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I was chatting with a friend recently about routines and some of the helpful or silly things we do. I mentioned that when I played adult baseball, we often had Sunday morning games and a routine of mine was to drive to town, stop at a 7-11, and get a large cup of coffee and an apple fritter. It was a comforting habit that I still have today, often stopping when I have a morning flight to do the same thing in the way to the airport.
Only in Denver though, not when I'm flying out of other cities and returning home.
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I went to San Francisco for Small Data SF, a conference sponsored by Mother Duck. The premise of the event was that smaller sets of data are both very useful and prevalent. The manifesto speaks to me, as I am a big fan of smaller sets of data for sure. I also think that most of the time we can use less data than we think we need, especially when it's recent data. That often is more relevant and we end up with contorted queries that try to weight new or old data differently to reflect this. Maybe the best line for me is this one:
Bigger data has an opportunity cost: Time.
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When I was younger, I had a variety of jobs, but in most of the positions I had to work hard for stretches. Really hard, as in more than 8 hours a day or 40 hours a week. Often as I was starting a new position, it took some time for me to develop some understanding, some skill, and some muscle memory. In some jobs, especially in restaurants, I also had to build the physical skills to be on my feet for many hours.
In technology, I've often found myself unsure of how to approach a new position, aware I had knowledge gaps about how things worked, and often, I was naïve or ignorant of some piece of technology my employer used. Even at jobs where I started as a developer or DBA on a known platform (ASP or SQL Server), I sometimes encountered some aspects of the technology that I hadn't used in the past (like clustering).
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I was asked this question recently: is it more likely that AI will replace humans or assist them in their work?
It's a good question. If you think about the way AI is being hyped in 2024, many people think AI is, or will soon be, replacing people and we need less of them in work. I guess the simplified view is that AI can do the jobs of many people, but I'm not sure the world is that simple. What I think is more likely is that AI becomes a lever that assists a few people in getting more work done and potentially replacing other, less knowledgable humans.
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It seems there's quite a dichotomy in the technology workforce. On one hand I hear about the Great Resignation where many employees are leaving their jobs because of RTO (return to office) mandates or some other dissatisfaction with their job. On the other, I've seen quite a few people who were laid off and are struggling to find new positions. On the third hand, there is no shortage of companies who report they are struggling to find and hire talented people for some positions.
I don't quite know what to think, but I do know that employment seems to be harder to come by. If you are looking for a job, I believe you need your own sustainable learning plan. If your organization can't find qualified talented staff, then the organization definitely needs a continuous learning plan.
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I was listening to someone at Microsoft talk about their product recently. I can't disclose which one it was, but lots of people use this product and are impacted by changes. The particular thing that caught my attention was that the presenter noted there was a breaking change in the new version for some people. This wasn't a huge change or one that would affect a lot of people, but it was a breaking change for a few.
Another attendee asked this question: I would tend to do xxx instead of what you showed, so why would you do this?
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I was watching a video called Microservices are Technical Debt. In it, the person being interviewed said that a lot of people really have a distributed monolith. That caught my eye since I've worked with a number of customers who are trying to adopt microservice architectures for their applications. I think this is less a performance/scaling choice than a reworking of their software development teams, and I'm not sure they will end up with a better system.
What is a distributed monolith? I am not an expert, but this appears to be a place where all the services still depend on each other. For example, I might have a service getting user profile info that an app calls, with another service getting previous orders, and a third service that returns inventory. In a monolith, if any of these are down, the others don't work. In a distributed monolith, these might be built independently, but perhaps the core app/web page still requires all these to be working to show the user something.
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There are lots of software development methodologies. This page lists a few, among them waterfall, agile, iterative, rapid, and more. What's been interesting to me is that the process of deciding what to code and then whether it works doesn't change much between different ways of building software.
Instead, the cycle time between when we ask a client what to do and when we deliver it changes. The more agile/lean we are, the lower the cycle time. The more waterfall-ish, the larger the cycle time. I guess that analysis and breakdown of problems into work also changes, as the scope in modern DevOps styles of development is smaller (more contained) than in waterfall.
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I've been very pleased with the direction of SSMS the last few years. As it's been separated from SQL Server releases and gets updated more often, I think the changes from v17 though v20 have been improvements. There are still issues, but it's been better. Now we finally have SSMS moving to a modern shell with the v21 preview and I'm excited to see how this changes the future of our tooling.
However, the PM for SSMS, Erin Stellato, posted a note on LinkedIn recently asking why people don't read documentation. She also asks what you want to see in 21, so respond if you think there are holes in the SSMS docs. I think this post came about because of many responses that came from people who clearly hadn't read some documentation.
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Thanksgiving is tomorrow in the US and it is supposed to be a holiday when we give thanks for our blessings in life. My wife usually has everyone in our family tell what they are thankful for this year. I also see many people posting things they are grateful for during the month of December.
Last month I was lucky enough to have dinner with Bob Ward and we were talking about some of the things we'd seen in travels, often some stressful times for ourselves or others. We've seen many people get upset or angry or have some other reaction. Both of us have some empathy for others, recognizing that we don't really know their history or experiences, and it doesn't make sense for us to get upset. If we knew those things, we might better understand the reaction that someone displays to a situation.
