The Challenge: High-Volume Extraction from Low-Tech Sources
Our client required a comprehensive dataset of demographic trends spread across thousands of slow-loading, legacy public portals. The sheer volume was massive—over 1 Terabyte of text data.
Standard tools failed because the source servers would timeout or block IP addresses after a few hundred requests. We needed a way to extract data at scale without triggering security blocks or overwhelming the fragile infrastructure.
The Engineering: A "Polite" Distributed Cluster
Brute force wasn't an option. We built a smart, distributed ingestion engine:
- Distributed Workers: We deployed 50+ lightweight scrapers across multiple availability zones.
- Smart Throttling: Unlike standard scrapers, our engine monitored the "health" of the source server. If the server slowed down, our bots automatically paused to let it recover.
- IP Rotation: We implemented a rotating proxy network to ensure our traffic looked like normal user behavior rather than a botnet.
The Outcome
The pipeline ran continuously for 3 weeks, successfully harvesting 1TB of clean, structured JSON data. This enabled the client to perform deep demographic analysis that was previously impossible due to data fragmentation.