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Difficulty: MediumDatabase Migration and Schema Conversion using DMS and SCT

A company is planning to migrate its on-premises IBM DB2 LUW database to an Amazon Aurora PostgreSQL-Compatible Edition DB cluster. The database contains complex PL/SQL-equivalent stored procedures and is approximately 8 TB in size. The company requires a migration strategy that minimizes downtime to under 30 minutes and converts the database schema and application logic with minimal manual effort. Which of the following approaches should a solutions architect recommend to meet these requirements?

  1. Use the AWS Schema Conversion Tool (SCT) to generate an assessment report, convert the database schemas and stored procedures, and apply the converted schema to the target Aurora DB cluster. Enable archival logging and transaction logging on the source DB2 database to meet AWS DMS Change Data Capture (CDC) prerequisites. Create an AWS DMS replication instance, configure a Full Load and CDC task, and switch application traffic to Aurora when replication lag is near zero.Answer
  2. B
    Create an AWS DMS replication instance and configure a migration task to migrate the database directly from DB2 to Aurora PostgreSQL. Rely on AWS DMS to dynamically convert the source stored procedures and tables during the initial migration phase. Do not configure any additional transaction logging on the source database to maximize write performance, and cut over to the new database immediately after the full load completes.
  3. C
    Use the AWS Schema Conversion Tool (SCT) to convert the source database schema and stored procedures, and apply the converted schema to the target Aurora DB cluster. Set up an AWS DMS replication instance and initiate a migration task with Full Load and Change Data Capture (CDC). Do not modify the source DB2 logging configuration to avoid CPU overhead on the production database, and rely on standard AWS DMS querying to replicate ongoing changes.
  4. D
    Order an AWS Snowball Edge device to copy DB2 database backup files to Amazon S3. Use the AWS Schema Conversion Tool (SCT) to convert the schema and apply it to Aurora PostgreSQL. Configure Aurora Auto Scaling replicas on the target cluster to scale horizontally to handle the incoming write traffic from the restore process, and use AWS DMS to synchronize the remaining delta changes.

Answer

Use the AWS Schema Conversion Tool (SCT) to generate an assessment report, convert the database schemas and stored procedures, and apply the converted schema to the target Aurora DB cluster. Enable archival logging and transaction logging on the source DB2 database to meet AWS DMS Change Data Capture (CDC) prerequisites. Create an AWS DMS replication instance, configure a Full Load and CDC task, and switch application traffic to Aurora when replication lag is near zero.
The correct approach uses the AWS Schema Conversion Tool (SCT) to assess and convert the database schema and PL/SQL-equivalent stored procedures from DB2 to PostgreSQL. It also configures the required transaction logging on the source DB2 database so that AWS DMS can perform Change Data Capture (CDC) alongside the initial Full Load. This enables continuous replication, allowing the final switchover to occur with minimal downtime.

Step-by-Step Solution

1
Assess and convert the schema using the AWS Schema Conversion Tool (SCT).
An assessment report outlining migration challenges and a converted schema ready to be applied to the Amazon Aurora PostgreSQL-Compatible target database.
Because DB2 and PostgreSQL are heterogeneous database engines, direct schema replication is not possible, requiring schema conversion before data migration.
2
Configure transaction and archival logging on the source IBM DB2 database.
The source database writes transaction logs in a format that AWS DMS can read for ongoing changes.
AWS DMS CDC relies on the database's native transaction logs to capture data changes. Neglecting this configuration will prevent replication of live updates.
3
Create an AWS DMS replication instance and run a Full Load + CDC task.
Existing data is loaded, and subsequent changes are replicated continuously with minimal lag.
Running both full load and CDC allows the target database to catch up to the source, minimizing downtime during the final switchover.

Key Concept

Heterogeneous database migration using AWS SCT to convert schemas/stored procedures, and AWS DMS with CDC enabled via source engine logging prerequisites to minimize downtime.
Estimated Time:2m 0s
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Topics

Database Migration and Schema Conversion using DMS and SCT

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