- [Instructor] Hello, and welcome to this final lecture where I just want to quickly summarize what we have learned throughout the course. I started off by covering what is mean by compute whereby I explained that compute can be considered the brains and processing power required by applications and systems to carry out computational abilities via a series of instructions. So essentially compute is closely related to common server components which many of you will be more familiar with such as CPUs and RAM.

Following this we then started to get into the meat of the AWS compute resources to provide you with an understanding of the fundamentals of the different AWS compute services and features. During the EC2 lecture we learned that it is one of most common compute services in use today and what it provided from a compute perspective.

I also discussed the different components of the service, which covered Amazon Machine Images, AMIs, instance types, the instance purchasing options, tenancy, user data, storage options, and security.

Following EC2 we looked at how elastic load balancing and auto scaling has a relationship with EC2 allowing you create a highly scalable and load balanced architecture. I explained that the main function of ELB is to direct and route traffic destined for you fleet of EC2 instances across an even distribution which helps to maintain high availability and resiliency of your environment, whereas auto scaling is a mechanism that automatically allows you to increase or decrease your resources to meet demand based off of custom defined metrics and thresholds.

I also talked through how to create an ELB using the following steps. By defining the load balancer, assigning security groups, configuring security settings, configuring a health check, adding your EC2 instances, and adding tags. When we discussed auto scaling I pointed out some of the main advantages of using it. As auto scaling provides automatic visioning based off of custom defined thresholds, your infrastructure will start to manage itself preventing you from having to monitor and perform manual deployments.

This will ultimately provide a better experience for your users. If there is always enough capacity within your environment it's unlikely your end user will experience performance problems which may prevent them from using your services again. Cost reduction. With the ability to automatically reduce the amount of resources you have when demand drops you will stop paying for those resources. You only pay for an EC2 resource when it is up and running.

I gave an overview of Elastic Beanstalk, which is an AWS managed service that takes your uploaded web application code and automatically provisions and deploys the required resources within AWS to make the web application operational. The components that make up Elastic Beanstalk are applications, applications versions, environments, environment and configurations, and configuration templates. I then covered how Elastic Beanstalk operates a very simple workflow process for your application deployment in four simple steps.

Firstly, you create an application. Next, you must upload your application version of your application to Elastic Beanstalk. The environment is then created by Elastic Beanstalk with the appropriate resources to run your code. Any management of the application can then take place. Next, was an overview of AWS Lambda, which is the service that lets you run your own code in response to events in a scalable and highly available serverless environment. To reiterate again, serverless means that you do not need to worry about provisioning and managing your own compute resources to run your own code.

Instead, this is managed and provisioned by AWS. Although it's named serverless it does of course require servers or at least compute power to carry out your code request. But because the AWS user does not need to be concerned with what compute power is use, or where it's coming from, it's considered serverless from the user perspective. I explained what Lambda Functions were and the elements that form them. Required resources, maximum execution timeout, IAM role, and handler name. Once we understood what Lambda

Functions were I covered how to create them and the three simple steps to do so. Select a new blueprint. Configure the triggers. And configure in the function. Be sure to give the labs a go that I've mentioned throughout this course as they will really help you to implant what we've covered and help you understand how some of the services are put together. Thank you for taking the time to view this course, and if you have any feedback, positive or negative, I would very much appreciate your comments. That now brings us to the end of this course. I wish you continued success with any future development learning of cloud computing. Thank you.