Month: November 2016

Spark in a general cluster-computing framework, and in our case we will use it to process data from the Cassandra cluster. As we saw in Part I, we cannot run any type of query on a Cassandra table. But by running a Spark worker on each host running a Cassandra node, we can efficiently read/analyse all of its data in a distributed way. Each Spark worker (slave) will read the data from its local Cassandra node and send the result back to the Spark driver (master). Spark on Cassandra Cluster

Docker