Real-Time Market Data Processing: Designing Systems for Low Latency and High Throughput

In financial markets, real-time data processing is critical for trading, risk management, and decision-making. Market data systems must ingest and process millions of updates per second while ensuring ultra-low latency. During my time at Bloomberg and Two Sigma, I worked on optimizing such systems for speed and reliability.

By the means of this article, I’d like to explore key challenges in real-time market data processing, design strategies, and optimizations—with code snippets where applicable. I’ll try my best to keep things succinct without going into too much details!

High-Performance Data Ingestion

Market data systems need to handle a continuous stream of updates from multiple exchanges with minimal latency. Traditional approaches using TCP-based brokers like Kafka introduce too much overhead. Instead, many trading firms rely on UDP multicast for market data distribution.

UDP Multicast for Low Latency

A lightweight, high-performance approach uses UDP multicast to distribute data to multiple ...