Global versus local scope
One of the things that you, as a prospective PySpark user, need to get used to is the inherent parallelism of Spark. Even if you are proficient in Python, executing scripts in PySpark requires shifting your thinking a bit.
Spark can be run in two modes: Local and cluster. When you run Spark locally your code might not differ to what you are currently used to with running Python: Changes would most likely be more syntactic than anything else but with an added twist that data and code can be copied between separate worker processes.
However, taking the same code and deploying it to a cluster might cause a lot of head-scratching if you are not careful. This requires understanding how Spark executes a job on the cluster.
In ...
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