Time and date dimension in data warehouse

My guess is that it depends on your reporting requirement. If you need need something like

WHERE "Hour" = 10

meaning every day between 10:00:00 and 10:59:59, then I would use the time dimension, because it is faster than

WHERE date_part('hour', TimeStamp) = 10  

because the date_part() function will be evaluated for every row. You should still keep the TimeStamp in the fact table in order to aggregate over boundaries of days, like in:

WHERE TimeStamp between '2010-03-22 23:30' and '2010-03-23 11:15' 

which gets awkward when using dimension fields.

Usually, time dimension has a minute resolution, so 1440 rows.

Time should be a dimension on data warehouses, since you will frequently want to aggregate about it. You could use the snowflake-Schema to reduce the overhead. In general, as I pointed out in my comment, hours seem like an unusually high resolution. If you insist on them, making the hour of the day a separate dimension might help, but I cannot tell you if this is good design.

Kimball recommends having separate time- and date dimensions:

design-tip-51-latest-thinking-on-time-dimension-tables

In previous Toolkit books, we have recommended building such a dimension with the minutes or seconds component of time as an offset from midnight of each day, but we have come to realize that the resulting end user applications became too difficult, especially when trying to compute time spans. Also, unlike the calendar day dimension, there are very few descriptive attributes for the specific minute or second within a day. If the enterprise has well defined attributes for time slices within a day, such as shift names, or advertising time slots, an additional time-of-day dimension can be added to the design where this dimension is defined as the number of minutes (or even seconds) past midnight. Thus this time-ofday dimension would either have 1440 records if the grain were minutes or 86,400 records if the grain were seconds.