Data deduplication looks for redundancy of sequences of bytes across very large comparison windows. Sequences of data (over 8 KB long) are compared to the history of other such sequences. The first uniquely stored version of a sequence is referenced rather than stored again. This process is completely hidden from users and applications so the whole file is readable after it's written.
Deduplication segments an incoming data stream, uniquely identifies data segments, and then compares the segments to previously stored data. If the segment is unique, it's stored on disk. However, if an incoming data segment is a duplicate of what has already been stored, a reference is created to it and the segment isn't stored again.
For example, a file or volume that's backed up every week creates a significant amount of duplicate data. Deduplication algorithms analyze the data and store only the compressed, unique segments of a file. This process can provide an average of 10 to 30 times reduction in storage capacity requirements, with average backup retention policies on normal enterprise data. This means that companies can store 10 TB to 30 TB of backup data on 1 TB of physical disk capacity, which has huge economic benefits.
liminating redundant data can significantly shrink storage requirements and improve bandwidth efficiency. Because primary storage has gotten cheaper over time, enterprises typically store many versions of the same information so that new workers can reuse previously done work. Some operations like backup store extremely redundant information.
Deduplication lowers storage costs as fewer disks are needed. It also improves disaster recovery since there's far less data to transfer. Backup and archive data usually includes a lot of duplicate data.
The same data is stored over and over again, consuming unnecessary storage space on disk or tape, electricity to power and cool the disk or tape drives, and bandwidth for replication. This creates a chain of cost and resource inefficiencies within the organization.
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