ServerlessMicroservice with AWS - A Complete Guide!: 3-in-1
4.2 (29 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
312 students enrolled

ServerlessMicroservice with AWS - A Complete Guide!: 3-in-1

The perfect course to implementing cost-effective, and scalable Microservices using Serverless Computing on AWS
4.2 (29 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
312 students enrolled
Created by Packt Publishing
Last updated 8/2018
English [Auto-generated]
Current price: $129.99 Original price: $199.99 Discount: 35% off
16 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 11.5 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • Improve the reusability, composability, and maintainability of code.
  • Create a highly available serverlessmicroservice data API.
  • Build, deploy and run your serverless configuration and code.
  • Speed up delivery, flexibility and time to market using serverlessmicroservices.
  • Add your microservices to a continuous integration & continuous delivery pipeline.
  • Estimate, and reduce maintenance and running costs.
  • Implement over 15 microservices architecture patterns without needing containers or EC2 instances.
  • Scale up without significant changes to tooling, architecture, or development practices.
  • Reduce the risk and cost of operating a cloud platform.
Course content
Expand all 88 lectures 11:37:59
+ Building Microservices on AWS
23 lectures 01:35:37

This video will give you an overview about the course. 

Preview 01:54

This video will give you an overview of the key concepts of Microservices.                         

  • Understand about better flexibility

  • Discuss about agility

  • Look  how it’s easy to change

The Concepts of Microservices

In this video will look into the benefits of Microservices.                         

  • Learn to deploy independently

  • Learn about the  different programming languages

  • Get to know about different backend databases

Benefits of Microservices

The aim of this video is to design key elements for Microservices.                         

  • Learn to keep separate databases

  • Learn to keep services small

  • Learn to have an API layer

Key Design Elements for Microservices

The aim of this video is to split services based on machine.                         

  • Explore the utilities of small and separate EC2 Instances

  • Look at the demand

  • Walkthrough the utility of Application Load Balancer

Understanding AWS EC2 and ELB

The aim of this video is to make you understand why does the service need a backend.                         

  • Explore different databases

  • Explore databases based on requirement

  • Explore database clusters

Decentralizing AWS Data Options

The aim of this video is to make you understand why we need to provision environment for developers.                         

  • Take a look at the languages supported

  • Look at the environments needed

  • Keep initial deployments

AWS Elastic Beanstalk

The aim of this video is to make you understand why we need to consider Serverless computing.                         

  • Take a look at supported programming languages

  • Take a look at provisioning functions for each service

  • Use multiple versions

Working with AWS Lambda

The aim of this video is to make you understand about the Serverless API functionality.                         

  • Use API gateway for multiple services

  • Control access to the gateway

  • Learn to allow users to invoke functions

AWS API Gateway

The aim of this video is to make you understand how to direct traffic using AWS Route 53.                         

  • Take a look at Blue Green deployments

  • Take a look at failover deployments

  • Route traffic to microservices

AWS Route 53

The aim of this video is to make you understand how to monitor microservices.                         

  • Explore CloudWatch metrics

  • Use CloudWatch logs for central logging

  • Explore vendor products for custom requirements

Monitoring Microservices

This video will walk you through the better deployment mechanisms.                         

  • Create your production environment

  • Create your test environment

  • Conduct Blue Green Deployments

Exploring Blue Green Deployments

This video will make you understand the need of elasticity for your application.                         

  • Use Application Load Balancers

  • Place your instances behind the ELB

  • Use Route53 for traffic routing

Using Elastic Load Balancer for Blue Green Deployments

This video will make you understand the need of easy deployment mechanisms.                         

  • Create your production environment

  • Create your test environment

  • Use the swap URL feature

Using Elastic Beanstalk for Blue Green Deployments

The aim of this video will give you an overview of Continuous Integration.                         

  • Learn to automate builds

  • Get to know about faster releases

  • Ensure to test

Getting Started with Continuous Integration

In this learn how to perform source code versioning using AWS code commit.                         

  • Based on Git

  • Learn to manage service

  • Learn to work from the command line

Using AWS CodeCommit

In this video learn how to automate builds.                         

