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Transformations are lazy evaluated operations on RDD that create one or many new RDDs, e.g. map, filter, reduceByKey, join, cogroup, randomSplit. Transformations are functions which take an RDD as the input and produces one or many RDDs as output. They don’t change the input RDD as RDDs are immutable and hence cannot be changed or modified but always produces new RDD by applying the computations operations on them. By applying transformations you incrementally build an RDD lineage with all the ancestor RDDs of the final RDD(s).
Transformations are lazy, i.e. are not executed immediately. Transformations can be executed only when actions are called. After executing a transformation, the result RDD(s) will always be different from their ancestors RDD and can be smaller (e.g. filter, distinct, sample), bigger (e.g. flatMap, union, cartesian) or the same size (e.g. map) or it can vary in size.
RDD allows you to create dependencies b/w RDDs. Dependencies are the steps for producing results in a program. Each RDD in lineage chain, string of dependencies has a function for operating its data and has a pointer dependency to its ancestor RDD. Spark will divide RDD dependencies into stages and tasks and then send those to workers for execution.
Follow below guide to know more about transformation in Apache Spark:
http://data-flair.training/blogs/rdd-transformations-actions-apis-apache-spark/
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