Hammerspace announced enterprise hybrid cloud services for protecting and securing data. Hammerspace, using its Data-as-a-Service platform, abstracts complex and diverse infrastructure to simplify multi-cloud data sharing, on-demand cloud bursting, disaster recovery, and Kubernetes data management.
The global accessibility of data across cloud and Kubernetes environments are some of the biggest challenges for improving data agility, control, and efficiency. Data must be readily accessible for users without any impact on performance, while at the same time protected and secured across sites.
With Hammerspace, users and apps access data anywhere while machine learning (ML) continuously optimizes for performance and cost.
“We are excited to announce updates to Hammerspace’s cloud-native data services that enhance the protection and security of data as it is accessed across the hybrid multi-cloud,” said Douglas Fallstrom, VP Products & Operations at Hammerspace. “Now, our customers can accelerate their cloud journey while maintaining tight security over their data anywhere across the infrastructure.”
NEW SERVICES INCLUDE:
Global undelete for files and snapshots that allows users to self-service data recovery
Automated data classification
Integration with customer-managed key management systems (KMS) for multi-cloud security
Metadata harvesting integration with cloud analytics services to detect and tag files with content information
Hammerspace customers taking advantage of these services can be found across diverse markets, including legal services, financial services, media and entertainment, higher education, and healthcare. The common thread among them is that they have large amounts of file data that must be made available across sites and clouds, while using their existing infrastructure investments.
With Hammerspace customers can take a data-centric approach to data management instead of navigating infrastructure to find and use it.
Hammerspace frees customers from the lock-in of storage silos abstracting away the infrastructure by separating the control place (metadata) from the data plane (data). This approach allows users to self-service their access to data with the support of machine learning-driven automation to deliver Data-as-a-Service. With Hammerspace, file data becomes cloud-native so that workloads can be deployed anywhere.