Optimize your database storage for Oracle workloads on AWS, Part 2: Using hybrid partitioning and ILM data movement policies

This article discusses techniques to optimize database storage for Oracle workloads on AWS by using a combination of hybrid partitioning, Information Lifecycle Management (ILM) data movement policies, and Heat Map statistics.

Specifically, the article covers:

• Using hybrid partitioning and an automated procedure to move older data partitions to cost-effective external storage (e.g., Amazon EFS or S3) based on Heat Map access patterns
• Implementing ILM data movement policies to automatically move data between different storage tiers (e.g., moving cold data to lower-cost gp3/gp2 volumes) based on access patterns
• Step-by-step instructions for both use cases, including creating policies, procedures, and verifying the data movement
• Conclusion highlighting the storage optimization and cost savings achieved by these techniques