Power BI is without question the best way to report on data in SharePoint lists. The query tools available in Power Query make working with SharePoint data relatively painless, an the cached dataset means that reports are run against an optimized copy of the list data, not the data itself.
This latter distinction, while removing the performance issues of systems that query lists directly, also introduces problems with data latency. The report will never be fully “up to date”, as it needs to be refreshed on a periodic basis.
Consider the following scenario. A Power BI report has been built that uses data from a SharePoint list. That report has been embedded on a SharePoint page in the same site. A user adds an item to the list, and then navigates to the page to see the updated report. Unfortunately, that report won’t get updated until the next scheduled refresh.
This has been a significant problem, until the recent release of the new “Refresh a dataset” action in Microsoft Flow.
It is a relatively simple procedure to add a simple 1 step flow to any SharePoint list that is triggered when an item is created, updated, or deleted. This flow simply needs to add the “Refresh a dataset” action, that is configured for the relevant dataset, and these embedded reports will be updated very shortly after the data is modified.
Alternatively, the flow can be triggered by a timer, allowing you to create your own schedule (every 5 minutes, etc) that is not hardwired to run at the top or bottom of any given hour.
A few caveats should be kept in mind when using this action however.
While this action gives us much finer grained control over when refreshes happen, all of the current license restrictions remain in place. For datasets located in the shared capacity, only 8 refreshes per day are allowed.
For datasets in dedicated capacities (Premium), there are no limits to the number of refreshes. The limit of 48 per day is a UI restriction, not a licensing restriction. However, refresh can utilize significant resources, particularly memory, so you’ll want to ensure that you have significant resources to support the update frequency.
Finally, the load on the source data system should be considered. Refresh will pull a significant amount of data every time it is run.
Caveats aside, this new flow action is a welcome relief to those that need greater control of how their reports are updated.
At the end of 2018, SharePoint received something that we haven’t seen for a long time – a new column type, Location. Location columns will look up an address and geocode it as it is being entered in a form. It will also separate all the constituent parts of the address as well as the latitude and longitude into separate display only columns. These columns are used primarily in views but can also be used in reports. Given that I put together a series of posts recently on using Power BI to work with complex SharePoint report types, I was interested on how to report on this new column type. As it turns out, it is relatively straightforward.
This post will delve into the nuances involved with reporting on this new SharePoint Location column in Power BI..
The Location Column
To begin with, the Location column is a “modern” SharePoint column. This means that it can be added to a list via the Add column button in the list view, but NOT through the list settings page as other column types are.
List view creation
List settings creation
If the Add column does not appear for you, you may be using a “classic” SharePoint site, or you may be using one or more column types that are not supported in “modern” which causes a classic view to be used. Removing these columns from the view is often enough to light up the add button.
Once created, you will have the option to add any or all of the address components to the view. These are display elements only and will be available to reports (or other views) whether or not they are added to the view at creation time.
Once created, entering data is as simple as typing in an address, or the name of a location into the column. The typeahead feature will attempt to find the location and fill in the details.
Once selected, the full address will be filled in, and all the constituent address properties will be populated. If they are on the view, the list can be sorted, filtered, etc. by these elements.
Reporting on the Location Column
Internally, the location is saved as a BLOB of JSON content within a column. When the column itself is used in the view, its friendly display format is displayed. When constituent items are displayed (City for example) their values are extracted from the column and displayed as discrete elements. For other SharePoint column types, this can provide complications, but the developers of the location feature seem to have had reporting in mind when it was built. Consider the following list that contains a Location column named Location:
Loading the Data
We first launch Power BI Desktop, select “Get Data” and then choose SharePoint Online list. 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 Properties. 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.
With all other complex SharePoint column types, the FieldValuesAsText column will retrieve the textual representation of required column values. This is the way that the column value appears in a view. However, it appears that the Location column type is an exception to this rule. When the Location column is used, the JSON value itself is returned, which renders FieldValuesAsText relatively useless. THis value is also available using the Location column value itself. The steps for extracting FiedValuesAsText are covered in previous posts in this series. Given that ultimately this will not be a good approach for the Location column, we won’t go into it further here.
