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In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs.
The term deduplication refers generally to eliminating duplicate or redundant information. Data deduplication, in computer storage, refers to the elimination of redundant data; Record linkage, in databases, refers to the task of finding entries that refer to the same entity in two or more files
In engineering, science, and statistics, replication is the process of repeating a study or experiment under the same or similar conditions to support the original claim, which is crucial to confirm the accuracy of results as well as for identifying and correcting the flaws in the original experiment. [1]
The additional data can simply be a complete copy of the actual data (a type of repetition code), or only select pieces of data that allow detection of errors and reconstruction of lost or damaged data up to a certain level.
Verifying that the data is unique is an important aspect of data editing to ensure that all data provided was only entered once. This reduces the possibility for repeated data that could skew analytics reporting. See the example below. In the above table is an example of data with duplicate entries. See Sr.
Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases).
A winter weather advisory was put in place late Sunday for many counties across the tri-state for potential winter weather threats.
"Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.