keywordbarrage/bauhaus-scraper

Python Code:

import sqlite3
from datasets import load_dataset
import pandas as pd

# Method 1: Load via Hugging Face datasets library
# Note: This loads a verified sample. Full real-time stream via enterprise SLA.
dataset = load_dataset("daas-intelligence/enterprise-bauhaus-sku-depletion-and-waf-telemetry")
print("Hub Sample Record (train split):")
print(dataset["train"][0])

# Method 2: Load zero-latency local pre-indexed SQLite deliverable
# This assumes you have received the full enterprise deliverable file.
try:
    conn = sqlite3.connect("bauhaus_enterprise_feed.sqlite")
    df = pd.read_sql_query("SELECT sku_id, product_title, live_price_try, inventory_status, timestamp FROM sku_depletion_feed ORDER BY timestamp DESC LIMIT 5", conn)
    print("\nSample from local SQLite deliverable:")
    print(df)
    conn.close()
except Exception as e:
    print(f"\nCould not load local SQLite file. Ensure 'bauhaus_enterprise_feed.sqlite' is available. Error: {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 - sku-depletion-rate - cloudflare-bypass - time-series-forecasting - pre-indexed-sqlite - 1500-usdt-sla-retainer size_categories: - 10K<n<100K configs: - config_name: default data_files: - split: train path: "data/train-.parquet" - split: validation path: "data/val-.parquet"

Enterprise Bauhaus SKU Depletion & WAF Telemetry Feed

This repository contains a 1,000-row verified sample of the Bauhaus (bauhaus.com.tr) SKU depletion and anti-bot telemetry feed. Full high-frequency real-time stream is delivered via our enterprise 1,500 USDT/mo SLA pipeline.

Dataset Summary & B2B Motivation (1,500 USDT/mo Real-Time Pipeline)

For CTOs, Hedge Funds, and E-Commerce Leaders, maintaining consistent, high-fidelity market intelligence from dynamic e-commerce platforms like Bauhaus is a critical challenge. Brittle in-house scraping pipelines, constantly broken proxy pools, and escalating WAF (Web Application Firewall) defenses (such as Cloudflare Edge & Turnstile Shield on bauhaus.com.tr) routinely destroy data pipelines, leading to data staleness, missed market signals, and significant engineering overhead.

This dataset provides a production-grade, bypass-proof data feed for bauhaus.com.tr, offering real-time insights into SKU availability, pricing elasticity, and stock depletion rates. Our fully managed pipeline eliminates the burden of proxy rotation, WAF bypass, and infrastructure maintenance, delivering clean, structured data directly to your systems under a guaranteed 1,500 USDT/month SLA Retainer with 99.9% uptime and zero operational overhead. This allows your data and analytics teams to focus on generating alpha and business intelligence, not fighting anti-bot challenges.

Zero-Code Enterprise Deliverables Matrix

Our enterprise data feeds are engineered for direct ingestion into your existing data architecture, providing four distinct, zero-maintenance formats:

1. Pre-indexed SQLite (.sqlite):
   • Benefit: Instant local querying, ideal for rapid prototyping, ad-hoc analysis, and small to medium-sized applications. Pre-built indices on sku_id, live_price_try, inventory_status, and timestamp ensure lightning-fast query performance without requiring a database setup.
2. Snappy Parquet (.parquet):
   • Benefit: Optimized for cloud data warehouses and big data analytics platforms (e.g., Snowflake, Google BigQuery, AWS Athena). Columnar storage with Snappy compression ensures efficient storage and ultra-fast analytical query performance, minimizing compute costs.
3. Zero-Dependency Offline DataGrid (.html):
   • Benefit: A self-contained, interactive HTML file that allows business stakeholders, sales teams, or non-technical users to browse, filter, and sort the entire dataset directly in their web browser, requiring no backend, database, or internet connection.
4. CDC JSONL Stream (.jsonl):
   • Benefit: Change Data Capture (CDC) format for real-time updates. Each line is a JSON object representing a data record, ideal for streaming into enterprise RAG pipelines, vector databases, or event-driven architectures to keep your machine learning models and knowledge bases fresh.

Dataset Schema & Data Dictionary

The enterprise-bauhaus-sku-depletion-and-waf-telemetry dataset is meticulously structured to provide comprehensive market intelligence from bauhaus.com.tr.

Column Name	Type	Nullable	Description
product_title	STRING	NO	Full product title as displayed on bauhaus.com.tr.
brand	STRING	NO	Product brand (e.g., Bosch, Makita, Gardena).
manufacturer_part_number	STRING	YES	Manufacturer Part Number (MPN) or Article Number (Art. No.).
ean_gtin_barcode	STRING	YES	EAN-13 / GTIN barcode for the product.
bauhaus_sku	STRING	NO	Unique internal Bauhaus SKU or model code.
category_l1	STRING	NO	Top-level DIY & Hardware taxonomy (e.g., Werkstatt & Maschinen, Garten & Freizeit).
category_l2	STRING	YES	Second-level DIY & Hardware taxonomy.
live_price_try	FLOAT	NO	Current live price in Turkish Lira (TRY), including VAT.
strikethrough_price_try	FLOAT	YES	Original strikethrough price in TRY, if a discount is active.
volume_discounts_json	STRING	YES	JSON array of volume/bulk discount tiers (e.g., [{'min_qty': 5, 'price_per_unit': 120.00}]).
inventory_status	STRING	NO	Availability status (e.g., 'In Stock', 'Limited Stock', 'Out of Stock').
store_stock_json	STRING	YES	JSON array of physical store stock levels by branch location.
technical_specs_json	STRING	YES	JSON object of technical specifications (e.g., {'wattage': '1200W', 'voltage': '230V'}).
energy_efficiency_rating	STRING	YES	Energy efficiency rating (e.g., 'A+++', 'A-G Label').
environmental_certifications_json	STRING	YES	JSON array of environmental certification badges.
warranty_period	STRING	YES	Product warranty period (e.g., 'Bauhaus 5-Year Quality Guarantee').
safety_data_sheet_url	STRING	YES	URL to the product's Safety Data Sheet (SDS), if available.
fulfillment_modes_json	STRING	YES	JSON array of available fulfillment modes (e.g., 'Parcel Courier Delivery', 'Reserve & Collect').
timestamp	TIMESTAMP	NO	UTC timestamp of when the data was extracted.
target_domain	STRING	NO	The target domain from which data was extracted (e.g., bauhaus.com.tr).
detected_waf	STRING	NO	Detected Web Application Firewall/Anti-Bot system (e.g., 'Cloudflare Edge & Turnstile Shield').
tls_ja4_signature	STRING	YES	TLS JA4 fingerprint of the client used for extraction, providing telemetry on bypass methods.
delivery_status	STRING	NO	Status of data delivery for the record (e.g., 'SUCCESS', 'WAF_CHALLENGE', 'BAN').

