When I first started working in technology in the 90s, it was a time of outsourcing lots of work overseas. Many large companies followed the wave of manufacturing in the 70s and 80s by many companies, including lots of semi-conductor manufacturers. I watched as a number of jobs moved overseas, though fortunately not mine.

In the early 2000s, I worked at a company as a manager, where I was involved in some of the discussions about outsourcing a lot of our IT operations to another company. It was a scary time for my friends, as the move would have left a lot of them looking for work. The CIO tried to say that many would get jobs with the outsourcing supplier, but couldn't say how many.

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The senior advantage means more to me all the time, though often I feel there are more disadvantages to being around longer than advantages. Getting older is hard, especially physically, and I struggle with that. Having more wisdom, more tolerance, and more patience, are good things, but I'm not sure I would consciously make that trade.

In a business, senior often equates to more experience and time working on systems. Sometimes it means more skill as well, but usually, the internal knowledge of how our systems work inside of our environment is more valuable, even though it can be hard to get management to realize that fact.

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I had never heard of a vector database. I assumed this was a specialist type of database used for a particular problem domain, like a streaming database or graph database. There is a need for specialized platforms in certain situations, but I wasn't sure what a vector was. The description I saw for a vector database was that they "... are specifically designed to work with the unique characteristics of vector embeddings. They index data in a way that makes it easy to search and retrieve objects according to their numerical values."

That sounds like any database. However, I saw a few more articles on the hype and then some details about the ways in which this type of database is helpful. Essentially, this is a database designed to store the outputs from various Artificial Intelligence (AI) and Machine Learning (ML) models that examine unstructured data. Things like images, video, audio, and even text are turned into numerical values, or vectors. The vector database is designed to help index and then search these vectors.

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Monitoring your SQL Server instances is important to ensure you can meet your SLAs. Availability, performance, reliability, quality, whatever you care about, it's important that whoever is responsible is looking at how the database is performing. At Redgate, we have multiple teams working on SQL Monitor to enhance and grow it to meet your needs.

A short while ago there was an internal conversation recently about page life expectancy. We've had some customers ask about this and setting alerts to watch this value. Our developers and sales engineers asked for a few thoughts from Grant and others on how to respond. There are a variety of opinions, some saying monitor it, some saying don't bother.

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The #SQLFamily is amazing, at least I think it is. Like many families, it's welcoming, supportive, and comforting. It's also maddening, frustrating, and exacerbating at times. Like most families, or at least the ones I know, it's not perfect, but it's what we have and at the end of the day, most of us get along with each other.

It's also an open group of people. In general, we welcome people with open arms and smiles. Those of us that are more visible or prominent are willing to listen to, help, and support anyone. I was overjoyed during the recent PASS Data Community Summit, where I had the chance to see so many people that I haven't seen in person in 2-3 years. I met many other interesting people for the first time and enjoyed the experience.

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I saw this article a few months ago, which talks about engineers at Facebook not knowing where their customers' personal data is stored. The engineers were being questioned in a legal matter, where they were asked to definitively state where all personal PII data for any human was stored by Facebook. Their answer was that they didn't think anyone in the company would be able to answer that question.

Facebook has been controversial over the years, and plenty of people dislike the way the company conducts business. I noticed no shortage of data people (and many others) commenting on this situation, saying that Facebook should be shut down because they don't know where data is being stored.

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We collect a lot of data in our databases. Not as much in bytes as a lot of the video/audio/TikTok/Instagram sites, but still enough that many of us are constantly adding storage to our systems. All this data is not only a challenge to manage, but it also means that we are regularly dealing with query tuning issues. Better code, indexes, and more become regular challenges with large volumes of data.

I am a big fan of trying to reduce the data you manage where possible. Archive, delete, remove older data, do something. This not only makes your systems easier to manage and improves performance, but it reduces your risk. Any PII data you have that might store is an ongoing risk in the event of a data breach. I don't pretend this is easy to do in any way, but it's a good idea.

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It couldn't have happened to a worse set of people, and I'm glad it did, but I'll still take a positive lesson out of this. A hacker sent a typo in a command to a botnet and lost control. That's kind of funny, and I'm glad it happened. The less botnets, the better, IMHO.

The coding in this software didn't have good error handling, which is a lesson in and of itself. Overall it seems many developers do a good job of error handling, but I still encounter more pieces of software that allow problematic input than I'd like. While we don't have great error handling in T-SQL, you can make some checks, and you should.

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A while back I ran across a blog post that talks about the difference between database snapshots and database backups. There are certainly some similarities and some differences, as well as an overlap in the places where you might choose to use each of these technologies. Both might be useful as a way to recover from a bad code deployment, but both aren't necessarily helpful for a DR situation where the primary server has a catastrophic hardware failure.

It's often the case that we learn only a bit about some of the technologies in SQL Server. That's understandable as the platform has grown very complex, encompassing a vast array of technologies and options. There is often some overlap between them and possibly different places where you might choose to use one or the other and solve a problem.

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Many of you reading this probably work primarily in SQL. Even if you are a developer whose main language is something else, you write a lot of SQL. Even if you have an ORM writing the SQL that goes into production, I bet a lot of you are writing queries against a database to check that the data coming back in your application is correct.

As for me, I mostly work in SQL, with PowerShell and Python being second and third. I tried R for a while, but I think Python does everything R can do and it's much cleaner. I find R very cumbersome. I rarely write C# or experiment with anything else, but that's the nature of my job. PowerShell is important, as I do a bunch of DevOps and PoSh is a good choice to work with on the command line for gluing processes together.

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