PySpark is a programming language that supports Apache Spark and can be used to create a variety of data platforms and handle big data analytics. It is a Python API developed as part of the collaboration between Apache Spark and Python was created. PySpark can allow users to write a number of different programs efficiently and is also compatible with other external library sources, including PySparkSQL, MLib and GraphFrames. You can also easily integrate it with other languages, such as Scala and Java.  

PySpark is primarily used for processing large amounts of data. This includes RDD, Resilient Distributed Datasets, which allow users to test their data via transformations and actions. Transformations focus on input data and how to apply it, while actions focus on how to encode these results for the right output.

Data frameworks and machine learning can also be used as part of PySpark, which is particularly effective at filtering and sorting data that would otherwise require the use of Scala to create a custom spreadsheet instead. Ultimately, this Python language is essential for those who want to combine Spark's frameworks and data intelligence with Python's efficiency. It's a really effective way to sort and process data to get the best output variant.