Foreword: The approach described in this blog post only uses the Databricks REST API and therefore should work with both, Azure Databricks and also Databricks on AWS!
It recently had to migrate an existing Databricks workspace to a new Azure subscription causing as little interruption as possible and not loosing any valuable content. So I thought a simple Move of the Azure resource would be the easiest thing to do in this case. Unfortunately it turns out that moving an Azure Databricks Service (=workspace) is not supported:
Resource move is not supported for resource types ‘Microsoft.Databricks/workspaces’. (Code: ResourceMoveNotSupported)
I do not know what is/was the problem here but I did not have time to investigate but instead needed to come up with a proper solution in time. So I had a look what needs to be done for a manual export. Basically there are 5 types of content within a Databricks workspace:
Workspace items (notebooks and folders)
Security (users and groups)
For all of them an appropriate REST API is provided by Databricks to manage and also exports and imports. This was fantastic news for me as I knew I could use my existing PowerShell module DatabricksPS to do all the stuff without having to re-invent the wheel again. So I basically extended the module and added new Import and Export functions which automatically process all the different content types:
They can be further parameterized to only import/export certain artifacts and how to deal with updates to already existing items. The actual output of the export looks like this and of course you can also modify it manually to your needs – all files are in JSON except for the notebooks which are exported as .DBC file by default:
A very simple sample code doing and export and an import into a different environment could look like this:
Having those scripts made the whole migration a very easy task. In addition, these new cmdlets can also be used in your Continuous Integration/Continuous Delivery (CI/CD) pipelines in Azure DevOps or any other CI/CD tool!
So just download the latest version from the PowerShell gallery and give it a try!
In my previous post I showed how you can use Microsoft Power BI to create a Data Virtualization layer on top of multiple relational data sources querying them all at the same time through one common model. As I already mentioned in the post and what was also pointed out by Adam Saxton (b, t) in the comments is the fact, that this approach can cause serious performance problems at the data source and also on the Power BI side. So in this post we will have a closer look on what actually happens in the background and which queries are executed when you join different data sources on-the-fly.
We will use the same model as in the previous post (you can download it from there or at the end of this post) and run some basic queries against it so we can get a better understanding of the internals. Here is our relationship diagram again as a reference. Please remember that each table comes from a different SQL server:
In our test we will simply count the number of products for each Product Subcategory:
Even though this query only touches two different data sources, it is a good way to analyze the queries sent to the data sources. To track these queries I used the built-in Performance Analyzer of Power BI desktop which can be enabled on the “View”-tab. It gives you detailed information about the performance of the report including the actual SQL queries (under “Direct query”) which were executed on the data sources. The plain text queries can also be copied using the “Copy queries” link at the bottom. In our case 3 SQL queries were executed against 2 different SQL databases:
The query basically selects two columns from the DimProductSubcategory table:
ProductSubcategoryKey – which is used in the join with DimProduct
ProductSubcategoryName – which is the final name to be displayed in the visual
The inner sub-select (line 7-14) represents the original Power Query query. It selects all columns from the DimProductSubcategory table and renames [EnglishProductSubcagetoryName] to [ProductSubcategoryName] (line 10). Any other Power Query steps that are supported in direct query like aggregations, groupings, filters, etc. would also show up here.
(SELECTN'Mountain Bikes'AS[c67],1AS[c29])UNION ALL
(SELECTN'Road Bikes'AS[c67],2AS[c29])UNION ALL
(SELECTN'Touring Bikes'AS[c67],3AS[c29])UNION ALL
(SELECTN'Bottom Brackets'AS[c67],5AS[c29])UNION ALL
(The query was shortened at line 16 and line 29 as the removed columns/rows are not relevant for the purpose of this example.)
Similar to Query 1 above, the innermost sub-select (line 13-17) in the FROM clause returns the results of the Power Query query for DimProduct whereas the outer sub-select (line 7-20) groups the result by the common join-key [ProductSubcategoryKey]. This result is then joined with a static table which is made up from hard-coded SELECTs and UNION ALLs (line 24-30). If you take a closer look, you will realize that this table actually represents the original result of Query 1! Additionally it also includes a special NULL-item (line 30) that is used to handle non-matching entries. The last step is to group the joined tables to obtain the final results.
(The query was shortened at line 9 as the removed columns/rows are not relevant for the purpose of this example.)
The last query is necessary to display the correct grand total across all products and product sub-categories.
As you can see, most of the “magic” happens in Query 2. The virtual join or virtualization is done by hard-coding the results of the remote table/data source directly into the SQL query of the current table/data source. This works fine as long as the results of the remote query are small enough – both, in terms of numbers of rows and columns – but the more limiting factor is the number of rows. Roughly speaking, if you have more than thousand items that are joined this way, the queries tend to get slow. In reality this will very much depend on your data so I would highly recommend to test this with your own data!
I ran a simple test and created a join on the SalesOrderNumber which has about 27,000 distinct items. The query never returned any results and after having a look at the Performance Analyzer I realized, that the query similar to Query 2 above was never executed. I do not know yet whether this is because of the large number of items and the very long SQL query that is generated (27,000 times SELECT + UNION ALL !!!) or a bug.
At this point you may ask yourself if it makes sense to use Power BI for data virtualization or use another tool that was explicitly designed for this scenario. (Just google for “data virtualization”). These other tools may perform better even on higher volume data but they will also reach their limits if the joins get too big and, what is even more important, the are usually quite expensive.
So I think that Power BI is still a viable solution for data virtualization if you keep the following things in mind: – keep the items in the join columns at a minimum – use Power Query to pre-aggregate the data if possible – don’t expect too much in terms of performance – only use it when you know what you are doing 🙂
Again, this works pretty well and is explained in detail in the blog posts.
Once you have implemented this change the business users usually complain that Total is wrong. This depends on how you implemented the TopN measure and what the users actually expect. I have seen two scenarios that cause confusion: 1) The Total is the SUM of the TopN items only – not reflecting the actual Grand Total 2) The Total is NOT the SUM of the TopN items only – people complaining that Power BI does not sum up correctly
As I said, this pretty much depends on the business requirements and after discussing that in length with the users, the solution is usually to simply add an “Others” row that sums up all values which are not part of the TopN items. For regular business users this requirement sounds really trivial because in Excel the could just add a new row and subtract the values of the TopN items from the Grand Total.
These work fine even if I do not like the DAX as it is unnecessarily complex (from my point of view) but the general approach is the same as the one that will I show in this blog post and follows these steps: 1) create a new table in the data model (either with Power Query or DAX) that contains all our items that we want to use in our TopN calculation and an additional row for “Others” 2) link the new table also to the fact table, similar to the original table that contains your items 3) write a measure that calculates the rank for each item, filters the TopN items and assigns the rest to the “Others” item 4) use the new measure in combination with the new table/column in your visual
Step 1 – Create table with “Others” row
I used a DAX calculated table that does a UNION() of the existing rows for the TopN calculation and a static row for “Others”. I used ROW() first so I can specify the new column names directly. I further use ALLNOBLANKROW() to remove to get rid of any blank rows.
The new table is linked to the same table to which the original table was linked to. This can be the fact-table directly or an intermediate table that then filters the facts in a second step (as shown below)
Step 3 – Create DAX measure
That’s actually the tricky part about this solution, but I think the code is still very easy to read and understand:
Top Measure ProductSubCategory=
/* get the items for which we want to calculate TopN + Others */
One of the benefits of this approach is that it also allows you to use the “Others” value in slicers, for cross-filtering/-highlight and even in drill-downs. To do so we need to configure our visual with two levels. The first one is the column that contains the “Others” item and the second level is the original column that contains the items. The DAX measure will take care of the rest.
