Normalization and denormalization are two opposing database design techniques aimed at achieving different goals. Let’s explore each concept:

Normalization: Normalization is the process of organizing the data in a database to minimize redundancy and dependency. The main objective of normalization is to ensure data integrity and reduce anomalies during data manipulation.

Normalization typically involves dividing large tables into smaller, related tables and defining relationships between them. This is usually achieved by applying a series of normalization forms, such as First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), and beyond.

The normalization process usually results in the following benefits:

1. Reducing data redundancy: By eliminating duplicate data, normalization reduces storage space requirements and ensures data consistency.
2. Improving data integrity: By organizing data into smaller, related tables and enforcing referential integrity constraints, normalization helps maintain data integrity and prevent anomalies like update, insertion, and deletion anomalies.
3. Simplifying database maintenance: Normalized databases are typically easier to maintain and modify, as changes made to one part of the database are less likely to affect other parts.

Denormalization: Denormalization is the process of intentionally introducing redundancy into a database schema to improve query performance or simplify data retrieval. Unlike normalization, which aims to minimize redundancy, denormalization deliberately duplicates data to optimize read performance.

Denormalization is often applied in scenarios where:

1. There are frequent read operations and relatively fewer write operations.
2. Queries frequently involve joining multiple tables, and performance is a primary concern.
3. The application requires real-time or near-real-time data retrieval, and the overhead of normalization is deemed too high.

Denormalization can lead to the following benefits:

1. Improved query performance: By reducing the need for joins and simplifying data retrieval, denormalization can improve query performance, especially for complex queries involving multiple tables.
2. Reduced computational overhead: Denormalized schemas can minimize the computational overhead associated with join operations, aggregation, and other query processing tasks.
3. Better scalability: In some cases, denormalization can improve database scalability by reducing the complexity of queries and distributing the workload more evenly across database servers.

However, denormalization also comes with certain trade-offs, including increased storage requirements, potential data inconsistency (if updates are not properly synchronized), and added complexity in maintaining data integrity. Therefore, denormalization should be carefully considered and balanced against the specific performance requirements and constraints of the application.