In this section, we provide guides and references to use the Databricks Pipeline connector.
Configure and schedule Databricks Pipeline 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.
### Python Requirements
To run the Databricks Pipeline ingestion, you will need to install:
**Host and Port**: Enter the fully qualified hostname and port number for your Databricks Pipeline deployment in the Host and Port field.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Token**: Generated Token to connect to Databricks Pipeline.
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Databricks during the connection. These details must be added as Key-Value pairs.
- In case you are using Single-Sign-On (SSO) for authentication, add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "sso_login_url"`
- In case you authenticate with SSO using an external browser popup, then add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "externalbrowser"`
The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/pipelineServiceMetadataPipeline.json):
**dbServiceNames**: Database Service Name for the creation of lineage, if the source supports it.
**includeTags**: Set the Include tags toggle to control whether or not to include tags as part of metadata ingestion.
**markDeletedPipelines**: Set the Mark Deleted Pipelines toggle to flag pipelines as soft-deleted if they are not present anymore in the source system.
**pipelineFilterPattern** and **chartFilterPattern**: Note that the `pipelineFilterPattern` and `chartFilterPattern` both support regex as include or exclude.
{% /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: databrickspipeline
serviceName: local_databricks_pipeline
serviceConnection:
config:
type: DatabricksPipeline
```
```yaml {% srNumber=1 %}
hostPort: localhost:443
```
```yaml {% srNumber=2 %}
token: <databrickstoken>
```
```yaml {% srNumber=3 %}
connectionArguments:
http_path: <httppathofdatabrickscluster>
```
```yaml {% srNumber=4 %}
sourceConfig:
config:
type: PipelineMetadata
# markDeletedPipelines: True
# includeTags: True
# includeLineage: true
# pipelineFilterPattern:
# includes:
# - pipeline1
# - pipeline2
# excludes:
# - pipeline3
# - pipeline4
```
```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 = """
<yourYAMLconfiguration>
"""
```
```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",
