Data merge purge3/28/2023 Parsing an attribute to identify smaller subcomponents, or merging two or more attributes together to form one column,.Eliminating or replacing incorrect values,.Transforming data types of certain attributes,.Cleaning, parsing, and filtering data – Once you have the data profile reports and are aware of the differences present between your datasets, you can now begin to fix things that may cause issues during the merge purge process.On the contrary, lexical heterogeneity has to do with the contents present within a column, for example the Full Name column in one database stores the name as Jane Doe, while the other dataset stores it as Doe, Jane. An example of structural heterogeneity is when one dataset contains three columns for a name ( First, Middle, and Last Name), while the other just contains one ( Full Name). Eliminating data heterogeneity – structural and lexical Data heterogeneity refers to the structural and lexical differences present between two or more datasets.With this information, you can understand the differences present in the connected datasets and what you need to consider and fix before merging data. For example, a data profile will show you a list of all attributes present in each database, as well as their fill rate, data type, maximum character length, common pattern, format, and other such details. Profiling data to uncover structural details – Data profiling means running aggregational and statistical analysis on your imported data to uncover its structural details and identify potential cleansing and transforming opportunities. This may require you to pull data from a number of places, such as local files, databases, cloud storage, or other third-party applications. This is done to bring data together in one place so that the merge process can be better planned by considering all sources and data involved.
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