QuestionQ204

Data Store Management

A data engineer at a large company must create centralized datasets optimized for Amazon Redshift performance. The company has multiple downstream teams that use their own AWS accounts and dedicated Amazon Redshift clusters with RA3 nodes. All downstream teams require access to the centralized datasets.

Which solution will give immediate access to the datasets while maintaining current Amazon Redshift performance?

  • A Copy the datasets to an Amazon S3 bucket by using the UNLOAD command. Register the table definitions in a dedicated AWS Glue Data Catalog schema. Share the schema with the other AWS accounts by using AWS Lake Formation. Use Amazon Redshift Spectrum to access the data.
  • B Create a daily extract, transform, and load (ETL) job to unload the data to an Amazon S3 staging area. Instruct the teams to copy the data into their Amazon Redshift clusters.
  • C Set up Amazon Redshift data sharing between the Amazon Redshift producer clusters and the consumer clusters to provide access to the centralized datasets.
  • D Set up an AWS DataSync job that automatically syncs the data between the Amazon Redshift producer clusters and the consumer clusters.
Explanation

Amazon Redshift data sharing securely exposes live data from producer clusters to consumer clusters, including clusters in different AWS accounts, without copying or moving the data. Consumers can query the shared datasets immediately, and RA3 node types support this model.

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