As you might know, in Business Central and NAV windows client, we can view and save the report dataset from the preview page, similar to the page inspector. For the development of complex reports, it is very important to be able to analyze the data before print. This would be very useful for creating and debugging RDLC reports.
Today’s post covers some programming that is not of my own making. My colleague Andreas Rascher has built a damn cool functionality that allows you to export the dataset of RDLC reports in different formats. Since we think the functionality is extremely useful, we didn’t want to keep it from you.
Now recently I came across a post from Bert De Temmerman on Yammer: https://www.yammer.com/dynamicsnavdev/threads/736254082498560 and that got my inspiration flowing…
He suggests that when you add this action to the “Report Layout Selection” page, you can export any dataset by first running the request-page:
Power BI incremental refresh is a very powerful feature and now it’s available in Shared capacity (not just Premium) everyone can use it. It’s designed for scenarios where you have a data warehouse running on a relational database but with a little thought you can make it do all kinds of other interesting things; Miguel Escobar’s recent blog post on how to use incremental refresh for files in a folder is a great example of this.
A few weeks ago I showed how XMLA Endpoints allow you to connect SQL Server Profiler to Power BI Premium. As well as looking at query execution times this also means you can see in more detail what happens when a dataset is refreshed, for example so you can find out exactly how long a refresh took, understand which tables inside the dataset contribute most to refresh times or which calculated columns or calculated tables take the longest to create.
Query objects can also perform calculations on data, such as finding the sum or average of all values in a column of the dataset.
Business Central query objects enable you to retrieve records from one or more tables and then combine the data into rows and columns in a single dataset.