Compute - Summary


Storage and Databases
Amazon S3
27m 3s
Amazon RDS
11m 28s
Amazon EBS
11m 38s
Services at a glance
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In this course we learn to recognize and explain AWS compute and storage fundamentals, and to recognise and explain the family of AWS services relevant to the certified developer exam. This course provides you with snapshots of each service, and covering just what you need to know, gives you a good, high-level starting point for exam preparation. It includes coverage of:


Amazon Simple Queue Service (SQS)
Amazon Simple Notification Service (SNS)
Amazon Simple Workflow Service (SWF)
Amazon Simple Email Service (SES)
Amazon CloudSearch
Amazon API Gateway
Amazon AppStream
Amazon WorkSpaces
Amazon Data Pipeline
Amazon Kinesis
Amazon OpsWorks
AWS Elastic Beanstalk
Amazon CloudFormation

Storage and database
Amazon Simple Storage Service (S3)
Amazon Elastic Block Store (EBS)
AWS Relational Database Service (RDS)
Other Database Services
Amazon Glacier

Elastic Cloud Compute (EC2)
Elastic Load Balancing (ELB)
Auto Scaling
Amazon ECS
AWS Lambda

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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 meant 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, which are:

During the EC2 lecture, we learned that it is one of the most common compute services in use today, and what it provided from a compute perspective. I also discussed the different components of this service, which covered:

  • Amazon Machine Images, AMIs
  • Instance types
  • the Instance Purchasing Options
  • Tenancy
  • User Data
  • Storage Options
  • and Security

I then performed a demonstration that showed you how to create a new EC2 instance, from within the AWS management console.

Following EC2, we looked at how Elastic Load Balancing and Auto Scaling has a relationship with EC2, allowing you to create a highly scalable and low balance architecture. I explained that the main function of ELB is to direct and route traffic destined for your fleet of EC2 instances, across an even distribution, which helps to maintain high evaluability 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 follow 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 provisioning 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 performer problems, which may prevent them from using your services again. Cost reduction. With the ability to automatically reduce the number 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 then demonstrated how to create an auto-scaling group from an existing launch configuration.

The next service we looked at was the Amazon ECS container service. This can be defined as a service that allows you to run Docker-enabled applications, packaged as containers across a cluster of EC2 instances, without requiring you to manage a complex and administratively heavy cluster management system. As a result, there is no need to install any management software for your cluster. Neither is there a need to install any monitoring software either.

All of this and more is taken care of by the service, allowing you to focus on building great applications and deploying them across your scalable cluster.

Following ECS, 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
  • application versions
  • environments
  • environment configurations
  • and configuration templates

I then covered how elastic beanstalk operates a very simple workflow process for your application deployment in four simple steps.

  1. Firstly, you create an application
  2. next, you must upload your application version of your application to elastic beanstalk
  3. the environment is then created by elastic beanstalk with the appropriate resources to run your code
  4. any management of your application can then take place

Next was an overview of AWS Lambda, which is a service that lets you run your own code in response to events in a scalable and highly evaluable 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's used, 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:

  1. Select a new blueprint
  2. configuring the triggers
  3. and configuring the function

Following lambda, I introduced AWS batch, which is used to manage and run batch computing workloads within AWS. Batch computing is primarily used in specialist use cases, which require a vast amount of compute power across a cluster of compute resources, to complete batch processing, executing a series of jobs or tasks. To understand how AWS batch works, I covered some of the key parts:

  • Jobs. A job is classed as the unit of work that is to be run by AWS batch
  • Job definitions. These define specific parameters for the jobs themselves
  • Job queues. Jobs that are scheduled are placed into a job queue until they run
  • Job scheduling. The job scheduler takes care of when a job should be run and from which compute environment
  • And finally, compute environments. And these are the environments containing the compute resources to carry out the job

Finally, we looked at Amazon Lighstail, which provides a Virtual Private Server, a VPS. It has been designed to be simple, quick, and very easy to use for small scale use cases. A Lighstail VPS provides you with the following features:

  • the virtual instance itself
  • an operating system
  • optional pre-installed applications
  • solid state drives
  • data transfer allowance
  • DNS management
  • and static IP addresses

An Amazon Lighstail instance can be launched and configured all from a single page, making this a simple solution.

You should now have a good understanding of the different AWS compute services and features available, allowing you to select the most appropriate service for your project. Each have their advantages by providing something of value that's different to others.

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 have covered and helped 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 at learning of cloud computing.

Thank you!

About the Author
Learning Paths

Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data center and network infrastructure design, to cloud architecture and implementation.

To date, Stuart has created 150+ courses relating to Cloud reaching over 180,000 students, mostly within the AWS category and with a heavy focus on security and compliance.

Stuart is a member of the AWS Community Builders Program for his contributions towards AWS.

He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.

In January 2016 Stuart was awarded ‘Expert of the Year Award 2015’ from Experts Exchange for his knowledge share within cloud services to the community.

Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages.