Welcome back to the Deep Dive. Today we are strapping
ourselves in really getting into the engine room
of the AWS cloud. Yeah, absolutely. Forget databases,
storage, networking for a minute. We're focusing
just on compute. The core stuff. Exactly. These
services, they're the foundation, right? The
virtual servers, containers, the places your
code actually runs. If compute's the engine,
then well... understanding it is like having
the driver's manual for a cloud architecture.
You just can't build anything scalable or resilient
or, you know, cost -effective without getting
this layer right. So our mission today is pretty
specific. Yeah, we want to give you that critical
shortcut, sort of distill the must -know info
about EC2, Lambda, ELB, auto scaling. And how
they all work together. Exactly. For scalability,
elasticity, and crucially, keeping costs down.
And for you, listening, this is probably the
area to focus on for quick expertise gains. It's
not just knowing the names. No, not at all. It's
about understanding the trade -offs. The why
behind choosing one over another. If someone
asks where we run this code, well, the answer
is always complex. Always. But for traditional
apps, the kind that are always on, the starting
point is almost always the same thing, isn't
it? It is. Amazon EC2. Elastic compute cloud.
Right. When you think virtual machine or server
in the cloud, nine times out of 10, you're thinking
EC2. Honestly, it's probably the single most
important service to really get your head around
first. Because it's infrastructure as a service.
Exactly. It gives you that secure, resizable
compute capacity. Those virtual servers are called
instances. It's the closest thing you get to,
like, bare metal in the cloud. OK. Let's unpack
that, then. EC2, the virtual server. Launching
one isn't just a button click, though. You have
to define things. The recipe, the power, the
access. Right. You need to configure it. We can
break it down into three core components you
must define. OK. First up, the Amazon machine
image, or AMI. AMI. Think of the AMI as the template.
It's the recipe for the server's brain, basically.
OK. It contains all the software config, the
OS, maybe an application server like Apache or
applications. It defines what the server actually
is. So the AMI sets the software blueprint. Got
it. But servers need hardware, too. How do we
define the horsepower for this virtual machine?
That's the instance type. And this is where.
Frankly, architects spend a lot of time optimizing.
Makes sense. The instance type determines the
whole hardware profile of the computer it's running
on. We're talking virtual CPUs, vCPUs, RAM, storage
options, network performance. It's the whole
package. And it's not just one size fits all,
right? AWS has loads of these. Different families.
Oh, absolutely. Tons. And knowing the families
is key, it often gets glossed over. You really
need to pick the right tool for the job. So what
are the main ones we should know? Well, The most
common you'll bump into are probably the M, C,
and R families. MCR, OK. M family, think M for
main, or maybe multipurpose. It's general purpose.
Good balance of CPU, memory, network. Great starting
point for web servers, small databases, that
kind of thing. OK, general stuff. Then the C
family. C for compute optimized. These are CPU
powerhouses, high -performance computing, batch
jobs, video encoding, anything that really hammers
the processor. Got it. CPU heavy. And conversely,
the R family. R for RAM. Memory optimized. These
are for things that need loads of memory. Big
databases, memory caches, data analytics that
chew through RAM. So M for balanced, C for CPU,
R for RAM. That makes sense. So if I'm running
just a standard website, probably start with
an M. But if I'm doing heavy data crunching...
You'd look at an R, definitely. Or maybe a C
if the crunching is CPU bound. Making that choice
right saves money. You're not paying for RAM
you don't need. or a CPU you're not using. That
makes a lot of sense. Are there other important
types? Yeah, a couple of others worth mentioning.
The T family, like T2, T3, T4G, those are the
burst instances. Burst. Yeah, they have a baseline
CPU level but can burst higher for short periods.
Great for dev test environments, things that
are mostly idle but need occasional power. Very
cost effective for that. OK. And then you get
into the really specialized stuff. PNG families
for machine learning with GPUs, X families for
massive memory needs, but MC, R, and T cover
a huge range of use cases. Choosing the instance
type is fundamentally about performance versus
cost. Right. OK, so we have the recipe AMI and
the power instance type. We need to weigh in
securely. That's the third piece. That's the
key pair. absolutely essential for secure admin
access, like SSHing into a Linux box or RDPing
into Windows. How does that work? It's a pair
of cryptographic keys. There's a public key,
which AWS stores and puts on the instance when
it launches. And then there's a private key.
