This is the code repository for Building Serverless Microservices in Python, published by Packt.
A practical guide for developing end-to-end serverless microservices in Python for developers, DevOps, and architects.
Over the last few years, there has been a massive shift from monolithic architecture to microservices, thanks to their small and independent deployments that allow increased flexibility and agile delivery. Traditionally, virtual machines and containers were the principal mediums for deploying microservices, but they involved a lot of operational effort, configuration, and maintenance. More recently, serverless computing has gained popularity due to its built-in autoscaling abilities, reduced operational costs, and increased productivity.
This book covers the following exciting features:
- Discover what microservices offer above and beyond other architectures
- Create a serverless application with AWS
- Gain secure access to data and resources
- Run tests on your configuration and code
- Create a highly available serverless microservice data API Build, deploy, and run your serverless configuration and code
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
"phoneNumbers": [
{
"type": "home",
"number": "212 555-1234"
},
{
Following is what you need for this book: If you are a developer with basic knowledge of Python and want to learn how to build, test, deploy, and secure microservices, then this book is for you. No prior knowledge of building microservices is required.
With the following software and hardware list you can run all code files present in the book (Chapter 1-).
| Chapter | Software required | OS required |
|---|---|---|
| All | Python 3 | Windows/macOS/Linux |
| All | Docker | Windows/macOS/Linux |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Richard Takashi Freeman has an M.Eng. in computer system engineering and a PhD in machine learning and natural language processing from the University of Manchester, United Kingdom. His current role is as a lead data engineer and architect, but he is also a data scientist and solutions architect. He has been delivering cloud-based, big data, machine learning, and data pipeline serverless and scalable solutions for over 14 years, and has spoken at numerous leading academic and industrial conferences, events, and summits.
He has written various technical blog posts, and has acquired in excess of three years' serverless production experience in connection with large-scale, consumer-facing websites and products.
Click here if you have any feedback or suggestions.