While normalization is a cornerstone of good database design, aimed at reducing redundancy and improving data integrity, there are situations where a deliberate departure from strict normalization, known as denormalization, can be beneficial. Denormalization is the process of intentionally introducing controlled redundancy into a database schema to improve query performance.

It's crucial to understand that denormalization is an optimization technique, not a design starting point. You should first design your database in a normalized form (typically 3NF or BCNF) and then consider denormalization only if performance bottlenecks are identified and cannot be resolved through other means (e.g., indexing, query optimization).

1. Improved Query Performance: This is the primary driver. Joins between multiple normalized tables can be computationally expensive, especially with large datasets and complex queries. By combining data into fewer tables or duplicating data, denormalization can reduce the number of joins required for frequently accessed queries, leading to significantly faster data retrieval.
2. Simpler Queries: Some complex analytical or reporting queries that would otherwise require multiple intricate joins can be simplified when data is pre-joined or aggregated in a denormalized structure.
3. Optimizing for Reporting and Analytical Needs (Data Warehousing): Data warehouses and Online Analytical Processing (OLAP) systems are prime examples where denormalization is widely used. They often employ denormalized schemas like star schemas or snowflake schemas (which are variations of denormalized structures) to optimize for aggregation and slice-and-dice operations on large volumes of historical data. The focus here is on fast reads for analysis, not transactional updates.
4. Specific Application Requirements: Sometimes, an application's unique data access patterns or the need for very low latency for specific operations might lead to a denormalized structure.

The benefits of denormalization come with significant trade-offs:
1. Increased Data Redundancy: This is the inherent nature of denormalization. Storing the same data in multiple places increases storage space requirements.
2. Increased Risk of Data Inconsistency: If redundant data is not updated consistently across all its occurrences, the database will contain conflicting information. Maintaining data integrity becomes much more complex, often requiring application-level logic, triggers, or batch processes to ensure consistency. This is the biggest danger.
3. Increased Complexity for Update, Insertion, and Deletion Operations: Modifying data in a denormalized table often means updating multiple instances of the same data, which can be slower and more complicated to manage transactionally.
4. More Complex Design and Maintenance: Denormalized schemas can be harder to design, understand, and maintain. The explicit relationships and clear single source of truth provided by normalized forms are lost, making schema evolution more challenging.

Denormalization should always be a strategic decision made after a thorough analysis of:
- Identified Performance Bottlenecks: Do not denormalize speculatively. Only consider it when specific queries are demonstrably performing poorly due to normalization.
- Read vs. Write Patterns: Denormalization is generally more suitable for read-heavy systems (where data is frequently queried but rarely updated). For systems with frequent updates, the overhead of maintaining consistency in a denormalized schema often outweighs the read performance benefits.
- Tolerance for Redundancy and Consistency: Understand the business implications of potential data inconsistencies. Can your application tolerate temporary inconsistencies, or does it require absolute real-time consistency?
- Availability of Other Optimization Techniques: Before denormalizing, explore other performance tuning options such as:
- Indexing: Properly indexing frequently queried columns.
- Query Optimization: Rewriting inefficient queries.
- Caching: Storing frequently accessed data in memory.
- Hardware Upgrades: Improving server resources.

Common Denormalization Techniques:
- Adding Redundant Columns: Duplicating a column from a "parent" table into a "child" table to avoid a join. (e.g., adding DepartmentName to an Employee table if it's always associated with DepartmentID).
- Creating Summary (Aggregate) Tables: Storing pre-calculated aggregates (e.g., DailySalesTotal, MonthlyRevenue) that would otherwise require complex and time-consuming calculations on the fly. This is common in data warehousing.
- Storing Derived Data: Storing a calculated value (e.g., OrderTotal in an Order table, instead of calculating it from UnitPrice * Quantity for each item) if the calculation is expensive and frequently needed.
- Combining Tables (Horizontal or Vertical Partitioning): Merging two or more tables into a single table, even if it violates normal forms, to reduce joins.