Diskless database architectures separate compute and storage for real-time data
Updated
Updated · InfoWorld · May 5
Diskless database architectures separate compute and storage for real-time data
9 articles · Updated · InfoWorld · May 5
The approach keeps data in memory for immediate indexing and querying while using object storage for durable, elastic persistence at terabyte-to-petabyte scale.
It aims to cut latency that hampers time-series workloads in AI, IoT, observability and industrial systems, while allowing compute and storage to scale independently without planned downtime.
Advocates say removing local disks simplifies high availability, fault isolation and upgrades, supporting continuous telemetry analysis, faster anomaly response and live model training across cloud, on-premise and hybrid environments.
Diskless databases slash costs, but will hidden network fees and latency become the next bottleneck for real-time AI?
As data systems move storage to the cloud, does this architecture create an over-reliance on a few tech giants?
With Kafka's official diskless feature years away, what are the real-world risks of adopting third-party solutions now?
Ultra-Low Latency Diskless Kafka and Stateless Compute: The 2026 Real-Time Analytics Architecture Guide
Overview
In 2026, AutoMQ revolutionized diskless Kafka by integrating AWS FSx for NetApp ONTAP as a write-ahead log with S3 storage, achieving ultra-low P99 write latencies of 13 ms and cutting costs nearly tenfold. This breakthrough enables real-time applications like financial trading with sub-20ms latency, though it introduces some cloud vendor lock-in risks. Meanwhile, ClickHouse Cloud transformed its architecture to a stateless compute model with a Shared Catalog, allowing seamless scaling to hundreds of replicas, instant node warm-up, and fast global schema changes. It uses distributed caching and asynchronous prefetching to maintain low query latency despite relying on object storage, though write-heavy workloads face higher latency. StarRocks’ multi-tiered caching notably improved Pinterest’s query latency and cost, while Aiven’s leaderless Kafka design offers moderate cost savings but higher latency compared to AutoMQ. Together, these innovations highlight the evolving balance between cost, latency, and scalability in cloud-native data streaming and analytics.