Build a Social Media Data Pipeline That Actually Scales

Your scraper runs fine for 100 profiles. At 10,000 it crashes. At 100,000 it's a dumpster fire. You've got timeouts. Duplicate records. Missing data. A Postgres database that takes 30 seconds to query. And a cron job that silently failed three days ago — nobody noticed until a client complained. I've built data pipelines that process millions of social media records daily. The architecture isn't complex. But it's very different from "fetch in a loop and save to DB." Here's the exact pipeline I use. The Stack Node.js – orchestration SociaVault API – social media data source BullMQ + Redis – job queue PostgreSQL – storage Cron – scheduling The Problem With "Fetch and Save" Here's what most people start with: // ❌ This doesn't scale for (const username of usernames) { const profile = await fetchProfile(username); await db.insert('profiles', profile); } Why this breaks: One failure kills everything — if request #5,001 fails, you lose your place No parallelism — sequential = slow No retry l