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

Announcing Dragonfly SSD Data Tiering: Cost-Effective Scaling for Massive Workloads
Dragonfly Data Tiering extends RAM with SSDs for massive, cost-effective datasets. Deliver high performance for scaled workloads at a fraction of the cost.

Scaling Geospatial Queries: Dragonfly’s Inverted GEO Index
Dragonfly natively supports fast geospatial indexing and radius searches. Learn how its R-Tree implementation enables efficient querying of location data at scale.

1.5 Years After the Valkey Fork: The In-Memory Data Landscape at the End of 2025
Analyze the evolving in-memory data landscape post-Valkey fork, comparing solutions for context-rich AI/ML workloads and performance.