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Difficulty: MediumDatabase Migration and Schema Conversion using DMS and SCT

An enterprise is planning to migrate its legacy on-premises Oracle database to an Amazon Aurora PostgreSQL-Compatible DB cluster. The database contains complex PL/SQL packages with custom functions that do not have direct equivalents in PostgreSQL. The migration must minimize downtime for the dependent applications. Which of the following represents the correct sequence of steps to successfully perform this migration while ensuring application compatibility and data consistency?

  1. Convert the database schema and PL/SQL code using the AWS Schema Conversion Tool (SCT), apply the AWS SCT extension pack to the target Aurora PostgreSQL DB cluster, enable supplemental logging on the source Oracle database, and run an AWS DMS replication task configured for full load and Change Data Capture (CDC).Answer
  2. B
    Convert the database schema and PL/SQL code using the AWS Schema Conversion Tool (SCT), apply the AWS SCT extension pack to the target Aurora PostgreSQL DB cluster, and run an AWS DMS replication task configured for full load and Change Data Capture (CDC), relying on default Oracle logging configurations to capture ongoing transactional changes.
  3. C
    Classify the migration as a database replatforming path, bypass the AWS Schema Conversion Tool (SCT) by running the native pg_restore utility on the target Aurora PostgreSQL DB cluster, and configure an AWS DMS replication task with Change Data Capture (CDC) to capture ongoing transactions.
  4. D
    Convert the database schema and PL/SQL code using the AWS Schema Conversion Tool (SCT), apply the AWS SCT extension pack to the target Aurora PostgreSQL DB cluster, configure AWS DMS to write full load data to the primary DB instance and CDC updates to the Aurora Replicas to optimize performance, and enable supplemental logging on the source database.

Answer

The correct answer states that the schema and code must be converted using AWS SCT, the SCT extension pack applied to the target Aurora DB cluster, supplemental logging enabled on the source Oracle database, and an AWS DMS replication task run with full load and Change Data Capture (CDC).
Converting the schema and code via the AWS Schema Conversion Tool (SCT) and applying the extension pack ensures that proprietary PL/SQL functions are emulated correctly in PostgreSQL. Enabling supplemental logging on the source Oracle database is a mandatory requirement for AWS DMS to read redo and archive logs during Change Data Capture (CDC). Running a DMS task with full load and CDC ensures that the target database is fully populated and kept in sync, minimizing cutover downtime.

Step-by-Step Solution

1
Use AWS Schema Conversion Tool (SCT) to convert Oracle schema and PL/SQL code, then apply the generated extension pack to the target Aurora PostgreSQL cluster.
The database schema is created on the target, and custom PL/SQL functions are emulated via the extension pack SQL routines.
Ensures schema and code compatibility for the heterogeneous migration.
2
Enable supplemental logging on the source Oracle database.
Oracle database starts writing necessary supplemental information to the redo logs.
AWS DMS CDC requires supplemental logging to reconstruct changes from the database logs.
3
Create and execute an AWS DMS replication task configured for 'Full load + CDC' pointing to the primary writer endpoint of the Aurora PostgreSQL cluster.
The target database is seeded with initial data and kept continuously updated with ongoing source transactions.
Minimizes cutover downtime by keeping the target database in sync with the source.

Key Concept

Heterogeneous database migration sequence using AWS SCT and AWS DMS with Change Data Capture (CDC)
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Topics

Database Migration and Schema Conversion using DMS and SCT

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