  • Finalize the Source location

  • Ensure that target environment is ready

  • Learn to check for build outputs

Implementing AWS CodeBuild

In this video learn how to automate builds automate pipeline.                         

  • Explore the number of stages

  • Segregate the stages

  • Test the pipeline

Using AWS CodePipeline

The aim of this video will give you an overview of complete Continuous Integration Pipeline.                         

  • Learn to have proper tools in place

  • Learn to have proper processes in place

  • Learn to have proper governance in place

The Complete Continuous Integration Pipeline

Developing Microservices based applications.                         

  • Learn to use containers

  • Look at isolation of components

  • Look at integration of various components

Working with Containers

In this video learn how to get speed with using orchestration from a Microservices perspective.                         

  • Learn to use orchestration

  • Explore tools

  • Explore processes

Using Orchestration

In this video learn the benefits of Kubernetes.                         

  • Learn how to manage container deployments

  • Scale container deployments

  • Learn to ensure uptime for container deployments

Exploring Kubernetes

In this video learn how to host Docker containers using AWS Elastic container service.                         

  • Learn to use manage service

  • Learn to use AWS Cloud

  • Learn to use Elastic Container Service

Using AWS Elastic Container Service
Test your knowledge
4 questions
+ Building a Scalable Serverless Microservice REST Data API
23 lectures 02:46:13

This video gives an overview of the entire course. 

Preview 09:34

There are many architectures that exist before Microservices. In  this video, we will see what are they, what are the benefits and  drawbacks,  how do they compare to microservices.           

  • See the overview of monolithic multi-tier architecture and Service Orientated Architecture (SOA)

  • See the overview of micsoservices architecture

  • See the benefits and drawbacks of monolithic and micsoservices architecture

Monolithic and Microservice Architectures

In this video, we will show where does serverless computing fit in with other public cloud offerings.

  • See the overview of virtual machines benefits and drawbacks

  • See the overview of container benefits and drawbacks

  • See the overview of serverless computing  benefits and drawbacks

Virtual Machines, Containers, and Serverless Computing

In this video, we will see what the serverless computing offerings available in AWS are and how these service s fit together.

  • See the overview of the serverless managed services

  • See the example: how these can fit together to solve a data transformation task

Serverless Computing in AWS

In this video, we will set up a new user. Also, we will see how  can I ensure that the user access is secure and how  I setup my local  serverless environment.           

  • Use the AWS Management Console to setup a new user

  • Set up multi-factor authentication for a user

  • Set up your local environment credentials, AWS Python SDK and CLI

Setting Up Your Serverless Environment in AWS

In this video, we will see that with data breaches and compliance requirements security is a key consideration.           

  • See the security Examples and impacts on an organization

  • Explain the security at rest, in transit, authentication, and authorization

  • Learn that AWS has a shared security responsibility model that should be followed

Overview of Security in AWS

In this video, we will see how can we configure AWS security to help secure user authorization and access to resources.           

  • Explain AWS Identity and Access Management (IAM)

  • Learn IAM policies, users, groups to help secure your users

  • Explain IAM roles to  help secure your trusted entity

Overview of AWS Identity and Access Management (IAM)

In this video, we will see how can we ensure that your microservice is secure and what are some of the best practices.           

  • Explain Lambda security

  • Learn API gateway and DynamoDB security

  • Explain how to monitor and alert

Securing Your Serverless Microservice

In this video, we will see what is the architecture of serverless data provider API.

  • Make use of API gateway, Lambda, and DynamoDB

  • Overview of JSON and time series data

  • Explain about request/response data flows

Building a Serverless Microservice Data API

In this video, we will see how can I create my first Lambda function that parses the given URL parameters.

  • Setup the IAM policies and role

  • Write the Lambda code that parses the given URL parameters

  • Test the Lambda function works

Setting Up a Lambda in the AWS Management Console

In this video, we will configure the API gateway to call a Lambda function.

  • Create the API gateway resources and method

  • Integrate the GET method with Lambda

  • Test the API invoked the Lambda function correctly

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

In this video, we will see how can I create, add data to, and query my DynamoDB table.

  • Create a DynamoDB table using boto3

  • Insert data into DynamoDB

  • Query DynamoDB by partition and sort keys

Creating and Writing to a NoSQL Database Called DynamoDB

In this video, we will see how can I query DynamoDB from within a Lambda function.

