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Category: Microsoft Fabric

Visualizing a Power BI Refresh with the semantic-link Library

Phil Seamark builds a notebook:

A few blogs back I shared a technique using Power BI Profiler (or VS Code) to run and capture a trace over a refresh of a Power BI semantic model (the object formally known as a dataset).

I’ve since received a lot of positive feedback from people saying how useful it was to visualize each internal step within a problematic Power BI refresh. Naturally, in the age of Fabric, I’m keen to share how the same approach works using a Microsoft Fabric Notebook.

Click through to see how you can do it.

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Enabling Microsoft Fabric

Tomaz Kastrun continues a series on Microsoft Fabric:

If you have used Power BI services in the past, you will be on board immediately. The outlook is the as it is with the Power BI. You will only need additional credentials to access the services. In general, you will need Azure subscription, Power BI service already enabled, and the ability for your organization to enable Fabric with Admin roles

Click through to see how to enable Microsoft Fabric in your environment.

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Generating Reports in Azure ML with Copilot

Soheil Bakhshi automates report creation:

In Nov 2023, Microsoft announced Microsoft Fabric’s general availability and Public Preview of Copilot in Microsoft Fabric. In a previous post, I explained what Copilot means to Power BI developers, which is valid for other Fabric developers such as data engineers and data scientists as Copilot for Fabric helps with those experiences as well. But the main focus of this blog post is to discuss the requirements, how to enable Copilot, and how to use it from a Power BI development point of view. So, this blog will not discuss other aspects of Copilot in Microsoft Fabric. With that, let’s begin.

I haven’t been particularly impressed with the reports it generates, but I suppose this is like the proverbial bear riding a unicycle: it’s not a question of how well it does the task that makes it interesting, but rather that it does it at all.

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What Is Microsoft Fabric?

Tomaz Kastrun starts a new series:

Microsoft Fabric is a next-gen platform, that brings all-in-one data and analytics solution for end users, small, medium and large enterprises. Services offer the complete data cycle movement (data ingestion, data engineering, data integration, data storing with warehouse using one lake), delivering data insights and building predictive models.

Read on for the overview and stay tuned for plenty more where that came from.

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Operating the Data Wrangler in Microsoft Fabric Notebooks

Gilbert Quevauvilliers rustles up some data:

In this blog post I am going to show you an easy way to clean your data (which is often fixing data issues or mis-spelt data) using the new feature Launch Data Wranger using DataFrames

I had previously blogged about using Pandas data frames but this required extra steps and details, if you are interested in that blog post you can find it here: Did you know that there is an easy way to shape your data in Fabric Notebooks using Data Wrangler?

In this blog post I am going to show you how I cleaned up the data in my location column.

Read on for a demonstration of what you can do.

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Microsoft Fabric and Tabular Editor

Johnny Winter is excited:

Why the excitement on my part? Well to take advantage of all the great features in Tabular Editor, you really need to be able to connect and write via XMLA, be that for doing CI/CD pipelines or by making edits directly on the dataset.

What great new features does Tabular Editor unlock that you can’t just do in the online Power BI modelling experience in Fabric… tons!

Read on to see how Tabular Editor plays with Microsoft Fabric.

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Constraints in Microsoft Fabric Data Warehouses

Brian Bønk slips out of the constraints:

When working with data and building data models, I personally seldom use the constraints feature on a database. Call me lazy – but I think constraints are adding unnessesary complexity when building data models for reporting. Especially if you are working with the some of new platforms – like Microsoft Fabric, where you are using staleless compute, aka. data storage is seperated from the compute layer.

I understand the need for contraints on other database systems like OLTP systems.

In reporting models it can be somewhat usefull to have constraints between tables, as they help/force you to some level of governance in your datamodel.

But how can we use this in Microsoft Fabric and are they easy to work with?

Read on for those answers. I will note that I’m a stickler about constraints in transactional systems, though I agree that constraints in warehouses are not critical—assuming, at least, that you’re following the Kimball approach and have one and only one mechanism to write data, and that you have other mechanisms for vetting data quality.

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Scraping the Microsoft Fabric Road Map with Microsoft Fabric

Prathy Kamasani wants a report, not a webpage:

Like many I am also playing with Fabric, many of my clients are also excited about Fabric and want to know more about it. Being a solution architect in the consulting world one of the most common questions I get asked is: “When certain features will be available, Where are they in the roadmap?”. That’s what sparked the idea of scraping the Microsoft Fabric Roadmap and creating this Power BI report. It is based on a Direct Lake connection, so it has been a bit temperamental.

So, what did I do it? If you are not interested in the whole story. Here is Python code you can run to get a road map. If you are interested in my process carry on reading 

Click through for the process and explanation.

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Controlling Fallback Behavior in Direct Lake

Sandeep Pawar talks about fallback options:

When you create a Direct Lake semantic model, by default it is in Direct Lake mode, i.e. you will directly query the delta table from the lakehouse/warehouse. This is what we want because the query performance will be very much comparable to the import mode. However, under certain circumstances, the DAX query can fallback to DirectQuery if Direct Lake limitations are hit.

Read on to learn more about circumstances in which this could happen and ways to change the default behavior.

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