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Documentation Index

Fetch the complete documentation index at: https://openmetadata-feat-feat-2mbfixdeploy.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

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Run the ingestion from the OpenMetadata UI

When you create and manage ingestion workflows from OpenMetadata, under the hood we need to communicate with an orchestration system. It does not matter which one, but we need it to have a set of APIs to create, run, fetch the logs, etc. of our workflows. openmetadata-orchestration OpenMetadata supports two orchestration backends:
OrchestratorDescription
Apache AirflowThe traditional approach - uses Airflow DAGs to manage pipelines
Kubernetes NativeNew in 1.12 - Uses native K8s Jobs and CronJobs without requiring Airflow

Airflow Setup

Continue below for Airflow configuration

Kubernetes Native

Use native K8s Jobs (no Airflow required)

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Airflow as Orchestrator

Out of the box, OpenMetadata comes with integration for Airflow. In this guide, we will show you how to manage ingestions from OpenMetadata by linking it to an Airflow service.
Advanced note for developers: We have an interface that can be extended to bring support to any other orchestrator. You can follow the implementation we have for Airflow or Kubernetes as starting points.
  1. If you do not have an Airflow service up and running on your platform, we provide a custom Docker image, which already contains the OpenMetadata ingestion packages and custom Airflow APIs to deploy Workflows from the UI as well. This is the simplest approach.
  2. If you already have Airflow up and running and want to use it for the metadata ingestion, you will need to install the ingestion modules to the host. You can find more information on how to do this in the Custom Airflow Installation section.

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Airflow permissions

These are the permissions required by the user that will manage the communication between the OpenMetadata Server and Airflow’s Webserver: 
[
    (permissions.ACTION_CAN_DELETE, permissions.RESOURCE_DAG),
    (permissions.ACTION_CAN_CREATE, permissions.RESOURCE_DAG),
    (permissions.ACTION_CAN_EDIT, permissions.RESOURCE_DAG),
    (permissions.ACTION_CAN_READ, permissions.RESOURCE_DAG),
]
User permissions is enough for these requirements. You can find more information on Airflow’s Access Control here.

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Shared Volumes

The Airflow Webserver, Scheduler and Workers - if using a distributed setup - need to have access to the same shared volumes with RWX permissions.
We have specific instructions on how to set up the shared volumes in Kubernetes depending on your cloud deployment here.

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Using the OpenMetadata Ingestion Image

If you are using our openmetadata/ingestion Docker image, there is just one thing to do: Configure the OpenMetadata server. The OpenMetadata server takes all its configurations from a YAML file. You can find them in our repo. In openmetadata.yaml, update the pipelineServiceClientConfiguration section accordingly. 
# For Bare Metal Installations
[...]

pipelineServiceClientConfiguration:
  className: ${PIPELINE_SERVICE_CLIENT_CLASS_NAME:-"org.openmetadata.service.clients.pipeline.airflow.AirflowRESTClient"}
  apiEndpoint: ${PIPELINE_SERVICE_CLIENT_ENDPOINT:-http://localhost:8080}
  metadataApiEndpoint: ${SERVER_HOST_API_URL:-http://localhost:8585/api}
  hostIp: ${PIPELINE_SERVICE_CLIENT_HOST_IP:-""}
  verifySSL: ${PIPELINE_SERVICE_CLIENT_VERIFY_SSL:-"no-ssl"} # Possible values are "no-ssl", "ignore", "validate"
  sslConfig:
    certificatePath: ${PIPELINE_SERVICE_CLIENT_SSL_CERT_PATH:-""} # Local path for the Pipeline Service Client

  # Default required parameters for Airflow as Pipeline Service Client
  parameters:
    username: ${AIRFLOW_USERNAME:-admin}
    password: ${AIRFLOW_PASSWORD:-admin}
    timeout: ${AIRFLOW_TIMEOUT:-10}

[...]
If using Docker, make sure that you are passing the correct environment variables: 
PIPELINE_SERVICE_CLIENT_ENDPOINT: ${PIPELINE_SERVICE_CLIENT_ENDPOINT:-http://ingestion:8080}
SERVER_HOST_API_URL: ${SERVER_HOST_API_URL:-http://openmetadata-server:8585/api}
If using Kubernetes, make sure that you are passing the correct values to Helm Chart: 
# Custom OpenMetadata Values.yaml
openmetadata:
   config:
      pipelineServiceClientConfig:
      enabled: true
      # endpoint url for airflow
      apiEndpoint: http://openmetadata-dependencies-web.default.svc.cluster.local:8080
      auth:
         username: admin
         password:
            secretRef: airflow-secrets
            secretKey: openmetadata-airflow-password

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Custom Airflow Installation

  • Note that the openmetadata-ingestion only supports Python versions 3.9, 3,10, and 3.11.
    • The supported Airflow versions for OpenMetadata include 2.3, 2.4, 2.5, 2.6, and 2.7. Starting from release 1.6, OpenMetadata supports compatibility with Airflow versions up to 2.10.5. Specifically, OpenMetadata 1.5 supports Airflow 2.9, 1.6.4 supports Airflow 2.9.3, and 1.6.5 supports Airflow 2.10.5. Ensure that your Airflow version aligns with your OpenMetadata deployment for optimal performance.
You will need to follow three steps:
  1. Install the openmetadata-ingestion package with the connector plugins that you need.
  2. Install the openmetadata-managed-apis to deploy our custom APIs on top of Airflow.
  3. Configure the Airflow environment.
  4. Configure the OpenMetadata server.

