Activity #5: Research Normalization and Denormalization in Databases

A. Research Database Normalization

What is Normalization?

Normalization is the process of organizing data within a database to minimize redundancy and enhance data integrity. This technique is vital for structuring data efficiently. By dividing data into smaller, manageable tables that are linked through relationships, normalization reduces redundancy and improves data integrity and overall database performance.

Importance of Normalization

Normalization is crucial for several reasons:

1. Minimization of Redundancy:

   • By organizing data into related tables, normalization helps eliminate duplicate entries. This leads to a more efficient use of storage and reduces the chances of inconsistencies within the database.

2. Enhancement of Data Integrity:

   • Normalization helps maintain accuracy and consistency in the data. It ensures that each piece of information is stored in only one place, reducing the risk of errors that can arise from having multiple copies of the same data.

3. Improved Database Performance:

   • A normalized database can lead to faster query performance as there is less data to search through. Queries become simpler and more efficient when they are based on a well-structured database.

4. Prevention of Anomalies:

   • Without normalization, databases can encounter various anomalies that compromise their reliability:

     • Insert Anomaly: This occurs when you cannot add new data to a table without including related data, which can hinder data entry processes.

     • Update Anomaly: Caused by redundancy, this anomaly leads to inconsistencies during data updates, making it challenging to maintain accurate records.

     • Delete Anomaly: This happens when deleting certain attributes results in the loss of other important data, potentially compromising the integrity of the database.

5. Facilitation of Future Growth:

   • A normalized structure allows for easier adjustments and scalability. As the database grows, adding new data types or relationships can be managed more smoothly, ensuring that the database can evolve without significant restructuring.

Normalization is an essential practice for anyone managing a database. By minimizing redundancy, enhancing data integrity, and preventing anomalies, normalization not only improves the efficiency of data management but also lays a strong foundation for future scalability and performance.

(Database tables in a Normalized manner)

Levels of Normalization

Database normalization is typically divided into several levels, referred to as normal forms. The most commonly used normal forms are:

Normal Forms:

1. • First Normal Form (1NF):

     Ensures each column contains atomic values and each record is unique.

Consider an unnormalized table that stores customer orders:

This table violates 1NF because the Products column contains a list of items. To bring it to 1NF, we split the products into separate rows:

Now, each cell contains an atomic value, and the table is in 1NF.

2. Second Normal Form (2NF):

   Builds on 1NF by ensuring that all non-key attributes are fully dependent on the primary key.

Consider a table that stores information about students and their courses:

This table violates 2NF because the Instructor attribute depends on both StudentID and CourseID. To achieve 2NF, we split the table into two separate tables:

Students Table:

Courses Table:

Now, the Instructor attribute depends only on the CourseID, and the table is in 2NF.

3. Third Normal Form (3NF):

   Ensures there are no transitive dependencies, meaning non-key attributes depend only on the primary key.

Consider a table that stores information about employees and their projects:

This table violates 3NF because the Manager attribute depends on the EmployeeID, not directly on the primary key. To bring it to 3NF, we split the table into two separate tables:

Employees Table:

Projects Table:

EmployeeProjects Table:

Now, the Manager attribute depends on the ProjectID, and the table is in 3NF.

Boyce-Codd Normal Form (BCNF):

4. A stricter version of 3NF, ensuring precise handling of functional dependencies.

BCNF is a stricter version of 3NF. To illustrate BCNF, consider a table that stores information about professors and their research areas:

This table violates BCNF because there is a non-trivial functional dependency between ResearchArea and OfficeNumber (i.e., the office number depends on the research area). To achieve BCNF, we split the table into two separate tables:

Professors Table:

ResearchAreas Table:

ProfessorResearch Table:

Now, the table is in BCNF because there are no non-trivial functional dependencies.

5. Fourth Normal Form (4NF)

4NF deals with multi-valued dependencies. Consider a table that stores information about books and their authors:

This table violates 4NF because there is a multi-valued dependency between BookID and Authors. To achieve 4NF, we split the table into three separate tables:

Books Table:

Authors Table:

BookAuthors Table:

Now, each table is in 4NF, and multi-valued dependencies are removed.

Advantages of Normalization:

Normalization offers numerous benefits that make it a compelling choice in the context of Relational Database Management Systems (RDBMS):

1. Reduction of Database Size:

   • Normalization eliminates duplicate data, resulting in a smaller overall database size.

2. Enhanced Performance:

   • A reduced database size leads to quicker data retrieval, improving response times and overall speed.

3. More Efficient Table Design:

   • Tables created through normalization are more streamlined, often containing fewer columns. This allows for more records to be stored per page, optimizing space.

4. Simplified Maintenance Tasks:

   • With fewer records in each table, maintenance operations like indexing become faster and more manageable.

5. Focused Data Management:

   • Normalization allows for the selection of only the necessary tables during queries, streamlining the data retrieval process.

6. Elimination of Redundancy:

   • By ensuring that each piece of data is stored only once, normalization minimizes data redundancy and the risk of inconsistencies, thereby enhancing data accuracy.

7. Improved Data Integrity:

   • Data is broken down into more specific tables, ensuring that each table retains relevant information, which bolsters overall data integrity.

8. Streamlined Data Updates:

   • Updates are simplified, as changes need to be made in only one location, rather than multiple instances across the database.

9. Easier Database Design:

   • The systematic approach provided by normalization simplifies database design, making it easier to develop and maintain over time.

