Simple quickstart to dbt Core DAG with bashoperator using BigQuery

dbt_example_dag.py

# This is an example DAG that is a good reference for your portfolio

import os
import tempfile
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from airflow.models import Connection
from airflow.operators.bash import BashOperator


def save_keyfile_to_temp_file(gcp_conn_id):
    conn = Connection.get_connection_from_secrets(conn_id=gcp_conn_id)
    keyfile_dict = conn.extra_dejson['keyfile_dict']
    # keyfile_json = json.dumps(keyfile_dict)
    
    # Create a temporary file with a .json extension
    fd, temp_file_path = tempfile.mkstemp(suffix='.json', text=True)
    
    with os.fdopen(fd, 'w') as f:
        f.write(keyfile_dict)

    return temp_file_path

# TODO: replace the conn_id with your own
GCP_CONN_ID="your_bigquery_conn_id"
JSON_KEYFILE_PATH = save_keyfile_to_temp_file(GCP_CONN_ID)

PATH_TO_DBT_VENV = "/home/astro/dbt_venv/bin/activate"
# TODO replace these vars with your own
GIT_REPO = "dbt-bigquery-example"
PROJECT_ID = "dbt-demo-386220"
GIT_BRANCH = "main"

git_clone_cmds = f"""
    git clone https://github.com/sungchun12/dbt-bigquery-example.git"""

dbt_setup_cmds = f"""
    {git_clone_cmds} &&
    cd {GIT_REPO} &&
    git checkout {GIT_BRANCH} &&
    export PROJECT_ID={PROJECT_ID} &&
    export DBT_GOOGLE_BIGQUERY_KEYFILE={JSON_KEYFILE_PATH} &&
    dbt deps"""

dbt_run_cmd = f"""
    {dbt_setup_cmds} &&
    dbt seed &&
    dbt run
"""

# Set default arguments for the DAG
default_args = {
    "owner": "airflow",
    "depends_on_past": False,
    "email_on_failure": False,
    "email_on_retry": False,
    "retries": 1,
    "retry_delay": timedelta(minutes=5),
}

# Define the DAG
dag = DAG(
    "dbt_example_dag",
    default_args=default_args,
    description="An example DAG with extract, load tasks and dbt build using DockerOperator",
    schedule_interval=timedelta(days=1),
    catchup=False,
    start_date=datetime(2023, 5, 9),
)


# DummyOperator for the extract task
extract_task = DummyOperator(
    task_id="extract_task",
    dag=dag,
)

# DummyOperator for the load task
load_task = DummyOperator(
    task_id="load_task",
    dag=dag,
)

dbt_run_task = BashOperator(
    task_id="dbt_run_task",
    bash_command=f"source $PATH_TO_DBT_VENV && {dbt_run_cmd}",
    env={"PATH_TO_DBT_VENV": PATH_TO_DBT_VENV},
)

# Define the task dependencies
extract_task >> load_task >> dbt_run_task

Dockerfile

FROM quay.io/astronomer/astro-runtime:7.4.2

# Install apache-airflow-providers-google package
RUN pip install apache-airflow-providers-google

# Switch to root user for installing git
USER root

# Install git
RUN apt-get update && apt-get install -y git

# Clean up APT cache
RUN apt-get clean && rm -rf /var/lib/apt/lists/*

# Switch back to the original user
USER astro

# Create a Python virtual environment and install dbt-bigquery
ENV DBT_VENV_PATH /home/astro/dbt_venv
RUN python -m venv $DBT_VENV_PATH && \
    . $DBT_VENV_PATH/bin/activate && \
    pip install dbt-bigquery==1.5.0

Evidence it works