Datahub

In the previous ten posts, I analyzed the most common SQL generation errors. I used BIRD and Spider benchmarks, tested several models, and grouped wrong answers into types such as COLUMN_BINDING, VALUE_BINDING, and DERIVED_METRIC. As I kept looking at those SQL errors, I realized I wanted to check something different. “So how should generated SQL and supporting context appear in the UI?” I had been focused on accuracy and had missed what users actually need from the interface. In a workplace data analysis tool, the UI has to make it clear where numbers come from. Without traceable context, users end up re-running the SQL even when the result looks correct. ...

We tried using DataHub’s Business Glossary as an ontology store. What worked, what didn’t, and how we worked around it.

How we decided on DataHub + Vanna + ApeRAG + DozerDB for DataNexus. What got eliminated from the candidate list, and why.