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Category: Microsoft Fabric

Licensing for Microsoft Fabric

Reza Rad explains how licensing of Microsoft Fabric will work:

To understand the licensing for Microsoft Fabric, You first need to understand the Capacity structure. In Fabric, there are three important sections that the content can be organized into those; Tenant, Capacity, and Workspace.

Tenant is the most fundamental part of the structure of Fabric. Each domain can have one or multiple tenants.

The capacity is the substructure under the tenant. You can have one or multiple capacities in each tenant. Each capacity is a pool of resources that can be used for Microsoft Fabric services. There are different SKUs for different levels of resources. I’ll explain the pricing and SKUs shortly after.

Inside capacities, you will have workspaces. Workspaces are sharing units that will be used for developers and users. For example, you will create Lakehouse, Data Pipeline, and Dataflow inside a workspace, and you can share them with the rest of the developer team. A workspace is assigned to a capacity. However, you can have more than one capacity associated with one workspace. The screenshot below shows how Tenant, Capaicy, and Workspace work together.

Read on to understand at what level billing occurs, what the options are, and what it means. My gut is saying that F8 is probably the lowest acceptable tier for a real company’s production environment and F2 is more for dev environments or people trying things out. But we’ll know more, I think, in the next few months as people try things out.

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Configuring Compliance in Microsoft Fabric

Kevin Chant checks a box:

Compliance is a very important aspect when working for data. Especially when you must work to standards like PCI-DSS. With this in mind I looked into the compliance story for Microsoft Fabric.

By the end of this post, you will have a better idea of how to test configuring compliance for Microsoft Fabric. Along the way I share plenty of links.

Read on for step-by-step instructions, as well as those links.

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Thoughts on Fabric OneLake

Teo Lachev shares some thoughts:

In a previous post, I shared my overall impression of Fabric. In this post, I’ll continue exploring Fabric, this time sharing my thoughts on OneLake. If you need a quick intro to Fabric OneLake, the Josh Caplan’s “Build 2023: Eliminate data silos with OneLake, the OneDrive for Data” presentation provides a great overview of OneLake, its capabilities, and the vision behind it from a Microsoft perspective. If you prefer a shorter narrative, you can find it in the “Microsoft OneLake in Fabric, the OneDrive for data” post. As always, we are all learning and constructive criticism would be appreciated if I missed or misinterpreted something.

I think some of Teo’s criticism comes from the idea that OneLake should also mean one lakehouse or one data lake, but the abstraction is one level higher than that. I would like to see some of Teo’s ideas make it into GA, though.

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Microsoft Fabric and Process Unification

Paul Andrew gets to the heart of things:

Moving on and assuming you have seen the event sessions, I want to give you my point of view to help explain what Microsoft Fabric is. Firstly, lets clear up call out was terminology to support this understanding. Is this software offering a resource, service, platform, or solution? To answer this question, perspective is key, perspective with a timeline (2018 to 2023). We could simply say that Microsoft Fabric is all these things. All things to all data professionals and beyond. But, to understand this, let’s consider the journey Microsoft has been on and how this technology has evolved. I believe this journey is the best way to help explain what Microsoft Fabric is, rather than focusing on all the new and shiny bits.

Click through for Paul’s take on the matter and how this whole area of “modern data warehousing” has evolved over the past several years in Azure.

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Building a Data Warehouse in Microsoft Fabric

Reza Rad continues a video series on Microsoft Fabric:

Microsoft Fabric Data Warehouse is a database system that stores data in OneLake and provides a medium to interact with the database using SQL commands. The Fabric Data Warehouse, which is also called Data Warehouse, or in short, Warehouse, also provides a powerful computing engine behind the scene to account for large volumes of data and support a fast-performing database system. The term Data Warehouse comes from the fact that this is not usually a place to store transactional data for an operational system (for that, you can use Azure SQL Database). A Data Warehouse, in generic Business Intelligence terminology, is a place where you would store the data that needs to be analyzed.

