Sometimes people are annoyed by the term ‘Database DevOps’. Why not call it simply ‘DevOps’? After all, Database DevOps follows the same core principles.
The answer is simple: implementing DevOps is tricky by itself, but most teams are set up to fail when it comes to implementing DevOps for databases. This makes it worth defining as a specialization.
Twenty years ago, database administrators (DBAs) were the primary career path when it came to specializing in data management.
Much has changed: development patterns transformed from Waterfall to Agile, DevOps drives automation and shared ownership of code, and cloud services have made many more kinds of PAAS databases, data lakes, and data lakehouses available to organizations of all sizes.
These changes have introduced new and varied career paths for data folks which have different emphases on skill sets. In this post, I talk through the commonalities and differences between DBAs, Database Reliability Engineers (DBREs), and Data Engineers (DEs). Whether you’re a hiring manager or data professional, it’s worth knowing about these roles.
Have you ever received advice that was technically correct, but it was too hard to understand?
I think of this as “accidental bad advice,” because it can lead to confusion and bad outcomes. There’s a LOT of accidental bad advice out there on index maintenance for SQL Server and cloud versions like Azure SQL, even in the official documentation.
In this post I’m answering a common index maintenance question, and we’re going to keep it simple.
I was doing a bit of data analysis, and the resulting numbers didn’t quite add up.
I double-checked my queries. Had I goofed in my sql? Nope. Next, I looked into if some of the data was in an inconsistent state.
What I found was worse than what I’d imagined. As a data person, it made me feel sad and icky.
I’m thrilled to be heading to Seattle in November for the PASS Data Community Summit. My favorite things about the PASS Summit are making connections, learning from folks, broading my horizons, helping build a vibrant community, and teaching.
Teaching is a great privilege, and I’m excited to be giving both a pre-conference session and a regular session. I chat about the sessions in this ~4 minute video:
Not in the mood for a video? Read on for the written version and a comic.
I redid my static site’s contact form with a free option from my host, Netlify.
Here’s why I changed my static site host and how I got a Netlify contact form to work with a blog built on Hugo and the Mainroad theme.
I’m answering two questions from Brent Ozar’s list of user questions open for answers.
Q: What’s your opinion of entering confidential info in chat gpt? Will we see AI therapist chat bots?
Q: In terms of security, is it OK to expose your database to tools like GitHub Copilot in Azure Data Studio? Someone will know that your email address column is not encrypted or a stored procedure is not parsing its input parameters when dynamic T-SQL is built.
I recently posted on LinkedIn that I was interviewing for a SQL Server database administrator role for the first time in a long time. I invited folks: “Ask me your favorite interview question!”
Stephen Vakil had a great one: “when should you use SQL Server to store your data?”
For situations where SQL Server is already in use and there’s a relatively low barrier to entry, I think it’s simpler to turn this question around and ask, “When is SQL Server not a good choice for storing your data?”
This post is also available as a podcast episode, and on YouTube.
Reorganizations and layoffs are now commonplace in the tech industry: a daily occurrence. It’s increasingly necessary for tech workers mentally prepare for potential job loss and uncertainties that arise. It’s a common antipattern to believe that it’s “too late” to prepare for a layoff or unexpected reorganization if a layoff has already occurred in your organization.
It’s never too late, and it’s always the right time these days to start mentally preparing yourself for change– because there’s reliably another change around the corner.
One thing I enjoy about AI chatbots is that they can help me recognize when I’ve missed something obvious.
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