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This code creates a Spark RDD from a list of strings and then collects the elements of the RDD into a list. The `parallelize` function distributes the data across the nodes in your Spark cluster, allowing you to process it in parallel. The `collect` function retrieves the data from the cluster and returns it to the driver program. Run the cell. You should see the output `['Hello', 'World', 'Spark']` displayed below erebus disaster the cell. This confirms that you can create and manipulate RDDs in your Spark environment. Spark RDDs are the fundamental building blocks of Spark applications. They are immutable, distributed collections of data that can be processed in parallel. Spark provides a rich set of transformations and actions that you can use to manipulate RDDs. Transformations create new RDDs from existing RDDs, while actions trigger computations and return results to the driver program.
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