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Category: Microsoft Fabric

Tips for Adopting Microsoft Fabric

Published 2024-10-18 by Kevin Feasel

Paul Turley shares some thoughts:

Hello, friends. I’ve spent the past few months working with several new Fabric customers who were seeking guidance and recommendations for Fabric architecture decisions. What have we learned about using Fabric in enterprise data settings in the past 11 months? This post covers some of the important decisions points and Fabric solution design patterns.

Much of the industry’s experience with Microsoft Fabric over the past several months has been at a high-level as organizations were dipping their toe in the pool to test the water. So far, our Data & AI team have assisted around 50 clients with Fabric projects of various sizes. We have also implemented a handful of production scale projects with enterprise workloads, comparing notes with community leaders and the product teams who develop the product. What lessons have we learned?

Click through for several bits of high-level architectural guidance intended to make that adoption easier.

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Configuring the Fabric Service Principal to Support Storage APIs

Published 2024-10-17 by Kevin Feasel

Gilbert Quevauvilliers grants some permissions:

This blog post explains how to configure access for my Service Principal to interact with the Azure Storage API to use the API to get details for Microsoft Fabric Storage.

This is part of a blog post series where I am going to show you how to “View Total Storage consumed in Microsoft Fabric”

When I started this blog post I realized that I first need to explain how to configure the Service Principal authentication to interact with the Azure Storage API permissions. This is because in my notebook these steps are required for the notebook to run successfully.

Read on to find out how.

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Writing Data to an Unattached Lakehouse via Fabric Notebook

Published 2024-10-16 by Kevin Feasel

Prathy Kamasani does a bit of movement:

Regardless of which architecture we follow, during stages of data integration and transformation there’s always a step to move data from one location to another. And, we work with multiple tables, schemas, and even lake houses.Same goes with Fabric Notebooks. I often find myself in scenarios where I don’t want to attach Lakehouse to my notebook, but I do want to read or write data from various bakehouses.

I recently blogged about a way to achieve this as part of documenting your workspaces. In that post, I described how to write data to a workspace that was not attached to the notebook. I used MsSparkUtil(renamed to NotebookUtils) to mount and then write data in the Lakehouse as Delta tables.

Read on for the answer.

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Dealing with the Lack of Identity Columns in Microsoft Fabric

Published 2024-10-14 by Kevin Feasel

Nikola Ilic forges a new identity:

If you’ve ever worked with traditional relational database management systems (RDBMS) and/or data warehouses, and you’re now trying to be a “modern data platform professional” and apply your skills in Microsoft Fabric, you may find yourself in uncharted territory. Not only because of the SaaS-ification of the environment, but also due to many puzzling “solutions”, or maybe it’s better to say – lack of the features that we were taking for granted in the “previous” (pre-Fabric) life.

The goal of this article is to introduce you with different approaches for overcoming the limitation of non-existency of the identity columns in Microsoft Fabric. Please keep in mind that all of these approaches are considered workarounds and it may happen that Microsoft in the future provide the out-of-the-box solution

Missing the identity column attribute can be a bit annoying when building out dimensions, so Nikola provides a few tips on how to emulate this functionality.

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Invoking a Fabric Data Factory Pipeline from a Parent Pipeline

Published 2024-10-14 by Kevin Feasel

Andy Leonard takes us through a design pattern:

In an earlier post, I demonstrated one way to build a basic parent-child design pattern in Fabric Data Factory by calling one pipeline (child) from another (parent). In this post, I modify the parent and child pipelines to demonstrate calling a child pipeline that contains a parameter. In this post, we will:

  • Clone and edit the child pipeline
  • Clone and edit the parent pipeline
  • Test

Read on to see how it works.

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Tips for Orchestrating Fabric Notebooks

Published 2024-10-14 by Kevin Feasel

Stepan Resl talks orchestration:

Let’s start by introducing what orchestration is and why it’s important to talk about shared resources. Orchestration is a discipline focused on managing and coordinating individual items or control elements to collectively manage the flow of our data operations. In the context of Fabric, this involves managing notebooks, dataflows, pipelines, stored procedures, semantic model updates, and many other items, activities, and services that may even be outside of Fabric.

Read on for some of the options, how they work in Microsoft Fabric, and tips for success.

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Cloud Connections in Microsoft Fabric

Published 2024-10-10 by Kevin Feasel

Dennes Torres makes a connection:

I 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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Exploring Semantic Model Relationships with Sempy

Published 2024-10-09 by Kevin Feasel

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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Referencing a Microsoft Fabric ML Model from another Workspace

Published 2024-10-08 by Kevin Feasel

Sandeep Pawar crosses workspaces:

I have written a couple of blogs about working with ML models in Microsoft Fabric. Creating experiments and logging and scoring models in Fabric is very easy, thanks to the built-in MLflow integration. However, the Fabric Data Science experience has one limitation. There are no model endpoints yet, and you cannot load a model from another workspace because the model URI, unlike in Databricks, does not reference a workspace. If you use MLFlowTransformer as shown in this blog, only the model from the workspace where the notebook is hosted is loaded. However, there is a workaround.

Read on for that workaround, as well as the core limitation associated with it.

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