3. In what scenarios is it more beneficial to denormalize data in the context of One-to-Many relationships?

Denormalization of data in the context of One-to-Many relationships can be beneficial in several scenarios:

1. Improved Query Performance: Denormalization can reduce the number of joins needed to execute a query, which can significantly improve query performance. This is particularly beneficial in scenarios where read operations are more frequent than write operations.

2. Simplified Queries: Denormalization can simplify the structure of the data, making it easier to understand and query. This can be beneficial in scenarios where the complexity of the data structure is a barrier to efficient data analysis.

3. Data Aggregation: In scenarios where aggregated data is frequently accessed, denormalization can be beneficial. By storing the aggregated data in a denormalized table, the need to calculate the aggregated data each time it is accessed can be eliminated.

4. Data Replication: In distributed database systems, denormalization can be used to replicate data across multiple servers. This can improve the performance and reliability of the system.

However, it's important to note that denormalization also has its drawbacks, such as increased storage costs and the potential for data anomalies. Therefore, the decision to denormalize data should be made carefully, taking into account the specific requirements and constraints of the scenario.