Voice of the DBA

Category: Editorial

  • Deep Learning and Craftsmanship Matter

    There’s concern about the future of AI and how it may affect jobs and employment for the masses. I see plenty of people on both sides of the issue. Some are sure AI technologies won’t replace people; some are concerned their jobs will be eliminated, and some are hoping that we will eliminate some jobs and create many more.

    Sometimes that’s the same person.

    A GenAI can replicate a human, or maybe more accurately, mimic one. That might work well enough for some people to trust the technology more than humans. Or maybe it works well enough, enough of the time, and lots of us are OK with well-enough. After all, I think a lot of us already work with “well-enough” or “good-enough” code on a daily basis.

    However, the GenAI is based on what other humans have already done. It’s “trained” on lots of existing ideas, decisions, codebases, etc. It can recall and use those quicker, and often as well as many humans. It might be a light craftsman, but it can’t be a great one.

    Humans will be able to deeply understand problems and create better craftsmanship for many systems. Across time, an AI can learn from these craftsman and repeat their work in other systems, but an AI will often struggle to understand the entire context of whether we would apply that solution or a slightly different one this time.

    That’s the human advantage. Deep learning and craftsmanship will differentiate us from the AIs because we can contextualize things better than an AI. Or really, we can internalize the context better than another human can express it to the AI. That will be the difficult part of working with AI LLMs, agents, and whatever comes next: explaining what is really needed in a new situation.

    Communication is hard. Because many humans aren’t good at communicating, they won’t be able to use an AI to replace other humans. They’ll struggle with the results, and they will need to hire a craftsman to help. However, that also implies that more of us need to become craftsmen, not only for the advantage it gives us over AI, but because those skills will help us better judge AI output, as well as express what we want to see the AI do.

    There will be lots of work in the future, even with AI, but I also believe that the jobs that are desired, that will pay better, will go to those who learn to use AI tech and who can judge when the quality of the work is appropriate for the situation.

    Steve Jones

    Listen to the podcast at Libsyn, Spotify, or iTunes.

    Note, podcasts are only available for a limited time online.

  • Republish: Chopping Off Data

    It’s our first big volleyball tournament of the season. Today, Saturday, and Sunday I’ll be downtown in Denver coaching a team of 13 year olds.

    My break is your break (from me). Re-read Chopping Off Data today.

  • Learning From Breakage

    I’ve had the fortunate, or maybe unfortunate, experience of being thrown into a few jobs with no training. At a couple of my bartending jobs, I had to start working without any training, calling over someone to help run the ordering machine while I made and served drinks. I managed to slowly learn how things worked throughout that first shift, so I was ready to work on my own the second night. I had a similar experience at a tech job, starting as the lead DBA/IT Manager in a crisis, having to try and solve problems after ask others how things were supposed to work. I ended up fixing a bit of code, adjusting networking, and directing others on my first day.

    When we have a crisis, we often learn a lot from the situation. I’ve been through crashed upgrades, virus breakouts, hardware failures, and more in my career. While each was stressful and often not enjoyable, I learned a lot each time and came through the incident a more capable developer/DBA/whatever. When we work through a tough time, we are often better equipped for the next time something goes wrong.

    I ran across a great piece that says you never really know a system unless you’ve broken one. This is Tim O’Brien, a software architect who has learned a lot about databases from failure. In fact, I love his interview question for data professionals: “tell me about the worst database schema you ever created. What did it teach you to avoid?” I’ve certainly learned a few things over time from my schema designs, but those are stories for another piece.

    The piece draws parallels to today’s use of GenAI technology and vibe coders who seem to have success that they highlight in posts without discussing the problems. I do believe AI technology is going to make a lot of things easier (and faster) to build and then fix when they break. And they are going to break, partially because AI tech might not do a great job, and partially because we might not direct it well enough. Clear communication is key when working with AI.

    I’ve started to build some skills with AI, but as I try to tackle more complex tasks or scale up my work, I realize that I often don’t know enough about either the problem or AI technology, and I’m going to make mistakes. I’m going to break things and then have to fix them, or more likely, learn how to get the AI to reduce the number of broken things in some way before I have to take over.

    And learning to take over might be the number one skill with AI tech, but that’s something that you will only learn from the AI not working well for you in a variety of situations.

    Steve Jones

    Listen to the podcast at Libsyn, Spotify, or iTunes.

    Note, podcasts are only available for a limited time online.

  • Republish: Representative Data Challenges

    It’s a holiday in the US, though I’m in the UK, having arrived this morning and heading to work. I’ve got a long day ahead, and a long week as I spend a few days here and then head home on Thursday.

    In the meantime, you can get re-read Representative Data Challenges. I wish I’d talked more about this, especially the idea that so many bugs in software systems are data related, because we haven’t tested on representative data.