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

What is the AWS Glue service?

AWS has more than 100 services. When you integrate data between AWS and other sources, you might need to load data from the source, manipulate it with some transformations, and store it in a service. AWS Glue meets these requirements and provides a service that allows the preparation of data. In the following figure, you can see a very high-level overview of Glue. As you can see, Glue extracts the data from different sources, carries out some transformation, and loads the data in another source:

Figure 11.1 – AWS Glue

For example, let us assume you have data in S3 that is loaded by a batch process. To make it searchable, you have a requirement to store it in DynamoDB. Between these processes, one requirement is to filter, clean, and manipulate the data with some transformations. For that requirement, AWS Glue is a good option for data integration with some data manipulation.

Features of AWS Glue

AWS Glue has the...