Activity #5: Research Normalization and Denormalization in Databases

Step 1: Research Database Normalization

• What is Normalization?

  • Normalization is a database design technique that aims to reduce data redundancy (duplicate information) and improve data integrity (consistency and accuracy). It involves organizing data into tables and defining relationships between them in a structured way.

• Purpose:

  • Reduce Data Redundancy: By eliminating duplicate data, normalization saves storage space and prevents inconsistencies. If you change a piece of data in one place, you don't have to update it in multiple locations.

  • Improve Data Integrity: Normalization ensures that data is consistent and accurate across the database. Changes are applied uniformly, reducing the risk of errors.

• Importance:

  • Efficient Database Structure: A normalized database is more efficient because it requires less storage space and can be accessed faster.

  • Maintain Consistency: Normalization helps prevent data anomalies, ensuring that data is accurate and reliable.

  • Easier Maintenance: Normalized databases are easier to maintain and update because changes are applied consistently across tables.

• Normal Forms:

  • First Normal Form (1NF): Each column contains atomic (indivisible) values, and each record is unique. This means no repeating groups of columns and each cell contains a single value.

  • Second Normal Form (2NF): Builds on 1NF by ensuring that all non-key attributes (columns that are not part of the primary key) are fully dependent on the primary key. This eliminates partial dependencies.

  • Third Normal Form (3NF): Ensures that there are no transitive dependencies, meaning non-key attributes depend only on the primary key, not on other non-key attributes. This eliminates dependencies that are not directly related to the primary key.

  • Boyce-Codd Normal Form (BCNF): A stricter version of 3NF that ensures even more precise handling of functional dependencies. It eliminates any potential for anomalies related to functional dependencies.

• Advantages:

  • Reduced Redundancy: Less storage space is needed.

  • Data Consistency: Data is consistent across the database.

  • Improved Data Organization: Data is structured logically, making it easier to understand and manage.

Step 2: Research Denormalization

• What is Denormalization?

  • Denormalization is the process of intentionally adding redundancy to a database to improve query performance. It involves combining data from multiple tables into a single table, even if it violates normalization rules.

• Purpose:

  • Optimize Query Performance: Denormalization can significantly speed up queries, especially in large databases where read-heavy operations are common. By reducing the number of joins required to retrieve data, queries become faster.

• Why Denormalization is Necessary:

  • Read-Heavy Operations: In systems where data is primarily read (like reporting or data warehousing), denormalization can be beneficial.

  • Large-Scale Databases: Denormalization can be useful for large databases where performance is critical.

• When to Use Denormalization:

  • Data Warehousing: Data warehouses often use denormalization to optimize reporting and analysis.

  • Reporting Systems: Reporting systems often require fast retrieval of aggregated data, making denormalization a good choice.

  • Read-Optimized Databases: Databases designed for primarily read operations can benefit from denormalization.

• Trade-offs:

  • Performance Improvement: Faster queries.

  • Data Anomalies: Increased risk of data inconsistencies and anomalies.

  • Increased Storage: More storage space is required due to redundancy.

Step 3: Compare Normalization vs. Denormalization

Feature:

Denormalization

Redundancy

Query Performance

Data Integrity

Storage

Normalization :

Minimizes redundancy

Generally slower

High

Less storage space required

Denormalization :

Introduces redundancy

Generally faster

Potential for anomalies

More storage space required

• Practical Examples:

  • E-commerce Website: A normalized database might store customer information, order details, and product data in separate tables. For reporting purposes, a denormalized table could be created to combine customer, order, and product information for faster analysis.

  • Data Warehouse: A data warehouse might denormalize data from multiple source systems to create a single, integrated view for reporting and analysis.

• Normalization First, Then Denormalization:

  • It's common to start with a normalized database for data integrity and then selectively denormalize specific tables or views to optimize performance for specific queries.