Book Image

Python Essentials for AWS Cloud Developers

By : Serkan Sakinmaz
4 (1)
Book Image

Python Essentials for AWS Cloud Developers

4 (1)
By: Serkan Sakinmaz

Overview of this book

AWS provides a vast variety of services for implementing Python applications, which can pose a challenge for those without an AWS background. This book addresses one of the more predominant problems of choosing the right service and stepping into the implementation of exciting Python apps using AWS. The book begins by showing you how to install Python and create an AWS account, before helping you explore AWS Lambda, EC2, Elastic Beanstalk, and S3 for Python programming. You'll then gain hands-on experience in using these services to build the Python application. As you advance, you'll discover how to debug Python apps using PyCharm, and then start deploying the Python applications on Elastic Beanstalk. You’ll also learn how to monitor Python applications using the CloudWatch service, along with creating and publishing APIs on AWS to access the Python application. The concluding chapters will help you get to grips with storing unstructured and semi-structured data using NoSQL and DynamoDB, as well as advance your knowledge using the Glue serverless data integration service in AWS. By the end of this Python book, you’ll be able to take your application development skills up a notch with AWS services and advance in your career.
Table of Contents (18 chapters)
1
Part 1: Python Installation and the Cloud
4
Part 2: A Deep Dive into AWS with Python
9
Part 3: Useful AWS Services to Implement Python

A Lambda skeleton

When you implement a Lambda function via Python, you need to follow some rules in order to execute the application. When a Lambda function is run, it calls the handler method, which is shown with the following syntax:

def lambda_handler(event, context):
    ...
    return some_value

As you see, the first parameter is the event object. An event object consists of JSON in order to process data as a parameter. You can see a sample parameter here:

{
  "Temperature": 10,
  "Wind": -5
}

The second parameter shows information about the Lambda runtime. You can see some of the runtime fields here:

  • function_name (the name of the function)
  • function_version (the version of the function)
  • memory_limit_in_mb (the Lambda function memory limit)

We've looked at the main skeleton of the Python Lambda function. In the next section, we'll see how to return a value...