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Category: Microsoft Fabric

Notebook Concurrency in Microsoft Fabric

Published 2023-12-22 by Kevin Feasel

Ed Oldham takes us through a common problem:

If you are currently using Microsoft Fabric you will have some sort of capacity associated with your account. This will have a large impact on what you can run concurrently. If you are on a Fabric Trial, you will have access to a trial capacity and if you are paying you will be on a certain capacity tier based on how much you pay. The following diagram shows information about each level of capacity and the Trial. The Trial resembles F64 capacity but is apparently different in some important ways (More on that later).

Read on to learn more about capacity and what that means for concurrent notebooks and Spark jobs.

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Power BI, Event Streaming, and Notebooks in Microsoft Fabric

Published 2023-12-21 by Kevin Feasel

Tomaz Kastrun continues a series on Microsoft Fabric. Day 18 has us looking at Power BI:

We have created a Power BI report directly from the datalake and today we will check how to do same with dashboard and paginated reports.

Day 19 covers event streaming:

In Fabric, you can create streaming semantic model and when selecting you will get the usual sources:

Day 20 shows how you can work with notebooks in Microsoft Fabric:

Notebooks have been around for a long time and people, community, and professionals have proven the usability, practicality, versioning and reliability of notebooks. Not to mention the clarity and hygiene. But opinions are also divided.

The purpose of this post today is to check for a couple of functionalities that might not be that straightforward when it comes to notebooks.

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Making REST API Calls against Microsoft Fabric

Published 2023-12-21 by Kevin Feasel

Sandeep Pawar digs into the REST API:

Accessing Fabric REST endpoints in Fabric notebooks was already easy but it became easier and straightforward with semantic-link version 0.4.0. You can use the FabricRestClient class from sempy to set up a REST client and call the APIs. Authentication is automatically managed for you.

Click through to see how it works, as well as some warnings or things to keep in mind along the way.

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Looping through Lakehouses in Microsoft Fabric Spark Jobs

Published 2023-12-20 by Kevin Feasel

Dennes Torres builds a loop:

I have published videos and articles before about Lakehouse maintenance. In this article I want to address a missing point for a lot of Fabric administrators: How to do maintenance on multiple lakehouses that are located in different workspaces.

One of the videos I have published explains the maintenance of multiple lakehouses, but only addresses maintenance in a single workspace. Is it a good idea to keep multiple lakehouses in the same workspace? Probably not.

Click through for the process.

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Generating Fabric Delta Tables from Power BI Semantic Models

Published 2023-12-19 by Kevin Feasel

Nikola Ilic is excited:

A few days ago, while preparing materials for the customer training on Microsoft Fabric, I stumbled upon a very interesting article at Microsoft Learn. The article describes how to integrate Power BI semantic models (aka datasets) into OneLake.

At first glance, this doesn’t sound like something epic, but when I started thinking more and more about it, I realized that this really might be a huge thing in many different scenarios. First of all, at the moment of writing, this feature is still in preview – this means, it can change to some extent in the coming months, before eventually becoming GA. Nevertheless, I decided to take a shot and explore what can be done with OneLake integration for semantic models.

Read on to learn more about what this is doing and what you can do with it.

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Trying to Load a Table in Microsoft Fabric

Published 2023-12-19 by Kevin Feasel

Eugene Meidinger walks onto a field of rakes:

Last week, I struggled to load the data into Fabric, but finally got it into a Lakehouse. I was starting to run into a lot of frustration, and so it seemed like a good time to back up and get more oriented about the different pieces of Fabric and how they fit together. In my experience, it’s often most effective to try to do something, review some learning, and alternate. Without a particular pain point, it’s hard for the information to stick.

Read on for some thoughts on andragogy, learning paths, and travails loading data.

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Warehousing and Power BI in Microsoft Fabric

Published 2023-12-18 by Kevin Feasel

Tomaz Kastrun continues a series on Microsoft Fabric. Day 15 covers building a warehouse:

I have named my as “Advent2023_DWH”.

You can create a warehouse using T-SQL scripts, from data flow gen2, from data pipelines and from the sample data. Let’s select the sample data and grab a coffee.

Day 16 looks at data pipelines:

With the Fabric warehouse created and explored, let’s see, how we can use pipelines to get the data into Fabric warehouse.

In the existing data warehouse, we will introduce new data. By clicking “new data”, two options will be available; pipelines and dataflows. Select the pipelines and give it a name.

And Day 17 provides a primer on how Power BI can read Fabric assets:

Within the Power BI in Fabric, you will find many of the components, that can be used to create a final report. And here are the components:

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ML Models and Data Warehouses in Microsoft Fabric

Published 2023-12-15 by Kevin Feasel

Tomaz Kastrun continues a series on Microsoft Fabric. First up is creating ML models:

Protip: Both experiments and the ML model version look similar, and you can intuitively switch between both of them. But do not get confused, as the ML Model version applies the best-selected model from the experiment and can be used for inference.

Then we switch context to data warehousing:

Today we will start exploring the Fabric Data Warehouse.

With the data lake-centric logic, the data warehouse in Fabric is built on a distributed processing engine, that enables automated scaling. The SaaS experience creates a segway to easier analysis and reporting, and at the same time gives the ability to run heavy workloads against open data format, simply by using transact SQL (T-SQL). Microsoft OneLake gives all the services to hold a single copy of data and can be consumed in a data warehouse, datalake or SQL Analytics.

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Creating Charts in Microsoft Fabric Notebooks using Vega

Published 2023-12-14 by Kevin Feasel

Phil Seamark tries out Vega in a Microsoft Fabric notebook:

I recently needed to generate a quick visual inside a Microsoft Fabric notebook. After a little internet searching, I found there are many good quality charting libraries in Python, however it was going to take too long to figure out how to create a very specific type of chart.

This is where Vega came to the rescue. The purpose of this short article is to share a very simple implementation of generating a Vega chart using a Microsoft Fabric notebook.

Click through for the example code.

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