Author: Archana Sharma

This is the second post of a two-part series. In Part 1, we explored how you can use Automatic Data Optimization (ADO) and Oracle Information Lifecycle Management (ILM) policies for data compression. In this post, we demonstrate how to use Heat Map statistics to monitor data usage and integrate this information with hybrid partitioning and ILM data movement policies to move data to more cost-effective storage solutions.

In this two-part series, we demonstrate how to optimize storage for Oracle database workloads on AWS by using Oracle’s built-in features, such as Heat Map, Automatic Data Optimization (ADO), and hybrid partitioning. These features help classify data by its lifecycle stage and automate data management tasks to significantly reduce storage costs, while enhancing database performance, especially for growing datasets. In this post, we explore how to use ADO and Oracle ILM policies to automatically compress data based on usage patterns.

End of support notice: On March 31, 2027, AWS will end support for Amazon RDS Custom for Oracle. Existing customers can continue using the service until March 31, 2027. After March 31, 2027, you will no longer be able to access RDS Custom for Oracle resources including database instances, snapshots, and custom engine versions. We […]