What database design practice is utilized to prevent the duplication of data elements?

Normalization is a database design practice aimed at organizing data in a way that reduces redundancy and dependency. By applying normalization, data is structured into tables, where each piece of information is stored only once. This helps to ensure that each data element is unique and minimizes the chances of data duplication.

Normalization typically involves dividing a database into two or more tables and defining relationships between the tables. This process includes various normal forms that further refine the data structure, leading to efficient data management and integrity. The primary goal is to preserve data accuracy and efficiency while facilitating easier updates and queries.

In contrast, practices like de-normalization, data warehousing, and data lakes do not specifically focus on the elimination of duplicated data. De-normalization may actually introduce redundancy to optimize read performance. Data warehousing is intended for reporting and analysis, storing integrated data from multiple sources, while data lakes serve as raw storage repositories for various data types, often lacking the strict organization found in normalized databases.