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    Simplifying S3 Bucket Listing with Python Lambda Functions

    Simplifying S3 Bucket Listing with Python Lambda Functions


    AWS Lambda functions provide a serverless computing environment that allows you to run code without provisioning or managing servers. In this blog post, we will explore how to use a Python Lambda function to list S3 buckets in your AWS account. By leveraging Lambda and the AWS SDK for Python (Boto3), you can automate the process of retrieving a list of S3 buckets, making it easier to manage and monitor your cloud storage resources.


    Before we begin, ensure that you have an AWS account and the necessary IAM permissions to create and execute Lambda functions. Additionally, make sure you have Python and the Boto3 library installed on your local machine for development.

    Step 1: Create a New Lambda Function:

    1. Open the AWS Management Console and navigate to the AWS Lambda service.

    2. Click on "Create Function" and choose the "Author from scratch" option.

    3. Provide a name for your Lambda function, select the Python runtime, and choose an existing or create a new execution role with appropriate permissions.

    4. Click on "Create Function" to create the Lambda function.

    Step 2: Write the Python Code:

    1. In the function's code editor, write the Python code to list S3 buckets using Boto3. Here's an example:


    import boto3

    def lambda_handler(event, context):

        s3 = boto3.client('s3')

        response = s3.list_buckets()

        buckets = [bucket['Name'] for bucket in response['Buckets']]

        print("List of S3 Buckets:")

        for bucket in buckets:





    import json
    import boto3

    s3 = boto3.resource('s3')

    def lambda_handler(event, context):

     bucketlist = []

     for bucket in s3.buckets.all():

     return {
      "statusCode": 200,
      "body": bucketlist


    2. In the code, we import the `boto3` library, create an S3 client, and call the `list_buckets` method to retrieve the list of buckets. We then extract the bucket names from the response and print them.

    Step 3: Configure the Lambda Function:

    1. Scroll down to the "Basic settings" section and specify the desired memory allocation and timeout values for the Lambda function.

    2. Under the "Environment variables" section, you can define any environment variables required for your code.

    3. Click on "Save" to save the Lambda function configuration.

    Step 4: Test the Lambda Function:

    1. In the Lambda function editor, click on the "Test" button.

    2. Choose a sample test event or create a new test event.

    3. Click on "Test" to execute the Lambda function.

    Step 5: View the Results:

    After executing the Lambda function, you can view the output in the Lambda function logs. The logs will display the list of S3 buckets retrieved by the function.


    Using a Python Lambda function with Boto3, you can easily list S3 buckets in your AWS account. This approach eliminates the need for manual intervention and allows you to automate the retrieval of bucket information. By leveraging the serverless capabilities of Lambda, you can run this function on demand or schedule it to execute at specific intervals. With this automation in place, you can efficiently manage and monitor your S3 bucket resources in AWS, saving time and effort in the process.

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