Power BI dataflows are the first place that many users will encounter the new Power Query web based interface. Until now Power Query has been restricted to Power BI Desktop and Excel. This new web interface is, well, new, and it doesn’t contain all of the capabilities of the more mature client based interface. The good news is that you can take advantage of both the reusability of dataflows, and the maturity of the Power BI Desktop interface.
A quick glance at the user interfaces for Power Query on the web and in Power BI Desktop reveals the feature difference.
Power Query editor in dataflows
Power Query editor in Power BI Desktop
The Desktop editor has a full ribbon interface with a wide array of capabilities, while the web interface has a simple button bar with a subset of features. In the images above (which show the exact same set of queries in the two interfaces) it is easy to see that the combine binaries, or expand tables feature is not there for the “Content” column.
The key to bringing all of these capabilities in Power BI Desktop to Power Query in dataflows is the Advanced editor. Power Query is at its essence an interface that is used to construct queries using the M language. This core code is available to you in both Desktop and dataflows.
In Desktop, the Advanced editor is available from the ribbon both in the Home tab and in the View tab. In the web based editor it is available by right clicking on an entity (query) and selecting Advanced editor.
Advanced editor in Power BI Desktop – Home Tab
Advanced editor in Power BI Desktop – View Tab
Advanced editor in dataflow editor
The code revealed by this editor can appear rather daunting for a complex query, but all you really need to understand is how to copy and paste it. Build up whatever query you need using Desktop (or Excel!), open the Advanced editor and copy it to your clipboard. Then, either create a new dataflow or add an entity to a new dataflow using the Blank Query data source. Once the editor is open, right click on the query, open the Advanced editor, and paste the query from your clipboard.
Done. Well, almost. While both environments execute M code, there are a few differences to be aware of.
Some functions may not work
There are subtle differences between the M engine in Excel and the one in Power BI Desktop. This approach works very well with these two products, but occasionally an incompatibility can crop up. This is no different with the M engine for dataflows. If you do encounter an incompatibility, try achieving the same thing a different way in Desktop and trying again.
Dataflows do not support all of the data sources that Power BI Desktop and Excel do. This will of course change over time, but as of this writing, dataflows are in preview, and currently support 24 data sources compared to the almost 100 in Power BI Desktop.
Data sources supported in dataflows – Nov 2018
Queries posted into a dataflow that use an unsupported data source will therefore likely not work. However, there’s nothing stopping you from trying, I’ve been pleasantly surprised by a few.
Functions ARE supported in dataflows. They can be created using a blank query (and copying function from Power BI Desktop). However, if that’s all that you do, you may receive an error like “This dataflow contains computed entities, which require Premium to refresh” or “We cannot convert a value of type Table to Function”.
You do NOT need Premium to use functions, but a function must its “Enable Load” function disabled. This is done by right clicking on the function and toggling the Enable Load item to off.
Turn off Enable Load for a function
Computed entities (or calculated queries) are supported by dataflows but because the type of calculation can’t be predicted, they require the isolation that dedicated capacity (Premium) provides.
Referenced tables are an example of computed entities. If you are in the habit of designing a base query that does not load data, and then creating variants of that table that do in your reports, you will need to change that design in dataflows in order to avoid the Premium requirement.
In Power BI Desktop, this is the difference between Duplicate and Reference when creating a new query from a base query. Duplicate will simply create a new query with the same steps, while Reference will create a computed entity. If you want to avoid Premium, you’ll need to use Duplicate.
New query create options in Power BI Desktop
The Power Query capabilities in dataflows are more powerful that they might appear at first glance. Power BI Desktop is the key to unlocking them, unless you’re already a total wizard at writing M code. Even then, the new editing features in Power BI Desktop likely put it over the top as an editor.
For now we need to cut and paste, but I would love to see a day when Power BI Desktop could connect directly to a dataflow and edit it in place.
