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

title	slug
Run Metabase Connector using Airflow SDK	/connectors/dashboard/metabase/airflow

Run Metabase using the Airflow SDK

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

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

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

• Requirements
• Metadata Ingestion

Requirements

{%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.

Note: We have tested Metabase with Versions 0.42.4 and 0.43.4.

Python Requirements

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

pip3 install "openmetadata-ingestion[metabase]"

Metadata Ingestion

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

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

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

username: Username to connect to Metabase, for ex. user@organization.com. This user should have access to relevant dashboards and charts in Metabase to fetch the metadata.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

password: Password of the user account to connect with Metabase.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

hostPort: The hostPort parameter specifies the host and port of the Metabase instance. This should be specified as a string in the format http://hostname:port or https://hostname:port. For example, you might set the hostPort parameter to https://org.metabase.com:3000.

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=4 %}

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=5 %}

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

{% /codeInfo %}

Workflow Configuration

{% codeInfo srNumber=6 %}

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" %}

source:
  type: metabase
  serviceName: <service name>
  serviceConnection:
    config:
      type: Metabase

      username: <username>

      password: <password>

      hostPort: <hostPort>

  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 %}

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=7 %}

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=8 %}

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=9 %}

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

{% /codeInfo %}

{% codeInfo srNumber=10 %}

• 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=11 %}

• 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 %}