Understanding Transformations and Actions in Apache Spark

Transformations and Actions are two fundamental concepts in Apache Spark that play a crucial role in data processing pipelines.

Transformations

Transformations are operations that are applied to RDDs (Resilient Distributed Datasets) to create a new RDD. Examples of transformations include map, filter, and reduceByKey. These operations are lazy, meaning they are not executed immediately but instead create a lineage of transformations that will be executed when an action is called.

Actions

Actions are operations that trigger the execution of the previously defined transformations and produce a result. Examples of actions include count, collect, and saveAsTextFile. When an action is called, all the transformations in the lineage are executed to generate the final output.

In summary, transformations define the sequence of operations to be performed on the data, while actions actually trigger the execution of these operations and produce the final result.

Understanding the difference between transformations and actions is essential for optimizing data processing workflows in Apache Spark.