As the amount and types of data that constitute an organization's information increases exponentially, it becomes more difficult to manage and protect that data. Data deduplication technology, which is maturing in the backup space and emerging in nearline and primary storage solutions, is becoming a necessity for any organization managing large amounts of data. But why is data deduplication so critical? Simple: it has the potential to change the economics of data management and data protection solutions by greatly reducing the costs of storing and transferring data.

An IT manager drawn to the promise of deduplication will want to know how data deduplication delivers disruptive benefits, how it compares to other reduction technologies, what its limitations are and what customers must know when deploying deduplication solutions.

Deduplication compared to other data reduction technologies

First-generation data reduction technologies such as data compression and file single instancing have been available for some time and have been widely adopted. Although these technologies can reduce data footprint and transfer costs, these gains are modest, usually in the range of 2-8X.

The goal of most compression schemes is to reduce the footprint of a single file by removing redundancies and efficiently encoding the results. Little is done to address duplicate or versioned files. Emerging application-aware compression technologies can deliver significantly improved reduction rates for narrow classes of data, but often with significant performance penalties and little or no improvement handling cross-file redundancies.

The goal of file single instance storage (SIS) technologies is to reduce the data footprints of a repository by eliminating redundant files and replacing them with references. But file level SIS does little to reduce the footprint of a single file and doesn't handle sub-file redundancies. Block level SIS technologies improve reduction ratios by eliminating duplicate fixed length blocks across files in a repository, but results are still limited. File level changes that affect data alignment (e.g. byte insertions) typically defeat fixed block schemes. Related, redundant data in dissimilar files are rarely suitably block aligned, further limiting block level SIS technologies, making them a poor choice for versioned environments.

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