The software development lifecycle in Microsoft Fabric

In the fast-paced world of data analytics, agility and reliability are everything. Users want up to date, flexible reporting, with minimal disruption. That’s where development environments and deployment pipelines, two essential components for improving your software development practices, elevate a Microsoft Fabric solution.

Why development lifecycle matters

Imagine you’re developing a new report. You tweak a measure, adjust a visual, and publish the update. But what if that change breaks a key calculation, which ends up confusing end users? Without a structured deployment process, even small updates can cause big headaches.

Deployment pipelines solve this problem by introducing a software development lifecycle to Power BI. You can define up to three distinct environments – e.g. Development, Test and Production – each with its own purpose and audience.

This separation ensures that:

• developers can experiment freely
• testers can validate changes without affecting live users
• end-users always see stable, trusted reports.

How the development lifecycle works in Fabric

Microsoft Fabric is a cloud-based end-to-end data and analytics platform that brings data flows, transformations and analytics together in one place.

A unified platform can also create unique challenges. For example, a common requirement set up is as follows:

• developers build data transformation objects and test data loads into lakehouses and warehouse
• data analysts build semantic models, measures and reports
• business users want access to transformed data, as well as semantic model to build their own reports and perform analysis
• end consumers want to view the reports.

Here’s a typical setup that we’ve used to meet those requirements:

• Development Workspace: Used to build pipelines, dataflows and notebooks. Also to test loading into warehouses and lakehouses. Build and unit test your model, visuals, and DAX measures.
• Test Workspace: Hosts objects that are used for end-user testing. Objects in the Development workspace are deployed to this environment, and then test data is loaded. Grant business users viewer access to the workspace and read access on the data, so the users can validate data, reports and the user experience.
• Production Workspace: Hosts the production objects. Publish objects from Test to Production, after all testing is successful. Load all historical data. Grant business users the same read access, and grant end consumers access to reports via a Power BI app and read permissions on the warehouse/lakehouse.

Keep in mind Fabric deployment pipelines have some limitations – no parametrisation option for data source when deploying a direct lake model, and the need to manually update dataflows with a new environment destination. For example, a Dataflow deployed from Dev to Test must be updated manually after deployment to change the data destination to the Test warehouse.

In addition, environment and pipeline design will vary depending on the use case. For example, a small organisation might just have Dev and Prod. Another case we have seen is where an organisation has multiple tenancies that each require the same set of objects, which requires a more sophisticated source control and release design.

Real-world impact

Development environments and deployment pipelines in Fabric help deliver flexible, reliable reporting solutions. The incorporation of different environments and the ability to sync between them enables Fabric to play the role of a robust one-stop shop for data transformations, storage and analytics.