And that's the one I keep. That's the one you
keep. And you guard it carefully on your own
machine. AWS doesn't keep the private key, lose
it, and you can't log in. Oh, OK. And this is
a perfect, really clear example of the shared
responsibility model. Right. AWS secures the
cloud itself. The hardware, the network, the
facilities. But I'm responsible for security
in the cloud. Exactly. Managing that private
key, patching your OS, configuring firewalls.
It's on you. That distinction is so important.
Okay, let's pivot now. We know what an EC2 instance
is, how do we pay for it? Because this is where
decisions have huge financial impact for pricing
models. Yep, four main ways. And getting this
right is, like, foundational for cost optimization,
which is a pillar of the well -architected framework.
Let's start with the default, the most flexible
one, on -demand instances. Simplest model. Pay
by the hour, or even by the second now, for Linux
instances. Zero upfront commitment. Need capacity.
You get it immediately. So when would I use that?
Perfect for short term stuff. Irregular workloads.
Things you can't predict. Development. Testing.
Anything where you need capacity now, you don't
want it interrupted, but you don't know how long
you'll need it for. But you pay the highest price
for that flexibility. You do. It's the baseline
rate. OK. So if on -demand is about flexibility,
the next one is about commitment for savings.
Reserved instances and the newer savings plans.
Exactly. This is the core strategy if you know
you need servers running long term. Think steady
state. predictable workloads. Like my main production
web servers. Precisely. If you commit to using
compute for a one -year or three -year term,
AWS gives you a significant discount, up to 72
% off the on -demand price. 72%. That's massive.
It really is. And the newer savings plans are
generally better, more flexible than the older
IIs. How so? Well, traditional IIs locked you
into a specific instance, family, size, region,
OS. Quite rigid. Savings plans are more flexible.
You commit to a certain dollar amount of spend
per hour, say $10 an hour, for one or three years.
Okay. And that discount applies much more broadly.
If you change instance families within that commitment,
say from an M5 to an M6, or even move region
sometimes, the savings plan discount often still
applies. Much easier to manage. So the bottom
line, if it runs 247, commit to a savings plan.
Absolutely. That's a primary way to save big
on predictable usage. Okay, now the extreme end
of savings, the one that sounds almost too good
to be true, spot instances. Up to 90 % off. Yep,
a spot can offer truly massive discounts. You're
basically bidding on spare, unused EC2 capacity
that AWS has lying around. So what's the catch?
There's always a catch with 90 % off. The catch
is interruption. Because it's bare capacity,
AWS can reclaim it whenever they need it back
for on -demand or reserves customers. Reclaim
it, like, just turn it off. Pretty much. They
give you a two -minute warning and then boom,
the instance is terminated. Two minutes? That's
not a lot of time. It's not. Which means Spot
is only suitable for certain types of workloads.
Like, what? How do you use that warning? Your
application has to be genuinely fault tolerant
and, ideally, stateless. It needs to be able
to handle that interruption gracefully, meaning
it shouldn't store critical data only on that
instance. It needs to be able to, say, checkpoint
its work, save its state somewhere durable, like
S3 or a database, and shut down cleanly within
those two minutes. Then another instance, maybe
the spot instance and on -demand one, can pick
up where it left off. OK, so things like batch
processing, big data jobs, rendering. Exactly.
Tasks that can be stopped and restarted without
losing everything. Things that are maybe distributed
across many instances anyway. If it's your main
customer -facing database or a critical web server
that absolutely cannot go down unexpectedly.
Use Spot. Never use Spot for that. It's a huge
architectural trade -off. Stability versus massive
cost savings. Got it. Okay, one more pricing
model. The niche one. Dedicated hosts. Right.
A dedicated host is just what it sounds like.
A physical server in an AWS data center that's
entirely dedicated to your use. No other AWS
customer shares that underlying hardware. Why
would someone need that? Isn't the whole point
of cloud -shared infrastructure? Usually it's
not about cost savings. It's almost always about
compliance or licensing. Ah, licensing. Like
software that's licensed per physical core? Exactly.
Some legacy software, maybe... Bring your own
license BYOL scenarios. Have licensing terms
tied to physical hardware sockets or cores. Dedicated
hosts let you meet those terms. Or, sometimes,
very strict regulatory requirements might mandate
physical isolation. So it's a compliance tool,
not really a cost -saving tool. Primarily, yes.
