In this episode, we talk with Ian Cook, co-founder and CEO of Columnar and a member of the Apache Arrow Project Management Committee, about ADBC (Arrow Database Connectivity) and why the way applications connect to databases is overdue for a rethink.
Ian traces the history from the row-oriented database APIs of the 1990s, ODBC and JDBC, to today's world where nearly every analytic database and destination tool is columnar under the hood. He explains why keeping data in a columnar format end to end, using Apache Arrow, can deliver 10x to 100x speedups by turning a CPU-bound conversion problem back into a fast, network-friendly one, and why ADBC's flexible driver model works even against row-oriented systems like Postgres.
We then dig into the AI angle: how fast structured data retrieval matters more than ever when agents reason in milliseconds and bottleneck on tool calls, why today's models still lean on tool calls to understand tabular data, and what tabular foundation models might change. Along the way we cover Parquet, DuckDB, Snowflake, string view, the DeWitt clause and benchmarking culture, and Arrow's philosophy of growing an ecosystem by building consensus rather than enemies.
Ian also shares how to try ADBC yourself with the dbc CLI, a UV-inspired installer that makes it easy to install drivers for 20+ databases (columnar.tech/dbc).
Chapters
00:00 Introduction to Ian Cook and Columner
01:53 Understanding ADBC and Its Relationship with Arrow
10:10 The Need for Columnar Paradigms in Database Connectivity
14:37 Exploring Use Cases for ADBC in Modern Applications
18:41 Performance Impacts of ADBC in Various Systems
27:38 Integrating ADBC with AI and LLMs
37:26 The Future of ADBC and Its Role in Data Infrastructure
