Voice of the DBA

Category: Editorial

  • Investing in Another’s Career

    Years ago my son asked me to buy him a copy of The Unincorporated Man. It’s a science fiction book about the future, economics, and sentient AI systems. It’s the first part of a series of four, and I’ve enjoyed reading them all. I highly recommend them if you like science fiction.

    In any case, I was reminded of the series when I saw this article from the New Yorker about selling shares in yourself. It’s part of the premise behind the Unincorporated Man, where every person is their own corporation and sells shares of themselves to finance their careers. You could sell shares in your future earnings to finance education or some other investment in your future. In return, you have to pay back that value through dividends.

    I don’t know if this is a good idea, and it goes a bit sideways in the books as your “shareholders” can hold sway over which jobs you take and when. They do this to maximize their return on investment, putting their interests over your desires.

    While selling yourself in your early education or career is one thing, what about doing after you have had some success? The article linked above talks about this, where some entrepreneurs who have had some success decided to put all their assets and debts into a kind of fund and sell that on the stock market. They are doing this as individuals, not as a company. Essentially, you invest in their careers and their potential.

    They, and others, want to bring this to others, essentially bringing venture capital to people who are early in their careers. This allows others to speculate and invest in artists, athletes, and other professionals. This might allow people who aren’t rich to absorb the risk of pursuing their career goals.

    It’s an interesting idea, but I do worry about the influence investors might have over their investments. It’s one thing to invest in a hands-off manner and hope things improve. It’s quite another to attempt to manage the investment because of the number of shares you hold. Plenty of companies have learned that investors are not always willing to trust in existing management. What happens when that management is you?

    I loved the book, but I don’t think I’d want to follow this model. Instead, I like the Kickstarter and Indiegogo models where someone can produce a product or work of art, and others can choose to invest or not for that one project. To me, that’s a better way of allowing investors to support those that want to change their lives through by pursuing their craft.  Pursue some endeavor, selling shares to fund your efforts with future dividends or perhaps access to the end result.

    Steve Jones

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

  • Republish: Securing Your Instances

    I’m off today, enjoying the Thanksgiving holiday with family, so you get Securing Your Instances again.

  • AI Medical Record Scanning

    Artificial Intelligence (AI) systems continue to pervade many industries, usually where there is a lot of data and human developers struggle to build solutions that handle the complexities of the problem. Often the experts in these subject areas can’t fully articulate the reasoning behind how and why they use data to arrive at some conclusion. We developers often struggle to get clear specifications from clients in simple cases, so I certainly understand why AI might be attractive in complex problem spaces.

    I think AI is a promising way to try to tackle some of these issues, some of which are important to humans. These systems can achieve a focus and analysis of complex data in a way that very few people, if any, can. The sheer volume of data and myriad of relationships among the various different metrics captured eludes the ability of most people to properly analyze.

    I found an article that looks a neat non-intrusive way of analyzing medical records from patients to detect blood issues. In this case, an AI looks at the records of treatment and test results, looking behind the doctors and nurses to catch patterns that can indicate blood poisoning. The signs are subtle, and in today’s world where the humans are often overloaded, hand-offs between people can be incomplete. This means that doctors and nurses sometimes miss things. The AI doesn’t order treatment or prescribe anything, but raises a flag to alert humans. The medical staff can then review things, examine the patient, and decide on the treatment.

    Early results seem promising, and more importantly, this is the type of lever that computing can bring to leverage human expertise and help humans. Not only can they better treat patients, but potentially this can help enhance the understanding of what signs a doctor or nurse should look for in future patients. When a flag is raised and an issue detected, the staff can go over notes or discuss how they might have caught this themselves. A little continuous learning applied between humans and machines, just what we’d want in a DevOps software pipeline in technology.

    Steve Jones

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

  • The Challenge of New Platforms

    I saw a customer asking about Exasol recently, which is an in-memory, columnar database. I know nothing about it, and it might work great, but we don’t support it. I didn’t think much of it, as I’m sure the customer has a reason for choosing this platform.

    Later, however, I wondered if this was a good idea. Another customer had inquired about Knack, which I had never heard of either. Not only that, it’s an online database that appears to assemble its “tables” from data stored in other systems. Strange, but I’m sure it works well for some companies, especially those without many software developers.

    I often find there are developers, or even analysts, that find a new platform that appears to work better for their particular problem. Sometimes they’re excited, sometimes they have some experience in the past, but there seems to be a regular push to add new types of technology to many organizations. Often technology that isn’t substantially different in function from something that already is in place.

    Whenever you add a new technology to your organization, you are adding more than the capabilities. You are also adding the support of this system, which means not only training end users, but also training the staff that has to support the software. If you do this too often, you risk having staff that don’t know how to keep things running efficiently. This is why we rarely see companies changing their core database platform. The change is a big disruption.

    What’s more, our staff is not consistent across time. People come and go, and finding new people can be hard. Especially those that know all of our technology stacks. I appreciate and would like to see more customers working with new platforms, but I also think this has to be something an org considers carefully. After all, many software companies limit the number of products and versions they support for this very reason.

    There’s also the problem of technology becoming end-of-life’d. While we might think database platforms are around forever, some of them have disappeared over time, and even if they exist, support goes away. We might find ourselves with the need to upgrade multiple platforms, each of which requires different knowledge. Our staff might spend a lot of time learning and practicing upgrades for disparate platforms, knowledge that doesn’t transfer to the next upgrade.

    I am not advocating for everyone to run SQL Server (or PostgreSQL or MySQL or DB2 or Snowflake, etc.) I do think there are reasons why we might choose to use Synapse or Teradata instead of an Oracle database. However, I think the list of platforms ought to be limited in some way. Just like our list of programming languages should be limited. Having a handle on our domain of skills makes it easier to find, train, grow, and build knowledgeable staff. Adding to the list ought to be done slow and carefully, after some debate, discussion, and voting.

    I am glad that there are so many RDBMS platforms, and NOSQL platforms. It’s great to see people building new and improved databases. This work is how we get amazing datastores like Neo4J, Redis, and ElasticSearch. At the same time, I do think caution about adding new platforms is warranted inside of organizations. Reusing the knowledge we have should be the first thought, with the decision to grow based on a true need, not just someone’s desire to play with something new.

    Steve Jones

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