A Fine Slice Of SQL Server
Steven Sanderson isn’t content with regressing against a single variable:
Multiple linear regression is a powerful statistical method that allows us to examine the relationship between a dependent variable and multiple independent variables.
Read on to see how you can do this in R, as well as some of the types of things you should think about along the way, including the concept of multicollinearity.
Comments closedI draw the logical conclusion: the opposite of normal forms is, of course, abnormal forms:
This video covers a variety of topics, effectively wrapping up the series on normalization. We look at data warehousing, including why the Kimball-style star schema is a really bad design in theory but a perfectly reasonably design in practice. We cover the chimera of “overnormalization” and I throw out a hot take. And we finally slag on denormalization.
Click through for the video.
Comments closedOlivier Van Steenlandt sorts out a problem:
While I was writing another data recipe, I ran into an issue. For some reason, the “Create Calculation Group” was not visible / enabled in Tabular Editor.
I tried to create a Calculation Group by right-clicking on the Table Folder –> Create but the option wasn’t available.
I tried an alternative way: going to the Model Section in the Tabular Editor toolbar. But unfortunately, the option to create a new Calculation Group was disabled.
Read on to see how Olivier solved this problem.
Comments closedNikola Ilic puts on the green eyeshades:
Microsoft Fabric is finally generally available! Yaaaay! Now, when the dust settles and the initial allure goes away, the most obvious question that one might ask is – how much will Fabric cost me?
Because, yes, it’s cool to have a unified analytics solution and all Fabric’s “weapons” at your disposal, but at the end of the day, looking at the wallet is what matters most:)
So, let’s try to break down all the available options, plus explain some of the concepts that may affect your Fabric workloads in terms of pricing.
Read on to see what the major considerations are and how much you’ll shell out to use Fabric for real.
Comments closedChad Callihan gets the lead out:
Imagine we want to find gaps in post history for users. If a user is posting every day, we’re happy and can assume they’re happy. If they’re only posting once every few weeks, we want to investigate why that is.
Read on to see how you can do this with the LEAD() function.
Barney Lawrence shows off a nice enhancement to T-SQL in SQL Server 2022:
With SQL Server 2022 came a much requested additional feature added from the SQL standards – IGNORE_NULLS. You can probably guess what it does. Drop in IGNORE_NULLS after your function and you can blur the non null values over those gaps giving us results like this:
Read on for the pre-2022 version of the query and what it does, versus the version with IGNORE_NULLS specified. This small flag is extremely helpful in time series statistical analysis and I’m glad it’s in SQL Server now.
Ben Jarvis announces the news:
On the 15th November Microsoft announced the general availability of Microsoft Fabric. Having seen Microsoft Fabric develop through our involvement in the private and public preview this is a really exciting announcement and all of us at Adatis are looking forward to seeing how it will fit into the solutions we are deploying to customers.
It makes sense to announce going GA at Ignite, though I don’t think Fabric is ready for prime time yet. There are still so many gaps in the product, and even though many items are on the roadmap, I wonder if Fabric will get a bad reputation from going GA too early. My analogy is Cyberpunk 2077: when the game came out, it was a buggy mess. Most of the bugs have been fixed now, a couple of years later, but for many people, their everlasting impression of the game is how buggy it was.
Comments closedIn his invitation, Steve specifically asks for examples where Window functions provided a neat solution to a real world problem. Well, sorry Steve, but I am not going to do that. But your invitation did inspire to me write about the execution plans for these window functions. And there is, in fact, so much to write about it, that this is just the first part.
So that makes this post not only a T-SQL Tuesday contribution, but also part 23 in my ongoing plansplaining series: blog posts where I take an in depth look at execution plans to explain how exactly they work, and point out often overlooked details. In this post, I will look at the basics of window functions, as they have existed for over 10 years now. I will point out a few interesting performance gotchas. And there are some links to feedback items that you can upvote (if you are so inclined) to pressure Microsoft to make some changes to the optimizer and the execution engine, to get some small but still welcome performance gains.
Read on to get an in-depth understanding of how those window functions actually operate once the database engine gets ahold of your query.
Comments closed