It's more expensive than other options. OK, that
covers the single server EC2 really well. But
like we said, one server isn't resilient. It
can't handle big traffic swings. So we need to
move up a level, domain two, scaling and high
availability. Right. Moving beyond the single
instance to the services that make your application
resilient, available, and able to handle fluctuating
demand. This naturally brings us to elastic load
balancing. The load balancer. So this sits in
front of my EC2 instances. Correct. It acts as
the single front door, the single point of contact
for all your users or clients. Its main job is
to take that incoming traffic and distribute
it intelligently across your fleet of EC2 instances
running the application. Distributing the load
makes sense. But it does more than that, right,
for availability? Absolutely. Its second key
function is ensuring high availability. The best
practice is to run your EC2 instances across
multiple availability zones, or AZs. And AZs
are separate data centers within a region. Physically
separate, yes. Independent power, cooling, networking.
So the ELB spreads the traffic across instances
in, say, AZ1 and AZ2 if something catastrophic
happens, and the entire AZ1 goes offline. The
load balancer just sends traffic to the instances
still healthy in AZ2. Precisely. Your application
stays up. That's high availability in practice,
enabled by the ELB. You said it's a single point
of contact, but there isn't just one type of
load balancer anymore, is there? This trips people
up. That's a really important point. There are
a few types, but the main two you need to understand
are the application load balancer, ALB, and the
network load balancer, NLB. ALB and NLB. Difference.
They operated different layers of the network
stack. The ALB works at layer 7, the application
layer. Meaning it understands HTTP, HTTPS. Exactly.
It's smart. It can look inside the request at
the URL path, the host name, HTTP headers, and
make sophisticated routing decisions based on
that content. Want to send images traffic to
one set of servers and AP to another? ALB could
do that. perfect for modern microservices or
complex web apps. OK. ALB is the smart one for
web traffic. What about NLB? NLB operates a layer
4, the transport layer, TCP UDP. So it doesn't
look inside the application request? Nope. It
just looks at IP address and port information.
It's incredibly fast and designed for ultra high
performance, millions of requests per second
with very low latency. Because it's simpler.
it's faster. Great for TCP traffic, UDP, or situations
where you need extreme throughput or a static
IP address for your load balancer endpoint. So
ALB for smart application routing, NLB for raw
speed and throughput. Got it. And the load balancer
also checks if the instances behind it are actually
working. Health checks. Critically important,
yes. The ELB constantly pings its registered
targets, your EC2 instances, using configured
health checks. Could be checking a specific web
page, a port. And if an instance fails that check,
maybe the app crashed. The ELB immediately marks
it as unhealthy and stops sending any new traffic
to it automatically, instantly. It routes traffic
only to the healthy instances. This is key for
user experience. Nobody wants to hit a dead server.
OK, that covers spreading the load and staying
highly available. But what about handling those
traffic spikes? Staling up for a sale, scaling
down overnight, that elasticity part. Ah, that's
where the partner service comes in. Amazon EC2
auto scaling, ASG. Auto scaling. This is the
magic behind true elasticity. And honestly, probably
the single biggest cost optimization tool for
workloads with variable traffic. ASG automatically
adds more EC2 instances that scale out when demand
goes up and removes instances scale in when demand
drops. So you're only paying for the capacity
you actually need right now. Exactly. No more
over -provisioning massive fleets just to handle
peak load, which was the old data center nightmare.
ASG matches capacity to demand dynamically. How
do you set it up? You must tell it how to scale.
You do. You define an auto -scaling group, and
there are three key capacity settings you can
figure. The minimum capacity. the absolute smallest
number of instances you always want running,
maybe for base availability. The maximum capacity,
the upper limit, your cost ceiling stops it running
away. And the desired capacity, what the ASG
should aim for under normal conditions or what
it starts with. Min, max, desired, the boundaries.
And what actually triggers the scaling? Adding
or removing instances. Those are the scaling
policies you define the conditions It could be
a simple policy like if average CPU across the
fleet goes above 70 % for five minutes add two
instances Okay, but the more modern and usually
recommended approach is target tracking scaling
target tracking Yeah, it's much simpler to manage.