10. Flexible Querying:

    • Normalization enables varied querying options since data is organized into smaller, specific tables that can be joined as needed.

11. Scalability Support:

    • By reducing redundancy and structuring data efficiently, normalization facilitates the database's ability to scale to meet future needs.

12. Consistency Across Applications:

Normalization helps maintain data consistency across various applications that utilize the same database, enhancing integration and accuracy for all users.

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Disadvantages of Normalization

Despite its advantages, normalization has its drawbacks:

1. Increased Joins:

   • The dispersion of data across multiple tables necessitates more join operations, which can complicate queries and slow down performance.

2. Use of Codes Instead of Data:

   • Data may be stored as codes instead of actual values, requiring additional steps to reference the original data.

3. Complex Data Models:

   • The complexity of the data model can hinder ad-hoc queries, making it difficult to extract information without prior knowledge of the model.

4. Performance Impact with Complexity:

   • As the structure becomes more complex, the performance of the database may decrease due to the additional processing required.

5. Need for In-Depth Knowledge:

   • Successful normalization requires a good understanding of different normal forms. Improper application can lead to poor design and data anomalies.

6. Increased Complexity:

   • If not executed correctly, normalization can complicate the database design, making maintenance and updates more challenging.

7. Reduced Flexibility:

   • The strict organization required can limit the database's flexibility, making it difficult to accommodate changes or generate varied reports.

8. Higher Storage Requirements:

   • More tables and join operations may lead to increased storage needs, raising the overall cost of hardware.

9. Performance Overhead:

   • The need for additional joins can result in performance overhead and slower query execution times.

10. Loss of Context:

    • Normalization can result in data being split across tables, which may obscure the relationships between different data elements.

11. Risk of Update Anomalies:

    • Without proper design and maintenance, normalization can introduce risks of insert, update, and delete anomalies.

12. Expertise Requirement:

    • Proper implementation demands expert knowledge of database design. Without it, the benefits of normalization may not be fully realized, potentially compromising data consistency.

B. Research Denormalization

Denormalization is the process of intentionally introducing redundancy into a database by merging tables or introducing redundant data. The goal is to improve query performance by reducing the need for joins and aggregations.

When to Denormalize:

• Read-Heavy Workloads: In scenarios where the database is queried more frequently than it is updated, denormalization can be beneficial.

• Complex Queries: For complex queries that involve multiple joins, denormalization can simplify and speed up the retrieval of data.

• Reporting and Analytics: Denormalization is often employed in data warehousing and reporting systems.

Example:

Consider a denormalized version of the “Books” table where author information is reintroduced:

Book’s Table

Observations:

• Author information (Author and AuthorEmail) is reintroduced directly into the “Books” table.

• Redundancy is present, as the same author information is repeated for each book.

Benefits of Denormalization:

• Improved Query Performance: By reducing the need for joins, queries can be executed more quickly.

• Simplified Queries: Denormalized databases often lead to simpler and more straightforward queries.

Drawbacks of Denormalization:

• Data Redundancy: Introducing redundancy increases the risk of data inconsistency.

• Increased Update Complexity: Updates become more complex as changes need to be propagated across redundant data.

• Potential for Anomalies: Denormalization can reintroduce certain types of anomalies, especially in the presence of updates, insertions, or deletions.

The decision to normalize or denormalize depends on the specific requirements of the application. Striking the right balance between normalization and denormalization is often the key to designing an efficient and maintainable database.

• Hybrid Approaches: Many databases adopt a hybrid approach, normalizing critical tables for data integrity and denormalizing for performance where necessary.

• Use Cases: Consider the nature of the application and its primary use cases. Transactional systems may benefit more from normalization, while analytical systems may lean towards denormalization.

In conclusion, normalization and denormalization are essential tools in the database designer’s toolkit. The key is to understand the specific needs of the application and strike a balance that ensures both data integrity and optimal performance. The art of database design lies in making informed decisions based on the unique requirements

Source Citations:

GeeksforGeeks. (2024, September 13). What is Data Normalization and Why Is It Important? GeeksforGeeks. https://www.geeksforgeeks.org/what-is-data-normalization-and-why-is-it-important/

Vpadmin. (2023, September 15). A Comprehensive Guide to Database Normalization with Examples - Visual Paradigm Guides. Visual Paradigm Guides. https://guides.visual-paradigm.com/a-comprehensive-guide-to-database-normalization-with-examples/

Maiqani, E. (2024, January 19). Database Normalization vs. Denormalization - Analytics Vidhya - Medium. Medium. https://medium.com/analytics-vidhya/database-normalization-vs-denormalization-a42d211dd891

Vpadmin. (2023b, September 18). Balancing Data Integrity and Performance: Normalization vs. Denormalization in Database Design - Visual Paradigm Guides. Visual Paradigm Guides. https://guides.visual-paradigm.com/balancing-data-integrity-and-performance-normalization-vs-denormalization-in-database-design/

GeeksforGeeks. (2023, April 22). Advantages and disadvantages of normalization. GeeksforGeeks. https://www.geeksforgeeks.org/advantages-and-disadvantages-of-normalization/

Faisal, T. M. A. (2023, December 5). Database Normalization Explained with Real-World Examples. Medium. https://medium.com/@nabilt59/database-normalization-explained-with-real-world-examples-9fe0b3a8e021