sayFUSE S3 Object Storage
Scalable Object Storage for Backup, Archiving, Data Lakes and Data-Intensive Applications
A storage system that grows with your data — without architectural changes, additional storage silos or disruptive migrations.
sayFUSE S3 Object Storage is a horizontally scalable S3 platform for large and continuously growing datasets. Capacity and overall performance can be expanded incrementally — first by adding drives to existing nodes, then by integrating additional nodes into the cluster.
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Grow instead of migrate. Expand existing infrastructure according to demand while protecting long-term investments.
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Scale capacity and performance together. Each additional node contributes storage drives, CPU, I/O and network resources, increasing overall cluster performance.
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Unified S3 architecture. Manage active data, backups and archives across appropriate storage tiers.
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Automated integration. Authorized nodes adopt the cluster configuration and participate in data distribution, redundancy and processing.
Designed For
Backup & Long-Term Archiving
Scalable S3 storage for growing backup datasets and long-term retention.
Enterprises & Government Agencies
Centralized object storage for demanding operational requirements.
Data Lakes & Analytics
Parallel data storage for data-intensive analytics.
Critical Infrastructure Environments
Resilient architecture with configurable redundancy.
ARCHITECTURE & SCALABILITY
Growth Within the Same Platform
The scale-out approach combines flexible capacity expansion with a consistent operating model.
Two Ways to Scale
1.In-Node Scaling
Available drive bays can be populated with additional storage devices, making it easy to increase capacity when required.
2.Cluster Scaling
Additional nodes expand storage capacity while providing more CPU, I/O and network performance.
Simply Connect. Automatically Integrate.
After entering the authorized system key, a new node connects to the existing cluster, adopts the required configuration and joins data storage, distribution, redundancy and processing.
A complete reinstallation of the platform is not required.
Why Performance Scales with Capacity
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Distributed data paths: Objects are processed in parallel across multiple nodes, drives and network paths.
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More resources with every expansion: Each additional node contributes CPU, I/O and network capacity.
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No central scaling bottleneck: The overall cluster can grow according to object sizes, data volumes, network capacity, redundancy schemes and workload requirements.