We extract vintage furniture listings, designer catalogues, pricing signals, and dealer intelligence from Pamono. 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 pamono.com. All fields typed and schema-versioned.
"sku": "PM-193847", "title": "Mid-Century Lounge Chair by Charles Eames for Herman Miller", "designer": "Charles Eames", "maker": "Herman Miller", "era": "1960s", "price": 4500.0, "currency": "EUR", "condition": "Very Good", "materials": "['Rosewood', 'Leather', 'Aluminum']"
| # | sku | title | designer | maker | era | category |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Designer & Maker Data objects from pamono.com. All fields typed and schema-versioned.
"designer_id": "DS-8472", "designer_name": "Hans J. Wegner", "active_years": "1940-1990", "country_of_origin": "Denmark", "item_count": 1243, "top_categories": "['Seating', 'Tables', 'Storage']", "price_range_min": 800.0, "price_range_max": 25000.0
| # | designer_id | designer_name | biography | active_years | country_of_origin | item_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Dealer Intelligence objects from pamono.com. All fields typed and schema-versioned.
"dealer_id": "DL-9921", "dealer_name": "Berlin Vintage Design", "location_city": "Berlin", "location_country": "Germany", "rating": 4.9, "response_rate": "98%", "total_items": 412, "active_listings": 87
| # | dealer_id | dealer_name | location_city | location_country | rating | response_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Condition & Provenance objects from pamono.com. All fields typed and schema-versioned.
"sku": "PM-193847", "condition_grade": "Very Good", "wear_details": "Minor scratches on the base, leather patinated.", "restoration_history": "Reupholstered in 1990.", "authentication_marks": "Original Herman Miller sticker on underside.", "provenance_notes": "Acquired from a private collection in Munich.", "authenticity_guarantee": true
| # | sku | condition_grade | wear_details | restoration_history | authentication_marks | provenance_notes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Shipping & Logistics objects from pamono.com. All fields typed and schema-versioned.
"sku": "PM-193847", "origin_city": "Berlin", "origin_country": "Germany", "dispatch_time_days": 5, "ships_worldwide": true, "domestic_fee": 150.0, "international_fee": 850.0, "return_policy_days": 14
| # | sku | origin_city | origin_country | dispatch_time_days | ships_worldwide | domestic_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Pamono scraper captures the entire taxonomy of 20th-century design: from specific material compositions and era tags to dynamic dealer pricing and high-resolution image assets.
Title, designer, maker, era, category, and every metadata field Pamono surfaces. Scraped at the item level.
Capture current asking prices, currency normalisation, and discount flags across the entire inventory.
Extract structured width, depth, and height measurements alongside precise material composition tags.
Collect direct CDN URLs for all product gallery images, vital for visual machine learning models and cataloguing.
Dealer name, location, inventory size, and response rate for every listing on the marketplace.
Extract detailed condition grades, wear notes, restoration history, and authentication marks.
Monitor origin locations, dispatch times, and estimated shipping costs for domestic and international freight.
Map items to specific designers and manufacturers, tracking market volume per creator.
Run continuous pipelines with change-detection diffing. Only ingest new listings or price changes.
Brief in. Clean data out.
Provide target categories, designers, or dealer storefronts. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for pamono.com.
Schema validation, null-rate checks, and data type normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Marketplaces like Pamono rely on complex infinite scrolls and dynamic pricing modules. Here is how we maintain data integrity.
Pamono category pages use complex JavaScript-based infinite scrolling. We execute full Playwright sessions to intercept XHR requests and trigger lazy-loaded elements, ensuring complete category capture without missing items.
Prices and shipping costs change based on the user session location. We route requests through region-specific residential proxies to capture accurate, localised pricing arrays.
Condition reports and provenance details are often written in free text. We use structured data extraction (LD+JSON) combined with regex parsing to normalise this text into queryable fields.
Extracting high-resolution image URLs triggers aggressive rate limits. We distribute requests across a large pool of EU residential IPs to maintain throughput without triggering blocks.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs: new items, sold items, or price drops, reducing your downstream processing load.
Auction houses and appraisers track historical asking prices for specific designers to establish accurate market valuations.
Vintage furniture dealers monitor peer pricing strategies and inventory turnover rates across specific categories.
Interior designers and procurement teams aggregate listings into internal tools to source specific pieces for client projects.
Market analysts track the volume of specific eras (e.g., Mid-Century Modern vs Art Deco) to forecast consumer demand.
B2B service providers extract dealer profiles and inventory sizes to qualify leads for restoration or logistics services.
Computer vision teams train classification models on Pamono's extensive, high-quality image catalogue and designer metadata.
"Pamono holds the definitive taxonomy of 20th-century design and vintage furniture, but extracting structured provenance and pricing requires a managed pipeline."
Scraping highly curated marketplaces involves navigating infinite scrolls, dynamic shipping calculators, and complex metadata structures. DataFlirt manages the underlying proxy rotation, JavaScript execution, and schema mapping so your data engineering team receives normalised tables ready for immediate analysis.
Everything supported by our pamono.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 deduplication. Playwright manages JavaScript rendering and XHR interception for infinite scrolls.
We maintain pools of residential ISP proxies across the EU. Rotation happens per-request to prevent IP bans and ensure accurate geographic pricing.
Pipelines run on AWS Lambda and ECS. 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 pamono.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Pamono is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and dealer data. We do not extract personal data or circumvent authentication walls.
We use Playwright to execute JavaScript and intercept the underlying XHR/API requests that populate the grid. This ensures we capture 100% of the listings without relying on brittle UI scrolling scripts.
Yes. We extract the direct CDN URLs for all gallery images. We do not download the image files directly, but provide the URLs in the dataset for your systems to ingest.
No. Trade discounts and wholesale prices are gated behind authenticated accounts. We only extract the publicly listed retail prices.
We configure pipelines based on your requirements. Daily refreshes are standard for tracking sold items and price adjustments, though weekly cadences are often sufficient for category analysis.
Yes. Pamono listings often contain unstructured condition reports and dimensions. We apply parsing logic to split dimensions into discrete width, depth, and height columns in centimetres.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full extraction of mid-century seating or continuous price monitoring for specific dealers, we scope, build, and operate the pipeline. Tell us what you need.
