• Unified storage architecture with the flexibility to provide high read and write performance for a wide range of research workloads
• Use industry-standard hardware, protocols, and interfaces to ensure full compatibility with clinical systems such as MRI and large-scale analysis projects data using tools like Hadoop
• Scalable from 10 Gb/s to 40 Gb/s and plans to support 100 Gb/s connections with more than 1 million IOPS to support multi-threaded projects
• Supports flexible workloads, including uploading to AWS, S3 and other cloud big data analysis machines

Current tasks requiring large-scale data warehouses, such as DNA sequencing and MRI image analysis, can pave the way for the next generation of applications requiring even greater storage volumes, along with increased efficiency for new breakthroughs.

And for research, and for the clinical community, data storage should be able to adapt to a wide range of access schemes with the flexibility of easily assigning to different teams and departments. With limited IT resources, storage should provide complete reliability and ease of management, which will allow projects to scale up and down with minimal human intervention.