What does "De-Normalization" mean in the context of data migration?

What does "De-Normalization" mean in the context of data migration?

• A. The process of simplifying and optimizing data structure for better performance
• B. The process of duplicating data for redundancy
• C. The procedure for increasing data complexity
• D. The act of mixing different data types

Answer: (A)

In the context of data migration, "De-Normalization" refers to the process of simplifying and optimizing a data structure to improve performance. In relational database design, normalization is used to eliminate redundancy and ensure data integrity by organizing data into related tables. However, in some scenarios, such as when preparing for data migration or when performance is critical for certain applications, de-normalization is employed. By combining tables or allowing for some redundancy, de-normalization can enhance the speed of data retrieval and reduce the complexity of queries. This can be particularly important in situations where data needs to be accessed quickly and efficiently, such as in reporting or analytical scenarios where performance can significantly impact user experience. Therefore, the correct answer highlights the benefits of de-normalization in optimizing data structures specifically for improved performance during data migration.

In the context of data migration, "De-Normalization" refers to the process of simplifying and optimizing a data structure to improve performance. In relational database design, normalization is used to eliminate redundancy and ensure data integrity by organizing data into related tables. However, in some scenarios, such as when preparing for data migration or when performance is critical for certain applications, de-normalization is employed.

By combining tables or allowing for some redundancy, de-normalization can enhance the speed of data retrieval and reduce the complexity of queries. This can be particularly important in situations where data needs to be accessed quickly and efficiently, such as in reporting or analytical scenarios where performance can significantly impact user experience.

Therefore, the correct answer highlights the benefits of de-normalization in optimizing data structures specifically for improved performance during data migration.