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Tag: Power BI

Connect to Application Insights and Log Analytics with Direct Query in Power BI

Application Insights (AI) and Log Analytics (LA) from Microsoft Azure provide easy and inexpensive ways to instrument applications. Using just an instrumentation key, any application can send operational data to AI which can then provide a rich array of tools to monitor the operation of the application. In fact, the blog that you are reading uses an Application Insights plugin for WordPress that registers each view of a page into an instance of AI in my Azure tenant.

Application Insights data can be queried directly in the Azure portal to provide rich insights. In addition, the data can be exported to Excel for further analysis, or, it can be queried using Power Query in either Excel or Power BI. The procedure for using Power Query can be found in this article. The approach for doing so, uses the Web connector in Power Query, which can be automatically refreshed on a regular basis. The Web connector does not however support Direct Query, so the latency of the data in this scenario will be limited by the refresh schedule configured in Power BI. Any features that depend on Direct Query (Aggregations, Automatic Page Refresh) will also not work.

If you’ve worked with AI or LA, and dropped down to the Query editor, you’ve been exposed to KQL – The Kusto Query Language. This is the language that is used by Azure Data Explorer (ADX), or as its code name, “Kusto”. This is of course not a coincidence, as the Kusto engine powers both AI and LA.

Power BI contains a native connector for ADX, and if you can configure an ADX cluster for yourself, populate it, and work with it in Power BI for both imported and Direct Query datasets. Given that ADX is what powers AI and LA, it should be possible to use this connector to query the data for AI and LA. It turns out that the introduction of a new feature known as the ADX proxy will allow us to do just that.

The ADX proxy is designed to allow the ADX user interface to connect to instances of AI and LA and run queries from the same screens as native ADX clusters. The entire process is described in the document Query data in Azure Monitor using Azure Data Explorer. The document explains the process, but what we are particularly interested in is the syntax used to express an AI or LA instance as an ADX cluster. Multiple variations are described in the document, but the ones that we are most interested in are here:

For LA:<subscription-id>/resourcegroups/<resource-group-name>/providers/microsoft.operationalinsights/workspaces/<workspace-name>

For AI:<subscription-id>/resourcegroups/<resource-group-name>/providers/microsoft.insights/components/<ai-app-name>

By substituting in your subscription ID, resource group name, and resource name, you can treat these resources as if they were ADX clusters, and query them in Power BI using Direct Query. As an example, a simple query on this blog can be formed using the ADX connector:

And the result will appear as:

The precise query is provided in the query section of the connector above.

Once the report is built, it can be deployed to the PowerBI service, and refreshed using AAD credentials.

It is important to note that this method does NOT require you to configure an ADX cluster of your own. We are simply utilizing the cluster provided to all instances of AI and LA. We therefore do not have any control over performance levels, as we would have in a full ADX cluster. However, if the performance is adequate,(and the queries designed appropriately), this can be a good approach to work with AI and LA data that has low latency (near real time) requirements.

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Formatting the X Axis in Power BI Charts for Date and Time

Dates and times are probably the most commonly used dimensions in Power BI charts, or any charts for that matter. Power BI contains a number of features that help to display data with respect to time. Features like the automated date hierarchy reduce the need for users to construct or connect to a date dimension table (even though they likely should), which helps casual users get to solution more quickly. This is particularly true of using date/time on the axis of a chart. There are a lot of options for displaying this data, and they may not all be that well understood. This article will attempt to explain a number of them.

The scenario

We will be working with thermostat data taken in 5 minute increments over several months. The shape of the data is relatively simple. There are measures for outdoor temperature and heating/cooling system run times in seconds, as well as a date/time dimensions names DateAndTime. An example can be seen below

We want to plot these runtimes over time, and we will be working with a “Line and clustered column chart” to do this. The 4 different heating/cooling runtimes are used for the column values, the Outdoor temperature is used for the line values (with average being the default aggregation behaviour). This gets us to our point – what is the best way to display time on the X axis?

