Amazon Web Services (AWS) with Python & Boto3

In this course we are going to learn Amazon Web Services (AWS) with Python & Boto3, so Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers including the fastest growing startups, largest enterprises, and leading government agencies are using AWS to lower costs. And you can use AWS with different programming languages, in this course we want to learn AWS with Python Programming language.

What is Python ?

Python is a high-level general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small- and large-scale projects

This course is divided in to different sections.

• In this first section we are going to talk about IAM, so IAM is AWS Identity and Access Management. With IAM, you can specify who can access which services and resources, and under which conditions, we will create some examples with AWS console and after that we go through Python Programming Language.

  
  

• In the second section we want to learn about AWS Dynamodb, so DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB lets you offload the administrative burdens

  of operating and scaling a distributed database so that you don't have to worry about hardware provisioning, setup and configuration, replication, software patching, or cluster scaling. You don’t need to worry about these, because all of them are done by dynamodb.

  
  

• In the third section want to talk about amazon S3, so S3 stands for Simple Storage Service, it is an object storage service that offers industry-leading scalability, data availability, security, and performance.

  
  

• In the fourth section we want to learn about Amazon RDS or Amazon Relational Database Services and we want to learn about three relational databases and their integration with python like MySQL, Postgres and Mariadb.

  
  

• In the fifth section we are going to learn about Amazon EC2 or we can say elastic compute cloud and it provides scalable computing capacity in the Amazon Web Services (AWS) Cloud. We create some examples using the AWS console and after that we go through Python Language, also we are going to deploy our Django project in EC2.

  
  

• In this sixth section we want to talk about AWS lambda function so it is server less computing service that lets you run code without provisioning or managing servers.

  
  

• In the seventh section we want to learn about AWS CloudFormation so it is a service that helps you model and set up your AWS resources using JSON or YAML template.

  
  

• In the eight section we want to learn about AWS SES or we can say Simple Email Services, and using this service we can send emails to our customers.

  
  

• In the ninth section we are going to learn about Elastic Beanstalk, so it is an easy-to-use service for deploying and scaling web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS, also in this section we create a simple project in Django with RDS functionalities and after that we deploy that to elastic beanstalk.

• In the tenth section we are going to create a complete practical Blog project with Python and Django, we add Amazon RDS functionality to our Python web project, after that we deploy our web project to elastic beanstalk, after deploying to Amazon Elastic Beanstalk we add a custom domain name from Amazon Route53 to our project and at the end we secure our Python Web project with Amazon SSL Certificate Manager.

• In the eleventh section we are going to create a complete practical Blog project with Python and Flask,  particularly in this section we are going to focus that how we can deploy our Python Flask project in Elastic Beanstalk using Amazon Code Pipeline.