Explicit Measures Podcast cover image

Explicit Measures Podcast

403: Discussing Composite Models In Power BI (Again)

Mar 4, 2025
Discover the evolving landscape of composite models in Power BI! The hosts explore the advantages and challenges of integrating PySpark within lake house environments. Learn about new features like Direct Lake and their impact on reporting tools. They discuss the necessity of clear governance when using composite models to ensure data integrity. Plus, insights on how Microsoft Fabric redefines data storage and management highlight the potential for better analytics. Tune in for a deep dive into effective data modeling strategies!
53:37

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Quick takeaways

  • Composite models in Power BI enable users to blend various data sources while maintaining existing relationships for enhanced report flexibility.
  • Performance issues arise from filtering data across different connection modes, necessitating careful model design to prevent latency.

Deep dives

Overview of Composite Models

Composite models in Power BI allow users to blend data from different sources and connection types, such as Direct Query and Import mode, into a single model. This approach enables the combination of existing live connections and additional data sources without losing relationships or creating entirely separate datasets. For instance, users can combine a live connection to a semantic model with an Excel file, enhancing flexibility in report generation and data analysis. However, understanding the complexities and performance implications of working with composite models is crucial, as interacting with multiple connection modes could lead to slower query performance and unexpected data behavior.

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