Databases and Normalization Concepts

Databases

• Databases are structured collections of data stored electronically.
• They facilitate the efficient storage, retrieval, and manipulation of data.
• Examples include simple systems like Excel spreadsheets and complex systems like SQL databases.

Normalization

• The process of organizing data in a database to reduce redundancy and improve data integrity.
• It's a structured approach to improve efficiency in databases.
• Its main goals are:
  • Eliminate Redundancy
  • Ensure Data Integrity
  • Optimize Queries

Relational Modeling

• A methodology for designing a database that is structured to minimize redundancy and dependency.
• It involves:
  • Defining Tables: Identifying the tables needed to store data, with columns and data types for each table.
  • Defining Relationships: Establishing relationships between tables, utilizing foreign keys, to relate data across tables.
  • Normalization: Applying normalization rules for efficient database design and minimizing redundancy.

Entity Relationship Diagram (ERD)

• A visual representation of the data models and relationships within a database.
• Consists of:
  • Entities: Represent tables.
  • Attributes: Represent columns within tables.
  • Relationships: Define how tables relate to one another.
• They are essential during the database design phase, helping with:
  • Visualizing Database Structure: Providing a graphical overview of interconnected data elements.
  • Communicating Design: Acting as a communication tool for understanding the database design.
  • Guiding Database Creation: Serving as blueprints for creating the actual database.

Integration

• Normalization ensures data consistency and quality, preventing redundancy.
• This is crucial in data warehouses where data is integrated from multiple sources.
• Normalization and relational models optimize data storage, important for handling large datasets.
• A well-structured relational model facilitates efficient reporting and analytics.

Practical Considerations

• Denormalization in Data Warehousing:
  • While normalization is central in OLTP (Online Transaction Processing) databases, data warehouses sometimes utilize denormalization for improved query performance.
  • This involves combining tables to reduce the number of joins needed, speeding up read operations.
  • This approach is common in data warehouses that are optimized for read-heavy operations, rather than write-heavy operations.
• Design Complexity:
  • Both ERDs and relational modeling demand careful planning and understanding of business requirements.
  • Over-normalization can result in complex queries and slower performance.
  • Under-normalization can lead to data inconsistency.