"The S3 buckets have millions of objects" If there are million of objects then you should use Batch operations. https://aws.amazon.com/blogs/storage/encrypting-objects-with-amazon-s3-batch-operations/

The analytics team wants to copy only a subset of the data, run the copy as soon as data is ready, and receive notifications when the process completes. AWS DataSync supports the use of manifest files to control exactly which files or objects are transferred. By generating a manifest file on premises that lists only the relevant data and uploading that manifest to Amazon S3, the company can precisely limit what DataSync copies, which reduces data transfer costs and processing overhead.
Option A satisfies the requirement to identify only analytics-relevant data by creating and uploading a manifest file that lists the objects to be transferred. This avoids copying unnecessary data and is cost-effective.
Option D uses Amazon S3 Event Notifications to trigger an AWS Lambda function as soon as the manifest file is uploaded. The Lambda function starts the DataSync task and references the manifest file. This ensures that the copy process begins immediately after data generation, rather than waiting for a fixed schedule, which meets the "as soon as possible" requirement.
Option E provides a fully managed and scalable notification mechanism. AWS DataSync publishes task execution state changes as events. Amazon EventBridge can capture SUCCESS or ERROR states and route those events to Amazon SNS, which can then send email notifications. This approach avoids custom polling logic and minimizes operational overhead.
Option B and C introduce Amazon DynamoDB unnecessarily. DataSync can directly consume a manifest file from Amazon S3, so loading manifest data into DynamoDB and orchestrating copy logic manually adds cost and complexity without benefit.
Option F relies on a custom Lambda function to monitor task state changes, which increases operational overhead compared to the native EventBridge integration with DataSync.
Therefore, using S3-based manifests, event-driven invocation of DataSync, and EventBridge-based notifications is the most cost-effective and operationally efficient solution.
References:AWS documentation on AWS DataSync manifest files for selective data transfer.AWS documentation on integrating AWS DataSync with Amazon S3 Event Notifications and AWS Lambda.AWS documentation on AWS DataSync task state change events and Amazon EventBridge integration with Amazon SNS for notifications.

This solution allows developers to quickly launch resources using pre-approved configurations and instance types, while also ensuring that the resources launched comply with the company's architectural patterns. This can help reduce data transfer and compute costs associated with the resources. Using AWS Service Catalog also allows the company to control access to the approved configurations and resources through the use of IAM roles, while also allowing developers to quickly provision resources without negatively affecting their ability to perform their tasks.
Reference:
AWS Service Catalog:https://aws.amazon.com/service-catalog/
AWS Service Catalog Constraints:https://docs.aws.amazon.com/servicecatalog/latest/adminguide/constraints.
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AWS Service Catalog Launch Constraints:https://docs.aws.amazon.com/servicecatalog/latest/adminguide
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The requirements emphasize minimum downtime, rapid migration, managed database services, high availability, and support for stored procedures. For Oracle-to-AWS migrations with minimal downtime, AWS DMS can replicate ongoing changes from the source database to the target, which reduces cutover downtime.
However, a critical constraint is that the database contains stored procedures. If the company moves to a different engine, it must convert schema objects and procedural code.
Option D is the only option that combines an online replication approach (AWS DMS) with a managed, highly available target database (Amazon Aurora PostgreSQL) and explicitly includes schema and stored procedure conversion with AWS SCT. This aligns with a fast migration approach: SCT prepares the converted schema and stored procedures, while DMS performs ongoing replication of data changes until cutover, minimizing downtime. Aurora PostgreSQL is a managed service and supports high availability through multi-AZ architecture and managed failover capabilities within a Region.
Option A is incorrect because Amazon RDS does not use an S3 bucket "as database storage" in the way described. RDS storage is managed by the RDS service, and S3 replication is not the mechanism used to provide database high availability. High availability for RDS is typically achieved through Multi-AZ configurations and/or cluster-based architectures depending on engine. The described storage approach is not a valid managed RDS architecture.
Option B does not meet the managed database requirement because it restores Oracle onto EC2 instances, which is self-managed. It also does not meet minimum downtime because it requires shutting down the on- premises database to avoid new transactions before restoring the backup. This is a longer outage model than an online migration with continuous replication.
Option C is not feasible and would require major redesign. DynamoDB is not a relational database replacement for Oracle stored procedures. DAX is a DynamoDB cache and cannot run stored procedures. The option also incorrectly suggests running stored procedures "in DAX." This does not meet the "migrate as quickly as possible with minimum downtime" requirement due to extensive refactoring and redesign.
Therefore, migrating to Aurora PostgreSQL using AWS SCT for schema and stored procedure conversion, and AWS DMS for ongoing replication to minimize downtime, is the best fit.
References:AWS documentation and best practices describing AWS DMS as a tool for minimal-downtime database migration through continuous replication.AWS documentation describing AWS SCT for converting schema objects and procedural code when migrating between different database engines.AWS documentation describing Amazon Aurora as a managed, highly available database service with built-in multi-AZ resilience.