05_boto3

Install boto3

To install the Boto3 library, you have to run the following command in your terminal:

pip install boto3

The command above will install the Boto3 library globally in your system. Alternatively, you can configure a Python development environment to isolate your dependencies and manage them separately

Install AWS CLI tools

To install theAWS CLI tools, you have to run another command in your terminal:

pip install awscli

Configuring AWS environment

AWS CLI is a set of command-line tools for accessing AWS from the terminal shell. Those tools are available for you through the aws command. In this section, we’ll use a subcommand named configure to set up an AWS environment on your laptop, workstation, or server.

To configure the AWS environment, type the following command in your terminal:

aws configure

This command will walk you through an environment configuration process and ask you for 4 things:

• AWS Access Key: just press enter
• AWS Secret Access Key: just press and press enter
• Default region name: type -> your [aws-region-1] and enter
• Default output format: type -> json and press enter

The aws configure tool allows you not to store your AWS credentials (the AWS Access and Secret Keys) in your Python scripts.

Note: even storing AWS Access and Secret Keys in a plain text file (~/.aws/credentials) is not very secure. The better and more secure way is to store AWS Access and Secret Keys in the encrypted store, for example, aws-vault.

Testing AWS credentials

As soon as you’ve configured your AWS credentials, you can test that everything’s ready to move forward.

Test your Credentials here -> Test_AWS_Credentials.md

List Buckets example

from urllib import response
import boto3
from datetime import date

# Let's use Amazon S3
# s3 = boto3.resource('s3')

# Print out bucket names
# for bucket in s3.buckets.all():
#     print(bucket.name)

client = boto3.client("s3")

response = client.list_buckets()

#print(response)
#print(response["ResponseMetadata"]["RequestId"])
#print(response["Buckets"][0]["Name"])

for bucket in response["Buckets"]:
    print(date.strftime(bucket["CreationDate"], "%H-%m-%Y %H:%M"), bucket["Name"])

Exercises:

• Write a boto3 script that prints out all VPCs and Subnets in your lab account.

• Then for each resource found (VPC and subnets), attach a new AWS tag "Project: Talent-Academy" where tag key is "Project" and tag value is "Talent-Academy".