PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core.
Spark SQL and DataFrame
Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine.
pandas API on Spark
pandas API on Spark allows you to scale your pandas workload out. With this package, you can:
Be immediately productive with Spark, with no learning curve, if you are already familiar with pandas.
Have a single codebase that works both with pandas (tests, smaller datasets) and with Spark (distributed datasets).
Switch to pandas API and PySpark API contexts easily without any overhead.
Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics.
Built on top of Spark, MLlib is a scalable machine learning library that provides a uniform set of high-level APIs that help users create and tune practical machine learning pipelines.
Spark Core is the underlying general execution engine for the Spark platform that all other functionality is built on top of. It provides an RDD (Resilient Distributed Dataset) and in-memory computing capabilities.