AWS Certified Data Engineer Associate DEA-C01 Practice Question

An organization must migrate a 5 TB on-premises Oracle 12c OLTP database that contains hundreds of PL/SQL packages and triggers to Amazon Aurora PostgreSQL. The team wants to minimize manual effort when converting database code objects and ensure the converted schema is created in Aurora before they launch full-load and ongoing replication tasks. Which solution will meet these requirements while following AWS best practices?

Export the Oracle database with Oracle Data Pump and import the dump file directly into Aurora PostgreSQL using pg_restore, then configure AWS DMS for ongoing replication.
Create an AWS DMS Schema Conversion project, connect it to the Oracle source and Aurora PostgreSQL target, auto-convert the schema and code, apply the converted objects to Aurora, and then start DMS full-load and CDC tasks.
Run an AWS Glue crawler on the Oracle database to discover its schema and automatically create matching tables in Aurora PostgreSQL.
Use AWS Data Pipeline to export the Oracle DDL, manually edit the scripts for PostgreSQL syntax, and run them on the Aurora cluster before starting AWS DMS data migration.

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Answer Description

AWS Database Migration Service (AWS DMS) includes the Schema Conversion feature, which connects to both the Oracle source and Aurora PostgreSQL target, automatically converts tables, indexes, and most PL/SQL code into compatible PostgreSQL objects, and can apply the converted schema directly to Aurora. Performing the schema conversion step first lets subsequent AWS DMS migration tasks load and replicate data into an already-prepared target. Glue crawlers discover data structure but do not convert procedural code or create equivalent objects. AWS Data Pipeline and Oracle Data Pump export/import require extensive manual editing to translate PL/SQL to PostgreSQL and do not integrate with DMS replication tasks.

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