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I was a bit of a math nerd in high school and college. Some of you might have been as well, but I took advanced math all through high school, culminating with AP Calculus as a senior with 11 other kids (of about 320). In college, I started with Calculus III freshman year and went on to take 7 more semesters of various high level maths. One of those classes included analyzing data with linear regression, which we did with hand calculators and formulae.
At SQL Saturday Pittsburgh 2024, I watched a talk from Jeff Moden on linear regression. It was a trip down memory lane, with Jeff explaining how the process worked, the flaws, and how this technique could be used to do some predictions on data stored in SQL Server. It was a great session on the topic, but I liked that Jeff showed how you can use SQL Server to do various math calculations that might be useful to analyze data. I see applications sometimes programming various formulas, but I don't often see people doing this in database queries.
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In most of the organizations I've worked for or consulted with, patching was always a challenge. Patching hasn't usually been given a priority and is often skipped when operations staff is busy. This has resulted in lots of un-patched, or slowly patched systems. I assume this is one reason Microsoft continues to release RTM-GDR patches because some people won't patch at all unless there are critical fixes.
I also know that much of IT management sees patching systems like patching parking lots. Needs to be done, but tomorrow, after we do other important work today.
Read the rest of How Do You Patch 100 Database Servers?
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Microsoft constantly releases new features and products in the data platform space. Many of us have seen the SQL Server product grow in new ways, some of which are very useful to us. As an example the changes from log shipping to clustering to Availability Groups has improved our HA/DR options as well as the capabilities available to us in different situations.
With that in mind, I saw someone recently that wanted to deploy SQL Server on Kubernetes, which is something that could be a very interesting way of managing your different systems. However, this individual wanted to know when Microsoft would release their own supported solution with a Microsoft operator to manage the instance. There is guidance from Microsoft, but no official operator.
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Recently I was watching a presentation on how to scale performance in your SQL Server environment and one of the suggestions was setting up Availability Groups (AGs) and having read-intent connections that would query the secondary and not the primary. It's not a bad idea, and the SQL Native Client (and other drivers) support this and make it easy to implement.
The pattern of using multiple connections in an application, one for reads and one for writes, has been suggested often. However, in practice, I've rarely seen this work. Apparently having a connection variable, named dbConn, for writes and a second one, named dbConnReadOnly, for reads is too complex for most developers or teams.
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I assume most of you work with others in a team. Even if you are the data specialist and others work on different technologies, you still have a team. How long has your current team been together in this form? Have you had a stable team that might have grown, but the rest of the individuals and roles/responsibilities stay the same? Or has your team changed makeup, roles, responsibilities, or something else?
I don't see a lot of organizations that change their team structures often. There may be people who come and go from a team, but the core structure remains the same. Even when your company might reorganize a bit, often it's teams that shuffle between managers, but mostly remain the same. There certainly are exceptions, and some large orgs (Microsoft, Amazon, etc.) regularly shuffle lots of people around, but I'm not sure the teams change their makeup or their mandate much.
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Recently I had a friend traveling who is not very tech savvy. This person has traveled before and has a routine, but in this case, they were struggling to get an airline's mobile app to work. They also struggled with the website, and just before the trip, they were thinking to cancel because they didn't have a ticket in their hand before driving to the airport.
This turned out to be a login issue, and between friends and the airline's customer service, they were able to print out a ticket at home and take it in hand to the airport.
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Today is the first day at the PASS Data Community Summit and I'm in Seattle where I'll get to open the conference and introduce the Microsoft keynote. I'm sure the keynote will be full of announcements on something, but what?
I'm writing this a little over a week before the event, and I have no idea what Microsoft will do. Actually, by the time you read this I may have some ideas as there was a practice session yesterday, but I can't tell you anything. NDA, and really, by the time I got something organized, the keynote will be done.
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For a long time I've felt that my recommendation for people wanting to enter technology wasn't to go to college and get a degree, but rather start to learn on your own and get an entry level job (help desk, tech support, etc.) and start to work in the industry. That's a good way to both experiment and understand what you're considering undertaking as a career, as well as limiting your investment. It's also nice to get paid to learn something.
College is great, but it's also expensive. I find that for many people, it can be hard to get a good ROI from college these days. The fast rising cost, not to mention the uncertain opportunities after college lead me not to recommend pursuing a CS degree, or really any degree, as a default view. There are exceptions, but for many people, I'd prefer to work and try to better understand where they should invest in education.
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Your management gets a great demo from a cloud vendor and decides that the organization needs to implement the new service/application/etc. quickly. Your team tries to comply, furiously learning and experimenting with integrations, software changes, infrastructure configuration, and more. Things get deployed are working. Clients and management are happy with the new capabilities and you breathe a sigh of relief.
After a bit of time there's a security issue and all of a sudden there's blame pouring down on everyone. The vendor takes a hit because it's a public security problem, but the reality might be that your organization didn't completely understand how to configure strong security. The public doesn't blame your organization, but internally your team don't know how to make changes to ensure future security.
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