A Guide to Airflow with GCP for Streamlined Data Orchestration

Overview

In the first part of this blog, we delved into the fundamental concepts of Airflow and its efficacy within the GCP cloud environment for streamlined data orchestration. As we venture into the second part, the focus shifts towards practical implementation, sharpening the intricacy between Airflow and GCP Composer. This installment aims to provide readers with a hands-on understanding of Airflow’s functionality within the GCP ecosystem, showcasing its practical applications and the seamless orchestration it brings to data workflows. Building on the groundwork laid in the first part, we’re set to unravel the implementation intricacies, ensuring that readers understand how Airflow becomes a powerful ally in data management on GCP.

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Introduction

Data is an asset, and processing it efficiently is crucial for extracting meaningful insights. In this blog post, we’ll guide you through creating a data pipeline using Apache Airflow to take Google Cloud Storage (GCS) files, transform them, and store the results in BigQuery.

By the end of this guide, you’ll have a robust and automated solution for managing your data workflows in the cloud.

Prerequisites

Before you begin, ensure you have the following set up:

A GCP account with billing enabled.
A Google Cloud Storage bucket.
A BigQuery dataset.
Apache Airflow installed, either locally or on GCP Composer.

Step-by-Step Guide

Step 1: Set Up GCP Resources

Create a GCS Bucket:
In the GCP Console, navigate to “Storage” > “Browser.”
Create a new bucket and note the bucket name.

step1

Set Up Google Cloud BigQuery
In the GCP Console, navigate to “BigQuery.”
Create a new dataset within your project.

step1b

Step 2: Write Python Script for Transformation

Create a Python script:

Write a Python script that performs the desired transformation on your data.
Save the script, e.g., transform_data.py.

Python code

# transform_data.py
import pandas as pd

def transform_data(input_data):
    # Perform your data transformation here
    # Example: Convert CSV to DataFrame and add a new column
    df = pd.read_csv(input_data)
    df['new_column'] = df['existing_column'] * 2

    # Save the transformed data
    output_data = 'transformed_data.csv'
    df.to_csv(output_data, index=False)

    return output_data

# transform_data.py

import pandas as pd

def transform_data(input_data):

# Perform your data transformation here

# Example: Convert CSV to DataFrame and add a new column

df = pd.read_csv(input_data)

df['new_column'] = df['existing_column'] * 2

# Save the transformed data

output_data = 'transformed_data.csv'

df.to_csv(output_data, index=False)

return output_data

Step 3: Set Up Airflow DAG:

Create a new DAG file:

Create a new Python file in your Airflow DAGs folder, e.g., airflow.py.
Define the DAG structure and import the transform_data function.

step3

Python code

# data_pipeline_dag.py
from Airflow import DAG
from Airflow.operators.python_operator import PythonOperator
from datetime import datetime, timedelta
from transform_data import transform_data

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2023, 1, 1),
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

dag = DAG(
    'data_pipeline_dag',
    default_args=default_args,
    schedule_interval='0 0 * * *',  # Daily at midnight
)
transform_task = PythonOperator(
    task_id='transform_data_task',
    python_callable=transform_data,
    op_args=['gs://your-gcs-bucket/your-input-data.csv'],  # Replace with your GCS input data path
    provide_context=True,
    dag=dag,
)
transform_task

# data_pipeline_dag.py

from Airflow import DAG

from Airflow.operators.python_operator import PythonOperator

from datetime import datetime, timedelta

from transform_data import transform_data

default_args = {

'owner': 'airflow',

'start_date': datetime(2023, 1, 1),

'retries': 1,

'retry_delay': timedelta(minutes=5),

}

dag = DAG(

'data_pipeline_dag',

default_args=default_args,

schedule_interval='0 0 * * *', # Daily at midnight

)

transform_task = PythonOperator(

task_id='transform_data_task',

python_callable=transform_data,

op_args=['gs://your-gcs-bucket/your-input-data.csv'], # Replace with your GCS input data path

provide_context=True,

dag=dag,

)

transform_task

Step 4: Upload DAG to Airflow

Upload the DAG file:

Copy the airflow.py file to the DAGs folder in your Airflow instance.
Airflow will automatically detect the new DAG and add it to the DAG list.

step4

Step 5: Schedule and Monitor

Set up a schedule:

In the data_pipeline_dag.py file, adjust the schedule_interval parameter to your desired schedule.
Save the file.

