Client Details:
A rapidly growing enterprise needing large-scale, location-specific data aggregation to drive market intelligence, product tracking, and competitive insights across digital platforms.
Challenge:
The client required a centralized solution to capture dynamic, high-volume data across hundreds of websites with granular detail at the store level.
Manual scraping was not sustainable given the frequency, scale, and accuracy expectations—especially with the need to monitor over 3,000+ retail locations for a single retailer.
The challenge demanded a fully automated, cloud-native system capable of processing over 1 billion records weekly, with high reliability and data quality standards.
Solution:
We built an advanced web scraping architecture leveraging the latest Python modules, and a cloud-based orchestration framework. The system is designed to handle both real-time triggers and scheduled scrapes, tightly integrated with internal applications for end-to-end automation.
Key Highlights:
- 200+ Websites Scraped Weekly: Custom scraping logic for diverse site structures ensures robust and frequent data capture.
- 1 Billion+ Records Processed Weekly: The pipeline seamlessly scales with business demand while maintaining accuracy and consistency.
- Location-Based Job Execution: Supports store-level data collection for 3,000+ locations under a single retail brand, enabling hyper-local analysis.
- Flexible Scraping Modes: Includes full-page, product ID (UPC/PID), and category-based scraping options.
- Jobs Centrally Managed & Distributed: Jobs are centrally managed through a secure queueing mechanism and distributed intelligently to a pool of dedicated scraping servers based on resource availability. This ensures optimal performance and minimal downtime.
- Advanced Anti-Blocking Mechanism: Bypasses industry-leading bot detection platforms using in-house strategies like CAPTCHA solving, proxy rotation, dynamic human-like fingerprints, and cutting-edge AI-driven evasion techniques.
- Full Automation: From job scheduling and distribution to scraping, validation, and delivery—every stage is fully automated for operational efficiency and scale.
- End-to-End QA Automation: Data is cleaned and validated using Databricks, Python, and PySpark, ensuring it’s ready for downstream system.
The Impact:
✅ Scalable Intelligence Infrastructure:
Enabled fast, reliable data ingestion at enterprise scale with minimal manual intervention.
✅ Granular Competitive Visibility:
Delivered deep location-wise insights for smarter decision-making and precision targeting.
✅ Significant Time Savings:
