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Difficulty: MediumDatabase Migration and Schema Conversion using DMS and SCT

A company is planning to migrate its legacy on-premises Oracle database to an Amazon Aurora MySQL-Compatible Edition DB cluster. The database contains critical transaction tables, and the migration must minimize downtime by using continuous replication. The database size is 50 TB50\text{ TB}, and the network connection between the on-premises data center and AWS is a 50 Mbps50\text{ Mbps} VPN link. Which two of the following actions must the solutions architect perform to convert the schema and replicate the data successfully?

  1. Use the AWS Schema Conversion Tool (SCT) to generate an assessment report, convert the Oracle database schema, and apply the converted schema to the target Aurora MySQL DB cluster.Answer
  2. Enable supplemental logging on the source Oracle database for the tables being replicated, and configure the AWS DMS replication task to perform a full load followed by ongoing replication (Change Data Capture).Answer
  3. C
    Classify the migration as a replatforming strategy and deploy the AWS Application Migration Service (MGN) agent on the source database host to replicate the server block-by-block directly into Amazon RDS.
  4. D
    Configure the AWS DMS replication task to capture ongoing changes using default settings without modifying source database logging, allowing DMS to parse standard Oracle redo logs directly.
  5. E
    Replicate the initial full load of the 50 TB50\text{ TB} database directly over the existing 50 Mbps50\text{ Mbps} site-to-site VPN connection using AWS DMS to save costs.

Answer

Use the AWS Schema Conversion Tool (SCT) to convert the schema, and enable supplemental logging on the source Oracle database while setting up an AWS DMS task for full load and Change Data Capture (CDC).
For a heterogeneous database migration (Oracle to Aurora MySQL), the database schema must be converted using the AWS Schema Conversion Tool (SCT) because the source and target database engines use different SQL dialects and structures. To support ongoing replication (Change Data Capture) and minimize downtime, the source database must be configured to provide transaction logs with sufficient detail. For an Oracle source database, this requires enabling supplemental logging so that AWS DMS can capture row-level changes from the redo logs.

Step-by-Step Solution

1
Perform schema analysis and conversion using the AWS Schema Conversion Tool (SCT).
Oracle-specific database objects (such as tables, indexes, views, stored procedures, and triggers) are converted into MySQL-compatible equivalents, and the schema is applied to the target Aurora MySQL DB cluster.
SCT is required for heterogeneous migrations to automatically translate SQL dialects and schema structures before data migration starts.
2
Configure database-level logging and supplemental logging on the source Oracle database.
The source database writes supplemental details to the redo logs for all columns in the replicated tables.
AWS DMS requires supplemental logging to reconstruct the SQL statements for ongoing Change Data Capture (CDC) replication.
3
Create and execute an AWS DMS replication task configured for full load and Change Data Capture (CDC).
The initial data is copied to the target Aurora MySQL cluster, and subsequent changes are streamed continuously with minimal lag.
This minimizes the migration downtime by keeping the target database synchronized with the source until the cutover window.

Key Concept

Heterogeneous database migration and schema conversion using SCT and DMS with CDC
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Topics

Database Migration and Schema Conversion using DMS and SCT

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