LEDD: Large Language Model-Empowered Data Discovery in Data Lakes
This addresses the problem of efficient data management for users dealing with large datasets in data lakes, though it appears incremental as it builds on existing LLM capabilities for specific tasks.
The paper tackles the challenge of data discovery in data lakes by proposing LEDD, an end-to-end system that uses large language models to enable semantic table search and generate hierarchical global catalogs, resulting in a tool that returns semantically related tables based on natural-language queries.
Data discovery in data lakes with ever increasing datasets has long been recognized as a big challenge in the realm of data management, especially for semantic search of and hierarchical global catalog generation of tables. While large language models (LLMs) facilitate the processing of data semantics, challenges remain in architecting an end-to-end system that comprehensively exploits LLMs for the two semantics-related tasks. In this demo, we propose LEDD, an end-to-end system with an extensible architecture that leverages LLMs to provide hierarchical global catalogs with semantic meanings and semantic table search for data lakes. Specifically, LEDD can return semantically related tables based on natural-language specification. These features make LEDD an ideal foundation for downstream tasks such as model training and schema linking for text-to-SQL tasks. LEDD also provides a simple Python interface to facilitate the extension and the replacement of data discovery algorithms.