Introduction
Normalization, a systematic database design methodology, aims to enhance data organization by reducing redundancy and eliminating anomalies through table decomposition and relationship establishment. This process ensures data consistency and integrity, mitigating the risk of inconsistencies or errors.
Functional Dependencies in Normalization
Functional dependencies, which articulate attribute relationships in tables, are crucial in the normalization process. For instance, in an employee table, the employee ID may uniquely determine both the employee's name and department, establishing functional dependencies: Employee ID -> Employee Name and Employee ID -> Department. Identifying these dependencies aids in restructuring tables and establishing relationships.
Anomalies in Non-Normalized Databases
Non-normalized databases are susceptible to anomalies:- Insertion Anomalies: Adding new records becomes challenging due to incomplete or irrelevant data for certain attributes, resulting in NULL values.
- Update Anomalies: Changes to an attribute in one record necessitate updating multiple other records, causing inconsistencies in non-unique attributes.
- Deletion Anomalies: Deleting a record might lead to the loss of relevant information, causing data inconsistencies.
Levels of Normalization
First Normal Form (1NF):- Removes duplicate rows.
- Each column contains atomic values.
- Example: An employee table with unique rows and atomic columns.
Second Normal Form (2NF):
- Builds on 1NF by eliminating partial dependencies.
- Attributes are fully dependent on the entire primary key.
- Example: Splitting tables to ensure no partial dependencies exist.
Third Normal Form (3NF)
- Builds on 2NF by removing transitive dependencies.
- Attributes are solely dependent on the primary key.
- Example: Splitting tables to eliminate transitive dependencies.
- A stricter form of 3NF.
- Every determinant is a candidate key.
- Example: Ensuring no non-trivial functional dependencies exist.
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