And that’s it! You can now use the column that contains the artificial “Others” in combination with the new measure wherever you like. In a slicer, in a chart or in a table/matrix!
Over the last year I worked a lot with Databricks on Azure and I have to say that I was (and still am) very impressed how well it works and how it integrates with other services of the Microsoft Azure Data Platform like Data Lake Store, Data Factory, etc.
Some of the projects I worked on also included CI/CD like pipelines using Azure DevOps where Databricks did not really shine so bright in the beginning. There are no native tasks for it or anything. But this is OK as for those scenarios, where you need to automate/script something, Databricks offers a REST API (Azure, AWS).
As most of our deployments use PowerShell I wrote some cmdlets to easily work with the Databricks API in my scripts. These included managing clusters (create, start, stop, …), deploying content/notebooks, adding secrets, executing jobs/notebooks, etc. After some time I ended up having 20+ single scripts which was not really maintainable any more. So I packed them into a PowerShell module and also published it to the PowerShell Gallery (https://www.powershellgallery.com/packages/DatabricksPS) for everyone to use!
The module works for Databricks on Azure and also if you run Databricks on AWS – fortunately the API endpoints are almost identical.
The usage is quite simple as for any other PowerShell module:
Install it using Install-Module cmdlet
Setup the Databricks environment using API key and endpoint URL
All the cmdlets are documented and contain links to official documentation of the Rest API call used by the cmdlet. Some API endpoints support different variations of parameters – this was implemented using different parameter sets in PowerShell. There are still some ongoing tests (especially on AWS) and improvements but I general all cmdlets work as expected. I hope this helps anyone else who also has to deal with the Databricks APIs frequently or has to integrate it in a CI/CD pipeline.
As some of you probably remember, when PowerPivot was still only available in Excel and Power Query did not yet exist, it was possible to load images from a database (binary column) directly into the data model and display them in PowerView. Unfortunately, this feature did not work anymore in PowerBI Desktop and the only way to display images in a visual was to provide the URL of the image which is public accessible. The visual would then grab the image on-the-fly from the URL and render it. This of course has various drawbacks:
The image needs to be available via a public URL (e.g. upload it first to an Azure Blob Store)
The image cannot be displayed when you are offline
The link may break in the future or point to a different image as initially when the model was built
Until today I was sure that we have to live with this limitation but then I came across this blog post from Jason Thomas aka SqlJason. He shows a workaround to store images directly in the PowerBI data model and display them in the report as if they were regular images loaded from an URL. This is pretty awesome and I have to dedicate at least 99.9% of this blog post to Jason and his solution!
However, with this blog post I would like to take Jasons’ approach a step further. He creates the Base64 string externally and hardcodes it in the model using DAX. This has some advantages (static image, no external dependency anymore, …) but also a lot of disadvantages (externally create the Base64 string, manually copy&paste the Base64 string for each image, hard to maintain, cannot dynamically add images …). For scenarios where you have a local folder with images, a set of [private] URLs pointing to images or images stored in a SQL table (as binary) which you want to load into your PowerBI data model, this whole process should be automated and ideally done within PowerBI.
Fortunately, this turns out to be quite simple! Power Query provides a native function to convert any binary to a Base64 encoded string: Binary.ToText() . The important part to point out here is to use the second parameter which allows you to set the encoding of the resulting text. It supports two values: BinaryEncoding.Base64 (default) and BinaryEncoding.Hex. Once we have the Base64 string, we simply need to prefix it with the following meta data: “data:image/jpeg;base64, “
To make it easy, I wrote to two custom PowerQuery functions which convert and URL or a binary image to the appropriate string which can be used by PowerBI:
If your images reside in a local folder, you can simply load them using the “Folder” data source. This will give you a list of all images and and their binary content as separate column. Next add a new Custom Column where you call the above function to convert the binary to a prefixed Base64 string which can then be displayed in PowerBI (or Analysis Services) as a regular image. Just make sure to also set the Data Category of the column to “Image URL”:
And that’s it, now your visual will display the image stored in the data model without having to access any external resources!
