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The Difference between Transformation and Action in Apache Spark

When working with Apache Spark, it is crucial to differentiate between transformation and action. The focus keyword "Apache Spark" refers to a powerful distributed data processing framework. In this context:

Transformation:

A transformation in Apache Spark is a function that produces a new Resilient Distributed Dataset (RDD) from an existing RDD. These transformations are lazy in nature, meaning they are not executed immediately, but instead create a lineage of transformations that will be applied when an action is triggered.

Action:

An action in Apache Spark is a method that triggers the execution of the lineage of transformations on the RDD and computes a result. Examples of actions include actions like count(), saveAsTextFile(), or collect(), which initiate the computation process.

Understanding when to use transformations and actions efficiently can significantly impact the performance and optimization of data processing workflows in Apache Spark.

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In this HTML snippet, the focus keyword "Apache Spark" is strategically included in the title, meta description, and body content for SEO optimization. The explanation of the difference between transformation and action in Apache Spark is clear and informative for interview purposes.