1.Strongly-Typed API. Java and Scala use this API, where a DataFrame is essentially a Dataset organized into columns. Under the hood, a DataFrame is a row of a Dataset JVM object. 2.Untyped API. Python and R mak
No schematic view of data. RDDs have a hard time dealing withstructured data. A better option for handling structured data is through theDataFramesand Datasets APIs, which fully integrate with RDDs in Spark. Garbage collection. Since RDDs are in-memory objects, they rely heavily on Java'smem...
However, you may also persist an RDD in memory using the persist (or cache) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting RDDs on disk, or replicated across multiple nodes....