In-memory computing

A strategy that involves moving the working datasets entirely within a cluster's collective memory instead of reading it from hard disk, to reduce the processing time while omitting I/O bound operations. Intermediate calculations are not written to disk and are instead held in memory. This is the fundamental idea of projects such as Apache Spark. Because of this, it has huge advantages in speed over I/O bound systems such as MapReduce.