Answer / Naseem Ahmad

There are two main categories of transformations for DStreams in Apache Spark: (1) Basic Transformations: These include map, filter, flatMap, reduceByKey, and join. They allow users to perform various operations on the data, such as applying a function to each element, filtering out unwanted elements, combining key-value pairs, or joining two DStreams based on some condition. (2) Complex Transformations: These are higher-level transformations that build upon basic transformations, such as window functions, triggers, and aggregations. They allow users to process streaming data in a more sophisticated manner, like grouping data by time windows or processing data based on event timestamps.

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