A Fine Slice Of SQL Server
First things first. Before I show you how to leverage this feature in Microsoft Fabric, let’s first explain the feature itself.
But, before we explain the feature itself, we need to go one step back and examine the key logic behind the Microsoft Fabric workloads, so that you understand the full context of the Mirroring importance.
Take that context and then you get an idea of how mirroring becomes so important for the Microsoft Fabric experience.
Comments closedKoen Verbeeck double-checks the calendar:
That’s right, this is not an April Fool’s Joke! The most anticipated feature of Microsoft Fabric has arrived! I’m not talking about decent CI/CD support, or OneSecurity. Nope, this is all about the ability to create folders in your workspaces! Very important, since Fabric is a centralized SaaS data platform that allows you to create a gazillion different objects, but until now you had now way of actually organizing them.
To give you an idea about how many objects, this is what the filter currently shows (and some items are missing, like Eventhouse):
This is big. Even on a small proof of concept that I worked on, the lack of folders was annoying. On a full project, the pain becomes worse. Granted, it’s in public preview, so it might not be available to everybody right off the bat, but it’s certainly a step in the direction of usefulness.
Comments closedPaul 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.
Comments closedGilbert 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.
Comments closedJames 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.
Comments closedKoen 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.
Comments closedPaul 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.
Comments closedNikola 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.
Comments closedSiliang 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.
Comments closedIn 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.
Comments closed