Implementing Serverless Microservices Architecture Patterns

Building a microservices platform using virtual machines or containers, involves a lot of initial and ongoing effort and there is a cost associated with having idle services running, maintenance of the boxes and a configuration complexity involved in scaling up and down.

In this course, We will show you how Serverless computing can be used to implement the majority of the Microservice architecture patterns and when put in a continuous integration & continuous delivery pipeline; can dramatically increase the delivery speed, productivity and flexibility of the development team in your organization, while reducing the overall running, operational and maintenance costs.

We start by introducing the microservice patterns that are typically used with containers, and show you throughout the course how these can efficiently be implemented using serverless computing. This includes the serverless patterns related to non-relational databases, relational databases, event sourcing, command query responsibility segregation (CQRS), messaging, API composition, monitoring, observability, continuous integration and continuous delivery pipelines.

By the end of the course, you’ll be able to build, test, deploy, scale and monitor your microservices with ease using Serverless computing in a continuous delivery pipeline.

About the Author

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 rfreeman 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

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Frequently Asked Questions

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