Caution: As Jason also mentions at the end of his blog post, there is an internal limitation about the size of a text column. So this may cause issues when you try to load high-resolution images! In this case, simply lower the size/quality of the images before you load them. UPDATE May 2019: Chris Webb provides much more information and a solution(!) to this issue in his blog post: https://blog.crossjoin.co.uk/2019/05/19/storing-large-images-in-power-bi-datasets
This PowerBI Desktop model contains all samples from above including the PowerQuery functions!
As you probably know from my last blog post, I am currently upgrading the PowerBI reporting platform of one of my customer from a PowerBI backend (dataset hosted in PowerBI service) to an Azure Analysis Services backend. The upgrade/import of the dataset into Azure Analysis Services itself worked pretty flawless and after switching the connection of the reports everything worked as expected and everyone was happy. However, things got a bit tricky when it came to automatically refreshing the Azure Analysis Services database which was based on an Azure Data Lake Store. For the original PowerBI dataset, this was pretty straight forward as a scheduled refresh from an Azure Data Lake store data source works out of the box. For Azure Analysis Services this is a bit different.
When you build and deploy your data model from Visual Studio, your are prompted for the credentials to access ADLS which are then stored in the data source object of AAS. As you probably know, AAS uses OAuth authentication to access data from ADLS. And this also causes a lot of problems. OAuth is based on tokens and those tokens are only valid for a limited time, by default this is 2 hours. This basically means, that you can process your database for the next 2 hours and it will fail later on with an error message saying that the token expired. (The above applies to all OAuth sources!)
This problem is usually solved by using an Azure Service Principal instead of a regular user account where the token does not expire. Unfortunately, this is not supported at the moment for ADLS data sources and you have to work around this issue.
IMPORTANT NOTE: NONE OF THE FOLLOWING IS OFFICIALLY SUPPORTED BY MICROSOFT !!!
So the current situation that we need to solve is as follows:
we can only use regular user accounts to connect AAS to ADLS as service principals are not supported yet
the token expires after 2 hours
the database has to be processed on a regular basis (daily, hourly, …) without any manual interaction
manually updating the token is (of course) not an option
Back to our example – as we were already using Azure Automation for some other tasks, we decided to also use it here. Also, PowerShell integrates very well with other Azure components and was the language of choice for us. To accomplish our goal we had to implement 3 steps:
acquire a new OAuth token
update the ADLS data source with the new token
run our processing script
I could copy the code for the first step more or less from one of my older blog post (here) where I used PowerShell to acquire an OAuth token to trigger a refresh in PowerBI.
The second step is to update ADLS data source of our Azure Analysis Services model. To get started, the easiest thing to do is to simply open the AAS database in SQL Server Management Studio and let it script the existing datasource for you:
The resulting JSON will look similar to this one:
The important part for us is the “credential” field. It contains all the information necessary to authenticate against our ADLS store. However, most of this information is sensitive so only asterisks are displayed in the script. The rest of the JSON (except for the “credential” field) is currently hardcoded in the PowerShell cmdlet so if you want to use it, you need to change this manually!
The PowerShell cmdlet then combines the hardcoded part with an updated “credential”-field which is obtained by invoking a REST request to retrieve a new OAuth token. The returned object is modified a bit in order to match the required JSON for the datasource.
Once we have our final JSON created, we can send it to our Azure Analysis Services instance by calling the Invoke-ASCmd cmdlet from the SqlServer module.
Again, please see the original blog post mentioned above for the details of this approach.
After we have updated our datasource, we can simply call our regular processing commands which will then be executed using the newly updated credentials.
