This page is about running the Ingestion Framework externally!There are mainly 2 ways of running the ingestion:

Internally, by managing the workflows from Collate.
Externally, by using any other tool capable of running Python code.

If you are looking for how to manage the ingestion process from Collate, you can follow this doc.

Run the ingestion from your Airflow

OpenMetadata integrates with Airflow to orchestrate ingestion workflows. You can use Airflow to extract metadata and [deploy workflows] (/deployment/ingestion/openmetadata) directly. This guide explains how to run ingestion workflows in Airflow using three different operators:

Python Operator
Docker Operator
Python Virtualenv Operator

Using the Python Operator

Prerequisites

Install the openmetadata-ingestion package in your Airflow environment. This approach works best if you have access to the Airflow host and can manage dependencies.

Installation Command:

pip3 install "openmetadata-ingestion[&lt;plugin&gt;]~=x.y.z.0"

Replace <plugin> with the sources to ingest, such as mysql, snowflake, or s3.
Replace x.y.z with the Collate version matching your server. For example, use 2.0.5 for a Collate 2.0.5 server.

Example

pip3 install "openmetadata-ingestion[mysql,snowflake,s3]~=2.0.5.0"

Example DAG

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.workflow.metadata import MetadataWorkflow


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 = MetadataWorkflow.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,
    )

Key Notes

Function Setup: The python_callable argument in the PythonOperator executes the metadata_ingestion_workflow function, which instantiates the workflow and runs the ingestion process.
Drawback: This method requires pre-installed dependencies, which may not always be feasible. Consider using the DockerOperator or PythonVirtualenvOperator as alternatives.

Using the Docker Operator

For this operator, we can use the openmetadata/ingestion-base image. This is useful to prepare DAGs without any installation required on the environment, although it needs for the host to have access to the Docker commands.

Prerequisites

Ensure the Airflow host can run Docker commands. For Docker Compose setups, map the Docker socket as follows:

Example

volumes:
  - /var/run/docker.sock:/var/run/docker.sock:z  # Need 666 permissions to run DockerOperator

Example DAG

from datetime import datetime

from airflow import models
from airflow.providers.docker.operators.docker import DockerOperator


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

with models.DAG(
    "ingestion-docker-operator",
    schedule_interval='*/5 * * * *',
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["OpenMetadata"],
) as dag:
    DockerOperator(
        command="python main.py",
        image="openmetadata/ingestion-base:2.0.5",
        environment={"config": config, "pipelineType": "metadata"},
        docker_url="unix://var/run/docker.sock",  # To allow to start Docker. Needs chmod 666 permissions
        tty=True,
        auto_remove="True",
        network_mode="host",  # To reach the OM server
        task_id="ingest",
        dag=dag,
    )

Make sure to tune out the DAG configurations (schedule_interval, start_date, etc.) as your use case requires.

If you encounter issues such as missing task instances or Airflow failing to locate a deployed DAG (e.g., Dag '<DAG_ID>' could not be found), this may be due to a timezone mismatch in your Airflow configuration. To resolve this, set the following in your airflow.cfg:

default_timezone = system

This ensures that Airflow uses the system timezone, which is particularly important when OpenMetadata and Airflow are running on the same server.

Key Notes

Image Version: Ensure the Docker image version matches your Collate server version (for example, openmetadata/ingestion-base:2.0.5).
Pipeline Types: Set the pipelineType to metadata, usage, lineage, profiler, or other supported values.
No Installation Required: The DockerOperator eliminates the need to install dependencies directly on the Airflow host.

Another important point here is making sure that the Airflow will be able to run Docker commands to create the task. As our example was done with Airflow in Docker Compose, that meant setting docker_url="unix://var/run/docker.sock". The final important elements here are:

command="python main.py": This does not need to be modified, as we are shipping the main.py script in the image, used to trigger the workflow.
environment={"config": config, "pipelineType": "metadata"}: Again, in most cases you will just need to update the config string to point to the right connector.

Other supported values of pipelineType are usage, lineage, profiler, dataInsight, elasticSearchReindex, dbt, application or TestSuite. Pass the required flag depending on the type of workflow you want to execute. Make sure that the YAML config reflects what ingredients are required for your Workflow.

