Implementing Serverless Microservices Architecture Patterns [Video]

More Information
Learn
  • Implement over 15 microservices architecture patterns without needing containers or EC2 instances
  • Build, test, deploy and maintain serverless microservices
  • Speed up delivery, flexibility and time to market using serverless microservices
  • Get serverless best practices and recommendation on scaling out and enforcing security 
  • Debug, monitor and observe your serverless stack
  • Add your microservices to a continuous integration & continuous delivery pipeline
  • Estimate, and reduce maintenance and running costs
About

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.

Parts of the source code linked to this course are available at https://github.com/PacktPublishing/Implementing-Microservice-Architecture-using-Serverless-Computing-on-AWS

Style and Approach

This video tutorial is one of the most complete and practical courses on serverless microservices, with a lot of original content and offered through a recipe-based approach that teaches you the skills required to build, test, deploy and monitor Serverless microservices at scale on AWS. I’ve split the source code, packages and bash shell scripts into self contained examples that allow you to quickly grasp how to deploy individual patterns. The serverless configuration, IAM policies, source code and shell scripts will help you quickly understand the commands, so that you can adapt them for you needs. I’ve also mixed full code deployments with alternative AWS Console user interface steps in the screencasts for key areas, so you get to know both. This will give you a good grasp of all steps involved, giving you good working intuition, and allowing you to quickly and directly replicate what is shown. I only use the native AWS SDKs, SAM and CLI to do this, so any improvement and fixes will be available as soon as AWS releases them.

Features
  • Full step-by-step recipes including the use of AWS CLI tools and AWS console
  • Get fully working and extensive Serverless application, service and infrastructure source code and AWS configuration files
  • Gain practical experience and lessons learned from using Serverless microservices at scale in production
Course Length 7 hours 16 minutes
ISBN 9781788839570
Date Of Publication 29 May 2018

Authors

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 http://rfreeman.net 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