Presented by:

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Bhanu Ganesh Gudivada

Amazon Web Services
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Nishad Mankar

Amazon Web Services

Nishad Mankar is an experienced Cloud Migration Consultant with over 14 years of expertise in database analysis, design, and large-scale heterogeneous database migrations to AWS. He specializes in designing migration strategies and architectures to help organizations seamlessly transition their on-premise applications to the AWS Cloud. Nishad has deep knowledge of AWS solutions and technologies, including RDS, Aurora, DMS, IAM, and security, and is skilled in working with various database platforms such as Sybase, MSSQL, Oracle, and PostgreSQL.

Rajkumar Raghuwanshi is a Database Consultant with AWS Professional Services based out of Pune, India. With expertize in homogenous and heterogenous database migrations to PostgreSQL/Aurora/RDS and ELT/ETL Airflow/Redshif, he helps customers in assisment/migration/modernization to the AWS Cloud.

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The AWS Schema Conversion Tool (SCT) is indispensable for automating the migration of database schemas and SQL code across different engines. However, challenges persist in handling complex SQL code or intricate database structures, often requiring manual intervention. This presentation introduces a complementary tool powered by Large Language Models (LLMs) to address these challenges, improving the accuracy of SQL translation from source to target engines. By leveraging LLMs alongside AWS SCT, this tool fills the gaps and streamlines SQL conversion, especially in diverse and complex scenarios. We will demonstrate how providing additional 'useful' and 'in-context' examples to pre-trained LLMs further enhances the accuracy and reliability of SQL conversion.

Date:
2024 November 6 14:30 PST
Duration:
20 min
Room:
Dev: 422
Conference:
Seattle 2024
Language:
Track:
Dev
Difficulty:
Medium