There are more ways to create RDDs as well. You can create them from a JDBC connection. Basically any database that supports JDBC can also talk to Spark and have RDDs created from it. Cassandra, HBase, Elasticsearch, also files in JSON format, CSV format, sequence files object files, and a bunch of other compressed files like ORC can be used to create RDDs. I don't want to get into the details of all those, you can get a book and look those up if you need to, but the point is that it's very easy to create an RDD from data, wherever it might be, whether it's on a local filesystem or a distributed data store.

Again, RDD is just a way of loading and maintaining very large amounts of data and keeping track of it all at once. But, conceptually within your script, an RDD is just an object that contains a bunch of data. You don't have to think about the scale, because Spark does that for you.