Case Study: Leading Quantitative Research Firm achieves 1M+ TPS, sub-millisecond reads/writes and 12-minute 80TB data refreshes with Aerospike

How a leading quantitative research company leverages massive data sets and large-scale analysis with cutting-edge machine learning for trading advantages

A leading quantitative research firm that builds algorithmic trading strategies struggled to scale its time-series platform: researchers needed to monitor 200,000 products with 200 attributes each, keep up to 20 years of data online with five-minute refreshes, and retain multiple versions of each data point. Their SQL Server + caching architecture could not deliver the freshness, scale or predictable costs required, leading to long data-refresh times, compression inefficiencies and frustrated data science teams.

The firm replaced its stack with Aerospike’s Hybrid Memory Architecture™, enabling compute-on-data-in-place, strong compression (up to ~90% for some datasets) and enterprise security. Results included 1+ million transactions per second with 95% of reads/writes under 1 ms, previously impossible full data refreshes completed in 12 minutes with data 10–20% more accurate, the ability to keep 20 years of data online with five-minute updates, and linear scalability beyond petabyte scale with predictable TCO.