Month: November 2018

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.