This post covers how you can spread your SQL Server wings with Microsoft Fabric in 2026. As part of a long-running series of posts about spreading your SQL Server wings with the Microsoft Intelligent Data Platform.
Just after Microsoft Fabric was publicly announced during Microsoft Build 2023, I published a post that covered spreading your SQL Server wings with Microsoft Fabric.
A lot has changed since then. Including Microsoft Fabric becoming generally available and the introduction of more workloads. Since Data Days is currently taking place, I decided to publish an updated version.
To manage expectations, this post is aimed at SQL Server professionals. However, this post can be handy for those who want a holistic overview of Microsoft Fabric. For example, those with ambitions to become a Fabric architect which requires you to see the bigger picture about the entire product.
By the end of this post, you will have a good overview of Microsoft Fabric. In addition, where your SQL Server background can prove to be useful.
Along the way I share plenty of links. Plus, there are a couple of useful resources to help you get started towards the bottom of this post.
About Microsoft Fabric
Microsoft Fabric was first publicly announced during the keynote at Microsoft Build 2023.
It unifies the functionality that you can find in various applications together within one Software as a Service solution to deliver a unified analytics platform.
To help with some jargon here, you can find out more about what I mean when I refer to Software as a Service (SaaS) in an old blog post called ‘SaaS, PaaS, and IaaS.. Oh my!’.
Anyway, in a previous post in the series about Azure Synapse Analytics I mentioned that Azure Synapse Analytics was an integrated solution. Where you could work with various offerings under one roof.
Well, Microsoft Fabric takes it one step further. It allows you to work with multiple types of compute (aka workloads) that all connect together and have a unified storage solution. Which can be accessed by one central entry point based on the Power BI infrastructure.
So, all you have to do is go to one URL and all the functionality that you see below will be at your fingertips.
OneLake
To clarify something in the above diagram, all of the workloads in the top layer of the diagram store the data within OneLake. Which is the unified storage foundation for Microsoft Fabric.
OneLake is actually a layer of abstraction on top of multiple Azure Data Lake Storage Accounts. However, you just work with the presented OneLake. Typically the workloads store data either in Delta Lake format or general files. However, there are some exceptions.
For example, SQL databases in Fabric tend to store data in the native MDF and LDF format first. Before forwarding the data to OneLake.
When I say unified, I mean that the majority of the experiences in the top layer of the above diagram store data in OneLake as Delta lake tables. Allowing them to query and share the same data.
OneLake also raises the game further by providing the option to create shortcuts. Which are basically links to data stored elsewhere. More recently, Microsoft introduced the ability to perform shortcut transformations to which enables you to convert structured files into queryable Delta tables.
In addition to all the above, Microsoft Fabric has AI deeply integrated into it at various levels. Including multiple Copilot experiences in various workloads and Fabric Data Agents.
On top of all of this, there is also a level of Git integration with Azure DevOps. Which currently allows you to do version control for your reports, datasets and paginated reports.
Microsoft Fabric workloads
To give you an idea of what you can do within Microsoft Fabric, as you saw in the above diagram it currently offers the below workloads all under one roof.
 | Power BI to deliver Business Intelligence. One key point I want to highlight here is that Microsoft Fabric introduces a new Power BI mode called “Direct Lake” mode. Which allows Power BI to use the data that is stored locally in OneLake. |
 | Databases workload contains the Fabric native versions of SQL databases in Fabric and Cosmos DB. |
 | Data Factory to ingest data using either Data Pipelines or Dataflows which are based on Power Query. |
 | Industry solutions provides templated solutions for industry-specific data solutions. |
 | Fabric IQ for those who want to bring all their Fabric items together to create a shared business language that scales across your organization. Utilizing ontologies, semantic models, graph relationships, and enterprise data in OneLake. |
 | Real-Time Intelligence, which provides a complete streaming solution inside Microsoft Fabric based on Kusto. Including a KQL Database engine and a capability called eventstream. Which can be used to ingest streaming data. |
 | Data Engineering for those who want to work with Lakehouses at scale. Using similar experiences that you may have used with other services. |
 | Data Science capabilities. Including the ability to work with spark clusters and machine learning models. |
 | Data Warehousing which allows you to work with a powerful compute engine based on a serverless SQL relational engine. |
Custom workloads and Fabric apps
In addition to the above you can also create custom workloads with the Microsoft Fabric Extensibility Toolkit. Plus, at this years Microsoft Build Microsoft introduced Fabric apps. Which can utilize SQL database in Fabric.
Personally, I think that the ability to work with all of the above in a fully integrated way within one environment is mind-blowing. Due to the fact that has changed the landscape of how we work completely.
Friction
Microsoft Fabric as removed a lot of friction. Because it provides the opportunity to perform complex scenarios within one service. Therefore, removing the hassle of having to integrate multiple disparate services.
For example, in the past you had to integrate various services together. Including handling all the networking and security requirements to connect everything together. Like in the below diagram.
