Unveiling Microsoft Fabric’s Impact on Power BI Developers and Analysts

Unveiling Microsoft Fabric’s Impact on Power BI Developers and Analysts

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.

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Endorsement in Power BI, Part 2, How to Endorse?

Endorsement in Power BI, Part 2, How to Endorse?

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:

In this post, I explain the following:

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:

  1. After logging into Power BI Service, click the Settings button
  2. Click Admin Portal
  3. From the Tenant settings, scroll down to find the Export and sharing settings
  4. Find and expand the Certification setting
  5. Enable certification
  6. Put the certification process documentation URL (if any)
  7. It is not recommended to enable this feature for the entire organisation. So, select the Specific security groups option
  8. Type the security group name and select it from the list
  9. Click the Apply button

The following image shows the above steps:

Enabling certification from the Admin Portal in Power BI Service
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 artifact can promote it. Therefore, the users or security groups with one of the AdminMember, or Contributor roles in the Workspace can promote the artifacts.

However, one should not promote the artifacts just 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.

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Endorsement in Power BI, Part 1, The Basics

Content Endorsement in Power BI, Part 1, The Basics

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:

From those artifacts, the Reports, Dashboards and Apps are Contents.

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Thin Reports, Real-world Challenges

Power BI Thin Reports, Real-world Challenges

I previously explained in a blog post what thin reports are and why we should care about them. I also explained Report Level Measures in another blog post. In this post, I try to raise some real-world challenges we face when developing thin reports. I also provide a solution to those challenges.

Report Level Measure Related Challenges

Creating and using Report Level Measures is relatively easy, but there are some challenges that we face from time to time, such as:

  • Distinguishing Report Level Measures from Dataset Level Measures
  • Report Level Measure dependencies

Determining Report Level Measures from Dataset Level Measures

One of the challenges that Power BI Developers face is creating many report level measures. Unfortunately, Power BI Desktop currently uses the same iconography for both types of measures, making it hard to distinguish the actual measures created within the dataset from the report level measures. It gets even more challenging if we need to write technical documentation for an existing thin report. We have to open the PBIX file of the thin report in the Power BI Desktop and click every single measure. If the expression bar appears, the selected measure is a report level measure; otherwise, it is a dataset level measure.

So unless we use third-party tools, which I explain in this post, we must go through the manual process.

Report Level Measure dependencies

Another pain point related to the previous challenge is finding the dependencies between the report level measures. It is crucial to be aware of the interdependencies when doing impact analysis. We need to understand how a change in a report level measure impacts other report level measures. Again, Power BI Desktop does not currently have any options supporting that, so we have to click every measure and read through the DAX expressions to identify the dependencies or use the third-party tools to save development time.

Dataset and Thin Reports Dependency Challenges

The other challenges are even more difficult to overcome relate to interdependencies between datasets and thin reports. Power BI Service provides a lineage view that shows the dependencies between a dataset and its connected thin reports. But the challenges can get more complex to overcome manually. The following are some real-world examples of more complex situations:

  • What if we need to analyse the impact of changes in a dataset measure on all report level measures of the connected thin reports?
  • How do we analyse the impact of changes on a dataset measure on all connected thin reports, including the visuals, filters, etc…?
  • What if we need to tune the performance and we want to find a list of all unused tables or unused fields?

As you can see, the situation can get pretty complex, so manual operations are virtually impossible.

But there is a third party tool we can use which provides heaps of capabilities with a couple of clicks.

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