Data integration involves the movement of data from one data store to another. The process requires data to be directed between a source and target. A goal, and sometimes requirement, is to make the source data available in the format and structure required by the target. In the context of the add-on, this goal is addressed using the concept of mapping.
Mapping refers to:
The process of specifying the following:
Which data is extracted from a data source.
The data's destination in the target location.
Any required changes, or transformations, to the data along the way.
The implementation results of the above process. For example, the mappings between fields in a source and target table.
Data transformations are added to mappings when types, formatting, or other constraints prevent successful movement of the data. A transformation makes the data compatible with the structure of the target.
Transformation types can include:
Format changes: converting between technical formats, such as from String to Integer data types.
Structure changes: changing the data's form. For example, splitting a Full name field into First Name and Last Name fields.
Semantic conversion: converting the value to meet semantic requirements. For example, a source might use the following codes for states 1, 2, 3, etc. But, the target expects Alabama, Alaska, and Arizona. You can use a transformation to populate the expected value to the target.