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1. Install the Connector Modules

The current approach we are following here is preparing the metadata ingestion DAGs as PythonOperators. This means that the packages need to be present in the Airflow instances. You will need to install: 
pip3 install "openmetadata-ingestion[<connector-name>]==x.y.z"
And then run the DAG as explained in each Connector, where x.y.z is the same version of your OpenMetadata server. For example, if you are on version 1.0.0, then you can install the openmetadata-ingestion with versions 1.0.0.*, e.g., 1.0.0.0, 1.0.0.1, etc., but not 1.0.1.x.
You can also install openmetadata-ingestion[all]==x.y.z, which will bring the requirements to run any connector.
You can check the Connector Modules guide above to learn how to install the openmetadata-ingestion package with the necessary plugins. They are necessary because even if we install the APIs, the Airflow instance needs to have the required libraries to connect to each source.

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2. Install the Airflow APIs

The openmetadata-ingestion-apis has a dependency on apache-airflow>=2.2.2. Please make sure that your host satisfies such requirement. Only installing the openmetadata-ingestion-apis won’t result in a proper full Airflow installation. For that, please follow the Airflow docs.
The goal of this module is to add some HTTP endpoints that the UI calls for deploying the Airflow DAGs. The first step can be achieved by running: 
pip3 install "openmetadata-managed-apis==x.y.z"
Here, the same versioning logic applies: x.y.z is the same version of your OpenMetadata server. For example, if you are on version 1.0.0, then you can install the openmetadata-managed-apis with versions 1.0.0.*, e.g., 1.0.0.0, 1.0.0.1, etc., but not 1.0.1.x.

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3. Configure the Airflow environment

The ingestion image is built on Airflow’s base image, ensuring it includes all necessary requirements to run Airflow. For Kubernetes deployments, the setup uses community Airflow charts with a modified base image, enabling it to function seamlessly as a scheduler, webserver, and worker.
We need a couple of settings:

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AIRFLOW_HOME

The APIs will look for the AIRFLOW_HOME environment variable to place the dynamically generated DAGs. Make sure that the variable is set and reachable from Airflow.

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Airflow APIs Basic Auth

Note that the integration of OpenMetadata with Airflow requires Basic Auth in the APIs. Make sure that your Airflow configuration supports that. You can read more about it here. A possible approach here is to update your airflow.cfg entries for Airflow 3.x: 
[api]
auth_backends = airflow.api_fastapi.auth.backend.basic_auth

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DAG Generated Configs

Every time a DAG is created from OpenMetadata, it will also create a JSON file with some information about the workflow that needs to be executed. By default, these files live under ${AIRFLOW_HOME}/dag_generated_configs, which in most environments translates to /opt/airflow/dag_generated_configs. You can change this directory by specifying the environment variable AIRFLOW__OPENMETADATA_AIRFLOW_APIS__DAG_GENERATED_CONFIGS or updating the airflow.cfg with: 
[openmetadata_airflow_apis]
dag_generated_configs=/opt/airflow/dag_generated_configs
A safe way to validate if the configuration is properly set in Airflow is to run: 
airflow config get-value openmetadata_airflow_apis dag_generated_configs

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4. Configure in the OpenMetadata Server

After installing the Airflow APIs, you will need to update your OpenMetadata Server. The OpenMetadata server takes all its configurations from a YAML file. You can find them in our repo. In openmetadata.yaml, update the pipelineServiceClientConfiguration section accordingly. 
# For Bare Metal Installations
[...]

pipelineServiceClientConfiguration:
  className: ${PIPELINE_SERVICE_CLIENT_CLASS_NAME:-"org.openmetadata.service.clients.pipeline.airflow.AirflowRESTClient"}
  apiEndpoint: ${PIPELINE_SERVICE_CLIENT_ENDPOINT:-http://localhost:8080}
  metadataApiEndpoint: ${SERVER_HOST_API_URL:-http://localhost:8585/api}
  hostIp: ${PIPELINE_SERVICE_CLIENT_HOST_IP:-""}
  verifySSL: ${PIPELINE_SERVICE_CLIENT_VERIFY_SSL:-"no-ssl"} # Possible values are "no-ssl", "ignore", "validate"
  sslConfig:
    certificatePath: ${PIPELINE_SERVICE_CLIENT_SSL_CERT_PATH:-""} # Local path for the Pipeline Service Client

  # Default required parameters for Airflow as Pipeline Service Client
  parameters:
    username: ${AIRFLOW_USERNAME:-admin}
    password: ${AIRFLOW_PASSWORD:-admin}
    timeout: ${AIRFLOW_TIMEOUT:-10}

[...]
If using Docker, make sure that you are passing the correct environment variables: 
PIPELINE_SERVICE_CLIENT_ENDPOINT: ${PIPELINE_SERVICE_CLIENT_ENDPOINT:-http://ingestion:8080}
SERVER_HOST_API_URL: ${SERVER_HOST_API_URL:-http://openmetadata-server:8585/api}
If using Kubernetes, make sure that you are passing the correct values to Helm Chart: 
# Custom OpenMetadata Values.yaml
openmetadata:
   config:
      pipelineServiceClientConfig:
      enabled: true
      # endpoint url for airflow
      apiEndpoint: http://openmetadata-dependencies-web.default.svc.cluster.local:8080
      auth:
         username: admin
         password:
            secretRef: airflow-secrets
            secretKey: openmetadata-airflow-password
For installation validation, Git Sync guidance, SSL configuration, and troubleshooting Airflow pipeline issues, see the Airflow Troubleshooting & Advanced guide.