Data Mapping Components
Posted: Tue Feb 11, 2025 4:51 am
For instance, one system may call a customer’s age “Age” and another may use “Birth Year.” Simple mapping would just map “age” to “birth year,” and the values wouldn’t change. But if someone is 52, they weren’t born in ’52!
This is where data transformations come in and japan whatsapp number data where mapping is very powerful. Taking “Age” (field) and 52 (value) and creating a mapping rule that subtracts the age value from the present year will give the value of 1972, which can be put into the “Birth Year” field.
Source data is the original data that can be stored in databases, files, APIs, or other data repositories. Understanding the source data’s structure, format, and content is critical for effective mapping.
Target data, also known as destination data, is where the mapped data will be loaded or transformed.
Data elements are the data fields and their types. Armed with the schema of the source and the schema of the target, an important part of data mapping is identifying specific attributes between source and target data sets.
Mapping rules define how each of the data elements from the source maps to a corresponding element in the target and answer the question, “What needs to happen?” These rules cover data transformations, validations, default values, and the business logic applied during the mapping process.
This is where data transformations come in and japan whatsapp number data where mapping is very powerful. Taking “Age” (field) and 52 (value) and creating a mapping rule that subtracts the age value from the present year will give the value of 1972, which can be put into the “Birth Year” field.
Source data is the original data that can be stored in databases, files, APIs, or other data repositories. Understanding the source data’s structure, format, and content is critical for effective mapping.
Target data, also known as destination data, is where the mapped data will be loaded or transformed.
Data elements are the data fields and their types. Armed with the schema of the source and the schema of the target, an important part of data mapping is identifying specific attributes between source and target data sets.
Mapping rules define how each of the data elements from the source maps to a corresponding element in the target and answer the question, “What needs to happen?” These rules cover data transformations, validations, default values, and the business logic applied during the mapping process.