What type of mapping is essential when integrating systems with different data structures?

Data transformation mapping is essential when integrating systems with different data structures because it involves the process of converting data from one format or structure to another to ensure compatibility between systems. This transformation can include altering the data types, adjusting the hierarchies, and even merging or splitting data fields to meet the requirements of the target system.

In integration scenarios where data is coming from heterogeneous sources, it is common for structures to differ significantly. Data transformation mapping allows for seamless data exchange by standardizing data formats and making them interpretable across different systems. This ensures that the receiving system can accurately understand and use the data being sent from another system.

Other mapping types, such as data normalization, layout, and extraction, serve different purposes. Data normalization focuses on organizing data to reduce redundancy and improve data integrity within a single database, while data layout mapping deals with the physical arrangement of data within a storage environment. Data extraction mapping typically pertains to the process of retrieving data from a source, which may not necessarily involve the transformation needed for compatibility between systems. Thus, data transformation mapping stands out as the critical component in bridging the gaps created by diverse data structures during system integration.