Reporting on SharePoint data has been a requirement for a long time, and there have been many approaches to fulfill this need. Custom web parts, Data View web parts and SSRS direct connected reports have historically been some of the solutions, but they all suffer from the same problem. If you have any serious amount of SharePoint data, you’ll quickly begin to bump into capacity limits and performance limitations, and in some cases, you can impact the performance of the overall system. In order to avoid this problem, it is necessary to warehouse SharePoint data first, as I argued in this post from 2012.
Once your list-based data is in a relational database, the performance issue is taken care of. However, the means of getting it moved there have traditionally been problematic. For a long time, there was a CodePlex project called the SharePoint List Source and Destination. This solution provided read and write access to SharePoint lists from SQL Server Integration Services (SSIS). Unfortunately, it was last updated in 2012, it was unsupported by Microsoft, and it did not support authentication for Office 365. This of course rendered it useless for use with SharePoint Online. In 2015, SQL Server Integration Services got an OData source, and given that SharePoint lists have OData endpoints, this became a viable option, particularly given that it did support Office 365 authentication. The OData connection from SharePoint did however have some limitations as well.
For cloud scenarios, Power BI has emerged as a very competent way of reporting against SharePoint data. It has native connectors for SharePoint list data, both on premises and in the cloud and Power BI reports can be hosted in the cloud through the SharePoint Power BI web part. On premises, the same can be done with Power BI Report Server. The structure of Power BI reports mean that the data is cached in a data model, so reports are not run directly against the list data source. This avoids the performance issues listed earlier.
Earlier this year I published a series of articles detailing how to do exactly this. The only issue with this approach is that the data shaping and preparation is always specific to a single report. If I have 5 different reports that use one list, I must query and shape that data 5 different times – one for each report. This is where Power BI dataflows come in.
In this context, dataflows are essentially a data warehousing layer with transformation capability. Instead of each report connecting back to a source list, the dataflow connects to the list, shapes the data with Power Query online and stores it in a data lake. The Power BI reports then connect to the dataflow as their data source. Transformation and storage only need to happen once.
As of this writing, dataflows are in public preview, so be warned – some things could change.
Creating a dataflow
Creating a dataflow from a SharePoint list is relatively straightforward. In our examples below, we will work with the same sample list from the series of articles on SharePoint data earlier this year. To begin open Power BI and navigate to a workspace (your personal workspace will not have dataflows). Click on the workspace name in the navigation pane and the dataflows tab should be available.
To create a new dataflow, Select the Create button, and click dataflow.
Select the Add new entities button and the data source selection will appear. SharePoint list and SharePoint online list are both options. SharePoint list is for on premises list data which will work with the On-Premises Data Gateway. In our case we are working with SharePoint Online, so we select the SharePoint Online source.
At this point, you enter the URL for the site that you want to connect to (NOT the URL for the list) and select the Next button. Power BI Will connect to the site and you can then select which list you want to work with. In our case, we need our Listings data, so we select that list and click Next.
Finally, we’re in the Power Query editing screen. This should be quite familiar to those used to working with Power Query in either Power BI Desktop or in Excel. From here you can select the columns that you want to include in the dataflow.
Although this experience is similar that building queries in the Power BI Desktop, there are a few noticeable differences. Queries in a PBIX file are referred to as queries, but within a dataflow they are referred to as entities. These entities can be custom, or they can be mapped to Common Data Model object types. The Power Query web editor also does not include the full featured editing ribbon found in Power BI Desktop, but instead has a button bar. Many of the editing options available in Power BI Desktop are not available in the Power Query web experience.
If you have read through some of my earlier articles on working with SharePoint data in Power BI, you will notice that there are fewer columns available than we see in the Desktop Power Query editor. Most notably for us working with SharePoint data is the FieldValuesAsText column which is the convenient way of retrieving the text representation of complex SharePoint list column types. At first glance, this would appear to be quite limiting.
However, by right-clicking on the entity name, we can access the Advanced Editor.
This Advanced editor allows you to write queries by hand using the M language. The side benefit of the Advanced editor is that it makes queries portable between platforms -Desktop, Excel, and now dataflows. You can therefore build your queries in Power BI Desktop using its fully functional editor and then copy and paste it into a new blank query in the dataflow editor. Using this approach allows you take advantage of the SharePoint helpers built into Power BI Desktop as the FieldValuesAsText column, and other columns are available. Using this technique, the Listings example can be transformed into several normalized tables in the dataflow.
