DAS-C01 · Question #48
An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch…
The correct answer is C. Create an external table using Amazon Redshift Spectrum for the call center data and perform. Amazon Redshift Spectrum allows Redshift to query data directly in Amazon S3 using the AWS Glue Data Catalog as its metastore, without loading that data into Redshift. By creating external tables in Spectrum for the S3 CSV data, analysts can join it with the internal Redshift…
Question
An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well- functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity. Which solution meets these requirements?
Options
- AUnload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.
- BExport the call center data from Amazon Redshift using a Python shell in AWS Glue.
- CCreate an external table using Amazon Redshift Spectrum for the call center data and perform
- DExport the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop.
How the community answered
(20 responses)- A10% (2)
- B15% (3)
- C45% (9)
- D30% (6)
Explanation
Amazon Redshift Spectrum allows Redshift to query data directly in Amazon S3 using the AWS Glue Data Catalog as its metastore, without loading that data into Redshift. By creating external tables in Spectrum for the S3 CSV data, analysts can join it with the internal Redshift call center tables in a single query. Critically, Spectrum uses its own separate compute layer for scanning S3, offloading that work from the main Redshift cluster. This is fully managed, serverless, and requires minimal development effort. Options A and B require writing custom code to export data, and Option D (Apache Sqoop on EMR) adds operational overhead and does not reduce Redshift load.
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