In this case, there is only one file, sample.txt, but in general there can be more, and Hive will read all of them when querying the table. The OVERWRITE keyword in the LOAD DATA statement tells Hive to delete any existing files in the directory for the table. If it is omitted, the new files are simply added to the table's directory (unless they have the same names, in which case they replace the old files). Now that the data is in Hive, we can run a query against it: hive> > > > > 1949 1950 SELECT year, MAX(temperature) FROM records WHERE temperature != 9999 AND (quality = 0 OR quality = 1 OR quality = 4 OR quality = 5 OR quality = 9) GROUP BY year; 111 22 This SQL query is unremarkable. It is a SELECT statement with a GROUP BY clause for grouping rows into years, which uses the MAX() aggregate function to find the maximum temperature for each year group. But the remarkable thing is that Hive transforms this query into a MapReduce job, which it executes on our behalf, then prints the results to the console. There are some nuances, such as the SQL constructs that Hive supports and the format of the data that we can query--and we explore some of these in this chapter--but it is the ability to execute SQL queries against our raw data that gives Hive its power.