Data Deduplication is the process of removing duplicates or excessive copies of data to decrease the storage requirement and increase the efficiency of data processing. In this approach, the redundant data in a dataset will be deleted, leaving only a single copy to be stored.
To do this, the data deduplication technique will read and analyse a dataset to see if there are duplicates of byte patterns, then it will be replaced with reference points that will lead to the single remaining copy. Companies implement data deduplication within an enterprise to get rid of excessive data, as different departments tend to store the same set of data, which can lead to clogging up the storage capacity of the business.
For example, a follow-up of a press event sent attachments worth 5MB to 10 people in an editorial team, which means the workspace of the team would have 50MB less to their storage. With data deduplication, the excessive copies will be eliminated leaving only one 5MB copy, while the others will be turned into pointers, which will reference back to the remaining copy.
There are two types of data deduplication. First, the file-level, which eliminates duplicate copies per bytes in a file, leaving the non-identical elements (such as varying metadata) intact. The second one is block-level which frees up more space, eliminating duplicate data by blocks.
Data deduplication can be used to remove excessive or redundant copies in real-time streaming data, in storage devices, backup locations and disaster recovery applications. Companies can deduplicate before data can even be transferred to a storage location or in the storage device itself after the data is written.
