Rion.IO

Recently, I’ve been borderline addicted to Matt Dinniman’s Dungeon Crawler Carl series, which I cannot recommend enough.

Sometimes, when we’re getting ready to launch a new Apache Flink job — or even just roll out a major new feature — we run into a familiar problem: we need meaningful data in state before the job can do what we built it to do.

Recently, I was chatting with an engineer who was transitioning into a fully remote role for the first time in their career.

Last month, I wrote up a blog post discussing a three-phase cycle (profile, analyze, improve) for leveraging LLMs and other AI tooling to evaluate Apache Flink jobs for potential performance improvements.

Last year, I wrote a blog post discussing ideas on how to support use-cases in Apache Flink that required dynamically resolving (and potentially creating) sinks at runtime based on the incoming data.

As the mental (and possibly physical) hangover begins to wear off, I figured I’d take a moment to reflect on my recent trip to Confluent’s 2025 Current conference, conveniently held right here in my home state of Louisiana.

While performance always matters, streaming pipelines are where it can matter a lot.

Apache Flink makes it incredibly seamless to take your streaming data and throw it exactly where you want it to go.

This blog and in general most of my public presence on the internet has been pretty quiet over the better part of the last year or more.

Distributed systems are hard, and things can often get much more difficult when problems arise.

Without a doubt, the Java SDK is the most popular and full featured of the languages supported by Apache Beam and if you bring the power of Java's modern, open-source cousin Kotlin into the fold, you'll find yourself with a wonderful developer experience.

Everyone learns differently, especially when it comes to technology.

I’m probably one of the few folks out there that love a good technical book.

This situation sucks.

Containers are so awesome.