--- title: Run Glue Pipeline Connector using Airflow SDK slug: /connectors/pipeline/glue-pipeline/airflow --- # Run Glue Pipeline using the Airflow SDK In this section, we provide guides and references to use the Glue connector. Configure and schedule Glue metadata and profiler workflows from the OpenMetadata UI: - [Requirements](#requirements) - [Metadata Ingestion](#metadata-ingestion) ## Requirements To deploy OpenMetadata, check the Deployment guides. 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 Glue ingestion, you will need to install: ```bash pip3 install "openmetadata-ingestion[glue]" ``` ## 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/database/glueConnection.json) you can find the structure to create a connection to Glue. 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 Glue: ```yaml source: type: glue serviceName: local_glue serviceConnection: config: type: Glue awsConfig: awsAccessKeyId: KEY awsSecretAccessKey: SECRET awsRegion: us-east-2 # endPointURL: https://glue.us-east-2.amazonaws.com/ # awsSessionToken: TOKEN sourceConfig: config: type: PipelineMetadata # markDeletedPipelines: True # includeTags: True # includeLineage: true # pipelineFilterPattern: # includes: # - pipeline1 # - pipeline2 # excludes: # - pipeline3 # - pipeline4 sink: type: metadata-rest config: {} workflowConfig: # loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR openMetadataServerConfig: hostPort: authProvider: ``` #### Source Configuration - Service Connection - **awsAccessKeyId**: Enter your secure access key ID for your Glue connection. The specified key ID should be authorized to read all databases you want to include in the metadata ingestion workflow. - **awsSecretAccessKey**: Enter the Secret Access Key (the passcode key pair to the key ID from above). - **awsRegion**: Enter the location of the amazon cluster that your data and account are associated with. - **awsSessionToken**: The AWS session token is an optional parameter. If you want, enter the details of your temporary session token. - **endPointURL**: Your Glue connector will automatically determine the AWS Glue endpoint URL based on the region. You may override this behavior by entering a value to the endpoint URL. #### Source Configuration - Source Config 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. - `pipelineFilterPattern` and `chartFilterPattern`: Note that the `pipelineFilterPattern` and `chartFilterPattern` both support regex as include or exclude. E.g., - `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. ```yaml pipelineFilterPattern: includes: - users - type_test ``` #### Sink Configuration To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`. #### Workflow Configuration 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: ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: openmetadata securityConfig: jwtToken: '{bot_jwt_token}' ``` 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). You can find the different implementation of the ingestion below. ### Openmetadata JWT Auth ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: openmetadata securityConfig: jwtToken: '{bot_jwt_token}' ``` ### Auth0 SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: auth0 securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### Azure SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: azure securityConfig: clientSecret: '{your_client_secret}' authority: '{your_authority_url}' clientId: '{your_client_id}' scopes: - your_scopes ``` ### Custom OIDC SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### Google SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: google securityConfig: secretKey: '{path-to-json-creds}' ``` ### Okta SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: http://localhost:8585/api authProvider: okta securityConfig: clientId: "{CLIENT_ID - SPA APP}" orgURL: "{ISSUER_URL}/v1/token" privateKey: "{public/private keypair}" email: "{email}" scopes: - token ``` ### Amazon Cognito SSO The ingestion can be configured by [Enabling JWT Tokens](https://docs.open-metadata.org/deployment/security/enable-jwt-tokens) ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: auth0 securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### OneLogin SSO Which uses Custom OIDC for the ingestion ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### KeyCloak SSO Which uses Custom OIDC for the ingestion ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ## 2. Prepare the Ingestion DAG Create a Python file in your Airflow DAGs directory with the following contents: ```python import pathlib import yaml from datetime import timedelta from airflow import DAG try: from airflow.operators.python import PythonOperator except ModuleNotFoundError: from airflow.operators.python_operator import PythonOperator from metadata.config.common import load_config_file from metadata.ingestion.api.workflow import Workflow from airflow.utils.dates import days_ago 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) } config = """ """ 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() with DAG( "sample_data", default_args=default_args, description="An example DAG which runs a OpenMetadata ingestion workflow", start_date=days_ago(1), is_paused_upon_creation=False, schedule_interval='*/5 * * * *', catchup=False, ) as dag: ingest_task = PythonOperator( task_id="ingest_using_recipe", python_callable=metadata_ingestion_workflow, ) ``` 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.