E-commerce Price Intelligence Platform

E-commerce Price Intelligence Platform

Project Overview

Built a comprehensive price intelligence platform that helps e-commerce businesses stay competitive by monitoring competitor prices, stock levels, and product information across 500+ online retailers in real-time.

The Challenge

Our client, a major e-commerce retailer, needed to track competitor pricing across thousands of products daily. Manual monitoring was impossible, and they needed a solution that could:

  • Handle dynamic pricing that changes multiple times per day
  • Work across different e-commerce platforms with varying structures
  • Avoid detection and blocking
  • Process millions of data points efficiently

Technical Solution

Architecture

  • Distributed Scraping Network: Built using Scrapy with Redis for task distribution
  • Anti-Detection System: Implemented rotating proxies, user agents, and behavioral patterns
  • Data Pipeline: Real-time ETL using Apache Airflow and Kafka
  • Storage: PostgreSQL for structured data, S3 for raw HTML backup
  • API Layer: FastAPI serving processed data to client applications

Key Features

  1. Smart Scheduling: Adaptive crawling based on price volatility
  2. Data Quality Assurance: Automated validation and anomaly detection
  3. Real-time Alerts: Instant notifications for significant price changes
  4. Analytics Dashboard: React-based dashboard with real-time visualizations
  5. Historical Tracking: Complete price history with trend analysis

Technologies Used

  • Backend: Python, Scrapy, Celery, FastAPI
  • Data Processing: Pandas, Apache Airflow, Kafka
  • Frontend: React, Next.js, Chart.js, WebSocket
  • Database: PostgreSQL, Redis, MongoDB
  • Infrastructure: AWS EC2, S3, RDS, Docker, Kubernetes

Results

  • 📊 10M+ products monitored daily
  • ⚡ 99.9% uptime with fault-tolerant architecture
  • 📈 15% increase in client’s profit margins
  • 🚀 60% reduction in manual price monitoring costs
  • 🎯 Real-time insights enabling dynamic pricing strategies

Key Learnings

This project reinforced the importance of:

  • Building robust anti-detection mechanisms
  • Designing for scale from day one
  • Implementing comprehensive monitoring and alerting
  • Creating self-healing systems that handle failures gracefully

Client Testimonial

“Surendra’s price intelligence platform transformed our pricing strategy. The real-time insights and comprehensive coverage gave us a competitive edge we never had before. The system’s reliability and accuracy exceeded our expectations.”

— Head of E-commerce Strategy

Technical Highlights

# Example of the intelligent retry mechanism
class SmartRetryMiddleware:
    def process_response(self, request, response, spider):
        if response.status in [403, 429, 503]:
            # Implement exponential backoff
            retry_times = request.meta.get('retry_times', 0) + 1
            if retry_times <= self.max_retry_times:
                retryreq = request.copy()
                retryreq.meta['retry_times'] = retry_times
                retryreq.dont_filter = True
                # Switch proxy and user agent
                retryreq.meta['proxy'] = self.get_next_proxy()
                retryreq.headers['User-Agent'] = self.get_random_ua()
                return retryreq
        return response

This project showcases my ability to build enterprise-grade web scraping solutions that handle complex challenges at scale while delivering real business value.

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