Explosive data growth is pushing traditional storage architectures to their limits, creating urgent demands for scalable solutions and optimized total cost of ownership ( TCO ).
Massive, heterogeneous datasets create significant challenges for Time-to-Live (TTL) management in the absence of automated lifecycle controls.
Redundant data duplication reduces analysis efficiency, while independently building compute and storage platforms under a disaggregated architecture drives up costs.
Break through the limits of traditional storage. Our distributed object architecture handles hundreds of billions of files with ease, offering the exabyte-level scalability required for modern enterprise AI. By optimizing data ingestion and training workflows, we ensure your storage never slows down your innovation, providing a seamless, high-speed foundation for the entire AI pipeline.
Seamlessly support EB-level storage capacities and hundreds of billions of files.
Our architecture ensures that performance scales nearly linearly with capacity,
providing a robust solution for the most demanding unstructured data challenges.
Leverage flexible data redundancy configurations to ensure zero data loss and uninterrupted business continuity.
Our system is designed to withstand single drive, node, or rack failures without impacting upper-layer operations.
For geo-resilient protection, our multi-site, multi-live architecture provides comprehensive cross-regional disaster recovery.
Simplify the management of complex data environments with built-in lifecycle, cloud migration, and heterogeneous resource management.
By orchestrating the orderly flow of information across multiple sources, applications, and data centers, we reduce operational overhead and maintenance costs for even the most diverse unstructured data ecosystems.
High-Performance Scaling for Infinite Data Growth
1. Unified Namespace & Linear Elasticity Seamlessly manage hundreds of billions of objects within a single, unified namespace. Our architecture supports real-time horizontal expansion of storage servers, meeting the most aggressive growth demands for unstructured data. This eliminates the rigid scaling limitations and management silos common in traditional NAS environments.
2. Distributed Small-File Optimization By combining a fully distributed architecture with advanced small-file aggregation technology, we have pushed enterprise-level NAS capabilities from tens of millions to hundreds of billions of files. This enables organizations to efficiently orchestrate and manage the geometric growth of massive datasets without performance degradation.
Adaptive and Resilient Data Redundancy Framework
1.Tailored Redundancy & Dynamic Policy Management Customize your data protection based on criticality using advanced Replication or Erasure Coding (EC) strategies. Our framework supports real-time, dynamic adjustments to redundancy policies, ensuring that protection levels evolve alongside your data’s importance without interrupting operations.
2.DGeo-Distributed Resilience & Low-Latency Access Deploy across multiple data centers with total flexibility. AIDC provides high-availability solutions for remote disaster recovery and multi-branch edge access. By enabling cross-regional multi-live architectures, we facilitate seamless data flow and localized read/write performance—maximizing both system reliability and global access efficiency.
Intelligent Multi-Scenario Data Automation
1.Policy-Driven Lifecycle and Infrastructure Integration Automate the flow of hot, warm, and cold data across tiered resource pools based on custom lifecycle policies. Our platform seamlessly integrates with third-party NAS, providing a unified management layer that protects your legacy investments while enabling a frictionless transition to a modern, software-defined infrastructure.
2.Unified Hybrid Cloud and Edge-to-Center Orchestration Experience industry-leading hybrid cloud management with native support for the Alibaba Cloud (AliCloud) OSS object storage protocol. Our dual-tier architecture automates data aggregation and distribution between central and edge nodes, significantly reducing the complexity of managing distributed datasets across multiple regional branches.