**Normalization**: It is a way to reduce/minimize data redundancy (repetition of data). It can be achieved by splitting the table into many tables.

**First Normal Form (1NF)**:

-> It is the property of converting ER Diagram into the Relational Database.

-> The Relational Database is implicitly in 1NF.

-> All attributes should be atomic [ only one value in a tuple is allowed].

**Example**

Here, SMobile has multiple values which are not allowed. They have to be taken individually.

**Approach-1**

This approach creates a lot of redundancy in the table. Therefore it is not a suitable approach.

**Approach-2**

This approach also has redundancy but less when compared to the previous Approach. This is because the tables have now been split into two.

**Prime Attributes(Key):**

Those attributes which participate in the Candidate key.

**Example 1**

R(ABCD), CKs: {A, BD}

Therefore PA = {A,B,D}

**Example 2**

R(ABCDEFG), CKs: {AB, BC, CD, F}

Therefore PA = {A,B,C,D,F}

**Non-Prime Attributes(Non-Key):**

Those attributes which do not participate in Candidate Key.

**Example 1**

R(ABCD), CKs: {A, BD}

Therefore NPA = {C}

**Example 2**

R(ABCDEFG), CKs: {AB, BC, CD, F}

Therefore NPA = {E,G}

**Partial Dependency:**

If subset of any Candidate Key derives a non-key attribute.

**Example**

R(ABCD) FDs : {AB->CD, B->D}

CK => {AB}

B->D, B is a subset of AB and it derives non-key, therefore it is a partial dependency.

**Second Normal Form (2NF):**

=> If a relation 'R' is in **1NF** (and)

=> If every **non-prime attribute** is fully dependent on any complete CK (or) No **Partial Dependency** exists.

**Allowed in 2NF:**

=> Non-Prime determines Non-Prime

=> Non-Prime determines Prime

=> Prime determines Prime

**Not-Allowed in 2NF:**

=> Prime determines Non-Prime

## Discussion (0)