1.How do we create RDDs in Spark? 2.What do you understand by Transformations in Spark?

Question

Accepted Answer

Creating RDDs in Spark involves loading data from external sources or parallelizing an existing collection.

RDDs can be created by loading data from external sources like HDFS, local file system, or any other data source supported by Hadoop.
RDDs can also be created by parallelizing an existing collection in the driver program.
Transformations in Spark are operations that create a new RDD from an existing one.
Examples of transformations include map, filter, flatMap, groupByKey, reduceByKey, etc.
Transformations are lazily evaluated, meaning they are not executed until an action is called on the RDD.
Transformations are immutable, meaning they do not modify the original RDD but create a new one.

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