We extract wine catalogues, Angel vs guest pricing, winemaker profiles, and review data from Nakedwines. 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 nakedwines.com. All fields typed and schema-versioned.
"wine_id": "W12849", "name": "Stephen Millier Black Label Cabernet Sauvignon", "winemaker": "Stephen Millier", "vintage": "2021", "angel_price": 12.99, "guest_price": 19.99, "rating_pct": 92, "review_count": 4182
| # | wine_id | name | winemaker | vintage | region | country |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Stock objects from nakedwines.com. All fields typed and schema-versioned.
"wine_id": "W12849", "angel_price": 12.99, "guest_price": 19.99, "discount_pct": 35, "in_stock": true, "stock_status": "AVAILABLE", "currency": "USD"
| # | wine_id | angel_price | guest_price | discount_pct | in_stock | stock_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Winemaker Profiles objects from nakedwines.com. All fields typed and schema-versioned.
"winemaker_id": "M492", "name": "Stephen Millier", "location": "Calaveras County, California", "total_wines": 42, "average_rating": 91.4, "funding_status": "FULLY_FUNDED", "total_reviews": 128491
| # | winemaker_id | name | location | bio | joined_date | total_wines |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from nakedwines.com. All fields typed and schema-versioned.
"review_id": "R948210", "wine_id": "W12849", "angel_status": true, "rating": 5, "would_buy_again": true, "review_date": "2026-03-14", "vintage_reviewed": "2021"
| # | review_id | wine_id | user_name | angel_status | rating | would_buy_again |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Tasting & Pairings objects from nakedwines.com. All fields typed and schema-versioned.
"wine_id": "W12849", "body": "Full", "acidity": "Medium", "sweetness": "Dry", "food_pairings": "['Grilled steak', 'Aged cheddar', 'Roasted vegetables']", "serving_temp": "60-65F", "cellaring_potential": "Drink now or hold up to 5 years"
| # | wine_id | tasting_notes | aroma | body | acidity | sweetness |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Nakedwines scraper navigates age gates, dynamic session pricing, and infinite scroll layouts to deliver structured wine data, winemaker intelligence, and review corpora.
Name, vintage, ABV, grape variety, region, and style extracted at the individual bottle level.
Simultaneous capture of Angel pricing and standard guest pricing using session management.
Extract winemaker profiles, total wines produced, average ratings, and funding status.
Full review text, star ratings, and the critical 'would buy again' percentage across all vintages.
Body, acidity, sweetness profiles, and recommended food pairings for every listed wine.
Standardised extraction of country, region, and sub-region data for geographic analysis.
Automated session handling to clear 21+ verification modals without interrupting the crawl.
Playwright execution to handle infinite scroll wine lists and dynamic content hydration.
Run one-off bulk exports or configure continuous pipelines with change-detection diffing.
Brief in. Clean data out.
Provide wine styles, regions, or request the full catalogue. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and age-gate handling.
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.
Extracting accurate pricing and catalogue data from Nakedwines requires managing session states and dynamic layouts. Here is how we maintain pipeline stability.
Nakedwines displays different pricing depending on user state. Our crawlers maintain separate cookie jars to simultaneously extract both standard retail prices and discounted Angel prices, ensuring complete price intelligence.
Alcohol retail sites enforce age verification modals that block headless HTTP clients. We use Playwright to interact with these DOM elements, establishing validated sessions before initiating the main extraction routines.
Wine category pages rely on infinite scroll and AJAX pagination. We execute full browser sessions to trigger lazy-loading, ensuring every bottle in a category is captured rather than just the first 20.
Wine pages change layout depending on vintage availability and stock status. We use fallback selector chains across CSS, XPath, and LD+JSON to ensure data extraction continues even when UI elements shift.
We maintain a hash index of last-seen values per wine. Subsequent runs only push diffs for price updates, stock changes, or new reviews, reducing downstream processing load.
Wine retailers and distributors track Nakedwines Angel pricing models against traditional retail channels to optimise their own pricing strategies.
Beverage analysts monitor grape varieties, regional distribution, and style trends to identify shifting consumer preferences.
Brands mine tasting notes and 'would buy again' metrics to correlate wine characteristics with high consumer satisfaction.
Importers and distributors identify top-performing independent winemakers based on customer ratings and funding velocity.
Researchers track vintage transitions, pricing elasticity, and stock availability across different wine regions.
ML teams use structured tasting notes, body profiles, and food pairings to train wine recommendation engines.
"Nakedwines holds a unique dataset of independent winemaker performance and direct-to-consumer pricing that is critical for understanding modern wine retail."
Extracting this data requires handling complex age-gate modals, dynamic session-based pricing for Angel vs Guest states, and heavy JavaScript rendering. DataFlirt manages this infrastructure entirely, delivering clean, structured wine data directly to your warehouse so your team can focus on analysis.
Everything supported by our nakedwines.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 retry logic. Playwright handles JavaScript rendering, cookie sessions, and age-gate interactions.
We maintain pools of residential ISP proxies to avoid geographic blocking and rate limits, rotated per request with sticky sessions where required.
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 nakedwines.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Nakedwines is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We utilise session management within our Playwright nodes to maintain separate cookie states. One session navigates as a standard guest, while another simulates the state required to expose Angel pricing, allowing us to extract both data points simultaneously.
Our crawlers are programmed to detect the age-gate modal upon initial page load and interact with the necessary DOM elements to verify age, establishing a valid session before proceeding with data extraction.
Full catalogue refreshes at daily cadence complete within a 2-4 hour window. We can also configure intra-day runs targeting specific high-velocity categories or specific winemakers.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per wine ID, allowing you to track when a specific vintage sells out and transitions to the next year.
Our smallest packages start with weekly delivery of the full active Nakedwines catalogue. Contact us with your specific frequency requirements for a scoped quote.
Absolutely. We provide a sample run of up to 200 wines as part of the pre-engagement scoping process so you can validate schema fit, field completeness, and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across all vintages, we scope, build, and operate the pipeline. Tell us what you need.