Field value and value extracted from FieldValuesAsText
The text value of the location column is instead available through the derived DispName column.
With Power BI, it is possible to transform the JSON data contained in the original column, or the extracted FieldValuesAsText column. All of the extracted properties are available through more efficient means. The FieldValuesAsText column can therefore be ignored for the purposes of reporting on Location columns. In addition, in most cases, the original column (Locations in this case) can be removed, and the DispName column should be renamed in its place.
This behaviour is inconsistent with the behaviour of other complex SharePoint fields. It does not affect capability, but in the interests of consistency, my strong suggestion would be for the SharePoint team to eliminate the DispName field, and leverage FieldValuesAsText for the text conversion in the data feed.
Using Location Components
All the text components of the location column are separated out automatically as columns in Power Query. They can be used as any other column, and no additional action is necessary.
Automatically extracted location components
Power BI will automatically geocode data at the time the report is rendered. The text components can therefore be used by the reporting engine to place data on a map. However, geocoding is a relatively computationally expensive operation, especially if there is a lot of data, or poor internet connection. In addition, some visuals may require the use of specific latitude and longitude co-ordinates. These co-ordinates are available through the GeoLoc column if they are needed, but they do need to be extracted.
Within Power Query, locate the GeoLoc column, and click on the Expand icon in the right of the column header.
Select both the Latitude and Longitude columns and deselect Use original column name as prefix. In my testing, both Altitude and Measure do not return any meaningful data, so they can be safely ignored, however this could change in the future.
At this point, we are almost ready to do some reporting. Once all the required columns have been shaped, and their data types set, select the Close and Apply button from the ribbon.
Before using the location data on a map, it is important to categorize each of the components so that Power BI knows how to use it on a map. To categorize a data field, select it from the fields list. Then select the Modeling tab from the ribbon click the Data Category dropdown.
The category for most of the fields is obvious, but below is a table of recommended choices. In addition, both the longitude and latitude fields need to be set to the Decimal Number type.
State or Province
Once categorized, the data can be placed on a map according to any desired parameters. In this can, the below shows a map of listings colour coded by the asking price range.
The resulting report can then be published to the Power BI service, and then embedded into a SharePoint page through either the Power BI web part, or secure embedding if so desired.
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.
As discussed in a previous post, Working with Excel Files in the Power BI Service, Excel and Power BI have a rich, complex relationship. Power BI Desktop is the primary design tool for Power BI, and it has many feature overlaps with Excel as an analytic tool. Excel can be used both as an analytic tool and a data source, and the structure of the Excel file will dictate the way that Power BI Desktop can be used with it. If Excel is being used as an analytic tool (i.e. connected to data), the appropriate items in the file can be imported into Power BI Desktop. If it is being used as a data source (data in worksheets), Power BI Desktop will connect to it, and use its data to build a model. This post attempts to articulate the nuances of both scenarios.
Importing from Excel
Power BI Desktop has an unfortunate name in my opinion. It is a design tool and is not meant to replicate the capabilities of the Power BI service on the desktop, as the name might suggest. A better name for the product would I believe be Power BI Designer. Its purpose is to connect to and transform data (Power Query), build data models for Analysis (Power Pivot) and build reports (report designer).
Used as an analysis tool, Excel has all these capabilities as well. In fact, the first two (Power Query and Power Pivot) are identical to what is already in Power BI Desktop. Excel also has Power View for analytic reporting. Power View is very similar to the type of reporting in Power BI Desktop, but uses a different technology and has been deprecated for some time. As a result, Excel charts and pivot tables are the primary means of visualizing data in Excel.
So why would you need to use Power BI Desktop if you are using Excel? As explained in my previous post, the Power BI service can fully interact with Excel as an analysis tool, and allows you to interact with Excel right from the Power BI Service. If Excel is meeting all your analytics needs, then there may be no need to introduce Power BI Desktop at all. However, if you wish to take advantage of Power BI’s analytic reporting capabilities, and you have existing Excel assets, you may wish to convert them to the native Power BI format. Whatever the reason, moving from Excel to Power BI is relatively straightforward with Power BI Desktop.
