Category: Power BI Desktop

Azure SQL Database, Data Visualisation, Dataflows, Fabric Administrator, Lakehouse, Microsoft Fabric, Notebook, OneLake, Power BI, Power BI Desktop, Power BI Embedded Capacity, Power BI Premium Capacity, Power BI Service, Power Query, Self-Service BI, Synapse Data Engineering, Synapse Data Science, Synapse Data Warehouse

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.

Continue reading “Unveiling Microsoft Fabric’s Impact on Power BI Developers and Analysts” →
Azure DevOps, Data Visualisation, DAX, Microsoft Fabric, Power BI, Power BI Desktop, Power BI Embedded Capacity, Power BI Premium Capacity, Power BI Service, Self-Service BI, Source Control

Integrating Power BI with Azure DevOps (Git), part 2: Local Machine Integration

Integrating Power BI with Azure DevOps (Git), part 2: Local Machine Integration

This is the second part of the series of blog posts showing how to integrate Power BI with Azure DevOps, a cloud platform for software development. The previous post gave a brief history of source control systems, which help developers manage code changes. It also explained what Git is, a fast and flexible distributed source control system, and why it is useful. It introduced the initial configurations required in Azure DevOps and explained how to integrate Power BI (Fabric) Service with Azure DevOps.

This blog post explains how to synchronise an Azure DevOps repository with your local machine to integrate your Power BI Projects with Azure DevOps. Before we start, we need to know what a Power BI Project is and how we can create it.

What is Power BI Project (Developer Mode)

Power BI Project (*.PBIP) is a new file format for Power BI Desktop that was announced in May 2023 and made available for public preview in June 2023. It allows us to save our work as a project, which consists of a folder structure containing individual text files that define the report and dataset artefacts. This enables us to use source control systems, such as Git, to track changes, compare revisions, resolve conflicts, and review changes. It also enables us to use text editors, such as Visual Studio Code, to edit the artefact definitions more productively and programmatically. Additionally, it supports CI/CD (continuous integration and continuous delivery), where we submit changes to a series of quality gates before applying them to the production system.

PBIP files differ from the regular Power BI Desktop files (PBIX), which store the report and dataset artefacts as a single binary file. This made integrating with source control systems, text editors, and CI/CD systems difficult. PBIP aims to overcome these limitations and provide a more developer-friendly experience for Power BI Desktop users.

Since this feature is still in public preview when writing this blog post, we have to enable it from the Power BI Desktop Options and Settings.

Enable Power BI Project (Developer Mode) (Currently in Preview)

As mentioned, we first need to enable the Power BI Project (Developer Mode) feature, introduced for public preview in the June 2023 release of Power BI Desktop. Power BI Project files allow us to save our Power BI files as *.PBIP files deconstruct the legacy Power BI report files (*.PBIX) into well-organised folders and files.
With this feature, we can:

  • Edit individual components of our Power BI file, such as data sources, queries, data model, visuals, etc.
  • Use any text editor or IDE to edit our Power BI file
  • Compare and merge changes
  • Collaborate with other developers on the same Power BI file

To enable Power BI Project (Developer Mode), follow these steps in Power BI Desktop:

Continue reading “Integrating Power BI with Azure DevOps (Git), part 2: Local Machine Integration” →
Azure, Business Intelligence - BI, Microsoft Fabric, Power BI, Power BI Desktop, Power BI Premium Capacity, Power BI Service

Integrating Power BI with AzureDevOps (Git), part 1: Cloud Integration


Power BI is a powerful tool for creating and sharing interactive data visualizations. But how can you collaborate with other developers on your Power BI projects and ensure quality and consistency across your reports? In this series of blog posts, I will show you how to integrate Power BI with Azure DevOps, a cloud-based software development and delivery platform. We can integrate Azure DevOps with Power BI Service (Fabric) as well as Power BI Desktop.
The current post explains how to set up Azure DevOps and connect a Power BI Workspace.
The next blog post will explain how to use it on your local machine to integrate your Power BI Desktop projects with Azure DevOps.

A brief history of source control systems

Before we dive into the details of Power BI and Azure DevOps integration, let’s take a moment to understand what source control systems are and why they are essential for any software project.

