OpenMetadata/openmetadata-docs/content/v1.0.x/connectors/dashboard/mode/airflow.md

---
title: Run Mode Connector using Airflow SDK
slug: /connectors/dashboard/mode/airflow
---

# Run Mode using the Airflow SDK

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

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

Configure and schedule Mode 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.

OpenMetadata relies on Mode's API, which is exclusive to members of the Mode Business Workspace. This means that only resources that belong to a Mode Business Workspace can be accessed via the API.

### Python Requirements

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

```bash
pip3 install "openmetadata-ingestion[mode]"
```

## 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/modeConnection.json)
you can find the structure to create a connection to Mode.

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

This is a sample config for Mode:

{% codePreview %}

{% codeInfoContainer %}

#### Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

**hostPort**: Host and Port Mode Dashboard.

The hostPort parameter specifies the host and port of the Mode server. This should be specified as a string in the format `https://app.mode.com`.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

**accessToken**: Access Token for Mode Dashboard.

Get the Access Token by following below mentioned steps:
- Navigate to your Mode homepage.
- Click on your name in the upper left corner and click My Account.
- Click on API Tokens on the left side.
- To generate a new API token and password, enter a token name and click `Create token`.
- Copy the generated access token and password.

For detailed information visit [here](https://mode.com/developer/api-reference/introduction/).

{% /codeInfo %}

{% codeInfo srNumber=3 %}

**accessTokenPassword**: Access Token Password for Mode Dashboard.

Copy the access token password from the step above where a new token is generated.

For detailed information visit [here](https://mode.com/developer/api-reference/introduction/).

{% /codeInfo %}

{% codeInfo srNumber=4 %}

**workspaceName**: Mode Workspace Name.

Name of the mode workspace from where the metadata is to be fetched.

{% /codeInfo %}

#### Source Configuration - Source Config

{% codeInfo srNumber=5 %}

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 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.

{% /codeInfo %}

#### Sink Configuration

{% codeInfo srNumber=6 %}

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

{% /codeInfo %}

#### Workflow Configuration

{% codeInfo srNumber=7 %}

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: mode
  serviceName: local_mode
  serviceConnection:
    config:
      type: Mode
```
```yaml {% srNumber=1 %}
      hostPort: https://app.mode.com
```
```yaml {% srNumber=2 %}
      access_token: access_token
```
```yaml {% srNumber=3 %}
      access_token_password: access_token_password
```
```yaml {% srNumber=4 %}
      workspace_name: workspace_name
```
```yaml {% srNumber=5 %}
  sourceConfig:
    config:
      type: DashboardMetadata
      # dbServiceNames:
      #   - service1
      #   - service2
      # dashboardFilterPattern:
      #   includes:
      #     - dashboard1
      #     - dashboard2
      #   excludes:
      #     - dashboard3
      #     - dashboard4
      # chartFilterPattern:
      #   includes:
      #     - chart1
      #     - chart2
      #   excludes:
      #     - chart3
      #     - chart4
```
```yaml {% srNumber=6 %}
sink:
  type: metadata-rest
  config: {}
```

```yaml {% srNumber=7 %}
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=8 %}

#### 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=9 %}

**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=10 %}

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

{% /codeInfo %}

{% codeInfo srNumber=11 %}

- **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=12 %}

- **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=8 %}
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=9 %}
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=10 %}
config = """
<your YAML configuration>
"""

```

```python {% srNumber=11 %}
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=12 %}
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 %}