IBM Cloud Analytics Engine decouples compute and storage to provide independent scaling and cost

management capabilities. This approach allows organizations to scale compute resources (such as

CPU and memory) separately from storage resources, optimizing both performance and cost.

Independent Scaling: Decoupling compute and storage means that users can scale the computational

power (e.g., number of nodes, processing capabilities) independently of the storage capacity (e.g.,

data stored in IBM Cloud Object Storage). This is particularly useful in data analytics workloads where

the compute requirements may vary significantly over time, but the storage requirements remain

relatively constant.

Cost Control: By allowing compute and storage to be managed separately, users have greater

flexibility to control costs. For example, users can increase compute power temporarily to handle a

peak workload without the need to increase storage costs. Conversely, they can store large datasets

without paying for unused compute capacity. This decoupling leads to a more cost-effective and

efficient use of cloud resources.

Advantages in Cloud Environments: Decoupling compute and storage aligns with the best practices in

modern cloud environments, where elasticity, scalability, and cost efficiency are paramount. It allows

organizations to adapt quickly to changing business needs and workload demands, reducing

overhead and improving resource utilization.

Reference:

IBM Cloud Analytics Engine Documentation

IBM Cloud Architect Exam Study Guide

IBM Cloud Object Storage