Airflow Integration

DataHub supports integration of

• Airflow Pipeline (DAG) metadata
• DAG and Task run information as well as
• Lineage information when present

There are a few ways to enable these integrations from Airflow into DataHub.

Using Datahub's Airflow lineage plugin (new)

::: note

We recommend you use the lineage plugin if you are on Airflow version >= 2.0.2 or on MWAA with an Airflow version >= 2.0.2 :::

1. You need to install the required dependency in your airflow.

     pip install acryl-datahub-airflow-plugin

2. Disable lazy plugin load in your airflow.cfg. On MWAA you should add this config to your Apache Airflow configuration options.

   core.lazy_load_plugins : False

3. You must configure an Airflow hook for Datahub. We support both a Datahub REST hook and a Kafka-based hook, but you only need one.

   # For REST-based:
   airflow connections add  --conn-type 'datahub_rest' 'datahub_rest_default' --conn-host 'http://localhost:8080'
   # For Kafka-based (standard Kafka sink config can be passed via extras):
   airflow connections add  --conn-type 'datahub_kafka' 'datahub_kafka_default' --conn-host 'broker:9092' --conn-extra '{}'

4. Add your datahub_conn_id and/or cluster to your airflow.cfg file if it is not align with the default values. See configuration parameters below

   Configuration options:

   Name	Default value	Description
   datahub.datahub_conn_id	datahub_rest_default	The name of the datahub connection you set in step 1.
   datahub.cluster	prod	name of the airflow cluster
   datahub.capture_ownership_info	true	If true, the owners field of the DAG will be capture as a DataHub corpuser.
   datahub.capture_tags_info	true	If true, the tags field of the DAG will be captured as DataHub tags.
   datahub.graceful_exceptions	true	If set to true, most runtime errors in the lineage backend will be suppressed and will not cause the overall task to fail. Note that configuration issues will still throw exceptions.

5. Configure inlets and outlets for your Airflow operators. For reference, look at the sample DAG in lineage_backend_demo.py, or reference lineage_backend_taskflow_demo.py if you're using the TaskFlow API.

6. [optional] Learn more about Airflow lineage, including shorthand notation and some automation.

How to validate installation

1. Go and check in Airflow at Admin -> Plugins menu if you can see the Datahub plugin

2. Run an Airflow DAG and you should see in the task logs Datahub releated log messages like:

   Emitting Datahub ...

Using Datahub's Airflow lineage backend

caution

The Airflow lineage backend is only supported in Airflow 1.10.15+ and 2.0.2+. For managed services like MWAA you should use the Datahub Airflow plugin as the lineage backend is not supported there

note

If you are looking to run Airflow and DataHub using docker locally, follow the guide here. Otherwise proceed to follow the instructions below.

Setting up Airflow to use DataHub as Lineage Backend

1. You need to install the required dependency in your airflow. See https://registry.astronomer.io/providers/datahub/modules/datahublineagebackend

  pip install acryl-datahub[airflow]

2. You must configure an Airflow hook for Datahub. We support both a Datahub REST hook and a Kafka-based hook, but you only need one.

   # For REST-based:
   airflow connections add  --conn-type 'datahub_rest' 'datahub_rest_default' --conn-host 'http://localhost:8080'
   # For Kafka-based (standard Kafka sink config can be passed via extras):
   airflow connections add  --conn-type 'datahub_kafka' 'datahub_kafka_default' --conn-host 'broker:9092' --conn-extra '{}'

3. Add the following lines to your airflow.cfg file.

   [lineage]
   backend = datahub_provider.lineage.datahub.DatahubLineageBackend
   datahub_kwargs = {
       "datahub_conn_id": "datahub_rest_default",
       "cluster": "prod",
       "capture_ownership_info": true,
       "capture_tags_info": true,
       "graceful_exceptions": true }
   # The above indentation is important!

   Configuration options:

   • datahub_conn_id (required): Usually datahub_rest_default or datahub_kafka_default, depending on what you named the connection in step 1.
   • cluster (defaults to "prod"): The "cluster" to associate Airflow DAGs and tasks with.
   • capture_ownership_info (defaults to true): If true, the owners field of the DAG will be capture as a DataHub corpuser.
   • capture_tags_info (defaults to true): If true, the tags field of the DAG will be captured as DataHub tags.
   • capture_executions (defaults to false): If true, it captures task runs as DataHub DataProcessInstances. This feature only works with Datahub GMS version v0.8.33 or greater.
   • graceful_exceptions (defaults to true): If set to true, most runtime errors in the lineage backend will be suppressed and will not cause the overall task to fail. Note that configuration issues will still throw exceptions.

4. Configure inlets and outlets for your Airflow operators. For reference, look at the sample DAG in lineage_backend_demo.py, or reference lineage_backend_taskflow_demo.py if you're using the TaskFlow API.

5. [optional] Learn more about Airflow lineage, including shorthand notation and some automation.

Emitting lineage via a separate operator

Take a look at this sample DAG:

• lineage_emission_dag.py - emits lineage using the DatahubEmitterOperator.

In order to use this example, you must first configure the Datahub hook. Like in ingestion, we support a Datahub REST hook and a Kafka-based hook. See step 1 above for details.