Upgrading your reports from PowerBI to Azure Analysis Services

Since April 2017 it is possible to build reports on top of datasets that are hosted in the PowerBI service. This was announced and described here and here in more detail. This might not seem like a big deal at first sight, but it can have a huge impact on how you work with PowerBI. By separating the data model from the report, you can have two or more independent people working with the same dataset. Also, the people who build the reports in the end most not necessarily have the knowledge to build a data model – the just need to use it. So, there are some clear advantages when you split up your workbook:

  • separation of duty (data modeler vs. report builder)´
  • any number of reports on top of the same model
  • easy control over reports as the files are quite small (they only contain the definition of the report)

This is all pretty cool and, from my point of view, the way to go once you want to use the reports in production and/or have several people working on/with the same reports.

But lets go a step further. After some time, as your data model grows, you realize that the reports get slow and also the processing takes a considerable amount of time to finish. The official upgrade path will then guide you to Azure Analysis Services and you will migrate your data model to deal with the larger data volumes and make use of the flexibility in processing you gained by your upgrade. This migration process is very well described here.

So far so good, but what happens to your reports? Last week I was in exactly the position described above and we had to migrate the existing reports (which were base on a dataset hosted in PowerBI) to Azure Analysis Services. As of now, there is now simple way to simply change the connection string from PowerBI to Azure Analysis Services neither in PBI Desktop nor in the Service. But we could think of some options how it might work:

  • rebuild all reports
  • use the REST API to update the connection string of the existing reports
  • modify the .pbix file manually (NOT OFFICIALLY SUPPORTED)

As you can imagine, rebuilding all reports was not really an option.

The next option, the PowerBI REST API looked pretty promising at first sight. It allows you to retrieve and set the dataset that is used by your report. So the idea is to simply create a new dataset which points to Azure Analysis Services in Live Query mode, take the existing report and use the Rebind API call to bind it to the new AAS dataset. Even though this is supposed to work, I could not make it work in my environment. I tried all things that I could think of but nothing work and I also gave up on this.

So I was stuck there but knew that the information of the data source has to be somewhere in the .pbix file. In the past I already did something similar with Excel/PowerPivot files (“Restoring a SSAS Tabular model to PowerPivot”) so I thought I would also give it a try for .pbix files. And it turns out that they are quite similar. For those of you who are new to this, most (if not all) of the files that are associated with a Microsoft tool and end with “x” (e.g. .xlsx/docx/…) are just ZIP-files in the end. To unzip them, simply rename them to .zip and use your favorite zip-tool to open them. You will see a file-structure similar to the one below:
pbix zip file content

(If your file contains a data model, you see a file called “DataModelSchema” instead of “Connections”. The next steps will not work in this case!). However, in our case, as the report is linked to a dataset hosted in the PowerBI service, our file does not contain any data itself but only the connection information to our data source. As you can guess, this information is stored in the “Connections” file.

To see what a connection to an Azure Analysis Services dataset looks like, I simply created a new PowerBI desktop model and established a Live Connection. Saved it and opened it again as zip file. The Connection file itself is just a JSON but the details are not really relevant here. I simply replaced the Connections file from my original report with the one from my new workbook linked to AAS. Renamed it back to pbix, opened it and voilà, my report was connected to AAS!

This saved us a lot of time and we could move all of our reports within a couple of hours!

Please keep in mind, that this is not officially supported and might break your model. So make sure to always create a backup before you modify the contents of a pbix file manually!
I do not take any responsibility for any broken models or anything else that might happen!

Refresh PowerBI Datasets using PowerShell and Azure Runbooks

In June 2017, Microsoft announced a new set of API function to manage data refreshes in PowerBI. The new API basically allows you to trigger a refresh or retrieve the history of previously executed refreshes. The full specification can be found in the official MSDN documentation, or using this direct links: Refresh dataset and Get dataset refresh history

So besides the scheduled and manual refreshes from within the PowerBI service directly, we now have a third option to trigger refreshes but this time also from an external caller! This itself is already pretty awesome and some people already did some cool stuff leveraging the new API functions:

Charles Sterling: Running the Power BI Refresh API’s Headless
Sirui Sun: Git-Repository powerbi-powershell

The basic idea is to use object from pre-built Azure Management DLLs to generate the OAuth Access token that is necessary to use the API. This works very well locally but cannot be used in the cloud – e.g. in combination with Azure Automation Runbooks or Azure Functions where you cannot install or reference any custom DLLs.

