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jjoerg
New Member

Choosing connection method to SharePoint Online

I'm trying to set up a robust solution to report on data contained in a SharePoint list and want to create a Dataflow for this purpose.

 

I've noticed that if I start creating a query from the web based query Dataflow query editor it connects to the source like this:

 

 

Source = SharePoint.Tables("https://site.sharepoint.com/sites/porto"),
  Navigation = Source{[Name = "MyListName"]}[Content],

 

 

 

However, if I go through the same steps in Power BI Desktop it looks like this:

 

 

Source = SharePoint.Tables("https://site.sharepoint.com/sites/porto", [ApiVersion = 15]),
    #"14d14e15-f81f-4056-b280-cb3131c2fec9" = Source{[Id="14d14e15-f81f-4056-b280-cb3131c2fec9"]}[Items],

 

 

 

The resulting dataset is also very different, with the latter returning a large number of SharePoint "system" columns etc., affecting how I approach the transformation (eg. the latter method returns a column called "FieldValuesAsText" which can be expanded to show actual values for SharePoint 'Choice' or 'Lookup' fields more or less in one step).

 

My SharePoint list has about 100 records and won't grow significantly. It's got about 80 columns, with all sort of column types (Simple, Rich text, Currency, Choice, Yes/No, People & Groups, Lookup, calculated...)

 

My question is simply, which approach should I choose? Is there a best practice? Is one way the "new way" of doing things and the other will eventually be deprecated?

 

Or is it simply a question of picking the approach that fits best with the task at hand? If so, does anyone know the major pros and cons? 

 

Thanks in advance for any input!

 

Jorn

1 REPLY 1
Anonymous
Not applicable

Hi @jjoerg

Generally speaking, the smallest dataset that solves your needs is the best. Even if there is a performance difference between these two approaches on a couple of hundred rows it should not be visible.

I would not be worring a out possible deprecation of functionality in this case. This is different fields of the same dataset, ut is quite unlikely it will change in future (at least in 3-4 years).

Kind regrads

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