You’ll need to have completed an introduction course to the programming language you’re using Spark on. 

All of which you can find here:

Introduction to Python

Introduction to R

Introduction to SQL

Beyond that, anyone can get started with Spark through simple, interactive exercises on DataCamp.

If you're already familiar with Python and libraries such as Pandas, then PySpark is a good language to learn to create more scalable analyses and pipelines.

Apache Spark is basically a computational engine that works with huge sets of data by processing them in parallel and batch systems. 

Spark is written in Scala, and PySpark was released to support the collaboration of Spark and Python.

You’ll gain the ability to analyze data and train machine learning models on large-scale datasets—a valuable skill for becoming a data scientist. 

Having the expertise to work with big data frameworks like Apache Spark will set you apart.

Apache Spark is an open-source, distributed processing system used for big data workloads. 

It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size. 

It provides development APIs in Java, Scala, Python, and R, and supports code reuse across multiple workloads—batch processing, interactive queries, real-time analytics, machine learning, and graph processing.