airflow-provider-hightouch 3.0.0

Hightouch Provider for Airflow

Apache Airflow Provider for Hightouch

Provides an Airflow Operator and Hook for Hightouch. This allows the user to initiate a run for a sync from Airflow.

Installation

Pre-requisites: An environment running apache-airflow >= 1.10, including >= 2.

pip install airflow-provider-hightouch

Configuration

In the Airflow Connections UI, create a new connection for Hightouch.

• Conn ID: hightouch_default
• Conn Type: HTTP
• Host: https://api.hightouch.io
• Password: enter the API key for your workspace. You can generate an API key from your Workspace Settings

The Operator uses the hightouch_default connection id by default, but if needed, you can create additional Airflow Connections and reference them in the operator

Modules

HightouchTriggerSyncOperator

Starts a Hightouch Sync Run. Requires the sync_id or the sync_slug for the sync you wish to run.

The run is synchronous by default, and the task will be marked complete once the sync is successfully completed.

However, you can request a asynchronous request instead by passing synchronous=False to the operator.

If the API key is not authorized or if the request is invalid the task will fail. If a run is already in progress, a new run will be triggered following the completion of the existing run.

Examples

Creating a run is as simple as importing the operator and providing it with a sync_id. An example dag is available as well.

from airflow_provider_hightouch.operators.hightouch import HightouchTriggerSyncOperator

with DAG(....) as dag:
...
    my_task = HightouchTriggerSyncOperator(task_id="run_my_sync", sync_id="123")
    my_other_task = HightouchTriggerSyncOperator(task_id="run_my_sync", sync_slug="my-sync-slug")

Issues

Please submit issues and pull requests in our official repo: https://github.com/hightouchio/airflow-provider-hightouch

We are happy to hear from you, for any feedback please email the authors at [e-mail].

Acknowledgements

Special thanks to Fivetran for their provider and Marcos Marx's Airbyte contribution in the core Airflow repo for doing this before we had to so we could generously learn from their hard work.