What is the difference between Map and FlatMap in Apache Spark?

In Apache Spark, both `map` and `flatMap` are transformations used to apply a function to each element of an RDD (Resilient Distributed Dataset). - `map` applies a function to each element of the RDD and returns a new RDD where each element is the result of the function applied to the corresponding element of the original RDD. The output RDD will have the same number of elements as the input RDD. - `flatMap`, on the other hand, applies a function to each element of the RDD and flattens the results. It returns a new RDD where each element of the output RDD is a result of the function applied to an element of the input RDD, but the results are flattened into a single collection. This can result in an output RDD with a different number of elements compared to the input RDD, as each input element can produce zero or more output elements. In summary, `map` maintains the structure of the RDD, while `flatMap` can transform each input element into zero or more output elements and flattens the results into a single RDD. read less