Faster Discovery for Life Sciences

The medical innovations of tomorrow will be driven by the big data analysis of today. Accelerate your research – and your time to discovery – with Pure life sciences solutions.

FlashBlade for Genomics

The Challenge of Big Data

Modern genomics has seen the volume of sequencing rise dramatically as a result of lower costs, and massive data growth has followed – greatly increasing analytical challenges and placing ever-expanding demands on storage infrastructure. Against this wave of data, legacy storage architectures simply can't keep up.

Next-Gen Storage Solutions for Life Sciences

Advantage: FlashBlade

The FlashBlade array is an all-flash, scale-out storage architecture perfectly suited to the high concurrency genomic sequencing and analysis workflows that will drive tomorrow’s medical discoveries and research. Easily deployed, scaled, and managed, FlashBlade delivers competitive advantages over existing storage architectures.

FlashBlade All-Flash Storage for Life Sciences Solutions

Innovative Architecture

FlashBlade provides consistent, multi-dimensional performance in concurrency – including IOPS, throughput, and latency – and can handle large volumes of data access from 1000s of users.

Fast Genomic Sequencing

All-flash storage optimized for multiple pipelines in the workflow to accelerate read alignments during sequencing for interpretation and discovery.

Simplicity of Use

A compact 4U form factor scales out performance and capacity easily just by adding blades. Cloud-based management and Pure1 Support means there's almost nothing you need to do.

"When our researchers want to visualize a pattern or interaction, it creates a significant tax on our storage systems. Visualizations inherently require so much data to run, that they are nearly impossible to run smoothly. FlashBlade removes that roadblock for our researchers, which means our findings positively impact public health faster than ever before. If a doctor knows exactly which treatment to prescribe a patient based on that patient’s Addgenetic makeup, that patient is going to recover sooner. It’s that simple.”

Professor Anthony D. Joseph

UC Berkeley Center for Computational Biology

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