AWS Certified Data Engineer Associate DEA-C01 Practice Question

An analytics team runs a provisioned Amazon Redshift cluster that loads 3 TB of data nightly and is queried by business analysts. Queries arrive unpredictably, with some days heavy ad-hoc activity and most days almost no usage. The company wants to cut costs and remove cluster management tasks while keeping the existing Redshift schema and SQL. Which solution best meets these requirements?

Resize the cluster to RA3 nodes and enable Redshift Concurrency Scaling.
Snapshot the cluster into a workgroup and run it with Amazon Redshift Serverless.
Query the nightly data files directly from Amazon S3 by using Amazon Athena.
Migrate the workload to an on-demand Amazon EMR cluster running Apache Hive.

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Answer Description

Amazon Redshift Serverless eliminates the need to size, scale, or turn on and off a cluster. You can restore the existing cluster snapshot into a serverless workgroup, so the current schema and SQL continue to run unchanged. Because compute capacity automatically spins up only when queries arrive and pauses when idle, the company pays for RPU-seconds consumed instead of for a constantly running cluster. Athena would avoid cluster management but would require moving data to open-format tables and adapting queries that rely on Redshift-specific functions. An EMR on-demand cluster still needs provisioning and administration. Switching to RA3 nodes with concurrency scaling retains fixed baseline node costs, so idle time would still accrue charges. Therefore, Redshift Serverless is the most cost-effective and fully managed choice.

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