The most effective approach is to import both tables into a star schema model. This structure is optimized for analytical queries in Power BI. Designating Product as a dimension and Sales as a fact table allows DAX measures to efficiently aggregate data using the dimension’s hierarchies (like Product Category). Importing the data provides the VertiPaq engine with the opportunity to use columnar storage and compression, leading to faster query performance for aggregations compared to row-level DirectQuery operations or mixing modes unnecessarily.

In a composite model, relationships between an Import mode table and a DirectQuery table are considered ‘cross-database’. When a slicer from the Import date table filters a visual that queries the DirectQuery fact table, Power BI must translate the filter values into a query sent to the source database. This process can be inefficient and slow, especially with complex date hierarchies. This is a classic performance pitfall in composite models and should be the primary area of investigation, potentially by reviewing query plans or considering moving the date table to DirectQuery as well to enable a single-source query.

The SAMEPERIODLASTYEAR function requires a contiguous selection of dates in the current context to correctly identify the parallel period in the prior year. When a non-date filter (like Product Color) is applied, it may filter the Sales table in such a way that sales only occur on non-contiguous dates. The time intelligence function then cannot find a valid corresponding contiguous period in the prior year, resulting in a blank. This tests the understanding of how time intelligence functions interact with the overall filter context.

The most maintainable and scalable approach is to create a separate table (e.g., ‘UserRegion’) that defines the permission mapping. This table can be imported or brought in via a dynamic data source. The RLS role then uses a DAX filter on the dimension table (e.g., `’Region'[RegionID] IN VALUES(‘UserRegion'[RegionID])`). This separates security logic from fact data, makes it easy to update mappings without touching the core model, and aligns with best practices for dynamic RLS patterns.

The ideal place for stable, row-level business logic that does not depend on report filters is as close to the source as possible, typically in a SQL view or table. This follows the principle of ‘push-down’ processing. It improves refresh performance in Power BI (as the calculation is done by the source engine), ensures consistency if the same logic is needed elsewhere, simplifies the Power BI model by reducing calculated columns, and makes the logic easier to discover and manage for database administrators.

The measure `SUMX( Sales, … )` iterates row-by-row over the Sales table (or the filtered portion of it). Each iteration requires calculating the expression `Sales[Quantity] * (Sales[UnitPrice] – Sales[Discount])` in a row context. This forces the storage engine to materialize these rows and columns, leading to potentially large scans and poor performance. The best practice is to create a calculated column (e.g., `Sales[LineTotal] = Sales[Quantity] * (Sales[UnitPrice] – Sales[Discount])`) during data load, and then create a simple, aggregatable measure: `Sum of Discounted Sales = SUM( Sales[LineTotal] )`. This allows the VertiPaq engine to use highly efficient columnar aggregation.

The built-in Power BI Service deployment pipelines feature is designed for this exact scenario. When you deploy a dataset from one stage to the next, you have the option to deploy the accompanying reports. This process preserves report-level artifacts like bookmarks, custom visuals, and formatting. It is the most straightforward and supported method for promoting both datasets and reports while maintaining the integrity of the user-facing reports. Other methods like XMLA or ALM Toolkit focus primarily on the data model itself.

For a table where rows can be updated (signaled by `ModifiedDate`), the correct incremental refresh pattern uses a rolling window. The policy refreshes a recent period (e.g., last 60 days where changes are likely) in full. Data outside this window is stored in static, partitioned historical data that is refreshed less frequently (e.g., once a month). The filter in Power Query should be on the `ModifiedDate` column (`[ModifiedDate] >= RangeStart and [ModifiedDate] < RangeEnd`). This ensures new rows and updates to recent rows are captured efficiently, while minimizing the volume of data processed each refresh.

This is a classic use case for ALLSELECTED. When a product is selected, the filter context includes that product. To find other products bought in the same transactions, you need a measure that counts sales but *ignores* the filter on the specific product being analyzed. A pattern like `CALCULATE( COUNTROWS(Sales), ALLSELECTED( ‘Product'[ProductName] ) )` within an iterator like TOPN can achieve this. ALLSELECTED removes the filter from the specified column(s) while preserving other filters in the context (like date range), allowing the calculation to see all sales for the selected transactions.

The most effective approach is to import both tables into a star schema model. This structure is optimized for analytical queries in Power BI. Designating Product as a dimension and Sales as a fact table allows DAX measures to efficiently aggregate data using the dimension’s hierarchies (like Product Category). Importing the data provides the VertiPaq engine with the opportunity to use columnar storage and compression, leading to faster query performance for aggregations compared to row-level DirectQuery operations or mixing modes unnecessarily.0