Now you can do all of this under one roof within Microsoft Fabric. Which also introduces the option to mount and transform data that is stored elsewhere, as you can see below.
Below is another good example that shows how you can work with real-time intelligence within Microsoft Fabric. Instead of working with multiple cloud-based services.
Personally, I think that the above examples show that having everything integrated provides easier integration.
Plus, due to the environment being so integrated a lot of the items provide options that allow you to integrate with other items within a few clicks.
For example, when working within a Data Warehouse you can select a button to start working with a visual query editor. You can then refine your data and create new reports within the visual query editor.
Utilizing SQL Server background in Microsoft Fabric
In reality, you can utilize your SQL Server background in many different ways within Microsoft Fabric.
Below are just some examples to show where your SQL Server background can come in handy. Based on OneLake and the experiences that I covered earlier.
| You can use a lot of your SQL Server background within the Power BI experience. For example, when looking to optimize reports within Microsoft Fabric or perform data modelling. In reality, a lot of people who work with Power BI already have a lot of SQL Server knowledge. Due to the fact that a lot of them come from a SQL Server background. |
| You can utilize your SQL Server background to work with SQL databases in Fabric. I published a pot that covers how you can spread your SQL Server wings with SQL database in Fabric. |
| You can use your SQL Server Integration Services (SSIS) background to adapt to working with Pipeline and Dataflow functionality in Data Factory. Plus, T-SQL background when working with Spark SQL in notebooks. |
| You can utilize your graph knowledge when working with Graph in Microsoft Fabric. |
| You can query within Real-Time Intelligence using the Kusto Query Language (KQL). So, those of you with a SQL Server development background you can look to transfer your skills over to work with KQL as well. |
| Within the Data Engineering experience, you can work with various languages within notebooks. Including Spark SQL, which is similar to T-SQL. Plus, look to migrate your development skills by learning Python. |
| If you have worked with the Predict functionality in SQL Server, you can look to use it as part of the Data Science capabilities. |
| In the Data Warehousing experience, you can work with a T-SQL query editor. In addition, it comes with a visual query editor which can be an interesting experience for those with a T-SQL background. |
To help you visualize where your SQL Server background can come in handy, below is an example of some T-SQL being run within a Data Warehouse.
Spread your SQL Server wings with CI/CD
Those of you who performed CI/CD for SQL Server with either Azure DevOps or GitHub can look to migrate their knowledge to perform CI/CD for Microsoft Fabric in various ways.
You can store the metadata for various items in a Microsoft Fabric workspace by utilizing Microsoft Fabric Git integration. Which allows you to store the metadata of items in a Git repository stored in either Azure DevOps or GitHub. Allowing you to utilize your existing knowledge of Git and either or those offerings.
In addition, there are multiple ways to deploy various Fabric items together. Such as Microsoft Fabric deployment pipelines and the fabric-cicd Python library.
You can look to extend your existing deployment experience with SQL Server in whichever direction you choose. For example, look to extend your YAML pipeline and Python knowledge when looking to deploy with fabric-cicd and Azure DevOps.
I covered ways to perform deployments with deploy with fabric-cicd and Azure DevOps in various posts. Including how to orchestrate configuration-based deployments to Microsoft Fabric with YAML Pipelines. Microsoft updated their overview about CI/CD workflow options recently which provides further insights.
Another key point I want to highlight is that there is that it perfectly feasible to create database projects for both SQL database in Fabric and Data Warehouses. In order to perform CI/CD with them.
Like in the below diagram from my post that covers two ways to perform CI/CD for SQL databases in Fabric using GitHub Actions.
Online Microsoft Fabric material to help spread your SQL Server wings
To align with the announcement back in 2023 a Microsoft Fabric Launch Digital event took place. You can watch both the Day 1 video and the Day 2 video on YouTube.
There is other online material available for those who are interested in finding out more. For instance, there is a website that covers Microsoft Fabric. One thing I want to clarify is that this website includes a guide on how to enable Microsoft Fabric for your organization for existing Power BI users.
Another way to improve your knowledge is by studying for certifications. Microsoft provides some good learning material for the DP-600 and DP-700 exams
In addition, Microsoft provides some really good end-to-end tutorials in Microsoft Fabric to help you get up to speed with it. Plus, in Microsoft Learn you can find a learning path to help you get started with Microsoft Fabric.
I also added a couple of other posts in the spreading your SQL Server wings series that focuses on the below specific parts of Microsoft Fabric.
There are plenty of community resources relating to Microsoft Fabric. Furthermore, there is an online Fabric community, a Microsoft Fabric subreddit and a Microsoft Fabric Discord community.
Final words about spreading your SQL Server wings with Microsoft Fabric
I hope showing how you can spread your SQL Server wings with Microsoft Fabric in 2026 has proved to be useful. Especially when you consider just how much you can do in Microsoft Fabric alone. Because there has been a lot of updates since the original post.
I must admit, I did enjoy doing this updated version for the community and I hope you all learned at least one new thing from it.
1 thought on “Spread your SQL Server wings with Microsoft Fabric in 2026”