Category: Data Modeling

Bert Wagner gives us several reasons why VARCHAR(8000) can be a bad fit for data types:

When first learning SQL Server, I had the brilliant idea of defining every column on my table as VARCHAR(8000). After all, the VARCHAR datatype only consumes as much storage space as the string itself (plus two bytes), so even if I defined the max length of a column as 8000, I wouldn’t be wasting storage space if my data was smaller than that.

My development life would be easier too since I wouldn’t have to think about the kind of data I was storing in my columns; I could define everything as VARCHAR(8000) once and never have to go back to make any changes. Brilliant!

While I was correct about not wasting storage space, it turns out the idea of making every column VARCHAR(8000) is a terrible idea.

Click through to learn why.

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Caitlin Hudon shares some great advice on building data dictionaries:

The best defense I’ve found against relying on an oral history is creating a written one.

Enter the data dictionary. A data dictionary is a “centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format”, and provides us with a framework to store and share all of the institutional knowledge we have about our data.

As part of my role as a lead data scientist as a start-up, building a data dictionary was one of the first tasks I took on (started during my first week on the job). Learning about our data is a crucial part of onboarding for data-focused roles, and documenting that journey in the form of a data dictionary provides a useful data asset for the company (which helps to preserve institutional knowledge) and simultaneously provides a good resource for analyzing the data.

Read the whole thing.

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Niko Neugebauer is happy about an update to Master Data Services in SQL Server 2019:

Before we continue, let me ask you one question, have you heard about Silverlight?
Or in other words, and with a kind of evil voice “DID YOU EVER INSTALLED SILVERLIGHT ON A PRODUCTION SERVER”?.
If you have worked with MDS oh yes, you did! At least in order to check if everything is configured/upgraded correctly and nothing is broke, I will do a wild guess and claim that you did! So am I … :s

Because in order to make things work in MDS correctly, one needs this old, for a very long time deprecated framework, that is supported only in deprecated browser that is called Internet Explorer v.11, and that pain-in-the-neck framework is called Silverlight and if you dare to work with any SQL Server versions before SQL Server 2019, the picture on the left will appear in front of you at the moment you will try to explore the master data in the MDS Explorer – ensuring that unless you install a totally abandoned (and obviously unnecessary product, that represents another risk on your server) is a necessary thing. That is alone is the reason for some people would use some development VM in order to work with MDS, but that is not a good excuse to include that product in SQL Server 2016 or in SQL Server 2017.

The interface still has problems, as Niko points out, but hopefully this is the first step and not the last one.

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Phil Factor shows how to use Markov chain generation in T-SQL to generate realistic-looking country names:

How did we do this? We started with a table that took each word, added two spaces at the beginning and a |, followed by two subsequent spaces, at the end. This allowed us to map the frequency of each three-letter combination in a collection of words. Any language is made up of common combinations of characters with a few wild exceptions. For words to look right, they must follow this distribution. This distribution will change in various parts of a word, so you need all this information.

So what would happen if, instead of feeding the name of countries into the batch, we do the names of people?

My favorite name from the list was Kuwatian Samoa.

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