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AWS, or Amazon Web Services, is a cloud computing platform that provides infrastructure and managed services over the internet.
Instead of buying and maintaining physical servers for every workload, companies can use AWS resources for areas such as:
AWS resources can be provisioned programmatically and scaled as application requirements change.
Companies may use a handful of AWS services for a simple web application or dozens across a large cloud architecture.
AWS infrastructure is organized geographically.
A Region is a separate geographic area where AWS operates cloud infrastructure.
Organizations choose Regions based on factors such as:
Resources generally belong to a specific Region unless the service is designed to operate globally.
Availability Zones are isolated locations within an AWS Region.
Production architectures can distribute workloads across several Availability Zones to reduce dependence on one physical location.
For example:
Application servers → multiple Availability Zones
Database → Multi-AZ deployment
Load balancer → routes traffic across healthy resources
This architecture can continue serving users even when individual infrastructure components fail.
AWS can support nearly every layer of a modern software stack.
Companies can host web applications using services such as:
The exact architecture depends on the application.
AWS can provide infrastructure for SaaS applications involving:
Teams can build APIs using several architectures.
Examples include:
API Gateway + Lambda
or:
Application Load Balancer + ECS
or:
EC2 + application framework
Each approach creates different tradeoffs around cost, scaling, operational effort, and flexibility.
Mobile applications may use AWS for:
AWS supports data workloads involving:
AWS provides services for building, training, hosting, and consuming AI systems.
Examples include:
Companies can use AWS for:
AWS can support:
AWS allows architectures to scale capacity as demand changes.
Scaling may involve:
Compute determines where application code runs.
AWS provides several approaches.
Amazon Elastic Compute Cloud provides virtual machines in AWS.
Developers can control:
EC2 provides substantial flexibility.
It also requires more infrastructure management than highly managed compute services.
Auto Scaling can increase or decrease EC2 capacity according to demand and health.
This can help applications respond to:
Load balancers distribute incoming traffic across several application resources.
This can support:
AWS Lambda runs code without teams provisioning traditional servers.
Developers provide functions that execute in response to events.
Common triggers include:
Lambda can work especially well for:
A serverless application might look like:
API Gateway → Lambda → DynamoDB
Another workflow might be:
S3 upload → Lambda → processing → S3
The infrastructure still runs on servers. AWS manages much more of the server provisioning and scaling.
Lambda functions use IAM roles to determine which AWS resources they can access.
A function processing uploaded files might need access to:
It generally shouldn't receive broad administrative permissions.
API Gateway allows teams to build and expose APIs.
It can support:
It is commonly paired with Lambda for serverless applications.
API Gateway can provide capabilities around:
Companies running containerized applications have several AWS options.
Elastic Container Service orchestrates containers within AWS.
It can manage:
Fargate provides serverless compute for containers.
Teams define container requirements without managing the underlying EC2 instances directly.
Elastic Kubernetes Service provides managed Kubernetes.
Teams can use EKS when Kubernetes is important to their infrastructure strategy.
For deeper container concepts, explore Kubernetes and Docker.
Choosing compute based on fashion can create unnecessary complexity.
The right choice depends on application behavior, operational requirements, and team experience.
Amazon Simple Storage Service provides object storage.
Companies commonly use S3 for:
Objects can be accessed programmatically and protected through permissions and policies.
Elastic Block Store provides block storage commonly attached to EC2 instances.
It can store:
Elastic File System provides shared file storage that several compute resources can access.
Amazon CloudFront is AWS's content delivery network.
It can cache and deliver content closer to users.
Common use cases include:
A common architecture is:
User → CloudFront → S3
This reduces the distance between users and static content.
AWS supports relational, NoSQL, caching, analytics, and specialized databases.
Relational Database Service manages relational database infrastructure.
Supported engines include technologies such as:
RDS can reduce operational work around areas such as:
Aurora is an AWS relational database compatible with MySQL and PostgreSQL.
It provides a managed architecture designed specifically for cloud workloads.
DynamoDB is AWS's managed NoSQL database.
It can support applications requiring:
Strong DynamoDB design starts with understanding access patterns before creating the schema.
