We extract wine profiles, vintage variations, merchant pricing, flavour characteristics, and global review corpora from Vivino. 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 Profiles objects from vivino.com. All fields typed and schema-versioned.
"wine_id": "1127394", "name": "Opus One", "winery": "Opus One", "vintage": "2018", "region": "Napa Valley", "country": "United States", "vivino_rating": 4.8, "rating_count": 14205, "wine_style": "Napa Valley Bordeaux Blend"
| # | wine_id | name | winery | vintage | region | country |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Merchant Pricing objects from vivino.com. All fields typed and schema-versioned.
"wine_id": "1127394", "merchant_name": "Total Wine & More", "price": 385.0, "currency": "USD", "bottle_size": "750ml", "availability": "In Stock", "shipping_cost": 15.0, "scraped_at": "2026-05-12T10:15:22Z"
| # | wine_id | vintage | merchant_name | merchant_id | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Flavour Profiles objects from vivino.com. All fields typed and schema-versioned.
"wine_id": "1127394", "light_bold": 85, "smooth_tannic": 72, "dry_sweet": 15, "soft_acidic": 60, "primary_notes": "['Blackberry', 'Cassis', 'Plum']", "oak_presence": "High", "earthy_notes": "['Leather', 'Tobacco']"
| # | wine_id | light_bold | smooth_tannic | dry_sweet | soft_acidic | primary_notes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from vivino.com. All fields typed and schema-versioned.
"review_id": "84729105", "wine_id": "1127394", "user_rating": 5.0, "review_text": "Exceptional balance of dark fruit and structured tannins.", "language": "en", "review_date": "2026-04-20", "likes_count": 34, "vintage": "2018"
| # | review_id | wine_id | vintage | user_id | user_rating | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Winery Data objects from vivino.com. All fields typed and schema-versioned.
"winery_id": "8392", "name": "Opus One", "region": "Napa Valley", "country": "United States", "total_wines": 14, "average_rating": 4.7, "global_rank": 12, "regional_rank": 2
| # | winery_id | name | region | country | total_wines | average_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Vivino scraper handles every layer of the platform: wine profiles, dynamic merchant pricing, flavour characteristics, and the review corpus — with JavaScript rendering, session management, and anti-bot circumvention built in.
Name, winery, vintage, region, grape variety, Vivino rating, and image URLs — scraped at the individual wine level.
Capture merchant prices, shipping costs, availability, and bottle sizes across different shipping destinations — timestamped per crawl.
Extract Taste Characteristics including the bold-light slider values, tannin levels, acidity, and primary tasting notes.
Full review text, star ratings, user IDs, language, and review dates — paginated across all user reviews.
Winery aggregate ratings, total wine counts, regional rankings, and global standing data.
Extract recommended food pairings for specific wine styles and regions directly from the Vivino database.
Compare ratings, pricing, and tasting notes across different vintages of the exact same wine.
Extract pricing and availability data relative to specific user shipping countries and currencies.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide wine URLs, winery names, regional parameters, or search terms. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for vivino.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Vivino employs strict rate limiting and bot mitigation. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Vivino's bot detection operates on TLS fingerprints, browser headers, and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management — trained on real user behaviour patterns.
Vivino relies heavily on React for dynamic content loading, especially for pricing widgets and paginated reviews. We run full Playwright browser sessions with JavaScript execution to capture data that headless HTTP clients miss entirely.
Vivino changes its DOM structure frequently. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, and JSON state extraction — so a layout change doesn't break your data pipeline overnight.
For large wine 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. You get a clean changelog rather than full re-dumps.
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. SLA uptime is contractual, not aspirational.
Wine merchants and distributors monitor global pricing, shipping costs, and availability to optimise their own pricing strategies.
Importers track trending regions, rising ratings, and vintage variations to identify high-value procurement opportunities.
ML teams use Vivino's structured Taste Characteristics and food pairing datasets to train recommendation engines and NLP classifiers.
Analysts track regional popularity shifts, grape variety trends, and consumer sentiment across different demographics.
Wineries audit their own vintages, track consumer sentiment in reviews, and monitor how merchants are pricing their allocations.
Sommeliers and hospitality groups curate wine lists based on global ratings, stylistic trends, and optimal price-to-quality ratios.
"Vivino holds the world's most comprehensive index of wine flavour profiles and consumer sentiment — but extracting it requires navigating aggressive bot mitigation."
Most teams underestimate the investment required: reliable Vivino scraping requires residential proxies, full JavaScript rendering for React hydration, CAPTCHA handling, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our vivino.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. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across EU/US 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 vivino.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Vivino is generally permissible under applicable law. DataFlirt targets only public, non-authenticated wine, pricing, and review data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review Vivino's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes don't break the pipeline. We monitor for 403/CAPTCHA rate spikes in real time and trigger pool rotation or solver queues automatically.
Yes. We configure the crawlers to simulate requests from specific geographical regions, allowing us to capture localised merchant pricing, shipping costs, and availability for targeted markets.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined wine set. Full catalogue refreshes at daily cadence complete within a 6-12 hour window depending on size.
We extract all currently listed vintages and their associated ratings. For historical price tracking, we maintain a time-series table per wine from the date your pipeline starts.
Our smallest packages start at a defined list (typically 1,000-50,000 wines) with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Yes — including full pagination across all user reviews. Each review record includes the star rating, review text, user ID, language, likes count, and review date.
Absolutely. We provide a sample run of up to 500 wines or 50 search result 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 wine catalogue dump or a continuous price-monitoring feed across 100K vintages — we scope, build, and operate the pipeline. Tell us what you need.