Reza also explains how the warehouse differs from a lakehouse.

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Data Governance and Microsoft Fabric

Matthew Roche digs deeper into data governance in Microsoft Fabric:

One of the most underappreciated benefits of Power BI as a managed SaaS data platform has been the “managed” part. When you create a report, dataset, dataflow, or other item in Power BI, the Power BI service knows everything about it. Power BI is the authoritative system for all items it contains, which means that Power BI can answer questions related to lineage (where does the data used by this report come from?) and impact analysis (where is the data in this dataset used?) and compliance (who has permissions to access this report?) and more.

If you’ve ever tried to authoritatively answer questions like these for a system of any non-trivial scope, you know how hard it is. Power BI has made this information increasingly available to administrators, through logs and APIs, and the community has built a wide range of free and paid solutions to help admins turn this information into insights and action. Even more excitingly, Power BI keeps getting better and better even as the newer parts of Fabric seem to be getting all of the attention.

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First Look at Loading Data into Fabric

Reitse Eskens digs into Microsoft Fabric:

In my previous blog, I wrote about some first impression working with Fabric and mostly following the Lakehouse tutorial provided by Microsoft. Well structured as this one may be, the sizes are not like the sizes I’m seeing in the wild. So I decided to give Fabric a bit more of a challenge by letting it loose on my TPC-H dataset.

Click through for Reitse’s early analysis.

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Data Pipelines and Dataflows in Fabric Data Factory

Reza Rad has two videos and posts for us. First up is a primer on data pipelines in Microsoft Fabric Data Factory:

The Pipeline comes from Azure Data Factory. A Pipeline is a group of activities bundled together into a workflow. For example, a Pipeline can generate a process around the Dataflow. For example, you may want to run a Dataflow in a loop until something happens, and with the failure or success of each execution, you want to perform a task such as sending out an email, copying data somewhere, running a stored procedure, etc.

Reza then gets into Dataflows:

Through the years, the Data Transformation engines evolved. In the past, much coding was involved, and the user interface was not the best experience. These days, most actions can be done through pre-built transformations; less coding is needed, and a hardcore developer is not needed for preliminary tasks. This enables citizen data engineers to work with these tools.

Power Query is the data transformation engine of the new generation of Microsoft Data Integration tools and services. Power Query is the data transformation engine used in Power BI. However, Power Query can be used as a standalone cloud-based data transformation service when it is used as Dataflow. Dataflow is the ETL in the cloud offered by Microsoft, which uses the Power Query engine.

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Microsoft Fabric vs Synapse

Nikola Ilic shares some thoughts:

I’ve already introduced Microsoft Fabric in the previous article, so if you’re still not sure what is it all about and why you can think of Fabric as your “data football team”, I strongly encourage you to check that article. Additionally, there are many great articles and videos, both from Microsoft and the community, where you can find out more about Fabric and its various scenarios and components.

In the above-mentioned article, I scratched the surface of the inevitable topic that now comes into focus: “What now for Azure Synapse Analytics?” Since I’ve been asked this exact question multiple times in the previous days, I’ve decided to put down my thoughts and share them in this article.

Read the whole thing. My thoughts, which are generally similar to Nikola’s:

  • There are no plans (at this time) to remove Synapse, and even if there were, prior history—like with Azure SQL DW—says that the deprecation timeframe is something we can measure in years rather than months
  • Fabric is intended to replace Synapse one of these days, and new customers should start with Fabric
  • Current Synapse customers should stay on Synapse for now, especially given that there is currently no easy migration plan. Give partners and Microsoft some time to sort that out, though, and I expect you’ll see tools and products for this by the time Fabric goes GA
  • PaaS and SaaS are quite different and that can be an influential factor. My personal preference is for SaaS, especially knowing how difficult it can be to secure Synapse while still enabling developer functionality
  • We’re on day 4 of Fabric being a thing (at least in public), and it’ll probably be in a public preview for a while, so there’s still plenty of baking left to do
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