Database Normalization

• Database normalization organizes data to reduce redundancy and improve integrity.
• This involves dividing databases into multiple tables and defining relationships between them.
• The main goal is to isolate data so that amendments to an attribute can be made in one table only and propagate through the rest of the database automatically.
• Consistency is ensured and the risk of anomalies is reduced.
• Edgar F. Codd introduced normalization in 1970 as part of his relational model.

Benefits of Normalization

• Reduces data redundancy, minimizing storage space and costs.
• Improves data consistency by reducing the risk of update, insertion, and deletion anomalies.
• Enhances data integrity through the enforcement of constraints and rules.
• Simplifies data modification, making database updates and maintenance easier.
• Increases query performance due to reduced table size and optimized indexing.
• Makes database schema easier to understand and maintain.

Anomalies in Databases

• Anomalies may occur if databases aren't properly normalized, which leads to inconsistencies and errors.
• Three main types of anomalies include:

Update Anomaly

• This happens when the same data item is stored multiple times in a database, and updating it in one place means updating it in all places.
• Missing an instance will result in data inconsistency.

Insertion Anomaly

• Insertion Anomaly happens when inserting new data into the database without also inserting data about another entity is not possible.
• This occurs when the primary key is not known, or the table contains attributes that are not fully dependent on the primary key.

Deletion Anomaly

• This is when deleting data about one entity unintentionally deletes data about another entity.
• This can happen when two entities are stored in the same table, and deleting one removes all information about the other.

Normal Forms

• Normal forms are guidelines for achieving different levels of normalization.
• Each normal form builds upon the previous one, with higher normal forms providing greater data integrity.
• The most commonly used forms are:

First Normal Form (1NF)

• A table is in 1NF if each column contains only atomic values.
• Atomic value indicates that each column should hold a single, indivisible value.
• Achieve 1NF by eliminating repeating groups by creating separate tables or using composite keys.

Second Normal Form (2NF)

• A table is in 2NF if it is in 1NF and all non-key attributes are fully functionally dependent on the primary key.
• Functional dependency means that the value of one attribute is determined by the value of another attribute.
• To achieve 2NF, partial dependencies are removed by creating new tables and relating them to the original table using foreign keys.

Third Normal Form (3NF)

• A table is in 3NF if it is in 2NF and no non-key attribute is transitively dependent on the primary key.
• Transitive dependency means that a non-key attribute is dependent on another non-key attribute.
• Achieve 3NF by removing transitive dependencies by creating new tables and relating them to the original table using foreign keys.

Boyce-Codd Normal Form (BCNF)

• BCNF is a stronger version of 3NF that addresses redundancy not handled by 3NF.
• A table is in BCNF if every determinant is a candidate key.
• A determinant is an attribute or set of attributes that determines another attribute.
• Achieving BCNF may require further decomposition of tables.

Fourth Normal Form (4NF)

• A table is in 4NF if it is in BCNF and does not contain more than one multi-valued dependency.
• Multi-valued dependency occurs when the presence of one attribute implies the presence of multiple values of another attribute.
• Achieving 4NF involves eliminating multi-valued dependencies by creating new tables.

Fifth Normal Form (5NF)

• A table is in 5NF if it is in 4NF and cannot be decomposed into smaller tables without losing data or creating redundancy.
• 5NF deals with more complex dependencies than 4NF.
• Achieving 5NF is often difficult and may not be practical in many cases.

Denormalization

• Adding redundancy to a database to improve performance is called denormalization.
• It involves combining tables or adding redundant data to existing tables.
• Denormalization improves query performance by reducing the need for joins and complex calculations.
• There is an increased risk of data inconsistency and the database can become more difficult to maintain.
• Denormalization should be used judiciously and only when the benefits outweigh the risks.

When to Denormalize

• To optimize read-heavy workloads where query performance is critical.
• To simplify complex queries that involve multiple joins.
• To cache frequently accessed data for faster retrieval.
• To support reporting requirements that require aggregated data.
• To improve the performance of specific queries that are slow due to normalization.

Techniques for Denormalization

• Adding redundant columns to a table to avoid joins.
• Creating summary tables that pre-compute aggregated data.
• Combining tables into a single table to reduce the number of joins.
• Storing derived data in a table to avoid calculations at query time.
• Introducing repeating groups to reduce the number of tables.

Considerations for Normalization and Denormalization

• The specific requirements of the application dictate the choice between normalization and denormalization.
• Weighing the trade-offs between data integrity, query performance, and development effort is necessary.
• Normalization is generally preferred for transactional systems with high data integrity requirements.
• Denormalization is often used for data warehouses and reporting systems where query performance is critical.
• Regular database audits are necessary to identify and address any anomalies or performance issues.
• Thorough testing of the database is essential after making any changes to the schema to ensure data integrity and performance.