Since launching Social Intel, our mission has been straightforward: build the most comprehensive, structured archive of public social media conversations available anywhere. Today, our pipeline spans 100+ platforms and processes millions of posts daily. Here's how we built it.
The Architecture
At the core of our system is a distributed collector architecture. Each platform gets a dedicated scraper or API integration, designed to handle its specific rate limits, authentication requirements, and data formats. We run these collectors across a pool of workers that dynamically scale based on queue depth.
For API-based platforms like GitHub, Reddit, and YouTube, we implement exponential backoff and intelligent retry logic. For scraped platforms, we maintain rotating proxy pools and browser automation where needed.
Data Processing Pipeline
Raw data flows through several stages:
Challenges at Scale
Archiving at this scale presents unique challenges. Platform API changes can break collectors without warning. Rate limits vary wildly — some platforms allow thousands of requests per minute, others permit only a handful. We've built monitoring that alerts us within minutes of a collector failure.
What's Next
We're actively expanding to more platforms and improving our enrichment pipeline. Our heuristic engine recently replaced our LLM-based enrichment, delivering identical quality at a fraction of the cost and latency.
— Heshan Sanjuka, Founder