Yesterday I ran a simple Twitter poll about the relative ease of learning R vs. Python. Although a correct answer to this query will ALWAYS have to be based on nuances like pre-existing skills and the scope of need, this originates from people telling me they encounter job or career profiles that list a need for R and/or Python. If they don’t have either, if they prioritised the pursuit of just one, which would be possible to develop a degree of competency more easily, more quickly and more efficiently?
Andy has also created a Twitter moment from the responses.
My thought, based only on the question itself, is that R would be better than Python because the hypothetical person has no additional programming skills. For someone with additional programming skills, the breakdown for me starts with, if your background is statistics, database development, or functional programming, you probably want R; if your background is object-oriented development or imperative programming, you probably want Python. And then it gets nuanced.
You have four options from which to choose: two-class classification, multi-class classification, regression, or Choose Your Own Adventure. Today, we’re going to create a two-class classification model. Incidentally, they’re not kidding about things changing in preview—last time I looked at this, they didn’t have multi-class classifiers available.
Once you select Sentiment Analysis (that is, two-class classification of text), you can figure out how to feed data to this trainer.
I think this is fine for developers who are looking to add a machine learning component as a small part of a bigger product. I don’t think it will beat a trained human using R or Python, but it’s an interesting avenue.
We’re excited to announce the release of SQL Server Management Studio (SSMS) 18.1. It’s been just over a month since we released SSMS 18.0. While we brought in many fantastic capabilities, we also regressed some functionality for some of our users. We are happy to share that we’ve fixed those and are also bringing in some new features along with bug fixes.
The big thing for a lot of people is that database diagrams have returned. I was never the biggest fan of those, but there was enough of an uproar to bring them back.
Here we have a T-SQL statement that we can either create as a stored procedure or just run as T-SQL in a job step. It looks back over the last 10 minutes and looks for job failures. I would recommend scheduling this T-SQL to run every 5 minutes, you will get duplicate entries for a short period of time, but ideally, you shouldn’t get any failures anyway right? Plus once you’re notified, you can turn this off while you work on it or you can specify in the where clause to remove this job until fixed.
Click through for the script and do read the instructions.
One of my favorite recent examples was a company who came to me saying, “We’re spending about $2M per year in the cloud just on our databases alone. Can you help us reduce those costs?” Absolutely: with just a couple of days spent query & index tuning, we chopped their biggest database expenses in half while increasing performance.
At the end of that engagement, the CTO told me, “I thought I’d save money in the cloud by not having a DBA, but what I’m learning is that in the cloud, I actually get a return on my DBA investments.”
I completely agree with this post. The exact tools DBAs use will change, but the role will still be around decades from now. And that’s at the companies which move quickly.
Tables were very narrow with just a few columns and my expectations for data growth were very modest. However, after just a little while I was very surprised when my database showed huge unexpected growth and size of the data became multiple times higher than I’ve expected.
After very little research I’ve found and fixed the problem. In this post I’ll describe how I’ve done it.
Read on to learn how Slava figured this out and how a clustered index fixed the problem.
In my blog post here https://the.agilesql.club/blogs/ed-elliott/2019-06-10/steps-to-automated-database-deployments I described the steps you need to go through so you can build up your confidence that you are capable of deploying databases using automation. I mean, afterall, knowing that it is possible to automate your deployments and having confidence that they will succeed are two very different things.
Even with the best tooling in the world, automated database deployments are still a struggle and there is one key thing that you can do, no matter what tools you choose and that is to make the deployments re-runnable. (Insert discussion here on the word idempotent and how it means re-runnable but sounds far cooler and intellectual). If you make your deployments re-runnable then you can, by their very definiton, re-run them.
Click through for two options. I definitely prefer option number 1 as well.
In this post you will see some recommended tools and best practices that you should apply while doing performance comparison. The recommended performance comparison process has three stages:
1. Compare the environment settings on SQL Server and Managed Instance.
2. Create performance baseline on source SQL Server
3. Compare performance on Managed Instance with the baseline
In the following sections will be described the best practices and the recommended approaches
This is a good bit more involved than installing some product, clicking a few buttons, and comparing numbers.