The script I wrote allows you to specify which objects to process in different ways:
whole database (by leaving AASTableName and AASPartitionName empty)
a single or multiple table and all its partitions (by leaving only AASPartitionName empty)
or multiple partitions of a single table (by specifying exactly one AASTableName and multiple AASPartitionNames
If multiple tables or partitions are specified, the elements are separated by commas (“,”)
So to make the Runbook work in your environment, follow all the initial steps as described in the original blog post from Microsoft. In addition, you also need to create an Application (Type = “Native”) in your Azure Active Directory to obtain the OAuth token programmatically. This application needs the “Sign in and read user profile” permission from the API “Windows Azure Active Directory (Microsoft.Azure.ActiveDirectory)”:
Also remember the ApplicationID, it will be used as a parameter for the final PowerShell Runbook (=parameter “ClientID”!
When it comes to writing the PowerShell code, simply use the code from the download at the end of this blog post.
For the actual credential that you are using, make sure that it has the following permissions:
to update the AAS datasource (can be set in the AAS model or for the whole server)
has access to the required ADLS files/folders which are processed (can be set e.g. via ADLS Data Explorer)
(if you previously used your own account to do all the AAS and ADLS development, this should work just fine)
In general, a similar approach should work for all kinds of datasources that require OAuth authentication but so far I have only tested it with Azure Data Lake Store!
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:
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:
There are no service accounts in PowerBI so we will always use a “real” user
you need to supply the credentials of a “real” user
The user needs to have appropriate access to the dataset in order to refresh it
the dataset refresh must succeed if you do it manually in PowerBI
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!
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:
$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:
CredentialName – the name of the Azure Automation credential that you created and which stores the PowerBI username and password
ClientID – the ID of your Azure Active Directory Application which you created in the first step
PBIDatasetName – the name of the PowerBI dataset that you want to refresh
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!
Some time ago, when Microsoft released the first version of Power BI Rest API I already wrote a wrapper for C# which allowed you to map the object from the API into regular C# objects and work with them locally. However, there have been some major upgrades since then (Actually they were already announced in July 2016 but I did not find anytime to work on this again until now ). Anyway, I just published a new version of my C# wrapper on my GitHub site: https://github.com/gbrueckl/PowerBI.API.Client
To use it, you first need to create an Azure AD application and get an ApplicationID – this is very well described here or can be done directly at https://dev.powerbi.com/apps
A new PowerBI API Client object can then be created using the ApplicationID:
Basically, most of the features described in the API reference are also included in the API Wrapper. So you can now use C# to create your PowerBI model locally and deploy it to the PowerBI service! The only to keep in mind is that the dataset you create via the API can only be sourced by pushing data into it using the Push/Streaming API. As this can be quite cumbersome sometimes, I also added the functionality to publish a whole C# DataTable with basically just two lines of code to publish your reference data/dimensions:
Publish DataTable to PowerBI
// create a regular DataTable – but could also be derived from a SQL Database! DataTable dataTable = newDataTable(); /* populate the dataTable */ // create a PBI table from a regular DataTable object PBITable productsTable = newPBITable(dataTable); // publish the table and push the rows from the dataTable to the PowerBI table productsTable.PublishToPowerBI(true);
This snippet basically deploys the table structure to the PowerBI service and populates it with data from the DataTable:
For your “fact”-data you can also create single rows on your own using the PBIRow-object and publish them manually e.g. for WriteBack-scenarios:
Of course, all the features that were already supported in the first version, are still supported:
Get Embed-URLs of Reports and Tiles
List Reports, Dashboards, Datasets, …
The new version also supports Streaming and PushStreaming datasets in the same way as it does for regular Push datasets. For details on Streaming datasets please take a look at Real-time streaming in PowerBI
I recommend to explore the API on your own by simply building your first PowerBI Push/Streaming Model on your own! For the latest features and improvements please refer to the GitHub repository which will be updated frequently.
Any feedback and participation in the further development is highly appreciated and will be done via the GitHub repository.
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)
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 and the actual code:
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!
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!
Download: 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