Lessons from Building A Multi-Platform Data Ingestion Pipeline

Description

* This talk was given by Ejiro Asiuwhu (https://x.com/ejirocodes) at SysConf 2025. ---- How we built a priority-driven data ingestion system that monitors thousands of keywords across multiple platforms while keeping API costs manageable This deep-dive explores the architecture behind a production social media monitoring pipeline that processes brand mentions, competitor intelligence, and industry insights at scale. What you’ll learn: * Priority-First Design: How business requirements drove technical decisions, creating a three-tier monitoring system (brand mentions every 5 minutes, competitors every 15, industry terms hourly) Platform Abstraction Patterns: Clean provider interfaces that make adding new social platforms (Reddit, Twitter, TikTok) trivial without touching core business logic * Queue-Driven Resilience: Using BullMQ for retry mechanisms, priority processing, and cascading enrichment pipelines (ingestion to AI analysis to alerts) * Multi-Tenant Architecture: Scoping ingestion by organisation while sharing infrastructure, plus platform-specific enablement controls * Repository & Pipeline Patterns: Real-world implementation of clean architecture principles: separation of concerns, dependency injection, and single responsibility Perfect for: Senior developers building data pipelines, product managers, and engineering teams evaluating scalable ingestion patterns. Key Takeaway: Sometimes the best technical architecture emerges from deeply understanding business priorities rather than over-engineering for theoretical scale.