OpenMetadata/openmetadata-docs/content/connectors/messaging/redpanda/airflow.md

title	slug
Run Redpanda Connector using Airflow SDK	/connectors/messaging/redpanda/airflow

Run Redpanda using the Airflow SDK

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

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

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

pip3 install "openmetadata-ingestion[redpanda]"

Metadata Ingestion

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

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 Redpanda:

source:
  type: redpanda
  serviceName: local_redpanda
  serviceConnection:
    config:
      type: Redpanda
      bootstrapServers: localhost:9092
      schemaRegistryURL: http://localhost:8081  # Needs to be a URI
      consumerConfig: {}
      schemaRegistryConfig: {}
  sourceConfig:
    config:
      type: MessagingMetadata
      # markDeletedTopics: true
      topicFilterPattern:
        excludes:
          - _confluent.*
        # includes:
        #   - topic1
      generateSampleData: true
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

Source Configuration - Service Connection

• bootstrapServers: Redpanda bootstrap servers. Add them in comma separated values e.g.: host1:9092,host2:9092.
• schemaRegistryURL: Redpanda Schema Registry URL. URI format.
• consumerConfig: Redpanda Consumer Config.
• schemaRegistryConfig: Redpanda Schema Registry Config.

To ingest the topic schema schemaRegistryURL must be passed

Source Configuration - Source Config

The sourceConfig is defined here:

• generateSampleData: Option to turn on/off generating sample data during metadata extraction.
• topicFilterPattern: Note that the topicFilterPattern supports regex as include or exclude. E.g.,
• markDeletedTopics: Set the Mark Deleted Topics toggle to flag topics as soft-deleted if they are not present anymore in the source system.

topicFilterPattern:
  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:

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. You can find the different implementation of the ingestion below.

Openmetadata JWT Auth

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

Auth0 SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: auth0
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Azure SSO

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

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Google SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: google
    securityConfig:
      secretKey: '{path-to-json-creds}'

Okta SSO

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

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

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

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:

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 = """
<your YAML configuration>
"""

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.