Amazon Redshift Operators¶

Amazon Redshift manages all the work of setting up, operating, and scaling a data warehouse: provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine. You can focus on using your data to acquire new insights for your business and customers.

Airflow provides operators to manage your Redshift clusters.

Prerequisite Tasks¶

To use these operators, you must do a few things:

Create necessary resources using AWS Console or AWS CLI.
Install API libraries via pip.
pip install 'apache-airflow[amazon]'
Detailed information is available Installation
Setup Connection.

Manage Amazon Redshift Clusters¶

Amazon Redshift Cluster Sensor¶

To check the state of an Amazon Redshift Cluster until it reaches the target state or another terminal state you can use RedshiftClusterSensor.

airflow/providers/amazon/aws/example_dags/example_redshift_cluster.py[source]

task_wait_cluster_available = RedshiftClusterSensor(
    task_id='sensor_redshift_cluster_available',
    cluster_identifier=REDSHIFT_CLUSTER_IDENTIFIER,
    target_status='available',
    poke_interval=5,
    timeout=60 * 15,
)

Resume an Amazon Redshift Cluster¶

To resume a ‘paused’ Amazon Redshift Cluster you can use RedshiftResumeClusterOperator

airflow/providers/amazon/aws/example_dags/example_redshift_cluster.py[source]

task_resume_cluster = RedshiftResumeClusterOperator(
    task_id='redshift_resume_cluster',
    cluster_identifier=REDSHIFT_CLUSTER_IDENTIFIER,
)

Pause an Amazon Redshift Cluster¶

To pause an ‘available’ Amazon Redshift Cluster you can use RedshiftPauseClusterOperator

airflow/providers/amazon/aws/example_dags/example_redshift_cluster.py[source]

task_pause_cluster = RedshiftPauseClusterOperator(
    task_id='redshift_pause_cluster',
    cluster_identifier=REDSHIFT_CLUSTER_IDENTIFIER,
)

Reference¶

AWS boto3 Library Documentation for Amazon Redshift