OpenMetadata/openmetadata-docs/content/v1.0.0/connectors/metadata/atlas/cli.md

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Run Atlas Connector using the CLI	/connectors/metadata/atlas/cli

Run Atlas using the metadata CLI

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

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

• Requirements
• Metadata Ingestion

Requirements

Before this, you must ingest the database / messaging service you want to get metadata for. For more details click here

{%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 Atlas ingestion, you will need to install:

pip3 install "openmetadata-ingestion[atlas]"

Metadata Ingestion

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

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

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=12 %}

hostPort: Atlas Host of the data source.

{% /codeInfo %}

{% codeInfo srNumber=13 %}

username: Username to connect to the Atlas. This user should have privileges to read all the metadata in Atlas.

{% /codeInfo %}

{% codeInfo srNumber=14 %}

password: Password to connect to the Atlas.

{% /codeInfo %}

{% codeInfo srNumber=15 %}

databaseServiceName: source database of the data source(Database service that you created from UI. example- local_hive).

{% /codeInfo %}

{% codeInfo srNumber=16 %}

messagingServiceName: messaging service source of the data source.

{% /codeInfo %}

{% codeInfo srNumber=17 %}

entity_type: Name of the entity type in Atlas.

{% /codeInfo %}

Sink Configuration

{% codeInfo srNumber=18 %}

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

{% /codeInfo %}

Workflow Configuration

{% codeInfo srNumber=19 %}

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" %}

source:
  type: Atlas
  serviceName: local_atlas
  serviceConnection:
    config:
      type: Atlas

      hostPort: http://localhost:10000

      username: username

      password: password

      databaseServiceName: ["local_hive"] # create database service and messaging service and pass `service name` here

      messagingServiceName: []

      entity_type: Table
  sourceConfig:
    config:
      type: DatabaseMetadata

sink:
  type: metadata-rest
  config: {}

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.

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.

workflowConfig:
  openMetadataServerConfig:
    hostPort: "http://localhost:8585/api"
    authProvider: openmetadata
    securityConfig:
      jwtToken: "{bot_jwt_token}"

• You can refer to the JWT Troubleshooting section link 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.

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.