The decision to upgrade database servers can be a complicated one that involves features, costs, and support requirements. While I think many people would love to run database systems for ten years, often there are concerns about support, which effectively ends after five years for SQL Server. While you can purchase extended support, is that worth the cost? A hard question to answer, but one I've had to confront lately. When do you upgrade a database instance?

I've discussed this with a few customers, but it also came up in the context of SQL Server Central. We run a SQL Server 2016 database on Windows Server 2016. Someone recently sent a note that since 2016 is out of support, they'd like to upgrade the systems. They were thinking SQL Server 2019 on Windows Server 2022, but with the release of SQL Server 2022 that doesn't make sense.

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Time is the most valuable asset I have most days. Time is often a limiting factor that dictates my priorities at work. It also often determines my stress and enjoyment levels in life. Using time effectively can lower the former and increase the latter.

A few years ago I read Making Work Visible, which talks about work flow and how time can be wasted in teams. The book covers the 5 types of time thieves, and it helped me to better think about how I organize both my tasks at work and in my personal life. I think that has sometimes helped me find a way to get more done with less effort, and less stress.

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Watching the evolution of SQL Server and the Azure SQL Database (ASD) variant has been interesting across the last decade. For a long time, ASD felt crippled compared to the on-premises product. The last few years, however, it seems that ASD is getting features first, which then slip into a release that I can download and run locally. It's a cloud-first database now, even though there isn't parity with both products.

In my career, I've worked with a number of platforms in production environments. One of those is Db2, though that's often felt like a legacy platform. I've rarely seen users starting new projects on Db2, though they might add some new databases, especially on AS/400, mainframe, or Linux platforms. I never thought Db2 was less capable than other choices, but I just don't see it used very often.

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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.

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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.

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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.

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I saw this posted on Twitter recently: "If you're always right, you're not learning. If you're never failing, you're not reaching. The objective is to be right. The objective is to succeed. But if you're always winning, you're undershooting your potential." –@JamesClear

I found that to be a very interesting view, especially as I think about moving my career, life, business, or anything else forward. I think about this in terms of the goals I've set for myself each year for work, or each season of coaching. In the past, I've sometimes tried to pick those items that I think I can accomplish.

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When I first started work as a software developer, I knew that getting an environment set up where I could compile a project might take a few hours or a few days. The complexities of how people built software projects, the dependencies, and more were handled in a very immature manner. These days I can often download a project from GitHub and compile it in minutes, with many of the issues handled automatically with various tools such as complex config files and package managers.

In the world of database development, I often find people struggle to get a database set up with a project. Whether you are coding in SQL, C#, or Java, many organizations haven't got a standard way of bringing up a database environment for developers. Some have a shared database for a team, which can be better, but can also lead to collisions of code as the pressure to get software built grows and team size increases.

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Quite a few of the bugs we see in production systems come from data that isn't handled well. Perhaps the developer never considered this data, or another bug lets data into a system that should never be recorded. These are often NULL values, but they could be other data values that are far out of the ordinary.

Where do we draw the line for edge cases? Is it anything that doesn't fit 95% of the data range? I see this number used in many fields, often manufacturing and other "physical endeavors". Is it the 80% rule, where we ensure 80% of data cases are covered, but 20% represent special handling?

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