ElastiCache provides managed caching technologies.
Redis-compatible caching can support:
Caching can reduce database load and improve response time.
Networking determines how applications and cloud resources communicate.
Amazon Virtual Private Cloud allows organizations to define isolated network environments inside AWS.
A VPC may contain:
Public subnets can host resources that require direct internet connectivity.
Private subnets commonly contain resources that shouldn't receive direct inbound internet traffic.
Examples may include:
Security groups control allowed network traffic for AWS resources.
Permissions should be intentionally scoped rather than allowing every source and port.
Amazon Route 53 provides DNS and routing capabilities.
It can direct domains toward:
AWS Identity and Access Management, or IAM, controls authentication and authorization within AWS.
AWS environments contain many identities, including:
Each identity should receive the permissions required to perform its work.
IAM policies define allowed or denied actions.
They can specify:
Least privilege means granting only the access required for a particular job.
For example:
A Lambda function that reads one S3 bucket shouldn't automatically receive access to every S3 bucket in the AWS account.
Roles provide temporary permissions that applications, AWS services, or users can assume.
They are widely used for:
Applications need safe ways to manage secrets.
Secrets Manager can store values such as:
Applications can retrieve secrets programmatically rather than hard-coding them inside source code.
Parameter Store can hold configuration and selected secret values.
AWS Key Management Service manages encryption keys used by AWS services and applications.
Encryption may protect information:
The precise security architecture depends on the data and threat model involved.
Production AWS environments should generally be reproducible.
Infrastructure as code represents cloud resources through version-controlled configuration.
CloudFormation defines AWS infrastructure using templates.
Teams can manage resources such as:
as code.
AWS Cloud Development Kit allows developers to define cloud infrastructure using programming languages.
CDK then synthesizes infrastructure definitions for deployment.
Terraform is another widely used infrastructure-as-code tool.
It can manage AWS alongside many other cloud and SaaS providers.
Distributed systems often communicate asynchronously.
AWS provides several services for these patterns.
Simple Queue Service provides message queues.
A queue can separate two parts of an application.
For example:
Order API → SQS → Fulfillment Worker
The API can respond before every downstream operation finishes.
Simple Notification Service distributes messages to multiple subscribers.
This can be useful when one event needs to reach several systems.
EventBridge routes application and service events.
It can support event-driven architectures where services react to business or infrastructure events.
Step Functions coordinates workflows made from several steps.
A workflow might contain:
Validate → Process → Wait → Review → Complete
This can be useful when business processes require retries, branches, or several services.
Teams need to understand how production applications behave.
CloudWatch collects operational information such as:
Developers can monitor:
CloudTrail records AWS API activity.
It can help teams understand:
X-Ray can help trace requests across distributed applications.
This becomes useful when one request travels through:
API Gateway → Lambda → DynamoDB → external service
and the team needs to understand where latency or failure occurs.
Cloud architecture needs to assume individual components can fail.
Applications can distribute critical components across Availability Zones.
Backups need to be:
A backup strategy isn't complete until restoration has been validated.
Disaster-recovery planning considers how an application would recover after a larger outage or data-loss event.
Strategies vary according to:
Load balancers and other AWS services can identify unhealthy resources and route traffic toward healthy infrastructure.
Horizontal scaling adds more application instances.
Vertical scaling increases the resources available to one instance.
AWS can automatically change capacity according to metrics or demand.
Serverless services can handle much of the capacity management automatically, although concurrency and service quotas still need consideration.
Cloud resources can be created quickly.
That means costs can also grow quickly.
Strong AWS architecture considers cost during design rather than only after the monthly bill increases.
Costs may come from:
Teams should avoid running resources significantly larger than the workload requires.
Autoscaling can reduce unused capacity during lower demand.
Older objects can sometimes move to less expensive storage classes.
Tags and account structures can help teams understand which:
creates cloud spending.
Cloud development usually includes automated deployment pipelines.
A typical workflow might be:
Developer pushes code → tests run → build created → infrastructure validated → application deployed
Tools may include:
The exact CI/CD platform matters less than having repeatable and auditable deployment processes.
