Some recent changes to the primary server in my office left the fans running louder than previously. I do have some noise canceling headphones but I still don’t want to hear the sound of jet fans from the other room, with a little bit of free time I was able to nail down and correct the behavior using ipmitool(1) and magic bytes.

Whenever a system comes offline I try to always make multiple updates at once. When I recently received some new mounting hardware for one of my big FreeBSD servers, I decided to plonk an idle PCI-e device into the chassis while it was out of the rack.

A colleague once told me about their boss whose office door decorated by a single 8x11 piece of paper with “speed wins” scrawled upon it. I didn’t even work for them and I find that motivating. I think about it a lot, particularly when I’m waiting for Rust builds to complete. Speed wins, every second counts, and “why is this so fscking slow!” all run through my mind as units are compiled and linked.

I have recently been on a quest to get more speed from my Rust development environment, and today’s “why is this so fscking slow!” culprit is rust-analyzer.A larger project like delta-rs benefits from IDE-like machinery to help work across a sprawling codebase written in Python and Rust. rust-analyzer helps greatly with that, but it comes with a speed penalty.

Today I launched a new rework of buoyantdata.com thanks to the work of a designer I found in the fediverse! The original “design” of the site was something I had cobbled together with a Jekyll theme I originally ported to Cobalt, but it was always lacking.

Purchasing new hardware to run Linux on used to be so perilous that I would only buy hardware which was at least 6 months out of date. The ecosystem has changed dramatically such that when my Dell XPS experienced a chassis failure, I went to a big box store and came home with a Lenovo Slim 7. I immediately installed a Linux distribution on it and started setting up my new portable workstation without even considering hardware support.

Nothing quite feels like “I made it!” like being published. Which is why I am thrilled to share that Delta Lake: The Definitive Guide is available for purchase, and I kind of helped! I wanted to share a little bit about how my contributions (Chapter 6!) came about, because my entrance into the Delta Lake ecosystem was about as unplanned as my authorship of part of this wonderful book.

This year has been so jam packed full of activities that I forgot to share some videos from Data and AI Summit 2024 this past summer! The annual conference hosted by Databricks has become one of my favorites to meet with other Delta Lake users and developers to discuss the future of large-scale data ingestion and processing. This year however, I overdid it a little bit.

The staggering complexity of modern software makes it impossible for us to truly understand what is happening while our code runs, but when it fails there is always something we can learn. At the beginning of my career we, the industry, generally understood that programs were getting complex. Without hesitation we made things more complex, more distributed, and somehow more coupled. Failure is a “learning opportunity”, and those opportunities are in abundance.

I asked a large language model this question: