A life sciences team generates about 200 GB per genome from sequencers. They run 10–15 jobs per day. Each job can take several hours with ideal compute. Sequencing data is stored on an on-prem SAN and the company has a high-speed AWS Direct Connect link. Final results must be stored in Amazon S3. The team wants to move the genomics analysis platform to AWS to scale with demand and reduce turnaround time. Which solution meets these requirements?

The correct solution leverages AWS DataSync for efficient, high-speed transfer of 200 GB per genome data from on-premises to Amazon S3, utilizing the existing Direct Connect link. S3 events trigger a Lambda function, which orchestrates complex, multi-step genomics workflows via AWS Step Functions. AWS Batch, using Docker images stored in Amazon ECR, provides scalable and managed compute for the analysis jobs, optimizing turnaround time. Incorrect options: Snowball Edge is for large, one-time or infrequent data migrations, not for daily transfers of 200 GB per job. AWS Data Pipeline is primarily for moving and transforming data between different AWS services, not ideal for high-speed, large-volume on-premises to S3 transfers. Triggering an EC2 Auto Scaling group directly from S3 events for complex processing might lack the orchestration capabilities of Step Functions and the managed nature of AWS Batch. AWS Storage Gateway File Gateway provides file-based access to S3 but might not offer the same transfer performance and management as DataSync for this specific use case. Triggering AWS Batch directly from S3 events is possible, but a Step Functions workflow offers better orchestration for multi-step genomics analysis.