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Category: Microsoft Fabric

Preview-Only Steps in Microsoft Fabric Dataflows

Chris Webb covers a new feature:

I have been spending a lot of time recently investigating the new performance-related features that have rolled out in Fabric Dataflows over the last few months, so expect a lot of blog posts on this subject in the near future. Probably my favourite of these features is Preview-Only steps: they make such a big difference to my quality of life as a Dataflows developer.

The basic idea (which you can read about in the very detailed docs here) is that you can add steps to a query inside a Dataflow that are only executed when you are editing the query and looking at data in the preview pane; when the Dataflow is refreshed these steps are ignored. This means you can do things like add filters, remove columns or summarise data while you’re editing the Dataflow in order to make the performance of the editor faster or debug data problems. It’s all very straightforward and works well.

First up, that feature is pretty interesting, though I could see things break if you only do your testing in the preview pane. Second, what Chris does with this is quite interesting.

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Creating Fabric Linked Service Parameters for ADO Deployment

Koen Verbeeck glues together several technologies:

Quite the title, so let me set the stage first. You have an Azure Data Factory instance (or Azure Synapse Pipelines) and you have a couple of linked services that point to Fabric artifacts such as a lakehouse or a warehouse. You want to deploy your ADF instance with an Azure Devops build/release pipeline to another environment (e.g. acceptance or production) and this means the linked services need to change as well because in those environments the lakehouse or warehouse are in a different workspace (and also have different object Ids).

When you want to deploy ADF, you typically use the ARM template that ADF automatically creates when you publish (when your instance is linked with a git repo). More information about this setup can be found in the documentation. To parameterize certain properties of a linked service, you can use custom parameterization of the ARM template. Anyway, long story short, I tried to parameterize the properties of the Fabric linked service. 

Read on to see how that went, as well as what you need to do to solve this issue.

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Getting Help in MicrosoftFabricMgmt

Rob Sewell continues a series on the Microsoft Fabric management Powershell module:

Most of this blog post is going to be more about PowerShell in general than this specific module. The MicrosoftFabricMgmt module has over 295 cmdlets, which can be overwhelming at first glance. But PowerShell’s built-in discovery tools make it easy to find what you need. Knowing how to use a command is always available in the shell itself. You can find out how to use a function, what parameters it takes, and see examples of its usage without ever leaving the command line.

I have been using PowerShell for over a decade, and I still rely heavily on Get-Command and Get-Help to explore new modules and refresh my memory on ones I haven’t used in a while. In this post, I’ll show you how to use these tools effectively to navigate the MicrosoftFabricMgmt module.

Read on to see how you can get help. At least, on that front.

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Restoring Backed-Up Items in Microsoft Fabric

Gilbert Quevauvilliers grabs an item from backup:

In my previous blog post I had shown you how to backup your Microsoft Fabric Items: Backing Up Your Microsoft Fabric Workspace: A Notebook-Driven Approach to Disaster Recovery – FourMoo | Microsoft Fabric | Power BI

The next natural question is what happens when you want to restore one if the items that were previously backed up.

In the steps below I will show you how to do this.

Read on to see how it works and a bit of pain that you might experience.

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DevOps in Microsoft Fabric

Hamish Watson lays out what DevOps means in the context of Microsoft Fabric:

Microsoft Fabric (not to be confused with the more general term “fabric” in DevOps) is an integrated data and analytics platform designed for modern data-driven workloads, such as data engineering, business intelligence, and machine learning. With the introduction of Git integration in Microsoft Fabric, DevOps practices are becoming more accessible in the platform, allowing teams to implement collaborative, automated workflows that are common in DevOps environments.

Read on for some of the high-level concepts of what we do with DevOps and how they apply directly to Microsoft Fabric workspaces.

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Evaluating Power Query Programmatically in Microsoft Fabric

Mihir Wagle announces a new preview capability:

Power Query has long been at the center of data preparation across Microsoft products—from Excel and Power BI to Dataflows and Fabric. We’re introducing a major evolution: the ability to execute Power Query programmatically through a public API.

This capability turns Power Query into a programmable data transformation engine that can be invoked on demand through a REST API from notebooks, pipelines, and applications. Whether you’re orchestrating data pipelines, building custom data apps, or integrating Power Query into larger workflows, this API unlocks new flexibility and automation.

Click through for an overview of what’s available.

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A Primer on Microsoft Fabric Deployment Pipelines

Hamish Watson asks a question:

In the realm of software development and content creation, the deployment pipeline serves as a crucial bridge between innovation and implementation. Whether you are fine-tuning code, testing new features, or releasing a polished product to end-users, the deployment pipeline guides your content through distinct stages, each playing a vital role in ensuring a smooth and efficient journey from development to production.

Read on for a high-level overview of deployment pipeline structure and methods.

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MicrosoftFabricMgmt Powershell Pipeline Operations

Rob Sewell chains together some operations:

Last week I showed you how to work with workspaces — creating, updating, removing, assigning capacities. But we were doing each operation in isolation. Today I want to show you what happens when you connect those operations together using the PowerShell pipeline.

This is one of my favourite aspects of PowerShell and therefore it was imperative that Jess Pomfret B S L and I revamped the module to fully support pipeline operations. Every cmdlet that makes sense in a pipeline is built to work in one.

Click through for some examples of what this means.

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Pain Points around Direct Lake

Teo Lachev describes a pair of problems:

I’m helping an enterprise client modernize their data analytics estate. As a part of this exercise, a SSAS Multidimensional financial cube must be converted to a Power BI semantic model. The challenge is that business users ask for almost real-time BI during the forecasting period, where a change in the source forecasting system must be quickly propagated to the reporting the layer, so the users don’t sit around waiting to analyze the impact. An important part of this architecture is the Fabric Direct Lake storage to eliminate the refresh latency, but it came up with a couple of gotchas.

Click through for those two problems.

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Building Power BI Reports from the Desktop or Fabric

James Serra clears up some confusion:

If you’re a Power BI report author who’s just getting into Microsoft Fabric, you’ve probably asked the same question I hear over and over: am I supposed to stop using Power BI Desktop now?

It’s a fair question. Power BI Desktop is a Windows app that has traditionally been the place where report authors do everything: get data, transform it, model it, and build the report. Microsoft even describes that “connect, shape/transform, then load” experience as part of how Power BI Desktop works with Power Query.

Fabric changes the feel of that workflow because Power BI is now also a first-class experience in the browser inside the Fabric portal. And that browser experience isn’t just “view and share” anymore. You can edit semantic models in the service, including using Power Query for import models and building reports directly from that same environment.

Read on to see, for a brand new report, which of the two models can make the most sense.

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