There are several types of normalization, each with its own set of rules for eliminating data redundancy and improving data integrity.
The most commonly used normalization levels are:
- First Normal Form (1NF): This is the most basic level of normalization, and it requires that each column in a table be atomic (i.e., indivisible) and contain only one value. This means that a table should not contain repeating groups of data, such as multiple phone numbers in a single column.
- To achieve 1NF, you must create separate tables for each set of related data and use a primary key to link the tables.
- Second Normal Form (2NF): 2NF builds on 1NF by requiring that all non-key attributes be fully dependent on the primary key. This means that any column that does not depend on the primary key should be moved to a separate table.
- To achieve 2NF, you must identify the functional dependencies between columns in a table and remove any columns that do not depend on the primary key.
- Third Normal Form (3NF): 3NF builds on 2NF by requiring that all non-key attributes be directly dependent on the primary key, rather than on other non-key attributes.
- This means that any column that depends on another non-key column should be moved to a separate table. To achieve 3NF, you must identify and eliminate all transitive dependencies in a table.
- Boyce-Codd Normal Form (BCNF): BCNF is similar to 3NF, but it applies to tables with multiple candidate keys. It requires that all functional dependencies be based on candidate keys, rather than on other non-key attributes.
- To achieve BCNF, you must identify all candidate keys in a table and ensure that all functional dependencies are based on these keys.
There are also higher levels of normalization, such as Fourth Normal Form (4NF), Fifth Normal Form (5NF), and Domain-Key Normal Form (DK/NF), but they are less commonly used in practice.
What are the benefits of data normalization?
There are several benefits to data normalization in a database:
- Reduced data redundancy: Normalization eliminates duplicate data by breaking up tables into smaller, more specialized units.
- This reduces data redundancy and ensures that each piece of data is stored in only one place in the database, which helps to maintain data consistency.
- Improved data consistency: Normalization helps to improve data consistency by ensuring that each piece of data is stored in a standardized format.
- This reduces the likelihood of errors and inconsistencies in the data, which can lead to incorrect or incomplete results.
- Simplified database structure: Normalization simplifies the database structure by breaking up tables into smaller, more specialized units. This makes it easier to understand and maintain the database over time, which can save time and reduce errors.
- Better database performance: Normalization can help to improve database performance by reducing the amount of redundant data that needs to be stored and retrieved.
- The real result is you get faster query times and better overall database performance.
- Easier data updates: Normalization makes it easier to update data in the database because each piece of data is stored in only one place.
- As a result, this reduces the likelihood of errors and inconsistencies when updating data, which can help to ensure data accuracy and completeness.
Overall, data normalization is an important process for maintaining data consistency, reducing redundancy, and improving database performance. By breaking up tables into smaller, more specialized units, normalization can help to simplify the database structure and make it easier to manage over time.