Click on Done to save your entities, and then the Save button to save your dataflow. You will be prompted to Refresh Now which is a good idea because by default, the dataflow has no data contained within it. To keep the data up to date, you need to set a refresh schedule by clicking the schedule refresh icon under actions for the dataflow in question. From here, you schedule data refresh in the same manner as you would with ta Power BI Report.
Using the dataflow
Once data is loaded into the dataflow it becomes a source for a Power BI report. You must use Power BI Desktop to create this report, there is no way to connect a report to a dataflow in the pure web interface. Start Power BI Desktop and select “Get Data”. Choose the Power BI blade and then Power BI dataflows.
After clicking Connect, you will be presented with a set of Power BI workspaces that contain dataflows. Opening the workspace will allow you to open the dataflow and select the desired entities.
Once loaded, the report can be built just like any other. When it is refreshed, it will be refreshed from the data stored in the dataflow, NOT directly from the SharePoint list. It is therefore important to keep the dataflow itself up to date.
Any number of reports can be created from the dataflow. Instead of having all the transformation logic tied up within a single report, dataflows allow them to be centralized and consistent. With a little work, these transformations allow you work with your SharePoint data just as though it were relational. Power BI dataflows really are the best way to perform data warehousing with your SharePoint data, whether you SharePoint is on line or on-premises.
The recent availability of the SharePoint 2019 public preview, and the supporting information that accompanies it has clarified the status of Business Intelligence features in SharePoint 2019. This release, with one exception, is the culmination of the process of decoupling BI from SharePoint which began in SharePoint 2016 through the removal of Excel Services. This decoupling strategy was initially articulated in the fall of 2015 with the document Microsoft Business Intelligence – our reporting roadmap which stated that SQL Server Reporting Services was to be the cornerstone of their on-premises BI investment (and not SharePoint).
The embedded BI features now run with SharePoint as opposed to on SharePoint. These changes do however require some planning and some effort on behalf of those that have already invested in the current platform and wish to move forward on-premises. With this in mind, and the fact that concise information around these changes is a bit difficult to find, I wanted to put this reference together. This post does not get into migration strategies, only the changes themselves.
A summary of the changes to BI features, and a brief discussion of each is below.
SQL Server Reporting Services Integrated Mode
BISM file connections
PerformancePoint – Decomposition Trees
Power Pivot for SharePoint
Scheduled workbook data refresh
Workbook as a data source
PowerPivot management dashboard
SQL Server Reporting Services Integrated Mode
SSRS Integrated mode was deprecated in November 2016, as was not a part of SQL Server 2017. However, organizations could continue to use SSRS versions from 2016 and prior in SharePoint 2016. This is not supported in SharePoint 2019, which means that integrated mode isn’t an option at all with SharePoint 2019. The good news is that the recent Report Viewer web part fully replicates the capabilities of the SSRS Integrated mode web part.
Power View was a feature of SSRS Integrated mode and is available in Excel. When Excel Services was removed in 2016, Power View in Excel required SSRS Integrated mode to work. Both supporting platforms are now gone, and thus Power View is not supported in SharePoint 2019.
BISM file connections
The BISM file connection type was used by Excel and SSRS to connect Power View reports to SQL Server Analysis Services data sources. This connection type has been removed along with Power View.
PerformancePoint is a combination of capabilities that includes dashboarding, scorecards, and analytic reports. Very few new features have been added to PerformancePoint in the last few versions, and this one even loses a few. Many of of these features are also available in Power BI and Power BI report server, and Microsoft has taken the decision to deprecate this product. This gives customers with a PerformancePoint investment time to migrate their assets but is a clear indication that it will also be removed in a subsequent release.
PerformancePoint – Decomposition Trees
The Decomposition Tree feature in PerformancePoint came originally from ProClarity – one of the three products that made up the original PerformancePoint product. These visuals are based on Silverlight, and have been removed from the product accordingly.
