We extract global merchant inventory, pricing trends, critic scores, and vintage data from Wine-Searcher. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Wine Profiles objects from wine-searcher.com. All fields typed and schema-versioned.
"wine_id": "12345", "name": "Chateau Margaux", "producer": "Chateau Margaux", "region": "Bordeaux", "country": "France", "wine_type": "Red", "critic_score_avg": 96
| # | wine_id | name | producer | region | country | appellation |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from wine-searcher.com. All fields typed and schema-versioned.
"vintage": "2015", "merchant_name": "Total Wine", "price": 750.0, "currency": "USD", "bottle_size": "750ml", "in_stock": true, "tax_status": "ex-tax"
| # | offer_id | wine_id | vintage | merchant_name | merchant_country | merchant_state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Critic Scores objects from wine-searcher.com. All fields typed and schema-versioned.
"critic_name": "Robert Parker", "score": 98, "review_date": "2018-04-15", "tasting_notes": "Full bodied and rich...", "drinking_window": "2025-2050", "publication": "Wine Advocate"
| # | wine_id | vintage | critic_name | score | review_date | tasting_notes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Market Data objects from wine-searcher.com. All fields typed and schema-versioned.
"search_rank": 12, "average_price": 820.5, "price_change_1yr": 5.4, "price_change_5yr": 22.1, "availability_trend": "stable", "demand_percentile": 99
| # | wine_id | search_rank | search_rank_change | average_price | price_change_1yr | price_change_5yr |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Merchant Profiles objects from wine-searcher.com. All fields typed and schema-versioned.
"merchant_name": "Berry Bros & Rudd", "country": "UK", "total_wines_listed": 4521, "merchant_rating": 4.8, "website_url": "bbr.com", "shipping_policy": "Global shipping available"
| # | merchant_id | merchant_name | website_url | contact_email | contact_phone | address |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Wine-Searcher scraper navigates complex regional IP rules, tax state variations, and pagination limits to deliver normalised pricing and inventory data across global merchants.
Extract inventory and pricing across tens of thousands of merchants globally, capturing local market variations.
Monitor pricing trends over time across different vintages and bottle formats to identify market movements.
Capture ratings from Jancis Robinson, Robert Parker, Wine Spectator, and other major critics linked directly to specific vintages.
Isolate pricing and scores by specific vintage years, including NV tracking for Champagne and fortified wines.
Standardise prices across in-bond, ex-tax, and duty-paid listings for accurate cross-border comparison.
Monitor local currency listings and map them against historical exchange rates for financial modelling.
Extract search rank and popularity trends to gauge secondary market liquidity and consumer interest.
Map wines to correct AOC, DOC, or AVA regions and extract their primary grape blend compositions.
Extract data for whisky, bourbon, rum, and craft beer listings alongside the fine wine catalogue.
Brief in. Clean data out.
Provide target producers, regions, or merchant lists. We design the extraction schema together.
We configure Playwright crawlers, CAPTCHA solvers, and proxy rotation specifically for wine-searcher.com.
Schema validation, outlier detection on pricing, and null-rate checks before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or data warehouse on your defined cadence.
Extracting wine data requires navigating strict regional limits and unstandardised merchant formats. Here is how we maintain data integrity.
Wine-Searcher employs strict rate limiting and Cloudflare bot protection. We use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain high extraction throughput.
Wine pricing and availability change based on the user IP location. We route requests through region-specific proxies to capture localised merchant offers and tax regulations accurately.
Merchants list prices in different currencies, bottle sizes, and tax states. Our pipeline normalises these fields into a unified schema for direct comparison across borders.
Results per search query are capped. We mathematically matrix search parameters across regions, vintages, and price bands to extract the full long-tail catalogue without hitting pagination limits.
For large catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Funds and collectors track market prices, liquidity metrics, and critic scores to identify undervalued vintages.
Retailers monitor competitor pricing, stock availability, and shipping policies to adjust their own inventory strategies.
Restaurant groups track wholesale and retail pricing trends to optimise their wine list margins.
Producers monitor global retail channels to ensure grey-market stock is not undercutting official distributors.
Analysts track search ranks and demand percentiles to forecast emerging regions and varietals.
Insurers use aggregated historical pricing data to accurately value private cellars and commercial inventories.
"Wine-Searcher holds the definitive dataset for global wine pricing, but extracting normalised, cross-currency merchant data requires serious infrastructure."
Most teams underestimate the complexity of scraping wine data: strict rate limits, regional IP variations, unstandardised merchant listings, and complex tax states. DataFlirt handles the proxy rotation and schema normalisation so your analysts can focus on market trends, not broken web scrapers.
Everything supported by our wine-searcher.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 handles JavaScript rendering and Cloudflare challenge execution.
We maintain pools of residential ISP proxies across key wine markets. Rotation happens per-request to capture localised pricing.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About wine-searcher.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated wine pricing and merchant data. We do not extract personal data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We route our extraction requests through region-specific residential proxies. This ensures we capture the exact pricing, tax status, and availability that a local buyer would see in that specific market.
Yes. We extract historical time-series data from the public market trend charts available on wine profile pages, allowing you to track appreciation over 1-year and 5-year horizons.
Pipelines can be configured to run daily, weekly, or monthly. For highly liquid investment-grade wines, we can configure sub-daily tracking on specific merchant URLs.
Yes. Our pipeline standardises varying bottle formats into standard 750ml equivalents where requested, and clearly separates in-bond, ex-tax, and duty-paid pricing into distinct schema fields.
Absolutely. We provide a sample run of specific producers or regions during the scoping process so you can validate schema fit and data quality before committing to a production pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of top Bordeaux pricing or a full catalogue extraction of global merchants, we scope, build, and operate the pipeline. Tell us what you need.
