There's Too Much to Learn I remember getting started on SQL Server and trying to upskill myself in the mid-1990s. At that time, my employer was running a SQL Server 4.2 instance for a third-party application, but we wanted to rewrite our internal bespoke sales app to run on SQL Server. We were upgrading from Foxpro to Visual Foxpro and looking to move from shared dbf files to a SQL Server. There was a new release of SQL Server 6.5 during our development, and I wanted to learn more about it. I purchased Inside SQL Server 6.5 and read the entire thing, getting prepared to finish development and then manage a new platform in production. I had updated copies of that book as SQL Server released new versions until SQL Server 2005. When that came out, there weren't one, but rather 4 books to cover the Inside SQL Server details (Programming, Query Tuning, T-SQL, and The Storage Engine). A similar thing happened with the SQL Server Bible, which grew in size to over 1400 pages for the 2012 version. It was a backache in a book if you put it in with your laptop. Since then, the growth of the Microsoft Data Platform, which includes SQL Server, has been incredible with lots of changes not only to the box/on-premises product many of us install, but to the related systems in Azure. The SQL variants, Fabric, Power BI, Azure Data Factor, and more. It feels like there is way too much to learn I know I can't keep up, and I expect most people feel the same way. Coping with the load and the accompanying imposter syndrome is hard. It's hard on your psyche and it's hard when others in your organization, especially your boss, expect you to understand how to work with T-SQL, even when they might refer to SQL Server in one request and SQL Database in Fabric in another. They might even expect that you know "everything database-related", including how to troubleshoot their Azure PostgreSQL Flex server performance issue. AI can help, as can some strong Google skills and an eye that spots the information you need. Whether to do actual work or help guide an AI LLM along a path. I would argue that improving your ability to differentiate what's better from what's worse is becoming even more important in the age of vibe coding. Even if you aren't a YOLO, let-Claude-Code-do-it's-thing, others are and they'll call you when they don't get the results they want. Or when their system performs poorly against your database system. Who knows how many implicit conversion issues or RBAR pieces of code an LLM will write. There's a lot of that sample code out on the Internet, and much of that code goes into training these models. I don't have a magic solution for keeping up, other than build the habit of learning. Practice new techniques, play with code, conduct experiments in the things that plague your environment, or that you see others working with. I don't mess with Hyperscale much as I don't see it with customers, but I do see other technologies, like System-versioned tables. So I spend time there, learning what works well and what doesn't. Those are the skills that help me keep up with the knowledge I need to work with LLMs and humans. Steve Jones - SSC Editor Join the debate, and respond to today's editorial on the forums | 