  • Add the previous Lambda code that parses the given URL parameters

  • Add the previous code that queries DynamoDB by Partition and sort keys

  • Test the Lambda function with  sample request data

Creating a Lambda to Query DynamoDB

In this video, we will see how  the API Gateway, Lambda and DynamoDB integrate together and is it all working.

  • Understanding the integration

  • Test the data with a request URL

  • Understand the overall serverless microservices API

Connecting API Gateway, Lambda, and DynamoDB

In this video, we will see how we can make sure that the code is  still running correctly and be more productive writing Lambda code. 

  • See that testing allows better collaboration, improves product  quality, and leads to shorter release cycles

  • Explain how unit tests help  to verify that our functions work as expected

  • Mock to replace parts of the system under test

Unit Testing Your Python Lambda Code

In this video, we will see how can I simulate and debug a Lambda locally with real data without deploying it to AWS.

  • Use sample event data

  • Pass it into the Lambda function

  • Debug the Lambda code step by step

Running and Debugging Your AWS Lambda Code Locally

Once the Lambda function is deployed to AWS, this video explains how I can test it to make sure it’s running correctly.

  • Use sample event data

  • Submit the data via the AWS CLI

  • Make sure we get the expected response

Integration Testing Using Real Test Data

In this video, we will see how I know if my API is performing as  expected with a low latency and how can I improve the latency.   

  • Use Python Locust tool

  • Load test for concurrent  users

  • See that the latency can be improved by changing DynamoDB and Lambda settings

Performance and End-to-End Testing at Scale

In this video, we will get to know that there are many challenges  of provisioning infrastructure manually which include the cost, effort,  lack of repeatable processes and limited scalability.

  • Use Infrastructure as a Code

  • Learn that serverless infrastructure and resources can be deployed using Serverless Application Model (SAM)

  • Learn that serverless infrastructure and resources can be deployed using the AWS CLI other frameworks

Overview of Serverless Stack Build and Deploy Options

In this video, we will get to know that resource and infrastructure need to be provisioned.

  • Create an S3 bucket for the Lambda deployment package

  • Deploy IAM policies for the Lambda function

  • Deploy IAM roles for the Lambda function

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

In this video, we will see what are the steps needed to prepare and deploy the serverless stack.

  • Create a Lambda deployment package

  • Create API gateway, lambda, and dynamo using SAM

  • Load data into DynamoDB

Building and Deploying API Gateway, Lambda, and DynamoDB

In this video, we’ll summarize what we have covered in this course.

Building a Scalable Serverless Microservice Data API Conclusions

In this video, we’ll have a quick look at what is going to be covered in the next course. 

Next Course
Test your knowledge
6 questions
+ Implementing Serverless Microservices Architecture Patterns
42 lectures 07:16:09

This video gives an overview of the entire course. 

Preview 12:13

In building serverless microservices how can we benefit from what  is available from leading companies and open source communities.                         

  • Make use of design patterns

  • Use software design patterns and principles

  • Use and understand serverless  microservice pattern categories

Overview of Microservice Integration Patterns

How do we ensure efficient communication between microservices? How are microservices created and designed?                         

  • Understand synchronous versus asynchronous communication

  • Understand one-to-one and many-to-many communication microservice patterns

  • Understand  decomposition pattern by business capability

Communication Styles and Decomposition Microservice Patterns

What are the serverless microservice patterns that can leverage in AWS to reduce development, maintenance, and running costs?                         

  • Understand lambda event sources types

  • Understand lambda stream event sources

  • Leverage AWS managed services and serverless offerings in each category and use case

Serverless Computing to Implement Microservice Patterns

Lambda functions are stateless, so you need to store state  externally which is best practice. What is important in distributed  systems and what are the best ways to store state?                         

  • Understand that CAP Theorem stands for  Consistency, Availability, and Partition tolerance and is important for  data storage in distributed systems

  • Understand the shared  database and database per service patterns, and how they are used in distributed systems

  • Understand how these patterns can be implemented with DynamoDB, RDS, Aurora or Aurora serverless

Implementing Database Per Service and Shared Database Patterns

How can we expose data in DynamoDB externally?                         

