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 driver in Apache Spark's architecture?

• It provides a user interface for Spark applications.

• It stores the data processed by Spark.

• It coordinates the execution of a Spark application.

• It manages the resources of the cluster.

Which component is responsible for managing resources in a Spark cluster?

• Spark Context

• Spark Driver

• Spark Executor

• Cluster Manager

What does the cluster manager determine when a Spark program is submitted?

• The number of Spark sessions required

• The amount of RAM and CPU needed for each worker node

• The user interface settings

• The data format for processing

How does the Spark driver interact with the cluster manager?

• By storing data on worker nodes

• By launching Spark executors on worker nodes

• By managing the cluster's network settings

• By creating a user interface

What is the main entry point of a Spark application?

• Spark Session and Context

• Cluster Manager

• Spark Executor

• User Interface