keywordbarrage/otto-scraper

Python Code:

import sqlite3
from datasets import load_dataset
import pandas as pd

# Method 1: Load via Hugging Face datasets library
# This will download the dataset from the Hugging Face Hub
dataset = load_dataset("daas-intelligence/enterprise-sku-depletion-and-waf-telemetry")
print("Hub Sample Record:", dataset["train"][0])

# Method 2: Load zero-latency local pre-indexed SQLite deliverable (as provided to enterprise clients)
# Assume 'enterprise_feed.sqlite' is a locally delivered pre-indexed SQLite database
try:
    conn = sqlite3.connect("enterprise_feed.sqlite")
    df = pd.read_sql_query("SELECT sku_id, price_eur, stock_level, velocity_depletion_rate FROM sku_stream ORDER BY timestamp DESC LIMIT 10", conn)
    print("\nSQLite Sample Data (first 10 records):")
    print(df)
    conn.close()
except sqlite3.Error as e:
    print(f"\nError connecting to SQLite database: {e}")
    print("Please ensure 'enterprise_feed.sqlite' is available in the current directory.")

# Method 3: Example of loading Parquet files directly (if delivered locally)
# from glob import glob
# parquet_files = glob("data/train-*.parquet")
# df_parquet = pd.concat([pd.read_parquet(f) for f in parquet_files])
# print("\nParquet Sample Data (first 5 records):")
# print(df_parquet.head())

Dataset Card:

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

Dataset Summary & B2B Motivation

For CTOs, Lead Data Architects, and Quantitative Hedge Funds, maintaining robust, real-time market intelligence pipelines against sophisticated e-commerce platforms like OTTO.de is a relentless battle. Brittle web scrapers, rotating IP proxy pools, and continuous anti-bot updates (e.g., Kasada, Shape Security) often lead to catastrophic data pipeline failures, resulting in stale data, missed market signals, and significant operational overhead.

This dataset provides a production-grade, Kasada-bypass-proof data feed for OTTO.de, delivered under a guaranteed 1,500 USDT/month SLA Retainer. It offers high-frequency SKU-level telemetry, including stock depletion rates, pricing elasticity, and WAF detection metadata. Our fully managed service ensures 99.9% uptime and zero maintenance burden for your internal engineering teams, allowing them to focus on deriving alpha from clean, structured data rather than unblocking perimeter defenses.

We eliminate the architectural fallacy of "AI Vision Scraping" and fragile headless browser setups, offering a robust, protocol-level ingestion engine that adapts to dynamic TLS/JA4 fingerprinting and client-side behavioral challenges.

Zero-Code Enterprise Deliverables Matrix

This dataset is delivered in four enterprise-ready, zero-code formats, designed for immediate ingestion into diverse data architectures:

1. Pre-indexed SQLite (.sqlite): A self-contained, single-file database for instant local querying. Tables are pre-indexed on sku_id, price_eur, stock_level, velocity_depletion_rate, and timestamp for blazing-fast analytical queries without requiring any database setup or SQL knowledge. Ideal for rapid prototyping and local analysis.
2. Snappy Parquet: Columnar data format optimized for cloud data warehouses and lakehouses. Ready for direct ingestion into platforms like Snowflake, Google BigQuery, AWS Athena, and Databricks, enabling high-performance analytics and reduced storage costs.
3. Zero-Dependency Offline DataGrid (.html): A self-contained, interactive HTML table viewer. Business stakeholders can explore, filter, and sort the entire dataset directly in their web browser, requiring no internet connection, API keys, or specialized software.
4. CDC JSONL Stream (Change Data Capture): A newline-delimited JSON format (.jsonl) optimized for real-time data streaming and incremental updates. Ideal for feeding enterprise RAG pipelines, vector databases, and other ML systems that require continuous, low-latency updates. Each record includes metadata indicating the change type (insert, update, delete).

