Run Tableau using the Airflow SDK


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

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

To ingest tableau metadata, minimum Site Role: Viewer is requried for the tableau user.

OpenMetadata 0.12 or later

To deploy OpenMetadata, check the Deployment guides.

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 create lineage between tableau dashboard and any database service via the queries provided from Tableau Metadata API, please enable the Tableau Metadata API for your tableau server. For more information on enabling the Tableau Metadata APIs follow the link here

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

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

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 Tableau:

For Basic Authentication:

Username: The name of the user whose credentials will be used to sign in.

Password: The password of the user.

For Access Token Authentication:

Personal Access Token: The personal access token name. For more information to get a Personal Access Token please visit this link.

Personal Access Token Secret: The personal access token value. For more information to get a Personal Access Token please visit this link.

env: The config object can have multiple environments. The default environment is defined as tableau_prod, and you can change this if needed by specifying an env parameter.

hostPort: URL or IP address of your installation of Tableau Server.

siteName: Tableau Site Name. This corresponds to the contentUrl attribute in the Tableau REST API. The site_name is the portion of the URL that follows the /site/ in the URL.

siteUrl: Tableau Site URL. Tableau Site Url. To be kept empty if you are using the default Tableau site

apiVersion: Tableau API version. A lists versions of Tableau Server and of the corresponding REST API and REST API schema versions can be found 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:


For a default tableau site siteName and siteUrl fields should be kept as empty strings as shown in the below config.

For a non-default tableau site siteName and siteUrl fields are required.

Note: If represents the homepage url for your tableau site, the sitename from the url should be entered in the siteName and siteUrl fields in the config below.

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.