PowerPivot for SharePoint
PowerPivot for SharePoint is not supported in SharePoint 2019. PP4SP was originally a combination of two technologies – a specialized version of SQL Server Analysis Services, and a SharePoint service application. In the 2016 version, these two parts were split into two – the SSAS component became a part of the SQL Server installation media as SSQL – PowerPivot mode, and the service application, which continued the name PowerPivot for SharePoint. To be clear, it is the second of the two that has been removed. SSAS PowerPivot mode continues to be an important component and is used by Office Online Server for working with Excel files that have embedded models.
Scheduled workbook data refresh
This feature allowed for the automatic refresh of the data stored within Excel workbooks in SharePoint. It requires a PowerPivot data model to work, but the refresh operation would refresh all connected data in the workbook on a scheduled basis. This was a component of PowerPivot for SharePoint. It has recently been announced that this capability will soon be available in Power BI Report Server.
Workbook as a data source
With PowerPivot for SharePoint deployed, it is possible to use the data model in a published Excel workbook as the data source for another workbook. This feature will no longer be available, and there are no plans at present to reintroduce it.
PowerPivot Management Dashboard
Originally a part of SharePoint Central Administration, the management dashboard provided status updates on all PowerPivot for SharePoint operations. Being a part of PowerPivot for SharePoint, this has been removed accordingly.
The PowerPivot Gallery is a modified SharePoint Document library form that displays worksheet thumbnails contained in published Excel workbooks. This component is Silverlight based, and part of PowerPivot for SharePoint. It has been removed accordingly.
Power View, Decomposition trees, and the PowerPivot gallery were the last remaining features that carried a Silverlight dependency. SharePoint 2019 no longer has any Silverlight dependencies.
These changes are significant for anyone with an existing Business Intelligence investment that plans to move to SharePoint 2019. I intent to write more about migration strategies and will be addressing these topics at various conferences in the future.
Power BI has been able to work with Excel files since it was first introduced. Indeed, it was born from the analytic capabilities in Excel. Users can connect directly to Excel files by using the Power BI service and nothing but a browser. However, depending on the content of the Excel file, and the method of connecting, the resulting products can be very different. In this post I will attempt to clarify this behavior. A subsequent post will detail the options available when working with Excel files in Power BI Desktop.
Excel is a multi-purpose tool. It contains all the building blocks of Power BI, and as such, it is an excellent Business Intelligence client. Excel files are also often used (much to my chagrin) as a data storage container, or as a data transport medium. Understanding how the file is structured, and what you want to do with it is key to making the right choice when combining it with Power BI.
Originally Excel files (workbooks) were collections of worksheets. Analysts could import data into those worksheets and then analyze them with the tools that Excel provided. Although Excel was never intended to be a database, it’s ease of use and familiarity led many people to begin using it that was, and “spreadmarts” (spreadsheet data marts) quickly became a problem. The problems arose because the instant data was extracted from a source it became stale, and the fact that it was being stored in worksheets meant that it could be edited (changing history) and became subject to the data size limitations of a worksheet.
To take advantage of Excel’s analytic capabilities without being subject to the issues involved in worksheet data storage, the data model was introduced, initially through PowerPivot. The data model is a “miniaturized” version of the SQL Server Analysis Services tabular engine that runs in Excel. This data model is read only, refreshable, and highly compressed which importantly means that its only data limitation is the amount of available memory available on the machine running it. Importantly, this engine is the same engine that is used by Power BI – the advantages of which we’ll explore shortly.
Excel of course still needs to be able to use worksheets and be Excel, so we can’t just remove the worksheet capability (which incidentally is effectively what Power BI Desktop is – Excel without worksheets). Therefore, today from a data perspective, Excel files can have data in the data model, worksheets or both. From the Power BI service perspective, the important thing is whether the file contains a data model, as it treats the two cases differently.
Getting Excel Data
From the Power BI service, you click the Get Data button, and then the Get button in the Files tile. You are then presented with one of two dialogs depending on whether you are using a personal workspace, or an app workspace.