In this blog post I will show you how you can accomplish exactly this  – create an Azure Automation Runbook to refresh your PowerBI dataset!
But first of all there are some things that you need to keep in mind:

  1. There are no service accounts in PowerBI so we will always use a “real” user
  2. you need to supply the credentials of a “real” user
  3. The user needs to have appropriate access to the dataset in order to refresh it
  4. the dataset refresh must succeed if you do it manually in PowerBI
  5. you are still limited to 8 refreshes/day through the API

OK, so lets get started. First of all we need an Azure Application which has permissions in PowerBI. The easiest way to do this is to use the navigate to https://dev.powerbi.com/apps, log in with your account and simply follow the steps on the screen. The only import thing is to select the App Type “Native app”. At the end, you will receive a ClientID and a ClientSecret – Please remember the ClientID for later use!

Next step is to create the Azure Runbook. There are plenty of tutorials out there on how to do this: My first PowerShell workflow runbook or Creating or importing a runbook in Azure Automation so I will no go into much more detail here. Besides the runbook itself you also need to create an Automation Credential to store the username and password in a secure way – here is a tutorial for this: Credential Assets in Azure Automation

Now lets take a look at the PowerShell code. Instead of using any pre-built DLLs I removed all unnecessary code and do all the communication using Invoke-RestMethod. This is a very low-level function and is part of the standard PowerShell modules so there is no need to install anything! The tricky part is to acquire an Authentication Token using username/password as it is nowhere documented (at least I could not find it) what the REST call has to look like. So I used Fiddler to track the REST calls that the pre-built DLLs use and rebuilt them using Invoke-RestMethod. This is what I came up with:

Get Authentication Token
$authUrl = "https://login.windows.net/common/oauth2/token/"
$body = @{
    "resource" =https://analysis.windows.net/powerbi/api";
    "client_id" = $clientId;
    "grant_type" = "password";
    "username" = $pbiUsername;
    "password" = $pbiPassword;
    "scope" = "openid"

$authResponse = Invoke-RestMethod -Uri $authUrlMethod POST -Body $body

$clientId is the ClientID of the Azure AD Application
$pbiUsername is the email address of the PowerBI user.
$pbiPassword is the password of the PowerBI user.
The $authRepsonse then contains our Authentication token which we can use to make our subsequent calls:

Trigger Refresh in PowerBI
$restURL = "https://api.powerbi.com/v1.0/myorg/datasets/$pbiDatasetId/refreshes"
$headers = @{
    "Content-Type" = "application/json";
    "Authorization" = $authResponse.token_type + " " + $authResponse.access_token

$restResponse = Invoke-RestMethod -Uri $restURLMethod POST -Headers $headers

And that’s all you need. I wrapped everything into a PowerShell function that can be used as an Azure Runbook. The username/password is derived from an Azure Automation Credential.

The final runbook can be found here: PowerBI_Refresh_Runbook.ps1


It takes 4 Parameters:

  1. CredentialName – the name of the Azure Automation credential that you created and which stores the PowerBI username and password
  2. ClientID – the ID of your Azure Active Directory Application which you created in the first step
  3. PBIDatasetName – the name of the PowerBI dataset that you want to refresh
  4. PBIGroupName – (optional) the name of the group/workspace in which the PowerBI dataset from 3) resides

When everything is working as expected, you can create custom schedules or even create webhooks to trigger the script and refresh you PowerBI dataset! As you probably know, this is really powerful as you can now make the refresh of the PowerBI dataset part of your daily ETL job!

