Which solution will meet these requirements?
Use the AWS Schema Conversion Tool (AWS SCT) to convert the Oracle schema to Aurora PostgreSQL schema. Use the AWS Database Migration Service (AWS DMS) full-load migration task to migrate the data.
Use AWS DataSync to migrate the data to an Amazon S3 bucket. Import the S3 data to Aurora PostgreSQL by using the Aurora PostgreSQL aws_s3 extension.
Use the AWS Schema Conversion Tool (AWS SCT) to convert the Oracle schema to Aurora PostgreSQL schema. Use AWS Database Migration Service (AWS DMS) to migrate the existing data and replicate the ongoing changes.
Use an AWS Snowball device to migrate the data to an Amazon S3 bucket. Import the S3 data to Aurora PostgreSQL by using the Aurora PostgreSQL aws_s3 extension.
Explanations:
While AWS SCT can convert the schema, using AWS DMS for only a full-load migration does not capture ongoing changes from the Oracle database, which is a requirement.
AWS DataSync is not suitable for database migrations and cannot capture ongoing changes. Also, importing data from S3 using the aws_s3 extension does not ensure that changes in the Oracle database are tracked during migration.
This option uses AWS SCT for schema conversion and AWS DMS to not only migrate existing data but also replicate ongoing changes, meeting the requirement of capturing changes during migration.
Using an AWS Snowball device is unnecessary for this scenario since the company already has a Site-to-Site VPN. Additionally, Snowball does not facilitate capturing ongoing changes during migration.
I evaluate that the answer is:
Use the AWS Schema Conversion Tool (AWS SCT) to convert the Oracle schema to Aurora PostgreSQL schema. Use AWS Database Migration Service (AWS DMS) to migrate the existing data and replicate the ongoing changes.