Final answer:
PySpark and SparkR are the two Spark libraries with native shells, allowing Python and R users, respectively, to interact with Spark's data processing capabilities.
Step-by-step explanation:
The two Spark libraries that provide a native shell are A) PySpark and B) SparkR. PySpark is the Python API for Apache Spark, offering a Python-based shell for interacting with Spark's data processing capabilities.
SparkR is the R language API for Apache Spark, giving R users a way to perform large-scale data analysis using Spark's distributed computation engine through an R shell.
While SparkSQL is used for executing SQL queries within Spark, and SparkMLib is Spark's machine learning library, they do not provide a native shell environment for interaction like PySpark and SparkR do.