Run Airflow using the metadata CLI

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

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

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.

To run the Airflow ingestion, you will need to install:

pip3 install "openmetadata-ingestion[airflow]"

Note that this installs the same Airflow version that we ship in the Ingestion Container, which is Airflow 2.3.3 from Release 0.12.

The ingestion using Airflow version 2.3.3 as a source package has been tested against Airflow 2.3.3 and Airflow 2.2.5.

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

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

This is a sample config for Airbyte:

  type: airflow
  serviceName: airflow_source
      type: Airflow
      hostPort: http://localhost:8080
      numberOfStatus: 10
      # Connection needs to be one of Mysql, Postgres, Mssql or Sqlite
        type: Mysql
        username: airflow_user
        password: airflow_pass
        databaseSchema: airflow_db
        hostPort: localhost:3306
        # #
        # type: Postgres
        # username: airflow_user
        # password: airflow_pass
        # database: airflow_db
        # hostPort: localhost:3306
        # #
        # type: Mssql
        # username: airflow_user
        # password: airflow_pass
        # database: airflow_db
        # hostPort: localhost:3306
        # uriString: http://... (optional)
        # #
        # type: Sqlite
        # username: airflow_user
        # password: airflow_pass
        # database: airflow_db
        # hostPort: localhost:3306
        # databaseMode: ":memory:" (optional)
      type: PipelineMetadata
      # includeLineage: true
      # pipelineFilterPattern:
      #   includes:
      #     - pipeline1
      #     - pipeline2
      #   excludes:
      #     - pipeline3
      #     - pipeline4
  type: metadata-rest
  config: { }
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
    hostPort: http://localhost:8585/api
    authProvider: no-auth

Source Configuration - Service Connection

  • hostPort: URL to the Airflow instance.
  • numberOfStatus: Number of status we want to look back to in every ingestion (e.g., Past executions from a DAG).
  • connection: Airflow metadata database connection. See these docs for supported backends.

In terms of connection we support the following selections:

  • backend: Should not be used from the UI. This is only applicable when ingesting Airflow metadata locally by running the ingestion from a DAG. It will use the current Airflow SQLAlchemy connection to extract the data.
  • MySQL, Postgres, MSSQL and SQLite: Pass the required credentials to reach out each of these services. We will create a connection to the pointed database and read Airflow data from there.

Source Configuration - Source Config

The sourceConfig is defined here:

  • dbServiceName: 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.,
    - 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:

    hostPort: http://localhost:8585/api
    authProvider: no-auth

We support different security providers. You can find their definitions here. You can find the different implementation of the ingestion below.

chevron_rightConfigure SSO in the Ingestion Workflows

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.

Still have questions?

You can take a look at our Q&A or reach out to us in Slack

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