Building a Scalable Serverless Microservice REST Data API

In the past few years, there has been a shift away from monolithic architecture (with for example its large centralized deployments) to microservice architectures with small independent deployments, allowing much more flexibility and agile delivery. Traditionally virtual machines and containers were the main options for deploying microservices but they involve a lot of operational effort, configuration, and maintenance. More recently, there has been a growing interest in Serverless computing due to the increase in developer productivity, built in auto-scaling abilities, and reduced operational costs. In combining both microservices and serverless computing, organizations will benefit from having the servers and capacity planning managed by the cloud provider, making them much easier to deploy and run at scale.

In this course we show you how to build an end-to-end serverless application for your organization. We have selected a data API use case that could reduce costs and give you more flexibility in how you and your clients consume or present your application, metrics and insight data. We make use of the latest serverless deployment and build framework, share our experience on testing, and provide best practices for running a serverless stack in a production environment.

What You Will Learn

• Understand how microservices compares with existing architectures
• Understand how to create a serverless application in AWS
• Learn how to 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

Audience

This video is for developers who need practical solutions to common problems while building their serverless application. Programming knowledge is assumed.

About The Author

Richard Takashi Freeman: Richard Takashi Freeman has an M.Eng. in computer systems engineering and a Ph.D. in machine learning and natural language processing from the University of Manchester, UK. He is currently a lead big data and machine learning engineer at JustGiving; and a cloud architect, serverless computing, and machine learning freelance SME and consultant at Starwolf. He previously worked at PageGroup and Capgemini, and has been delivering cloud-based, big data, machine learning, serverless, and scalable solutions for over 14 years across different sectors.

He is a blogger; a speaker, presenting at various events; and the author of two video courses. You can visit his website, titled Richard Freeman, PhD, for his blog posts, presentations, and courses.