often starts with tools like pandas. They are intuitive, powerful, and perfect for small to medium-sized datasets. But as soon as your data grows beyond what fits comfortably in memory, performance issues begin to surface. This is where PySpark comes in.

Note that in this article I’ll often use the terms Spark and PySpark interchangeably. For our purposes, it doesn’t matter, but you should remember that they are different. Spark is the overarching distributed computing framework (written in Scala), and PySpark is a dedicated Python API to Spark.

What is PySpark?

PySpark is the Python API for Apache Spark, a distributed computing framework for efficiently processing large volumes of data.…