This also isn't a straight either or proposition. I build local command line pipelines and do testing and/or processing. When either the amount of data needed to be processed passes into the range where memory or network bandwidth makes the processing more efficient on a Hadoop cluster I make some fairly minimal conversions and run the stream processing on the Hadoop cluster in streaming mode. It hasn't been uncommon for my jobs to be much faster than the same jobs run on the cluster with Hive or some other framework. Much of the speed boils down to the optimizer and the planner.

Overall I find it very efficient to use the same toolset locally and then scale it up to a cluster when and if I need to.