Plotting with DateTime

When the DateAndTime column is added to the X axis, by default it is converted to a date hierarchy. This behaviour is on by default but can be turned off (and in many cases, should be). We initially want to work with the raw datetime value, so we can control that by setting the dropdown option in the shared axis section of the chart and selecting the name of the dimension instead of “Date Hierarchy”.

Doing this with our data results in a rather messy looking chart.

The data here is far too granular to display all of it across all of the available times. By default, using a date or datetime dimension on an X axis will display continuously like this. However, we can control this behaviour through an X axis property on the chart itself.

Opening up the chart display properties, and then opening the X axis section reveals that “Continuous” is selected for the Type property. This is the display mode that will scale the axis to include all available date/time values. The other option is “Categorical”. The Categorical option displays each date/time value as a discrete data element. Changing the axis type property to continuous results in the chart appearing as follows.

The continuous and categorical options are only available for date and date/time dimensions. If the dimension in the X axis is not one of these types, the “Type” option will not appear.

Using Continuous, each and every date and time value is displayed on the X axis, and the data values are clearly resolved. However, in our case, there are far too many values to make this useful. Finding what we’re after would take a lot of scrolling. It’s best in this case (and in most cases) to view the data in aggregate, which is to say totals and averages across different time periods, years, months, days etc. This is where the Date Hierarchy shows value.

Formatting with Date Hierarchy

Selecting our “DateAndTime” dimension back to “Date Hierarchy” immediately changes the chart to show all of the data aggregated by Year. It is also possible to see the detail of the hierarchy in the Shared axis property for the chart.

The top level of the hierarchy is shown, which is all of the data aggregated to the Year level.

I rarely use the “Quarter” level of the hierarchy, so I simply remove it, and have done so for the remainder of the operations. It can be removed simply by selecting the x beside it in the Shared axis property box.

If we want to see our data in a more granular fashion, we have three options – Drill down, Go down one level, and Expand all down one level, which are the icons listed left to right in the highlighter section in the image above. Drilling down is meant to be interactive. With Drill down selected, clicking on the data point in the chart will go down to the next level in the chart for that data point. It replaces the standard cross filtering or cross highlighting that would normally happen when selecting a data point. For example, with drilldown turned on, clicking on any column for 2019 results in the chart below.

Notice that the X axis now shows month names instead of the year. This cart is showing our measures by month now, but only for the year 2019. The up arrow in the upper left arrow can be selected at any time to go back up to year, or selecting one of the months will drill down further to show the values for all of the days in the selected month.

The second option, Go down one level behaves in a similar fashion, but it does not filter to the year, it simply takes the chart down one level in the hierarchy without first filtering by year. This could be useful when comparing months to each other in aggregate. The X axis changes in the same way as drill down, showing the values for that level of the hierarchy.

If we want to show the data more granularly than the year level, but we don’t want to aggregate all of the same month names together, we can use the third option – Expand all down one level, or as I like to call it, “drill down and out”. Selecting this option results in the chart below.

We can see the data broken out by both year and month in order. This is a much richer view and still understandable. For example, you can see that 2018 was generally warmer than 2019 due to the amount of cooling necessary at a glance. The title is automatically changed (if it wasn’t set manually) to reflect this configuration, and the X axis also shows both year and month.

In this particular example the X axis is still readable, but drilling down and out more than one level can be cumbersome, and very wordy. At the same time, you do need to know which year, month, and day a particular data point pertains to. The Z axis formatting pane has some further options that help with this. By default, all of the hierarchy levels are concatenated together when a hierarchy is expanded in this way. Going into the chart format tab, and selecting the X axis, we can see an option for this – “Concatenate Labels”. Turning this off presents each level categorically on different lines. This to my mind is much easier to read and is the configuration that I use.

The concatenate labels option only takes effect when a hierarchy is expanded past its root level.

