What long-term solution is recommended if an Amazon Redshift cluster is expected to become undersized due to increased ingestion rates?

The recommended long-term solution involves creating a daily job to unload records older than 13 months to Amazon S3 and managing them using Redshift Spectrum. This approach is effective because it allows for cost-effective storage of historical data while still enabling access to that data when needed. By leveraging Redshift Spectrum, you can query data stored in S3 directly, seamlessly integrating it with your existing Redshift queries. This strategy mitigates the risk of the Redshift cluster becoming undersized due to increased ingestion rates by efficiently managing data volume and retention within the cluster.

Using this method, you can keep the operational data within Redshift lean and optimized for performance while offloading older data to S3, where it can be stored at lower costs. This balances the need for quick access to recent data against the necessity of maintaining cluster performance, effectively preventing the situation where the Redshift cluster becomes under-resourced due to excessive data load.

The other options may not provide the ideal long-term strategy. Increasing the number of Redshift nodes regularly may solve immediate capacity issues but can lead to substantial costs without addressing the underlying data management problem. Migrating all data into Amazon S3 might reduce dependency on Redshift, but it would also hinder performance and accessibility for data that needs