![]() In other words, adding descriptions can indirectly improve performance. ![]() not causing cartesian products) which in turn makes performance better for all users. After all, users who know the model better will likely use it better, thereby creating better queries (i.e. Prior to publishing the model to the service, in Power Query Editor, add another filter to the ProductKey column that filters out any value other than 0, effectively or filtering out all data from the FactInternetSales table. Thus, if the model has no descriptions, the Data Dictionary is not so helpful and it can be said that the development team could be doing more to aid its users. For more information, see Incremental refresh in the Tabular editor documentation. This dictionary includes descriptions for each object in the model and helps users acquaint themselves with elements in the model. From the Power Query Home menu, select Advanced Editor. If you don’t see the Tabular Editor button, install the program. On the External Tools ribbon, select Tabular Editor. In Power BI Desktop, create the model that will define your OLS rules. ![]() Configure object level security using tabular editor. This last point is aided by creating a Data Dictionary. To copy data from REST endpoint to tabular I want to import some reports into Excel using rest. To create roles on Power BI Desktop datasets, use external tools such as Tabular Editor. The user experience includes aspects such as a star-schema architecture which is easy to navigate, proper formatting and naming conventions, as well as generally ensuring that the user understands the model as best as possible. While much of the focus of modeling is spent on performance (which of course is completely valid), there is another component which is very important.
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