Spark allows us to perform several data transformations to manipulate and manipulate data. Some commonly used transformations include:

• Map: Applies a function to each element of an RDD and returns a new RDD with the transformed values.

• Filter: Filters out elements from an RDD based on a specified condition.

• Reduce: Aggregates elements of an RDD using a specified function.

• Join: Combines two RDDs based on a common key.

These transformations act on RDDs, allowing us to process and transform large datasets efficiently. For example, if we have an RDD of sales data, we can use the map transformation to extract the prices of each item and calculate the total revenue using the reduce transformation.