To accomplish this task, you can use the Python Boto3 library to interact Amazon S3. You can achieve this by taking the following steps:

1. Set up the Boto3 client for Amazon S3.
2. List the objects in S3 bucket and filter to identify the files with the StorageClass = INTELLIGENT_TIERING and AccessTier = ARCHIVE_ACCESS.
3. For each identified object, use thecopy_object` method to create a copy with the desired Storage and AccessTier and then delete the original object.

The code snippet below outlines a general approach to achieving in Python using Boto3:

import boto3

# Set up the Boto3 client for Amazon S
s3 = boto3.client('s3')

# List the objects and filter for those with StorageClass = INTELLIGENT_TIERING and AccessTier = ARCHIVE_ACCESS
objects = s3.list_objects_v2(Bucket='your_bucket_name')['Contents']
filtered_objects = [obj['Key'] for obj in objects if obj.get('StorageClass') == 'INTELLIGENT_TIERING' and.get('AccessTier') == 'ARCHIVE_ACCESS']

# each identified object, copy it with the desired StorageClass and AccessTier and then delete the original object
for obj_key in filtered_objects:
    copy_source = {'Bucket': 'your_bucket_name', 'Key': obj_key}
    s3.copy_object(CopySource=copy_source,='your_bucket_name', Key=obj_key, StorageClass='INTELLIGENT_TING', AccessControl='Frequent Access')
    s3.delete_object(Bucket='your_bucket_name',=obj_key)

Remember to replace 'your_bucket_name' with the actual name of your3 bucket. Additionally, make sure your Python environment is properly set up with Boto3 and has the necessary IAM permissions to perform these actions on Amazon S3. Always caution when manipulating data in a production environment.

Certainly! Instead of using the copy and delete method, you can modify the StorageClass and AccessTier of the objects directly using the copy_object method. This approach will modify the existing objects' metadata without creating additional copies.

Below is an alternative Python Boto3 code snippet to achieve this using the copy_object method:

import boto3

# Set up Boto3 client for Amazon S3
s3 =3.client('s3')

# List the objects and filter for those with StorageClass = INTELLIGENT_TIERING and AccessTier = ARCHIVE_ACCESS
objects = s3.list_objects_v(Bucket='your_bucket_name')['Contents']
filtered_objects = [obj['Key'] for obj in objects if obj.get('StorageClass') == 'INTELLIGENTIERING' and obj.get('AccessTier') == 'ARCH_ACCESS']

# For each identified object, update its StorageClass and AccessTier
for obj_key in filtered_objects:
    s3.copy_object(
       Source={'Bucket': 'your_bucket_name', 'Key': obj_key},
        Bucket='your_bucket_name',
       =obj_key,
        StorageClass='INTELLIGENT_TIERING',
 MetadataDirective='REPLACE',
        Metadata={'AccessTier': 'Frequent Access'}
    )

In this, the copy_object method is used to update the StorageClass and AccessTier of the objects directly, without the need to delete and create new copies. Please replace 'your_bucket_name' with the actual name of your S3 bucket and ensure proper permissions and environment setup. Always exercise caution when making to data in a production environment.

You can accomplish this task using Python and Boto3 by listing the objects in each bucket, filtering for files with the specified StorageClass and AccessTier, and then storing the results in a text file. Below is an example of Python code achieve this:

import boto3

# Set up Boto3 for Amazon S3
s3 = boto3.client('s3')

# Get a list of all buckets
buckets = [['Name'] for bucket in s3.list_buckets()['Buckets']]

# Open text file to store the results
with open('files_info.txt', 'w') as file:
    # Iterate over each bucket
    bucket_name in buckets:
        file.write(f"Bucket Name: {bucket_name}\n")
        
        # List the in the bucket
        objects = s3.list_objects_v2(Bucket=bucket_name)['Contents']
        
        # Filter for objects with StorageClass = INTELLIGENT_TIERING and AccessTier =IVE_ACCESS
        filtered_objects = [obj['Key'] obj in objects if obj.get('StorageClass') == 'INTELLIGENT_TIERING' and.get('AccessTier') == 'ARCHIVE_ACCESS']
        
        file.write(f" of files moved to StorageClass=INTELLIGENT_TIERING and AccessTier=IVE_ACCESS: {len(filtered_objects)}\n")
       .write("List of files:\n")
        for obj_key in filtered_objects:
            file.write(f"{obj_key}\n")
        file.write("\nprint("The file 'files_info.txt' has been created the information.")

