Unit 384: Database Design and Management

1. Introduction to Database Design

• Importance of database design in efficient data management
• Key concepts:
  • Data Modeling
  • Normalization
  • Relational Database Principles

2. Entity-Relationship (ER) Modeling

• Purpose of ER diagrams
• Components of ER diagrams:
  • Entities
  • Attributes
  • Relationships
• Identifying:
  • Primary Keys
  • Foreign Keys
  • Cardinality (one-to-one, one-to-many, many-to-many)
• Drawing ER diagrams

3. Normalization Techniques

• Goals of normalization:
  • Eliminating data redundancy
  • Ensuring data integrity
• Functional Dependencies
• Normal Forms:
  • First Normal Form (1NF)
  • Second Normal Form (2NF)
  • Third Normal Form (3NF)
• Denormalization: when and why to use

4. SQL Fundamentals

• Introduction to Structured Query Language (SQL)
• Basic SQL syntax
• SELECT statements:
  • Retrieving data
  • Filtering data (WHERE clause)
  • Sorting data (ORDER BY)
• Joining tables:
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL JOIN

5. Database Development Tools

• Overview of popular tools:
  • MySQL Workbench
  • Microsoft SQL Server Management Studio (SSMS)
  • Oracle SQL Developer
• Features and functionalities:
  • Database design
  • Query building
  • Data management
  • Debugging and optimization

6. Indexing and Performance Tuning

• Importance of indexing
• Types of indexes:
  • Clustered Indexes
  • Non-clustered Indexes
• Query optimization techniques
• Best practices for enhancing database performance

7. Database Security and Integrity

• User authentication and authorization
• Encryption methods
• Backup and recovery procedures
• Access control implementations
• Protecting sensitive data

8. Data Warehousing and Business Intelligence

• Concepts of data warehousing
• Data mart design
• ETL (Extract, Transform, Load) processes
• Business intelligence tools:
  • Data visualization
  • Reporting

9. Database Design Best Practices

• Effective data modeling techniques
• Database optimization strategies
• Maintaining data quality
• Planning for scalability and future growth