Most disaster recovery strategies focus exclusively on the data center but most users use laptops and other endpoints to get their work done. And those endpoints often have unique, business critical data on them. Data center servers and storage as…
Most disaster recovery strategies focus exclusively on the data center but most users use laptops and other endpoints to get their work done. And those endpoints often have unique, business critical data on them. Data center servers and storage as…
Software-defined networking (SDN) represents the future of networking. A “software-defined” network enables an organization to virtualize their network, automate operations to enable efficient network configuration, and integrate network functions across dozens of switches creating a unified network architecture that is…
The General Data Protection Regulation (GDPR) forces organizations to evolve from a data protection mindset to a data management mindset. IT can no longer let backups store data on secondary storage as giant blobs of ones and zeros. The process…
Enterprise File Sync and Share (EFSS) is a “must deliver” for IT since most users perceive the capability to be a “must have”. In our last blog we discussed what to look for in an EFSS and explained the advantages…
It seems like backup vendors forgot about protecting NoSQL environments. Environments like Cassandra, MongoDB, Hortonworks, Couchbase, and Hadoop all need point-in-time protection. One reason for the lack of data protection solutions for these environments is that there is an assumption…
A question that came up on our on demand webinar “Next Question: How to protect Office 365” was “Can’t I just use the Deleted Items Folder?” The deleted items folder does help prevent some data loss. Microsoft Office 365 allows…
The cloud holds much promise for data protection, but support for the cloud varies depending on the solution. Traditional backup applications and appliances have, at best, rudimentary support for the cloud. They may only mirror the on-premises copy of data…
When an organization moves Artificial Intelligence (AI) and Deep Learning (DL) projects from the test and design phase to production, the responsibility for maintaining that project often lands in IT’s lap. IT then has to put together an architecture that…