Source control systems, also known as version control systems or revision control systems, are tools that help developers manage the changes made to their code over time. They allow developers to track, compare, and roll back changes when necessary and collaborate with other developers on the same project.

There are two main types of source control systems: centralised and distributed. Centralised source control systems use Client-server approach to store all the code and its history in a single server, and developers need to connect to that server to access or modify the code. Examples of centralised source control systems are Microsoft’s Team Foundation Server (TFS) which rebranded to Azure DevOps Server in 2018, IBM’s ClearCase, and Apache’s Subversion.

On the other hand, distributed source control systems use a peer-to-peer approach, allowing each developer to have a local copy of the entire code repository, including its history. Developers can work offline and sync their changes with other developers through a remote server. Examples of distributed source control systems are Git Software and Mercurial, which takes us to the next section. Let’s see what Git is.

What is Git, and why use it?

Git is one of the world’s most popular and widely used distributed source control systems. It was created by Linus Torvalds, the creator of Linux, in 2005. Git has many advantages over centralised source control systems, such as:

  • Speed: Git is fast and efficient, performing most operations locally without network access.
  • Scalability: Git can easily handle large and complex projects, as it does not depend on a single server.
  • Flexibility: Git supports various workflows and branching strategies, allowing developers to choose how they want to organise their code and collaborate with others.
  • Security: Git uses cryptographic hashes to ensure the integrity and authenticity of the code.
  • Open-source: Git is free and open-source, meaning anyone can use it, modify it, or contribute to it.

While Git is pretty good, it has some disadvantages compared with a centralised source control system. Here are some:

  • Complexity: Git has a steep learning curve, especially for users who are new to distributed version control systems. Understanding concepts such as branching, merging, rebasing, and resolving conflicts can be challenging for beginners and sometimes even seasoned Git users.
  • Collaboration challenges: While distributed version control systems like Git enable easy collaboration, they can also lead to collaboration issues. Multiple developers working on the same branch simultaneously may encounter conflicts that need to be resolved, which can introduce complexities and require extra effort.
  • Performance with large repositories: While Git performs pretty well on most operations, it can get abortive when working with large repositories containing many files or a long history of commits. Operations such as cloning or checking out large repositories can be time-consuming.

What is Azure DevOps, and what does it relate to Git?

Azure DevOps is Microsoft’s cloud-based platform providing a set of tools and services for software development. It encompasses a range of capabilities for managing, planning, developing, testing, and delivering software applications. Azure DevOps offers:

  • Azure Boards: A tool for planning, tracking, and managing work items, such as user stories, tasks, bugs, etc.
  • Azure Repos: A tool for hosting Git repositories online, which is the main focus of this blog post.
  • Azure Pipelines: A tool for automating builds, tests, and deployments.
  • Azure Test Plans: A tool for creating and running manual and automated tests.
  • Azure Artifacts: A tool for managing packages and dependencies.

Azure DevOps also integrates with other tools and platforms, such as GitHub, Visual Studio Code, and now, Power BI. This takes us to the next section of this blog post, Integrating Power BI with Azure DevOps.

How to integrate Power BI with Azure DevOps

Now that we understand what Git and Azure DevOps are let’s see how we can integrate Power BI with Azure DevOps.

Integrating Power BI with Azure DevOps has two different integrations. Cloud integration and local machine integration have the following requirements.

Prerequisites

To follow along with this tutorial, you will need:

  • In the cloud:
    • An Azure DevOps Service
    • A Power BI account with one of the following licenses to enable Power BI Workspace integration with Azure DevOps.:
      • Power BI PPU (Premium Per User)
      • Premium Capacity
      • Embedded Capacity (EM/A)
      • Fabric Capacity
  • On your local machine:
    • The latest version of Power BI Desktop (June 2023 or later)
    • Either Visual Studio or VS Code

As stated earlier, this post explains the Cloud integration partTherefore, we require to have an Azure DevOps Service and a Power BI account with a Premium licencing plan in order to integrate Power BI with Azure DevOps.

In the following few sections, we look into more details and go through them together step-by-step.

Continue reading “Integrating Power BI with AzureDevOps (Git), part 1: Cloud Integration” →