ExaGrid understands that deduplication is required, but how you implement it changes everything in backup. Data deduplication reduces the amount of storage required and also the amount of bandwidth for replication; however, if not implemented correctly, it will dramatically slow down backups, slow down restores and VM boots, and the backup window will grow as data grows. This is due to the fact that data deduplication is highly compute intensive; you don’t want to perform deduplication during the backup window and you also don’t want to restore or boot from a pool of deduplicated data.
ExaGrid provides the best level of data deduplication and has implemented data deduplication in a way that provides 6X the backup performance and up to 20X the restore and VM boot performance of other approaches. ExaGrid has a unique landing zone where backups can land straight to disk without any inline deduplication processing. Backups are fast and the backup window is short. Deduplication and offsite replication occur in parallel with the backups and never impede the backup process as they are always second order priority. ExaGrid calls this “adaptive deduplication.”
Fastest Backup/Shortest Backup Window
Since backups write directly to the landing zone, the most recent backups are in their full, undeduplicated form ready for any request. Local restores, instant VM recoveries, audit copies, tape copies, and all other requests do not require rehydration and are as fast as disk. As an example, instant VM recoveries occur in seconds to minutes versus hours for the inline deduplication approaches that store only deduplicated data that has to be rehydrated for every request.
Fastest Restores, Recoveries, VM Boots, and Tape Copies
Scalability: Fixed-length Backup Window and Data Growth
ExaGrid provides full appliances (processor, memory, bandwidth, and disk) in a scale-out system. As data grows, all resources are added, including additional landing zone, additional bandwidth, processor, and memory as well as disk capacity. The backup window stays fixed in length regardless of data growth, which eliminates expensive forklift upgrades. Unlike the inline, scale-up approach where you need to guess at which sized front-end controller is required, the ExaGrid approach allows you to simply pay as you grow by adding the appropriate sized appliances as your data grows. ExaGrid has various-sized appliance models, and any size or age appliance can be mixed and matched in a single system, which allows IT departments to buy compute and capacity as they need it. This evergreen approach also eliminates product obsolescence.