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Author: Kevin Feasel

Name Conflicts In DAX

Published 2019-01-17 by Kevin Feasel

Marco Russo takes us through an issue with naming in DAX:

You deploy this model and start creating reports using the Sales Returning Customers measure. So far, so good. One day, you need to extend the data model importing a new table that you decide to name ReturningCustomers. As soon as you import the new table named ReturningCustomers, your measure Sales Returning Customers stops working. The reason is that the ReturningCustomers variable generates a name conflict with the table that has the same name, as you can see from the error message.

‘ReturningCustomers’ is a table name and cannot be used to define a variable.

Marco has some advice if you’re in a situation where you are liable to see this pop up.

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Java With Visual Studio Code

Published 2019-01-17 by Kevin Feasel

Niels Berglund learns about Visual Studio Code and writing Java in VS Code:

I mentioned above how Maven is a build automation tool for primarily Java projects. There are other build tools as well, but in this post, I use Maven as it is – which I mentioned above – the de-facto standard for Java-based projects.

Above we saw how we installed Maven as well as the VS Code Maven extension, however before we start to use it let us talk a little about Maven archetypes and naming conventions.

If I were in Java day in and day out, I’d probably go with IntelliJ IDEA but for occasional work, I can see VS Code doing well. That’s the interesting use case for Visual Studio Code: with all of the extensions, it’s a really good multi-purpose IDE while still being worse than the “natural” IDE for almost any single language.

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SQL Undercover Inspector V1.3

Published 2019-01-17 by Kevin Feasel

Adrian Buckman announces a new version of the SQL Undercover team’s Inspector:

We know some of you really hate linked servers so we have been working on a powershell collection which will allow you to install the inspector without using linked servers to centrally log the information and instead the powershell function Invoke-SQLUndercoverInspector will do the rest for you (We will be writing a blog post about how you can use this soon) – this is currently a pre-release version so it’s a work in progress – I must say a massive thank you to Shane O’Neill (b | t) without his powershell skills this wouldn’t turned out as well as it has, thanks Shane!

If you’ve already downloaded this version, be aware that there is a hotfix.

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Management Studio Query Shortcuts

Published 2019-01-17 by Kevin Feasel

Michelle Haarhues shows how you can use query shortcuts in SQL Server Management Studio:

Back in the day, with the introduction of programs like Word and Excel, I used keyboard shortcuts to make my job easier.  Then we started using a mouse and reduced the number of keyboard shortcuts I used.  It took me a long time to switch from the keyboard shortcuts to the mouse.  Now I am back to using shortcuts, especially in SQL Server Management Studio (SSMS).  Microsoft allows users to create shortcuts that, if you use them, could make your job easier.  Setting up the shortcuts in SSMS are pretty simple.

My shortcuts are all around running sp_whoisactive: Ctrl+F1 gets results back the way I want, Ctrl+3 shows only my sessions, and Ctrl+4 gives me more details (like execution plans) when I’m willing to wait the extra time to get them.

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Review: dbForge Studio For Database Modeling

Published 2019-01-17 by Kevin Feasel

Randolph West is looking for a product for database modeling and tries out dbForge Studio:

These days I still design new databases from scratch with pen and paper (or iPad and Apple Pencil), where the entity relationship diagram (ERD) is rudimentary and crows’ feet relationships are badly-scrawled. But it got me wondering which database modelling tools are on the market today (commercial and free).

My ideal tool should be able to design a new database from scratch and generate creation scripts in T-SQL without failing over common issues like referential integrity and dependencies. More importantly though, it should be able to reverse-engineer a database (like Microsoft Visio used to be able to). This is extremely useful for consulting engagements when I need to get a picture in my head of the database I’m looking at. This was the one place I’ve used the Database Designer in SSMS more than I had initially remembered.

Randolph also mentions SQL Database Modeler, which I used on a consulting engagement where I wanted to replicate Visio’s database reverse engineer functionality.

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Where Machine Learning And Econometrics Collide

Published 2019-01-16 by Kevin Feasel

Dave Giles shares some thoughts on how machine learning and econometrics relate:

What is Machine Learning (ML), and how does it differ from Statistics (and hence, implicitly, from Econometrics)?

Those are big questions, but I think that they’re ones that econometricians should be thinking about. And if I were starting out in Econometrics today, I’d take a long, hard look at what’s going on in ML.

Click through for some quick thoughts and several resources on the topic.

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Odd Behavior With Altering Columns

Published 2019-01-16 by Kevin Feasel

Solomon Rutzky points out a few things which you can unintentionally change when running an ALTER TABLE [tbl] ALTER COLUMN [col] command:

If the column is NOT NULL, then not specifying NOT NULL will cause it to become NULLable. The documentation for ALTER TABLE even states:

ANSI_NULL defaults are always on for ALTER COLUMN; if not specified, the column is nullable.

Let’s see for ourselves. 

Solomon also has a couple collation-related items, including unexpected silent truncation when working with UTF-8 collations.

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Auto ML With SQL Server 2019 Big Data Clusters

Published 2019-01-16 by Kevin Feasel

Marco Inchiosa has a model scenario for using Big Data Clusters to scale out a machine learning problem:

H2O provides popular open source software for data science and machine learning on big data, including Apache SparkTM integration. It provides two open source python AutoML classes: h2o.automl.H2OAutoML and pysparkling.ml.H2OAutoML. Both APIs use the same underlying algorithm implementations, however, the latter follows the conventions of Apache Spark’s MLlib library and allows you to build machine learning pipelines that include MLlib transformers. We will focus on the latter API in this post.

H2OAutoML supports classification and regression. The ML models built and tuned by H2OAutoML include Random Forests, Gradient Boosting Machines, Deep Neural Nets, Generalized Linear Models, and Stacked Ensembles.

The post only has a few lines of code but there are a lot of working parts under the surface.

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Deploying SQL Server To Kubernetes The Easy Way

Published 2019-01-16 by Kevin Feasel

Andrew Pruski doesn’t want to mess with a bunch of yaml files:

In previous posts I’ve run through how to deploy sql server to Kubernetes using yaml files. That’s a great way to deploy but is there possibly an easier way?
Enter Helm. A package manager for Kubernetes.
Helm packages are called charts and wouldn’t you know it? There’s a chart for SQL Server!
Helm comes in two parts. Helm itself is the client side tool, and tiller, which is the server side component. Details of what each part does can be found here.

They’re making it too easy now…

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Filtered Index Trickiness

Published 2019-01-16 by Kevin Feasel

Greg Low explains some of the tricky bits behind using filtered indexes:

If you think about it, if all we’re ever going to use is one part of the index, i.e. just the unfinalized rows, having an entry in there for every single row is quite wasteful, as although the vast majority of the index will never be used, it still has to be maintained.
So in SQL Server 2008, we got the ability to create a filtered index. Now these were actually added to support sparse columns. But on their own, they’re incredibly useful anyway.

I use these on occasion but less than I want to, and a big part of the reason why is in this post, particularly around parameters.

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