Connecting to shared semantic models in Power BI is a powerful capability that enables collaboration and promotes data consistency across an organization. A semantic model (formerly known as a dataset) contains the data definitions, relationships, measures, and calculations that form the foundation…Connecting to shared semantic models in Power BI is a powerful capability that enables collaboration and promotes data consistency across an organization. A semantic model (formerly known as a dataset) contains the data definitions, relationships, measures, and calculations that form the foundation of your reports and dashboards.
Shared semantic models allow multiple report creators to build reports using the same underlying data structure. This approach ensures that everyone works with identical business logic, calculations, and data definitions, reducing inconsistencies and errors across the organization.
To connect to a shared semantic model, you can use Power BI Desktop or the Power BI service. In Power BI Desktop, navigate to the Home ribbon and select 'Power BI semantic models' from the data source options. This displays a list of semantic models you have access to within your organization's workspaces. You can browse through workspaces or search for specific models.
Once you select a semantic model, Power BI establishes a live connection to it. This means your report queries the semantic model in real-time rather than importing data locally. Any updates made to the underlying semantic model automatically reflect in your connected reports.
Key benefits of connecting to shared semantic models include reduced data duplication, consistent metrics across reports, simplified governance, and improved performance since data processing occurs centrally. Organizations can designate certified semantic models that meet quality standards, making it easier for users to identify trusted data sources.
When connecting, you inherit the security settings of the semantic model, including row-level security definitions. This ensures users only see data appropriate for their roles.
You can create new measures and calculated columns on top of shared semantic models using the 'Make changes to this model' feature, allowing customization while maintaining the connection to the shared foundation. This balance of standardization and flexibility makes shared semantic models essential for enterprise Power BI deployments.
Connect to Shared Semantic Models - Complete Guide for PL-300 Exam
Why Connecting to Shared Semantic Models is Important
Shared semantic models (formerly known as shared datasets) are a cornerstone of enterprise Power BI implementations. They enable organizations to create a single source of truth for business metrics and definitions, reducing data inconsistencies and eliminating redundant data preparation efforts across teams. This approach promotes governance, ensures consistency in reporting, and significantly reduces the workload for data analysts.
What is a Shared Semantic Model?
A shared semantic model is a published dataset in the Power BI service that other report creators can connect to and build reports on top of. Instead of each analyst creating their own data model, multiple teams can leverage a centrally managed, certified semantic model. This creates a hub-and-spoke architecture where:
• The hub is the semantic model containing all data transformations, relationships, measures, and calculations • The spokes are the reports built on top of that shared model
How Shared Semantic Models Work
1. Publishing the Model: A data modeler creates and publishes a semantic model to a Power BI workspace
2. Setting Permissions: The model owner grants Build permission to users or groups who need to create reports using the model
3. Connecting from Power BI Desktop: Report creators use the Power BI datasets connector (Home tab → Get Data → Power BI datasets) to connect to the shared model
4. Live Connection: When you connect to a shared semantic model, you establish a live connection. This means you cannot modify the underlying data model, add new tables, or change relationships
5. Creating Reports: You can create visuals, report pages, and even add report-level measures using the shared model's data
Key Features and Limitations
• Build Permission Required: Users must have Build permission on the semantic model to create reports • No Data Modification: You cannot add new data sources or tables to a live connection • Report-Level Measures: You CAN add measures that exist only in your report file • Certified Models: Models can be endorsed as Certified or Promoted to indicate quality and trustworthiness • Lineage View: Track dependencies between semantic models and reports in the workspace
How to Connect to a Shared Semantic Model
1. Open Power BI Desktop 2. Go to Home → Get Data 3. Select Power BI datasets or Power BI semantic models 4. Sign in to Power BI service if prompted 5. Browse or search for the semantic model you need 6. Select the model and click Create
Exam Tips: Answering Questions on Connect to Shared Semantic Models
• Remember the connector name: Questions may test whether you know to use the Power BI datasets connector, not DirectQuery or Import
• Understand Build permission: Expect questions about what permission is needed to create reports on shared models - the answer is Build permission
• Know the limitations: You CANNOT add new tables, data sources, or modify relationships with a live connection to a shared model
• Report-level measures: Be aware that you CAN create measures that are local to your report even when using a shared model
• Endorsement levels: Know the difference between Certified (IT-approved) and Promoted (recommended by owner) endorsements
• Workspace permissions vs Build permission: Having access to a workspace does not automatically grant Build permission on semantic models within it
• Scenario-based questions: When asked about enabling report reusability or establishing a single source of truth, shared semantic models are typically the correct answer
• Composite models: Note that Power BI now allows adding local data to shared models through composite models - watch for questions distinguishing between pure live connections and composite model scenarios