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AWS Cloud Practitioner Essentials - Coggle Diagram
AWS Cloud Practitioner Essentials
Module 1
Pay for what you need
Pay as you go
Client-server model
Client = web browser or desktop app that a person interacts with to make request to a server. E.g. from video - the guy ordering a drink at a cafe. On the internet asking for a video about kittens
Server = services such as Amazon Elastic Compute Cloud AC2. e.g. from video - the barista who checks/validates you can pay and then makes the client's drinks.
Cloud computing
Models to choose from
Cloud based deployment
we (our IT team) have to manage them
could consist of virtual servers, databases and network components all based in the cloud
good for migrating existing applications to the cloud
if we wanted to design and build a new application
all components are cloud based in this model
On-premises deployment
"Private cloud" deployment
Ask Tim if this is what we do for REDCap
Hybrid deployment
Where cloud-based resources connect to on-premises infrastructure.
legacy IT applications - maybe consideration for Program Guides
If part of a solution can be managed in the cloud, but relies on legacy applicaitons that can't be migrated
How you might make the decision for Cloud computing
Upfront expenses for physical on-premesis infrastructure. AWS allows you to pay for what you need and only what you use.
Maintenance. Spending time and money managing infrastructure and servers. This shifts to AWS to manage.
Guessing capacity - you don't have to predict what you might need.
Note: Learnings from REDCap. in reality, for a new deployment and working with the business costs for non-prod and prod environments the first costs are high. It's not really until the first month after go-live that you get a really clear indication of what's being used. Business usually wants to know in advance "how much will this cost" so they can budget but in reality, you won't know the actuals until it's used
Economy of scale - AWS gives lower variable cost than what you can get on your own - translates to lower-pay-as-you-go price as more customers use AWS.
Speed and agility - more time to experiment, quicker to develop and deploy.
Deploy applications to customers around the world with low latency.
Amazon Elastic Compute Cloud - Amazon EC2
(Module 2)
Servers are virtual but the service you use to gain access to the servers IS called EC2
Hypervisor - shares the underlying physical resources between the virtual machines - called Multitenancy
It's in the cloud but Amazon has to have physical servers that use virualisation technolgy
When you "provision"/spin up an instance
you get to choose he OS windows/linux
choose the apps that run on the EC2 instance
can size - small to star and then add more later "Vertical scaling"
control the network - public or private
"CaaS" model - compute as a service
Steps
Launch - First, you launch an instance. Begin by selecting a template with basic configurations for your instance. These configurations include the operating system, application server, or applications. You also select the instance type, which is the specific hardware configuration of your instance.
Connect
Next, connect to the instance. You can connect to the instance in several ways. Your programs and applications have multiple different methods to connect directly to the instance and exchange data. Users can also connect to the instance by logging in and accessing the computer desktop.
Use
After you have connected to the instance, you can begin using it. You can run commands to install software, add storage, copy and organize files, and more.
EC2 Instance types
General purpose
applicaions, gaming, backend servers for enterpise applications, small-med databases
Compute optimised
if you need a high performance processor. or high workload e.g. web applications
Memory opimised
For lots of data processing witth high workload and high memory needs
Storage optimised
great for read-write access to large data sets on local storage
Accelerated compuing
floating-point number calculations, graphics processing and datta patern matching. Great for game streaming or application streaming.
Auto Scaling
Dynamic scaling - respond to changing demand
Predictive scaling - automatically schedule the number of instances based on predicted demand
You can use both together
minimum capacity - when you first create an auto scaling group
then set desired capacity, even though application might need a minimum/single instance to run. Auto sets to minimum capacity if no desired capacity is set
Maximum capacity - scale out to increased demand but only o a maximum of instances
Direct traffic with Elastic Load Balancing