OpenMetadata/openmetadata-docs-v1/content/v1.0.0/connectors/dashboard/looker/airflow.md

---
title: Run Looker Connector using Airflow SDK
slug: /connectors/dashboard/looker/airflow
---

# Run Looker using the 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:

- [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%}

There are two types of metadata we ingest from Looker:
- Dashboards & Charts
- LookML Models

In terms of permissions, we need a user with access to the Dashboards and LookML Explores that we want to ingest. You can
create your API credentials following these [docs](https://cloud.google.com/looker/docs/api-auth).

However, LookML Views are not present in the Looker SDK. Instead, we need to extract that information directly from
the GitHub repository holding the source `.lkml` files. In order to get this metadata, we will require a GitHub token
with read only access to the repository. You can follow these steps from the GitHub [documentation](https://docs.github.com/en/enterprise-server@3.4/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token).

{% note %}

The GitHub credentials are completely optional. Just note that without them, we won't be able to ingest metadata
out of LookML Views, including their lineage to the source databases.

{% /note %}

### Python Requirements

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

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

## 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/lookerConnection.json)
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](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 Looker:

{% codePreview %}

{% codeInfoContainer %}

#### Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

**clientId**: Specify the Client ID to connect to Looker. It should have enough privileges to read all the metadata.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

**clientSecret**: Client Secret to connect to Looker.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

**hostPort**: URL to the Looker instance.

{% /codeInfo %}

{% codeInfo srNumber=4 %}

**githubCredentials** (Optional): GitHub API credentials to extract LookML Views' information by parsing the source `.lkml` files. There are three
properties we need to add in this case:

- **repositoryOwner**: The owner (user or organization) of a GitHub repository. For example, in https://github.com/open-metadata/OpenMetadata, the owner is `open-metadata`.
- **repositoryName**: The name of a GitHub repository. For example, in https://github.com/open-metadata/OpenMetadata, the name is `OpenMetadata`.
- **token**: Token to use the API. This is required for private repositories and to ensure we don't hit API limits.

{% /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: looker
  serviceName: local_looker
  serviceConnection:
    config:
      type: Looker
```
```yaml {% srNumber=1 %}
      clientId: Client ID
```
```yaml {% srNumber=2 %}
      clientSecret: Client Secret
```
```yaml {% srNumber=3 %}
      hostPort: http://hostPort
```
```yaml {% srNumber=4 %}
      githubCredentials:
        repositoryOwner: open-metadata
        repositoryName: OpenMetadata
        token: XYZ
```
```yaml {% srNumber=5 %}
  sourceConfig:
    config:
      type: DashboardMetadata
      overrideOwner: True
      # 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 %}