We extract wine catalogues, pricing signals, vintage details, tasting notes, and expert ratings from Vinos.de. 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 vinos.de. All fields typed and schema-versioned.
"wine_id": "V-12345", "name": "Marques de Riscal Reserva", "vintage": "2018", "bodega": "Marques de Riscal", "region_do": "Rioja DOCa", "grape_varieties": "['Tempranillo', 'Graciano']", "wine_type": "Red Wine", "price": 18.95
| # | wine_id | name | vintage | bodega | region_do | grape_varieties |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Bundles objects from vinos.de. All fields typed and schema-versioned.
"wine_id": "V-12345", "base_price": 18.95, "discount_price": 15.95, "bundle_size": 6, "price_per_litre": 21.26, "discount_pct": 15, "promotion_name": "Sommer-Paket", "timestamp": "2026-05-12T14:30:00Z"
| # | wine_id | base_price | discount_price | bundle_size | price_per_litre | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Expert Ratings objects from vinos.de. All fields typed and schema-versioned.
"wine_id": "V-12345", "rater_name": "Guia Penin", "score": 93, "rating_text": "Intense cherry red. Ripe fruit, sweet spices...", "publication": "Guia Penin 2020", "vintage_rated": "2018", "rating_scale": 100, "scraped_at": "2026-05-12T14:30:15Z"
| # | wine_id | rater_name | score | rating_text | publication | vintage_rated |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tasting Notes objects from vinos.de. All fields typed and schema-versioned.
"wine_id": "V-12345", "visual": "Deep ruby red with garnet rim", "nose": "Complex aromas of vanilla and dark berries", "palate": "Structured tannins, long finish", "serving_temp": "16-18 C", "food_pairing": "['Roast Lamb', 'Cured Cheeses']", "drinking_window": "2023-2030", "description": "A classic Rioja Reserva..."
| # | wine_id | visual | nose | palate | serving_temp | food_pairing |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Customer Reviews objects from vinos.de. All fields typed and schema-versioned.
"review_id": "R-98765", "wine_id": "V-12345", "star_rating": 5, "review_date": "2026-04-10", "reviewer_name": "Klaus M.", "review_text": "Excellent value for money. Will buy again.", "verified_buyer": true, "helpful_votes": 12
| # | review_id | wine_id | star_rating | review_date | reviewer_name | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Vinos.de scraper extracts the complete Spanish wine catalogue, including complex bundle pricing, expert ratings, and tasting notes, while managing age-verification sessions automatically.
Extract every wine, spirit, and sherry listed. Capture bodega, vintage, DO region, and grape variety metadata accurately.
Vinos.de relies heavily on 'Pakete' (bundles). We extract single bottle prices, bundle discounts, and price-per-litre metrics.
Capture and structure scores from Guia Penin, Robert Parker, Falstaff, and James Suckling directly from the product pages.
Extract structured tasting profiles, including visual, nose, palate, food pairings, and recommended drinking windows.
Paginate through customer reviews to extract star ratings, text, verified buyer status, and review dates.
Monitor inventory status, low-stock warnings, and delivery timeframes for every SKU in real time.
Automated session handling to bypass the mandatory 18+ age verification overlay without interrupting the crawl.
Run continuous pipelines with hash-based diffing. Receive updates only when prices, stock, or ratings change.
Capture German-language descriptions and tasting notes exactly as presented on the target storefront.
Brief in. Clean data out.
Provide category URLs, specific bodegas, or request a full catalogue crawl. We design the extraction schema together.
We configure Scrapy crawlers, session management for age gates, and proxy rotation for Vinos.de.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting wine data requires managing sessions, parsing complex pricing tables, and handling dynamic content. Here is how we build it.
Vinos.de requires users to confirm they are over 18. Our pipeline automatically manages cookie sessions and headers to clear this gate before extraction begins, ensuring zero dropped requests.
Wine pricing is rarely static. We parse single bottle prices, 6-bottle bundle discounts, seasonal promotions, and calculate the exact price-per-litre to normalise the dataset.
Scores from Parker, Penin, and Falstaff are presented in varying formats. We use regex and structured parsing to separate the rater, the score, and the textual review into clean database columns.
Certain stock indicators and dynamic search filters rely on client-side rendering. We deploy Playwright to execute JavaScript and capture the final DOM state.
Retailers update their front-end frameworks constantly. Our observability stack monitors null rates on critical fields like price and vintage, alerting our engineers to update selectors immediately.
Wine merchants and importers track Vinos.de pricing, bundle discounts, and promotions to adjust their own retail strategies.
Beverage analysts monitor the distribution of Spanish DO regions, grape varieties, and vintages available in the German market.
Machine learning teams ingest thousands of structured tasting notes and food pairings to train recommendation engines.
Spanish bodegas track how their wines are presented, priced, and reviewed by consumers in Northern Europe.
Supply chain analysts track out-of-stock indicators across specific vintages to predict market scarcity and demand.
NLP models process customer reviews to gauge consumer sentiment towards specific grape varieties or winemaking styles.
"Vinos.de holds the definitive catalogue of Spanish wines in the European market. Querying this data requires navigating age gates and complex bundle pricing."
Extracting wine data requires more than simple HTTP GET requests. You need session management for age verification, JavaScript execution for dynamic pricing bundles, and structured parsing for tasting notes. DataFlirt handles the extraction layer so your team can focus on analysis, not infrastructure maintenance.
Everything supported by our vinos.de 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 DE regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 vinos.de scraping, legality, and pipeline operations.
Ask us directly →Yes. Vinos.de frequently uses 'Pakete' (bundles). We extract the single bottle price, the bundle price, the discount percentage, and calculate the price-per-litre for standardisation.
Our pipeline automatically injects the necessary session cookies and headers to clear the age gate before initiating the crawl, ensuring complete access to the catalogue.
Yes. We parse the product descriptions and metadata to extract specific scores, the name of the rater, and the textual review provided by the critic.
Yes. We separate tasting notes into discrete fields such as visual appearance, nose, palate, recommended serving temperature, and food pairings.
We can configure pipelines to run daily, weekly, or at custom intervals. Change detection ensures we only deliver updated records, minimising processing overhead on your end.
Yes. We paginate through all available customer reviews for each wine, extracting the star rating, review text, date, and verified buyer status.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous price monitoring across thousands of vintages, we scope, build, and operate the pipeline. Tell us what you need.
