Run Looker using Airflow SDK

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

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

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.

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

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 Looker:

Source Configuration - Service Connection

  • hostPort: URL to the Looker instance.
  • username: Specify the User to connect to Looker. It should have enough privileges to read all the metadata.
  • password: Password to connect to Looker.
  • env: Looker Environment.

The sourceConfig is defined here:

  • dbServiceName: Database Service Name for the creation of lineage, if the source supports it.
  • dashboardFilterPattern and chartFilterPattern: Note that the dashboardFilterPattern and chartFilterPattern both support regex as include or exclude. E.g.,
        - users
        - type_test

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:

    hostPort: http://localhost:8585/api
    authProvider: no-auth

We support different security providers. You can find their definitions here. An example of an Auth0 configuration would be the following:

    hostPort: http://localhost:8585/api
    authProvider: auth0
      clientId: <client ID>
      secretKey: <secret key>
      domain: <domain>

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

import pathlib
import yaml
from datetime import timedelta
from airflow import DAG

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

from metadata.config.common import load_config_file
from metadata.ingestion.api.workflow import Workflow
from airflow.utils.dates import days_ago

default_args = {
    "owner": "user_name",
    "email": [""],
    "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)

with DAG(
    description="An example DAG which runs a OpenMetadata ingestion workflow",
    schedule_interval='*/5 * * * *',
) as dag:
    ingest_task = PythonOperator(

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.

Still have questions?

You can take a look at our Q&A or reach out to us in Slack

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