We extract wine catalogues, professional scores, tasting notes, winery profiles, and regional appellation data. 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 Wine Listings objects from wine.com. All fields typed and schema-versioned.
"wine_id": "W194827", "name": "Caymus Napa Valley Cabernet Sauvignon", "vintage": 2021, "varietal": "Cabernet Sauvignon", "region": "California", "appellation": "Napa Valley", "price": 89.99, "abv_pct": 14.5
| # | wine_id | name | vintage | varietal | region | appellation |
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Complete list of extractable fields for Professional Ratings objects from wine.com. All fields typed and schema-versioned.
"wine_id": "W194827", "reviewer": "James Suckling", "score": 94, "review_text": "A rich and layered cabernet with dark berries, chocolate and hints of vanilla.", "publication": "JamesSuckling.com", "drinking_window": "2024-2035"
| # | wine_id | reviewer | score | review_text | review_date | publication |
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Complete list of extractable fields for Pricing & Inventory objects from wine.com. All fields typed and schema-versioned.
"wine_id": "W194827", "base_price": 95.0, "sale_price": 89.99, "discount_pct": 5, "stewardship_price": 89.99, "in_stock": true, "stock_limit": 12
| # | wine_id | base_price | sale_price | discount_pct | stewardship_price | in_stock |
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Complete list of extractable fields for Winery Details objects from wine.com. All fields typed and schema-versioned.
"winery_id": "WIN482", "winery_name": "Caymus Vineyards", "location": "Rutherford, California", "founded_year": 1972, "winemaker": "Chuck Wagner", "total_wines_listed": 18
| # | winery_id | winery_name | location | description | founded_year | winemaker |
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Complete list of extractable fields for User Reviews objects from wine.com. All fields typed and schema-versioned.
"review_id": "REV92817", "wine_id": "W194827", "user_name": "CabLover99", "star_rating": 5, "review_title": "Consistently excellent", "review_date": "2023-11-14", "helpful_votes": 24
| # | review_id | wine_id | user_name | star_rating | review_title | review_body |
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Our Wine.com scraper handles dynamic search filters, pagination, and regional pricing variations with full JavaScript rendering and proxy rotation built in.
Extract varietal, vintage, region, ABV, volume, and winery details across the entire catalogue.
Capture structured ratings and text from Wine Spectator, Robert Parker, James Suckling, and Wilfred Wong.
Extract flavour profiles, food pairings, and winemaker notes for detailed product analysis.
Monitor base price, sale price, discounts, and StewardShip membership pricing variations.
Map country, region, and sub-region hierarchies accurately for every listed bottle.
Extract winemaker details, estate history, and full portfolio listings per producer.
Track stock status, low stock alerts, and purchase limits timestamped per crawl.
Extract star ratings, review text, and helpful votes paginated across all user feedback.
Capture pricing and availability variations based on target delivery state via session cookies.
Brief in. Clean data out.
Provide varietal lists, regions, or URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for wine.com.
Schema validation, null-rate checks, and data sampling before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Wine.com uses dynamic inventory, state-based pricing, and aggressive bot mitigation. Here is how we maintain data continuity.
Wine.com alters pricing and availability based on the shipping destination. Our crawlers manage state-specific session cookies to extract accurate regional pricing matrices.
The catalogue relies heavily on AJAX-driven filters. We execute full Playwright sessions to trigger lazy-loading and ensure complete extraction of paginated results.
We utilise residential ISP proxies with realistic browser fingerprints and randomised request timing to bypass aggressive rate limits and IP bans.
Professional reviews are often embedded in unstructured text blocks. Our pipeline parses and normalises these into strict schema fields for reviewer, score, and tasting notes.
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.
Retailers monitor competitor pricing, discount strategies, and shipping thresholds to maintain market parity.
Distributors track stock depletion rates and out-of-stock signals to optimise supply chain operations.
Analysts identify trending varietals, emerging regions, and consumer preferences based on review velocity.
Applications train machine learning models on tasting notes, professional scores, and food pairings.
Collectors monitor vintage scores and price appreciation across premium appellations.
Wineries track retail presence, promotional compliance, and user sentiment across their portfolio.
"Wine.com holds the definitive digital cellar - combining professional scores, tasting notes, and retail pricing in one highly structured dataset."
Extracting data from Wine.com requires managing state-specific cookies, parsing unstructured tasting notes, and bypassing aggressive rate limits. DataFlirt absorbs that complexity so your engineers can focus on analysis, not infrastructure maintenance.
Everything supported by our wine.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required for state-specific 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 wine.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
Yes. Our crawlers manage state-specific session cookies to extract accurate regional pricing matrices and inventory availability based on the target shipping destination.
Yes. We extract and normalise professional scores, reviewer names, and tasting notes from all listed authorities including Wine Spectator, Robert Parker, and James Suckling.
We track wines at the vintage level. When a new vintage replaces an old one on the same product page, we log it as a distinct record to maintain accurate historical pricing and rating data.
Pipelines can be configured at hourly, daily, or weekly cadences depending on your requirements. Daily refreshes are standard for most inventory monitoring use cases.
Yes. We capture all structured and unstructured product metadata, including flavour profiles, winemaker notes, and recommended food pairings.
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 100K vintages - we scope, build, and operate the pipeline. Tell us what you need.
