In Part 1 of this little series I described the core-Flow on how to automatically validate E-mail attachments with Flow and Power BI. It automatically sends an e-mail to a business partner who sent an attachment, that didn’t meet the agreed specifications:
Automatically validate e-mail attachments – Part1
But before going live with this Flow, you should consider the following aspects:
Sometimes I need to retrieve the textual representation of a type in Power Query and I’m using a fairly verbose function that I’ve stolen ages ago (I believe it was here: https://ssbi-blog.de/technical-topics-english/power-query-editor-using-notepad/ ) :
The following steps show how to create a new column in a table using existing custom function code. This works in Power BI as well as in Power Query in Excel:
Generally it’s a very bad idea to execute commands in Power Query that write out data to a source, as these commands might be executed multiple times (https://blog.crossjoin.co.uk/2018/10/07/power-query-write-data/)
However, as with most good rules, there are exceptions. I leave it to you to decide whether my use case here is a valid candidate for it. It doesn’t execute the code twice, because I execute the query only from the query editor and none of the other queries is referencing its results. But please see for yourself – Writing data to GitHub using just Power Query:
As per the time of writing, the native QuickBooks connector in Power BI has some shortcomings for the Time Activity-data: It will not return employee details (so you will not know who did the hours) and it will not return hours (if they haven’t been entered by start- and end-date).
But fortunately the connector has 2 functions, who can return the full data that the QBO-API has to offer. At the end of the list in the navigation pane there are the functions “Entity” and “Report”:
In this blogpost I show you my M-Python-function that I use to export data from Power BI to csv files (Export Python).
I prefer it to R mostly because I don’t have to create the csv-file(names) in advance before I import data to it. This is particularly important for scenarios where I want to append data to an existing file. The key for this task is NOT to use the append-option that Python offers, because M-scripts will be executed multiple times and this would create a total mess in my file. Instead I create a new file with the context to append and use the Import-from-folder method instead to stitch all csvs back together. Therefore I have to dynamically create new filenames for each import. So when the M-Python-scripts are executed repetitively here, the newly created file will just be overwritten – which doesn’t do any harm.