AWS Certified Solutions Architect – ProfessionalAccelerate Workload Migration and ModernizationMedium
A global manufacturing company is modernizing its on-premises data processing pipeline. The current pipeline uses a proprietary scheduling system and processes large batches of data daily, taking several hours to complete. The company wants to move this workload to AWS, improve scalability, reduce operational overhead, and allow for more dynamic, event-driven processing without a complete re-architecture of the core processing logic. Which AWS service combination offers the MOST effective modernization strategy?
- AMigrate to AWS Batch for job orchestration and Amazon S3 for data storage.
- BRefactor to AWS Lambda functions triggered by Amazon SQS.
- CRehost on Amazon ECS with Fargate and use Amazon EFS for shared storage.
- DMigrate to Amazon EC2 instances and use Auto Scaling Groups.
Show answer & explanationAnswer & explanation
Correct answer: A. Migrate to AWS Batch for job orchestration and Amazon S3 for data storage.
AWS Batch is specifically designed for running large-scale batch computing workloads efficiently, handling job orchestration, scheduling, and scaling. Storing data in Amazon S3 provides highly scalable and durable storage, aligning with the goal of reducing operational overhead and enabling dynamic processing by decoupling storage from compute.
Why the other options are wrong
- B. Refactoring to Lambda for large batch processing might require significant re-architecture and may hit Lambda's execution limits for long-running jobs, contrary to minimizing core logic changes.
- C. ECS with Fargate can host containerized applications, but AWS Batch is more specialized for the orchestration and scheduling needs of large batch workloads, and EFS might not be the most cost-effective or scalable solution for raw batch data storage compared to S3.
- D. While EC2 and Auto Scaling provide scalability, they don't inherently reduce operational overhead for job orchestration compared to a managed service like AWS Batch.
AWS Batch
A fully managed service that enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS.
- Dynamically provisions compute resources.
- Manages job queueing, scheduling, and execution.
- Integrates with other AWS services like S3 and EC2.
Memory trick: Batch processing is like a factory assembly line, AWS Batch manages the flow.