OpenMetadata/openmetadata-docs/content/v1.1.0/connectors/pipeline/fivetran/cli.md

title	slug
Run Fivetran Connector using the CLI	/connectors/pipeline/fivetran/cli

Run Fivetran using the metadata CLI

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

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

• Requirements
• 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.

To access Fivetran APIs, a Fivetran account on a Standard, Enterprise, or Business Critical plan is required.

Python Requirements

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

pip3 install "openmetadata-ingestion[fivetran]"

Metadata Ingestion

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

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 Fivetran:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

apiKey: Fivetran API Key.

Follow the steps mentioned below to generate the Fivetran API key and API secret:

• Click your user name in your Fivetran dashboard.
• Click API Key.
• Click Generate API key. (If you already have an API key, then the button text is Generate new API key.)
• Make a note of the key and secret as they won't be displayed once you close the page or navigate away.

For more detailed documentation visit here.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

apiSecret: Fivetran API Secret.

From the above step where the API key is generated copy the the API secret

{% /codeInfo %}

{% codeInfo srNumber=3 %}

hostPort: HostPort of the Fivetran instance.

Hostport of the Fivetran instance that the connection will be made to By default OpenMetadata will use https://api.fivetran.com to connect to the Fivetran APIs.

{% /codeInfo %}

{% codeInfo srNumber=4 %}

limit: Fivetran API Limit For Pagination.

This refers to the maximum number of records that can be returned in a single page of results when using Fivetran's API for pagination.

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=5 %}

The sourceConfig is defined here:

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=6 %}

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" %}

source:
  type: fivetran
  serviceName: local_fivetran
  serviceConnection:
    config:
      type: Fivetran

      apiKey: <fivetran api key>

      apiSecret: <fivetran api secret>

      # hostPort: https://api.fivetran.com (default)

      # limit: 1000 (default)

  sourceConfig:
    config:
      type: PipelineMetadata
      # markDeletedPipelines: True
      # includeTags: True
      # includeLineage: true
      # pipelineFilterPattern:
      #   includes:
      #     - pipeline1
      #     - pipeline2
      #   excludes:
      #     - pipeline3
      #     - pipeline4

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:

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.