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Get the guideSetting up AWS Glue with dbt Developer Hub requires an AWS Identity and Access Management (IAM) role with the necessary permissions to run an AWS Glue interactive session. This role will allow AWS Glue to access the necessary resources and perform tasks on your behalf.
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "glue:StartInteractiveSession",
"Resource": "*"
}
]
}
The above JSON policy document allows the IAM role to start an AWS Glue interactive session. This policy should be attached to the IAM role that you create for AWS Glue.
After creating the IAM role, the next step is to install dbt in the new Airflow environment. dbt is a transformation tool that allows you to define, test, and execute data transformations in SQL.
pip install dbt
The above command installs dbt using pip, which is a package installer for Python. You should run this command in your Airflow environment.
For AWS Glue to work with dbt Developer Hub, you need to add certain dependencies to your requirements.txt file. These dependencies include boto3, botocore, dbt-redshift, dbt-postgres, and Python.
boto3>=1.17.54
botocore>=1.20.54
dbt-redshift>=1.3.0
dbt-postgres>=1.3.0
The above lines should be added to your requirements.txt file. Each line specifies a package and its minimum required version.
Once the dependencies are installed, you can create Directed Acyclic Graphs (DAGs) that focus on dbt transformation. DAGs are a set of tasks that run in a particular order, without any cycles.
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators.python_operator import PythonOperator
def dbt_transform():
# dbt transformation code here
dag = DAG('dbt_dag', description='A simple dbt DAG',
schedule_interval='0 12 * * *',
start_date=datetime(2017, 3, 20), catchup=False)
dummy_operator = DummyOperator(task_id='dummy_task', retries=3, dag=dag)
dbt_operator = PythonOperator(task_id='dbt_transform', python_callable=dbt_transform, dag=dag)
dummy_operator >> dbt_operator
The above Python script creates a simple Airflow DAG with two tasks. The first task is a dummy task, and the second task is a Python task that calls a function for dbt transformation.
The final step in setting up AWS Glue with dbt Developer Hub is to configure your AWS profile for Glue Interactive Session. This involves setting your AWS access key ID, secret access key, and default region in your AWS configuration file.
[default]
aws_access_key_id = YOUR_ACCESS_KEY
aws_secret_access_key = YOUR_SECRET_KEY
region = YOUR_REGION
The above lines should be added to your AWS configuration file, which is usually located at ~/.aws/config. Replace YOUR_ACCESS_KEY, YOUR_SECRET_KEY, and YOUR_REGION with your actual AWS access key ID, secret access key, and default region, respectively.
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