In some of my old posts, which are most popular ones, I explained how to Export data Power BI Desktop or Power BI Service data to different destinations like CSV, Excel and SQL Server. In this quick tip I explain a very easy way to export the model data as a whole to either CSV or SQL Server with DAX Studio.
Daniil from XXL BI well explained this method, but I’d rather quickly explain how it works and add some more information.
After release 2.8 of DAX Studio, you can now quickly export the whole model to CSV and SQL Server in one shot.
In real world SSAS Tabular projects, you need to run many different testing scenarios to prove your customer that the data in Tabular model is correct. If you are running a Tabular Model on top of a proper data warehouse then your life would be a bit easier than when you build your semantic model on top of an operational database. However it would be still a fairly time-consuming process to run many test cases on Tabular Model, then run similar tests on the data warehouse and compare the results. So your test cases always have two sides, one side is your source database that can be a data warehouse and the other side is the Tabular Model. There are many ways to test the system, you can browse your Tabular Model in Excel, connecting to your Data Warehouse in Excel and create pivot tables then compare the data coming from Tabular Model and the data coming from the Data Warehouse. But, for how many measures and dimensions you can do the above test in Excel?
The other way is to run DAX queries on Tabular Model side. If your source database is a SQL Server database, then you need to run T-SQL queries on the database side then match the results of both sides to prove the data in Tabular Model is correct.
In this post I’d like to share with you a way to automate the DAX queries to be run on a Tabular model.
Straight away, this is going to be a long post, so you can make or take a cup of coffee while enjoying your reading.
While I will not cover the other side, the source or the data warehouse side, it is worth to automate that part too as you can save heaps of times. I’m sure a similar process can be developed in SQL Server side, but, I leave that part for now. What I’m going to explain in this post is just one of many possible ways to generate and run DAX queries and store the results in SQL Server. Perhaps it is not perfect, but, it is a good starting point. If you have a better idea it would be great to share it with us in the comments section below this post.
SQL Server Analysis Services Tabular 2016 and later (Compatibility Level 1200 and higher)
An instance of SQL Server
SQL Server Management Studio (SSMS)
How does it work
What I’m going to explain is very simple. I want to generate and run DAX queries and capture the results. The first step is to get all measures and their relevant dimensions, then I slice all the measures by all relevant dimensions and get the results. At the end I capture and store the results in a SQL Server temp table. Let’s think about a simple scenario:
you have just one measure, [Internet Sales], from ‘Internet Sales’ table
The measure is related to just one dimension, “Date” dimension
The “Date” dimension has only four columns, Year, Month, Year-Month and Date
you want to slice [Internet Sales] by Year, Month, Year-Month and Date
It is easy isn’t it? But, wait. What if you have 10 measures related to 4 dimension and each dimension has 10 columns? That sounds laborious doesn’t it? Well, in real world scenarios you won’t slice all measures by all relevant dimensions, but, you still need to do a lot. What we are going to do is to generate and run the DAX queries and store the results in a table in SQL Server. How cool is that?
OK, this is how it works…
Creating a Linked Server for SSAS Tabular instance from SQL Server
Generating DAX queries using Tabular DMVs
Running the queries through Tabular model and getting/storing the results in a SQL Server temp table
But, what if you don’t want to go with R? If you are more involved with BI than analytics, then using R might not really be your cup of tea. Luckily, there is another way to export your Power BI data to SQL Server which is more BI friendly. You can export Power BI data to SQL Server using SSIS (SQL Server Integration Services). So if you are familiar with SSIS, then it might be your your preferred choice.
With respect to Hans, in this post, I explain his method of exporting data from Power BI Desktop to SQL Server more in details so that anyone who is not that familiar with R can make it work. I also explain how to export data from Power BI Desktop to SQL Server using SSIS. If there is any other methods you’re aware of please let me know in the comment section below.
Exporting Data from Power BI Desktop to SQL Server with R
As stated before, Hans has already explained this method here. So I don’t explain exactly what he did, but, I use his method to export data from existing Power BI Desktop model to SQL Server and I explain it step-by-step.
To make this method work you need to:
Latest version of Power BI Desktop, you can download it from here
Have access to an instance of SQL Server, either on your own machine or on a server in your local network to export the data to
Either install R for Windows, you can download it from here OR using an existing R-Server OR install SQL Server 2016 R Services
Install RODBC library for R, you can download the library from here
Note: I haven’t installed R Studio and nothing went wrong.
Installing RODBC Library for R and SQL Server R Services
As mentioned earlier, you can install R OR SQL Server R Services OR R-Server, but, as I haven’t tried R-Server myself I just explain how to install RODBC in R and SQL Server R Services.
You have to download the library from the link provided above, then extract the contents of the zip file which contains a “RODBC” folder. Then all you really need to do is to copy the “RODBC” to the “library” folder exists in either R or SQL Server 2016 folders in your “Program Files” folder.
How Does It Work?
Open an existing Power BI Desktop model that you’re willing to export its data to a SQL Server table and follow the steps below: (I use “Internet Sales” model created on top of AdventureWorksDW2016CTP3. You can download my Power BI Desktop model at the end of this post.)