Leveraging Chatgpt for Multi-Language Data Engineering Code Generation in Distributed Analytics Systems

Abstract

The rapid expansion of distributed analytics systems has increased the demand for multilingual programming capabilities across diverse data engineering workflows. Traditional development processes require engineers to manually translate logic across languages such as Python, SQL, Scala, and Java, resulting in time-consuming and error-prone transitions between components of ETL pipelines, data orchestration, and streaming architectures. This study explores the role of ChatGPT as an intelligent assistant capable of generating multi-language data engineering code tailored for distributed analytics ecosystems. By analyzing its ability to produce syntactically correct, semantically aligned, and performance-oriented code across languages, the research evaluates the potential of ChatGPT to accelerate pipeline development, reduce cognitive load, and unify cross-linguistic engineering practices. Experimental results demonstrate that ChatGPT significantly improves productivity in constructing ETL transformations, Spark workflows, schema definitions, and orchestration scripts while maintaining consistency with large-scale distributed data systems. The findings position ChatGPT as a transformative tool for enabling multi-language interoperability in next-generation data engineering environments.