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Month: October 2024

Cloud Connections in Microsoft Fabric

Dennes Torres makes a connection:

wrote about cloud connections when they were in a very early stage.

Cloud connections evolved and are now sharable. We call the “regular” connection as “personal connection”.

The problem with the “personal connections” is the difficult to make teamwork. The personal connections belong to you and different developers can’t use them. When a different developer needs to work with the same objects, they are required to create their own connection.

Using cloud connections, we can create a single, reusable connection to the data source and share it with all the developers in the team.

Read on to learn more about how they work now that the feature is a bit more mature.

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Online DR from SQL Server 2022 and Azure SQL MI Now Available

Djordje Jeremic announces general availability of one of the key selling points from SQL Server 2022:

Today, we are announcing the general availability of the following two major capabilities of the Managed Instance link feature with SQL Server 2022:

  • Two-way failover between SQL Server 2022 and SQL Managed Instance through the link to unlock true disaster recovery (DR) with Azure
  • Creating a link from SQL Managed Instance to SQL Server 2022 to unlock off-PaaS data mobility for regulatory and dev/test scenarios 

Click through for more detail.

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Permissions and BACPAC Files in Azure SQL DB

Roberto Yonekawa diagnoses an error:

We had a support request where the customer was getting an error when trying to export his Azure SQL Database to a bacpac file using the SqlPackage command-line utility.

Error message:

Microsoft.Data.Tools.Diagnostics.Tracer Error: 19 : 2024-08-21T16:10:56 : Microsoft.SqlServer.Dac.DacServicesException: One or more unsupported elements were found in the schema used as part of a data package.

Error SQL71627: The element Permission has property Permission set to a value that is not supported in Microsoft Azure SQL Database v12.

Click through for the specific issue Roberto found. I’d imagine that there are other permission sets that are incompatible with Azure SQL Database and would cause this error message to pop up as well.

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Client Diagnostics in Cosmos DB

Arthur Daniels digs into diagnostic data:


This topic keeps coming up with my customers so the purpose of this blog post is to keep all the useful information in one post. Think of this post as a consolidation of different pieces of information. If you’re stuck on what “pipelined”, “created”, or “transit time” means, you’re in the right spot.

We are not talking about Diagnostic Settings/Log Analytics in this post, I’d describe those as server-side rather than client-side diagnostics. They are useful in different ways, our client diagnostics will help us understand the path that a request takes from the application to Cosmos DB and back, along with any latency on that path.

Note: before we get started, check to see if you’re using Direct or Gateway mode. Ideally sending your requests Direct mode in the .NET or Java SDKs will usually result in faster requests.

Read on to see how to retrieve and interpret this diagnostic data.

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Exploring Semantic Model Relationships with Sempy

Prathy Kamasani builds a graph:

Understanding the relationships between datasets is crucial in data analytics, especially in the world of self-service BI. Sempy, a Python library unique to Microsoft Fabric, allows users to visualise these relationships seamlessly. This post explores using Sempy to visualise semantic model relationships and view them in a Power BI Report. Viewing them in Notebook is easy and has been documented on MS Docs.

Click through for a notebook and explanation of the underlying code.

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Why Take Backups in SQL Server?

I’ve started a new video series:

In this video, I explain why it is so important to back up your databases. From there, we see one technique for taking these backups in SQL Server: using SQL Server Management Studio. We review some of the backup metadata you can collect and also see how to compress and encrypt backups.

The first few videos in the series have all gone a bit longer than I expected, and that’s even with me pointedly ignoring most everything to do with tape drives.

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Data Masking in Snowflake

Kevin Wilkie puts on the mask:

The premise behind data masking is fairly simple – make it so that only certain people can see the data in a certain column. For example, social security numbers or credit card numbers probably shouldn’t be seen by everyone who has access to your database.

I like to set it up as follows: (Obviously, this can change depending on your business’ policies.)

  1. Admins have full access.
  2. Call Center Representatives have access to the last 4 digits of the social.
  3. All others see nothing useful.

Read on to see how. I’ll even save my normal “data masking isn’t really a security feature” rant. This time.

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Data Types and Arithmetical Calculations in DAX

Marco Russo and Alberto Ferrari do some counting:

The VertiPaq engine is basically data type-independent. This means that it does not matter whether a column is a string, a floating point, or a date: because of the dictionary encoding happening inside VertiPaq, all these data types use around the same amount of memory and perform at nearly the same speed.

However, when mixing different data types in the same expression, DAX will likely need to perform conversions between data types. Some of these conversions are nearly free, whereas others require the intervention of the formula engine, with a related performance impact.

We have already written about possible errors occurring during data type conversion here: Understanding numeric data type conversions in DAX and here: Rounding errors with different data types in DAX. The issue with conversion errors is mostly due to the fact that the precisions of fixed decimals (also known as Currency) and decimals (also known as floating point) are different. This article starts with a focus on performance.

Read on to see what Marco and Alberto have for us this time.

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The Most Important Tool for a Data Detective

Andy Yun wants you to use your earholes:

The All Powerful…

… Question. That is what I now believe is the most important tool for a Data Detective.

Asking Questions Effectively

This nuance involves HOW you ask a question. Some of this involves knowing your audience. Is this the right place or the right time? Sometimes there comes a point where asking questions is just counter-productive because your audience has no interest in answering. And it also means you need to make sure you’re asking the correct audience in the first place.

Asking questions is difficult, so instead, I just strawman my way to success.

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Building a Data Detective Toolkit

Deb Melkin talks tools:

Happy T-SQL Tuesday! I wasn’t really sure I’d be able to crank something out for this one but somehow I managed to squeeze it in. Tim Mitchell ( b ) is hosting and he has a great topic for us: What’s in our Data Detective toolkit?

I love this topic for so many reasons. Partly because I feel like I’m asked to look at so many projects where I’m dropped in and asked to figure things out, usually performance related but occasionally new functionality or features. But as I’m asked to do this fairly often, I may have to see if Data Detective can be my new title… hmm…

Being a Data Detective in a film noir. On the one hand, that sounds like a really neat idea. On the other hand, things usually don’t turn out so well for the detective.

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