Building a Scalable Serverless Microservice REST Data API | Software Engineering Online Course by Udemy

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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. About the AuthorRichard T. Freeman, Ph.D. currently works for JustGiving, a tech-for-good company and the worlds most trusted social platform for online giving thats 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

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