Which model approach is preferred when importing data from an OLTP database into Power BI?

Which model approach is preferred when importing data from an OLTP database into Power BI?

• A. Normalizing the data model
• B. Using a star schema
• C. Denormalizing the data model
• D. Creating a multidimensional cube

Answer: (C)

When importing data from an Online Transaction Processing (OLTP) database into Power BI, the preferred approach is to denormalize the data model. Denormalization is the process of combining tables and reducing the level of normalization to optimize query performance and enhance the data retrieval experience. In the context of Power BI, denormalizing is particularly beneficial because it allows for faster report generation and more efficient data modeling. OLTP systems are typically highly normalized to reduce redundancy and maintain data integrity during transaction processing. However, for analytical purposes in Power BI, having a more simplified and flattened structure can significantly improve the performance of visualizations, as this reduces the complexity of joins between tables. Denormalization helps in creating a user-friendly and accessible dataset where data can be quickly aggregated and analyzed without complex relationships. This approach aligns well with Power BI's strengths in handling larger datasets and performing in-memory computations. In contrast to denormalization, a normalized data model might lead to slower performance because it often requires more complex queries to retrieve related data across multiple tables. While a star schema is an effective modeling technique used in data warehouses, it can still involve degrees of normalization depending on its design. Similarly, creating a multidimensional cube is more suited for specific OLAP systems that

When importing data from an Online Transaction Processing (OLTP) database into Power BI, the preferred approach is to denormalize the data model. Denormalization is the process of combining tables and reducing the level of normalization to optimize query performance and enhance the data retrieval experience.

In the context of Power BI, denormalizing is particularly beneficial because it allows for faster report generation and more efficient data modeling. OLTP systems are typically highly normalized to reduce redundancy and maintain data integrity during transaction processing. However, for analytical purposes in Power BI, having a more simplified and flattened structure can significantly improve the performance of visualizations, as this reduces the complexity of joins between tables.

Denormalization helps in creating a user-friendly and accessible dataset where data can be quickly aggregated and analyzed without complex relationships. This approach aligns well with Power BI's strengths in handling larger datasets and performing in-memory computations.

In contrast to denormalization, a normalized data model might lead to slower performance because it often requires more complex queries to retrieve related data across multiple tables. While a star schema is an effective modeling technique used in data warehouses, it can still involve degrees of normalization depending on its design. Similarly, creating a multidimensional cube is more suited for specific OLAP systems that