- 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.
About the Author
Richard T. Freeman^ Ph.D. currently works for JustGiving^ a tech-for-good company and the world’s most trusted social platform for online giving that’s helped 22 million users in 164 countries raise $4.5 billion for over 27^000 good causes. He is also offering short-term freelance cloud architecture &, machine learning consultancy.
He is a highly accomplished results-driven hands-on certified AWS Solutions Architect^ Data Engineer and Data Scientist with proven success in delivering cloud-based big data analytics^ unstructured data^ 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.
He has worked in charity^ insurance^ retail banking^ recruitment^ financial services^ financial regulators^ central government and e-commerce sectors^ where he:
- Provided design^ 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^ 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 three years of production experience with Serverless computing on AWS