Deploying an Azure Data Factory project as ARM Template

In my last post I wrote about how to Debug Custom .Net Activities in Azure Data Factory locally. This fixes one of the biggest issues in Azure Data Factory at the moment for developers. The next bigger problem that you will run into is when it comes to deploying your Azure Data Factory project. At the moment, you can only do it manually from Visual Studio which, for bigger projects, can take quite some time. So I extended and advanced the code from my CustomActivityDebugger. Well, actually I rewrote some major parts of it and moved it into a new GitHub repository: Azure.DataFactory.LocalEnvironment

The new code base now includes the functionality to export an existing ADF project to an ARM template which can then be deployed very easily using Azure standard deployment mechanisms.

So basically, these are the changes and new Features that I made:

Export as ARM template:

  • Export all ADF objects and properties
  • Support for configurations
  • obey dependencies between ADF objects
  • parameterized Data Factory name
  • automatic upload of ADF dependencies (e.g. custom activities)
  • specify the region where ADF should be deployed (ADF is not available in all regions yet!)


Custom Activity Debugger:

  • simplified usability – just select the pipeline, activity and set the slice-dates
  • Support for configurations
  • no need to add any namespaces
  • no need to add any references
  • write activity log to console output



  • Load from the ADF Project file (.dfproj) instead of a whole folder
  • implemented as Assembly
  • can be used in a Console Application for automation
  • will be published via NuGet in the future! (coming soon)


Everything else is described in the Git-Repository itself!

Hope you enjoy it!

Monitoring Azure Data Factory using PowerBI

Some time ago Microsoft released the first preview of a tool which allows you to monitor and control your Azure Data Factory (ADF). It is a web UI where you can select your Pipeline, Activity or Dataset and check its execution state over time. However, from my very personal point of view the UI could be much better, especially much clearer(!) as it is at the moment. But that’s not really a problem as the thing I like the most about ADF is that its quite open for developers (for example Custom C#/.Net Activities) and it also offers a quite comprehensive REST API to control an manage it.
For our monitoring purposes we are mainly interested in the LIST interface but we could do basically every operation using this API. In my example I only used the Dataset API, the Slices API and the Pipeline API.

First we start with the Dataset API to get a list of all data sets in our Data Factory. This is quite simple as we just need to build our URL of the REST web service like this:

  1. https://management.azure.com/subscriptions/{SubscriptionID}/resourcegroups/{ResourceGroupName}/providers/Microsoft.DataFactory/datafactories/{DataFactoryName}/datasets?api-version={api-version}

You can get all of this information for the Azure Portal by simply navigating to your Data Factory and checking the URL which will be similar to this one:

  1. https://portal.azure.com/#resource/subscriptions/1234567832324a04a0a66e44bf2f5d11/resourceGroups/myResourceGroup/providers/Microsoft.DataFactory/dataFactories/myDataFactory

So this would be my values for the API Call:
– {SubscriptionID} would be “12345678-3232-4a04-a0a6-6e44bf2f5d11”
– {ResourceGroupName} would be “myResourceGroup”
– {DataFactoryName} would be “myDataFactory”
– {api-version} would be a fixed value of “2015-10-01”

Once you have your URL you can use PowerBI to query the API using Get Data –> From Web
Next you need to authenticate using your Personal or Organizational Account – the same that you use to sign in to the Portal – and also the level for which you want to use the credentials. I’d recommend you to set it either to the subscription level or to the data-factory itself, depending on your security requirements. This ensures that you are not asked for credentials for each different API:

This works in a very similar way also for the Slices API, the Pipeline API and all other APIs available! The other transformations I used are regular PowerQuery/M steps done via the UI so I am not going to describe them in more detail here. Also, setting up the relationships in our final PowerPivot model should be straight forward.

Now that we have all the required data in place, we can start with our report. I used some custom visuals for the calendar view, some slicers and a simple table to show the details. I also used a Sankey Chart to visualize the dependencies between the datasets.


Compared to the standard GUI for monitoring this provides a much better overview of slices and their current states and it also allows easy filtering. I am sure there are a lot of other PowerBI visualizations which would make a lot of sense here, these are just to give you an idea how it could look like, but of course you have all the freedom PowerBI offers you for reporting!

