Data Management
Handling large datasets by leveraging cloud storage solutions for fast access and seamless data transfer between HPC and cloud environments.
The Distributed Data Challenge
In 2026, the bottleneck of High-Performance Computing is rarely the CPU—it is the **latency and gravity of data**. Integrating cloud services requires an intelligent **Data Fabric** that synchronizes on-premise scratch storage with cloud object stores. Malgukke provides the architectures to move data at line-rate, ensuring that compute nodes never wait for I/O.
Hybrid Data Pipelines
Utilizing high-speed asynchronous transfer protocols to bridge the gap between local BeeGFS/Lustre clusters and cloud-native S3 storage. Our solutions minimize "egress" costs while maximizing "ingress" speed for real-time processing.
- Latency-optimized data "bursting"
- Automated cache-proxy management
Unified Namespace
Presenting disparate storage tiers as a single, logical volume. Whether data resides on a local NVMe array or a deep-cloud archive, researchers access it through a unified interface, eliminating the complexity of manual data movement.
- Cross-platform metadata synchronization
- Policy-driven Information Lifecycle Management (ILM)
Cloud-HPC Integration Logic
| Integration Pillar | HPC-Cloud Action | Operational Outcome |
|---|---|---|
| Data Access | Deployment of Parallel Cluster-Mounts (BeeOND/FSx). | Sub-millisecond access to remote datasets |
| Synchronization | Real-time object-to-file mirroring via low-latency links. | Immediate availability of results globally |
| Efficiency | Transparent data tiering to low-cost archival clouds. | 60% reduction in long-term storage TCO |