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Category: Microsoft Fabric

Designing for Direct Lake Mode

Published 2024-04-01 by Kevin Feasel

Paul Turley shares some advice:

Since the introduction of Power Pivot for Excel, SQL Server Analysis Services Tabular, Azure Analysis Services and Power BI; the native mode for storing data in a semantic data model (previously called a “dataset” in Power BI) has been a proprietary file structure consisting of binary and XML files. These file structures were established in the early days of multidimensional SSAS back in 2000 and 2005. When an Import mode model is published to the Power BI service, deployed to an SSAS server or when Power BI Desktop is running, data for the model is loaded into memory where it remains as long as the service is running. When users interact with a report or when DAX queries are run against the model, results are retrieved very quickly from the data residing in memory. There are some exceptions for very large models or when many models in the service don’t all fit into memory at the same time, the service will page some or all of the model in and out of memory to make sure that the most-often used model pages remain in memory for the next user request. But, for argument’s sake, the entire semantic model sits in memory, waiting for the next report or user request.

Rather than the proprietary SSAS file structure, Direct Lake models use the native Delta-parquet files that store structured data tables for a Fabric lakehouse or warehouse in One Lake. And rather than making a copy of the data in memory, the semantic model is a metadata structure that shares the same Delta-parquet file storage. As soon as a report runs against a model, all of the model data is paged into memory which then behaves a lot like an Import mode model. This means than while the model remains in memory, performance should about the same as Import, with a few exceptions.

Read on to see what the capabilities of Direct Lake mode are today, as well as a few design considerations for your Microsoft Fabric architecture.

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Mirroring an Azure SQL Database in Microsoft Fabric

Published 2024-03-29 by Kevin Feasel

Gilbert Quevuavilliers holds up a mirror:

Creating a Mirrored Azure SQL Database in Fabric

This week they announced Announcing the Public Preview of Database Mirroring in Microsoft Fabric | Microsoft Power BI Blog | Microsoft Power BI

I decided to see how easy it was to create a mirrored database in Fabric and below are my findings (PS it is AMAZING)

Click through for the demo. Though it does look like Gilbert has mirrored the contents of the blog post a few times as well, at least as of the time of my post here.

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Announcements from the Microsoft Fabric Community Conference

Published 2024-03-29 by Kevin Feasel

James Serra gives us the round-up:

A ton of new features for Microsoft Fabric were announced at the Microsoft Fabric Community Conference. Here are all the new features I am aware of, with some released now and others coming soon:

  • Mirroring is now in public preview for Cosmos DB, Azure SQL DB and Snowflake. See Announcing the Public Preview of Database Mirroring in Microsoft Fabric
  • You get a free terabyte of Mirroring storage for replicas for every capacity unit (CU) you have purchased and provisioned. For example, if you purchase F64, you will get sixty-four free terabytes worth of storage for your mirrored replicas

Click through for a couple dozen more announcements. They’ve been quite busy on Microsoft Fabric.

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Changing the Timeout of a Spark Session in Microsoft Fabric

Published 2024-03-20 by Kevin Feasel

Koen Verbeeck doesn’t have time to wait:

You might know the feeling: you’re writing code in a Notebook in Microsoft Fabric and suddenly you have to leave your workstation for a while. Someone ran the doorbell (you’re working from home and you get some parcels delivered), or you took a coffee break with some colleagues. When you return to your notebook, the Spark session has timed out and when you run a cell, you have to wait for the damn thing to restart again. The agony, waiting for 2-3 minutes for the session to start, and only after that the actual code can start running.

Read on to see how you can set the timeout to a custom value, assuming you’re okay with paying for the Spark cluster to sit around until it times out.

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Migrating from Power BI to Microsoft Fabric

Published 2024-03-20 by Kevin Feasel

Paul Turley gives us an overview:

Fabric is here but what does that mean if you are using Power BI? What do you need to know and what, if anything, will you need to change if you are a Power BI report designer, developer or BI solution architect? What parts of Fabric should you use now and how do you plan for the near-term future? As I write this in March of 2024, I’m at the Microsoft MVP Summit at the Microsoft campus in Redmond, Washington this week learning about what the product teams will be working on over the next year or so. Fabric is center stage in every conversation and session. To say that Fabric has moved my cheese would be a gross understatement. I’ve been working with data and reporting solutions for about 30 years and have seen many products come and go. Everything I knew about working with databases, data warehouses, transforming and reporting on data has changed recently BUT it doesn’t mean that everyone using Power BI must stop what they are doing and adapt to these changes. The core product is unchanged. Power BI still works as it always has.

Read on to learn more about Paul’s thesis and how the world changes with Microsoft Fabric.

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A Dive into Direct Lake

Published 2024-03-19 by Kevin Feasel

Nikola Ilic digs into Direct Lake:

The most common question I’m hearing these days from clients is – how can we refresh the Direct Lake semantic model? It’s a fair question. Since they have been relying on Import mode for years, and Direct Lake promises an “import mode-like performance”…So, there has to be a similar process in place to keep your data up to date, right?

Read on to learn the answer to this question and quite a few more.

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Feature Engineering with Azure ML and Microsoft Fabric

Published 2024-03-18 by Kevin Feasel

Siliang Jiao, et al, talk architecture:

Feature engineering is the process of using domain knowledge to extract features (characteristics, properties, attributes) from raw data. The extracted features are used for training the models that can predict values for relevant business scenarios. A feature engineering system provides the tools, processes, and techniques used to perform feature engineering consistently and efficiently. 

This article elaborates on how to build a feature engineering system based on Azure Machine Learning managed feature store and Microsoft Fabric. 

Click through to see how the pieces fit together.

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Copying a Direct Lake Semantic Model between Fabric Workspaces

Published 2024-03-18 by Kevin Feasel

Kevin Chant makes a copy:

In this post I introduce scripts to improve copying a Direct Lake semantic model to another workspace using Microsoft Fabric Git integration.

I wanted to do this follow-up after my previous post about my initial tests to copy a Direct Lake semantic model to another workspace using Microsoft Fabric Git integration.

Due to the fact that I want to show how you can work with scripts locally to create the repository that contains the Direct Lake semantic model. Plus, how to do this in a way that includes the new Tabular Model Definition Language (TMDL) semantic file format.

Read on to see how it all fits together.

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Copilot in Microsoft Fabric Dataflows Gen2

Published 2024-03-18 by Kevin Feasel

Reza Rad shows off a capability:

There has been a lot of hype recently about Generative AI and Copilot in Microsoft. Microsoft Fabric incorporates many of those features, and one of the areas it has been added to is the Dataflow Gen2 in Microsoft Fabric, or we can also call it Power Query in Power BI Service Dataflows. In this article and video, I will describe how the Copilot works with Data Factory Dataflow Gen2, its requirements, and its examples.

Click through for the video and the article. The thing that I believe will keep many people from using this is that you need a Microsoft Fabric capacity of F64 or greater to get access to Copilot. That’s a pretty hefty requirement.

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Retrieving Spark Session Config Variables from Microsoft Fabric

Published 2024-03-14 by Kevin Feasel

Koen Verbeeck gets some settings:

I was trying some stuff out in a notebook on top of a Microsoft Fabric Lakehouse. I was wondering what some of the default values are of the configuration variables, and if there’s an easy way to retrieve them all. Luckily there is. In the code, I’m using Scala because it has a nice GetAll() function.

Click through for an example of how to use this. And bonus points for using Scala instead of Python here.

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