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.
There’s a lot of hype, concern, and fear regarding generative AI lately. Tools like ChatGPT are so good at generating groups of words that it feels like magic– however, generative AI doesn’t have the ability to understand or verify the language it generates. For example, it’s been used to write news stories for CNET – but sometimes included facts that are just plain wrong.
One positive use case of a generative AI tool like ChatGPT is to ask it to make a case for different perspectives than your own, and use this as a starting point to broaden your understanding – with the knowledge that we need to verify everything ChatGPT tells us. As an example, let’s chat to ChatGPT about Object Relational Mapping tools (ORMs).
Hosted cloud databases make a lot of administrative tasks easier, or take care of them for you altogether.
But here are three things that I’ve found a little too easy to forget about Azure SQL Database.
We’ve just published a new article in the SQL docs, Tune nonclustered indexes with missing index suggestions . The article explains what the missing index feature is, limitations of the feature, and how to use missing index DMVs and missing index suggestions in Query Store to tune indexes.
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