Leveraging Generative AI for Database Migration: A Comprehensive Approach for Heterogeneous Migrations

Abstract

In this paper, the author seeks to determine the extent to which generative AI particularly the Large Language Models can redefine Database Migration. The conventional techniques that are used for migrating data to next generation databases entail scripting as well as mapping manual work which are prone to errors, cumbersome and demand the services of an expert. This research aims at developing an integral solution based on LLMs that can assist at specific and critical phases of the migration process, especially for heterogeneous migration between distinct platforms of databases. The authors specifically point out how LLMs are used for analyzing the source database schema, for handling schema translation and data type mapping automatically and for interpreting and converting other database-dependent code like stored procedures and functions. The use of LLMs in the research also seeks to achieve a major reduction in manual work, enhancement of accuracy, and the general time taken in the migration processes. The paper also considers the position of LLMs within the performance enhancement, security. Experimentations on a modified version of a Gemini model on a sample Oracle to PostgreSQL database migration justify the proposed approach. The analysis points out significant gains in precision and performance besides noticeable reduction in the likelihood of errors from the use of traditional techniques.