Apache Spark 3 for Data Engineering and Analytics with Python - The Spark Architecture

The video tutorial explains how Apache Spark operates within a master-slave architecture, where the master node is the driver and the slave nodes are the workers. It details the role of the Spark session and context as entry points for applications, and how operations are executed on worker nodes. The tutorial also covers the function of cluster management systems like Apache Yarn and Mesos, which manage resources and determine the allocation of RAM and CPU for Spark programs. The Spark driver is highlighted as the central coordinator, interacting with the cluster manager to execute processing logic on worker nodes. The video concludes with a brief mention of the Spark unified stack.

What is the role of the master node in Apache Spark's architecture?

Explain how a Spark session is created and its significance in a Spark application.

What are the main components of a cluster manager in a resource management system?

Describe the interaction between the Spark driver and the cluster manager.

What is the purpose of the Spark executor in the Spark architecture?