In addition to simply reducing the amount of space consumed by duplicate files, there can be some performance benefits associated with using data deduplication software as well since fewer reads/writes are needed across multiple disks or tapes, there may be less disk I/O contention and thus overall increase in system speed and response times. Once duplicates have been identified, the software will then either delete entire copies (known as single instance deletion) or selectively remove only those portions which are deemed redundant (known as partial instance deletion). The exact method used by a particular piece of data deduplication software varies depending on the application, but generally it will involve some combination of hashing algorithms (e.g., SHA256) and pattern matching to detect redundant areas within files or over entire datasets. ![]() Post-process deduplication, on the other hand, involves periodic scans that look for duplicated files and delete them from the storage media after they have already been written. ![]() Inline deduplication involves comparing new data against existing stored data and eliminating any areas that contain identical information before it is written to storage media. There are two primary types of deduplication techniques - inline deduplication and post-process deduplication. This can result in significant savings in terms of costs, as well as improved efficiency when dealing with large amounts of data. The goal of deduplication is to reduce the amount of physical or logical storage required for the data by eliminating redundant copies. Data deduplication software is a type of application used to detect and remove duplicate copies of data stored in different places.
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