keywordbarrage/1688-scraper

Python Code:

import sqlite3
from datasets import load_dataset
import pandas as pd

# Method 1: Load via Hugging Face datasets library for ML training
dataset = load_dataset("your-username/enterprise-sku-depletion-and-waf-telemetry")
print("Hub Sample Record:", dataset["train"][0])

# Method 2: Direct query on the pre-indexed SQLite deliverable
# Optimized for high-frequency market intelligence ingestion
conn = sqlite3.connect("data/sku_depletion_feed.sqlite")
query = """
SELECT sku_id, price_usd, stock_level, velocity_depletion_rate 
FROM inventory_stream 
WHERE timestamp > datetime('now', '-24 hours')
ORDER BY velocity_depletion_rate DESC 
LIMIT 100
"""
df = pd.read_sql_query(query, conn)
print(df.head())

---

Dataset Card: Enterprise High-Frequency SKU Depletion & WAF Telemetry

1. Executive Summary & Research Motivation

This repository contains an open 1,000-row verified benchmark sample of the 1688 SKU depletion feed. In modern quantitative finance and retail intelligence, data pipelines are frequently crippled by brittle scrapers and aggressive WAF challenge loops (e.g., Alibaba Cloud AliSecGuard). This dataset provides a blueprint for protocol-level extraction that bypasses TLS/JA4 fingerprinting and browser entropy checks, maintaining 99.9% pipeline availability.

2. Enterprise Deliverables Matrix

We provide this intelligence in four production-ready formats:

• Pre-indexed SQLite (.sqlite): Instant local querying with B-Tree indices on sku_id and timestamp.
• Snappy Parquet: Columnar storage optimized for high-throughput ingestion into Snowflake, BigQuery, or AWS Athena.
• Zero-Dependency DataGrid (.html): A self-contained, interactive viewer for quantitative stakeholders to audit market signals without requiring SQL expertise.
• CDC JSONL Stream: A change-data-capture format optimized for real-time RAG pipelines and vector database synchronization.

3. Dataset Schema & Data Dictionary

Column	Type	Nullable	Description
sku_id	STRING	No	Unique B2B product identifier (OfferID).
timestamp	DATETIME	No	ISO-8601 UTC execution time.
price_usd	FLOAT	No	Normalized unit price in USD.
stock_level	INTEGER	No	Real-time factory inventory count.
velocity_depletion_rate	FLOAT	No	Inventory change rate (units/hour).
detected_waf	STRING	No	WAF signature (e.g., 'Alibaba-Cloud-WAF').
tls_ja4_signature	STRING	No	TLS/JA4 handshake fingerprint.
delivery_status	STRING	No	Pipeline execution verification status.

Sample JSON Record:

{
  "sku_id": "1688-9928471-X",
  "timestamp": "2026-09-24T14:30:00Z",
  "price_usd": 12.45,
  "stock_level": 450,
  "velocity_depletion_rate": 4.2,
  "detected_waf": "Alibaba-Cloud-WAF",
  "tls_ja4_signature": "t13d1516181a_c4c4c4c4c4c4",
  "delivery_status": "verified"
}

4. Data Quality & Reproducibility

• Availability: 99.9% uptime via distributed edge nodes.
• Parity: Deterministic schema parity across all format exports.
• Performance: Sub-120ms protocol execution benchmark for catalog traversal.
• Compliance: This dataset is provided as a verified open sample for quantitative research and data engineering evaluation.

5. Terms of Engagement

This repository serves as a technical benchmark for enterprise data procurement. Our managed ingestion services provide dedicated proxy infrastructure and full-catalog delta streams, ensuring pipeline resilience against evolving bot mitigation. For production-grade integration, we offer a dedicated service level agreement (SLA) covering 24/7 monitoring, schema evolution support, and zero-maintenance delivery.