4 Ways to Create a Data Frame in Spark
Learn how to create or transform your data to a Spark DataFrame object
Introduction
Spark is known as a language to deal with parallelized data, as well as to work with Big Data. But it’s not that uncommon when you need to create a data frame using PySpark.
There are a handful of cases when creating a data frame can be useful: make some data for testing purposes, create a data frame out of a filtered list as a base for a join operation, and transform other objects into a data frame. Well… you name it. You might be thinking of other use cases right now.
The fact is that at one time or another, we’ll need to create a data frame, and in this post we will learn a couple of ways to do that in Spark, using Databricks.
As you may or may not know, Spark can be used in a Jupyter Notebook, upon installation or you can take advantage of Databricks, which is a platform on top of Apache Spark. In this post, we’ll consider the use of Databricks.
Let’s get to work.
Data Frame from a List
The first option we will cover is creating a data frame from a Python list.
