Building an Agentic Analytics System over an Iceberg Lakehouse

Sat, 18 Jul 2026 00:00:00 +0000

Generative AI has made conversational analytics feel within reach, yet direct text-to-SQL systems remain hard to operate reliably. A database schema tells you the tables, columns, and types, but it says nothing about which datasets are canonical, which join paths are approved, how a governed metric is calculated, or what the business actually means by “revenue” or “active customer”. Ask a language model to infer all of that from raw tables and, sooner or later, it will confidently invent an answer.

I built a proof of concept to explore a more disciplined approach: put a semantic layer between the model and the data, and let the agent reason over governed business concepts instead of guessing at raw schemas. It runs entirely locally on an open-source stack, and it happens to tie together two of my other projects along the way.

Introducing odctl: One CLI for a Local Open Data Stack

Thu, 16 Jul 2026 00:00:00 +0000

Anyone who has tried to stand up a realistic data platform on their laptop knows the pain. You want Kafka talking to Flink, Spark writing to Iceberg, Trino querying the result, and maybe a catalog and a lineage tool watching over all of it. What you actually get is an afternoon lost to dependency conflicts, port clashes, and a Docker Compose file that grows a new bug every time you touch it.

I built odctl to make that afternoon disappear. It is a curated collection of open-source data technologies wrapped in a small CLI, and it is now available on PyPI.

Odctl on Jaehyeon Kim

Building an Agentic Analytics System over an Iceberg Lakehouse

Introducing odctl: One CLI for a Local Open Data Stack