We learned in the introduction that MapReduce is primarily a data processing engine since it enables the processing of data and is therefore a data processing engine. Spark, on the other hand, is a framework that powers whole analytical solutions or applications; this characteristic makes it a logical choice for data scientists to use Spark as a data analytics engine. When it comes to MapReduce, the processing pace is slowed down since reading and writing operations are conducted from and to a disc. This causes the speed of the processing to be slowe Along with reducing the number of read/write cycles, Spark also minimises the amount of data that is stored in memory, which enables it to be ten times faster. However, if the data cannot be stored in memory, spark's performance may significantly degrade.