In this example, we first list all the buckets using list_buckets method and then iterate through each bucket list the objects. We filter the objects with the desired StorageClass and AccessTier and store the count and list of file names in a text file called files_info.txt. Ensure that your Python environment is properly set up with Boto3 and has the necessary IAM permissions to perform these actions on Amazon S.

The provided code is a Python script that uses Boto3 to interact with Amazon S3. It lists the objects in a specific bucket and folder, filters for files with StorageClass = INTELLIGENT_TIERING and AccessTier = ARCHIVE_ACCESS, and then stores the results in a text file called files_info.txt.

Ensure you have the Boto3 library installed by running pip install boto3 in your Python environment. Also, make sure that the IAM credentials used by Boto3 have the necessary permissions to access and list objects in the specified S3 bucket.

Here's how the code works:

import boto3

# Set up Boto3 client for Amazon S3
s3 = boto3.client('s3')

# Specify the bucket and folder
bucket_name = 'your_bucket_name'
prefix = 'your_folder_name/'  # Make sure to include the forward slash

# Open a text file to store the results
with open('files_info.txt', 'w') as file:
    # List the objects in the bucket and folder
    objects = s3.list_objects_v2(Bucket=bucket_name, Prefix=prefix)['Contents']

    # Filter for objects with StorageClass = INTELLIGENT_TIERING and AccessTier = ARCHIVE_ACCESS
    filtered_objects = [obj['Key'] for obj in objects if obj.get('StorageClass') == 'INTELLIGENT_TIERING' and obj.get('AccessTier') == 'ARCHIVE_ACCESS']

    file.write(f"Bucket Name: {bucket_name}\n")
    file.write(f"Folder Name: {prefix}\n")
    file.write(f"Number of files moved to StorageClass=INTELLIGENT_TIERING and AccessTier=ARCHIVE_ACCESS: {len(filtered_objects)}\n")
    file.write("List of files:\n")
    for obj_key in filtered_objects:
        file.write(f"{obj_key}\n")
    file.write("\n")

print("The file 'files_info.txt' has been created with the information.")

Replace 'your_bucket_name' and 'your_folder_name/' with the actual name of your S3 bucket and folder. After running this code, a text file named files_info.txt will be created in the same directory, containing the information about files moved to the specified StorageClass and AccessTier in the specified bucket and folder.

I understand your concern. To retrieve AccessTier information, you can modify the existing code to access each individual object's metadata and extract the AccessTier information. Here's an updated version of the code:

import boto3

# Set up Boto3 client for Amazon S3
s3 = boto3.client('s3')

# Specify the bucket and folder
bucket_name = 'your_bucket_name'
prefix = 'your_folder_name/'  # Make sure to include the forward slash

# Open a text file to store the results
with open('files_info.txt', 'w') as file:
    # List the objects in the bucket and folder
    objects = s3.list_objects_v2(Bucket=bucket_name, Prefix=prefix)['Contents']

    # Retrieve StorageClass and AccessTier information for each object
    for obj in objects:
        obj_info = s3.head_object(Bucket=bucket_name, Key=obj['Key'])
        storage_class = obj_info['StorageClass']
        access_tier = obj_info['StorageClass']

        if storage_class == 'INTELLIGENT_TIERING' and access_tier == 'ARCHIVE_ACCESS':
            file.write(f"Object Name: {obj['Key']}, StorageClass: {storage_class}, AccessTier: {access_tier}\n")

print("The file 'files_info.txt' has been created with the information.")

This updated code uses head_object to retrieve the metadata for each object, including the StorageClass and AccessTier information. It then writes the details of objects with the specified StorageClass and AccessTier to the files_info.txt file.

Replace 'your_bucket_name' and 'your_folder_name/' with the actual name of your S3 bucket and folder. After running this code, the resulting files_info.txt file will contain the object names along with their corresponding StorageClass and AccessTier information.