Version Control in Power BI: Why PBIP Changes Everything

Version Control in Power BI – Why it matters, how it works, and what the PBIP format brings

During a workshop about Fabric, my attention was drawn to the fact that PBIP will become the default format for Power BI reports. This motivated me to look a bit deeper into the topic: version control in Power BI and the Power BI Project file format itself.

If you work in Power BI, you have probably encountered the problem: you need to manage multiple report pages, measures, or data models at the same time, and it is easy to lose control over the changes.

Version control solves this problem – we can easily track who changed what and when in the project.

This article reviews what this means in practice, how PBIP can be used with Git-based version control, which workflows work well, what limitations exist, and how all of this connects to AI-driven development.

Why is version a control problem in Power BI?

If you have ever built a complex data model or report, you probably know the feeling:

which is the latest working version of a measure?
who changed the structure of the page?
can we revert to a previously working state?
how can 2–3 developers work on the same model at the same time without overwriting each other?

The PBIX file is binary, so it is not possible to check line by line what changed. This practically made the classic Git workflow impossible.

PBIP offers a solution to this problem.

What is PBIP and how does it help version control?

PBIP (Power BI Project) is a text-based file format that separates the Power BI model into structured components. The project structure typically looks like this:

.pbi\
json – contains settings related to the user and the local machine. Git ignores this by default.
CustomVisuals\ – this folder contains metadata for custom visualizations
StaticResources\
RegisteredResources\ – contains resources such as custom themes, images, and custom visuals. Developers can easily modify the company theme here without changing every report individually.
json – contains the structure of the semantic model and the description of the model diagrams.
pbir – this file contains the full definition of the report and references to the semantic model.
json – contains layout and behavior settings for the mobile-optimized report.
json – This file contains the report definition in the Power BI Report Legacy format (PBIR-Legacy) and doesn’t support external editing.
definition\ folder – This folder is only available if the Power BI project is saved using the Power BI enhanced report format (PBIR). It replaces the report.json file.
.platform – Fabric platform file that holds properties vital for establishing and maintaining the connection between Fabric items and Git.

The most important thing is that every component is human-readable: JSON, M code, and DAX.

This is perfect for Git because:

changes can be seen line by line
versions can be compared
conflicts can be resolved
multiple developers can work in parallel

Version Control in Power BI

Traceability

Every modification is stored as a commit. You can revert to an earlier state without losing the latest changes.

Security

If your machine fails or something is accidentally overwritten, every commit can be restored.
No more “lost PBIX files”.

Team collaboration without conflicts

Git can handle situations such as:

two developers modifying the same measure
a new page and a model change appearing in parallel
two developers editing the same visualization

Transparency

Every commit describes:

what changed
who made the change
why it happened (if the commit message is good)

Technical details – how version control works with PBIP

Repository creation

Place the PBIP project folder into a Git repository (GitHub, Azure DevOps, GitLab).

Frequent and well-named commits

Branch strategy

A well-designed branching strategy is important for the development process.

Pull Request templates and code review

Two developers can review the changes before they are merged into the main branch.

When PBIP is not perfect (limitations)

Parts prone to conflicts

JSON files of complex visualizations
bookmarks
filter states
large M-code transformations
page layout changes (if edited simultaneously)

Things not yet version-controllable in PBIP

workspace settings
data refresh schedules
roles and permissions
Deployment Pipeline configuration

CI/CD in Power BI – what can be automated?

PBIP enables the creation of CI/CD workflows. For example, the following can be automated:

Continuous Integration

Measure quality checks (Tabular Editor BPA)
Model validation
DAX formatting
M code syntax validation
Automatic build pipelines

Continuous Delivery

Automatic publishing to a Power BI workspace
Deployment across DEV → TEST → PROD environments
Parameterized data sources
Semantic model deployments

AI + PBIP – the future of Power BI development

The text-based format of PBIP is an ideal environment for AI. It can be easily applied to tasks such as:

transforming or refactoring measures
analyzing an entire model structure
automatically understanding an unknown report
bulk renaming of visuals and fields
automatic documentation generation
performance analysis and optimization suggestions

If you also use an MCP server (An MCP (Model Context Protocol) server acts as a bridge between an AI system and external tools or data sources. It exposes structured capabilities (such as files, databases, or APIs) so the AI can safely read information and perform actions in those systems), AI can directly execute operations on the model — opening an entirely new dimension.

Summary

The introduction of PBIP and version control is a milestone in the evolution of Power BI. BI development is aligning more closely with classical software development practices: version control, collaborative work, CI/CD, automated validation, and transparent deployment processes.