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 SelectStar, and she joined Sean

Falconer to discuss solving metadata curation challenges, managing data context at scale,

using LLM's first SQL generation, emerging trends in metadata management, and more.

This episode is hosted by Sean Falconer.

Check the show notes for more information on Sean's work and where to find him.

Shinji, welcome to the show.

Thanks, Sean.

Great to be here.

Yeah, I probably should have said welcome back since you've been here before,

although it's been a couple years.

Yeah, more than three years ago to introduce Select Star. But I am really excited to be back, and software engineering daily has always been, yeah, also

morphing and changing a lot.

...