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Zorluk: ZorDatabase Migration and Schema Conversion using DMS and SCT

A financial enterprise is migrating an on-premises Oracle database to an Amazon Aurora PostgreSQL-Compatible Edition DB cluster. The migration must achieve the lowest possible downtime and ensure that schema customizations, secondary indexes, and referential integrity constraints are preserved. The database size is 8 TB, and the application generates a high volume of daily transactions. The Solutions Architect has already run the AWS Schema Conversion Tool (SCT) to generate the target PostgreSQL DDL. Which migration strategy will meet these requirements with the shortest replication cutover window and the highest performance during the initial load?

  1. Apply the table structures and primary keys from the SCT DDL to the Aurora DB cluster. Enable supplemental logging on the source Oracle database. Configure the AWS DMS replication task to use the 'Do nothing' table preparation mode, and execute the Full Load phase. After the Full Load phase completes, apply the secondary indexes, foreign key constraints, and triggers from the SCT DDL to the Aurora database, then initiate the Change Data Capture (CDC) replication phase.Cevap
  2. B
    Apply the complete SCT DDL, including all tables, indexes, and constraints, to the target Aurora DB cluster. Configure a single AWS DMS task to perform both Full Load and Change Data Capture (CDC) replication with 'Do nothing' table preparation mode, leaving supplemental logging disabled on the source Oracle database to avoid source performance impact.
  3. C
    Skip the AWS SCT process to save time, and directly configure an AWS DMS replication task with 'Drop tables on target' preparation mode. Rely on AWS DMS to automatically convert the source Oracle database schemas, data types, and PL/SQL packages into PostgreSQL format on the fly during the Full Load and Change Data Capture (CDC) phases.
  4. D
    Apply the complete SCT DDL to the target Aurora DB cluster. To scale the write performance of the initial load, configure the AWS DMS task to write data concurrently to the Aurora PostgreSQL primary writer instance and its Multi-AZ standby instance. Enable supplemental logging on the source Oracle database, and run the DMS task with 'Truncate' preparation mode.

Cevap

Apply only table structures and primary keys from the SCT DDL to the Aurora DB cluster, enable supplemental logging on the source Oracle database, run the DMS Full Load with 'Do nothing' table preparation, and then apply secondary indexes, foreign keys, and triggers before initiating the CDC phase.
Applying only table structures and primary keys before the full load prevents index maintenance and constraint verification overhead. Enabling supplemental logging on the source Oracle database is required for AWS DMS to perform CDC replication. Applying the secondary indexes, foreign keys, and triggers after the full load completes ensures optimal performance and preserves referential integrity.

Adım Adım Çözüm

1
Apply the base schema DDL (tables and primary keys only) from SCT to the target Aurora DB cluster.
The target database contains empty tables with primary keys, but no secondary indexes, foreign keys, or triggers.
This avoids write overhead and constraint failures during the high-speed data migration.
2
Configure supplemental logging on the source Oracle database.
The source Oracle database logs the necessary columns in its redo logs for update and delete statements.
This is a prerequisite for AWS DMS CDC replication to track and apply ongoing changes.
3
Run the AWS DMS Full Load task with target table preparation mode set to 'Do nothing'.
Data is populated into the target tables quickly without constraint violations or index maintenance latency.
Ensures the pre-created schema structures are preserved and not dropped by DMS.
4
Apply secondary indexes, foreign keys, and triggers, and start the DMS Change Data Capture (CDC) replication phase.
The database index structure is created, referential integrity is established, and incremental changes are replicated.
Completes the schema migration and synchronizes the target with the source for cutover.

Anahtar Kavram

Heterogeneous database migration optimization using AWS SCT and AWS DMS
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Konular

Database Migration and Schema Conversion using DMS and SCT

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