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.
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Featured In-memory Data Resources

How Instacart Achieved 50% Better Performance with 70% Fewer Nodes
How Instacart migrated its ad-serving feature store from managed Valkey to Dragonfly, cutting cluster size ~70% and average and P99 latency 50% in weeks.

Dragonfly vs. Valkey 9.0 on AWS Graviton: An Honest Head-to-Head
Valkey 9.0 closed real ground on I/O. We reran the benchmarks on m7g and c7gn.metal to find out where, and where Dragonfly still wins.

SSD Data Tiering Is Generally Available
Dragonfly SSD Data Tiering is now GA. Scale beyond RAM limits on a single node — no resharding, no code changes. The only source-available Redis-compatible store with SSD tiering.