Apache Spark Certification Practice Test

In Apache Spark, transformations are operations on RDDs (Resilient Distributed Datasets) that define a new dataset derived from an existing one. However, these transformations are not executed immediately when they are called. Instead, they create a logical plan that Spark uses later when an action is invoked.

The execution of a transformation occurs when an action is performed. Actions are operations that trigger the execution of the transformations by requiring Spark to compute the datasets. Examples of actions include collect(), count(), and saveAsTextFile(). When one of these actions is called, Spark evaluates the transformations, optimizes the execution plan, and then processes the data accordingly.

This laziness in execution is an essential feature of Spark's design. It allows Spark to optimize the overall processing workflow and only perform the necessary computations when required by the action, instead of executing transformations immediately.