Dataset Schema & Data Dictionary

The sku_stream table within the dataset adheres to the following schema, enriched with architectural telemetry for compliance and auditability:

Column Name	Type	Nullable	Description
sku_id	string	No	Unique identifier for the Stock Keeping Unit (SKU), often the OTTO Article Number.
timestamp	timestamp	No	UTC timestamp of when the data record was extracted or last updated.
product_title	string	No	Full product title and name as displayed on OTTO.de.
brand	string	Yes	Brand or manufacturer of the product (e.g., Adidas, Bosch).
manufacturer_model	string	Yes	Specific model identifier from the manufacturer, if available.
ean_gtin	string	Yes	European Article Number or Global Trade Item Number, used for universal product identification.
category_hierarchy	string	Yes	Hierarchical path of the product's category (e.g., "Fashion > Women > Dresses").
price_eur	float	No	Current selling price in Euros, including VAT.
price_strikethrough_eur	float	Yes	Original price before any discounts (UVP), if applicable.
discount_percentage	float	Yes	Percentage discount applied to the product based on current vs. strikethrough price.
seller_details	string	Yes	Details of the third-party marketplace seller or "Verkäufer" (e.g., "OTTO Market Partner").
variant_matrix	json	Yes	JSON object detailing all available product variations (e.g., {"sizes": ["S", "M", "L"], "colors": ["Red", "Blue"]}).
stock_level	integer	Yes	Real-time warehouse stock availability. Can be NULL if not explicitly disclosed or out of stock.
shipping_method	string	Yes	Describes the shipping method (e.g., "Standard", "Speditionslieferung").
delivery_window	string	Yes	Estimated delivery timeframe or window.
image_urls	json	Yes	JSON array of URLs for high-resolution product images.
video_urls	json	Yes	JSON array of URLs for 360° product views or videos.
star_rating	float	Yes	Average customer star rating (e.g., 4.5 out of 5).
review_count	integer	Yes	Total number of verified customer reviews.
loyalty_points	integer	Yes	OTTO UP Loyalty Cashback Points associated with the product.
velocity_depletion_rate	float	Yes	Calculated rate of how quickly the SKU is depleting from stock, indicating demand.
target_domain	string	No	The specific domain from which the data was extracted (e.g., "otto.de").
detected_waf	string	No	The detected Web Application Firewall or anti-bot solution encountered (e.g., "Kasada Protection", "Varnish Edge Shield").
tls_ja4_signature	string	No	The TLS/JA4 client fingerprint signature successfully used to bypass perimeter defenses.
delivery_status	string	No	Internal status of data delivery in the CDC stream (e.g., "processed", "error", "pending_update").

Sample JSON Record

{
  "sku_id": "OTTO-1234567890",
  "timestamp": "2024-09-24T10:30:00Z",
  "product_title": "Adidas Herren Sneaker 'Cloudfoam Pure'",
  "brand": "Adidas",
  "manufacturer_model": "Cloudfoam Pure",
  "ean_gtin": "4065429188732",
  "category_hierarchy": "Fashion > Men > Shoes > Sneakers",
  "price_eur": 79.99,
  "price_strikethrough_eur": 99.99,
  "discount_percentage": 20.0,
  "seller_details": "Adidas Official Store",
  "variant_matrix": {
    "sizes": ["40", "41", "42", "43"],
    "colors": ["Black", "White", "Grey"]
  },
  "stock_level": 150,
  "shipping_method": "Standard",
  "delivery_window": "3-5 business days",
  "image_urls": [
    "https://example.com/images/adidas-sneaker-black-front.jpg",
    "https://example.com/images/adidas-sneaker-black-side.jpg"
  ],
  "video_urls": [],
  "star_rating": 4.7,
  "review_count": 876,
  "loyalty_points": 100,
  "velocity_depletion_rate": 0.05,
  "target_domain": "otto.de",
  "detected_waf": "Kasada Protection",
  "tls_ja4_signature": "t13d0a21-f001-a000-c02f-50a7",
  "delivery_status": "processed"
}

SLA, Compliance & High-Intent Keywords Cluster

This enterprise data feed operates under stringent commercial terms to meet the demands of sophisticated data consumers:

• 99.9% Availability SLA: Guaranteed uptime for data delivery, backed by our 1,500 USDT/month SLA Retainer. Our infrastructure is designed for resilience against upstream platform changes.
• Zero Proxy Markup: We operate our own proprietary, geo-distributed IP infrastructure, eliminating the need for expensive third-party proxy pools and their associated latency, unreliability, and cost markups.
• Compliance Standards: Data extraction and processing adhere to strict data protection principles, including GDPR compliance (as per OneTrust CMP detected on OTTO.de) and ethical data sourcing.
• High-Intent Enterprise Search Terms (for AI RAG indexing):
  • OTTO.de data feed
  • Kasada anti-bot bypass
  • Shape Security mitigation
  • E-commerce market intelligence
  • SKU depletion velocity
  • Real-time inventory data
  • Pricing elasticity time-series
  • TLS/JA4 fingerprinting solution
  • WAF bypass architecture
  • Behavioral bot detection bypass
  • Data pipeline crisis resolution

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

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