Connecting file-based data to a personal workspace
When importing into a personal workspace, there are 4 possible data sources.
A local file is one that is stored on a file system local to the machine being used. Selecting this option will allow you to work with the Excel file stored in that location, but if the file is being used as a data source (data is in the worksheets), then a Data Gateway will be required for any data refreshes. Power BI will also connect to a file stored in OneDrive, either Personal or Business (through office 365). Finally, the service can work with files stored in any accessible SharePoint site (not simply Team sites as the name would indicate).
Connecting file-based data to an App workspace
When importing into an App workspace, there are 3 possible data sources. The Local File and SharePoint – Team Sites options are precisely the same as when importing into a personal workspace. The difference is the OneDrive – Workspace name option replaces the two other OneDrive options. Choosing this option allows you to work with files stored in the “Group OneDrive”. Since every App workspace is backed by an Office 365 or “Modern” group, it also has access to the SharePoint site for that group. The “Group OneDrive” is the Documents library within that SharePoint site. Therefore, choosing SharePoint – TeamSites and navigating to the Documents library will render the same results in a few more mouse clicks, but also give access to all other document libraries within that site.
Connect vs Import
Once you navigate to the Excel file that you want to work with, you select it, and click connect. You will then be presented with two options for the file, Import or Connect.
This choice dictates how the file is brought into the Power BI service. The structure of the file determines exactly what is brought in to the service in both cases.
Clicking the Connect button allows Power BI to connect to and work with the Excel file in place. The workbook is displayed as an Excel workbook in full fidelity in the Power BI interface using Excel Online. The file itself is shown in the Workbooks section in the Power BI interface, and it stands alone from other Power BI elements (except that regions of it can be pinned to a dashboard). Connecting to an Excel report will not create a Power BI Dataset, Report, or Dashboard. All operations, including refresh (see below) are controlled through the workbook.
At no point is the file moved, or “brought in” to the Power BI service. If the file is being stored in SharePoint, or OneDrive, anything done to the file in the Power BI service will be visible to anyone with access to the file itself, whether they are a Power BI user or not. This includes refresh, which will be discussed further below, but the important part to remember here is that if the data in the connected file is refreshed through the Power BI service, and it is being stored in SharePoint (or OneDrive), all users will be able to see updated data the next time that they open the file.
Connecting to an Excel file behaves the same way whether the file contains a data model or not, but the file must contain a data model in order to be refreshed by the Power BI service.
Connected Excel file within Power BI
Importing an Excel file behaves totally differently from connecting to it. When an Excel file is imported, it is treated as a data source to Power BI, and the assets within that file are brought into the Power BI service. Subsequent changes to the source file are not immediately reflected within the Power BI service, but are retrieved through the refresh process.
The way that the assets are brought into the service depends very much on the structure of the file, specifically whether it contains a data model or not. If the file does not contain a data model, then Power BI will use the data contained in the Excel worksheets to construct a new one. This is similar to what happens when a CSV file is imported into the service. If the file does contain a data model, then the worksheet data is imported, and that data model is brought into the service as-is. One important exception to this is if worksheet data uses the same query as an existing model, the worksheet data is ignored, and the data model is brought in as-is. This is important because Excel’s Power Pivot editor can be used to edit the model, creating calculated columns, calculated measures and relationships prior to import. The model that is automatically created when the file does not contain a model has no editing capabilities.
When an Excel file with a data model is imported, the data model (imported or created) is added to datasets, and a link to the dataset is added to the default dashboard for the workspace. If no default dashboard exists, one will be created. A report can then be authored in the service. If the workbook contains any PowerView reports, these will be converted to native Power BI reports and added to the service as well. Any embedded 3D maps are not brought in.
Imported Excel File showing calculated measures
Data refresh options, and behavior depend on both the Get Data choice (connect or import) and the structure of the Excel file.
If the workbook is connected to the service, and it does not contain a data model, it cannot be refreshed. This is true even if the worksheets in the workbook contain data from Power Query queries. This is the only scenario that does not support refresh in any way.
