Vipul Kumar does a bit of work with resilient distributed datasets:
PySpark, the Python API for Apache Spark, is widely used for big data processing and distributed computing. It enables data engineers and data scientists to efficiently process large datasets using resilient distributed datasets (RDDs) and DataFrames. Two commonly used transformations in PySpark are map() and flatMap(). These functions allow users to perform operations on RDDs and are pivotal in distributed data processing.
In this blog, we’ll explore the key differences between map() and flatMap(), their use cases, and how they can be applied in PySpark.
The DataFrame approach has all but obviated having developers use the original Hadoop-like map-reduce approach to writing code in Spark. Even so, I do think it’s useful to know how it all works.