You just tell the ASG. I want to maintain an
average CPU utilization of say 60 % across all
instances. And the ESG does the math automatically
adding or removing instances as needed to keep
that target metric stable. You can target CPU,
network traffic, request count per instance from
the ELB. It's quite powerful. Oh, that sounds
much easier to set the target. It generally is.
So we see the synergy here. The ELB takes the
incoming traffic, spreads it across AZs for availability.
The ASG watches the instances behind the ELB
and automatically adjusts the fleet size to match
the demand the ELB is seeing based on metrics
like CPU. That's a classic bread -and -butter
architecture for a scalable, resilient web application
on AWS. You see two instances managed by an ASG
sitting behind an ELB, usually an ELB. You'll
see that pattern everywhere. Okay, but... What
if I don't want to manage any of that? No EC2
instances, no AMIs, no OS patching, no configuring
ASGs. Ah, now you're talking about fundamental
shift. That brings us neatly into Domain 3. Serverless
compute, and the poster child for that is AWS
Lambda. Serverless, which as you always have
to point out, doesn't mean no servers. Right.
It's a bit of a misnomer. The servers definitely
exist. The crucial difference is you don't manage
them. AWS handles all the underlying infrastructure.
And Lambda is the core service here, function
as a service or face. Exactly. Lambda lets you
run your code without thinking about provisioning
or managing servers at all. What's the key principle?
What burden does it lift? Basically everything
related to infrastructure management. AWS handles
provisioning the compute capacity, the automatic
scaling, patching the operating systems, all
the underlying administration. You, the developer,
just focus on writing the code for your function.
That's it. That sounds like a massive reduction
in operational work. It is. Huge. And unlike
an EC2 web server that's running 24 -7 waiting
for requests, Lambda works differently. Fundamentally
different. Lambda is event -driven. Event -driven.
Meaning... Your Lambda function sits there, dormant,
until something triggers it. An event happens
in your AWS environment, and that event invokes
the function. What kind of events? All sorts.
An image file gets uploaded to an S3 bucket,
a message arrives on an SQS queue, a record gets
updated in a DynamoDB table, a user logs in via
Cognito, or very commonly an HTTP request comes
in through API Gateway. The function only runs
in response to these triggers. It's reactive.
Totally reactive. It sleeps until woken up by
an event. And that has a huge impact on cost,
right? Yeah. The pay -per -use billing. This
is the killer feature for cost optimization on
intermittent workloads. If your Lambda function
isn't running, if it's idle, you pay absolutely
nothing. Zero. Wow. You only get charged for
two things. The number of times your function
is invoked, the number of requests, and the total
time your code spends executing, measured in
milliseconds. Milliseconds. Down to the millisecond.
If your function runs for, say, 500 milliseconds,
you pay for exactly 500 milliseconds of compute
time. Compare that to an EC2 instance that you
pay for 247, even if it's idle 90 % of the time.
The efficiency is incredible for the right workload.
And scaling. So I'd only get 1 ,000 S3 uploads
at once. The scaling is also totally different
from EC2. It's automatic and near instantaneous.
Lambda has massive built -in concurrency. If
the thousand events hit simultaneously, Lambda
will try to spin up a thousand parallel execution
environments to run your function concurrently
to handle that burst. You don't configure min
-max instances or scaling policies like with
ASG. It just happens. It just handles it. Up
to your counts concurrency limits, of course,
but those are typically very high. That seamless
scaling sounds amazing, but there must be a trade
-off. What about performance? Especially for
that first request, I've heard about cold starts.
Ah, yes, the cold start. That is the main operational
trade -off you need to be aware of, especially
for latency -sensitive applications. What is
it exactly? Well, if your Lambda function has
been invoked for a while, AWS might tear down
the underlying container or execution environment
to save resources. When the next request comes
in, Lambda has to do some setup work first. Find
capacity, provision the environment, download
your code, initialize the runtime. Before your
code even starts running. Exactly. That initialization
phase is the cold start. It can add latency sometimes
just tens or hundreds of milliseconds, but sometimes
for complex functions or certain runtimes, it
could be a second or two. And that extra latency
happens only on the first request after a period
of inactivity. Generally, yes, or when scaling
up rapidly requires new environments. Subsequent
requests to a warm environment are usually much
faster. So that cold start latency could be unacceptable
for, say, a real -time API endpoint where users
expect an instant response. It absolutely could.
