Published Paper Details:

The integration of Large Language Models (LLMs) with SQL-oriented data pipelines is an emerging area that seeks to make databases more functional and usable based on the paradigm of natural language processing (NLP) methods. Although the impressive capabilities demonstrated by LLMs in the domain of text-to-SQL translation are well documented, the wider potential of LLMs for the domain of database systems is relatively unexplored. Existing academic contributions have been mostly focused on niche applications, such as query construction; however, concerns related to database schema understanding, query optimization, and scalability in dynamic environments are still open. There is a pressing need for well-tuned models with the capability to handle diverse domain-specific data, as well as the incorporation of LLMs in data preprocessing and real-time querying, which is an open research gap. Furthermore, existing solutions are not robust enough for large-scale, real-time applications and are usually beset with challenges of ensuring data privacy and security when handling sensitive data. This research effort seeks to address these gaps by suggesting an end-to-end system for the incorporation of LLMs in SQL-oriented data pipelines, with a focus on important considerations such as query construction efficiency, query optimization, and dynamism in heterogeneous domains. Through the exploration of pre-trained and well-tuned LLM approaches, this research seeks to close the gap between state-of-the-art NLP methods and real-world database management, thus improving the effectiveness and scalability of SQL-based systems for a range of real-world applications. The expected outcomes are expected to provide insights into the construction of more intelligent, autonomous database systems with reduced human query construction and enabling more natural interaction with data.