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HPE Q9Q94AAE WekaIO Matrix
- High-performance parallel file system.
- Optimized for AI, HPC, and data-intensive workloads.
- Software-defined storage solution.
- Provides low-latency, high-throughput data access.
- Scalable architecture for growing data needs.
- Simplifies data management and deployment.
- Supports NVMe, SSD, and HDD storage tiers.
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Product Overview
HPE Q9Q94AAE WekaIO Matrix is a high-performance, scalable parallel file system designed for demanding AI, HPC, and data-intensive workloads. It provides a software-defined storage solution that accelerates data access and simplifies data management.
Technical Information
| Product Type | Storage Software |
| File System | Parallel File System |
| Performance | High-Performance |
Additional Specifications
| Target Workloads | AI, HPC, Big Data |
| Architecture | Software-Defined |
Product Description
WekaIO Matrix, as offered by HPE under SKU Q9Q94AAE, is a cutting-edge, software-defined storage solution that delivers a high-performance parallel file system. It is engineered to address the most demanding data-intensive workloads, including artificial intelligence (AI), high-performance computing (HPC), and large-scale analytics. The core innovation lies in its ability to provide extremely low-latency and high-throughput data access, which is critical for accelerating complex computational tasks and research. The architecture of WekaIO Matrix is designed for massive scalability, allowing organizations to seamlessly expand their storage capacity and performance as their data needs grow. It intelligently tiers data across different storage media, including NVMe SSDs, standard SSDs, and HDDs, optimizing cost and performance. This dynamic tiering ensures that frequently accessed data is always available at the highest speeds, while less critical data resides on more cost-effective storage. Beyond its performance capabilities, WekaIO Matrix simplifies data management and deployment. It offers a unified namespace across distributed storage nodes, making it easier for users and applications to access data without needing to understand the underlying storage infrastructure. This ease of use, combined with its robust performance and scalability, makes it an ideal solution for organizations pushing the boundaries of data science and scientific discovery.

