Quantitative trading data is different.

Market data is fast, voluminous, and complicated, and the tech landscape continues to evolve, merge, and morph quickly. Regulation reigns.

Thanks to pioneering work in streaming analytics, time-series data management, and, now, generative AI, data management on Wall Street has come a long way from the first digitized trading rooms of the 80s. The challenge, of course, is time. The time between a market micro-movement and the action that must be taken. The time to build new trading strategies that out-alpha your competitors. The time it takes to build systems that implement new ideas yet comply with ever-increasing compliance demands.

The other challenge is change. There are new types of data to unlock, new opportunities thanks to the rise of generative AI and vector databases, and new demands to run applications as managed services in the cloud, but safely, securely, and with the same low-latency demands as on-premise, bare-metal performance. 

From handling latency to massive volumes and throughput to time-series comparison on the fly, trading data is just different. But, hey, this is Wall Street – let’s look at how trading data is different by the numbers.