Using the Python Virtualenv Operator

Prerequisites

As stated in Airflow’s docs, install the virtualenv package on the Airflow host.If using a different Python version in the virtual environment (e.g., Python 3.9 while Airflow uses 3.7), install additional packages such as:

gcc python3.9-dev python3.9-distutils

Example DAG

from datetime import timedelta
from airflow import DAG

try:
    from airflow.operators.python import PythonVirtualenvOperator
except ModuleNotFoundError:
    from airflow.operators.python_operator import PythonVirtualenvOperator

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(seconds=10),
    "execution_timeout": timedelta(minutes=60),
}


def metadata_ingestion_workflow():
    from metadata.workflow.metadata import MetadataWorkflow

    import yaml
    config = """
        source:
          type: postgres
          serviceName: local_postgres
          serviceConnection:
            config:
              type: Postgres
              username: openmetadata_user
              authType:
                password: openmetadata_password
              hostPort: localhost:5432
              database: pagila
          sourceConfig:
            config:
              type: DatabaseMetadata
        sink:
          type: metadata-rest
          config: {}
        workflowConfig:
          # loggerLevel: INFO # DEBUG, INFO, WARN or ERROR
          openMetadataServerConfig:
            hostPort: http://localhost:8585/api
            authProvider: openmetadata
            securityConfig:
              jwtToken: "eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5
    """

    workflow_config = yaml.safe_load(config)
    workflow = MetadataWorkflow.create(workflow_config)
    workflow.execute()
    workflow.raise_from_status()
    workflow.print_status()
    workflow.stop()


with DAG(
    "ingestion_dag",
    default_args=default_args,
    description="An example DAG which runs a OpenMetadata ingestion workflow",
    start_date=days_ago(1),
    is_paused_upon_creation=True,
    catchup=False,
) as dag:
    ingest_task = PythonVirtualenvOperator(
        task_id="ingest_using_recipe",
        requirements=[
            'openmetadata-ingestion[mysql]~=2.0.5.0',  # Specify any additional Python package dependencies
        ],
        system_site_packages=False,  # Set to True if you want to include system site-packages in the virtual environment
        python_version="3.10",
        python_callable=metadata_ingestion_workflow
    )

Key Notes

Function Rules:

Use a def function (not part of a class).
All imports must occur inside the function.
Avoid referencing variables outside the function’s scope.

Ingestion Workflow classes

We have different classes for different types of workflows. The logic is always the same, but you will need to change your import path. The rest of the method calls will remain the same. For example, for the Metadata workflow we’ll use:

import yaml

from metadata.workflow.metadata import MetadataWorkflow

def run():
    workflow_config = yaml.safe_load(CONFIG)
    workflow = MetadataWorkflow.create(workflow_config)
    workflow.execute()
    workflow.raise_from_status()
    workflow.print_status()
    workflow.stop()

The classes for each workflow type are:

Metadata: from metadata.workflow.metadata import MetadataWorkflow
Lineage: from metadata.workflow.metadata import MetadataWorkflow (same as metadata)
Usage: from metadata.workflow.usage import UsageWorkflow
dbt: from metadata.workflow.metadata import MetadataWorkflow
Profiler: from metadata.workflow.profiler import ProfilerWorkflow
Data Quality: from metadata.workflow.data_quality import TestSuiteWorkflow
Data Insights: from metadata.workflow.data_insight import DataInsightWorkflow
Elasticsearch Reindex: from metadata.workflow.metadata import MetadataWorkflow (same as metadata)

​Run the ingestion from your Airflow

​Using the Python Operator

​Prerequisites

​Installation Command:

​Example

​Example DAG

​Key Notes

​Using the Docker Operator

​Prerequisites

​Example

​Example DAG

​Key Notes

​Using the Python Virtualenv Operator

​Prerequisites

​Example DAG

​Key Notes

​Ingestion Workflow classes

Run the ingestion from your Airflow

Using the Python Operator

Prerequisites

Installation Command:

Example

Example DAG

Key Notes

Using the Docker Operator

Prerequisites

Example

Example DAG

Key Notes

Using the Python Virtualenv Operator

Prerequisites

Example DAG

Key Notes

Ingestion Workflow classes