When dealing with large datasets, optimizing data storage is crucial for performance and efficiency. The Date column, containing date-time values, can be optimized by reducing its precision, thereby decreasing the storage requirements.

Changing the data type to text: Converting the Date column to text would likely increase the storage size and complicate date-time operations, as text representations are less efficient for date-time computations.

Trimming the date: This option is ambiguous. If it refers to removing time components, it could lead to loss of essential information.

Rounding the hour to the start of the hour: By adjusting the time to the top of the hour (e.g., 10:45 becomes 10:00), the precision is reduced, which can decrease the storage size and improve performance without significant loss of essential information.

Splitting into two columns—time and date: This approach increases the number of columns and may not effectively reduce the overall data size.

Therefore, rounding the hour to the start of the hour is the most effective method to reduce the data model's size while preserving essential temporal information.