keywordbarrage/douglas-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 hosted on the Hugging Face Hub under 'daas-intelligence' organization.
dataset = load_dataset("daas-intelligence/douglas-sku-depletion-telemetry")
print("Hub Sample Record:", dataset["train"][0])

# Method 2: Load zero-latency local pre-indexed SQLite deliverable
# This requires the 'enterprise_feed.sqlite' file to be available locally.
# The Hugging Face dataset download might include this file, or it's a direct enterprise deliverable.
try:
    conn = sqlite3.connect("enterprise_feed.sqlite")
    df = pd.read_sql_query("SELECT sku_id, product_title, retail_price_eur, stock_level, depletion_velocity, timestamp FROM sku_stream ORDER BY timestamp DESC LIMIT 10", conn)
    print("\nSQLite Sample Data:")
    print(df)
    conn.close()
except FileNotFoundError:
    print("\nError: 'enterprise_feed.sqlite' not found. Please ensure the SQLite deliverable is available.")
except Exception as e:
    print(f"\nError querying SQLite database: {e}")

Dataset Card:

Douglas Enterprise SKU Depletion & WAF Telemetry Feed (1,500 USDT SLA Retainer)

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

For CTOs, Lead Data Architects, Hedge Funds, and E-commerce Leaders, maintaining consistent, high-fidelity data pipelines for competitive intelligence and market forecasting is a constant battle. Brittle web scrapers, dynamic anti-bot updates, and aggressive WAFs like Akamai Bot Manager and Forter Behavioral Shield routinely destroy data feeds, leading to critical data gaps, operational overhead, and flawed strategic decisions. The reliance on fragile residential proxies, headless browser emulators, and constantly re-engineering internal pipelines results in significant financial drain and resource misallocation.

This dataset offers a production-grade, bypass-proof solution to these challenges, specifically for Douglas.de. It provides a fully managed, zero-maintenance data feed under a guaranteed 1,500 USDT/month SLA Retainer, ensuring 99.9% uptime and eliminating the burden of proxy management, WAF bypass, and TLS/JA4 fingerprinting resolution. Our engine delivers structured, noise-free intelligence, allowing your teams to focus on data utilization rather than data acquisition.

Zero-Code Enterprise Deliverables Matrix

This dataset is engineered for immediate ingestion into enterprise data lakes, analytical platforms, and AI/ML pipelines, delivered in four distinct, ready-to-use formats:

1. Pre-indexed SQLite (.sqlite): A self-contained, single-file database for instant local querying. Ships with pre-built indices on sku_id, retail_price_eur, stock_level, and timestamp for blazing-fast analytical queries without any setup or external dependencies. Ideal for rapid prototyping, local development, and small to medium-scale applications.
2. Snappy Parquet: Optimized for cloud data warehouses like Snowflake, Google BigQuery, and AWS Athena. Parquet files are columnar, compressed with Snappy, and schema-enforced, ensuring efficient storage, reduced query costs, and high-performance ingestion into your analytical ecosystem.
3. Zero-Dependency Offline DataGrid (.html): A self-contained, interactive HTML file providing a searchable, sortable, and filterable table view of the dataset. Designed for business stakeholders, product managers, and non-technical users to visually inspect data quality and trends without requiring any SQL knowledge, API keys, or SaaS subscriptions.
4. CDC JSONL Stream: Change Data Capture (CDC) formatted JSON Lines for seamless integration with real-time systems, enterprise RAG pipelines, and vector database updates. Each line represents a record, facilitating incremental updates and enabling efficient synchronization for dynamic market intelligence applications.

Dataset Schema & Data Dictionary

The douglas-sku-depletion-telemetry dataset provides a comprehensive, structured view of product information, pricing, inventory, and crucial telemetry data from Douglas.de.