You're essentially trading potentially higher,
unpredictable latency on some invocations for
the benefit of lower cost and zero server management.
So it's a conscious choice based on the workload's
priorities. Always. Is consistent low latency
paramount? Maybe EC2 is better. Is cost efficiency
and minimal ops overhead the driver? Lambda looks
very attractive. This leads us right into the
core comparison, EC2 versus Lambda. Let's really
nail down the key differences, because this comes
up all the time. Definitely. Let's break it down.
OK, first, the basic model. EC2 is Infrastructure
as a Service, IAS. You rent the virtual server
hardware. Lambda is Function as a Service, AAS,
or Serverless. You rent code execution time.
Server management. With EC2, it's all you. OS
patching, installing software, managing scaling,
security hardening, Lambda. AWS manages everything
under the hood. You just manage your function
code. EC2 needs you to configure auto -scaling
groups. To find policies, set min -max limits.
Lambda scaling is built -in, automatic, instantaneous.
Billing. EC2. Pay while it's running, even if
idle. You pay for that standby capacity. Lambda.
Pay only for requests and execution time in milliseconds.
Zero costs when idle. And the big constraint.
How long can they run? EC2 instances can run
indefinitely, 247 for years if you want. Lambda
functions have a maximum execution timeout. Currently,
it's 15 minutes. 900 seconds. Only 15 minutes.
Yep. If your task takes 16 minutes to complete,
lambda is simply not an option. You'd need EC2
or maybe containers. OK. So that clearly defines
the sweet spot for each, EC2 -4. long -running
applications, web servers, APIs needing consistent
low -latency databases, anything that needs to
run continuously or for longer than 15 minutes,
persistent services. That distinction is critical.
Get that wrong, and you're either overspending
or using the wrong tool. Absolutely fundamental.
Okay, moving on. We've done VMs, EC2, functions,
Lambda. What about containers? Docker is huge
now. Packaging apps and dependencies together.
That brings us to domain four. Container services.
Right. Containers solve that classic, well, it
works on my machine problem by packaging everything
needed into a single unit. Ensures consistency.
But running containers at scale, that needs management.
An orchestrator. And AWS gives us choices for
orchestration. Two main ones. First, there's
Amazon Elastic Container Service, ECS. ECS. That's
AWS's own one. Yep. Their native homegrown container
orchestration service. It's highly scalable,
fast, and designed for really tight integration
with other AWS services like IAM, VPC, load balancers.
Often preferred if you're already deep in the
AWS ecosystem and want something simpler to manage.
than the alternative. And the alternative is?
The industry standard, Kubernetes. AWS offers
Amazon Elastic Kubernetes Service, EKS. EKS for
Kubernetes. Right. EKS is a managed service that
lets you run standard open source Kubernetes
on AWS without having to deal with the nightmare
of installing and managing the Kubernetes control
plane yourself, which is notoriously complex.
Teams already using Kubernetes or wanting that
open source portability, often choose EKS. So
ECS is the AWS native way. EKS is the managed
open source Kubernetes way. But with both of
these, traditionally, you still had to run your
containers on something, right? Like on EC2 instances?
Exactly. You'd set up ECS or EKS, but then you'd
also need to provision and manage a cluster of
EC2 instances to actually host your container
tasks or pods. You still have to worry about
patching those EC2 instances, scaling them with
ASG's, optimizing the instance types. Which sounds
like we're back to managing servers again, even
with containers. It does, yeah. And that's where
the real game changer comes in, combining containers
with that serverless idea we talked about, AWS
Fargate. Fargate. Okay, what is Fargate? Is it
another orchestrator? No, that's the key thing.
Fargate is not an orchestrator. It's a serverless
compute engine that works with both ECS and EKS.
A serverless engine for containers? Precisely.
When you use Fargate, you completely eliminate
the need to provision, manage, patch, or scale
that underlying cluster of EC2 virtual machines.
How does that work? You just define your container
task for ECS or pod for EKS as usual. You specify
the CPU and memory resources your container needs.
Then you tell ECS or EKS to launch it using the
Fargate launch type. And Fargate just runs it
without me picking an EC2 instance. Exactly.
Fargate finds the capacity and runs your container
on fully managed infrastructure. You don't see
or manage any EC2 instances at all. It's like
Lambda. but for containers. Wow. So the takeaway
is Fargate is the serverless way to run containers
on AWS using either ECS or EKS as the orchestrator.