The only drawback at the moment is that you cannot reschedule/reset slices from PowerBI but for my monitoring-use-case this was not a problem at all. Also, I did not include the SliceRun API in my report as this would increase the size of the data model a lot, so detailed log information is not available in my sample report.

The whole PowerBI template is available for download on my GitHub site: https://github.com/gbrueckl/Azure.DataFactory.PowerBIMonitor

Debugging Custom .Net Activities in Azure Data Factory

UPDATE 2017-02-22:
I released a new toolset for Azure Data Factor which also integrates the Customer .Net Activity Debugger from this blob post. Please refer to the new GitHub project: https://github.com/gbrueckl/Azure.DataFactory.LocalEnvironment

(all links have been changed to refer to the new repository!)

Azure Data Factory (ADF) is one of the newer tools of the whole Microsoft Data Platform on Azure. It is Microsoft’s Data Integration tool, which allows you to easily load data from you on-premises servers to the cloud (and also the other way round). It comes with some handy templates to copy data fro various sources to any available destination. However, when the Extract-Transform-Load (ETL) or ELT steps get more complicated you will hit the (current) out-of-the-box limits of Azure Data Factory pretty soon. But this is OK as ADF is a very open platform and allows you to integrate so called “Custom Activities”. These can either be .Net/C# Activities or HDInsight Activities. In this post we will focus on .Net Activities and how to develop and debug them in an efficient way.

A .Net Activity is basically just a .dll which implements a specific Interface (IDotNetActivity)and is then executed by the Azure Data Factory. To be more precise here, the .dll (and all dependencies) are copied to an Azure Batch Node which then executes the code when the .Net Activity is scheduled by ADF. So far so good, but the tricky part is to actually develop the .Net code, test, and debug it. Well, not the code itself but the more or less complex integration with the ADF Interface which you are very likely not familiar with in the beginning. In such cases it usually helps to run the code locally, step into the different code paths and examine the C# objects and their values. The problem is that you do not have a local instance of ADF on your workstation which you could use the start the .Net Activity and debug it interactively in Visual Studio.
So I wrote my own tool which you can add to the Solution that already contains the code of your Custom .Net Activity. Then you can simply link the CustomActivityDebugger to the JSON definitions and configurations of your ADF project, reference your custom code, configure some other things like SliceStart/SliceEnd and you are ready to go.
Once you start the CustomActivityDebugger it will read all ADF files and settings and basically create a local ADF environment which helps you to debug your custom .Net Activity using all settings and parameters as they would be passed in when the code is executed on the Azure Batch Node.

This little picture shows the CustomActivityDebugger in action – debugging custom .Net activities is now like debugging any other code:

All the sources including a simple ADF Project, a simple Custom Activity and setup instructions are available on my GitHub site:


Feel free to use it as it is and/or extend it to your needs.

Score whole PowerBI DataSets dynamically in Azure ML

One of the most requested features when it comes to Azure ML is and has always been the integration into PowerBI. By now we are still lacking a native connector in PowerBI which would allow us to query a published Azure ML web service directly and score our datasets. Reason enough for me to dig into this issue and create some Power Query M scripts to do this. But lets first start off with the basics of Azure ML Web Services.

Every Azure ML project can be published as a Web Service with just a single click. Once its published, it can be used like any other Web Service. Usually we would send a record or a whole dataset to the Web Service, the Azure ML models does some scoring (or any other operation within Azure ML) and then sends the scored result back to the client. This is straight forward and Microsoft even supplies samples for the most common programming languages. The Web Service relies on a standardized REST API which can basically be called by any client. Yes, in our case this client will be PowerBI using Power Query.
Rui Quintino has already written an article on AzureML Web Service Scoring with Excel and Power Query and also Chris Webb wrote a more generic one on POST Request in Power Query in general Web Service and POST requests in Power Query. Even Microsoft recently published an article how you can use the R Integration of Power Query to call a Azure ML Web Service here.

