Managing data connections in Microsoft Fabric can be challenging if you’re unsure where to start. This blog post and its detailed YouTube video will help you find, manage, and share the existing data connections, making your workflow more efficient and streamlined. A meaningful use case for this feature is to reuse the existing connections leading to more controlled connections to the data sources. More on this later in this blog.
Understanding Data Connections in Microsoft Fabric
In Microsoft Fabric, a data connection links the platform to various data sources, whether in the cloud or on-premises. Different items in Microsoft Fabric, such as Data Factory Pipelines, Dataflows, Paginated reports, Semantic Models, KQL databases, and Mirrored Azure SQL databases (currently in preview), create these data connections.
Finding Data Connections
To find data connections in Microsoft Fabric:
Click on Settings at the top right of the page.
Select Manage connections and gateways.
Navigate to the Connections tab.
This tab displays all the connections shared with you or created by you. From here, you can check the status of each connection, remove old connections, and manage them as needed.
Manage connections and gateways
This page used to be called Manage Gateways where we could configure and manage on-premises data gateways. I have a very old blog post explaining the gateway setup and configuration in the cloud and on your local server here. While it’s an old post, the topics are still relevant, so check it out if you are interested in the gateway configuration.
Note
As the preceding image shows, the Data page is currently in publicPreview, hence, it is subject to change. It is also worthwhile to mention that not all connections are currently accessible via this page such as connections that are natively created by KQL databases within Fabric.
Check Connection Status
To check the connection status, click the status button of each connection. The result shows if the connection is online or offline.
Microsoft Fabric is a new platform designed to bring together the data and analytics features of Microsoft products like Power BI and Azure Synapse Analytics into a single SaaS product. Its goal is to provide a smooth and consistent experience for both data professionals and business users, covering everything from data entry to gaining insights. A new data platform comes with new keywords and terminologies, so to get more familiar with some new terms in Microsoft Fabric, check out this blog post.
As mentioned in one of my previous posts, Microsoft Fabric is built upon the Power BI platform; therefore we expect it to provide ease of use, strong collaboration, and wide integration capabilities. While Microsoft Fabric is getting more attention in the market, so we see more and more organisations investigating the possibilities of migrating their existing data platforms to Microsoft Fabric. But what does it mean for seasoned Power BI developers? What about Power BI professional users such as data analysts and business analysts? In this post, I endeavor to answer those questions.
I have been blogging predominantly around Microsoft Data Platforms and especially Power BI since 2013. But I have never written about the history of Power BI. I believe it makes sense to touch upon the history of Power BI to better understand the size of its user base and how introducing a new data platform that includes Power BI can affect them. A quick search on the internet provides some interesting facts about it. So let’s take a moment and talk about it.
The history of Power BI
Power BI started as a top-secret project at Microsoft in 2006 by Thierry D’Hers and Amir Netz. They wanted to make a better way to analyse data using Microsoft Excel. They called their project “Gemini” at first.
In 2009, they released PowerPivot, a free extension for Excel that supports in-memory data processing. This made it faster and easier to do calculations and create reports. PowerPivot got quickly popular among Excel users, but it had some limitations. For example, it was hard to share large Excel files with others, and it was not possible to update the data automatically.
In 2015, Microsoft combined PowerPivot with another extension called Power Query, which lets users get data from different sources and clean it up. They also added a cloud service that lets users publish and share their reports online. They called this new product Power BI, which stands for Power Business Intelligence.
In the past few years, Power BI grasped a lot of attention in the market and improved a lot to cover more use cases and business requirements from data transformation, data modelling, and data visualisation to combining all these goods with the power of AI and ML to provide predictive and prescriptive analysis.
Who are Power BI Users?
Since its birth, Power BI has become one of the most popular and powerful data analysis and data visualisation tools in the world used by a wide variety of users. In the past few years, Power BI generated many new roles in the job market, such as Power BI developer, Power BI consultant, Power BI administrator, Power BI report writer, and whatnot, as well as helping many others by making their lives easier, such as data analysts and business analysts. With Power BI, the data analysts could efficiently analyse the data and make recommendations based on their findings. Business analysts could use Power BI to focus on more practical changes resulting from their analysis of the data and show their findings to the business much quicker than before. As a result, millions of users interact with Power BI on a daily basis in many ways. So, introducing a new data platform that sort of “Swallows Power BI” may sound daunting to those whose daily job relates to content creation, maintenance, or administrating Power BI environments. For many, the fear is real. But shall the developers and analysts be afraid of Microsoft Fabric? The short answer is “Absolutely not!”. Does it change the way we used to work with Power BI? Well, it depends.
To answer these questions, we first need to know who are Power BI users and how they interact with it.
In the previous post I explained the basic concepts around endorsement in Power BI. We discussed that users’ ability to collaborate in creating and sharing artifacts is one of the key aspects of users’ experience in Power BI. But it would be hard, if not impossible, to identify the quality of the artifact without a mechanism to identify the artifact’s quality in large organisations. Endorsement is the answer to this challenge. We discussed the following in the previous post:
How do Power BI administrators enable certification and grant rights to security groups?
In the previous post, we discussed that a Power BI administrator must enable certification and grant sufficient rights to the security groups. Therefore, all members of the specified security group are authorised to certify the artifacts. If you are a Power BI administrator, follow these steps to do so:
After logging into Power BI Service, click the Settings button
Click Admin Portal
From the Tenant settings, scroll down to find the Export and sharing settings
Find and expand the Certification setting
Enable certification
Put the certification process documentation URL (if any)
It is not recommended to enable this feature for the entire organisation. So, select the Specific security groups option
Type the security group name and select it from the list
Click the Apply button
The following image shows the above steps:
Enabling certification from the Admin Portal in Power BI Service
It may take up to 15 minutes for the changes to go through. After that, all the members of the specified security can certify the artifacts. In the next section, we see how to certify the supported artifacts.
Note
Everyone who has “write” permission on the Workspace containing the artifactcan promote it. Therefore, the users or security groups with one of the Admin, Member, or Contributorroles in the Workspace can promote the artifacts.
However, one should not promote the artifactsjust because he/she can. The organisations usually have a promotion process to follow, but the boundaries around promoting are often much more relaxed than certifying it.
As you may already know, Power BI is not a report-authoring tool only. Indeed, it is much more than that. Power BI is an all-around data platform supporting many aspects you’d expect from such a platform. You can ingest the data from various data sources, transform it, model it, visualise and share it with others. Read more about what Power BI is here.
One of the key aspects of users’ experience in Power BI is their ability to collaborate in creating and sharing artifacts, making it an easy-to-use and convenient platform. But the convenience comes with the cost of having a lot of shared artifacts in large organisations raising concerns about the artifact’s quality and trustworthiness. It would be hard, if not impossible, to identify the quality of the artifacts without a mechanism to identify the quality of the artifacts. Endorsement is the answer to this.
In this series of blog posts, I answer the following questions:
But before we start, we need to know what content means in Power BI.
What does Content Mean in Power BI?
Update: Microsoft lately updated the “Content” terminology, which is slightly different from when I wrote this blog. So I replaced content with artifact that is a more generic term. While the term content is not relevant to the topic anymore, I decided to keep this section explaining what content means in Power BI.
When we use the term Content in the context of Power BI, we refer to the artifacts related to visuals in Power BI Service. We currently have the following artifacts in Power BI:
