Research Normalization and Denormalization in Databases

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Step 1: Research Database Normalization

1. What is Normalization?

   • Research the purpose of normalization in database design. Understand how it reduces data redundancy and improves data integrity.

   • Document why normalization is important for maintaining an efficient and consistent database structure.

2. Normal Forms:

   • Research the different levels of normal forms (1NF, 2NF, 3NF, BCNF).

     • First Normal Form (1NF): Ensures that each column contains atomic (indivisible) values, and each record is unique.

     • Second Normal Form (2NF): Builds on 1NF by ensuring that all non-key attributes are fully dependent on the primary key.

     • Third Normal Form (3NF): Ensures that there are no transitive dependencies, meaning non-key attributes depend only on the primary key.

     • Boyce-Codd Normal Form (BCNF): A stricter version of 3NF that ensures even more precise handling of functional dependencies.

3. Advantages of Normalization:

   • Research the benefits of normalization, such as reducing redundancy, ensuring data consistency, and improving data organization.

Step 2: Research Denormalization

1. What is Denormalization?

   • Research denormalization and understand its purpose in database design. Learn how denormalization intentionally adds redundancy to optimize data retrieval speed.

   • Document why denormalization is sometimes necessary to improve performance, especially in large-scale databases where read-heavy operations are common.

2. When to Use Denormalization:

   • Research scenarios where denormalization is beneficial, such as in data warehousing, reporting systems, and read-optimized databases.

   • Understand the trade-offs, including how denormalization can speed up queries but may lead to data anomalies and increased storage requirements.

Step 3: Compare Normalization vs. Denormalization

1. Differences:

   • Research and document the key differences between normalization and denormalization. Understand the trade-offs between the two approaches in terms of data redundancy, query performance, and data integrity.

2. Practical Examples:

   • Find real-world examples or case studies where both normalization and denormalization are applied.

   • Research situations where databases are first normalized for data integrity and then selectively denormalized to optimize performance.