OpenMetadata/openmetadata-docs/content/v1.1.0/connectors/pipeline/databricks-pipeline/cli.md

---
title: Run Databricks Pipeline Connector using the CLI
slug: /connectors/pipeline/databricks-pipeline/cli
---

# Run Databricks Pipeline using the metadata CLI

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:

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

## 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/pipeline/databricksPipelineConnection.json)
you can find the structure to create a connection to Databricks Pipeline.

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 Databricks Pipeline:

{% codePreview %}

{% codeInfoContainer %}

#### Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

**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"`


**HTTP Path**: Databricks Pipeline compute resources URL.

{% /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/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 %}

{% partial file="workflow-config.md" /%}

{% /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: <databricks token>

```
```yaml {% srNumber=3 %}
      connectionArguments:
        http_path: <http path of databricks cluster>
```
```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: {}
```

{% partial file="workflow-config-yaml.md" /%}

{% /codeBlock %}

{% /codePreview %}

### 2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

```bash
metadata ingest -c <path-to-yaml>
```

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.