AWS Cloud

Amazon Web Services is a distributed cloud computing platform built on a foundation of virtualized compute (EC2), object storage (S3), and networking primitives (VPC) that together form the substrate for over 200 higher-level managed services. Its architecture is region/availability-zone based, with 34+ regions and 100+ AZs providing physical isolation for fault tolerance, and a global edge network (CloudFront, Route 53, Global Accelerator) for low-latency delivery. Core compute options range from raw EC2 instances and Auto Scaling Groups to container orchestration via ECS/EKS (Kubernetes-compatible) and event-driven serverless execution through Lambda, which supports Node.js, Python, Java, Go, .NET, Ruby, and custom runtimes via container images. On the data layer, AWS offers a spectrum from fully relational (RDS supporting Postgres, MySQL, MariaDB, SQL Server, Oracle) to purpose-built NoSQL (DynamoDB, a single-digit-millisecond key-value/document store with on-demand or provisioned throughput), to Aurora Serverless v2 for auto-scaling Postgres/MySQL-compatible workloads, plus specialized stores like DocumentDB (Mongo-compatible), Neptune (graph), Timestream (time-series), and ElastiCache (Redis/Memcached). Infrastructure is provisioned via CloudFormation, CDK (TypeScript/Python/Java/Go), or Terraform, and virtually every service is exposed through REST APIs, the AWS CLI, and SDKs in 12+ languages (boto3 for Python, aws-sdk for JS/Java/Go/.NET/Ruby/PHP). The target audience spans solo developers prototyping serverless apps to Fortune 500 enterprises running multi-region, compliance-heavy workloads (HIPAA, FedRAMP, PCI-DSS). AWS is typically chosen over narrower PaaS competitors when teams need deep IAM-level access control, hybrid/on-prem connectivity (Direct Connect, Outposts), or a single vendor capable of covering everything from IoT device management to GPU-backed ML training (SageMaker, Trainium/Inferentia chips).