That's the perfect summary. You choose your orchestrator,
ECS or EKS, and then you choose your launch type,
either EC2 if you want control over the VMs or
Fargate if you want serverless. That feels like
a really powerful option for teams who love containers
but hate managing VMs. It really is. It simplifies
operations massively. Focus on your containers,
not the infrastructure they run on. Okay, we've
covered the big three execution models. IAS with
EC2, FAS with Lambda, and serverless containers
with Fargate. Let's quickly touch on Domain 5,
other key compute -related services. These seem
like tools for specific situations. Yeah, these
are more specialized, often providing higher
levels of extraction or solving niche problems
like location. Let's start with AWS Elastic Beanstalk.
I hear this described as Patas platform as a
service. That's exactly what it is. Elastic Beanstalk
sits between raw IS like EC2 and pure Fados like
Lambda. What's its job? Its core function is
to make it super easy to deploy and scale standard
web applications and services. Think apps written
in Java, .NET, PHP, Node .js, Python, Ruby, Go,
Docker. Okay, so how does it make it easy? The
magic of Beanstalk is abstraction. You basically
just upload your application code. My war file
or my Python code. Yep. And Elastic Beanstalk
automatically handles provisioning and managing
the entire underlying environment stack needed
to run it. The whole stack, what does that include?
It provisions the EC2 instances, sets up the
auto scaling group. configures the elastic load
balancer, creates security groups, manages application
versions, handles deployments like rolling updates
or immutable deployments. All of that infrastructure
complexity is managed for you based on your application
type. So it's using EC2, ASG, ELB under the covers,
but hiding the configuration details. Precisely.
It abstracts away that lower -level infrastructure
configuration, letting developers focus just
on writing code, not on wiring together AWS services.
So, if you hear about a developer wanting to
deploy a web app quickly, needing scaling and
load balancing, but explicitly not wanting to
manage the underlying AWS resources, Elastic
Beanstalk sounds like the answer. It's very often
the answer in that scenario. The perfect big
yeast is middle ground. Okay. Next up, something
quite different. AWS Outposts. This sounds like
bringing AWS home. It kind of is. Outposts is
a unique service that literally extends AWS infrastructure,
the same hardware services, APIs, and tools you
use in an AWS region, directly into your own
on -premises data center or co -location facility.
Why would you need to do that? Isn't the point
of cloud not running stuff in your own data center?
Usually for two main reasons. Latency or data
residency? Latency. Yeah. Imagine you have an
application that requires extremely low latency,
like single -digit millisecond response times
maybe for factory automation or real -time financial
trading. The physical distance of the nearest
AWS region might introduce too much network latency.
Outposts lets you run those AWS services locally
right next to your end users or equipment. And
data residency. Some industries or countries
have very strict regulations that require certain
data to physically remain within a specific location,
like the customer's own building or country.
Outposts allows you to process that data using
familiar AWS services while ensuring it never
leaves your premises. So outposts is the answer
when you need AWS compute locally. Exactly. Cloud
infrastructure. on your turf. Okay, one more
in this category. Amazon LightSail. This sounds
simpler. Much simpler. LightSail is designed
to be the absolute easiest way to get started
with a virtual private server, VPS, on AWS. Easier
than EC2. Way easier. Think of people coming
from traditional VPS providers like DigitalOcean
or Linode. EC2, with all its auctions, AMIs,
instance types, security groups, EBS volumes,
VPCs, can be overwhelming for beginners. So how
does LightSale simplify it? It bundles everything
you need into a single, simple package with a
predictable low monthly price. You choose a plan
like $5 per month, and it includes a virtual
machine with a set amount of CPU, RAM, SSD storage,
and data transfer allowance, plus networking
and a static IP address. Ah, fixed monthly cost.
Very predictable. Very predictable. It's perfect
for beginners, students, developers wanting to
run a simple website, blog, dev environment,
or small application without needing the granular
control and potential cost variability of full
EC2. Simple fixed price VPS. Got it. Easy mode
VPS. Okay, that was a whirlwind tour of the compute
landscape. Let's try to bring it all together
in Domain 7, Application and Core Cloud Concept
Review. How do these services map back to those
key cloud pillars we started with? Right, this
is where we connect the dots. The pillars guide
our choices. Let's start with elasticity, scaling
up and down automatically. The key service for
EC2 is Amazon EC2 Auto Scaling, ASG. That's what
provides the automatic adjustment. For serverless,
like Lambda and Fargate, elasticity is built
in, handled by the platform. Okay. What about
scalability, handling growth over time? For EC2
-based applications, scalability is really achieved
by the ELB and ASG working together. The ELB
distributes increasing load, and the ASG adds
instances to handle that load. High availability,
running across multiple AZs surviving failures.
