Airflow Cloud ETL is a simple Data Pipeline repository using Airflow and Google Cloud for Cloud ETL and Batch Processing.

Use git to clone this repository

git clone https://github.com/ghazimuharam/airflow-cloud-etl.git

Make sure you have python 3.7 installed on your machine

> python --version
Python 3.7.10

To run the script in this repository, you need to install the prerequisite library from requirements.txt

pip install -r requirements.txt

Store all your service account json files to ./conf directory

Before running Airflow DAGs, you have to configure Airflow Variables and Airflow Connections

To run airflow docker container, use Initializing Environment step on this Airflow Official Page, make sure you have docker-compose installed on your machine.

Change gce_region, gce_zone, project_id, transaction_output_table_id value with your configuration

Change

• Keyfile Path to /opt/airflow/conf/key.json
• Project Id to your-project-name

To run composer_transaction_integrator DAG, you have to configure 2 service account json files.

• json_input_config_files (Where the big query stored)
• json_output_config_files (Where to output the processed big query execution)

transaction_integration = IntegrateTransactionOperator(
        task_id='integrate_transaction',
        json_input_config_files="/opt/airflow/conf/bigquery.json",
        json_output_config_files="/opt/airflow/conf/key.json",
        ...

The main application program will create 2 Dataset, 3 Table, and 1 Bucket. The main application program only executed once when initializing the Google Cloud Storage requirements. Before running the main program, run the command below

export GOOGLE_APPLICATION_CREDENTIALS="./path/to/file.json"

Change ./main.py cloud configuration

# Your project name
project_name = 'linen-patrol-285921'

# Your dataset location
location = 'US'

# Your bucket name
bucket_name = 'week2-blank-space'

After setting up the configuration, you can run ./main.py using command below

python main.py

MIT