Data Deduplication
Data deduplication is a technique used in data science and artificial intelligence to eliminate redundant copies of data. It involves identifying and removing identical or similar data elements, such as files, records, or blocks, from a dataset. This process helps to reduce storage space, improve data management, and increase efficiency in data processing. Data deduplication can be performed at different levels, including file-level, block-level, and byte-level, depending on the granularity of the data. It can be implemented through software or hardware solutions, such as backup systems, storage arrays, or cloud services. Data deduplication is particularly useful in scenarios where large amounts of data need to be stored or transferred, such as in backup and recovery, archiving, or data migration.
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