Real-Time Data Pipeline for Financial Analytics
Fintech Startup
The Problem
A fast-growing fintech startup needed to process real-time market data from multiple exchanges, run analytics computations, and deliver results to their platform with sub-millisecond latency. Their existing Python-based pipeline couldn't keep up with the data volume, dropping events under peak load and introducing unacceptable latency for their algorithmic trading clients.
Our Approach
We redesigned the pipeline from the ground up in Rust, leveraging its zero-cost abstractions and memory safety guarantees. The architecture used Kafka for reliable event streaming, custom lock-free data structures for the hot path, and a tiered storage strategy with PostgreSQL for historical queries and in-memory caches for real-time access.
The Solution
The resulting system processes over 2 million events per second with p99 latency under 500 microseconds. Built-in backpressure handling ensures graceful degradation under extreme load, and the system has operated with zero data loss since deployment.
Results
2M+
Events per second
<500μs
p99 latency
0
Events dropped
85%
Cost reduction vs. prior system