streamhouse

Hey everyone! 👋 I'm excited to share StreamHouse - a Go package I built to solve the pain of setting up reliable data streaming pipelines. The Problem I Was Solving: I kept running into the same challenges across projects - needing to stream events reliably, validate data schemas, handle backpressure, and store everything for analytics. The usual approach meant stitching together multiple tools and writing tons of boilerplate. What Makes StreamHouse Different: 1. Schema-first approach - Define your data structure once, get validation and auto-generated ClickHouse tables 2. Dual backend design - Redis Streams for reliable queuing + ClickHouse for blazing-fast analytics 3. Production ready - Built-in consumer groups, error handling, health checks, and graceful shutdown 4. Developer friendly - Simple builder API, comprehensive docs, and sensible defaults Real-world use cases I've tested: 1. User activity tracking (signups, page views, purchases) 2. IoT sensor data streaming 3. Application performance monitoring 4. Audit logging for compliance Questions I'd love feedback on: 1. What data streaming challenges are you facing in your projects? 2. Any features you'd find useful that aren't covered? 3. How do you currently handle schema evolution in your pipelines? Check out the quick start example - you can have it running in under 5 minutes. Looking forward to your thoughts and contributions! 🚀