Today I discovered JSON (…confess) and Quandl. Mates – this is powerful!
Here comes just a quick function to automatically expand all columns of a time-series at once:
Today I discovered JSON (…confess) and Quandl. Mates – this is powerful!
Here comes just a quick function to automatically expand all columns of a time-series at once:
Reading Gerhard Brueckl’s post on how to visualize SSAS calculation dependencies reminded me of my post about a similar technique from December last year.
His solution has features that would do my version good as well:
So wouldn’t it be cool if we could just add a documentation page to our current model – “all in one” so to speak? Here you find how to “hack”-connect with Excel to your current Power BI Desktop-Model.
So what works with Excel should work with PBI as well – just that we need to connect via the query-editor, using M. And of course: As we’re hacking ourselves here (i.e. the file we’re currently working on), we need to save our changes in order to make them being shown.
Marco Russo has shared bunch of very valuable PowerBI best practice tricks for designing your PowerBI model here: http://www.sqlbi.com/articles/data-import-best-practices-in-power-bi/?platform=hootsuite
One of them being never to directly import a database table, but to use a view instead as a kind of abstraction layer. It separates your business logic (that determines how the tables should be structured that will be passed into the data model) from your actual/incoming data (your source systems, whose table structure you cannot change and which might change itself over time).
This is really important in my eyes, as it will be only a question of time when your underlying data will change and the last thing you want to do is to edit all datamodels or even reports in your (multiple and possibly decentralized) PowerBI files!
But what do you do if your data don’t come from databases or you have no possibility to create views on them?