Spark is designed to handle very large datasets by distributing processing across a cluster, which is why working with Spark DataFrames unlocks these scalability benefits. In contrast, Python and Pandas are not inherently distributed; Pandas dataframes are eagerly evaluated and executed locally, so you can encounter memory issues when working with large datasets. For instance, exceeding around 95 GB of data in Pandas often leads to out-of-memory errors because only the driver node handles all computation, regardless of cluster size.

To bridge this gap, consider using the Pandas API on Spark, which is part of the Spark ecosystem. This API provides Pandas-equivalent syntax and functionality, while leveraging Spark’s distributed processing to handle larger data volumes efficiently. You can learn more here: https://docs.databricks.com/aws/en/pandas/pandas-on-spark.

In short, the Pandas API on Spark lets you write familiar Pandas-style code but benefit from distributed computation. It greatly reduces memory bottlenecks and scales to bigger datasets than native Pandas workflows allow.

Hope this helps, Louis.