keywordbarrage/yoox-scraper

Python Code:

import sqlite3
from datasets import load_dataset
import pandas as pd

# Method 1: Load via Hugging Face datasets library
# This assumes the dataset is pushed to the Hugging Face Hub under 'daas-intelligence' organization.
# For private datasets, ensure proper authentication (e.g., `huggingface-cli login`).
dataset = load_dataset("daas-intelligence/enterprise-sku-depletion-telemetry")
print("Hub Sample Record:", dataset["train"][0])

# Method 2: Load zero-latency local pre-indexed SQLite deliverable
# This demonstrates querying the delivered 'enterprise_feed.sqlite' file directly.
# Ensure 'enterprise_feed.sqlite' is in your working directory.
try:
    conn = sqlite3.connect("enterprise_feed.sqlite")
    df = pd.read_sql_query("SELECT sku_id, price_usd, stock_level, depletion_velocity FROM sku_stream ORDER BY timestamp DESC LIMIT 10", conn)
    print("\nSQLite Sample Data:")
    print(df)
    conn.close()
except sqlite3.OperationalError:
    print("\nSQLite database 'enterprise_feed.sqlite' not found. Please ensure it's delivered and in the current directory.")
except Exception as e:
    print(f"\nAn error occurred while querying SQLite: {e}")

Dataset Card:

---

pretty_name: "Enterprise High-Frequency SKU Depletion & WAF Telemetry Feed (1,500 USDT SLA Retainer)" annotations_creators: - machine-generated language: - en - tr license: apache-2.0 task_categories: - tabular-classification - time-series-forecasting tags: - e-commerce-intelligence - akamai-bypass - time-series-sku-depletion - pre-indexed-sqlite - 1500-usdt-sla-retainer - luxury-retail-fashion size_categories: - 10K<n<100K configs: - config_name: default data_files: - split: train path: "data/train-.parquet" - split: validation path: "data/val-.parquet"

Enterprise High-Frequency SKU Depletion & WAF Telemetry Feed (1,500 USDT SLA Retainer)

Dataset Summary & B2B Motivation (1,500 USDT/month SLA Retainer)

For CTOs, Lead Data Architects, Hedge Funds, and E-Commerce Leaders, the integrity and uptime of market intelligence feeds are paramount. Traditional web scraping pipelines are inherently brittle, constantly breaking due to aggressive anti-bot measures, WAF updates, and TLS fingerprinting changes. This leads to costly engineering overhead, inconsistent data, and missed market signals.

This dataset provides a fully managed, production-grade YOOX luxury e-commerce data extraction service, specifically engineered to bypass sophisticated perimeter defenses like Akamai Bot Manager and Riskified Anti-Fraud Shield. We deliver clean, structured data with 99.9% uptime and zero maintenance burden under a guaranteed 1,500 USDT/month SLA Retainer. Our architecture ensures protocol-level parity and session decoupling, extracting critical SKU information, pricing elasticity time-series, and inventory depletion rates directly from Yoox.com, without triggering behavioral heuristics or TLS/JA4 fingerprint bans. This eliminates the need for internal proxy management, CAPTCHA solving, or continuous pipeline refactoring for your engineering teams, allowing them to focus on high-value analytics and strategic decision-making.

Zero-Code Enterprise Deliverables Matrix

This dataset is delivered in four enterprise-ready formats, ensuring seamless integration into diverse data architectures and business workflows:

1. Pre-indexed SQLite (.sqlite): A self-contained, single-file database for instant local querying. Tables are pre-indexed on sku_id, price_delta, and timestamp for blazing-fast analytical queries without requiring a dedicated database server or complex setup. Ideal for rapid prototyping, local development, and small-to-medium scale analytics.
2. Snappy Parquet: Optimized for cloud data warehouses and big data platforms. Parquet files are columnar, compressed with Snappy, and schema-aware, making them ideal for high-performance ingestion into Snowflake, Google BigQuery, AWS Athena, and other modern data lakes.
3. Zero-Dependency Offline DataGrid (.html): A self-contained, interactive HTML file that provides a rich, sortable, and filterable table view of the dataset. Business stakeholders and non-technical users can explore the data instantly in any web browser, without needing SQL, API keys, or specialized software. This enables rapid data validation and sharing across teams.
4. CDC JSONL Stream: A Change Data Capture (CDC) format, delivered as a stream of JSON Lines (JSONL). Each line represents a record, facilitating incremental updates for Enterprise RAG pipelines, vector database synchronization, and real-time data streaming architectures. This format is crucial for maintaining up-to-date knowledge bases and dynamic ML models.

Dataset Schema & Data Dictionary

The enterprise-sku-depletion-telemetry dataset provides comprehensive, structured information about luxury fashion products, their pricing, inventory, and associated telemetry from Yoox.com.

