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Power BI
powerbi semantic models dax dashboards and reports
Why learn Power BI?
Connects data preparation, reusable measures, and business reporting. Model relationships, refresh behavior, and access controls determine whether published numbers are trustworthy.
Where it came from
Power BI is Microsoft's business analytics platform for connecting data, building semantic models, creating reports, and sharing analysis. Desktop authoring and the online service play different roles.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Import a sample dataset and inspect its types.
- Build a star schema with explicit relationships.
- Create a DAX measure and test its filter context.
- Design a report whose totals reconcile to source data.
- Document refresh and row-level access requirements before publishing.
Documentation and sources
- Official documentation (learn.microsoft.com)
Catalog research notes
- Research snapshot: September 9, 2026. This entry summarizes the linked primary documentation; it does not assert a latest release version.
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Real World Example
Unified Logistics Semantic Modeling
Logistics managers overseeing regional distribution centers.
The business problem
Fragmented data across multiple SQL databases leads to inconsistent inventory reporting and manual reconciliation errors.
How this topic solves it
Centralizing data into a Power BI semantic model allows for a single source of truth using DAX to define consistent business logic and relationships across disparate logistics tables.
- Connect Power BI Desktop to regional SQL databases using Power Query to clean and shape incoming data.
- Define relationships between inventory, shipment, and warehouse tables within the model view.
- Create DAX measures to calculate standardized KPIs like stock turnover and transit duration.
- Publish the semantic model to the Power BI service to enable shared reporting across the organization.
- Configure row-level security in the service to ensure managers only access data relevant to their specific region.
This diagram shows how disparate logistics entities are joined in a semantic model to support unified reporting.
Stock levels
Transit logs
DAX measures and relationships
- Warehouse → 1:Many → Inventory
- Inventory → Aggregation → Semantic Model
- Shipment → Aggregation → Semantic Model
Expected business outcome
Improved decision-making through consistent, automated reporting that eliminates manual data reconciliation.
Limitations and checks
The primary tradeoff is model complexity; as the number of tables and DAX measures grows, performance can degrade if relationships are not optimized, which can be checked using the Performance Analyzer in Power BI Desktop.