DAS-C01 · Question #40
A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined…
The correct answer is A. Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and. This is a classic hot/warm storage tiering problem. The solution must handle both frequent recent queries (13 months) and infrequent historical queries (7 years) while controlling cost and administrative overhead. Option A uses an AWS Glue job to UNLOAD data older than 13…
Question
A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that, at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4 months. A long- term solution is needed. The data analyst has indicated that most queries only reference the most recent 13 months of data, yet there are also quarterly reports that need to query all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about the costs, administrative effort, and performance of a long-term solution. Which solution should the data analyst use to meet these requirements?
Options
- ACreate a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and
- BTake a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense
- CExecute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than
- DUnload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tiering.
How the community answered
(32 responses)- A56% (18)
- B13% (4)
- C25% (8)
- D6% (2)
Explanation
This is a classic hot/warm storage tiering problem. The solution must handle both frequent recent queries (13 months) and infrequent historical queries (7 years) while controlling cost and administrative overhead. Option A uses an AWS Glue job to UNLOAD data older than 13 months from Redshift to Amazon S3, then uses Amazon Redshift Spectrum to query S3 data for quarterly historical reports. This keeps only the hot 13-month window in Redshift (controlling cluster size and cost), while S3 stores cold data cheaply. Redshift Spectrum allows querying the S3 data using standard SQL without loading it back. Option B (dense compute nodes) increases cost without solving the growth problem. Option C (CTAS) just reshuffles data within Redshift. Option D (unloading all tables to S3) eliminates the fast Redshift tier entirely, hurting query performance for the frequent recent-data workloads.
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