Apache Spark is a popular (because it is fast) big data engine. The speed comes from keeping data in memory. This is an update to my older post: it is still Spark in standalone mode, using the nodes assigned by GE as the worker nodes. I have an update for using Spark 2.2.0, with Java 1.8.0.
It is mostly the same, except only one file needs to be modified: sbin/slaves.sh The Parallel Environment (PE) startup script update only adds an environment variable for defining where the worker logs go. (Into a Grid Engine job-specific directory under the job directory.) And it now specifies Java 1.8.0.
As before, the modifications to sbin/slaves.sh handle using the proper spark-env script based on the user's shell. Since that spark-env script is set up by the PE script to generate job-specific conf and log directories, everything job-specific is separated.