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---
title: Run Metabase Connector using Airflow SDK
slug: /connectors/dashboard/metabase/airflow
---
# Run Metabase using the Airflow SDK
2023-04-26 17:41:42 +05:30
| 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](#requirements)
- [Metadata Ingestion](#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:
```bash
pip3 install "openmetadata-ingestion[metabase]"
```
## Metadata Ingestion
All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/dashboard/metabaseConnection.json)
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](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)
### 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](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/dashboardServiceMetadataPipeline.json):
**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" %}
```yaml
source:
type: metabase
serviceName: <service name>
serviceConnection:
config:
type: Metabase
```
```yaml {% srNumber=1 %}
username: <username>
```
```yaml {% srNumber=2 %}
password: <password>
```
```yaml {% srNumber=3 %}
hostPort: <hostPort>
```
```yaml {% srNumber=4 %}
sourceConfig:
config:
type: DashboardMetadata
markDeletedDashboards: True
# dbServiceNames:
# - service1
# - service2
# dashboardFilterPattern:
# includes:
# - dashboard1
# - dashboard2
# excludes:
# - dashboard3
# - dashboard4
# chartFilterPattern:
# includes:
# - chart1
# - chart2
# excludes:
# - chart3
# - chart4
```
```yaml {% srNumber=5 %}
sink:
type: metadata-rest
config: {}
```
```yaml {% srNumber=6 %}
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](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client).
## 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](/deployment/security/enable-jwt-tokens).
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
```
- You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) 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](/deployment/security/workflow-config-auth).
### 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](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html#declaring-a-dag).
{% /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" %}
```python {% srNumber=7 %}
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
```
```python {% srNumber=8 %}
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)
}
```
```python {% srNumber=9 %}
config = """
<your YAML configuration>
"""
```
```python {% srNumber=10 %}
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()
```
```python {% srNumber=11 %}
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 %}