The examples used above utilize a “Line and clustered column bar chart” but pertain to all of the standard visuals that employ an x and y axis.

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Fixing Power BI Report Builder Connection Errors

Power BI Report Builder is Microsoft’s design tool for building Paginated reports in Power BI. It is based on Microsoft Report Builder (formerly SQL Server Reporting Services Report Builder), but has been optimized for the Power BI service.

One of the most important capabilities of Power BI Report Builder is the ability to connect to datasets that have been published to the service. If you have done this, and spent any significant amount of time building reports, you may have come across some puzzling connection errors that are caused by the same thing.

After initially creating a connection and building a “Paginated dataset” (not to be confused with a Power BI service dataset), and then spending some time designing your report, when you select the “Run” option from the ribbon, you may be presented with the “Failed to preview report” error shown at the top of this article. Selecting the details button reveals more information:

A similar error can be found under the same conditions when editing a Paginated dataset’s query with the Query designer tool. Selecting this tool can result in the error “Unable to connect to data source xxxxxxxx”, and the details button reveals another “Unauthorized” error.

What’s worse in this case is that when you select OK, a dialog box appears prompting you to enter a set of credentials.

There are no combination of credentials that you can enter that will fix the connection to the data source. This dialog box was designed for classic paginated connections, not for connections to published data sets. You should select cancel if you see this dialog box.

What is happening in both of these cases is that the token acquired from the Power BI service has expired, and Report Builder does not automatically fetch a new one. There are a couple of ways to deal with this problem.

If you have saved the RDL file to a local file system, you can close Report Builder and reopen it. That will re-establish the data connection. You could also choose to save the RDL directly into the Power BI workspace. This will also re-establish the connection. You can do this by selecting File – Save as and selecting Power BI Service.

You can then choose which workspace to save the file in. This also removes the need to upload the file into the service when you want to publish it – saving and publishing are the same thing in this scenario.

If you are editing a file directly in the service, these errors will still appear after periods of no data retrieval activity, but the connection can re-established simply by saving the report. You can look at the errors as a way of prompting you to save your work .

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Connecting Power BI workspaces and SharePoint sites

Power BI V2 workspaces recently (May 2019) entered into general availability. The biggest difference between a V1 and V2 Power BI workspace is the fact that a V2 workspace is not backed by an Office 365 group, and a V1 workspace is. One area that this change affects a great deal is the “Get data” experience in the Power BI service (browser). This post outlines the differences, and describes the configuration options.

Data connections to files stored in SharePoint and OneDrive have certain unique characteristics when they are created in the browser. For example, these connections are automatically refreshed hourly unless that option is disabled.

V1 workspaces automatically offer the connection to the Documents library in the underlying SharePoint site. V2 workspaces do not automatically offer this option, as there is no underpinning SharePoint site. However, any V2 workspace can be connected to any Modern SharePoint site, and in this way, the option is more flexible. For the sake of clarity, a Modern SharePoint site is one that is backed by an Office 365 Group, and has an email address.

Let’s explore the 4 possible experiences when using “Get Data” and then choosing “Files” in the Power BI service. There are 4 possible experiences, depending on the type and configuration of the workspace;

  1. Personal workspace
  2. V1 workspace
  3. V2 workspace not connected to a site
  4. V2 workspace connected to a site

In each example below, the options are reached by selecting “Get Data” and then choosing “Files”. The type of files that can be imported are CSV, Excel, PBIX (Power BI Desktop files) and RDL (paginated reports).

Personal workspace

The personal workspace is the only workspace available using the free Power BI license. It is not connected to any SharePoint sites, and provides 4 options for importing.

“Local File” can be used for importing files from a local file store. Files imported in this manner are not automatically refreshed, and without the use of a gateway, cannot be. This option is available for every workspace type and will not be discussed further. “Learn about importing files” is a simple help link, likewise available to all workspace types.

OneDrive – Business connects to the currently logged in user’s OneDrive for Business storage. This is the OneDrive that is associated with “School or Organization” account which is stored in Azure Active Directory.

