--- title: Run Airbyte Connector using the CLI slug: /connectors/pipeline/airbyte/cli --- # Run Airbyte 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: - [Requirements](#requirements) - [Metadata Ingestion](#metadata-ingestion) ## Requirements {%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {% /inlineCallout %} 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 Airbyte ingestion, you will need to install: ```bash pip3 install "openmetadata-ingestion[airbyte]" ``` ## 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/pipeline/airbyteConnection.json) 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](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 Airbyte: {% codePreview %} {% codeInfoContainer %} #### Source Configuration - Service Connection {% codeInfo srNumber=1 %} **hostPort**: Pipeline Service Management UI URL {% /codeInfo %} #### Source Configuration - Source Config {% codeInfo srNumber=2 %} 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. **includeTags**: Set the 'Include Tags' toggle to control whether 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. **pipelineFilterPattern** and **chartFilterPattern**: Note that the `pipelineFilterPattern` and `chartFilterPattern` both support regex as include or exclude. {% /codeInfo %} #### Sink Configuration {% codeInfo srNumber=3 %} To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`. {% /codeInfo %} {% partial file="workflow-config.md" /%} {% /codeInfoContainer %} {% codeBlock fileName="filename.yaml" %} ```yaml source: type: airbyte serviceName: airbyte_source serviceConnection: config: type: Airbyte ``` ```yaml {% srNumber=1 %} hostPort: http://localhost:8000 ``` ```yaml {% srNumber=2 %} sourceConfig: config: type: PipelineMetadata # markDeletedPipelines: True # includeTags: True # includeLineage: true # pipelineFilterPattern: # includes: # - pipeline1 # - pipeline2 # excludes: # - pipeline3 # - pipeline4 ``` ```yaml {% srNumber=3 %} sink: type: metadata-rest config: {} ``` {% partial file="workflow-config-yaml.md" /%} {% /codeBlock %} {% /codePreview %} ### 2. Run with the CLI First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run: ```bash metadata ingest -c ``` 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.