A range of key industries, including life sciences, engineering design, energy and university research, rely on workflows that involve storing, managing, and moving large numbers of files. Quantum helps companies meet this problem head-on by reducing both operating and capital costs associated with managing large amounts of file-based data across complex storage environments.
New storage strategies and technologies to reduce costs and grow your business.
Data archive for a constantly changing environment.
Improve Operational and Storage Efficiency of Project-Based Workflows with DataFrameworks and Quantum
As organizations continue to generate new types of valuable data, there is an increasing need to retain data and maintain visibility and access to it. However, keeping all that data on high-performance disk is proving cost-prohibitive and difficult to manage. Quantum offers an integrated solution set that has proven effective in managing some of the world's most demanding big data environments.
When projects are current, data access is direct from the fast tier of storage. Then, as time passes and data is accessed less frequently, Quantum automatically stores it to a less-expensive disk or tape tier on a policy-driven basis.
As a massively scalable file system, Quantum allows you to seamlessly evolve and adapt your data infrastructure to remain competitive. Users can rapidly share data across Unix, Linux, Windows and Mac operating systems, and enjoy uninterrupted data access, even as you flex and expand your server and storage environment.
As data sets grow in scale, the importance of having a well-designed storage and data management infrastructure becomes more critical. Quantum supports easily adding capacity as the total volume of data under management grows, without disrupting operations—and adding the right kind of capacity.
Performance requirements vary depending on where the data is in the workflow. As data is created or captured, it must be quickly stored or ingested. Quantum optimizes performance for a particular analysis workload depending on both the data characteristics and how the files are accessed.