Why is Normalization Important?
Normalization is crucial as it helps eliminate redundant data and inconsistencies, ensuring more accurate, lean, and efficient databases. It also simplifies data management and enhances the speed and performance of the overall database system, thereby proving to be advantageous.
Example
Let us assume the library database that maintains the required details of books and borrowers. In an unnormalized database, the library records in one table the book details and the member who borrowed it, as well as the member’s detail. This would result in repetitive information every time a member borrows a book.
Normalization splits the data into different tables — ‘Books’, “Members” and “Borrowed” and connects “Books” and “Members” with “Borrowed” through a biunique key. This removes redundancy, which means data is well managed, and there is less space utilization.
Conclusion
The concepts of normalization, and the ability to put this theory into practice, are key to building and maintaining comprehensive databases which are both strong and impervious to data anomalies and redundancy. Properly applied and employed at the right times, normalization boosts database quality, making it structured, small, and easily manageable.
What is Normalization in DBMS?
The normalization concept for relational databases, developed by E.F. Codd, the inventor of the relational database model, is from the 1970s. Before Codd, the most common method of storing data was in large, cryptic, and unstructured files, generating plenty of redundancy and lack of consistency. When databases began to emerge, people noticed that stuffing data into them caused many duplications and anomalies to emerge, like insert, delete, and update anomalies. These anomalies could produce incorrect data reporting, which is harmful to any business. Normalization is a methodological method used in the design of databases to create a neat, structured, and structured table in which each table relates to just one subject or one-to-one correspondence.
The objective is to extensively reduce data redundancy and dependency. In essence, normalization was introduced and has continually been improved to rectify these specific aspects of data management. By organizing data in such a rigorous and stringent manner, normalization facilitates a significantly enhanced level of data integrity and enables more efficient data operations.