Consider that databases are often used for storing time-specific data like Sales Information, Server Logs, and Flight Timetables all immediately come to mind as prime candidates in this regard. As you might imagine, though, such data stores can easily become excessively large and quite unwieldy. As a result, a common storage strategy is to break the data up into numerous tables, with each name pertinent to a particular time block. For example, 12 identical tables might be used to store server log data, with each assigned a name corresponding to each month of the year. However, reports based on data spread across all 12 tables are necessary, meaning multitable queries will need to be written and updated to reflect the information found within these tables. Rather than write such potentially error-prone queries, the tables can be merged together and a single query can be used instead. The MERGE table can later be dropped without affecting the original data.