Enriching metadata for accurate text-to-SQL generation for Amazon Athena

This article discusses how to generate accurate SQL queries for Amazon Athena using generative AI models from Amazon Bedrock. It demonstrates the importance of enriched metadata for tables and columns in generating precise SQL queries.

Specifically, the article covers:

• An overview of the solution architecture and workflow for text-to-SQL generation using Amazon Bedrock
• Prerequisites and steps to set up the necessary AWS resources and Jupyter Notebooks
• Examples showcasing the significance of metadata details like column descriptions, possible values, foreign key constraints, etc. in generating accurate SQL queries
• Challenges faced in maintaining up-to-date and accurate metadata
• Strategies to enrich metadata, such as using generative AI models, data profiling, and crawler options
• Enhancing prompts with query optimization rules and instructions