What characterizes a denormalized database like OLAP?

A denormalized database, such as one used in Online Analytical Processing (OLAP), is characterized primarily by its emphasis on performance, particularly in query execution, at the expense of redundancy. In OLAP systems, the need for fast retrieval of data for analysis takes precedence over minimizing duplicates, which is a hallmark of normalized databases often used in Online Transaction Processing (OLTP). This design choice enables OLAP systems to aggregate and summarize large volumes of data quickly, making them ideal for reporting and analysis tasks.

In OLAP, denormalization might involve combining tables or including redundant data, which simplifies queries and prevents the need to perform multiple joins when accessing data. Consequently, this strategy results in improved performance and quicker response times when executing complex analytical queries, as it allows for operations over a flatter data structure rather than a highly normalized one.

The other options contrast the nature of denormalization in OLAP databases. For instance, having more tables than OLTP is usually not the case, as denormalization typically means fewer, more complex tables. Stricter normalization rules are not a characteristic of a denormalized structure; rather, they are typically applied in OLTP environments to reduce redundancy and maintain data integrity. Lastly, a denormalized database