https://github.com/julioaranajr/05_boto3
How install boto3, awscli. Configuring AWS environment, testing AWS credentials, exercises.
https://github.com/julioaranajr/05_boto3
aws-config aws-credentials aws-environment aws-sdk awscli boto3 boto3-script
Last synced: 4 months ago
JSON representation
How install boto3, awscli. Configuring AWS environment, testing AWS credentials, exercises.
- Host: GitHub
- URL: https://github.com/julioaranajr/05_boto3
- Owner: julioaranajr
- Created: 2022-10-07T08:12:06.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-11T13:28:59.000Z (over 2 years ago)
- Last Synced: 2025-01-09T07:12:55.762Z (5 months ago)
- Topics: aws-config, aws-credentials, aws-environment, aws-sdk, awscli, boto3, boto3-script
- Language: Python
- Homepage:
- Size: 293 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 05_boto3
## Install boto3
To install the Boto3 library, you have to run the following command in your terminal:
```sh
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:
```sh
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](https://github.com/julioaranajr/05_boto3/blob/main/Test_AWS_Credentials.md)
### List Buckets example
```py
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".