A common challenge in data-rich organizations is that critical context about the data is often hard to capture and even harder to keep up to date. As more people across the organization use data and data models get more complex, simply finding the right dataset can be slow and create bottlenecks.
Select Star is a data discovery and metadata platform that builds a continuously updated knowledge graph of an organization’s data by analyzing both its structure and how it’s actually used. It enriches data with context such as popularity, lineage, and semantic models, making it easier for AI and teams to discover, trust, and use the right data. These enriched metadata layers are also highly valuable for large language models, significantly improving the accuracy of generated SQL queries.
Shinji Kim is the founder and CEO of Select Star, and she joins Sean Falconer to discuss solving metadata curation challenges, managing data context at scale, using LLMs for SQL generation, emerging trends in metadata management, and more.
Full Disclosure: This episode is sponsored by Select Star.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Context-Aware SQL and Metadata with Shinji Kim appeared first on Software Engineering Daily.
