Patterns for a High-Performance Data Architecture
For fast-growing startups in e-commerce, gaming, media, or other consumer sectors, facing challenges in scaling data infrastructure is almost inevitable. As products gain traction, increased data volumes, pipelines, and sources often lead to longer response times, higher error rates, escalated resource costs, and more frequent service downtimes.
At this critical juncture, the scalability of infrastructure and how it accesses data becomes pivotal in delivering a seamless user experience. A lack of a strategic approach can compromise not only the performance and reliability of services but also the reputation and trust built with the audience.
This guide offers best practice recommendations for a high-performance data architecture, with a focus on reducing data latency and enhancing scalability.
Trusted by the best
Featured In-memory Data Resources

How Dailymotion unified its key-value stores on Dragonfly and saved $450K a year
Dailymotion moved three key-value engines onto Dragonfly in seven regions, cut nodes up to 75%, and halved recommendation cache latency on Dragonfly 2.0.
.png%3F2026-09-16T22%3A11%3A31.167Z&w=3840&q=100)
Announcing Dragonfly 2.0
Three years after 1.0, Dragonfly 2.0 delivers 54% more throughput, 35% lower average latency, and 30-40% less memory. Here is what changed.

Infrastructure for the Agent Era
AI agents will outnumber humans by orders of magnitude. Why legacy in-memory data stores can't scale for the agentic era.