Data Normalization and Functional Dependency

• Data Normalization: A systematic process of organizing data in a database to reduce data redundancy and improve data integrity. It involves decomposing tables into smaller, well-structured tables and defining relationships between them. The goal is to avoid anomalies like insertion, update, and deletion problems.

Functional Dependency

• Functional Dependency (FD): A constraint between attributes in a relation. If knowing the value of attribute 'A' determines the value of attribute 'B', then 'B' is functionally dependent on 'A'. This is written as A → B.

• Full Functional Dependency: An attribute is fully functionally dependent on a set of attributes if it is functionally dependent on the entire set but not on any proper subset of that set.

• Transitive Dependency: If attribute 'B' is functionally dependent on attribute 'A', and attribute 'C' is functionally dependent on 'B', then 'C' is transitively dependent on 'A'. This often indicates the need for further normalization.

Normal Forms

• First Normal Form (1NF): All attributes in a relation must be atomic (indivisible). No repeating groups of data are allowed within a single table.

• Second Normal Form (2NF): The table must be in 1NF and every non-key attribute must be fully functionally dependent on the entire primary key. Eliminates redundant data associated with part of a composite key.

• Third Normal Form (3NF): The table must be in 2NF and no transitive dependencies exist. This means removing attributes that are functionally dependent on non-key attributes.

• Boyce-Codd Normal Form (BCNF): A stronger version of 3NF. BCNF ensures that every determinant (attribute or set of attributes that determine other attributes) is a candidate key. This eliminates certain anomalies not caught by 3NF.

• Higher Normal Forms (4NF, 5NF): These are less commonly used but handle more complex data structures and dependencies.

Database Design

• Database Design: The process of designing the structure of a database, including tables, attributes, and relationships between them. Good database design is crucial for maintaining data integrity and efficiency.

• ER Diagrams (Entity-Relationship Diagrams): Visual representations of database schemas, showing entities (objects), attributes, and relationships between them. Used as a blueprint for database design.

Data Integrity

• Data Integrity: Ensuring the accuracy, consistency, and reliability of data within a database. Normalization plays a significant role in achieving this.

• Constraints: Rules defined to limit the values that can be stored in a database. These help to maintain data integrity; e.g. a primary key constraint ensures unique identifiers, not null constraints prevent null values on essential attributes.

• Data Validation: Process of checking data for accuracy and consistency before storing it in the database. This is often crucial prior to normalization.

Lossless Decomposition

• Lossless Decomposition: A crucial aspect of database design. A decomposition of a relation into smaller relations is lossless if all the information in the original relation can be recovered from the smaller relations. Techniques such as dependency preservation and losslessness properties are needed during decomposition when designing multiple tables.

• Dependency Preservation: Decomposition is dependency preserving if the dependencies in the original relation are reflected within the new relations, helping to maintain the integrity of the data.

This quiz covers the concepts of data normalization and functional dependencies in database design. Learn about organizing data to reduce redundancy, as well as different types of functional dependencies, including full and transitive dependencies. Test your understanding of these essential database concepts!