The linchpin there is the elastic load balancer,
ELB. It's what distributes traffic across those
multiple AZs and routes around failures. And
the big one, cost optimization, getting the best
bang for your buck. Here, the choices are critical,
using spot instances for fault -tolerant work
for massive savings, committing via savings plans
or IIs for predictable workloads, or choosing
lambda for intermittent tasks to eliminate idle
costs entirely. It's about matching the pricing
model to the workload pattern. And that final
concept, serverless, where AWS manages the infrastructure.
That's embodied by AWS Lambda for functions and
AWS Fargate for containers, shifting operational
responsibility to AWS. Okay, let's make this
really concrete. Scenario time. Let's force ourselves
to pick the right service. Ready? Let's do it.
Scenario one, an e -commerce site. Global traffic,
huge spikes during sales, needs to be always
up, needs to scale automatically. Okay, variable
traffic, high availability needed, automatic
scaling. That classic pattern, you need the trifecta.
EC2 instances for the compute, managed by an
auto -scaling group for the elasticity and cost
savings, sitting behind an application load balancer,
ALB, for the intelligent traffic distribution
and HA across AZs. Makes perfect sense. Scenario
2, a simple script. Resizes images whenever they
land in an S3 bucket. runs for maybe 10 seconds,
needs to be super cheap. Keywords, event -driven
S3 upload, short execution time 10 seconds, cost
is paramount. That screams AWS Lambda. EC2 would
be massive overkill and way more expensive because
you'd pay for idle time. Lambda only charges
for those 10 seconds. Perfect. Scenario 3. A
big bank runs an analytics app using Docker containers.
The ops team loves containers but hates managing
VMs. They want the simplest possible way to run
these containers. Containers, yes. No VM management.
That points directly to AWS Fargate. They've
used Fargate as a launch type with either ECS
or EKS as their orchestrator. Serverless containers.
Got it. Takes the EC2 burden away. Scenario 4.
A developer has a new Ruby on Rails web app.
needs HA, load balancing, scaling, the works.
But they don't want to mess with cloud formation
or manually configure ASGs and ELBs. They just
want to deploy the code. They're asking for that
platform as a service abstraction. The answer
is AWS Elastic Beanstalk. Upload the Ruby code.
Beanstalk builds and manages the whole scalable
load balanced environment automatically. Excellent.
Those scenarios really help crystallize the choices.
Well, that brings us towards the end of this
deep dive. We've really traced the evolution,
haven't we? From managing the virtual machine
completely yourself with EC2. Right. Maximum
control, maximum responsibility. To outsourcing
a lot of that configuration complexity with Elastic
Beanstalk. Picking up some control for operational
ease. All the way to letting AWS handle basically
the entire operational environment for your code
with Lambda, Faylis, or Fargate. Minimum operational
overhead, maximum focus on code, but with certain
constraints like execution time or cold starts.
Understanding those three models, IES, PICE,
Face Serverless, is really the core takeaway,
isn't it? It absolutely is. Because your choice
isn't just technical. It's a strategic decision
about where your team invests this time and effort,
managing infrastructure or building features.
And that really sets up our final provocative
thought for you, the listener, to mull over.
The deepest insight here isn't just knowing what
EC2 or Lambda is. It's understanding that every
single architectural choice you make, using Spot,
accepting a Lambda cold start, paying for a dedicated
host. It's all fundamentally a trade -off. A
trade -off between operational control on one
side and cost optimization on the other. So the
question to leave you with is, which of those
two sides control or cost matters most to you
for your next project or your biggest bottleneck
right now? Where are you willing specifically
to maybe give up some fine -grained control to
slash operational overhead and cost? And conversely,
where is that absolute control, maybe over latency
or specific hardware, so critical that it justifies
the higher cost and management effort? We hope
this dive has given you a clearer framework for
making those crucial decisions.