Voice of the DBA

Author: way0utwest

  • My 2024 in Data: Travels

    This is my last week of the year working (I guess I come back on the 30th for a minute), so I decided to do some analysis of my year. I like data and numbers, so I’m looking at a few aspects of life this year with data I’ve compiled from previous years.

    Today we look at travels, which is something I do a lot each year.

    The Report

    Let’s look at my Power BI Report. Quite the year, traveling to three countries and spending nights in 35 cities. My home city/country are included, so the next is other places. As you can see from the map, I was lucky to get to Australia, the UK, and Italy.

    2024-12_0145

    One of my favorite spots this year was Montepulciano, where my wife and I spent a relaxing day.

    2024-12_0146

    A few other numbers (ignoring home):

    • US States visited: 15
    • Nights in hotels: 119
    • Nights in hotels not for work: 36
    • Trips: 26
    • Flights: 68
    • Overnight train trips: 1

    The non work hotels are likely a bit low as my wife was with me and we extended some work trips by a day and relaxed. Otherwise, I’d have likely tried to miss a few hotel nights and get home.

    I have no idea how many miles I traveled, but it was a lot. I had some long trips this year as well, which were made easier (for most of them) with my wife coming along. Australia was 19 days and the UK/Italy/UK was 14 days.

    I also had some crazy stretches, which were hard, but fun trips. The first was this turnaround:

    • Sun – Wed – London, UK for an event
    • Thur – Cambridge
    • Fri – fly to Colorado
    • Sat/Sun – Colorado, coaching
    • Mon – fly to Sydney, AUS

    My body was a bit messed up after that one. The next one was a easier.

    • Fri – leave Melbourne to fly home
    • Sat-Sun home
    • Mon – fly to NY

    Another almost crossing half the world trip. The last one was also a bit crazy.

    • Wed – Fly to Dallas
    • Thur – event + Fly back to Colorado
    • Fri – Fly to Syr
    • Sat – speak, fly to New Jersey, then London overnight

    That last one was OK since I spent 5 days in Cambridge and then a week in Tuscany with my wife. Glad I went home for a night though.

    All in all, this was one of my biggest and not biggest travel years. I had the same number of trips as last year, but they were more spread out. No trips in Dec, and some nice stretches at home throughout the year. Last year was more hectic with lots of small trips that kept me leaving home almost every week for a lot of the year.

    I also didn’t see as many countries this year, and no new ones, but we had some memorable and amazing vacations, which made a better year.

    I also crossed 1 million flight miles on United this year, which is a nice milestone for someone that travels a lot. My wife gets to share my status, which is nice as she often travels solo and meets me in places.

    Looking forward to 2025, though I have no idea what to expect for travel.

  • Learning to Grind

    When I was younger, I had a variety of jobs, but in most of the positions I had to work hard for stretches. Really hard, as in more than 8 hours a day or 40 hours a week. Often as I was starting a new position, it took some time for me to develop some understanding, some skill, and some muscle memory. In some jobs, especially in restaurants, I also had to build the physical skills to be on my feet for many hours.

    In technology, I’ve often found myself unsure of how to approach a new position, aware I had knowledge gaps about how things worked, and often, I was naïve or ignorant of some piece of technology my employer used. Even at jobs where I started as a developer or DBA on a known platform (ASP or SQL Server), I sometimes encountered some aspects of the technology that I hadn’t used in the past (like clustering).

    In those situations, something I learned from my parents and a few youth coaches came to mind. I needed to bust my butt to be successful. The lessons I learned weren’t expressed so politely, but they boiled down to putting in extra time and focus, and continuing on that path until I was competent in the eyes of someone else, usually my boss.

    I’ve encountered many people in the last decade that have much to learn. I’ve met far too many that didn’t understand their environments as well as I’d expect them to as a manager. I have encountered far too many people who wish they could be more skilled in some way, but they haven’t made a commitment do the work to further that wish. I’ve met far too few people who are working to improve themselves on a regular basis.

    How do we teach people to grind away at something to improve themselves?

    I don’t know. I’ve tried to motivate people, I try to give them examples, I’ve tried to provide suggestions. It seems that many people have lost the drive to invest in themselves to prepare for the future. Too many want their boss to train them and then re-train them when they don’t use a skill and forget it. Or they want their time in a position to count as experience. Or they want their boss to give them time out of their 40 hours, without having to make their own investment of time at night or on weekends.

