Airflow : Using Boto3 in Airflow

Apache Airflow

Boto3 is the Amazon Web Services (AWS) SDK for Python, which allows Python developers to write software that makes use of services like Amazon S3 and Amazon EC2. Airflow is an open-source platform to programmatically author, schedule, and monitor workflows.

Using Boto3 in Airflow can be helpful for performing various AWS-related tasks, such as uploading files to S3, starting and stopping EC2 instances, or sending notifications via SNS. Here’s an example of how you can use Boto3 in Airflow to upload a file to S3:

Install the Boto3 library in your Airflow environment:

pip install boto3

Create a Python function that uses Boto3 to upload a file to S3. Here’s an example function:

import boto3

def upload_file_to_s3(bucket_name, file_path, s3_key):
    s3 = boto3.client('s3')
    with open(file_path, 'rb') as file:
        s3.upload_fileobj(file, bucket_name, s3_key)

This function takes three arguments: the name of the S3 bucket to upload the file to, the path to the local file to be uploaded, and the S3 key (i.e., the object key in S3) to use for the uploaded file. The function uses the boto3.client method to create a client object for the S3 service, and then uses the upload_fileobj method of the client to upload the file.

Create an Airflow DAG that uses this function to upload a file to S3. Here’s an example DAG:

from datetime import datetime
from airflow import DAG
from airflow.operators.python_operator import PythonOperator

default_args = {
    'owner': 'me',
    'start_date': datetime(2022, 4, 1),
    'depends_on_past': False,
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=5)

def upload_file():
    upload_file_to_s3('my-bucket', '/path/to/local/file.txt', 's3-key-for-file.txt')

with DAG('upload_to_s3', default_args=default_args, schedule_interval=None) as dag:
    t1 = PythonOperator(


In this DAG, we define a single task that calls the upload_file function we defined earlier. We use the PythonOperator to create this task, and specify the function to be called using the python_callable argument. We also specify some default arguments for the DAG, such as the owner, start date, and email settings.

When you run this DAG in Airflow, it will upload the specified file to the specified S3 bucket using the specified key.

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