OneDrive – Personal connects to a user’s personal, or consumer OneDrive account. This is the type of OneDrive that is accessed using a “personal” account (otherwise known as a Microsoft account, or MSA). The personal workspace is the only type of workspace that allows a connection to personal OneDrive content.

SharePoint – Team Sites allows files stored in any SharePoint Online library to be loaded. Files stored in SharePoint on-premises can be loaded into Power BI, but only through Power BI Desktop. This method is online only.

Data imported in this fashion will be updated hourly with the exception of “Local File”. This will also be true of any OneDrive or SharePoint source referenced below.

V1 workspace

A Power BI V1 workspace is connected to an Office 365 Group, and therefore backed by a SharePoint site. This is reflected in the Files experience in the service.

Here we see 3 import options. Local File, SharePoint – Team Sites, and “Learn about..” are exactly the same as with personal workspaces. However, both OneDrive options from there are unavailable. The “OneDrive – XXXX” option is different, and bears some explanation.

In the image above, “Demos” is the name of the V1 workspace. Selecting this option will open the SharePoint library named “Documents” in the SharePoint site that is associated with this workspace and Office 365 group.

In my opinion, this option is poorly named, which leads to confusion. This container truly has nothing to do with OneDrive – it is a SharePoint library. We already have enough different “OneDrives” to keep track of, but I digress.

V2 workspace (not connected to a site)

The V2 workspace is not associated with a SharePoint site, and therefore, there is no Documents library to connect to. The option is instead replaced with the ability to connect to the user’s OneDrive for Business (OneDrive – Business) storage, as in the personal workspace. In essence, this experience is identical to the personal workspace experience minus the ability to connect to personal OneDrives.

V2 workspace (connected to a site)

Although a V2 workspace is not inherently connected to a SharePoint site, it can be manually connected to one. This restores the capability missing from V1 workspaces, while being more flexible. The workspace is no longer bound to a specific site, but can be configured to work with any Modern SharePoint site. In addition, the same site can be bound to multiple workspaces.

The “Modern” distinction above is important. The SharePoint site itself must be backed by an Office 365 group, as that is how it is identified in Power BI.

Associating a workspace with a SharePoint site

With V2 workspaces, site connection is now a property of the workspace. To edit workspace properties, select either the workspace settings button in the ribbon, or the ellipsis beside the workspace in the workspace list.

The connection setting is in the advanced section, and is identified as the “Workspace OneDrive”.

The important thing to note here is that you do NOT enter the URL of the SharePoint site in this field. This field is expecting the address of it in email format (ie All Modern Sharepoint sites are bound to an Office 365 group, and the email address is the address of that group.

Get Data – File options for a V2 connected site

Once connected, the “Get Data” – “File” options will be much the same as with an unconnected workspace, but with the “OneDrive – SiteName” option added.

I still take exception with the name presented above, in my opinion it should be “Site – SiteName” or “SharePoint – SiteName site” and use a SharePoint option. However, once connected files in the connected site can be imported easily into the Power BI service.


It is important to understand what the connected site is used for in Power BI. Connecting a site allows for files stored in a SharePoint library to be either imported into the service (all supported file types), or connected to (Excel files). This feature does NOT allow Power BI content to be stored in a SharePoint library

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Connect to Power BI dataflows and datasets using Power BI Desktop with multiple accounts

Image result for multiple accounts

Power BI datasets and dataflows are the two native data sources for Power BI reports. Connecting to a datasets allows a report to be built against an existing Power BI dataset in place, and dataflows represent a source of data that has had transformations applied to it. When connecting to Power BI dataflows, data is imported, into a data model but the connection to a Power BI dataset is a direct connection.

The two sources handle identity in drastically different ways, and this can lead to confusion when dealing with multiple accounts and tenants. This post is an attempt to help clarify this confusion

Connecting to a dataset

To connect to a Power BI dataset, select the “Get Data” button from the ribbon, select the “Power BI” tab, select Power BI dataset, and finally the “Connect” button.

