Building a Scalable Serverless Microservice REST Data API [Video]

More Information
  • 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

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.

Style and Approach

This video tutorial adopts a recipe-based approach to teaching you the skills required to develop end-to-end serverless microservices for developers, DevOps, and architects. We have chosen a particular use case—creating a serverless data REST API—but the steps remain similar for other use cases. This use case was chosen, as traditionally it would have required an always-on fleet of machines or containers, which would have had to sit behind a load balancer and scale up and down depending on the number of requests. That approach is costly, and required complex configuration and on-going management. In this course, we will show you how you can overcome those limitations and quickly build a fully functional system using serverless microservices.

  • Create a system where the infrastructure, scalability, and security are managed by AWS
  • Reduce your support, maintenance, and infrastructure costs
  • Speed up your development process through rapid iterations
Course Length 2 hours 46 minutes
ISBN 9781788622318
Date Of Publication 28 Feb 2018


Richard T. Freeman, PhD

Richard T. Freeman, PhD currently works for JustGiving, a tech-for-good social platform for online giving that’s helped 25 million users in 164 countries raise $5 billion for good causes. He is also offering independent and short-term freelance cloud architecture & machine learning consultancy services.

Richard is a hands-on certified AWS Solutions Architect, Data & Machine Learning Engineer with proven success in delivering cloud-based big data analytics, data science, high-volume, and scalable solutions. At Capgemini, he worked on large and complex projects for Fortune Global 500 companies and has experience in extremely diverse, challenging and multi-cultural business environments. Richard has a solid background in computer science and holds a Master of Engineering (MEng) in computer systems engineering and a Doctorate (Ph.D.) in machine learning, artificial intelligence and natural language processing. See his website for his latest blog posts and speaking engagements.

He has worked in nonprofit, insurance, retail banking, recruitment, financial services, financial regulators, central government and e-commerce sectors, where he:

  • Provided the delivery, architecture and technical consulting on client site for complex event processing, business intelligence, enterprise content management, and business process management solutions.
  • Delivered in-house production cloud-based big data solutions for large-scale graph, machine learning, natural language processing, serverless, cloud data warehousing, ETL data pipeline, recommendation engines, and real-time streaming analytics systems.
  • Worked closely with IBM and AWS and presented at industry events and summits
  • Published research articles in numerous journals, presented at conferences and acted as a peer-reviewer
  • Has over four years of production experience with Serverless computing on AWS