  • Use API Gateway and a lambda function as a proxy

  • Create API methods such as GET and POST

  • Use the lambda function to shape the request into a DynamoDB query and parse the returned data for the API

Accessing DynamoDB from API Gateway Via a Lambda Function

Is there a simpler way to expose DynamoDB data externally?                         

  • Use API gateway direct DynamoDB integration

  • Use API gateway to  shape the query in the integration request step

  • Use API gateway to shape the response in the integration response step

Accessing DynamoDB Directly from API Gateway

How can I implement the transaction log trailing pattern?                         

  • Use DynamoDB streams

  • Use a lambda function to consume  the stream as an event source

  • Use a lambda to write the steam data to another table in DynamoDB

Implementing the Transaction Log Tailing Pattern

How can I implement the saga pattern as a serverless stack?                         

  • Use SNS and lambda combination

  • Use step functions to build up a state machine in JSON using the Amazon states language

  • Use step functions states to invoke lambda functions

Implementing the Saga Pattern

How can I ensure that my DynamoDB database is secure?                         

  • Enable encryption at rest

  • Enable the VPC endpoint

  • Use IAM policies that are locked down as much as possible to a specific table and with minimum permissions

Securing Your DynamoDB Databases

What are the main differences between relational and non-relational databases?                         

  • Understand the BASE versus ACID consistency models

  • Understand the  scalability for non-relational databases

  • Understand SQL flexibility for relational databases

Relational Versus Non-Relational Databases

What is a VPC and why is it needed?                         

  • Understand VPC is a virtual network isolated from other networks

  • Understand that a VPC contains one or more Subnets

  • You have to use a VPC to access RDS or Aurora from a Lambda function

Overview of Amazon Virtual Private Cloud

What are the main steps for setting up a VPC?                         

  • Create VPC and subnets

  • Attach an internet gateway and Egress only gateway

  • Configure the to route tables and associate it with the subnets

Setting Up Amazon Virtual Private Cloud for Accessing RDS and Aurora

What are the main steps to look out for when accessing RDS from your local network?                         

  • Configure you corporate or local firewall

  • Configure the route tables with your IP

  • Configure the VPC security groups with your IP

Setting Up RDS and Accessing It from Your Local Network

Why and how can a lambda access an RDS instance?                         

  • Access MySQL RDS from API gateway via a lambda function

  • Understand the code and configuration to set it up

  • Ensure the lambda role has a policy that allows it to attach and detach network interfaces within a VPC

Accessing RDS from API Gateway Via a Lambda Function

What are the main differences for a lambda to access Aurora over RDS MySQL?                         

  • Use the same code to access MySQL RDS, MySQL Aurora,  and Aurora Serverless

  • Understand that Aurora is clustered by default

  • Understand the  additional read replica database endpoint in Aurora

Accessing Aurora from API Gateway Via a Lambda Function

What are the main steps to ensure your RDS or Aurora databases communication is secure?                         

  • Understand RDS and Aurora encryption in transit code and configuration

  • Set up RDS or Aurora IAM database authentication

  • Use IAM database authentication to generate an authorization token rather than use a password

Securing Your RDS and Aurora Databases

We need a way to create an efficient API that can be consumed by third parties and abstract away the underlying complexities.                         

  • Understand API Gateway Pattern

  • Understand API Composition Pattern

  • Understand the benefits and drawback of API Composition Pattern

API Gateway and API Composition Patterns

What can we use to implement the API gateway and API composition patterns?                         

  • Understand Amazon Route 53 and use it as a sclable cloud DNS

  • Understand implement Amazon API Gateway and AWS Lambda

  • Use Amazon CloudFront and Amazon S3

Implementing the Serverless API Composition Patterns

How can we overcome the database persistence limitation such as  lack of full history on row changes, and efficient distributed  synchronisation/replication challenges?                         

  • Use the event sourcing pattern where every action is an event appended to an event store

  • Use the Command Query Responsibility Segregation (CQRS) pattern to split write and read methods

  • Understand that in CQRS, commands are methods that mutates state and queries are methods that return data

Event Sourcing and CQRS Patterns

What AWS managed services are core to architect the serverless event sourcing pattern?                         

