Description: Normalization is a fundamental process in database design that aims to organize data efficiently and coherently. Its main objective is to reduce redundancy and improve data integrity by dividing large tables into smaller, related tables. This approach allows data to be stored in a way that minimizes duplication, which in turn facilitates the updating and maintenance of the database. Normalization is carried out through a series of ‘normal forms’, each of which has specific criteria that must be met. These normal forms, ranging from first to fifth, establish rules on how tables and their relationships should be structured. By applying normalization, a more logical and efficient structure is achieved, resulting in better query performance and greater ease in conducting data analysis. In summary, normalization is essential for software development involving databases, as it ensures that data is handled effectively and maintains its integrity over time.
History: Database normalization was introduced in the 1970s by Edgar F. Codd, a pioneer in the field of relational databases. Codd published his seminal work in 1970, where he presented the relational model and laid the groundwork for normalization. Over the years, normalization has evolved, and several normal forms have been developed to address different types of data redundancy and dependency.
Uses: Normalization is primarily used in the design of relational databases to ensure data integrity and reduce redundancy. It is applied in database management systems (DBMS) to structure data in a way that optimizes queries and minimizes errors. It is also fundamental in the development of applications that require efficient handling of large volumes of data across various fields of technology.
Examples: A practical example of normalization is the design of a database for an online store. Instead of having a single table containing information about products, customers, and orders, normalization would allow these data to be divided into separate tables: one for products, another for customers, and another for orders. This not only reduces redundancy but also facilitates data management and improves query performance.
