Normalization in Databases

In the process of working with databases, one must have been asked "Is that database normalized?" Usually, the reply is "Uh, yeah." Normalization is often forgotten about, touted as a luxury that only scholars have time for. However, knowing the principles of normalization and applying them to daily database design tasks really isn't all that complicated and it could drastically improve the performance of DBMS. The main problem plaguing database design is redundancy. Redundancy is storing the same data item in more one place. Redundancy creates several problems, taking up extra storage space, entering same data more than once, deleting data from more than one place, and modifying data in more than one place. It creates inconsistency and unreliability in the database. In order to solve these problems, the “raw” database needs to be normalized.
Normalization is the process of efficiently organizing data in a database. There are two goals of the normalization process. The first goal of normalization is to eliminate redundant data (for example, storing the same data in more than one table). The second goal is to ensure data dependencies make sense (only storing related data in a table). Both of these are important goals as they reduce the amount of space a database consumes and ensure that data is logically stored.
Un-Normalized Form (UNF)
If a table contains non-atomic values at each row, it is said to be in UNF. An atomic value is something that cannot be broken down further. A non-atomic value, as the name suggests, can be further decomposed and simplified

First Normal Form (1NF)
A relation is said to be in 1NF if it contains no non-atomic values and each row can provide a unique combination of values. The above table in UNF can be processed to create the following table in 1NF. As you can see now, each row contains unique