In Power BI development in Microsoft Fabric, understanding and utilising source control mechanisms is crucial for efficient collaboration and version management. This blog post delves into the essential aspects of source control for Power BI. This blog also includes the recording of my session at Saudi Arabia’s Excel User Group on the 26th of August 2023. The event was organised by Microsoft MVP, Faraz Sheik, where we walked through all the topics discussed in this blog.
Understanding Source Control
At its core, source control is a system that records changes to a file or set of files over time. This lets developers recall specific versions later, ensuring efficient collaboration and error management. It’s particularly vital for development teams, allowing multiple contributors to work on the same codebase without overwriting each other’s work.
For Power BI developers, this means tracking changes made to reports, and data models that are the most crucial components of every Power BI project.
In Nov 2023, Microsoft announced Microsoft Fabric’s general availability and Public Preview of Copilot in Microsoft Fabric. In a previous post, I explained what Copilot means to Power BI developers, which is valid for other Fabric developers such as data engineers and data scientists as Copilot for Fabric helps with those experiences as well. But the main focus of this blog post is to discuss the requirements, how to enable Copilot, and how to use it from a Power BI development point of view. So, this blog will not discuss other aspects of Copilot in Microsoft Fabric. With that, let’s begin.
Requirements
Right off the bat, Copilot is only available on Power BI Premium capacities or their equivalentFabric capacities. So, NO it is NOT available on Power BI Pro or Premium Per User or Power BI Embedded Analytics. So the Power BI items you want to use Copilot on must be in a Workspace assigned to a Power BI Premium P1 or Microsoft Fabric F64 capacities or higher.
You also need to have a Contributor role on the premium workspace.
To use Copilot, your Microsoft Fabric Administrator must enable it from the Fabric Admin Portal. This setting is not available in all regions yet, but Microsoft is gradually rolling it out to more regions.
As mentioned before, your Fabric Administrator must enable Copilot features within the Admin Portal. Follow these steps to enable Copilot on your tenant after logging into Microsoft Fabric:
Click Settings (the gear icon on the top right of the page)
Click Admin portal
Ensure that the Tenant setting tab is selected
Scroll all the way down to the Copilot and Azure OpenAI Service (preview) section
Note
You can also use the search box and search for OpenAI to find the Copilot and Azure OpenAI Service (preview) section.
Enable the Users can use a preview of Copilot and other features powered by Azure OpenAI
Click the Apply button
Enable the Data sent to Azure OpenAI can be processed outside your tenant’s geographic region, compliance boundary, or national cloud instance
Click the Apply button again
That is it. You enabled the Copilot capabilities on your tenant.
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:
Microsoft Fabric is a new and unified analytics platform in the cloud that integrates various data and analytics services, such as Azure Data Factory, Azure Synapse Analytics, and Power BI, into a single product that covers everything from data movement to data science, real-time analytics, and business intelligence. Microsoft Fabric is built upon the well-known Power BI platform, which provides industry-leading visualization and AI-driven analytics that enable business analysts and users to gain insights from data.
Basic concepts
On May 23rd 2023, Microsoft announced a new product called Microsoft Fabric at the Microsoft Build conference. Microsoft Fabric is a SaaS Analytics Platform that covers end-to-end business requirements. As mentioned earlier, it is built upon the Power BI platform and extends the capabilities of Azure Synapse Analytics to all analytics workloads. This means that Microfot Fabric is an enterprise-grade analytics platform. But wait, let’s see what the SaaS Analytics Platform means.
What is an analytics platform?
An analytics platform is a comprehensive software solution designed to facilitate data analysis to enable organisations to derive meaningful insights from their data. It typically combines various tools, technologies, and frameworks to streamline the entire analytics lifecycle, from data ingestion and processing to visualisation and reporting. Here are some key characteristics you would expect to find in an analytics platform:
Data Integration: The platform should support integrating data from multiple sources, such as databases, data warehouses, APIs, and streaming platforms. It should provide capabilities for data ingestion, extraction, transformation, and loading (ETL) to ensure a smooth flow of data into the analytics ecosystem.
Data Storage and Management: An analytics platform needs to have a robust and scalable data storage infrastructure. This could include data lakes, data warehouses, or a combination of both. It should also support data governance practices, including data quality management, metadata management, and data security.
Data Processing and Transformation: The platform should offer tools and frameworks for processing and transforming raw data into a usable format. This may involve data cleaning, denormalisation, enrichment, aggregation, or advanced analytics on large data volumes, including streaming IOT (Internet of Things) data. Handling large volumes of data efficiently is crucial for performance and scalability.
Analytics and Visualisation: A core aspect of an analytics platform is its ability to perform advanced analytics on the data. This includes providing a wide range of analytical capabilities, such as descriptive, diagnostic, predictive, and prescriptive analytics with ML (Machine Learning) and AI (Artificial Intelligence) algorithms. Additionally, the platform should offer interactive visualisation tools to present insights in a clear and intuitive manner, enabling users to explore data and generate reports easily.
Scalability and Performance: Analytics platforms need to be scalable to handle increasing volumes of data and user demands. They should have the ability to scale horizontally or vertically. High-performance processing engines and optimised algorithms are essential to ensure efficient data processing and analysis.
Collaboration and Sharing: An analytics platform should facilitate collaboration among data analysts, data scientists, and business users. It should provide features for sharing data assets, analytics models, and insights across teams. Collaboration features may include data annotations, commenting, sharing dashboards, and collaborative workflows.
Data Security and Governance: As data privacy and compliance become increasingly important, an analytics platform must have robust security measures in place. This includes access controls, encryption, auditing, and compliance with relevant regulations such as GDPR or HIPAA. Data governance features, such as data lineage, data cataloging, and policy enforcement, are also crucial for maintaining data integrity and compliance.
Flexibility and Extensibility: An ideal analytics platform should be flexible and extensible to accommodate evolving business needs and technological advancements. It should support integration with third-party tools, frameworks, and libraries to leverage additional functionality.
Ease of Use: Usability plays a significant role in an analytics platform’s adoption and effectiveness. It should have an intuitive user interface and provide user-friendly tools for data exploration, analysis, and visualisation. Self-service capabilities empower business users to access and analyse data without heavy reliance on IT or data specialists. These characteristics collectively enable organisations to harness the power of data and make data-driven decisions. An effective analytics platform helps unlock insights, identify patterns, discover trends, and drive innovation across various domains and industries.
