Microsoft Graph data connect (GDC) is a connector technology that allows an organization to extract data in bulk from the Microsoft Graph. Using Azure Data Factory, extraction jobs can be scheduled that can securely extract Graph data while respecting an organization’s data control policies. On a scheduled basis, GDC stages the data behind the scenes, and stores it in an Azure storage account. The storage can either be Azure Blob storage, Azure Data lake Gen 1, or Azure Data Lake Gen 2. This article describes a procedure to process the output from GDC and store it in a Power BI dataflow.
Details on how to configure GDC can be found here, and an excellent video tutorial here.
Azure Data Lake Gen 2 Storage
Azure Data Lake Gen 2 (ADLG2) brings a hierarchical namespace to Azure Blob storage. This storage system is designed for big data analytics and is highly cost effective. It is one of the three data sink (destination) options for GDC, and it is the required storage system for the “bring your own”, or external storage option of Power BI Dataflows. Given that n ADLG2 account is required for the Power BI Dataflows, it is logical to use the same account as the GDC data sink, but it is not required.
In order to use an ADLG2 account for external storage with Power BI dataflows, it must be in the same data center as the Power BI tenant. The data center for a tenant can be determined by navigating to the Power BI web application, selectin the “?” icon in the upper right, and then selecting “About Power BI”.
In order to be able to use an external storage account for Power BI dataflows, it MUST be created in the data center listed in “Your data is stored in”.
Connecting Power BI to ADLGen2 Storage
When a dataflow is created in Power BI, it is stored in an ADLG2 storage system managed by Microsoft. If Power BI is the only platform that will access the data, this is perfectly adequate, but an organization may wish to use the data with other tools. If this is the case, a Power BI tenant can be connected to an ADLG2 account that is accessible to other tools. A workspace administrator can then decide to have all the dataflows in that workspace store their data in the custom storage account. These are known as “external dataflows”. Dataflows are all stored in Common Data Model (CDM) folders which are described in detail here.
Detailed instructions on configuring external dataflow storage for Power BI can be found here . The process consists of several steps. It should be noted that as of this writing, external dataflows are in preview, and these steps could change.
- If one does not already exist, create an ADLG2 account in the same tenant as Power BI
- In Azure, Grant the Reader role to the Power BI service identity for the account in #1
- Create a file system for Power BI. The file system MUST be named “powerbi”
- Using Azure Storage Explorer, grant file system access to three Power BI service principals, Power BI Premium, Power BI Service, and Power Query Online (see the above link for details)
- Connect the Power BI tenant to the ADLG1 account
- Enable workspace administrators to assign workspaces to external storage.
As of this writing, step #5 above is irreversible. Care should be taken with its name.
Once configured, a workspace administrator can assign their workspace to their external storage. This setting is a property of the workspace, and can be accessed via its settings with the “Storage” tab.
Once this setting has been enabled, all dataflows will be stored in external storage. A folder is created within the file system created in step #3 above with the name of the workspace. Each dataflow in the workspace will be added within that folder, and each entity of the dataflow as a folder of its own. The dataflow folder will contain a file named model.json which describes the entities, and the entity folders contain multiple csv files which house the data itself. Within Azure Data Explorer, the structure appears as below.
- Azure Data Lake Gen 2 account (connected to the Power BI tenant)
- File system created for Power BI dataflows (always named powerbi)
- Workspace folder
- Dataflow folder
- JSON file describing the dataflow
- Entity folder containing entity data
Once configured, Power Query Online (part of the process of creating a dataflow) can be used to acquire and transform data. The data will be stored in these folders according to the Common Data Model specification and can be accessed by other applications. However, the reverse of this is also true. Any CDM folder that is stored in the Power BI connected file system can be connected to Power BI as an external dataflow. The process for doing this is described here. The order of operations is important. The user that will make the connection needs to be granted access to the CDM folder before it is populated with data.
An external dataflow is read only with respect to Power BI (Power BI only sees the data; it does not transform it). The goal is therefore to transform the data created by Graph data connect into the CDM format. Azure Databricks provides support for doing so.
Azure Databrick is a suite of serverless big data technologies that encompass Hadoop, Apache Spark, SQL, Python and Scala technologies. Databricks clusters can be created and used when needed and discarded or suspended when not as needed. A discussion of how to create and use Databricks is beyond the scope of this post, but there is a great deal of documentation on it here. In addition, Microsoft provides a free 14-day trial of Azure Databricks.
Databricks is particularly useful in this scenario, as it has libraries that support Azure Data Lake Gen 2, and libraries that support the Common Data Model. Databricks notebooks can be called from Azure Data Factory, so that when a GDC extraction job is completed, the resulting files can be processed with Databricks to populate the CDM folders.
An excellent tutorial on using Databricks with dataflows and CDM folders can be found on GitHub here. The scenario in the tutorial involves using dataflows to produce data instead of consuming it, but it does cover off several important concepts. The tutorial is part of the project that includes the CDM library for Databricks which is used to transform GDC data into CDM folders.
As of this writing, the CDM library requires a Databricks 4.3.x-scala2.11 cluster. This is an older configuration that is not available to the standard user interface when creating a Databricks cluster. Subsequent versions of the CDM library will most likely support newer clusters, but at present, it is necessary to take a few additional steps during cluster creation.
From the cluster creation UI, specify window.prefs.set(“enableCustomSparkVersions”, true) in the browser debug console, and then navigate to the cluster page, and specify the image tag below. Refresh the browser and then 4.3.x-scala2.11 will be listed as a custom version.
Once a cluster has been created, and the CDM library loaded into it, a notebook can be created to process the GDC data. Processing consists of four main steps. Connecting Databricks to ADLG2, Reading the JSON files from GDC, extracting the desired data into dataframes, and writing the data out to CDM folders.