If the workbook contains a data model refresh is supported. The interesting part is that refresh will be triggered not only for the data model itself, but for any worksheets that have Power Queries as a data source. Therefore, a workaround to the lack of refresh support for a worksheet with no data model is to add a blank data model.
For refresh to work, the data source must be available to the Power BI service. This means that the source must be available in the cloud or registered on an available On-Premises Data Gateway.
The important thing to note about connected workbooks is that the refresh options that are performed on them are permanent – refreshed data is stored with the workbook. This means that if the connected workbook is stored in SharePoint, or shared through OneDrive, updated data is available to all users with access regardless of whether they are Power BI users.
Refresh options for imported workbooks are slightly more complicated. As mentioned above data is either imported from the worksheets, a data model imported into the service or both.
If data was imported from worksheets, then the Excel file is the data source from the standpoint of Power BI. If the file is stored in SharePoint or OneDrive, it will automatically be refreshed every hour by default. This means that changes to the underlying Excel file will be reflected back in the Power BI service within an hour. This feature can be disabled, but it is not possible to change to hourly schedule, nor precisely when it will occur.
Refresh options for workbooks in OneDrive/SharePoint
If the file is stored on a file system, it can be scheduled more granularly, but you will need to connect to it through an On-Premises Data Gateway.
If the file contained a data model that was imported into the service, then the original source of data for that data model (the query) is what the Power BI service will refresh from (NOT the Excel file itself). In this case, the refresh options are the same as with most other Power BI data sources – Excel is taken out of the picture completely, and any changes to the source Excel file will not be reflected into the service. The exception to this is if the file had both a data model, and worksheet data that was imported.
In the case of an Excel with both a data model and worksheet data, both cases above will apply. The workbook is used as a data source for the table that was created by Power BI on import, and the original data model’s source is updated independently. This means that changes to the worksheet data are reflected in the Power BI service when refreshed, but any model changes to the original Excel file are not. Both the OneDrive and regular refresh schedules are used for imported files of this type.
Refresh options for a combined data source
The following table summarizes the refresh options available for file structure and connection type.
Get Data option
Refresh from worksheet
Data model only
Refresh from model source
Refresh from model source
Data model plus worksheet data
Refresh from model source and worksheet source
Refresh from model source and worksheet
Both Excel and Power BI are powerful tools in their own rights, and the decision to use one does not preclude using the other and in fact there are many good reasons for doing so. Bringing refreshability to Excel files stored in SharePoint is just one of them. It is however important to understand how it all works in order to get the maximum impact.
This post is the sixth and final post in a series exploring Power BI and complex data types in SharePoint. This post examines the various options in Power BI for working with lookup fields. The previous posts are:
A lookup field in SharePoint contains values looked up from another list in the same SharePoint site. Strictly speaking, the field contains only the ID from the item in the source list, and the value(s) is/are looked up whenever the field is displayed. The lookup field can also be used to display multiple field values from the target list items.
Consider the following list that contains a lookup field named “Neighbourhood”:
We can see from the screenshot above that the text value for neighbourhood is displayed in the view, although only the row identifier is stored in the column. We will be able to get both values and more if desired in a Power BI report, but first we need to build the report using Power BI Desktop.
Loading the Data
We first launch Power BI Desktop, select “Get Data” and then choose SharePoint Online list (if connecting to SharePoint Online) or SharePoint List (if using SharePoint Server). We are then prompted for the URL of the SharePoint Site. The dialog is titled SharePoint lists, but the value is the URL of the site, NOT the list itself. Once this is entered we are prompted for credentials if we haven’t connected to this site before. After entering credentials, we can select the list that we want to report on. In our case, it’s named “Listings”. We select it, and then click on the Edit button.
Once the data loads in, one of the first things that you’ll notice is that there are a lot of columns to choose from, and it’s a good idea to remove the columns that you don’t need. We can do this by right clicking on the desired column titles and selecting “Remove”. In this case, we can remove the ContentTypeId column and everything to the right of it, with two important exceptions. We want to keep the “FieldValuesAsText” in addition to the special “Neighbourhood” column at the far right of the columns, as we’ll be needing them for our options below.