Column Name	Type	Nullable	Description
sku_id	TEXT	NO	Unique YOOX Item Code (Codice Articolo) or Global Trade Item Number (GTIN/EAN-13) for the product.
timestamp	TIMESTAMP	NO	UTC timestamp of when the data record was extracted.
product_title	TEXT	NO	Full product title including designer brand and collection name (e.g., "Gucci GG Marmont Mini Bag").
designer_brand	TEXT	NO	The luxury designer brand (e.g., "Prada", "Balenciaga", "Saint Laurent").
department_taxonomy	TEXT	NO	Categorization of the product (e.g., "Women's Clothing", "Men's Footwear", "Kids", "Design+Art").
price_usd	REAL	NO	Discounted outlet price in USD. Converted from original currency if needed.
original_msrp_usd	REAL	YES	Original Manufacturer's Suggested Retail Price in USD, if available.
discount_rate_pct	REAL	YES	Percentage discount from MSRP to the current price.
currency	TEXT	NO	Original currency of the price (e.g., "EUR", "USD", "GBP").
stock_level	INTEGER	YES	Current available stock quantity. Can be low/out of stock.
low_stock_indicator	BOOLEAN	YES	True if the product is marked as low stock, False otherwise.
size_availability	JSON	YES	JSON array of available sizes (e.g., ["IT 38", "US 6", "FR 36"]).
color_variant	TEXT	YES	Primary color variant of the product (e.g., "Black", "Navy", "Ecru").
high_res_image_url	TEXT	YES	URL to the high-resolution runway photography or product image.
material_composition	TEXT	YES	Detailed material composition (e.g., "100% Recycled Nylon", "Leather and Rubber Sole").
country_of_origin	TEXT	YES	Country where the product was manufactured (e.g., "Italy", "France").
garment_fit_details	TEXT	YES	Description of the garment fit (e.g., "Regular fit", "Slim fit", "Oversized").
shipping_rates_info	TEXT	YES	Information on global shipping rates and customs clearance.
returns_window_days	INTEGER	YES	Number of days for free returns.
velocity_depletion_rate	REAL	YES	Calculated rate of SKU depletion based on historical stock changes.
target_domain	TEXT	NO	The target domain from which data was extracted (e.g., "yoox.com").
detected_waf	TEXT	NO	The Web Application Firewall detected during extraction (e.g., "Akamai Bot Manager", "Riskified Shield").
tls_ja4_signature	TEXT	NO	The TLS/JA4 fingerprint used during the successful bypass.
delivery_status	TEXT	NO	Status of the data point delivery (e.g., "SUCCESS", "WAF_CHALLENGE_BYPASSED").

Sample JSON Record

{
  "sku_id": "13123456BC",
  "timestamp": "2024-07-26T14:30:00Z",
  "product_title": "Prada Re-Nylon Padded Puffer Jacket",
  "designer_brand": "Prada",
  "department_taxonomy": "Men's Outerwear",
  "price_usd": 1850.00,
  "original_msrp_usd": 2500.00,
  "discount_rate_pct": 26.0,
  "currency": "USD",
  "stock_level": 12,
  "low_stock_indicator": false,
  "size_availability": ["IT 48", "IT 50", "IT 52"],
  "color_variant": "Black",
  "high_res_image_url": "https://www.yoox.com/images/prada_jacket_hires.jpg",
  "material_composition": "100% Recycled Nylon, Goose Down Padding",
  "country_of_origin": "Italy",
  "garment_fit_details": "Regular fit, slightly oversized silhouette",
  "shipping_rates_info": "Free global shipping, duties included",
  "returns_window_days": 100,
  "velocity_depletion_rate": 0.05,
  "target_domain": "yoox.com",
  "detected_waf": "Akamai Bot Manager & Riskified Shield",
  "tls_ja4_signature": "t13d180101h2-e0a7-1901-0101-0101",
  "delivery_status": "SUCCESS"
}

Python Usage Code

To enable immediate integration and analysis, this dataset can be accessed directly via the Hugging Face datasets library or by utilizing the pre-indexed SQLite deliverables provided under the SLA.

import sqlite3
from datasets import load_dataset
import pandas as pd

# Method 1: Load via Hugging Face datasets library
# This assumes the dataset is pushed to the Hugging Face Hub under 'daas-intelligence' organization.
# For private datasets, ensure proper authentication (e.g., `huggingface-cli login`).
dataset = load_dataset("daas-intelligence/enterprise-sku-depletion-telemetry")
print("Hub Sample Record:", dataset["train"][0])

# Method 2: Load zero-latency local pre-indexed SQLite deliverable
# This demonstrates querying the delivered 'enterprise_feed.sqlite' file directly.
# Ensure 'enterprise_feed.sqlite' is in your working directory.
try:
    conn = sqlite3.connect("enterprise_feed.sqlite")
    df = pd.read_sql_query("SELECT sku_id, price_usd, stock_level, depletion_velocity FROM sku_stream ORDER BY timestamp DESC LIMIT 10", conn)
    print("\nSQLite Sample Data:")
    print(df)
    conn.close()
except sqlite3.OperationalError:
    print("\nSQLite database 'enterprise_feed.sqlite' not found. Please ensure it's delivered and in the current directory.")
except Exception as e:
    print(f"\nAn error occurred while querying SQLite: {e}")

SLA, Compliance & High-Intent Keywords Cluster

This enterprise data feed is backed by a stringent Service Level Agreement (SLA) guaranteeing 99.9% availability and delivery consistency. We operate with zero proxy markup and transparent pricing, ensuring that your investment translates directly into high-quality data. Our ingestion pipelines are designed for compliance standards, focusing on ethical data sourcing and robust data protection.

For enterprises seeking high-frequency market intelligence, e-commerce intelligence, and alternative data streams, this dataset offers a critical edge. It addresses challenges like Akamai Bot Manager bypass, Riskified Shield mitigation, and overcoming TLS/JA4 fingerprinting for luxury retail fashion data. Data architects and quantitative analysts can leverage this for time-series SKU depletion analysis, pricing elasticity models, competitive intelligence, and supply chain analytics. The structured outputs are ideal for enterprise RAG pipelines, vector database updates, and robust retail market insights. This solution ensures data pipeline resilience and provides actionable intelligence for strategic e-commerce decision-making, without the burden of managing complex anti-bot solutions or WAF bypass infrastructure.

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

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