10 Facts About Data transformation

In computing, data transformation is the process of converting data from one format or structure into another format or structure.

Data transformation can be simple or complex based on the required changes to the data between the source data and the target data.

Data transformation is typically performed via a mixture of manual and automated steps.

Tools and technologies used for data transformation can vary widely based on the format, structure, complexity, and volume of the data being transformed.

Data transformation can be divided into the following steps, each applicable as needed based on the complexity of the transformation required.

Data transformation mapping is the process of defining how individual fields are mapped, modified, joined, filtered, aggregated etc.

Typically, the data transformation technologies generate this code based on the definitions or metadata defined by the developers.

Traditionally, data transformation has been a bulk or batch process, whereby developers write code or implement transformation rules in a data integration tool, and then execute that code or those rules on large volumes of data.

Batch data transformation is the cornerstone of virtually all data integration technologies such as data warehousing, data migration and application integration.

Interactive data transformation is an emerging capability that allows business analysts and business users the ability to directly interact with large datasets through a visual interface, understand the characteristics of the data, and change or correct the data through simple interactions such as clicking or selecting certain elements of the data.