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From pandas to Arrow: Wes McKinney on the Future of Data Infrastructure
Manage episode 522227342 series 3594857
Treść dostarczona przez Kostas Pardalis, Nitay Joffe. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Kostas Pardalis, Nitay Joffe lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.
Summary
In this episode of Tech on the Rocks, Kostas and Nitay sit down with Wes McKinney the creator of pandas and co-creator of Apache Arrow and Ibis, and long-time leader in the Python data ecosystem. Wes walks us through his journey from building pandas in 2008 to rethinking how we represent and move columnar data with Arrow, and why Arrow is fundamentally different from file formats like Parquet and ORC.
We get into the future of data file formats, DataFusion and the new generation of query engines, the rise of open data lakes (Iceberg, Delta, Hudi), and why “big metadata” is becoming just as important as big data. Wes also shares candid thoughts on open source sustainability, how companies and infrastructure projects really survive, and how AI coding agents like Claude Code are changing the day-to-day work of software engineers, especially for complex systems work.
If you care about the foundations of modern data infrastructure, or you’ve ever called import pandas as pd, this is an episode you won’t want to miss.
Chapters
00:00 Intro — Wes McKinney & his journey in the Python data ecosystem
02:15 How pandas evolved & why UX first mattered for data science
06:14 Open source sustainability, funding & the Posit model
07:31 From pandas to Datapad, Cloudera & the origins of Apache Arrow and Ibis
13:38 What is Apache Arrow? In‑memory columnar data, batches & schemas
22:23 Inside Arrow IPC — zero‑copy, Flatbuffers & cross‑language interop
24:34 Arrow vs Parquet — columnar memory format vs columnar storage format
29:28 The next generation of columnar file formats & GPU‑friendly encodings
36:03 Big metadata, table formats & the rise of Iceberg/Delta/Hudi
43:05 Rethinking data systems: from big data to DuckDB, Rust & “no JVM” stacks
54:11 DataFusion as a modular Rust query engine for modern startups
57:58 Open source, the composable data stack & why infra is “AI‑resistant”
01:00:07 Vibe‑coding with AI agents — using Claude Code in real projects
01:09:49 AI, open source maintainers & the risks of AI‑generated contributions
01:18:57 Bridging LLMs and data: ADBC, data context & the future of infra + AI
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