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 Dragonfly Improves Large Collection Replication
Dragonfly's new tagged chunks replication format doubles throughput and cuts P99 latency up to 87%. See how fine-grained locking removes stalls.

How Dragonfly Cuts Celery & Sidekiq Queue Memory by 3-4x with Dictionary Compression
Dragonfly has gained a new type of compression for lists. This new feature enables efficient compression of small records which are traditionally very hard to compress.

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.