The semantic model in Power BI (also called the tabular model or data model) is designed primarily for data analysis and consumption, not for complex data transformation. Here’s why it has limitations on data transformation:
🔹 1.
Performance and Optimization Focus
- Semantic models are optimized for fast querying, aggregations, and visualizations.
- Allowing heavy transformations at query time would slow down performance, defeating the purpose of a semantic model.
🔹 2.
Design Separation: Transform vs Model
Power BI follows a separation of concerns:
- Power Query (M) handles data transformation and shaping (ETL).
- Semantic model (DAX) handles calculated columns, measures, relationships, and business logic.
This ensures:
- Cleaner models
- Reusability
- Efficient refresh and query performance
🔹 3.
DAX Is Not Meant for ETL
- DAX (used in the semantic model) is designed for calculated logic on already loaded data, not for:
- Complex joins
- Row-level transformations
- Column reshaping or unpivoting
- These tasks are meant to be done in Power Query or upstream ETL tools (like Alteryx, SQL, etc.).
🔹 4.
Storage Engine Constraints
- The semantic model uses VertiPaq, an in-memory columnar storage engine.
- VertiPaq is efficient only if data is clean and structured. Transformations can:
- Increase model size
- Reduce compression efficiency
- Slow down queries
🔹 5.
Governance and Maintainability
- If complex transformations are done within the semantic model:
- It becomes harder to audit, manage, or debug.
- Data lineage becomes less transparent.
- Data governance is weakened.
✅ What the Semantic Model
Should Do
- Define relationships
- Add business logic (measures, KPIs, hierarchies)
- Handle role-level security
- Enable efficient slicing and dicing of clean, transformed data
🧠Best Practice
Perform all heavy transformations in Power Query or a data warehouse. Use the semantic model only for modeling and business logic.