A serverless application might combine:
API Gateway
↓
Lambda
↓
DynamoDB
↓
EventBridge
↓
SQS
↓
Another Lambda
This approach can remove much of the server-management work.
Developers still need to design:
Serverless architecture moves operational responsibilities rather than eliminating architecture decisions.
A container architecture might instead use:
CloudFront
↓
Application Load Balancer
↓
ECS or EKS
↓
RDS
↓
ElastiCache
This model can fit long-running services and applications requiring greater control over runtime behavior.
AWS also provides infrastructure for generative AI applications.
Amazon Bedrock provides access to foundation models and services for building generative AI applications.
Applications may use it for:
Amazon SageMaker provides tools for developing, training, deploying, and operating machine-learning systems.
For deeper coverage, explore Amazon SageMaker.
A modern AWS application might look like this:
AWS becomes the cloud layer connecting application code, data, networking, infrastructure, security, deployments, and operations.
An AWS Developer builds applications and services that run deeply within AWS.
A Cloud Engineer may focus more heavily on infrastructure, networking, platform operations, and cloud governance.
A DevOps Engineer may use AWS alongside CI/CD, Kubernetes, Terraform, monitoring, and automation.
A Back-End Developer may use Lambda, ECS, RDS, DynamoDB, S3, and other AWS services to operate application backends.
A Site Reliability Engineer may use AWS extensively while focusing on reliability, observability, incident response, and scalability.
A Cloud Architect focuses more heavily on broad cloud architecture, networking, security, account strategy, governance, and platform standards.
A Data Engineer may use AWS services for data lakes, pipelines, warehouses, streaming, and analytics.
An ML Engineer may use SageMaker, Bedrock, S3, EKS, and other AWS services to build and operate machine-learning workloads.
Cloud computing is the broader discipline.
AWS is one cloud platform.
Cloud computing may also involve:
AWS skills represent platform-specific knowledge within that broader discipline.
For broader concepts, explore Cloud Computing.
AWS and Azure are major cloud platforms offering overlapping capabilities around:
The service names, APIs, operational models, and surrounding ecosystems differ.
Companies should usually prioritize the cloud platform already central to their architecture rather than treating cloud experience as completely interchangeable.
Traditional hosting may provide one or several servers for an application.
AWS exposes a much broader set of programmable services around:
This gives teams more architectural flexibility while also requiring stronger cloud engineering discipline.
AWS is a cloud platform.
Kubernetes is a container-orchestration system.
AWS can run Kubernetes through EKS.
It can also run applications without Kubernetes through:
A company can therefore use AWS extensively without adopting Kubernetes.
AWS is a cloud computing platform providing services for compute, storage, databases, networking, identity, analytics, AI, security, and application infrastructure.
Important AWS skills include EC2, Lambda, S3, RDS, DynamoDB, VPC, IAM, ECS/EKS, queues, monitoring, infrastructure as code, security, reliability, and cost optimization.
Lambda is AWS's serverless function-compute service.
It allows applications to execute code in response to events without developers provisioning traditional servers.
A VPC is an isolated virtual network within AWS where teams can organize cloud resources, subnets, routing, and security.
IAM controls authentication and authorization for AWS users, roles, services, and resources.
Infrastructure as code represents cloud infrastructure using version-controlled files or programming languages.
AWS environments may use CloudFormation, CDK, or Terraform.
Yes.
AWS provides several container options, including ECS, EKS, and Fargate.
No.
AWS can support small applications, startups, growing SaaS companies, and large organizations.
The appropriate architecture should reflect actual workload complexity.
Yes.
AWS provides services including Amazon Bedrock and SageMaker for generative AI and machine-learning workloads.
AWS Developers, Cloud Engineers, DevOps Engineers, Back-End Developers, Site Reliability Engineers, Cloud Architects, Data Engineers, and ML Engineers may all use different parts of AWS.
Understanding AWS helps you identify whether your platform needs stronger serverless architecture, containers, networking, databases, IAM, infrastructure as code, observability, reliability, or cost optimization.
If you need someone dedicated to building production applications on AWS, South can help you hire AWS Developers in Latin America.
Schedule a free call and find remote cloud and engineering talent in Latin America with South.
