High-Speed Inline Deduplication Storage

The Data Domain Appliance Series delivers the highest throughput and scalability of any nearline storage system in the industry and was designed to be seamlessly and cost-effectively integrated into existing infrastructures.

Unlike other disk-based backup products that serve as mere front-ends to a tape library infrastructure, the Appliance Series provides cost-effective long-term onsite retention and highly efficient WAN vaulting for disaster recovery (DR), remote office protection and tape consolidation. Fundamental to these economic and operational qualities is Data Domain's Global Compression™ high speed inline deduplication and compression technology, which reduces backup data by an average of 20x.

Key features include:

• Massive Data Reduction: Driven by Data Domain's fast, in-line deduplication and compression technology, Global Compression, the Appliance Series offers an average 20x data reduction for backup images, enabling cost-efficient, long-term retention.
• Fully Compatible with All Leading Enterprise Backup and Archiving Software: The Appliance Series is designed to easily integrate into the existing backup infrastructure - either as a file server or virtual tape library (VTL) - supporting all leading backup and archiving software.
• High Throughput and Extended Retention: The Appliance Series delivers up to 800 GB/hr of throughput (exceeding LTO-4 performance) and offers up to 1.25 PB of logical capacity, allowing months of online backup image retention ensuring fast and reliable data recoveries from disk.
• Local and Remote Site Data Protection: The Data Domain Replicator software option enables Data Domain's nearline storage systems to function as a highly efficient WAN vaulting solution for DR, remote office data protection and multi-site tape consolidation.
• Ultimate Data Integrity: Data Domain's Data Invulnerability Architecture provides the best defense against data integrity issues to continuously verify, detect and protect against data recoverability issues throughout the life cycle of the data.