OVERVIEW Apache spark is a Distributed Computing framework. By distributed it doesn’t imply that it can run only on a cluster. Spark can be configured on our local system also. But Since spark works great in clusters and in real time , it is being implemented in multi node clusters like Hadoop, we will consider a Hadoop cluster for explaining spark here. We can Execute spark on a spark cluster in following ways Interactive clients(scala shell,pyspark etc): Usually used for exploration while coding like python shell Submit a job (using spark submit utility):Always used for submitting a production application Basic Architecture Spark follows a Master/Slave Architecture. That is For every submitted application, it creates a Master Process and multiple slave processes. Master is the Driver and Slaves are the executors. Say If from a client machine, we have submitted a spark job to a cluster. Spark will create a driver process and multiple executors. Similraly if