Examining our columns, we can see that amongst the simpler text fields, we don’t have a “Neighbourhood” column, but instead, a “NeighbourhoodId” column, with numeric values. We do have a Neighbourhood column further off to the right, but it doesn’t display simple text (we’ll come back to this shortly). If we simply want the text value of our lookup target, we can use the “FieldValuesAsText” column quickly.
Scrolling right in the Query editor view, we find the “FieldValuesAsText” column. The record values represent a one to one relationship with the text values of the list row, so we can click on the column expander at the right of the column title. From there we can extract the text value of any column, including our lookup field, “Neighbourhood”.
With “Neighbourhood” checked, and nothing else, including the “Use original column name..” option, we can click OK, and the “FieldNameAsText” column is replaced by a new column, “Neighbourhood” that contains the text values for Neighbourhood.
If this value is all that is needed, then this is a totally valid approach, and we can move on to report building. However, this is only one way to achieve this goal. If more information is needed, then other methods may be more suitable.
Retrieving all Lookup Field Values from the Extended Column
Given that the lookup target item is a SharePoint list item, all that item’s properties are available to us. We can access them from the extended column set up for the field. In our case, the original “Neighbourhood” column is the extended column. We can expand this column by selecting its column expander.
We then deselect all of the columns except the ones that we want to use in the report. The fields available are the fields available in the target list. In our case, we select the “Title” field, as it is the one being looked up. We can however retrieve any of the fields that we need from the target list.
Keep in mind that “Title” in our example is a simple text field, so no further action is necessary. The retrieved fields can be complex (person, MMS, etc), but keep in mind that if a complex field type is retrieved, it will need to be transformed just like any from the list in question.
The field name in the target list may not adequately describe its function for the report. In our case, “Title” actually means “Neighbourhood” in this report. It’s a good idea to rename it.
Finally, if multiple field values are to be retrieved, the data model could grow significantly. This is because the values for every field are repeated in every row of data. Given that the original lookup column adds a measure of relational behaviour to SharePoint, using this relationship is the most efficient way to work with this data. Power Query allows us to do just that.
Working with Related Tables
To work with related tables, we need not only the original data table (in our case, “Listings”) but also the table for the lookup list itself. To do this, from the Query Editor, we create a new data source like the one created above for “Listings”, but instead we select the lookup list (“Neighbourhoods”).
Once imported, we can remove any extraneous columns, and then set the data type for the ID field to be “Whole Number”.
We also need to set the data type of the “NeighbourhoodId” column in the Listings table to “Whole Number”. Once these options have been set, we are ready to work with the data model and the report. We select “Close and Apply” from the ribbon to load the data into the model.
Once loaded, we launch the relationship builder in the design pane in order to establish the relationship between the two tables.
We can see that Power BI has already detected a relationship. However, it is not correct. The model designer assumes that because both tables contain an “id” column, then they must be related. However, the true relationship is between the “Id” column in our “Neighbourhoods” table, and the “NeighbourhoodId” column in our “Listings” table.
We must first delete the detected relationship by selecting the connector between the two tables and pressing “Delete”. We can then create the proper relationship by dropping one of our related columns onto the other. Once this is created, we also need to ensure that the “Cross filter direction” is set to “Both”. We do this by double clicking on the relationship connector and selecting the appropriate option.
Once the relationship has been established, we can return to the design pane and construct a rudimentary report. We drag a few fields from Listings into a table, create a calculated measure for the number of listings, and we add the “Title” field (renamed to “Neighbourhood”) to the canvas separately. Once we set the visualization for “Neighbourhood” to a slicer, we can easily slice our listings data by neighbourhood.
We can therefore see that there are several options for accessing data for a lookup field, ranging from simple to complex. The trade-off for simplicity is flexibility. Which approach used will depend on your requirements but storing the lookup table separately is the most efficient as the data is only stored once and referenced.