An e-commerce company needs the orders table in Amazon RDS for PostgreSQL reflected in an Amazon Redshift warehouse within minutes, including updates and deletes made at the source after the initial load. Which approach fits best?
Show answer and explanation
Answer: B
AWS DMS performs an initial full load, then switches to ongoing replication that reads the database change log and applies inserts, updates and deletes to Redshift. This keeps the warehouse close to current without rescanning the table. Full-table Glue jobs repeat expensive scans, hourly exports are batch snapshots rather than change capture, and per-row Lambda invocations do not scale reliably for high write volumes.
Why the other options are wrong
- A. A full scan every night reprocesses the entire table, so it cannot keep the warehouse current within minutes or capture deletes made between runs.
- C. Export and COPY move data in scheduled batches, so they lack the change-level capture needed to reflect updates and deletes promptly.
- D. Invoking code for every row write ties the database to Lambda throughput limits and is not a dependable way to stream high-volume changes.