While learning Hadoop is a good idea, if you're bored in grad school, you're doing it wrong.
EDIT: its also worth pointing out that Hadoop is not the 'be all, end all' framework for large-scale data analysis. Many problems do not fit the map-reduce paradigm, and are better suited for other frameworks (looking into GraphLab once I get free time, for example).
I know Hadoop is the poster child of all things good, but its API really makes me fall asleep. Not a big fan. Add to that the fact that Google's implementation is (or atleast used to be) 4~6 times faster for similar sized processing jobs and more fun to code in (the latter may be entirely subjective). The funny part is that Google's clusters then were made of weaker machines ! I dont know how it is now.
EDIT: It is indeed in C++ that alone cannot fully explain the discrepancy though. Java can be slower but shouldnt be that much slower. I am sure caching, memory footprint and as you said data access latencies and overall design plays a big role. If I remember correctly UIUC has an open source mapreduce framework written in C++ and they claim a similar speedup over hadoop.
4-6 times is crazy though. You sure it just wasn't due to data access latencies?