Normalization is a process that helps organize relational databases into logical and efficient structures by eliminating data redundancy and ensuring data integrity. It involves decomposing large tables into smaller ones based on specific rules and dependencies. The most common normalization forms include First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF).
First Normal Form (1NF):
1NF requires that each attribute within a table contains only atomic (indivisible) values. It eliminates repeating groups and ensures that each row has a unique identifier (primary key).
Second Normal Form (2NF):
2NF builds upon 1NF and addresses partial dependencies. It requires that all non-key attributes depend on the entire primary key, rather than just a portion of it. This form ensures each attribute is functionally dependent on the primary key.
Third Normal Form (3NF):
3NF further refines the normalization process by addressing transitive dependencies. It mandates that no non-key attribute depends on other non-key attributes within the same table. In other words, it removes indirect relationships between non-key attributes.
** Benefits of Normalization:**
Data integrity:
Normalization helps prevent anomalies such as insertion, deletion, and update anomalies, ensuring data consistency.
Reduced redundancy: **
By eliminating redundant data, normalization reduces storage requirements and avoids inconsistencies.
**Improved query performance:
Normalized tables are typically optimized for efficient data retrieval, leading to faster query execution.
Denormalization:
Denormalization is the process of selectively reintroducing redundancy into a database to improve query performance or simplify data retrieval. While normalization aims to minimize redundancy, denormalization acknowledges that in certain scenarios, duplicating data can be beneficial.
Types of Denormalization:
Flattening:
Combining related tables into a single table to eliminate the need for joins.
Redundant Columns: Adding duplicate data to a table to avoid joins and improve query performance.
Summary Tables:
Creating aggregated tables that contain pre-computed summaries of data to speed up analytical queries.
Materialized Views:
Storing the results of complex queries as physical tables to enhance query performance.
Benefits of Denormalization:
Improved query performance: **
By reducing the number of joins or eliminating them altogether, denormalization can significantly enhance query execution speed.
**Simplified data model:
Denormalized structures can simplify application development and reduce complexity.
Reduced resource consumption:
Denormalization can decrease the demand for computational resources, such as CPU and memory, during query execution.
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