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In Spark, you perform operations on data using two main categories: transformations and actions. Transformations are operations that create a new RDD from an existing one, like `map()`, `filter()`, and `groupByKey()`. Transformations are *lazy*, which means they are not executed immediately. Instead, Spark remembers the transformations and applies them when an action is called. This lazy evaluation helps Spark optimize the execution plan. Actions, on the other hand, trigger the execution of the transformations. Actions return a result to the driver program, like `count()`, `collect()`, and `saveAsTextFile()`. When an action is called, Spark executes all the necessary transformations to compute the result. Understanding the difference between transformations and actions is crucial for writing efficient Spark code. Transformations define the data processing steps, and actions trigger their execution and return the results. Mastering these concepts will allow you to build complex data pipelines.
