Transformations in Spark are operations that transform an existing RDD into a new RDD, such as map, filter, and reduceByKey. They are executed in a lazy manner, meaning that Spark will not immediately execute the transformations but will instead wait for an action to be called. Actions, on the other hand, are operations that produce a final result or write data to an external storage system, such as counting the number of elements in an RDD using the count action or saving an RDD to a file using the save action.

Transformations in Spark are operations on RDDs (Resilient Distributed Datasets) that return a new RDD, such as map or filter. They are lazily evaluated, meaning that they are not executed until an action is called on the RDD. Actions, on the other hand, are operations that perform computation and return a result or write data to an external storage system. Examples of actions include count, collect, and save.

Transformations are evaluated in a lazy manner, which means they are not executed immediately. Instead, Spark builds up a directed acyclic graph (DAG) of the transformations and optimizes the execution plan. Actions, on the other hand, trigger the execution of the DAG and produce a result. This lazy evaluation and the ability to optimize execution plans are key features that make Spark efficient and scalable.