SparkContext and SparkSession are two fundamental components in Apache Spark. They serve different purposes but are closely related.

SparkContext

Here are key responsibilities of SparkContext:

• Cluster Communication - connects to the Spark cluster and manages the distribution of tasks across the cluster nodes;

• Resource Management - handles resource allocation by communicating with the cluster manager (like YARN, Mesos, or Kubernetes);

• Job Scheduling - distributes the execution of jobs and tasks among the worker nodes;

• RDD Creation - facilitates the creation of RDDs;

• Configuration - manages the configuration parameters for Spark applications.

SparkSession

Practically, it's an abstraction that combines SparkContext, SQLContext, and HiveContext.

Here are some of the key features:

Key Functions:

• Unified API - it provides a single interface to work with Spark SQL, DataFrames, Datasets, and also integrates with Hive and other data sources;

• DataFrame and Dataset Operations - SparkSession allows you to create DataFrames and Datasets, perform SQL queries, and manage metadata;

• Configuration - it manages the application configuration and provides options for Spark SQL and Hive.