  • Use Amazon Kinesis streams

  • Use Amazon DynamoDB

  • Use Amazon SQS

Architectures of the Serverless Event Sourcing Pattern

How can we implement the serverless event sourcing pattern?                         

  • Have API Gateway with or without a lambda proxy write to DynamoDB

  • Have API Gateway with or without a lambda proxy write to Kinesis Streams

  • Have API Gateway with or without a lambda proxy write to SNS or SQS. The lambda the uses CloudWatch trigger

Implementing the Serverless Event Sourcing Pattern

What AWS managed services are core to architect a serverless CQRS pattern?                         

  • Use Aurora, Aurora Serverless, or DynamoDB

  • Use SQS / SNS

  • Use Kinesis Streams and Kinesis Firehose

Architectures of the Serverless CQRS Pattern

How can we implement the serverless CQRS pattern command and event processor?                         

  • Have API gateway with or without a lambda proxy write to DynamoDB. Then a lambda writes to DynamoDB and Kinesis Firehose

  • Have API gateway with or without a lambda proxy write to SQS/SNS. Then a lambda writes to DynamoDB and Kinesis Firehose

  • Have API gateway with or without a lambda  proxy write to Kinesis Streams. Then a Lambda writes to DynamoDB and  Kinesis Streams write to Kinesis Firehose

Implementing the Serverless CQRS Pattern

How can we secure the serverless resources?                         

  • Use roles specific to the resource

  • Attach policies to the role

  • Specify the permitted actions on the resources in the policy

Securing Your Event Streams and Queries

Why is it important to monitor your microservices and what patterns are available to do so?                         

  • 1000s of microservices, governance, compliance, operational auditing, risk auditing, and so on

  • Use application metrics and health checks patterns

  • Use centralized logging, audit logging, and distributed tracing patterns

Monitoring and Observability Patterns

How can serverless application metrics and health check API patterns be implemented?                         

  • Use Amazon CloudWatch

  • Use a lambda triggered by CloudWatch to run health checks

  • Use a lambda to write the results of the  check to CloudWatch metrics and set an Alarm that sends an email  notification upon failure

Implementing Serverless Metrics and Health Check API Patterns

What are the different ways to send logs to CloudWatch logs?                         

  • Use API Gateway built-in logging or custom logging

  • Use Lambda print function, native logging or external JSON logging packages

  • For non-lambda microservice (e.g. traditional EC2 or container-based) use CloudWatch logs agent

Implementing the Serverless Centralized Logging Pattern

How can I monitor my AWS account activity?                         

  • Enable AWS CloudTrail

  • Push logs to CloudWatch logs, optionally have a lambda scan them for suspicious activity

  • Create Alarms that trigger on suspicious activates

Implementing the Serverless Audit Logging Pattern

How can I configure Lambda, DynamoDB and other microservices to send traces to X-Ray?                         

  • Enable built-in Lambda X-Ray integration with the correct policy

  • For Lambda and DynamoDB  in Python use aws-xray-sdk via xray_recorder begin and end, or decorators

  • For non-lambda services use aws-xray-daemon

Implementing the Serverless Distributed Tracing Pattern

How can I architect a serverless discovery and catalogue service?                         

  • For batch discovery use a lambda function that lists all resources, and persists results to DynamoDB

  • For near-real-time use AWS CloudTrail that sends logs to CloudWatch logs that a lambda scans, and persists results to DynamoDB

  • Expose the DynamoDB catalogue using API gateway via a Lambda proxy

Creating a Serverless Discovery Service and Catalogue

What are the focus and differences of Continuous Integration, and Continuous Delivery, and Continuous Deployment?                         

  • Use continuous Integration to focus on automating build and testing to detect issues early

  • Use Continuous Delivery to focus on automated release process with a human approval step for the production release

  • Use Continuous Deployment to focus on a fully automated release process without a human approval step for the production release

Continuous Integration and Continuous Delivery

What AWS managed services are used in a serverless CI/CD pipeline and what are they for?                         

