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.
**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.
Follow these [steps](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token#creating-a-fine-grained-personal-access-token) in order to create a fine-grained personal access token.
When configuring, give repository access to `Only select repositories` and choose the one containing your LookML files. Then, we only need `Repository Permissions` as `Read-only` for `Contents`.
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.
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 = """
<yourYAMLconfiguration>
"""
```
```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",
