MapReduce and Spark

MapReduce is a technique for performing aggregate processing on large amounts of data in parallel; it's a particularly common technique in data analytics applications. Cassandra does not offer built-in MapReduce capabilities, but it can be integrated with Hadoop in order to perform MapReduce operations across Cassandra data sets, or Spark for real-time data analysis. The DataStax enterprise product provides integration with both of these tools out of the box.
Spark is a fast, distributed, and expressive computational engine used for large-scale data processing similar to MapReduce. It is much more efficient than MapReduce and runs with resource managers such as Mesos and Yarn. It can read data from various sources such as Hadoop or Cassandra or even streams such as Kafka. DataStax provides a Spark-Cassandra connector to load data from Cassandra into Spark and run batch computations on the data.