Run PowerBI using the Airflow SDK


In this section, we provide guides and references to use the PowerBI connector.

Configure and schedule PowerBI metadata and profiler workflows from the OpenMetadata UI:

OpenMetadata 0.12 or later

To deploy OpenMetadata, check the Deployment guides.

Please follow the steps mentioned here to setup the permissions required for the PowerBI connector.

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment.

To run the PowerBI ingestion, you will need to install:

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to PowerBI.

In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.

The workflow is modeled around the following JSON Schema

This is a sample config for PowerBI:

clientId: PowerBI Client ID.

To get the client ID (also know as application ID), follow these steps:

  • Log into Microsoft Azure.
  • Search for App registrations and select the App registrations link.
  • Select the Azure AD app you're using for embedding your Power BI content.
  • From the Overview section, copy the Application (client) ID.

clientSecret: PowerBI Client Secret.

To get the client secret, follow these steps:

  • Log into Microsoft Azure.
  • Search for App registrations and select the App registrations link.
  • Select the Azure AD app you're using for embedding your Power BI content.
  • Under Manage, select Certificates & secrets.
  • Under Client secrets, select New client secret.
  • In the Add a client secret pop-up window, provide a description for your application secret, select when the application secret expires, and select Add.
  • From the Client secrets section, copy the string in the Value column of the newly created application secret.

tenantId: PowerBI Tenant ID.

To get the tenant ID, follow these steps:

  • Log into Microsoft Azure.
  • Search for App registrations and select the App registrations link.
  • Select the Azure AD app you're using for Power BI.
  • From the Overview section, copy the Directory (tenant) ID.

scope: Service scope.

To let OM use the Power BI APIs using your Azure AD app, you'll need to add the following scopes:


Instructions for adding these scopes to your app can be found by following this link:

authorityUri: Authority URI for the service.

To identify a token authority, you can provide a URL that points to the authority in question.

If you don't specify a URL for the token authority, we'll use the default value of

hostPort: URL to the PowerBI instance.

To connect with your Power BI instance, you'll need to provide the host URL. If you're using an on-premise installation of Power BI, this will be the domain name associated with your instance.

If you don't specify a host URL, we'll use the default value of to connect with your Power BI instance.

Pagination Entity Per Page:

The pagination limit for Power BI APIs can be set using this parameter. The limit determines the number of records to be displayed per page.

By default, the pagination limit is set to 100 records, which is also the maximum value allowed.

Use Admin APIs:

Option for using the PowerBI admin APIs:

  • Enabled (Use PowerBI Admin APIs) Using the admin APIs will fetch the dashboard and chart metadata from all the workspaces available in the PowerBI instance.

When using the PowerBI Admin APIs there are no limitations on the Datasets that are retrieved for creating lineage information.

  • Disabled (Use Non-Admin PowerBI APIs) Using the non-admin APIs will only fetch the dashboard and chart metadata from the workspaces that have the security group of the service principal assigned to them.

When using the PowerBI Non-Admin APIs, the lineage information can only be generated if the dataset is a Push Dataset. For more information please visit the PowerBI official documentation here.

The sourceConfig is defined here:

  • dbServiceNames: Database Service Names for ingesting lineage if the source supports it.
  • dashboardFilterPattern, chartFilterPattern, dataModelFilterPattern: Note that all of them support regex as include or exclude. E.g., "My dashboard, My dash.*, .*Dashboard".
  • includeOwners: Set the 'Include Owners' toggle to control whether to include owners to the ingested entity if the owner email matches with a user stored in the OM server as part of metadata ingestion. If the ingested entity already exists and has an owner, the owner will not be overwritten.
  • includeTags: Set the 'Include Tags' toggle to control whether to include tags in metadata ingestion.
  • includeDataModels: Set the 'Include Data Models' toggle to control whether to include tags as part of metadata ingestion.
  • markDeletedDashboards: Set the 'Mark Deleted Dashboards' toggle to flag dashboards as soft-deleted if they are not present anymore in the source system.

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

For a simple, local installation using our docker containers, this looks like:


We support different security providers. You can find their definitions here.

  • JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.
  • You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.

Create a Python file in your Airflow DAGs directory with the following contents:

The Workflow class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.

Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.

Default arguments for all tasks in the Airflow DAG.

  • Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.
  • config: Specifies config for the metadata ingestion as we prepare above.
  • metadata_ingestion_workflow(): This code defines a function metadata_ingestion_workflow() that loads a YAML configuration, creates a Workflow object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.
  • DAG: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
  • For more Airflow DAGs creation details visit here.

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.