Even if the data isn't big, there can be a benefit from the Hadoop infrastructure. Say you have just 86,400 rows of data but each row takes 1 second. That adds up to 24 hours of elapsed time, and waiting for that run can be painful, especially if you are trying to experiment, iterate. With HDFS/MapReduce you can distribute that work across N machines and divide the elapsed time by N, speeding up the pace of iteration. I've worked on a project that had exactly this challenge, before Hadoop was available, and so we had to invent our own crappy ways of distributing the data to the N machines, monitoring them, collecting the results. Hadoop HDFS and Map/Reduce, with Job Tracker, etc, would have been much better than what we came up with.