Which solution will meet these requirements with the LEAST operational overhead?
Use AWS DataSync to transfer the data to Amazon S3. Use AWS Glue to transform the data and integrate the data into a data lake.
Use AWS Snowball to transfer the data to Amazon S3. Use AWS Batch to transform the data and integrate the data into a data lake.
Use AWS Database Migration Service (AWS DMS) to transfer the data to Amazon S3. Use AWS Glue to transform the data and integrate the data into a data lake.
Use an Amazon EC2 instance to transfer the data to Amazon S3. Configure the EC2 instance to transform the data and integrate the data into a data lake.
Explanations:
AWS DataSync is primarily used for data transfer and synchronization, but it does not provide ongoing updates efficiently. While AWS Glue can transform data, this option does not facilitate continuous updates from the on-premises Oracle database.
AWS Snowball is designed for large-scale data transfers but is not suitable for ongoing updates. AWS Batch is for batch processing jobs rather than continuous data integration, making this option inefficient for real-time data lake updates.
AWS Database Migration Service (AWS DMS) supports ongoing replication of data from the on-premises Oracle database to Amazon S3. Combined with AWS Glue for data transformation, this option offers a low-overhead solution for continuously updating the data lake.
Using an EC2 instance introduces higher operational overhead, as it requires manual configuration and management. It is not an efficient solution for ongoing data transfers and transformations compared to AWS DMS.
I arrange that the answer is:
Use AWS Database Migration Service (AWS DMS) to transfer the data to Amazon S3. Use AWS Glue to transform the data and integrate the data into a data lake.
As far as I’m aware, the answer is:
Use AWS Database Migration Service (AWS DMS) to transfer the data to Amazon S3. Use AWS Glue to transform the data and integrate the data into a data lake.