Migrating On-Premises Oracle Databases to Amazon Aurora Using AWS Database Migration Service (DMS) and Schema Conversion Tool (SCT)

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Migrating databases from on-premises environments to the cloud can be daunting, especially when dealing with legacy systems like Oracle. However, this process can be streamlined with the right tools and a strategic approach, minimizing downtime and ensuring data integrity. This post will explore how to use AWS Database Migration Service (DMS) and Schema Conversion Tool (SCT) to migrate your on-premises Oracle databases to Amazon Aurora.

Why Migrate to Amazon Aurora?

Amazon Aurora is a fully managed relational database engine compatible with MySQL and PostgreSQL. It offers high performance, scalability, and availability at a fraction of the cost of commercial databases like Oracle. Key benefits include:

Cost-Effectiveness: Pay only for what you use, without the need for expensive licensing fees.
Scalability: Easily scale your database up or down based on demand.
High Availability: Automated backups, snapshots, and replication ensure your data is always available.
Performance: Aurora delivers up to five times the throughput of standard MySQL databases and three times the standard PostgreSQL databases.

Prerequisites

Before starting the migration process, ensure you have the following:

An AWS account with necessary permissions.
An existing on-premises Oracle database.
AWS DMS and SCT installed and configured.

Step 1: Assess and Convert the Schema with AWS SCT

Download and Install SCT: Download the AWS Schema Conversion Tool from the AWS website and install it on your local machine.
Create a New Project: Open SCT and create a new project. Select the source database (Oracle) and the target database (Amazon Aurora MySQL or PostgreSQL).
Connect to Source and Target Databases: Provide the connection details for your Oracle database and Aurora instance.
Analyze the Schema: SCT will analyze your Oracle database schema and generate a conversion report. This report will highlight any incompatibilities and provide recommendations for resolving them.
Convert the Schema: Use SCT to convert the schema from Oracle to Aurora. Review the converted schema to ensure it matches your expectations.
Apply the Converted Schema: Apply the converted schema to your Aurora database using SCT.

Step 2: Migrate the Data with AWS DMS

Create a Replication Instance: In the AWS Management Console, navigate to AWS DMS and create a replication instance. This instance will handle the data migration.
Configure Source and Target Endpoints: Set up endpoints for your Oracle source database and Aurora target database. Test the connections to ensure they are working correctly.
Create a Migration Task: Define a migration task in DMS. Specify the source and target endpoints, select the migration type (full load, change data capture, or both), and map the source tables to the target tables.
Start the Migration Task: Begin the migration task and monitor its progress. DMS will handle the data transfer, ensuring data consistency and integrity.
Validate Data: Once the migration is complete, validate the data in Aurora to ensure it matches the source data.

Post-Migration Steps

Optimize Performance: After migration, optimize the performance of your Aurora database by fine-tuning instance sizes, configuring read replicas, and adjusting parameter groups.
Monitor and Maintain: Use Amazon CloudWatch and other AWS monitoring tools to monitor your Aurora database’s health and performance.

Conclusion

Migrating your on-premises Oracle databases to Amazon Aurora using AWS DMS and SCT can significantly enhance your database’s performance, scalability, and cost-efficiency. By following the above steps, you can ensure a smooth and successful migration.