A data-engineering team packages a nightly ETL routine into a Docker container. They want to define the container's vCPU and memory requirements, pay only for those resources while the job is running, and avoid provisioning or patching any EC2 instances or Kubernetes worker nodes. Which AWS service best satisfies these goals?
AWS Fargate
Amazon EKS with self-managed worker nodes
AWS Lambda
Amazon EC2 Auto Scaling group using Spot Instances
AWS Fargate is a serverless, pay-as-you-go compute engine for Amazon ECS/EKS that lets you specify vCPU and memory at the task or pod level and removes all server and cluster management. Because billing starts when the container image begins downloading and stops when the task ends, the team pays only for actual vCPU- and memory-seconds consumed.
AWS Lambda is also serverless, but it is optimized for short, event-driven functions; although it can run container images, it does not give separate vCPU sizing and has runtime limits that make it less suitable for long-running ETL containers. EC2 Spot Instances in an Auto Scaling group and EKS with self-managed worker nodes both require the team to provision, patch, and scale EC2 infrastructure, which contradicts the requirement to avoid server management.
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