Connecting to a Power BI dataset

Next, if a user is signed into Power BI Desktop a list of workspaces and their datasets are presented. If not, the user is prompted to sign in. The dataset can then be connected.

Selecting a Power BI dataset

The important thing to notice here is the list of workspaces itself. The list presented is a list of the workspaces available in the tenant belonging to the currently signed in user. It is the same list of workspaces that can be chosen as a publishing target. It should also be noted that the identity of the user is displayed in the upper right corner of the dialog box, and the identity can be changed directly from there.

Connecting to a dataset in a different tenant

The signed in user can be changed by selecting “Sign in” at the upper right of the Power BI Desktop client, or within the connection dialog itself. If the user signs into a different account (in a different tenant), a different list of workspaces and datasets will then be offered. The dataset source is hard linked to the currently signed in user. In this way, the Power BI dataset source behaves differently than all other data sources, which maintain connection credentials separately.

Connecting to a Dataflow

Connecting to a dataflow follows the same steps as a dataset, with the exception that the “Power BI dataflows” option is chosen.

Connecting to a Power BI datflow

At this point, a Power BI data connection dialog will be shown.

Signing in to a Power BI dataflow

There is only one authentication option because Power BI dataflows only support one authentication option.

Unlike datasets, dataflows are NOT linked to the currently signed in user. The connection is authenticated, not the current user. The “Sign In” button must be selected, and authentication completed to connect to a Power BI dataflow.

Once signed in, selecting the “Connect” button will display a list of workspaces that contain dataflows. Expanding the workspace and then the dataflow will expose a list of entities that can be imported into the Power BI data model.

Selecting a Power BI dataflow

The connection information for the dataflow is cached with Power BI Desktop, and subsequent connections to dataflows will not require the user to sign in. The same authentication credentials will be used.

it should be noted that unlike the dataset connection dialog, this one does not show the current credentials and does not allow those credentials to be changed. This makes the process of changing credentials to use dataflows in multiple tenant somewhat less than intuitive.

Connecting to a dataflow in a different tenant

With datasets, changing the currently signed in user will result in a different set of datasets being presented when the dataset option is chosen. This is different with dataflows. No matter what user is currently logged in, the cached credentials will be used.

This behaviour can be confusing when multiple tenants need to be accessed. With most other data sources, the cached credentials are linked with the specific data source. For example, when two different SQL databases are connected, Power BI caches two different sets of credentials.

To connect to dataflows in a different tenant, the current connection information needs to be cleared. This can be done with any data source, but it is particularly important to dataflows as it is the only way to switch connection credentials.

To clear the credentials for the dataset, select “File”, “Options and Settings” and the “Data Source Settings”. The Data source settings dialog will then be presented.

Power BI Data source settings
Clearing the credentials for a Power BI dataflow

Unlike most other data sources which can have multiple entries in the list, one for each unique data source, there will only be one source for dataflows. It is named “Power BI dataflows. For example, if the current instance of Power BI Desktop has authenticated to 3 different SQL servers, there will be three SQL Server connections in this list, but there will only be one for dataflows, no matter how many tenants that have been connected.

To switch tenants, the current credentials must be either cleared, or edited. The cached credentials can be fully removed by selecting “Clear Permissions” or they can be changed by selecting “Edit Permissions”. If cleared, the user will be prompted for credentials the next time the dataflow option is selected. If edited, the new credentials will be stored.

Conclusions, and recommendations

It is possible to work with multiple tenants for both connected datasets and dataflows. However, the methods for doing so are completely different for either option. This can obviously lead to some confusion.

It is my opinion that this behaviour should be changed, and that the behaviour or connected datasets is the more intuitive. If the credentials for the currently logged in user were user for both types of connection, it would be much more intuitive, and also easier to user for report designers.

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