Voice of the DBA

Tag: career

  • The Team You Want

    Brent wrote about what a good DBA looks like and challenged people to write a testimonial for them. There are some great comments on the post, and some funny ones. It’s worth a read when you need a break from other work. I especially chuckled at the picture Brent used. I think ours was better.

    That got me thinking: what do I want in a team? Or maybe, who do I want in a team?

    I have a great team now, but we are fairly distributed and work independently. I have great co-workers at Redgate, though I don’t often work that closely with any of them. I work often with some of them, for specific things, but usually it’s coordination rather than the tight, back and forth I’ve often had with technical teams.

    I realized at some point in my career that I was often being asked to do the same work. I needed to manage systems. I needed to write and tune queries (or rewrite those for others). I needed to manage security. Most importantly, I needed to find solutions to the constant set of questions, problems, and demands from others in my organization. I learned how to teach myself things, how to research, how to test, and how to talk to others. I became good at clarifying what people needed and then finding a way to meet those needs. I think I turned into part of the Incredible DBA Team, even though often it was a team of just me.

    The characteristics Brent talked about were important to me.

    A little. I have always felt that I could work with others if they want to learn from me and teach me things. They have to want to collaborate and get things done as a team. That led me to worry more about who I worked with than what the job was. If the compensation was good, then the decision to take a job often came down to who would I work with. After all, the work was often very similar.

    Think about your situation. What do you want to see in your coworkers? I’m sure you want people that you get along with and carry their weight. Maybe you want to learn from them. Maybe you want to be able to teach them. Maybe you have specific skills you wish were stronger on your team.

    Leave a comment and let us know what your ideal team looks like. As always, if you want to leave an anonymous comment, send me a PM on the site with what you’d like posted in the discussion.

    Steve Jones

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

  • Future Data Driven is Coming Sept 27 for FREE

    The Future Data Driven 2023 virtual conference is coming on September 27. Register today and save a note in your calendar.

    I have been honored to speak at this event in the past. I’m not speaking this year as I’ve been buried with work and travel and need a break. However, I see a great schedule (scroll down) with some sessions that I will try to watch. A few on my mind:

    I can get information in other ways, but a conference is a good chance to hear what someone else has learned and shares with me. Even if I listen in the background, I’m learning some new things.

    There are lots of other great sessions, so register today.

  • The IT Jobs AI Can’t Do

    This was a provocative title: 6 ITOps Skills That Will Never Be Automated. In a time when AI use is growing quickly and many people fear for their jobs, it’s nice to see someone writing about areas that AI will struggle to handle.

    Ironically, lots of these articles are written by writers without much technical expertise and who are more likely than others to struggle in the age of AI. If a computer can mimic styles, less writers will be employed. That might be fitting with so many journalists hyping technologies without really understanding them, doing so in a way that creates stress among actual IT professionals.

    The article lists 6 jobs: policy config, incident response, complex scaling, app feedback, ITOps tool deployments, and end-user support. Of these, I’m not sure that app feedback and end-user support are good-paying jobs that many people want, but I know there certainly are people working in these areas who depend on those jobs.

    The others are often jobs that few people do. I do think that these are complex jobs where a computer can’t really take in enough information to do a great job here, but I do think that one smart worker could learn to guide an AI that does a lot of the busy work. Someone could specify a policy from an AI and then ask the AI to tweak it as holes or gaps are identified. I think over time a lot of quick incident response items could be handled by an AI, albeit with a human guiding it. Again, that reduces the need for humans in these roles.

    To me, that’s where the AI revolution becomes scary. AI is a lever to get a lot done. Fewer humans can be used to accomplish tasks with the aid of AIs, which reduces a lot of the labor needed. After all, we know that in any area, there are lots of beginners and advanced beginners doing work. If we can use an AI assistant to help one or two humans, we might remove the need for a dozen others.

    Maybe that’s what a future 10x engineer looks like. Someone that’s way more productive than many others because they’ve leveraged the computer (in the form of an AI) to get a tremendous amount of work done.

    I do think there are likely jobs that AIs can’t easily do, but I also think that a lot of the work in these areas might get copy/pasted between organizations. Policies, scaling choices, and more are things that one human might learn from others and mimic those efforts. With better judgment than an AI, but not as much as if a true expert were coming up with the best solution for a particular situation.

    Unfortunately, most of us don’t come up with optimal solutions, and our organizations run fine with sub-optimal code/policies/decisions/whatever. Will companies get by with very few highly paid people or a few more low ones and accept mediocre software?

    That already happens far too often already. I suspect like most trends, we’ll see quite a few organizations move in this direction. However, I think the human ingenuity will win out and as a few companies realize that the creativity of humans can better target their goals than an AI that merely summarizes other work, the pendulum will swing back.

    Unfortunately, a lot of people will get caught in bad situations as this happens. Work on your skills, technical and soft, and be sure that you are in the best position you can be in as the world embraces and evolves with AI technology.

    Steve Jones

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

  • Using AI with Data Tasks

    The hot new technology of the year is AI. Between ChatGPT, Copilot, and generative AI, it seems that this is invading the world of computing at an incredible rate. Whether this becomes really useful and valuable or not is something that we will seem over time. There have been plenty of trends in this area in the past that haven’t become as ubiquitous as the hype would lead you to believe.

    I’ve done some light experiments with AI on my blog. To date, I haven’t found this to be that useful, other than a few cases where I basically used an AI to search the web for me. Rather than read a bunch of SSC or Stack Overflow results, the AI summarized things.

    Somewhat.

    I definitely had to test and verify the code more than I feel I’ve done with code posted in a forum. Of course, I do less experimenting because the AI results were a little more targeted to what I needed, rather than my cobbling knowledge and partial solutions together. I’m also not sure which I prefer.

    I would like to use AI for data work, and there is an article that talks about some of the ways that we’ve used AI in the past. Data profiling has made sense, and I can see value here. For data security, I’m not sure how helpful AIs have been. I’ve looked at some products, and I don’t know that I think any of them do a great job of identifying data. They do make it easy for whoever is assigned the task by doing some of the work, but they aren’t a panacea. They make mistakes, just like humans do.

    I do think data observation and looking for anomalies is a place where AI can really shine, but that’s not the data work that many of us do. It matters, but for most of us, this isn’t something we deal with.

    The future of AI was more interesting. The idea of data homogenization, taking data from different sources, and fitting it to a data model is interesting. Of course, the AI can’t make too many mistakes, or the time correcting might overwhelm the time saved. I think we see that now with humans who we ask to ETL data. If they aren’t good at it, or make lots of mistakes, those of us overseeing them might just do the entire job ourselves.

    I know that AIs are still new and immature, and while there is a lot of potential, they feel like junior staffers now, needing more handholding and micromanaging than I like to do. Perhaps they will change our careers and the way we work, but I don’t know how quickly, or even how deeply. Already I find lots of companies putting restrictions on what their employees can do with AI, which makes me think this might be more a targeted, niche technology more than a general, use-it-everywhere solution.

    Steve Jones