Sample Record (JSON)

{
  "product_title": "Bosch Akülü Darbeli Matkap GSB 18V-55 Professional",
  "brand": "Bosch",
  "manufacturer_part_number": "06019H5100",
  "bauhaus_sku": "27658930",
  "category_l1": "Werkstatt & Maschinen",
  "category_l2": "Akülü El Aletleri",
  "live_price_try": 3499.00,
  "strikethrough_price_try": 4299.00,
  "inventory_status": "In Stock",
  "technical_specs_json": {
    "voltaj": "18V",
    "akü_kapasitesi": "2.0 Ah",
    "tork": "55 Nm",
    "darbe_sayısı": "27000 bpm"
  },
  "timestamp": "2026-09-24T14:30:00Z",
  "target_domain": "bauhaus.com.tr",
  "detected_waf": "Cloudflare Edge & Turnstile Shield",
  "delivery_status": "SUCCESS"
}

Python Usage Code

To get started with the sample data on Hugging Face Hub or integrate with your local enterprise deliverables:

import sqlite3
from datasets import load_dataset
import pandas as pd

# Method 1: Load via Hugging Face datasets library
# Note: This loads a verified sample. Full real-time stream via enterprise SLA.
dataset = load_dataset("daas-intelligence/enterprise-bauhaus-sku-depletion-and-waf-telemetry")
print("Hub Sample Record (train split):")
print(dataset["train"][0])

# Method 2: Load zero-latency local pre-indexed SQLite deliverable
# This assumes you have received the full enterprise deliverable file.
try:
    conn = sqlite3.connect("bauhaus_enterprise_feed.sqlite")
    df = pd.read_sql_query("SELECT sku_id, product_title, live_price_try, inventory_status, timestamp FROM sku_depletion_feed ORDER BY timestamp DESC LIMIT 5", conn)
    print("\nSample from local SQLite deliverable:")
    print(df)
    conn.close()
except Exception as e:
    print(f"\nCould not load local SQLite file. Ensure 'bauhaus_enterprise_feed.sqlite' is available. Error: {e}")

SLA, Compliance & 26 High-Intent Keywords Cluster

Our enterprise data pipelines are backed by a stringent 99.9% availability SLA, ensuring continuous, reliable data delivery. We operate with zero proxy markup, leveraging a proprietary network of clean, high-reputation IPs and advanced WAF bypass techniques.

This dataset is designed to address high-intent enterprise search and RAG indexing requirements for:

• E-commerce intelligence Bauhaus
• SKU depletion rates bauhaus.com.tr
• Real-time inventory Bauhaus
• Competitive pricing analysis DIY retail
• Cloudflare bypass data feed
• Anti-bot solutions for web scraping
• WAF telemetry data
• TLS fingerprinting bypass
• Enterprise data extraction services
• Managed scraping pipeline
• Market trend analysis hardware
• Supply chain insights e-commerce
• Alternative data for quant funds
• High-frequency data feeds
• Business intelligence retail sector
• Data-as-a-Service (DaaS) enterprise
• Bauhaus product catalog API
• Price elasticity modeling
• Stock level forecasting
• DIY market analytics
• Turkish e-commerce data
• Global retail data solutions
• Zero-maintenance data pipelines
• WAF-resilient data collection
• 1500 USDT SLA data
• Production-grade data delivery

#akamai-bot-manager #datadome-bypass #cloudflare-turnstile #kasada-shape-security #perimeterx-human-security #incapsula-imperva #aws-waf-mitigation #ja4-fingerprint #tls-client-hello #http2-fingerprinting #canvas-entropy #browser-telemetry-audit #ja3-spoofing #tcp-ip-fingerprint #managed-data-feed #shopify-plus-intelligence #magento-variant-extraction #salesforce-commerce-cloud #sap-commerce-cloud #time-series-sku-tracking #inventory-depletion-engine #storefront-graphql #nextjs-ssr-extraction #nuxt-payload-parsing #internal-rest-api-extraction #sqlite-dataset-delivery

📦 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):

• 📊 Universal Multi-Vendor Showcase — Live search, faceted multi-variant filtering & delta analytics.
• 🛍️ Shopify Direct-to-Consumer Grid — High-velocity catalog indexing & SKU tracking.
• 🎨 Etsy Marketplace Grid — Handmade seller taxonomy, real reviews & fee calculations.