The Spark platform is easily accessible through Spark APIs available in Python, Scala, R, and Java. Together they make working with data in Spark simple and broadly accessible. During the inception of the Spark project, it only supported Scala/Java as the primary API. However, since one of the overarching objectives of Spark was to provide an easy interface to a diverse set of developers, the Scala API was followed by a Python and R API.

In Python, the PySpark package has become a widely used standard for writing Spark applications by the Python developer community. In R, users interact with Spark via the SparkR package. This is useful for R developers who may also be interested in working with data stored in a Spark ecosystem. Both of these languages are very prevalent in the Data Science community, and hence, the introduction of the Python and R APIs set the groundwork for democratizing Big Data Analytics on Spark for analytical use cases.