Which function would you use to apply a transformation to every element in an RDD?

The function used to apply a transformation to every element in an RDD (Resilient Distributed Dataset) is the map function. When you use map, it takes a function as an argument and applies that function to each element in the RDD, resulting in a new RDD containing the transformed elements. This is particularly useful when you want to transform or compute a new value from the existing dataset without changing the number of elements in the output.

For example, if you have an RDD of numbers and you wish to square each number, the map function allows you to define a function that squares a number and then applies it to every element in the RDD.

In contrast, the flatMap function is used when you want to apply a transformation to each element but may produce zero or more elements for each input element, leading to a potentially different number of elements in the output RDD. The filter function is utilized to select elements that satisfy a certain condition and produces a new RDD consisting of only those elements. The reduce function is an action that aggregates the elements of an RDD using a specified associative function, reducing the dataset to a single value based on the processing logic applied.

By using map, you achieve element-wise transformation efficiently, which is