11_180425.pdf"
| # | item_id | dimensions | weight | power_output | battery_capacity | interfaces |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from pearl.de. All fields typed and schema-versioned.
"item_id": "ZX-1234-919", "current_price": 29.99, "recommended_retail_price": 49.9, "discount_pct": 40, "shipping_costs": 4.95, "price_timestamp": "2026-05-12T09:14:00Z"
| # | item_id | current_price | recommended_retail_price | discount_pct | discount_abs | volume_pricing |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from pearl.de. All fields typed and schema-versioned.
"review_id": "REV-98765", "item_id": "ZX-1234-919", "rating": 5, "review_title": "Top Powerbank", "review_text": "Ladet mein Smartphone 4 mal komplett auf.", "date": "2026-04-12", "verified_purchase": true
| # | review_id | item_id | reviewer_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from pearl.de. All fields typed and schema-versioned.
"keyword": "solar powerbank", "position": 1, "item_id": "ZX-1234-919", "price": 29.99, "rating": 4.2, "is_bestseller": true, "scraped_at": "2026-05-12T09:15:22Z"
| # | keyword | position | item_id | title | price | rating |
|---|---|---|---|---|---|---|
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Our pearl.de scraper handles specific catalogue structures, legacy HTML layouts, and German regional pricing to deliver clean electronics data.
Parse detailed technical tables and identify PDF manual links for every gadget.
Track Pearl's unique item identifiers to map products across categories.
Capture current prices versus recommended retail prices and calculate discount depths.
Monitor availability text and estimated delivery windows for inventory intelligence.
Extract linked compatible products and spare parts associated with main items.
Isolate embedded test scores and press quotes from German tech magazines.
Map the deep electronics and gadget category trees to understand site structure.
Extract high-resolution image URLs and embedded product video links.
Run daily pipelines to capture price drops and stock changes efficiently.
Brief in. Clean data out.
Provide category URLs or keyword sets. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and parsers for pearl.de.
Schema validation, null-rate checks, and data normalisation before launch.
JSON, CSV, or Parquet pushed to your AWS S3 bucket on agreed cadence.
Extracting structured data from legacy retail sites requires strict parsing rules. We maintain the parsers so you do not have to.
Pearl.de serves region-specific content. We route requests through German residential proxies. This ensures accurate pricing and stock data.
The pearl.de HTML structure contains legacy table layouts. Our parsers use specific CSS and XPath fallback chains to normalise this into clean JSON.
Technical specifications are often buried in linked PDF documents. We identify and extract these URLs for downstream processing.
Pearl frequently embeds test scores from German tech magazines. We isolate these citations from standard product descriptions.
We maintain a hash index of last-seen values per Bestell-Nr. Subsequent runs only push diffs, reducing downstream processing load.
Track gadget prices against Amazon and local German retailers to adjust your own pricing strategy.
Analyse Pearl's private label strategy across electronics categories to identify product gaps.
Monitor stock depth indicators and delivery delays to predict market shortages.
Enrich internal PIM systems with technical specs and dimensions from legacy listings.
Aggregate German language user feedback on budget electronics to inform product development.
Collect product images and video links for affiliate marketing databases.
"Pearl.de houses a massive, highly specific catalogue of budget electronics and gadgets. Extracting its technical specs requires precise, reliable parsing."
Many scraping tools fail on legacy HTML structures and complex category trees. DataFlirt handles the proxy routing, DOM normalisation, and daily scheduling. We deliver clean, structured data so your engineering team can focus on analysis instead of fixing broken XPath selectors.
Everything supported by our pearl.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 retry logic. Playwright handles JavaScript execution for dynamic elements.
We route traffic through German residential IPs to ensure accurate region-specific pricing and stock.
Pipelines run on AWS infrastructure. Airflow handles scheduling and dependency management.
Data delivered to where your team already works — no new tooling required.
About pearl.de scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use custom parsers designed for their legacy HTML tables. Our selectors have fallback chains to ensure data extraction remains stable.
Yes. We isolate embedded magazine citations and test scores from the main product description text.
Pipelines can be configured to run daily or at custom intervals to capture price changes and stock availability updates.
Yes. We locate and extract the URLs for PDF manuals, technical data sheets, and driver downloads.
We build pipelines for specific category trees or full catalogue extraction. Contact us with your target volume for a scope.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full catalogue dump or continuous price monitoring across thousands of gadgets, we build the pipeline. Tell us what you need.
