Understanding Spark Application concepts and Running our first PySpark application

Explains the key concepts of a Spark application including application, spark session, job, stage, and task.

Details the spark session as the entry point of every Spark application and the job as a parallel computation consisting of multiple tasks distributed in response to Spark actions.

Discusses the job, stage, and task components in detail, highlighting their roles in the execution of a Spark application.

Explains how the Spark driver transforms a Spark application into multiple jobs and stages, submitting them to worker nodes for execution.

Describes tasks as units of execution assigned to each Spark executor, working in parallel on different partitions of data.

Explores lazy evaluation in Spark, classifying operations into transformations and actions, and how it optimizes query execution.

Distinguishes between narrow and wide transformations in Spark, highlighting their impact on data partitioning and computation efficiency.

Introduces the Spark UI, a graphical interface for monitoring and managing Spark applications, providing insights into jobs, stages, and tasks.

Demonstrates the execution of a Spark application, including importing packages, creating a Spark session, defining a custom schema, and performing transformations and actions on a CSV file data frame.