Monitor in Airflow UI:

Access the Airflow web UI.
Navigate to the “DAGs” section and find the “data_pipeline_dag.”
Trigger the DAG manually to test the data transformation.

Step 6: Load Transformed Data into Google Cloud BigQuery

Write Python Script for Google Cloud BigQuery Load.
Write a Python script to load the transformed data into Google Cloud BigQuery.
Use the BigQuery client library to interact with Google Cloud BigQuery.

Python code

# load_to_bigquery.py
from google.cloud import bigquery

def load_to_bigquery(input_data, dataset_id, table_id):
    client = bigquery.Client()

    # Set up the destination table
    dataset_ref = client.dataset(dataset_id)
    table_ref = dataset_ref.table(table_id)
    job_config = bigquery.LoadJobConfig()
    job_config.source_format = bigquery.SourceFormat.CSV

    # Load data into BigQuery
    with open(input_data, 'rb') as source_file:
        job = client.load_table_from_file(
            source_file, table_ref, job_config=job_config
        )

    job.result()

if __name__ == "__main__":
    load_to_bigquery('transformed_data.csv', 'your-dataset-id', 'your-table-id')

# load_to_bigquery.py

from google.cloud import bigquery

def load_to_bigquery(input_data, dataset_id, table_id):

client = bigquery.Client()

# Set up the destination table

dataset_ref = client.dataset(dataset_id)

table_ref = dataset_ref.table(table_id)

job_config = bigquery.LoadJobConfig()

job_config.source_format = bigquery.SourceFormat.CSV

# Load data into BigQuery

with open(input_data, 'rb') as source_file:

job = client.load_table_from_file(

source_file, table_ref, job_config=job_config

)

job.result()

if __name__ == "__main__":

load_to_bigquery('transformed_data.csv', 'your-dataset-id', 'your-table-id')

Step 7: Extend Airflow DAG for Google Cloud BigQuery Load:

Modify the DAG file:

Edit the data_pipeline_dag.py file to include the BigQuery load task.

Python code

# data_pipeline_dag.py
from Airflow.operators.python_operator import PythonOperator
from load_to_bigquery import load_to_bigquery

# ... (previous code)

load_to_bigquery_task = PythonOperator(
    task_id='load_to_bigquery_task',
    python_callable=load_to_bigquery,
    op_args=['transformed_data.csv', 'your-dataset-id', 'your-table-id'],  # Replace with your BigQuery dataset and table
    provide_context=True,
    dag=dag,
)

transform_task >> load_to_bigquery_task

# data_pipeline_dag.py

from Airflow.operators.python_operator import PythonOperator

from load_to_bigquery import load_to_bigquery

# ... (previous code)

load_to_bigquery_task = PythonOperator(

task_id='load_to_bigquery_task',

python_callable=load_to_bigquery,

op_args=['transformed_data.csv', 'your-dataset-id', 'your-table-id'], # Replace with your BigQuery dataset and table

provide_context=True,

dag=dag,

)

transform_task >> load_to_bigquery_task

Conclusion

You’ve successfully built a data pipeline using Apache Airflow to take files from Google Cloud Storage, transform them, and store the results in Google Cloud BigQuery. This scalable and automated solution showcases the power of Airflow in orchestrating complex workflows. Feel free to customize the script and DAG to fit your requirements and explore additional Airflow features for more advanced data processing tasks. Automation like this enhances the efficiency of your data workflows, ultimately leading to more informed decision-making and insights from your data.

Drop a query if you have any questions regarding Apache Airflow and we will get back to you quickly.

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FAQs

1. How can I version-control and manage the code for my Airflow DAGs when using GCP Composer?

ANS: – Version-controlling Airflow DAGs in GCP Composer is best achieved by storing your DAG definitions in a version-controlled repository like Git. You can then use GCP Composer’s environment versioning feature to update your environment with the latest version of your DAGs. This ensures that your DAGs are consistent across different environments and allows for easy rollback in case of issues. GCP Composer allows you to upload DAGs directly through the web UI or the gcloud command-line tool.

2. Can I trigger Google Cloud Functions or other GCP services from Apache Airflow DAGs?

ANS: – Absolutely! Apache Airflow has operators that enable interaction with various GCP services. For triggering Google Cloud Functions, you can use the GoogleCloudFunctionOperator. Similarly, operators for other GCP services like BigQuery, Dataflow, and more exist. You can seamlessly integrate and orchestrate workflows involving multiple GCP services within your Airflow DAGs by leveraging these operators.