Having tried these solutions, I have to admit that they have some major issues:
1) very static / hard coded
2) complex to write
3) operate on row-by-row basis and might run into the API Call Limits as discussed here.
4) need a local R installation

As Azure ML usually deal with tables, which are basically Power Query DataSets, a requirement would be to directly use a Power Query DataSet. The DataSet has to be converted dynamically into the required JSON structure to be POSTed to Azure ML. The returned result, usually a table again, should be converted back to a Power Query DataSet. And that’s what I did, I wrote a function that does all this for you. All information that you have to supply can be found in the configuration of your Azure ML Web Service:
– Request URI of your Web Service
– API Key
– the [Table to Score]

the [Table to Score] can be any Power Query table but of course has to have the very same structure (including column names and data types) as expected by the Web Service Input. Then you can simply call my function:

The whole process involves a lot of JSON conversions and is kind of complex but as I encapsulated everything into M functions it should be quite easy to use by simply calling the CallAzureMLService-function.

However, here is a little description of the used functions:
ToAzureMLJson – converts any object that is passed in as an argument to a JSON element. If you pass in a table, it is converted to a JSON-array. Dates and Numbers are formatted correctly, etc. so the result can the be passed directly to Azure ML.

AzureMLJsonToTable – converts the returned JSON back to a Power Query Table. It obeys column names and also data types as defined in the Azure ML Web Service output. If the output changes (e.g. new columns are added) this will be taken care of dynamically!

CallAzureMLService – uses the two function from above to convert a table to JSON, POST the JSON to Azure ML and convert the result back to a Power Query Table.

Known Issues:
As the [Table to Score] will probably come from a SQL DB or somewhere else, you may run into issues with Privacy Levels/Settings and the Formula Firewall. In this case make sure to enable Fast Combine for your workbook as described here.

The maximum timeout of a Request/Response call to an Azure ML Web Service is 100 seconds. If your call exceeds this limit, you might get an error message returned.I ran a test and tried to score 60k rows (with 2 numeric columns) at once and it worked just fine, but I would assume that you can run into some Azure ML limits here very easily with bigger data sets. As far as I know, these 100 seconds are for the Azure ML itself only. If it takes several minutes to upload your dataset in the POST request, than this is not part of this 100 seconds. If you are still hitting this issue, you could further try to split your table into different batches, score them separately and combine the results again afterwards.


So these are the steps that you need to do in order to use your Azure ML Web Service together with PowerBI:
1) Create an Azure ML Experiment (or use an existing)
2) Publish the Experiment as a Web Service
3) note the URL and the API Key of your Web Service
4) run PowerBI and load the data that you want to score
5) make sure that the dataset created in 4) has the exact same structure as expected by Azure ML (column names, data types, …)
6) call the function “CallAzureMLWebService” with the parameters from 3) and 5)
7) wait for the Web Service to return the result set
8) load the final table into PowerBI (or do some further transformations before)

And that’s it!

You can find a PowerBI workbook which contains all the functions and code here: CallAzureMLWebService.pbix
I used a simple Web Service which takes 2 numeric columns (“Number1” and “Number2”) and returns the [Number1] * [Number2] and [Number1] / [Number2]

PS: you will not be able to run the sample as it is as I changed the API Key and also the URL of my original Azure ML Web Service

Using Self-Signed Certificates for your Power BI DMG

In my previous post I showed how to setup a Power BI Data Management Gateway on a non-domain Azure VM. The final setup is also the starting-point for this post where we will use self-signed certificates to use HTTPS/SSL connectivity to our DMG. So make sure that you have all prerequisites up and running before you continue reading.

Basically, the process to switch to HTTPS is pretty straight forward. Simply open your DMG, go to Settings and change from HTTP to HTTPS. Finally select your certificate and you are ready to go!
This may work in a corporate hybrid environment where everything is set up correctly but for a non-Azure VM this is a bit more complicated and this is what this post is about.

Besides the initial setup from my previous post there are some steps you need to do in advance in order for HTTPS connectivity to work:
1) Open the port that the DMG HTTPS connection uses in your Windows Firewall (default is port 8050)
2) Create an Endpoint for your Azure VM for the very same port
3) Create a self-signed certificate to be used to establish a secure connection

You should already be familiar with 1) and 2) as you needed to do the same steps also for your HTTP port of your DMG (default is port 8051 here). To create a self-signed certificate you can simply follow the steps as described here. The important thing here is to use the full qualified server name: CN=myserver.cloudapp.net
This is very import, otherwise the final connection will not work!

