AWS Database Migration Service (DMS) is a cloud service designed to simplify the migration of databases to AWS. It supports both homogeneous migrations, like Oracle to Oracle, and heterogeneous migrations, like Oracle to Amazon Aurora. A key feature of AWS DMS is Change Data Capture (CDC), which enables the continuous replication of data changes.
### Migration from On-Premises Databases using AWS DMS
1. **Initial Setup:**
– **Source and Target Endpoints:** Define your on-premises database as the source endpoint and your AWS database (e.g., Amazon RDS, Amazon Aurora, Amazon Redshift, or a target on an EC2 instance) as the target endpoint.
– **Replication Instance:** Deploy a DMS replication instance within your VPC. This instance will perform the migration tasks and should have adequate resources to handle the data load.
2. **Security and Networking:**
– Ensure network connectivity between your on-premises environment and AWS. This often involves configuring VPNs or Direct Connect for secure data transfer.
– Properly configure security groups, IAM roles, and permissions to allow DMS to access the source and target databases.
3. **Migration Task:**
– **Full Load:** Begin with a full data load to copy existing data from the source to the target.
– **Change Data Capture (CDC):** After completing the full load, enable CDC to capture ongoing changes. DMS captures changes from the database transaction logs of the source and applies them to the target.
### Batch Change Data Capture Pipeline
A batch CDC pipeline refers to processing changes at intervals rather than streaming them continuously. While DMS itself primarily provides continuous CDC, you can implement a batch CDC pipeline using additional AWS services and configurations.
1. **Using AWS DMS for CDC:**
– Configure DMS tasks to perform CDC where changes are continuously captured from the source and applied to the target.
– Use the DMS task settings to filter out specific tables, columns, or changes if needed.
2. **Batch Processing:**
– Implement a solution to periodically trigger the application of captured changes in batches. This can reduce load on the target during peak times.
– Use AWS Lambda or AWS Step Functions to schedule and manage the timing of when CDC changes are applied to the target database.
3. **Data Transformation and Storage:**
– For transformations, AWS Glue can be integrated where incoming CDC data can be pre-processed before being applied to the target.
– Store captured changes temporarily in a durable service like Amazon S3 to decouple capture from application and accommodate batch sizes effectively.
4. **Monitoring and Logging:**
– Utilize Amazon CloudWatch for logging and monitoring DMS tasks to ensure that batch processes are running smoothly and to diagnose any issues that arise.
– Set up alerts for errors or performance issues to maintain the reliability of the migration pipeline.
### Considerations
– **Latency:** Batch processing introduces latency, so evaluate the frequency of batches based on your business needs.
– **Consistency:** Ensure transactional consistency by using DMS’s built-in mechanisms to manage data integrity during and after migration.
– **Scalability:** Adjust the resources of the DMS replication instance as needed based on data size and change intensity.
By leveraging AWS DMS for batch CDC, you can effectively migrate and keep your on-premises databases synchronized with AWS databases, taking full advantage of the scalability and flexibility of AWS infrastructure.