This page covers essential concepts in designing databases and ensuring they meet processing and storage efficiency through normalization.

Data Modeling

Conceptual Data Modeling

• Definition: High-level view representing business concepts and their relationships
• Components: Entities, relationships, and attributes without implementation details
• Purpose: To understand and communicate business requirements
• Techniques: Entity-Relationship Diagrams (ERDs) with minimal technical specifications
• Audience: Business stakeholders and analysts

Logical Data Modeling

• Definition: Translation of conceptual model into a database-oriented structure
• Components: Tables, columns, keys, and relationships with data types
• Purpose: Bridge between business requirements and technical implementation
• Techniques: Normalized relational schema, detailed ERDs
• Key Considerations: Data integrity, referential constraints, and cardinality

Physical Data Modeling

• Definition: Implementation-specific design for the target database system
• Components: Indexes, partitions, tablespaces, storage parameters
• Optimization Focus: Performance, storage efficiency, query execution plans
• DBMS-Specific Features: Utilizing vendor-specific capabilities (Oracle, SQL Server, PostgreSQL)
• Implementation Concerns: Denormalization strategies, indexing strategies, clustering

Normalization Principles

First Normal Form (1NF)

• Rule: Eliminate repeating groups; each cell must contain a single value
• Process: Identify and remove repeating columns or multi-valued attributes
• Benefits: Simplifies queries and updates
• Example: Converting a spreadsheet with multiple phone numbers in one cell into separate records

Second Normal Form (2NF)

• Rule: Must be in 1NF, and all non-key attributes must depend on the entire primary key
• Process: Remove partial dependencies by creating separate tables
• Benefits: Reduces data redundancy and update anomalies
• Example: Separating order details from customer information in an order processing system

Third Normal Form (3NF)

• Rule: Must be in 2NF, and no non-key attributes can depend on other non-key attributes
• Process: Eliminate transitive dependencies
• Benefits: Further reduces redundancy and improves data integrity
• Example: Moving city and state information from a customer table to a separate locations table

Boyce-Codd Normal Form (BCNF)

• Rule: For any dependency A → B, A must be a superkey
• Process: Restructuring to ensure all determinants are candidate keys
• When to Apply: Complex systems with multiple candidate keys
• Trade-offs: Increased complexity vs. improved data integrity

Fourth and Fifth Normal Forms

• 4NF: Addresses multi-valued dependencies
• 5NF: Addresses join dependencies
• Advanced Applications: Complex data systems requiring high levels of data integrity
• Implementation Considerations: Performance impacts of high normalization levels

Entity-Relationship Diagrams (ERD)

ERD Components

• Entities: Rectangles representing business objects (e.g., Customer, Product)
• Attributes: Properties of entities (e.g., CustomerID, ProductName)
• Relationships: Lines connecting entities with cardinality notations
• Cardinality Types: One-to-one, one-to-many, many-to-many
• Primary and Foreign Keys: Representation in diagrams

ERD Notation Systems

• Chen Notation: Original notation focused on clarity for business stakeholders
• Crow's Foot Notation: Popular notation showing cardinality constraints visually
• UML Class Diagrams: Object-oriented approach to data modeling
• IDEF1X: Government and military standard for data modeling

Translating Business Rules to ERDs

• Identifying Entities: Converting nouns in requirements to entities
• Establishing Relationships: Interpreting verbs and business processes
• Determining Cardinality: Analyzing how entities relate quantitatively
• Validation Techniques: Verifying the model against user stories and use cases
• Iterative Refinement: Methods for evolving the model based on stakeholder feedback

Advanced ERD Concepts

• Weak Entities: Entities dependent on others for identification
• Associative Entities: Resolving many-to-many relationships
• Subtype/Supertype Relationships: Modeling hierarchies and inheritance
• Recursive Relationships: Entities related to themselves
• Constraints and Business Rules: Documenting additional requirements on ERDs