Open Data Stack on Jaehyeon Kimhttps://jaehyeon.me/tags/open-data-stack/Recent content in Open Data Stack on Jaehyeon KimHugo -- gohugo.ioenCopyright © 2023-2026 Jaehyeon Kim. All Rights Reserved.Thu, 16 Jul 2026 00:00:00 +0000Introducing odctl: One CLI for a Local Open Data Stackhttps://jaehyeon.me/blog/2026-07-16-odctl-open-data-stack/Thu, 16 Jul 2026 00:00:00 +0000https://jaehyeon.me/blog/2026-07-16-odctl-open-data-stack/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.

]]>Productionizing an Online Product Recommender using Event Driven Architecturehttps://jaehyeon.me/blog/2026-02-23-productionize-recommender-with-eda/Mon, 23 Feb 2026 00:00:00 +0000https://jaehyeon.me/blog/2026-02-23-productionize-recommender-with-eda/In Part 1, we built a contextual bandit prototype using Python and Mab2Rec. While effective for testing algorithms locally, a monolithic script cannot handle production scale. Real-world recommendation systems require low-latency inference for users and high-throughput training for model updates.

This post demonstrates how to decouple these concerns using an event-driven architecture with Apache Flink, Kafka, and Valkey.

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