DBMS TOPIC 4 : Database Modeling – ERD vs Normalization

Database Modeling – ERD vs Normalization

Database Modeling

The process of creating a structured framework of entities, attributes, and relationships to support a database's design.

Entity Relationship Diagram (ERD)

A graphical representation of entities, attributes, and relationships in a database.

Entity

An object or concept about which data is stored (e.g., Student, Course).

Attribute

A property or detail of an entity (e.g., Name, ID).

Relationship

An association between two or more entities (e.g., Student enrolls in Course).

Primary Key

A unique identifier for a record in a table.

Foreign Key

A field that creates a link between two tables, referencing the primary key of another table.

Chen Notation

A conceptual modeling style for ERD with shapes like ovals for attributes.

Crow’s Foot Notation

A practical, implementation-focused ERD style using symbols like lines and "crow's feet" to indicate cardinality.

Top-Down Approach

Database design that starts with ERD modeling and refines into more specific structures.

Bottom-Up Approach

Database design that begins with normalization by analyzing attributes and grouping them into entities.

Conceptual Design

High-level ERD-based modeling; independent of DBMS implementation.

Logical Design

Converts conceptual models to logical structures using rules like normalization.

Physical Design

Translates the logical model into actual database structures like tables and indexes.

Normalization

The process of structuring data to minimize redundancy and improve data integrity.

1NF (First Normal Form)

Ensures each table cell contains a single value and each record is unique.

2NF (Second Normal Form)

Satisfies 1NF and removes partial dependencies from composite primary keys.

3NF (Third Normal Form)

Satisfies 2NF and removes transitive dependencies between non-key attributes.

Transitive Dependency

A situation where a non-key attribute depends on another non-key attribute.

Insert Anomaly

Inability to add data due to missing fields or required dependencies.

Update Anomaly

Inconsistencies due to updating data in multiple places.

Delete Anomaly

Accidental data loss when deleting a record that contains multiple data points.