A star schema is a foundational best practice in Power BI for performance and usability. Separating dimensions (Date, Product, Store) from facts (Sales) and using integer-based relationships allows the VertiPaq engine to compress data efficiently and enables fast filter propagation. This structure is specifically designed to handle large datasets with complex filtering needs.

Time intelligence functions like TOTALYTD require a properly marked date table to work correctly. If the ‘Date’ table is not marked as a date table, Power BI cannot reliably interpret the date hierarchy and context, leading to incorrect calculations where the function may default to evaluating over the entire table, producing a constant total.

The on-premises data gateway acts as a secure bridge between the cloud Power BI service and the on-premises data source. When installed in ‘Personal Mode’ (or a standard gateway in a managed IT scenario) and associated with a Premium capacity, it allows scheduled data refreshes or live queries. This lets authorized users in the Power BI service view report data sourced from the internal database without having direct network access to it.

The most straightforward and efficient pattern is to create a single measure that sums the necessary column from each fact table. DAX measures are dynamic and respect filter context from visuals. This approach is clean, performant, and easy to understand and maintain. Using RELATED would not work as it’s a row context function for columns, not an aggregate function for measures across tables.

When connected to a live connection source like Azure Analysis Services, the underlying model is read-only. Power Query, calculated tables, and managing aggregations are disabled. The primary way to extend the model for reporting purposes is by creating local measures using DAX. These measures exist only in the .pbix file and do not affect the source AAS model.

For a slicer to filter another visual, there must be a filter propagation path through the data model. The most fundamental requirement is a relationship between the tables involved. If the ‘Product Category’ field and the ‘Sales Amount’ field reside in tables that are not related (directly or indirectly), no filtering will occur, regardless of slicer or visual settings. This is always the first point of diagnosis for filtering problems.

The SELECTEDVALUE function is specifically designed for this scenario. It returns the value of a column when the context has been filtered down to one distinct value; otherwise, it returns a specified alternate result (e.g., ‘Multiple Selected’). A measure like `Dynamic Title = “Pending Items: ” & SELECTEDVALUE(‘Table'[Status])` would be the core component, though it would need to be combined with logic to count rows when ‘Pending’ is selected.

Power Query is Power BI’s data connectivity and preparation engine (also known as the Transform Data experience). Its core function is to connect to a vast array of data sources (like SQL, Excel, Web, etc.), apply necessary transformations (cleaning, pivoting, merging, appending), and shape the data before it is loaded into the in-memory model for analysis. It handles the ‘E’ and ‘T’ (Extract, Transform) of the ETL process.

A single-directional filter (the most common type) from the ‘one’ side (Customer) to the ‘many’ side (Sales) is correct and allows the table visual to work. Slicers inherently filter other visuals and the report page. An error when using the field in a slicer is unusual if the model relationship is valid. The most likely scenario is a misleading error message; the real cause might be something else (e.g., a broken measure reference elsewhere on the page). The other options are less probable: duplicate values are allowed in slicers, inactive relationships don’t cause immediate errors in field selection, and data type issues are rare for text fields in slicers.

A star schema is a foundational best practice in Power BI for performance and usability. Separating dimensions (Date, Product, Store) from facts (Sales) and using integer-based relationships allows the VertiPaq engine to compress data efficiently and enables fast filter propagation. This structure is specifically designed to handle large datasets with complex filtering needs.0