A company has an on-premises Oracle data warehouse and is building a data lake on Amazon S3. They need to load warehouse tables into S3 and keep them synchronized with daily incremental data. Each table has a monotonically increasing column, sizes under 50 GB, nightly refresh between 1 AM and 2 AM, and BI queries run from 10 AM to 8 PM. Which approach is the most operationally efficient?

The most operationally efficient approach is to use AWS DMS for an initial full load and continuous Change Data Capture (CDC). This leverages DMS's native ability to capture changes from Oracle, ensuring data synchronization with minimal custom effort. The CDC stream can then be processed by AWS Glue to append daily incremental data to the S3 data lake. This avoids repeatedly transferring full datasets, which is inefficient for tables up to 50 GB and only requires daily incremental updates. Using an AWS Glue JDBC connection with job bookmarks is less efficient because Glue is primarily an ETL service, not a dedicated replication service like DMS. While it can read incremental data, managing the initial full load and continuous synchronization with custom logic is more complex and error-prone than using DMS. Performing a daily full load with DMS or AWS Glue, overwriting the previous day's data, is inefficient. This approach transfers the entire 50 GB dataset daily, which is unnecessary given that only incremental updates are needed and tables have monotonically increasing columns. It also increases the risk of data unavailability during the overwrite process, especially during the specified nightly refresh window.