Your MakeCert-command should look similar to this:
makecert -r -pe -n “CN=myserver.cloudapp.net” -b 01/01/2000 -e 01/01/2050 -eku -ss my -sr localMachine -sky exchange -sp “Microsoft RSA SChannel Cryptographic Provider” -sy 12

After you run the command the new certificate is automatically added to your users personal certificates and can be used when setting up HTTPS connectivity for your DMG:

Once you click [OK] it takes some time (~1 Minute) until everything is updated and HTTPS connectivity can be used. Now you can use Excel and Power Query to search for your data sources that are published via OData. You will find all of them but as soon as you try to load the data you will receive the following error:

That’s a bit surprising as the DMG is configured correctly using HTTPS and the very same OData feed worked just fine with HTTP. But here comes the error in my thinking that I was not aware of before talking to Benjamin Tang and Samuel Zhang from the product team. Until that point I always thought that the data is load through the cloud and there is no direct connection from my client to the server:
But this is not how it works!

What actually happens in the background is that the request to the Power BI OData service gets redirected to the server and the client connects directly to the server:

And this is also where our PQ error originates as the certificate used is not a trusted certificate on the client. In order to make it a trusted certificate you need to install it on the client. This can be done by following these steps:
1) Launch Internet Explorer using “Run as Administrator”
    (I’m serious here, this only works with IE but not with e.g. Chrome!)
2) navigate to https://myserver.cloudapp.net:8050 (or whatever servername/port you used)
3) continue to the website and ignore the certificate error
4) press [Cancel] at the popup the asks for credentials
5) now click on the “Certificate error” in the menu bar and press “View certificates”
6) Now install the certificate:
(Please note that this option is only available if you are using Internet Explorer launched as Administrator!!!)
7) select the location where you want to store the certificate (Current User or Local Machine depending whether it should be installed for you only or for all users)
8) whichever storage location you used, just make sure that you place the certificate in the “Trusted Root Certification Authorities” on the next page:

Once you have installed the certificate to your Trusted Root Certificate Authorities store the Power Query connections works again but now it is using HTTPS!

Of course this solution is only for demo and testing purposes, in a real world scenario you would already have your certificates in place and everything should indeed work out-of-the-box.

Using Power BI DMG on Non-Domain Azure VMs – August 2014 Update

In one of my recent posts I explained how to use the Power BI Data Management Gateway to access data hosted in a SQL Server running on an Azure VM. At the time of writing that post the steps to establish connectivity were not quite intuitive. With the latest Update of the Data Management Gateway (Version 1.2.5303.1 and later) things got a bit easier. However, there is still a little thing that you have to configure to make everything work smoothly. First of all, I highly recommend you to read my first post on this topic to fully understand the actual issue and why it does not work out-of-the-box.

When creating a new Data Source the DMG has to be reachable from the machine on which the Data Source Manager (the Click-Once application where you enter your SQL credentials) is executed. The hostname is derived from the DMG and for Azure VMs this does by default not reflect the hostname under which the VM is reachable from public. The hostname would be “MyServer” whereas the public DNS name is “MyServer.cloudapp.net”. To check what hostname the DMG is using you can execute the following Power Shell command:

  1. [System.Net.Dns]::GetHostEntry("localhost")

In order to change this hostname you can either join the VM to a domain (which is not what we want to do here) or use the following approach:

Open the System settings of your server:
You will notice that both, “Computer name” and “Full computer name” show the same name, and both without the suffix “.cloudapp.net”. In order to change this we need to click the “Change settings” button right next to the names to open the System Properties:

Again, click [Change …] to open the computers domain settings:
As you can see, the “Full computer name” does not show our required suffix “.cloudapp.net” yet. We can change this in the dialog available via the [More …] Button:
Here we can set our “Primary DNS suffix” – we set it to “cloudapp.net” (without leading dot) to reflect our public DNS name.