  • Use IAM roles and polices for security and access

  • Use AWS CodeCommit for code version control. Use AWS CodeBuild to  manage build service

  • Use AWS CodePipeline for  continuous delivery, and as a service that helps you build a CI/CD  pipeline

Serverless Continuous Integration and Continuous Delivery Setup

How can you access AWS CodeCommit?                         

  • With Git credentials use HTTPS or SSH Keys

  • Generate them in IAM

  • Attach the CodeCommit access policy to the IAM User

Using CodeCommit for the Serverless Data API Code

How do you setup a CodeBuild project?                         

  • Use  source code from CodeCommit or GitHub

  • Use an IAM role for CodeBuild to access the AWS resources or code

  • Generate a personal access token to access GitHub over HTTPS

Using CodeBuild to Build-Test the Serverless Data API Stack

How can you create a CodePipeline?                         

  • Use An IAM role for CodePipeline to access the AWS resources or code

  • Setup CodePipeline stages

  • Configure each CodePipeline stage with one or more actions such as source, build, deploy, and approve

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

How can you setup other CI/CD tools for serverless?                         

  • Use their YAML files

  • Use a Makefile

  • Use CloudFormation with SAM to build and deploy you serverless stack

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

When is it best to use serverless computing versus containers?                         

  • Look at your non-functional requirements

  • Use containers for very low latency <  50ms, large number of concurrent requests > 3000, large deployments  packages and fixed costs

  • Use serverless when you require latency > 50ms, focus on business logic code, PAYG costs, and you are already on AWS

When to Use and Not Use Serverless Computing?

How can we estimate the serverless costs?                         

  • See the number of concurrent and average length of the requests

  • See data sizes and read/write capacities

  • Monitor costs in billing dashboard

Estimating Serverless Stack Costs

How do you scale database and event streaming resources?                         

  • Scale DynamoDB using GSI, creating a batch API and using DAX

  • Scale Aurora using read-replicas, DB optimization. Runload testing with production loads and monitor performance

  • Scale Kinesis Streams look at the number of consumers required, and load test by replying full production volumes

Database and Event Streaming Scalability

How do I ensure Lambda functions work at web scale?                         

  • Set lambda concurrency limits

  • Use lambda pre-warming to reduce their launch time

  • Use lambda code optimizations such as reducing package size, logging and declare objects outside  lambda_handler()

Web Scale Best Practices

This video concludes the entire course. 

Test your knowledge
5 questions
  • Prior experience to traditional application development is assumed.
  • Basic understanding of microservices and serverless architecture will be useful.

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.

Building a microservices platform using virtual machines or containers, involves a lot of initial and ongoing effort. 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 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 3-in-1 course is a step-by-step tutorial which is a perfect course to implementing Microservices using Serverless Computing on AWS. Build highly availableMicroservices 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.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Building Microservices on AWS, covers building highly available Microservices to power applications of any size and scale. This course shows you how to build Microservices-based applications on AWS. Overcome the limitations and challenges you experience in traditional monolith deployments. By the end of the course, you'll have learned to apply AWS tools to create and deploy Microservices-based applications. You'll be able to make your applications cost-effective, easier to scale, and faster to develop.

The second course, Building a Scalable ServerlessMicroservice 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 third course, Implementing ServerlessMicroservices 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 the course, you’ll create a secure, scalable, and Serverless data API to build highly available Microservices to power applications of any size and scale.

About the Authors

●        Alan Rodrigues has been working on software components such as Docker containers and Kubernetes for the last 2 years. He has extensive experience working on the AWS Platform, currently being certified as an AWS Solution Architect Associate, a SysOps Administrator, and a Developer Associate. He has seen that organizations are moving towards using containers as part of their Microservices architecture. And there is a strong need to have a container orchestration tool in place. Kubernetes is by far the most popular container orchestration on the market.

●        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.

Who this course is for:
  • Developers, software architects, and software engineers. Developers familiar with traditional application development but interested in using Microservices in a DevOps environment will also benefit. Microservices are appropriate to large-scale enterprise environments so this course should appeal to people interested in developing for those environments.
  • Developers who need practical solutions to common problems while building their serverless application. Programming knowledge is assumed.
  • Developers, architects, DevOps, administrators and operations who would like to deploy Serverless computing and microservices in their organization.