Real-time Analytics Platform
Designed and built a streaming analytics platform processing 1M+ events per second
1M+ events processed per second
Sub-100ms query latency
60% infrastructure cost reduction
The Problem
A data company needed to provide real-time insights to customers but their batch-processing architecture couldn't deliver sub-second query responses.
Our Approach
We architected a streaming-first platform using Kafka, ClickHouse, and a custom query layer. The system processes events in real-time while maintaining historical queryability.
Tech Stack
The Challenge
The existing system relied on hourly batch jobs. Customers were demanding real-time visibility into their metrics, but the architecture couldn't support it.
Our Approach
Rather than incrementally improving the existing system, we designed a parallel streaming architecture:
- Event Ingestion - Kafka cluster with exactly-once semantics
- Stream Processing - Go-based processors for real-time aggregation
- Analytical Store - ClickHouse for sub-second OLAP queries
- Query Layer - Custom API with intelligent caching
Results
The platform now powers dashboards for thousands of customers with true real-time updates. The streaming architecture also reduced infrastructure costs by consolidating multiple batch jobs.