Serverless Microservices On Aws In 10 Hours

The perfect course to implementing cost-effective, and scalable Microservices and APIs using serverless computing on AWS

What you'll learn
Improve the reusability, composability, and maintainability of code
Create a highly available serverless microservice data API
Build, deploy and run your serverless configuration and code
Implement over 15 microservices architecture patterns without needing containers or EC2 instances
Speed up delivery, flexibility and time to market using serverless microservices
Add your microservices to a continuous integration & continuous delivery pipeline

Requirements
Basic knowledge of programming and AWS is required. Familiarity with DevOps will be beneficial, but not necessary.

Description
Microservices are a popular new approach to building maintainable, scalable, cloud-based applications. AWS is the perfect platform for hosting Microservices. 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.This comprehensive 2-in-1 course is a step-by-step tutorial which is a perfect course to implementing microservices using serverless computing on AWS. Build highly available microservices to power applications of any size and scale. Get to grips with microservices and overcome the limitations and challenges experienced in traditional monolithic deployments. Design a highly available and cost-efficient microservices application using AWS. Create a system where the infrastructure, scalability, and security are managed by AWS. Finally, reduce your support, maintenance, and infrastructure costs.This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Building a Scalable Serverless Microservice REST Data API, covers practical solutions to building Serverless applications. 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. The second course, Implementing Serverless Microservices Architecture Patterns, covers implementing Microservices using Serverless Computing on AWS. 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. 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.By the end of this course, you will be able to build, test, deploy, scale, and monitor your APIs and microservices with ease using serverless computing in a continuous delivery pipeline. Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth: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.

Overview
Section 1: Building a Scalable Serverless Microservice REST Data API

Lecture 1 The Course Overview

Lecture 2 Monolithic and Microservice Architectures

Lecture 3 Virtual Machines, Containers, and Serverless Computing

Lecture 4 Serverless Computing in AWS

Lecture 5 Setting Up Your Serverless Environment in AWS

Lecture 6 Overview of Security in AWS

Lecture 7 Overview of AWS Identity and Access Management (IAM)

Lecture 8 Securing Your Serverless Microservice

Lecture 9 Building a Serverless Microservice Data API

Lecture 10 Setting Up a Lambda in the AWS Management Console

Lecture 11 Setting Up the API Gateway and Integrating It with a Lambda Proxy

Lecture 12 Creating and Writing to a NoSQL Database Called DynamoDB

Lecture 13 Creating a Lambda to Query DynamoDB

Lecture 14 Connecting API Gateway, Lambda, and DynamoDB

Lecture 15 Unit Testing Your Python Lambda Code

Lecture 16 Running and Debugging Your AWS Lambda Code Locally

Lecture 17 Integration Testing Using Real Test Data

Lecture 18 Performance and End-to-End Testing at Scale

Lecture 19 Overview of Serverless Stack Build and Deploy Options

Lecture 20 Creating an S3 Bucket, IAM Policies, and IAM Roles Resources

Lecture 21 Building and Deploying API Gateway, Lambda, and DynamoDB

Lecture 22 Building a Scalable Serverless Microservice Data API Conclusions

Lecture 23 Next Course

Section 2: Implementing Serverless Microservices Architecture Patterns

Lecture 24 The Course Overview

Lecture 25 Overview of Microservice Integration Patterns

Lecture 26 Communication Styles and Decomposition Microservice Patterns

Lecture 27 Serverless Computing to Implement Microservice Patterns

Lecture 28 Implementing Database Per Service and Shared Database Patterns

Lecture 29 Accessing DynamoDB from API Gateway Via a Lambda Function

Lecture 30 Accessing DynamoDB Directly from API Gateway

Lecture 31 Implementing the Transaction Log Tailing Pattern

Lecture 32 Implementing the Saga Pattern

Lecture 33 Securing Your DynamoDB Databases

Lecture 34 Relational Versus Non-Relational Databases

Lecture 35 Overview of Amazon Virtual Private Cloud

Lecture 36 Setting Up Amazon Virtual Private Cloud for Accessing RDS and Aurora

Lecture 37 Setting Up RDS and Accessing It from Your Local Network

Lecture 38 Accessing RDS from API Gateway Via a Lambda Function

Lecture 39 Accessing Aurora from API Gateway Via a Lambda Function

Lecture 40 Securing Your RDS and Aurora Databases

Lecture 41 API Gateway and API Composition Patterns

Lecture 42 Implementing the Serverless API Composition Patterns

Lecture 43 Event Sourcing and CQRS Patterns

Lecture 44 Architectures of the Serverless Event Sourcing Pattern

Lecture 45 Implementing the Serverless Event Sourcing Pattern

Lecture 46 Architectures of the Serverless CQRS Pattern

Lecture 47 Implementing the Serverless CQRS Pattern

Lecture 48 Securing Your Event Streams and Queries

Lecture 49 Monitoring and Observability Patterns

Lecture 50 Implementing Serverless Metrics and Health Check API Patterns

Lecture 51 Implementing the Serverless Centralized Logging Pattern

Lecture 52 Implementing the Serverless Audit Logging Pattern

Lecture 53 Implementing the Serverless Distributed Tracing Pattern

Lecture 54 Creating a Serverless Discovery Service and Catalogue

Lecture 55 Continuous Integration and Continuous Delivery

Lecture 56 Serverless Continuous Integration and Continuous Delivery Setup

Lecture 57 Using CodeCommit for the Serverless Data API Code

Lecture 58 Using CodeBuild to Build-Test the Serverless Data API Stack

Lecture 59 Using CodePipeline as CI/CD for the Serverless Data API Stack

Lecture 60 Using Other CI/CD Solutions with the Serverless Data API Stack

Lecture 61 When to Use and Not Use Serverless Computing?

Lecture 62 Estimating Serverless Stack Costs

Lecture 63 Database and Event Streaming Scalability

Lecture 64 Web Scale Best Practices

Lecture 65 Conclusion

This course is for developers, architects, and DevOps administrators who would like to build and deploy serverless APIs and microservices with AWS for their organizations.

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