OpenMetadata/openmetadata-docs/content/v0.13.3/connectors/dashboard/tableau/airflow.md

title	slug
Run Tableau Connector using Airflow SDK	/connectors/dashboard/tableau/airflow

Run Tableau using the Airflow SDK

Stage	PROD
Dashboards	{% icon iconName="check" /%}
Charts	{% icon iconName="check" /%}
Owners	{% icon iconName="check" /%}
Tags	{% icon iconName="cross" /%}
Lineage	{% icon iconName="check" /%}

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:

• Requirements
• Metadata Ingestion

Requirements

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

{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {%/inlineCallout%}

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

Python Requirements

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

pip3 install "openmetadata-ingestion[tableau]"

Metadata Ingestion

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

1. Define the YAML Config

This is a sample config for Tableau:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

hostPort: URL to the Tableau instance.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

username: Specify the User to connect to Tableau. It should have enough privileges to read all the metadata.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

password: Password for Tableau.

{% /codeInfo %}

{% codeInfo srNumber=4 %}

apiVersion: Tableau API version.

{% /codeInfo %}

{% codeInfo srNumber=5 %}

siteName: Tableau Site Name. To be kept empty if you are using the default Tableau site

{% /codeInfo %}

{% codeInfo srNumber=6 %}

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

{% /codeInfo %}

{% codeInfo srNumber=7 %}

personalAccessTokenName: Access token. To be used if not logging in with user/password.

{% /codeInfo %}

{% codeInfo srNumber=8 %}

personalAccessTokenSecret: Access token Secret. To be used if not logging in with user/password.

{% /codeInfo %}

{% codeInfo srNumber=9 %}

env: Tableau Environment.

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=10 %}

The sourceConfig is defined here:

dbServiceNames: Database Service Name for the creation of lineage, if the source supports it.

dashboardFilterPattern, chartFilterPattern: Note that the they support regex as include or exclude. E.g.,

includeTags: Set the Include tags toggle to control whether or not 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.

{% /codeInfo %}

Sink Configuration

{% codeInfo srNumber=11 %}

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

{% /codeInfo %}

Workflow Configuration

{% codeInfo srNumber=12 %}

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:

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

```yaml
source:
  type: tableau
  serviceName: local_tableau
  serviceConnection:
    config:
      type: Tableau

      username: username

      password: password

      env: tableau_prod

      hostPort: http://localhost

      siteName: site_name

      siteUrl: site_url

      apiVersion: api_version

      # If not setting user and password
      # personalAccessTokenName: personal_access_token_name

      # personalAccessTokenSecret: personal_access_token_secret

  sourceConfig:
    config:
      type: DashboardMetadata
      markDeletedDashboards: True
      # dbServiceNames:
      #   - service1
      #   - service2
      # dashboardFilterPattern:
      #   includes:
      #     - dashboard1
      #     - dashboard2
      #   excludes:
      #     - dashboard3
      #     - dashboard4
      # chartFilterPattern:
      #   includes:
      #     - chart1
      #     - chart2
      #   excludes:
      #     - chart3
      #     - chart4

sink:
  type: metadata-rest
  config: {}

workflowConfig:
  openMetadataServerConfig:
    hostPort: "http://localhost:8585/api"
    authProvider: openmetadata
    securityConfig:
      jwtToken: "{bot_jwt_token}"

{% /codeBlock %}

{% /codePreview %}

Example Source Configurations for default and non-default tableau sites

1. Sample config for default tableau site

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

source:
  type: tableau
  serviceName: local_tableau
  serviceConnection:
    config:
      type: Tableau
      hostPort: http://localhost
      username: username
      password: password
      apiVersion: api_version
      siteName: ""
      siteUrl: ""
      env: tableau_prod
      # If not setting user and password
      # personalAccessTokenName: personal_access_token_name
      # personalAccessTokenSecret: personal_access_token_secret
  sourceConfig:
    config:
      type: DashboardMetadata
      # dbServiceNames:
      #   - service1
      #   - service2
      # dashboardFilterPattern:
      #   includes:
      #     - dashboard1
      #     - dashboard2
      #   excludes:
      #     - dashboard3
      #     - dashboard4
      # chartFilterPattern:
      #   includes:
      #     - chart1
      #     - chart2
      #   excludes:
      #     - chart3
      #     - chart4
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

1. Sample config for non-default tableau site

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

Note: If https://xxx.tableau.com/#/site/sitename/home 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.

source:
  type: tableau
  serviceName: local_tableau
  serviceConnection:
    config:
      type: Tableau
      username: username
      password: password
      env: tableau_prod
      hostPort: http://localhost
      siteName: openmetadata
      siteUrl: openmetadata
      apiVersion: api_version
      # If not setting user and password
      # personalAccessTokenName: personal_access_token_name
      # personalAccessTokenSecret: personal_access_token_secret
  sourceConfig:
    config:
      type: DashboardMetadata
      overrideOwner: True
      # dbServiceNames:
      #   - service1
      #   - service2
      # dashboardFilterPattern:
      #   includes:
      #     - dashboard1
      #     - dashboard2
      #   excludes:
      #     - dashboard3
      #     - dashboard4
      # chartFilterPattern:
      #   includes:
      #     - chart1
      #     - chart2
      #   excludes:
      #     - chart3
      #     - chart4
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

Workflow Configs for Security Provider

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

Openmetadata JWT Auth

• JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

workflowConfig:
  openMetadataServerConfig:
    hostPort: "http://localhost:8585/api"
    authProvider: openmetadata
    securityConfig:
      jwtToken: "{bot_jwt_token}"

• 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.

2. Prepare the Ingestion DAG

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

{% codePreview %}

{% codeInfoContainer %}

{% codeInfo srNumber=13 %}

Import necessary modules

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.

{% /codeInfo %}

{% codeInfo srNumber=14 %}

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.

{% /codeInfo %}

{% codeInfo srNumber=15 %}

• config: Specifies config for the metadata ingestion as we prepare above.

{% /codeInfo %}

{% codeInfo srNumber=16 %}

• 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.

{% /codeInfo %}

{% codeInfo srNumber=17 %}

• 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.

{% /codeInfo %}

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.

{% /codeInfoContainer %}

{% codeBlock fileName="filename.py" %}

import pathlib
import yaml
from datetime import timedelta
from airflow import DAG
from metadata.config.common import load_config_file
from metadata.ingestion.api.workflow import Workflow
from airflow.utils.dates import days_ago

try:
    from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
    from airflow.operators.python_operator import PythonOperator

default_args = {
    "owner": "user_name",
    "email": ["username@org.com"],
    "email_on_failure": False,
    "retries": 3,
    "retry_delay": timedelta(minutes=5),
    "execution_timeout": timedelta(minutes=60)
}

config = """
<your YAML configuration>
"""

def metadata_ingestion_workflow():
    workflow_config = yaml.safe_load(config)
    workflow = Workflow.create(workflow_config)
    workflow.execute()
    workflow.raise_from_status()
    workflow.print_status()
    workflow.stop()

with DAG(
    "sample_data",
    default_args=default_args,
    description="An example DAG which runs a OpenMetadata ingestion workflow",
    start_date=days_ago(1),
    is_paused_upon_creation=False,
    schedule_interval='*/5 * * * *',
    catchup=False,
) as dag:
    ingest_task = PythonOperator(
        task_id="ingest_using_recipe",
        python_callable=metadata_ingestion_workflow,
    )

{% /codeBlock %}

{% /codePreview %}