Solutions

Digital enterprises need a data management solution built with scale-out architecture and designed to handle the requirements of massive unstructured data.
Single-threaded protocols in legacy software don’t cut it for petabytes of data and billions of files. Highly parallel, multi-threaded data movement is a must for modern secondary storage infrastructure.
IT budgets aren’t growing at the same rate as data. Modern data management must be delivered as-a-Service to reduce management overhead.

For machine learning, the more data the better—so being able to ingest enormous amounts of data is a necessity.
After ingestion, this data requires a strong, modern, and API-driven archive infrastructure that enables other applications to use the data.
The end users are data scientists, who need to focus on analyzing the data rather than managing it. IT support needs to be lean even as data requirements grow, necessitating a solution that’s delivered as-a-Service to reduce management overhead.

Additional resources:

Machine Learning/Artificial Intelligence Solution Brief

Enterprises utilizing EDA software generate large amounts of unstructured file data, which stresses legacy solutions. These companies need a secondary storage solution built to handle petabyte-scale data and file-dense workloads to protect their valuable digital assets.
Archiving data is an essential part of EDA data management strategy, necessitating a modern, project-based archive tier.
Since EDA data often lives in secondary storage, where it’s cheaper to store than recreate, companies must be able to move data from where it lives to where it’s needed. Easy-to-use and efficient data movement is needed to move large amounts of data quickly, without impacting other work.

For life science and research organizations, storing data on the cloud provides cost savings, on-demand high-performance computing capabilities, and cross-team collaboration. Solutions with policy-based workflows make it easy to automatically replicate and tier data to cloud.
Data must be economically managed without impacting the critical work of scientists and researchers, but legacy solutions rely on expensive disk-to-disk replication, or slow down organizations with backup windows. Latency-aware, parallelized data movement specifically architected for unstructured data provides high performance without impacting research or overwhelming budgets.
Because science is moving faster than IT can keep up, life sciences and research organizations often cannot scale their IT departments with their exploding data. A solution that’s delivered as-a-Service keeps costs down without compromising on protection and effectiveness.

Digital assets generated by media and entertainment companies still offer tremendous value, even after projects have finished. These companies need to migrate large amounts of data quickly without impacting other work, from expensive primary NAS storage to a cool tier for long-term archive.
Media and entertainment companies need a modern, project-based archive solution to manage their petabytes of unstructured data.
An archive solution that offers easy tiering to public cloud enables companies to take advantage of the agility of public cloud storage.

The Modern Data Management Solution