Apache Spark comprises several key components, each designed to handle different types of data processing tasks:

• Spark Core - the foundation of Spark, providing basic functionalities like task scheduling, memory management, and fault tolerance;
• Spark SQL - enables querying of structured data using SQL or DataFrame APIs and integrates with various data sources (e.g., Hive, Avro, Parquet);
• Spark Streaming - allows to process real-time data streams with micro-batch processing;
• MLlib - a library for scalable machine learning algorithms, including classification, regression, clustering, and collaborative filtering.
• GraphX - provides a framework for graph processing and analytics, supporting operations like graph traversal and computation.

Spark operates on a cluster architecture that consists of a master node and worker nodes.

The master node manages job scheduling and resource allocation, while worker nodes execute tasks and store data.

Basic Workflow:

1. Submit Application - users submit Spark applications to a cluster manager (e.g., YARN, Mesos, or Kubernetes);
2. Job Scheduling - the cluster manager schedules the job and distributes tasks across worker nodes;
3. Task Execution - worker nodes execute the tasks, performing computations on data stored in memory or on disk;
4. Result Collection - the results of the tasks are collected and returned to the user.