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

title	slug
Run Dagster Connector using the CLI	/connectors/pipeline/dagster/cli

Run Dagster using the metadata CLI

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

Configure and schedule Dagster metadata and profiler workflows from the OpenMetadata UI:

• Requirements
• 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 Dagster ingestion, you will need to install:

pip3 install "openmetadata-ingestion[dagster]"

Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Dagster.

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

1. Define the YAML Config

This is a sample config for Dagster:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

• host: host and port for dagster pipeline

Note: If dagster is deployed on localhost and entering https://localhost:3000 into hostPort gives a connection refused error, please enter https://127.0.0.1:3000 into the hostPort and try again.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

Token : Need pass token if connecting to dagster cloud instance

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=3 %}

The sourceConfig is defined here:

dbServiceNames: Database Service Name for the creation of lineage, if the source supports it.

includeTags: Set the 'Include Tags' toggle to control whether 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=4 %}

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" %}

source:
  type: dagster
  serviceName: dagster_source
  serviceConnection:
    config:
      type: Dagster

        host: "https://<yourorghere>.dagster.cloud/prod" # or http://127.0.0.1:3000

        token: token

  sourceConfig:
    config:
      type: PipelineMetadata
      # markDeletedPipelines: True
      # includeTags: True
      # includeLineage: true
      # pipelineFilterPattern:
      #   includes:
      #     - pipeline1
      #     - pipeline2
      #   excludes:
      #     - pipeline3
      #     - pipeline4

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:

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.