DataFrames Battle Royale | Pandas vs Polars vs Spark
Pandas operates with an in-memory, single-threaded architecture ideal for small to medium datasets, providing simplicity and immediate feedback. Polars, built with Rust, offers multi-threaded, in-memory processing and supports both eager and lazy execution, optimizing performance for larger datasets. Apache Spark uses a distributed computing architecture with lazy execution, designed for processing massive datasets across clusters, ensuring scalability and fault tolerance.
