A Fine Slice Of SQL Server
Koen Verbeeck copies some data:
We have a couple of Snowflake databases and would like to have that data available in Microsoft Fabric as well. Is there an easy solution to get the data quickly in Fabric? We don’t have many technical people on staff, so writing complex ETL is not an option.
Read on for more information on how it works. Mind you, you’re probably still writing the T and some of the L after using mirroring.
Comments closedGilbert Quevauvilliers takes on a challenge:
I always enjoy a good challenge and I got it working! In this blog post I will use the same method where I am sending an email to scale up or scale down my Fabric Capacity.
The good news is that this works if the Capacity is paused or running (It might take a bit more time when running).
brb, sending Gilbert’s task an e-mail.
Actually, Gilbert does a good job in making sure that the sender has to be his e-mail address and not just some rando’s.
Comments closedMarc Lelijveld breaks out the green eyeshade:
P-SKUs, A-SKUs, EM-SKUs and now we also have F-SKUs… all these different capacities that are out there today each have their own specifics. Lately, I’ve been in a lot of conversations around Fabric capacities. There seems to be some unclarity around what you pay for in the end and how it compares to Power BI Premium capacities. Therefore, I thought, maybe this is the right time to write it down – besides the Microsoft documentation that is already out there.
In this blog I will elaborate on differences in purchasing, billing and buying the capacities. I will not deep dive in capacity metrics or how capacity units are consumed.
There’s a lot of good information in the article, especially if you’re looking to price out Microsoft Fabric in your organization.
Comments closedReza Rad gives people a primer on Apache Spark:
Microsoft Fabric runs some workloads under the Spark engine, but what is it really? In this article, I’ll take you through the question of what Spark is, What benefits it has, how it is associated with Fabric, what configurations you have, and other things you need to know about it.
Reza talks a bit about history, interaction with languages, etc. As a quick addition to the languages list, you can use .NET languages like F# and C# with Spark, though it does involve setting up dotnet/spark and there are some open questions about its future. And I’m not even sure you could get it to work with Microsoft Fabric.
Comments closedNikola Ilic shows us the current options:
Options, options, options…Having the possibility to perform a certain task in multiple different ways is usually a great “problem” to have, although very often not each option is equally effective. And, Microsoft Fabric is all about “options”…You want to ingest the data? No problem, you can use notebooks, pipelines, Dataflows, or T-SQL. Data transformation needed? No worries at all – again, you may leverage notebooks, T-SQL, Dataflows…Data processing, you asked? Lakehouse (Spark), Warehouse (SQL), Real-Time Intelligence (KQL), Power BI…The choice is yours again.
In a nutshell, almost every single task in Microsoft Fabric can be completed in multiple ways, and there is no “right” or “wrong” tool, as long as it gets the job done (of course, as efficiently as possible).
Nikola lays out two pre-requisites and then shows us two options we can currently use, and three potential options we currently cannot use.
Comments closedKoen Verbeeck doesn’t want to hard-code the connection string:
When you develop data pipelines in Microsoft Fabric (the Azure Data Factory equivalent in Fabric, not to be confused with deployment pipelines), you will most likely have some activities with a connection to a warehouse, a lakehouse or a KQL database (for the remainder of the blog post I’ll talk about a warehouse, but it can be any of those three data stores). For example, in a Script, Lookup, or Copy activity. When you deploy your data pipeline to another workspace – using, you might’ve guessed it, deployment pipelines – the pipeline itself is copied to the other workspace. E.g., we deploy a pipeline from the development workspace to the test workspace.
Read on to see what this means for warehouse connections and how you can work around the existing messiness.
Comments closedSandeep Pawar makes it look good:
Last year I shared a blog post on formatting DAX expressions using DAX Formatter API (thanks to SQL BI). I extend the same idea in this blog to format all and save the formatted DAX in all measures in a semantic model using semantic link.
Click through for an important warning, as well as how to do this.
Comments closedReza Rad offers up some advice:
The Lakehouse or Warehouse comes with a default Power BI Sematic model, which can be used for reporting and analytics. However, you can also build and use a customized semantic model. There are significant differences when using the semantic model in real-world analytics projects. In this article, I’ll explain the difference between these two, which one is recommended, and why.
Click through for the video, as well as the article.
Comments closedKoen Verbeeck helps us choose:
This doesn’t mean no code has to be written. On the contrary, in this article we’re going to focus on two services of Fabric: the lakehouse and the warehouse. The first one is part of the Data Engineering experience in Fabric, while the latter is part of the Data Warehousing experience. Both require code to be written to create any sort of artefact. In the warehouse we can use T-SQL to create tables, load data into them and do any kind of transformation. In the lakehouse, we use notebooks to work with data, typically in languages such as PySpark or Spark SQL.
Read on for the comparison. I tend to go more for the lakehouse experience rather than warehouse, but Koen provides a lot of the info you’d need in order to make the right decision for yourself.
Comments closedMarc Lelijveld shares some advice:
Recently, I architected a solution for a client for their Microsoft Fabric data platform. The client works with Dynamics Finance & Operations as one of their main ERP system. Fabric offers easy ways to bring data from various standard Microsoft services into the platform, however it is not always as easy as it looks like. In this blog I will elaborate on the gotcha’s encountered in architecting this solution.
Read on for the challenges that Marc ran into along the way.
Comments closed