    Skill and experience don’t magically appear. They take work. They take grinding away, making mistakes, achieving small successes, taking a step backward, then driving forward in new ways. It’s effort, and it’s time. Read any story about a person who’s achieved success and you’ll find tales of study, work, practice on their own time.

    If you want something different in your career, or in life, you have to work at that thing. Make a plan, but then work at it. Give up some leisure time. Not all, but some. Give up something fun to achieve something else later. Learn to sharpen your saw, polish your craft, grow your marketability, whatever you want to call it.

    Just start doing it.

    If you want to read a few examples, I have a short series of posts on grinding away at life.

    Steve Jones

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

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

  • Playing with the Data API Builder

    I published an article today on the Data API Builder, which is a way of presenting your database tables as a REST or GraphQL API for developers. You can read the article to learn how to get started, but I’m adding a small thing in this post.

    This is part of my posts on the Data API Builder.

    Setting the Page

    The default page for data is 100 elements (and the next URL). That’s a lot for display, and I might not want developers to get that much data by default. Perhaps I only want 5 rows.

    In the JSON file, I’ll add an item below the development mode element (but a level above. I’ve shown this below:

    2024-12_0164

    The JSON is a bit fiddly, so you need to make sure you know where you’re adding this. The options are below the “runtime” at the same level as “rest” and other items.

    Once I do this, if I query from Postman, I see this, only 5 elements (and the next)

    2024-12_0165

    A quick add, which will be in the next (or next after that) DAB article.

  • The AI/Human Spectrum

    I was asked this question recently: is it more likely that AI will replace humans or assist them in their work?

    It’s a good question. If you think about the way AI is being hyped in 2024, many people think AI is, or will soon be, replacing people and we need less of them in work. I guess the simplified view is that AI can do the jobs of many people, but I’m not sure the world is that simple. What I think is more likely is that AI becomes a lever that assists a few people in getting more work done and potentially replacing other, less knowledgable humans.

    Maybe it’s the ultimate, do-more-with-less pressure that the management in many organizations places on workers that has people looking to AI to help. Get more done this year, but we’re not adding staff, and potentially we’re removing a few staffers. Maybe AI can do your job?

    AI can be a lever, and I do think that there are tedious tasks that we might have AI take on for us. We’ll need more of an agent/proxy approach to AI systems, as now I can ask AI to do some things that relate to text/images, but not actually do work for me. I want an AI to actually set my Out-of-Office for me, not give me instructions on how to do it or write the message for respondents.

    The current Generative AI/LLM systems aren’t really smart or intelligent, but they do process vast amounts of data and mimic the responses that other humans might give. If you work in an area that can benefit from that type of interaction, maybe an AI works well as a lever and lets you get more done on any given day. For some jobs.

    However, getting more done might not be enough. If I pair program with someone else and they write a bunch of poorly performing code, it takes me time to judge the quality of what they’ve written and then additional time to fix it. Getting more done in that situation can be a burden because the additional code produced requires additional rework. I might get less work done in some cases if the code is low-quality and I use a lot of time to rewrite or improve the code.

    However, if I am tackling simple tedious tasks, perhaps basic CRUD work in an application, maybe an AI can generate enough SQL, web, C#, etc. code that the job is done quicker. Maybe not at the most efficient level, but how many of you think the code for your internal applications is amazing? Is it good enough? Can an AI do “good-enough” work?

    As with a lot of dramatic changes to technology, I find myself going back and forth on the value produced. Some days I think the tech is amazing and some days I think it’s akin to the stuff I shovel out of horse stalls. AI is in the same boat with me, and while I think there is potential, I know that there are also downfalls and potential detractions from its widespread use.

    Certainly, the need to evaluate and judge quality is a challenge with AI, which leans me towards the lever that assists talented humans and replaces less talented ones. The other issue is cost. The LLMs are expensive, and use a lot of compute power. I’ve seen some smaller models, perhaps tailored with RAG or other methods of refining (and limiting) their use will overcome that, but who knows. The current models, however, cost something to run and someone is going to have to pay for that. Is there enough ROI to do that?

    Lastly, trust. Can we really trust an AI to give us accurate responses, or even perform work on our behalf? We have that problem with other humans now, but they work slowly compared to a computer. Can you imagine the problems that a rogue computer system could create with access to change things in the real world?

    Answer my question today. Are AIs more likely to assist or replace people?

    Steve Jones

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

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