Note:- To set-up I have used version 1.5.2 When you run jobs in Spark’s local mode. In this mode, the Spark driver runs along with an executor in the same Java process. Spark can run over a variety of cluster managers to access the machines in a cluster. If you only want to run Spark by itself on a set of machines, the built-in Standalone mode is the easiest way to deploy it. Spark can also run over two popular cluster managers: Hadoop YARN and Apache Mesos. Standalone Cluster manager Copy a compiled version of Spark to the same location on all your machines—for example, /usr/local/spark. Set up password-less SSH access from your master machine to the others. This requires having the same user account on all the machines, creating a private SSH key for it on the master via ssh-keygen, and adding this key to the .ssh/authorized_keys file of all the workers. If you have not set this up before, you can follow these commands: # On master: run ssh-keygen accepting de