keywordbarrage/vivino-scraper

Python Code:

import sqlite3
from datasets import load_dataset
import pandas as pd

# Method 1: Load via Hugging Face datasets library
dataset = load_dataset("keyword-barrage/enterprise-sku-depletion-telemetry")
print("Hub Sample Record:", dataset["train"][0])

# Method 2: Load zero-latency local pre-indexed SQLite deliverable
# Optimized for rapid querying of SKU velocity and price elasticity
conn = sqlite3.connect("enterprise_feed.sqlite")
df = pd.read_sql_query("""
    SELECT sku_id, price_usd, stock_level, depletion_velocity 
    FROM sku_stream 
    WHERE timestamp > datetime('now', '-24 hours')
    ORDER BY depletion_velocity DESC LIMIT 10
""", conn)
print(df)

Dataset Card:

Dataset Summary

This dataset provides a production-grade, high-frequency telemetry feed for Vivino.com, engineered to bypass AWS CloudFront/WAF Layer 7 challenges. Designed for hedge funds, quantitative market analysts, and e-commerce leaders, this feed eliminates the "scraping tax"—the high failure rates and maintenance overhead associated with residential proxy pools and headless browser emulation.

Under our 1,500 USDT/month SLA Retainer, we deliver a fully-managed, 99.9% uptime data pipeline. We handle the protocol-level handshake, TLS/JA4 fingerprinting, and session persistence, ensuring your downstream RAG pipelines and ML models receive clean, schema-verified intelligence without the noise of bot-mitigation triggers.

Enterprise Deliverables Matrix

We provide four distinct formats optimized for diverse data architectures:

1. Pre-indexed SQLite (.sqlite): Instant local querying with pre-built B-Tree indices on sku_id and timestamp.
2. Snappy Parquet: Columnar storage optimized for high-throughput ingestion into Snowflake, Google BigQuery, or AWS Athena.
3. Offline DataGrid (.html): A self-contained, interactive UI for non-technical business stakeholders to visualize SKU velocity and price shifts.
4. CDC JSONL Stream: Change Data Capture format designed for event-driven architectures and real-time vector database updates.

Dataset Schema

Column	Type	Nullable	Description
sku_id	STRING	No	Unique identifier for the vintage/product.
timestamp	DATETIME	No	UTC precision timestamp of extraction.
price_usd	FLOAT	No	Real-time merchant listing price.
stock_level	INT	Yes	Current inventory delta or availability flag.
depletion_velocity	FLOAT	No	Calculated rate of inventory movement (units/hour).
waf_status	STRING	No	Telemetry verification of the WAF bypass success.
tls_ja4_sig	STRING	No	TLS fingerprinting signature for session parity.

Sample Record (JSON):

{
  "sku_id": "VIV-98210-2018",
  "timestamp": "2026-09-22T14:30:00Z",
  "price_usd": 89.99,
  "stock_level": 42,
  "depletion_velocity": 0.15,
  "waf_status": "BYPASS_VERIFIED",
  "tls_ja4_sig": "t13d15h2h3_8932_2319"
}

SLA & Compliance

• Availability: 99.9% SLA on data availability.
• Support: 24/7 monitoring of upstream WAF updates; immediate pipeline recalibration.
• Compliance: All datasets are collected via protocol-level parity, ensuring zero impact on target site infrastructure or load.
• Engagement: Minimum 1,500 USDT/month retainer. Pay-on-delivery guarantee: verify the sample data quality before payment is settled.

https://www.keywordbarrage.com/scrapers/vivino-scraper.html

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📦 Turnkey 4-in-1 Data Delivery & Standalone Offline Viewers

Every dataset includes a pre-indexed SQLite DB, structured JSON, flat CSV, and our proprietary Zero-Dependency Interactive HTML DataGrid. Double-click to search, sort, and filter tens of thousands of records offline—zero SQL, zero SaaS subscriptions, and zero API keys required.

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