To accommodate a recent increase of 4 TB of user data in Amazon Redshift, which cluster adjustment is recommended?

When considering the appropriate method to resize an Amazon Redshift cluster in response to an increase in user data, it is essential to understand the differences between elastic and classic resizing methods, as well as the types of nodes.

Choosing to resize the cluster using classic resize with dense compute nodes is a sound approach for accommodating increased data, particularly when it involves a significant amount of data like 4 TB, which suggests that it will require a substantial increase in resources. Classic resizing allows for a complete reallocation of resources, ensuring that the new cluster utilizes the optimal configuration for storage and compute capacity needed to handle the influx of data effectively.

Dense compute nodes, in particular, are designed to deliver higher performance for demanding workloads by providing more memory and CPU power per node. This increased capacity is crucial for efficiently processing larger datasets and improving query performance, which would likely be impacted by the new data volume.

On the other hand, elastic resizing offers flexibility for real-time scaling without downtime, but it may not always provide enough capacity in terms of processing power and performance for such a large increase in data. The classic resize method, while potentially requiring some downtime during the migration process, ensures that you adequately prepare for a sustained increase in workloads.

The choice of dense compute nodes further complements