By clicking [OK] on all open windows you will see the new full name “MyServer.cloudapp.net” now being used as “Full computer name” everywhere. Also our Power Shell command from above now shows the correct hostname. Note that this change also requires a reboot of the VM.

Once the machine is rebooted and DMG is running again you can now use any client machine to create your Data Source which was previously only possible from the server directly and required a RDP connection. Also HTTPS connectivity with self-signed certificates works with this approach which I will show in one of my next posts – so stay tuned!

Using Power BI Data Management Gateway on Non-Domain Azure VM

There were some changes to the DMG in August 2014. Please refer to my new blog post which addresses the issues with the new version! However, I still recommend you to read this post first in order to fully understand the original issue!
The new post can be found here.


I am currently preparing some demos and examples for Power BI. As you can expect for demos you do not want to put too much effort in building up any infrastructure so I decided to use an Azure VM to host my SQL databases and SSAS cubes. Keeping things simple the Azure VM is not joined to a domain which is fine for SQL where I can use SQL authentication, for SSAS I use msmdpump.dll. After everything was set up I wanted to install the Data Management Gateway to expose my SQL tables via OData to Power Query and Online Search.
Bryan C. Smith recently published an article on that very same topic Creating a Demo Power BI Data Gateway using an Azure Virtual Machine but for some reasons it did not work for me. Further, as Bryan already mentions in the first paragraph, his setup is not supported and  its also a bit of a hack (modifying hosts-file, and so on).
So I started my own investigations and came up with another solution, which only uses out-of-the-box features and tools and is actually quite simple. Another thing to mention here is that it will (probably) not work for scheduled data refreshes but only for exposing the SQL database via OData and make it searchable in Power Query.
Having that said, here are the steps to follow:

1) Setup the Data Management Gateway itself on the Azure VM as described here: Create a Data Management Gateway. This should work just fine and the Gateway should be in the “Registered”-state on the Azure VM and in “Ready”-state in the Power BI Admin Center:

2) Create a new Data Source on top of the previously created Gateway as described here: Create a Data Source and Enable OData Feed in Power BI Admin Center

Here you will usually receive an error when you want to enter credentials for the SQL Database:

By Clicking on the [credentials]-button a new window pops up. Please note that this is a click-once application that actually runs on your client and is independent of your actual browser!

If the Gateway is running on an Azure VM, or basically any machine which cannot be reached from your current client you will receive an error that a connection could not be established or something similar.
Assuming you called your Azure VM “MyCloudServer” and is perfectly reachable via “MyCloudServer.cloudapp.net” you will receive an error saying that “MyCloudServer” (without “.cloudapp.net”) could not be resolved. Which is actually true as the correct server would be “MyCloudServer.cloudapp.net”. Unfortunatelly, this server name cannot be changed anywhere as far as I know. As the name cannot be changed we need to make the name somehow “resolveable”. Bryan manually modifies the hosts file and makes “MyCloudServer” point to the public IP address of “MyCloudServer.cloudapp.net”. This should usually work just fine, but somehow did not work for me. Also the public IP address may change if you reboot your Azure VM and so you would need to modify the hosts-file again.

So these are the findings we mad so far:
– the Data Source Manager is a click-once application which runs on the client
– the client must be able to resolve “MyCloudServer”

After some thinking I ended up with the following:
The only machine in my scenario that can correctly resolve “MyCloudServer” is the Azure VM itself! So instead of running the Data Source Manager on my client I simply connected to the Power BI Admin Center from my server and repeated the steps from above there.
Now everything works fine and we can proceed:
This connectivity check is only done once and has no further impact (I am not 100% sure on this Smile ). Though, the Username and Password are stored and used for all subsequent connection through the gateway, e.g. for OData access so make sure the user has the necessary access rights.

In the next step you can select the tables and views that you want to expose:

Those can then be searched and queried using Excel and Power Query from any client:

And that’s it – The simple trick is to run the Power BI Admin Center from the server itself and create the data source there!

Hope this helps everyone who is dealing with the same issue or wants to setup a demo environment too.