Redgate recently launched their State of the Database Landscape 2024 survey results, from almost 4,000 database professionals from around the globe. A clear picture emerged from the results, suggesting that 2024 is the year that skill diversification among database professionals is imperative. There’s the need to manage multiple databases, to migrate to the cloud, to introduce continuous delivery with DevOps, and even incorporating Generative AI into the mix.
In a recent livestream to launch the survey results, there were lots of questions from the live audience about what these changes might mean for data careers in 2024 and beyond. Below are the answers from the livestream speakers: Redgate’s Steve Jones and Ryan Booz (both Advocates) and Beca Parker (Software Engineer).
“I’m quite junior but I’m ambitious and I’d love to get ahead in my career. If you were to choose a priority between learning a new database platform type, cloud migration or AI in 2024, which would you choose?”??
Beca: I’ll give a typical developer answer… it depends! In this scenario, I would look at your organization’s 2024 strategy or even at the job description for the next rung on your career ladder. Is there a named focus on any of these areas? Also consider which area you are most interested in as this will drive your motivation to progress when the learning gets tough.
Steve: I’d look for an opportunity at your employer. This will help you decide where to focus your efforts. Ask managers, yours, or others, where you might grow and get a chance to make a difference. Then work on that. The AI thing is shaky, so this might be an area I focus efforts on outside of work, for personal growth. If you have a chance to work on cloud stuff, there is lots of opportunity. I think a new platform is probably the easiest option out of all three.
“Any thoughts on what careers in the database space will look like in 5 years’ time? How might they evolve?”
Ryan: At least one aspect of database careers will entail data management for ML (LLM). In the Postgres space there are multiple “vector” extensions being made that can help companies create their own LLM models using databases. I’m sure we’ll see more in this space from MS (as part of Fabric?), too.
Otherwise, I think we’ll see more consolidation in job roles on the traditional DBA side of things to really become part of the DevOps teams/space. It won’t be enough to tune servers/queries, the top performers will understand where and how the data layer is incorporated into the overall pipeline/workflow of the business.
Steve: In many cases, your job will be the same. You will still be responsible for the same tasks and situations. You might get additional responsibility with new tech (new RDBMS, cloud work, new versions of your DB, reviewing more PoSh/Python code, notebooks (spark, python), new ETL, data lakes). If you keep the same job.
That’s an if. If you go elsewhere, the “DBA” job might look different. Even the DB developer job might be different. That’s a good reason why you and your team ought to be looking at new and related technologies. You might run into them.
At the same time, you ought to deepen your knowledge in the area that you use the most. If that’s writing queries, then work on that. If you are tuning, learn more about what makes good queries go faster or use fewer resources. If that’s admin, make sure you are better at HA / replication / deployments etc.
“As a team lead of 7, how can I best support their career development to keep all of us – and the organization – ahead of the game?”??
Steve: Add 10% time (or whatever you can get, 5%?). Put up a Medium blog and have engineers post what they are using this time for, share resources, and then periodically have them share what they’ve learned. Create a book club, where you assign everyone a book to read and go over small sections each week for a short period of time. Drive learning and make this a part of their review. Note, this can be soft skills as well as tech ones. I might recommend “The Trusted Sales Advisor” book, which isn’t just for sales folks. I see things in there I’d use to present tech to my colleagues, or even how I work with internal clients when meeting their needs. A lot of good soft skill advice in that book. The Phoenix Project / The Goal are also good to help people think about how to improve their jobs.
Above all, tolerate mistakes and failures. Not big ones, and repeated ones, but give people room to apologize, learn, and get better safely. Psychological safety does a lot of good for teams.
“What is the best way to start learning a new database platform type when I have a limited amount of L&D time?”?
Beca: For me, the most impactful way to get learning a new database platform was to spin up an instance (either installing one on your local machine or use a tool like Redgate Clone or Docker). Get one of the sample databases for your preferred platform onto your instance (e.g. Pagila for Postgres, Sakila for MySQL). There are various step-by-step guides and tutorials online that will walk you through this depending on your chosen platform. Now pause to celebrate this achievement ?; you’re already learning loads. Then try executing some queries you’re previously familiar with and get curious about the results. For trouble shooting, try asking your search engine or AI chat bot to rewrite your query in ANSI SQL (which should work against any platform) and get that working first. As you gain more confidence, learn ways to optimize queries for your specific database platform.
Steve: What I suggest is twofold. First, dedicate a time each week. It can be as little as 15 minutes but put this on your work or personal calendar. Start making an investment in yourself. Second, work through organized learning on a platform. Get a book, use Ryan’s PostgreSQL 101 series, something that moves you through step by step. From there, you can look for more specific educational things on certain features/functions you don’t understand.
“What is everyone on the panel doing to educate themselves on AI developments with regards to database management?”?
Steve: I am starting to ask Bing Copilot, Prompt + AI, or ChatGPT a question before searching. I’m trying to evaluate if I get good responses, or if I know how to adjust a prompt to get a better response. This often is just a few extra minutes, but I’m learning to work with AI. I’m also slowly going through an AI course from MS, trying to better understand the tech. Not that I plan on training a model, but knowing how tech works has often helped me better understand how and when to use it.
“As DBAs, should we be worried about our future jobs anytime soon because of AI?”
Steve: No. Vendors hype this, and many products are “adding AI”, but the reality I see is that talented people can use AI as a lever to get more work done, but junior people get AI to produce junior results, which isn’t necessarily going to work well.
I think it’s worth learning how to work with AI as a tool. It’s hard at first, cumbersome, but learning where and when it helps you is worthwhile because it’s not going away and it’s going to get better.
At the same time, the best insurance for your job is to become better at your job. Look at what others do, answer questions on SQL Server Central (or at least test yourself to see if you can). Invest in your career. Doctors, nurses, lawyers, accountants, etc are required to train and upskill every year. Hold yourself to that same professional standard.