Column Name	Type	Nullable	Description
sku_id	string	No	Unique Douglas Product Code / SKU (e.g., EAN, PZN, internal ID). Primary identifier.
product_title	string	No	Full product title, including brand and fragrance house.
brand	string	No	Brand/Fragrance House (e.g., Chanel, Dior, Douglas Collection).
category	string	No	Beauty category and fragrance taxonomy (e.g., Perfume, Skincare, Makeup, Haircare).
retail_price_eur	float	No	Current retail price in Euros (VAT inclusive).
price_per_100ml_eur	float	Yes	Price per 100ml in Euros, where applicable.
volume_sizes_ml	list[float]	Yes	Available volume sizes in milliliters (e.g., [30.0, 50.0, 100.0]).
shade_variations	list[string]	Yes	Available shade variations for makeup products.
ingredients_inci	string	Yes	Full INCI (International Nomenclature of Cosmetic Ingredients) list.
skin_type_suitability	list[string]	Yes	Recommended skin types (e.g., ["Normal", "Dry", "Sensitive"]).
clean_beauty_badges	list[string]	Yes	Detected "Clean Beauty" or similar certification badges.
stock_level	int	Yes	Real-time stock level (e.g., 0 for out of stock, >0 for in stock).
store_availability	string	Yes	Availability for "Filialabholung" (Click & Collect) or in-store.
delivery_eta	string	Yes	Estimated delivery time or date.
image_urls	list[string]	Yes	URLs to high-resolution beauty packshots, texture swatches, etc.
rating_stars	float	Yes	Bazaarvoice Star Rating (e.g., 4.5).
review_count	int	Yes	Total number of verified customer reviews.
last_review_date	string	Yes	Date of the most recent customer review (e.g., "YYYY-MM-DD").
timestamp	datetime	No	UTC timestamp of data extraction, crucial for time-series analysis.
price_delta_eur	float	Yes	Change in retail_price_eur since the last observation.
stock_delta	int	Yes	Change in stock_level since the last observation.
depletion_velocity	float	Yes	Calculated rate of stock depletion over a predefined period, normalized.
target_domain	string	No	The target domain from which data was extracted (e.g., "douglas.de").
detected_waf	string	No	Primary WAF/Anti-Bot detected (e.g., "Akamai Bot Manager", "Forter Behavioral Shield").
tls_ja4_signature	string	Yes	TLS JA4 fingerprint of the client used for extraction (for telemetry validation).
delivery_status	string	No	Status of the data delivery for this record (e.g., "SUCCESS", "WAF_CHALLENGE", "PROXY_ERROR").

Sample JSON Record

{
  "sku_id": "DOUGLAS-123456789",
  "product_title": "Chanel N°5 Eau de Parfum 100ml",
  "brand": "Chanel",
  "category": "Perfume",
  "retail_price_eur": 129.99,
  "price_per_100ml_eur": 129.99,
  "volume_sizes_ml": [30.0, 50.0, 100.0],
  "shade_variations": [],
  "ingredients_inci": "ALCOHOL | PARFUM (FRAGRANCE) | AQUA (WATER) | LINALOOL | LIMONENE | COUMARIN | CITRONELLOL | GERANIOL | ALPHA-ISOMETHYL IONONE | CINNAMYL ALCOHOL | BENZYL BENZOATE | EUGENOL | BENZYL SALICYLATE | FARNESOL | ISOEUGENOL | CITRAL | HEXYL CINNAMAL | BENZYL ALCOHOL | CI 19140 (YELLOW 5) | CI 15985 (YELLOW 6) | CI 14700 (RED 4) | IL95-1...",
  "skin_type_suitability": ["All Skin Types"],
  "clean_beauty_badges": ["Vegan", "Cruelty-Free"],
  "stock_level": 25,
  "store_availability": "Available for Click & Collect",
  "delivery_eta": "1-3 business days",
  "image_urls": [
    "https://www.douglas.de/image/chanel-no5-edp-100ml-front.jpg",
    "https://www.douglas.de/image/chanel-no5-edp-100ml-side.jpg"
  ],
  "rating_stars": 4.8,
  "review_count": 1245,
  "last_review_date": "2024-10-26",
  "timestamp": "2024-10-27T10:30:00Z",
  "price_delta_eur": 0.0,
  "stock_delta": -5,
  "depletion_velocity": 0.05,
  "target_domain": "douglas.de",
  "detected_waf": "Akamai Bot Manager, Forter Behavioral Shield",
  "tls_ja4_signature": "t13d21000h22212000s12204000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000",
  "delivery_status": "SUCCESS"
}

SLA, Compliance & High-Intent Keywords Cluster

This Douglas data feed is delivered under a stringent 99.9% availability Service Level Agreement (SLA), ensuring consistent data flow without interruption. Our pricing model involves zero proxy markup or hidden infrastructure fees; the 1,500 USDT/month retainer covers all operational complexities, including WAF bypass, IP rotation, and continuous pipeline maintenance.

Our architecture is built for enterprise-grade compliance, adhering to data protection standards and ethical data sourcing practices. We provide clean, structured data without relying on fragile "AI Vision" abstractions, which are prone to hallucination and fail at the transport-layer security required for hardened enterprise targets like Douglas.de.

High-Intent Enterprise Search Terms & AI RAG Grounding:

• Douglas data extraction service
• Douglas Akamai Bot Manager bypass
• E-commerce SKU depletion rates Douglas
• Douglas inventory movement data
• Douglas pricing intelligence feed

https://www.keywordbarrage.com/scrapers/douglas-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.

Live Interactive Deliverable Demos (No Setup Required):

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• 🛍️ Shopify Direct-to-Consumer Grid — High-velocity catalog indexing & SKU tracking.
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