Connecting Databricks to ADLG2
The recommended way to connect Databricks to ADLG2 storage is through a Service Principal. The same principal that GDC itself uses can be used, and if the same ADLG2 account is being used, no further configuration is necessary.
Databricks will need to read from the file system that houses the GDC data. Several lines of code (Python) in a Databricks notebook will establish the required connections:
DLG2AccessKey = “Storage Account Access Key” ADLG2AccountName = “ADLG2 Account” spark.conf.set("fs.azure.account.key." + ADLG2AccountName + ".dfs.core.windows.net", DLG2AccessKey) spark.conf.set("fs.azure.createRemoteFileSystemDuringInitialization", "true") filesystem = "abfss://GDCFileSystem@ADLG2AccountName.dfs.core.windows.net/"
Once connected, files in the GDC folder can be listed using the built in dbutils library:
dbutils.fs.ls(filesystem + “/GDCFolderName”)
While the above and below examples shows account names and keys being explicitly defined in the notebook, this is not recommended beyond any testing or demonstration environments. Instead, it is recommended to store such secure strings in Azure Key Vault and retrieve them at runtime. For instructions on how this is done, see the document Secret Scopes.
Reading JSON Files
Databricks can read all JSON files in a folder (as well as other text-based formats) into a dataframe. A dataframe is an in-memory table that can be hierarchical and queried via standard SQL commands. The schema of the dataframe will be implied through the structure of the JSON files contained within. To load all of the GDC JSON files from a particular folder into a dataframe, the following line of Python can be used:
contactbasedf = spark.read.json(filesystem + “/Contacts Folder”)
The read is recursive, which means that subfolders are interrogated as well. GDC folders typically contains a metadata folder with files of differing schemas than the data files themselves. For this reason, it is a good idea to move the data files to a dedicated folder before reading them into a dataframe. This can be done with the dbutils.fs.mv command.
Extracting the desired data
Once the files have been read into a dataframe, the dataframe can be saved to a temporary table. This table can be queried through standard SQL commands. For example, the query creates a temporary table from the initial dataframe (contactbasedf) that was created by reading JSON files created by GDC for organizational contacts. The relevant details are then queried and saved into another dataframe, named df1 in this case.
contactbasedf.createOrReplaceTempView("contactbaseTemp") df1 = spark.sql("SELECT AssistantName, Birthday, BusinessHomePage, CompanyName, Department, DisplayName, GivenName, Initials, JobTitle, Manager, MiddleName, NickName, PersonalNotes, Profession, Surname, Title from contactbaseTemp")
Writing to CDM folders
Once the CDM libraries are loaded into a Databrick cluster, writing data to them is a relatively simple method call from a dataframe. The call itself requires several parameters, and those parameters are:
- cdmModelName – The name of the Model (dataflow) that houses all entities
- entity – the name of the entity within the dataflow (a dataflow can contain multiple entities or tables)
- cdmFolder – The folder in ADLG2 to save the model.
- appId – The service principal ID of an application with Blob Contributor access to the ADLG2 account
- appKey – The secret key for the appId specified above
- tenantId – The tenant ID for the ADLG2 account
Using the dataframe defined above, the contents of the dataframe can be written out to the CDM folder with the following (Python) code:
appID = “Service Principal ID” appKey = “Service Principal Key” tenantID = “Tenant ID” Workspace = “Workspace Name” cdmModelName = "AllContacts" outputLocation = "https://" + ADLG2AccountName + ".dfs.core.windows.net/powerbi/" + Workspace + "/” + cdmModelName (df.write.format("com.microsoft.cdm") .option("entity", "Contacts") .option("appId", appID) .option("appKey", appKey) .option("tenantId", tenantID) .option("cdmFolder", outputLocation) .option("cdmModelName", cdmModelName) .save())
The above code will output the contents of the dataframe to an entity named “Contacts” in a model named “AllContacts” stored in a folder named “AllContacts” within the workspace folder specified in the “Workspace” variable.
Creating an external Dataflow
Once the GDC data has been written to a CDM folder, it can be connected to Power BI as an external dataflow. In order to do so, as mentioned above, the user making the connection must have explicit access to the model folders.
From a Power BI V2 workspace (V1 workspaces are not supported), go to the dataflows tab, and select Create – Dataflow from the toolbar. If Power BI has been connected to the ADLG2 storage, and the workspace has been configured for external storage, the “Attach a Common Data Model folder” option should appear.
Selecting “Create and attach” brings up the Attach Common Data Model folder dialog box, where two items must be entered.
The Name of the dataflow is the name with respect to Power BI. It can be completely different than the name of the model folder, or the internal name of the model created above, but it’s likely a good idea to keep it consistent. The CDM folder path is actually the absolute path to the model.json file that describes the model, and it’s vital that model.json be included at the end of the path. Failing to do so will result in an error.
Once completed, Power BI Desktop can be used to connect to the external dataflow, just like any other dataflow. The only difference is that external dataflows are not refreshed in the Power BI service, but will be updated by Databricks. The same Azure Data Factory jobs that extract data from Graph data connect can be used to call into the Databricks notebooks when the data has been extracted.
If you are interested in a product that leverages the data produced by Graph data connect, I would be remiss if I did not suggest our tyGraph for Exchange, which is currently in preview. It combines all of the technology listed above with a rich set of reports in concert with other Office 365 workloads. If you are interested, please contact me directly, or email email@example.com .
Finally, for an in depth conversation with Abram Jackson and Tyler Lenig from the Graph data connect team, check out their recent appearance on our BIFocal podcast.
[…] Using Microsoft Graph Data Connect with PowerBI dataflows […]
[…] Using Microsoft Graph Data Connect with Power BI Dataflows (@diverdown1964) […]