From the File menu in Power BI Desktop, select Import, and then Excel Workbook Contents.
You are then prompted to select an Excel file. Once selected, you are then presented with a warning dialog.
The dialog does a very good job of explaining what will happen, specifically the fact that data from workbooks will not be brought into this new file. Any Power Pivot data models or Power Queries will be brought in. If the workbook contains legacy Power View sheets, they will be converted to native Power BI visuals. In addition, any legacy (non-Power Query) data connections used by the source file’s Power Pivot data model will be converted to Power Query and imported.
Legacy Power View Sheet converted to Power BI visuals
A complete list of workbook content and what is/isn’t converted is below:
Import to PBI Desktop Support
Data in sheets
Data model (Power Pivot)
Converted to Power Query and imported
Power View Sheets
Converted to PBI visuals and imported
Once imported, the new Power BI file (PBIX) lives on its own and contains no connection or any other type of relationship to the original source Excel file. If the source Excel file is changed, there is no way to update the PBIX file. Any imported data connections are between the PBIX file and the original data source. The new PBIX file can be published to the Power BI service like any other.
Connecting to Excel
Connecting to Excel as a data source is a very different thing than importing from it. In this scenario, the data in the worksheets and only the data in the worksheets is brought into the data model. The is very different behaviour than that of connecting to Excel files to the Power BI Service, where both the model and the worksheet data is brought in.
Using the Excel Connector
The easiest, and most obvious way to connect to Excel worksheet data is by using the Excel connector. From the ribbon in Power BI Desktop, select Get Data. The Excel connector is right at the top of the list.
Selecting it allows you select your source file, and then the workbooks within it, and then build out the data model.
This approach works well but carries with it an important limitation. The new queries are connected to the file using a local file system. This means that to be refreshed, an on-premises data gateway is required. In order to eliminate the gateway requirement, you can connect to the file in SharePoint using the SharePoint folder connector.
Using the SharePoint Folder Connector
The SharePoint Folder connects to all the files stored in libraries of a SharePoint site. It allows you to report on file metadata, but it also allows you to drill into file contents.
From Power BI Desktop select Get Data but instead of selecting Excel, Search for SharePoint and select SharePoint folder.
Once selected, enter the URL of the SharePoint site (NOT the URL of a library or folder) in the dialog box.
Next, you will be presented with a preview of all the files in your site. Unless you are only interested in file metadata, click on the Edit button to bring up the Power Query editor.
The initial view will contain all the files in the site, but we are interested in the content of just one of those files. Every file in this view will contain the hyperlinked value “Binary” in the Content column. Clicking that link for the file that you want to connect to will drill down into the contents.
From this level, you can build your Power Query, data model, and report as needed just as if you had used the Excel connector. The difference is that now when you publish your report to Power BI, it will know the file is stored in SharePoint and will connect directly to it. It will not require a gateway for refresh purposes. Once credentials are registered, the report will refresh itself directly from the workbook stored in SharePoint.
XLS vs XLSX
A note of caution. The above SharePoint folder approach only works for XLSX files. The Power BI Desktop and the Power BI service both support both Excel file formats (XLS and XLSX). However, refresh does not. If the source file format is XLS, and a refresh is attempted, you will receive the classic “microsoft.ace.oledb.12.0 provider” error in the Power BI service.
The older Excel file format (XLS) requires an Access driver to refresh, which is not a part of the Power BI service. The newer XLSX file does not require this driver. As a result, if the source file is XLS, refreshing it requires going through an On-Premises Data gateway, and that gateway machine must also have the ACE components installed.
To recap, you can bring Excel assets into Power BI Desktop by using the import function, and you can load data from Excel files through Power Query. The two operations have very different results, and the can be combined if a source workbook contains both analyses and data.
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.