The modern data engineering landscape frequently demands seamless transitions and interoperability between established SQL-based systems and the popular ecosystem of Dataframe-centric frameworks like Pandas, Apache Spark and Polars. Migrating legacy applications or building hybrid systems often requires translating SQL queries into their DataFrame API equivalents. While manual rewriting is feasible for small-scale projects, it quickly becomes a bottleneck, prone to errors and challenging to maintain as complexity grows.

This article delves into leveraging ANTLR (ANother Tool for Language Recognition), a powerful parser generator, to construct a robust and extensible SQL to DataFrame converter. We will explore the core principle, implementation steps and challenges involved in building such system.