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
Related Resources
Featured Posts
Case Study: Abusix Eliminates Bottlenecks and Scales Threat Intelligence with Dragonfly
Abusix scaled real-time threat intelligence by migrating to Dragonfly Cloud, eliminating bottlenecks with high-performance sorted sets.
Redis 8.0 vs. Valkey 8.1: A Technical Comparison
A deep technical comparison of Redis 8.0 vs. Valkey 8.1: threading models, performance benchmarks, feature sets, and licensing.
Feature Stores: Architecture and Online/Offline Storage
Explore feature store fundamentals: architecture and offline/online storage options—the essential concepts before hands-on deployment.