We extract daily wine deals, marathon sale events, pricing histories, and tasting notes from Last Bottle. Delivered as clean JSON, CSV, or Parquet to S3 or via Webhook for real-time alerts.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Daily Deals objects from lastbottlewines.com. All fields typed and schema-versioned.
"deal_id": "LB-9482", "wine_name": "Chateau Montelena Cabernet Sauvignon", "vintage": 2018, "varietal": "Cabernet Sauvignon", "last_bottle_price": 89.0, "retail_price": 150.0, "discount_pct": 40, "stock_status": "in_stock"
| # | deal_id | wine_name | vintage | varietal | region | winery |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Marathon Events objects from lastbottlewines.com. All fields typed and schema-versioned.
"event_id": "MAR-2026-SPRING", "event_name": "Spring Marathon", "total_wines_offered": 450, "average_discount": 48.5, "fastest_sellout_seconds": 14, "active_wine_id": "LB-9921", "current_price": 24.0, "timestamp": "2026-04-10T14:22:01Z"
| # | event_id | event_name | start_time | end_time | total_wines_offered | average_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Wine Metadata objects from lastbottlewines.com. All fields typed and schema-versioned.
"wine_id": "W-8841", "producer": "Opus One", "appellation": "Napa Valley", "country": "USA", "alcohol_pct": 14.5, "bottle_size": "750ml", "critic_scores": "{"James Suckling": 99, "Wine Advocate": 97}", "drinking_window": "2025-2040"
| # | wine_id | producer | appellation | country | alcohol_pct | bottle_size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tasting Notes & Copy objects from lastbottlewines.com. All fields typed and schema-versioned.
"deal_id": "LB-9482", "headline": "Napa Royalty at 40% Off", "flavor_profile": "["Blackberry", "Cassis", "Graphite"]", "pairings": "["Ribeye steak", "Aged cheddar"]", "staff_review": "Absolutely singing right now. Massive structure.", "review_author": "Last Bottle Team", "source_url": "https://www.lastbottlewines.com/"
| # | deal_id | headline | subheadline | body_text | flavor_profile | pairings |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory Tracking objects from lastbottlewines.com. All fields typed and schema-versioned.
"deal_id": "LB-9482", "wine_name": "Chateau Montelena", "initial_detection_time": "2026-05-12T09:00:00Z", "sold_out_time": "2026-05-12T11:42:15Z", "time_to_sell_out_seconds": 9735, "inventory_status": "sold_out", "price": 89.0, "volume_sold_estimate": 120
| # | deal_id | wine_name | initial_detection_time | sold_out_time | time_to_sell_out_seconds | inventory_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Last Bottle scraper handles the high-velocity nature of daily deals and marathon events. We extract pricing, critic scores, and inventory status with sub-second polling.
Sub-second monitoring for marathon events to capture every single wine offered before it sells out.
Extract Last Bottle price, stated retail price, and calculate exact discount percentages for every deal.
Track precise sell-out times to calculate deal velocity and consumer demand.
Parse flavour profiles, pairing suggestions, and staff reviews from unstructured deal copy.
Use regex patterns to extract James Suckling, Wine Advocate, and Vinous scores hidden in the text.
Maintain a continuous log of rapid-fire sales, capturing wines that appear and disappear in seconds.
Standardise appellations and regions to build a clean dataset of Napa, Bordeaux, and Piedmont offers.
Trigger instant HTTP POST requests the millisecond a new deal goes live.
Access our repository of past sales data to analyse seasonal pricing trends.
Brief in. Clean data out.
Choose between daily deal monitoring or high-frequency marathon event tracking.
We configure Scrapy crawlers, sub-second polling loops, and proxy rotation for lastbottlewines.com.
Schema validation, price-outlier detection, and critic score regex testing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, or Webhooks fired for real-time arbitrage.
Flash sale sites require specialized polling infrastructure. Here is how we capture data that standard daily crawlers miss entirely.
During Last Bottle Marathons, wines can sell out in under 30 seconds. We deploy distributed polling clusters that check for state changes multiple times per second without triggering rate limits.
Rather than relying solely on DOM parsing, we intercept API responses and extract the underlying JSON state, capturing precise inventory flags and unformatted pricing data.
Aggressive polling triggers IP bans. We distribute requests across a massive pool of US residential proxies, ensuring continuous access during critical marathon events.
Last Bottle often embeds valuable critic scores (JS 99, WA 97) deep within their conversational copy. We use tested regex patterns to extract and normalise these numerical values.
For arbitrage and consumer alert applications, data is useless if it is five minutes late. Our webhook delivery system fires HTTP POST payloads within milliseconds of a deal changing.
Wine retailers track flash sale pricing to adjust their own promotional strategies and margin expectations.
Buyers monitor extreme discounts on blue-chip wines for immediate resale on secondary platforms.
Funds track historical discount rates and sell-out velocities to gauge true market demand for specific vintages.
Developers power notification services that alert collectors when specific varietals or regions drop.
Wineries analyse how their past vintages are being discounted to inform future direct-to-consumer pricing.
Importers track which regions and varietals sell out fastest during marathons to guide procurement.
"Last Bottle flash sales evaporate in seconds. Capturing that pricing history requires sub-second polling and zero-latency webhooks, not nightly batch jobs."
Flash sale sites present unique scraping challenges. Traditional crawlers miss inventory drops entirely. We deploy high-frequency polling clusters with residential proxies to monitor Last Bottle marathons in real time, extracting pricing, critic scores, and sell-out velocity before the deal vanishes. DataFlirt handles the infrastructure so you never miss a drop.
Everything supported by our lastbottlewines.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.
Distributed asynchronous workers poll endpoints multiple times per second, using Redis to manage state and detect changes instantly.
Custom Python NLP pipelines extract structured attributes like critic scores, drinking windows, and varietals from conversational marketing copy.
Pipelines run on AWS Lambda for burst scaling during marathons. Airflow handles scheduling, and all state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About lastbottlewines.com scraping, legality, and pipeline operations.
Ask us directly →During standard daily deals, we poll at a configurable interval (typically every minute). During announced Marathons, we scale to sub-second polling clusters to capture deals that may only exist for 15 seconds.
Yes. Marathon events require a completely different architecture than standard daily scraping. We use distributed asynchronous workers and massive residential proxy pools to sustain high-frequency polling without triggering rate limits.
Yes. Last Bottle embeds scores from James Suckling, Wine Advocate, Vinous, and others in their copy. We use regex patterns to extract the critic name and the numerical score into structured JSON fields.
We use Webhook delivery. The millisecond our polling engine detects a new deal or a price change, we fire an HTTP POST request to your endpoint with the complete JSON payload.
Yes. We monitor the stock status flag and record the exact timestamp when a deal transitions to sold out, allowing you to calculate deal velocity.
Scraping publicly available pricing and product information is generally permissible. DataFlirt targets only public, non-authenticated deal data. We do not extract personal data or circumvent authentication walls.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need historical pricing analysis or real-time marathon alerts - we scope, build, and operate the pipeline. Tell us what you need.
